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Biology subjects

Metcalf, G.

Publications and source records attributed to Metcalf, G..

8 recordsLinked to original sources

Functional Genomics of Gastrointestinal Escherichia coli Isolated from Patients with Cancer and Diarrhea

We describe the epidemiology and clinical characteristics of 29 patients with cancer and diarrhea in whom Enteroaggregative Escherichia coli (EAEC) was initially identified by GI BioFire panel multiplex. E. coli strains were successfully isolated from fecal cultures in 14 of 29 patients. Six of the 14 strains were identified as EAEC and 8 belonged to other diverse E. coli groups of unknown pathogenesis. We investigated these strains by their adherence to human intestinal organoids, cytotoxic responses, antibiotic resistance profile, full sequencing of their genomes, and annotation of their functional virulome. Interestingly, we discovered novel and enhanced adherence and aggregative patterns for several diarrheagenic pathotypes that were not previously seen when co-cultured with immortalized cell lines. EAEC isolates displayed exceptional adherence and aggregation to human colonoids compared not only to diverse GI E. coli, but also compared to prototype strains of other diarrheagenic E. coli. Some of the diverse E. coli strains that could not be classified as a conventional pathotype also showed an enhanced aggregative and cytotoxic response. Notably, we found a high carriage rate of antibiotic resistance genes in both EAEC strains and diverse GI E. coli isolates and observed a positive correlation between adherence to colonoids and the number of metal acquisition genes carried in both EAEC and the diverse E. coli strains. This work indicates that E. coli from cancer patients constitute strains of remarkable pathotypic and genomic divergence, including strains of unknown disease etiology with unique virulomes. Future studies will allow for the opportunity to re-define E. coli pathotypes with greater diagnostic accuracy and into more clinically relevant groupings.

microbiology↗

INTRA- AND INTER-HOST EVOLUTION OF HUMAN NOROVIRUS IN HEALTHY ADULTS

BackgroundHuman noroviruses are a leading cause of acute and sporadic gastroenteritis worldwide. The evolution of human noroviruses in immunocompromised persons has been evaluated in many studies. Much less is known about the evolutionary dynamics of human norovirus in healthy adults. MethodsWe used sequential samples collected from a controlled human infection study with GI.1/Norwalk/US/68 virus to evaluate intra- and inter-host evolution of a human norovirus in healthy adults. Up to 12 samples from day 1 to day 56 post-challenge were sequenced using a norovirus-specific capture probe method. ResultsComplete genomes were assembled, even in samples that were below the limit of detection of standard RT-qPCR assays, up to 28 days post-challenge. Analysis of 123 complete genomes showed changes in the GI.1 genome in all persons, but there were no conserved changes across all persons. Single nucleotide variants resulting in non-synonymous amino acid changes were observed in all proteins, with the capsid VP1 and nonstructural protein NS3 having the largest numbers of changes. ConclusionsThese data highlight the potential of a new capture-based sequencing approach to assemble human norovirus genomes with high sensitivity and demonstrate limited conserved immune pressure-driven evolution of GI.1 virus in healthy adults.

microbiology↗

Examining intra-host genetic variation of RSV by short read high-throughput sequencing

Every viral infection entails an evolving population of viral genomes. High-throughput sequencing technologies can be used to characterize such populations, but to date there are few published examples of such work. In addition, mixed sequencing data are sometimes used to infer properties of infecting genomes without discriminating between genome-derived reads and reads from the much more abundant, in the case of a typical active viral infection, transcripts. Here we apply capture probe-based short read high-throughput sequencing to nasal wash samples taken from a previously described group of adult hematopoietic cell transplant (HCT) recipients naturally infected with respiratory syncytial virus (RSV). We separately analyzed reads from genomes and transcripts for the levels and distribution of genetic variation by calculating per position Shannon entropies. Our analysis reveals a low level of genetic variation within the RSV infections analyzed here, but with interesting differences between genomes and transcripts in 1) average per sample Shannon entropies; 2) the genomic distribution of variation hotspots; and 3) the genomic distribution of hotspots encoding alternative amino acids. In all, our results suggest the importance of separately analyzing reads from genomes and transcripts when interpreting high-throughput sequencing data for insight into intra-host viral genome replication, expression, and evolution.

