Biology subjects

Pham, D. T.

Publications and source records attributed to Pham, D. T..

13 recordsLinked to original sources

bioRxiv2024

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

neuroscience↗

bioRxiv2023

Lagrangian Formalism in Biology: II. Non-Standard and Null Lagrangians for Nonlinear Dynamical Systems and their Role in Population Dynamics

Non-standard Lagrangians do not display any discernible energy-like terms, yet they give the same equations of motion as standard Lagrangians, which have easily identifiable energy-like terms. A new method to derive non-standard Lagrangians for second-order nonlinear differential equations with damping is developed and the limitations of this method are explored. It is shown that the limitations do not exist only for those nonlinear dynamical systems that can be converted into linear ones. The obtained results are applied to selected population dynamics models for which non-standard Lagrangians and their corresponding null Lagrangians and gauge functions are derived, and their roles in the population dynamics are discussed.

ecology↗

bioRxiv2023

Multidrug resistance plasmids underlie clonal expansions and international spread of Salmonella enterica serotype 4,,12,i:- ST34 in Southeast Asia

Salmonella enterica serotype 4,[5],12,i:- (Typhimurium monophasic variant) of sequence type (ST) 34 has emerged as the predominant pandemic genotype in recent decades. Despite increasing reports of resistance to antimicrobials in Southeast Asia, Salmonella ST34 population structure and evolution remained understudied in the region. Here we performed detailed genomic investigations on 454 ST34 genomes collected from Vietnam and diverse geographical sources to elucidate the pathogens epidemiology, evolution and antimicrobial resistance. We showed that ST34 has been introduced into Vietnam in at least nine occasions since 2000, forming five co-circulating major clones responsible for paediatric diarrhoea and bloodstream infection. Most expansion events were associated with acquisitions of large multidrug resistance plasmids of IncHI2 or IncA/C2. Particularly, the self-conjugative IncA/C2 pST34VN2 (co-transferring blaCTX-M-55, mcr-3.1, and qnrS1) underlies local expansion and intercontinental spread in two separate ST34 clones. At the global scale, Southeast Asia was identified as a hotspot for the emergence and dissemination of multidrug resistant Salmonella ST34, and mutation analysis suggests of selection in antimicrobial responses and key virulence factors. Our work enriches the understanding on epidemiology and evolution of this variant in Southeast Asia, and determines that multidrug resistance plasmids have driven its local and potentially global success.

genomics↗

bioRxiv2022

The origins of haplotype 58 (H58) Salmonella enterica serovar Typhi

Antimicrobial resistance (AMR) poses a serious threat to the clinical management of typhoid fever. AMR in Salmonella Typhi (S. Typhi) is associated with the H58 lineage, which arose comparatively recently before becoming globally disseminated. To better understand when and how this lineage emerged and became dominant, we performed detailed phylogenetic and phylodynamic analyses on contemporary genome sequences from S. Typhi isolated in the period spanning the emergence. Our dataset, which contains the earliest described H58 S. Typhi, indicates that the prototype H58 organisms were multi-drug resistant (MDR). These organisms emerged spontaneously in India in 1987 and became radially distributed throughout South Asia and then globally in the ensuing years. These early organisms were associated with a single long branch, possessing mutations associated with increased bile tolerance, suggesting that the first H58 organism was generated during chronic carriage. The subsequent use of fluoroquinolones led to several independent mutations in gyrA. The ability of H58 to acquire and maintain AMR genes continues to pose a threat, as extensively drug-resistant (XDR; MDR plus resistance to ciprofloxacin and third generation cephalosporins) variants, have emerged recently in this lineage. Understanding where and how H58 S. Typhi originated and became successful is key to understand how AMR drives successful lineages of bacterial pathogens. Additionally, these data can inform optimal targeting of typhoid conjugate vaccines (TCVs) for reducing the potential for emergence and the impact of new drug-resistant variants. Emphasis should also be placed upon the prospective identification and treatment of chronic carriers to prevent the emergence of new drug resistant variants with the ability to spread efficiently.

microbiology↗

bioRxiv2022

Genetic dissection of the pluripotent proteome through multi-omics data integration

