Biology subjects

Banaei-Esfahani, A.

Publications and source records attributed to Banaei-Esfahani, A..

6 recordsLinked to original sources

bioRxiv2026

Proteogenomic discovery of novel small proteins in clinical Mycobacterium tuberculosis strains

Even though our meta-analysis ranks Mycobacterium tuberculosis genomes among the bacterial pathogens that are most straightforward to assemble, most available assemblies relied on short-read sequencing and contain genomic blind spots that miss functionally important genes. Complete genomes are essential for functional genomics, particularly for identifying small ORF-encoded proteins (SEPs; [≤]100 amino acids), which can play critical biological roles yet are frequently missed by standard annotations. Here, we generated complete long-read assemblies for six clinical reference strains representing lineage 1 and the more pathogenic lineage 2, followed by comparative genomic and proteogenomic analyses. We additionally provide software to predict comprehensive sets of mycobacteria-specific proline-glutamic acid (PE) and PPE family genes, including lineage-specific variants. Using parallel accumulation-serial fragmentation mass spectrometry, we detected approximately two-thirds of each strains annotated proteome from unfractionated cell extracts. Extending our proteogenomic framework across related strains, and adding rigorous control of proteogenomic discovery rates using entrapment strategies, we revealed 12-24 previously unannotated proteins per strain, predominantly SEPs, 56-60 alternative translation start sites, and 9-17 expressed pseudogenes. Newly identified proteins included conserved and lineage-specific SEPs, an antitoxin, candidate antimicrobial peptides and novel proteins under purifying selection. Overall, applying this improved proteogenomics method to phylogenomically selected clinical reference strains provides a valuable approach for discovering candidate diagnostics or therapeutics, as illustrated here for a WHO-listed critical bacterial pathogen.

microbiology↗

bioRxiv2025

Epistasis between drug resistance-conferring mutations in Mycobacterium tuberculosis

Studies in model organisms show that mutations conferring resistance to different antibiotics can interact epistatically, but the biological and epidemiological consequences of such interactions are unknown. Here we show that in Mycobacterium tuberculosis (Mtb), positive sign epistasis between RpoB and GyrA mutations causing resistance to rifampicin and fluoroquinolone, respectively, can lead to double-resistant strains with high in vitro fitness. Two of these RpoB-GyrA mutation combinations account for 53% in a global collection of highly drug-resistant Mtb clinical isolates, compared to <0.7% for RpoB-GyrA combinations with low in vitro fitness. Moreover, the two high-fitness RpoB-GyrA combinations are associated with a more benign and idiosyncratic proteome perturbation compared to low-fitness combinations. Our findings highlight the relevance of epistasis for the emergence and spread of antimicrobial resistance.

microbiology↗

bioRxiv2020

SWATH-MS co-expression profiles reveal paralogue interference in protein complex evolution

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

evolutionary biology↗

bioRxiv2020

Network analysis identifies regulators of lineage-specific phenotypes in Mycobacterium tuberculosis

The Mycobacterium tuberculosis (Mtb) complex includes ten phylogenetically distinct human-adapted lineages with varying geographical distributions and pathogenicity. Lineage 1 (L1) is associated with low virulence, while Lineage 2 (L2) is linked to hyper-virulence, increased transmission, and drug resistance. We performed multi-layer comparative analyses integrating whole-genome sequencing with quantitative transcriptomic and proteomic profiling of a panel of L1 and L2 clinical strains, each grown under two in vitro conditions. Our data revealed variable correlations between transcript and protein levels across strains and gene categories, indicating lineage-specific post-translational regulation. Transcriptional and translational differences scaled with phylogenetic distance, with one in three SNPs on average leading to gene expression changes. A newly developed genome-scale transcriptional regulatory model identified master transcription factors--closely linked to the sigma factor network--whose targets were differentially expressed between L1 and L2. Notably, DosR proteins showed higher basal levels and a stronger nitric oxide (NO) response in L2. Time-course validation using an Rv1985c induction and wild-type H37Rv under hypoxia and reaeration confirmed the role of Rv1985c in the differentially expressed genes between L1 and L2. These findings suggest that limited genetic variation can translate into significant phenotypic differences via differential regulation of key transcriptional networks. HighlightsO_LIProteomic and transcriptomic characterization of fully sequenced diverse L1 and L2 clinical strains of Mtb. C_LIO_LIPost translational control mechanisms for regulatory and virulence genes are mitigated in Mtb L2. C_LIO_LIA genome-scale transcriptional framework identifies DosR, Rv1985c, Lsr2 and Rv0691c as master transcription factors responsible for differential target gene expression in L2 compared to L1 strains. C_LIO_LIL1 and L2 DosR proteins respond differently to nitric oxide stress, thus determining a relevant phenotype. C_LIO_LITime-course phenotypic and transcriptomic data under hypoxia and reaeration validate Rv1985c as a key transcription factor differentiating L2 and L1 strains. C_LI

systems biology↗

bioRxiv2018

Resource misallocation as a mediator of fitness costs in antibiotic resistance.

Antimicrobial resistance poses a threat to global health and the economy. It is widely accepted that, in the absence of antibiotics, drug resistance mutations carry a fitness cost. In the case of rifampicin resistance in fast-growing bacteria, this cost stems from a reduced transcription rate of the RNA polymerase resulting in slower ribosome biosynthesis. However, this relationship does not apply in the slow-growing Mycobacterium tuberculosis, where the true mechanism of fitness cost of rifampicin resistance as well as the impact of compensatory evolution remain unknown. Here we show, using global transcriptomic and proteomic profiling of selected M. tuberculosis mutants and clinical strains, that the fitness cost of rifampicin resistance in M. tuberculosis is the result of the physiological burden caused by aberrant gene expression. We further show that the perceived burden can be increased, effectively suppressing the emergence of drug resistance.

microbiology↗

bioRxiv2018

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance.\n\nIn this paper we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps.\n\nOur analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2127 protein subunits. The technique identifies novel subcomplexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible, and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.\n\nHighlightsO_LIIntroduction of the concept of complex-centric proteome profiling\nC_LIO_LIDevelopment of CCprofiler, a software framework for complex-centric data analysis\nC_LIO_LIDetection and quantification of subunit distribution of 462 distinct protein complexes containing 2127 proteins from a SEC-SWATH-MS dataset of HEK293 cells, and identification of novel complex variants such as assembly intermediates\nC_LIO_LIStatistical target-decoy model to estimate accurate false discovery rates for complexes quantified by complex-centric analysis\nC_LIO_LISECexplorer, an online platform to support custom complex-centric exploration of SEC-SWATH-MS datasets.\nC_LI

systems biology↗