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Background and Purpose: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). Methods: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. Results: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance ( P −9 ) and 4 novel loci at genome-wide significance ( P −8 ), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke ( P −6 ). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2 , HDAC9 , ZFHX3 , and LRCH1 . Conclusions: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

European journal of cancer 160, 80-91 (2022). doi:10.1016/j.ejca.2021.10.007, Published by Elsevier, Amsterdam [u.a.]

Background: Addressing policy change in traumatic injury care demands a strong evidence base from which to inform necessary amendments, and measure the impact of any change. Current recommendations for acute traumatic spinal cord injury include admission to a specialist Spinal Cord Injury Unit within 24 hours from injury. This study aimed to document pathways for patients with traumatic spinal cord injury across a state-wide Health Service in a historical cohort, prior to and in order to inform trauma policy changes. Methods: Retrospective analysis of a large Ambulance service record-linked dataset, containing 2.04 million Ambulance records linked with hospital and death records (2006-09). Incident cases of traumatic spinal cord injury were identified using ICD-10-AM codes. Multivariate analysis aimed to identify factors associated with admission to specialist units within 24 hours. Results: Of 311 patients with confirmed traumatic spinal cord injury, 177 (56.9%) were admitted to a specialist Spinal Cord Injury Unit, with 130 of these (73.4%) being within 24 hours post injury. The remaining 47 (26.6%) had up to several months delayed transfer to SCIU. Patients were significantly more likely to have timely admission to SCIU with a cervical level cord injury (OR 2.05), aeromedical transfer to a specialist unit (OR 2.5), outer regional geographic location of injury (OR 2.05), or a surgical spinal procedure within 24 hours (OR 3.1). Patients were significantly less likely to be admitted to a specialist unit within 24 hours were those who experienced more than one hospital transfer (OR 0.28), and patients >75 years (OR 0.35). Conclusion: Historically across this state-wide Health Service, patients with traumatic spinal cord injury did not experience consistent treatment pathways. Publication of this study importantly provides a baseline from which changes to clinical policies that have occurred since 2009 can be evaluated.

PURPOSE OF REVIEW: Decades of research in acute respiratory distress syndrome (ARDS) have led to few interventions that impact clinical outcomes. The pandemic of patients with ARDS due to the novel SARS-CoV-2 infection has stressed the need for more effective therapies in ARDS. Phenotyping may enable successful trials and precision therapeutics in this patient population. RECENT FINDINGS: Clinical phenotypes that group patients by shared cause, time-course or radiographic presentation are of prognostic value, but their use is limited by misclassification. Physiological phenotypes, including the P/F ratio, ventilatory ratio and dead space fraction, predict poor outcomes but can rapidly change, making them unstable over time. Biologic phenotypes have prognostic value with composite clinical and biomarker sub-phenotypes additionally impacting treatment response but are yet to be prospectively validated. SUMMARY: Although much progress has been made in ARDS phenotyping, implementation of precision medicine practices will depend on conducting phenotype-aware trials using rapid point of care assays or machine learning algorithms. Omics studies will enhance our understanding of biologic determinants of clinical outcomes in ARDS sub-phenotypes. Whether biologic ARDS sub-phenotypes are specific to this syndrome or rather more broadly identify endotypes of critical illness remains to be determined.

The slowdown, inhibition, or reversal of age-related decline (as a composite of disease, dysfunction, and, ultimately, death) by diet or natural compounds can be defined as dietary geroprotection. While there is no single reliable biomarker to judge the effects of dietary geroprotection, biomarker signatures based on omics (epigenetics, gene expression, microbiome composition) are promising candidates. Recently, omic biomarkers started to supplement established clinical ones such as lipid profiles and inflammatory cytokines. In this review, we focus on human data. We first summarize the current take on genetic biomarkers based on epidemiological studies. However, most of the remaining biomarkers that we describe, whether omics-based or clinical, are related to intervention studies. Then, because of their promising potential in the context of dietary geroprotection, we focus on the effects of berry-based interventions, which up to now have been mostly described employing clinical markers. We provide an aggregation and tabulation of all the recent systematic reviews and meta-analyses that we could find related to this topic. Finally, we present evidence for the importance of the "nutribiography," that is, the influence that an individual's history of diet and natural compound consumption can have on the effects of dietary geroprotection.

