Functional & Integrative Genomics最新文献

At the dawn of new millennium, policy makers and researchers focused on sustainable agricultural growth, aiming for food security and enhanced food quality. Several emerging scientific innovations hold the promise to meet the future challenges. Nanotechnology presents a promising avenue to tackle the diverse challenges in agriculture. By leveraging nanomaterials, including nano fertilizers, pesticides, and sensors, it provides targeted delivery methods, enhancing efficacy in both crop production and protection. This integration of nanotechnology with agriculture introduces innovations like disease diagnostics, improved nutrient uptake in plants, and advanced delivery systems for agrochemicals. These precision-based approaches not only optimize resource utilization but also reduce environmental impact, aligning well with sustainability objectives. Concurrently, genetic innovations, including genome editing and advanced breeding techniques, enable the development of crops with improved yield, resilience, and nutritional content. The emergence of precision gene-editing technologies, exemplified by CRISPR/Cas9, can transform the realm of genetic modification and enabled precise manipulation of plant genomes while avoiding the incorporation of external DNAs. Integration of nanotechnology and genetic innovations in agriculture presents a transformative approach. Leveraging nanoparticles for targeted genetic modifications, nanosensors for early plant health monitoring, and precision nanomaterials for controlled delivery of inputs offers a sustainable pathway towards enhanced crop productivity, resource efficiency, and food safety throughout the agricultural lifecycle. This comprehensive review outlines the pivotal role of nanotechnology in precision agriculture, emphasizing soil health improvement, stress resilience against biotic and abiotic factors, environmental sustainability, and genetic engineering.

Previous studies have demonstrated the clinical relevance of aberrant serum immunoglobulin G (IgG) N-glycomic profiles in liver fibrosis and the pathogenic effects of agalactosyl IgG on activating hepatic stellate cells (HSCs). However, the dynamics of gene expression changes during HSC activation by agalactosyl IgG remain poorly understood. We performed RNA sequencing to analyze the mRNAome of human LX-2 HSCs at multiple time points after treatment with agalactosyl IgG and then compared these results with those obtained after normal IgG and transforming growth factor (TGF)-β1 treatments. Gene expression changes were significantly pronounced on day 5 and subsided by day 11 after HSC activation. A high degree of similarity in gene expression patterns between HSCs treated with agalactosyl IgG and TGF-β1 was observed, of which 1796 and 1785 differentially expressed genes (DEGs) were identified, respectively. Disease ontology analyses revealed that 114 and 105 DEGs in activated HSCs following agalactosyl IgG and TGF-β1 treatments, respectively, were linked to liver cirrhosis, hepatitis, fatty liver disease, hepatitis B, and alcoholic hepatitis, with CCL5 and FAS being the most commonly affected genes. DEGs associated with liver fibrosis or aforementioned liver diseases involved in gene annotation, physiological functions, and signaling pathways regarding secretion of cytokines and chemokines, expression of fibrosis-related growth factors and their receptors, modification of extracellular matrices, and regulation of cell viability in activated HSCs. In conclusion, this study characterized the dynamics of mRNAome and gene networks and identified the liver fibrosis-related DEGs during HSC activation by agalactosyl IgG and TGF-β1.

Microsatellites, or simple sequence repeats (SSRs), are repetitive DNA sequences typically composed of 1–6 nucleotides. These repetitive sequences are found in almost all genomes, including chloroplasts and mitochondria, and are widely distributed throughout the genomes. Microsatellites are highly polymorphic, and their length may differ from species to species. Consequently, microsatellites are widely used as molecular markers and play pivotal roles in various biological research. However, comprehensive information about the length variation of microsatellites in various organellar genome sequences is not available. Therefore, to provide mined information and explore the variability in the length of microsatellites across species, we developed a comprehensive resource named pSATdb 2.0 (polymorphic microSATellites database; https://bioinfo.icgeb.res.in/psatdb/). This upgraded version of its predecessor pSATdb provides comprehensive information on the frequency and distribution of 348,894 microsatellites identified in organellar genome sequences. These sequences originate from 15,681 organisms spanning 3252 genera within Metazoa and Viridiplantae. Remarkably, pSATdb 2.0 is the only database that offers information on common and polymorphic microsatellites detected between organisms, along with unique microsatellites specific to each genus. Furthermore, this database features unrestricted access and includes pioneer functionalities such as Advanced Search, BLAST, and JBrowse, which facilitate user-specific microsatellite search and its visualization within the database. The pSATdb holds immense potential for the research community to support diverse studies, including genetic diversity, genetic mapping, marker-assisted selection, and comparative population investigations.

