Molecular Genetics and Genomics最新文献

Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of stroke risk factors requires advanced approaches for accurate prediction. The objective of this study is to identify key risk factors for stroke and develop a predictive model using machine learning techniques to enhance early detection and improve clinical decision-making. Data from the China Health and Retirement Longitudinal Study (2011-2020) were analyzed, classifying participants based on baseline characteristics. We evaluated correlations among 12 chronic diseases and applied machine learning algorithms to identify stroke-associated parameters. A dose-response relationship between these parameters and stroke was assessed using restricted cubic splines with Cox proportional hazards models. A refined predictive model, incorporating age, sex, and key risk factors, was developed. Stroke patients were significantly older (average age 69.03 years) and had a higher proportion of women (53%) compared to non-stroke individuals. Additionally, stroke patients were more likely to reside in rural areas, be unmarried, smoke, and suffer from various diseases. While the 12 chronic diseases were correlated (p < 0.05), the correlation coefficients were generally weak (r < 0.5). Machine learning identified nine parameters significantly associated with stroke risk: TyG-WC, WHtR, TyG-BMI, TyG, TMO, CysC, CREA, SBP, and HDL-C. Of these, TyG-WC, WHtR, TyG-BMI, TyG, CysC, CREA, and SBP exhibited a positive dose-response relationship with stroke risk. In contrast, TMO and HDL-C were associated with reduced stroke risk. In the fully adjusted model, elevated CysC (HR = 2.606, 95% CI 1.869-3.635), CREA (HR = 1.819, 95% CI 1.240-2.668), and SBP (HR = 1.008, 95% CI 1.003-1.012) were significantly associated with increased stroke risk, while higher HDL-C (HR = 0.989, 95% CI 0.984-0.995) and TMO (HR = 0.99995, 95% CI 0.99994-0.99997) were protective. A nomogram model incorporating age, sex, and the identified parameters demonstrated superior predictive accuracy, with a significantly higher Harrell's C-index compared to individual predictors. This study identifies several significant stroke risk factors and presents a predictive model that can enhance early detection of high-risk individuals. Among them, CREA, CysC, SBP, TyG-BMI, TyG, TyG-WC, and WHtR were positively associated with stroke risk, whereas TMO and HDL-C were opposite. This serves as a valuable decision-support resource for clinicians, facilitating more effective prevention and treatment strategies, ultimately improving patient outcomes.

Chili pepper (Capsicum spp.) fruits are used as vegetables, spices, and ornamental plants, necessitating various fruit characteristics. However, their genetic improvement is challenging through conventional crossbreeding due to the quantitative traits, which makes it difficult to predict phenotypes in the progeny. As a breakthrough, we focused on phenotypic simulation via genomic prediction (GP) and aimed to clarify its utility for fruit-related traits in chili peppers. The present study used 291 C. annuum accessions, including two populations: inbred lines and F₁ accessions derived from 20 inbred parents. We collected data of fruit length, width, shape index (length/width), weight, and pericarp thickness, and obtained single nucleotide polymorphism data via multiplexed inter-simple sequence repeat genotyping by sequencing. We simulated the fruit-related traits in the F₁ accessions by inputting their estimated genotypes (based on their parents) into the GP model using the GBLUP-GAUSS model, which was shown to be the most accurate regardless of population or trait differences in the present study. As a result, we observed strong positive correlations (r = 0.833-0.908) between the simulated and observed phenotypic values across all traits, suggesting that accurate ranking of F₁ progenies based on fruit-related traits can be achieved using parental information. This is the first report demonstrating the utility of phenotypic simulation via GP in chili pepper breeding, offering valuable insights for its application in this field.

Acute kidney injury (AKI) is one of the most serious and common complications in the course of sepsis, known for its poor prognosis and high mortality rate. Recently, ferroptosis, as a newly discovered regulatory cell death, might be closely associated with the progression of AKI. METTL14 is a writer of RNA m6A, an abundant epigenetic modification in transcriptome with broad function. Hence, the purpose of our study is to explore the potential function and mechanism of METTL14 on the ferroptosis in sepsis-induced AKI. In this paper, TCMK-1 cells and mice treated with LPS were used to constructe AKI model in vitro and in vivo. Pathological changes of renal tissue were observed by HE staining. The fluorescent probe C11-BODIPY and 4HNE kits were used to measure the lipid peroxidation. The ferroptosis index was evaluated by MDA, GSH and Fe²⁺ kits. The total m6A levels were analyzed by EpiQuik M6A RNA methylation kit, and the m6A levels of LPCAT3 were examined by Me-RIP assay. Finally, the interaction between LPCAT3 and METTL14 was clarified using RIP and dual-luciferase reporter gene assays. Our works revealed that the m6A level and ferroptosis was markedly ascended in LPS-induced TCMK-1 cells. The silence of METTL14 lowered the cell viability, the levels of MDA, Fe²⁺ and lipid peroxidation in the LPS-stimulated AKI model in vitro and in vivo, but increase GSH levels. Moreover, the up-regulation of ferroptosis-related proteins by LPS was notably inhibited by the knockdown of METTL14. In addition, silencing METTL14 reduced the m6A and mRNA levels of LPCAT3. Furthermore, the efficacy of METTL14 downregulation on the ferroptosis in the LPS-induced TCMK-1 cells were antagonized by LPCAT3 overexpression. Taken together, our findings revealed that METTL14 knockdown resisted ferroptosis in sepsis-induced AKI through lessening the level of LPCAT3 mediated by m6A modification.

