Genes最新文献

Background/Objectives: Identifying candidate genes underlying quantitative trait loci (QTL) in poultry has traditionally required labor-intensive positional cloning. Previous studies using an F₂ population derived from native Japanese Nagoya (NAG) and White Plymouth Rock (WPR) breeds revealed a major QTL on chromosome 2 affecting 3-week body weight and 4-week pectoralis muscle weight. This study aimed to identify candidate genes for this QTL using a hypothesis-free integrative genetic approach. Methods: We employed a multi-step analytical framework combining QTL remapping, transcriptome analysis, gene enrichment analysis, haplotype frequency comparison, and correlation analysis. QTL remapping was performed using individual traits and their first principal component (PC1) in 239 F₂ chickens. RNA-sequencing (RNA-seq) of liver tissue was conducted for F₂ individuals with extreme PC1 scores, followed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) validation. Results: QTL remapping refined the 95% confidence interval to a chromosome 2 region containing 329 genes. RNA-seq analysis identified 23 differentially expressed genes (DEGs) within this interval. Although gene enrichment analysis initially highlighted GATA binding protein 6 (GATA6) as a potential candidate, RT-qPCR in NAG, WPR, and F₁ chickens showed no significant expression differences, excluding GATA6. Haplotype frequency and correlation analyses prioritized cadherin-17 (CDH17) as the strongest candidate gene and ring finger protein 151 (RNF151) as a secondary candidate. Conclusions: Our hypothesis-free integrative approach effectively refined candidate genes for a chromosome 2 QTL influencing early growth and pectoralis muscle weight. CDH17 and RNF151 represent promising targets for functional validation and may support marker-assisted selection to improve muscle-related traits in chickens.

Background/Objectives: The aim of this study was to simultaneously evaluate alterations in apoptosis-related biomarker gene expression accompanied by electroconvulsive therapy (ECT) in treatment-resistant depression (TRD) patients. Methods: A total of 25 subjects (15 healthy controls; 10 TRD patients) were initially tested for baseline values of relative mRNA expression of apoptosis-related markers (Bax, Bcl-2, p53, and cytochrome c) in peripheral blood samples and MADRS score. Results: Healthy subjects showed significantly lower values in MADRS, and Bax and p53, with increased Bcl-2 expression. The four-week ECT protocol (bitemporal, three sessions per week, with MADRS evaluation and blood sampling after each week) in TRD patients resulted in a concomitant significant decrease in MADRS, Bax, and p53 and an increase in Bcl-2 expression. Conclusions: Our results confirmed that the benefits observed by clinical outcome may also be attributed to the anti-apoptotic impact of ECT.

Hereditary polyneuropathies represent a genetically and clinically heterogeneous group of disorders affecting the peripheral nervous system, characterized by progressive motor, sensory, and autonomic impairment. Advances in molecular genetics have identified key causative genes, including PMP22, MPZ, MFN2, TTR, EGR2, and CX32 (GJB1), which are implicated in Charcot-Marie-Tooth disease, Dejerine-Sottas syndrome, and related neuropathies. These conditions display substantial allelic and locus heterogeneity. Pathogenetically, mechanisms involve impaired myelin maintenance, disrupted axonal transport, mitochondrial dysfunction, and aberrant Schwann cell biology. Despite these insights, therapeutic options remain limited, and there is a pressing need to translate genetic findings into effective interventions. This review aims to provide a comprehensive synthesis of current knowledge compiling all known mutations resulting in hereditary polyneuropathies. In addition, it underscores the molecular pathomechanisms of hereditary polyneuropathies and evaluates emerging therapeutic strategies, including adeno-associated virus mediated RNA interference, CRISPR-based gene editing, antisense oligonucleotide therapy, and small-molecule modulators of axonal degeneration. Furthermore, the integration of precision diagnostics, such as next-generation sequencing and functional genomic approaches, is discussed in the context of personalized disease management. Collectively, this review underscores the need for patient-centered approaches in advancing care for individuals with hereditary polyneuropathies.

Feingold syndrome (FS) is a rare congenital disorder with an autosomal dominant inheritance pattern. Two distinct subtypes are recognized based on their molecular pathology: FS type 1 (FS1) and FS type 2 (FS2). Both types share skeletal anomalies such as microcephaly, brachymesophalangia, and clinodactyly; however, gastrointestinal atresia is unique to FS1. Herein, we report a rare prenatal diagnosis of FS1 in a female fetus. The second-trimester ultrasound revealed bilateral clinodactyly and fetal microcephaly, and the subsequent molecular karyotyping identified a ~342 kb deletion at 2p24.3 encompassing the MYCN gene, confirming the diagnosis. The same deletion was detected in the father, verifying the hereditary pattern. The pregnancy was also complicated by preeclampsia and fetal growth restriction, leading to preterm caesarean delivery at 33 + 3 weeks of gestation. The neonate had microcephaly and clinodactyly but no gastrointestinal defects. In conclusion, high clinical suspicion aroused by identifying ultrasound features of FS can lead to early prenatal diagnosis via molecular karyotyping. Detecting accompanying gastrointestinal disorders that require early operation is crucial for the prognosis, genetic counseling, and prenatal management of the affected families.

