Structured Review

Illumina Inc icc169 476
PFGE profile of different C. rodentium isolates. PFGE generated after Xba I cleavage of genomic DNA isolated from different strains and isolates of C. rodentium . Strain ICC168 shows the same PFGE pattern as for ICC169, ICC169-474, ICC169-335 and <t>ICC169-476.</t> Two isolates displayed significant differences in their PFGE profiles (indicated by red arrows); ICC169-407 has a band missing at approximately 340 kb and additional bands of approximately 280 kb and 420 kb; ICC169-496 is also missing the 340 kb band and has two extra bands between 145 and 200 kb. Markers are from New England BioLabs. Band sizes are indicated in kb.
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1) Product Images from "Citrobacter rodentium is an Unstable Pathogen Showing Evidence of Significant Genomic Flux"

Article Title: Citrobacter rodentium is an Unstable Pathogen Showing Evidence of Significant Genomic Flux

Journal: PLoS Pathogens

doi: 10.1371/journal.ppat.1002018

PFGE profile of different C. rodentium isolates. PFGE generated after Xba I cleavage of genomic DNA isolated from different strains and isolates of C. rodentium . Strain ICC168 shows the same PFGE pattern as for ICC169, ICC169-474, ICC169-335 and ICC169-476. Two isolates displayed significant differences in their PFGE profiles (indicated by red arrows); ICC169-407 has a band missing at approximately 340 kb and additional bands of approximately 280 kb and 420 kb; ICC169-496 is also missing the 340 kb band and has two extra bands between 145 and 200 kb. Markers are from New England BioLabs. Band sizes are indicated in kb.
Figure Legend Snippet: PFGE profile of different C. rodentium isolates. PFGE generated after Xba I cleavage of genomic DNA isolated from different strains and isolates of C. rodentium . Strain ICC168 shows the same PFGE pattern as for ICC169, ICC169-474, ICC169-335 and ICC169-476. Two isolates displayed significant differences in their PFGE profiles (indicated by red arrows); ICC169-407 has a band missing at approximately 340 kb and additional bands of approximately 280 kb and 420 kb; ICC169-496 is also missing the 340 kb band and has two extra bands between 145 and 200 kb. Markers are from New England BioLabs. Band sizes are indicated in kb.

Techniques Used: Generated, Isolation

2) Product Images from "Quantitative genome re-sequencing defines multiple mutations conferring chloroquine resistance in rodent malaria"

Article Title: Quantitative genome re-sequencing defines multiple mutations conferring chloroquine resistance in rodent malaria

Journal: BMC Genomics

doi: 10.1186/1471-2164-13-106

Scans of chloroquine selection (LGS-pyro) . Allele proportions (sensitive strain, AJ) in uncloned progeny of genetic crosses using AS/AJ SNPs (pyrosequencing). A . Genome-wide - AS-30CQ × AJ parasites surviving 1.5 (black, ■), 3 (blue, ♦), 10 (green, ▲) or 20 (orange, + ) mg CQ kg -1 day -1 . The positions of mutations in aat1 , PCHAS_031370 and ubp1 are indicated, and the proportions of the wild-type (AJ) base at these positions (as estimated by proportional sequencing [ 54 ]) are included. B . Chromosome 11 selection valley - parasites surviving 3 mg CQ kg -1 day -1 , with position of aat1 mutation indicated; AS-30CQ × AJ backcross (blue, ♦), AS-15MF × AJ backcross (red, ■). The region previously defined by classical genetic linkage analysis [ 20 ] is shown (gradient shaded green box).
Figure Legend Snippet: Scans of chloroquine selection (LGS-pyro) . Allele proportions (sensitive strain, AJ) in uncloned progeny of genetic crosses using AS/AJ SNPs (pyrosequencing). A . Genome-wide - AS-30CQ × AJ parasites surviving 1.5 (black, ■), 3 (blue, ♦), 10 (green, ▲) or 20 (orange, + ) mg CQ kg -1 day -1 . The positions of mutations in aat1 , PCHAS_031370 and ubp1 are indicated, and the proportions of the wild-type (AJ) base at these positions (as estimated by proportional sequencing [ 54 ]) are included. B . Chromosome 11 selection valley - parasites surviving 3 mg CQ kg -1 day -1 , with position of aat1 mutation indicated; AS-30CQ × AJ backcross (blue, ♦), AS-15MF × AJ backcross (red, ■). The region previously defined by classical genetic linkage analysis [ 20 ] is shown (gradient shaded green box).

Techniques Used: Selection, Genome Wide, Sequencing, Mutagenesis

3) Product Images from "A non-mosaic humanized mouse model of Down syndrome, trisomy of a nearly complete long arm of human chromosome 21 in mouse chromosome background"

Article Title: A non-mosaic humanized mouse model of Down syndrome, trisomy of a nearly complete long arm of human chromosome 21 in mouse chromosome background

Journal: bioRxiv

doi: 10.1101/862433

Mosaicism analysis in TcMAC21. ( A ) Nine organs showed uniform GFP expression when illuminated with UV light. ( B ) RT-PCR analyses for HSA21 gene expression in different TcMAC21 tissues demonstrated expected patterns of expression. ( C ) Representative FISH image of cells from TcMAC21 tissues (brain, spleen, intestine and muscle). Red signals indicate the HSA21q-MACs. ( D ) Percentage of cells showing retention of the HSA21q-MAC in various TcMAC21 tissues analyzed by FISH. ( E ) Retention rate of the HSA21q-MAC in three lymphocyte populations analyzed by FCM. ( F ) Mosaicism analysis of HSA21q-MAC in Purkinje cells (PC) of TcMAC21 by immunostaining. White arrows indicate cell bodies of randomly selected PC in RFP-channel, and red arrows indicate corresponding locations in GFP-channel.
Figure Legend Snippet: Mosaicism analysis in TcMAC21. ( A ) Nine organs showed uniform GFP expression when illuminated with UV light. ( B ) RT-PCR analyses for HSA21 gene expression in different TcMAC21 tissues demonstrated expected patterns of expression. ( C ) Representative FISH image of cells from TcMAC21 tissues (brain, spleen, intestine and muscle). Red signals indicate the HSA21q-MACs. ( D ) Percentage of cells showing retention of the HSA21q-MAC in various TcMAC21 tissues analyzed by FISH. ( E ) Retention rate of the HSA21q-MAC in three lymphocyte populations analyzed by FCM. ( F ) Mosaicism analysis of HSA21q-MAC in Purkinje cells (PC) of TcMAC21 by immunostaining. White arrows indicate cell bodies of randomly selected PC in RFP-channel, and red arrows indicate corresponding locations in GFP-channel.

Techniques Used: Expressing, Reverse Transcription Polymerase Chain Reaction, Fluorescence In Situ Hybridization, Magnetic Cell Separation, Immunostaining

Learning and memory deficits in TcMAC21. (A-F) Classic MWM (female: Eu (n=10), TcMAC21 (n=11); male: Eu (n=11), TcMAC21 (n=12); female and male data are consolidated in (A, C and E) and separated in (B, D and F)): (A and B) acquisition trials (average distance of 8 trials per day); (C and D) short delay probe trials; (E and F) long delay probe trials. (G-J) RRWM (female: Eu (n=8), TcMAC21 (n=9)): (G) acquisition trials in RRWM1; (H) short delay probe trials in RRWM1; long delay probe trials from day 1 to day 3 for comparing abilities of learning and memorizing a new target (I) and inhibiting memory of previous target (J). (K) TBS-induced LTP at SC-CA1 synapses in Eu and TcMAC21. Normalized fEPSP slopes are plotted every 1 min. Sample traces represent fEPSPs taken before “1” and 60 min after TBS stimulation “2”. Arrow indicates LTP induction, and scale bars represent 0.5 mV (vertical), 5 ms (horizontal). (L) The amplitude of fEPSP slopes are averaged at 60 min after the stimulation (Eu (n=5, 13 slices), MAC21 (n=5, 14 slices)). Repeated measures ANOVA with LSD-post hoc test was used in (A-J) and two-tailed t-test was used in (K and L); data are expressed as mean ± SEM and see also Data file S4.
Figure Legend Snippet: Learning and memory deficits in TcMAC21. (A-F) Classic MWM (female: Eu (n=10), TcMAC21 (n=11); male: Eu (n=11), TcMAC21 (n=12); female and male data are consolidated in (A, C and E) and separated in (B, D and F)): (A and B) acquisition trials (average distance of 8 trials per day); (C and D) short delay probe trials; (E and F) long delay probe trials. (G-J) RRWM (female: Eu (n=8), TcMAC21 (n=9)): (G) acquisition trials in RRWM1; (H) short delay probe trials in RRWM1; long delay probe trials from day 1 to day 3 for comparing abilities of learning and memorizing a new target (I) and inhibiting memory of previous target (J). (K) TBS-induced LTP at SC-CA1 synapses in Eu and TcMAC21. Normalized fEPSP slopes are plotted every 1 min. Sample traces represent fEPSPs taken before “1” and 60 min after TBS stimulation “2”. Arrow indicates LTP induction, and scale bars represent 0.5 mV (vertical), 5 ms (horizontal). (L) The amplitude of fEPSP slopes are averaged at 60 min after the stimulation (Eu (n=5, 13 slices), MAC21 (n=5, 14 slices)). Repeated measures ANOVA with LSD-post hoc test was used in (A-J) and two-tailed t-test was used in (K and L); data are expressed as mean ± SEM and see also Data file S4.

Techniques Used: Two Tailed Test

Morphological analysis of the heart, brain and skull in TcMAC21. ( A-C ) CHD analysis: (A) wet dissection of hearts from E18.5 Eu (top, normal) and TcMAC21(bottom, DORV and VSD (black arrow)), and a small slit-like conus VSD was also seen in TcMAC21; (B) histology of E14.5 Eu (normal) and two TcMAC21 mice with different heart defects, VSD and AVSD (black arrow indicating locations of defects); AO, aorta; AoV, aortic valve; LA, left atrium; LV, left ventricle; PT, pulmonary trunk; PulV, pulmonary valve; RA, right atrium; RV, right ventricle. (C) summary data of CHD analysis. (D) Representative T2-weighted MR images (top, ex vivo ) and statistical analysis of whole brain volume, percentage of cerebellum and hippocampus relative to brain; mid-sagittal slices from Eu (S1) and TcMAC21 (S2) and coronal slices from Eu (C1) and TcMAC21 (C2), scale bar (5 mm); n=7 per group and data are analyzed by two-way ANOVA and expressed as mean ± SEM. ( E and F ) Results of a principal components analysis (PCA) of 3D geometric morphometric analysis of Eu and TcMAC21 cranial shape. PC1 shows a 24.2% separation between Eu and TcMAC21 mice while PC2 captures intraspecific variation (Eu (n=10) and TcMAC21 (n=9)).
Figure Legend Snippet: Morphological analysis of the heart, brain and skull in TcMAC21. ( A-C ) CHD analysis: (A) wet dissection of hearts from E18.5 Eu (top, normal) and TcMAC21(bottom, DORV and VSD (black arrow)), and a small slit-like conus VSD was also seen in TcMAC21; (B) histology of E14.5 Eu (normal) and two TcMAC21 mice with different heart defects, VSD and AVSD (black arrow indicating locations of defects); AO, aorta; AoV, aortic valve; LA, left atrium; LV, left ventricle; PT, pulmonary trunk; PulV, pulmonary valve; RA, right atrium; RV, right ventricle. (C) summary data of CHD analysis. (D) Representative T2-weighted MR images (top, ex vivo ) and statistical analysis of whole brain volume, percentage of cerebellum and hippocampus relative to brain; mid-sagittal slices from Eu (S1) and TcMAC21 (S2) and coronal slices from Eu (C1) and TcMAC21 (C2), scale bar (5 mm); n=7 per group and data are analyzed by two-way ANOVA and expressed as mean ± SEM. ( E and F ) Results of a principal components analysis (PCA) of 3D geometric morphometric analysis of Eu and TcMAC21 cranial shape. PC1 shows a 24.2% separation between Eu and TcMAC21 mice while PC2 captures intraspecific variation (Eu (n=10) and TcMAC21 (n=9)).

Techniques Used: Dissection, Mouse Assay, Ex Vivo

Evaluation of DS-like pathology in TcMAC21. ( A-C ) APP protein levels and amyloid plaques in brains of 15-24-month-old TcMAC21 and Eu (n=6 per group): (A) western blot of total APP in hippocampus and cortex; (B) ELISA of total Aβ40 and Aβ42 levels in hippocampus and cortex; (C) amyloid plaques visualized by immunostaining with APP antibody 6E10; APPswe/PS1ΔE9 served as a positive control for plaque formation, scale bar (1 mm). ( D ) CFU level of GM and GEMM in TcMAC21 spleen and bone marrow (n=6 per group). ( E-G ) Chromatid and chromosome aberrations in bone marrow cells after X-ray irradiation (n=3 per group), (E) chromatid aberration, (F) chromosome aberration and (G) chromatid and/or chromosome exchange. Data are analyzed by two-tailed t-test and expressed as mean ± SEM.
Figure Legend Snippet: Evaluation of DS-like pathology in TcMAC21. ( A-C ) APP protein levels and amyloid plaques in brains of 15-24-month-old TcMAC21 and Eu (n=6 per group): (A) western blot of total APP in hippocampus and cortex; (B) ELISA of total Aβ40 and Aβ42 levels in hippocampus and cortex; (C) amyloid plaques visualized by immunostaining with APP antibody 6E10; APPswe/PS1ΔE9 served as a positive control for plaque formation, scale bar (1 mm). ( D ) CFU level of GM and GEMM in TcMAC21 spleen and bone marrow (n=6 per group). ( E-G ) Chromatid and chromosome aberrations in bone marrow cells after X-ray irradiation (n=3 per group), (E) chromatid aberration, (F) chromosome aberration and (G) chromatid and/or chromosome exchange. Data are analyzed by two-tailed t-test and expressed as mean ± SEM.

Techniques Used: Western Blot, Enzyme-linked Immunosorbent Assay, Immunostaining, Positive Control, Irradiation, Two Tailed Test

Growth profile, nesting and open field in TcMAC21. ( A-B ) The mass of TcMAC21 and Eu were measured during postnatal development at P1, P6, P14, P20, P27, P35 and P90: (A) female and male were consolidated (n≥23 per group; the overall genetic effect is analyzed using two-way ANOVA; genetic effect at each timepoint is analyzed using two-tailed t-test, *P
Figure Legend Snippet: Growth profile, nesting and open field in TcMAC21. ( A-B ) The mass of TcMAC21 and Eu were measured during postnatal development at P1, P6, P14, P20, P27, P35 and P90: (A) female and male were consolidated (n≥23 per group; the overall genetic effect is analyzed using two-way ANOVA; genetic effect at each timepoint is analyzed using two-tailed t-test, *P

Techniques Used: Two Tailed Test

HSA21q PCG expression pattern in P1 TcMAC21. ( A ) RNA-Seq analysis of HSA21q and its mouse orthologs in TcMAC21 and Eu in P1 forebrain identified 117 HSA21 genes with mouse orthologs whose FPKM is ≥1 in Eu. Three expression values are shown: 1. the ratio of HSA21q transcript in TcMAC21 relative to its ortholog in Eu mice (gray open squares); 2. the ratio of the mouse ortholog in TcMAC21 relative to that of Eu (blue circles); and 3. the ratio of total expression (FPKM of HSA21q + its mouse ortholog) in TcMAC21 relative to Eu (red circles). The positions of deleted regions are indicated in red. ( B ) Correlation between relative quantification by Taqman assay and by RNA-Seq for 10 HSA21q genes and mouse actin (mACTB) in TcMAC21. ( C ) Taqman assay comparing expression of 10 HSA21 genes between forebrain, hindbrain, and heart using the same amount of total RNA. (n = 2 TcMAC21 and 3 Eu littermates at P1).
Figure Legend Snippet: HSA21q PCG expression pattern in P1 TcMAC21. ( A ) RNA-Seq analysis of HSA21q and its mouse orthologs in TcMAC21 and Eu in P1 forebrain identified 117 HSA21 genes with mouse orthologs whose FPKM is ≥1 in Eu. Three expression values are shown: 1. the ratio of HSA21q transcript in TcMAC21 relative to its ortholog in Eu mice (gray open squares); 2. the ratio of the mouse ortholog in TcMAC21 relative to that of Eu (blue circles); and 3. the ratio of total expression (FPKM of HSA21q + its mouse ortholog) in TcMAC21 relative to Eu (red circles). The positions of deleted regions are indicated in red. ( B ) Correlation between relative quantification by Taqman assay and by RNA-Seq for 10 HSA21q genes and mouse actin (mACTB) in TcMAC21. ( C ) Taqman assay comparing expression of 10 HSA21 genes between forebrain, hindbrain, and heart using the same amount of total RNA. (n = 2 TcMAC21 and 3 Eu littermates at P1).

Techniques Used: Expressing, RNA Sequencing Assay, Mouse Assay, TaqMan Assay

Construction of TcMAC21 mice (HSA21q-MAC). ( A ) Schematic diagram of HSA21q-MAC construction. ( B ) Chimeric mice were obtained via injection of mouse ES cells carrying the HSA21q-MAC. The arrow indicates a GFP-positive, TcMAC21 trisomic mouse. ( C ) FISH and ( D ) G-banding based karyotype of TcMAC21 containing the HSA21q-MAC. ( E ) WGS showing the positions of 4 deletions in HSA21q (A, B, C, and D). These are shown normalized to gene numbers, not physical length. The regions of homology with mouse chromosomes 16, 17 and 10 are indicated. ( F ) Genome positions of deletions and numbers of affected PCGs and non-PCGs on the HSA21q-MAC (based on GRCh38.p12, BioMart-Ensembl, May 2019).
Figure Legend Snippet: Construction of TcMAC21 mice (HSA21q-MAC). ( A ) Schematic diagram of HSA21q-MAC construction. ( B ) Chimeric mice were obtained via injection of mouse ES cells carrying the HSA21q-MAC. The arrow indicates a GFP-positive, TcMAC21 trisomic mouse. ( C ) FISH and ( D ) G-banding based karyotype of TcMAC21 containing the HSA21q-MAC. ( E ) WGS showing the positions of 4 deletions in HSA21q (A, B, C, and D). These are shown normalized to gene numbers, not physical length. The regions of homology with mouse chromosomes 16, 17 and 10 are indicated. ( F ) Genome positions of deletions and numbers of affected PCGs and non-PCGs on the HSA21q-MAC (based on GRCh38.p12, BioMart-Ensembl, May 2019).

Techniques Used: Mouse Assay, Injection, Fluorescence In Situ Hybridization

4) Product Images from "16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife"

Article Title: 16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife

Journal: mSystems

doi: 10.1128/mSystems.00032-16

Prevalence of Mycoplasma lineages in Senegalese rodents, by site, and phylogenetic associations between Mycoplasma lineages and rodent species. (A) Comparison of phylogenetic trees based on the 16S rRNA V4 sequences of Mycoplasma and on the mitochondrial cytochrome b gene and the two nuclear gene fragments (IRBP exon 1 and GHR) for rodents (the tree was drawn based on data from reference 92 ). Lines link the Mycoplasma lineages detected in the various rodent species (for a minimum site prevalence exceeding 10%). The numbers next to the branches are bootstrap values (shown only if > 70%). (B) Plots of OTU prevalences, with 95% confidence intervals calculated by Sterne’s exact method ( 93 ) according to rodent species and site (see reference 69 for more information about site codes and their geographic locations). The gray bars on the x axis indicate sites from which the rodent species concerned is absent.
Figure Legend Snippet: Prevalence of Mycoplasma lineages in Senegalese rodents, by site, and phylogenetic associations between Mycoplasma lineages and rodent species. (A) Comparison of phylogenetic trees based on the 16S rRNA V4 sequences of Mycoplasma and on the mitochondrial cytochrome b gene and the two nuclear gene fragments (IRBP exon 1 and GHR) for rodents (the tree was drawn based on data from reference 92 ). Lines link the Mycoplasma lineages detected in the various rodent species (for a minimum site prevalence exceeding 10%). The numbers next to the branches are bootstrap values (shown only if > 70%). (B) Plots of OTU prevalences, with 95% confidence intervals calculated by Sterne’s exact method ( 93 ) according to rodent species and site (see reference 69 for more information about site codes and their geographic locations). The gray bars on the x axis indicate sites from which the rodent species concerned is absent.

Techniques Used:

Taxonomic assignment of the V4 16S rRNA sequences in wild rodents and in negative controls for extraction and PCR. The histograms show the percentages of sequences for the most abundant bacterial genera in the two MiSeq runs combined. Notice the presence in the controls of several bacterial genera, which was likely due to the inherent contamination of laboratory reagents by bacterial DNA (termed “contaminant genera”). These contaminant genera are also present (to a lesser extent) in the rodent samples. The insertions represent the proportion of sequences from rodent samples which were incorrectly assigned to the controls. See Fig. S1 for separate histograms for the two MiSeq runs.
Figure Legend Snippet: Taxonomic assignment of the V4 16S rRNA sequences in wild rodents and in negative controls for extraction and PCR. The histograms show the percentages of sequences for the most abundant bacterial genera in the two MiSeq runs combined. Notice the presence in the controls of several bacterial genera, which was likely due to the inherent contamination of laboratory reagents by bacterial DNA (termed “contaminant genera”). These contaminant genera are also present (to a lesser extent) in the rodent samples. The insertions represent the proportion of sequences from rodent samples which were incorrectly assigned to the controls. See Fig. S1 for separate histograms for the two MiSeq runs.

Techniques Used: Polymerase Chain Reaction

Workflow of the wet laboratory, bioinformatics, and data filtering procedures in the process of data filtering for 16S rRNA amplicon sequencing. Reagent contaminants were detected by analyzing the sequences in the NC ext and NC PCR controls. Sequence number thresholds for correcting for cross-contamination (T CC ) are OTU and run dependent and were estimated by analyzing the sequences in the NC mus , NC ext , NC PCR , and PC index controls. Sequence number thresholds for correcting for false-index-pairing (T FA ) values are OTU and run dependent and were estimated by analyzing the sequences in the NC index and PC alien controls. A result was considered positive if the number of sequences was > T CC and > T FA . Samples were considered positive if a positive result was obtained for both PCR replicates. *, see Kozich et al. ( 18 ) for details on the sequencing.
Figure Legend Snippet: Workflow of the wet laboratory, bioinformatics, and data filtering procedures in the process of data filtering for 16S rRNA amplicon sequencing. Reagent contaminants were detected by analyzing the sequences in the NC ext and NC PCR controls. Sequence number thresholds for correcting for cross-contamination (T CC ) are OTU and run dependent and were estimated by analyzing the sequences in the NC mus , NC ext , NC PCR , and PC index controls. Sequence number thresholds for correcting for false-index-pairing (T FA ) values are OTU and run dependent and were estimated by analyzing the sequences in the NC index and PC alien controls. A result was considered positive if the number of sequences was > T CC and > T FA . Samples were considered positive if a positive result was obtained for both PCR replicates. *, see Kozich et al. ( 18 ) for details on the sequencing.

