Human Gene最新文献

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a significant challenge worldwide. Rapid genome sequencing of SARS-CoV-2 is going on across the globe to detect mutations and genomic modifications in SARS-CoV-2. In this study, we have sequenced 23 SARS-CoV-2-positive samples collected during the first pandemic from the state of Uttar Pradesh, India. We observed a range of already reported mutations (2−22), including D614G, L452R, Q613H, Q677H, and T1027I in the S gene; S194L in the N gene; and Q57H, L106F, and T175I in the ORF3. A few unreported mutations, such as P309S in the ORF1ab gene, T379I in the N gene, and L52F and V77I in the ORF3a gene, were also detected. Phylogenetic genome analysis showed similarity with other SARS-CoV-2 viruses reported from Uttar Pradesh. The observed mutations may be associated with SARS-CoV-2 virus pathogenicity or disease severity.

Microsatellites, also called short tandem repeats (STRs) or simple sequence repeats (SSRs) are short DNA sequence segments consisting of tandemly repeated motifs. SSRs are regarded as one of the most effective tools for genetic research. In this study, we conducted an in silico analysis to investigate the presence, distribution, and characteristics of SSRs within the genes associated with spinal cord astrocytoma. The aim of this study was to identify SSRs in coding and non-coding regions, analyze their correlation with gene parameters, and determine the GC content in the gene sequences. The results revealed that the widespread presence of SSRs within the genes is associated with spinal cord astrocytoma (SCA). The distribution of SSR motifs varied among the analyzed genes, and certain motifs were common across multiple genes. Additionally, we observed a strong positive correlation between the total number of SSRs and gene size, indicating that larger genes tend to have a higher number of microsatellites. Furthermore, we identified SSRs in both coding and non-coding regions of the genes. The incidence of SSRs and cSSRs differed among genes, suggesting potential functional implications for gene expression and regulation. Our study provides valuable insights into the genetic diversity within the astrocytoma genes and highlights the potential significance of SSRs in gene regulation. In conclusion, this study contributes to a better understanding of the role of microsatellites within the genes associated with spinal cord astrocytoma. The observed patterns of SSR distribution and characteristics suggest their potential functional relevance in the development and progression of astrocytoma.

The most common oncologic cause of mortality in children is pediatric glioblastoma, an extremely dangerous brain tumor. The tumor progress is almost inevitable and recurs after first-line standard care. Because surgical resection is often more effective when tumors are localized and smaller, early identification and action may be essential to assure favourable outcomes for the recurring disease. This study aims to employ single-cell RNA-Sequencing data (scRNA-Seq data) for clustering and explainable Artificial intelligence framework to find gene biomarkers and signature cell types for the diagnosis and prognosis of reoccurring pediatric glioblastoma. Distinct cell types and statistically significant DEGs were found using scRNA-Seq data retrieved from the Gene Expression Omnibus database. Random forest (RF) and extreme gradient boosting (XGBoost) machine learning (ML) classifiers were constructed to select genes significantly contributing to the disease using Shapley (SHAP) values, an explainable artificial intelligence (EAI) framework. Potential biomarkers were chosen based on the shared genes among statistically discovered DEGs and SHAP-based relevance. B cells, macrophages, CD8+ T cells, T cells, and NK cells were identified as distinct cell types, which played an essential role in disease recurrence. Also, five significant genes, namely HMGB2, H2AFZ, HIST1H4C, KIAA0101, and DUT, were screened and in silico validated through survival analysis and feature plot, hence, proposed as biomarkers for recurring pediatric glioblastoma. Utilising these five genes may improve disease prognosis and provide a crucial understanding of the molecular causes of recurrent pediatric glioblastoma.

Background

Congenital heart defects (CHD) emerge prominently as anomalies during the intricate process of fetal development, manifesting when the heart or adjacent blood vessels deviate from their customary developmental trajectory before birth. The prevailing hypothesis is that polymorphisms within two pivotal candidate genes, specifically MTHFD1 G1958A and CBS 844ins68, may contribute to an increased susceptibility to CHD.

Aim

In light of this, the primary objective of this meta-analysis is to ascertain the statistical risk of CHD associated with the variations present in the MTHFD1 and CBS genes.

