"Генетика", 2026 г., № 3

Статьи, опубликованные только в Russian J. of Genetics, № 3 – 2026 г.

Grain Yield Gene Sequence Analysis across 3K RGP Panel Provides Elite Haplotypes and Haplotype Combinations for Rice Yield Improvement.

T. T. Zhu, H. L. Chen, G. Li, X. Z. Hu, Z. H. Luan, Z. W. Gao, J. Y. Zhong, J. Q. Wu, Y. Song, X. N. Li, L. Z. Meng

College of Agricultural and Biology, Liaocheng University, 252000, Liaocheng, China
Correspondence to Y. Song or X. N. Li or L. Z. Meng

Grain size is a quantitative trait controlled by multiple genes. Though many genes regulating grain size have been reported in rice, their superior haplotypes or haplotype combinations for developing elite varieties remain elusive. In this study, we found that haplotypes of 35 previously functionally characterized genes governing grain yield significantly varied in the 3000 rice genome project (3K RGP) panel. Twenty-four of these genes had 2–6 haplotypes based on non-synonymous single nucleotide polymorphisms (SNPs), while the remaining 11 genes had only one haplotype. A total of 75 haplotypes in the 24 genes were identified. Also, we conducted an association analysis between the 75 haplotypes and 100-grain weight, and found that haplotype SG1c had the highest grain weight, followed by GW8e and DEP1e, while GW8f and GLW7d had the lowest. Furthermore, OsBAK1, D2, TGW6, BG1, SRS3, GL7, GW8 and GW6a haplotypes could distinguish japonica and indica, and thus were associated with rice domestication. Haplotype combination analysis showed that four haplotypes including MAPK6b, BAK1b, GL7d and SRS3b were the superior haplotype combination, and explained a possible genetic basis for accession superiority with the highest grain weight. Haplotype identification of genes controlling grain size variation will provide the theoretical basis for developing elite rice varieties with superior haplotypes or haplotype combinations of target genes and provide valuable molecular breeding targets.

DOI: 10.1134/S1022795425701583
К статье на сайте SpringerLink

Transcriptomic Identification and Validation of FGF5/FGF18 Associated with Cashmere Fiber Fineness in Yanshan Cashmere Goats

D. X. Wang¹, Z. Z. Liu¹, C. C. Ge¹, X. Qiao¹, Y. C. Xie¹, H. X. Sun², H. F. Gao³, F. Zhang¹, H. Ding², X. L. Li¹, Y. F. Gong¹

¹. Hebei Normal University of Science and Technology, College of Animal Science and Technology, Key Laboratory of Exploration and Innovation of Characteristic Animal Seed Resources in Hebei Province, 066004, Qinhuangdao, Hebei, China
². Hebei Institute of Animal Husbandry and Veterinary Medicine, 071000, Baoding, Hebei, China
³. Baotou Light Industry Vocational Technical College, Department of Animal Husbandry and Veterinary Medicine, 014013, Baotou, Inner Mongolia, China
Correspondence to X. L. Li or Y. F. Gong

This study aims to accelerate the breeding of Yanshan cashmere goats and explore key genes affecting the cashmere fiber fineness. In this study, two groups of 5 individuals with significant differences in cashmere fiber fineness (coarse-cashmere group and fine-cashmere group) were selected from 101 female Yanshan cashmere goats. Using RNA-seq technology, transcriptome sequencing was performed on the skin tissues of the two groups of goats. Candidate genes with significant differential expression and closely related to fineness of cashmere fibers were verified using RT-qPCR technology. Screening was performed according to the criteria of |Log2 (fold change)| > 1, P-value < 0.05, padj < 0.05. RNA-seq identified 167 significantly differentially expressed genes, with 135 upregulated and 32 downregulated. Significantly differentially expressed genes were analyzed by GO functional annotation and KEGG pathway enrichment analysis, it was found that FGF5 and FGF18 genes were present in multiple pathways such as fibroblast growth factor receptor signaling pathway, response to fibroblast growth factor, growth factor activity and receptor binding in GO functional annotation. The KEGG pathway enrichment mainly includes MAPK signaling pathway, PI3K Akt signaling pathway, Rap1 signaling pathway, etc. This suggests that these two genes may regulate the cashmere fiber fineness through fibroblast growth. RT-qPCR verification indicated that the mRNA expression levels of both FGF5 and FGF18 genes were lower in the fine-cashmere group compared to the coarse-cashmere group, with a particularly significant difference in mRNA expression levels between the two groups for the FGF18 gene (P < 0.05). The results of RT-qPCR and RNA-seq for the two genes were basically consistent. It is speculated that both FGF5 and FGF18 are key genes regulating the fineness of cashmere fibers in Yanshan Cashmere goats. The research results have enriched our understanding of the molecular genetic mechanisms of cashmere fiber fineness, providing a new theoretical basis for improving the fiber fineness of Yanshan cashmere goats and facilitating the breeding of high-quality individuals.

DOI: 10.1134/S1022795425701637
К статье на сайте SpringerLink

Computational Identification of Differentially Expressed Genes in Prostate Cancer Using Integrated Public Datasets

V. K. Aydin

Department of Biophysics, Faculty of Medicine, Pamukkale University, Denizli, Türkiye
Correspondence to V. K. Aydin

Prostate cancer (PC) is a major global health challenge, yet the molecular drivers of its aggressive forms remain incompletely understood. Computational integration of publicly available datasets can identify molecular signatures and candidate biomarkers requiring experimental validation. In this study, I demonstrate that robust transcriptomic analyses can be performed by integrating publicly available RNA-seq datasets, even when control and tumour samples originate from different studies and technical batches. I analysed RNA-seq data from RWPE-1 normal prostate epithelial cells and PC3 androgen-independent prostate adenocarcinoma cells using nf-core pipelines and open-source tools, applying rigorous quality control and correcting for batch effects with Surrogate Variable Analysis (SVA). My differential expression analysis identified 12 337 significantly altered genes (p-adj < 0.05), with 6462 upregulated and 5875 downregulated in PC3 cells. This analysis confirmed established oncogenes such as YBX2 and CX3CL1, and revealed novel candidates in prostate cancer, including the upregulation of PRSS21 and the downregulation of PAX6, THSD7A, and the metabolic regulator PDK4. Functional enrichment analyses consistently highlighted pathways critical to cancer progression, including extracellular matrix organization, cell adhesion, and neural signalling. This work provides a resource of potential biomarkers and therapeutic candidates and demonstrates a methodology that enables researchers to extract possible clinically relevant molecular mechanisms from public data using reproducible workflows.

DOI: 10.1134/S1022795425701698
К статье на сайте SpringerLink