Biology subjects

Tran, T. N.

Publications and source records attributed to Tran, T. N..

9 recordsLinked to original sources

bioRxiv2026

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

General-purpose large language models (LLMs) are trained on large corpora to acquire broad knowledge, but whether LLMs can replace, or augment, task-specific models is unclear. We evaluated LLMs on three real-world, clinically important tumor genomic interpretation tasks, in order of increasing difficulty: (i) distinguishing tumor from non-tumor mutations (n=34,415 variants), (ii) distinguishing driver from passenger mutations (n=13,469 variants), and (iii) inferring cancer type from tumor sequencing reports across multiple assays and institutions (n=102,791 samples). The best general-purpose LLMs performed as well as the benchmark tailor-made predictor for task (i). Ensembling tailor-made models with zero-shot LLMs improved their performance for tasks (i) and (ii). For task (iii), LLMs outperformed or supplemented tailor-made models on out-of-distribution data. Without fine-tuning, current LLMs already can be useful in clinical genomic interpretation by adding complementary expertise to tailor-made, state-of-the-art predictors.

genomics↗

bioRxiv2026

A Long-lived Avatar for Modeling Age-Related Vascular Disease

BackgroundCurrent microphysiological models do not support long-term investigations into the chronic effects of vascular risk factors and the development of vascular diseases. Prolonged culture frequently leads to cellular senescence and loss of functional integrity, resulting in variability and inconsistency in modeling chronic vascular responses. Here we aimed to develop and sustain a long-term multicellular human vascular avatar, addressing the critical need for long-term disease modeling and drug testing. MethodsTo identify the optimal media for longevity, cell identity and function were assessed by morphology, qPCR, beta-gal staining, ELISA, bulk RNA-seq and single cell RNA-seq analysis. After optimizing the culture media, iPSCs-derived ECs and VSMCs from unaffected and Hutchinson-Gilford Progeria Syndrome (HGPS) donors were grown in Gravitational Lumen Patterning (GLP) Vessel- Chips for 1-6 months to generate a long-lived vascular avatar for the study of vascular aging. ResultsGuided by quantitative morphological analyses and bulk RNAseq profiling, we generated a novel optimized culture media VSL (VEGF, SB431542 as a TGF-{beta} inhibitor, low fetal bovine serum) that enhances the long-term health of vascular endothelial cells (ECs). Furthermore, we modified the VSL formulation (mVSL) by modulating 8Br-cAMP, FGF, PDGF, and a cell viability enhancer HMH1015 levels to enhance EC-VSMC (vascular smooth muscle cell) crosstalk and support long-term cellular viability. Subsequently, we maintained and characterized a human vascular avatar with a three-dimensional extracellular matrix environment and 3D vascular architecture for over 180 days. Finally, we demonstrated that this long-lived human vascular avatar enabled modeling vascular aging using iPSC-derived vascular cells from patients with Hutchinson-Gilford Progeria Syndrome (HGPS). ConclusionsWe have successfully engineered and maintained a human vascular avatar for over 180 days. The vascular avatar provides a robust platform for modeling disease-associated vascular aging and for evaluating therapeutic strategies targeting chronic vascular disorders.

bioengineering↗

bioRxiv2024

TIA1 Mediates Divergent Inflammatory Responses to Tauopathy in Microglia and Macrophages

The RNA binding protein TIA1 is known to regulate stress responses. Here we show that TIA1 plays a much broader role in inflammatory cells, being required for the microglial sensome. We crossed TIA1 cKO mice (using a CX3CR1 driven cre element) to PS19 MAPT P301S tauopathy mice. The peripheral macrophages of TIA1 cKO mice exhibited a hyper-inflammatory phenotype with increased cytokine signaling, as expected. Surprisingly, the brains of these mice showed striking reductions in inflammation, including decreases in microglial inflammatory cytokines (TNF and IL-1{beta}) and sensome markers (CLEC7A, TREM2, ITGAX); these reductions were accompanied by corresponding decreases in tau pathology. Analysis of the brain TIA1 protein interactome identified brain selective TIA1 protein mediated pathways, including strong interactions with the microglial protein C1q, which directs pruning of dystrophic neurons. These results uncover a previously unknown regulatory role for TIA1 in microglial activation in the context of neurodegenerative disease and highlights the divergent regulation of two mononuclear phagocytic lineages: microglia and macrophages.