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

PURPOSE: Due to growing concerns about the obesity pandemic as a worldwide phenomenon, a global effort has been made for managing it and associated disorders. Accordingly, metabolomics as a promising field of “OMICS” is presented for investigating different molecular pathways in obesity and related disorders through the evaluation of specific metabolites in both animal and human subjects. Herein, the aim of the present study as the first systematic review is to evaluate all available studies about different mechanisms and their biomarkers discovery using metabolomics approaches. METHOD: The study was designed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Using a comprehensive search strategy we searched in databases including; Web of Science, PubMed, and Scopus using specific keywords. Based on predefined inclusion/exclusion criteria study selection has been conducted considering the type of studies, participant, and outcome measures. Quality assessment was done using CASP (Critical Appraisal Skills Programme) checklist followed by data extraction according to a predefined data extraction sheet. RESULTS: Among the articles that resulted from electronic search, a total of 74 articles met our inclusion criteria. The most prevalent studied metabolites were amino acids and lipid derivatives and both targeted and non-targeted approaches were applied for metabolomics studies. CONCLUSION: This systematic review summarized a wide range of studies regardless of the age, history, language, and type of the study. Further studies are needed to compare the application of emerging methods in the treatment of obesity and related disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40200-021-00917-w.

Pancreatic ductal adenocarcinoma (PDAC), already among the deadliest epithelial malignancies, is rising in both incidence and contribution to overall cancer deaths. Decades of research have improved our understanding of PDAC carcinogenesis, including characterizing germline predisposition, the cell of origin, precursor lesions, the sequence of genetic alterations, including simple and structural alterations, transcriptional changes and subtypes, tumour heterogeneity, metastatic progression and the tumour microenvironment. These fundamental advances inform contemporary translational efforts in primary prevention, screening and early detection, multidisciplinary management and survivorship, as prospective clinical trials begin to adopt molecular-based selection criteria to guide targeted therapies. Genomic and transcriptomic data on PDAC were also included in the international pan-cancer analysis of approximately 2,600 cancers, a milestone in cancer research that allows further insight through comparison with other tumour types. Thus, this is an ideal time to review our current knowledge of PDAC evolution and heterogeneity, gained from the study of preclinical models and patient biospecimens, and to propose a model of PDAC evolution that takes into consideration findings from varied sources, with a particular focus on the genomics of human PDAC. This Review outlines our current understanding of the evolution and heterogeneity of pancreatic ductal adenocarcinoma (PDAC), which has advanced owing to the study of preclinical models and patient samples, and presents an evolutionary model of PDAC progression based primarily on genomics studies of human PDAC.

Sepsis remains a significant cause of neonatal mortality and morbidity, especially in low- and middle-income countries. Neonatal sepsis presents with nonspecific signs and symptoms that necessitate tests to confirm the diagnosis. Early and accurate diagnosis of infection will improve clinical outcomes and decrease the overuse of antibiotics. Current diagnostic methods rely on conventional culture methods, which is time-consuming, and may delay critical therapeutic decisions. Nonculture-based techniques including molecular methods and mass spectrometry may overcome some of the limitations seen with culture-based techniques. Biomarkers including hematological indices, cell adhesion molecules, interleukins, and acute-phase reactants have been used for the diagnosis of neonatal sepsis. In this review, we examine past and current microbiological techniques, hematological indices, and inflammatory biomarkers that may aid sepsis diagnosis. The search for an ideal biomarker that has adequate diagnostic accuracy early in sepsis is still ongoing. We discuss promising strategies for the future that are being developed and tested that may help us diagnose sepsis early and improve clinical outcomes. IMPACT: Reviews the clinical relevance of currently available diagnostic tests for sepsis. Summarizes the diagnostic accuracy of novel biomarkers for neonatal sepsis. Outlines future strategies including the use of omics technology, personalized medicine, and point of care tests.

