Biology subjects

Nguyen, A.

Publications and source records attributed to Nguyen, A..

119 records · Page 7Linked to original sources

bioRxiv2019

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

AimUnsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.\n\nLocationAnnamite mountains, Vietnam and Laos.\n\nMethodsWe conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.\n\nResultsAnalyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.\n\nMain conclusionIdentifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

ecology↗

bioRxiv2019

A nutritional memory impairs survival, transcriptional and metabolic response to dietary restriction in old mice

Dietary restriction (DR) during adulthood can greatly extend lifespan and improve metabolic health in diverse species. However, whether DR in mammals is still effective when applied for the first time at old age remains elusive. Here, we conducted a late-life DR switch experiment employing 800 mice, by switching old animals from ad libitum (AL) to DR and vice versa. Strikingly, the switch from DR-to-AL acutely increased mortality, while the switch from AL-to-DR caused only a weak and gradual increase in survival, highlighting a memory of earlier nutrition. A significant association between fat preservation and survival response pointed to the white adipose tissue (WAT) as a potential memory source. Consistently, post-switch RNA-seq profiling in liver and WAT demonstrated that the transcriptional and metabolic program of chronic DR remained largely refractory to the AL-to-DR switch specifically in adipose tissue. Integration of lipidomics confirmed impaired membrane lipogenesis and limited mitochondrial copy number increase under late-life DR as functional consequences of this memory effect. Together, our results provide evidence for a nutritional memory as a limiting factor for DR-induced longevity and metabolic remodeling of WAT in mammals.

physiology↗

bioRxiv2019

Enhancing DNA metabarcoding performance and applicability with bait capture enrichment and DNA from conservative ethanol

Metabarcoding is often presented as an alternative identification tool to compensate for coarse taxonomic resolution and misidentification encountered with traditional morphological approaches. However, metabarcoding comes with two major impediments which slow down its adoption. First, the picking and destruction of organisms for DNA extraction are time and cost consuming and do not allow organism conservation for further evaluations. Second, current metabarcoding protocols include a PCR enrichment step which induces errors in the estimation of species diversity and relative biomasses. In this study, we first evaluated the capacity of capture enrichment to replace PCR enrichment using controlled freshwater macrozoobenthos mock communities. Then, we tested if DNA extracted from the fixative ethanol (etDNA) of the same mock communities can be used as an alternative to DNA extracted from pools of whole organisms (bulk DNA). We show that capture enrichment provides more reliable and accurate representation of species occurrences and relative biomasses in comparison with PCR enrichment for bulk DNA. While etDNA does not permit to estimate relative biomasses, etDNA and bulk DNA provide equivalent species detection rates. Thanks to its robustness to mismatches, capture enrichment is already an efficient alternative to PCR enrichment for metabarcoding and, if coupled to etDNA, is a time-saver option in studies where presence information only is sufficient.

molecular biology↗

bioRxiv2019

Perinatal Programming of Mucosal Stromal Cell Identity by the Lymphotoxin Pathway Regulates Mucosal Immune Responses in the Adult

Redundant mechanisms support IgA responses to intestinal antigens. These include multiple priming sites (mesenteric lymph nodes (MLN), Peyers patches and isolated lymphoid follicles) and various cytokines that promote class switch to IgA, even in the absence of T cells. In spite of these back-up mechanisms, vaccination against enteric pathogens such as Rotavirus has limited success in some populations. Genetic and environmental signals experienced during early life are known to influence mucosal immunity, yet the mechanisms for how these exposures operate remain unclear. Here we used Rotavirus infection to follow antigen-specific IgA responses through time and in different gut compartments. Using genetic and pharmacological approaches, we tested the role of a pathway known to support IgA responses (Lymphotoxin - LT) at different developmental stages. We found that LT-beta receptor (LT{beta}R) signalling in utero programs intestinal IgA responses in adulthood by affecting antibody class switch recombination to IgA and subsequent generation of IgA antibody-secreting cells within an intact MLN. In addition, in utero LT{beta}R signalling dictates the phenotype and function of MLN stromal cells in order to support IgA responses in the adult. Collectively, our studies uncover new mechanistic insights into how in utero LT{beta}R signalling impacts mucosal immune responses during adulthood. One Sentence SummaryEarly life LT{beta}R signalling is critical for programming the mesenteric lymph node stromal cell environment, impacting both antibody isotype switching to IgA and the differentiation of IgA+ antibody secreting cells. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/557652v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@4780dborg.highwire.dtl.DTLVardef@5cbe85org.highwire.dtl.DTLVardef@e0465eorg.highwire.dtl.DTLVardef@2bcae6_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2018