Recent therapeutic strategies have highlighted the potential of β-hydroxybutyrate (BHB) and α-ketoglutarate (α-KG) as effective anticancer agents, particularly for colon cancer. These metabolites can modulate cellular metabolism and induce epigenetic changes, inhibiting tumor growth. Nonetheless, certain cancer cells may utilize ketone bodies, like BHB as nutrient sources under hypoxic conditions, potentially reducing treatment efficacy. Understanding these mechanisms is crucial for optimizing cancer therapies. This study evaluated the effects of BHB and α-KG on HCT-116 colorectal cancer cell viability under normoxic and low-glucose hypoxic conditions. HCT-116 cell lines were treated with different doses of BHB and α-KG in normoxic and low-glucose hypoxic conditions, and then cell viability was assessed by the MTT assay. Moreover, the mRNA expression levels of SRC, hypoxia-inducible factor 1α (HIF-1α), acetyl-CoA acetyltransferase 1 (ACAT1), and sirtuin 2 (SIRT2) genes were determined using quantitative reverse transcriptase-polymerase chain reaction (q RT-PCR). BHB significantly increased the proliferation of HCT-116 colon cancer cells under low-glucose hypoxic conditions, while α-KG maintained cell viability in normoxic conditions but not in hypoxia. BHB treatment reduced SIRT2 mRNA levels and increased ACAT1, SRC, and HIF-1α expression. Conversely, α-KG decreased ACAT1, SRC, and HIF-1α expression and increased SIRT2 levels in normoxia but could not reverse gene expression during hypoxia. Our study demonstrated that BHB and α-KG exhibited complex interactions with colon cancer cell viability under varying oxygen and glucose conditions. While BHB promoted cell proliferation in hypoxic environments, α-KG showed protective effects in normoxic conditions. This research contributed to the growing body of evidence supporting the role of metabolic modulators in cancer therapy and emphasized the importance of understanding tumor microenvironments to optimize treatment outcomes. However, the need for further research into the metabolic pathways is underscored to enhance therapeutic strategies for cancer treatment.

Brassica villosa is characterized by its dense hairiness and high resistance against the fungal pathogen Sclerotinia sclerotiorum. Information on the genetic and molecular mechanisms governing trichome development in B. villosa is rare. Here, we analyzed an F₂ population, derived from a cross between B. villosa and the glabrous B. oleracea by QTL mapping and transcriptomic analyses. As a result, the phenotyping of 171 F₂ progenies revealed a wide range of variation in trichome development. Subsequent genotyping with the 15-k Illumina SNP array resulted in a genetic map with 970 markers and a total length of 812 cM. Four QTLs were identified, which explained phenotypic variation from 3.2% to 40.3%. Interestingly, one of these was partially co-localized with the major QTL for Sclerotinia-resistance previously detected in the same F₂ population. However, only a moderate correlation between trichomes and Sclerotinia-resistance was observed. In total, 133 differentially expressed genes (DEGs) associated with trichome development were identified, from which only BoTRY, an orthologue of Arabidopsis TRY encoding a MYB transcription factor negatively regulating trichome development, was located within the major QTL. Expression of BoTRY was tissue-specific and highly variable between the hairy and glabrous species, suggesting that BoTRY may also act as a master-switch regulator of trichome development in B. villosa. This study provides valuable data for further understanding the genetic architecture of trichome development and identifying related genes and mechanisms in Brassica species. Molecular markers can be developed to facilitate the introgression and selection of this trait in oilseed rape breeding.

Precursors of microRNAs (pre-miRNAs) are less used in silico to mine miRNAs. This study developed PmiR-Select^® based on covariance models (CMs) to identify new pre-miRNAs, detecting conserved secondary structural features across RNA sequences and eliminating the redundancy. The pipeline preceded PmiR-Select^® filtered 20% plant pre-miRNAs (from 38589 to 8677) from miRBase. The second filter reduced pre-miRNAs by 7% (from 8677 to 8045) through length limit to pre-miRNAs (70-300 nt) and miRNAs (20-24 nt). The 80% redundancy threshold was statistically the best, eliminating 55% pre-miRNAs (from 8045 to 3608). Angiosperms retained the highest number of pre-miRNAs and their families (2981 and 2202), followed by gymnosperms (362 and 271), bryophytes (183 and 119), and algae (82 and 78). Thirty-seven conserved pre-miRNA families happened among plant land clades, but none with algae. The PmiR-Select^® was applied to the rice genome, producing 8536 pre-miRNAs from 36 families. The 80% redundancy threshold retained 3% pre-miRNAs (n = 264) from 36 families, valuable experimental and computational research resources. 14% (n = 1216) of 8536 were new pre-miRNAs from 19 new families in rice. Only 16 new sequences from six families overlapped (39 to 54% identities) with rice pre-miRNAs and five species on miRBase. The validation against mature miRNAs identified 8086 pre-miRNAs from 13 families. Eleven ones have already been recorded, but two new and abundant pre-miRNAs [miR437 (n = 296) and miR1435 (n = 725)] scattered in all 12-rice chromosomes. PmiR-Select^® identified pre-miRNAs, decreased the redundancy, and discovered new miRNAs. These findings pave the way to delineating benchtop and computational experiments.

