Daily Archives: 2025年10月23日

Toxin–Antitoxin (TA) Systems & Pulldown Experiments — Practical Guide

TA_operon_shared_promoter_v3_en

A consolidated reference covering TA gene organization and regulation, promoter vs RBS roles, co-transcription criteria, start-codon troubleshooting, RNA-seq analysis strategy, pulldown experiment design/controls/statistics, and multi-omics integration.


1) TA System Overview

  • Composition: Paired genes encoding toxin (protein) and antitoxin (protein).
  • Typical organization: Same strand, antitoxin upstream, toxin downstream, forming a bicistronic operon.
  • Transcriptional control: Frequently transcribed from a shared σ⁷⁰-like promoter (−35/−10) upstream of the antitoxin.
  • Autoregulation: Antitoxin or TA complex often binds operator sites near the promoter to repress transcription. Under stress (e.g., antitoxin proteolysis), repression is relieved → toxin increases.
  • Functions: Stress response, persistence, plasmid maintenance, virulence modulation (family-specific).

2) Promoter vs RBS — Who Does What?

  • Promoter → transcription start.
    • Recognized by RNA polymerase holoenzyme (core RNAP + σ factor; often σ⁷⁰).
    • −35/−10 boxes typically spaced 16–19 bp; TSS sits downstream of −10.
  • RBS (Shine–Dalgarno) → translation start.
    • 16S rRNA (30S) anti-SD tail base-pairs with the RBS to position the start codon (usually ATG, also GTG/TTG).
    • RBS–start codon spacing commonly 5–10 nt.
  • Cheats: Promoter decides where transcription begins; RBS decides where translation begins.

3) Evidence Framework for a Shared Promoter / Co-transcription

Goal: Decide whether antitoxin & toxin belong to the same transcript and quantify co-expression.

3.1 Structural / Sequence Evidence

  1. Genomic context: Same strand; short intergenic (<50–100 bp) or slight overlap.
  2. Promoter prediction: Clear −35/−10 upstream of antitoxin; no strong independent promoter upstream of toxin.
  3. RBS: SD-like motifs upstream of both ORFs.
  4. Terminator: No strong Rho-independent terminator between the pair; terminator at operon end.

3.2 RNA-seq Evidence (Strand-specific libraries preferred)

  1. Coverage continuity: Same-strand coverage crosses the intergenic region.
  2. Spanning fragments: Paired-end insert spans the antitoxin↔toxin boundary.
  3. Expression correlation: From all samples (e.g., 27), compute TPM/CPM correlations; Pearson/Spearman r ≥ 0.8, p<0.01; remains high within each timepoint subset.
  4. DE consistency: For each timepoint’s treated vs control, log2FC for both genes are same direction with FDR<0.05.
  5. (Optional) TSS evidence: 5′-enriched or TSS-seq reveals shared TSS cluster upstream of antitoxin.

Note: Non-strand-specific libraries weaken strand-continuity evidence; interpret cautiously.


4) Why Your Provided Sequences Start with “TTA,” Not “ATG”

  • Observed “TTA” starts suggest: 1) Sequences include 5′ UTR/promoter (CDS not cut at true start). 2) Sequences could be reverse-complement relative to coding strand. 3) Bacteria can use GTG/TTG as starts, but TTA is not a typical start codon.
  • Standard resolution steps:
    • BLAST the fragments to the genome to get strand & coordinates.
    • Six-frame translate; on the correct strand, locate the longest ORF starting with ATG/GTG/TTG and ending at a stop.
    • Verify RBS distance (5–10 nt) and domain homology (BLASTX/HMM against TA families).
    • Use RNA-seq coverage shape/TSS to refine the start site.

5) RNA-seq Analysis Plan for 27 Samples (Example Design)

Design: Same strain × 3 conditions (untreated / Mitomycin C / Moxifloxacin) × 3 timepoints × 3 biological replicates = 27 samples.

