Toward a Taxonomy for Multi-Omics Science? Terminology Development for Whole Genome Study Approaches by Omics Technology and Hierarchy

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Integration of Physiological, Transcriptomic, and Metabolomic Analyses Reveal Molecular Mechanisms of Salt Stress in Maclura tricuspidata

Salt stress is a universal abiotic stress that severely affects plant growth and development. Understanding the mechanisms of Maclura tricuspidate's adaptation to salt stress is crucial for developing salt-tolerant plant varieties. This article discusses the integration of physiology, transcriptome, and metabolome to investigate the mechanism of salt adaptation in M. tricuspidata under salt stress conditions. Overall, the antioxidant enzyme system (SOD and POD) of M. tricuspidata exhibited higher activities compared with the control, while the content of soluble sugar and concentrations of chlorophyll a and b were maintained during salt stress. KEGG analysis revealed that deferentially expressed genes were primarily involved in plant hormone signal transduction, phenylpropanoid and flavonoid biosynthesis, alkaloids, and MAPK signaling pathways. Differential metabolites were enriched in amino acid metabolism, the biosynthesis of plant hormones, butanoate, and 2-oxocarboxylic acid metabolism. Interestingly, glycine, serine, and threonine metabolism were found to be important both in the metabolome and transcriptome-metabolome correlation analyses, suggesting their essential role in enhancing the salt tolerance of M. tricuspidata. Collectively, our study not only revealed the molecular mechanism of salt tolerance in M. tricuspidata, but also provided a new perspective for future salt-tolerant breeding and improvement in salt land for this species.

Towards a Multi-Omics of Male Infertility

Infertility is a common problem affecting one in six couples and in 30% of infertile couples, the male factor is a major cause. A large number of genes are involved in spermatogenesis and a significant proportion of male infertility phenotypes are of genetic origin. Studies on infertility have so far primarily focused on chromosomal abnormalities and sequence variants in protein-coding genes and have identified a large number of disease-associated genes. However, it has been shown that a multitude of factors across various omics levels also contribute to infertility phenotypes. The complexity of male infertility has led to the understanding that an integrated, multi-omics analysis may be optimal for unravelling this disease. While there is a vast array of different factors across omics levels associated with infertility, the present review focuses on known factors from the genomics, epigenomics, transcriptomics, proteomics, metabolomics, glycomics, lipidomics, miRNomics, and integrated omics levels. These include: repeat expansions in AR, POLG, ATXN1, DMPK, and SHBG, multiple SNPs, copy number variants in the AZF region, disregulated miRNAs, altered H3K9 methylation, differential MTHFR, MEG3, PEG1, and LIT1 methylation, altered protamine ratios and protein hypo/hyperphosphorylation. This integrative review presents a step towards a multi-omics approach to understanding the complex etiology of male infertility. Currently only a few genetic factors, namely chromosomal abnormalities and Y chromosome microdeletions, are routinely tested in infertile men undergoing intracytoplasmic sperm injection. A multi-omics approach to understanding infertility phenotypes may yield a more holistic view of the disease and contribute to the development of improved screening methods and treatment options. Therefore, beside discovering as of yet unknown genetic causes of infertility, integrating multiple fields of study could yield valuable contributions to the understanding of disease development. Future multi-omics studies will enable to synthesise fragmented information and facilitate biomarker discovery and treatments in male infertility.

