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Motivation Target enrichment strategies generate genomic data from multiple pathogens in a single process, greatly improving sensitivity over metagenomic sequencing and enabling cost-effective, high-throughput surveillance and clinical applications. However, uptake by research and clinical laboratories is constrained by an absence of computational tools that are specifically designed for the analysis of multi-pathogen enrichment sequence data. Here we present an analysis pipeline, Castanet, for use with multi-pathogen enrichment sequencing data. Castanet is designed to work with short-read data produced by existing targeted enrichment strategies, but can be readily deployed on any BAM file generated by another methodology. Also included are an optional graphical interface and installer script. Results In addition to genome reconstruction, Castanet reports method-specific metrics that enable quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality. Castanet can be used as a traditional end-to-end pipeline for consensus generation, but its strength lies in the ability to process a flexible, pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments. In our tests, Castanet consensus sequences were accurate reconstructions of reference sequences, including in instances where multiple strains of the same pathogen were present. Castanet performs effectively on standard computers and can process the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command, in $<$2 h. Availability and implementation Source code freely available under GPL-3 license at https://github.com/MultipathogenGenomics/castanet , implemented in Python 3.10 and supported in Ubuntu Linux 22.04. The data underlying this article are available in Europe Nucleotide Archives, at https://www.ebi.ac.uk/ena/browser/view/PRJEB77004. [ABSTRACT FROM AUTHOR]

Host–pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host–pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human–HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant–pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion. [ABSTRACT FROM AUTHOR]

Studies have shown that hepatitis C virus subtype 3a (HCV-3a) is likely to have been circulating in South Asia before its global spread. However, the time and route of this dissemination remain unclear. For the first time, we generated host and virus genome-wide data for more than 500 patients infected with HCV-3a from the UK, North America, Australia, and New Zealand. We used the host genomic data to infer the ancestry of the patients and used this information to investigate the epidemic history of HCV-3a. We observed that viruses from hosts of South Asian ancestry clustered together near the root of the tree, irrespective of the sampling country, and that they were more diverse than viruses from other host ancestries. We hypothesized that South Asian hosts are more likely to have been infected in South Asia and used the inferred host ancestries to distinguish between the location where the infection was acquired and where the sample was taken. Next, we inferred that three independent transmission events resulted in the spread of the virus from South Asia to the UK, North America, and Oceania. This initial spread happened during or soon after the end of World War II. This was subsequently followed by many independent transmissions between the UK, North America, and Oceania. Using both host and virus genomic information can be highly informative in studying the virus epidemic history, especially in the context of chronic infections where migration histories need to be accounted for. [ABSTRACT FROM AUTHOR]

Targeted metagenomics using strand-specific libraries with target enrichment is a sensitive, generalized approach to pathogen sequencing and transcriptome profiling. Using this method, we recovered 13 (76%) complete human respiratory syncytial virus (RSV) genomes from 17 clinical respiratory samples, reconstructed the phylogeny of the infecting viruses, and detected differential gene expression between 2 RSV subgroups, specifically, a lower expression of the P gene and a higher expression of the M2 gene in RSV-A than in RSV-B. This methodology can help to relate viral genetics to clinical phenotype and facilitate ongoing population-level RSV surveillance and vaccine development. Clinical Trials Registration. NCT03627572 and NCT03756766. [ABSTRACT FROM AUTHOR]

The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realization from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is, however, often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated data sets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications. [ABSTRACT FROM AUTHOR]

In eukaryotes, detailed surveys of recombination rates have shown variation at multiple genomic scales and the presence of “hotspots” of highly elevated recombination. In bacteria, studies of recombination rate variation are less developed, in part because there are few analysis methods that take into account the clonal context within which bacterial evolution occurs. Here, we focus in particular on identifying “hot regions” of the genome where DNA is transferred frequently between isolates. We present a computationally efficient algorithm based on the recently developed “chromosome painting” algorithm, which characterizes patterns of haplotype sharing across a genome. We compare the average genome wide painting, which principally reflects clonal descent, with the painting for each site which additionally reflects the specific deviations at the site due to recombination. Using simulated data, we show that hot regions have consistently higher deviations from the genome wide average than normal regions. We applied our approach to previously analyzed Escherichia coli genomes and revealed that the new method is highly correlated with the number of recombination events affecting each site inferred by ClonalOrigin, a method that is only applicable to small numbers of genomes. Furthermore, we analyzed recombination hot regions in Campylobacter jejuni by using 200 genomes. We identified three recombination hot regions, which are enriched for genes related to membrane proteins. Our approach and its implementation, which is downloadable from https://github.com/bioprojects/orderedPainting, will help to develop a new phase of population genomic studies of recombination in prokaryotes. [ABSTRACT FROM PUBLISHER]

Patterns of linkage disequilibrium, homoplasy, and incompatibility are difficult to interpret because they depend on several factors, including the recombination process and the population structure. Here we introduce a novel model-based framework to infer recombination properties from such summary statistics in bacterial genomes. The underlying model is sequentially Markovian so that data can be simulated very efficiently, and we use approximate Bayesian computation techniques to infer parameters. As this does not require us to calculate the likelihood function, the model can be easily extended to investigate less probed aspects of recombination. In particular, we extend our model to account for the bias in the recombination process whereby closely related bacteria recombine more often with one another. We show that this model provides a good fit to a data set of Bacillus cereus genomes and estimate several recombination properties, including the rate of bias in recombination. All the methods described in this article are implemented in a software package that is freely available for download at http://code.google.com/p/clonalorigin/. [ABSTRACT FROM AUTHOR]

Background. Clostridium difficile is a major cause of nosocomial diarrhea, with 30-day mortality reaching 30%. The cell surface comprises a paracrystalline proteinaceous S-layer encoded by the slpA gene within the cell wall protein (cwp) gene cluster. Our purpose was to understand the diversity and evolution of slpA and nearby genes also encoding immunodominant cell surface antigens.Methods. Whole-genome sequences were determined for 57 C. difficile isolates representative of the population structure and different clinical phenotypes. Phylogenetic analyses were performed on their genomic region (>63 kb) spanning the cwp cluster.Results. Genetic diversity across the cwp cluster peaked within slpA, cwp66 (adhesin), and secA2 (secretory translocase). These genes formed a 10-kb cassette, of which 12 divergent variants were found. Homologous recombination involving this cassette caused it to associate randomly with genotype. One cassette contained a novel insertion (length, approximately 24 kb) that resembled S-layer glycosylation gene clusters.Conclusions. Genetic exchange of S-layer cassettes parallels polysaccharide capsular switching in other species. Both cause major antigenic shifts, while the remainder of the genome is unchanged. C. difficile genotype is therefore not predictive of antigenic type. S-layer switching and immune escape could help explain temporal and geographic variation in C. difficile epidemiology and may inform genotyping and vaccination strategies. [ABSTRACT FROM AUTHOR]