Biology subjects

Crudu, V.

Publications and source records attributed to Crudu, V..

9 recordsLinked to original sources

bioRxiv2025

The global phylogeography of rapidly expanding multidrug resistant Ural lineage 4.2 Mycobacterium tuberculosis

Multidrug resistant tuberculosis (MDR-TB) is a leading global health challenge. Recent studies identified a reproductively fit Ural lineage 4.2 MDR M. tuberculosis (Mtb) variant circulating within the Republic of Moldova. We searched a large publicly available dataset of [~]200,000 Mtb whole genome sequences and identified a clade of 1604 drug-resistant Mtb sequences harboring conserved resistance-conferring mutations that are closely related to the MDR Mtb variant circulating in Moldova. We identified the Russian Federation as the most likely country of origin for this clade, and we inferred several independent migration events from Russia and Moldova to other former Soviet Republics and several European countries. Using local branching index, we found that this clade is expanding more rapidly than comparable clades of lineage 4.2. This evolutionarily successful strains presence beyond Moldova poses a threat to MDR-TB control, and enhanced surveillance is necessary to reduce the risk of MDR-TB epidemics in the region. FundingThe authors report funding from the National Institutes of Health (R01AI180209, P01AI159402)

microbiology↗

bioRxiv2025

Minimal immune cell subset differences in a cohort of close contacts of tuberculosis index cases

Understanding the perturbations in immune response across the spectrum of TB infection is still unclear. In this study, we followed a cohort of close contacts of pulmonary TB patients with serial QFT testing at 0, 3, 6, and 12 months, and stratified them into six subgroups: QFT-increasing (low/high), QFT converters (QFT- to QFT+), QFT+ stable, and QFT- individuals. Despite these distinct QFT trajectories, we observed minimal differences in immune cell frequencies, activation profiles, and T helper subset distributions among the QFT subgroups, suggesting limited immunological stratification based on QFT dynamics alone. Ex vivo immune phenotyping, including analysis of CD4, CD8, and NKT cell frequencies, memory T cell subsets, and activated T cells (HLA-DRCD38), failed to distinguish between QFT subgroups. Antigen-specific CD4 T cell responses assessed by the activation-induced marker (AIM) assay were elevated in QFT+ compared to QFT- individuals. These findings suggest that blood-based immune profiling may not capture subtle immunological transitions among QFT converters or individuals with increasing QFT responses. In contrast, individuals with active TB (ATB) showed clear immune perturbations. ATB patients at diagnosis exhibited significantly elevated frequencies of antigen-specific CD4 T cells, increased activated T cells, and higher frequencies of intermediate monocytes and NK cells compared to QFT+/QFT- contacts. Many of these immune features declined with treatment, indicating therapy-associated immune resolution. Additionally, shifts in T helper subsets and effector memory populations were observed over the course of treatment. These results suggest that while ex vivo immune profiling can robustly distinguish active TB from non-diseased states, it lacks the resolution to differentiate QFT subgroups based on QFT dynamics alone. This could reflect either immunological similarity among close contacts regardless of QFT status or limitations of blood-based phenotyping in detecting early or subclinical immune shifts. Our study highlights the challenge of immunologically distinguishing QFT-defined subgroups within close contacts using conventional ex vivo profiling approaches.

immunology↗

bioRxiv2024

Tiled Amplicon Sequencing Enables Culture-free Whole-Genome Sequencing of Pathogenic Bacteria From Clinical Specimens

Pathogen sequencing is an important tool for disease surveillance and demonstrated its high value during the COVID-19 pandemic. Viral sequencing during the pandemic allowed us to track disease spread, quickly identify new variants, and guide the development of vaccines. Tiled amplicon sequencing, in which a panel of primers is used for multiplex amplification of fragments across an entire genome, was the cornerstone of SARS-CoV-2 sequencing. The speed, reliability, and cost-effectiveness of this method led to its implementation in academic and public health laboratories across the world and adaptation to a broad range of viral pathogens. However, similar methods are not available for larger bacterial genomes, for which whole-genome sequencing typically requires in vitro culture. This increases costs, error rates and turnaround times. The need to culture poses particular problems for medically important bacteria such as Mycobacterium tuberculosis, which are slow to grow and challenging to culture. As a proof of concept, we developed two novel whole-genome amplicon panels for M. tuberculosis and Streptococcus pneumoniae. Applying our amplicon panels to clinical samples, we show the ability to classify pathogen subgroups and to reliably identify markers of drug resistance without culturing. Development of this work in clinical settings has the potential to dramatically reduce the time of diagnosis of drug resistance for multiple drugs in parallel, enabling earlier intervention for high priority pathogens.

genomics↗

bioRxiv2024

A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission

BackgroundMixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections. MethodsWe employed a Gaussian mixture model to cluster allele frequencies at mixed sites (hSNPs) in each sample to identify signals of multiple strains. Building upon our previous tool, MixInfect, we increased the accuracy of classifying in vitro mixed samples through multiple improvements to the bioinformatic pipeline. Major and minor proportion constituent strains were reconstructed using three approaches and assessed by comparing the estimated sequence to the known constituent strain sequence. Lastly, mixed infections in a real-world Mycobacterium tuberculosis population from Moldova were detected with MixInfect2 and clusters of recent transmission that included major and minor constituent strains were built. ResultsAll 36/36 in vitro mixed and 12/12 non-mixed samples were correctly classified with MixInfect2, and major strain proportions estimated with high accuracy, outperforming previous tools. Reconstructed major strain sequences closely matched the true constituent sequence by taking the allele at the highest frequency at hSNPs, while the best performing approach to reconstruct the minor proportion strain sequence was identifying the closest non-mixed isolate in the same population, though no approach was effective when the minor strain proportion was at 5%. Finally, fewer mixed infections were identified in Moldova than previous estimates (6.6% vs 17.4%) and we found multiple instances where the constituent strains of mixed samples were present in transmission clusters. ConclusionsMixInfect2 accurately detects samples with evidence of mixed infection from WGS data and provides an excellent estimate of the mixture proportions. While there are limitations in reconstructing the constituent strain sequences of mixed samples, we present recommendations for the best approach to include these isolates in further analyses.

genomics↗

bioRxiv2023

The appearance of sugI mixed loci in three individuals during treatment for MDR-TB, supports the involvement of sugI in Mycobacterium tuberculosis d-cycloserine resistance in vivo.

