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coding sequence (codon optimized for e. coli) for the e-diii þ ns1  (GenScript corporation)

 
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    GenScript corporation coding sequence (codon optimized for e. coli) for the e-diii þ ns1
    Coding Sequence (Codon Optimized For E. Coli) For The E Diii þ Ns1, supplied by GenScript corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Average 90 stars, based on 1 article reviews
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    GenScript corporation e. coli codon-optimized coding sequence
    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
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    Thermo Fisher codon-optimized coding sequences e. coli mgsa, glda yqhd
    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following <t>ZZEF1</t> knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.
    Codon Optimized Coding Sequences E. Coli Mgsa, Glda Yqhd, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following ZZEF1 knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: (A) Schematic representation of the workflow integrating network analysis and experimental validation to identify transcriptional regulators of tRNA expression in breast cancer cells. NBP, nucleic acid-binding proteins. (B) Heatmap displaying tRF expression profiles in MDA cells after knockdown of identified transcriptional regulator candidates. Hierarchical clustering was applied to group the resulting profiles. (C) tRF expression changes in MDA cells following ZZEF1 knockdown, compared to control cells. (D) Correlation analysis showing a significant positive association between tRNA-Lys UUU abundance and ZZEF1 expression across different cancer cell lines. tRNA-Lys UUU abundance was calculated as the sum of the corresponding isodecoders. Spearman’s ρ and the associated P -value are shown. (E) Mature tRNA expression alterations in MDA cells with ZZEF1 knockdown, highlighting significant changes in tRNA-Lys UUU compared to control cells. tRNA molecules with significant abundance alterations ( P < 0.01) after ZZEF1 knockdown are colored in blue. tRNA-Lys UUU isodecoders are colored in orange, and tRNA-Thr UGU is colored in yellow. (F) Northern blot analysis of tRNA-Lys UUU and U1 RNA (loading control) in MDA cells with ZZEF1 knockdown and control cells.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: Biomarker Discovery, Expressing, Binding Assay, Knockdown, Control, Northern Blot

    (A) Volcano plot showing alterations in translational efficiency in MDA cells following ZZEF1 knockdown compared to control cells. TER, translational efficiency ratio. Proteins with an absolute logTER greater than 0.5 and P > 0.01 are labeled in green; proteins with an absolute logTER less than 0.5 and P < 0.01 are labeled in blue; and proteins with significant logTER alterations (absolute logTER > 0.5 and P < 0.01) are colored in red. (B) Enrichment of the lysine (AAR codons) content as a function of logTER between ZZEF1 knockdown and control cells. mRNAs are distributed into equally populated bins according to their logTER (the red bars on the black background show the range of values in each bin). Bins with significant enrichment (hypergeometric test, corrected P < 0.05; red) or depletion (blue) of AAR codons are denoted with a bolded border. Also included are mutual information (MI) value and its associated z-score. (C) Similar to (B) , but showing results of enrichment patterns of transcripts with high AAR codon content after restoration of tRNA-Lys UUU supply. (D) Bar plot showing transcripts with higher AAR codon content exhibited elevated ZZEF1-tRNA-Lys UUU translational effect scores. (E) Ribosomal AAR codon dwelling times estimated using Consistent Excess of Loess Predictions (CELP) bias coefficients in ZZEF1-deficient cells with tRNA-Lys UUU overexpression compared to controls. Higher coefficients indicate longer dwelling times. (F) Functional characterization of ZZEF1-tRNA-Lys UUU targets obtained by overlapping proteins that (i) showed significant translational downregulation following ZZEF1 knockdown, (ii) showed significant translational upregulation after tRNA-Lys UUU overexpression in ZZEF1-deficient cells, and (iii) frequently utilized AAR codons. (G) Scatter plot showing that proteins with reduced translational efficiency after ZZEF1 knockdown exhibited higher translational efficiency following tRNA-Lys UUU overexpression. Pearson’s r and the associated P -value are shown. (H) Western blot analysis of GAPDH (control) and STK3 in MDA cells with ZZEF1 knockdown compared to control cells ( P = 0.038). Mean values and standard errors of STK3 quantification are shown. P -value was calculated with one-tailed t -test.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: (A) Volcano plot showing alterations in translational efficiency in MDA cells following ZZEF1 knockdown compared to control cells. TER, translational efficiency ratio. Proteins with an absolute logTER greater than 0.5 and P > 0.01 are labeled in green; proteins with an absolute logTER less than 0.5 and P < 0.01 are labeled in blue; and proteins with significant logTER alterations (absolute logTER > 0.5 and P < 0.01) are colored in red. (B) Enrichment of the lysine (AAR codons) content as a function of logTER between ZZEF1 knockdown and control cells. mRNAs are distributed into equally populated bins according to their logTER (the red bars on the black background show the range of values in each bin). Bins with significant enrichment (hypergeometric test, corrected P < 0.05; red) or depletion (blue) of AAR codons are denoted with a bolded border. Also included are mutual information (MI) value and its associated z-score. (C) Similar to (B) , but showing results of enrichment patterns of transcripts with high AAR codon content after restoration of tRNA-Lys UUU supply. (D) Bar plot showing transcripts with higher AAR codon content exhibited elevated ZZEF1-tRNA-Lys UUU translational effect scores. (E) Ribosomal AAR codon dwelling times estimated using Consistent Excess of Loess Predictions (CELP) bias coefficients in ZZEF1-deficient cells with tRNA-Lys UUU overexpression compared to controls. Higher coefficients indicate longer dwelling times. (F) Functional characterization of ZZEF1-tRNA-Lys UUU targets obtained by overlapping proteins that (i) showed significant translational downregulation following ZZEF1 knockdown, (ii) showed significant translational upregulation after tRNA-Lys UUU overexpression in ZZEF1-deficient cells, and (iii) frequently utilized AAR codons. (G) Scatter plot showing that proteins with reduced translational efficiency after ZZEF1 knockdown exhibited higher translational efficiency following tRNA-Lys UUU overexpression. Pearson’s r and the associated P -value are shown. (H) Western blot analysis of GAPDH (control) and STK3 in MDA cells with ZZEF1 knockdown compared to control cells ( P = 0.038). Mean values and standard errors of STK3 quantification are shown. P -value was calculated with one-tailed t -test.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: Knockdown, Control, Labeling, Over Expression, Functional Assay, Western Blot, One-tailed Test

