Biology subjects

Strong, M.

Publications and source records attributed to Strong, M..

7 recordsLinked to original sources

bioRxiv2023

Worms on the Cape: an integrative survey of polydorid infestation in wild and cultivated oysters (Crassostrea virginica) from Massachusetts, USA

Polydorid infestations pose a significant challenge to shellfish aquaculture by impacting marketability and profitability of farms. In this study, we investigated the prevalence, intensity, identity and biogeography of shell-boring worms infecting both farmed and wild oysters (Crassostrea virginica) from three sites in Wellfleet Harbor, Massachusetts - an economically important shellfishing region in the northeastern United States. DNA barcoding revealed that Polydora websteri was the sole culprit responsible for infecting oysters from all three sites, reaching maximum prevalence (100% infection) and intensity (mean intensity: 38.63) in the Herring River. The oysters in the Herring River are subjected to restricted tidal flow due to the presence of a physical barrier (dike), and this could be responsible for the high infestation levels of P. websteri observed in this population. In addition, a population genetic analysis incorporating COI sequence data from Wellfleet P. websteri in addition to newly published sequences from the Black Sea and the Sea of Azov found very low levels of genetic differentiation across several intercontinental populations (0.000 - 0.399), which is likely being driven by multiple introductory events such as oyster importations. These findings are discussed in relation to the future of shellfish aquaculture in the United States.

zoology↗

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↗

bioRxiv2021

Analysis of SARS-CoV-2 Mutations Over Time Reveals Increasing Prevalence of Variants in the Spike Protein and RNA-Dependent RNA Polymerase

Amid the ongoing COVID-19 pandemic, it has become increasingly important to monitor the mutations that arise in the SARS-CoV-2 virus, to prepare public health strategies and guide the further development of vaccines and therapeutics. The spike (S) protein and the proteins comprising the RNA-Dependent RNA Polymerase (RdRP) are key vaccine and drug targets, respectively, making mutation surveillance of these proteins of great importance. Full protein sequences for the spike proteins and RNA-dependent RNA polymerase proteins were downloaded from the GISAID database, aligned, and the variants identified. Polymorphisms in the protein sequence were investigated at the protein structural level and examined longitudinally in order to identify sequence and strain variants that are emerging over time. Our analysis revealed a group of variants in the spike protein and the polymerase complex that appeared in August, and account for around five percent of the genomes analyzed up to the last week of October. A structural analysis also facilitated investigation of several unique variants in the receptor binding domain and the N-terminal domain of the spike protein, with high-frequency mutations occurring more commonly in these regions. The identification of new variants emphasizes the need for further study on the effects of these mutations and the implications of their increased prevalence, particularly as these mutations may impact vaccine or therapeutic efficacy.

bioinformatics↗

bioRxiv2020

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

bioinformatics↗

bioRxiv2020

A composite method to infer drug resistance with mixed genomic data

BackgroundThe increasing incidence of drug resistance in tuberculosis and other infectious diseases poses an escalating cause for concern, emphasizing the urgent need to devise robust computational and molecular methods identify drug resistant strains. Although machine learning-based approaches using whole-genome sequence data can facilitate the inference of drug resistance, current implementations do not optimally take advantage of information in public databases and are not robust for small sample sizes and mixed attribute types. ResultsIn this paper we introduce the Composite MetaDistance method, an approach for feature selection and classification of high-dimensional, unbalanced datasets with mixed attribute features from various data sources. We introduce a mixed-attribute, multi-view distance function to calculate distances between samples, with optimal handling of nominal features and different feature views. We also introduce a novel feature set for drug resistance prediction in Mycobacterium tuberculosis, using data from diverse sources. We compare the performance of Composite MetaDistance to multiple machine learning algorithms for Mycobacterium tuberculosis drug resistance prediction for three drugs. Composite MetaDistance consistently outperforms existing algorithms for small sample training sets, and performs as well as other algorithms for training sets with larger sample sizes. ConclusionThe feature set formulation introduced in this paper is utilizes mutational and publicly available information for each gene, and is much richer than ever devised previously. The prediction algorithm, Composite MetaDistance, is sample size agnostic and robust especially given small sample sizes. Proper handling of nominal features improves performance even with a very small number of nominal features. We expect Composite MetaDistance to be even more robust for datasets with a higher percentage of nominal features. The algorithm is application independent and can be used for any mixed attribute dataset.

