How Can Omic Science be Improved?

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND

Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.

RESULTS

In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.

CONCLUSIONS

Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma

Improving the reliability of quantification in lipidomic analyses is crucial for its successful application in the discovery of new biomarkers or in clinical practice. In this study, we propose a workflow to improve the accuracy and precision of lipidomic results issued by the laboratory. Lipid species from 11 classes were analyzed by a targeted RPLC-MRM/MS method. The peak areas of species were used to estimate concentrations by an internal standard calibration approach (IS-calibration) and by an alternative normalization signal calibration schema (NS-calibration). The latter uses a long-term reference plasma material as a matrix-matched external calibrator whose accuracy was compared to the NIST SRM-1950 mean consensus values reported by the Interlaboratory Lipidomics Comparison Exercise. The bias of lipid concentrations showed a good accuracy for 69 of 89 quantified lipids. The quantitation of species by the NS-calibration schema improved the within- and between-batch reproducibility in quality control samples, in comparison to the usual IS-calibration approach. Moreover, the NS-calibration workflow improved the robustness of the lipidomics measurements reducing the between-batch variability (relative standard deviation <10% for 95% of lipid species) in real conditions tested throughout the analysis of 120 plasma samples. In addition, we provide a free access web tool to obtain the concentration of lipid species by the two previously mentioned quantitative approaches, providing an easy follow-up of quality control tasks related to lipidomics.

Extracellular vesicles and domestic animal reproduction

Embryo implantation is a complex process in which significant changes occur continually in both the corpora lutea and in the endometrium of females and which varies depending on the embryonic, pre-implantation, or fetal stages. However, at all stages, correct maternal-embryonic communication is essential. In the last few years, a new intercellular communication tool, mediated by extracellular vesicles (EVs), has emerged. Many authors agree on the relevant role of EVs in correct communication between the mother and the embryo, as a fundamental system for the pregnancy to reach term and embryonic development to occur correctly. This review analyzes current information on known EVs, their main functions, and their role in implantation and embryonic development in domestic animals.

The nephrology crystal ball: the medium-term future

In January 2019, the ERA-EDTA surveyed nephrologists with questions on kidney care and kidney research designed to explore comprehension of the impact of alterations to organization of renal care and of advancements in technology and knowledge of kidney disease. Eight hundred and twenty-five ERA-EDTA members, ∼13% of the whole ERA-EDTA membership, replied to an ad hoc questionnaire. More than half of the respondents argued that kidney centres will be increasingly owned by large dialysis providers, nearly a quarter of respondents felt that many medical aspects of dialysis will be increasingly overseen by non-nephrologists and a quarter (24%) also believed that the care and long-term follow-up of kidney transplant patients will be increasingly under the responsibility of transplant physicians caring for patients with any organ transplant. Nearly half of the participants (45%, n = 367) use fully electronic clinical files integrating the clinical ward, the outpatient clinics, the haemodialysis and peritoneal dialysis units, as well as transplantation. Smartphone-based self-management programmes for the care of chronic kidney disease (CKD) patients are scarcely applied (only 11% of surveyed nephrologists), but a substantial proportion of respondents (74%) are eager to know more about the potential usefulness of these apps. Finally, European nephrologists expressed a cautious optimism about the application of omic sciences to nephrology and on wearable and implantable kidneys, but their expectations for the medium term are limited.

A Comprehensive Functional Characterization of Escherichia coli Lipid Genes

Lipid membranes are the border between living cells and their environments. The membrane's lipid composition defines fluidity, thickness, and protein activity and is controlled by the intricate actions of lipid gene-encoded enzymes. However, a comprehensive analysis of each protein's contribution to the lipidome is lacking. Here, we present such a comprehensive and functional overview of lipid genes in Escherichia coli by individual overexpression or deletion of these genes. We developed a high-throughput lipidomic platform, combining growth analysis, one-step lipid extraction, rapid LC-MS, and bioinformatic analysis into one streamlined procedure. This allowed the processing of more than 300 samples per day and revealed interesting functions of known enzymes and distinct effects of individual proteins on the phospholipidome. Our data demonstrate the plasticity of the phospholipidome and unexpected relations between lipid classes and cell growth. Modeling of lipidomic responses to short-chain alcohols provides a rationale for targeted membrane engineering.