    (A) Bioluminescence imaging showing increased lung colonization in MDA cells with tRNA-Lys UUU knockdown compared to control cells. N = 5 mice per cohort. P value was calculated using a two-way analysis of variance (ANOVA). (B) Similar to (A) , but showing reduced lung colonization in MDA cells with tRNA-Lys UUU overexpression compared to control cells. N = 5 mice per cohort. (C) Bioluminescence imaging showing increased lung colonization in MDA cells with ZZEF1 knockdown and STK3 knockdown compared to control cells. N = 4 mice per cohort. (D) Bar plot showing the distribution of 10-year relapse-free survival P -values (two-sided log-rank test results, reported as –log P for positive association and log P for negative association) for the correlation between ZZEF1 expression and clinical outcomes across listed breast cancer datasets. Purple bars show associations that pass the statistical threshold ( P < 0.01). (E) Kaplan-Meier survival curve illustrating the correlation between tumor ZZEF1 levels and distant metastasis-free survival in a collection of breast cancer patient cohorts. (F) Boxplot showing ZZEF1 expression levels across clinical samples, including tissues from various breast tumor stages. ANOVA was used to calculate the P -value. (G) Kaplan-Meier survival curve illustrating the correlation between tumor STK3 protein levels and progression-free survival.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: (A) Bioluminescence imaging showing increased lung colonization in MDA cells with tRNA-Lys UUU knockdown compared to control cells. N = 5 mice per cohort. P value was calculated using a two-way analysis of variance (ANOVA). (B) Similar to (A) , but showing reduced lung colonization in MDA cells with tRNA-Lys UUU overexpression compared to control cells. N = 5 mice per cohort. (C) Bioluminescence imaging showing increased lung colonization in MDA cells with ZZEF1 knockdown and STK3 knockdown compared to control cells. N = 4 mice per cohort. (D) Bar plot showing the distribution of 10-year relapse-free survival P -values (two-sided log-rank test results, reported as –log P for positive association and log P for negative association) for the correlation between ZZEF1 expression and clinical outcomes across listed breast cancer datasets. Purple bars show associations that pass the statistical threshold ( P < 0.01). (E) Kaplan-Meier survival curve illustrating the correlation between tumor ZZEF1 levels and distant metastasis-free survival in a collection of breast cancer patient cohorts. (F) Boxplot showing ZZEF1 expression levels across clinical samples, including tissues from various breast tumor stages. ANOVA was used to calculate the P -value. (G) Kaplan-Meier survival curve illustrating the correlation between tumor STK3 protein levels and progression-free survival.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: Imaging, Knockdown, Control, Over Expression, Expressing