Using omics approaches to dissect the therapeutic effects of Chinese herbal medicines on gastrointestinal cancers

Chinese herbal medicines offer a rich source of anti-cancer drugs. Differences between the pharmacology of Chinese herbal medicines and modern synthetic chemicals hinder the development of drugs derived from herbal products. To address this challenge, novel omics approaches including transcriptomics, proteomics, genomics, metabolomics, and microbiomics have been applied to dissect the pharmacological benefits of Chinese herbal medicines in cancer treatments. Numerous Chinese herbal medicines have shown potential anti-tumor effects on different gastrointestinal (GI) cancers while eliminating the side effects associated with conventional cancer therapies. The present study aimed to provide an overview of recent research focusing on Chinese herbal medicines in GI cancer treatment, based on omics approaches. This review also illustrates the potential utility of omics approaches in herbal-derived drug discovery. Omics approaches can precisely and efficiently reveal the key molecular targets and intracellular interaction networks of Chinese herbal medicines in GI cancer treatment. This study summarizes the application of different omics-based approaches in investigating the effects and mechanisms of Chinese herbal medicines in GI cancers. Future research directions are also proposed for this area of study.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Integrative Map of HIF1A Regulatory Elements and Variations

Hypoxia-inducible factor (HIF) family of transcription factors (HIF1A, EPAS1, and HIF3A) are regulators of the cellular response to hypoxia. They have been shown to be involved in development of various diseases such as cancer, diabetes, and erythrocytosis. A complete map of connections between HIF family of genes with various omics types has not yet been developed. The main aim of the present analysis was to construct the integrative map of genomic elements associated with HIF1A gene and prioritize potentially deleterious variants. Various genomic databases and bioinformatics tools were used, including Ensembl, MirTarBase, STRING, Cytoscape, MethPrimer, CADD, SIFT, and UALCAN. Integrative HIF1A gene map was visualized and includes transcriptional and post-transcriptional regulators, downstream targets, and genetic variants. One CpG island overlaps transcription start site of the HIF1A gene. Out of over 450 missense variants, four have predicted deleterious effect on protein function by at least five bioinformatics tools. Currently there are 85 miRNAs reported to target HIF1A. HIF1A downstream targets include protein-coding genes, long noncoding RNAs, and microRNAs (hypoxamiRs). The study presents the first integration of heterogeneous molecular interactions associated with HIF1A gene enabling a holistic view of the gene and lays the groundwork for supplementing the data in the future.

Current opinion on risk assessment of cosmetics

Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and invitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.

A Multi-Omics Analysis of PON1 Lactonase Activity in Relation to Human Health and Disease

Paraoxonase 1 (PON1) enzyme has antioxidative properties and is present in mammalian blood and several other body fluids. In blood, PON1 is usually integrated into the high-density lipoprotein (HDL) cholesterol. PON1 is a highly versatile enzyme displaying diverse functions such as arylesterase, lactonase, and paraoxonase, among others. PON1 activities are usually investigated with artificial substrates, for example, dihydrocoumarin and thiobutyl butyrolactone for lactonase activity. The PON1 enzyme activities measured with different substrates tend to be falsely assumed as being equivalent in the literature, although there are poor or weak correlations among the PON1 enzyme activities with different substrates. In addition, and despite our knowledge of the factors influencing PON1 paraoxonase and arylesterase activities, there is little knowledge of PON1 lactonase activity variations and attendant mechanisms. This is important considering further that the lactonase activity is the native activity of PON1. We report here a multi-omics analysis of PON1 lactonase activity. The influence of genetic variations, particularly of single nucleotide polymorphisms and epigenetic, proteomic, and lipidomic variations on PON1 lactonase activity are reviewed. In addition, the influence of various environmental, clinical, and demographic variables on PON1 lactonase activity is discussed. Finally, we examine the associations between PON1 lactonase activity and health states and common complex diseases such as atherosclerosis, dementias, obesity, and diabetes. To the best of our knowledge, this is the first multi-omics analysis of PON1 lactonase activity with an eye to future applications in basic life sciences and translational medicine and the nuances of critically interpreting PON1 function with lactones as substrates.

