Guoqing Liu

Nucleosome positioning critically regulates chromatin functions, yet species-specific mechanisms remain incompletely understood. This study revisits nucleosome organization in Schizosaccharomyces pombe ( S. pombe) using a DNA deformation energy model and a high-resolution nucleosome map. We demonstrate that DNA bending energy—not shearing energy—accurately predicts rotational positioning (72.2–77.4% accuracy) and nucleosome-depleted regions (NDRs) near transcription start sites (TSSs) in S. pombe. Gene-end analyses reveal that NDRs and nucleosome phasing are, at least partly, encoded in DNA sequence. Strikingly, nucleosome enrichment at RNA splice sites is determined primarily by trans-acting factors (e.g., transcription factors Pcr1/Atf1), not by DNA sequence preference for nucleosome positioning, and correlates with splice site usage rates. Highly transcribed genes exhibit reduced nucleosome occupancy upstream of splice sites, while frequently used splice sites show elevated nucleosome occupancy. Furthermore, 3D chromatin architecture analysis indicates that highly transcribed intron-poor genes display enhanced medium-range chromatin looping (10–100 kb), potentially reflecting their preferential aggregation at sub-nuclear environments enriched in transcriptional machinery and splicing factors. Our work identifies DNA bending properties as an important contributor to S. pombe nucleosome organization and reveals the involvement of nucleosome positioning and chromatin architecture in co-transcriptional splicing. The online version contains supplementary material available at 10.1186/s13072-026-00675-0.

Microhaplotypes are playing an increasingly important role in forensic evidence analysis. However, the impact of meiotic recombination events on the effica

We investigate the spin-photogalvanic effect (SPGE) in armchair-edged monolayer Janus ZrSSe via the combination of non-equilibrium Green’s function and density functional theory. It is found that defects and doping can optimize the spin photocurrent and spin polarization properties of ZrSSe. The introduction of S vacancies, Se vacancies, Cr doping, and Ni doping induces localized states, breaks symmetry, and engineers the electronic structure. These modifications, in turn, enhance the response of the SPGE and broaden its spectral adaptability. After the introduction of defects and doping, both high-energy domain light absorption and spin polarization are significantly enhanced; among all modified systems, the Cr-doped one exhibits the most remarkable increase in spin photocurrent. This finding highlights that doping and defect engineering can effectively optimize the spin-related properties of ZrSSe, thereby providing a theoretical basis for the design of high-efficiency spin-photonic devices.

K-mer analysis is a powerful tool for understanding genome structure and evolution. A “k-mer” refers to a short DNA sequence made up of k nucleotides (where k is a specific integer), while an “m-mer” is a similar concept but with a shorter sequence length. The functional mechanisms of CG-containing k-mers, as well as their potential role in evolutionary processes, remain unclear. To explore this issue, we analyzed 8-mers in several species with varying genomic complexities and evolutionary divergences: Homo sapiens, Saccharomyces cerevisiae, Bombyx mori, Ciona intestinalis, Danio rerio, and Caenorhabditis elegans, which were grouped by CG dinucleotide content (0CG, 1CG, and 2CG). We examined the relative frequencies of shorter m-mers (with m = 3 and 4) within each CG-defined group, using information-theoretic, distance-based, and angular metrics. Our results show that 0CG motifs follow random patterns, while 1CG and 2CG motifs display significant deviations, likely due to functional constraints such as nucleosome-binding and CpG island association. The observed unimodal distribution of 8-mers arises from the convergence of the three CG-defined groups. Among them, the 2CG group shows the highest divergence in m-mer composition, followed by 1CG, reflecting varying degrees of selective pressure. Furthermore, species-specific differences in CG-classified 8-mer patterns could provide valuable insights into phylogenetic relationships. Through extensive comparison, we explore how CG content and sequence composition influence genomic organization and contribute to evolutionary divergence across different taxa. These findings deepen our understanding of short motif functions, genome organization, and sequence evolution.

Background: Epigenomic instability accelerates mutations in tumor suppressor genes and oncogenes, contributing to malignant transformation. Histone modifications, particularly methylation and acetylation, significantly influence tumor biology, with chromo-, bromo-, and Tudor domain-containing proteins mediating these changes. This study investigates how genes encoding these domain-containing proteins affect colorectal cancer (CRC) prognosis. Methods: Using CRC data from the GSE39582 and TCGA datasets, we identified domain-related genes via GeneCards and developed a prognostic signature using LASSO-COX regression. Patients were classified into high- and low-risk groups, and comparisons were made across survival, clinical features, immune cell infiltration, immunotherapy responses, and drug sensitivity predictions. Single-cell analysis assessed gene expression in different cell subsets. Results: Four domain-related genes (AKAP1, ORC1, CHAF1A, and UHRF2) were identified as a prognostic signature. Validation confirmed their prognostic value, with significant differences in survival, clinical features, immune patterns, and immunotherapy responses between the high- and low-risk groups. Drug sensitivity analysis revealed top candidates for CRC treatment. Single-cell analysis showed varied expression of these genes across cell subsets. Conclusions: This study presents a novel prognostic signature based on domain-related genes that can predict CRC severity and offer insights into immune dynamics, providing a promising tool for personalized risk assessment in CRC.

Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism–related gene sets, WMRCA + outperformed widely used clustering algorithms—including iCluster, SNF, NMF, CC, and CNMF—achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA.

Klebsiella pneumoniae, an opportunistic pathogen, is pervasively distributed across the world. Its escalating antibiotic resistance poses a serious threat to global public health. The mechanisms behind this resistance remain largely elusive. In this study, we performed antibiotic susceptibility testing on several clinical isolates of Klebsiella pneumoniae , and a reference strain ATCC13883, and then analysed their transcriptomic profiles to identify genes and pathways associated with antibiotic resistance. Our results showed that a clinical isolate DY16KPN may counteract antibiotics by enhancing the biosynthesis of building blocks of bacterial cell, such as fatty acids, proteins, and DNA, and reducing transmembrane transport. Increased butanoate metabolism and lipopolysaccharide biosynthesis may also contribute to the drug‐resistance of Klebsiella pneumoniae . Additionally, we identified resistance‐promoting mutations in gene promoter regions, which regulate the activity of downstream drug‐resistant genes and pathways. Our results also demonstrated that DY16KPN counterbalances the trimethoprim/sulfamethoxazole‐mediated inhibitory effect on the synthesis of tetrahydrofolates and DNA by up‐regulating the expression of targeted enzymes of trimethoprim/sulfamethoxazole, dihydrofolate reductase and dihydropteroate synthase.

Metabolic reprogramming is a hallmark of cancers, but pan-cancer level roles of lipid metabolism in cancer development are remains poorly understood. We investigated the possible roles of lipid metabolic genes (LMGs) in 14 cancer types. The results indicate that: (1) there is strong evidence for increased lipid metabolism in THCA and KICH. (2) Although the overall levels of lipid metabolic processes are down-regulated in some cancer types, fatty acid synthase activity and fatty acid elongation are moderately up-regulated in more than half of the cancer types. Cholesterol synthesis is up-regulated in five cancers including KICH, BLCA, COAD, BRCA, UCEC, and THCA. (3) The catabolism of cholesterols, triglycerides and fatty acids is repressed in most cancers, but a specific form of lipid degradation, lipophagy, is activated in THCA and KICH. (4) Lipid storage is enhanced in in kidney cancers and thyroid cancer. (5) Similarly to primary tumors, metastatic tumors tend to up-regulate biosynthetic processes of diverse lipids, but down-regulate lipid catabolic processes, except lipophagy. (6) The frequently mutated lipid metabolic genes are not key LMGs. (7) We established a LMG-based model for predicting cancer prognosis. Our results are helpful in expanding our understanding of the role of lipid metabolism in cancer.

Background: Meiotic recombination is an important source of genetic variation, but how recombination shapes the genome is not clearly understood yet. Objective: Here, we investigate the roles of recombination on human genome evolution from two aspects: How does recombination shape single nucleotide polymorphism (SNP)-related genomic variation features? Whether recombination drives genome evolution through a neighbor-dependent mutational bias? Methods: We analyzed the relationship of recombination rate with mutational bias and selection effect at SNP sites derived from the 1000 Genomes Project. Results: Our results show that SNP density, Ts/Tv, nucleotide diversity, and Tajima's D were positively correlated with the recombination rate, while Ka/Ks were negatively correlated with the recombination rate. Moreover, compared with non-coding regions, gene exonic regions have lower nucleotide diversity but higher Tajima's D, suggesting that coding regions are subject to stronger negative selection but have fewer rare alleles. Gene set enrichment analysis of the protein-coding genes with extreme Ka/Ks ratio implies that under the effect of high recombination rates, the genes involved in the cell cycle, RNA processing, and oocyte meiosis are subject to strong negative selection. Our data also support S (G or C) > W (A or T) mutational bias and W>S fixation bias in high recombination regions. In addition, the neighbor-dependent mutational bias was found to be stronger at high recombination regions. Conclusion: Our data suggest that genetic variation patterns, particularly the neighbor-dependent mutational bias at SNP sites in the human genome, are mediated by recombination.