Journal of Applied Genetics最新文献

Hyponatremia can occur in endocrinological diseases, neoplasms, kidney diseases, and acquired or genetically conditioned disorders of antidiuretic hormone levels. Nephrogenic syndrome of inappropriate antidiuresis (NSIAD) is a rare X-linked disease caused by a point mutation of the type 2 vasopressin receptor (AVPR2) gene. This mutation results in constitutive activation of the AVPR2 and a low sodium level.We reported the first familial NSIAD in Poland in siblings with hyponatremia. Case 1. A 2.5-year-old boy, during a respiratory tract infection, showed the following laboratory test results: Na 125 mmol/L, serumosmolality 260 mOsm/kg H2O, low uric acid level, and increased fractional sodium and uric acid excretions. Thyroid, adrenal, and renal function were normal. Copeptin level was low. Case 2. A 7-month-old brother presented with reduced activity and muscle tone, a sodium level of 117 mmol/L, and a serum osmolality of 249 mOsm/kg H2O. They were both confirmed to be hemizygous for the R137C mutation on the AVPR2 gene. The boys were advised to restrict their oral fluid intake and supplement sodium orally, aiming for sodium levels of 133-140 mmol/L. Conclusions: Genetic testing for an AVPR2 mutation is crucial in patients with hyponatremia, normovolemia, hypoosmolality, and low copeptin level.

Despite the growing recognition of microRNAs (miRNAs) as critical biomarkers in cancer, current approaches to their analysis remain fragmented, disjointed, and poorly integrated with emerging computational advances. This lack of cohesion limits progress toward reproducible and clinically actionable biomarker discovery. To address this unmet need, we present a review that unifies the latest findings and tools in bioinformatics, machine learning (ML), and large language models (LLMs) for miRNA analysis in oncology, thereby bridging a significant methodological gap in the field. We begin by critically synthesizing, benchmarking, and evaluating algorithms, including miRDeep2 and DIANA-miRPath, within a functional pipeline that spans next-generation sequencing (NGS) data processing to multi-omics integration. Building on this foundation, we review ML-augmented layers incorporating supervised and deep learning (DL) algorithms, specifically support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to enable robust miRNA signature identification, classification, and target prediction. Furthermore, we explore the integration of generative models and LLMs to support hypothesis generation and enhance reproducibility in biomarker discovery workflows. This comprehensive framework enhanced with artificial intelligence (AI) is contextualized through cancer-specific datasets, with particular emphasis on translational applications for early detection, prognosis, and therapy selection. By systematically organizing fragmented methodologies into a scalable and reproducible pipeline, our work provides a strategic roadmap to accelerate the development of miRNA-based precision cancer.

This study aimed to evaluate the effects of selection for post-weaning weight on reproductive traits in Nellore cattle by (i) estimating genetic parameters and trends for birth weight (BIW), body weight at selection (BW), days to calving (DC), and pregnancy rate (PR); and (ii) performing a genome-wide association study (GWAS), gene annotation, and functional enrichment analyses to uncover genomic regions, candidate genes, biological processes, and metabolic pathways underlying DC and PR. The dataset contained 12,865 Nellore animals from the experimental breeding program of the Institute of Animal Science (IZ, Sertãozinho, Brazil), including three selection lines: Nellore Control (NeC, stablishing selection for post-weaning weight), Nellore Selection (NeS, selected for higher post-weaning weight), and Nellore Traditional (NeT, selected for higher post-weaning weight and lower residual feed intake). Genomic data were available for 2,326 animals and 384,521 autosomal SNP markers after quality control. Genetic parameters were estimated using Bayesian inference under the ssGBLUP framework. Genetic trends from 1981 to 2021 were derived from linear regressions considering genomic estimated breeding values (GEBVs). The weighted single-step GWAS (WssGWAS) was used to identify genomic regions that explained more than 1.0% of the additive genetic variance for DC and PR, which were further analyzed for gene annotation and functional enrichment. Heritability estimates were high for BIW (0.46 ± 0.02) and BW (0.41 ± 0.02), and low for DC and PR (0.10 ± 0.02 for both). Moderate genetic correlations were observed between BIW and DC, especially in lines selected for higher growth (NeS: 0.38 ± 0.12; NeT: 0.56 ± 0.09), in contrast, BW showed weak genetic correlations with reproductive traits, with estimates for DC of - 0.11 ± 0.18 (NeC), 0.15 ± 0.15 (NeS), and 0.36 ± 0.14 (NeT), and for PR of 0.25 ± 0.22 (NeC), - 0.12 ± 0.17 (NeS), and - 0.44 ± 0.16 (NeT). Genetic trends indicated consistent increases in BW and BIW in NeS and NeT, while NeC showed more favorable trends for DC and PR. The GWAS identified 13 and 9 genomic windows associated with DC and PR, respectively, with pleiotropic regions on chromosome 14 influencing both traits. Key candidate genes annotated included PLAG1, MOS, MAPK13, MAPK14, and FKBP5. Functional enrichment revealed biological processes related to hormone metabolism, immune modulation, and oocyte development. Selection for increased growth does not directly impair reproductive traits; however, it indirectly influences fertility due to correlated response in BIW, which is genetically associated with DC.

