Biology subjects

Walter, K. S.

Publications and source records attributed to Walter, K. S..

3 recordsLinked to original sources

bioRxiv2026

Population structure of human-infecting and environmental isolates of Coccidioides immitis, the fungus causing Valley fever

Coccidioides is a soil-dwelling fungus that can cause serious disease (coccidioidomycosis or Valley fever) when its spores are inhaled. Limiting exposure to environmental spores is critical for disease prevention, yet how Coccidioides is distributed in the soil and where people become exposed remain almost entirely unknown. Here we report the first genomes of environmental isolates of C. immitis, from sampling of rodent burrows in the highly endemic region--California's San Joaquin Valley. We sequenced 16 soil-derived isolates alongside 26 novel patient-derived isolates collected from the same region to investigate genetic structure and diversity in C. immitis, and the potential origins of human infection. We found evidence of regional population structure among environmental C. immitis strains, though with extensive allele sharing between clusters. Most patient isolates derived nearly 100% of their ancestry from a genetic cluster shared with soil isolates, suggesting that the Coccidioides strain inhaled by a human host represents a random draw from the environmental pathogen pool. Further, we found that high levels of genomic diversity in environmental C. immitis are partitioned into effectively clonal lineages within individual rodent burrows, with isolates from neighboring burrows differing by over 13,000 SNPs. Together, these data support a picture of diverse, regionally structured environmental populations of C. immitis, suggesting that strategies to assign patient exposures to general regions within the San Joaquin Valley may be possible.

ecology↗

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↗

bioRxiv2019

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet there is no current consensus on how to measure genomic variation. We investigated the effects of variant identification approaches on transmission inferences for M. tuberculosis by comparing variants identified by five different groups in the same sequence data from a clonal outbreak. We then measured the performance of commonly used variant calling approaches in recovering variation in a simulated tuberculosis outbreak and tested the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant calling approaches used by different groups do not recover consistent sets of variants, often leading to conflicting transmission inferences. Further, performance in recovering true outbreak variation varied widely across approaches. Finally, stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. We conclude that measurements of genetic distance and phylogenetic structure are dependent on variant calling approach. Variant calling algorithms trained upon true sequence data outperform other approaches and enable inclusion of repetitive regions typically excluded from genomic epidemiology studies, maximizing the information gleaned from outbreak genomes.

genomics↗