Integration of metabolomics, lipidomics and clinical data using a machine learning method

High hypoxia status in pancreatic cancer is associated with multiple hallmarks of an immunosuppressive tumor microenvironment

Introduction

Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a particularly lethal disease that is often diagnosed late and is refractory to most forms of treatment. Tumour hypoxia is a key hallmark of PDAC and is purported to contribute to multiple facets of disease progression such as treatment resistance, increased invasiveness, metabolic reprogramming, and immunosuppression.

Methods

We used the Buffa gene signature as a hypoxia score to profile transcriptomics datasets from PDAC cases. We performed cell-type deconvolution and gene expression profiling approaches to compare the immunological phenotypes of cases with low and high hypoxia scores. We further supported our findings by qPCR analyses in PDAC cell lines cultured in hypoxic conditions.

Results

First, we demonstrated that this hypoxia score is associated with increased tumour grade and reduced survival suggesting that this score is correlated to disease progression. Subsequently, we compared the immune phenotypes of cases with high versus low hypoxia score expression (HypoxiaHI vs. HypoxiaLOW) to show that high hypoxia is associated with reduced levels of T cells, NK cells and dendritic cells (DC), including the crucial cDC1 subset. Concomitantly, immune-related gene expression profiling revealed that compared to HypoxiaLOW tumours, mRNA levels for multiple immunosuppressive molecules were notably elevated in HypoxiaHI cases. Using a Random Forest machine learning approach for variable selection, we identified LGALS3 (Galectin-3) as the top gene associated with high hypoxia status and confirmed its expression in hypoxic PDAC cell lines.

Discussion

In summary, we demonstrated novel associations between hypoxia and multiple immunosuppressive mediators in PDAC, highlighting avenues for improving PDAC immunotherapy by targeting these immune molecules in combination with hypoxia-targeted drugs.

Plasma Lipidomic Profiling Using Mass Spectrometry for Multiple Sclerosis Diagnosis and Disease Activity Stratification (LipidMS)

This investigation explores the potential of plasma lipidomic signatures for aiding in the diagnosis of Multiple Sclerosis (MS) and evaluating the clinical course and disease activity of diseased patients. Plasma samples from 60 patients with MS (PwMS) were clinically stratified to either a relapsing-remitting (RRMS) or a chronic progressive MS course and 60 age-matched controls were analyzed using state-of-the-art direct infusion quantitative shotgun lipidomics. To account for potential confounders, data were filtered for age and BMI correlations. The statistical analysis employed supervised and unsupervised multivariate data analysis techniques, including a principal component analysis (PCA), a partial least squares discriminant analysis (oPLS-DA) and a random forest (RF). To determine whether the significant absolute differences in the lipid subspecies have a relevant effect on the overall composition of the respective lipid classes, we introduce a class composition visualization (CCV). We identified 670 lipids across 16 classes. PwMS showed a significant increase in diacylglycerols (DAG), with DAG 16:0;0_18:1;0 being proven to be the lipid with the highest predictive ability for MS as determined by RF. The alterations in the phosphatidylethanolamines (PE) were mainly linked to RRMS while the alterations in the ether-bound PEs (PE O-) were found in chronic progressive MS. The amount of CE species was reduced in the CPMS cohort whereas TAG species were reduced in the RRMS patients, both lipid classes being relevant in lipid storage. Combining the above mentioned data analyses, distinct lipidomic signatures were isolated and shown to be correlated with clinical phenotypes. Our study suggests that specific plasma lipid profiles are not merely associated with the diagnosis of MS but instead point toward distinct clinical features in the individual patient paving the way for personalized therapy and an enhanced understanding of MS pathology.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Coenzyme A Restriction as a Factor Underlying Pre-Eclampsia with Polycystic Ovary Syndrome as a Risk Factor

Pre-eclampsia is the most common pregnancy complication affecting 1 in 20 pregnancies, characterized by high blood pressure and signs of organ damage, most often to the liver and kidneys. Metabolic network analysis of published lipidomic data points to a shortage of Coenzyme A (CoA). Gene expression profile data reveal alterations to many areas of metabolism and, crucially, to conflicting cellular regulatory mechanisms arising from the overproduction of signalling lipids driven by CoA limitation. Adverse feedback loops appear, forming sphingosine-1-phosphate (a cause of hypertension, hypoxia and inflammation), cytotoxic isoketovaleric acid (inducing acidosis and organ damage) and a thrombogenic lysophosphatidyl serine. These also induce mitochondrial and oxidative stress, leading to untimely apoptosis, which is possibly the cause of CoA restriction. This work provides a molecular basis for the signs of pre-eclampsia, why polycystic ovary syndrome is a risk factor and what might be done to treat and reduce the risk of disease.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes

Simple Summary

We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers.