Glycomics: Immunoglobulin G N-Glycosylation Associated with Mammary Gland Hyperplasia in Women

Mammary gland hyperplasia (MGH) is very common, especially among young and middle-aged women. New diagnostics and biomarkers for MGH are needed for rational clinical management and precision medicine. We report, in this study, new findings using a glycomics approach, with a focus on immunoglobulin G (IgG) N-glycosylation. A cross-sectional study was conducted in a community-based population sample in Beijing, China. A total of 387 participants 40-65 years of age were enrolled in this study, including 194 women with MGH (cases) and 193 women who had no MGH (controls). IgG N-glycans were characterized in the serum by ultra-performance liquid chromatography. The levels of the glycan peaks (GPs) GP2, GP5, GP6, and GP7 were lower in the MGH group compared with the control group, whereas GP14 was significantly higher in the MGH group (p < 0.05). A predictive model using GP5, GP21, and age was established and a receiver operating characteristic curve analysis was performed. The sensitivity and specificity of the model for MGH was 61.3% and 63.2%, respectively, likely owing to receptor mechanisms and/or inflammation regulation. To the best of our knowledge, this is the first study reporting on an association between IgG N-glycosylation and MGH. We suggest person-to-person variations in IgG N-glycans and their combination with multiomics biomarker strategies offer a promising avenue to identify novel diagnostics and individuals at increased risk of MGH.

Omics biomarkers for frailty in older adults

Frailty is a clinical state characterized by an age-related unsteady state of the body, a decline in physiological function, and an increased vulnerability to adverse outcomes. Early diagnosis of frailty is important for improving the quality of life in older adults and promoting healthy aging. The biological mechanisms underlying frailty have been extensively studied in recent years. Combining assessment tools and biomarkers can facilitate the early diagnosis of frailty. However, there is a lack of stable and reliable frailty-related biomarkers for use in clinical practice. Advances in the multi-omics platforms have provided new information on the molecular mechanisms underlying frailty. Thus, identifying biomarkers using omics-based approaches helps explore the physiological mechanisms underlying frailty, and aids the evaluation of the risk of frailty development and progression. This article reviews the current status of frailty biomarkers from the genomics, transcriptomics, proteomics, and metabolomics perspectives.

Molecular signature of eutopic endometrium in endometriosis based on the multi-omics integrative synthesis

PURPOSE

To synthesise data from genome-wide studies reporting molecular signature of eutopic endometrium through the phases of the menstrual cycle in endometriosis.

METHODS

Extraction of data from publications reporting genetic signatures characterising endometrium associated with endometriosis. The nomenclature of extracted differentially expressed transcripts and proteins was adopted according to the HUGO Gene Nomenclature Committee (HGNC). Loci were further sorted according to the different phases of the menstrual cycle, i.e. menstrual (M), proliferative (P), secretory (S), early-secretory (ES), mid-secretory (MS), late-secretory (LS), and not specified (N/S) if the endometrial dating was not available. Enrichment analysis was performed using the DAVID bioinformatics tool.

RESULTS

Altered molecular changes were reported by 21 studies, including 13 performed at the transcriptomic, 6 at proteomic, and 2 at epigenomic level. Extracted data resulted in a catalogue of total 670 genetic causes with available 591 official gene symbols, i.e. M = 3, P = 188, S = 81, ES = 82, MS = 173, LS = 36, and N/S = 28. Enriched pathways included oestrogen signalling pathway, extracellular matrix organization, and endothelial cell chemotaxis. Our study revealed that knowledge of endometrium biology in endometriosis is fragmented due to heterogeneity of published data. However, 15 genes reported as dysregulated by at least two studies within the same phase and 33 significantly enriched GO-BP terms/KEGG pathways associated with different phases of the menstrual cycle were identified.

CONCLUSIONS

A multi-omics insight into molecular patterns underlying endometriosis could contribute towards identification of endometrial pathological mechanisms that impact fertility capacities of women with endometriosis.

Rise of Systems Glycobiology and Personalized Glycomedicine: Why and How to Integrate Glycomics with Multiomics Science?