Techniques Used: Amplification, Sequencing, Polymerase Chain Reaction

5) Product Images from "Genome-wide profiling of RNA editing sites in sheep"

Article Title: Genome-wide profiling of RNA editing sites in sheep

Journal: Journal of Animal Science and Biotechnology

doi: 10.1186/s40104-019-0331-z

IGV screenshot for RNA editing sites in BLCAP ( a ) and BEIL1 ( b ). The two IGV screenshots showing the alignments of genomic and RNA reads in BLCAP ( a ) and BEIL1 ( b ). The two editing sites (NC_019470.2:65916359 and NC_019475.2:32339099) were at the center lines. From top to bottom, the tracks are as follows: genomic DNA reads, RNA reads in Kidney, RNA reads in Spleen, reference sequences and transcripts
Figure Legend Snippet: IGV screenshot for RNA editing sites in BLCAP ( a ) and BEIL1 ( b ). The two IGV screenshots showing the alignments of genomic and RNA reads in BLCAP ( a ) and BEIL1 ( b ). The two editing sites (NC_019470.2:65916359 and NC_019475.2:32339099) were at the center lines. From top to bottom, the tracks are as follows: genomic DNA reads, RNA reads in Kidney, RNA reads in Spleen, reference sequences and transcripts

Techniques Used:

6) Product Images from "Long Noncoding RNA MEG3 Is an Epigenetic Determinant of Oncogenic Signaling in Functional Pancreatic Neuroendocrine Tumor Cells"

Article Title: Long Noncoding RNA MEG3 Is an Epigenetic Determinant of Oncogenic Signaling in Functional Pancreatic Neuroendocrine Tumor Cells

Journal: Molecular and Cellular Biology

doi: 10.1128/MCB.00278-17

m-Meg3 requires PRC2 components to repress the m-c-Met transcript. (A and B) Effect of the EZH2 inhibitor GSK343 on the expression of m-Meg3 and m-c-Met transcripts. RNA was isolated from MIN6-4N and Men1 cells after vehicle or GSK343 treatments at the indicated times. The RNA isolated was used in qPCR analyses using primers specific for m-c-Met, m-Meg3-1, and m-EZH2. The qPCR transcript data shown are from representative experiments where technical replicates were set up in triplicate. ***, P ≤ 0.001; *, P ≤ 0.05; N.s., nonsignificant. (C) Depletion of H3K27me3 is a confirmatory readout for the inhibition of EZH2 methyltransferase activity. Representative Western blots for H3K27me3, using whole-cell extracts (WCEs) from MIN6-4N and Men1 cells after 7-day and 6-day treatments, respectively, with 2.5 μM GSK343 or dimethyl sulfoxide (DMSO) vehicle controls are shown. β-Actin served as the loading control.
Figure Legend Snippet: m-Meg3 requires PRC2 components to repress the m-c-Met transcript. (A and B) Effect of the EZH2 inhibitor GSK343 on the expression of m-Meg3 and m-c-Met transcripts. RNA was isolated from MIN6-4N and Men1 cells after vehicle or GSK343 treatments at the indicated times. The RNA isolated was used in qPCR analyses using primers specific for m-c-Met, m-Meg3-1, and m-EZH2. The qPCR transcript data shown are from representative experiments where technical replicates were set up in triplicate. ***, P ≤ 0.001; *, P ≤ 0.05; N.s., nonsignificant. (C) Depletion of H3K27me3 is a confirmatory readout for the inhibition of EZH2 methyltransferase activity. Representative Western blots for H3K27me3, using whole-cell extracts (WCEs) from MIN6-4N and Men1 cells after 7-day and 6-day treatments, respectively, with 2.5 μM GSK343 or dimethyl sulfoxide (DMSO) vehicle controls are shown. β-Actin served as the loading control.

Techniques Used: Expressing, Isolation, Real-time Polymerase Chain Reaction, Inhibition, Activity Assay, Western Blot

ChIRP-Seq reveals m-Meg3 enrichment at multiple m-c-Met loci. (A) Representative agarose gel image showing the specificity of the m-Meg3 ChIRP probes. RNA was isolated after m-Meg3 ChIRP from the V3 (vector) and the M5 (stable MIN6-4N cells stably expressing the m-Meg3-3 isoform which lacks exon 4) cell lines. The RNA was then used for RT-PCR. Input corresponds to the RT-PCR product using RNA isolated before m-Meg3 ChIRP-Seq. Odd and even correspond to RT-PCR using RNA after m-Meg3 ChIRP with probes located at odd and even locations on the m-Meg3 RNA. The gel image represents products of the RT-PCR performed with primers 1F/1R (flanking exon 7 and exon 8) that recognize all m-Meg3 isoforms. gapdh served as the negative control. cDNAs from three replicates of V3 and M5 ChIRP-Seq were pooled due to low yields and sequenced. (B) m-Meg3 ChIRP-PCR in a stable cell line expressing full-length m-Meg3. RNA was isolated after m-Meg3 ChIRP from the 9V (vector) and the 14M (stable MIN6-4N cells with the m-Meg3-1 isoform, which encompasses all 10 exons) cell lines. The RNA was then used for RT-PCR. Input corresponds to RT-PCR from RNA isolated before m-Meg3 ChIRP. RT-PCR was performed with primers 1F/1R (flanking exons 7 and 8) that recognize all m-Meg3 isoforms and further confirmed with the ex3F/ex4R primer pair (flanking exons 3 and 4), specific for the m-Meg3-1 isoform. gapdh served as the negative control. (C) m-Meg3 enrichment patterns at discrete m-c-Met genomic regions in different m-Meg3 stable cell lines. DNA was isolated after m-Meg3 ChIRP from two different m-Meg-3 stable MIN6-4N cell lines and their respective vector controls. The DNA was then subjected to whole-genome amplification (WGA) and subsequent purification. The purified WGA DNA was then used to set up qPCRs in duplicate with primers specific for m-c-Met genomic regions identified by m-Meg3 ChIRP-Seq, namely, the m-c-Met upstream region, the m-c-Met exon 18 region, the m-c-Met exon 20 region, and also the previously identified kb +63 enhancer. The qPCR data are represented as percent input of DNA.
Figure Legend Snippet: ChIRP-Seq reveals m-Meg3 enrichment at multiple m-c-Met loci. (A) Representative agarose gel image showing the specificity of the m-Meg3 ChIRP probes. RNA was isolated after m-Meg3 ChIRP from the V3 (vector) and the M5 (stable MIN6-4N cells stably expressing the m-Meg3-3 isoform which lacks exon 4) cell lines. The RNA was then used for RT-PCR. Input corresponds to the RT-PCR product using RNA isolated before m-Meg3 ChIRP-Seq. Odd and even correspond to RT-PCR using RNA after m-Meg3 ChIRP with probes located at odd and even locations on the m-Meg3 RNA. The gel image represents products of the RT-PCR performed with primers 1F/1R (flanking exon 7 and exon 8) that recognize all m-Meg3 isoforms. gapdh served as the negative control. cDNAs from three replicates of V3 and M5 ChIRP-Seq were pooled due to low yields and sequenced. (B) m-Meg3 ChIRP-PCR in a stable cell line expressing full-length m-Meg3. RNA was isolated after m-Meg3 ChIRP from the 9V (vector) and the 14M (stable MIN6-4N cells with the m-Meg3-1 isoform, which encompasses all 10 exons) cell lines. The RNA was then used for RT-PCR. Input corresponds to RT-PCR from RNA isolated before m-Meg3 ChIRP. RT-PCR was performed with primers 1F/1R (flanking exons 7 and 8) that recognize all m-Meg3 isoforms and further confirmed with the ex3F/ex4R primer pair (flanking exons 3 and 4), specific for the m-Meg3-1 isoform. gapdh served as the negative control. (C) m-Meg3 enrichment patterns at discrete m-c-Met genomic regions in different m-Meg3 stable cell lines. DNA was isolated after m-Meg3 ChIRP from two different m-Meg-3 stable MIN6-4N cell lines and their respective vector controls. The DNA was then subjected to whole-genome amplification (WGA) and subsequent purification. The purified WGA DNA was then used to set up qPCRs in duplicate with primers specific for m-c-Met genomic regions identified by m-Meg3 ChIRP-Seq, namely, the m-c-Met upstream region, the m-c-Met exon 18 region, the m-c-Met exon 20 region, and also the previously identified kb +63 enhancer. The qPCR data are represented as percent input of DNA.

Techniques Used: Agarose Gel Electrophoresis, Isolation, Plasmid Preparation, Stable Transfection, Expressing, Reverse Transcription Polymerase Chain Reaction, Negative Control, Polymerase Chain Reaction, Whole Genome Amplification, Purification, Real-time Polymerase Chain Reaction

m-Meg3 TFO-9 regulates the m-c-Met transcript. (A) Schematic of full-length m-Meg3-1 exon structure showing the TFO-9 coordinates. Full-length m-Meg3-1 is depicted, with exons 1 to 10 numbered (top) and the sequence length in base pairs (bottom). TFO-9, a GA- and GT-rich 16-mer sequence, is shown mapping to the C-terminal portion of exon 10 in m-Meg3-1. TFO-9, spanning bp 1822 to 1838, was predicted by Triplexator to form triplexes with double-stranded DNA. (B) Effect of TFO-9 on the expression of m-Meg3 and m-c-Met transcripts. RNA was isolated at 48 h and 96 h posttransfection from MIN6-4N cells transiently transfected with TFO RNA oligonucleotides. Purified RNA converted to cDNA was subjected to qPCR analyses with primers specific for m-Meg3-1 or m-c-Met. The data represent an average from three independent experiments and multiple technical replicates (mean ± SD) *, P ≤ 0.05.
Figure Legend Snippet: m-Meg3 TFO-9 regulates the m-c-Met transcript. (A) Schematic of full-length m-Meg3-1 exon structure showing the TFO-9 coordinates. Full-length m-Meg3-1 is depicted, with exons 1 to 10 numbered (top) and the sequence length in base pairs (bottom). TFO-9, a GA- and GT-rich 16-mer sequence, is shown mapping to the C-terminal portion of exon 10 in m-Meg3-1. TFO-9, spanning bp 1822 to 1838, was predicted by Triplexator to form triplexes with double-stranded DNA. (B) Effect of TFO-9 on the expression of m-Meg3 and m-c-Met transcripts. RNA was isolated at 48 h and 96 h posttransfection from MIN6-4N cells transiently transfected with TFO RNA oligonucleotides. Purified RNA converted to cDNA was subjected to qPCR analyses with primers specific for m-Meg3-1 or m-c-Met. The data represent an average from three independent experiments and multiple technical replicates (mean ± SD) *, P ≤ 0.05.

Techniques Used: Sequencing, Expressing, Isolation, Transfection, Purification, Real-time Polymerase Chain Reaction

m-Meg3 associates with PRC2 components in mouse insulinoma cell line models. (A and B) Association of m-Meg3 RNA with EZH2 and H3K27me3. RNA-ChIP in Men1 cells (Meg3 proficient) was performed using the antibodies directed toward EZH2 (A) and H3K27me3 (B). qPCR was performed on cDNA obtained from RNA isolated after RNA-ChIP. qPCR data were calculated as percent RNA input and are shown as the average from two independent biological replicates and multiple technical replicates (mean ± SD). IgG served as the negative control for the RNA-ChIP assay. (C and D) EZH2 and H3K27me3 enrichment at the m-c-Met regions identified by m-Meg3 ChIRP-Seq. ChIP assay was performed in Men1 cells using the indicated antibodies. DNA isolated after ChIP was used for qPCR, with primers specific for the m-c-Met upstream region, the +63-kb enhancer, the m-c-Met exon 18 region, and the m-c-Met exon 20 region. EZH2 (C) and H3K27me3 (D) enrichment was calculated as percent chromatin DNA input and constitutes the average from three independent biological replicates and multiple technical replicates (mean ± SD). (E and F) Enhancer-signature histone modifications at the m-c-Met loci identified by m-Meg3 ChIRP-Seq. MIN6-4N cells were subjected to ChIP assays and qPCR analyses to detect the enrichment of H3K27Ac (E) and H3K4me1 (F) at the m-c-Met upstream region, the kb +63 enhancer, the m-c-Met exon 18 region, and the m-c-Met exon 20 region. The data represent an average from two independent biological replicates and multiple technical replicates (mean ± SD).
Figure Legend Snippet: m-Meg3 associates with PRC2 components in mouse insulinoma cell line models. (A and B) Association of m-Meg3 RNA with EZH2 and H3K27me3. RNA-ChIP in Men1 cells (Meg3 proficient) was performed using the antibodies directed toward EZH2 (A) and H3K27me3 (B). qPCR was performed on cDNA obtained from RNA isolated after RNA-ChIP. qPCR data were calculated as percent RNA input and are shown as the average from two independent biological replicates and multiple technical replicates (mean ± SD). IgG served as the negative control for the RNA-ChIP assay. (C and D) EZH2 and H3K27me3 enrichment at the m-c-Met regions identified by m-Meg3 ChIRP-Seq. ChIP assay was performed in Men1 cells using the indicated antibodies. DNA isolated after ChIP was used for qPCR, with primers specific for the m-c-Met upstream region, the +63-kb enhancer, the m-c-Met exon 18 region, and the m-c-Met exon 20 region. EZH2 (C) and H3K27me3 (D) enrichment was calculated as percent chromatin DNA input and constitutes the average from three independent biological replicates and multiple technical replicates (mean ± SD). (E and F) Enhancer-signature histone modifications at the m-c-Met loci identified by m-Meg3 ChIRP-Seq. MIN6-4N cells were subjected to ChIP assays and qPCR analyses to detect the enrichment of H3K27Ac (E) and H3K4me1 (F) at the m-c-Met upstream region, the kb +63 enhancer, the m-c-Met exon 18 region, and the m-c-Met exon 20 region. The data represent an average from two independent biological replicates and multiple technical replicates (mean ± SD).

Techniques Used: Chromatin Immunoprecipitation, Real-time Polymerase Chain Reaction, Isolation, Negative Control

Model depicting the mechanisms of Meg3-mediated c-Met regulation in normal pancreatic islets and tumor cells. The model presented here suggests that in the context of intact menin in normal islet beta cells, the presence of Meg3 deters the proto-oncogenic activity of c-Met by multiple mechanisms. c-Met signaling can be repressed by genomic Meg3 binding, perhaps through contact with Meg3 TFO regions and Meg3 interaction with PRC2 components. Meg3 downregulation driven by menin loss and menin-independent Meg3 loss could both favor gene-activating epigenetic changes. These epigenetic alterations in turn potentiate aberrant c-Met signaling, leading to pancreatic islet β-cell tumor formation.
Figure Legend Snippet: Model depicting the mechanisms of Meg3-mediated c-Met regulation in normal pancreatic islets and tumor cells. The model presented here suggests that in the context of intact menin in normal islet beta cells, the presence of Meg3 deters the proto-oncogenic activity of c-Met by multiple mechanisms. c-Met signaling can be repressed by genomic Meg3 binding, perhaps through contact with Meg3 TFO regions and Meg3 interaction with PRC2 components. Meg3 downregulation driven by menin loss and menin-independent Meg3 loss could both favor gene-activating epigenetic changes. These epigenetic alterations in turn potentiate aberrant c-Met signaling, leading to pancreatic islet β-cell tumor formation.

Techniques Used: Activity Assay, Binding Assay

7) Product Images from "Citrobacter rodentium is an Unstable Pathogen Showing Evidence of Significant Genomic Flux"

Article Title: Citrobacter rodentium is an Unstable Pathogen Showing Evidence of Significant Genomic Flux

Journal: PLoS Pathogens

doi: 10.1371/journal.ppat.1002018

Genetic organisation of the C. rodentium prophages showing transcriptionally active genes. The genomes of each of the five intact prophages in the C. rodentium genome are shown aligned with mapped sequence reads for the whole genome transcriptome. The prophage remnant CRPr20 is also included due to its high similarity to CRP38 and the difficulty in mapping repetitive sequences. The RNA-seq data are represented as a plot showing the depth of sequences mapped to the forward strand (blue) and reverse strand (red) above each genome (window size = 200 bp). The majority of prophage genes, including those predicted to encode phage structural and lysis genes (see key), are expressed. Putative cargo genes can be identified by their relatively high levels of expression (numbered CDSs; see Table 4 for details). The scale bar indicates genome length. This figure was produced using Easyfig [67] and Artemis [68] .
Figure Legend Snippet: Genetic organisation of the C. rodentium prophages showing transcriptionally active genes. The genomes of each of the five intact prophages in the C. rodentium genome are shown aligned with mapped sequence reads for the whole genome transcriptome. The prophage remnant CRPr20 is also included due to its high similarity to CRP38 and the difficulty in mapping repetitive sequences. The RNA-seq data are represented as a plot showing the depth of sequences mapped to the forward strand (blue) and reverse strand (red) above each genome (window size = 200 bp). The majority of prophage genes, including those predicted to encode phage structural and lysis genes (see key), are expressed. Putative cargo genes can be identified by their relatively high levels of expression (numbered CDSs; see Table 4 for details). The scale bar indicates genome length. This figure was produced using Easyfig [67] and Artemis [68] .

Techniques Used: Sequencing, RNA Sequencing Assay, Lysis, Expressing, Produced

8) Product Images from "Diverse Non-genetic, Allele-Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain"

Article Title: Diverse Non-genetic, Allele-Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain

Journal: Neuron

doi: 10.1016/j.neuron.2017.01.033

Identification of Autosomal DAEEs and Allele CoEEs in the Primate Brain (A) Schematic of the strategy to profile allele co-expression in the DRN region of juvenile female cynomolgus macaques. For ten parent-offspring trios, we performed whole-genome sequencing of the parents and transcriptome sequencing of RNA extracted from the DRN of the daughters. SNPs that distinguish maternal from paternal alleles in the daughters are determined from the parental genomes and RNA-seq datasets. (B) By analyzing r a values for the 838 genes with maternal and paternal allele RNA-seq data in all ten daughters, we defined genes with allele CoEEs ( AGAP1 ) and potential DAEEs ( CNTN1 ) in the juvenile female macaque DRN. (C) Plots of the non-genetic r ab 95% CIs for 17 X-linked genes and 821 autosomal genes, colorized according to the categories of high-confidence allelic effects indicated in the legend. Most X-linked genes exhibit AAEEs or DAEEs. Most autosomal genes exhibit allele CoEEs (red) or do not exhibit sufficiently robust allelic effects to be categorized with high confidence (gray); however, high-confidence DAEEs were discovered for RBM48 and HTT . In addition, more modest putative DAEEs were observed for several autosomal genes (see main text). (D) The r a values and non-genetic r ab 95% CIs for examples of primate genes with allele CoEEs ( ABCA1 ), putative DAEEs ( NARS , TPNC1 ), high-confidence DAEEs ( RBM48 , HTT) , and uncategorized genes ( ATP1A3 , ABAT). (E) Boxplots comparing the primate DRN expression level of autosomal and X-linked genes with allele CoEEs, putative DAEEs (pDAEEs), DAEEs, and uncategorized (UNCAT) genes. A significant main effect of gene class was observed (one way ANOVA, p
Figure Legend Snippet: Identification of Autosomal DAEEs and Allele CoEEs in the Primate Brain (A) Schematic of the strategy to profile allele co-expression in the DRN region of juvenile female cynomolgus macaques. For ten parent-offspring trios, we performed whole-genome sequencing of the parents and transcriptome sequencing of RNA extracted from the DRN of the daughters. SNPs that distinguish maternal from paternal alleles in the daughters are determined from the parental genomes and RNA-seq datasets. (B) By analyzing r a values for the 838 genes with maternal and paternal allele RNA-seq data in all ten daughters, we defined genes with allele CoEEs ( AGAP1 ) and potential DAEEs ( CNTN1 ) in the juvenile female macaque DRN. (C) Plots of the non-genetic r ab 95% CIs for 17 X-linked genes and 821 autosomal genes, colorized according to the categories of high-confidence allelic effects indicated in the legend. Most X-linked genes exhibit AAEEs or DAEEs. Most autosomal genes exhibit allele CoEEs (red) or do not exhibit sufficiently robust allelic effects to be categorized with high confidence (gray); however, high-confidence DAEEs were discovered for RBM48 and HTT . In addition, more modest putative DAEEs were observed for several autosomal genes (see main text). (D) The r a values and non-genetic r ab 95% CIs for examples of primate genes with allele CoEEs ( ABCA1 ), putative DAEEs ( NARS , TPNC1 ), high-confidence DAEEs ( RBM48 , HTT) , and uncategorized genes ( ATP1A3 , ABAT). (E) Boxplots comparing the primate DRN expression level of autosomal and X-linked genes with allele CoEEs, putative DAEEs (pDAEEs), DAEEs, and uncategorized (UNCAT) genes. A significant main effect of gene class was observed (one way ANOVA, p

Techniques Used: Expressing, Sequencing, RNA Sequencing Assay

9) Product Images from "Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer"

Article Title: Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer

Journal: EBioMedicine

doi: 10.1016/j.ebiom.2018.04.007

Analysis of therapeutic response defines signature of ADT resistance. (a) Schematic representation of a treatment time-course. Scenario 1: Time to treatment-related event: event occurred during the course of the treatment or within 1.5 years after the treatment end. Time between treatment start and a treatment-related event indicated. Scenario 2: Time to follow-up: time between treatment-start and latest follow-up indicated. (b) ADT response in the TCGA-PRAD cohort. Red vertical bars correspond to time between treatment start and event. Blue circles define censored patients (without events), indicating time between treatment start and latest follow-up. Non-responder (n = 4) and responder (n = 4) (Table S1) patients are indicated. (c) Schematic depiction of the differential methylation signature between non-responder and responder patients, sorted from sites whose methylation did not change (left tail) to sites with significant differential methylation (right tail). Signature was defined as a list of sites ordered by –log10 (p-value) from the two-sample two-tail t -test comparing non-responder and responder patient groups.
Figure Legend Snippet: Analysis of therapeutic response defines signature of ADT resistance. (a) Schematic representation of a treatment time-course. Scenario 1: Time to treatment-related event: event occurred during the course of the treatment or within 1.5 years after the treatment end. Time between treatment start and a treatment-related event indicated. Scenario 2: Time to follow-up: time between treatment-start and latest follow-up indicated. (b) ADT response in the TCGA-PRAD cohort. Red vertical bars correspond to time between treatment start and event. Blue circles define censored patients (without events), indicating time between treatment start and latest follow-up. Non-responder (n = 4) and responder (n = 4) (Table S1) patients are indicated. (c) Schematic depiction of the differential methylation signature between non-responder and responder patients, sorted from sites whose methylation did not change (left tail) to sites with significant differential methylation (right tail). Signature was defined as a list of sites ordered by –log10 (p-value) from the two-sample two-tail t -test comparing non-responder and responder patient groups.

Techniques Used: Methylation, Significance Assay

Treatment-related survival analysis of candidate 5 site-gene panel in TCGA, Beltran et al, and PROMOTE patient cohorts. (a) Treatment-related Kaplan-Meier survival analysis of candidate 5 site-gene panel in TCGA Gleason 7 and Gleason 8-9, demonstrating that therapeutic predictive ability of the identified 5 site-gene panel is independent of Gleason score. Log-rank p-values are indicated. (b) ROC analysis comparing TCGA-PRAD (group1) with Beltran et al (green) and PROMOTE (brown) patient cohorts. AUROC is indicated.
Figure Legend Snippet: Treatment-related survival analysis of candidate 5 site-gene panel in TCGA, Beltran et al, and PROMOTE patient cohorts. (a) Treatment-related Kaplan-Meier survival analysis of candidate 5 site-gene panel in TCGA Gleason 7 and Gleason 8-9, demonstrating that therapeutic predictive ability of the identified 5 site-gene panel is independent of Gleason score. Log-rank p-values are indicated. (b) ROC analysis comparing TCGA-PRAD (group1) with Beltran et al (green) and PROMOTE (brown) patient cohorts. AUROC is indicated.

Techniques Used:

10) Product Images from "Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis"

Article Title: Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

Journal: PeerJ

doi: 10.7717/peerj.6425

Differential gene expression and differential gene hydroxymethylation. (A) GSE51588 microarray dataset. (B) GSE114007 mRNA expression profile dataset. (C) GSE64393 high-throughput hydroxymethylation dataset. Red indicates upregulation, green indicates downregulation, and blue indicates no significant change in gene expression or hydroxymethylation based on the criteria of an absolute log2 (fold change) > 1 and P
Figure Legend Snippet: Differential gene expression and differential gene hydroxymethylation. (A) GSE51588 microarray dataset. (B) GSE114007 mRNA expression profile dataset. (C) GSE64393 high-throughput hydroxymethylation dataset. Red indicates upregulation, green indicates downregulation, and blue indicates no significant change in gene expression or hydroxymethylation based on the criteria of an absolute log2 (fold change) > 1 and P

Techniques Used: Expressing, Microarray, High Throughput Screening Assay

Heat maps of the aberrantly hydroxymethylated differentially expressed genes in the mRNA dataset. (A) GSE51588 dataset; (B) GSE114007 dataset. Red represents upregulation; blue, downregulation; N, normal; and OA, osteoarthritis.
Figure Legend Snippet: Heat maps of the aberrantly hydroxymethylated differentially expressed genes in the mRNA dataset. (A) GSE51588 dataset; (B) GSE114007 dataset. Red represents upregulation; blue, downregulation; N, normal; and OA, osteoarthritis.

