Medicine in Omics最新文献

Clostridium botulinum strain Hall produces potent botulinum neurotoxin type A1, which causes food-borne, infant, and wound botulism in humans. Antibiotics and botulinum antitoxins can control growth and prevent botulinum toxicity. However, limited information on a protein with an unknown function hinders the discovery of new drug targets for this disease. In this study, a combined bioinformatics approach with literature support was applied to predict, assign, and validate operome functions. Our functional annotation scheme was based on sequence motifs, conserved domains, structures, protein folds, and evolutionary relationships. Approximately 14.62 % of the operome exhibited sequence similarity to known proteins, with 6.65 % predicted functions for 293 proteins, including 121 proteins exclusive to C. botulinum. Structural analysis revealed a significant presence of the Rossmann fold (26 %) and miscellaneous folds (43 %) among the operome. Transporters (>85) and transcriptional regulators (>45) were prevalent, underscoring their importance in C. botulinum adaptive strategies. The newly identified operome contributed to the diverse cellular and metabolic processes of this organism. The function of its operome was involved in amino acid metabolism and botulinum neurotoxin biosynthesis. In this study, we identified and characterized 13 new virulence proteins from the operome to determine their structure–function relationships. These new metabolic and virulence proteins allow the organism to colonize and interact with the human gastrointestinal tract. This study provides a quest for new drugs and targets for treating the underlying diseases of C. botulinum in humans.

Currently, vaccines have shown efficacy against new SARS-CoV-2 variants. This study aimed to develop a systems medicine framework that can predict and validate drug combinations that can be repurposed for treating COVID-19 and its comorbidities, specifically type 2 diabetes and hypertension. This study found that gut microbes could potentially influence the action of drugs, innate immune response, intestinal dysfunction, and susceptibility to the virus in individuals with these comorbidities. It was also discovered that the spike protein of the virus interacts with 57 human genes, many of which are linked to food-borne bacteria. An analysis of disease enrichment showed that arthritis and hypertension were frequently observed as comorbidities in patients infected with SARS-CoV-2. Several drugs, including Fluvoxamine, Donepezil, and Ifenprodil, have been identified as potentially repurposable drugs for treating COVID-19 in individuals with hypertension. Moreover, nitazoxanide and tocilizumab (antivirals), bacitracin (antibacterial), and gliclazide (antidiabetics) were also identified as potential repurposable drugs. Tocilizumab and gliclazide are effective drug combinations for treating COVID-19 in individuals with type 2 diabetes. A combination of tocilizumab and lidocaine has also been suggested for treating COVID-19 in individuals infected with food-borne bacteria.

In the recent years, the development of so-called omics technologies has greatly contributed to the discovery of new biomarkers and targets, spanning different areas from diagnosis to therapy, and helping to accelerate the progress of precision and personalized medicine. In addition to classic omics, including genomics, transcriptomics, proteomics, and metabolomics, newer-generation omics technologies and related platforms, such as microbiomics and nutrigenomics, are emerging. At the same time, the use of liquid biopsies is becoming established as optimal biological samples, consisting in biological fluids (i.e. blood, saliva, and urine), that are easy to collect, and whose components (cells, nucleic acids, exosome) can be analysed using throughput techniques. In addition, it is becoming attractive, because it consents the extrapolation of big data via multi-omics technologies. Here, we report a brief description and discussion of such technologies, highlighting applications and possible limitations.