5.1 Pipeline Outline

  1. QC & Alignment: FastQC/MultiQC → trimming → align to reference (confirm strand-specificity).
  2. Quantification: featureCounts/Salmon → DESeq2/edgeR normalization.
  3. Differential Expression:
    • For each timepoint, contrast treated vs untreated (include batch if needed).
    • Output per contrast: log2FC, SE, p, FDR.
  4. TA Co-transcription Checks:
    • IGV views: same-strand continuity across intergenic; spanning fragments.
    • Correlation between antitoxin & toxin across 27 samples (r, p).
    • DE direction consistency for both genes.
  5. Pulldown Targets in RNA-seq:
    • For candidate target list, extract log2FC/FDR; produce volcano/heatmaps.
    • Perform functional enrichment (GO/KEGG/COG) with overlap to pulldown hits.
  6. Deliverables:
    • IGV screenshots with annotated −35/−10, TSS, RBS, terminator.
    • MA/volcano plots, sample PCA, correlation plots.
    • Tables summarizing DEGs per timepoint and pulldown×RNA-seq overlaps.

6) Pulldown Experiments — Types, Controls, Statistics

6.1 Types

  • Protein–protein pulldown / affinity purification: Bait = toxin/antitoxin protein (His/FLAG/biotin); ID by LC–MS/MS.
  • Nucleic-acid pulldown:
    • DNA pulldown: bait = promoter/operator DNA; identify bound proteins (MS).
    • RNA pulldown: bait = specific RNA; identify bound proteins (MS) or enriched RNAs.

6.2 Critical Controls

  • Empty vector/beads, irrelevant protein, mutant bait (disrupt binding), competition elution.
  • 3 biological replicates recommended.

6.3 Hit Calling (Proteomics Example)

  • Use SAINT / MSstats / DEP or log2FC + FDR thresholds, e.g. log2FC ≥ 1 & FDR ≤ 0.05, consistently detected in ≥2 replicates.
  • Remove sticky background proteins (CRAPome) and ubiquitous ribosomal/chaperones where appropriate.
  • Deliver a high-confidence candidate list.

6.4 Integration with RNA-seq

  • Cross-table: pulldown hits vs RNA-seq log2FC/FDR across conditions/timepoints.
  • Enrichment/pathways: overlap enrichment for hits and DEGs.
  • Evidence ladder: 1) Pulldown enrichment (binding); 2) RNA-seq co-expression / DE (regulatory consistency); 3) Biophysical/functional assays (EMSA/SPR/ChIP-qPCR, reporter assays) for validation.

7) Validation Roadmap (Low→High Effort)

  1. RT-qPCR: Junction-spanning primers across antitoxin→toxin.
  2. EMSA/SPR: Direct binding & affinity to operator by antitoxin/TA complex.
  3. Reporter / Mutagenesis: Disrupt operator/−35/−10 or RBS, assess transcription/translation impact.
  4. ChIP-qPCR/ChIP-seq: In vivo occupancy (if antitoxin has DNA-binding domain).
  5. RACE/TSS-seq: Precise TSS mapping to confirm shared promoter.

8) Practical Criteria & Verdict Grades

  • Structure: Same strand; intergenic <100 bp; no strong terminator in-between.
  • Promoter: Clear −35/−10 upstream of antitoxin; toxin lacks strong independent promoter.
  • RNA-seq: Same-strand continuity across intergenic; boundary-spanning fragments; r ≥ 0.8 (p<0.01) across all samples; per-timepoint log2FC same direction (FDR<0.05).
  • Conclusion grades: Strong support / Support / Insufficient evidence.

9) Schematic Figures (Generated)

  • Chinese-labeled (non-overlapping):
    TA_operon_shared_promoter_v3_cn.pngopen/download
  • English-labeled (non-overlapping):
    TA_operon_shared_promoter_v3_en.pngopen/download

Each figure depicts: shared σ⁷⁰-like promoter (−35/−10, TSS)antitoxin (upstream)toxin (downstream) with each RBS, a terminal Rho-independent terminator, and stylized same-strand RNA-seq coverage that spans the intergenic region.