Techniques Used:

11) Product Images from "Function and Evolution of DNA Methylation in Nasonia vitripennis"

Article Title: Function and Evolution of DNA Methylation in Nasonia vitripennis

Journal: PLoS Genetics

doi: 10.1371/journal.pgen.1003872

DNA methylation and gene conservation. (A) Phylogenetic tree of eight insect species: Nasonia vitripennis , Apis mellifera , Tribolium castaneum , Bombyx mori , Anopheles gambiae , Drosophila melanogaster , Pediculus humanus and Acyrthosiphon pisum . The methylation status and correlating factors were plotted in (B–F) for four groups of genes: all 5,039 Nasonia single-copy genes with one or zero ortholog in seven other insect species, 2,374 genes with one orthologs in all eight insect species, 443 genes with one orthologs in Apis and Nasonia but missing in other six species, and 320 genes present only in Nasonia . The y -axes plotted in (B–F) are (B): proportion of methylated (blue) and non-methylated genes (red); (C): percentage of methylated CpG sites in methylated genes; (D): adult RNA-seq expression levels (log 10 FPKM); (E): coefficient of variation of expression level in tiling array across six developmental stages; (F): number of expressed tissues. (G) Top: Phylogenetic tree of three Nasonia species: N. longicornis (L), N. giraulti (G) and N. vitripennis (V). Bottom: boxplots of nucleotide substitution rates between V–L, V–G and L–G.
Figure Legend Snippet: DNA methylation and gene conservation. (A) Phylogenetic tree of eight insect species: Nasonia vitripennis , Apis mellifera , Tribolium castaneum , Bombyx mori , Anopheles gambiae , Drosophila melanogaster , Pediculus humanus and Acyrthosiphon pisum . The methylation status and correlating factors were plotted in (B–F) for four groups of genes: all 5,039 Nasonia single-copy genes with one or zero ortholog in seven other insect species, 2,374 genes with one orthologs in all eight insect species, 443 genes with one orthologs in Apis and Nasonia but missing in other six species, and 320 genes present only in Nasonia . The y -axes plotted in (B–F) are (B): proportion of methylated (blue) and non-methylated genes (red); (C): percentage of methylated CpG sites in methylated genes; (D): adult RNA-seq expression levels (log 10 FPKM); (E): coefficient of variation of expression level in tiling array across six developmental stages; (F): number of expressed tissues. (G) Top: Phylogenetic tree of three Nasonia species: N. longicornis (L), N. giraulti (G) and N. vitripennis (V). Bottom: boxplots of nucleotide substitution rates between V–L, V–G and L–G.

Techniques Used: DNA Methylation Assay, Methylation, RNA Sequencing Assay, Expressing

12) Product Images from "Empowering Shotgun Mass Spectrometry with 2DE: A HepG2 Study"

Article Title: Empowering Shotgun Mass Spectrometry with 2DE: A HepG2 Study

Journal: International Journal of Molecular Sciences

doi: 10.3390/ijms21113813

Distribution of the number of potential amino acid sequences per gene in different referent libraries. Grey dots correspond to standard UniProt library and green to the list of potential protein sequences of the HepG2 cell line (custom data).
Figure Legend Snippet: Distribution of the number of potential amino acid sequences per gene in different referent libraries. Grey dots correspond to standard UniProt library and green to the list of potential protein sequences of the HepG2 cell line (custom data).

Techniques Used:

Two-dimensional electrophoresis (2DE) gel maps of the HepG2 cell line proteome: original stained gel with resolved proteins ( a ) [ 10 ]; heatmaps of peptide ( b ) and protein ( c ) identifications. Gel cells with a larger number of identifications are colored with high-intensity colors. The most intensive colors correspond to 4919 peptides and 193 proteins, respectively. Gel cells with a lower number of identifications appear pale. The least intensive colors correspond to 158 peptides and 5 proteins, respectively. Isoelectric points (p I ) are marked as the x-axis , molecular weights (MW) are marked as y-axis .
Figure Legend Snippet: Two-dimensional electrophoresis (2DE) gel maps of the HepG2 cell line proteome: original stained gel with resolved proteins ( a ) [ 10 ]; heatmaps of peptide ( b ) and protein ( c ) identifications. Gel cells with a larger number of identifications are colored with high-intensity colors. The most intensive colors correspond to 4919 peptides and 193 proteins, respectively. Gel cells with a lower number of identifications appear pale. The least intensive colors correspond to 158 peptides and 5 proteins, respectively. Isoelectric points (p I ) are marked as the x-axis , molecular weights (MW) are marked as y-axis .

Techniques Used: Electrophoresis, Two-Dimensional Gel Electrophoresis, Staining

13) Product Images from "Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition"

Article Title: Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition

Journal: Scientific Reports

doi: 10.1038/srep25376

Association mapping of milk fat percentage. Manhattan plots showing association of sequence variants with milk fat percentage in an outbred cow population of 64,139 animals. Panel 3A shows a 2 Mbp interval of variants (chr5:92945655-94945655) centred on the SNP of greatest effect from Bayes B GWAS (BovineHD0500026662), panel 3B shows a zoomed, 50 kbp view of the locus showing peak association (chr5:93920738-93970738). The nine genes annotated to the 2 Mbp locus are indicated, with the zoomed panel showing the distribution of associated variants across the MGST1 gene structure. Variants are coloured relative to their linkage disequilibrium (R 2 ) relationships with the top variant from sequence-based association (g.93945738C > T), with variants highlighted by the blue box (3B) comprising the cluster of 17 highly associated, highly correlated SNPs considered as potential functional candidates for the QTL. The top-associated Illumina BovineHD and SNP50 panel SNPs are indicated by the triangle (BovineHD0500026662), and square (Hapmap36414-SCAFFOLD150043_20489) respectively.
Figure Legend Snippet: Association mapping of milk fat percentage. Manhattan plots showing association of sequence variants with milk fat percentage in an outbred cow population of 64,139 animals. Panel 3A shows a 2 Mbp interval of variants (chr5:92945655-94945655) centred on the SNP of greatest effect from Bayes B GWAS (BovineHD0500026662), panel 3B shows a zoomed, 50 kbp view of the locus showing peak association (chr5:93920738-93970738). The nine genes annotated to the 2 Mbp locus are indicated, with the zoomed panel showing the distribution of associated variants across the MGST1 gene structure. Variants are coloured relative to their linkage disequilibrium (R 2 ) relationships with the top variant from sequence-based association (g.93945738C > T), with variants highlighted by the blue box (3B) comprising the cluster of 17 highly associated, highly correlated SNPs considered as potential functional candidates for the QTL. The top-associated Illumina BovineHD and SNP50 panel SNPs are indicated by the triangle (BovineHD0500026662), and square (Hapmap36414-SCAFFOLD150043_20489) respectively.

Techniques Used: Sequencing, GWAS, Variant Assay, Functional Assay

Accuracy of genome sequence-resolution imputation. Scatter plots showing imputation accuracy (minor allele sensitivity) of imputed sequence variants relative to minor allele frequency (2A), and allelic R 2 (2B) values. These comparisons used 790 SNPs common to both imputed genome and RNA-seq-derived genotype sets, where variants called from the RNA-seq alignments were used to assess sequence imputation accuracy in these 406 animals. Red variants shown in panel 2A represent the subset of real (i.e. not imputed) genotypes in the genome sequence-derived dataset (70 SNPs from the Illumina BovineHD chip), giving an indication of the accuracy of variant calls derived from the RNA-seq-alignments.
Figure Legend Snippet: Accuracy of genome sequence-resolution imputation. Scatter plots showing imputation accuracy (minor allele sensitivity) of imputed sequence variants relative to minor allele frequency (2A), and allelic R 2 (2B) values. These comparisons used 790 SNPs common to both imputed genome and RNA-seq-derived genotype sets, where variants called from the RNA-seq alignments were used to assess sequence imputation accuracy in these 406 animals. Red variants shown in panel 2A represent the subset of real (i.e. not imputed) genotypes in the genome sequence-derived dataset (70 SNPs from the Illumina BovineHD chip), giving an indication of the accuracy of variant calls derived from the RNA-seq-alignments.

Techniques Used: Sequencing, RNA Sequencing Assay, Derivative Assay, Chromatin Immunoprecipitation, Variant Assay

14) Product Images from "Epigenetic changes in the CDKN2A locus are associated with differential expression of P16INK4A and P14ARF in HPV-positive oropharyngeal squamous cell carcinoma"

Article Title: Epigenetic changes in the CDKN2A locus are associated with differential expression of P16INK4A and P14ARF in HPV-positive oropharyngeal squamous cell carcinoma

Journal: Cancer Medicine

doi: 10.1002/cam4.374

(A) Dot plot of normalized DNMT1 expression, as determined by beadchip probe fluorescence intensity (ILMN_1760201 probe) in total RNA samples of 9 HPV-negative and 6 HPV-positive OPSCC primary tumors obtained previously from the Albert Einstein head and neck cancer database. Statistical significance was calculated by unpaired t -test with Welsch correction (*** P
Figure Legend Snippet: (A) Dot plot of normalized DNMT1 expression, as determined by beadchip probe fluorescence intensity (ILMN_1760201 probe) in total RNA samples of 9 HPV-negative and 6 HPV-positive OPSCC primary tumors obtained previously from the Albert Einstein head and neck cancer database. Statistical significance was calculated by unpaired t -test with Welsch correction (*** P

Techniques Used: Expressing, Fluorescence

(A) Genomic organization of the chromosome 9p21 CDKN2A locus encoding p16 INK4A and p14 ARF . Proteins p16(INK4A) and p14(ARF) share exons 2 and 3 but have a distinct exon 1. RNA transcripts are translated in different reading frames to generate two separate protein products. Alternate promoter sites are indicated by arrows. Location of the CpG island corresponding to the downstream region (9:21958106-21958899) is indicated by a grey box. Also shown are two DNA segments located within the CDKN2A downstream region that was tested for DNA methylation by bisulfite sequencing. Individual CpG loci to be tested within each region are indicated by open circles; those indicated with an asterisk correspond to the location of Illumina beadchip loci cg10895543 and cg07752420. (B) Measurement of CpG methylation observed in the CDKN2A downstream region of HPV+ and HPV− primary OPSCC tumors by bisulfite sequencing. Patient identifiers are indicated on the top; individual CpG loci for each region are shown at the left. Methylated CpG loci are indicated by closed circles; unmethylated CpG loci are indicated by open circles. Positions of CpG loci included on the Illumina HumanMethylation27 BeadChip are indicated by an asterisk. (C) Real-time PCR measurements of gene expression for p14(ARF), p16(INK4A), and p16 gamma in oropharyngeal tumor and adjacent normal tissues from HPV-positive and HPV-negative oropharyngeal cancer cases. The panel shows expression changes (tumor/normal ratio) in 8 HPV+ and HPV− tumor samples following log 2 transformation. Statistical significance of gene expression differences between HPV-positive and HPV-negative cases were assessed by Wilcoxon Rank Sum. (D) Analysis of p14(ARF), p16(INK4A), and p16 gamma expression looking individually at normal adjacent mucosa and primary OPSCC tumors as independent populations in HPV-positive and HPV-negative patients.
Figure Legend Snippet: (A) Genomic organization of the chromosome 9p21 CDKN2A locus encoding p16 INK4A and p14 ARF . Proteins p16(INK4A) and p14(ARF) share exons 2 and 3 but have a distinct exon 1. RNA transcripts are translated in different reading frames to generate two separate protein products. Alternate promoter sites are indicated by arrows. Location of the CpG island corresponding to the downstream region (9:21958106-21958899) is indicated by a grey box. Also shown are two DNA segments located within the CDKN2A downstream region that was tested for DNA methylation by bisulfite sequencing. Individual CpG loci to be tested within each region are indicated by open circles; those indicated with an asterisk correspond to the location of Illumina beadchip loci cg10895543 and cg07752420. (B) Measurement of CpG methylation observed in the CDKN2A downstream region of HPV+ and HPV− primary OPSCC tumors by bisulfite sequencing. Patient identifiers are indicated on the top; individual CpG loci for each region are shown at the left. Methylated CpG loci are indicated by closed circles; unmethylated CpG loci are indicated by open circles. Positions of CpG loci included on the Illumina HumanMethylation27 BeadChip are indicated by an asterisk. (C) Real-time PCR measurements of gene expression for p14(ARF), p16(INK4A), and p16 gamma in oropharyngeal tumor and adjacent normal tissues from HPV-positive and HPV-negative oropharyngeal cancer cases. The panel shows expression changes (tumor/normal ratio) in 8 HPV+ and HPV− tumor samples following log 2 transformation. Statistical significance of gene expression differences between HPV-positive and HPV-negative cases were assessed by Wilcoxon Rank Sum. (D) Analysis of p14(ARF), p16(INK4A), and p16 gamma expression looking individually at normal adjacent mucosa and primary OPSCC tumors as independent populations in HPV-positive and HPV-negative patients.

Techniques Used: DNA Methylation Assay, Methylation Sequencing, CpG Methylation Assay, Methylation, Real-time Polymerase Chain Reaction, Expressing, Transformation Assay

15) Product Images from "The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1"

Article Title: The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1

Journal: BMC Gastroenterology

doi: 10.1186/s12876-018-0917-5

Simplified 1000IBD data model. 1000IBD-ID is the 1000IBD identifier used in every data-layer, also referred to as primary key (PK) and foreign key 1 (FK1). RNAseq: RNA-sequencing, 16S: Sequencing data of the microbial 16S rRNA gene; WGS: whole genome shotgun sequencing
Figure Legend Snippet: Simplified 1000IBD data model. 1000IBD-ID is the 1000IBD identifier used in every data-layer, also referred to as primary key (PK) and foreign key 1 (FK1). RNAseq: RNA-sequencing, 16S: Sequencing data of the microbial 16S rRNA gene; WGS: whole genome shotgun sequencing

Techniques Used: RNA Sequencing Assay, Sequencing, Shotgun Sequencing

Flow of research data from the 1000IBD project. In Stage 1, data that has been generated or will be generated is announced. In Stage 2, summary statistics will be made available. In Stage 3, the data itself will be publicly released
Figure Legend Snippet: Flow of research data from the 1000IBD project. In Stage 1, data that has been generated or will be generated is announced. In Stage 2, summary statistics will be made available. In Stage 3, the data itself will be publicly released

Techniques Used: Flow Cytometry, Generated

1000IBD Project Logo. This logo depicts the intestine and the multifaceted character of the project
Figure Legend Snippet: 1000IBD Project Logo. This logo depicts the intestine and the multifaceted character of the project

Techniques Used:

16) Product Images from "Characterisation of the changing genomic landscape of metastatic melanoma using cell free DNA"

Article Title: Characterisation of the changing genomic landscape of metastatic melanoma using cell free DNA

Journal: NPJ genomic medicine

doi: 10.1038/s41525-017-0030-7

Coverage uniformity of WGS libraries. a Represented is the cumulative proportion of sequencing coverage per cumulative proportion of sequence in whole genome sequencing across normal germline DNA (gDNA) from peripheral blood mononuclear cells, two cfDNA time points, and an archival FFPE tissue biopsy from a metastatic melanoma patient. If coverage was perfectly uniform across the genome coverage the relationship would be linear with gradient one. b Mapped depth of coverage distribution for WGS sequencing runs. The range of the coverage distribution is truncated at 150. Each trace is annotated with the mode of the distribution. c Insert size distribution of sequencing reads for the sequencing runs. The distribution is truncated at 300 base pairs. Each trace is annotated with the mode of the distribution
Figure Legend Snippet: Coverage uniformity of WGS libraries. a Represented is the cumulative proportion of sequencing coverage per cumulative proportion of sequence in whole genome sequencing across normal germline DNA (gDNA) from peripheral blood mononuclear cells, two cfDNA time points, and an archival FFPE tissue biopsy from a metastatic melanoma patient. If coverage was perfectly uniform across the genome coverage the relationship would be linear with gradient one. b Mapped depth of coverage distribution for WGS sequencing runs. The range of the coverage distribution is truncated at 150. Each trace is annotated with the mode of the distribution. c Insert size distribution of sequencing reads for the sequencing runs. The distribution is truncated at 300 base pairs. Each trace is annotated with the mode of the distribution

Techniques Used: Sequencing, Formalin-fixed Paraffin-Embedded

17) Product Images from "In depth analysis of the Sox4 gene locus that consists of sense and natural antisense transcripts"

Article Title: In depth analysis of the Sox4 gene locus that consists of sense and natural antisense transcripts

Journal: Data in Brief

doi: 10.1016/j.dib.2016.01.045

RNA FISH of Sox4 and Hmbs sense and NATs. The type of transcripts analyzed is shown at the top of the figure and the origins of cells are shown to the left of the micrographs.
Figure Legend Snippet: RNA FISH of Sox4 and Hmbs sense and NATs. The type of transcripts analyzed is shown at the top of the figure and the origins of cells are shown to the left of the micrographs.

Techniques Used: Fluorescence In Situ Hybridization

18) Product Images from "Data-independent acquisition mass spectrometry to quantify protein levels in FFPE tumor biopsies for molecular diagnostics"

Article Title: Data-independent acquisition mass spectrometry to quantify protein levels in FFPE tumor biopsies for molecular diagnostics

Journal: Journal of proteome research

doi: 10.1021/acs.jproteome.8b00699

Differential SDHB status. (A) Chromatographic traces of four extracted fragment ions (y3, y5, y6, y8) (± 10 ppm) of a unique SDHB peptide, QQYLQSIEER are shown for three biopsies exhibiting different levels (high, intermediate, absent) of SDHB protein. (B) Depth of tumor DNA, germline DNA, and tumor RNA sequencing reads mapped to SDHB in three biopsies corresponding to the patient samples shown in DIA-MS data left. Red bar indicates the number of reads that contain Arg90ter.
Figure Legend Snippet: Differential SDHB status. (A) Chromatographic traces of four extracted fragment ions (y3, y5, y6, y8) (± 10 ppm) of a unique SDHB peptide, QQYLQSIEER are shown for three biopsies exhibiting different levels (high, intermediate, absent) of SDHB protein. (B) Depth of tumor DNA, germline DNA, and tumor RNA sequencing reads mapped to SDHB in three biopsies corresponding to the patient samples shown in DIA-MS data left. Red bar indicates the number of reads that contain Arg90ter.

Techniques Used: RNA Sequencing Assay, Mass Spectrometry

19) Product Images from "Signal-transducing adapter protein-1 is required for maintenance of leukemic stem cells in CML"

Article Title: Signal-transducing adapter protein-1 is required for maintenance of leukemic stem cells in CML

Journal: Oncogene

doi: 10.1038/s41388-020-01387-9

STAP-1 affects JAK/STAT signaling as well as PPAR signaling pathways. Gene expression profiles of CML LSCs at day 11 after first BMT between WT and STAP-1 KO mice were compared by RNA-seq analysis. a Scatter plot represents the mean of normalized counts. In all, 3792 genes (2536 downregulated genes; 1256 upregulated genes) were differentially expressed (twofold change) in STAP-1 KO LSCs ( y -axis) relative to WT LSCs ( x -axis). b – d Gene expression data were subjected to bioinformatics analysis by Ingenuity Pathway Analysis. For each signaling pathway, an enrichment p value and a z -score of activation were calculated. Pathways that were predicted to be downregulated or upregulated in STAP-1 KO CML LSCs compared with WT CML LSCs are shown in ( b ). Pathways involved in inflammatory responses are shown in ( c ). Upstream regulatory candidates are shown in ( d ). Candidates of interest are labeled.
Figure Legend Snippet: STAP-1 affects JAK/STAT signaling as well as PPAR signaling pathways. Gene expression profiles of CML LSCs at day 11 after first BMT between WT and STAP-1 KO mice were compared by RNA-seq analysis. a Scatter plot represents the mean of normalized counts. In all, 3792 genes (2536 downregulated genes; 1256 upregulated genes) were differentially expressed (twofold change) in STAP-1 KO LSCs ( y -axis) relative to WT LSCs ( x -axis). b – d Gene expression data were subjected to bioinformatics analysis by Ingenuity Pathway Analysis. For each signaling pathway, an enrichment p value and a z -score of activation were calculated. Pathways that were predicted to be downregulated or upregulated in STAP-1 KO CML LSCs compared with WT CML LSCs are shown in ( b ). Pathways involved in inflammatory responses are shown in ( c ). Upstream regulatory candidates are shown in ( d ). Candidates of interest are labeled.

Techniques Used: Expressing, Mouse Assay, RNA Sequencing Assay, Activation Assay, Labeling

20) Product Images from "Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition"

Article Title: Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition

Journal: Scientific Reports

doi: 10.1038/srep25376

Association mapping of milk fat percentage. Manhattan plots showing association of sequence variants with milk fat percentage in an outbred cow population of 64,139 animals. Panel 3A shows a 2 Mbp interval of variants (chr5:92945655-94945655) centred on the SNP of greatest effect from Bayes B GWAS (BovineHD0500026662), panel 3B shows a zoomed, 50 kbp view of the locus showing peak association (chr5:93920738-93970738). The nine genes annotated to the 2 Mbp locus are indicated, with the zoomed panel showing the distribution of associated variants across the MGST1 gene structure. Variants are coloured relative to their linkage disequilibrium (R 2 ) relationships with the top variant from sequence-based association (g.93945738C > T), with variants highlighted by the blue box (3B) comprising the cluster of 17 highly associated, highly correlated SNPs considered as potential functional candidates for the QTL. The top-associated Illumina BovineHD and SNP50 panel SNPs are indicated by the triangle (BovineHD0500026662), and square (Hapmap36414-SCAFFOLD150043_20489) respectively.
Figure Legend Snippet: Association mapping of milk fat percentage. Manhattan plots showing association of sequence variants with milk fat percentage in an outbred cow population of 64,139 animals. Panel 3A shows a 2 Mbp interval of variants (chr5:92945655-94945655) centred on the SNP of greatest effect from Bayes B GWAS (BovineHD0500026662), panel 3B shows a zoomed, 50 kbp view of the locus showing peak association (chr5:93920738-93970738). The nine genes annotated to the 2 Mbp locus are indicated, with the zoomed panel showing the distribution of associated variants across the MGST1 gene structure. Variants are coloured relative to their linkage disequilibrium (R 2 ) relationships with the top variant from sequence-based association (g.93945738C > T), with variants highlighted by the blue box (3B) comprising the cluster of 17 highly associated, highly correlated SNPs considered as potential functional candidates for the QTL. The top-associated Illumina BovineHD and SNP50 panel SNPs are indicated by the triangle (BovineHD0500026662), and square (Hapmap36414-SCAFFOLD150043_20489) respectively.

Techniques Used: Sequencing, GWAS, Variant Assay, Functional Assay

Accuracy of genome sequence-resolution imputation. Scatter plots showing imputation accuracy (minor allele sensitivity) of imputed sequence variants relative to minor allele frequency (2A), and allelic R 2 (2B) values. These comparisons used 790 SNPs common to both imputed genome and RNA-seq-derived genotype sets, where variants called from the RNA-seq alignments were used to assess sequence imputation accuracy in these 406 animals. Red variants shown in panel 2A represent the subset of real (i.e. not imputed) genotypes in the genome sequence-derived dataset (70 SNPs from the Illumina BovineHD chip), giving an indication of the accuracy of variant calls derived from the RNA-seq-alignments.
Figure Legend Snippet: Accuracy of genome sequence-resolution imputation. Scatter plots showing imputation accuracy (minor allele sensitivity) of imputed sequence variants relative to minor allele frequency (2A), and allelic R 2 (2B) values. These comparisons used 790 SNPs common to both imputed genome and RNA-seq-derived genotype sets, where variants called from the RNA-seq alignments were used to assess sequence imputation accuracy in these 406 animals. Red variants shown in panel 2A represent the subset of real (i.e. not imputed) genotypes in the genome sequence-derived dataset (70 SNPs from the Illumina BovineHD chip), giving an indication of the accuracy of variant calls derived from the RNA-seq-alignments.