Detecting genome-wide SNP-SNP interactions (epistasis) efficiently is essential to harnessing the vast data now available from modern biobanks. With millions of SNPs and genetic information from hundreds of thousands of individuals, researchers are positioned to uncover new insights into complex disease pathways. However, this data scale brings significant computational and statistical challenges. To address these, recent approaches leverage GPU-based parallel computing for high-throughput, cost-effective analysis and refine algorithms to improve time and memory efficiency. In this survey, we systematically review GPU-accelerated methods for exhaustive epistasis detection, detailing the statistical models used and the computational strategies employed to enhance performance. Our findings indicate substantial speedups with GPU implementations over traditional CPU approaches. We conclude that while GPU-based solutions hold promise for advancing genomic research, continued innovation in both algorithm design and hardware optimization is necessary to meet future data challenges in the field.

Root nodule symbiosis is traditionally recognized in the Fabales, Fagales, Cucurbitales, and Rosales orders within the Rosid I clade of angiosperms. However, ambiguous root nodule formation has been reported in Zygophyllaceae and Roystonea regia (Arecaceae), although a detailed analysis has yet to be conducted. We aimed to perform morphological analyses of root structures in these plants and utilize metagenomic techniques to identify and characterize the bacterial populations within the nodule-like structures. We collected root samples of Tribulus terrestris (Zygophyllaceae) and Roystonea regia from West Asia and the Caribbean, respectively. We conducted detailed morphological analyses of nodule-like structures, isolated and genome-sequenced the endophytes, and employed metagenomic techniques to identify the bacterial populations within these formations. We observed nodule-like structures in both plant species. Symbiosomes, which are hallmark structures of nodulating plants, were not detected. Metagenome sequence data analysis revealed potential nodulating and nitrogen-fixing bacteria in the nodule-like structures of both species. Canonical nodulation and nitrogen-fixation genes were identified in microbes associated with the nodules. However, the phylogenomic analysis showed that the bacteria isolated from T. terrestris and R. regia are within Gammaproteobacteria and Bacilli, which are not typically known as nodulating bacteria. The observed structures differ significantly from traditional nodules found in legumes and actinorhizal plants, suggesting unique characteristics with hosting nitrogen-fixing bacteria. Although bacteria identified through in silico analysis or culture are well-known nitrogen-fixers, their specific role in root nodule formation remains to be investigated.

Patients with multiple sclerosis (MS) face a heightened risk of developing chronic obstructive pulmonary disease (COPD). Despite this widely reported association, the pathogenic contributors and processes that may favor the development of COPD in MS patients have yet to be identified. Recent studies have suggested peripheral blood leukocytes as a potential link between COPD and autoimmune disorders. Therefore, this study aimed to unveil shared molecular signatures between MS and COPD using blood transcriptomes. To this end, gene expression datasets obtained from MS and COPD blood specimens were retrieved from the Gene Expression Omnibus (GEO) database. By integrating datasets belonging to each disorder, differentially expressed genes (DEGs) were determined for each disease. Then, the protein-protein interaction (PPI) network was constructed for shared DEGs between MS and COPD. Subsequently, the network was analyzed to identify hub genes and key regulatory miRNAs. The integrated data for MS encompassed 51 samples (28 from MS patients and 23 from controls), and the integrated data for COPD included 450 samples (275 from COPD patients and 175 from controls). A total of 246 genes were found to exhibit identical directions of expression in both MS and COPD. By applying a high confidence threshold (0.7), a PPI network with 74 nodes was constructed. TP53, H4C6, SNRPE, and RPS11 were identified as hub genes according to the degree measure. In addition, 8 miRNAs were identified as key regulators, each interacting with 6 mRNAs. Among these miRNAs, miR-218-5p and miR-142-5p have been previously reported to contribute to the pathogenesis of these diseases, and here they were identified as key regulators of the shared PPI network, suggesting a potential epigenetic link between MS and COPD. In conclusion, the results highlighted the potential role of peripheral blood leucocytes as a bridge between MS and COPD. These findings broaden our understanding of pathogenic contributors linking MS and COPD. While this transcriptomics study identified multiple key players, such as TP53, miR-218-5p, and miR-142-5p, the assessment of their therapeutic efficacy demands further experimental studies.