Biomedical subjects

Mei Liu

Publications and source records attributed to Mei Liu.

4 recordsLinked to original sources

PubMed2026

Identification of Freezing-Responsive microRNAs and Their Targets in Chinese Jujube by Small RNA and Degradome Sequencing.

The jujube tree fruit remains a primary fruit in northern China, yet its geographical distribution and yield are significantly constrained by freezing stress during winter. Numerous studies have highlighted the pivotal regulatory function of microRNAs (miRNAs) in plant responses to low-temperature stress. Nevertheless, the specific miRNAs involved in the response to low temperatures and their associated gene networks in Ziziphus jujuba Mill are not well understood. In this investigation, we utilized high-throughput sequencing to analyze small RNA libraries from branches subjected to temperatures of 4 °C and -30 °C. Our analysis identified a total of 342 miRNAs, comprising 123 known miRNAs and 219 novel miRNAs. The differential expression analysis revealed that under low-temperature conditions, 177 miRNAs underwent significant changes. Among them, specific upregulation of miR319 in the less cold-resistant variety and miR6483 in sensitive variety was observed. By employing degradome sequencing, we identified a total of 1551 target genes corresponding to 3059 unique miRNA target interaction pairs involving 299 miRNAs. Functional analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicated that these target genes are primarily associated with transcriptional regulation, metabolic pathways, and genetic information processing. Through a comprehensive analysis, we pinpointed 11 genes corresponding to 9 miRNAs that are implicated in jujube tree cold stress, and 7 target genes of 7 miRNAs were confirmed by 5'-RACE analysis. These miRNAs are likely to exert crucial regulatory functions in the context of jujube tree cold stress. This study is the first to systematically identify miRNAs and their target genes in the response of Ziziphus jujuba Mill to low-temperature stress, which provides important resources for in-depth analysis of the molecular mechanism of jujube tree cold resistance and for cold-resistant breeding.

Ziziphus↗

PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗

PubMed2025

Diagnostic performance of the Sanity 2.0 assay to detect resistance to rifampicin, isoniazid, and fluoroquinolones in tuberculosis.

UNLABELLED: Effective tuberculosis (TB) management relies on prompt diagnosis of Mycobacterium tuberculosis complex (MTBC) and associated drug resistance. The Sanity 2.0 assay is a high-resolution melting assay designed for direct respiratory sample testing, enabling simultaneous detection of MTBC and resistance to rifampicin (RIF), isoniazid (INH), and fluoroquinolones (FQ) in a single step. This study evaluated its diagnostic performance in two registered multicenter trials among bacteriologically confirmed TB patients. Diagnostic performance was evaluated for MTBC detection, as well as for the identification of resistance to RIF, INH, and FQ, using phenotypic drug susceptibility testing, whole-genome sequencing, and a composite reference standard. Agreement analyses were conducted between the Sanity 2.0 assay and Xpert MTB/RIF and Xpert MTB/XDR. Among 611 patients, the Sanity 2.0 assay detected MTBC in 563 patients, exhibiting a sensitivity of 92.1% (95% CI: 89.7-94.0). For detecting resistance to RIF, INH, and FQ, sensitivities exceeded 90%, with specificities of 95.8% (95% CI: 88.5-98.6), 100.0% (95% CI: 96.4-100.0), and 97.8% (95% CI: 93.8-99.3) against the composite reference standard, respectively. The agreement with Xpert MTB/RIF for RIF detection was 98.6% (95% CI: 96.9-99.3). For INH and FQ resistance, the agreement with Xpert MTB/XDR was 92.0% (95% CI: 88.5-94.5) and 94.3% (95% CI: 91.2-96.3), respectively. The Sanity 2.0 assay is a rapid and user-friendly platform capable of detecting both MTBC and key drug resistance. It demonstrated good diagnostic performance and could potentially be an effective alternative to guide individualized anti-TB treatment, especially in resource-limited settings. IMPORTANCE: Rapid and accurate detection of both Mycobacterium tuberculosis complex (MTBC) and key drug resistance is critical to improving tuberculosis treatment outcomes and reducing transmission. However, current molecular diagnostic workflows often require sequential testing, which can delay the initiation of effective and individualized therapy. We evaluated the Sanity 2.0 assay, an integrated high-resolution melting test that simultaneously detects MTBC and resistance to rifampicin, isoniazid, and fluoroquinolone resistance directly from respiratory samples in about 2-3 hours. The assay demonstrated excellent performance, with MTBC detection sensitivity of 92.1% and drug resistance sensitivities exceeding 90% and specificities over 95% against a composite reference standard, as well as strong concordance with World Health Organization-endorsed molecular assays. Implementation of the Sanity 2.0 assay could streamline TB diagnostic workflows; enable rapid, single-step resistance profiling; and facilitate timely, individualized treatment-particularly in resource-limited settings where rapid and comprehensive resistance testing remains a critical unmet need.

Humans↗

PubMed2025

16S rRNA and Metagenomic Datasets of Gastrointestinal Microbiota in Fetal and 7-Day-Old Goat Kids.

The perinatal period (from late gestation to the neonatal stage) in ruminants is a critical phase for fetal organ maturation, where ecological succession of gastrointestinal microbial communities significantly impacts livestock production efficiency. However, research remains insufficient regarding the distribution patterns and functional annotation of microbial communities across different gastrointestinal compartments during this period. This study characterized early microbiota dynamics in Hutianshi Goats using 16S rRNA sequencing (4 fetal goats at 90 ± 10 gestational days) and metagenomics (3 7-day-old goat kids). The fetal goat group generated 852,694 valid reads, yielding 688,277 high-quality reads after chimera removal for downstream analysis. The 7-day-old goat kids group produced 1,081,588,182 final valid reads, after data processing and assembly, 8,561,345 contigs were generated. Gene prediction identified 6,095,352 genes. Multi-database annotations (NR, KEGG, CAZy, etc.) revealed functional potential and antimicrobial resistance traits. The public release of this dataset facilitates academic understanding of microbial community dynamics and host-microbe interactions during this developmental stage, providing both theoretical foundations and data resources for ruminant developmental biology and precision breeding regulation.

Animals↗