Techniques Used: Sequencing, RNA Sequencing Assay, Derivative Assay, Chromatin Immunoprecipitation, Variant Assay

21) Product Images from "Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient"

Article Title: Sequencing of cancer cell subpopulations identifies micrometastases in a bladder cancer patient

Journal: Oncotarget

doi: 10.18632/oncotarget.17312

Analysis schematic Radical cystoprostatectomy and extended pelvic lymph node dissection were performed, and fresh tissue from four regions of the bladder tumor and representative left and right lymph nodes were dissociated into single cell suspensions and fluorescence activated cell sorting isolated the EPCAM + CD44 + CD49f + cancer cell subpopulation. DNA and RNA were isolated from the bulk tumor regions (samples A-D) and lymph nodes (samples L and R) and the corresponding cancer cell subpopulations (denoted with′) and utilized for whole-exome sequencing and RNA-sequencing (A) . Representative histology of primary tumor, left and right sampled lymph nodes (B) .
Figure Legend Snippet: Analysis schematic Radical cystoprostatectomy and extended pelvic lymph node dissection were performed, and fresh tissue from four regions of the bladder tumor and representative left and right lymph nodes were dissociated into single cell suspensions and fluorescence activated cell sorting isolated the EPCAM + CD44 + CD49f + cancer cell subpopulation. DNA and RNA were isolated from the bulk tumor regions (samples A-D) and lymph nodes (samples L and R) and the corresponding cancer cell subpopulations (denoted with′) and utilized for whole-exome sequencing and RNA-sequencing (A) . Representative histology of primary tumor, left and right sampled lymph nodes (B) .

Techniques Used: Dissection, Fluorescence, FACS, Isolation, Sequencing, RNA Sequencing Assay

22) Product Images from "Semiconductor-based sequencing of genome-wide DNA methylation states"

Article Title: Semiconductor-based sequencing of genome-wide DNA methylation states

Journal: Epigenetics

doi: 10.1080/15592294.2014.1003747

Scalability of Ion Torrent-compatible MeDIP-Seq protocol. (a) Global 5-methylcytosine (5mC) content measured as a percentage of the genome in DNA from WT and DKO cells determined by an ELISA assay. (b) Number of peaks identified by MACS v2.1.0 software 64 from MeDIP-Seq data in WT and DKO cells. (c) Sequence coverage of genome-wide CpGs for WT and DKO cells on the Ion Torrent Proton sequencer calculated using MEDIPS v1.14.0 software. 39 Pie charts illustrate the fraction of CpGs covered by the indicated reads according to their fold-coverage in relation to the total genomic CpG content from MeDIP-Seq libraries of the indicated sample sequenced on the Proton with the total number of non-redundant mapped reads in parentheses. (d) Sequence coverage of genome-wide CpGs for WT and DKO cells in MeDIP-Seq on the Ion Torrent PGM sequencer. Data displayed as in c . (e) Scaled chromosomal view of the 5mC MeDIP-Seq enrichment of methylation over the MX1 gene in WT and DKO cells sequenced on the 2 different Ion Torrent sequencers as indicated. MeDIP-Seq data are displayed as RPM (Reads Per Million mapped reads) below the RefSeq annotation of the gene (blue lines). Methylation enrichment is displayed in gray, with red vertical lines corresponding to areas containing CpGs. Chromosome position and gene orientation are displayed.
Figure Legend Snippet: Scalability of Ion Torrent-compatible MeDIP-Seq protocol. (a) Global 5-methylcytosine (5mC) content measured as a percentage of the genome in DNA from WT and DKO cells determined by an ELISA assay. (b) Number of peaks identified by MACS v2.1.0 software 64 from MeDIP-Seq data in WT and DKO cells. (c) Sequence coverage of genome-wide CpGs for WT and DKO cells on the Ion Torrent Proton sequencer calculated using MEDIPS v1.14.0 software. 39 Pie charts illustrate the fraction of CpGs covered by the indicated reads according to their fold-coverage in relation to the total genomic CpG content from MeDIP-Seq libraries of the indicated sample sequenced on the Proton with the total number of non-redundant mapped reads in parentheses. (d) Sequence coverage of genome-wide CpGs for WT and DKO cells in MeDIP-Seq on the Ion Torrent PGM sequencer. Data displayed as in c . (e) Scaled chromosomal view of the 5mC MeDIP-Seq enrichment of methylation over the MX1 gene in WT and DKO cells sequenced on the 2 different Ion Torrent sequencers as indicated. MeDIP-Seq data are displayed as RPM (Reads Per Million mapped reads) below the RefSeq annotation of the gene (blue lines). Methylation enrichment is displayed in gray, with red vertical lines corresponding to areas containing CpGs. Chromosome position and gene orientation are displayed.

Techniques Used: Methylated DNA Immunoprecipitation, Enzyme-linked Immunosorbent Assay, Magnetic Cell Separation, Software, Sequencing, Genome Wide, Methylation

Validation of Ion Torrent-compatible MeDIP-Seq data by integration with the single-nucleotide resolution 450K array. ( a ) Histogram of the frequency and distribution of CpG cytosine methylation levels of probes on the 450K array in HCT116-WT (green) and -DKO (orange) cells stratified into categories based on their percentage of methylation. ( b ) Percentage of CpGs on the 450K array (black) overlapping with those underlying MeDIP-Seq peaks (white) of WT cells. ( c ) Distribution of methylation levels measured in WT cells of all probes on the 450K array that integrate with MeDIP-Seq peaks. ( d ) Distribution of methylation levels measured in DKO cells of all probes on the 450K array that integrate with MeDIP-Seq peaks in WT cells. ( e ) Scaled chromosomal view of the MeDIP-Seq enrichment of methylation over the ICR of the SNRPN gene. MeDIP-Seq from WT and DKO cells are displayed as indicated below the RefSeq annotation of SNRPN/SNURF (blue lines) and a CpG island (green). CpG methylation data of each 450K array probe over this region from WT and DKO cells are shown to scale as colored bars representing the degree of methylation (blue, low-; violet, intermediate-; and orange, high-levels of methylation). Percent methylation (calculated from β-values) of these CpGs is shown under each bar. The expanded region below these values shows a scaled depiction of CpG methylation of alleles sequenced after bisulfite-PCR in WT and DKO cells as indicated. Each circle represents a CpG cytosine in the amplicon sequenced, the color of which depicts its methylation state (white, unmethylated; black, methylated). *, the CpG on the 450K array also covered by the bisulfite-PCR amplicon. ( f ) The average expression of SNURF is significantly elevated in DKO compared to WT cells. Average expression values and s.e.m. from triplicate qPCR are shown. ( g ) DNA methylation over the SNURF promoter region is significantly higher in WT compared to DKO cells. Using bisulfite-PCR sequencing data described in e , percent methylation was calculated from the sum of methylated CpGs over the total CpGs evaluated. Average methylation values and s.e.m from individual clones are shown. P -values shown in f and g were calculated using 2-sided Student's t -test.
Figure Legend Snippet: Validation of Ion Torrent-compatible MeDIP-Seq data by integration with the single-nucleotide resolution 450K array. ( a ) Histogram of the frequency and distribution of CpG cytosine methylation levels of probes on the 450K array in HCT116-WT (green) and -DKO (orange) cells stratified into categories based on their percentage of methylation. ( b ) Percentage of CpGs on the 450K array (black) overlapping with those underlying MeDIP-Seq peaks (white) of WT cells. ( c ) Distribution of methylation levels measured in WT cells of all probes on the 450K array that integrate with MeDIP-Seq peaks. ( d ) Distribution of methylation levels measured in DKO cells of all probes on the 450K array that integrate with MeDIP-Seq peaks in WT cells. ( e ) Scaled chromosomal view of the MeDIP-Seq enrichment of methylation over the ICR of the SNRPN gene. MeDIP-Seq from WT and DKO cells are displayed as indicated below the RefSeq annotation of SNRPN/SNURF (blue lines) and a CpG island (green). CpG methylation data of each 450K array probe over this region from WT and DKO cells are shown to scale as colored bars representing the degree of methylation (blue, low-; violet, intermediate-; and orange, high-levels of methylation). Percent methylation (calculated from β-values) of these CpGs is shown under each bar. The expanded region below these values shows a scaled depiction of CpG methylation of alleles sequenced after bisulfite-PCR in WT and DKO cells as indicated. Each circle represents a CpG cytosine in the amplicon sequenced, the color of which depicts its methylation state (white, unmethylated; black, methylated). *, the CpG on the 450K array also covered by the bisulfite-PCR amplicon. ( f ) The average expression of SNURF is significantly elevated in DKO compared to WT cells. Average expression values and s.e.m. from triplicate qPCR are shown. ( g ) DNA methylation over the SNURF promoter region is significantly higher in WT compared to DKO cells. Using bisulfite-PCR sequencing data described in e , percent methylation was calculated from the sum of methylated CpGs over the total CpGs evaluated. Average methylation values and s.e.m from individual clones are shown. P -values shown in f and g were calculated using 2-sided Student's t -test.

Techniques Used: Methylated DNA Immunoprecipitation, Methylation, CpG Methylation Assay, Polymerase Chain Reaction, Amplification, Expressing, Real-time Polymerase Chain Reaction, DNA Methylation Assay, Sequencing, Clone Assay

Workflow of Ion Torrent-compatible MeDIP-Seq protocol. Schematic representation of the MeDIP-Seq protocol developed for the Ion Torrent platform to profile DNA methylation genome wide. (a) Genomic DNA (1 μg) is isolated from a sample of interest and sonicated for 300 s on a Covaris Focused-ultrasonicator (Peak intensity power: 50, Duty: 20%, Cycles: 200, Temp: 20°C) to a mean fragment size of 300 bp. (b) Fragmented DNA is nick repaired and ligated with Ion Torrent sequencing adapters. (c) Ligated DNA is enriched for either 5mC (data shown) or 5hmC variants of methylation. Efficiency of MeDIP reaction is determined by using spiked in methylated and unmethylated DNA and compared to a 10% input DNA control. (d) Fragments of immunoprecipitated DNA are separated on a 2% agarose gel and size selected from 200–400 bp. (e) Size-selected fragmented DNA library is amplified for 18 cycles. (f) Amplified MeDIP library is cleaned up and size selected again on a 2% E-gel. (g) MeDIP library (1 μl) is used on a bioanalyzer instrument to assess quality and determine concentration. Figure displays bioanalyzer traces for WT (top) and DKO (bottom) 5mC MeDIP libraries. (h) High quality MeDIP library is templated on ISP beads appropriate to the capacity of sequencing (Life Technologies). (i) Templated MeDIP library is loaded onto an Ion Torrent semi-conductor sequencing chip and sequenced for 500 flows (Life Technologies). An example of the Ion Torrent 318 chip is shown in the figure using scanning electron microscopy to visualize the wells of a semi-conductor sequencing chip. (j) Sequencing data is processed on the Ion Torrent software (Life Technologies). An example of a loaded P1 chip after an Ion Proton sequencing run is shown. WT, HCT116-WT; DKO, HCT116-DKO.
Figure Legend Snippet: Workflow of Ion Torrent-compatible MeDIP-Seq protocol. Schematic representation of the MeDIP-Seq protocol developed for the Ion Torrent platform to profile DNA methylation genome wide. (a) Genomic DNA (1 μg) is isolated from a sample of interest and sonicated for 300 s on a Covaris Focused-ultrasonicator (Peak intensity power: 50, Duty: 20%, Cycles: 200, Temp: 20°C) to a mean fragment size of 300 bp. (b) Fragmented DNA is nick repaired and ligated with Ion Torrent sequencing adapters. (c) Ligated DNA is enriched for either 5mC (data shown) or 5hmC variants of methylation. Efficiency of MeDIP reaction is determined by using spiked in methylated and unmethylated DNA and compared to a 10% input DNA control. (d) Fragments of immunoprecipitated DNA are separated on a 2% agarose gel and size selected from 200–400 bp. (e) Size-selected fragmented DNA library is amplified for 18 cycles. (f) Amplified MeDIP library is cleaned up and size selected again on a 2% E-gel. (g) MeDIP library (1 μl) is used on a bioanalyzer instrument to assess quality and determine concentration. Figure displays bioanalyzer traces for WT (top) and DKO (bottom) 5mC MeDIP libraries. (h) High quality MeDIP library is templated on ISP beads appropriate to the capacity of sequencing (Life Technologies). (i) Templated MeDIP library is loaded onto an Ion Torrent semi-conductor sequencing chip and sequenced for 500 flows (Life Technologies). An example of the Ion Torrent 318 chip is shown in the figure using scanning electron microscopy to visualize the wells of a semi-conductor sequencing chip. (j) Sequencing data is processed on the Ion Torrent software (Life Technologies). An example of a loaded P1 chip after an Ion Proton sequencing run is shown. WT, HCT116-WT; DKO, HCT116-DKO.

Techniques Used: Methylated DNA Immunoprecipitation, DNA Methylation Assay, Genome Wide, Isolation, Sonication, Sequencing, Methylation, Immunoprecipitation, Agarose Gel Electrophoresis, Amplification, Concentration Assay, Chromatin Immunoprecipitation, Electron Microscopy, Software

Ion Torrent-compatible MeDIP-Seq detects expected differences in DNA methylation between DNMT -proficient and - deficient cells. Distribution of 5mC methylation over the indicated bp window of all RefSeq annotated TSS ( a ), gene bodies ( b ), CpG islands ( c ), and exons ( d ). Methylation of WT (green line) and DKO (orange line) HCT116 cells are displayed as mean RPM values with s.e.m. indicated as a semi-transparent shade around the mean curve. Yellow shaded areas highlight regions of significant difference between WT and DKO methylation calculated using a one-sided KS test ( P -values shown). In c and d , a KS test was performed using data over the non-shaded areas as well ( P -values shown).
Figure Legend Snippet: Ion Torrent-compatible MeDIP-Seq detects expected differences in DNA methylation between DNMT -proficient and - deficient cells. Distribution of 5mC methylation over the indicated bp window of all RefSeq annotated TSS ( a ), gene bodies ( b ), CpG islands ( c ), and exons ( d ). Methylation of WT (green line) and DKO (orange line) HCT116 cells are displayed as mean RPM values with s.e.m. indicated as a semi-transparent shade around the mean curve. Yellow shaded areas highlight regions of significant difference between WT and DKO methylation calculated using a one-sided KS test ( P -values shown). In c and d , a KS test was performed using data over the non-shaded areas as well ( P -values shown).

Techniques Used: Methylated DNA Immunoprecipitation, DNA Methylation Assay, Methylation

Ion Torrent-compatible MeDIP-Seq confirms a role for DNA methylation in alternative splicing. (a) Distribution of 5mC methylation over alternatively spliced exons (ASE) flanked by constitutively spliced exons (5’ or 3’ exons) in WT and DKO cells. Mean RPM values are displayed over ± 500 bp windows relative to the splice acceptor and donor sites at each of the exons as indicated, with s.e.m. depicted by shaded areas for WT (green) and DKO (orange) profiles. A schematic representation of cassette exons is shown, with the number of exons aberrantly included (blue) and excluded (gray) in WT vs. DKO cells indicated. P- values shown were calculated using one-sided KS test. (b) The ASE of the FAM204A gene (exon 2) is aberrantly excluded in DKO compared to WT cells. Schematic shows the 2 known isoforms for the FAM204A gene (blue bars depict the first 3 exons of the gene in the orientation indicated), under which a Sashimi plot generated by MISO analysis of RNA-Seq data measured as RPKM in WT and DKO cells illustrates the number of exon-exon junction reads as indicated to infer isoform expression. The left graphs show the MISO calculated distribution of a percent exon inclusion score (Psi-value; 95% confidence intervals in brackets) for the FAM204A ASE from WT (top) and DKO (bottom) RNA-Seq data. (c) Demethylation of the FAM204A ASE correlates with its aberrant exclusion. Scaled chromosomal view at the FAM204A gene region (ASE highlighted in yellow) of the indicated WT and DKO distribution of RNA-Seq (top pair) and DNA methylation (bottom pair) data displayed as RPKM. The first 3 exons of the gene are represented (blue bars) with a CpG island in the promoter region indicated (green bar). In the MeDIP-Seq data, red vertical lines correspond to areas containing CpGs.
Figure Legend Snippet: Ion Torrent-compatible MeDIP-Seq confirms a role for DNA methylation in alternative splicing. (a) Distribution of 5mC methylation over alternatively spliced exons (ASE) flanked by constitutively spliced exons (5’ or 3’ exons) in WT and DKO cells. Mean RPM values are displayed over ± 500 bp windows relative to the splice acceptor and donor sites at each of the exons as indicated, with s.e.m. depicted by shaded areas for WT (green) and DKO (orange) profiles. A schematic representation of cassette exons is shown, with the number of exons aberrantly included (blue) and excluded (gray) in WT vs. DKO cells indicated. P- values shown were calculated using one-sided KS test. (b) The ASE of the FAM204A gene (exon 2) is aberrantly excluded in DKO compared to WT cells. Schematic shows the 2 known isoforms for the FAM204A gene (blue bars depict the first 3 exons of the gene in the orientation indicated), under which a Sashimi plot generated by MISO analysis of RNA-Seq data measured as RPKM in WT and DKO cells illustrates the number of exon-exon junction reads as indicated to infer isoform expression. The left graphs show the MISO calculated distribution of a percent exon inclusion score (Psi-value; 95% confidence intervals in brackets) for the FAM204A ASE from WT (top) and DKO (bottom) RNA-Seq data. (c) Demethylation of the FAM204A ASE correlates with its aberrant exclusion. Scaled chromosomal view at the FAM204A gene region (ASE highlighted in yellow) of the indicated WT and DKO distribution of RNA-Seq (top pair) and DNA methylation (bottom pair) data displayed as RPKM. The first 3 exons of the gene are represented (blue bars) with a CpG island in the promoter region indicated (green bar). In the MeDIP-Seq data, red vertical lines correspond to areas containing CpGs.

Techniques Used: Methylated DNA Immunoprecipitation, DNA Methylation Assay, Methylation, Generated, RNA Sequencing Assay, Expressing

23) Product Images from "Detection of canonical A-to-G editing events at 3′ UTRs and microRNA target sites in human lungs using next-generation sequencing"

Article Title: Detection of canonical A-to-G editing events at 3′ UTRs and microRNA target sites in human lungs using next-generation sequencing

Journal: Oncotarget

doi:

Validation of A-to-G editing by Sanger sequencing A. Integrative Genome view of Ctsc , RhoA and Adam19 is shown. The RNA- and DNA-seq traces are shown in top and bottom panel, respectively. B. Validation of editing sites in Ctsc , RhoA and Adam19 is shown. For Ctsc , three editing sites were validated (chr11:88,055,689, 88,055,690, 88,055,691). For RhoA , we validated one site (chr3:49,397,323). For Adam19 , we validated five sites chr5: (156,905,567, 156,905,566, 156,905, 565 in the left panel and 156,905,561, and 156,905,560) in the right panel. The sites validated by Sanger are shown and the nucleotide changes are labelled. Samples were sequenced using both forward and reverse M13 primers. We show few representative images of each.
Figure Legend Snippet: Validation of A-to-G editing by Sanger sequencing A. Integrative Genome view of Ctsc , RhoA and Adam19 is shown. The RNA- and DNA-seq traces are shown in top and bottom panel, respectively. B. Validation of editing sites in Ctsc , RhoA and Adam19 is shown. For Ctsc , three editing sites were validated (chr11:88,055,689, 88,055,690, 88,055,691). For RhoA , we validated one site (chr3:49,397,323). For Adam19 , we validated five sites chr5: (156,905,567, 156,905,566, 156,905, 565 in the left panel and 156,905,561, and 156,905,560) in the right panel. The sites validated by Sanger are shown and the nucleotide changes are labelled. Samples were sequenced using both forward and reverse M13 primers. We show few representative images of each.

Techniques Used: Sequencing, DNA Sequencing

24) Product Images from "PGBD5 promotes site-specific oncogenic mutations in human tumors"

Article Title: PGBD5 promotes site-specific oncogenic mutations in human tumors

Journal: Nature genetics

doi: 10.1038/ng.3866

DNA end-joining repair is required for survival of cells expressing active PGBD5 (a) Western blot of PGBD5 protein after 24 h of doxycycline (500 ng/ml) treatment of isogenic PAXX +/+ and PAXX -/- RPE cells stably expressing doxycycline-inducible PGBD5 . (b) Representative photomicrograph of PAXX +/+ and PAXX -/- RPE cells after 48 h treatment with doxycycline (500 ng/ml) or vehicle control stained for DAPI (blue) and γH2AX (red). Scale bar = 100 μm. (c) Fraction of apoptotic cells as measured by cleaved caspase-3 staining and flow cytometric analysis of PAXX +/+ and PAXX -/- RPE cells after treatment with doxycycline or vehicle control. * p = 8.7 × 10 -4 for PAXX +/+ vs. PAXX -/- with doxycycline. (d) Number of viable PAXX +/+ and PAXX -/- RPE cells per cm 2 in monolayer culture as measured by Trypan blue staining after 48 h of expression of GFP-PGBD5, as compared to GFP-PGBD5 D168A/D194A/D386 mutant and GFP -expressing control cells. * p = 7.4 × 10 -5 for PAXX -/- GFP-PGBD5 vs. GFP control. Error bars represent standard deviations of 3 independent experiments.
Figure Legend Snippet: DNA end-joining repair is required for survival of cells expressing active PGBD5 (a) Western blot of PGBD5 protein after 24 h of doxycycline (500 ng/ml) treatment of isogenic PAXX +/+ and PAXX -/- RPE cells stably expressing doxycycline-inducible PGBD5 . (b) Representative photomicrograph of PAXX +/+ and PAXX -/- RPE cells after 48 h treatment with doxycycline (500 ng/ml) or vehicle control stained for DAPI (blue) and γH2AX (red). Scale bar = 100 μm. (c) Fraction of apoptotic cells as measured by cleaved caspase-3 staining and flow cytometric analysis of PAXX +/+ and PAXX -/- RPE cells after treatment with doxycycline or vehicle control. * p = 8.7 × 10 -4 for PAXX +/+ vs. PAXX -/- with doxycycline. (d) Number of viable PAXX +/+ and PAXX -/- RPE cells per cm 2 in monolayer culture as measured by Trypan blue staining after 48 h of expression of GFP-PGBD5, as compared to GFP-PGBD5 D168A/D194A/D386 mutant and GFP -expressing control cells. * p = 7.4 × 10 -5 for PAXX -/- GFP-PGBD5 vs. GFP control. Error bars represent standard deviations of 3 independent experiments.

Techniques Used: Expressing, Western Blot, Stable Transfection, Staining, Flow Cytometry, Mutagenesis

Human rhabdoid tumors exhibit genomic rearrangements associated with PGBD5-specific signal sequence breakpoints (a) Aggregate Circos plot of somatic structural variants identified in 31 human rhabdoid tumors using laSV, as marked for PSS-containing breakpoints (outer ring, arrowheads), recurrence (middle ring histogram, rearrangements occurring in ≥3 out of 31 samples and highlighted in red for rearrangements with recurrence frequency greater than 13%), and structural variant type (inner lines, as color-labeled). Recurrently rearranged genes are labeled. (b) Representation of 21 structural variant breakpoints in rhabdoid tumors identified to harbor PSS sequences (red) within 10 bp of the breakpoint junction (arrowhead). (c) Recurrent structural variants of CNTNAP2 (red) with gene structure (black) and Sanger sequencing of the rearrangement breakpoints. (d) CNTNAP2 mRNA expression in primary rhabdoid tumors as measured using RNA sequencing in CNTNAP2 mutant (red) as compared to CNTNAP2 intact (blue) specimens (* p = 0.017 by t -test for intact vs. mutant CNTNAP2 ).
Figure Legend Snippet: Human rhabdoid tumors exhibit genomic rearrangements associated with PGBD5-specific signal sequence breakpoints (a) Aggregate Circos plot of somatic structural variants identified in 31 human rhabdoid tumors using laSV, as marked for PSS-containing breakpoints (outer ring, arrowheads), recurrence (middle ring histogram, rearrangements occurring in ≥3 out of 31 samples and highlighted in red for rearrangements with recurrence frequency greater than 13%), and structural variant type (inner lines, as color-labeled). Recurrently rearranged genes are labeled. (b) Representation of 21 structural variant breakpoints in rhabdoid tumors identified to harbor PSS sequences (red) within 10 bp of the breakpoint junction (arrowhead). (c) Recurrent structural variants of CNTNAP2 (red) with gene structure (black) and Sanger sequencing of the rearrangement breakpoints. (d) CNTNAP2 mRNA expression in primary rhabdoid tumors as measured using RNA sequencing in CNTNAP2 mutant (red) as compared to CNTNAP2 intact (blue) specimens (* p = 0.017 by t -test for intact vs. mutant CNTNAP2 ).