Method

We employed a set of keywords to systematically review the literature following PRISMA guidelines from E-databases. Our investigation into the impact of selected gene variants on CHD utilized a 95% confidence interval and odds ratio. To assess the heterogeneity of the studies, the meta-analysis employed the chi-squared Cochran's Q Test alongside the I-square test. Evaluation of reporting bias and other publishing biases involved Begg's and Egger's tests. All analyses were conducted using the “Meta-Genyo online Statistical Analysis System software.

Result

Regarding the risk assessment of G1958A and its association with CHD, no statistically significant connection was found across various genetic models, such as the allele model (1.09, CI [0.84–1.40], p-value = 0.50). However, upon subgrouping based on ethnicity, a notable association emerged within the Caucasian population (allele- OR: 2.11, CI [1.007–4.41], p-value = 0.04). Conversely, the analysis of CBS-844ins68 indicated a probable risk attribution and a statistically significant correlation in allelic terms (OR: 1.88, CI [1.88–3.36), p-value = 0.0005).

Conclusion

In summary, our current meta-analysis identifies CBS 844ins68 as a noteworthy marker for CHD, while MTHFD1 exhibits a risk attribution that is dependent on the population under consideration.

CRISPR, or clustered regularly interspaced short palindromic repeats, is a groundbreaking gene-editing technology derived from the bacterial immune system. CRISPR has quickly become a cornerstone in biological research and biotechnology due to its simplicity, efficiency, and versatility. It holds enormous potential for applications ranging from correcting genetic defects and engineering disease-resistant crops to advancing treatments for genetic disorders and cancer. However, the widespread adoption of CRISPR also raises ethical concerns and challenges related to off-target effects, unintended consequences, and equitable access to genetic modification technologies. Artificial Intelligence (AI) has emerged as a transformative force in the field of gene editing, contributing to a revolution in the way genetic modifications are approached and executed. The integration of AI into gene editing processes, such as CRISPR technology, brings forth a multitude of advantages that significantly enhance the precision and efficiency of genetic manipulation. This review article explores how these two powerful technologies are working together. It considers the beneficial relationship between these cutting-edge technologies, demonstrating how AI enhances the accuracy, effectiveness, and adaptability of CRISPR while opening up new possibilities for genome editing, disease detection, drug discovery, and other fields. The article also looks at the problems, ethical concerns, and what might come next as these two technologies team up, giving us a clear picture of how biotechnology is changing in the CRISPR-AI era.

Background

Preeclampsia, a main contributor to maternal mortality worldwide, impacts women beyond 20 weeks of gestation, presenting with elevated blood pressure and proteinuria. The role of oxidative stress in the development of preeclampsia is considerable due to the increased oxygen requirement of placenta. The aim of this study is to evaluate the association between MnSOD-Ala16Val genetic variation and the risk of preeclampsia in a group of pregnant women in accompanying a bioinformatics analysis.

Materials and methods

In this case-control study, 136 pregnant women with preeclampsia vs. 173 healthy women without any history of known diseases as control group, were selected from cases who have referred to the Shahid Beheshti hospital of Kashan (Isfahan province, Iran). After sampling and DNA extraction, MnSOD-Ala16Val polymorphism was evaluated using PCR-RFLP for genotype assessment. Eventually, some bioinformatics tools were used for evaluation of the above-mentioned polymorphism on gene function.

Results

Our data illustrate the association between CT heterozygote genotype of MnSOD-Ala16Val polymorphism with enhanced risk of susceptibility to preeclampsia (OR = 1.865, 95%CI = 1.03–3.37, p = 0.039). Indeed, bioinformatics analysis demonstrated the significant impact of the above mentioned polymorphism on the structure of protein (Score: 49, Expected accuracy: 71%) and RNA (Distance: 0.1815, p-value: 0.1676; A p-value <0.2 is significant).

Conclusions

As a preliminary study, our findings suggest a potential association between the MnSOD-Ala16Val polymorphism and the risk of preeclampsia. However, more investigations with a larger sample size and in different ethnicities are essential for achievement of more precise outcomes.

Background

Dystrophin is a structural protein primarily found in skeletal muscles that connects trans-membrane component of dystrophin-glycoprotein complex to the intracellular cytoskeleton network. N-terminal domain of dystrophin binds to F-actin and its C-terminal domain attaches to dystrophin-associated complex (DAG) in the membrane. It serves as the bridge connecting the extracellular matrix to the actin based cytoskeleton of muscle cells across the plasma membrane. This complex works together to strengthen muscle fibers and shield them from damage as they contract and relax.

