Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity. Trial registration. The study is registered as NCT00221052 in clinicaltrials.gov database. © 2010 van Erk et al; licensee BioMed Central Ltd

Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics

Diseases of the Central Nervous System (CNS) affect millions of people worldwide, with the number of people affected quickly growing. Unfortunately, the successful development of CNS-acting drugs is less than 10%, and this is attributed to the complexity of the CNS, unexpected side effects, difficulties in penetrating the blood-brain barrier and lack of biomarkers. Areas covered: Herein, the authors first review how pharmacokinetic/pharmacodynamic (PK/PD) models are designed to predict the dose-dependent time course of effect, and how they are used to translate drug effects from animal to man. Then, the authors discuss how pharmacometabolomics gives insight into system-wide pharmacological effects and why it is a promising method to study interspecies differences. Finally, the authors advocate the application of PK/PD-metabolomics modeling to advance translational CNS drug development by discussing its opportunities and challenges. Expert opinion: It is envisioned that PK/PD-metabolomics will increase our understanding of CNS drug effects and improve translational CNS drug development, thereby increasing success rates. The successful future development of this concept will require multi-level and longitudinal biomarker evaluation over a large dose range, multi-tissue biomarker evaluation, and the generation of a proof of principle by application to multiple CNS drugs in multiple species. INTRODUCTIONArticle / Letter to editorLeiden Academic Centre for Drug Researc

Identification of factors associated with non-responders to total joint replacement and sustained knee pain in primary osteoarthritis patients by epidemiological and multi-omic studies

Osteoarthritis (OA) is among the most common rheumatic diseases, affecting 30% of the world’s population over 60 years. Currently, total joint replacement (TJR) is considered the most effective treatment for end-stage OA. However, up to 20% of patients do not see clinically significant improvement in pain or function after the surgery. This thesis aims to identify epidemiological, metabolic, and genetic factors which are significantly associated with non-responders to TJR and patients with sustained, treatment-resistant pain in a large cohort from Newfoundland and Labrador (NL), Canada. First, we identified a number of epidemiological factors significantly associated with non-responders to TJR including clinical depression, younger age, and multisite musculoskeletal pain (MSMP). This highlighted potential roles for altered pain perception and pain sensitization in non-responders. Subsequently, we used a targeted metabolomic approach which profiled 186 metabolites in plasma and identified three metabolite ratios and two metabolite networks which were significantly associated with pain or function non-responders. Our findings highlighted phosphatidylcholines (PCs), branched chain amino acids (BCAAs), and acylcarnitines, all of which are involved in inflammatory processes, as metabolites of interest for further study in non-responders. Next, we used the same metabolomic approach to assess metabolites and metabolite ratios associated with sustained knee pain in two independent cohorts, one from NL and the other from Ontario, Canada. We identified one metabolite and three metabolite ratios to be associated with sustained pain, further highlighting roles for PCs, acylcarnitines, and sphingomyelins (SMs) in OA knee pain. We then investigated mechanisms underlying sustained pain in the NL cohort using a multi-omic approach which identified KALRN as a candidate gene and a significant role for central pain sensitization in sustained knee pain. Finally, we developed and evaluated a method to profile eicosanoids and endocannabinoids, a large group of inflammatory mediators involved in pain generation, in plasma for use in future studies on non-responders and patients with sustained knee pain. Overall, our findings highlighted potential roles for inflammation and pain sensitization in OA pain and non-response to TJR and offer interesting routes for future studies in this area and could have potential utility in predicting surgical outcome or as druggable targets to modify outcomes

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation

Metamarker: differential correlation network methodology and software for metabolomic data analysis

Biomarkers are the substances with quantitative properties present within organisms indicating disease progression. Metabolomics is a newer approach towards understanding the human body following the footsteps of other "omics" techniques (genomics, proteomics, transcriptomics). Metabolomics refers to the scientific study of low molecular intracellular elements called metabolites. With the advancement of technology, it is now easier to extract different sets of metabolites from various forms of biological samples such as cells, tissues, bio-fluids, etc. Metabolomic data analysis is a complex workflow. It requires sophisticated data processing and statistical analysis. Various tools have been developed, such as data cleaning and preprocessing tools, modeling tools, validation/ result visualizations, and many more. Most of these software tools are developed for comprehensive studies rather than precisely focusing on metabolomic biomarker discovery. As a result, their capacity, in most cases, is limited. The modeling techniques commonly used in these tools are also not adequate. Many of these software tools provide basic analysis methods rather than more advanced machine learning techniques. The high throughput metabolomic datasets require compound analysis techniques. This thesis designed and developed a software tool that encompasses the general metabolomic biomarker research workflow. Our software platform is equipped with many basic to advanced analysis techniques, interactive visualizations, delicate result analysis, and comparison modules (The first version release can be found at, http://18.189.6.35:8000/). Our software is designed so that users do not have to switch in between different tools during the study since the platform provides necessary features that are commonly used throughout the workflow. Some of the software’s significant features are outlier handling of the uploaded datasets, analyzing the dataset with principal component analysis or partial least square discriminant analysis, and comparing different models. The software makes the study process fast and convenient. We employed a differential correlation network analysis model for the biomarker discovery studies, which is advantageous in finding key metabolites that influence diseases through interaction