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the third-leading cause of death due to the lack of definite biomarkers for early-stage detection. HCC progression occurs by the dysregulation of several genes. Though several studies focus on biomarkers for HCC diagnosis, stage-specific marker identification remains elusive. Hence, the present study aims to identify early-stage biomarkers for the detection of HCC through integrated in silico analysis. The differential gene expression was performed using GEO2R for the datasets (GSE14520, GSE63898, GSE121248, GSE124535, GSE94660, and GSE6764) retrieved from Gene Expression Omnibus (GEO) of patients with cirrhotic liver or HCC. The common differentially expressed gene enrichment analysis was performed using Funrich for Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomics (KEGG) gene mapping. The Protein-Protein Interaction (PPI) network was performed using the Search Tool for the Retrieval of Interacting Genes (STRING). The hub genes were identified using the CytoHubba plug-in of Cytoscape software. The identified genes were verified for their prognostic value using the Kaplan-Meier plotter and Immunohistochemistry micrographs obtained from the Human Protein Atlas database. An overall of 243 common differentially expressed genes (DEGs) were identified containing 171 upregulated and 72 downregulated genes. With the help of PPI network construction, ten hub genes were identified as CDK1, AURKA, CCNB1, CCNB2, CENPF, CDC20, TOP2A, BUB1, RRM2, and HMMR, which are dysregulated owing to HCC proliferation, tumorigenesis and poor prognosis in patients. These hub genes are suitable waypoints for the diagnosis and targeted therapy against early-stage HCC.

Background

COVID-19 caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome) has created an alarming situation worldwide. The surface (S) glycoprotein of novel coronavirus, encoded by the genome of SARS-CoV-2, plays an role in attachment, fusion as well as entry into the host cell. The spike glycoprotein plays vital role in not only infection but pathogenesis and adaptive immunity, and, therefore, the S glycoprotein is considered as the main target for the development of effective and durable vaccine against SARS-CoV-2. Present study aims to compare the SARS-CoV-2 spike sequence obtained from first Wuhan virus with those of Asian SARS-CoV-2 isolates.

Result

A total of 1165 mutations from 657 sequences of Asia submitted in the month of November 2020 to February 2021 were detected. Further, secondary structure prediction followed by protein modeling analysis was performed which revealed, these mutations, considerably altered the stability of Spike protein. Additionally, Physiochemical properties, toxicity, allergenicity and stability of spike glycoprotein were estimated to demonstrate the specificity of the epitope candidates. Subsequently, we identified a total of 34B-cell and 10 T-cell immune epitopes. Among the predicted epitopes, 26 B-cell and 9T-cell epitopes showed non-allergenic, non-toxic and highly antigenic properties.

Conclusion

Taken together, our study showed spike glycoprotein of SARS-CoV-2 can be a potentially good candidate for the development of vaccine to curb SARS-CoV-2 infections.

Colorectal cancer (CRC) is the third most prevalent cancer worldwide. Vitamin D receptor (VDR) gene mutations and Vitamin D deficiency contribute to CRC development and progression. Certain long non-coding RNAs (lncRNAs) directly inhibit VDR gene transcription, leading to VDR mutation. Thus, targeting oncogenic lncRNAs and VDR expression is a promising strategy for effective cancer treatment. Here, we green-synthesized Lactobacillus plantarum loaded nickel oxide nanoparticles (LpNiONPs) to assess their anticancer potential in CRC by targeting long non-coding RNA EIF3J- divergent transcript (lncRNA EIF3J-DT) and VDR. The potent bioactive component present in L. plantarum was identified via gas chromatography-mass spectrometry (GC–MS) analysis, and its interaction with VDR, as well as the functional interaction with lncRNA EIF3J-DT, were evaluated using the PyRx program and RPISeq-software, respectively. The LpNiONPs were characterized using UV–Vis spectroscopy, Zeta Potential, dynamic light scattering (DLS), fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray (EDX) and X-ray diffraction (XRD) techniques. The anticancer potential of LpNiONPs against HT-29 cells was assessed through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, scratch assay, 4′,6-diamidino-2-phenylindole (DAPI)/ acridine orange-ethidium bromide (AO-EtBr) staining experiments, and reverse transcriptase-PCR to evaluate the expression of lncRNA EIF3J-DT/VDR and apoptotic-related genes. The potent bioactive compound Pyrrolo (1,2-a) pyrazine-1,4-dione in L. plantarum strongly interacts with VDR, highlighting its drug design potential. The formation of LpNiONPs was confirmed via UV–Vis spectroscopy with an absorption peak at 394 nm. LpNiONPs were positively charged, monodispersed, and stable square-shaped nanoparticles. LpNiONPs show dose-dependent cytotoxicity and induced apoptosis, confirmed by staining images in HT-29 cells. Moreover, LpNiONPs downregulated lncRNA EIF3J-DT, CYP24-A1 and BCL2 genes while upregulating VDR, cas-3, cas-9 and BAX in HT-29 cells. These findings suggest that LpNiONPs exhibit anticancer activity by promoting VDR-associated apoptosis by inhibiting lncRNA EIF3J-DT in CRC cells.