Habitat-Net: Segmentation of habitat images using deep learning

Understanding environmental factors that influence forest health, as well as the occurrence and abundance of wildlife, is a central topic in forestry and ecology. However, the manual processing of field habitat data is time-consuming and months are often needed to progress from data collection to data interpretation. Computer-assisted tools, such as deep-learning applications can significantly shortening the time to process the data while maintaining a high level of accuracy. Here, we propose Habitat-Net: a novel method based on Convolutional Neural Networks (CNN) to segment habitat images of tropical rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution, produces a binary segmentation of the input image. We worked on two different types of habitat datasets that are widely used in ecological studies to characterize the forest conditions: canopy closure and understory vegetation. We trained the model with 800 canopy images and 700 understory images separately and then used 149 canopy and 172 understory images to test the performance of Habitat-Net. We compared the performance of Habitat-Net with a simple threshold based method, a manual processing by a second researcher and a CNN approach called U-Net upon which Habitat-Net is based. Habitat-Net, U-Net and simple thresholding reduced total processing time to milliseconds per image, compared to 45 seconds per image for manual processing. However, the higher mean Dice coefficient of Habitat-Net (0.94 for canopy and 0.95 for understory) indicates that accuracy of Habitat-Net is higher than that of both the simple thresholding (0.64, 0.83) and U-Net (0.89, 0.94). Habitat-Net will be of great relevance for ecologists and foresters, who need to monitor changes in their forest structures. The automated workflow not only reduces the time, it also standardizes the analytical pipeline and, thus, reduces the degree of uncertainty that would be introduced by manual processing of images by different people (either over time or between study sites). Furthermore, it provides the opportunity to collect and process more images from the field, which might increase the accuracy of the method. Although datasets from other habitats might need an annotated dataset to first train the model, the overall time required to process habitat photos will be reduced, particularly for large projects.

ecology↗

bioRxiv2018

neoepiscope Improves Neoepitope Prediction with Multi-variant Phasing

The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false positive and false negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels), and herein illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to [~]5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible, and supports several major histocompatibility complex binding affinity prediction tools. We have released neoepiscope as open-source software (MIT license, https://github.com/pdxgx/neoepiscope) for broad use.\n\nKEY POINTSO_LIGermline context and somatic variant phasing are important for neoepitope prediction\nC_LIO_LIMany popular neoepitope prediction tools have issues of performance and reproducibility\nC_LIO_LIWe describe and provide performant software for accurate neoepitope prediction from DNA-seq data\nC_LI

bioinformatics↗

bioRxiv2018

Multi-omics analysis of AML cells treated with azacitidine reveals highly variable cell surface proteome remodeling

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor (DNMTi) widely used to treat MDS and AML, yet the impact of AZA on the cell surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines (KG1a, HL60, HNT34, and AML193), representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome (methylation array), the transcriptome (gene expression array), and the cell surface proteome (glycoprotein capture with SILAC labeling). Untreated AML cell lines showed substantial overlap in their methylomes, transcriptomes, and cell surface proteomes. AZA treatment globally reduced DNA methylation in all cell lines, but changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteomics analysis found no commonly regulated proteins. Gene Set Enrichment Analysis (GSEA) of differentially-regulated RNA and surface proteins showed a decrease in metabolism pathways and an increase in immune defense response pathways. As such, AZA treatment in four AML cell lines had diverse effects at the individual gene and protein level, but converged to regulation of metabolism and immune response at the pathway level. Given the heterogeneous response of AZA in the four cell lines at the gene and protein level, we discuss potential therapeutic strategies for combinations with AZA.

cancer biology↗

bioRxiv2018

How small-molecule inhibitors of dengue-virus infection interfere with viral membrane fusion

Dengue virus (DV) is a compact, icoshedrally symmetric, enveloped particle, covered by 90 dimers of envelope protein (E), which mediates viral attachment and membrane fusion. Fusion requires a dimer-to-trimer transition and membrane engagement of hydrophobic \"fusion loops\". We previously characterized the steps in membrane fusion for the related West Nile virus (WNV), using recombinant, WNV virus-like particles (VLPs) for single-particle experiments. Trimerization and membrane engagement are rate-limiting; fusion requires at least two adjacent trimers; availability of competent monomers within the contact zone between virus and target membrane creates a trimerization bottleneck. We have extended that work to dengue VLPs, from all four DV serotypes, finding an essentially similar mechanism. Small-molecule inhibitors of DV infection that target E block its fusion-inducing conformation change. We show that [~]15 bound molecules per particle ([~]8.5 % occupancy) completely prevent fusion, in accord with the proposed mechanism and the likely inhibitor binding site on E.\n\nImpact statementSingle-particle studies of dengue-virus membrane fusion and the effect of small-molecule inhibitors of infection clarify the viral fusion mechanism.