genomics↗

A pan-genome approach to decipher variants in the highly complex tandem repeat of LPA

Lp(a) is an important factor in Coronary Heart disease risk, and its expression is correlated with the length of the LPA gene. The KIV-2 region of LPA consists of variable tandem repeats whose number is highly variable across individuals. Little is known about the inner diversity of the KIV-2 repeats themselves. Here we utilize a pan-genome graph approach across 47 haplotype resolved assemblies to identify unexpected variation across KIV-2.

bioinformatics↗

Oligonucleotide Capture Sequencing of the SARS-CoV-2 Genome and Subgenomic Fragments from COVID-19 Individuals

The newly emerged and rapidly spreading SARS-CoV-2 causes coronavirus disease 2019 (COVID-19). To facilitate a deeper understanding of the viral biology we developed a capture sequencing methodology to generate SARS-CoV-2 genomic and transcriptome sequences from infected patients. We utilized an oligonucleotide probe-set representing the full-length genome to obtain both genomic and transcriptome (subgenomic open reading frames [ORFs]) sequences from 45 SARS-CoV-2 clinical samples with varying viral titers. For samples with higher viral loads (cycle threshold value under 33, based on the CDC qPCR assay) complete genomes were generated. Analysis of junction reads revealed regions of differential transcriptional activity and provided evidence of expression of ORF10. Heterogeneous allelic frequencies along the 20kb ORF1ab gene suggested the presence of a defective interfering viral RNA species subpopulation in one sample. The associated workflow is straightforward, and hybridization-based capture offers an effective and scalable approach for sequencing SARS-CoV-2 from patient samples.

genomics↗

Oligonucleotide capture sequencing of the SARS-CoV-2 genome and subgenomic fragments from COVID-19 individuals

The newly emerged and rapidly spreading SARS-CoV-2 causes coronavirus disease 2019 (COVID-19). To facilitate a deeper understanding of the viral biology we developed a capture sequencing methodology to generate SARS-CoV-2 genomic and transcriptome sequences from infected patients. We utilized an oligonucleotide probe-set representing the full-length genome to obtain both genomic and transcriptome (subgenomic open reading frames [ORFs]) sequences from 45 SARS-CoV-2 clinical samples with varying viral titers. For samples with higher viral loads (cycle threshold value under 33, based on the CDC qPCR assay) complete genomes were generated. Analysis of junction reads revealed regions of differential transcriptional activity and provided evidence of expression of ORF10. Heterogeneous allelic frequencies along the 20kb ORF1ab gene suggested the presence of a defective interfering viral RNA species subpopulation in one sample. The associated workflow is straightforward, and hybridization-based capture offers an effective and scalable approach for sequencing SARS-CoV-2 from patient samples.

microbiology↗

Multiethnic catalog of structural variants and their translational impact for disease phenotypes across 19,652 genomes

Genome sequencing at population scale provides unprecedented access to the genetic foundations of human phenotypic diversity, but genotype-phenotype association analyses limited to small variants have failed to comprehensively characterize the genetic architecture of human health and disease because they ignore structural variants (SVs) known to contribute to phenotypic variation and pathogenic conditions1-3. Here we demonstrate the significance of SVs when assessing genotype-phenotype associations and the importance of ethnic diversity in study design by analyzing SVs across 19,652 individuals and the translational impact on 4,156 aptamerbased proteomic measurements across 4,021 multi-ethnic samples. The majority of 304,533 SVs detected are rare, although we identified 2,336 protein-coding genes impacted by common SVs.\ We identified 64 significant SV-protein associations that comprise 36 cis- and 28 trans-acting relationships, and 21 distinct SV regions overlapped with genome-wide association study loci. These findings represent a more comprehensive mapping of regulatory and translational endophenotypes underlying health and disease.

genomics↗

xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

MotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.\n\nResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30x short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.\n\nAvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/xatlas.\n\nContactfarek@bcm.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