The official recording outbreak COVID-19 virus was in Dec 2019. When it affects humans, it affects almost all age groups, especially aged people. COVID-19 becomes a Global pandemic within a short period. The primary consequence of this infection is that it targets the individual's respiratory system and causes severe acute respiratory syndrome (SARS-CoV-2). Research efforts were made internationally to find a proper vaccine. Here, with the mechanism of action, this review provides the infection mechanism, Immunological changes, and associated organ damage.

Tuberculosis (TB) continues to be a global health problem due to its high morbidity and death rates. Standardized regimens have been used in traditional TB treatment methods, frequently leading to less-than-ideal results and the establishment of drug-resistant strains. The development of personalized medicine provides a potentially effective remedy to individual patients' by adjusting therapeutic approaches to particular genotypic and phenotypic traits. Detecting TB strains, drug resistance indicators, and host genetic variants that affect treatment results is made possible by genomic and molecular diagnostic approaches. These developments offer helpful information for predicting therapy outcomes and choosing the best treatment plan for each individual.

Integrating phenotypic data, such as clinical characteristics, immunological state, and comorbidities, improves diagnostic and treatment decision-making accuracy. The use of targeted drug therapies, such as innovative anti-TB medicines and repurposed medications, which have the potential to overcome drug resistance and boost treatment effectiveness, can be guided by personalized therapy. Personalized interventions based on genetic and phenotypic factors can improve patient outcomes by identifying those at risk of treatment failure or disease progression. This article discusses the importance of personalized therapy for TB patients. It specifically highlights the benefits of using “omics” data to enhance therapeutic results and decrease the risk of drug resistance.

The present study involves the use of combined network pharmacology and molecular modelling approach for identifying important phytoconstituents that could modulate the functions of multiple therapeutic targets in Alzheimer’s disease. A list of botanicals reported in the literature for their efficacy in Alzheimer’s disease, the phytochemicals present in the botanicals were identified with the help of network pharmacology approach. The pharmacokinetic properties like blood brain barrier penetration and Lipinski’s rule of five for the selected phytoconstituents were analyzed. The major targets involved in the pathogenesis of Alzheimer’s disease were collected from the DisGeNET database. The selected proteins were subjected to topological analysis using Cytoscape software to identify the important targets in the network. The top 7 phytoconstituents and 5 proteins were subjected to molecular docking, MM-GBSA and molecular dynamics studies. A total of 15 plants and 1443 phytoconstituents were identified through a literature survey and from several databases. The pharmacokinetics study revealed that 7 phytoconstituents - glycyrrhisoflavone, eugenol, ferulic acid, methyl jasmonate, geranyl formate, formononetin, and elemicin- exhibited favourable pharmacokinetic properties. Five targets, HMOX1, CNR1, STAT3, HDAC2, and MAOB were found to be important in the network of 3300 proteins based on degree centrality and betweenness centrality. Among the seven phytoconstituents, glycyrrhisoflavone exhibited good dock scores and free energy value. Based on this, the stability of glycyrrhisoflavone with the five selected targets were analyzed using molecular dynamics study. Glycyrrhisoflavone showed good stability with most of the selected therapeutic targets. The current study reveals that the selected phytoconstituents i.e glycyrrhisoflavone, eugenol, ferulic acid, methyl jasmonate, geranyl formate, formononetin, and elemicin could serve as good lead molecules in treatment and management of Alzheimer’s disease through modulation of multiple targets.