Introduction: Early diagnosis of Alzheimer disease (AD) through the use of biomarkers could assist in the implementation and monitoring of early therapeutic interventions, and has the potential to significantly modify the course of the disease. Development: The classic cerebrospinal fluid and approved structural and functional neuroimaging biomarkers are of limited clinical application given their invasive nature and/or high cost. The identification of more accessible and less costly biomarkers, such as blood biomarkers, would increase their use in clinical practice. We review the available published evidence on the main blood biochemical biomarkers potentially useful for diagnosing AD. Conclusions: Blood biomarkers are more cost- and time-effective than CSF biomarkers. However, immediate applicability in clinical practice is relatively unlikely. The main limitations come from the difficulty of measuring and standardising thresholds between different laboratories and the failure to replicate results. Of all the molecules studied, apoptosis and neurodegeneration biomarkers and the biomarker panels obtained through “omics” approaches, such as isolated or combined metabolomics, offer the most promising results. Resumen: Introducción: El diagnóstico precoz de la enfermedad de Alzheimer mediante la utilización de biomarcadores podría facilitar la instauración y monitorización de intervenciones terapéuticas tempranas con potencial capacidad para modificar significativamente el curso de la enfermedad. Desarrollo: Los biomarcadores clásicos de líquido cefalorraquídeo y de neuroimagen estructural y funcional aprobados tienen una aplicación clínica limitada, dado su carácter invasivo o su elevado coste. La identificación de biomarcadores más accesibles y menos costosos, como los sanguíneos, facilitaría su aplicación en la práctica clínica. Se presenta una revisión bibliográfica de los principales biomarcadores bioquímicos sanguíneos con potencial utilidad para el diagnóstico de la enfermedad de Alzheimer. Conclusiones: Los biomarcadores sanguíneos son coste y tiempo efectivos con respecto a los marcadores de líquido cefalorraquídeo. Sin embargo, la aplicabilidad inmediata de los biomarcadores bioquímicos sanguíneos en la práctica clínica es poco esperable. Las principales limitaciones estriban en la dificultad para la medición y estandarización de los umbrales entre los diferentes laboratorios y en los fallos de replicación de resultados. Entre todas las moléculas estudiadas, los biomarcadores de apoptosis y neurodegeneración, al igual que los paneles de biomarcadores obtenidos mediante aproximaciones ómicas – como la metabolómica de forma aislada o combinada – ofrecen los resultados más prometedores.

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Mitochondrial disorders are one of the most common neurometabolic disorders affecting all age groups. The phenotype-genotype heterogeneity in these disorders can be attributed to the dual genetic control on mitochondrial functions, posing a challenge for diagnosis. Though the advancement in the high-throughput sequencing and other omics platforms resulted in a "genetics-first" approach, the muscle biopsy remains the benchmark in most of the mitochondrial disorders. This review focuses on the myopathological aspects of primary mitochondrial disorders. The utility of muscle biopsy is not limited to analyse the structural abnormalities; rather it also proves to be a potential tool to understand the deranged sub-cellular functions.

Background Primary prevention trials have demonstrated that the traditional Mediterranean diet is associated with a reduction in cardiovascular mortality and morbidity. However, this benefit has not been proven for secondary prevention after an acute coronary syndrome (ACS). We hypothesized that a high-intensity Mediterranean diet intervention after an ACS decreases the vulnerability of atherosclerotic plaques by complex interactions between anti-inflammatory effects, microbiota changes and modulation of gene expression. Methods The MEDIMACS project is an academically funded, prospective, randomized, controlled and mechanistic clinical trial designed to address the effects of an active randomized intervention with the Mediterranean diet on atherosclerotic plaque vulnerability, coronary endothelial dysfunction and other mechanistic endpoints. One hundred patients with ACS are randomized 1:1 to a monitored high-intensity Mediterranean diet intervention or to a standard-of-care arm. Adherence to diet is assessed in both arms using food frequency questionnaires and biomarkers of compliance. The primary endpoint is the change (from baseline to 12 months) in the thickness of the fibrous cap of a non-significant atherosclerotic plaque in a non-culprit vessel, as assessed by repeated optical coherence tomography intracoronary imaging. Indices of coronary vascular physiology and changes in gastrointestinal microbiota, immunological status and protein and metabolite profiles will be evaluated as secondary endpoints. Discussion The results of this trial will address the key effects of dietary habits on atherosclerotic risk and will provide initial data on the complex interplay of immunological, microbiome-, proteome- and metabolome-related mechanisms by which non-pharmacological factors may impact the progression of coronary atherosclerosis after an ACS. Trial registration ClinicalTrials.govNCT03842319. Registered on 13 May 2019

Researchers have endeavored to identify the etiology of inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis. Though the pathogenesis of inflammatory bowel diseases remains unknown, dysregulation of the immune system in the host gastrointestinal tract is believed to be the major causative factor. Omics is a powerful methodological tool that can reveal biochemical information stored in clinical samples. Lipidomics is a subset of omics that explores the lipid classes associated with inflammation. One objective of the present systematic review was to facilitate the identification of biochemical targets for use in future lipidomic studies on inflammatory bowel diseases. The use of high-resolution mass spectrometry to observe alterations in global lipidomics might help elucidate the immunoregulatory mechanisms involved in inflammatory bowel diseases and discover novel biomarkers for them. Assessment of the characteristics of previous clinical trials on inflammatory bowel diseases could help researchers design and establish patient selection and analytical method criteria for future studies on these conditions. In this study, we curated literature exclusively from four databases and extracted lipidomics-related data from literature, considering criteria. This paper suggests that the lipidomics approach toward research in inflammatory bowel diseases can clarify their pathogenesis and identify clinically valuable biomarkers to predict and monitor their progression.

Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed. This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics. We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020. Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies. Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.