Background: Genetic mutations have proven to be the epicenters of cancer and disease progression. Traditional WXS sequencing and BLOSUM scoring can be used to infer the evolutionary conservation of amino acid substitutions, though these approaches are not informed by probable base pair sequence changes. Within gene mutation analysis, most tools focus on amino acid conservation or codon switching independently, limiting their ability to contextualize observed mutations against stochastic mutational processes. In the clinical setting, variants of unspecified significance remain difficult to interpret, as clinicians are often unable to determine whether observed mutations arise from oncogenic selection or from stochastic mutational degradation.

Methods: We analyzed mutation sequences from the TCGA BRCA cohort for TP53 and PIK3CA and developed a model that integrates BLOSUM scoring with statistical modeling of base pair changes to evaluate deviation from codon-aware neutral expectations. Observed mutational distributions were compared against a stochastic neutral model to assess statistical significance.

Results: Within the TCGA BRCA cohort, TP53 mutations were significantly more evolutionarily radical than expected under the codon-aware neutral model, while PIK3CA mutations were significantly more evolutionarily conservative, as determined using chi-square testing. These opposing patterns are consistent with the distinct functional roles of TP53 and PIK3CA in oncogenesis, where TP53 is inhibited through disruptive loss-of-function mutations, whereas PIK3CA is recurrently mutated in a manner that preserves protein structure and promotes constitutive pathway activation. This contrast reflects selective pressure toward disabling tumor suppressor function while maintaining persistent oncogenic signaling.

Conclusions: Codon-aware neutral modeling provides a statistical framework for distinguishing mutations that deviate from stochastic expectations and may aid in the interpretation of variants of unspecified significance. By contextualizing mutational severity relative to neutral processes, this approach offers insight into tumor evolution and may support prognostic assessment without relying on predefined gene-level neutrality.

Background: Rapid advances in biotechnology provide researchers with the opportunity to integrate omics profiles (genomics, epigenomics, transcriptomics, proteomics, etc.) with multiple phenotypes or experimental conditions. In cancers such as acute myeloid leukemia (AML), where combination therapies are standard of care, identifying genetic drivers of drug resistance requires evaluating how genes are associated with multiple drug response phenotypes. Statistical analyses associating omics profiles with multiple phenotypes yield multiple significance values and rankings for each of many genes. There is a great need to consolidate these multiple rankings into a consensus ranking to prioritize specific genes for detailed follow-up wet-lab or clinical studies.

Methods/results: Here, we evaluate the well-known Fisher's method, the sum of squared z-statistics (SSz), and the recently published cellMCD method as tools for gene prioritization. In simulation studies, cellMCD showed very similar or highly superior performance to the widely used Fisher's and SSz methods. These advantages were also observed in an example application involving a CRISPR drug screen of an acute myeloid leukemia cell line.

Conclusions: In summary, our results indicate that cellMCD should be more widely used for prioritizing discoveries from multiple omic association studies. These methods are available as an R package on github.

Background/Objectives We re-analyzed publicly available gene expression profiles from the male mouse cortex under conditions of sleep deprivation (SD) using tensor decomposition-based unsupervised feature extraction, originally proposed by one of the authors in 2017. Methods We focused on two distinct expression patterns: genes whose levels were altered in SD and failed to normalize during recovery sleep (RS), and genes that overshot normal levels during RS. This selection excluded the expected "altered in SD and recovered in RS" pattern, which was not significantly observed. These two gene sets showed substantial overlap but were still distinct from each other. Results The analysis revealed that the selected gene sets were enriched in various brain regions as evidenced through clustering in the Allen Brain Atlas. This suggests that the successful selection identified biologically meaningful genes. Furthermore, somatostatin (Sst)-expressing neuronal clusters were among the most highly enriched. Conclusions Given that sst is already implicated in SD and RS, our fully data-driven transcriptomic analysis successfully identified the activity of sst during SD and RS. These findings reveal that Sst-expressing neurons may play a key role in SD. These results were further validated using AlphaGenome by uploading the selected genes to it.

