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Biology subjects

Nguyen, P. N.

Publications and source records attributed to Nguyen, P. N..

3 recordsLinked to original sources

Home is where the host is: Evolutionary history of geographic spread, host switching, and adaptive genomic signatures in two generalist Group B Streptococcus clonal groups

Group B Streptococcus (GBS) is a pathogen of global relevance in neonatal and maternal disease as well as bovine mastitis. Two closely related clonal groups, denoted 103 and 314 (CG103/314) have been detected in humans and cattle on multiple continents in recent decades but are poorly characterised compared to other host-generalist clades. We examined their potential origins, host-switching events and presence of a suite of genetic markers for antimicrobial resistance, virulence and host association using a newly assembled dataset of 248 CG103/314 genomes from humans, cattle, and food originating from five continents. We detected multiple host switches between humans and cattle, and significant regional differences in AMR gene distribution, possibly reflecting local differences in antimicrobial use across countries and hosts and indicating a capacity for regional adaptation to selective pressures. Across the evolutionary history of CG103/314 from both host species, the prevalence of the Lac.2 operon, a genetic marker associated with bovine host adaptation, was high, whereas the prevalence of the scpB-lmb gene pair, a genetic marker of human host adaptation in other GBS clonal groups, was very low. All isolates with scpB-lmb were associated with human disease rather than carriage. Our dataset displayed biases typical of research into multi-host pathogens, when sampling is often focused on a specific host species or setting. Consistent, balanced, contemporaneous and sympatric sampling efforts across host species and sources are needed for a full understanding of the distribution and emergence of CG103/314 and similar multi-host pathogens impacting food safety and public health. Impact statementThis study provides a comprehensive, global genomic overview of generalist clonal groups 103/314 of the human and animal pathogen Group B Streptococcus (GBS). By analysing host switching, antimicrobial resistance and virulence-associated markers, we show that these clonal groups display adaptation patterns shaped by region- and host-specific selective pressures. Our findings include potential expansion of the host range from humans and cattle into porcupines and pigs, and provides detailed discussion around anthropocentric sampling bias, highlighting the importance of balanced, multi-host sampling of generalist GBS lineages and One Health pathogens in general. This work reinforces the need for coordinated One Health surveillance to monitor emerging sub-lineages with relevance for food safety, human and animal health.

evolutionary biology↗

Vibrio harveyi plasmids as drivers of virulence in barramundi (Lates calcarifer)?

Vibrio species are an emerging public and animal health risk in marine environments and the opportunistic bacterial pathogen Vibrio harveyi is a major disease risk for tropical aquaculture. Current understanding of virulence in V. harveyi is limited by strain-specific variability and complex host-pathogen dynamics. This study sought to integrate genomic investigation, phenotypic characterisation and in vivo challenge trials in barramundi (Lates calcarifer) to increase our understanding of V. harveyi virulence. We identified two hypervirulent isolates, Vh-14 and Vh-15 that caused 100% mortality in fish within 48 hours, and that were phenotypically and genotypically distinct from other V. harveyi isolates. Virulent isolates contained multiple plasmids, including a 105,412 bp conjugative plasmid with type III secretion system genes originally identified in Yersinia pestis. The emergence of this hypervirulent plasmid-mediated patho-variant poses a potential threat to the sustainable production of marine finfish in Southeast Asia, the Mediterranean and Australia. In addition, we observed an effect of temperature on phenotypic indicators of virulence with an increase in activity at 28{degrees}C and 34{degrees}C compared to 22{degrees}C. This suggests that temperature fluctuations associated with climate change may act as a stressor on bacteria, increasing virulence gene secretion and host adaptation. Our results utilising a myriad of technologies and tools, highlights the importance of a holistic view to virulence characterisation.

microbiology↗

Supervised non-negative matrix factorization on cell-free DNA fragmentomic features enhances early cancer detection

BackgroundCell-free circulating DNA (cfDNA) fragments exhibit non-random patterns in their length (FLEN), end-motif (EM), and distance to nucleosome position (ND). While these cfDNA features have shown promise as inputs for machine learning and deep learning models in early cancer detection, most studies utilize them as raw inputs, overlooking the potential benefits of pre-processing to extract cancer-specific features. This study aims to enhance cancer detection accuracy by developing a novel approach to feature extraction from cfDNA fragmentomics. MethodsWe implemented a supervised non-negative matrix factorization (SNMF) algorithm to generate embedding vectors capturing cancer-specific signals within cfDNA fragmentomic features. These embeddings served as input for a machine learning model to classify cancer patients from healthy individuals. ResultsWe validated our framework using two datasets: an in-house cohort of 431 cancer patients and 442 healthy individuals (dataset 1), and a published cohort comprising 90 hepatocellular carcinoma (HCC) patients and 103 individuals with cirrhosis or hepatitis B (dataset 2). In dataset 1, we achieved an AUC of 94% in pan-cancer detection. In dataset 2, our framework achieved an AUC of 100% for HCC vs healthy classification, 99% for HCC vs non-HCC patients classification, and 96% for identifying HCC patients among a mixed group of non-HCC patients and healthy donors. ConclusionThis study demonstrates the efficiency of SNMF-transformed features in improving both pan-cancer detection and specific HCC detection. Our approach offers a significant advancement in leveraging cfDNA fragmentomics for early cancer detection, potentially enhancing diagnostic accuracy in clinical settings.

bioinformatics↗