The growing human population is currently facing an unprecedented challenge on global food production and sustainability. Despite recognizing poultry as one of the most successful and rapidly growing food industries to address this challenge; poultry health and safety remain major issues that entail immediate attention. Bacterial diseases including colibacillosis, salmonellosis, and necrotic enteritis have become increasingly prevalent during poultry production. Likewise, outbreaks caused by consumption of undercooked poultry products contaminated with zoonotic bacterial pathogens such as Salmonella, Campylobacter and Listeria, are a serious public health concern. With antimicrobial resistance problem and restricted use of antibiotics in food producing animals, bacteriophages are increasingly recognized as an attractive natural antibacterial alternative. Bacteriophages have recently shown promising results to treat diseases in poultry, reduce contamination of carcasses, and enhance the safety of poultry products. Omics technologies have been successfully employed to accurately characterize bacteriophages and their genes/proteins important for interaction with bacterial hosts. In this review, the potential of using lytic bacteriophages to mitigate the risk of major poultry-associated bacterial pathogens are explored. This study also explores challenges associated with the adoption of this technology by industries. Furthermore, the impact of omics approaches on studying bacteriophages, their host interaction and applications is discussed.

Background Traditional Chinese Medicine (TCM) is distinguished by Syndrome differentiation, which prescribes various formulae for different Syndromes of same disease. This study aims to investigate the underlying mechanism. Methods Using a strategy which integrated proteomics, metabolomics study for clinic samples and network pharmacology for six classic TCM formulae, we systemically explored the biological basis of TCM Syndrome differentiation for two typical Syndromes of CHD: Cold Congealing and Qi Stagnation (CCQS), and Qi Stagnation and Blood Stasis (QSBS). Results Our study revealed that CHD patients with CCQS Syndrome were characterized with alteration in pantothenate and CoA biosynthesis, while more extensively altered pathways including D-glutamine and D-glutamate metabolism; alanine, aspartate and glutamate metabolism, and glyoxylate and dicarboxylate metabolism, were present in QSBS patients. Furthermore, our results suggested that the down-expressed PON1 and ADIPOQ might be potential biomarkers for CCQS Syndrome, while the down-expressed APOE and APOA1 for QSBS Syndrome in CHD patients. In addition, network pharmacology and integrated analysis indicated possible comorbidity differences between the two Syndromes, that is, CCQS or QSBS Syndrome was strongly linked to diabetes or ischemic stroke, respectively, which is consistent with the complication disparity between the enrolled patients with two different Syndromes. These results confirmed our assumption that the molecules and biological processes regulated by the Syndrome-specific formulae could be associated with dysfunctional objects caused by the Syndrome of the disease. Conclusion This study provided evidence-based strategy for exploring the biological basis of Syndrome differentiation in TCM, which sheds light on the translation of TCM theory in the practice of precision medicine. Supplementary Information The online version contains supplementary material available at 10.1186/s13020-021-00521-3., Highlights 1. Our work was based on clinical samples rather than pure data analysis or animal models. 2. We conducted multiple omics studies. Especially, as for metabolomics study, we performed both untargeted and targeted metabolomics experiments. 3. We performed network pharmacological study to cross-validated the results of multi-omics study. Although the data sources of network pharmacology were completely unrelated with our omics data, they came to the same conclusion about the difference of the two Syndromes. 4. In the network pharmacological study, we made efforts to collect and screen high-quality data. We collected data from multiple TCM databases and conducted drug likeness screening. Especially, we added quality markers of each herb, whose pharmacological relevance had been validated. To enhance the reliability of targets, for each Syndrome, we only studied common targets of 3 different TCM formulae prescribed for this Syndrome. Supplementary Information The online version contains supplementary material available at 10.1186/s13020-021-00521-3.

Objective This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC). Methods Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set. Results The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p Conclusions These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

BackgroundDifferential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.ResultsWe present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available athttps://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available athttps://diffcornet.github.io/CorDiffViz/demo.html.ConclusionsOur software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Lupus nephritis (LN) is severe renal comorbidity associated with systemic lupus erythematosus (SLE), a complex autoimmune disorder with high morbidity and mortality. Diagnosis and monitoring of LN patients still rely on renal biopsy, a procedure that exposes patients to a variety of risks and is not capable of providing longitudinally information about disease prognosis. In this review, we summarized current data of recent promising biomarkers developed in the precision medicine era, particularly under genomic, transcriptomic, proteomic, and metabolomic techniques. Genome-wide association studies have been evaluating the role of endogenous elements beyond the autoimmunity in LN. Transcriptomic methods, including single-cell sequencing, are potential tools in identifying inflammatory signatures, miRNAs, and gene expression. Proteomic measures, including anti-C1q antibodies, cytokines, TLRs, VCAM-1, NGAL osteopontin, angiostatin, have been considered helpful to provide a more profound comprehension of the disease pathogenic processes. Metabolomic approaches may identify several abnormal metabolite profiles related to the impairment of cellular functions. Together, these accurate, non-invasive, and moderate-cost propedeutic resources may be the novel tools for recognizing, distinguishing, and predicting LN progression and prognosis. Furthermore, omics evaluation may also predict responsiveness to treatment and, consequently, change the way we manage LN cases in the near future.