10) “Ready-to-Ask” Template for Collaborators

Objective: Determine if the TA pair shares a promoter and is co-transcribed; call DEGs per timepoint across conditions; test RNA-seq changes for pulldown targets.

Please deliver:

  1. IGV tracks with −35/−10, TSS, RBS, terminator, and boundary-spanning reads.
  2. DE tables (per timepoint per contrast), with log2FC/FDR.
  3. Correlation stats (antitoxin↔toxin r, p) across 27 samples and within timepoints.
  4. Pulldown×RNA-seq cross table (+ enrichment analyses).
  5. One-page verdict: shared promoter? co-transcription? evidence grade & key screenshots.

Inputs we’ll provide: Reference genome/annotation (FASTA/GFF/GTF), BAM/BAI, sample sheet, pulldown target list.


One-liner Summary

Promoter = transcription start; RBS = translation start.
For TA pairs, antitoxin→toxin often sits in a single operon driven by a shared promoter; RNA-seq continuity, spanning fragments, correlation, and concordant DE together provide strong evidence for co-transcription.

克隆≠表型完全相同 —— 详细阐述与具体例子

“克隆性”是指细菌在基因型上几乎无差异,属于高度同源的一组,但这并不意味着它们在耐药性等表型上一定一模一样。克隆株的表型可以有差异,原因包括调控机制、基因表达水平、外排泵活性、膜蛋白变化、插入序列等导致的基因功能或表达的不同。

具体例子:

在临床实践中,研究发现铜绿假单胞菌(Pseudomonas aeruginosa)的同一克隆菌株,虽然基因组完全一致,但对美罗培南(Meropenem)的最小抑菌浓度(MIC)可能不同。进一步检测发现:某些菌株因oprD基因突变、插入等,导致外膜通道蛋白表达下调,从而表现为高MIC(耐药),而同簇内oprD完整的菌株则敏感。1

这说明:即使是同一克隆簇的菌株,耐药表型可以因调控突变或基因表达等后天因素存在差异。


科学文章(中文发布版)

克隆性≠表型一致:基因同源不等于耐药全同 —— Holger邮件讨论解读

在近期的菌群全基因组分析中,我们通过cgMLST/WGS技术识别出了若干克隆相关的菌株簇。以Acinetobacter baumannii和Klebsiella pneumoniae为例,每个克隆簇内的菌株,其耐药基因和毒力因子分布高度一致,且AST(药敏)表型大多数时间点表现相近。

但邮件交流中,Gradientech团队指出:“仅凭MLST等判断克隆性,不能保证所有基因组无差异。即使核心基因型相同,也可能存有表型差异,例如耐药性或毒力不同。” 这很有道理。

举例说明:临床细菌如铜绿假单胞菌,同簇内部分菌株,因为外排泵或通道蛋白基因(如oprD)表达下降、插入序列影响或失活突变,导致美罗培南敏感性变弱(MIC升高),表型变为耐药。而同克隆簇的另一株也许表达正常,表现为敏感。这种“基因型同源但表型不完全一致”的现象,正是精准医学面临的挑战之一。1

针对克隆株是否要在分析中剔除,Holger建议保持信息透明,在方法和讨论部分如实披露、科学解释,不做删减。讨论段推荐补充一句:“克隆性并不必然对应表型耐药表达的一致,实际还受调控机制等多种因素影响,因此本研究保留所有分离株评估表型检测方法的性能。如果后续审核需要,可以按簇去重再行敏感性分析。”


总结

  • 克隆唯一区分于基因型层面,表型如耐药往往还会受基因调控、表达水平、特殊突变等多种影响,同一克隆内“表型一致”不是绝对规律。
  • 这一认识对临床耐药菌株流行病学追踪和新型方法学评估极其关键,有助于提升科学结论的严谨性。1 2345678910