Abstract

Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches

Lipids are essential for cellular functioning considering their role in membrane composition, signaling, and energy metabolism. The brain is the second most abundant organ in terms of lipid concentration and diversity only after adipose tissue. However, in the central system (CNS) lipid dysregulation has been linked to the etiology, progression, and severity of neurodegenerative diseases such as Alzheimeŕs, Parkinson, and Multiple Sclerosis. Advances in the human genome and subsequent sequencing technologies allowed us the study of lipidomics as a promising approach to diagnosis and treatment of neurodegeneration. Lipidomics advances rapidly increased the amount and quality of data allowing the integration with other omic types as well as implementing novel bioinformatic and quantitative tools such as machine learning (ML). Integration of lipidomics data with ML, as a powerful quantitative predictive approach, led to improvements in diagnostic biomarker prediction, clinical data integration, network, and systems approaches for neural behavior, novel etiology markers for inflammation, and neurodegeneration progression and even Mass Spectrometry image analysis. In this sense, by exploiting lipidomics data with ML is possible to improve the identification of new biomarkers or unveil new molecular mechanisms associated with lipid impairment across neurodegeneration. In this review, we present the lipidomic neurobiology state-of-the-art highlighting its potential applications to study neurodegenerative conditions. Also, we present theoretical background, applications, and advances in the integration of lipidomics with ML. This review opens the door to new approaches in this rising field.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

MOTIVATION

The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data.

RESULTS

Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions.

CONTACT

hguzzi@unicz.it, sroy01@cus.ac.in.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin

An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography - time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

A random forest based biomarker discovery and power analysis framework for diagnostics research

Background

Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale –omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.

Methods

In this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.

Results

We present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface (https://joelarkman.shinyapps.io/PowerTools/) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.

Conclusions

We developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.

An Optimized Dual Extraction Method for the Simultaneous and Accurate Analysis of Polar Metabolites and Lipids Carried out on Single Biological Samples

The functional understanding of metabolic changes requires both a significant investigation into metabolic pathways, as enabled by global metabolomics and lipidomics approaches, and the comprehensive and accurate exploration of specific key pathways. To answer this pivotal challenge, we propose an optimized approach, which combines an efficient sample preparation, aiming to reduce the variability, with a biphasic extraction method, where both the aqueous and organic phases of the same sample are used for mass spectrometry analyses. We demonstrated that this double extraction protocol allows working with one single sample without decreasing the metabolome and lipidome coverage. It enables the targeted analysis of 40 polar metabolites and 82 lipids, together with the absolute quantification of 32 polar metabolites, providing comprehensive coverage and quantitative measurement of the metabolites involved in central carbon energy pathways. With this method, we evidenced modulations of several lipids, amino acids, and energy metabolites in HepaRG cells exposed to fenofibrate, a model hepatic toxicant, and metabolic modulator. This new protocol is particularly relevant for experiments involving limited amounts of biological material and for functional metabolic explorations and is thus of particular interest for studies aiming to decipher the effects and modes of action of metabolic disrupting compounds.

A Pilot Integrative Analysis of Colonic Gene Expression, Gut Microbiota, and Immune Infiltration in Primary Sclerosing Cholangitis-Inflammatory Bowel Disease: Association of Disease With Bile Acid Pathways

BACKGROUND

Although a majority of patients with PSC have colitis [PSC-IBD; primary sclerosing cholangitis-inflammatory bowel disease], this is phenotypically different from ulcerative colitis [UC]. We sought to define further the pathophysiological differences between PSC-IBD and UC, by applying a comparative and integrative approach to colonic gene expression, gut microbiota and immune infiltration data.