Motivation Precise prediction of cancer subtypes is of significant importance in cancer diagnosis and treatment. Disease etiology is complicated existing at different omics levels; hence integrative analysis provides a very effective way to improve our understanding of cancer. Results We propose a novel computational framework, named Deep Subspace Mutual Learning (DSML). DSML has the capability to simultaneously learn the subspace structures in each available omics data and in overall multi-omics data by adopting deep neural networks, which thereby facilitates the subtype’s prediction via clustering on multi-level, single-level and partial-level omics data. Extensive experiments are performed in five different cancers on three levels of omics data from The Cancer Genome Atlas. The experimental analysis demonstrates that DSML delivers comparable or even better results than many state-of-the-art integrative methods. Availability and implementation An implementation and documentation of the DSML is publicly available at https://github.com/polytechnicXTT/Deep-Subspace-Mutual-Learning.git.

Gastric cancer (GC) has been classified based on molecular profiling like The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG), and attempts have been made to establish therapeutic strategies based on these classifications. However, it is difficult to predict the survival according to these classifications especially in radically resected patients. We aimed to establish a new molecular classification of GC which predicts the survival in patients undergoing radical gastrectomy. The present study included 499 Japanese patients with advanced GC undergoing radical (R0/R1) gastrectomy. Whole-exome sequencing, panel sequencing, and gene expression profiling were conducted (High-tech Omics-based Patient Evaluation [Project HOPE]). We classified patients according to TCGA and ACRG subtypes, and evaluated the clinicopathologic features and survival. Then, we attempted to classify patients according to their molecular profiles associated with biological features and survival (HOPE classification). TCGA and ACRG classifications failed to predict the survival. In HOPE classification, hypermutated (HMT) tumors were selected first as a distinctive feature, and T-cell-inflamed expression signature-high (TCI) tumors were then extracted. Finally, the remaining tumors were divided by the epithelial-mesenchymal transition (EMT) expression signature. HOPE classification significantly predicted the disease-specific and overall survival (p

The past two decades have witnessed significant advances in high-throughput “omics” technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive “omics” profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR) - a framework that includes a variety of regression methods - in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data.

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This ‘one measure does not fit all’ situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of ‘precision medicine’ [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn’s and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person.This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

Medial arterial calcification (MAC) is a chronic systemic vascular disorder distinct from atherosclerosis that is frequently but not always associated with diabetes mellitus, chronic kidne...

Background and Purpose: The diagnosis of cardioembolic stroke can be challenging for patient management in secondary stroke prevention, particularly in the case of covert paroxysmal atrial fibrillation. The molecular composition of a cerebral thrombus is related to its origin. Therefore, proteomic and metabolomic analyses of the retrieved thrombotic material should allow the identification of biomarkers or signatures to improve the etiological diagnosis of stroke. Methods: In this pilot study, the proteome and metabolome of cerebral thrombi from atherothrombotic and cardioembolic stroke patients were studied according to ASCOD phenotyping (A: atherosclerosis; S: small-vessel disease; C: cardiac pathology; O: other causes; D: dissection), with the highest causality grade, from the ThrombiOMIC cohort (consecutive patients with stroke recanalized by mechanical thrombectomy in an acute phase). Proteomic and metabolomic results were used separately or combined, and the obtained omic signatures were compared with classical cardioembolic stroke predictors using pairwise comparisons of the area under receiver operating characteristics. Results: Among 59 patients of the ThrombiOMIC cohort, 34 patients with stroke showed a cardioembolic phenotype and 7 had an atherothrombotic phenotype. Two thousand four hundred fifty-six proteins and 5019 molecular features of the cerebral thrombi were identified using untargeted proteomic and metabolomic approaches, respectively. Area under receiver operating characteristics to predict the cardioembolic origin of stroke were calculated using the proteomic results (0.945 [95% CI, 0.871–1]), the metabolomic results (0.836 [95% CI, 0.714–0.958]), and combined signatures (0.996 [95% CI, 0.984–1]). The diagnostic performance of the combined signatures was significantly higher than that of classical predictors such as the plasmatic BNP (B-type natriuretic peptide) level (area under receiver operating characteristics, 0.803 [95% CI, 0.629–0.976]). Conclusions: The combined proteomic and metabolomic analyses of retrieved cerebral thrombi is a very promising molecular approach to predict the cardioembolic cause of stroke and to improve secondary stroke prevention strategies.

