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Biomedical subjects

Jiaqi Li

Publications and source records attributed to Jiaqi Li.

4 recordsLinked to original sources
PubMed2026

PAFAH1B1 governs follicular development by modulating the protein complex of CCNE1-CDK2-CDK1 to induce cell cycle arrest.

BACKGROUND: Ovarian follicle development plays a crucial role in mammalian fertility, which is primarily regulated by granulosa cell (GC) proliferation and cell cycle. Cell cycle dysregulation collectively might drive follicular atresia through GC dysfunction. However, the underlying molecular mechanisms remain largely unexplored. METHODS: The scRNA-seq and integrative analysis revealed that PAFAH1B1 was involved in cell cycle. Functional assays, including overexpression/knockdown, flow cytometry, EdU, HE, and TUNEL, confirmed that PAFAH1B1 regulated cell cycle and follicular development in vitro and in vivo. CoIP showed that PAFAH1B1 bound CCNE1-CDK2-CDK1 to arrest G2/M phase. Chromatin accessibility and CRISPR/dCas9-TET1 demonstrated that DNA methylation modulated PAFAH1B1 transcription. RESULTS: A novel regulator of cell cycle, PAFAH1B1, was identified in Pig Genotype-Tissue Expression (PigGTEx). During GC proliferation, we found that PAFAH1B1 transcription was correlated with the distribution rate of G1 phase in GCs. PAFAH1B1 protein was confirmed to specifically bind to CCNE1-CDK2-CDK1 to arrest G2/M phase. Notably, PAFAH1B1 appeared to hinder the development of follicles. Furthermore, the demethylation significantly promoted the transcription activity and chromatin accessibility of CpG island (-7 bp to +170 bp) of PAFAH1B1. Taken together, PAFAH1B1 physically interacted with the CCNE1-CDK2-CDK1 complex to arrest G2/M phase and inhibit the GCs proliferation and follicular development. Additionally, demethylation of CpG island significantly promoted the transcription of PAFAH1B1. CONCLUSION: These findings not only advance understanding of cell proliferation and cycle regulation but also identify PAFAH1B1 as a candidate gene for further investigation in follicular development.

CCNE1-CDK2-CDK1 complex↗
PubMed2026

DeepGeSeq: deep learning library for genomic sequence modeling and analysis.

MOTIVATION: Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. RESULTS: By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq's versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/JiaqiLi1024/DeepGeSeq.

Deep Learning↗
PubMed2026

Prospective clinical validation of targeted long-read sequencing for preimplantation genetic testing of α-thalassaemia.

BACKGROUND: Preimplantation genetic testing for monogenic disorders (PGT-M) can prevent transmission of severe α-thalassaemia, but conventional workflows remain limited by family-specific assay design for direct variant detection, dependence on additional family samples for haplotype construction, and labour-intensive multi-step procedures across several platforms. Targeted long-read sequencing-based PGT-M for α-thalassaemia (tlrPGT-α-thal) integrates direct variant detection and haplotype linkage analysis within a single assay, but prospective clinical validation is lacking. METHODS: This prospective clinical study enrolled 103 families at high risk of transmitting α-thalassaemia at a reproductive medicine centre between August 2024 and March 2025. All families underwent blinded parallel analysis using both conventional NGS-based PGT-M (comparator) and tlrPGT-α-thal. RESULTS: In the primary concordance analysis, tlrPGT-α-thal was fully concordant with conventional NGS-based PGT-M (507/507, 100.0%; exact 95% CI, 99.3-100.0). Direct variant detection was successful in 501/507 embryos (98.82%; 95% CI, 97.4-99.6), haplotype linkage was established in 505/507 embryos (99.61%; 95% CI, 98.6-100.0), and one meiotic recombination event was identified. Among 93 families proceeding to embryo transfer, 57 pregnancies underwent invasive prenatal diagnosis, and all were concordant with the corresponding tlrPGT-α-thal results. Of the 26 comparator-inconclusive embryos, tlrPGT-α-thal resolved 6 complex cases, including cases with incomplete pedigrees or insufficient informative SNPs. Among the remaining 20 embryos with HBA-region aneuploidies, genotype and parental origin could be determined in 12. CONCLUSIONS: The findings show that tlrPGT-α-thal enables direct detection of diverse α-thalassaemia-causing variants together with efficient haplotype linkage analysis within a single workflow, without requiring family-specific assay design or additional family samples. The method demonstrated high diagnostic accuracy while providing added value in complex scenarios. Taken together, tlrPGT-α-thal represents a simplified and broadly applicable strategy for α-thalassaemia PGT-M.

Humans↗
PubMed2025

A global survey of taxa-metabolic associations across mouse microbiome communities.

Host-microbiota mutualism is rooted in the exchange of dietary and metabolic molecules. Microbial diversity broadens the metabolite pool, with each taxon contributing distinct compounds in varying proportions. In the human microbiome, high variability in consortial composition is largely compensated by similar metabolic functions across different taxa. However, the extent of compensation in lower diversity mouse models, and whether vivaria are metabolically equivalent, is unknown. We provide a searchable resource of microbiome composition variability across 51 murine vivaria and 12 wild mouse colonies worldwide, with vivarium-specific variants mapped according to predicted 3D structures for each microbial species. Our matched metabolomics data show that realized metabolic potential has relatively low variability, providing functional evidence for metabolic compensation. Additionally, variability is related to taxonomic composition rather than vivarium, revealing taxa-metabolite associations that are potentially relevant to phenotypic differences between vivaria. Collectively, this resource offers tools to strengthen microbiome studies and collaborative science.

Animals↗