Biology subjects

Sobkowiak, B.

Publications and source records attributed to Sobkowiak, B..

6 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↗

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

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogens genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIMs capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

bioinformatics↗

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↗

bioRxiv2022

Characterising indel diversity in a large Mycobacterium tuberculosis outbreak - implications for transmission reconstruction

Genomic sequencing of Mycobacterium tuberculosis (Mtb), the primary aetiological agent of tuberculosis (TB) in humans, has been used to understand transmission dynamics and reconstruct past outbreaks. Putative transmission events between hosts can be predicted by linking cases with low genomic variation between pathogen strains, though typically only variation in single nucleotide polymorphisms (SNPs) is used to calculate divergence. In highly clonal Mtb populations there can be many strains that appear identical by SNPs, reducing the utility of genomic data to disentangle potential transmission routes in these settings. Small insertions and deletions (indels) are found in high numbers across the Mtb genome and can be an important source of variation to increase the observed diversity in outbreaks. Here, we examine the value of including indels in the transmission reconstruction of a large Mtb outbreak in London, UK, characterised by low levels of SNP diversity between 1998 and 2013. Our results show that including indel polymorphism decreases the number of strains in the outbreak with at least one other identical sequence by 43% compared to using only SNP variation and reduces the size of largest clonal cluster by 53%. Considering both SNPs and indel polymorphisms alters the reconstructed transmission network and decreases likelihood of direct transmission between hosts with variation in indels. This work demonstrates the importance of incorporating indels into Mtb transmission reconstruction and we provide recommendations for further work to optimise the inclusion of indel diversity in such analyses.

genomics↗

bioRxiv2022

Comparing transmission reconstruction models with Mycobacterium tuberculosis whole genome sequence data

Pathogen genomic epidemiology is now routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools that reconstruct transmission networks by coupling genomic data with epidemiological modelling have been developed. The resulting inferences are often used to inform outbreak investigations, yet to date, the performance of these transmission reconstruction tools has not been compared specifically for tuberculosis, a disease process with complex epidemiology that includes variable latency periods and within-host heterogeneity. Here, we carried out a systematic comparison of seven publicly available transmission reconstruction tools, evaluating their accuracy in predicting transmission events in both simulated and real-world Mycobacterium tuberculosis outbreaks. No tool was able to fully resolve transmission networks, though both the single-tree and multi-tree input implementations of TransPhylo identified the most epidemiologically supported transmission events and the fewest false positive links. We observed a high degree of variability in the transmission networks inferred by each approach. Our findings may inform an end-users choice of tools in future tuberculosis transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

bioinformatics↗