Glycomics is a rapidly emerging subspecialty of system sciences that evaluates the structures and functions of glycans in biological systems. Moreover, glycomics informs allied scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between-population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. For glycomics to make greater, systems-scale, contributions to biology and medical research, it is facing a new developmental challenge: transition from single omics to multiomics integrative technology platforms. A comprehensive map of all possible connections between glycomics and other omics types has not yet been developed. Glycomics aims to discover a complex interplay of molecular interactions; however, little is known about the regulatory networks controlling these complex processes. In addition, the glycomics knowledgebase is presently scattered across various publications and databases, and therefore does not enable a holistic or systems view of this study field. Therefore, researchers are not always aware, for example, that a given analyzed genetic locus is linked with glycans, and that there are also glycomics determinants of complex phenotypes in health and biology. This review presents several published examples of glycomics science in association with other omics levels, such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, ncRNomics, lipidomics, and interactomics. I also highlight the salient knowledge gaps and suggest future research directions. Understanding the interconnections of glycomics with other omics technologies will facilitate multiomics science and knowledge integration, enhance development of systems glycobiology and personalized glycomedicine.

How Do Postgenomic Innovations Emerge? Building Legitimacy by Proteomics Standards and Informing the Next-Generation Technology Policy

How do postgenomic innovations emerge and become legitimate? Proteomics, a frequently utilized postgenomic technology, provides a valuable case study of the sociotechnical strategies used by an emergent scientific field to establish its legitimacy and assert political power. Chief among these strategies is standard making, an inherently political process that requires examination through a critical social science lens. We report in this study an original case study from interviews with proteomics scientists and observations at conferences of the Human Proteome Organization and Australasian Proteomics Society over a 5-year period (2011-2015). The study contributes new knowledge on how an emerging postgenomic science uses standard-setting practices to politically legitimize a hitherto contested technology. Drawing on legitimacy theory, we show how proteomics scientists and organizations used standards as strategic tools to establish the legitimacy of this postgenomic field and affirm that proteomics can generate verifiable and reproducible results, thereby establishing it as a legitimate scientific field. Notably, legitimacy can be leveraged, at the same time, to maximize political power vis-à-vis other fields of science and as such embodies power relationships. These data collectively inform the broader context, in which postgenomic innovations emerge and legitimize, both technically and politically, through standards making. These findings have relevance for the design of next generation technology policies by demonstrating that standards are not "just" standards or neutral constructs but also tools to leverage political power of and by science and innovation actors, as shown in this case study of the emerging early phase of proteomics from 2011 to 2015.

A Map of the microRNA Regulatory Networks Identified by Experimentally Validated microRNA-Target Interactions in Five Domestic Animals: Cattle, Pig, Sheep, Dog, and Chicken

Domestic animals are members of the broader ecological context, in which humans are situated. Yet, genomics and systems science research have lagged behind and been relatively underappreciated in domestic animals compared to human genetics/genomics. Harnessing big data calls for omics data mapping studies in a broad range of mammals. To this end, microRNAs (miRNAs) regulate posttranscriptional expression of target genes, hence, governing different biological pathways and physiological processes. The knowledge of miRNA regulatory networks and maps is important for understanding regulation of gene expression and functions in both humans and domestic animals. However, complete miRNA regulatory networks have not yet been described in all species, particularly in domestic animals. We report here an original analysis so as to map the miRNA regulatory networks in domestic animals based on miRNA-target interactions (MTIs). Validated MTIs for five species; cattle, pig, sheep, dog, and chicken were extracted from the miRTarBase. miRNA regulomes were visualized using the Cytoscape software. The data in cattle, chicken, and pig were sufficient to visualize networks, identify central molecules, and subnetworks associated with the same phenotype; however, the MTI data in dog and sheep are still limited. We found several hub genes with large number of interactions, for example, 1 miRNA (bta-miR-17-5p) interacting with 27 genes and 7 miRNAs interacting with the same gene (tumor necrosis factor [TNF]) in cattle. In addition, two single-nucleotide polymorphisms were identified within the seed region of a previously demonstrated MTI, namely, between HMGB3 (high mobility group box 3) gene and bta-miR-17-5p. In summary, this miRNA regulome mapping study will enable and guide further studies of genome function in mammals with a view to applications in human as well as veterinary medicine. Furthermore, these miRNA regulomes can help to clarify fundamental pathways in cell biology and reveal molecular insights on phenotypic trait variability in common complex diseases and response phenotypes of drugs or other health interventions for precision medicine in the future.

