Biology subjects

Gabrielian, A.

Publications and source records attributed to Gabrielian, A..

4 recordsLinked to original sources

bioRxiv2024

Genetic diversity within diagnostic sputum samples is mirrored in the culture of Mycobacterium tuberculosis

Culturing Mycobacterium tuberculosis (MTB) from tuberculosis cases is the basis for many research and clinical applications. Paradoxically, it is assumed to impose a diversity bottleneck, which, if true, would entail unexplored consequences. The alternative, culture-free sequencing from diagnostic samples, is a promising but challenging approach both to obtain and analyse the MTB genome from the complex sample. This study obtains high-quality genomes of sputum-culture pairs from two different settings after developing a workflow for sequencing from sputum and a tailored bioinformatics pipeline. Our approach reveals that 88% of variants called in culture-free sequencing analysis are false positives due to supplementary alignments, mostly in enriched-sputa samples. Overall, contrary to the bottleneck dogma, we identify a 97% variant agreement within sputum-culture pairs, with a high correlation also in the variants frequency (0.98). Our findings extrapolate to all publicly available data, thus demonstrating that in most cases culture accurately mirrors clinical samples.

genomics↗

bioRxiv2022

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain-specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular microbial strain. Due to insufficient data on most microbial strains, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target microbial strains. New models can perform predictions of AMPs for microbial strains that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased significantly--on average by 7%--relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at https://dbaasp.org/tools?page=genome-prediction

bioinformatics↗

bioRxiv2020

Metabolic fate of human immunoactive sterols in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) infection is among top ten causes of death worldwide, and the number of drug-resistant strains is increasing. The direct interception of human immune signaling molecules by Mtb remains elusive, limiting drug discovery. Oxysterols and secosteroids regulate both innate and adaptive immune responses. Here we report a functional, structural, and bioinformatics study of Mtb enzymes initiating cholesterol catabolism and demonstrated their interrelation with human immunity. We show that these enzymes metabolize human immune oxysterol messengers. Rv2266, the most potent among them, can also metabolize vitamin D3 (VD3) derivatives. High-resolution structures show common patterns of sterols binding and reveal a site for oxidative attack during catalysis. Finally, we designed a compound that binds and inhibits three studied proteins. The compound shows activity against Mtb H37Rv residing in macrophages. Our findings contribute to molecular understanding of suppression of immunity and suggest that Mtb has its own transformation system resembling the human phase I drug-metabolizing system.Competing Interest StatementA.Gi. and E.S. are employees of MT-Medicals LLC. The other authors declare no competing interests.View Full Text

biochemistry↗

bioRxiv2019

Prediction of multiple drug resistant pulmonary tuberculosis against drug sensitive pulmonary tuberculosis by CT nodular consolidation sign

Multidrug-resistant tuberculosis (mdrtb) refers to TB infection resistant to at least two most powerful anti-TB drugs, isoniazid and rifampincin. It has been estimated that globally 3.5% (which can be much higher in some regions) of newly diagnosed TB patients, and 20.5% of previously treated patients had mdrtb. Extensively drug-resistant TB (xdrtb) has resistance to rifampin and isoniazid, as well as to any member of the quinolone family and at least one of the second line injectable drugs: kanamycin, amikacin and capreomycin. xdrtb accounts for 4-20% of mdrtb. Early detection and targeted treatment are priorities for mdrtb/xdrtb control. The suspicion of mdr/xdr -pulmonary TB (mdrptb or xdrptb) by chest imaging shall suggest intensive diagnostic testing for mdrptb/xdrptb. We hypothesize that multiple nodular consolidation (NC) may serve one of the differentiators for separating dsptb vs mdrptb/xdrptb cases. For this study, mdrptb cases (n=310) and XDR-PTB cases ([xcap]=I58) were from the NIAID TB Portals Program (TBPP) . Drug sensitive pulmonary TB (dsptb) cases were from the TBPP collection (n=112) as well as the Shenzhen Center for Chronic Disease Control (n=111), Shenzhen, China, and we excluded patients with HIV(+) status. Our study shows NC, particularly multiple NCs, is more common in mdrptb than in dsptb, and more common in xdrptb than in mdrptb. For example, 2.24% of dsptb patients, 13.23% of mdrptb patients, and 20.89% of xdrptb patients, respectively, have NCs with diameter >= 10mm equal or more than 2 in number.

pathology↗