METHODS

Colonic biopsies were collected from patients with PSC-IBD [n = 10], UC [n = 10], and healthy controls [HC; n = 10]. Shotgun RNA-sequencing for differentially expressed colonic mucosal genes [DEGs], 16S rRNA analysis for microbial profiling, and immunophenotyping were performed followed by multi-omic integration.

RESULTS

The colonic transcriptome differed significantly between groups [p = 0.01]. Colonic transcriptomes from HC were different from both UC [1343 DEGs] and PSC-IBD [4312 DEGs]. Of these genes, only 939 had shared differential gene expression in both UC and PSC-IBD compared with HC. Imputed pathways were predominantly associated with upregulation of immune response and microbial defense in both disease cohorts compared with HC. There were 1692 DEGs between PSC-IBD and UC. Bile acid signalling pathways were upregulated in PSC-IBD compared with UC [p = 0.02]. Microbiota profiles were different between the three groups [p = 0.01]; with inferred function in PSC-IBD also being consistent with dysregulation of bile acid metabolism. Th17 cells and IL17-producing CD4 cells were increased in both PSC-IBD and UC when compared with HC [p < 0.05]. Multi-omic integration revealed networks involved in bile acid homeostasis and cancer regulation in PSC-IBD.

CONCLUSIONS

Colonic transcriptomic and microbiota analysis in PSC-IBD point toward dysregulation of colonic bile acid homeostasis compared with UC. This highlights important mechanisms and suggests the possibility of novel approaches in treating PSC-IBD.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

Metabolic syndrome is a health condition that increases the risk of heart diseases, diabetes, and stroke. The prognostic variables that identify this syndrome have already been defined by the World Health Organization (WHO), the National Cholesterol Education Program Third Adult Treatment Panel (ATP III) as well as by the International Diabetes Federation. According to these guides, there is some symmetry among anthropometric prognostic variables to classify abdominal obesity in people with metabolic syndrome. However, some appear to be more sensitive than others, nevertheless, these proposed definitions have failed to appropriately classify a specific population or ethnic group. In this work, we used the ATP III criteria as the framework with the purpose to rank the health parameters (clinical and anthropometric measurements, lifestyle data, and blood tests) from a data set of 2942 participants of Mexico City Tlalpan 2020 cohort, applying machine learning algorithms. We aimed to find the most appropriate prognostic variables to classify Mexicans with metabolic syndrome. The criteria of sensitivity, specificity, and balanced accuracy were used for validation. The ATP III using Waist-to-Height-Ratio (WHtR) as an anthropometric index for the diagnosis of abdominal obesity achieved better performance in classification than waist or body mass index. Further work is needed to assess its precision as a classification tool for Metabolic Syndrome in a Mexican population.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Prospects and challenges of multi-omics data integration in toxicology

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry (MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes. The metabolomes of cells, tissues, and organisms comprise a variety of molecules including lipids, amino acids, sugars, organic acids, and so on. Metabolomics mainly focus on the hydrophilic classes, while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes. The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases, but system-level understanding is largely lacking, which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies. While scientists are continuously striving to develop high-coverage omics approaches, integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation. Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape, enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology, facilitating the study of interconnection between lipids and other metabolites in disease progression. In this review, we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health. We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.

High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage

OBJECTIVE

Metabolite profiling (or metabolomics) can identify candidate biomarkers for disease and potentially uncover new pathways for intervention. The goal of this study was to identify potential biomarkers of functional outcome after subarachnoid hemorrhage (SAH).

METHODS

The authors performed high-throughput metabolite profiling across a broad spectrum of chemical classes (163 metabolites) on plasma samples taken from 191 patients with SAH who presented to Massachusetts General Hospital between May 2011 and October 2016. Samples were drawn at 3 time points following ictus: 0-5, 6-10, and 11-14 days. Elastic net (EN) and LASSO (least absolute shrinkage and selection operator) machine learning analyses were performed to identify metabolites associated with 90-day functional outcomes as assessed by the modified Rankin Scale (mRS). Additional univariate and multivariate analyses were then conducted to further examine the relationship between metabolites and clinical variables and 90-day functional outcomes.

