Journal: Nature Communications

Article Title: Evolutionary origin and population diversity of a cryptic hybrid pathogen

doi: 10.1038/s41467-024-52639-1

Figure Lengend Snippet: Genome features and ohnolog information for the parental and hybrid isolates

Article Snippet: Library preparation was conducted using the Twist Whole Genome / Metagenomics_LowpassWGS-Twist Miniprep (Twist Bioscience).

Techniques:

Journal: Nature Communications

Article Title: Evolutionary origin and population diversity of a cryptic hybrid pathogen

doi: 10.1038/s41467-024-52639-1

Figure Lengend Snippet: A Among a total of 10,078 orthologous groups of genes, 7485 are categorized as core (present in 100% of isolates; N = 22) and 2593 as accessory (present in <100% of isolates; N < 22); among these accessory genes, 1448 are softcore (present in ≥95% and <100%; N = 21), 793 are shell (5-95% of isolates; 21 > N ≥ 2), and 352 are cloud (present in less than 5% of isolates; N = 1). B Among 95 biosynthetic gene cluster families (BGCFs), 46 are categorized as core and 49 as accessory (9 are software, 25 are shell, and 15 are cloud). C The number of accessory gene families increases as the number of strains increases, suggesting additional sequencing is needed to fully capture A. latus gene content variation. N values varied between 1 and 26,393 depending on the number of strains being analyzed. D The number of accessory BGCFs substantially increases with additional isolates suggesting the gene content variation of BGCs has also yet to be captured. Notably the core genome is larger among gene families, whereas the accessory portion is larger among BGCFs. Errors bars indicate standard deviation. E Protein sequence lengths differed among gene categories wherein core and softcore genes are longer than shell and cloud genes ( N = 10,079). F As genes were less frequently observed among isolates, they were also functionally annotated less frequently. G The number of genes in BGCs differed per category wherein softcore BGCs tend to be smaller than BGCs categorized as core, shell, and cloud ( N = 2511). H Few BGCs are predicted to make known secondary metabolites. Source data are provided as Source Data files. For panels E and G , statistical comparisons were made using a Kruskal–Wallis rank sum test ( p < 0.01 for both tests); pairwise comparisons were made using the Dunn’s test. One, two, and three asterisks represents a significance threshold of 0.05, 0.01, and 0.001, respectively. In panels C – E , G , average values are depicted and error bars indicate the standard deviation from the mean.

Article Snippet: Library preparation was conducted using the Twist Whole Genome / Metagenomics_LowpassWGS-Twist Miniprep (Twist Bioscience).

Techniques: Software, Sequencing, Standard Deviation

Journal: Nature Communications

Article Title: Evolutionary origin and population diversity of a cryptic hybrid pathogen

doi: 10.1038/s41467-024-52639-1

Figure Lengend Snippet: A. latus (purple), A. spinulosporus (blue), and A. quadrilineatus (red) are indistinguishable in culture. At the genomic level, A. latus isolates have larger genome sizes and gene repertoires than other Aspergillus species and can be distinguished from its close relatives through Fluorescence-Activated Cell Sorting (or FACS) analysis of DNA content. Furthermore, amplification and sequencing of single-locus molecular markers, including taxonomically informative loci, is expected to show evidence of two distinct loci that are phylogenetically distinct in a single-locus phylogeny. At the phenotypic level, A. latus spores are larger than those of other species due to their larger genome size.

Article Snippet: Library preparation was conducted using the Twist Whole Genome / Metagenomics_LowpassWGS-Twist Miniprep (Twist Bioscience).

Techniques: Fluorescence, FACS, Amplification, Sequencing

Journal: Molecular Cancer

Article Title: Multi-omic profiling provides insights into the heterogeneity, microenvironmental features, and biomarker landscape of small-cell lung cancer

doi: 10.1186/s12943-025-02514-4

Figure Lengend Snippet: Multi-omic profiling reveals the heterogeneity of SCLC. A Representative IHC staining images for the subtyping-defining biomarkers ASCL1, NEUROD1, and POUF23 for each of the IHC-defined subtypes. B Pathological subtypes across samples with IHC-defined SCLC subtypes ( n = 132). C representative images of the different pathological subtypes identified. D. Additional characterization of samples with IHC-defined subtypes by RNAseq subtype, NE subtype, pathological subtype, and ASCL1, NEUROD1, and POU2F3 H-score on IHC and Z-score on RNAseq and methylome analyses. E Concordance of SCLC subtyping by RNAseq and IHC in 97 samples evaluated by both methodologies and with available H-scores for all three biomarkers. IHC, immunohistochemistry; LCNEC, large cell neuroendocrine carcinoma of the lung; NE, neuroendocrine; NSCLC, non-small-cell lung cancer; RNAseq, RNA sequencing; SCLC, small-cell lung cancer

Article Snippet: The methylome capture technique developed by TWIST Bioscience was applied to 158 SCLC FFPE blocks with sufficient DNA.