bioinformatics↗

bioRxiv2019

In-host population dynamics of M. tuberculosis during treatment failure

BackgroundTuberculosis (TB) is a leading cause of death globally from an infectious agent. Understanding the population dynamics of TBs causative agent Mycobacterium tuberculosis (Mtb) in-host is vital for understanding the efficacy of antibiotic treatment. Here we use longitudinally collected clinical Mtb isolates that underwent Whole-Genome Sequencing (WGS) from the sputa of 307 subjects to investigate Mtb diversity during the course of active TB disease. Methods and findingsWe excluded cases suspected of reinfection or contamination to analyze data from 200 subjects, 167 of which met microbiological criteria for delayed culture conversion, treatment failure or relapse. Using technical and biological replicate samples, we defined an allele frequency threshold attributable to in-host evolution. Of the 167 subjects with unsuccessful treatment outcome, 27 (16%) developed new resistance mutations between sampling with 20/27 (74%) occurring in patients with pre-existing antibiotic resistance. Low abundance resistance variants at a purity of [≥]19% in the first isolate predicts fixation of these variants in the subsequent sample with 27.0% sensitivity and 95.8% specificity. We identify significant in-host variation in seven genes associated with antibiotic resistance and twenty other genes, including metabolic genes and genes known to modulate host innate immunity by interacting with TLR2. We confirm Rv0095c, Rv1944c, PPE18, PPE54 and PPE60 to be under positive selection by assessing phylogenetic convergence across a global and genetically diverse independent sample of 20,352 isolates. ConclusionsOur large sample provides a comprehensive picture of the mutational dynamics in-host during active TB disease. We demonstrate a framework to study temporal changes in Mtb population diversity using average depth WGS data. We show that minor variants can be used to inform antibiotic treatment regimens in patients with TB. Furthermore, we detect a signature of positive selection in-host, possibly stemming from innate immune pressure and informing our understanding of host-pathogen interactions.

genomics↗

bioRxiv2019

Population Genomics of Nontuberculous Mycobacteria Recovered from United States Cystic Fibrosis Patients

Nontuberculous mycobacteria (NTM) pose a threat to individuals with cystic fibrosis (CF) due to an increased prevalence of pulmonary infections, innate drug resistance of the bacteria, and potential transmission between CF patients. To explore the genetic diversity of NTM isolated from CF patients within the United States (US) and to identify potential transmission events, we sequenced and analyzed the genomes of 341 NTM isolates from 191 CF patients as part of a nationwide surveillance study. The most abundant species in the isolate cohort were Mycobacterium abscessus (59.5%), followed by species in the Mycobacterium avium complex (37.5%). Phylogenomic analyses of the three M. abscessus subspecies revealed that more than half of CF patients had isolates in one of four dominant clones, including two dominant clones of M. abscessus subspecies abscessus and two dominant clones of M. abscessus subspecies massiliense. M. avium isolates from US CF patients, however, do not have dominant clones and are phylogenetically diverse. Longitudinal NTM isolates were compared to determine genome-wide single nucleotide polymorphisms (SNPs) that occur within patients over time. This information was used to compare between and within-patient SNP distributions, to quantitatively define SNP thresholds suggestive of transmission, and calculate a posterior probability of recent transmission given the SNP distance between two isolates from different patients. Out of 114 patients with M. abscessus subspecies, ten clusters of highly similar isolates from 26 patients were identified. Among the 26 patients in the M. abscessus clusters, 12 attended the same CF care centers. No highly similar isolate clusters were observed in M. avium. Our study reveals the contrasting genomic diversity and epidemiology of two major NTM taxa and the potential for between-patient exposure and cross-transmission of these emerging pathogens.

microbiology↗