Techniques Used: Sequencing, Variant Assay, Labeling, Expressing, RNA Sequencing Assay, Mutagenesis

PGBD5 is physically associated with human genomic PSS sequences that are sufficient to mediate DNA rearrangements in rhabdoid tumor cells (a) Genomic distribution of PGBD5 protein in G401 rhabdoid tumor cells as a function of enrichment of PSS (red) as compared to scrambled PSS (orange) and RAG1 recombination signal sequence (RSS, blue) controls as measured using PGBD5 ChIP-seq ( p = 2.9 × 10 -29 for PSS, p = 0.28 for scrambled PSS, p = 1.0 for RSS by hypergeometric test). (b) Schematic of synthetic transposon substrates used for DNA transposition assays, including transposons with T. ni ITR marked by triangles in blue, transposons with PGBD5-specific signal sequence (PSS) marked by triangles in red and transposons lacking ITRs marked in black (top) and sequence alig nment of T. ni ITR compared to human PSS (bottom). (c) Representative photographs of Crystal violet-stained colonies obtained upon G418 selection in the transposon reporter assay. (d) Genomic DNA transposition assay as measured using neomycin resistance clonogenic assays in HEK293 cells co-transfected with human GFP-PGBD5 or control GFP and T.ni GFP-PiggyBac , and transposon reporters encoding the neomycin resistance gene flanked by human PSS (red), as compared to control reporters lacking inverted terminal repeats (-ITR, black) and T. ni piggyBac ITR (blue). ** p = 5.0 × 10 -5 . Lepidopteran T. ni PiggyBac DNA transposase and its piggyBac ITR serve as specificity controls. Errors bars represent standard deviations of three independent experiments. (e) Schematic model of transposition reporter assay in G401 rhabdoid tumor cells followed by flanking sequence exponential anchored-polymerase chain reaction (FLEA-PCR) and Illumina paired-end sequencing. (f) Genomic integration of synthetic NeoR transposons (red) by endogenous PGBD5 in G401 rhabdoid tumor cells at PSS site (arrowhead), as shown in the ChIP-seq genome track of PGBD5 (blue), as compared to its sequencing input (gray), and H3K27Ac and H3K4me3 (bottom), consistent with the bound PGBD5 transposase protein complex.
Figure Legend Snippet: PGBD5 is physically associated with human genomic PSS sequences that are sufficient to mediate DNA rearrangements in rhabdoid tumor cells (a) Genomic distribution of PGBD5 protein in G401 rhabdoid tumor cells as a function of enrichment of PSS (red) as compared to scrambled PSS (orange) and RAG1 recombination signal sequence (RSS, blue) controls as measured using PGBD5 ChIP-seq ( p = 2.9 × 10 -29 for PSS, p = 0.28 for scrambled PSS, p = 1.0 for RSS by hypergeometric test). (b) Schematic of synthetic transposon substrates used for DNA transposition assays, including transposons with T. ni ITR marked by triangles in blue, transposons with PGBD5-specific signal sequence (PSS) marked by triangles in red and transposons lacking ITRs marked in black (top) and sequence alig nment of T. ni ITR compared to human PSS (bottom). (c) Representative photographs of Crystal violet-stained colonies obtained upon G418 selection in the transposon reporter assay. (d) Genomic DNA transposition assay as measured using neomycin resistance clonogenic assays in HEK293 cells co-transfected with human GFP-PGBD5 or control GFP and T.ni GFP-PiggyBac , and transposon reporters encoding the neomycin resistance gene flanked by human PSS (red), as compared to control reporters lacking inverted terminal repeats (-ITR, black) and T. ni piggyBac ITR (blue). ** p = 5.0 × 10 -5 . Lepidopteran T. ni PiggyBac DNA transposase and its piggyBac ITR serve as specificity controls. Errors bars represent standard deviations of three independent experiments. (e) Schematic model of transposition reporter assay in G401 rhabdoid tumor cells followed by flanking sequence exponential anchored-polymerase chain reaction (FLEA-PCR) and Illumina paired-end sequencing. (f) Genomic integration of synthetic NeoR transposons (red) by endogenous PGBD5 in G401 rhabdoid tumor cells at PSS site (arrowhead), as shown in the ChIP-seq genome track of PGBD5 (blue), as compared to its sequencing input (gray), and H3K27Ac and H3K4me3 (bottom), consistent with the bound PGBD5 transposase protein complex.

Techniques Used: Sequencing, Chromatin Immunoprecipitation, Staining, Selection, Reporter Assay, Transfection, Polymerase Chain Reaction

Ectopic expression of PGBD5 in human cells leads to oncogenic transformation both in vitro and in vivo (a) Schematic for testing transforming activity of PGBD5. (b) Relative PGBD5 mRNA expression measured by quantitative RT-PCR in normal mouse tissues (brain, liver, spleen and kidney), as compared to human tumor cell lines (rhabdoid G401, neuroblastoma LAN1 and SK-N-FI, medulloblastoma UW-228 cells), primary human rhabdoid tumors (PAKHTL, PARRCL, PASYNF, PATBLF), and BJ and RPE cells stably transduced with GFP-PGBD5 and GFP . Error bars represent standard deviations of 3 independent measurements. (c) Representative images of GFP-PGBD5- transduced RPE cells grown in semisolid media after 10 days of culture, as compared to control GFP -transduced cells. (d) Number of refractile foci formed in monolayer cultures of RPE and BJ cells expressing GFP-PGBD5 or GFP, as compared to non-transduced cells ( p = 3.6 × 10 -5 and 3.9 × 10 -4 for GFP-PGBD5 vs. GFP for BJ and RPE cells, respectively). (e) Expression of T. ni GFP-PiggyBac does not lead to the formation of anchorage independent foci in monolayer culture (* p = 3.49 × 10 -5 for GFP-PGBD5 vs. T. ni GFP-PiggyBac ). Error bars represent standard deviations of 3 independent experiments. (f) Kaplan-Meier analysis of tumor-free survival of mice with subcutaneous xenografts of RPE cells expressing GFP-PGBD5 or GFP control, as compared to non-transduced cells or cells expressing SV40 large T antigen (LTA) and HRAS ( n = 10 mice per group, p
Figure Legend Snippet: Ectopic expression of PGBD5 in human cells leads to oncogenic transformation both in vitro and in vivo (a) Schematic for testing transforming activity of PGBD5. (b) Relative PGBD5 mRNA expression measured by quantitative RT-PCR in normal mouse tissues (brain, liver, spleen and kidney), as compared to human tumor cell lines (rhabdoid G401, neuroblastoma LAN1 and SK-N-FI, medulloblastoma UW-228 cells), primary human rhabdoid tumors (PAKHTL, PARRCL, PASYNF, PATBLF), and BJ and RPE cells stably transduced with GFP-PGBD5 and GFP . Error bars represent standard deviations of 3 independent measurements. (c) Representative images of GFP-PGBD5- transduced RPE cells grown in semisolid media after 10 days of culture, as compared to control GFP -transduced cells. (d) Number of refractile foci formed in monolayer cultures of RPE and BJ cells expressing GFP-PGBD5 or GFP, as compared to non-transduced cells ( p = 3.6 × 10 -5 and 3.9 × 10 -4 for GFP-PGBD5 vs. GFP for BJ and RPE cells, respectively). (e) Expression of T. ni GFP-PiggyBac does not lead to the formation of anchorage independent foci in monolayer culture (* p = 3.49 × 10 -5 for GFP-PGBD5 vs. T. ni GFP-PiggyBac ). Error bars represent standard deviations of 3 independent experiments. (f) Kaplan-Meier analysis of tumor-free survival of mice with subcutaneous xenografts of RPE cells expressing GFP-PGBD5 or GFP control, as compared to non-transduced cells or cells expressing SV40 large T antigen (LTA) and HRAS ( n = 10 mice per group, p

Techniques Used: Expressing, Transformation Assay, In Vitro, In Vivo, Activity Assay, Quantitative RT-PCR, Stable Transfection, Transduction, Mouse Assay

PGBD5 transposase activity is necessary to transform human cells (a) Western blot of GFP in RPE cells expressing GFP-PGBD5 , GFP-PGBD5 mutants, and GFP compared to RPE cells (DM = double mutant D194A/D386A; TM = triple mutant D168A/D194A/D386A). (b) Number of refractile foci formed in monolayer culture in RPE and BJ cells stably expressing GFP-PGBD5 or control GFP , as compared to non-transduced cells and cells expressing GFP-PGBD5 mutants (red = transposase deficient mutants, blue = transposase proficient mutants, * p = 2.1 × 10 -4 for D168A vs. GFP-PGBD5 , p = 2.7 × 10 -6 for D194A vs. GFP-PGBD5 , p = 1.8 × 10 -6 for D194A/D386A vs. GFP-PGBD5 , p = 2.4 × 10 -7 for D168A/D194A/D386A vs. GFP-PGBD5 ). Error bars represent standard deviations of 3 independent experiments. (c) Composite plot of ChIP-seq of GFP-PGBD5 (green), as compared to the GFP-PGBD5 D168A/D194A/D386A catalytic TM mutant (orange) and GFP control (purple). (d) Kaplan-Meier analysis of tumor-free survival of mice with subcutaneous xenografts of RPE cells expressing GFP-PGBD5 as compared to cells expressing GFP-PGBD5 mutants ( n = 10 per group, p
Figure Legend Snippet: PGBD5 transposase activity is necessary to transform human cells (a) Western blot of GFP in RPE cells expressing GFP-PGBD5 , GFP-PGBD5 mutants, and GFP compared to RPE cells (DM = double mutant D194A/D386A; TM = triple mutant D168A/D194A/D386A). (b) Number of refractile foci formed in monolayer culture in RPE and BJ cells stably expressing GFP-PGBD5 or control GFP , as compared to non-transduced cells and cells expressing GFP-PGBD5 mutants (red = transposase deficient mutants, blue = transposase proficient mutants, * p = 2.1 × 10 -4 for D168A vs. GFP-PGBD5 , p = 2.7 × 10 -6 for D194A vs. GFP-PGBD5 , p = 1.8 × 10 -6 for D194A/D386A vs. GFP-PGBD5 , p = 2.4 × 10 -7 for D168A/D194A/D386A vs. GFP-PGBD5 ). Error bars represent standard deviations of 3 independent experiments. (c) Composite plot of ChIP-seq of GFP-PGBD5 (green), as compared to the GFP-PGBD5 D168A/D194A/D386A catalytic TM mutant (orange) and GFP control (purple). (d) Kaplan-Meier analysis of tumor-free survival of mice with subcutaneous xenografts of RPE cells expressing GFP-PGBD5 as compared to cells expressing GFP-PGBD5 mutants ( n = 10 per group, p

Techniques Used: Activity Assay, Western Blot, Expressing, Mutagenesis, Stable Transfection, Chromatin Immunoprecipitation, Mouse Assay

PGBD5-induced cell transformation involves site-specific genomic rearrangements associated with PGBD5-specific signal sequence breakpoints (a) Circos plot of structural variants discovered in RPE-GFP-PGBD5 tumor cells using assembly-based genome analysis. Black arrows on outer circle indicate the presence of PSS at variant breakpoints. (b) Representation of 7 breakpoints identified to harbor PSS sequences (red) within 10 bp of the breakpoint junction (arrowhead) of structural variants in PGBD5 expressing RPE cells. Genomic sequence is annotated 5′ to 3′ as presented in the reference genome (+) strand. (c) Waterfall plot of enrichment of ENCODE regulatory DNA elements with structural variants in fetal (red) as compared to adult tissues (blue) in PGBD 5-transformed RPE cells ( p = 5.7 × 10 -8 ). (d) Schematic of the WWOX gene and its intragenic duplication in GFP-PGBD5-transformed RPE cells (top), with Sanger sequencing chromatogram of the rearrangement breakpoint (bottom). Arrowhead marks the breakpoint. (e) Western blot analysis of WWOX in 10 independent GFP-PGBD5-transformed RPE cell tumor xenografts, as compared to control GFP-transduced and non-transduced RPE cells. Actin serves as loading control. (f) Schematic model of the proposed mechanism of PGBD5-induced cell transformation, involving association of PGBD5 with genomic PSS sequences, their remodeling dependent on PAXX-meditated end-joining DNA repair, and generation of tumorigenic genomic rearrangements.
Figure Legend Snippet: PGBD5-induced cell transformation involves site-specific genomic rearrangements associated with PGBD5-specific signal sequence breakpoints (a) Circos plot of structural variants discovered in RPE-GFP-PGBD5 tumor cells using assembly-based genome analysis. Black arrows on outer circle indicate the presence of PSS at variant breakpoints. (b) Representation of 7 breakpoints identified to harbor PSS sequences (red) within 10 bp of the breakpoint junction (arrowhead) of structural variants in PGBD5 expressing RPE cells. Genomic sequence is annotated 5′ to 3′ as presented in the reference genome (+) strand. (c) Waterfall plot of enrichment of ENCODE regulatory DNA elements with structural variants in fetal (red) as compared to adult tissues (blue) in PGBD 5-transformed RPE cells ( p = 5.7 × 10 -8 ). (d) Schematic of the WWOX gene and its intragenic duplication in GFP-PGBD5-transformed RPE cells (top), with Sanger sequencing chromatogram of the rearrangement breakpoint (bottom). Arrowhead marks the breakpoint. (e) Western blot analysis of WWOX in 10 independent GFP-PGBD5-transformed RPE cell tumor xenografts, as compared to control GFP-transduced and non-transduced RPE cells. Actin serves as loading control. (f) Schematic model of the proposed mechanism of PGBD5-induced cell transformation, involving association of PGBD5 with genomic PSS sequences, their remodeling dependent on PAXX-meditated end-joining DNA repair, and generation of tumorigenic genomic rearrangements.

Techniques Used: Transformation Assay, Sequencing, Variant Assay, Expressing, Western Blot

Transient PGBD5 transposase expression is sufficient to transform human cells (a) Tumor volume of RPE cells as a function of time in primary (light gray box) and secondary (dark gray box) transplants, with PGBD5 expression induced using doxycycline (black), as indicated. RPE cells were treated with doxycycline in vitro for 10 days prior to transplantation. Arrowhead denotes withdrawal of doxycycline from the diet (red). Inset: Western blot of PGBD5 protein, as compared to actin control in cells derived from tumors after primary transplant. (b) Representative photomicrographs of hematoxylin and eosin stained tumor sections from doxycycline-inducible PGBD5 -expressing RPE tumors after continuous (+Dox) and discontinuous (-Dox) doxycycline treatment. (c) Western blot of PGBD5 in G401 and A204 rhabdoid tumor cells upon depletion of PGBD5 using two independent shRNAs, as compared to non-transduced cells and control cells expressing shGFP. (d) Relative number of viable G401 and A204 cells upon 72 hours of PGBD5 shRNA depletion. Errors bars represent standard deviations of 3 independent experiments.
Figure Legend Snippet: Transient PGBD5 transposase expression is sufficient to transform human cells (a) Tumor volume of RPE cells as a function of time in primary (light gray box) and secondary (dark gray box) transplants, with PGBD5 expression induced using doxycycline (black), as indicated. RPE cells were treated with doxycycline in vitro for 10 days prior to transplantation. Arrowhead denotes withdrawal of doxycycline from the diet (red). Inset: Western blot of PGBD5 protein, as compared to actin control in cells derived from tumors after primary transplant. (b) Representative photomicrographs of hematoxylin and eosin stained tumor sections from doxycycline-inducible PGBD5 -expressing RPE tumors after continuous (+Dox) and discontinuous (-Dox) doxycycline treatment. (c) Western blot of PGBD5 in G401 and A204 rhabdoid tumor cells upon depletion of PGBD5 using two independent shRNAs, as compared to non-transduced cells and control cells expressing shGFP. (d) Relative number of viable G401 and A204 cells upon 72 hours of PGBD5 shRNA depletion. Errors bars represent standard deviations of 3 independent experiments.

Techniques Used: Expressing, In Vitro, Transplantation Assay, Western Blot, Derivative Assay, Staining, shRNA

25) Product Images from "Diverse Non-genetic, Allele-Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain"

Article Title: Diverse Non-genetic, Allele-Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain

Journal: Neuron

doi: 10.1016/j.neuron.2017.01.033

Identification of Autosomal DAEEs and Allele CoEEs in the Primate Brain (A) Schematic of the strategy to profile allele co-expression in the DRN region of juvenile female cynomolgus macaques. For ten parent-offspring trios, we performed whole-genome sequencing of the parents and transcriptome sequencing of RNA extracted from the DRN of the daughters. SNPs that distinguish maternal from paternal alleles in the daughters are determined from the parental genomes and RNA-seq datasets. (B) By analyzing r a values for the 838 genes with maternal and paternal allele RNA-seq data in all ten daughters, we defined genes with allele CoEEs ( AGAP1 ) and potential DAEEs ( CNTN1 ) in the juvenile female macaque DRN. (C) Plots of the non-genetic r ab 95% CIs for 17 X-linked genes and 821 autosomal genes, colorized according to the categories of high-confidence allelic effects indicated in the legend. Most X-linked genes exhibit AAEEs or DAEEs. Most autosomal genes exhibit allele CoEEs (red) or do not exhibit sufficiently robust allelic effects to be categorized with high confidence (gray); however, high-confidence DAEEs were discovered for RBM48 and HTT . In addition, more modest putative DAEEs were observed for several autosomal genes (see main text). (D) The r a values and non-genetic r ab 95% CIs for examples of primate genes with allele CoEEs ( ABCA1 ), putative DAEEs ( NARS , TPNC1 ), high-confidence DAEEs ( RBM48 , HTT) , and uncategorized genes ( ATP1A3 , ABAT). (E) Boxplots comparing the primate DRN expression level of autosomal and X-linked genes with allele CoEEs, putative DAEEs (pDAEEs), DAEEs, and uncategorized (UNCAT) genes. A significant main effect of gene class was observed (one way ANOVA, p
Figure Legend Snippet: Identification of Autosomal DAEEs and Allele CoEEs in the Primate Brain (A) Schematic of the strategy to profile allele co-expression in the DRN region of juvenile female cynomolgus macaques. For ten parent-offspring trios, we performed whole-genome sequencing of the parents and transcriptome sequencing of RNA extracted from the DRN of the daughters. SNPs that distinguish maternal from paternal alleles in the daughters are determined from the parental genomes and RNA-seq datasets. (B) By analyzing r a values for the 838 genes with maternal and paternal allele RNA-seq data in all ten daughters, we defined genes with allele CoEEs ( AGAP1 ) and potential DAEEs ( CNTN1 ) in the juvenile female macaque DRN. (C) Plots of the non-genetic r ab 95% CIs for 17 X-linked genes and 821 autosomal genes, colorized according to the categories of high-confidence allelic effects indicated in the legend. Most X-linked genes exhibit AAEEs or DAEEs. Most autosomal genes exhibit allele CoEEs (red) or do not exhibit sufficiently robust allelic effects to be categorized with high confidence (gray); however, high-confidence DAEEs were discovered for RBM48 and HTT . In addition, more modest putative DAEEs were observed for several autosomal genes (see main text). (D) The r a values and non-genetic r ab 95% CIs for examples of primate genes with allele CoEEs ( ABCA1 ), putative DAEEs ( NARS , TPNC1 ), high-confidence DAEEs ( RBM48 , HTT) , and uncategorized genes ( ATP1A3 , ABAT). (E) Boxplots comparing the primate DRN expression level of autosomal and X-linked genes with allele CoEEs, putative DAEEs (pDAEEs), DAEEs, and uncategorized (UNCAT) genes. A significant main effect of gene class was observed (one way ANOVA, p

Techniques Used: Expressing, Sequencing, RNA Sequencing Assay

26) Product Images from "Existing and Emerging Technologies for Tumor Genomic Profiling"

Article Title: Existing and Emerging Technologies for Tumor Genomic Profiling

Journal: Journal of Clinical Oncology

doi: 10.1200/JCO.2012.46.5948

Categories of genomic alterations and technologies for detection. Many of the hallmark alterations in cancer are currently detected by using a multitude of existing technologies, often in a serial fashion, each using an appreciable amount of nucleic acid. Newer sequencing-based methodologies are capable of interrogating many types of cancer alterations in one composite, sensitive test. CGH, comparative genomic hybridization; ChIP-Seq, chromatin immunoprecipitation followed by massively parallel sequencing; FISH, fluorescent in situ hybridization; IHC, immunohistochemistry; PCR, polymerase chain reaction; RNA-Seq, RNA sequencing, also known as transcriptome sequencing; SNP, single nucleotide polymorphism; Targ-Seq, targeted sequencing; WES, whole-exome sequencing; WGS, whole-genome sequencing.
Figure Legend Snippet: Categories of genomic alterations and technologies for detection. Many of the hallmark alterations in cancer are currently detected by using a multitude of existing technologies, often in a serial fashion, each using an appreciable amount of nucleic acid. Newer sequencing-based methodologies are capable of interrogating many types of cancer alterations in one composite, sensitive test. CGH, comparative genomic hybridization; ChIP-Seq, chromatin immunoprecipitation followed by massively parallel sequencing; FISH, fluorescent in situ hybridization; IHC, immunohistochemistry; PCR, polymerase chain reaction; RNA-Seq, RNA sequencing, also known as transcriptome sequencing; SNP, single nucleotide polymorphism; Targ-Seq, targeted sequencing; WES, whole-exome sequencing; WGS, whole-genome sequencing.

