Multiple Omics Analyses Research Articles

Multiple omics analysis reveals that high fiber diets promote gluconeogenesis and inhibit glycolysis in muscle

BackgroundMeat quality is a complex trait affected by genotypic and environmental factors. In a previous study, it was found that feedstuffs have various effects on the growth rate and meat quality of lambs. However, the underlying mechanisms are still not entirely clear.ResultsIn this study, to investigate the mechanisms that impact meat quality in twin sheep fed either with high fiber low protein (HFLP) forage (Ceratoides) or low fiber high protein (LFHP) forage (alfalfa) diets, multi omics techniques were utilized for integration analysis based on the feed nutritional value and the sheep microbiome, transcriptome, metabolome, and fatty acid profile. Results showed that the production performance and the muscle components of lambs were significantly affected by feeds. The essential fatty acid (linoleic acid and arachidonic acid) content of the muscle, based on gas chromatography-mass spectrometry analysis, was increased when lambs were fed with HFLP. The microbes in the lambs’ rumen fed a HFLP diet were more diverse than those of the LFHP fed group. Besides, the ratio of Bacteroidetes and Firmicutes in the rumen of the sheep fed a LFHP diet was 2.6 times higher than that of the HFLP fed group. Transcriptome analysis of the muscle revealed that the genes related to glucose metabolic processes and fatty acid biosynthesis were significantly differentially expressed between the two groups. Potential cross talk was found between the sfour omics data layers, which helps to understand the mechanism by which feedstuffs affect meat quality of lambs.ConclusionFeed systems may affect the epigenetic regulation of genes involved in the glucose metabolic pathway. HFLP feeds could induce gluconeogenesis to maintain glucose levels in blood, resulting in decreased fat content in muscle. The multiple omics analysis showed that the microbiota structure is significantly correlated with the metabolome and gene expression in muscle. This study laid a theoretical foundation for controlling the nutrient intake of sheep; it suggested that its fatty acid spectrum modifications and the removal of meat quality detrimental material could guide sheep feeding for functional mutton.

Multiple omics analysis of the protective effects of SFN on estrogen-dependent breast cancer cells.

In recent years, sulforaphane (SFN) has been shown to have antitumor effects. To better understand the molecular basis of SFN intervention in estrogen-dependent breast cancer, integrated multi-omics data analysis was used to provide evidence and insights into molecular biology. MCF-7 breast cancer cells were treated with estradiol (E2) or/and SFN. Genome-wide DNA methylation analysis was performed by using microarray platforms. The protein profile was analyzed by TMT labeled HPLC-MS/MS. The metabolic profile was obtained by GC-MS and UPLC-MS methods. Multivariate statistics analyses, such as PCA and hierarchical clustering, were performed. The Gene Ontology (GO) and KEGG analysis were used to perform enrichment analysis of biological processes and pathways. A set of differentially methylated genes and differentially expressed proteins and metabolites were found, which indicated that SFN may reverse the adverse effects induced by E2. Integrated analysis revealed cancer genes that responded to estrogen and other pathways frequently associated with cancer. Co-pathway analysis revealed that the reversal effects of SFN were associated with purine metabolism and glutathione metabolism. The integrated omics analysis outlined a promising blueprint of the relationship of biological molecules in different dimensions, which will be beneficial for understanding the mechanism of anti-breast cancer effects and for new targets of medicines.

Genetic basis of otosclerosis

Introduction. Otosclerosis is a disorder of the bone labyrinth and stapes resulting in conductive hearing loss. The genetic basis of otosclerosis still remains unknown. We aimed at reporting a comprehensive review of up-to-date knowledge on genetic basis of otosclerosis. Methods. Narrative literature review was undertaken to summarize the data about genetics of otosclerosis. Results. Genetics of otosclerosis has not been studied extensively and the literature on this topic is scarce. However, knowledge of genetic basis of otosclerosis is recently increasing. We have presented an overview of the knowledge of association of genetic markers with otosclerosis, gained from linkage analyses, candidate-gene studies, and modern high-throughput genomic studies. Conclusion. Due to its complex pathophysiology, otosclerosis is not a disease whose genetic base will be easily understood. Multiple omics analysis and bioinformatics will lead to elucidation of genetic basis of otosclerosis.