RESULTS

One hundred thirty-seven (71.7%) patients with aneurysmal SAH met the criteria for inclusion. A good functional outcome (mRS score 0-2) at 90 days was found in 79 (57.7%) patients. Patients with good outcomes were younger (p = 0.002), had lower admission Hunt and Hess grades (p < 0.0001) and modified Fisher grades (p < 0.0001), and did not develop hydrocephalus (p < 0.0001) or delayed cerebral ischemia (DCI) (p = 0.049). EN and LASSO machine learning methods identified taurine as the leading metabolite associated with 90-day functional outcome (p < 0.0001). Plasma concentrations of the amino acid taurine from samples collected between days 0 and 5 after aneurysmal SAH were 21.9% (p = 0.002) higher in patients with good versus poor outcomes. Logistic regression demonstrated that taurine remained a significant predictor of functional outcome (p = 0.013; OR 3.41, 95% CI 1.28-11.4), after adjusting for age, Hunt and Hess grade, modified Fisher grade, hydrocephalus, and DCI.

CONCLUSIONS

Elevated plasma taurine levels following aneurysmal SAH predict a good 90-day functional outcome. While experimental evidence in animals suggests that this effect may be mediated through downregulation of pro-inflammatory cytokines, additional studies are required to validate this hypothesis in humans.

An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies

Human Biomonitoring (HB), the process for determining whether and to what extent chemical substances penetrated our bodies, serves as a useful tool to quantify human exposure to pollutants. In cases of nutrition and physiologic status, HB plays a critical role in the identification of excess or deficiency of essential nutrients. In pollutant HB studies, levels of substances measured in body fluids (blood, urine, and breast milk) or tissues (hair, nails or teeth) aid in the identification of potential health risks or associated adverse effects. However, even as a widespread practice in several countries, most HB studies reflect exposure to a single compound or mixtures which are measured at a single time point in lifecycle. On the other hand, throughout an individual's lifespan, the contact with different physical, chemical, and social stressors occurs at varying intensities, differing times and durations. Further, the interaction between stressors and body receptors leads to dynamic responses of the entire biological system including proteome, metabolome, transcriptome, and adductome. Bearing this in mind, a relatively new vision in exposure science, defined as the exposome, is postulated to expand the traditional practice of measuring a single exposure to one or few chemicals at one-time point to an approach that addresses measures of exposure to multiple stressors throughout the lifespan. With the exposome concept, the science of exposure advances to an Environment-Wide Association Perspective, which might exhibit a stronger relationship with good health or disease conditions for an individual (phenotype). Thus, this critical review focused on the current progress of HB and exposome investigations, anticipating some challenges, strategies, and future needs to be taken into account for designing future surveys.

Predicting nationwide obesity from food sales using machine learning

The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through different suppliers on a regular basis. This study applies a methodology to predict obesity prevalence at the country-level based on national sales of a small subset of food and beverage categories. Three machine learning algorithms for nonlinear regression were implemented using purchase and obesity prevalence data from 79 countries: support vector machines, random forests and extreme gradient boosting. The proposed method was validated in terms of both the absolute prediction error and the proportion of countries for which the obesity prevalence was predicted satisfactorily. We found that the most-relevant food category to predict obesity is baked goods and flours, followed by cheese and carbonated drinks.

-Omics biomarker identification pipeline for translational medicine

BACKGROUND

Translational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.

METHODS

We developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.

RESULTS

We applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.

CONCLUSIONS

We developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

The application of omics techniques to understand the role of the gut microbiota in inflammatory bowel disease

The aetiopathogenesis of inflammatory bowel diseases (IBD) involves the complex interaction between a patient's genetic predisposition, environment, gut microbiota and immune system. Currently, however, it is not known if the distinctive perturbations of the gut microbiota that appear to accompany both Crohn's disease and ulcerative colitis are the cause of, or the result of, the intestinal inflammation that characterizes IBD. With the utilization of novel systems biology technologies, we can now begin to understand not only details about compositional changes in the gut microbiota in IBD, but increasingly also the alterations in microbiota function that accompany these. Technologies such as metagenomics, metataxomics, metatranscriptomics, metaproteomics and metabonomics are therefore allowing us a deeper understanding of the role of the microbiota in IBD. Furthermore, the integration of these systems biology technologies through advancing computational and statistical techniques are beginning to understand the microbiome interactions that both contribute to health and diseased states in IBD. This review aims to explore how such systems biology technologies are advancing our understanding of the gut microbiota, and their potential role in delineating the aetiology, development and clinical care of IBD.

