Multilayer Omics Research Articles

Analysis of the aging-related biomarker in a nonhuman primate model using multilayer omics

BackgroundAging is a prominent risk factor for diverse diseases; therefore, an in-depth understanding of its physiological mechanisms is required. Nonhuman primates, which share the closest genetic relationship with humans, serve as an ideal model for exploring the complex aging process. However, the potential of the nonhuman primate animal model in the screening of human aging markers is still not fully exploited. Multiomics analysis of nonhuman primate peripheral blood offers a promising approach to evaluate new therapies and biomarkers. This study explores aging-related biomarker through multilayer omics, including transcriptomics (mRNA, lncRNA, and circRNA) and proteomics (serum and serum-derived exosomes) in rhesus monkeys (Macaca mulatta).ResultsOur findings reveal that, unlike mRNAs and circRNAs, highly expressed lncRNAs are abundant during the key aging period and are associated with cancer pathways. Comparative analysis highlighted exosomal proteins contain more types of proteins than serum proteins, indicating that serum-derived exosomes primarily regulate aging through metabolic pathways. Finally, eight candidate aging biomarkers were identified, which may serve as blood-based indicators for detecting age-related brain changes.ConclusionsOur results provide a comprehensive understanding of nonhuman primate blood transcriptomes and proteomes, offering novel insights into the aging mechanisms for preventing or treating age-related diseases.

Molecular pathological approach to cancer epigenomics and its clinical application.

Careful microscopic observation of histopathological specimens, accumulation of large numbers of high-quality tissue specimens, and analysis of molecular pathology in relation to morphological features are considered to yield realistic data on the nature of multistage carcinogenesis. Since the morphological hallmark of cancer is disruption of the normal histological structure maintained through cell-cell adhesiveness and cellular polarity, attempts have been made to investigate abnormalities of the cadherin-catenin cell adhesion system in human cancer cells. It has been shown that the CDH1 tumor suppressor gene encoding E-cadherin is silenced by DNA methylation, suggesting that a "double hit" involving DNA methylation and loss of heterozygosity leads to carcinogenesis. Therefore, in the 1990s, we focused on epigenomic mechanisms, which until then had not received much attention. In chronic hepatitis and liver cirrhosis associated with hepatitis virus infection, DNA methylation abnormalities were found to occur frequently, being one of the earliest indications that such abnormalities are present even in precancerous tissue. Aberrant expression and splicing of DNA methyltransferases, such as DNMT1 and DNMT3B, was found to underlie the mechanism of DNA methylation alterations in various organs. The CpG island methylator phenotype in renal cell carcinomawas identified for the first time, and its therapeutic targets were identified by multilayer omics analysis. Furthermore, the DNA methylation profile of nonalcoholic steatohepatitis (NASH)-related hepatocellular carcinomawas clarified in groundbreaking studies. Since then, we have developed diagnostic markers for carcinogenesis risk in NASH patients and noninvasive diagnostic markers for upper urinary tract cancer, as well as developing a new high-performance liquid chromatography-based diagnostic system for DNA methylation diagnosis. Research on the cancer epigenome has revealed that DNA methylation alterations occur from the precancerous stage as a result of exposure to carcinogenic factors such as inflammation, smoking, and viral infections, and continuously contribute to multistage carcinogenesis through aberrant expression of cancer-related genes and genomic instability. DNA methylation alterations at the precancerous stages are inherited by or strengthened in cancers themselves and determine the clinicopathological aggressiveness of cancers as well as patient outcome. DNA methylation alterations have applications as biomarkers, and are expected to contribute to diagnosis, as well as preventive and preemptive medicine.

Abstract 042: Therapeutic Administration Of L-lysine Leads To The Attenuation Of Salt-sensitive Hypertension

Our previous studies using a multilayered omics approach showed a deficiency of lysine metabolites in Dahl Salt-sensitive (SS) rats fed a high-salt (HS) diet. Consequent studies attempted to rewire these metabolic changes by treating the SS rats with lysine in drinking water while on an HS diet. Rats treated with lysine revealed that the treatment significantly attenuated the development of SS hypertension (HTN) and decreased kidney damage via increased diuresis, accelerated lysine metabolism, and inhibited tubular albumin uptake. Our current study hypothesizes that lysine administration will also be beneficial when blood pressure is already elevated. For this purpose, we used a therapeutic approach where we fed 8-week-old male rats with an HS diet for 10 days (at which point they develop moderate kidney damage (by historic data) and a mean arterial pressure (MAP) of ~140 mmHg, N=13) and consequently put on either L-lysine (17 mg/ml drinking water supplement) or vehicle treatment for 11 more days. MAP was continuously measured using radiotelemetry. At the end of the protocol, the L-lysine-treated group showed a significant decrease in MAP compared to the vehicle group (endpoint 145±5 vs. 176±8 mmHg, N=6-7). Next, using targeted metabolomics, we measured 42 amino acids and their derivatives in the kidney, plasma, and urine at the endpoint of these rats to determine the influence of the treatment on amino acid metabolism. Lysine was upregulated in the treatment group in the kidney, plasma, and urinary excretion compared to the control (log 2 FC & p-value: 0.24, 0.01; 1.22, 3.37E-06; 6.6, 0.04; N=4-5 per group). Lysine degradation product, alpha-Aminoadipic-acid was also increased by 1.56-fold and 1.86-fold in the kidney and plasma, respectively (p-value:0.03 & 0.006). Additionally, 13 other amino acids or derivatives significantly changed in the kidney, plasma, or urine. Our current data suggest that L-lysine administration in hypertensive subjects can have therapeutic effects on the progression and magnitude of SS HTN. As shown by our previous study, it is likely the mechanisms behind this beneficial effect lie in the increased lysine metabolism, which can affect critical downstream metabolic pathways such as the TCA cycle and Fatty Acid metabolism.

Multilayer omics landscape analyses reveal the regulatory responses of tea plants to drought stress

Adverse environments, especially drought conditions, deeply influence plant development and growth in all aspects, and the yield and quality of tea plants are largely dependent on favorable growth conditions. Although tea plant responses to drought stress (DS) have been studied, a comprehensive multilayer epigenetic, transcriptomic, and proteomic investigation of how tea responds to DS is lacking. In this study, we generated DNA methylome, transcriptome, proteome, and phosphoproteome data to explore multiple regulatory landscapes in the tea plant response to DS. An integrated multiomics analysis revealed the response of tea plants to DS at multiple regulatory levels. Furthermore, a set of DS-responsive genes involved in photosynthesis, transmembrane transportation, phytohormone metabolism and signaling, secondary metabolite pathways, transcription factors, protein kinases, posttranslational and epigenetic modification, and other key stress-responsive genes were identified for further functional investigation. These results reveal the multilayer regulatory landscape of the tea plant response to DS and provide insight into the mechanisms of these DS responses.

Multilayer omics reveals the molecular mechanism of early infection of Clonorchis sinensis juvenile

BackgroundClonorchiasis remains a non-negligible global zoonosis, causing serious socioeconomic burdens in endemic areas. Clonorchis sinensis infection typically elicits Th1/Th2 mixed immune responses during the course of biliary injury and periductal fibrosis. However, the molecular mechanism by which C. sinensis juvenile initially infects the host remains poorly understood.MethodsThe BALB/c mouse model was established to study early infection (within 7 days) with C. sinensis juveniles. Liver pathology staining and observation as well as determination of biochemical enzymes, blood routine and cytokines in blood were conducted. Furthermore, analysis of liver transcriptome, proteome and metabolome changes was performed using multi-omics techniques. Statistical analyses were performed using Student's t-test.ResultsHistopathological analysis revealed that liver injury, characterized by collagen deposition and inflammatory cell infiltration, occurred as early as 24 h of infection. Blood indicators including ALT, AST, WBC, CRP and IL-6 indicated that both liver injury and systemic inflammation worsened as the infection progressed. Proteomic data showed that apoptosis and junction-related pathways were enriched within 3 days of infection, indicating the occurrence of liver injury. Furthermore, proteomic and transcriptomic analysis jointly verified that the detoxification and antioxidant defense system was activated by enrichment of glutathione metabolism and cytochrome P450-related pathways in response to acute liver injury. Proteomic-based GO analysis demonstrated that biological processes such as cell deformation, proliferation, migration and wound healing occurred in the liver during the early infection. Correspondingly, transcriptomic results showed significant enrichment of cell cycle pathway on day 3 and 7. In addition, the KEGG analysis of multi-omics data demonstrated that numerous pathways related to immunity, inflammation, tumorigenesis and metabolism were enriched in the liver. Besides, metabolomic screening identified several metabolites that could promote inflammation and hepatobiliary periductal fibrosis, such as CA7S.ConclusionsThis study revealed that acute inflammatory injury was rapidly triggered by initial infection by C. sinensis juveniles in the host, accompanied by the enrichment of detoxification, inflammation, fibrosis, tumor and metabolism-related pathways in the liver, which provides a new perspective for the early intervention and therapy of clonorchiasis.Graphical

Network-Guided Multiomic Mapping of Aortic Valve Calcification.

Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.

PS-P05-9: A PROTOCOL OF DIGITAL PROSPECTIVE COHORT STUDY ON HEART FAILURE AND COGNITIVE DECLINE IN URBAN RESIDENTS: THE SUITA STUDY NEXT

Background: The aging rate in Japan is about 30%, which is the highest worldwide and will continue to increase. The prevalence of heart failure and dementia in Japan increases much more than the nationally known values. There is an urgent need for a drastic solution to this. Multi-employers’ community intervention prevents heart failure and dementia in Western countries, but Japan mainly relies on family care to prevent them. Although heart failure and dementia worsen each other, there are few cohort studies aimed at both at the same time. Also, there are few cohort studies of Japanese heart failure. Objective: This study aims to show the evidence of risk factors for heart failure and dementia in urban residents and to build evidence to create a town where multi- employers’ community intervention is possible Methods: We recommend that all people aged 65 or older who live in two predetermined (elementary school) districts of Suita City receive a medical examination (6,340 people). We will conduct the following tests accompanying the medical check-up: echocardiography, carotid artery echo, limb blood pressure, Holter electrocardiography, ophthalmic examination (visual acuity, fundus examination, color vision examination, tonometry, optical interference tomography), otolaryngology examination (auditory examination, olfactory test), physical fitness test (grip power, walking test), oral examination (dental prescale, masticatory ability measurement, and repeated saliva swallowing test), cognitive function test, depression test, and health and semi-quantitative food frequency questionnaires. Blood collection will be carried out collectively for research and biobank projects (only for consenters). In addition, we will receive prognosis data (annual medical examination data, medical expenses, long-term care, transfer information, and cause of death) of cohort subjects. We encourage and follow up every three years for people aged 65 and over who live in the same two specific districts. The Institutional Review Board approved this protocol. Informed consent is obtained from all subjects. Discussion: We started this cohort study in September 2021. After a three-year baseline survey, a follow-up survey will begin. In a cross-sectional study of the baseline survey, we prepare profile papers on the relationship between the prevalence of heart failure and cognitive decline and prognosis. We are studying multi-layered omics analysis using blood sampling and AI research using a lot of digital data. We will first provide evidence on the prevention of heart failure and dementia among the urban residents in Japan and present materials that contribute to policymaking that can extend healthy life expectancy.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

REALGAR: a web app of integrated respiratory omics data.

In the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL) and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic associations. We designed the Reducing Associations by Linking Genes and omics Results web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies. The REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar. Supplementary data are available at Bioinformatics online.

Integrated Multilayer Omics Reveals the Genomic, Proteomic, and Metabolic Influences of Histidyl Dipeptides on theHeart.

BackgroundHistidyl dipeptides such as carnosine are present in a micromolar to millimolar range in mammalian hearts. These dipeptides facilitate glycolysis by proton buffering. They form conjugates with reactive aldehydes, such as acrolein, and attenuate myocardial ischemia–reperfusion injury. Although these dipeptides exhibit multifunctional properties, a composite understanding of their role in the myocardium is lacking.Methods and ResultsTo identify histidyl dipeptide–mediated responses in the heart, we used an integrated triomics approach, which involved genome‐wide RNA sequencing, global proteomics, and unbiased metabolomics to identify the effects of cardiospecific transgenic overexpression of the carnosine synthesizing enzyme, carnosine synthase (Carns), in mice. Our result showed that higher myocardial levels of histidyl dipeptides were associated with extensive changes in the levels of several microRNAs, which target the expression of contractile proteins, β‐fatty acid oxidation, and citric acid cycle (TCA) enzymes. Global proteomic analysis showed enrichment in the expression of contractile proteins, enzymes of β‐fatty acid oxidation, and the TCA in the Carns transgenic heart. Under aerobic conditions, the Carns transgenic hearts had lower levels of short‐ and long‐chain fatty acids as well as the TCA intermediate—succinic acid; whereas, under ischemic conditions, the accumulation of fatty acids and TCA intermediates was significantly attenuated. Integration of multiple data sets suggested that β‐fatty acid oxidation and TCA pathways exhibit correlative changes in the Carns transgenic hearts at all 3 levels.ConclusionsTaken together, these findings reveal a central role of histidyl dipeptides in coordinated regulation of myocardial structure, function, and energetics.

Abstract 6375: Augmented intelligence to define drug targets associated with triple negative breast cancer (TNBC) metabolic reprogramming

Abstract Introduction: Basal-like breast cancer (BLBC; TNBC) cells use aerobic glycolysis at a higher rate than Luminal A (LumA) cells. Metabolic reprogramming using aerobic glycolysis (Warburg effect), is correlated with increased aggressiveness of cancer cells and poor outcome in patients. Therefore, genes involved in this pathway are promising targets for developing cancer therapeutics. Method: MIMs has developed a unique platform of augmented intelligence that combines bioinformatics, systems biology and artificial intelligence. It integrates multi-layered omics data with a knowledge-base that aggregates structured data from more than 140 databases as well as unstructured data from the scientific literature. Using this approach a genetic interaction graph (GI-Graph) is inferred per patient, capturing functional relationships between genes in the specific context of the tumor. The GI-Graphs are subsequently used to train supervised machine learning algorithms for predicting gene functionality and potential as a target. Preliminary analysis was performed on transcriptomic data from 321 LumA and 162 TNBC samples, from the TCGA Data Portal and a predictive model was developed using two GI-Graphs from the two subgroups. Briefly, subgraphs, containing genes with functional interactions with the known genes in OXPHOS and glycolysis pathways, were used to extract gene attributes, and to build an algorithm that predicts involvement of a gene in the Warburg effect. Using a testing gene set extracted from the literature the performance of the model was assessed, which showed a true positive rate of 18% and a false positive rate of 0.36%, and outperformed 5-times the classical bioinformatics tools. Results: This model predicted 108 genes as the top 1% genes being involved in the metabolic reprogramming of TNBC. Additional information from MIMs’ platform, including differential gene expression between LumA and TNBC, gene pleiotropy and essentiality and the topological metrics, enabled the life scientists to further refine the gene lists, based on the expected characteristics of a good target in oncology. Following this process, 30 genes were selected as potential targets controlling the metabolic reprogramming of TNBC, among which 4 genes were already evaluated in TNBC clinical trials. Conclusion: Our preliminary data strongly supports that the predictive model based on the GI-Graphs has the potential to identify promising therapeutic targets for TNBC. Citation Format: Sarah Jenna, Benjamin Boucher, Liebaut Dudragne, Abdoulaye Baniré Diallo. Augmented intelligence to define drug targets associated with triple negative breast cancer (TNBC) metabolic reprogramming [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6375.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

Galbase: a comprehensive repository for integrating chicken multi-omics data

BackgroundMulti-omics data can provide a stereoscopic view to explore potential causal variations and genes, as well as underlying genetic mechanisms of complex traits. However, for many non-mammalian species, including chickens, these resources are poorly integrated and reused, greatly limiting genetic research and breeding processes of the species.ResultsHere, we constructed Galbase, an easily accessible repository that integrates public chicken multi-omics data from 928 re-sequenced genomes, 429 transcriptomes, 379 epigenomes, 15,275 QTL entries, and 7,526 associations. A total of 21.67 million SNPs, 2.71 million InDels, and 488,583 cis-regulatory elements were included. Galbase allows users to retrieve genomic variations in geographical maps, gene expression profiling in heatmaps, and epigenomic signals in peak patterns. It also provides modules for batch annotation of genes, regions, and loci based on multi-layered omics data. Additionally, a series of convenient tools, including the UCSC Genome Browser, WashU Epigenome Browser, BLAT, BLAST, and LiftOver, were also integrated to facilitate search, visualization, and analysis of sequence features.ConclusionGalbase grants new opportunities to research communities to undertake in-depth functional genomic studies on chicken. All features of Galbase make it a useful resource to identify genetic variations responsible for chicken complex traits. Galbase is publicly available at http://animal.nwsuaf.edu.cn/ChickenVar.

Systemic proteomic and metabolomic signature of the SGLT2 inhibitor dapagliflozin

BackgroundDapagliflozin is a member of a new class of medication called gliflozins indicated for the treatment of diabetes. Gliflozins lower blood glucose levels by inhibition of the sodium glucose co‐transporter 2 (SGLT2), which is predominantly expressed in the S1 segment of renal proximal tubules and responsible for >90% of renal glucose reabsorption. Gliflozins also protect the kidney and heart, which cannot be solely explained by blood glucose lowering. To gain further mechanistic insights, we determined the metabolomic and proteomic signature of dapagliflozin in a murine model of diabetes.MethodsMale C57BL/6 non‐diabetic wildtype and littermate Ins2 (Akita) diabetic mice, both fed a Western diet, were given dapagliflozin (10 mg/kg diet) or vehicle for one week (4 groups, N=8/group). Blood and urine glucose levels confirmed the expected treatment response. We analyzed metabolome, proteome and phosphoproteome for a range of tissues (kidney, liver, heart, smooth muscle, white adipose tissue) and body fluids (serum, urine, erythrocytes), and extended the analysis to the gut metaproteome. For untargeted metabolomics, analytes were measured by HILIC‐MS/MS and RP‐MS/MS. For proteomics and phosphoproteomics, TMT labelled (TMT16plex) peptides were separated by high pH reverse phase fractionation and measured by LC‐MS/MS including FAIMS (Thermo Scientific Orbitrap Exploris 480 mass spectrometer).ResultsMulti‐layered omics data were obtained comprising 11341 proteins, 14762 phosphorylation sites, and 217 metabolites. In general, dapagliflozin‐induced changes were more distinct in Western diet‐fed non‐diabetic than diabetic mice. Metabolomics revealed effects of dapagliflozin on amino acid, carbohydrate and lipid metabolism with most changes observed in urine, serum and liver. In comparison, dapagliflozin‐induced perturbations on proteome and phosphoproteome level were observed predominantly in kidney and affected ATP/lipid/carbohydrate/nitrogen metabolism, mitochondria, mitophagy, methyl transferases, methyl CpG‐binding, kinases, deacetylases, endosomes, cell junctions and gene expression. Interestingly, also the gut metaproteome was affected by dapagliflozin with significant changes in Bacteroides and Bifidobacterium species.ConclusionThe SGLT2 inhibitor dapagliflozin induces distinct effects on kidney proteomics, associated with robust metabolomic signatures in plasma, urine and liver, as well as effects on the gut metaproteome. These effects occurred independent of blood glucose levels and support a concept of pleiotropic effects of SGLT2 inhibition that may originate in the kidney but induce wide‐ranged extrarenal metabolic effects and consequences.

Multilayered omics reveal sex- and depot-dependent adipose progenitor cell heterogeneity

Single-cell RNA sequencing (scRNA-seq) has revealed that adult white adipose tissue (WAT) harbors functionally diverse subpopulations of mesenchymal stromal cells that differentially impact tissue plasticity. To date, the molecular basis of this cellular heterogeneity has not been fully defined. Here, we describe a multilayered omics approach to dissect adipose progenitor cell heterogeneity in three dimensions: progenitor subpopulation, sex, and anatomical localization. We applied state-of-the-art mass spectrometry methods to quantify 4,870 proteins in eight different stromal cell populations from perigonadal and inguinal WAT of male and female mice and acquired transcript expression levels of 15,477 genes using RNA-seq. Our data unveil molecular signatures defining sex differences in preadipocyte differentiation and identify regulatory pathways that functionally distinguish adipose progenitor subpopulations. This multilayered omics analysis, freely accessible at http://preadprofiler.net/, provides unprecedented insights into adipose stromal cell heterogeneity and highlights the benefit of complementary proteomics to support findings from scRNA-seq studies.

Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.

Due to the rapid development of high-throughput sequencing technology, we can easily obtain not only the genetic variants at the whole-genome sequence level (e.g., from 1000 Genomes project and 1000 Bull Genomes project), but also a wide range of functional annotations (e.g., enhancers and promoters from ENCODE, FAANG, and FarmGTEx projects) across a wide range of tissues, cell types, developmental stages, and environmental conditions. This huge amount of information leads to a revolution in studying genetics and genomics of complex traits in humans, livestock, and plant species. In this chapter, we focused on and reviewed the genomic prediction methods that incorporate external biological information into genomic prediction, such as sequence ontology, linkage disequilibrium (LD) of SNPs, quantitative trait loci (QTL), and multi-layer omics data (e.g., transcriptome, epigenome, and microbiome).

Multilayer omics analysis reveals a non-classical retinoic acid signaling axis that regulates hematopoietic stem cell identity.

Hematopoietic stem cells (HSCs) rely on complex regulatory networks to preserve stemness. Due to the scarcity of HSCs, technical challenges have limited our insights into the interplay between metabolites, transcription, and the epigenome. In this study, we generated low-input metabolomics, transcriptomics, chromatin accessibility, and chromatin immunoprecipitation data, revealing distinct metabolic hubs that are enriched in HSCs and their downstream multipotent progenitors. Mechanistically, we uncover a non-classical retinoic acid (RA) signaling axis that regulates HSC function. We show that HSCs rely on Cyp26b1, an enzyme conventionally considered to limit RA effects in the cell. In contrast to the traditional view, we demonstrate that Cyp26b1 is indispensable for production of the active metabolite 4-oxo-RA. Further, RA receptor beta (Rarb) is required for complete transmission of 4-oxo-RA-mediated signaling to maintain stem cells. Our findings emphasize that a single metabolite controls stem cell fate by instructing epigenetic and transcriptional attributes.

Construction of the multi-layer omics data generation / analysis platform that contributes to the precision medicine of COVID-19

The clinical manifestations of COVID-19 vary from mild or almost asymptomatic to severe ones that require intensive cares such as mechanical ventilation and ECMO in the ICU. In humans with such complex genetic or environmental factors, multi-layered omics networks on the viral and the host side are involved in the formation of various pathologies across all organs. Precision medicine, which provides appropriate medical cares to individual patients based on the omics information including genomic one, is a key to control COVID-19 in humans with diverse biological backgrounds. We construct the multi-layer omics data generation / analysis platform, in which time-series multi-layered omics data of the virus and host, which are linked to medical information, are stored. Those our generating and public data, could be useful for clarifying the complex biological network of COVID-19, and the prediction of disease progression, complications, and effects of interventions for individual patients.

Multilayer OMICS characterization of APOE‐modulated isogenic hiPSCs elucidates cell‐type–specific mechanisms modified by APOE: A study of the IMI ADAPTED Consortium

AbstractBackgroundAlzheimer’s Disease (AD) is the most common cause of dementia in the elderly and affects over 35 million people worldwide, imposing increasing social and economic burden as the population ages. While it is widely known that the most prominent genetic risk factor for AD is the presence of the Apolipoprotein E (APOE) ε4 allele, the effects of APOE in the development of AD is still poorly understood. As part of the IMI ADAPTED consortium, we aim to clarify the role of APOE as a risk factor in the development of AD. Here we present an in‐depth analysis of the effect of the APOE genotype on the transcriptome of brain cells derived from human‐induced pluripotent stem cells (hiPSCs).MethodIsogenic hiPSC lines were modified to carry different APOE genotypes: ε3/ε3, ε4/ε4, ε3/ε4, ε2/ε2, as well as an APOE knock‐out (KO) cell line. Lines carrying each of these genotypes were differentiated into distinct cell types. Differential gene expression (DGE) and protein expression (DPE) was calculated, followed by gene set enrichment. Clustering approaches were used to identify shared and differing gene signatures across genotypes. We further applied upstream regulator and network analysis on the individual cell‐type results and integrated these results across cell types. The results were compared with DGE and DEP results from an APOE mouse model. Finally, the identified genes and mechanisms were combined with the results of data from postmortem human brain samples of AD cases and controls.ResultsThe observed transcriptional changes confirmed phenotypic observations made for the hiPSCs and refined the insight of genes identified human brain OMICS data. Several genes and pathways were identified, which showed consistent gene expression on transcriptome and proteome level. Further, shared patterns of expressions of genes across genotypes and potential mechanisms involved in this were detected.ConclusionsIn depth transcriptomics and proteomics analysis of APOE modified hiPSCs enabled to study cell type specific effects and contributed with this to the understanding of mechanisms affected by different APOE genotypes.

Niche-adaptation in plant-associated Bacteroidetes favours specialisation in organic phosphorus mineralisation

Bacteroidetes are abundant pathogen-suppressing members of the plant microbiome that contribute prominently to rhizosphere phosphorus mobilisation, a frequent growth-limiting nutrient in this niche. However, the genetic traits underpinning their success in this niche remain largely unknown, particularly regarding their phosphorus acquisition strategies. By combining cultivation, multi-layered omics and biochemical analyses we first discovered that all plant-associated Bacteroidetes express constitutive phosphatase activity, linked to the ubiquitous possession of a unique phosphatase, PafA. For the first time, we also reveal a subset of Bacteroidetes outer membrane SusCD-like complexes, typically associated with carbon acquisition, and several TonB-dependent transporters, are induced during Pi-depletion. Furthermore, in response to phosphate depletion, the plant-associated Flavobacterium used in this study expressed many previously characterised and novel proteins targeting organic phosphorus. Collectively, these enzymes exhibited superior phosphatase activity compared to plant-associated Pseudomonas spp. Importantly, several of the novel low-Pi-inducible phosphatases and transporters, belong to the Bacteroidetes auxiliary genome and are an adaptive genomic signature of plant-associated strains. In conclusion, niche adaptation to the plant microbiome thus appears to have resulted in the acquisition of unique phosphorus scavenging loci in Bacteroidetes, enhancing their phosphorus acquisition capabilities. These traits may enable their success in the rhizosphere and also present exciting avenues to develop sustainable agriculture.