Orb-weaving spider Araneus ventricosus genome elucidates the spidroin gene catalogue

Members of the family Araneidae are common orb-weaving spiders, and they produce several types of silks throughout their behaviors and lives, from reproduction to foraging. Egg sac, prey capture thread, or dragline silk possesses characteristic mechanical properties, and its variability makes it a highly attractive material for ecological, evolutional, and industrial fields. However, the complete set of constituents of silks produced by a single species is still unclear, and novel spidroin genes as well as other proteins are still being found. Here, we present the first genome in genus Araneus together with the full set of spidroin genes with unamplified long reads and confirmed with transcriptome of the silk glands and proteome analysis of the dragline silk. The catalogue includes the first full length sequence of a paralog of major ampullate spidroin MaSp3, and several spider silk-constituting elements designated SpiCE. Family-wide phylogenomic analysis of Araneidae suggests the relatively recent acquisition of these genes, and multiple-omics analyses demonstrate that these proteins are critical components in the abdominal spidroin gland and dragline silk, contributing to the outstanding mechanical properties of silk in this group of species.

Intermittent Fasting Alleviates Diabetes-induced Cognitive Decline via Gut Microbiota-metabolites-brain Axis (OR32-04-19)

ObjectivesCognitive decline is one of severe type 2 diabetes complications. Intermittent fasting (IF) is a promising dietary intervention for T2D risk reduction, but its protective effect and mechanism on diabetic cognitive dysfunction remain elusive. Gut microbiota plays a vital role interphasing diet and host physiology and pathology and highly affected by the dietary composition and patterns. It has been reported that the microbiota homeostasis is essential for maintenance of gut health and for modulating cognitive function. We hypothesized that gut microbiota might play a pivotal role in mediating protective effects of IF on diabetes-induced cognitive decline. MethodsAfter a 28-day IF regimen treatment, cognitive behavioral tests and brain insulin signaling were assessed on db/db mice. The microbiota-metabolites-brain axis alterations were detected by multiple-omics analysis (transciptomics, 16S rRNA sequencing and metabolomics). A intergrade multi-omics analysis was performed to analyze the correlation among gut microbiota, plasma metabolites, and hippocampal gene expression. ResultsHere we found that a 28-day Intermittent fasting (IF) regimen improved cognitive deficits in db/db mice via a microbiota-metabolites-brain axis assessed by behavioral tests and multiple-omics analysis: IF activated AMPK/PGC1α signaling, enhanced mitochondrial biogenesis in hippocampus and elevated genes enriched in hippocampal metabolic function. Moreover, IF re-structured gut microbiota and improved plasma microbial metabolites in relation to diabetes and cognitive function, e.g., serotonin, 3-Indolepropionic acid, and bile acids. Integration of multi-omics data demonstrated strong links between IF-related genes, gut microbiome and metabolites. Furthermore, removal of gut microbiota with antibiotics partly abolished the observed benefits of IF on cognition and hippocampal metabolic function. ConclusionsTaken together, the present study suggests a critical role of gut microbiota in connecting peripheral metabolism with brain function, which could lead to novel interventions against metabolism-implicated neurodegenerative pathophysiologies. Funding SourcesThis work was financially supported by the National Key Research and Development Program of China, National Natural Science Foundation of China.

Drug repositioning for dengue haemorrhagic fever by integrating multiple omics analyses

To detect drug candidates for dengue haemorrhagic fever (DHF), we employed a computational drug repositioning method to perform an integrated multiple omics analysis based on transcriptomic, proteomic, and interactomic data. We identified 3,892 significant genes, 389 proteins, and 221 human proteins by transcriptomic analysis, proteomic analysis, and human–dengue virus protein–protein interactions, respectively. The drug candidates were selected using gene expression profiles for inverse drug–disease relationships compared with DHF patients and healthy controls as well as interactomic relationships between the signature proteins and chemical compounds. Integrating the results of the multiple omics analysis, we identified eight candidates for drug repositioning to treat DHF that targeted five proteins (ACTG1, CALR, ERC1, HSPA5, SYNE2) involved in human–dengue virus protein–protein interactions, and the signature proteins in the proteomic analysis mapped to significant pathways. Interestingly, five of these drug candidates, valparoic acid, sirolimus, resveratrol, vorinostat, and Y-27632, have been reported previously as effective treatments for flavivirus-induced diseases. The computational approach using multiple omics data for drug repositioning described in this study can be used effectively to identify novel drug candidates.

Dissecting the Dynamics of N-Linked Glycosylation in Fed-Batch Cultures of Chinese Hamster Ovary Cells Through Time Course Omics Analyses

N-linked glycosylation is a post-translational modification that affects potency, safety, immunogenicity and pharmacokinetic clearance of several therapeutic proteins including monoclonal antibodies (mAbs). A robust control strategy is needed to accurately dial in appropriate glycosylation profile during the course of a cell culture process. However, N-glycosylation dynamics remain insufficiently understood due to the lack of integrative and quantitative analyses of factors that influence the dynamics, including nucleotide sugar donors (NSDs), glycosyltransferases and glycosidases. Here, an integrative approach is taken employing multiple omics analyses to dissect the temporal dynamics of glycoforms produced during fed-batch cultures of CHO cells. Several pathways linked with NSD metabolism, such as glycolysis, TCA cycle and purine and pyrimidine biosynthesis exhibit significant temporal dynamics over cell culture period. The steps involving galactose and sialic acid addition were determined as temporal bottlenecks in the N-glycosylation process. Our results show that galactose and not manganese is able to mitigate the temporal bottleneck, despite both being known effectors of galactosylation. Another bottleneck step, sialylation, is primarily governed by the sialyltransferases, but is also affected significantly by galactosylated precursors and the auto-regulation of CMP-sialic acid biosynthesis.

Introduction of the DiaGene study: clinical characteristics, pathophysiology and determinants of vascular complications of type 2 diabetes

BackgroundType 2 diabetes is a major healthcare problem. Glucose-, lipid-, and blood pressure-lowering strategies decrease the risk of micro- and macrovascular complications. However, a substantial residual risk remains. To unravel the etiology of type 2 diabetes and its complications, large-scale, well-phenotyped studies with prospective follow-up are needed. This is the goal of the DiaGene study. In this manuscript, we describe the design and baseline characteristics of the study.MethodsThe DiaGene study is a multi-centre, prospective, extensively phenotyped type 2 diabetes cohort study with concurrent inclusion of diabetes-free individuals at baseline as controls in the city of Eindhoven, The Netherlands. We collected anthropometry, laboratory measurements, DNA material, and detailed information on medication usage, family history, lifestyle and past medical history. Furthermore, we assessed the prevalence and incidence of retinopathy, nephropathy, neuropathy, and diabetic feet in cases. Using logistic regression models, we analyzed the association of 11 well known genetic risk variants with type 2 diabetes in our study.ResultsIn total, 1886 patients with type 2 diabetes and 854 controls were included. Cases had worse anthropometric and metabolic profiles than controls. Patients in outpatient clinics had higher prevalence of macrovascular (41.9% vs. 34.8%; P = 0.002) and microvascular disease (63.8% vs. 20.7%) compared to patients from primary care. With the exception of the genetic variant in KCNJ11, all type 2 diabetes susceptibility variants had higher allele frequencies in subjects with type 2 diabetes than in controls.ConclusionsIn our study population, considerable rates of macrovascular and microvascular complications are present despite treatment. These prevalence rates are comparable to other type 2 diabetes populations. While planning genomics, we describe that 11 well-known type 2 diabetes genetic risk variants (in TCF7L2, PPARG-P12A, KCNJ11, FTO, IGF2BP2, DUSP9, CENTD2, THADA, HHEX, CDKAL1, KCNQ1) showed similar associations compared to literature. This study is well-suited for multiple omics analyses to further elucidate disease pathophysiology. Our overall goal is to increase the understanding of the underlying mechanisms of type 2 diabetes and its complications for developing new prediction, prevention, and treatment strategies.

High Expression of CPT1A Predicts Adverse Outcomes: A Potential Therapeutic Target for Acute Myeloid Leukemia

Carnitine palmitoyl transferase 1A (CPT1A) protein catalyzes the rate-limiting step of Fatty-acid oxidation (FAO) pathway, which can promote cell proliferation and suppress apoptosis. Targeting CPT1A has shown remarkable anti-leukemia activity. But, its prognostic value remains unclear in Acute Myeloid Leukemia (AML). In two independent cohorts of cytogenetically normal AML (CN-AML) patients, compared to low expression of CPT1A (CPT1Alow), high expression of CPT1A (CPT1Ahigh) was significantly associated with adverse outcomes, which was also shown in European Leukemia Network (ELN) Intermediate-I category. Multivariable analyses adjusting for known factors confirmed CPT1Ahigh as a high risk factor. Significant associations between CPT1Ahigh and adverse outcomes were further validated whether for all AML patients (OS: P=0.008; EFS: P=0.002, n=334, no M3) or for National Comprehensive Cancer Network (NCCN) Intermediate-Risk subgroup (OS: P=0.021, EFS: P=0.024, n=173). Multiple omics analysis revealed aberrant alterations of genomics and epigenetics were significantly associated with CPT1A expression, including up- and down-regulation of oncogenes and tumor suppressor, activation and inhibition of leukemic (AML, CML) and immune activation pathways, hypermethylation enrichments on CpG island and gene promoter regions. Combined with the previously reported anti-leukemia activity of CPT1A's inhibitor, our results proved CPT1A as a potential prognosticator and therapeutic target for AML.

Omics and Integrated Omics for the Promotion of Food and Nutrition Science

Transcriptomics, proteomics, and metabolomics are three major platforms of comprehensive omics analysis in the science of food and complementary medicine. Other omics disciplines, including those of epigenetics and microRNA, are matters of increasing concern. The increased use of the omics approach in food science owes much to the recent advancement of technology and bioinformatic methodologies. Moreover, many researchers now put the combination of multiple omics analysis (integrated omics) into practice to exhaustively understand the functionality of food components. However, data analysis of integrated omics requires huge amount of work and high skill of data handling. A database of nutritional omics data was constructed by the authors, which should help food scientists to analyze their own omics data more effectively. In addition, a novel tool for the easy visualization of omics data was developed by the authors’ group. The tool enables one to overview the changes of multiple omics in the KEGG pathway. Research in traditional and complementary medicine will be further facilitated by promoting the integrated omics research of food functionality. Such integrated research will only be possible with the effective collaboration of scientists with different backgrounds.

Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes

In recent decades, our knowledge of the genetics and functional genomics of drug-metabolizing enzymes has increased and a wealth of data on drug-related 'omics' has become available. Despite the availability of large amounts of biological information on xenobiotic biotransformation, the number of available biotransformation pathway maps that can easily be used for visualization of multiple omics data is limited. Here, we created integrated biotransformation pathway maps suitable for multiple omics analysis using PathVisio. The ease of visualizing data on these maps was demonstrated by using published microarray data from human hepatocyte-like cell models, exemplifying - where a sufficient capacity for metabolizing chemicals is a prerequisite for a suited model - how the biotransformation pathway maps can be used for model selection.

INVERSE METABOLIC ENGINEERING BY INTEGRATION OF MULTIPLE OMICS ANALYSES

Abstract An inverse metabolic engineering method with “multiple omics analyses” was applied to creation of a stress tolerant strain of Saccharomyces cerevisiae. DNA microarray data of laboratory and brewing strains under high ethanol concentration condition (transcriptomics data) were compared and analyzed by a clustering method. Sensitivity analysis of gene knockout mutant library was further performed (phenomics data). The selection of candidate genes for conferring stress tolerance was successfully performed.

Comparative integrated omics approach sterically understanding hepatic metabolic dynamics in mouse model

Currently, the general approach of analysis is using a single omics technique , however the combined analysis of data by employing multiple omics analyses can enable a more fundamental understanding of the biological phenomena. Multi-layered combination of multiple omics technologies involves generation of a large amount of data, which leads to increased complexity and makes comprehension of bio-information more difficult. The objective of this study was to investigate the utility of incorporating multiple omics technologies in a multi-layered fashion. Transcriptomic, proteomic, and metabolomic analyses were carried out using a mouse model of diet-induced obesity. The present study reported the comprehensiveness of three omics analyses and the utility of using multiple omics analyses. Uniform changes were observed among changes at all stages but the majority of these specific to the omics approach. This data supports the fact that various molecules progress through the central dogma at differing speeds. Since the time axis differs for each molecule, combining multiple omics analyses makes it possible to investigate the reactions in organisms three-dimensionally. At first glance, it simply appears that combining a number of very large data sets produces even more complexity but, if multi-layered omics data are treated with an awareness of their meaning, benefits, and limitations, then the combination of multiple omics analyses can be extremely useful for research in molecular biology.