To study the adaptation of multi-drug resistant Mycobacterium tuberculosis (MDR-TB) during treatment patients diagnosed with MDR-TB were recruited into an observational study. Clinical data and M. tuberculosis DNA at diagnosis and between seven days and two months of MDR-TB treatment were collected. The drugs prescribed were recorded. Interpretable WGS data from 118 isolates from 54 participants was obtained (11 in Belarus and 43 in Moldova) and screened for the presence of unfixed single nucleotide polymorphisms (mixed SNPs / loci). This study was performed shortly after the publication of the 2019 WHO consolidated guidelines on drug-resistant tuberculosis treatment. Existing drug supplies and procurement in one country after the switch to the all oral MDR-TB regimen in addition to patient factors, influenced the selection of and exposure to drugs. Confidently mixed SNPs were identified in samples from multiple participants in only five genes (gyrA, pncA, Rv1129c, Rv1148c, and sugI). All other genes with confidently mixed SNPs were identified in isolates from only a single individual. A significant proportion of the participants (52 of 54 participants) received d-cycloserine as part of their initial treatment, most participants who initially received d-cycloserine did not receive bedaquiline in their initial regimen (all at one site). Three different mixed SNPs were identified in sugI gene from a follow up isolate from three participants (P7A, P7T, and Q6stop). Mutations in sugI have previously been reported in spontaneous in vitro d-cycloserine resistant mutants. Alterations in the sugI gene may indicate a sub optimal d-cycloserine containing regimen and potentially be of clinical significance with respect to adaptation to d-cycloserine. Monitoring the accumulation of low frequency escape mutants may help identify regimens insufficiently powerful to block the accumulation of antimicrobial resistance mutants and identify drug(s) at risk of resistance selection.

microbiology↗

bioRxiv2023

Identification of differentially recognized T cell epitopes in the spectrum of Mtb infection

Tuberculosis caused by Mycobacterium tuberculosis is one of the leading causes of death from a single infectious agent. Identifying dominant epitopes and comparing their reactivity in different tuberculosis (TB) infection states can help design diagnostics and vaccines. We performed a proteome-wide screen of 20,610 Mtb derived peptides in 21 Active TB (ATB) patients 3-4 months post-diagnosis of pulmonary TB (mid-treatment) using an IFN{gamma} and IL-17 Fluorospot assay. Responses were mediated exclusively by IFN{gamma} and identified a total of 137 unique epitopes, with each patient recognizing, on average, 8 individual epitopes and 22 epitopes (16%) recognized by 2 or more participants. Responses were predominantly directed against antigens part of the cell wall and cell processes category. Testing 517 peptides spanning TB vaccine candidates and ESAT- 6 and CFP10 antigens also revealed differential recognition between ATB participants mid-treatment and healthy IGRA+ participants of several vaccine antigens. An ATB-specific peptide pool consisting of epitopes exclusively recognized by participants mid-treatment, allowed distinguishing participants with active pulmonary TB from healthy interferon-gamma release assay (IGRA)+/- participants from diverse geographical locations. Analysis of longitudinal samples indicated decreased reactivity during treatment for pulmonary TB. Together, these results show that a proteome-wide screen of T cell reactivity identifies epitopes and antigens that are differentially recognized depending on the Mtb infection stage. These have potential use in developing diagnostics and vaccine candidates and measuring correlates of protection.

immunology↗

bioRxiv2021

Genomic sequence characteristics and the empiric accuracy of short-read sequencing

BackgroundShort-read whole genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences, and sequencing bias, reduce the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. For the clonal pathogen Mycobacterium tuberculosis (Mtb), researchers frequently exclude 10.7% of the genome believed to be repetitive and prone to erroneous variant calls. To benchmark short-read variant calling, we used 36 diverse clinical Mtb isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically study the short-read variant calling accuracy and the influence of sequence uniqueness, reference bias, and GC content. [a] ResultsReference based Illumina variant calling had a recall [≥]89.0% and precision [≥]98.5% across parameters evaluated. The best balance between precision and recall was achieved by tuning the mapping quality (MQ) threshold, i.e. confidence of the read mapping (recall 85.8%, precision 99.1% at MQ [≥] 40). Masking repetitive sequence content is an alternative conservative approach to variant calling that maintains high precision (recall 70.2%, precision 99.6% at MQ[≥]40). Of the genomic positions typically excluded for Mtb, 68% are accurately called using Illumina WGS including 52 of the 168 PE/PPE genes (34.5%). We present a refined list of low confidence regions and examine the largest sources of variant calling error. ConclusionsOur improved approach to variant calling has broad implications for the use of WGS in the study of Mtb biology, inference of transmission in public health surveillance systems, and more generally for WGS applications in other organisms.

bioinformatics↗

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↗

bioRxiv2018

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as \"transmission clusters\". The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.

genomics↗