Krüppel-like factor 14 (KLF14) is a pivotal trans-regulatory transcription factor that modulates diverse biological processes, including insulin sensitivity, glucose homeostasis, lipid metabolism, and potentially cancer suppression through its regulation of centrosome amplification and apoptosis in colorectal cancer cells. KLF14 has been associated with the pathogenesis of metabolic disorders, including obesity, insulin resistance, and type 2 diabetes (T2D), and contains CpG sites that exhibit age-associated methylation changes, which may serve as potential biomarkers for estimating an individual age. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) that are significantly associated with KLF14 expression in adipose tissue. The functional impact of KLF14 is influenced by both physiological and environmental factors, underscoring its complex role in disease pathogenesis. Population-based genetic studies reveal significant interethnic variability in KLF14 polymorphisms, highlighting the necessity of incorporating ethnic diversity into genetic research. Moreover, a deeper understanding of the molecular regulatory mechanisms and disease associations of KLF14 could inform the development of targeted therapies and personalized medicine strategies. Thus, the current study underscores the significance of KLF14 as a trans-regulatory gene and a potential therapeutic target, emphasizing its intricate role in biological regulation and disease pathogenesis.

Globodera pallida is a major pest that is responsible for huge losses in potato yields worldwide. Expanding the gene pool of cultivated potatoes with clones resistant to this pest is made possible by searching for resistance genes in wild Solanum species. The aim of this study was to identify quantitative trait loci (QTLs) for potato resistance to Globodera pallida derived from Solanum gourlayi. A resistant diploid potato clone, Sg 2/7 (Solanum gourlayi, accession CGN17592), was crossed with a susceptible potato hybrid clone, DW 94-4235, to generate an F1 mapping population. All clones were tested for nematode resistance using G. pallida, pathotypes Pa2 and Pa3, in 2 or 3 years (2017-2019), respectively. Diversity Array Technology (DArTseq) was used for genotyping and genetic map construction. QTLs for nematode resistance were identified on potato chromosomes II, IV, V, VI, VII, X, XI, and XII, explaining from 10.1 to 21.5% of phenotypic variance. The most significant QTL for resistance to G. pallida pathotype Pa2 was identified on chromosome XII, explaining 20.9% of the phenotypic variance in the dataset from 2017. The most significant QTL for resistance to the G. pallida Pa3 pathotype was identified on chromosome VI, with a CAPS marker Exp928 in its peak, explaining 21.5% of the phenotypic variance in the dataset from 2017. The novel QTLs for resistance to S. gourlayi may be useful for breeding resistant potato cultivars, further studies of candidate genes, and host responses of potato to G. pallida infection.

Proteasome inhibitors are currently at the forefront of multiple myeloma (MM) treatment. Drug resistance in MM challenges treatment, causing relapses and making the disease incurable. Urgent strategies are needed to combat resistance and understand its mechanisms. Targeting the metabolism of MM is a promising approach, as metabolic changes are associated with the disease and its adaptation to therapy. Metabolomics, the study of small molecule metabolites, is a powerful tool for identifying and analyzing a cell's metabolic phenotype. In this study, we aimed to investigate alterations in the metabolome of carfilzomib-resistant MM cells. We conducted global metabolomic comparative analyses of two carfilzomib-sensitive MM lines with their carfilzomib-resistant progenies. Additionally, we performed bioinformatic analysis to determine the top canonical pathways, biological functions, and upstream regulators linked to the differences in metabolomic profiles. Differential metabolite analysis showed increased amino acid and decreased fatty acid concentrations in carfilzomib-resistant cells. Functional analysis revealed increased glucose-6-phosphate oxidation and inhibited lipid accumulation in resistant lines. The bioinformatic analysis predicted PML, ARNT D-glucose, and UPC1 as upstream regulators of observed changes in carfilzomib-resistant cells. This study presents one of the first metabolomic profiles of two carfilzomib-resistant MM lines and the metabolome changes that may contribute to carfilzomib resistance.