Genetic background is a major driver of phenotypic variability in pluripotent stem cells (PSCs). Most studies of variation in PSCs have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the primary functional units in the cell, vary across genetically diverse PSCs, how protein abundance relates to variation in other cell characteristics, and how genetic background confers these effects. Here we present a comprehensive genetic study characterizing the pluripotent proteome of 190 unique mouse embryonic stem cell lines (mESCs) derived from genetically heterogeneous Diversity Outbred (DO) mice. The quantitative proteome is highly variable across DO mESCs, and we identified differentially activated pluripotency-associated pathways in the proteomics data that were not evident in transcriptome data from the same cell lines. Comparisons of protein abundance to transcript levels and chromatin accessibility show broad co-variation across molecular layers and variable correlation across samples, with some lines showing high and others low correlation between these multi-omics datasets. Integration of these three molecular data types using multi-omics factor analysis revealed shared and unique drivers of quantitative variation in pluripotency-associated pathways. QTL mapping localized the genetic drivers of this quantitative variation to a number of genomic hotspots, and demonstrated that multi-omics data integration consolidates the influence of genetic signals shared across molecular traits to increase QTL detection power and overcome the limitations inherent in mapping individual molecular features. This study reveals transcriptional and post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome, and in so doing provides a regulatory map for mouse ESCs that can provide a rational basis for future mechanistic studies, including studies of human PSCs.

genetics↗

bioRxiv2022

Mucosal microbiomes and Fusobacterium genomics in Vietnamese colorectal cancer patients

Perturbations in the gut microbiome have been linked to the promotion and prognosis of colorectal cancer (CRC), with the colonic overabundance of Fusobacterium nucleatum shown as the most consistent marker. Despite the increasing health burden inflicted by CRC in low- and middle-income countries like Vietnam, the CRC-specific microbiome in these populations remains underexplored. Here we conducted a study in Vietnam to enrol 43 CRC patients (cases) and 25 patients with non-cancerous colorectal polyps (controls) between December 2018 and January 2020. Our study investigated the mucosal microbiome signature and genomic diversity of Fusobacterium in Vietnamese CRC patients, using a combination of 16S rRNA gene profiling, anaerobic microbiology, and whole genome sequencing. We found that several oral bacteria, including F. nucleatum and Leptotrichia, were significantly more abundant in the tumour mucosa, and these two bacteria were also more enriched in tumours of advanced CRC stages (III-IV). We obtained 53 Fusobacterium genomes from the saliva, tumour and non-tumour mucosa of six CRC patients. Isolates from the gut mucosa belonged to diverse F. nucleatum subspecies (nucleatum, animalis, vincentii, polymorphum) and a potential new subspecies of F. periodonticum. The Fusobacterium population within each individual was distinct and in many cases diverse, with minimal intra-clonal variation. Phylogenetic analyses showed that within each individual, tumour-associated Fusobacterium were clonal to those isolated from non-tumour mucosa, but distantly related to those isolated from saliva. Genes encoding major virulence factors (Fap2 and RadD) showed variability in length and evidence of horizontal gene transfer. Our work provides a framework to understand the genomic diversity of Fusobacterium within the CRC patients, which can be exploited for the development of CRC diagnostic and therapeutic options targeting this oncobacterium.

microbiology↗

bioRxiv2021

Systems genetics uncovers microbe-lipid-host connections in the murine gut

The molecular bases of how host genetic variation impact gut microbiome remain largely unknown. Here, we used a genetically diverse mouse population and systems genetics strategies to identify interactions between molecular phenotypes, including microbial functions, intestinal transcripts and cecal lipids that influence microbe-host dynamics. Quantitative trait loci (QTL) analysis identified genomic regions associated with variations in bacterial taxa, bacterial functions, including motility, sporulation and lipopolysaccharide production, and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and cecal levels of ornithine lipids (OL). Follow-up studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that OL have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits. Collectively, these results suggest that OL are key players in A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

genetics↗

bioRxiv2020

The genomic epidemiology of multi-drug resistant nontyphoidal Salmonella causing invasive disease in sub-Saharan Africa

BackgroundInvasive nontyphoidal Salmonella (iNTS) is one of the leading causes of bacteraemia in sub-Saharan Africa. Multi-drug resistance (MDR) and further resistance to third generation cephalosporins and fluoroquinolones have emerged in multiple iNTS serotypes. Molecular epidemiological investigations of nontyphoidal Salmonella are needed to better understand the genetic characteristics and transmission dynamics associated with major MDR iNTS serotypes across the continent. MethodsA total of 166 nontyphoidal Salmonella isolates causing invasive disease were collected from a multi-centre study in eight African countries between 2010 and 2014, and whole-genome sequenced to investigate the geographical distribution, antimicrobial genetic determinants and population structure of iNTS serotypes-genotypes. Phylogeographical reconstruction was further conducted in context of the existing genomic framework of iNTS serotypes Typhimurium and Enteritidis. Population-based incidence of MDR-iNTS disease was also estimated. ResultsSalmonella enterica subsp. Enterica serotype Typhimurium (S. Typhimurium) sequence-type (ST) 313 and Salmonella enterica subsp. Enterica serotype Enteritidis (S. Enteritidis) ST11 were predominant, and both exhibited high frequencies of MDR. Salmonella enterica subsp. Enterica serotype Dublin (S. Dublin) ST10 emerged in West Africa. Mutations in the gyrA gene were identified in S. Enteritidis and S. Typhimurium in Ghana; and ST313 carrying blaCTX-M-15 was found in Kenya. Inter-country transmission of MDR ST313 lineage II and the West African Clade of MDR ST11 between Ghana and neighbouring countries including Mali, Burkina Faso, and Nigeria were evident. The incidence of MDR-iNTS disease exceeded 100/100,000-person years-of-observation (PYO) in children aged <5 years in several West African countries. ConclusionsMultiple MDR iNTS serotypes-sequence types, predominantly S. Typhimurium ST313 and S. Enteritidis ST11, are co-circulating in sub-Saharan Africa with evidence of transmission between West African countries. The development of safe and effective iNTS vaccines coupled with appropriate antimicrobial stewardship and adequate epidemiological monitoring are essential to limit the impact of these pathogens in Africa.