Multi-omics response of Pannonibacter phragmitetus BB to hexavalent chromium

The release of hexavalent chromium [Cr(VI)] into water bodies poses a major threat to the environment and human health. However, studies of the biological response to Cr(VI) are limited. In this study, a toxic bacterial mechanism of Cr(VI) was investigated using Pannonibacter phragmitetus BB (hereafter BB), which was isolated from chromate slag. The maximum Cr(VI) concentrations with respect to the resistance and reduction by BB are 4000 mg L^-1 and 2500 mg L^-1, respectively. In the BB genome, more genes responsible for Cr(VI) resistance and reduction are observed compared with other P. phragmitetus strains. A total of 361 proteins were upregulated to respond to Cr(VI) exposure, including enzymes for Cr(VI) uptake, intracellular reduction, ROS detoxification, DNA repair, and Cr(VI) efflux and proteins associated with novel mechanisms involving extracellular reduction mediated by electron transfer, quorum sensing, and chemotaxis. Based on metabolomic analysis, 174 metabolites were identified. Most of the upregulated metabolites are involved in amino acid, glucose, lipid, and energy metabolisms. The results show that Cr(VI) induces metabolite production, while metabolites promote Cr(VI) reduction. Overall, multi-enzyme expression and metabolite production by BB contribute to its high ability to resist/reduce Cr(VI). This study provides details supporting the theory of Cr(VI) reduction and a theoretical basis for the efficient bioremoval of Cr(VI) from the environment.

Identification of a Novel Splice Variant of Neural Cell Adhesion Molecule in Glioblastoma Through Proteogenomics Analysis

Splice variants are known to be important in the pathophysiology of tumors, including the brain cancers. We applied a proteogenomics pipeline to identify splice variants in glioblastoma (GBM, grade IV glioma), a highly malignant brain tumor, using in-house generated mass spectrometric proteomic data and public domain RNASeq dataset. Our analysis led to the identification of a novel exon that maps to the long isoform of Neural cell adhesion molecule 1 (NCAM1), expressed on the surface of glial cells and neurons, important for cell adhesion and cell signaling. The presence of the novel exon is supported with the identification of five peptides spanning it. Additional peptides were also detected in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel separated proteins from GBM patient tissue, underscoring the presence of the novel peptides in the intact brain protein. The novel exon was detected in the RNASeq dataset in 18 of 25 GBM samples and separately validated in additional 10 GBM tumor tissues using quantitative real-time-polymerase chain reaction (qRT-PCR). Both transcriptomic and proteomic data indicate downregulation of NCAM1, including the novel variant, in GBM. Domain analysis of the novel NCAM1 sequence indicates that the insertion of the novel exon contributes extra low-complexity region in the protein that may be important for protein-protein interactions and hence for cell signaling associated with tumor development. Taken together, the novel NCAM1 variant reported in this study exemplifies the importance of future multiomics research and systems biology applications in GBM.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

What Are the Multi-Omics Mechanisms for Adaptation by Microorganisms to High Alkalinity? A Transcriptomic and Proteomic Study of a Bacillus Strain with Industrial Potential

