Molecular Genetics and Genomics最新文献

This study aimed to perform noninvasive prenatal testing for structural chromosomal rearrangements (NIPT-SR) for a female pregnant proband carrying a t(4;8) balanced translocation, whose husband exhibited a normal karyotype. NIPT-SR could accurately detect transmission status of structural rearrangements in fetus through Hidden Markov Model (HMM) analysis, which requires the construction of parental haplotypes. To address the challenge of lacking genetic information from other family members of this proband, we developed a novel strategy to infer the fetal inheritance of structural variants by integrating Oxford Nanopore Technologies (ONT) with the NIPT-SR approach. Long-read sequencing was performed on the proband to directly detect the translocation and nearby single nucleotide polymorphisms (SNPs), and to link the structural variants with phased haplotypes. NIPT-SR method was used to infer the fetal inheritance of the constructed haplotypes and to evaluate the potential presence of unbalanced translocation in the fetus. Noninvasive prenatal testing (NIPT) was performed at 12 weeks of gestation, followed by copy number variation sequencing (CNV-seq) and karyotype analysis after birth respectively to confirm the accuracy of NIPT-SR results. Using nanopore sequencing, we identified the precise locations of the breakpoint junctions and successfully established the SNP-based haplotypes that were linked to the breakpoints on chr4 and chr8, without the need for retrieving genetic information of other family members. Haplotype-based analysis of cell-free DNA (cfDNA) indicated that the fetus inherited the normal haplotypes, which was consistent with the NIPT results and confirmed by the postnatal CNV-seq and karyotype analysis. In conclusion, the NIPT-SR method coupled with ONT platform could be used to perform NIPT-SR for those who carries balanced translocation circumventing the need for other family members as reference, providing an important supplement to birth defects prevention.

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.

CRP is a biomarker of acute inflammation linked to metabolic complications. Given the rising prevalence of these conditions in India, we investigated the genetic basis of CRP levels in Indian adolescents, an underrepresented group in genetic studies, to identify early markers of metabolic risk. We performed a two-phased genome-wide association study (GWAS; N = 5052) and an independent Exome-wide association study (ExWAS; N = 4547), to identify both common and rare genetic variants associated with CRP levels. The study identified intergenic variants near CRP and CRPP1 genes, and APOC1 gene as the key regulators of CRP levels establishing the universality of these associations. The GWAS identified the variant rs4247360 (PITPNC1) to be associated at a suggestive significance. The ExWAS single variant association identified novel associations in genes FGL1 (rs35431851), C19orf45 (rs608144, rs475923, rs484870), TRAPPC12 (rs11686212) and KIAA0087 (rs17153822). The SKATO analysis of the rare variants highlighted the role of loss of function and missense variants in genes EPS15, CCDC15, ZNF286A, ELF1, B3GNT8, ZNF850, MAP2, and PSG2. The GWAS and ExWAS in the present study validated the association of 56 variants previously reported for CRP levels. The meta-analysis with the CRP GWAS earlier reported in Indian adults revealed the shared genetic architecture of CRP levels across age groups. The gene-set enrichment analysis highlighted the role of CRP-associated genes in inflammatory and cardiometabolic pathways. The study enhances understanding of genetic predispositions to inflammation and metabolic disorders confirming known associations, identifying novel loci, and validating shared genetic architecture across age-groups, guiding targeted prevention for at-risk youth.

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.