Seeing the forest for the trees : retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed

Osteoartroosi kardiovaskulaarne ja metaboloomiline profileerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneOsteoartroos on krooniline liigeshaigus, mis haarab sagedamini käe-, põlve-, puusa- ja lülisamba liigeseid ning põhjustab valu ja liigesliikuvuse piiratust. Osteoartroosiga patsientide arv on suur ning tegemist on kõige kiiremini kasvava liigeshaiguse patsiendigrupiga. Tänaseni puudub haiguse tekkemehhanismidest piisav arusaam, et efektiivselt pidurdada haiguse kulgu. Osteoartroosi on pikalt peetud kõhre mehhaanilisest kulumisest tingitud haiguseks, aga viimastel aastatel on ilmnenud üha rohkem tõendeid, et haiguse arengus mängivad olulist rolli süsteemne alaäge põletik, muutused ainevahetuses ja arterite jäikuses. Osteoartroosiga patsientidel on leitud kõrgem südame- ja veresoonkonna haiguste risk, kuid täpsemad mehhanismid selle võimaliku seose taga on teadmata. Selle doktoritöö eesmärgiks oli selgitada osteoartroosi ja arterite funktsiooni ning oksüdatiivse stressi ja ainevahetushäirete (sh metaboolse sündroomi) vahelisi seoseid. Lisaks oli eesmärgiks kirjeldada osteoartroosi lipiidide ainevahetusega seotud madalmolekulaarsete ühendite profiili. Selle doktoritöö raames läbiviidud uuringusse kaasati 70 lõppstaadiumis osteoartroosiga patsienti Tartu Ülikooli Kliinikumi ortopeedia osakonnast ning kontrollgruppi 82 ilma osteoartroosita uuritavat Tartumaa perearstide nimistutest. Arterite jäikuse mõõtmised tehti Endoteeli Keskuses ning biokeemiliste markerite mõõtmised TÜ Kliinikumi ühendlaboris ning bio- ja siirdemeditsiini instituudi biokeemia osakonnas. Töö tulemusena leidsime uudse seose aordi jäikuse ja osteoartroosi raskusastme vahel. Osteoartroosiga patsientide arterid olid võrreldes kontrollgrupi liikmete arteritega jäigenenud. Lisaks leidsime, et osteoartroosi korral esineb organismis kõrgem oksüdatiivse stressi tase, mis on seotud ka osteoartroosi raskusastmega. Kirjeldasime, et osteoartroosi korral esinevad lipiidide ainevahetuse häired, mis on seotud arterite funktsiooniga ja aitavad selgitada osteoartroosi ning südame- ja veresoonkonnahaiguste vahelist seost. Selle uuringu tulemusena tuvastatud seosed osteoartroosi ja lipiidide ainevahetuse, oksüdatiivse stressi ja arterite funktsiooni vahel võivad tulevikus aidata välja töötada uusi ründepunkte osteoartroosi ravimisel.Osteoarthritis is a chronic joint disease that most frequently affects the hand, knee and hip joints, and the spine. Osteoarthritis manifests mainly as pain and serious loss of function. The prevalence of osteoarthritis is high and it is the fastest growing musculoskeletal disorder. Furthermore, osteoarthritis is the leading cause of pain in elderly. There is no cure for the disease and treatment focuses on pain relief and on preserving the function of affected joints. For a long time osteoarthritis was thought to be a non-inflammatory degenerative „wear and tear“ disease of the articular cartilage. However, in recent years several studies have found that systemic low-grade inflammation, metabolic disturbances and arterial dysfunction are involved in development of osteoarthritis. Osteoarthritis patients have increased cardiovascular disease risk, but the mechanisms behind the association are largely unknown. The aims of this thesis were to determine associations between osteoarthritis, and arterial function, metabolic disturbances and oxidative stress. In addition, we aimed to describe the metabolic profile of low molecular weight lipid related compunds in osteoarthritis patients. Seventy end-stage knee and hip osteoarthritis patients from the department of orthopaedics of Tartu University Hospital and 82 non-osteoarthritis controls from the same geographic regioon were enrolled in the study. Measurements of arterial stiffness were conducted at the Endothelial Centre, University of Tartu. Biochemical markers were measured at the Department of Biochemistry, Institute of Biomedicine and Translational Medicine and at the United Laboratories of the University of Tartu. As a result of the study, we found that severity of osteoarthritis was associated with aortic stiffness. The arterial stiffness of the osteoarthritis patients was significantly higher compared with the controls. In addition, the level of oxidative stress was higher in the osteoarthritis patients and correlated with severity of osteoarthritis. We found that lipid metabolism was associated with osteoarthritis and also with arterial stiffness, which might hence explain the association between osteoarthritis and cardiovascular disease. In conclusion, this thesis describes associations of osteoarthritis with arterial stiffness, high-grade oxidative stress and lipid metabolism and might help to design new targets in finding cure for the disease