Alkaliphilic organisms are among an industrially important class of extremophile microorganisms with the ability to thrive at pH 10-11.5. Microorganisms that exhibit alkaliphilic characteristics are sources of alkali-tolerant enzymes such as proteases, starch degrading enzymes, cellulases, and metabolites such as antibiotics, enzyme inhibitors, siderophores, organic acids, and cholic acid derivatives, which have found various applications in industry for human and environmental health. Yet, multi-omics mechanisms governing adaptation to high alkalinity have been poorly studied. We undertook the present work to understand, as a case study, the alkaliphilic adaptation strategy of the novel microorganism, Bacillus marmarensis DSM 21297, to alkaline conditions using a multi-omics approach that employed transcriptomics and proteomics. As alkalinity increased, bacteria remodeled the peptidoglycan layer by changing peptide moieties along with the peptidoglycan constituents and altered the cell membrane to reduce lipid motility and proton leakiness to adjust intracellular pH. Different transporters also contributed to the maintenance of this pH homeostasis. However, unlike in most well-known alkaliphiles, not only sodium ions but also potassium ions were involved in this process. Interestingly, increased pH has triggered the expression of neither general stress proteins nor gene encoding proteins associated with heat, salt, and nutrient stresses. Only an increase in the expression of oxidative stress related genes was evident. Endospore formation, also a phenomenon closely linked to stress, was unclear. This questioned if high pH was a real stress for B. marmarensis. These new findings, corroborated using the multi-omics approach of the present case study, broaden the knowledge on the mechanisms of alkaliphilic adaptation and might also potentially offer useful departure points for further industrial applications with other microorganisms.

Artificial Intelligence and Integrated Genotype⁻Phenotype Identification

The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes. Collaborations between biomedical and AI researchers will be highlighted in order to describe initial successes with an eye toward the future.

Evaluation of the impact of waterpipe tobacco smoke exposure on the activity and expression of rat hepatic CYP450: a pharmacokinetic study

Waterpipe smoke contains many toxic constituents that can alter drug pharmacokinetics. This study assessed the effect of waterpipe smoke exposure on the activity and expression of CYP450 enzymes in rats. Animals (n = 10/group) were exposed to either waterpipe smoke or side-stream cigarette smoke for 1 h/day (6 days/week) for 31 days, or fresh air (control). An intragastric cocktail solution containing three probe drugs, phenacetin, chlorzoxazone and testosterone was administered to assess the activity of CYP1A2, CYP2E1 and CYP3A, respectively. Serum concentrations were determined using LC-MS/MS and the pharmacokinetic parameters were calculated. The mRNA expression of hepatic enzymes was also quantified. Waterpipe and cigarette smoke exposure did not significantly alter the pharmacokinetics of phenacetin, chlorzoxazone and testosterone. For example, the clearance and drug exposure values were comparable among groups for all probe drugs. Additionally, there was no significant effect of waterpipe and cigarette smoke on mRNA expression of hepatic CYP1A2, CYP2E1 and CYP3A2. The results demonstrate that waterpipe smoke exposure had no effect on the functional expression of three key CYP450 isoforms in rats. Future research is required with longer exposure periods to waterpipe smoke. Such work serves to enhance current understanding of effect of waterpipe smoke exposure on pharmacokinetics.

Data integration strategies for predictive analytics in precision medicine

With the rapid growth of health-related data including genomic, proteomic, imaging and clinical, the arduous task of data integration can be overwhelmed by the complexity of the environment including data size and diversity. This report examines the role of data integration strategies for big data predictive analytics in precision medicine research. Infrastructure-as-code methodologies will be discussed as a means of integrating and managing data. This includes a discussion on how and when these strategies can be used to lower barriers and address issues of consistency and interoperability within medical research environments. The goal is to support translational research and enable healthcare organizations to integrate and utilize infrastructure to accelerate the adoption of precision medicine.

