Biology subjects

Tran, N.-T.

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

4 recordsLinked to original sources

bioRxiv2026

First-Trimester Non-Invasive Prediction of Preterm Birth Using Cell-Free DNA Fragmentomics

ObjectiveTo develop and validate a cell-free DNA (cfDNA) fragmentomic classifier for the early prediction of spontaneous preterm birth (PTB) using routine first-trimester non-invasive prenatal testing (NIPT) data. MethodsA nested case-control study was conducted within a prospective multicenter Vietnamese cohort comprising 286 pregnancies, including 82 spontaneous PTB cases and 204 term controls. Maternal plasma cfDNA collected during routine first-trimester NIPT (median gestational age, 12 weeks) was sequenced to a depth of approximately 20 million reads per sample. Five fragmentomic feature categories including copy number alterations, end-motif composition, nucleosome distance, fragment length, and joint fragment-lengthxend-motif were evaluated for PTB prediction. Machine learning classifiers were developed in a training cohort (n = 228, 65 PTB vs 163TB) and tested in a validation cohort (n = 58, 17 PTB vs 41 TB). ResultsAmong the five fragmentomic feature classes evaluated, 4-mer end-motif (EM) profiles exhibited the most pronounced differences between PTB and term control samples. Consistent with these findings, the EM-based classifier demonstrated the highest discriminative performance in the validation cohort, achieving an AUC of 0.970 (95% CI, 0.912-1.000). At a specificity >90%, the model achieved a sensitivity of 94% (95% CI, 78-100%). ConclusionThese findings demonstrate that cfDNA EM signatures derived from routine first-trimester NIPT can accurately identify pregnancies at risk of spontaneous preterm birth, without additional blood collection or sequencing, thereby extending the clinical utility of existing prenatal screening infrastructure. KEY POINTSO_ST_ABSWhat is already known about this topic?C_ST_ABSO_LICurrent first-trimester prediction strategies based on maternal characteristics, cervical length, and biochemical markers have limited predictive accuracy, particularly in nulliparous women. C_LIO_LIExisting cfDNA-based approaches have shown only modest performance or require additional assays, limiting clinical applicability. C_LI What does this study add?O_LIExisting NIPT sequencing data can be repurposed (without additional blood sampling or sequencing) for accurate prediction of spontaneous preterm birth (AUC=0.970). C_LIO_LIA classifier employing 4-mer end-motif (EM) profiles achieved an AUC of 0.970. At a specificity >90%, the model achieved a sensitivity of 94%. C_LI

genomics↗

bioRxiv2024

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↗

bioRxiv2020

Methylation of Dual Specificity Phosphatase 4 Controls Cell Differentiation

A collection of signaling and epigenetic events needs to be orchestrated for normal development of hematopoietic lineages. While mitogen-activated protein (MAP) kinases (MAPKs) and multiple epigenetic modulators have been implicated in the megakaryocytic (Mk) cell differentiation, the underlying molecular mechanisms of signaling-epigenetic crosstalk remain unclear. MAPKs are in general inactivated by dual specificity phosphatases (DUSPs), whose activities are tightly regulated by various posttranslational modifications. Using knockdown screening and single-cell transcriptional analysis, we determined that DUSP4 is the phosphatase that inactivates p38 MAPK in hematopoietic cells and serves as a key regulator to promote Mk differentiation. With the nextgeneration Bioorthogonal Profiling of Protein Methylation technology for live cells, we identified DUSP4 as a PRMT1 substrate. Mechanistically, PRMT1-mediated Arg351 methylation of DUSP4 triggers its ubiquitinylation by HUWE1 (an E3 ligase) and then degradation, which results in p38 MAPK activation and inhibition of Mk differentiation in vitro and in vivo. Interestingly, the mechanistic axis of the DUSP4 degradation and p38 activation is also associated with a transcriptional signature of immune activation and thus argues immunological roles of Mk cells. Collectively, these results demonstrate a critical role of PRMT1-mediated posttranslational modification of DUSP4 in regulation of Mk differentiation and maturation. In the context of thrombocytopenia observed in myelodysplastic syndromes (MDS), we demonstrated that high levels of p38 MAPK and PRMT1 are associated with low platelet counts and adverse prognosis, while pharmacological inhibition of p38 MAPK or PRMT1 stimulates megakaryopoiesis in MDS samples. These findings provide novel mechanistic insights into the role of the PRMT1-DUSP4-p38 axis on Mk differentiation and present a targeting strategy for treatment of thrombocytopenia associated with myeloid malignancies such as MDS.

cell biology↗

bioRxiv2020

Genetic landscape of recessive diseases in the Vietnamese population from large-scale clinical exome sequencing

PurposeAccurate profiling of population-specific recessive diseases is essential for the design of cost-effective carrier screening programs. However, minority populations and ethnic groups, including Vietnamese, are still under-represented in existing genetic studies. Here we reported the first comprehensive study of recessive diseases in the Vietnamese population. MethodsClinical exome sequencing (CES) data of 4,503 disease-associated genes obtained from a cohort of 985 Vietnamese individuals was analyzed to identify pathogenic variants, associated diseases and their carrier frequencies in the population. ResultsEighty-five recessive diseases were identified in the Vietnamese population, among which seventeen diseases had carrier frequencies of at least 1% (1 in 100 individuals). Three diseases were especially prevalent in the Vietnamese population with carrier frequencies of 2-12 times higher than in other East Asia or the world populations, including Beta-thalassemia (1 in 25), citrin deficiency (1 in 33) and phenylketonuria (1 in 40). Seven novel pathogenic and three likely pathogenic variants associated with nine recessive diseases were also discovered. ConclusionsThe comprehensive profile of recessive diseases identified in this study shall enable the design of cost-effective carrier screening programs specific to the Vietnamese population. The newly discovered pathogenic variants may also exist in other populations at extremely low frequencies, thus representing a valuable resource for future research. Our study has demonstrated the advantage of population-specific genetic studies to advance the knowledge and practice of medical genetics.

genetics↗