genomics↗

bioRxiv2020

Spontaneous emergence of azithromycin resistance in independent lineages of Salmonella Typhi in Northern India

BackgroundThe emergence and spread of antimicrobial resistance (AMR) pose a major threat to the effective treatment and control of typhoid fever. The ongoing outbreak of extensively drug resistant (XDR) Salmonella Typhi (S. Typhi) in Pakistan has left azithromycin as the only remaining broadly efficacious oral antimicrobial for typhoid in South Asia. Ominously, azithromycin resistant S. Typhi organisms have been subsequently reported in Bangladesh, Pakistan, and Nepal. MethodsHere, we aimed to understand the molecular basis of AMR in 66 S. Typhi isolated in a cross-sectional study performed in a suburb of Chandigarh in Northern India using whole genome sequencing (WGS) and phylogenetic analysis. ResultsWe identified seven S. Typhi organisms with the R717Q mutation in the acrB gene that was recently found to confer resistance to azithromycin in Bangladesh. Six out of the azithromycin-resistant S. Typhi isolates also exhibited triple mutations in gyrA (S83F and D87N) and parC (S80I) genes and were resistant to ciprofloxacin. These contemporary ciprofloxacin/azithromycin-resistant isolates were phylogenetically distinct from each other and from those reported from Bangladesh, Pakistan, and Nepal. ConclusionsThe independent emergence of azithromycin resistant typhoid in Northern India reflects an emerging broader problem across South Asia and illustrates the urgent need for the introduction of typhoid conjugate vaccines (TCVs) in the region. Key pointsWe identified ciprofloxacin/azithromycin-resistant Salmonella Typhi (S. Typhi) in Chandigarh in Northern India. The independent emergence of ciprofloxacin/azithromycin-resistant typhoid in Bangladesh, Pakistan, Nepal, and India and the continued spread of extensively-drug resistant (XDR) typhoid in Pakistan highlight the limitations of licensed oral treatments for typhoid fever in South Asia.

genomics↗

bioRxiv2020

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

molecular biology↗

bioRxiv2020

Regulation of protein abundance in genetically diverse mouse populations

Proteins constitute much of the structure and functional machinery of cells, forming signaling networks, metabolic pathways, and large multi-component complexes. Protein abundance is regulated at multiple levels spanning transcription, translation, recycling, and degradation to maintain proper balance and optimal function. To better understand how protein abundances are maintained across varying genetic backgrounds, we analyzed liver proteomes of three genetically diverse mouse populations. We observe strong concordance of genetic and sex effects across populations. Differences between the populations arise from the contributions of additive, dominance, and epistatic components of heritable variation. We find that the influence of genetic variation on proteins that form complexes relates to their co-abundance. We identify effects on protein abundance from mutations that arose and became fixed during breeding and can lead to unique regulatory responses and disease states. Genetically diverse mouse populations provide powerful tools for understanding proteome regulation and its relationship to whole-organism phenotypes. O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/296657v3_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1c8a941org.highwire.dtl.DTLVardef@e022b6org.highwire.dtl.DTLVardef@106e361org.highwire.dtl.DTLVardef@13a2c2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2020

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

bioinformatics↗

bioRxiv2020

Scalable and flexible gene-environment interaction analysis in millions of samples

MotivationGene-environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle, or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases. However, commonly-used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples. ResultsHere, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates, and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352,768 unrelated individuals from the UK Biobank, identifying 39 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of genomic contributions to complex traits. AvailabilityGEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/GEM. Contactakmanning@mgh.harvard.edu, Han.Chen.2@uth.tmc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