Biology subjects

Truong, B.

Publications and source records attributed to Truong, B..

10 recordsLinked to original sources

bioRxiv2026

Ancient DNA reveals that natural selection has upregulated the immune system over the last 10,000 years

The specific mechanisms through which human biology and disease susceptibility evolved with major shifts in West Eurasian environments and societies over the last 10,000 years(1)--particularly rising infectious burden(2)--remain poorly characterized, despite ancient DNA studies(3-6) identifying hundreds of candidate loci under positive selection(6). Here, we identify specific immune diseases/traits, genes/variants, pathways, and tissues/cell types impacted by natural selection by systematically integrating variant-level selection statistics with genome-wide association study (GWAS), quantitative trait locus (QTL), and molecular bulk/single-cell and gene pathway data. Genome-wide, positively-selected alleles are associated with reduced susceptibility to infectious diseases like tuberculosis (TB), influenza, and intestinal infections; consistent with selection-signal enrichments in immune cells within barrier tissues such as the respiratory tract and gut mucosa. In contrast, positively-selected alleles increase risk of intestinal inflammatory disease and autoimmune hypothyroidism, supportive of a tradeoff between infection and immune-mediated pathology, and consistent with adaptive alleles being QTLs for genes upregulating inflammation and other host-defense pathways. We reveal many novel adaptive loci with convergent signals from selection, infectious disease GWAS and immune-gene QTLs (including at FUT6 for intestinal infections; at ASAP1 for TB; and at LYZ, an antimicrobial enzyme), fine-mapping selection onto likely causal variants. Surprisingly, adaptive alleles had a protective effect on allergic conditions like asthma and dermatitis, challenging a common view that these conditions arose through evolutionary mismatch of present-day hygienic contexts relative to past, pathogen-rich environments(7).

evolutionary biology↗

bioRxiv2026

PARP1 directly disassembles nucleosomes to regulate DNA repair

Upon DNA damage, chromatin remodeling is rapidly initiated to promote chromatin accessibility, thereby facilitating the recruitment and assembly of repair factors. Although this enhanced accessibility has been linked to poly(ADP-ribose) polymerase (PARP) activity, the mechanism by which cells overcome the nucleosome barrier remains unclear. Using our designer chromatin system, we uncovered a previously uncharacterized activity of PARP1, whereby it directly and asymmetrically evicts histone dimers proximal to DNA strand breaks from nucleosomes to generate oriented hexasomes. In the presence of HPF1, PARP1 generates stable PARylated hexasomes, an open chromatin intermediate that can serve as a bifunctional hub for recruitment of DNA- and PAR-dependent factors. Using cellular assays, we demonstrated that PARP activity is both required and sufficient to drive chromatin accessibility and the recruitment of repair factors, with direct involvement of subnucleosomal species. Unexpectedly, we identified the C-terminal tail of histone H2A, a motif harboring recurrent cancer-associated mutations, as a critical determinant of efficient PARP1-mediated nucleosome disassembly. Deletion of the H2A tail sensitizes cells to DNA-damaging agents and PARP inhibitors, implicating a functional role of PARP1-mediated nucleosome disassembly in DNA repair. Together, our findings support a model in which PARP1 directly drives histone eviction, leading to the formation of subnucleosomes that facilitate efficient DNA repair.

molecular biology↗

bioRxiv2022

A novel hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter < 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP. Author SummaryDespite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor (), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.

genomics↗

bioRxiv2021

A novel method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.

genetics↗

bioRxiv2020

Nonlinearities between inhibition and T-type calcium channel activity bidirectionally regulate thalamic network oscillations

Absence seizures result from 3-5 Hz generalized thalamocortical oscillations that depend on highly regulated inhibitory neurotransmission in the thalamus. Efficient reuptake of the inhibitory neurotransmitter GABA is essential, and reuptake failure worsens seizures. Here, we show that blocking GABA transporters (GATs) in acute brain slices containing key parts of the thalamocortical seizure network modulates epileptiform activity. As expected, we found that blocking either GAT1 or GAT3 prolonged oscillations. However, blocking both GATs unexpectedly suppressed oscillations. Integrating experimental observations into single-neuron and network-level computational models shows how a non-linear dependence of T-type calcium channel opening on GABAB receptor activity regulates network oscillations. Receptor activity that is either too brief or too protracted fails to sufficiently open T-type channels necessary for sustaining oscillations. Only within a narrow range does prolonging GABAB receptor activity promote channel opening and intensify oscillations. These results have implications for therapeutics that modulate GABA transporters.

neuroscience↗

bioRxiv2020

The winning methods for predicting cellular position in the DREAM single cell transcriptomics challenge

MotivationPredicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM Challenge on Single Cell Transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single cell transcriptomic data. ResultsWe have developed over 50 pipelines by combining different ways of pre-processing the RNA-seq data, selecting the genes, predicting the cell locations, and validating predicted cell locations, resulting in the winning methods for two out of three sub-challenges in the competition. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web-application to facilitate the research on single cell spatial reconstruction. All the data and the example use cases are available in the Supplementary material. AvailabilityThe scripts of the package are available at https://github.com/thanhbuu04/SCTCwhatateam and the Shiny application is available at https://github.com/pvvhoang/SCTCwhatateam-ShinyApp ContactThuc.Le@unisa.edu.au Supplementary informationSupplementary data are available at Briefings in Bioinformatics online.

systems biology↗

bioRxiv2019

Loss of prdm1a accelerates melanoma onset and progression

Melanoma is an aggressive, deadly skin cancer derived from melanocytes, a neural crest cell derivative. Melanoma cells mirror the developmental program of neural crest cells in that they exhibit the same gene expression patterns and utilize similar cellular mechanisms, including increased cell proliferation, EMT and migration. Here we studied the role of neural crest regulator PRDM1 in melanoma onset and progression. In development, Prdm1a functions to promote neural crest progenitor fate, and in melanoma, we found that PRDM1 has reduced copy number and is recurrently deleted in both zebrafish and humans. When examining expression of neural crest and melanocyte development genes, we show that sox10 progenitor expression is high in prdm1a-/- mutants, while more differentiated melanocyte markers are reduced, suggesting that normally Prdm1a is required for differentiation. Data mining of human melanoma datasets indicate that high PRDM1 expression in human melanoma is correlated with better patient survival and decreased PRDM1 expression is common in metastatic tumors. When one copy of prdm1a is lost in the zebrafish melanoma model (Tg[mitfa:BRAFV600E];p53-/-; prdm1a+ /-), melanoma onset occurs more quickly, and the tumors that form have a larger area with increased expression of sox10. These data demonstrate a novel role for PRDM1 as a tumor suppressor in melanoma.

cancer biology↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗

bioRxiv2019

Identifying miRNA synergism using multiple-intervention causal inference

BackgroundStudying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test.\n\nResultsIn this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network.\n\nConclusionsTaken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

bioinformatics↗

bioRxiv2019

Personalized Annotation-based Networks (PAN) for the Prediction of Breast Cancer Relapse

The classification of clinical samples based on gene expression data is an important part of precision medicine. However, it has proved difficult to accurately predict survival outcomes and treatment responses for cancer patients. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks all have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. These databases organize genes into overlapping gene sets, called annotations, that we use to build a network where nodes represent functional terms and edges represent the similarity between them. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study (METABRIC and a super-set of GEO studies), we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary informationThe codes and data are available at https://github.com/thinng/PAN. ContactThuc.Le@unisa.edu.au

bioinformatics↗