Background/Objectives: Genetic variants in the cone-rod homeobox (CRX) gene, a transcription factor critical for the differentiation, function, and survival of photoreceptors, are a rare cause of inherited retinal diseases (IRDs). CRX-associated retinopathies can produce variable phenotypes, including Leber congenital amaurosis (LCA), maculopathy (M), cone-rod dystrophy (CRD), and rod-cone dystrophy (RCD), such as retinitis pigmentosa (RP). Based on clinical observations at our eye institute, we hypothesized that nasal retinal degeneration is a feature of CRX-associated maculopathy and M/CRD. Methods: We performed an IRB-approved, retrospective review of patients at our eye institute with CRX-associated retinopathy to assess the frequency of nasal degeneration and potential genotype-phenotype correlations. Results: A total of 15 patients with a CRX-associated retinopathy and meeting the inclusion criteria were identified (LCA 3, RCD/RP 2, M/CRD 10). Overall, nasal degeneration occurred in 8 of 15 patients (53.3%) in the cohort. Nasal retinal degeneration was seen in the M/CRD (6 of 10; 60.0%) as well as LCA groups (2 of 3; 66.6%). No significant differences in age, gender, or presenting visual acuity were observed between patients with and without nasal degeneration. Genetic variants associated with nasal degeneration are localized to both the homeobox motif and activation domain. Intronic variants were relatively more common in patients with nasal degeneration, while missense variants predominated in those without, although these differences were not statistically significant. Conclusions: We conclude that nasal degeneration is a feature of a subset of CRX-associated phenotypes, affects both genders, and can be caused by genetic variants in multiple locations and of various subtypes.

Plants secrete a heterogenous population of membrane-enclosed extracellular vesicles that harbour an incredible diversity of molecular cargo. It is the complexity of the molecular cargo encapsulated by plant extracellular vesicles (PEVs) which facilitates the fundamental role PEVs play in mediating communication and signalling. PEV molecular cargo is composed of a diverse mixture of lipids, metabolites, proteins, and nucleic acids. Among the nucleic acids, the microRNA (miRNA) class of small regulatory RNA can be viewed as one of the most biologically relevant. Plant miRNAs regulate the expression of genes essential for all aspects of development as well as to control the gene expression changes required to drive the adaptive and defensive responses of plants to environmental stress and pathogen attack. Furthermore, recent research has shown that specific miRNA cohorts are selectively packaged into PEVs as part of the molecular-level response of a plant to its growth environment. For example, PEVs are loaded with a specific miRNA population for their targeted delivery to sites of pathogen infection in the host plant, or for cross-kingdom delivery of host-plant-encoded miRNAs to the pathogen itself. Here we outline PEV physical properties, compare PEV biogenesis pathways, detail the composition of PEV molecular cargo, and go on to provide detailed commentary on the role of PEV-delivered miRNAs in plant development, environmental stress adaptation, and pathogen defence. We conclude this article with a proposal for the potential future use of PEVs and their miRNA cargo in agriculture and aquaculture.

Background/Objectives: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a multifactorial origin involving genetic, epigenetic, and environmental determinants as well as some risk factors. Genetic predisposition has been quantified through polygenic risk scores (PRS), which integrate the cumulative effect of multiple single nucleotide variants (SNVs) associated with disease risk. Despite extensive research on immune and inflammatory pathways in SLE, the interplay between genetic susceptibility and metabolic dysfunction remains poorly understood. This study aimed to explore associations between SLE-related PRS and metabolic, inflammatory, and clinical parameters in adults participating in the METAINFLAMACIÓN-CM project (Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain). Methods: Ninety-three participants were included: 56 SLE patients and 37 individuals with metabolic syndrome (MetS) as a reference group. PRS were computed based on validated lupus-associated SNVs. Results: SLE patients showed a distinct metabolic profile compared with the MetS group, characterized by lower BMI, visceral fat, blood pressure, glucose, and liver enzyme levels. Within the SLE cohort, PRS values varied markedly and correlated with specific clinical and biochemical features. Linear regression models revealed a significant inverse association between PRS in SLE and ferritin levels, whereas other metabolic and inflammatory markers (glucose, IL-6, LDL, CRP, neutrophils) were directly influenced by clinical factors. Conclusions: Polygenic predisposition contributes to variability in SLE metabolic phenotype but does not independently drive most inflammatory parameters. SLE patients displayed metabolic and inflammatory alterations relevant to cardiovascular risk, highlighting the importance of comprehensive cardiometabolic assessment. Integrating PRS with metabolic profiling may support precision personalized management and improve cardiovascular risk evaluation in SLE.