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID‐19 pandemic, multiomics‐based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID‐19. In order to help researchers and clinicians to keep up with the knowledge of COVID‐19, we summarized the most recent progresses reported in omics‐based research papers. This review discusses omics‐based approaches for studying COVID‐19, summarizing newly emerged SARS‐CoV‐2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID‐19. This review can help researchers and clinicians gain insight into COVID‐19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression., In the global COVID‐19 pandemic, multiomics‐based strategies have been applied into the surveillance of newly emerged SARS‐CoV‐2 variants and the study of diagnostic methods, risk factors, and pathological features of COVID‐19, which will contribute to the prevention and control of this pandemic.

Summary Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.

PURPOSE OF THE REVIEW: Asthma is the most common chronic disease of childhood. Investigations of the lower and upper airway microbiomes have significantly progressed over recent years, and their roles in pediatric asthma are becoming increasingly clear. RECENT FINDINGS: Early studies identified the existence of upper and lower airway microbiomes, including imbalances in both associated with pediatric asthma. The infant airway microbiome may offer predictive value for the development of asthma in later childhood, and it may also be influenced by external factors such as respiratory viral illness. The airway microbiome has also been associated with the clinical course of asthma, including rates of exacerbation and level of control. Advances in –omics sciences have enabled improved identification of the airway microbiome’s relationships with host response and function in children with asthma. Investigations are now moving towards application of the above findings to explore risk modification and treatment options. SUMMARY: The airway microbiome provides an intriguing window into pediatric asthma, offering insights into asthma diagnosis, clinical course, and perhaps treatment. Further investigation is needed to solidify these associations and translate research findings into clinical practice.

Merkel Cell Carcinoma (MCC) is a destructive form of neuroendocrine carcinoma that represents the 2nd highest cause of skin cancer related deaths despite being a relatively rare form of cancer. Different risk factors have been associated with MCC such as advancement age, immunosuppression, and ultraviolet light exposure, polyomavirus infection and a current, concurrent or previous diagnosis of Chronic Lymphocytic Leukaemia (CLL). However, the exact mechanism that leads to MCC and its inherent biology is yet to be fully researched and explored. The current treatments for MCC are a combination of surgery and radiation or chemo-radiation. So far, only two drugs have been approved by FDA for the treatment of MCC, with both drugs being immune checkpoint inhibitors. However, the drug approval from FDA was accelerated based on promising results from a relatively small number of patients, which means that the more research and trials are undergoing currently considering the fact that a very small cohort was used for clinical trials. Presently there are no biomarkers indicative of MCC and additional research is essential. With the advent of bioinformatics and microarray technology, in silico approaches pertaining to ‘omics’ data analysis, shall allow identification of hub genes and miRNA to broaden our understanding of MCC biology and their clinical utility.

Crop plants are hosting a variety of microorganisms in their surroundings, some are beneficial, but others are causing diseases that account for huge financial losses to the farmers. To prevent these, we need to understand molecular machinay of plant–pathogen interactions and genes/proteins involved in concerned biochemical pathways. Omics has provided us various approaches to unveil the different key components responsible for disease resistance/susceptibility. In this article, we are highlighting the existing omics techniques, i.e., genomics, transcriptomics, proteomics, metabolomics and phenomics which yield valuable data, along with bioinformatics, networks and system biology which aid in analyzing this enormous data for novel discovery. We are also highlighting the application of omics in the prevention and management of crop plant diseases, which include designing and development of markers, immunodiagnostic kits, defence inducers, agrochemicals and disease resistant transgenic plants. Thus, concluding that omics is a solution for safe, fast and efficient way to prevent and manage complex and recalcitrant diseases in crop plants.

Genetic improvement of soybean, one of the major crops providing edible oil and protein-rich food, is important to ensure balanced nutrition for the growing world population. To make soybean cultiv...

Background: The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is poorly understood and the relationships between systemic abnormalities in metabolism and AD/AMCI pathogenesis are unclear. Objective: The aim of the study was to compare the metabolomic and proteomic signature of saliva from cognitively normal and patients diagnosed with MCI or AD, to identify specific cellular pathways altered with the progression of the disease. Methods: We analyzed 80 saliva samples from individuals with MCI or AD as well as age- and gender-matched healthy controls. Saliva proteomic and metabolomic analyses were conducted utilizing mass spectrometry methods and data combined using pathway analysis. Results: We found significant alterations in multiple cellular pathways, demonstrating that at the omics level, disease progression impacts numerous cellular processes. Multivariate statistics using SIMCA showed that partial least squares-data analysis could be used to provide separation of the three groups. Conclusion: This study found significant changes in metabolites and proteins from multiple cellular pathways in saliva. These changes were associated with AD, demonstrating that this approach might prove useful to identify new biomarkers based upon integration of multi-omics parameters.