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

Biobanks are infrastructures for large-scale biology innovation. Governance of biobanks can be usefully informed by studies of publication trends, for example, on the types of biosamples employed in scientific publications. We examined trends in each of the serum, plasma, peripheral blood mononuclear cell (PBMC), buffy coat, tissue, and gut microbiome biosample-related scientific publications over the past 40 years, using data between 1977 and 2016 from the Scopus database. We found that the number of tissue-related publications was the highest in each year of our analysis than other biosamples, but was generally less than the sum of serum- and plasma-related publications. Importantly, the microbiome publications increased greatly starting in the 2010s, and currently overtook the number of publications on PBMC and buffy coat. Among serum-, plasma-, and tissue-related publications, the number of protein- and RNA-related publications was generally higher than cell-free DNA-, DNA-, and metabolite-related publications for the past 40 years. Mass spectrometry- and next-generation sequencing-related publications have increased dramatically since the 2000s and 2010s, respectively. Microbiome- and metabolite-related biosamples can help diversify future biosample collections, while tissue collections appear to maintain their importance in scientific publications. We also report here our observations on the countries that use biosample research (e.g., China, United Kingdom, United States, and others). These publication trends by the type of biosamples illuminate roadmaps by which biobanks might establish and diversify their biosample collections in the future. In addition, we note that biobanks need to secure biosamples appropriate for integrated analysis of multi-omics research data.

Genome-wide screening for smallest regions of overlaps in cryptorchidism

Cryptorchidism is a urogenital abnormality associated with increased rates of testicular neoplasia and impaired spermatogenesis. The field is facing expansion of genomics data; however, it lacks protocols for biomarker prioritization. Identification of smallest regions of overlap (SRO) presents an approach for candidate gene identification but has not yet been systematically conducted in cryptorchidism. The aim of this study was to conduct a genome-wide screening for SRO (GW-SRO) associated with cryptorchidism development. We updated the Cryptorchidism Gene Database to version 3, remapped genomic coordinates of loci from older assemblies to the GRCh38 and performed genome-wide screening for overlapping regions associated with cryptorchidism risk. A total of 73 chromosomal loci (68 involved in chromosomal mutations and five copy number variations) described in 37 studies associated with cryptorchidism risk in humans were used for SRO identification. Analysis resulted in 18 SRO, based on deletions, duplications, inversions, derivations and copy number variations. Screening for SRO was challenging owing to heterogeneous reporting of genomic locations. To our knowledge, this is the first GW-SRO study for cryptorchidism and it presents the basis for further narrowing of critical regions for cryptorchidism and planning functional experiments. The developed protocol could also be applied to other multifactorial diseases.

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics-DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

Genome-Wide Association Studies for Idiosyncratic Drug-Induced Hepatotoxicity: Looking Back-Looking Forward to Next-Generation Innovation

Idiosyncratic drug-induced hepatotoxicity is a formidable challenge for rational drug discovery and development, as well as the science of personalized medicine. There is evidence that hereditary factors, in part, contribute to drug toxicity. This expert analysis and review offer the insights gained, and the challenges ahead, for genome-wide association studies (GWASs) of idiosyncratic drug-induced hepatotoxicity. Published articles on genome-wide and subsequent replication studies were systematically searched in the PubMed electronic database. We found that the genetic risk variants that were identified genome-wide, and replication confirmed, are mainly related to polymorphisms in the human leukocyte antigen (HLA) region that include HLA-DQB1*06:02 for amoxicillin-clavulanate, HLA-B*57:01 for flucloxacillin, HLA-DRB1*15:01 for lumiracoxib, and HLA-DRB1*07:01 for lapatinib and ximelagatran-induced hepatotoxicity. Additionally, polymorphisms in ST6 β-galactosamide α-2, 6-sialyltranferase-1 (ST6GAL1), which plays a role in systemic inflammatory response, and variants in intron of family with sequence similarity-65 member-B (FAM65B) that play roles in liver inflammation displayed association with flucloxacillin and antituberculosis drug-induced hepatotoxicity, respectively. Taken together, these GWAS findings offer molecular leads on the central role that the immune system plays in idiosyncratic drug-induced hepatotoxicity. We conclude the expert review with a brief discussion of the salient challenges ahead. These include, for example, the need for discursive discovery paradigms that incorporate alternating GWASs and candidate gene studies, as well as the study of the environtome, the entire complement of environmental factors, including science and innovation policies that enact on global society and the human host, and by extension, on susceptibility for idiosyncratic drug-induced hepatotoxicity.