Evidence map›Paper›PMID 42219816›Full record

ArticlePulmonary medicine2026

Untargeted and Targeted Liquid Chromatography-Mass Spectrometry-Based Lipidomic Profiling Revealed a Potential Biomarker Panel to Distinguish Clinical Isolates of M. tuberculosis From Nontuberculous M. kansasii.

Meenakshi Chugh, Saif Hameed, Jitendra Singh, Zeeshan Fatima

Abstract read
In one paragraph

Article in Pulmonary medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Meenakshi ChughAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, India, amity.edu.ORCID https://orcid.org/0000-0003-4703-9183
Saif HameedAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, India, amity.edu.ORCID https://orcid.org/0000-0002-6477-1870
Jitendra SinghDepartment of Translational Medicine, All India Institute of Medical Sciences, Bhopal, India, aiims.edu.ORCID https://orcid.org/0000-0001-5626-6076
Zeeshan FatimaAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, India, amity.edu.ORCID https://orcid.org/0000-0002-5084-4450

Funding

Department of Biotechnology, Ministry of Science and Technology, India BT/PR/23016/NER/95/2017Indian Council of Medical Research 5/8/5/13/ITRC/Diag/2022/ECD-1
6 · The paper itself

Abstract

The rising incidence of nontuberculous mycobacteria (NTM) infections, particularly Mycobacterium kansasii (M. kansasii), and their overlapping clinical features with Mycobacterium tuberculosis (MTB) are leading to diagnostic ambiguity in tuberculosis-endemic regions. Accurate differentiation remains limited by conventional diagnostic methods, emphasizing the need for molecular- and lipid-based biomarkers. In the present era of "OMICS" sciences, herein we attempted a comprehensive analysis of MTB and M. kansasii lipid profiles to elucidate species-specific lipidomic features as potential biomarkers. Three clinical isolates each of MTB and M. kansasii were obtained from treatment-naïve patients with microbiologically confirmed pulmonary infections. Lipid profiling was performed by integrating untargeted and targeted profiling through ultraperformance liquid chromatography coupled with tandem mass spectrometry from bacilli total lipid and mycobacterial cell wall lipid extracts. Untargeted profiling exhibited a higher abundance of fatty acyls, GLs, selected GPLs (PE, PG, PI, and PA), and saccharolipids (SLs) specifically Ac2SGL in MTB, whereas M. kansasii isolates were characterized by elevated levels of GPLs like PC and LPC, polyketides, and specific SLs such as diacylated trehalose species. Targeted quantification confirmed differential expression of GL (TG and DG) and GPL (PC, PE, LPC, PI, and PS) species, supporting their diagnostic relevance. Additionally, biomarker analysis further identified five lipid species with strong discriminative potential. Collectively, these findings support the development of a robust lipidomic biomarker panel for the accurate differentiation of MTB and M. kansasii, with potential implications for improved diagnostics and targeted therapeutic strategies after further confirmation.

Indexed as

LipidomicsLipidsLiquid Chromatography-Mass SpectrometryMycobacterium Infections, NontuberculousMycobacterium kansasiiMycobacterium tuberculosisBiomarkersChromatography, LiquidHumansTandem Mass SpectrometryBiomarkersLipidsbiomarker panellipidomicsmass spectrometryMycobacterium tuberculosisnontuberculous mycobacteriatuberculosis

Identifiers

PMID42219816
PMCPMC13239167

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