Techniques Used: Sequencing, Hybridization, Chromatin Immunoprecipitation, Fluorescence In Situ Hybridization, In Situ Hybridization, Immunohistochemistry, Polymerase Chain Reaction, RNA Sequencing Assay

27) Product Images from "Genetic deletion of microRNA biogenesis in muscle cells reveals a hierarchical non-clustered network that controls focal adhesion signaling during muscle regeneration"

Article Title: Genetic deletion of microRNA biogenesis in muscle cells reveals a hierarchical non-clustered network that controls focal adhesion signaling during muscle regeneration

Journal: Molecular Metabolism

doi: 10.1016/j.molmet.2020.02.010

Genome-wide identification of differentially expressed genes and miRNA target regulation after single or combinatorial miRNA inhibition in human myotubes. Human myoblasts were transfected with the indicated antagomirs and 24 h after transfection differentiation was induced for two days. RNA was isolated for RNA deep sequencing, n = 3. A. Circos plot of differentially expressed genes (p-value
Figure Legend Snippet: Genome-wide identification of differentially expressed genes and miRNA target regulation after single or combinatorial miRNA inhibition in human myotubes. Human myoblasts were transfected with the indicated antagomirs and 24 h after transfection differentiation was induced for two days. RNA was isolated for RNA deep sequencing, n = 3. A. Circos plot of differentially expressed genes (p-value

Techniques Used: Genome Wide, Inhibition, Transfection, Isolation, Sequencing, Significance Assay

A combination of six miRNAs rescues myotube morphology and reverses induction of the focal adhesion gene cluster following DGCR8 deletion. Dgcr8 KO and control myoblasts were transfected with control mimics, the indicated individual six miRNA mimics or their combination (6× miRNA) two days after beginning of the tamoxifen incubation. Twenty-four hours after transfection, myoblast differentiation was induced for 48 h. Myotube morphology was analyzed using immunofluorescence for desmin (A) and brightfield microscopy (B), 10× magnification. Scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from control, Dgcr8 myotubes and Dgcr8 myotubes transfected with the combination of six miRNAs. D. RNA levels as determined by RNA deep sequencing in control and DGCR8 knockout cells with or without transfection of the six miRNA mimics, n = 3. Shown are all genes that are significantly upregulated in the knockout cells and significantly downregulated after transfection with the mimics (p value
Figure Legend Snippet: A combination of six miRNAs rescues myotube morphology and reverses induction of the focal adhesion gene cluster following DGCR8 deletion. Dgcr8 KO and control myoblasts were transfected with control mimics, the indicated individual six miRNA mimics or their combination (6× miRNA) two days after beginning of the tamoxifen incubation. Twenty-four hours after transfection, myoblast differentiation was induced for 48 h. Myotube morphology was analyzed using immunofluorescence for desmin (A) and brightfield microscopy (B), 10× magnification. Scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from control, Dgcr8 myotubes and Dgcr8 myotubes transfected with the combination of six miRNAs. D. RNA levels as determined by RNA deep sequencing in control and DGCR8 knockout cells with or without transfection of the six miRNA mimics, n = 3. Shown are all genes that are significantly upregulated in the knockout cells and significantly downregulated after transfection with the mimics (p value

Techniques Used: Transfection, Incubation, Immunofluorescence, Microscopy, Isolation, Sequencing, Knock-Out, Significance Assay

A combination of five miRNAs accelerates differentiation of human myoblasts and improves insulin sensitivity downstream of FAK signaling. Human primary myoblasts were transfected with equal concentrations of control antagomirs or antagomirs against the indicated miRNAs, either as single antagomirs or in combination (Ant-5x). Twenty-four hours after transfection, differentiation was induced for two days (A, B, C, E, G) or up to five days (D, F). A. Gene expression of myogenic regulatory factors and eMHC was analyzed by qRT-PCR and normalized for 18S RNA, n = 5. B. Protein expression of myogenin and eMHC was analyzed by western blot and normalized to GAPDH (n = 4–6). C. Luciferase vectors harboring either the myogenin 3′UTR (n = 4) or promoter region (n = 5) were transfected with miRNA mimics or antagomirs respectively. Myogenin mRNA from control and Ant-5x treated samples was measured by qRT-PCR at the indicated time points following Actinomycin D administration (n = 3). D. Time course of myogenin and eMHC protein expression during five days of differentiation, normalized to GAPDH (n = 4–6). E. Immunofluorescent analysis of myotube formation using anti-desmin and wheat germ agglutinin (WGA). Fusion index was calculated as percentage of nuclei present in cells containing at least two nuclei compared to all nuclei per well (scale bar 100um, n = 4). F. Phosphorylation of p38 MAPK, AKT and FAK during the first three days of differentiation, n = 4. G. Insulin-dependent glycogen synthesis, n = 3. ∗: p
Figure Legend Snippet: A combination of five miRNAs accelerates differentiation of human myoblasts and improves insulin sensitivity downstream of FAK signaling. Human primary myoblasts were transfected with equal concentrations of control antagomirs or antagomirs against the indicated miRNAs, either as single antagomirs or in combination (Ant-5x). Twenty-four hours after transfection, differentiation was induced for two days (A, B, C, E, G) or up to five days (D, F). A. Gene expression of myogenic regulatory factors and eMHC was analyzed by qRT-PCR and normalized for 18S RNA, n = 5. B. Protein expression of myogenin and eMHC was analyzed by western blot and normalized to GAPDH (n = 4–6). C. Luciferase vectors harboring either the myogenin 3′UTR (n = 4) or promoter region (n = 5) were transfected with miRNA mimics or antagomirs respectively. Myogenin mRNA from control and Ant-5x treated samples was measured by qRT-PCR at the indicated time points following Actinomycin D administration (n = 3). D. Time course of myogenin and eMHC protein expression during five days of differentiation, normalized to GAPDH (n = 4–6). E. Immunofluorescent analysis of myotube formation using anti-desmin and wheat germ agglutinin (WGA). Fusion index was calculated as percentage of nuclei present in cells containing at least two nuclei compared to all nuclei per well (scale bar 100um, n = 4). F. Phosphorylation of p38 MAPK, AKT and FAK during the first three days of differentiation, n = 4. G. Insulin-dependent glycogen synthesis, n = 3. ∗: p

Techniques Used: Transfection, Expressing, Quantitative RT-PCR, Western Blot, Luciferase, Whole Genome Amplification

Genetic deletion of the miRNA pathway induces the gene cluster “focal adhesion” in proliferating primary myoblasts. A. Myoblast cultures (Pax7 CE xDgcr8 flox/flox ) were incubated for 48 h with tamoxifen to induce Cre recombinase (Dgcr8 KO) or with vehicle (Control). Deletion of Dgcr8 induced a time-dependent decrease of DGCR8 protein (western blotting), miRNA levels (qRT-PCR normalized for sno234, n = 4, control represented by the dashed line) and onset of apoptosis (flow cytometry for annexin V staining, n = 6–11). B. Primary myoblasts were treated as in A and differentiated into myotubes for 48 h four days after the beginning of the tamoxifen incubation (day 4). Desmin (green), nuclear DAPI ((blue), 20× magnification, scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from proliferating Dgcr8 KO and control myoblasts at day 4. D. Expression of Dcgr8 and members of the KEGG pathway “focal adhesion” in Pax7 CE xDgcr8 wt/wt (control) and Pax7 CE xDgcr8 flox/flox myoblasts measured using qRT-PCR at day 4 after tamoxifen incubation, n = 4. The dashed line represents incubation with vehicle. All results are shown as mean ± SEM. ∗: p
Figure Legend Snippet: Genetic deletion of the miRNA pathway induces the gene cluster “focal adhesion” in proliferating primary myoblasts. A. Myoblast cultures (Pax7 CE xDgcr8 flox/flox ) were incubated for 48 h with tamoxifen to induce Cre recombinase (Dgcr8 KO) or with vehicle (Control). Deletion of Dgcr8 induced a time-dependent decrease of DGCR8 protein (western blotting), miRNA levels (qRT-PCR normalized for sno234, n = 4, control represented by the dashed line) and onset of apoptosis (flow cytometry for annexin V staining, n = 6–11). B. Primary myoblasts were treated as in A and differentiated into myotubes for 48 h four days after the beginning of the tamoxifen incubation (day 4). Desmin (green), nuclear DAPI ((blue), 20× magnification, scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from proliferating Dgcr8 KO and control myoblasts at day 4. D. Expression of Dcgr8 and members of the KEGG pathway “focal adhesion” in Pax7 CE xDgcr8 wt/wt (control) and Pax7 CE xDgcr8 flox/flox myoblasts measured using qRT-PCR at day 4 after tamoxifen incubation, n = 4. The dashed line represents incubation with vehicle. All results are shown as mean ± SEM. ∗: p

Techniques Used: Incubation, Western Blot, Quantitative RT-PCR, Flow Cytometry, Staining, Isolation, Expressing

28) Product Images from "A non-mosaic transchromosomic mouse model of Down syndrome carrying the long arm of human chromosome 21"

Article Title: A non-mosaic transchromosomic mouse model of Down syndrome carrying the long arm of human chromosome 21

Journal: eLife

doi: 10.7554/eLife.56223

HSA21 expression pattern in P1 TcMAC21 brain. ( A ) RNA-Seq summary of HSA21 PCG and non-PCG transcript levels in TcMAC21. ( B ) Transcript levels of individual PCG and non-PCGs across the length of HSA21q in TcMAC21. ( C ) HSA21 dosage imbalance analysis of TcMAC21 among 117 HSA21 mouse orthologs whose FPKM ≥1 in Eu. Three expression values are shown: 1. the FPKM ratio of HSA21 PCG of TcMAC21 to its ortholog of Eu (gray open squares); 2. the FPKM ratio of HSA21 mouse ortholog of TcMAC21 to that of Eu (blue dot); 3. the FPKM ratio of total expression (HSA21 PCG + its mouse ortholog) of TcMAC21 to the HSA21 mouse ortholog of Eu (red circles). The positions of deleted regions are indicated in red. Eu and Ts65Dn littermates, n = 2 per group. See also Figure 2—source data 1 for expression levels (FPKM) of all HSA21 and its orthologs in Eu and TcMAC21. ( D ) RNA-Seq verification by Taqman RT-PCR. Correlation between CT value from Taqman RT-PCR and Log 10 (FPKM) from RNA-Seq for 10 HSA21q genes and mouse actin (mACTB) in TcMAC21. ( E ) Taqman assay comparing expression of 10 HSA21 genes between forebrain, hindbrain, and heart using the same amount of total RNA. The sample size of Taqman assay in D and E is that n = 2 for TcMAC21 and n = 3 for Eu (negative control). RNA-seq of TcMAC21 and Eu. HSA21 expression pattern in P1 TcMAC21 brain. Effects of HSA21 on gene expression of other mouse chromosomes analyzed by RNA-seq. HSA21 expression pattern in P1 TcMAC21 brain, source data.
Figure Legend Snippet: HSA21 expression pattern in P1 TcMAC21 brain. ( A ) RNA-Seq summary of HSA21 PCG and non-PCG transcript levels in TcMAC21. ( B ) Transcript levels of individual PCG and non-PCGs across the length of HSA21q in TcMAC21. ( C ) HSA21 dosage imbalance analysis of TcMAC21 among 117 HSA21 mouse orthologs whose FPKM ≥1 in Eu. Three expression values are shown: 1. the FPKM ratio of HSA21 PCG of TcMAC21 to its ortholog of Eu (gray open squares); 2. the FPKM ratio of HSA21 mouse ortholog of TcMAC21 to that of Eu (blue dot); 3. the FPKM ratio of total expression (HSA21 PCG + its mouse ortholog) of TcMAC21 to the HSA21 mouse ortholog of Eu (red circles). The positions of deleted regions are indicated in red. Eu and Ts65Dn littermates, n = 2 per group. See also Figure 2—source data 1 for expression levels (FPKM) of all HSA21 and its orthologs in Eu and TcMAC21. ( D ) RNA-Seq verification by Taqman RT-PCR. Correlation between CT value from Taqman RT-PCR and Log 10 (FPKM) from RNA-Seq for 10 HSA21q genes and mouse actin (mACTB) in TcMAC21. ( E ) Taqman assay comparing expression of 10 HSA21 genes between forebrain, hindbrain, and heart using the same amount of total RNA. The sample size of Taqman assay in D and E is that n = 2 for TcMAC21 and n = 3 for Eu (negative control). RNA-seq of TcMAC21 and Eu. HSA21 expression pattern in P1 TcMAC21 brain. Effects of HSA21 on gene expression of other mouse chromosomes analyzed by RNA-seq. HSA21 expression pattern in P1 TcMAC21 brain, source data.

Techniques Used: Expressing, RNA Sequencing Assay, Reverse Transcription Polymerase Chain Reaction, TaqMan Assay, Negative Control

The experimental design for behavioral tests and electrophysiology to assess learning and memory in both Eu and TcMAC21.
Figure Legend Snippet: The experimental design for behavioral tests and electrophysiology to assess learning and memory in both Eu and TcMAC21.

Techniques Used:

Construction of TcMAC21 mice (HSA21q-MAC). ( A ) Information for vectors used to generate the hybrid chromosome, HSA21q-MAC. ( B–D ) FISH images to verify the critical steps: ( B ) before Cre-loxP mediated recombination, CHO cells containing a HSA21-loxP and a MAC; ( C ) after recombination, CHO cells containing the HSA21q-MAC (yellow arrow) and by-product (21p-cen) (white arrow); ( D ) mouse ES cell containing the HSA21q-MAC (yellow arrow). FISH probes: digoxigenin-labeled human COT-1 DNA for human chromosome detection (red, in B-D), biotin-labeled mouse COT-1 DNA for mouse chromosome detection (green, in B-C), biotin-labeled mouse minor satellite DNA for mouse centromere detection (green, in D), and chromosomal DNA counterstained with DAPI.
Figure Legend Snippet: Construction of TcMAC21 mice (HSA21q-MAC). ( A ) Information for vectors used to generate the hybrid chromosome, HSA21q-MAC. ( B–D ) FISH images to verify the critical steps: ( B ) before Cre-loxP mediated recombination, CHO cells containing a HSA21-loxP and a MAC; ( C ) after recombination, CHO cells containing the HSA21q-MAC (yellow arrow) and by-product (21p-cen) (white arrow); ( D ) mouse ES cell containing the HSA21q-MAC (yellow arrow). FISH probes: digoxigenin-labeled human COT-1 DNA for human chromosome detection (red, in B-D), biotin-labeled mouse COT-1 DNA for mouse chromosome detection (green, in B-C), biotin-labeled mouse minor satellite DNA for mouse centromere detection (green, in D), and chromosomal DNA counterstained with DAPI.

Techniques Used: Mouse Assay, Fluorescence In Situ Hybridization, Labeling

RRWM. ( A ) RRWM scheme: four full days of training, the targeting platform is in NW (relocated from SE of MWM) during reversal session 1 (R day one and R day 2) and then relocated to SW during reversal session 2 (R day three and R day 4). Each day has 10 trials, including eight acquisition trials (trial 2–9) and two probe trials (trial 10 of R day 1–4 is 30 min short delay, Trial 1 of R day one also known as the 72 hr delay probe trial of MWM, Trial 1 of R day 2–4 is 24 hr long delay). ( B ) Acquisition and short delay probe trials in reversal 1. ( C ) Acquisition, short delay probe and long delay probe trials in reversal 2. Data are analyzed by repeated measures ANOVA with LSD post-hoc and expressed as mean ± SEM. Female: Eu (n = 8), TcMAC21 (n = 9), and see Figure 7—source data 1C for detailed statistical analysis.
Figure Legend Snippet: RRWM. ( A ) RRWM scheme: four full days of training, the targeting platform is in NW (relocated from SE of MWM) during reversal session 1 (R day one and R day 2) and then relocated to SW during reversal session 2 (R day three and R day 4). Each day has 10 trials, including eight acquisition trials (trial 2–9) and two probe trials (trial 10 of R day 1–4 is 30 min short delay, Trial 1 of R day one also known as the 72 hr delay probe trial of MWM, Trial 1 of R day 2–4 is 24 hr long delay). ( B ) Acquisition and short delay probe trials in reversal 1. ( C ) Acquisition, short delay probe and long delay probe trials in reversal 2. Data are analyzed by repeated measures ANOVA with LSD post-hoc and expressed as mean ± SEM. Female: Eu (n = 8), TcMAC21 (n = 9), and see Figure 7—source data 1C for detailed statistical analysis.

Techniques Used:

Visual discrimination. ( A ) Water tank specifications. The inner circle, the target area, is defined as a circle inscribed in the platform quadrant, covering ~17% of water maze tank. ( B ) Design of visual discrimination: platform changes every two trials from W, to E, and to S. ( C–D ) Visual discrimination performance of TcMAC21 and Eu measured by escape latency (p=0.27, ( C ) and escape distance (p=0.19, ( D ). Female: Eu (n = 13), MAC21 (n = 11); male: Eu (n = 11), MAC21 (n = 12). Data are analyzed by repeated measures ANOVA with LSD post-hoc and expressed as mean ± SEM. See Figure 7—source data 1A for detailed statistical analysis.
Figure Legend Snippet: Visual discrimination. ( A ) Water tank specifications. The inner circle, the target area, is defined as a circle inscribed in the platform quadrant, covering ~17% of water maze tank. ( B ) Design of visual discrimination: platform changes every two trials from W, to E, and to S. ( C–D ) Visual discrimination performance of TcMAC21 and Eu measured by escape latency (p=0.27, ( C ) and escape distance (p=0.19, ( D ). Female: Eu (n = 13), MAC21 (n = 11); male: Eu (n = 11), MAC21 (n = 12). Data are analyzed by repeated measures ANOVA with LSD post-hoc and expressed as mean ± SEM. See Figure 7—source data 1A for detailed statistical analysis.

Techniques Used:

Mosaicism analysis. ( A ) Tails from GFP negative and positive TcMAC21 littermates are analyzed using Taqman RT-PCR assay for testing expression of human-APP (hAPP, centromere-proximal), human-PRMT2 (hPRMT2, telomere-proximal), and mouse actin (mActin, endogenous control). N = 12 per group. ( B ) TcMAC21 and Eu pups are visualized with GFP flashlights. ( C ) FISH of kidney cells of Eu and TcMAC21 with human COT-1 (red), and chromosomal DNA counterstained with DAPI.
Figure Legend Snippet: Mosaicism analysis. ( A ) Tails from GFP negative and positive TcMAC21 littermates are analyzed using Taqman RT-PCR assay for testing expression of human-APP (hAPP, centromere-proximal), human-PRMT2 (hPRMT2, telomere-proximal), and mouse actin (mActin, endogenous control). N = 12 per group. ( B ) TcMAC21 and Eu pups are visualized with GFP flashlights. ( C ) FISH of kidney cells of Eu and TcMAC21 with human COT-1 (red), and chromosomal DNA counterstained with DAPI.

Techniques Used: Reverse Transcription Polymerase Chain Reaction, Expressing, Fluorescence In Situ Hybridization

Lateral ventricle in ex vivo and in vivo MRI. ( A ) 3D rendering of Eu and TcMAC21 brains MRI images. No clear ventricle space is seen in PFA-fixed samples (ex vivo MRI) and clear ventricles and CSF are seen in live samples (in vivo MRI). LV: lateral ventricle, 3/4 V: the third and fourth ventricle; scale bar (5 mm) ( B ) Quantitative analysis of LV volume of live Eu and TcMAC21 mice. LV are visualized in four matching transverse levels of MRI images from the dorsal to the ventral (adjacent levels spaced 312.5 um apart, scale bar (5 mm)). Absolute and percentage of brain volumes of LV are compared (N = 3 pairs and data are analyzed by two-tailed t-test and expressed as mean ± SEM).
Figure Legend Snippet: Lateral ventricle in ex vivo and in vivo MRI. ( A ) 3D rendering of Eu and TcMAC21 brains MRI images. No clear ventricle space is seen in PFA-fixed samples (ex vivo MRI) and clear ventricles and CSF are seen in live samples (in vivo MRI). LV: lateral ventricle, 3/4 V: the third and fourth ventricle; scale bar (5 mm) ( B ) Quantitative analysis of LV volume of live Eu and TcMAC21 mice. LV are visualized in four matching transverse levels of MRI images from the dorsal to the ventral (adjacent levels spaced 312.5 um apart, scale bar (5 mm)). Absolute and percentage of brain volumes of LV are compared (N = 3 pairs and data are analyzed by two-tailed t-test and expressed as mean ± SEM).

Techniques Used: Ex Vivo, In Vivo, Magnetic Resonance Imaging, Mouse Assay, Two Tailed Test

29) Product Images from "Early loss of Crebbp confers malignant stem cell properties on lymphoid progenitors"

Article Title: Early loss of Crebbp confers malignant stem cell properties on lymphoid progenitors

Journal: Nature cell biology

doi: 10.1038/ncb3597

Loss of Crebbp in committed lymphoid cells attenuates disease generation and CREBBP mutations occur in HSPC from a lymphoma patient. a. Analysis of the Lin- and IL7Ra+ compartments in Cd19- Crebbp-/- mice. Box plots show the median with interquartile range and whiskers represent the minimum and maximum values, n = 6 mice per genotype were quantified. Non-significance confirmed using 2-sided t-test. b. Comparison of H2AXγ foci in the IL7Ra+ progenitor populations from Cd19- Crebbp-/- mice. Data represent the mean ± SEM from n = 3 independent experiments, non-significance confirmed using 2-sided t-test. See Supplementary Table 26 for scatter plots of > 10 foci. c. KM analysis showing that no significant alterations in survival were seen for Cd19- Crebbp -/- mice (n = 27 animals) vs WT mice (n = 25 animals). P value calculated using Log-rank (Mantel-Cox) test. d. in contrast to that seen in Mx-Crebbp-/- mice. e. B-cell disease was very rare in this cohort (Cd19- Crebbp -/- LPD 4 cases, WT LPD 1 case). f. Flow sorting strategy to functionally isolate specific HSPC populations from patients with CREBBP mutated lymphomas. g. Allele specific PCR demonstrates the presence of the same mutation (183 bp band) identified from the tumour (Mut lane) within the CD34+/CD38+ HSPC population. CFU-colony forming assay, Bl- blank, WT- wild type. h . Model of mechanisms and stage specific nature of Crebbp tumour suppression in lymphoid malignancies. Following early loss of Crebbp activity in the HSPC compartment, abnormal epigenetic regulation, post-translational modifications of substrate proteins and altered transcription lead to expansion of lymphoid progenitors, blocked differentiation, increased proliferation and clonogenicity within this expanded progenitor pool. In association with an alteration of the DNA damage response mediated through suboptimal Crebbp acetylation of p53, DNA double strand breaks accumulate and accompanied by a relative decrease in apoptosis, lead to the retention of mutations within B-cell progenitors. We illustrate the epigenetic priming of specific loci at earlier time points, where H3K27ac ChIP-Seq and RNA-Seq at the critical gene X locus read out in terms of downregulation of gene expression only at later timepoints during lymphoma evolution. We speculate that the molecular aberrations cumulatively acquired interact with this primed epigenetic state and lead to the restoration of self-renewal in this abnormal pre-malignant stem cell population and to the evolution of Lymphoma.
Figure Legend Snippet: Loss of Crebbp in committed lymphoid cells attenuates disease generation and CREBBP mutations occur in HSPC from a lymphoma patient. a. Analysis of the Lin- and IL7Ra+ compartments in Cd19- Crebbp-/- mice. Box plots show the median with interquartile range and whiskers represent the minimum and maximum values, n = 6 mice per genotype were quantified. Non-significance confirmed using 2-sided t-test. b. Comparison of H2AXγ foci in the IL7Ra+ progenitor populations from Cd19- Crebbp-/- mice. Data represent the mean ± SEM from n = 3 independent experiments, non-significance confirmed using 2-sided t-test. See Supplementary Table 26 for scatter plots of > 10 foci. c. KM analysis showing that no significant alterations in survival were seen for Cd19- Crebbp -/- mice (n = 27 animals) vs WT mice (n = 25 animals). P value calculated using Log-rank (Mantel-Cox) test. d. in contrast to that seen in Mx-Crebbp-/- mice. e. B-cell disease was very rare in this cohort (Cd19- Crebbp -/- LPD 4 cases, WT LPD 1 case). f. Flow sorting strategy to functionally isolate specific HSPC populations from patients with CREBBP mutated lymphomas. g. Allele specific PCR demonstrates the presence of the same mutation (183 bp band) identified from the tumour (Mut lane) within the CD34+/CD38+ HSPC population. CFU-colony forming assay, Bl- blank, WT- wild type. h . Model of mechanisms and stage specific nature of Crebbp tumour suppression in lymphoid malignancies. Following early loss of Crebbp activity in the HSPC compartment, abnormal epigenetic regulation, post-translational modifications of substrate proteins and altered transcription lead to expansion of lymphoid progenitors, blocked differentiation, increased proliferation and clonogenicity within this expanded progenitor pool. In association with an alteration of the DNA damage response mediated through suboptimal Crebbp acetylation of p53, DNA double strand breaks accumulate and accompanied by a relative decrease in apoptosis, lead to the retention of mutations within B-cell progenitors. We illustrate the epigenetic priming of specific loci at earlier time points, where H3K27ac ChIP-Seq and RNA-Seq at the critical gene X locus read out in terms of downregulation of gene expression only at later timepoints during lymphoma evolution. We speculate that the molecular aberrations cumulatively acquired interact with this primed epigenetic state and lead to the restoration of self-renewal in this abnormal pre-malignant stem cell population and to the evolution of Lymphoma.

