Journal of Applied Genetics最新文献

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.

Screening genotypes with robust root architecture is preeminent to increase considerably bean (Phaseolus vulgaris L.) seed yield. The survey consisted to evaluate the genetic variability, estimate  broad sense heritability, genetic advance, and determine  the correlations for root traits, seed yield, and its components for ten bean lines. The field trial was laid out in a randomized complete block design with three replications. Data were submitted to the analysis of variance (ANOVA) and multivariate analyses using STATGRAPHICS PLUS version 16.1 and R version 3.5.1 statistical package programmes, respectively. ANOVA showed that the results revealed significant differences among the bean lines denoting a wider genetic variability for root traits, seed yield and its components. Principal component analysis displayed the relationship between traits and genotypes, indicating a substantial level of variability among the genotypes for root traits, yield components and seed yield; hence, it could be exploited for more improvement bean yield. Cluster analysis confirmed a sufficient genetic variability; thereby, the distal clusters could be used for further improvement bean yield and root traits. All traits exhibited high values of broad sense heritability suggesting these characters could be improved by selection. High broad sense heritability associated to high genetic advance was observed for root growth angle and 100-seeds weight. This attests that additive gene effects were involved in the heredity of these traits, and they can be readily fixed in the genotypes by selection in the early generations. The number of basal roots exhibited significant and positive correlation with the number of adventitious roots; hence, these traits could be considered the main components and selection may be effective in improving the bean root system. The bean lines Bigarré, FEB192, GLP190 S, ECA-PAN021, Gros Rouge and Petit Rouge could be used as elite material in breeding programmes for seed yield and root traits.

Over the last few years, the number of publications regarding genetics and sports has notably increased. However, the way this information is actually used is often misinterpreted by those who do not apply genetics in their day-to-day training. Therefore, the purpose of this short communication is to exemplify, using the real cases of identical twin athletes, how genetic information must be applied in sports. Additionally, we present data on performance tests and individual preferences for training and competitions between the athletes. Accordingly, the study concludes with a distinction between genetic profile studies and genetic application studies, emphasizing the importance of further research in this area, especially case reports.

Head and neck squamous cell carcinoma (HNSCC), the seventh most common cancer worldwide, has become more closely linked to poor lifestyle habits. Despite improvements in cancer treatment approaches, patients with stage I-II HNSCC have a 70-90% 5-year survival rate, and for patients with advanced stages III-IV, this rate falls to about 40%. This controversy is all about the heterogeneity of HNSCC. Finding diagnosis and prognosis biomarkers has the potential to make significant improvements in the life expectancy and overall health of these patients. The combination of bioinformatics and machine learning has facilitated the finding of the best markers for HNSCC. In this regard, RNA expression data were obtained to identify genes that were expressed differently (DEGs) and utilize a deep learning algorithm to identify genes that exhibited significant variability. In addition, correlations between clinical data and DEGs, the building of a Receiver Operating Characteristic (ROC) curve, and the prediction of tumor-infiltrating immune cells were analyzed. Deep learning analysis identified diagnostic and prognostic biomarkers strongly associated with carcinogenesis, such as KRT33B, KRTAP3-3, C14orf34, and ACADM. In addition, after analyzing the ROC curve, it was found that the combination of ACADM, KRT33B, and C14orf34 is the most practical combination of diagnostic markers. This combination achieved sensitivity, specificity, and Area Under the Curve (AUC) values of 0.92, 0.86, and 0.93, respectively.

According to UNICEF India, an estimated 67,385 neonates are born daily in India, each striving to survive the initial 28 days of life, which are pivotal in determining their future health prognosis. A significant number of these neonates succumb to inborn errors of metabolism (IEM), resulting in a spectrum of either manageable or severe clinical consequences. The evolution of techniques from basics to next-generation sequencing (NGS) and cutting-edge bioinformatics has enabled the prompt and precise identification of metabolic defects during the early stages of life. But the limited awareness, facilities, and access to the screening program necessitate the urgent need for establishing a state-of-the-art screening initiative all over India. The program holds the potential to substantially diminish infant mortality rates and alleviate the national health burden. This article delineates inborn errors of metabolism, investigates the advancements in diagnostic methodologies, outlines the NGS technique, underscores the role of computational biology, and advocates for the establishment of a centralized screening initiative in India specifically for treatable IEM. Furthermore, a few case studies have been included to showcase the notable discoveries of genes and associated disorders facilitated by NGS along with some studies highlighting the advantages of employing computational biology.