    (A) Normalized ChIP-seq tracks showing enriched ZZEF1 binding in the proximal regions of tRNA-Lys UUU -3 loci using a ZZEF1-specific antibody compared to an IgG control in MDA cells. (B ) Bar plot presenting statistical analysis of ZZEF1 binding enrichment across target loci, non-target loci, and other isoacceptor loci. P value calculated using one-tailed Welch’s t -test. (C) Sequence logo depicting two distinct sequence features that define ZZEF1 binding preferences. Bar plot showing the statistical enrichment ( P -value) of the identified sequence features in ZZEF1-specific ChIP-seq data compared to genome-wide sequences. (D) Schematic representation of the identified sequence features located in the proximal regions of tRNA-Lys-TTT-3 loci.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: (A) Normalized ChIP-seq tracks showing enriched ZZEF1 binding in the proximal regions of tRNA-Lys UUU -3 loci using a ZZEF1-specific antibody compared to an IgG control in MDA cells. (B ) Bar plot presenting statistical analysis of ZZEF1 binding enrichment across target loci, non-target loci, and other isoacceptor loci. P value calculated using one-tailed Welch’s t -test. (C) Sequence logo depicting two distinct sequence features that define ZZEF1 binding preferences. Bar plot showing the statistical enrichment ( P -value) of the identified sequence features in ZZEF1-specific ChIP-seq data compared to genome-wide sequences. (D) Schematic representation of the identified sequence features located in the proximal regions of tRNA-Lys-TTT-3 loci.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: ChIP-sequencing, Binding Assay, Control, One-tailed Test, Sequencing, Genome Wide

    (A) Pulldown immunoprecipitation (IP) results demonstrating interactions between ZZEF1 domains and mononucleosomes. In, input. IP, biotin pulldown. His, anti-His-tag western blot. (B) Dot plot showing CHD6 as a top interactor identified through ZZEF1 co-IP/MS. (C) Normalized ChIP-seq tracks showing enriched CHD6 binding (orange) in the proximal regions of tRNA-Lys-TTT-3 loci using a CHD6-specific antibody, compared to an IgG control in MDA cells. The purple tracks represent ZZEF1 binding from . (D) Normalized ATAC-seq tracks showing chromatin accessibility at tRNA-Lys-TTT-3 loci in MDA cells with ZZEF1 knockdown, CHD6 knockdown, and CHD6 knockdown in a ZZEF1-deficient background, compared to control cells. (E) Boxplot showing statistical analysis of chromatin accessibility from ATAC-seq at the tRNA-Lys-TTT-3 loci and non-target loci in ZZEF1 knockdown, CHD6 knockdown, and CHD6 knockdown in a ZZEF1-deficient background compared to control cells.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: (A) Pulldown immunoprecipitation (IP) results demonstrating interactions between ZZEF1 domains and mononucleosomes. In, input. IP, biotin pulldown. His, anti-His-tag western blot. (B) Dot plot showing CHD6 as a top interactor identified through ZZEF1 co-IP/MS. (C) Normalized ChIP-seq tracks showing enriched CHD6 binding (orange) in the proximal regions of tRNA-Lys-TTT-3 loci using a CHD6-specific antibody, compared to an IgG control in MDA cells. The purple tracks represent ZZEF1 binding from . (D) Normalized ATAC-seq tracks showing chromatin accessibility at tRNA-Lys-TTT-3 loci in MDA cells with ZZEF1 knockdown, CHD6 knockdown, and CHD6 knockdown in a ZZEF1-deficient background, compared to control cells. (E) Boxplot showing statistical analysis of chromatin accessibility from ATAC-seq at the tRNA-Lys-TTT-3 loci and non-target loci in ZZEF1 knockdown, CHD6 knockdown, and CHD6 knockdown in a ZZEF1-deficient background compared to control cells.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: Immunoprecipitation, Western Blot, Co-Immunoprecipitation Assay, ChIP-sequencing, Binding Assay, Control, Knockdown

    This schematic illustrates the regulatory mechanism of ZZEF1 revealed through integration of EXTRNA, TCGA, and CCLE datasets. ZZEF1 activates transcription of tRNA-Lys UUU -3 by engaging chromatin remodeling machinery, thereby enhancing the translational efficiency of AAR codon-enriched transcripts, including the tumor suppressor STK3. This codon-dependent mechanism contributes to the regulation of metastatic potential in breast cancer.

    Journal: bioRxiv

    Article Title: Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

    doi: 10.1101/2025.04.26.650725

    Figure Lengend Snippet: This schematic illustrates the regulatory mechanism of ZZEF1 revealed through integration of EXTRNA, TCGA, and CCLE datasets. ZZEF1 activates transcription of tRNA-Lys UUU -3 by engaging chromatin remodeling machinery, thereby enhancing the translational efficiency of AAR codon-enriched transcripts, including the tumor suppressor STK3. This codon-dependent mechanism contributes to the regulation of metastatic potential in breast cancer.

    Article Snippet: E. coli codon-optimized DNA sequences coding ZZEF1 domains (EF hand (111-146 amino acid), DOC (226-405 amino acid), ZZ1 (1778-1833 amino acid), ZZ2 (1827-1882 amino acid), ZZ1/2 (1778-1882 amino acid)) were synthesized and cloned into a His-MBP fusion expression vector (Addgene #29654) via BamHI site and Gibson assembly.

    Techniques: