Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Mycotoxin deoxynivalenol affects myoblast differentiation via downregulating cytoskeleton and ECM-integrin-FAK-RAC-PAK signaling pathway

As a common mycotoxin, deoxynivalenol (DON) contaminates cereal grains and feed in field or during processing and storage. DON elicits a spectrum of adverse effects in animals including anorexia and growth retardation. Especially, the presence of DON has also been detected in muscle, suggesting that DON may has the potential to affect the development of muscle. However, the relevant research is very rare and the molecular mechanism remains unclear. Myoblasts differentiation into multinucleated myotubes is one of the crucial steps of skeletal muscle development. In the present study, we investigated the effects of DON on differentiation of myoblasts using murine C2C12 cells model. The results indicated that DON dose-dependent inhibited the formation of myotubes in C2C12 cells. After performing omics techniques, a total of 149 differentially expressed genes were identified. The expression of cytoskeleton proteins and extracellular matrix (ECM) proteins were downregulated by DON. Furthermore, DON significantly downregulated the expression of integrin αv and integrin β5, leading to inhibition of the ECM-integrin receptor interaction. The focal adhesion kinase (FAK) and phosphorylated forms, ras-related C3 botulinum toxin substrate (RAC) and p21-activated kinases 1 (PAK1) were also downregulated by DON. Taken together, our findings suggest that DON has the potent to affect the differentiation of myoblasts via downregulating of cytoskeleton and ECM-integrin-FAK-RAC-PAK signaling pathway.

Grouped-seq for integrated phenotypic and transcriptomic screening of patient-derived tumor organoids

Patient-derived tumor organoids (PDOs) have emerged as a reliable in vitro model for drug discovery. However, RNA sequencing-based analysis of PDOs treated with drugs has not been realized in a high-throughput format due to the limited quantity of organoids. Here, we translated a newly developed pooled RNA-seq methodology onto a superhydrophobic microwell array chip to realize an assay of genome-wide RNA output unified with phenotypic data (Grouped-seq). Over 10-fold reduction of sample and reagent consumption together with a new ligation-based barcode synthesis method lowers the cost to ∼$2 per RNA-seq sample. Patient-derived colorectal cancer (CRC) organoids with a number of 10 organoids per microwell were treated with four anti-CRC drugs across eight doses and analyzed by the Grouped-seq. Using a phenotype-assisted pathway enrichment analysis (PAPEA) method, the mechanism of actions of the drugs were correctly derived, illustrating the great potential of Grouped-seq for pharmacological screening with tumor organoids.

Does Low Grade Systemic Inflammation Have a Role in Chronic Pain?

One of the major clinical manifestations of peripheral neuropathy, either resulting from trauma or diseases, is chronic pain. While it significantly impacts patients’ quality of life, the underlying mechanisms remain elusive, and treatment is not satisfactory. Systemic chronic inflammation (SCI) that we are referring to in this perspective is a state of low-grade, persistent, non-infective inflammation, being found in many physiological and pathological conditions. Distinct from acute inflammation, which is a protective process fighting against intruders, SCI might have harmful effects. It has been associated with many chronic non-communicable diseases. We hypothesize that SCI could be a predisposing and/or precipitating factor in the development of chronic pain, as well as associated comorbidities. We reviewed evidence from human clinical studies indicating the coexistence of SCI with various types of chronic pain. We also collated existing data about the sources of SCI and who could have it, showing that those individuals or patients having SCI usually have higher prevalence of chronic pain and psychological comorbidities. We thus elaborate on the need for further research in the connection between SCI and chronic pain. Several hypotheses have been proposed to explain these complex interactions.

Structural Predictive Model of Presenilin-2 Protein and Analysis of Structural Effects of Familial Alzheimer's Disease Mutations

Alzheimer's disease manifests itself in brain tissue by neuronal death, due to aggregation of β-amyloid, produced by senile plaques, and hyperphosphorylation of the tau protein, which produces neurofibrillary tangles. One of the genetic markers of the disease is the gene that translates the presenilin-2 protein, which has mutations that favor the appearance of the disease and has no reported crystallographic structure. In view of this, protein modeling is performed using prediction and structural refinement tools followed by an energetic and stereochemical characterization for its validation. For the simulation, four reported mutations are chosen, which are Met239Ile, Met239Val, Ser130Leu, and Thr122Arg, all associated with various functional responses. From a theoretical analysis, a preliminary bioinformatic study is made to find the phosphorylation patterns in the protein and the hydropathic index according to the polarity and chemical environment. Molecular visualization was carried out with the Chimera 1.14 software, and the theoretical calculation with the hybrid quantum mechanics/molecular mechanics system from the semi-empirical method, with Spartan18 software and an AustinModel1 basis. These relationships allow for studying the system from a structural approach with the determination of small distance changes, potential surfaces, electrostatic maps, and angle changes, which favor the comparison between wild-type and mutant systems. With the results obtained, it is expected to complement experimental data reported in the literature from models that would allow us to understand the effects of the selected mutations.

Metabolomic profiling revealed altered lipid metabolite levels in childhood food allergy

BACKGROUND

The pathophysiology of childhood food allergy (FA) and its natural history are poorly understood. Clarification of the underlying mechanism may help identify novel biomarkers and strategies for clinical intervention in children with FA.

OBJECTIVE

This study aimed to identify metabolites associated with the development and resolution of FA.

METHODS

The metabolomic profiles of 20 children with FA and 20 healthy controls were assessed by liquid chromatography-tandem mass spectrometry. Comparative analysis was performed to identify metabolites associated with FA and FA resolution. For subjects with FA, serum samples were collected at the time of diagnosis and after resolution to identify the changes in metabolite levels. The selected metabolites were then quantified in a quantification cohort to validate the results. Finally, genome-wide association analysis of the metabolite levels was performed.

RESULTS

The study demonstrated a significantly higher level of sphingolipid metabolites and a lower level of acylcarnitine metabolites in children with FA than those in healthy controls. At diagnosis, subjects with resolving FA had a significantly high level of omega-3 metabolites and a low level of platelet-activating factors compared to persistent FA. However, the level of omega-3 metabolites decreased in children with resolving FA but increased in children with persistent FA during the same time. The quantification data of omega-3-derived resolvins, platelet-activating factor, and platelet-activating factor acetylhydrolase activity further supported these results.

CONCLUSION

The lipid metabolite profile is closely related to childhood FA and FA resolution. This study suggests potential predictive biomarkers and provides insight into the mechanisms underlying childhood FA.

Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics

BACKGROUND

Nanoparticles have been utilized in brain research and therapeutics, including imaging, diagnosis, and drug delivery, owing to their versatile properties compared to bulk materials. However, exposure to nanoparticles leads to their accumulation in the brain, but drug development to counteract this nanotoxicity remains challenging. To date, concerns have risen about the potential toxicity to the brain associated with nanoparticles exposure via penetration of the brain blood barrier to address this issue.

METHODS

Here the effect of silica-coated-magnetic nanoparticles containing the rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] were assessed on microglia through toxicological investigation, including biological analysis and integration of transcriptomics, proteomics, and metabolomics. MNPs@SiO2(RITC)-induced biological changes, such as morphology, generation of reactive oxygen species, intracellular accumulation of MNPs@SiO2(RITC) using transmission electron microscopy, and glucose uptake efficiency, were analyzed in BV2 murine microglial cells. Each omics data was collected via RNA-sequencing-based transcriptome analysis, liquid chromatography-tandem mass spectrometry-based proteome analysis, and gas chromatography- tandem mass spectrometry-based metabolome analysis. The three omics datasets were integrated and generated as a single network using a machine learning algorithm. Nineteen compounds were screened and predicted their effects on nanotoxicity within the triple-omics network.

RESULTS

Intracellular reactive oxygen species production, an inflammatory response, and morphological activation of cells were greater, but glucose uptake was lower in MNPs@SiO2(RITC)-treated BV2 microglia and primary rat microglia in a dose-dependent manner. Expression of 121 genes (from 41,214 identified genes), and levels of 45 proteins (from 5918 identified proteins) and 17 metabolites (from 47 identified metabolites) related to the above phenomena changed in MNPs@SiO2(RITC)-treated microglia. A combination of glutathione and citrate attenuated nanotoxicity induced by MNPs@SiO2(RITC) and ten other nanoparticles in vitro and in the murine brain, protecting mostly the hippocampus and thalamus.

CONCLUSIONS

Combination of glutathione and citrate can be one of the candidates for nanotoxicity alleviating drug against MNPs@SiO2(RITC) induced detrimental effect, including elevation of intracellular reactive oxygen species level, activation of microglia, and reduction in glucose uptake efficiency. In addition, our findings indicate that an integrated triple omics approach provides useful and sensitive toxicological assessment for nanoparticles and screening of drug for nanotoxicity.

Molecular alterations that precede the establishment of the hallmarks of cancer: An approach on the prevention of hepatocarcinogenesis

Chronic liver injury promotes the molecular alterations that precede the establishment of cancer. Usually, several decades of chronic insults are needed to develop the most common primary liver tumor known as hepatocellular carcinoma. As other cancer types, liver cancer cells are governed by a common set of rules collectively called the hallmarks of cancer. Although those rules have provided a conceptual framework for understanding the complex pathophysiology of established tumors, therapeutic options are still ineffective in advanced stages. Thus, the molecular alterations that precede the establishment of cancer remain an attractive target for therapeutic interventions. Here, we first summarize the chemopreventive interventions targeting the early liver carcinogenesis stages. After an integrative analysis on the plethora of molecular alterations regulated by anticancer agents, we then underline and discuss that two critical processes namely oxidative stress and genetic alterations, play the role of 'dirty work laborer' in the initial cell damage and drive the transformation of preneoplastic into neoplastic cells, respectively; besides, the activation of cellular senescence works as a key mechanism in attempting to prevent the onset and establishment of liver cancer. Whereas the detrimental effects of the binomial made up of oxidative stress and genetic alterations are either eliminated or reduced, senescence activation is promoted by anticancer agents. We argue that collectively, oxidative stress, genetic alterations, and senescence are key events that influence the fate of initiated cells and the establishment of the hallmarks of cancer.

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery

Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.

Cord blood metabolic signatures predictive of childhood overweight and rapid growth

INTRODUCTION

Metabolomics may identify biological pathways predisposing children to the risk of overweight and obesity. In this study, we have investigated the cord blood metabolic signatures of rapid growth in infancy and overweight in early childhood in four European birth cohorts.

METHODS

Untargeted liquid chromatography-mass spectrometry metabolomic profiles were measured in cord blood from 399 newborns from four European cohorts (ENVIRONAGE, Rhea, INMA and Piccolipiu). Rapid growth in the first year of life and overweight in childhood was defined with reference to WHO growth charts. Metabolome-wide association scans for rapid growth and overweight on over 4500 metabolic features were performed using multiple adjusted logistic mixed-effect models and controlling the false discovery rate (FDR) at 5%. In addition, we performed a look-up analysis of 43 pre-annotated metabolites, previously associated with birthweight or rapid growth.

RESULTS

In the Metabolome-Wide Association Study analysis, we identified three and eight metabolites associated with rapid growth and overweight, respectively, after FDR correction. Higher levels of cholestenone, a cholesterol derivative produced by microbial catabolism, were predictive of rapid growth (p = 1.6 × 10-3). Lower levels of the branched-chain amino acid (BCAA) valine (p = 8.6 × 10-6) were predictive of overweight in childhood. The area under the receiver operator curve for multivariate prediction models including these metabolites and traditional risk factors was 0.77 for rapid growth and 0.82 for overweight, compared with 0.69 and 0.69, respectively, for models using traditional risk factors alone. Among the 43 pre-annotated metabolites, seven and five metabolites were nominally associated (P < 0.05) with rapid growth and overweight, respectively. The BCAA leucine, remained associated (1.6 × 10-3) with overweight after FDR correction.

CONCLUSION

The metabolites identified here may assist in the identification of children at risk of developing obesity and improve understanding of mechanisms involved in postnatal growth. Cholestenone and BCAAs are suggestive of a role of the gut microbiome and nutrient signalling respectively in child growth trajectories.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Dysregulation of KIF14 regulates the cell cycle and predicts poor prognosis in cervical cancer: a study based on integrated approaches

Cervical cancer (CC) is the most common malignant tumor in females. Although persistent high-risk human papillomavirus (HPV) infection is a leading factor that causes CC, few women with HPV infection develop CC. Therefore, many mechanisms remain to be explored, such as aberrant expression of oncogenes and tumor suppressor genes. To identify promising prognostic factors and interpret the relevant mechanisms of CC, the RNA sequencing profile of CC was downloaded from the Cancer Genome Atlas and the Gene Expression Omnibus databases. The GSE63514 dataset was analyzed, and differentially expressed genes (DEGs) were obtained by weighted coexpression network analysis and the edgeR package in R. Fifty-three shared genes were mainly enriched in nuclear chromosome segregation and DNA replication signaling pathways. Through a protein-protein interaction network and prognosis analysis, the kinesin family member 14 (KIF14) hub gene was extracted from the set of 53 shared genes, which was overexpressed and associated with poor overall survival (OS) and disease-free survival (DFS) of CC patients. Mechanistically, gene set enrichment analysis showed that KIF14 was mainly enriched in the glycolysis/gluconeogenesis signaling pathway and DNA replication signaling pathway, especially in the cell cycle signaling pathway. RT-PCR and the Human Protein Atlas database confirmed that these genes were significantly increased in CC samples. Therefore, our findings indicated the biological function of KIF14 in cervical cancer and provided new ideas for CC diagnosis and therapies.

What the Jackson Heart Study Has Taught Us About Diabetes and Cardiovascular Disease in the African American Community: a 20-year Appreciation

PURPOSE OF REVIEW

The burden of cardiometabolic diseases such as cardiovascular disease (CVD) and type 2 diabetes (T2D) is pronounced among African Americans. Research has shown that behavioral, social, metabolic, psychosocial, and genetic risk factors of CVD and T2D are closely interwoven. Approximately 20 years ago, the Jackson Heart Study (JHS) was established to investigate this constellation of risk factors.

RECENT FINDINGS

Findings from neighborhood studies emphasize the importance of social cohesion and physical environment in the context CVD and T2D risk. Socioeconomic status factors such as income and education were significant predictors for CVD and T2D. Behavioral studies indicate that modifiable risk factors such as smoking, physical inactivity, lack of sleep, and poor nutrition are associated with CVD risk and all-cause mortality. Mental health also was found to be associated with CVD and T2D. Genetic influences are associated with disease etiology. This review summarizes the joint contributions of CVD and cardiometabolic risk factors in an African American population.

Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics

Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.

Diabetes and Heart Failure: Multi-Omics Approaches

Diabetes and heart failure, as important global issues, cause substantial expenses to countries and medical systems because of the morbidity and mortality rates. Most people with diabetes suffer from type 2 diabetes, which has an amplifying effect on the prevalence and severity of many health problems such as stroke, neuropathy, retinopathy, kidney injuries, and cardiovascular disease. Type 2 diabetes is one of the cornerstones of heart failure, another health epidemic, with 44% prevalence. Therefore, finding and targeting specific molecular and cellular pathways involved in the pathophysiology of each disease, either in diagnosis or treatment, will be beneficial. For diabetic cardiomyopathy, there are several mechanisms through which clinical heart failure is developed; oxidative stress with mediation of reactive oxygen species (ROS), reduced myocardial perfusion due to endothelial dysfunction, autonomic dysfunction, and metabolic changes, such as impaired glucose levels caused by insulin resistance, are the four main mechanisms. In the field of oxidative stress, advanced glycation end products (AGEs), protein kinase C (PKC), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) are the key mediators that new omics-driven methods can target. Besides, diabetes can affect myocardial function by impairing calcium (Ca) homeostasis, the mechanism in which reduced protein phosphatase 1 (PP1), sarcoplasmic/endoplasmic reticulum Ca2+ ATPase 2a (SERCA2a), and phosphorylated SERCA2a expressions are the main effectors. This article reviewed the recent omics-driven discoveries in the diagnosis and treatment of type 2 diabetes and heart failure with focus on the common molecular mechanisms.

Comparative transcriptome and proteome provide new insights into the regulatory mechanisms of the postharvest deterioration of Pleurotus tuoliensis fruitbodies during storage

The Pleurotus tuoliensis (Pt), a precious edible mushroom with high economic value, is widely popular for its rich nutrition and meaty texture. However, rapid postharvest deterioration depreciates the commercial value of Pt and severely restricts its marketing. By RNA-Seq transcriptomic and TMT-MS MS proteomic, we study the regulatory mechanisms of the postharvest storage of Pt fruitbodies at 25 ℃ for 0, 38, and 76 h (these three-time points recorded as groups A, B, and C, respectively). 2,008 DEGs (Differentially expressed genes) were identified, and all DEGs shared 265 factors with all DEPs (Differentially expressed proteins). Jointly, the DEGs and DEPs of two-omics showed that the category of the metabolic process contained the most DEGs and DEPs in the biological process by GO (Gene Ontology) classification. The top 17 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways with the highest sum of DEG and DEP numbers in groups B/A (38 h vs. 0 h) and C/A (76 h vs. 0 h) and pathways closely related to energy metabolism were selected for analysis and discussion. Actively expression of CAZymes (Carbohydrate active enzymes), represented by laccase, chitinase, and β-glucanase, directly leads to the softening of fruitbodies. The transcription factor Rlm1 of 1,3-β-glucan synthase attracted attention with a significant down-regulation of gene levels in the C/A group. Laccase also contributes, together with phenylalanine ammonia-lyase (PAL), to the discoloration reaction in the first 76 h of the fruitbodies. Significant expression of several crucial enzymes for EMP (Glycolysis), Fatty acid degradation, and Valine, leucine and isoleucine degradation at the gene or protein level supply substantial amounts of acetyl-CoA to the TCA cycle. Citrate synthase (CS), isocitrate dehydrogenase (ICDH), and three mitochondrial respiratory complexes intensify respiration and produce high levels of ROS (Reactive oxygen species) by significant up-regulation. In the ROS scavenging system, only Mn-SOD was significantly up-regulated at the gene level and was probably interacted with Hsp60 (Heat shock protein 60), which was significantly up-regulated at the protein level, to play a dominant role in antioxidation. Three types of stresses - cell wall stress, starvation, and oxidative stress - were suffered by Pt fruitbodies postharvest, resulting in cell cycle arrest and gene expression disorder.

Bacteria and Sepsis: Microbiome to the Rescue?

The microbiome is the metagenome of all microbes that live on and within every individual, and evidence for its role in the pathogenesis of a variety of diseases has been increasing over the past several decades. While there are various causes of sepsis, defined as the abnormal host response to infection, the host microbiome may provide a unifying explanation for discrepancies that are seen in septic patient survival based on age, sex, and other confounding factors. As has been the case for other human diseases, evidence exists for the microbiome to control patient outcomes after sepsis. In this review, associative data for the microbiome and sepsis survival are presented with causative mechanisms that may be at play. Finally, clinical trials to manipulate the microbiome in order to improve patient outcomes after sepsis are presented as well as areas of potential future research in order to aid in the clinical treatment of these patients.

Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels

MOTIVATION

The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms.

RESULTS

Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants.

AVAILABILITY AND IMPLEMENTATION

Models are available at https://github.com/ytzhong/DNAm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics in asthma: A platform for discovery

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Metabolome Shift in Both Metastatic Breast Cancer Cells and Astrocytes Which May Contribute to the Tumor Microenvironment

The role of astrocytes in the periphery of metastatic brain tumors is unclear. Since astrocytes regulate central nervous metabolism, we hypothesized that changes in astrocytes induced by contact with cancer cells would appear in the metabolome of both cells and contribute to malignant transformation. Coculture of astrocytes with breast cancer cell supernatants altered glutamate (Glu)-centered arginine–proline metabolism. Similarly, the metabolome of cancer cells was also altered by astrocyte culture supernatants, and the changes were further amplified in astrocytes exposed to Glu. Inhibition of Glu uptake in astrocytes reduces the variability in cancer cells. Principal component analysis of the cancer cells revealed that all these changes were in the first principal component (PC1) axis, where the responsible metabolites were involved in the metabolism of the arginine–proline, pyrimidine, and pentose phosphate pathways. The contribution of these changes to the tumor microenvironment needs to be further pursued.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Integrated proteomic and transcriptomic profiling identifies aberrant gene and protein expression in the sarcomere, mitochondrial complex I, and the extracellular matrix in Warmblood horses with myofibrillar myopathy

Background

Myofibrillar myopathy in humans causes protein aggregation, degeneration, and weakness of skeletal muscle. In horses, myofibrillar myopathy is a late-onset disease of unknown origin characterized by poor performance, atrophy, myofibrillar disarray, and desmin aggregation in skeletal muscle. This study evaluated molecular and ultrastructural signatures of myofibrillar myopathy in Warmblood horses through gluteal muscle tandem-mass-tag quantitative proteomics (5 affected, 4 control), mRNA-sequencing (8 affected, 8 control), amalgamated gene ontology analyses, and immunofluorescent and electron microscopy.

Results

We identified 93/1533 proteins and 47/27,690 genes that were significantly differentially expressed. The top significantly differentially expressed protein CSRP3 and three other differentially expressed proteins, including, PDLIM3, SYNPO2, and SYNPOL2, are integrally involved in Z-disc signaling, gene transcription and subsequently sarcomere integrity. Through immunofluorescent staining, both desmin aggregates and CSRP3 were localized to type 2A fibers. The highest differentially expressed gene CHAC1, whose protein product degrades glutathione, is associated with oxidative stress and apoptosis. Amalgamated transcriptomic and proteomic gene ontology analyses identified 3 enriched cellular locations; the sarcomere (Z-disc & I-band), mitochondrial complex I and the extracellular matrix which corresponded to ultrastructural Z-disc disruption and mitochondrial cristae alterations found with electron microscopy.

Conclusions

A combined proteomic and transcriptomic analysis highlighted three enriched cellular locations that correspond with MFM ultrastructural pathology in Warmblood horses. Aberrant Z-disc mechano-signaling, impaired Z-disc stability, decreased mitochondrial complex I expression, and a pro-oxidative cellular environment are hypothesized to contribute to the development of myofibrillar myopathy in Warmblood horses. These molecular signatures may provide further insight into diagnostic biomarkers, treatments, and the underlying pathophysiology of MFM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07758-0.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Integrated Analysis of Omics Data Reveal AP-1 as a Potential Regulation Hub in the Inflammation-Induced Hyperalgesia Rat Model

Inflammation-associated chronic pain is a global clinical problem, affecting millions of people worldwide. However, the underlying mechanisms that mediate inflammation-associated chronic pain remain unclear. A rat model of cutaneous inflammation induced by Complete Freund's Adjuvant (CFA) has been widely used as an inflammation-induced pain hypersensitivity model. We present the transcriptomics profile of CFA-induced inflammation in the rat dorsal root ganglion (DRG) via an approach that targets gene expression, DNA methylation, and post-transcriptional regulation. We identified 418 differentially expressed mRNAs, 120 differentially expressed microRNAs (miRNAs), and 2,670 differentially methylated regions (DMRs), which were all highly associated with multiple inflammation-related pathways, including nuclear factor kappa B (NF-κB) and interferon (IFN) signaling pathways. An integrated analysis further demonstrated that the activator protein 1 (AP-1) network, which may act as a regulator of the inflammatory response, is regulated at both the transcriptomic and epigenetic levels. We believe our data will not only provide drug screening targets for the treatment of chronic pain and inflammation but will also shed light on the molecular network associated with inflammation-induced hyperalgesia.

The plasma phospholipidome of Tursiops truncatus: From physiological insight to the design of prospective tools for managed cetacean monitorization

Plasma biochemical analysis remains one of the established ways of monitoring captive marine mammal health. More recently, complementary plasma lipidomic analysis has proven to be a valid tool in disease diagnosis and prevention, with the potential to validate and complement common biochemical analysis, providing a more integrative approach. In this study, we thoroughly characterized the plasma polar lipid content of Tursiops truncatus, the most common cetacean species held under human care. Our results showed that phosphatidylcholine, lysophosphatidylcholine, and sphingomyelins (CerPCho) are the most represented phospholipid classes in T. truncatus plasma. Palmitic, oleic, and stearic acids are the major fatty acid (FA) present esterified to the plasma polar lipids of this species, although some n-3 species are also remarkably present, namely eicosapentaenoic and docosahexaenoic acids. The polar lipidome identified by HILIC LC-MS allowed identifying 304 different lipid species. These species belong to the phosphatidylcholine (103 lipid species), lysophosphatidylcholine (35), phosphatidylethanolamine (71), lysophosphatidylethanolamine (20), phosphatidylglycerol (13), lysophosphatidylglycerol (5), phosphatidylinositol (15), lysophosphatidylinositol (3), phosphatidylserine (6) lysophosphatidylserine (1), and sphimgomyelin (32) classes. This was the first time that the dolphin plasma phospholipid profile was characterized, providing a knowledge that will be important to further understand lipid metabolism and physiological regulation in small cetaceans. Furthermore, this study proved the practicability of the use of plasma lipid profiling for health assessment in marine mammals under human care.

Integration of Immunome With Disease-Gene Network Reveals Common Cellular Mechanisms Between IMIDs and Drug Repurposing Strategies

Objective

Development and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in IMIDs has been challenging due to the underlying complex molecular mechanism.

Methods

Using transcriptome profiles of 40 different immune cells, unsupervised machine learning, and disease-gene networks, we constructed the Disease-gene IMmune cell Expression (DIME) network and identified top DACs and DAGs of 12 phenotypically different IMIDs. We compared the DIME networks of IMIDs to identify common pathways between them. We used the common pathways and publicly available drug-gene network to identify promising drug repurposing targets.

Results

We found CD4+Treg, CD4+Th1, and NK cells as top DACs in inflammatory arthritis such as ankylosing spondylitis (AS), psoriatic arthritis, and rheumatoid arthritis (RA); neutrophils, granulocytes, and BDCA1+CD14+ cells in systemic lupus erythematosus and systemic scleroderma; ILC2, CD4+Th1, CD4+Treg, and NK cells in the inflammatory bowel diseases (IBDs). We identified lymphoid cells (CD4+Th1, CD4+Treg, and NK) and their associated pathways to be important in HLA-B27 type diseases (psoriasis, AS, and IBDs) and in primary-joint-inflammation-based inflammatory arthritis (AS and RA). Based on the common cellular mechanisms, we identified lifitegrast as a potential drug repurposing candidate for Crohn's disease and other IMIDs.

Conclusions

Existing methods are inadequate in capturing the intricate involvement of the crucial genes and cell types essential to IMIDs. Our approach identified the key DACs, DAGs, common mechanisms between IMIDs, and proposed potential drug repurposing targets using the DIME network. To extend our method to other diseases, we built the DIME tool (https://bitbucket.org/systemsimmunology/dime/) to help scientists uncover the etiology of complex and rare diseases to further drug development by better-determining drug targets, thereby mitigating the risk of failure in late clinical development.

Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light?

Cardiovascular disease (CVD) is the leading cause of mortality in South Asia, with rapidly increasing prevalence of hypertension, type 2 diabetes (T2DM) and hyperlipidemia over the last two decades. Atherosclerotic CVD (ASCVD) affects South Asians earlier in life and at lower body weights, which is not fully explained by differential burden of conventional risk factors. Heart failure (HF) is a complex clinical syndrome of heterogeneous structural phenotypes including two major clinical subtypes, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The prevalence of HF in South Asians is also rising with other metabolic diseases, and HFpEF develops at younger age and leaner body mass index in South Asians than in Whites. Recent genome-wide association studies, epigenome-wide association studies and metabolomic studies of ASCVD and HF have identified genes, metabolites and pathways associated with CVD traits. However, these findings were mostly driven by samples of European ancestry, which may not accurately represent the CVD risk at the molecular level, and the unique risk profile of CVD in South Asians. Such bias, while formulating hypothesis-driven research studies, risks missing important causal or predictive factors unique to South Asians. Importantly, a longitudinal design of multi-omic markers can capture the life-course risk and natural history related to CVD, and partially disentangle putative causal relationship between risk factors, multi-omic markers and subclinical and clinical ASCVD and HF. In conclusion, combining high-resolution untargeted metabolomics with epigenomics of rigorous, longitudinal design will provide comprehensive unbiased molecular characterization of subclinical and clinical CVD among South Asians. A thorough understanding of CVD-associated metabolomic profiles, together with advances in epigenomics and genomics, will lead to more accurate estimates of CVD progression and stimulate new strategies for improving cardiovascular health.

Perspective: Planning and Conducting Statistical Analyses for Human Nutrition Randomized Controlled Trials: Ensuring Data Quality and Integrity

Appropriate planning, execution, and reporting of statistical methods and results is critical for research transparency, validity, and reproducibility. This paper provides an overview of best practices for developing a statistical analysis plan a priori, conducting statistical analyses, and reporting statistical methods and results for human nutrition randomized controlled trials (RCTs). Readers are referred to the other NURISH (NUtrition inteRventIon reSearcH) publications for detailed information about the preparation and conduct of human nutrition RCTs. Collectively, the NURISH series outlines best practices for conducting human nutrition research.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Holo-omics for deciphering plant-microbiome interactions

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets-referred to here as holo-omics-provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness. Video abstract.

Multi-omics approaches to improve malaria therapy

Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.

Eight-lncRNA signature of cervical cancer were identified by integrating DNA methylation, copy number variation and transcriptome data

BACKGROUND

Copy number variation (CNV) suggests genetic changes in malignant tumors. Abnormal expressions of long non-coding RNAs (lncRNAs) resulted from genomic and epigenetic abnormalities play a driving role in tumorigenesis of cervical cancer. However, the role of lncRNAs-related CNV in cervical cancer remained largely unclear.

METHODS

The data of messenger RNAs (mRNAs), DNA methylation, and DNA copy number were collected from 292 cervical cancer specimens. The prognosis-related subtypes of cervical cancer were determined by multi-omics integration analysis, and protein-coding genes (PCGs) and lncRNAs with subtype-specific expressions were identified. The CNV pattern of the subtype-specific lncRNAs was analyzed to identify the subtype-specific lncRNAs. A prognostic risk model based on lncRNAs was established by least absolute shrinkage and selection operator (LASSO).

RESULTS

Multi-omics integration analysis identified three molecular subtypes incorporating 617 differentially expressed lncRNAs and 1395 differentially expressed PCGs. The 617 lncRNAs were found to intersect with disease-related lncRNAs. Functional enrichment showed that 617 lncRNAs were mainly involved in tumor metabolism, immunity and other pathways, such as p53 and cAMP signaling pathways, which are closely related to the development of cervical cancer. Finally, according to CNV pattern consistent with differential expression analysis, we established a lncRNAs-based signature consisted of 8 lncRNAs, namely, RUSC1-AS1, LINC01990, LINC01411, LINC02099, H19, LINC00452, ADPGK-AS1, C1QTNF1-AS1. The interaction of the 8 lncRNAs showed a significantly poor prognosis of cervical cancer patients, which has also been verified in an independent dataset.

CONCLUSION

Our study expanded the network of CNVs and improved the understanding on the regulatory network of lncRNAs in cervical cancer, providing novel biomarkers for the prognosis management of cervical cancer patients.

Genetic factors contributing to skeletal class III malocclusion: a systematic review and meta-analysis

OBJECTIVES

The present systematic review aims to report and critically assess the findings of the available scientific evidence from genetic association studies examining the genetic variants underlying skeletal class III malocclusion and its sub-phenotypes.

MATERIAL AND METHODS

A pre-piloted protocol was registered and followed. The PubMed, Scopus, WOS, Cochrane Library, Gray Open literature, and CADTH databases were explored for genetic association studies following PICOS-based selection criteria. The research was reported in accordance with PRISMA statement and HuGE guidelines. The Q-genie tool was applied to assess the quality of genetic studies. Meta-analysis of genetic association studies was done by means of Meta-Genyo tool.

RESULTS

A total of 8258 articles were retrieved, of which 22 were selected for in-depth analysis. Most of the studies did not differentiate between sub-phenotypes, and the cohorts were heterogeneous regarding ethnicity. Four to five principal components of class III malocclusion explained the phenotypic variation, and gene variants at MYO1H(rs10850110), BMP3(rs1390319), GHR (rs2973015,rs6184, rs2973015), FGF7(rs372127537), FGF10(rs593307), and SNAI3(rs4287555) (p < .05) explained most of the variation across the studies, associated to vertical, horizontal, or combined skeletal discrepancies. Meta-analysis results identified a statistically significant association between risk of class III malocclusion of A allele of the FBN3 rs7351083 [OR 2.13; 95% CI 1.1-4.1; p 0.02; recessive model].

CONCLUSION

Skeletal class III is a polygenic trait substantially modulated by ethnicity. A multicentric approach should be considered in future studies to increase sample sizes, applying multivariate analysis such as PCA and cluster analysis to characterize existing sub-phenotypes warranting a deeper analysis of genetic variants contributing to skeletal class III craniofacial disharmony.

CLINICAL RELEVANCE

Grasping the underlying mechanisms of this pathology is critical for a fuller understanding of its etiology, allowing generation of preventive strategies, new individualized therapeutic approaches and more accurate treatment planification strategies.

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

A multi-omics view of the complex mechanism of vascular calcification

Vascular calcification is a high incidence and high risk disease with increasing morbidity and high mortality, which is considered the consequence of smooth muscle cell transdifferentiation initiating the mechanism of accumulation of hydroxyl calcium phosphate. Vascular calcification is also thought to be strongly associated with poor outcomes in diabetes and chronic kidney disease. Numerous studies have been accomplished; however, the specific mechanism of the disease remains unclear. Development of the genome project enhanced the understanding of life science and has entered the post-genomic era resulting in a variety of omics techniques used in studies and a large amount of available data; thus, a new perspective on data analysis has been revealed. Omics has a broader perspective and is thus advantageous over a single pathway analysis in the study of complex vascular calcification mechanisms. This paper reviews in detail various omics studies including genomics, proteomics, transcriptomics, metabolomics and multiple group studies on vascular calcification. Advances and deficiencies in the use of omics to study vascular calcification are presented in a comprehensive view. We also review the methodology of the omics studies and omics data analysis and processing. In addition, the methodology and data processing presented here can be applied to other areas. An omics landscape perspective across the boundaries between genomics, transcriptomics, proteomics and metabolomics is used to examine the mechanisms of vascular calcification. The perspective combined with various technologies also provides a direction for the subsequent exploration of clinical significance.

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.

Overtraining Syndrome as a Complex Systems Phenomenon

The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.