biophysics↗

bioRxiv2018

A novel xenograft model reveals invasive mesenchymal transition and ineffective angiogenic response during the evolution of resistance to anti-angiogenic therapy

Bevacizumab treatment of glioblastoma is limited by transient responses and acquired resistance. Because of the lengthy duration of treatment that can precede resistance in patients, in order to study changes underlying the evolution of bevacizumab resistance, we created a novel multigenerational xenograft model of acquired bevacizumab resistance. Glioblastoma xenografts were treated with bevacizumab or IgG, and the fastest growing tumor re-implanted into new mice, generating paired isogeneic responsive or resistant multigenerational xenografts. Microarray analysis revealed significant overexpression across generations of the mesenchymal subtype gene signature, paralleling results from patient bevacizumab-resistant glioblastomas (BRGs) that exhibited increasing mesenchymal gene expression correlating with increased bevacizumab treatment duration. Key mesenchymal markers, including YKL-40, CD44, SERPINE1, and TIMP1 were upregulated across generations, with altered morphology, increased invasiveness, and increased neurosphere formation confirmed in later xenograft generations. Interestingly, YKL-40 levels were elevated in serum of patients with bevacizumab-resistant vs. bevacizumab-naive glioblastomas. Finally, despite upregulation of VEGF-independent pro-angiogenic genes across xenograft generations, immunostaining revealed increased hypoxia and decreased vessel density with increasing generation of treatment, mirroring our findings in patient BRGs and suggesting tumor growth despite effective devascularization caused by VEGF blockade. Besides identifying novel targets for preventing the evolution of resistance and offering a xenograft model for testing resistance inhibitors, our work suggests YKL-40 as a blood biomarker of bevacizumab resistance worthy of further evaluation.

cancer biology↗

bioRxiv2017

Quantification of autism recurrence risk by direct assessment of paternal sperm mosaicism

De novo genetic mutations represent a major contributor to pediatric disease, including autism spectrum disorders (ASD), congenital heart disease, and muscular dystrophies1,2, but there are currently no methods to prevent or predict them. These mutations are classically thought to occur either at low levels in progenitor cells or at the time of fertilization1,3 and are often assigned a low risk of recurrence in siblings4,5. Here, we directly assess the presence of de novo mutations in paternal sperm and discover abundant, germline-restricted mosaicism. From a cohort of ASD cases, employing single molecule genotyping, we found that four out of 14 fathers were germline mosaic for a putatively causative mutation transmitted to the affected child. Three of these were enriched or exclusively present in sperm at high allelic fractions (AF; 7-15%); and one was recurrently transmitted to two additional affected children, representing clinically actionable information. Germline mosaicism was further assessed by deep (>90x) whole genome sequencing of four paternal sperm samples, which detected 12/355 transmitted de novo single nucleotide variants that were mosaic above 2% AF, and more than two dozen additional, non-transmitted mosaic variants in paternal sperm. Our results demonstrate that germline mosaicism is an underestimated phenomenon, which has important implications for clinical practice and in understanding the basis of human disease. Genetic analysis of sperm can assess individualized recurrence risk following the birth of a child with a de novo disease, as well as the risk in any male planning to have children.

genetics↗

bioRxiv2017

Population-level distribution and putative immunogenicity of cancer neoepitopes

BackgroundTumor neoantigens are a driver of cancer immunotherapy response; however, current neoantigen prediction tools produce many candidates that require further prioritization for research/clinical applications. Additional filtration criteria and population-level understanding may help to produce refined lists of putative neoantigens. Herein, we show neoepitope immunogenicity is likely related to measures of peptide novelty and report population-level behavior of these and other metrics.\n\nMethodsWe propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to tumor neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, as well as to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq [1].\n\nResultsWe show neoepitope burden varies across TCGA disease sites and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or even to bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity with an AUROC of 0.66.\n\nConclusionsOur proposed neoepitope prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but with respect to the entire human proteome, as well as bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of both cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

cancer biology↗