Techniques: Immunohistochemistry, RNA sequencing, RNA Sequencing

Journal: bioRxiv

Article Title: Automatic annotation of Cryo-EM maps with the convolutional neural network Haruspex

doi: 10.1101/644476

Figure Lengend Snippet: A. Reconstruction map for the human Ribonuclease P holoenzyme (EMDB entry 9627). Manual assignment of secondary structure features can be difficult, in particular if the composition of a macromolecular complex is unknown. The shown surface corresponds to an r.m.s.d. of 0.04 with no carving. B. Secondary structure as identified by our network in the map, is projected onto the surface. Orange corresponds to RNA/DNA; red to helices and blue to sheets. This was a fairly typical test case with 70.5% true positives, 18.8% false positives and 10.7% false negatives. Recall was 86.8% and precision 79.0%. Region (I) depicts a well-predicted α-helical structure, (II) a β-sheet and (III) RNA misinterpreted as an α-helix. C. The deposited model PDB 6AHU for this map is shown in comparison. The regions depicted in and are marked # and * , respectively.

Article Snippet: In this work, we demonstrate that deep neural networks are not only capable of annotating protein secondary structure, but also oligonucleotides (RNA/DNA) in Cryo-EM maps, and provide a pre-trained network, named Haruspex .

Techniques:

Journal: bioRxiv

Article Title: Automatic annotation of Cryo-EM maps with the convolutional neural network Haruspex

doi: 10.1101/644476

Figure Lengend Snippet: Top: Annotated map. Bottom: Deposited structure for comparison. Orange corresponds to RNA/DNA; red to helices; blue to sheets and grey regions were not assigned any secondary structure. A. Nucleosome from Xenopus laevis , average map resolution 3.8 Å (map: EMDB 4297/model: PDB 6FQ5): recall 98.5%, precision 94.0% B. Flavobacterium johnsoniae Type 9 protein translocon, average map resolution 3.5 Å (map: EMDB 0133 /model: PDB 6H3I): recall 96.3%, precision 49.3% C. Leucine dehydrogenase from Geobacillus stearothermophilus , average map resolution 3.0 Å (map: EMDB 9590 /model: PDB 6ACF): recall 89.8%, precision 85.7% D. Escherichia coli Type VI secretion system, average map resolution 4.0 Å (map: EMDB 9747/model:PDB 6IXH): recall 95.9%, precision 70.9% E. Homo sapiens metabotropic glutamate receptor 5, average map resolution 4.0 Å (map: EMDB 0345/model: PDB 6N51): recall 95.9%, precision 71.7% F. Bacterial RNA polymerase-sigma54 holoenzyme transcription open complex, average map resolution 3.4 Å (map: EMDB 0001 /model: PDB 6GH5): recall 94.2%, precision 67.5%.

Article Snippet: In this work, we demonstrate that deep neural networks are not only capable of annotating protein secondary structure, but also oligonucleotides (RNA/DNA) in Cryo-EM maps, and provide a pre-trained network, named Haruspex .

Techniques:

Journal: Nature

Article Title: Latent human herpesvirus 6 is reactivated in CAR T cells

doi: 10.1038/s41586-023-06704-2

Figure Lengend Snippet: a, Quantification of HHV-6B by qPCR at day 19 for four donors normalized to the cell count. Bars are shown in order of increasing abundance. b, Longitudinal qPCR surveillance of HHV-6B U31 gene copies in CAR T cell culture from two donors. c, Schematic of the single-cell sequencing workflow to detect HHV-6+ cells from the CAR T culture. Two models are presented that would explain HHV-6 reactivation: model 1 (top), in which all cells express HHV-6 transcripts; and model 2 (bottom), in which only a subset of cells express HHV-6B. Both the host and HHV-6B viral RNA can be directly quantified using the 10x Genomics scRNA-seq workflow. d, Summary of HHV-6B expression from an individual donor (98). The top 0.2% of cells contain 99% of the HHV-6B transcript UMIs from this experiment. e, Tabulated summary of scRNA-seq profiling for four CAR T donors, including number of cells profiled, percentage expressing HHV-6, U31 qPCR value and number of shared TCR clones between the HHV-6B+ cells. N/A, not applicable. f, Extended longitudinal sampling of HHV-6B through U31 qPCR. Number at right end of each plot line indicates the fold (×) increase from the first qPCR measurement (day 21) to the final measurement (day 27; black outline) for each donor. g, Schematic and summary of HHV-6B expression in donor 34 after 19 and 25 days, showing evidence of HHV-6B spreading in the culture as depicted in the schematic. h, Correlation analyses of host factor gene expression with HHV-6B RNA abundance in individual cells for donor 34 on day 25. The per-gene correlation statistics are shown in black against a permutation of the HHV-6B expression in grey. Select genes are indicated. i, Pathway enrichment analysis of Molecular Signatures Database Hallmark gene sets for gene set enrichment analysis. A positive normalized enrichment score corresponds to genes that are overexpressed in cells with large amounts of HHV-6B transcript.

Article Snippet: Both the host and HHV-6B viral RNA can be directly quantified using the 10x Genomics scRNA-seq workflow. d , Summary of HHV-6B expression from an individual donor (98).

Techniques: Cell Counting, Cell Culture, Sequencing, Expressing, Clone Assay, Sampling, Gene Expression

Journal: Nature

Article Title: Latent human herpesvirus 6 is reactivated in CAR T cells

doi: 10.1038/s41586-023-06704-2

Figure Lengend Snippet: (a) Schematic of the experiment where CAR T cells from D98 were profiled using the 10x Genomics Multiome workflow to detect both viral DNA and RNA. (b) Scatter plot of the abundance of viral DNA and RNA at single-cell resolution. Pearson correlation between the log10 abundances is shown. (c) Per-cell viral gene expression signatures. The proportion of viral gene expression belonging to each class (late, early, immediate early) per cell is shown. (d) Same plot as in (c) but colored by the log2 number of viral DNA fragments. The population of cells highly expressing early HHV-6 transcripts show a corresponding high HHV-6 DNA copy number is highlighted by the arrow. (e) Pearson correlation of HHV-6 transcript signatures with their log abundance of DNA fragments per cell. The two-sided p-value for the Pearson correlation test is noted by each bar. (f) Bulk-level RNA and DNA correspondence in the four donors studied in the day 19 allogeneic CAR products.

Article Snippet: Both the host and HHV-6B viral RNA can be directly quantified using the 10x Genomics scRNA-seq workflow. d , Summary of HHV-6B expression from an individual donor (98).

Techniques: Gene Expression, Expressing

Journal: Nature

Article Title: Latent human herpesvirus 6 is reactivated in CAR T cells

doi: 10.1038/s41586-023-06704-2

Figure Lengend Snippet: (a) Schematic of CAR T product reculture experiment. Donor D97, which at day 19 showed a low but detectable level of HHV-6, was selected for reculture for five days. (b) Summary of RT-qPCR at the control and two treatment levels of Foscarnet. Each dot represents a technical replicate over one biological replicate per condition (validated in panel d). HHV-6 was not detected (n.d.) at the 1 mM concentration. Error bars represent the standard error of the mean. Comparison of foscarnet treated to untreated resulted in significantly lower abundance of HHV-6 RNA (p = 0.00026; two-sided ordinary least squares linear model). (c) Schematic of D34 reculture +/− foscarnet at 1 mM. (d) Difference between untreated and treated in the abundance of HHV-6+ cells. Comparing the two 10x Genomics scRNA-seq data channels, foscarnet-treated cells had a lower incidence of HHV-6 positive cells (OR = 6.25; p = 8.3e-122; Fisher’s exact test, two-sided). (e) Reduced dimensionality analysis of treated and untreated D34 cells profiled with scRNA-seq. Host gene expression was used for the analysis, showing overlapping clustering of populations irrespective of treatment status. (f) Differential gene expression analysis comparing foscarnet treated and control CAR T cells. The three most significant differential genes are noted. 0 genes were differentially expressed with a minimum log2 fold-change exceeding 1 (noted by the red).

Article Snippet: Both the host and HHV-6B viral RNA can be directly quantified using the 10x Genomics scRNA-seq workflow. d , Summary of HHV-6B expression from an individual donor (98).

Techniques: In Vitro, Quantitative RT-PCR, Control, Concentration Assay, Comparison, Gene Expression