Biomedical subjects

Xiang Liu

Publications and source records attributed to Xiang Liu.

4 recordsLinked to original sources

PubMed2026

Parent-of-origin effects on allelic expression bias in interspecific poplar hybrids.

In hybrid plants, phenotypic outcomes are governed by interactions between the two parental genomes. However, the mechanisms underlying the interplay of divergent regulatory networks from these genomes remain poorly understood. In this study, we compared gene-level and allele-specific expression patterns, as well as differentially enriched pathways between F₁ and complex backcross (CBC) lines derived from a natural interspecific hybrid population of Populus fremontii (Pf) and P. angustifolia (Pa). Metabolic differences between Pf and Pa which exhibit low and high levels respectively of phenylpropanoid-derived condensed tannins were leveraged. Using individualized transcriptome references, differential expression and clustering analyses revealed CBC-biased and F₁-biased expression for genes involved in phenylpropanoid metabolism and photosynthesis, respectively. Biased expression of these genes at the allele level was also observed in F1. At the whole-transcriptome level, Pa-biased genes predominated in F₁ hybrids, and Pa alleles displayed more conserved expression patterns than Pf alleles across examined samples. Further analyses indicated that allelic expression bias was significantly associated with parental origin, which could be driven by sequence variations in cis-regulatory elements and differences in CpG island length. Our findings demonstrate strong parent-of-origin effects on divergent regulatory networks governing gene expression in poplar hybrids and provide clues for strategic parental selection tailored to specific metabolic pathways of interest.

cis-regulation↗

PubMed2026

Rapid glycomic analysis of serum EVs reveals altered N-glycosylation patterns in ASD.

Objective laboratory diagnostics for autism spectrum disorder (ASD) are lacking, necessitating rapid clinical screening tools. Because serum extracellular vesicle (EV) N-glycosylation captures critical neurodevelopmental signatures, we developed a fast, biologically interpretable diagnostic strategy. EVs from ASD patients with language impairment and neurotypical controls were isolated using a rapid extra-polyethylene glycol precipitation/filtration (EPF) workflow, benchmarked against ultracentrifugation. Following MALDI-TOF/MS profiling, machine learning was re-evaluated using repeated nested cross-validation to reduce optimistic bias and potential information leakage. Among five classifiers, Random Forest (RF) showed the best overall balance across discrimination, calibration, and classification metrics. RF-based SHAP analysis provided transparent interpretation, highlighting key discriminative glycans, including H4N3S1F1, H5N5S1F1, and H3N5F1. To elucidate molecular mechanisms, we integrated public EV transcriptomic data. This revealed significant dysregulation of N-glycosylation machinery genes (e.g., MAN1A1, NEU1, OSTC, RPN2), whose expression directionally aligned with observed glycan shifts in synaptic pathways. Collectively, this rapid serum EV N-glycomic workflow, combined with leakage-controlled RF-based interpretation, provides a promising foundation for non-invasive ASD biomarker discovery and future multicenter validation.

Humans↗

PubMed2026

Circle-seq analysis reveals the involvement of eccDNAs in salt stress response of bermudagrass (Cynodon dactylon).

Extrachromosomal circular DNAs (eccDNAs) have been identified in a wide variety of plant species and play a pivotal role in genomic plasticity, emerging as key drivers of stress adaptation. However, the putative roles of eccDNAs under environmental stress remain largely unexplored in plants. As a high-quality turfgrass, bermudagrass (Cynodon dactylon L.) is a pivotal species for the reclamation and improvement of saline-alkali soils. Therefore, we performed a comprehensive analysis of the eccDNA profiles in bermudagrass under salt stress. A total of 1,068 eccDNAs were identified across all chromosomes. These eccDNAs were characterized by short lengths (ranging from 100 bp to 1 kb) and low GC content. Their genomic distribution was not entirely random but rather exhibited a certain preference for intergenic regions and coding sequences (CDS). Crucially, null model analysis of A/T-rich junction sites revealed that these eccDNAs primarily originate from physically unstable scaffold/matrix attachment regions (S/MARs) via stochastic fragmentation, followed by opportunistic circularization predominantly mediated by the non-homologous end joining (NHEJ) pathway. Notably, salt stress specifically enriched eccDNAs derived from DNA transposons, including the Tc1/Mariner, CACTA and MITE superfamilies. Overall, our findings reveal complex extrachromosomal structural dynamics in bermudagrass, offering novel insights into its genomic adaptation under environmental stress.

Cynodon↗

PubMed2025

qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics.

SUMMARY: Clonal hematopoiesis (CH) is a molecular biomarker associated with various adverse outcomes in both healthy individuals and those with underlying conditions, including cancer. Detecting CH usually involves genomic sequencing of individual blood samples followed by robust bioinformatics data filtering. We report an R package, qcCHIP, a bioinformatics pipeline that implements permutation-based parameter optimization to guide quality control filtering and cohort-specific CH identification. We benchmark qcCHIP under various data settings, including different sequencing depths, ranges of cohort sizes, with and without normal-tumor paired samples, and across different cancer types. We show that qcCHIP allows users to customize analysis needs to generate CH calls based on cohort-specific data characteristics. AVAILABILITY AND IMPLEMENTATION: qcCHIP R package is freely accessible at GitHub https://github.com/tenglab/qcCHIP and DOI: 10.5281/zenodo.16421861.

Humans↗