The idea of precision medication is turning out progressively to be famous. The utilization of enormous information, genomics and other "omics" like metabolomics; proteomics and transcriptomics; could soon cause the fantasy of personalized medication to turn into a reality. The most recent advancements in precision medication permit the adjustment of helpful approaches in various pathologies based on the particular molecular interpretation of the patient. Precision medication (PM) represents a new way of thinking about infection detection; prevention and treatment. The use of PM grounds in an emerging COVID virulent disease is becoming more prominent. Various discoveries revealed that severe acute respiratory disorder COVID-2 (SARS CoV-2) is accountable for the Coronavirus disease -2019 (COVID-19), which caused slew of procedures to restrict viral spread, affecting people's habits and lifestyles. According to the viral genomic sequencing, the SARS-CoV-2 spike protein uses angiotensin-converting enzyme 2 (ACE2), which is established on the ciliated epithelial cells of the human lungs as its particular receptor. In this specific situation; precision medication is an integrative helpful methodology that thinks about conventional elements (i.e. age, sex, clinical aggregate), just as arising hereditary qualities and connections with natural components, to individualize prevention; diagnosis, treatments and prognosis. The aim of this review was to summarize how precision medicine is impactful in the management of COVID-19.

Autism Spectrum Disorder (ASD) is characterized by complicated phenotypic symptoms, including intervention with social activity, communication, and unusually behavioral abnormality. ASD is a lifelong developmental condition affecting one in 88 children and is considered one of today's most urgent public health challenges. Individuals with ASD tend to respond inappropriately in conversation and may struggle to build relationships. Currently, the prime cause of ASD remains unclear, even though emerging findings emphasize the role of genetic and environmental factors in the development of autistic behavior could be examined. At present, risks such as exposure to unknown chemicals as an environmental factor in ASD are less appreciated. This review will discuss potential risks include air pollution and particle matters in alignment with detection strategies, like multidimensional Omics and the transcriptomic approach, which may empower the capability of predicting potential risk from gene expression to phenotype level as a hallmark of transformation outcome. In addition, this genomic-driven validation process saves time and quality of accuracy in the process of finding molecular determinants in the early stage of disease onset. Currently, the genomics era brings prediction models with various algorithms, and its intervention alternatives speed up to analyze the environmental risk of chemical stressors, such as hazardous chemicals, air pollutants, and/or nanoparticles, in compliance with regulatory measures of exploring molecular determinants associated with chronic disease and metabolic disorders. The value chain of disease prevention along with surveillance platform closely interacts with the prediction of risk assessment using a molecular-based platform. Efficacy of a sequential workout, including exploring, monitoring, and the translational application process in cellular or in vitro systems, could crosstalk with a transgenic animal model. Targeting molecule implication, such as gain- or loss-of-functional reverse genetic technology to verify its functional analysis, multi-dimensional omics could be beneficial in the field of environmental risk assessment, including safety evaluation: food and drug screening in ASD combined with imaging technology.

Due to the metabolic nature of osteoporosis, this study was conducted to identify metabolomic studies investigating the metabolic profile of low bone mineral density (BMD) and osteoporosis. A comprehensive systematic literature search was conducted through PubMed, Web of Science, Scopus, and Embase databases up to April 08, 2020, to identify observational studies with cross-sectional or case-control designs investigating the metabolic profile of low BMD in adults using biofluid specimen via metabolomic platform. The quality assessment panel specified for the "omics"-based diagnostic research (QUADOMICS) tool was used to estimate the methodologic quality of the included studies. Ten untargeted and one targeted approach metabolomic studies investigating biomarkers in different biofluids through mass spectrometry or nuclear magnetic resonance platforms were included in the systematic review. Some metabolite panels, rather than individual metabolites, showed promising results in differentiating low BMD from normal. Candidate metabolites were of different categories including amino acids, followed by lipids and carbohydrates. Besides, certain pathways were suggested by some of the studies to be involved. This systematic review suggested that metabolic profiling could improve the diagnosis of low BMD. Despite valuable findings attained from each of these studies, there was great heterogeneity regarding the ethnicity and age of participants, samples, and the metabolomic platform. Further longitudinal studies are needed to validate the results and confirm the predictive role of metabolic profile on low BMD and fracture. It is also mandatory to address and minimize the heterogeneity in future studies by using reliable quantitative methods. Summary: Due to the metabolic nature of osteoporosis, researchers have considered metabolomic studies recently. This systematic review showed that metabolic profiling including different categories of metabolites could improve the diagnosis of low BMD. However, great heterogeneity was observed and it is mandatory to address and minimize the heterogeneity in future studies.