Holoprosencephaly (HPE) is the most common structural anomaly of developing forebrain, characterized by incomplete separation of the cerebral hemispheres. While mutations in the Sonic Hedgehog (SHH) signaling pathway remain the most established genetic cause, recent studies have identified an expanding list of genes and molecular networks involved in the pathogenesis of HPE. These include modulators of the NODAL, NOTCH, WNT/PCP, FGF, and RAS/ERK1/2 pathways as well as components of ciliary structures and cohesin complexes. Incomplete penetrance, broad phenotypic heterogeneity, and gene-environment interactions complicate diagnostic and counselling efforts. This review summarizes recent insights into the molecular mechanisms of HPE, highlighting key signalling networks, gene candidates, and phenotypic correlations. We also discuss under-recognised contributors such as cohesin and ciliary gene defects, which may account for a significant subset of unresolved cases. Finally, we propose a diagnostic framework incorporating clinical stratification, extended gene panels, and consideration of syndromic features.

The skin is one of the largest organs in humans and is formed by a layer (termed the epidermis) that enables the body to interact with the environment and protects it from various harmful agents. The epidermis includes a physical, biochemical, and adaptive immune barrier. The immune system in the human body is responsible for protecting organisms from potentially infectious microorganisms, allergens, and toxins, maintaining homeostasis, controlling inflammation processes, and tissue repair. Autoimmune and autoinflammatory diseases are of the immune system manifesting as aberrations in adaptive and innate immunity that lead to an inflammatory state and self-attack damage, also in the skin. The commonness of cutaneous autoinflammations has increased over the past decades, and the occurrence of the disease can have a crucial impact on a patient's quality of life due to their visible nature, discomfort caused by somatic symptoms, and emotional and social challenges. In this review, we summarize the current knowledge of four common autoinflammatory skin diseases-vitiligo, alopecia areata, systemic lupus erythematosus, and psoriasis-with particular emphasis on their molecular background, including the role of genetic susceptibility, epigenetic regulation, and immunological pathways.

Praecitrullus fistulosus (Stocks) Pangalo is one of the nutrient-rich vegetables crops with proven therapeutic value. This study was designed to investigate the inheritance of important physiological and biochemical traits of Praecitrullus fistulosus. A set of 15 cross combinations were developed from five lines and three testers and evaluated for two years. The results depicted significant (p < 0.05) variation among the genotypes (parents and crosses) with respect to flavonoids, phenolic compounds, total carbohydrates, vitamin C, and carotenoids during both studied years. Data were analysed with traditional line × tester analysis for inheritance pattern, and the genotypes (parents and hybrids) were further analysed using polar plots for heterosis and gene action, and principal component biplot analysis for graphical explanation of combining abilities. The physiological traits, i.e., flavonoids, antioxidants, and total soluble proteins, showed significant means square values and general combining ability for genotypes, i.e., 20 and 47. The F1 hybrids 20 × 42, 8 × 63, and 20 × 40 showed high and significant specific combining ability for flavonoids, antioxidants, vitamin C, and carotenoids. GCA, SCA, and the PCA biplot also showed comparable results. The studies of heterosis using polar plots showed the preponderance of overdominance for the majority of traits. Conclusively, both conventional and graphical attribution of data using line × tester analysis could lead Praecitrullus fistulosus breeders to the selection of suitable breeding methods.

Endometriosis is a chronic gynecological condition characterized by the presence of endometrial-like tissue outside the uterine cavity. Its diagnosis remains a significant clinical challenge, often delayed by 7 to 12 years, leading to considerable socio-economic burden and a substantial decline in patients' quality of life, including potential infertility. Consequently, there is an urgent need to identify reliable biomarkers that would allow for earlier and more accurate detection. This review provides a comprehensive and up-to-date analysis of potential biomarkers for the diagnosis of endometriosis, including hormonal, inflammatory, genetic, epigenetic, immunological, metabolic, and imaging-based markers. Their diagnostic value and limitations are critically assessed, with particular emphasis on the advantages of multimarker and integrated diagnostic approaches to enhance early detection. The findings of this review offer valuable insights for clinicians, researchers, and healthcare professionals working to develop better diagnostic methods and improve patient outcomes. Moreover, the integration of emerging technologies, such as artificial intelligence, offers promising opportunities to revolutionize endometriosis diagnostics through personalized and precise medical care.