neuroscience↗

bioRxiv2024

Machine learning predictions improve identification of real-world cancer driver mutations

Characterizing and validating which mutations influence development of cancer is challenging. Machine learning has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluated multiple computational methods for identifying cancer driver alterations. For identifying known drivers, methods incorporating protein structure or functional genomic data outperformed methods trained only on evolutionary data. We further validated VUSs annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N=7,965 and 977). "Pathogenic" VUSs in KEAP1 and SMARCA4 identified by several methods were associated with worse survival, unlike "benign" VUSs. "Pathogenic" VUSs exhibited mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. Despite training primarily on germline, rather than somatic, mutation data, computational predictions contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.

genomics↗

bioRxiv2021

In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes

Infertility is a heterogeneous condition, with genetic causes estimated to be involved in approximately half of the cases. High-throughput sequencing (HTS) is becoming an increasingly important tool for genetic diagnosis of diseases including idiopathic infertility, however, most rare or minor alleles revealed by HTS are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling. To determine the consequences of population polymorphisms in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXOI, FKBP6, MCM9, M1AP, MEI1, MSH4 and SEPT12 by generating genome-edited mouse models. Nine variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction in the yeast 2 hybrid assay. Even though these genes are known to be essential for normal meiosis or spermiogenesis in mice, only one of the tested human variants (rs1460351219, encoding p.R581H in MCM9), which was observed in a male infertility patient, compromised fertility or gametogenesis in the mouse models. To explore the disconnect between predictions and outcomes, we compared pathogenicity calls of missense variants made by ten widely-used algorithms to: 1) those present in ClinVar, and 2) those which have been evaluated in mice. We found that all the algorithms performed poorly in terms of predicting the effects of human missense variants that have been modeled in mice. These studies emphasize caution in the genetic diagnoses of infertile patients based primarily on pathogenicity prediction algorithms, and emphasize the need for alternative and efficient in vitro or vivo functional validation models for more effective and accurate VUS delineation to either pathogenic or benign categories. SignificanceAlthough infertility is a substantial medical problem that affects up to 15% of couples, the potential genetic causes of idiopathic infertility have been difficult to decipher. This problem is complicated by the large number of genes that can cause infertility when perturbed, coupled with the large number of VUS that are present in the genomes of affected patients. Here, we present and analyze mouse modeling data of missense variants that are classified as deleterious by commonly-used pathogenicity prediction algorithms but which caused no detectible phenotype when introduced into mice by genome editing. We find that augmenting pathogenicity predictions with preliminary screens for biochemical defects substantially enhanced the proportion of prioritized variants that caused phenotypes in mice. The results emphasize that, in the absence of substantial improvements of in silico prediction tools or other compelling pre-existing evidence, in vivo analysis is crucial for confident attribution of infertility alleles.

genetics↗

bioRxiv2021

MLH1/3 variants causing aneuploidy, pregnancy loss, and premature reproductive aging

Most spontaneous pregnancy losses are a result of embryonic aneuploidy stemming from mis-segregation of chromosomes during meiosis. Proper disjunction of homologous chromosomes is dependent upon precise control of crossing-over, a process requiring the mismatch repair (MMR) genes MLH1 and MLH3. Both are required for fertility and completion of meiosis in mice. People inheriting variants in these genes are often at high risk for colorectal cancer and Lynch syndrome, yet the potential impacts of variants upon reproduction are unclear. To determine if MLH1/3 variants (namely single nucleotide polymorphisms, or SNPs) in human populations can cause reproductive abnormalities, we used a combination of computational predictions, yeast two-hybrid assays, and assays of MMR and recombination in yeast to select nine MLH1 and MLH3 variants for modeling in mice via genome editing. We identified 7 alleles that caused reproductive defects in mice including subfertility in females, male infertility, reduced sperm counts, and increased spermatocyte apoptosis. Remarkably, these alleles in females caused age-dependent decreases in litter size, and increased resorption of embryos during pregnancy. These outcomes were likely a consequence of reduced meiotic chiasmata, in turn causing an increase in misaligned chromosomes and univalents in meiotic metaphase I (MI). Our data indicate that segregating hypomorphic alleles of meiotic recombination genes in populations can predispose females to increased incidence of pregnancy loss from gamete aneuploidy.