Techniques Used: Mouse Assay, Flow Cytometry, Polymerase Chain Reaction, Mutagenesis, Activity Assay, Chromatin Immunoprecipitation, RNA Sequencing Assay, Expressing

30) Product Images from "Stemness, Pluripotentiality, and Wnt Antagonism: sFRP4, a Wnt antagonist Mediates Pluripotency and Stemness in Glioblastoma"

Article Title: Stemness, Pluripotentiality, and Wnt Antagonism: sFRP4, a Wnt antagonist Mediates Pluripotency and Stemness in Glioblastoma

Journal: Cancers

doi: 10.3390/cancers11010025

( A – F ). Apoptosis related genes identified in sFRP4 chromatin immunoprecipitation (ChIP) and pull-down sequencing analysis. Schematic 1 represents the sequence of steps of ChIP and downstream sequencing. ChIP DNA resolved on agarose gel indicated a 150 bp DNA band ( A ), ChIP mapping statistics by Burrow-Wheeler Aligner software indicated 5,581,398 mapped reads ( B ); peak calling analysis output using MACS2 software revealed 34,711 peaks related to secreted frizzled-related protein 4 ( sFRP4 ) ( C , left panel), categorization of peak identities represented by pie chart and table, analysis gene list present within 5′UTR (enlarged) indicated the presence of miRNA 885 ( C , right panel), RNA sequencing data of 5′UTR revealed an upregulation of three 5′UTR genes in sFRP4 OE (OE) and downregulation in sFRP4 SI (SI) cells as indicated in the box ( D ), upregulation of active miR-885 in sFRP4 OE cells using an miR-885 5′LNA probe was detected as red fluorescence using a fluorescence microscope (scale bar = 10 μm) ( E ), quantitative RT-PCR indicated an over expression of miR885 in sFRP4 OE and downregulation in sFRP4 SI cells ( F ). Schematic 2 represents a model indicating the mode of action of miR-885 through its target genes CDK2 and MCM5 in cellular homeostasis via activation of p53 . Results are mean ± SD of three independent experiments performed in triplicates (* p value
Figure Legend Snippet: ( A – F ). Apoptosis related genes identified in sFRP4 chromatin immunoprecipitation (ChIP) and pull-down sequencing analysis. Schematic 1 represents the sequence of steps of ChIP and downstream sequencing. ChIP DNA resolved on agarose gel indicated a 150 bp DNA band ( A ), ChIP mapping statistics by Burrow-Wheeler Aligner software indicated 5,581,398 mapped reads ( B ); peak calling analysis output using MACS2 software revealed 34,711 peaks related to secreted frizzled-related protein 4 ( sFRP4 ) ( C , left panel), categorization of peak identities represented by pie chart and table, analysis gene list present within 5′UTR (enlarged) indicated the presence of miRNA 885 ( C , right panel), RNA sequencing data of 5′UTR revealed an upregulation of three 5′UTR genes in sFRP4 OE (OE) and downregulation in sFRP4 SI (SI) cells as indicated in the box ( D ), upregulation of active miR-885 in sFRP4 OE cells using an miR-885 5′LNA probe was detected as red fluorescence using a fluorescence microscope (scale bar = 10 μm) ( E ), quantitative RT-PCR indicated an over expression of miR885 in sFRP4 OE and downregulation in sFRP4 SI cells ( F ). Schematic 2 represents a model indicating the mode of action of miR-885 through its target genes CDK2 and MCM5 in cellular homeostasis via activation of p53 . Results are mean ± SD of three independent experiments performed in triplicates (* p value

Techniques Used: Chromatin Immunoprecipitation, Sequencing, Agarose Gel Electrophoresis, Software, RNA Sequencing Assay, Fluorescence, Microscopy, Quantitative RT-PCR, Over Expression, Activation Assay

Apoptotic genes TP53 and SMAD4 were detected in gene cluster analysis of ChIP genes and interlinking study. Schematic 3 represents LOGOS diagram of first rank motif homeobox- Cphx1 and second rank motif FoxK1 , and downstream effectors of Cphx1 (i) and a flow chart indicating activation of the Usp9x gene by Cphx1 via the reported role of Usp9x in the regulation of sensitization and apoptosis in cancer cells (ii). Gene cluster analysis using Cluster 3.0 software indicated linking of Usp9x , Foxk1 , and sFRP4 via TP53 and SMAD4 ( A ). Relative mRNA expression showed upregulation of FOXK1 , TP53 , USP9x , and SMAD4 in sFRP4 OE compared to control and sFRP4 SI cells ( B ). DNA instability was indicated in sFRP4 OE cells by immunocytochemical staining of H2AX and visualization using fluorescence microscopy (scale bar = 10 µm) ( C ). Results are mean ± SD of three independent experiments performed in triplicates (* p value
Figure Legend Snippet: Apoptotic genes TP53 and SMAD4 were detected in gene cluster analysis of ChIP genes and interlinking study. Schematic 3 represents LOGOS diagram of first rank motif homeobox- Cphx1 and second rank motif FoxK1 , and downstream effectors of Cphx1 (i) and a flow chart indicating activation of the Usp9x gene by Cphx1 via the reported role of Usp9x in the regulation of sensitization and apoptosis in cancer cells (ii). Gene cluster analysis using Cluster 3.0 software indicated linking of Usp9x , Foxk1 , and sFRP4 via TP53 and SMAD4 ( A ). Relative mRNA expression showed upregulation of FOXK1 , TP53 , USP9x , and SMAD4 in sFRP4 OE compared to control and sFRP4 SI cells ( B ). DNA instability was indicated in sFRP4 OE cells by immunocytochemical staining of H2AX and visualization using fluorescence microscopy (scale bar = 10 µm) ( C ). Results are mean ± SD of three independent experiments performed in triplicates (* p value

Techniques Used: Chromatin Immunoprecipitation, Flow Cytometry, Activation Assay, Software, Expressing, Staining, Fluorescence, Microscopy

31) Product Images from "Evaluation and application of RNA-Seq by MinION"

Article Title: Evaluation and application of RNA-Seq by MinION

Journal: DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes

doi: 10.1093/dnares/dsy038

Applications of MinION transcriptome sequencing. (A) Novel isoforms of the STRAP gene (left) and the ACTG1 gene (right). RefSeq transcripts and cufflink assembles using Illumina RNA-Seq are shown in the upper panel. FL-cDNA-Seq reads annotated as novel isoforms are shown in the middle panel. Illumina RNASeq reads are shown in the lower panel. (B) Three fusion genes detected by FL-cDNA. CCDC6-RET is known as a fusion gene of LC2/ad. Only the fusion chromosome of CCDC6-RET harbors a G to T SNP. WAC-SFMBT2 and ZSCAN22-CHMP2A were detected on LC2/ad and PC-9, respectively. (C) Sanger sequencing of the three fusion junctions. (D) Phased hetero SNP by FL-cDNA-Seq reads. We exemplified two phased genes, DSG2 (left) and SEPT9 (right), detected by the R9.4 reads of LC2ad. The distance between the phased SNP on the transcript and genome and hetero SNP patterns are shown at the top. The FL-cDNA-Seq reads are shown in the middle. The phased SNP pattern and number of reads covering all the SNPs are shown at the bottom.
Figure Legend Snippet: Applications of MinION transcriptome sequencing. (A) Novel isoforms of the STRAP gene (left) and the ACTG1 gene (right). RefSeq transcripts and cufflink assembles using Illumina RNA-Seq are shown in the upper panel. FL-cDNA-Seq reads annotated as novel isoforms are shown in the middle panel. Illumina RNASeq reads are shown in the lower panel. (B) Three fusion genes detected by FL-cDNA. CCDC6-RET is known as a fusion gene of LC2/ad. Only the fusion chromosome of CCDC6-RET harbors a G to T SNP. WAC-SFMBT2 and ZSCAN22-CHMP2A were detected on LC2/ad and PC-9, respectively. (C) Sanger sequencing of the three fusion junctions. (D) Phased hetero SNP by FL-cDNA-Seq reads. We exemplified two phased genes, DSG2 (left) and SEPT9 (right), detected by the R9.4 reads of LC2ad. The distance between the phased SNP on the transcript and genome and hetero SNP patterns are shown at the top. The FL-cDNA-Seq reads are shown in the middle. The phased SNP pattern and number of reads covering all the SNPs are shown at the bottom.

Techniques Used: Sequencing, RNA Sequencing Assay

Comparison of FL-cDNA-Seq and Illumina RNA-Seq. (A) The gene expression of FL-cDNA-Seq of LC2/ad (R9.4) was compared with that of TruSeq RNA (left) and SMART-Seq (right). Pearson correlation coefficients are shown on the graph. (B) Influence of sequencing depth on the estimation of gene expression level and gene detection. Reads for each method were randomly sampled in triplicate. The average of the Pearson correlation coefficients between TruSeq RNA and randomly sampled data for FL-cDNA-Seq and SMART-Seq is shown (left). The average number of genes with an expression level of more than 1 tpm or ppm is shown (right). (C) Comparison to qRT-qPCR. Forty-four genes detected by all methods were analyzed. The gene expression of these genes was normalized to GAPDH. Pearson correlation coefficients are shown on the graph. qRT-qPCR data of LC2/ad was obtained as in our previous study. 16
Figure Legend Snippet: Comparison of FL-cDNA-Seq and Illumina RNA-Seq. (A) The gene expression of FL-cDNA-Seq of LC2/ad (R9.4) was compared with that of TruSeq RNA (left) and SMART-Seq (right). Pearson correlation coefficients are shown on the graph. (B) Influence of sequencing depth on the estimation of gene expression level and gene detection. Reads for each method were randomly sampled in triplicate. The average of the Pearson correlation coefficients between TruSeq RNA and randomly sampled data for FL-cDNA-Seq and SMART-Seq is shown (left). The average number of genes with an expression level of more than 1 tpm or ppm is shown (right). (C) Comparison to qRT-qPCR. Forty-four genes detected by all methods were analyzed. The gene expression of these genes was normalized to GAPDH. Pearson correlation coefficients are shown on the graph. qRT-qPCR data of LC2/ad was obtained as in our previous study. 16

Techniques Used: RNA Sequencing Assay, Expressing, Sequencing, Real-time Polymerase Chain Reaction

32) Product Images from "A Spontaneous Fatp4/Scl27a4 Splice Site Mutation in a New Murine Model for Congenital Ichthyosis"

Article Title: A Spontaneous Fatp4/Scl27a4 Splice Site Mutation in a New Murine Model for Congenital Ichthyosis

Journal: PLoS ONE

doi: 10.1371/journal.pone.0050634

SNP mapping. Genomic DNA was analyzed from 4 heterozygous parents (2780, 2771, 3345 and 3377), and from 9 mutant offspring (PS118, 119, 121–127). Since the mutation occurred on an FVB background, the affected mice should be homozygous for FVB alleles (red color) that are linked to the mutation. Homozygosity for the C57BL/6 allele (B6) is indicated in blue. Carriers of both alleles are indicated in yellow. The critical region is centered around the SNP named rs13459062 (red arrow). This sequence is located near 30 MB on mouse chromosome 2. P, parental; A, affected; U, unaffected.
Figure Legend Snippet: SNP mapping. Genomic DNA was analyzed from 4 heterozygous parents (2780, 2771, 3345 and 3377), and from 9 mutant offspring (PS118, 119, 121–127). Since the mutation occurred on an FVB background, the affected mice should be homozygous for FVB alleles (red color) that are linked to the mutation. Homozygosity for the C57BL/6 allele (B6) is indicated in blue. Carriers of both alleles are indicated in yellow. The critical region is centered around the SNP named rs13459062 (red arrow). This sequence is located near 30 MB on mouse chromosome 2. P, parental; A, affected; U, unaffected.

Techniques Used: Mutagenesis, Mouse Assay, Sequencing

33) Product Images from "A multiplatform approach identifies miR-152-3p as a common epigenetically regulated onco-suppressor in prostate cancer targeting TMEM97"

Article Title: A multiplatform approach identifies miR-152-3p as a common epigenetically regulated onco-suppressor in prostate cancer targeting TMEM97

Journal: Clinical Epigenetics

doi: 10.1186/s13148-018-0475-2

Identification of miRNAs downregulated by DNA methylation in prostate cancer, using a combinatorial approach. a Venn diagram of the intersection of the miR expression (Exiqon) versus DNA methylation (Infinium HumanMethylation450 BeadChip) for miRNA promoters. Intersection is shown for the downregulated miRNAs and hypermethylated miRNAs. The five common miRNAs based on expression level and DNA methylation in PCa tissues are miR-10a, miR-23b, miR-27b, miR-34c, and miR-152-3p. b Independent validation using the TCGA Prostate RNA-seq cohort for miR-10a, miR-23b, miR-27b, and miR-152-3p in PCa samples compared to NAT samples. c MiRNA expression analysis of 52 matched normal and PCa samples pairs using TCGA cohort. Except for miR-10a, all miRs were significantly downregulated in PCa. d DNA methylation levels (β-Values) for each probe in specific miRNA loci, comparing normal and PCa samples using TCGA Prostate 450 K cohort. Overall, DNA methylation gain (hypermethylation) was found in PCa samples. NAT: Normal Adjacent Tissue; PCa: Prostate Cancer; Mann-Whitney U test: * p
Figure Legend Snippet: Identification of miRNAs downregulated by DNA methylation in prostate cancer, using a combinatorial approach. a Venn diagram of the intersection of the miR expression (Exiqon) versus DNA methylation (Infinium HumanMethylation450 BeadChip) for miRNA promoters. Intersection is shown for the downregulated miRNAs and hypermethylated miRNAs. The five common miRNAs based on expression level and DNA methylation in PCa tissues are miR-10a, miR-23b, miR-27b, miR-34c, and miR-152-3p. b Independent validation using the TCGA Prostate RNA-seq cohort for miR-10a, miR-23b, miR-27b, and miR-152-3p in PCa samples compared to NAT samples. c MiRNA expression analysis of 52 matched normal and PCa samples pairs using TCGA cohort. Except for miR-10a, all miRs were significantly downregulated in PCa. d DNA methylation levels (β-Values) for each probe in specific miRNA loci, comparing normal and PCa samples using TCGA Prostate 450 K cohort. Overall, DNA methylation gain (hypermethylation) was found in PCa samples. NAT: Normal Adjacent Tissue; PCa: Prostate Cancer; Mann-Whitney U test: * p

Techniques Used: DNA Methylation Assay, Expressing, RNA Sequencing Assay, MANN-WHITNEY

COPZ2 -miR-152-3p transcriptional unit’s DNA methylation and expression validation in IPO Porto’s cohort of prostate samples. a HumanMethylation450 BeadChip data for miR-152-3p locus, showing a significant increase in cg21384971, cg05096161, cg05850656, cg06598332, cg10382221, and cg24389730. b Significant miR-152-3p downregulation in PCa ( n = 100) compared to morphologically normal prostate tissues (MNPT, n = 14), as determined by RT-qPCR ( p
Figure Legend Snippet: COPZ2 -miR-152-3p transcriptional unit’s DNA methylation and expression validation in IPO Porto’s cohort of prostate samples. a HumanMethylation450 BeadChip data for miR-152-3p locus, showing a significant increase in cg21384971, cg05096161, cg05850656, cg06598332, cg10382221, and cg24389730. b Significant miR-152-3p downregulation in PCa ( n = 100) compared to morphologically normal prostate tissues (MNPT, n = 14), as determined by RT-qPCR ( p

Techniques Used: DNA Methylation Assay, Expressing, Quantitative RT-PCR

34) Product Images from "Isolation and genome sequencing of four Arctic marine Psychrobacter strains exhibiting multicopper oxidase activity"

Article Title: Isolation and genome sequencing of four Arctic marine Psychrobacter strains exhibiting multicopper oxidase activity

Journal: BMC Genomics

doi: 10.1186/s12864-016-2445-4

Phylogenetic tree of laccase-positive Psychrobacter species based on 16S rRNA gene sequence similarity. Psychrobacter strains with laccase-positive phenotype are indicated in bold font. Psychrobacter type strains were used as references, with Moraxella atlantae as an outgroup. The four strains selected for genome sequence determination are indicated with arrows
Figure Legend Snippet: Phylogenetic tree of laccase-positive Psychrobacter species based on 16S rRNA gene sequence similarity. Psychrobacter strains with laccase-positive phenotype are indicated in bold font. Psychrobacter type strains were used as references, with Moraxella atlantae as an outgroup. The four strains selected for genome sequence determination are indicated with arrows

Techniques Used: Sequencing

35) Product Images from "Solution Hybrid Selection with Ultra-long Oligonucleotides for Massively Parallel Targeted Sequencing"

Article Title: Solution Hybrid Selection with Ultra-long Oligonucleotides for Massively Parallel Targeted Sequencing

Journal: Nature biotechnology

doi: 10.1038/nbt.1523

Overview of hybrid selection method. Illustrated are steps involved in the preparation of a complex pool of biotinylated RNA capture probes (“bait”; top left), whole-genome fragment input library (“pond”; top right) and hybrid-selected enriched output library (“catch”; bottom). Two sequencing targets and their respective baits are shown in red and blue. Thin and thick lines represent single and double strands, respectively. Universal adapter sequences are grey. The excess of single-stranded non-self-complementary RNA (wavy lines) drives the hybridization. See main text and Methods for details.
Figure Legend Snippet: Overview of hybrid selection method. Illustrated are steps involved in the preparation of a complex pool of biotinylated RNA capture probes (“bait”; top left), whole-genome fragment input library (“pond”; top right) and hybrid-selected enriched output library (“catch”; bottom). Two sequencing targets and their respective baits are shown in red and blue. Thin and thick lines represent single and double strands, respectively. Universal adapter sequences are grey. The excess of single-stranded non-self-complementary RNA (wavy lines) drives the hybridization. See main text and Methods for details.

Techniques Used: Selection, Sequencing, Hybridization

36) Product Images from "In depth analysis of the Sox4 gene locus that consists of sense and natural antisense transcripts"

Article Title: In depth analysis of the Sox4 gene locus that consists of sense and natural antisense transcripts

Journal: Data in Brief

doi: 10.1016/j.dib.2016.01.045

RNA FISH of Sox4 and Hmbs sense and NATs. The type of transcripts analyzed is shown at the top of the figure and the origins of cells are shown to the left of the micrographs.
Figure Legend Snippet: RNA FISH of Sox4 and Hmbs sense and NATs. The type of transcripts analyzed is shown at the top of the figure and the origins of cells are shown to the left of the micrographs.

Techniques Used: Fluorescence In Situ Hybridization

37) Product Images from "Function and Evolution of DNA Methylation in Nasonia vitripennis"

Article Title: Function and Evolution of DNA Methylation in Nasonia vitripennis

Journal: PLoS Genetics

doi: 10.1371/journal.pgen.1003872

DNA methylation, gene expression and expression breadth. (A) Distribution of RNA-seq expression level (log 10 FPKM) in adult female for methylated (blue), non-methylated (red) and all genes (purple). (B) Distribution of RNA-seq expression level (log 10 FPKM) in adult female for groups of genes binned by percentage of methylated CpG sites in 5′ 1 kbp coding region. Red: non-methylation genes; blue: methylated genes. (C) Histograms for distribution of expression coefficient of variation (log 10 expression CV) in five developmental stages (early embryo, late embryo, larvae, pupae and adult) for methylated (blue), non-methylated (red) and all genes (purple). (D) Distribution of expression breadth measurement (log 10 expression CV) in six developmental stages for groups of genes binned by percentage of methylated CpG sites in 5′ 1 kbp coding region. Red: non-methylation genes; blue: methylated genes. (E) Scatterplot of expression breadth (log 2 expression CV) on y-axis against median expression level (log 2 signal intensity) in tiling array on x-axis, color-coded by adult female methylation status (blue: methylated genes; red: non-methylated genes). Fitted lines using non-parametric local regression are shown for methylated and non-methylated genes respectively. (F) Top right panel: Stacked barplot for expressed methylated and non-methylated genes with 0 to 6 expressed stages. Red: unmethylation genes; blue: methylated genes. Top left and bottom panel: boxplot for distribution of adult female RNA-seq expression level (log 10 FPKM) for methylated (in blue), non-methylated (in red) and all genes (in purple) expressed in 0–5 developmental stages.
Figure Legend Snippet: DNA methylation, gene expression and expression breadth. (A) Distribution of RNA-seq expression level (log 10 FPKM) in adult female for methylated (blue), non-methylated (red) and all genes (purple). (B) Distribution of RNA-seq expression level (log 10 FPKM) in adult female for groups of genes binned by percentage of methylated CpG sites in 5′ 1 kbp coding region. Red: non-methylation genes; blue: methylated genes. (C) Histograms for distribution of expression coefficient of variation (log 10 expression CV) in five developmental stages (early embryo, late embryo, larvae, pupae and adult) for methylated (blue), non-methylated (red) and all genes (purple). (D) Distribution of expression breadth measurement (log 10 expression CV) in six developmental stages for groups of genes binned by percentage of methylated CpG sites in 5′ 1 kbp coding region. Red: non-methylation genes; blue: methylated genes. (E) Scatterplot of expression breadth (log 2 expression CV) on y-axis against median expression level (log 2 signal intensity) in tiling array on x-axis, color-coded by adult female methylation status (blue: methylated genes; red: non-methylated genes). Fitted lines using non-parametric local regression are shown for methylated and non-methylated genes respectively. (F) Top right panel: Stacked barplot for expressed methylated and non-methylated genes with 0 to 6 expressed stages. Red: unmethylation genes; blue: methylated genes. Top left and bottom panel: boxplot for distribution of adult female RNA-seq expression level (log 10 FPKM) for methylated (in blue), non-methylated (in red) and all genes (in purple) expressed in 0–5 developmental stages.

Techniques Used: DNA Methylation Assay, Expressing, RNA Sequencing Assay, Methylation

DNA methylation and gene conservation. (A) Phylogenetic tree of eight insect species: Nasonia vitripennis , Apis mellifera , Tribolium castaneum , Bombyx mori , Anopheles gambiae , Drosophila melanogaster , Pediculus humanus and Acyrthosiphon pisum . The methylation status and correlating factors were plotted in (B–F) for four groups of genes: all 5,039 Nasonia single-copy genes with one or zero ortholog in seven other insect species, 2,374 genes with one orthologs in all eight insect species, 443 genes with one orthologs in Apis and Nasonia but missing in other six species, and 320 genes present only in Nasonia . The y -axes plotted in (B–F) are (B): proportion of methylated (blue) and non-methylated genes (red); (C): percentage of methylated CpG sites in methylated genes; (D): adult RNA-seq expression levels (log 10 FPKM); (E): coefficient of variation of expression level in tiling array across six developmental stages; (F): number of expressed tissues. (G) Top: Phylogenetic tree of three Nasonia species: N. longicornis (L), N. giraulti (G) and N. vitripennis (V). Bottom: boxplots of nucleotide substitution rates between V–L, V–G and L–G.
Figure Legend Snippet: DNA methylation and gene conservation. (A) Phylogenetic tree of eight insect species: Nasonia vitripennis , Apis mellifera , Tribolium castaneum , Bombyx mori , Anopheles gambiae , Drosophila melanogaster , Pediculus humanus and Acyrthosiphon pisum . The methylation status and correlating factors were plotted in (B–F) for four groups of genes: all 5,039 Nasonia single-copy genes with one or zero ortholog in seven other insect species, 2,374 genes with one orthologs in all eight insect species, 443 genes with one orthologs in Apis and Nasonia but missing in other six species, and 320 genes present only in Nasonia . The y -axes plotted in (B–F) are (B): proportion of methylated (blue) and non-methylated genes (red); (C): percentage of methylated CpG sites in methylated genes; (D): adult RNA-seq expression levels (log 10 FPKM); (E): coefficient of variation of expression level in tiling array across six developmental stages; (F): number of expressed tissues. (G) Top: Phylogenetic tree of three Nasonia species: N. longicornis (L), N. giraulti (G) and N. vitripennis (V). Bottom: boxplots of nucleotide substitution rates between V–L, V–G and L–G.

Techniques Used: DNA Methylation Assay, Methylation, RNA Sequencing Assay, Expressing

DNA methylation and gene length, exon number and gene locations. (A) Scatterplot for gene length (log 10 ) and percentage of methylated CpG sites for methylation genes in the entire transcript region (left) and in 5′ 1 kbp coding region (right). The fitted lines using non-parametric local regression are shown in red. (B) Left: Distance between neighboring methylated genes (MM), non-methylated genes (NN) and methylated-non-methylated genes (MN or NM). The expected distributions for the three classes calculated by permuting the methylation status (N = 5,000) were plotted (MM: blue; NN: red; MN or NM: purple). The observed mean distance for each group was shown using arrows. Right: Distribution of the distance for the four classes (MM, NN, MN and NM). (C) Distribution of observed (orange) and expected (blue) counts for consecutive run of methylated genes. The expected counts were computed assuming the methylation status is randomly distributed.
Figure Legend Snippet: DNA methylation and gene length, exon number and gene locations. (A) Scatterplot for gene length (log 10 ) and percentage of methylated CpG sites for methylation genes in the entire transcript region (left) and in 5′ 1 kbp coding region (right). The fitted lines using non-parametric local regression are shown in red. (B) Left: Distance between neighboring methylated genes (MM), non-methylated genes (NN) and methylated-non-methylated genes (MN or NM). The expected distributions for the three classes calculated by permuting the methylation status (N = 5,000) were plotted (MM: blue; NN: red; MN or NM: purple). The observed mean distance for each group was shown using arrows. Right: Distribution of the distance for the four classes (MM, NN, MN and NM). (C) Distribution of observed (orange) and expected (blue) counts for consecutive run of methylated genes. The expected counts were computed assuming the methylation status is randomly distributed.