Endometriosis research in the -omics era

Endometriosis is a pathological condition extensively studied, but its pathogenesis is not completely understood, since its pathophysiology stems from a broad spectrum of environmental influences and genetic factors. Moreover, the nature of this condition is heterogeneous and includes different anatomical entities. Scientists actively pursue discovery of novel biomarkers in the hope of better identifying susceptible individuals in early stages of the disease. High-throughput technologies have substantially revolutionized medical research and, as a first step, the advent of genotyping arrays led to large-scale genome-wide association studies (GWAS) and enabled the assessment of global transcript levels, thus giving rise to integrative genetics. In this framework, comprehensive studies have been conducted at multiple biological levels by using the "omics" platforms, thus allowing to re-examine endometriosis at a greater degree of molecular resolution. -Omics technologies can detect and analyze hundreds of markers in the same experiment and their increasing use in the field of gynecology comes from an urgent need to find new diagnostic and therapeutic tools that improve the diagnosis of endometriosis and the efficacy of assisted reproductive techniques. Proteomics and metabolomics have been introduced recently into the every day methodology of researchers collaborating with gynecologists and, importantly, multi-omics approach is advantageous to gain insight of the total information that underlies endometriosis, compared to studies of any single -omics type. In this review, we expect to present multiple studies based on the high-throughput-omics technologies and to shed light in all considerable advantages that they may confer to a proper management of endometriosis.

The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research

Big data-driven omics research has led to a steep rise in investigations involving two of the most functional omes, the metabolome and microbiome. The former is touted as the closest to the phenotype, and the latter is implicated in general well-being and a plethora of human diseases. Although some research publications have integrated the concepts of the two domains, most focus their analyses on evidence solely originating from one or the other. With a growing interest in connecting the microbiome and metabolome in the context of disease, researchers must also appreciate the disconnect between the two domains. In the present review, drawing examples from the current literature, tools, and resources, I discuss the connections between the microbiome and metabolome and highlight challenges and opportunities in linking them together for the basic, translational, clinical, and nursing research communities.

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

Coronary Artery Disease Risk and Lipidomic Profiles Are Similar in Hyperlipidemias With Family History and Population-Ascertained Hyperlipidemias

Background

We asked whether, after excluding familial hypercholesterolemia, individuals with high low‐density lipoprotein cholesterol (LDL‐C) or triacylglyceride levels and a family history of the same hyperlipidemia have greater coronary artery disease risk or different lipidomic profiles compared with population‐based hyperlipidemias.

Methods and Results

We determined incident coronary artery disease risk for 755 members of 66 hyperlipidemic families (≥2 first‐degree relatives with similar hyperlipidemia) and 19 644 Finnish FINRISK population study participants. We quantified 151 circulating lipid species from 550 members of 73 hyperlipidemic families and 897 FINRISK participants using mass spectrometric shotgun lipidomics. Familial hypercholesterolemia was excluded using functional LDL receptor testing and genotyping. Hyperlipidemias (LDL‐C or triacylglycerides >90th population percentile) associated with increased coronary artery disease risk in meta‐analysis of the hyperlipidemic families and the population cohort (high LDL‐C: hazard ratio, 1.74 [95% CI, 1.48–2.04]; high triacylglycerides: hazard ratio, 1.38 [95% CI, 1.09–1.74]). Risk estimates were similar in the family and population cohorts also after adjusting for lipid‐lowering medication. In lipidomic profiling, high LDL‐C associated with 108 lipid species, and high triacylglycerides associated with 131 lipid species in either cohort (at 5% false discovery rate; P‐value range 0.038–2.3×10⁻⁵⁶). Lipidomic profiles were highly similar for hyperlipidemic individuals in the families and the population (LDL‐C: r=0.80; triacylglycerides: r=0.96; no lipid species deviated between the cohorts).

