Multi-Omics Approaches in Immunological Research.
Ontology highlight
ABSTRACT:
SUBMITTER: Chu X
PROVIDER: S-EPMC8226116 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.
| S-EPMC8425417 | biostudies-literature
Project description:ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.
| S-EPMC7145565 | biostudies-literature
Project description:Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.
| S-EPMC4735675 | biostudies-other
Project description:The striped venus (Chamelea gallina) is an important economic resource in the Mediterranean Basin; this species has exhibited a strong quantitative decline in the Adriatic Sea. The aim of this work was to provide a comprehensive view of the biological status of C. gallina to elucidate the bioecological characteristics and genetic diversity of wild populations. To the best of our knowledge, this investigation is the first to perform a multidisciplinary study on C. gallina based on two omics approaches integrated with histological, ecotoxicological, and chemical analyses and with the assessment of environmental parameters. The results obtained through RNA sequencing indicated that the striped venus has a notable ability to adapt to different environmental conditions. Moreover, the stock reduction exhibited by this species in the last 2 decades seems not to have negatively affected its genetic diversity. Indeed, the high level of genetic diversity that emerged from our ddRAD dataset analyses is ascribable to the high larval dispersal rate, which might have played a "compensatory role" on local fluctuations, conferring to this species a good adaptive potential to face the environmental perturbations. These findings may facilitate the efforts of conservation biologists to adopt ad hoc management plans for this fishery resource.
| S-EPMC7645701 | biostudies-literature
Project description:BackgroundThe genus Allium (Family: Amaryllidaceae) is an economically important group of crops cultivated worldwide for their use as a vegetable and spices. Alliums are also well known for their nutraceutical properties. Among alliums, onion, garlic, leek, and chives cultivated worldwide. Despite their substantial economic and medicinal importance, the genome sequence of any of the Allium is not available, probably due to their large genome sizes. Recently evolved omics technologies are highly efficient and robust in elucidating molecular mechanisms of several complex life processes in plants. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, metagenomics, etc. have the potential to open new avenues in research and improvement of allium crops where genome sequence information is limited. A significant amount of data has been generated using these technologies for various Allium species; it will help in understanding the key traits in Allium crops such as flowering, bulb development, flavonoid biosynthesis, male sterility and stress tolerance at molecular and metabolite level. This information will ultimately assist us in speeding up the breeding in Allium crops.MethodIn the present review, major omics approaches, and their progress, as well as potential applications in Allium crops, could be discussed in detail.ResultsHere, we have discussed the recent progress made in Allium research using omics technologies such as genomics, transcriptomics, micro RNAs, proteomics, metabolomics, and metagenomics. These omics interventions have been used in alliums for marker discovery, the study of the biotic and abiotic stress response, male sterility, organ development, flavonoid and bulb color, micro RNA discovery, and microbiome associated with Allium crops. Further, we also emphasized the integrated use of these omics platforms for a better understanding of the complex molecular mechanisms to speed up the breeding programs for better cultivars.ConclusionAll the information and literature provided in the present review throws light on the progress and potential of omics platforms in the research of Allium crops. We also mentioned a few research areas in Allium crops that need to be explored using omics technologies to get more insight. Overall, alliums are an under-studied group of plants, and thus, there is tremendous scope and need for research in Allium species.
| S-EPMC7486827 | biostudies-literature
Project description:Amyotrophic lateral sclerosis (ALS) is a devastating and fatal neurodegenerative disorder, caused by the degeneration of upper and lower motor neurons for which there is no truly effective cure. The lack of successful treatments can be well explained by the complex and heterogeneous nature of ALS, with patients displaying widely distinct clinical features and progression patterns, and distinct molecular mechanisms underlying the phenotypic heterogeneity. Thus, stratifying ALS patients into consistent and clinically relevant subgroups can be of great value for the development of new precision diagnostics and targeted therapeutics for ALS patients. In the last years, the use and integration of high-throughput "omics" approaches have dramatically changed our thinking about ALS, improving our understanding of the complex molecular architecture of ALS, distinguishing distinct patient subtypes and providing a rational foundation for the discovery of biomarkers and new individualized treatments. In this review, we discuss the most significant contributions of omics technologies in unraveling the biological heterogeneity of ALS, highlighting how these approaches are revealing diagnostic, prognostic and therapeutic targets for future personalized interventions.
| S-EPMC7661549 | biostudies-literature
Project description:Campylobacter jejuni clone SA recently emerged as the predominant cause of sheep abortion in the United States and is also associated with foodborne gastroenteritis in humans. A distinct phenotype of this clone is its ability to induce bacteremia and abortion. To facilitate understanding the pathogenesis of this hypervirulent clone, we analyzed a clinical isolate (IA3902) of clone SA using multi-omics approaches. The genome of IA3902 contains a circular chromosome of 1,635,045 bp and a circular plasmid of 37,174 bp. Comparative genomic analysis revealed that IA3902 is most closely related to C. jejuni NCTC11168, which is a reference strain and was previously shown to be non-abortifacient in pregnant animals. Despite the high genomic synteny and sequence homology, there are 12 variable regions (VRs) and 8,696 single-nucleotide polymorphisms and indels between the two genomes. Notably, the variable genes in the capsular polysaccharides biosynthesis and O-linked glycosylation loci of IA3902 are highly homogenous to their counterparts in C. jejuni subsp. doylei and C. jejuni G1, which are known to be frequently associated with bacteremia. Transcriptomic and proteomic profiles were conducted to compare IA3902 with NCTC11168, which revealed that the pathways of energy generation, motility, and serine utilization were significantly up-regulated in IA3902, whereas the pathways of iron uptake and proline, glutamate, aspartate, and lactate utilization were significantly down-regulated. These results suggest that C. jejuni clone SA has evolved distinct genomic content and gene expression patterns that modulate surface polysacharide structures, motilitiy, and metabolic pathways. These changes may have contributed to its hyper-virulence in abortion induction.
| S-EPMC3845652 | biostudies-literature
Project description:Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.
| S-EPMC8074237 | biostudies-literature
Project description:As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
| S-EPMC10994553 | biostudies-literature
Project description:An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics' sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the 'Cancer' and 'Gene' sections. The 'Survival' panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, 'Survival Analysis' in 'Customized-analysis,' allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics' sophisticated information, and also constructed a Summary panel in the 'Cancer' and 'Gene' sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
| S-EPMC7145679 | biostudies-literature