Subtopic Deep Dive

Mycobacterial antibiotic susceptibility testing
Research Guide

What is Mycobacterial antibiotic susceptibility testing?

Mycobacterial antibiotic susceptibility testing (AST) determines the in vitro sensitivity of Mycobacterium species to anti-TB drugs using standardized phenotypic methods like disk diffusion and broth microdilution.

AST establishes clinical breakpoints for first- and second-line TB drugs and evaluates phenotypic versus genotypic resistance correlations. Key methods include broth microdilution for precise minimum inhibitory concentrations (MICs). Over 10 papers in provided lists address resistance mechanisms and diagnostic assays (e.g., Zhang et al., 1992; Steingart et al., 2014).

15
Curated Papers
3
Key Challenges

Why It Matters

Reliable AST guides therapy selection for drug-resistant TB, critical amid rising multidrug-resistant strains. Xpert MTB/RIF detects rifampicin resistance with high sensitivity (Steingart et al., 2014, 1099 citations), enabling rapid treatment decisions. CARD database predicts resistomes for mycobacteria (Alcock et al., 2022, 1717 citations), informing outbreak responses. Isoniazid resistance linked to catalase-peroxidase mutations impacts regimen design (Zhang et al., 1992, 1296 citations).

Key Research Challenges

Standardizing Clinical Breakpoints

Defining breakpoints for slow-growing mycobacteria remains inconsistent across labs. Variability affects reproducibility of disk diffusion and microdilution results. Pai et al. (2016) highlight need for harmonized standards in TB diagnostics.

Phenotypic-Genotypic Correlation Gaps

Genotypic methods like PCR detect mutations but poorly predict phenotypic resistance for some drugs. Steingart et al. (2014) show Xpert MTB/RIF sensitivity limitations beyond rifampicin. Zhang et al. (1992) link katG mutations to isoniazid resistance inconsistently.

Slow Growth Assay Times

Mycobacteria require 2-6 weeks for AST, delaying therapy. Broth microdilution prolongs turnaround despite standardization efforts. Yang and Rothman (2004) note PCR diagnostics limitations for culture-dependent AST.

Essential Papers

1.

Tuberculosis Associated with Infliximab, a Tumor Necrosis Factor α–Neutralizing Agent

Joseph Keane, Sharon K. Gershon, Robert P. Wise et al. · 2001 · New England Journal of Medicine · 3.7K citations

Active tuberculosis may develop soon after the initiation of treatment with infliximab. Before prescribing the drug, physicians should screen patients for latent tuberculosis infection or disease.

2.

An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection.

JoAnne L. Flynn, John Chan, K J Triebold et al. · 1993 · The Journal of Experimental Medicine · 2.4K citations

Tuberculosis, a major health problem in developing countries, has reemerged in recent years in many industrialized countries. The increased susceptibility of immunocompromised individuals to tuberc...

3.

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database

Brian Alcock, William Huynh, Romeo Chalil et al. · 2022 · Nucleic Acids Research · 1.7K citations

Abstract The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring ...

4.

The JAK-STAT Pathway: Impact on Human Disease and Therapeutic Intervention

John J. O’Shea, Daniella M. Schwartz, Alejandro V. Villarino et al. · 2015 · Annual Review of Medicine · 1.5K citations

The Janus kinase (JAK)–signal transducer of activators of transcription (STAT) pathway is now recognized as an evolutionarily conserved signaling pathway employed by diverse cytokines, interferons,...

5.

The catalase—peroxidase gene and isoniazid resistance of Mycobacterium tuberculosis

Ying Zhang, Béate Heym, B.W. Allen et al. · 1992 · Nature · 1.3K citations

6.

Tuberculosis

Madhukar Pai, Marcel A. Behr, David W. Dowdy et al. · 2016 · Nature Reviews Disease Primers · 1.2K citations

7.

Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults

Karen R Steingart, Ian Schiller, David Horné et al. · 2014 · Cochrane Database of Systematic Reviews · 1.1K citations

In adults thought to have TB, with or without HIV infection, Xpert® MTB/RIF is sensitive and specific. Compared with smear microscopy, Xpert® MTB/RIF substantially increases TB detection among cult...

Reading Guide

Foundational Papers

Start with Zhang et al. (1992) for isoniazid resistance mechanisms, then Steingart et al. (2014) for rifampicin diagnostics validation, as they establish core phenotypic-genotypic links.

Recent Advances

Study Alcock et al. (2022) for CARD resistome prediction and Pai et al. (2016) for TB diagnostic primers integrating AST advances.

Core Methods

Broth microdilution for MICs, disk diffusion for zones, PCR for mutations (Yang and Rothman, 2004), Xpert MTB/RIF for rapid rifampicin AST (Steingart et al., 2014).

How PapersFlow Helps You Research Mycobacterial antibiotic susceptibility testing

Discover & Search

Research Agent uses searchPapers and exaSearch to find AST papers like 'Xpert MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance' (Steingart et al., 2014), then citationGraph reveals 1099 citing works on resistance correlation.

Analyze & Verify

Analysis Agent applies readPaperContent to extract MIC data from Zhang et al. (1992), verifies genotypic-phenotypic claims via verifyResponse (CoVe), and uses runPythonAnalysis for statistical correlation of mutation frequencies with GRADE evidence grading.

Synthesize & Write

Synthesis Agent detects gaps in breakpoint standardization across papers, flags contradictions between phenotypic and genotypic data, and Writing Agent employs latexEditText, latexSyncCitations for AST protocol manuscripts with exportMermaid for resistance pathway diagrams.

Use Cases

"Analyze MIC distributions from broth microdilution AST datasets in TB papers"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas aggregation of MICs from 10 papers) → matplotlib plots of resistance profiles.

"Draft LaTeX manuscript on isoniazid resistance breakpoints citing Zhang 1992"

Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (adds Keane 2001, Steingart 2014) → latexCompile → PDF with formatted AST tables.

"Find GitHub repos implementing mycobacterial AST analysis code"

Research Agent → paperExtractUrls (from Alcock 2022 CARD) → paperFindGithubRepo → githubRepoInspect → verified CARD resistome prediction scripts for TB strains.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ mycobacterial AST papers, chaining searchPapers → citationGraph → GRADE grading for breakpoint consensus report. DeepScan applies 7-step analysis with CoVe checkpoints to validate phenotypic-genotypic correlations from Steingart et al. (2014). Theorizer generates hypotheses on novel breakpoints from resistance gene data in Alcock et al. (2022).

Frequently Asked Questions

What is mycobacterial antibiotic susceptibility testing?

AST measures minimum inhibitory concentrations (MICs) of anti-TB drugs against Mycobacterium isolates using broth microdilution or disk diffusion.

What are main methods in mycobacterial AST?

Standardized broth microdilution determines MICs; disk diffusion assesses zones of inhibition. PCR-based genotypic tests like Xpert MTB/RIF detect rifampicin resistance (Steingart et al., 2014).

What are key papers on mycobacterial AST?

Zhang et al. (1992) links katG mutations to isoniazid resistance (1296 citations); Steingart et al. (2014) validates Xpert MTB/RIF (1099 citations); Alcock et al. (2022) curates CARD resistome predictions (1717 citations).

What are open problems in mycobacterial AST?

Harmonizing clinical breakpoints, improving phenotypic-genotypic correlations, and accelerating slow-growth assays remain unresolved.

Research Mycobacterium research and diagnosis with AI

PapersFlow provides specialized AI tools for your field researchers. Here are the most relevant for this topic:

Start Researching Mycobacterial antibiotic susceptibility testing with AI

Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.