Conclusions

Hyperlipidemias with family history conferred similar coronary artery disease risk as population‐based hyperlipidemias. We identified distinct lipidomic profiles associated with high LDL‐C and triacylglycerides. Lipidomic profiles were similar between hyperlipidemias with family history and population‐ascertained hyperlipidemias, providing evidence of similar and overlapping underlying mechanisms.

Critical review: Grand challenges in assessing the adverse effects of contaminants of emerging concern on aquatic food webs

Much progress has been made in the past few decades in understanding the sources, transport, fate, and biological effects of contaminants of emerging concern (CECs) in aquatic ecosystems. Despite these advancements, significant obstacles still prevent comprehensive assessments of the environmental risks associated with the presence of CECs. Many of these obstacles center around the extrapolation of effects of single chemicals observed in the laboratory or effects found in individual organisms or species in the field to impacts of multiple stressors on aquatic food webs. In the present review, we identify 5 challenges that must be addressed to promote studies of CECs from singular exposure events to multispecies aquatic food web interactions. There needs to be: 1) more detailed information on the complexity of mixtures of CECs in the aquatic environment, 2) a greater understanding of the sublethal effects of CECs on a wide range of aquatic organisms, 3) an ascertaining of the biological consequences of variable duration CEC exposures within and across generations in aquatic species, 4) a linkage of multiple stressors with CEC exposure in aquatic systems, and 5) a documenting of the trophic consequences of CEC exposure across aquatic food webs. We examine the current literature to show how these challenges can be addressed to fill knowledge gaps. Environ Toxicol Chem 2019;38:46-60. © 2018 SETAC.

Lipidomics in Major Depressive Disorder

Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.

MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

On the Promising Role of Enzyme Activity Assay in Interpreting Comparative Proteomic Data in Plants

Comparative proteomics is widely used to detect protein changes, especially differential abundance proteins (DAPs) that are involved in plant responses to development, disease, or environment. Once DAPs are identified, it is essential to validate any change in their abundance, and their role in the biological process under study. In addition to common confirmation by quantitative RT-PCR, immunoblot, and multiple reaction monitoring analysis, it has been proposed that enzyme activity assay (EAA) can be complementary to the standard proteomics results, especially regarding the elucidation of protein (enzyme) function and the mechanism of enzyme-associated biochemical or metabolic pathways. The enzymes discussed here are the DAPs identified in comparative plant proteomics. Despite the small number of enzymes in a proteome, they often make up a substantial proportion of the DAPs identified in comparative studies. Currently, only a few studies have performed EAA to complement the interpretation of proteomic data, especially activity-based protein profiling. This viewpoint aims to arouse the attention of proteomic researchers on the promising role of EAA in plant proteomics and highlights the need for high-throughput assays of enzyme activities in comparative plant proteomics.

Exosomal lipid composition and the role of ether lipids and phosphoinositides in exosome biology

Exosomes are a type of extracellular vesicle released from cells after fusion of multivesicular bodies with the plasma membrane. These vesicles are often enriched in cholesterol, SM, glycosphingolipids, and phosphatidylserine. Lipids not only have a structural role in exosomal membranes but also are essential players in exosome formation and release to the extracellular environment. Our knowledge about the importance of lipids in exosome biology is increasing due to recent technological developments in lipidomics and a stronger focus on the biological functions of these molecules. Here, we review the available information about the lipid composition of exosomes. Special attention is given to ether lipids, a relatively unexplored type of lipids involved in membrane trafficking and abundant in some exosomes. Moreover, we discuss how the lipid composition of exosome preparations may provide useful information about their purity. Finally, we discuss the role of phosphoinositides, membrane phospholipids that help to regulate membrane dynamics, in exosome release and how this process may be linked to secretory autophagy. Knowledge about exosome lipid composition is important to understand the biology of these vesicles and to investigate possible medical applications.