Automated supervised learning pipeline for non-targeted GC-MS data analysis

Non-targeted analysis is nowadays applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. Conventional processing strategies for GC-MS data include baseline correction, feature detection, and retention time alignment before multivariate modeling. These techniques can be prone to errors and therefore time-consuming manual corrections are generally necessary. We introduce here a novel fully automated approach to non-targeted GC-MS data processing. This new approach avoids feature extraction and retention time alignment. Supervised machine learning on decomposed tensors of segmented chromatographic raw data signal is used to rank regions in the chromatograms contributing to differentiation between sample classes. The performance of this novel data analysis approach is demonstrated on three published datasets.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Incorporating machine learning approaches to assess putative environmental risk factors for multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) incidence has increased recently, particularly in women, suggesting a possible role of one or more environmental exposures in MS risk. The study objective was to determine if animal, dietary, recreational, or occupational exposures are associated with MS risk.

METHODS

Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of exposures with potential relevance to disease in a large population-based (Kaiser Permanente Northern California [KPNC]) case-control study. Variables with non-zero coefficients were analyzed in matched conditional logistic regression analyses, adjusted for established environmental risk factors and socioeconomic status (if relevant in univariate screening),± genetic risk factors, in the KPNC cohort and, for purposes of replication, separately in the Swedish Epidemiological Investigation of MS cohort. These variables were also assessed in models stratified by HLA-DRB1*15:01 status since interactions between risk factors and that haplotype have been described.

RESULTS

There was a suggestive association of pesticide exposure with having MS among men, but only in those who were positive for HLA-DRB1*15:01 (OR pooled = 3.11, 95% CI 0.87, 11.16, p = 0.08).

CONCLUSIONS

While this finding requires confirmation, it is interesting given the association between pesticide exposure and other neurological diseases. The study also demonstrates the application of LASSO to identify environmental exposures with reduced multiple statistical testing penalty. Machine learning approaches may be useful for future investigations of concomitant MS risk or prognostic factors.

A review of machine learning in obesity

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

Missing data in open-data era – a barrier to multiomics integration

The exploration of complex interactions in biological systems is one of the main aims in nature science nowadays. Progress in this area is possible because of high-throughput omics technologies and the computational surge. The development of analytical methods “is trying to keep pace” with the development of molecular biology methods that provide increasingly large amounts of data – omics data. Specialized databases consist of ever-larger collections of experiments that are usually conducted by one next-generation sequencing technique (e.g. RNA-seq). Other databases integrate data by defining qualitative relationships between individual objects in the form of ontologies, interactions, and pathways (e.g. GO, KEGG, and String). However, there are no open-source complementary quantitative data sets for the biological processes studied, including information from many levels of the organism organization, which would allow the development of multidimensional data analysis methods (multiscale and insightful overviews of biological processes). In the paper, the lack of omics complementary quantitative data set, which would help integrate the defined qualitative biological relationships of individual biomolecules with statistical, computational methods, is discussed.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

Multi-OMICS: a critical technical perspective on integrative lipidomics approaches

During the past decades, high-throughput approaches for analyzing different molecular classes such as nucleic acids, proteins, metabolites, and lipids have grown rapidly. These approaches became powerful tools for getting a fundamental understanding of biological systems. Considering each approach and its results separately, relations and causal connections between these classes have no chance to be revealed, since only separate molecular snapshots are provided. Only a combined approach, not fully established yet, with the integration of the corresponding data, might yield a comprehensive and complete understanding of biological processes, such as crosstalk and interactions in signaling pathways. Taking two or more omics-methods into consideration for analysis is referred to as a multi-omics approach, which is gradually evolving. In this critical note, we briefly discuss the relevance, challenges, current state, and potential of data integration from multi-omics approaches, with a special focus on lipidomics analysis, listing the advantages and gaps in this field. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.