Background To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics. Methods In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI. Results The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI. Conclusion The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical “big data” predictive modeling in the near future.

Type 2 diabetes mellitus (T2DM) is a burdensome global disease. In-depth understanding of its mechanism will help to optimize diagnosis and treatment, which reduces the burden. Multi-omics research has unparalleled advantages in contributing to the overall understanding of the mechanism of this chronic metabolic disease. In the past two decades, the study of multi-omics on T2DM-related intestinal flora perturbation and plasma dyslipidemia has shown tremendous potential and is expected to achieve major breakthroughs. The regulation of intestinal flora in diabetic patients has been confirmed by multiple studies. The use of metagenomics, 16S RNA sequencing, and metabolomics has comprehensively identified the overall changes in the intestinal flora and the metabolic disturbances that could directly or indirectly participate in the intestinal flora-host interactions. Lipidomics combined with other “omics” has characterized lipid metabolism disorders in T2DM. The combined application and cross-validation of multi-omics can screen for dysregulation in T2DM, which will provide immense opportunities to understand the mechanisms behind T2DM.

Sex and gender differences in lung health and disease are imperative to consider and study if precision pulmonary medicine is to be achieved. The development of reliable COPD biomarkers has been elusive, and the translation of biomarkers to clinical care has been limited. Useful and effective biomarkers must be developed with attention to clinical heterogeneity of COPD; inherent heterogeneity exists related to grouping women and men together in the studies of COPD. Considering sex and gender differences and influences related to -omics may represent progress in susceptibility, diagnostic, prognostic, and therapeutic biomarker development and clinical innovation to improve the lung health of men and women.

An increasing number of people are now living with cardiovascular disease (CVD), with concomitant CVD-related hospitalizations, operations, and prescriptions. To ultimately deliver optimal cardiovascular care, access to population-based biobanks with data on multiomics, phenotypes, and lifestyle risk factors are crucial. UK Biobank is a cohort study that incorporated data between 2006 and 2010 from over half a million individuals (40 to 69 years of age) at recruitment from across the United Kingdom. As one of the most accessible, largest, and in-depth cohort studies in the world, UK Biobank continues to enhance the resource with the addition of data from various omics platforms (eg, genomics, metabolomics, proteomics), multimodal imaging, self-reported risk factors and health outcomes, and linkage to electronic health records. The vision of UK Biobank is to allow as many researchers as possible to apply their expertise and imagination to undertake research to prevent, diagnose, and treat a wide range of chronic conditions, including CVD.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19., COVID-19 is a critical public health threat, but molecular characterizations of patients’ immunity is still lacking. Here the authors collected blood from patients with various disease severity, and prefiltered to exclude selected comorbidity, to obtain genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles to report a trans-omics landscape.

Albeit endometriosis is one of the most common gynecological diseases, its diagnosis and treatment remain controversial. The reasons behind this include: 1) multifactorial pathogenesis and insufficiently studied mechanisms of endometriosis; 2) relatively low diagnostic value of minimally invasive examination in relation to this disease; 3) inefficiency of current therapeutic approaches in many patient settings. In our opinion, uncovering the causes of endometriosis and factors promoting its progression is the cornerstone of its successful management. Here we review the lessons from genome-wide and candidate gene association studies, discuss the expression of regulatory miRNAs and describe the role of heat shock protein 90, annexin A2, and peroxiredoxin 2 in controlling DNA integrity in the eutopic endometrium. Further, we highlight the role of cytokeratin-19 in urine as a feasible diagnostic marker of endometriosis. Clinicians and basic researchers concur that the molecular basis of endometriosis is still in its infancy and current understanding of its pathophysiology remains poor. Recent progress in -omics approaches and bioinformatics paved the way for complex investigations of regulated cell death, proliferation, cell invasion and angiogenesis, opening the avenue for the novel approaches to treat endometriosis. Yet, the diversity of symptoms and an absence of sensitive and specific biomarkers frequently delay and complicate the diagnosis. In addition, surgery represents the only appropriate option to reliably confirm the diagnosis and to establish the disease extent, reducing patient adherence and postponing the start of the treatment. In this review, we discuss challenges in the diagnosis of endometriosis as well as relevant and potentially informative biomarkers.

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly ( p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.