genetics↗

bioRxiv2019

Tempora: cell trajectory inference using time-series single-cell RNA sequencing data

AO_SCPCAPBSTRACTC_SCPCAPSingle-cell RNA sequencing (scRNAseq) can map cell types, states and transitions during dynamic biological processes such as development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNAseq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNAseq data. In performance comparison tests, Tempora accurately inferred developmental lineages in human skeletal myoblast differentiation and murine cerebral cortex development, beating state of the art methods. Tempora uses biological pathway information to help identify cell type relationships and can identify important time-dependent pathways to help interpret the inferred trajectory. Our results demonstrate the utility of time information to supervise trajectory inference for scRNA-seq based analysis.

bioinformatics↗

bioRxiv2019

A segregating human allele of SPO11 modeled in mice disrupts timing and amounts of meiotic recombination, causing oligospermia and a decreased ovarian reserve.

A major challenge in medical genetics is to characterize variants of unknown significance (VUS), so as to better understand underlying causes of disease and design customized treatments. Infertility has presented an especially difficult challenge with respect to not only determining if a given patient has a genetic basis, but also to identify the causative genetic factor(s). Though genome sequencing can identify candidate variants, in silico predictions of causation are not always sufficiently reliable so as to be actionable. Thus, experimental validation is crucial. Here, we describe the phenotype of mice containing a nonsynonymous (proline-to-threonine at position 306) change in Spo11, corresponding to human SNP rs185545661. SPO11 is a topoisomerase-like protein that is essential for meiosis because it induces DNA double stranded breaks (DSBs) that stimulate pairing and recombination of homologous chromosomes.\n\nAlthough both male and female Spo11P306T/P306T mice were fertile, they had reduced sperm and oocytes, respectively. Spermatocyte chromosomes exhibited synapsis defects (especially between the X and Y chromosomes), elevated apoptotic cells, persistent markers of DSBs, and most importantly, fewer Type 1 crossovers that causes some chromosomes to have none. Spo11P306T/- mice were sterile and made fewer meiotic DSBs than Spo11+/- animals, suggesting that the Spo11P306T allele is a hypomorph and likely is delayed in making sufficient DSBs in a timely fashion. If the consequences are recapitulated in humans, it would predict phenotypes of premature ovarian failure, reduced sperm counts, and possible increased number of aneuploid gametes. These results emphasize the importance of deep phenotyping in order to accurately assess the impact of VUSs in reproduction genes.

genetics↗

bioRxiv2018

A putative human infertility allele of the meiotic recombinase DMC1 does not affect fertility in mice

Whole exome or genome sequencing is becoming routine in clinical situations for identifying mutations underlying presumed genetic causes of disease, including infertility. While this is a powerful approach for implicating polymorphisms or de novo mutations in genes plausibly related to the phenotype, a greater challenge is to definitively prove causality. This is a crucial requisite for treatment, especially for infertility, in which validation options are limited. In this study, we created a mouse model of a putative infertility allele, DMC1M200V. DMC1 encodes a RecA homolog essential for meiotic recombination and fertility in mice. This allele was originally implicated as being responsible for sterility of a homozygous African woman, a conclusion supported by subsequent biochemical analyses of the mutant protein and by studies of yeast with the orthologous amino acid change. Here, we found that Dmc1M200V/M200V male and female mice are fully fertile and do not exhibit any gonadal abnormalities. Detailed immunocytological analysis of meiosis revealed no defects suggestive of compromised fertility. This study serves as a cautionary tale for making conclusions about consequences of genetic variants, especially with respect to infertility, and emphasizes the importance of conducting relevant biological assays for making accurate diagnoses in the era of genomic medicine.

genetics↗