Techniques Used: DNA Methylation Assay, Methylation

DNA methylation and alternative splicing. (A) Counts of alternatively spliced and non-alternatively spliced genes with different methylation status from OGS2 gene models (left) and RNA-seq data (right). AS: alternatively spliced; nAS: non-alternatively spliced. Methylated is shown in blue and non-methylated shown in red. (B) Distribution of fraction of major spliced forms for alternatively spliced methylated (blue) and non-methylated genes (red). (C) Gene expression, DNA methylation and alternative splicing profile for a non-methylated gene Nasvi2EG003411. Plotted at the top is the IGV browser screenshot showing adult female RNA-seq coverage (on log scale) and read alignments in the gene region. Plotted at the bottom are the CpG methylation profile at covered CpG sites from WGBS-seq data and the exon model of the alternatively spliced transcripts from OGS2 gene models. A vertical bar was drawn for each CpG at its position in the gene, color-coded by the methylation percentage in proportion to the bar length (blue: methylated Cs; red: non-methylated Cs). All 587 covered CpGs in the gene region were non-methylated. Two of the three OGS2 transcript variants, Nasvi2EG003411t1 (labelled as t1) and Nasvi2EG003411t3 (labelled as t3), were covered in the RNA-seq data with 47% and 41% of the transcript abundance, respectively. Two of the remaining minor transcript variants (other1 and other2) were also plotted.
Figure Legend Snippet: DNA methylation and alternative splicing. (A) Counts of alternatively spliced and non-alternatively spliced genes with different methylation status from OGS2 gene models (left) and RNA-seq data (right). AS: alternatively spliced; nAS: non-alternatively spliced. Methylated is shown in blue and non-methylated shown in red. (B) Distribution of fraction of major spliced forms for alternatively spliced methylated (blue) and non-methylated genes (red). (C) Gene expression, DNA methylation and alternative splicing profile for a non-methylated gene Nasvi2EG003411. Plotted at the top is the IGV browser screenshot showing adult female RNA-seq coverage (on log scale) and read alignments in the gene region. Plotted at the bottom are the CpG methylation profile at covered CpG sites from WGBS-seq data and the exon model of the alternatively spliced transcripts from OGS2 gene models. A vertical bar was drawn for each CpG at its position in the gene, color-coded by the methylation percentage in proportion to the bar length (blue: methylated Cs; red: non-methylated Cs). All 587 covered CpGs in the gene region were non-methylated. Two of the three OGS2 transcript variants, Nasvi2EG003411t1 (labelled as t1) and Nasvi2EG003411t3 (labelled as t3), were covered in the RNA-seq data with 47% and 41% of the transcript abundance, respectively. Two of the remaining minor transcript variants (other1 and other2) were also plotted.

Techniques Used: DNA Methylation Assay, Methylation, RNA Sequencing Assay, Expressing, CpG Methylation Assay

Distribution of CpG DNA methylation in the Nasonia genome across protein-coding genes. (A) Distributions across genomic features for all 14 million CpG sites (Top left), 8 million covered CpG sites (Top middle) and methylated CpG sites (mCpGs, Top right). Plotted in the bottom panel are the distributions for percentage of mCpGs and methylation percentage at covered CpG sites. (B) Percentage of mCpGs in the 1 kbp upstream, 1 kbp downstream, UTR and intronic regions for methylated (blue), non-methylated (red) and all genes (purple). (C) Percentage of mCpGs in introns for methylated (blue), non-methylated (red) and all genes (purple), binned by the nearest distance to the exon-intron junctions. (D) Percentage of mCpGs across exons for methylated (blue), non-methylated (red) and all genes (purple). (E) Percentage of mCpGs in the coding region starting from first codon for methylated (blue), non-methylated (red) and all genes (purple). (F) Methylation level in 1 kbp upstream, 1 kbp 5′-UTR, first 2 kbp coding, 1 kbp 3′-UTR and 1 kbp downstream regions for 1,540 expressed transposable element genes (TE genes) and 16,186 non-TE genes. Dark blue line: percentage of mCpGs for non-TE genes; light blue line: average methylation percentage across covered CpGs for non-TE genes; red line: percentage of mCpGs for TE genes. (G) Methylation level in 1 kbp upstream, 1 kbp 5′-UTR, first 2 kbp coding, 1 kbp 3′-UTR and 1 kbp downstream regions for 4,751 methylated non-TE genes and 12,975 non-methylated non-TE genes. Dark blue line: percentage of mCpGs for methylated genes; light blue line: average methylation percentage across covered CpGs for methylated genes; red line: percentage of mCpGs for non-methylated genes. (H–I) Plot of Percentage of methylated CpG sites in the 5′UTR, the first four exons and introns (H) and 3′UTR, the last four exons and introns (I) for methylated (blue) and non-methylated genes (red). All exons, introns and UTRs were rescaled to the same length.
Figure Legend Snippet: Distribution of CpG DNA methylation in the Nasonia genome across protein-coding genes. (A) Distributions across genomic features for all 14 million CpG sites (Top left), 8 million covered CpG sites (Top middle) and methylated CpG sites (mCpGs, Top right). Plotted in the bottom panel are the distributions for percentage of mCpGs and methylation percentage at covered CpG sites. (B) Percentage of mCpGs in the 1 kbp upstream, 1 kbp downstream, UTR and intronic regions for methylated (blue), non-methylated (red) and all genes (purple). (C) Percentage of mCpGs in introns for methylated (blue), non-methylated (red) and all genes (purple), binned by the nearest distance to the exon-intron junctions. (D) Percentage of mCpGs across exons for methylated (blue), non-methylated (red) and all genes (purple). (E) Percentage of mCpGs in the coding region starting from first codon for methylated (blue), non-methylated (red) and all genes (purple). (F) Methylation level in 1 kbp upstream, 1 kbp 5′-UTR, first 2 kbp coding, 1 kbp 3′-UTR and 1 kbp downstream regions for 1,540 expressed transposable element genes (TE genes) and 16,186 non-TE genes. Dark blue line: percentage of mCpGs for non-TE genes; light blue line: average methylation percentage across covered CpGs for non-TE genes; red line: percentage of mCpGs for TE genes. (G) Methylation level in 1 kbp upstream, 1 kbp 5′-UTR, first 2 kbp coding, 1 kbp 3′-UTR and 1 kbp downstream regions for 4,751 methylated non-TE genes and 12,975 non-methylated non-TE genes. Dark blue line: percentage of mCpGs for methylated genes; light blue line: average methylation percentage across covered CpGs for methylated genes; red line: percentage of mCpGs for non-methylated genes. (H–I) Plot of Percentage of methylated CpG sites in the 5′UTR, the first four exons and introns (H) and 3′UTR, the last four exons and introns (I) for methylated (blue) and non-methylated genes (red). All exons, introns and UTRs were rescaled to the same length.

Techniques Used: DNA Methylation Assay, Methylation

38) Product Images from "Genetic deletion of microRNA biogenesis in muscle cells reveals a hierarchical non-clustered network that controls focal adhesion signaling during muscle regeneration"

Article Title: Genetic deletion of microRNA biogenesis in muscle cells reveals a hierarchical non-clustered network that controls focal adhesion signaling during muscle regeneration

Journal: Molecular Metabolism

doi: 10.1016/j.molmet.2020.02.010

Genome-wide identification of differentially expressed genes and miRNA target regulation after single or combinatorial miRNA inhibition in human myotubes. Human myoblasts were transfected with the indicated antagomirs and 24 h after transfection differentiation was induced for two days. RNA was isolated for RNA deep sequencing, n = 3. A. Circos plot of differentially expressed genes (p-value
Figure Legend Snippet: Genome-wide identification of differentially expressed genes and miRNA target regulation after single or combinatorial miRNA inhibition in human myotubes. Human myoblasts were transfected with the indicated antagomirs and 24 h after transfection differentiation was induced for two days. RNA was isolated for RNA deep sequencing, n = 3. A. Circos plot of differentially expressed genes (p-value

Techniques Used: Genome Wide, Inhibition, Transfection, Isolation, Sequencing, Significance Assay

A combination of six miRNAs rescues myotube morphology and reverses induction of the focal adhesion gene cluster following DGCR8 deletion. Dgcr8 KO and control myoblasts were transfected with control mimics, the indicated individual six miRNA mimics or their combination (6× miRNA) two days after beginning of the tamoxifen incubation. Twenty-four hours after transfection, myoblast differentiation was induced for 48 h. Myotube morphology was analyzed using immunofluorescence for desmin (A) and brightfield microscopy (B), 10× magnification. Scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from control, Dgcr8 myotubes and Dgcr8 myotubes transfected with the combination of six miRNAs. D. RNA levels as determined by RNA deep sequencing in control and DGCR8 knockout cells with or without transfection of the six miRNA mimics, n = 3. Shown are all genes that are significantly upregulated in the knockout cells and significantly downregulated after transfection with the mimics (p value
Figure Legend Snippet: A combination of six miRNAs rescues myotube morphology and reverses induction of the focal adhesion gene cluster following DGCR8 deletion. Dgcr8 KO and control myoblasts were transfected with control mimics, the indicated individual six miRNA mimics or their combination (6× miRNA) two days after beginning of the tamoxifen incubation. Twenty-four hours after transfection, myoblast differentiation was induced for 48 h. Myotube morphology was analyzed using immunofluorescence for desmin (A) and brightfield microscopy (B), 10× magnification. Scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from control, Dgcr8 myotubes and Dgcr8 myotubes transfected with the combination of six miRNAs. D. RNA levels as determined by RNA deep sequencing in control and DGCR8 knockout cells with or without transfection of the six miRNA mimics, n = 3. Shown are all genes that are significantly upregulated in the knockout cells and significantly downregulated after transfection with the mimics (p value

Techniques Used: Transfection, Incubation, Immunofluorescence, Microscopy, Isolation, Sequencing, Knock-Out, Significance Assay

A combination of five miRNAs accelerates differentiation of human myoblasts and improves insulin sensitivity downstream of FAK signaling. Human primary myoblasts were transfected with equal concentrations of control antagomirs or antagomirs against the indicated miRNAs, either as single antagomirs or in combination (Ant-5x). Twenty-four hours after transfection, differentiation was induced for two days (A, B, C, E, G) or up to five days (D, F). A. Gene expression of myogenic regulatory factors and eMHC was analyzed by qRT-PCR and normalized for 18S RNA, n = 5. B. Protein expression of myogenin and eMHC was analyzed by western blot and normalized to GAPDH (n = 4–6). C. Luciferase vectors harboring either the myogenin 3′UTR (n = 4) or promoter region (n = 5) were transfected with miRNA mimics or antagomirs respectively. Myogenin mRNA from control and Ant-5x treated samples was measured by qRT-PCR at the indicated time points following Actinomycin D administration (n = 3). D. Time course of myogenin and eMHC protein expression during five days of differentiation, normalized to GAPDH (n = 4–6). E. Immunofluorescent analysis of myotube formation using anti-desmin and wheat germ agglutinin (WGA). Fusion index was calculated as percentage of nuclei present in cells containing at least two nuclei compared to all nuclei per well (scale bar 100um, n = 4). F. Phosphorylation of p38 MAPK, AKT and FAK during the first three days of differentiation, n = 4. G. Insulin-dependent glycogen synthesis, n = 3. ∗: p
Figure Legend Snippet: A combination of five miRNAs accelerates differentiation of human myoblasts and improves insulin sensitivity downstream of FAK signaling. Human primary myoblasts were transfected with equal concentrations of control antagomirs or antagomirs against the indicated miRNAs, either as single antagomirs or in combination (Ant-5x). Twenty-four hours after transfection, differentiation was induced for two days (A, B, C, E, G) or up to five days (D, F). A. Gene expression of myogenic regulatory factors and eMHC was analyzed by qRT-PCR and normalized for 18S RNA, n = 5. B. Protein expression of myogenin and eMHC was analyzed by western blot and normalized to GAPDH (n = 4–6). C. Luciferase vectors harboring either the myogenin 3′UTR (n = 4) or promoter region (n = 5) were transfected with miRNA mimics or antagomirs respectively. Myogenin mRNA from control and Ant-5x treated samples was measured by qRT-PCR at the indicated time points following Actinomycin D administration (n = 3). D. Time course of myogenin and eMHC protein expression during five days of differentiation, normalized to GAPDH (n = 4–6). E. Immunofluorescent analysis of myotube formation using anti-desmin and wheat germ agglutinin (WGA). Fusion index was calculated as percentage of nuclei present in cells containing at least two nuclei compared to all nuclei per well (scale bar 100um, n = 4). F. Phosphorylation of p38 MAPK, AKT and FAK during the first three days of differentiation, n = 4. G. Insulin-dependent glycogen synthesis, n = 3. ∗: p

Techniques Used: Transfection, Expressing, Quantitative RT-PCR, Western Blot, Luciferase, Whole Genome Amplification

Genetic deletion of the miRNA pathway induces the gene cluster “focal adhesion” in proliferating primary myoblasts. A. Myoblast cultures (Pax7 CE xDgcr8 flox/flox ) were incubated for 48 h with tamoxifen to induce Cre recombinase (Dgcr8 KO) or with vehicle (Control). Deletion of Dgcr8 induced a time-dependent decrease of DGCR8 protein (western blotting), miRNA levels (qRT-PCR normalized for sno234, n = 4, control represented by the dashed line) and onset of apoptosis (flow cytometry for annexin V staining, n = 6–11). B. Primary myoblasts were treated as in A and differentiated into myotubes for 48 h four days after the beginning of the tamoxifen incubation (day 4). Desmin (green), nuclear DAPI ((blue), 20× magnification, scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from proliferating Dgcr8 KO and control myoblasts at day 4. D. Expression of Dcgr8 and members of the KEGG pathway “focal adhesion” in Pax7 CE xDgcr8 wt/wt (control) and Pax7 CE xDgcr8 flox/flox myoblasts measured using qRT-PCR at day 4 after tamoxifen incubation, n = 4. The dashed line represents incubation with vehicle. All results are shown as mean ± SEM. ∗: p
Figure Legend Snippet: Genetic deletion of the miRNA pathway induces the gene cluster “focal adhesion” in proliferating primary myoblasts. A. Myoblast cultures (Pax7 CE xDgcr8 flox/flox ) were incubated for 48 h with tamoxifen to induce Cre recombinase (Dgcr8 KO) or with vehicle (Control). Deletion of Dgcr8 induced a time-dependent decrease of DGCR8 protein (western blotting), miRNA levels (qRT-PCR normalized for sno234, n = 4, control represented by the dashed line) and onset of apoptosis (flow cytometry for annexin V staining, n = 6–11). B. Primary myoblasts were treated as in A and differentiated into myotubes for 48 h four days after the beginning of the tamoxifen incubation (day 4). Desmin (green), nuclear DAPI ((blue), 20× magnification, scale bar = 50 μm. C. KEGG pathway analysis of RNA isolated from proliferating Dgcr8 KO and control myoblasts at day 4. D. Expression of Dcgr8 and members of the KEGG pathway “focal adhesion” in Pax7 CE xDgcr8 wt/wt (control) and Pax7 CE xDgcr8 flox/flox myoblasts measured using qRT-PCR at day 4 after tamoxifen incubation, n = 4. The dashed line represents incubation with vehicle. All results are shown as mean ± SEM. ∗: p

Techniques Used: Incubation, Western Blot, Quantitative RT-PCR, Flow Cytometry, Staining, Isolation, Expressing

39) Product Images from "Long Noncoding RNA MEG3 Is an Epigenetic Determinant of Oncogenic Signaling in Functional Pancreatic Neuroendocrine Tumor Cells"

Article Title: Long Noncoding RNA MEG3 Is an Epigenetic Determinant of Oncogenic Signaling in Functional Pancreatic Neuroendocrine Tumor Cells

Journal: Molecular and Cellular Biology

doi: 10.1128/MCB.00278-17

ChIRP-Seq reveals m-Meg3 enrichment at multiple m-c-Met loci. (A) Representative agarose gel image showing the specificity of the m-Meg3 ChIRP probes. RNA was isolated after m-Meg3 ChIRP from the V3 (vector) and the M5 (stable MIN6-4N cells stably expressing the m-Meg3-3 isoform which lacks exon 4) cell lines. The RNA was then used for RT-PCR. Input corresponds to the RT-PCR product using RNA isolated before m-Meg3 ChIRP-Seq. Odd and even correspond to RT-PCR using RNA after m-Meg3 ChIRP with probes located at odd and even locations on the m-Meg3 RNA. The gel image represents products of the RT-PCR performed with primers 1F/1R (flanking exon 7 and exon 8) that recognize all m-Meg3 isoforms. gapdh served as the negative control. cDNAs from three replicates of V3 and M5 ChIRP-Seq were pooled due to low yields and sequenced. (B) m-Meg3 ChIRP-PCR in a stable cell line expressing full-length m-Meg3. RNA was isolated after m-Meg3 ChIRP from the 9V (vector) and the 14M (stable MIN6-4N cells with the m-Meg3-1 isoform, which encompasses all 10 exons) cell lines. The RNA was then used for RT-PCR. Input corresponds to RT-PCR from RNA isolated before m-Meg3 ChIRP. RT-PCR was performed with primers 1F/1R (flanking exons 7 and 8) that recognize all m-Meg3 isoforms and further confirmed with the ex3F/ex4R primer pair (flanking exons 3 and 4), specific for the m-Meg3-1 isoform. gapdh served as the negative control. (C) m-Meg3 enrichment patterns at discrete m-c-Met genomic regions in different m-Meg3 stable cell lines. DNA was isolated after m-Meg3 ChIRP from two different m-Meg-3 stable MIN6-4N cell lines and their respective vector controls. The DNA was then subjected to whole-genome amplification (WGA) and subsequent purification. The purified WGA DNA was then used to set up qPCRs in duplicate with primers specific for m-c-Met genomic regions identified by m-Meg3 ChIRP-Seq, namely, the m-c-Met upstream region, the m-c-Met exon 18 region, the m-c-Met exon 20 region, and also the previously identified kb +63 enhancer. The qPCR data are represented as percent input of DNA.
Figure Legend Snippet: ChIRP-Seq reveals m-Meg3 enrichment at multiple m-c-Met loci. (A) Representative agarose gel image showing the specificity of the m-Meg3 ChIRP probes. RNA was isolated after m-Meg3 ChIRP from the V3 (vector) and the M5 (stable MIN6-4N cells stably expressing the m-Meg3-3 isoform which lacks exon 4) cell lines. The RNA was then used for RT-PCR. Input corresponds to the RT-PCR product using RNA isolated before m-Meg3 ChIRP-Seq. Odd and even correspond to RT-PCR using RNA after m-Meg3 ChIRP with probes located at odd and even locations on the m-Meg3 RNA. The gel image represents products of the RT-PCR performed with primers 1F/1R (flanking exon 7 and exon 8) that recognize all m-Meg3 isoforms. gapdh served as the negative control. cDNAs from three replicates of V3 and M5 ChIRP-Seq were pooled due to low yields and sequenced. (B) m-Meg3 ChIRP-PCR in a stable cell line expressing full-length m-Meg3. RNA was isolated after m-Meg3 ChIRP from the 9V (vector) and the 14M (stable MIN6-4N cells with the m-Meg3-1 isoform, which encompasses all 10 exons) cell lines. The RNA was then used for RT-PCR. Input corresponds to RT-PCR from RNA isolated before m-Meg3 ChIRP. RT-PCR was performed with primers 1F/1R (flanking exons 7 and 8) that recognize all m-Meg3 isoforms and further confirmed with the ex3F/ex4R primer pair (flanking exons 3 and 4), specific for the m-Meg3-1 isoform. gapdh served as the negative control. (C) m-Meg3 enrichment patterns at discrete m-c-Met genomic regions in different m-Meg3 stable cell lines. DNA was isolated after m-Meg3 ChIRP from two different m-Meg-3 stable MIN6-4N cell lines and their respective vector controls. The DNA was then subjected to whole-genome amplification (WGA) and subsequent purification. The purified WGA DNA was then used to set up qPCRs in duplicate with primers specific for m-c-Met genomic regions identified by m-Meg3 ChIRP-Seq, namely, the m-c-Met upstream region, the m-c-Met exon 18 region, the m-c-Met exon 20 region, and also the previously identified kb +63 enhancer. The qPCR data are represented as percent input of DNA.

Techniques Used: Agarose Gel Electrophoresis, Isolation, Plasmid Preparation, Stable Transfection, Expressing, Reverse Transcription Polymerase Chain Reaction, Negative Control, Polymerase Chain Reaction, Whole Genome Amplification, Purification, Real-time Polymerase Chain Reaction

40) Product Images from "Metagenomic Analysis of a Biphenyl-Degrading Soil Bacterial Consortium Reveals the Metabolic Roles of Specific Populations"

Article Title: Metagenomic Analysis of a Biphenyl-Degrading Soil Bacterial Consortium Reveals the Metabolic Roles of Specific Populations

Journal: Frontiers in Microbiology

doi: 10.3389/fmicb.2018.00232

Diversity and composition of the biphenyl-degrading consortium. (A) Rarefaction curve of observed OTUs (≥97% sequence identity) over the number of 16S rRNA sequences and (B) relative abundance of genus based on 16S rRNA and CDSs taxonomic assignment. Only taxa with a minimum relative abundance of 0.15% for 16S rRNA and 0.9% for CDSs is represented.
Figure Legend Snippet: Diversity and composition of the biphenyl-degrading consortium. (A) Rarefaction curve of observed OTUs (≥97% sequence identity) over the number of 16S rRNA sequences and (B) relative abundance of genus based on 16S rRNA and CDSs taxonomic assignment. Only taxa with a minimum relative abundance of 0.15% for 16S rRNA and 0.9% for CDSs is represented.

Techniques Used: Sequencing

Related Articles

DNA Extraction:

Article Title: Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition
Article Snippet: .. Animals were typed across a range of SNP-chip platforms (see ‘DNA Extraction and SNP-chip Genotyping’ section above), and imputed using 712,164 SNPs from the Illumina BovineHD BeadChip from a reference population of 796 animals. .. Pairwise LD-based pruning was performed using Plink software (v1.07 ) implementing the following parameters: window size = 250, step size = 50, R2> 0.99999.

High Throughput Screening Assay:

Article Title: Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition
Article Snippet: .. All high throughput genotyping was conducted by GeneSeek (Lincoln, NE, USA), with animals typed across a range of platforms including the GeneSeek Genomic Profiler BeadChip (Super GGP; GeneSeek/Illumina; N = 26,878), the Illumina BovineSNP50 BeadChip (Illumina; N = 36,266), or the Illumina BovineHD BeadChip (Illumina; N = 1,038). .. Most (N = 377) of the 406 RNA-seq animals were typed using the BovineHD BeadChip, with the remainder (N = 29) typed using the BovineSNP50 platform.

Real-time Polymerase Chain Reaction:

Article Title: Expression Variants of the Lipogenic AGPAT6 Gene Affect Diverse Milk Composition Phenotypes in Bos taurus
Article Snippet: .. For the 12 non-FJX animals that were analysed by qPCR, the Illumina BovineHD BeadChip SNP ARS-BFGL-NGS-57448 was manually genotyped using PCR and Sanger sequencing, as described in . .. RNA Sequencing High-depth RNAseq was undertaken using mammary gland tissue samples from 217 lactating cows.

Sequencing:

Article Title: Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
Article Snippet: .. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. .. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.

Article Title: Expression Variants of the Lipogenic AGPAT6 Gene Affect Diverse Milk Composition Phenotypes in Bos taurus
Article Snippet: .. For the 12 non-FJX animals that were analysed by qPCR, the Illumina BovineHD BeadChip SNP ARS-BFGL-NGS-57448 was manually genotyped using PCR and Sanger sequencing, as described in . .. RNA Sequencing High-depth RNAseq was undertaken using mammary gland tissue samples from 217 lactating cows.

other:

Article Title: Genome-wide identification of copy number variation using high-density single-nucleotide polymorphism array in Japanese Black cattle
Article Snippet: Table S12 – Primers and probes for the basic transcriptional factor 3 . (XLSX 686 kb) Additional file 2: Detection number and mean size of CNVRs in 3, 5, 10, and 15 consecutive SNP windows. (PPTX 53 kb) Additional file 3: Autosomal SNPs intervals of Illumina BovineHD BeadChip Array. (PPTX 48 kb)

Polymerase Chain Reaction:

Article Title: Expression Variants of the Lipogenic AGPAT6 Gene Affect Diverse Milk Composition Phenotypes in Bos taurus
Article Snippet: .. For the 12 non-FJX animals that were analysed by qPCR, the Illumina BovineHD BeadChip SNP ARS-BFGL-NGS-57448 was manually genotyped using PCR and Sanger sequencing, as described in . .. RNA Sequencing High-depth RNAseq was undertaken using mammary gland tissue samples from 217 lactating cows.

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