Objective

Our Objective is to investigate the different pathogenic mutations in four distinct domains of dystrophin protein and to decipher how these mutations impact protein structure and function. In this study, we investigated the impact of disease causing missense mutations in isoforms of dystrophin – P.11532–1 (K18N.A165V, D3187G, F3228L); P11532–4 (Y223N, D3179G) and P-11532-11 (Y227N,D3183G).Occurrence of pathogenic mutations in dystrophin might compromise structural stability, interfere with protein-protein interactions or alter cellular signaling pathways collectively. All these can contribute to the progressive muscle degeneration observed in Duchene or Becker muscular dystrophy(DMD or BMD) and X-lined dilated cardiomyopathy(CMD3B) affected individuals.

Methods

Evolutionary conservation analysis using multiple sequence alignment followed by pathogenicity prediction using web based server was performed. Homology modeling of mutants was performed bySWISS MODEL a fully automated homology-modeling server, accessible through the Expasy web server. These models were then analyzed using various servers such as Dynamut. Pymol was used for visualization and structural analysis.

Results

Analyzed mutations cause structural instability by either gaining or losing specific interactions involved in the folding of protein. Loss of pi-pi stacking interactions has the potential to significant impact the overall stability of the protein. It is important to note that the majority of the mutations lead to stability defects rather than functional defects, ultimately contributing to different forms of muscular dystrophy.

Conclusión

The mutations in different domains of Dystrophin protein significantly alters the protein structure. This may in turn impact its ability to interact with other proteins. Understanding the specific impacts of these mutations can pave the way for development of targeted therapies that aims at restoring functionality of dystrophin and ameliorating the debilitating effects of muscular dystrophy.

Aim

Breast cancer ranks as the most prevalent cancer in women, with an estimated 508,000 deaths globally. Pim1, a proto-oncogene, has been implicated in the initiation and progression of malignant phenotypes. While the mutational status of Pim1 has been extensively studied in various cancers, its significance in breast cancer remains uncertain. This study aimed to identify novel mutations and potential hotspots within the coding region of Pim1 in breast cancer, and to assess their impact on protein expression and patients' progression-free and overall survival.

Methods

Ninety-six Indian subjects diagnosed with breast cancer and undergoing surgery at our hospital were included in the study. Genomic DNA was amplified and sequenced to detect mutations in the coding region of Pim1. Protein expression of Pim1 was assessed using FFPE sections.

Results

Mutations were found across exons 2 to 5, with exon 4 showing the highest mutation frequency. About 32% of the study population carried mutations, with many exhibiting double mutations. Aberrant Pim1 expression, characterized by nuclear membrane and cytoplasmic staining, was observed in 49% of the study cohort. Survival analysis revealed that patients with mutant Pim1 and negative protein expression had significantly improved survival outcomes compared to those with the wild-type gene and positive protein expression.

Conclusion

Our findings suggest that Pim1 mutation and negative Pim1 expression serve as independent prognostic factors for enhanced survival outcomes in breast cancer patients.

Background

Several studies suggested that mutations in the TMPRSS6 gene affect hepcidin production, which may lead to iron deficiency anemia (IDA). Hence, this meta-analysis explored the association of the V736A or the rs855791 polymorphism in the TMPRSS6 gene with IDA development.

Methods

Related studies were searched as of December 24, 2023. Two authors screened the resulting studies, and data were extracted and collated in a customized spreadsheet. Review Manager 5.4 and Meta-Essentials were used to compute the odds ratios (ORs) and 95% confidence intervals (CIs).

Results

A total of 187 studies were screened. From this, only six studies satisfied the inclusion criteria and were included in the meta-analysis. Analysis of the allelic model showed a high degree of inter-study heterogeneity, which prompted us to determine the cause through sub-group analysis. Sub-groups were conceptualized based on the participant characteristics. Based on this, homogenous findings were observed for the allelic, co-dominant, and dominant models of the association between the polymorphism and the development of IDA among females of reproductive age still experiencing menstruation. Non-significant and highly heterogeneous outcomes were noted for the other sub-groups.

Conclusion

Overall, based on the pooled findings, the V736A polymorphism is associated with IDA development in females of reproductive age still experiencing menstruation. However, further studies are needed to verify these claims.