Semi-automated NMR Pipeline for Environmental Exposures: New Insights on the Metabolomics of Smokers versus Non-smokers

Environmental exposure pathophysiology related to smoking can yield metabolic changes that are difficult to describe in a biologically informative fashion with manual proprietary software. Nuclear magnetic resonance (NMR) spectroscopy detects compounds found in biofluids yielding a metabolic snapshot. We applied our semi-automated NMR pipeline for a secondary analysis of a smoking study (MTBLS374 from the MetaboLights repository) (n = 112). This involved quality control (in the form of data preprocessing), automated metabolite quantification, and analysis. With our approach we putatively identified 79 metabolites that were previously unreported in the dataset. Quantified metabolites were used for metabolic pathway enrichment analysis that replicated 1 enriched pathway with the original study as well as 3 previously unreported pathways. Our pipeline generated a new random forest (RF) classifier between smoking classes that revealed several combinations of compounds. This study broadens our metabolomic understanding of smoking exposure by 1) notably increasing the number of quantified metabolites with our analytic pipeline, 2) suggesting smoking exposure may lead to heterogenous metabolic responses according to random forest modeling, and 3) modeling how newly quantified individual metabolites can determine smoking status. Our approach can be applied to other NMR studies to characterize environmental risk factors, allowing for the discovery of new biomarkers of disease and exposure status.

Advancing Food Allergy Through Omics Sciences

Since the publication of the first draft of the human genome, there has been an explosion of new technologies with increasing power to interrogate the totality of biological molecules (eg, DNA, RNA, proteins, metabolites) and their modifications (eg, DNA methylation, histone modifications). These technologies, collectively called omics, have been widely applied in the last 2 decades to study biological systems to gain deeper insight into mechanisms driving the physiology and pathophysiology of human health and disease. Because of its complex, multifactorial nature, food allergy is especially well suited to be investigated using omics approaches. In this rostrum, we review how omic technologies have been applied to explore diverse aspects of food allergy, including adaptive and innate immune processes in food-allergic responses, the role of the microbiome in food allergy risk, metabolic changes in the gut and blood associated with food allergy, and the identification of biomarkers and potential therapeutic targets for the condition. We discuss the strengths and limitations of the studies performed thus far and the need to adopt systems biology approaches that integrate data from multiple omics to fully leverage the potential of these technologies to advance food allergy research and care.

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

A Mouse Brain-based Multi-omics Integrative Approach Reveals Potential Blood Biomarkers for Ischemic Stroke

Stroke remains a leading cause of death and disability worldwide. Despite continuous advances, the identification of key molecular signatures in the hyper-acute phase of ischemic stroke is still a primary interest for translational research on stroke diagnosis, prognosis, and treatment. Data integration from high-throughput -omics techniques has become crucial to unraveling key interactions among different molecular elements in complex biological contexts, such as ischemic stroke. Thus, we used advanced data integration methods for a multi-level joint analysis of transcriptomics and proteomics data sets obtained from mouse brains at 2 h after cerebral ischemia. By modeling net-like correlation structures, we identified an integrated network of genes and proteins that are differentially expressed at a very early stage after stroke. We validated 10 of these deregulated elements in acute stroke, and changes in their expression pattern over time after cerebral ischemia were described. Of these, CLDN20, GADD45G, RGS2, BAG5, and CTNND2 were next evaluated as blood biomarkers of cerebral ischemia in mice and human blood samples, which were obtained from stroke patients and patients presenting stroke-mimicking conditions. Our findings indicate that CTNND2 levels in blood might potentially be useful for distinguishing ischemic strokes from stroke-mimicking conditions in the hyper-acute phase of the disease. Furthermore, circulating GADD45G content within the first 6 h after stroke could also play a key role in predicting poor outcomes in stroke patients. For the first time, we have used an integrative biostatistical approach to elucidate key molecules in the initial stages of stroke pathophysiology and highlight new notable molecules that might be further considered as blood biomarkers of ischemic stroke.

Predictors of stakeholders’ intention to adopt nutrigenomics

Background

Nutrigenomics is an emerging science that studies the relationship between genes, diet and nutrients that can help prevent chronic disease. The development of this science depends on whether the public accept its application; therefore, predicting their intention to adopt it is important for its successful implementation.

Objective

This study aims to analyse Malaysian stakeholders’ intentions to adopt nutrigenomics, and determines the factors that influence their intentions.

Methods

A survey was conducted based on the responses of 421 adults (aged 18 years and older) and comprising two stakeholder groups: healthcare providers (n = 221) and patients (n = 200) who were located in the Klang Valley, Malaysia. The SPSS software was used to analyse the descriptive statistics of intention to adopt nutrigenomics and the SmartPLS software was used to determine the predicting factors affecting their decisions to adopt nutrigenomics.

Results

The results show that the stakeholders perceived the benefits of nutrigenomics as outweighing its risks, suggesting that the perceived benefits represent the most important direct predictor of the intention to adopt nutrigenomics. The perceived risks of nutrigenomics, trust in key players, engagement with medical genetics and religiosity also predict the intention to adopt nutrigenomics. Additionally, the perceived benefits of nutrigenomics served as a mediator for four factors: perceived risks of nutrigenomics, engagement with medical genetics, trust in key players and religiosity, whilst the perceived risks were a mediator for engagement with medical genetics.

Conclusion

The findings of this study suggest that the intentions of Malaysian stakeholders to adopt nutrigenomics are a complex decision-making process where all the previously mentioned factors interact. Although the results showed that the stakeholders in Malaysia were highly positive towards nutrigenomics, they were also cautious about adopting it.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.