Biomedical subjects

Yanlin Zhang

Publications and source records attributed to Yanlin Zhang.

3 recordsLinked to original sources

PubMed2026

Construction and evaluation of an independently generated transgenic mouse model carrying mutated human HRAS genes for short-term carcinogenicity assessment.

The study aimed to construct and evaluate an independently generated transgenic mouse model applied to the short-term carcinogenicity assessment. Mutated human HRAS fragment containing an intron point-mutation was inserted into C57BL/6JGpt mice via bacterial artificial chromosome transgenic technology, eventually generating BALB/c;B6J-Tg(hHRAS)16/Gpt mice, abbreviated as HRAS mice. The inserted human HRAS fragment in HRAS mice was characterized, revealing five tandem copies at chromosome 19. Baseline profiles, including biochemical, hematological, immunophenotypic, survival, and carcinogenic data of HRAS mice, were collected. To evaluate the tumor susceptibility in HRAS mice, we applied N-Nitroso-N-methylurea (MNU) to HRAS mice in a short-term carcinogenicity assessment conducted according to Good Laboratory Practice. The genetic characteristics of HRAS mice include five tandem arrays of mutated human HRAS fragments located in genomic coordinate 7,755,606 on chromosome 19 and the duplication of a 9-kilobase genome sequence (genomic coordinate 7,755,606-7,746,509) located on chromosome 19. HRAS mice showed a relatively lower incidence and range of spontaneous tumor formation during long-term observation compared to CByB6F1-Tg(HRAS)2Jic (Tg.rasH2) transgenic mice. The short-term carcinogenicity assessment showed a strong tumor response to MNU, with high incidences of lymphoma (≥ 90%) and stomach squamous cell papilloma (≥ 90%) in both male and female HRAS mice. The HRAS mice showed susceptibility to MNU and exhibited baseline characteristics distinct from those of Tg.rasH2 mice. The co-expression of HRAS and MKI67 at the cellular localization level was found in neoplasms of HRAS mice. These findings preliminarily evaluated the feasibility of HRAS mice applied to the short-term carcinogenicity assessment.

Animals↗

PubMed2025

Genomic insights into the demographic history and local adaptation of wild boars across Eurasia.

Wild boars exhibit genetic and phenotypic diversity shaped by migrations and local adaptations. Their expansion across Eurasia, especially in Central Asia, remains underexplored. Here, we present newly sequenced whole-genome data of 47 wild boars from Eastern Asia, Central Asia, and Europe, combined with 49 existing genomes, creating a comprehensive dataset of 96 individuals. Our analyses show that Asian wild boars and Southeast Asian Suids split ∼3.6 million years ago (mya), with Central Asian and Southern Chinese ancestors diverging ∼1.8 mya. The split between Central Asian and European-Near East ancestors occurred ∼0.9 mya, followed by a European-Near East divergence ∼0.6 mya. We identify signatures of local adaptation in Central Asian populations, including two positively selected variants in LPIN1, associated with lipid metabolism, and a missense mutation in ALPK2, linked to meat traits. These findings provide insights into wild boar dispersal and adaptation and shed light on domestic pig breeding.

Animals↗

PubMed2025

MutBERT: probabilistic genome representation improves genomics foundation models.

MOTIVATION: Understanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. RESULTS: We present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. AVAILABILITY AND IMPLEMENTATION: https://github.com/ai4nucleome/mutBERT.

Humans↗