Gastric cancer (GC) is the third leading cause of death in developed countries. The reprogramming of energy metabolism represents a hallmark of cancer, particularly amplified dependence on aerobic glycolysis. Here, we aimed to illustrate the functional role of glutamate ionotropic receptor N-methyl-D-aspartate type subunit 2D (GRIN2D) in the regulation of glycolysis in GC and the mechanisms involved. Differentially expressed genes were analyzed using the GEO and GEPIA databases, followed by prognostic value prediction using the Kaplan-Meier Plotter database. The effect of GRIN2D knockdown on the malignant behavior and glycolysis of GC cells was explored. GRIN2D expression was upregulated in GC cells and promoted the malignant behavior of GC cells by activating glycolysis. Class E basic helix-loop-helix protein 40 (BHLHE40) was overexpressed in GC cells and mediated transcriptional activation of GRIN2D. The anti-tumor effects of BHLHE40 knockdown on GC cells in vitro and in vivo were reversed by GRIN2D overexpression. Knockdown of GRIN2D or BHLHE40 downregulated the expression of mRNA of electron transport chain subunits and phosphorylation of p38 MARK and inhibited calcium efflux in GC cells. Overexpression of GRIN2D promoted calcium efflux, phosphorylation of p38 MARK protein, and proliferation of GES1 cells. Altogether, the findings derived from this study suggest that BHLHE40 knockdown suppresses the growth, mobility, and glycolysis of GC cells by inhibiting GRIN2D transcription and disrupting the BHLHE40/GRIN2D axis may be an attractive therapeutic strategy for GC.

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient’s health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

This study investigates the performance of reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for the colorimetric detection of SARS-CoV-2 using fluorometric dye, namely, calcein. The detection limit (LoD) with the N-ID1 primer set resulted in superior performance, corresponding to ~ 2 copies/reaction or ~ 0.1 copies/μL of the RNA sample. The color development can be observed by the naked eye, using an ultraviolet (UV) transilluminator or a hand-UV light without the requirement of expensive devices. The average time-to-reaction (TTR) value was 26.2 min in high-copy number samples, while it was about 50 min in rRT-PCR. A mobile application was proposed to quantify the positive and negative results based on the three-color spaces (RGB, Lab, and HSB). Compared to rRT-PCR (n = 67), this assay allows fast and sensitive visual detection of SARS-CoV-2, with high sensitivity (90.9%), selectivity (100%), and accuracy (94.03%). Besides, the assay was sensitive regardless of variants. Since this assay uses a fluorescent dye for visual observation, it can be easily adapted in RT-LAMP assays with high sensitivity. Thus, it can be utilized in low-source centers and field testing such as conferences, sports meetings, refugee camps, companies, and schools.

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/.

Cytochrome P450s are a large family of protein-encoding genes in plant genomes, many of which have not yet been comprehensively characterized. Here, a novel P450 gene, CYP82D47, was isolated and functionally characterized from cucumber (Cucumis sativus L.). Quantitative real-time reverse-transcription polymerase chain reaction analysis revealed that CYP82D47 expression was triggered by salicylic acid (SA) and ethephon (ETH). Expression analysis revealed a correlation between CYP82D47 transcript levels and plant defense responses against powdery mildew (PM) and Fusarium oxysporum f. sp. cucumerinum (Foc). Although no significant differences were observed in disease resistance between CYP82D47-RNAi and wild-type cucumber, overexpression (OE) of CYP82D47 enhanced PM and Foc resistance in cucumber. Furthermore, the expression levels of SA-related genes (PR1, PR2, PR4, and PR5) increased in CYP82D47-overexpressing plants 7 days post fungal inoculation. The levels of ETH-related genes (EIN3 and EBF2) were similarly upregulated. The observed enhanced resistance was associated with the upregulation of SA/ETH-signaling-dependent defense genes. These findings indicate the crucial role of CYP82D47 in pathogen defense in cucumber. CYP82D47-overexpressing cucumber plants exhibited heightened susceptibility to both diseases. The study results offer important insights that could aid in the development of disease-resistant cucumber cultivars and elucidate the molecular mechanisms associated with the functions of CYP82D47.