OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma

Analysis of Key Differential Metabolites in Intervertebral Disc Degeneration Based on Untargeted Metabolomics

Background

Intervertebral disc degeneration disease (IVDD) is a prevalent orthopedic condition that causes chronic lower back pain, imposing a substantial economic burden on patients and society. Despite its high incidence, the pathophysiological mechanisms of IVDD remain incompletely understood.

Objective

This study aimed to identify metabolomic alterations in IVDD patients and explore the key metabolic pathways and metabolites involved in its pathogenesis.

Methods

Serum samples from 20 IVDD patients and 20 healthy controls were analyzed using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). The identified metabolites were mapped to metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

Significant alterations were observed in metabolites such as 2-methyl-1,3-cyclohexadiene, stearoyl sphingomyelin, methylcysteine, L-methionine, and cis, cis-muconic acid. These metabolites were involved in pathways including glycine, serine, and threonine metabolism, cyanoamino acid metabolism, and the citrate cycle (TCA cycle).

Conclusion

The identified metabolic alterations provide insights into the pathogenesis of IVDD and suggest potential therapeutic targets for future investigation.

Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Absolute Quantitative Lipidomics Reveals Different Granule-Associated Surface Lipid Roles in the Digestibility and Pasting of Waxy, Normal, and High-Amylose Rice Starches

Granule-associated surface lipids (GASLs) and internal lipids showed different lipid-amylose relationships, contents, and distributions, suggesting their differing biological origins and functions, among waxy, normal, and high-amylose rice starch. The GASL content mainly depended on the pore size, while internal lipids regulated starch biosynthesis, as indicated by correlations of internal lipids with the chain length distribution of amylopectin and amylose content. Of the 1346 lipids detected, 628, 562, and 408 differentially expressed lipids were observed between normal-waxy, high-amylose-waxy, and normal-high-amylose starch, respectively. After the removal of GASLs, the higher lysophospholipid content induced greater decreases in the peak and breakdown viscosity and swelling power, while the highest digestibility increase was found with the highest triacylglycerol content. Thus, different GASL compositions led to different digestibility, swelling, and pasting outcomes. This study sheds new light on the mechanism of the role of GASLs in the structure and properties of starch, as well as in potential modifications and amyloplast membrane development.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.

Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis [Formula: see text]. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

Application of metabolomics in osteoporosis research

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Phenylalanine Metabolism is Dysregulated in Human Hippocampus with Alzheimer's Disease Related Pathological Changes

BACKGROUND

Alzheimer's disease (AD) is one of the most challenging diseases causing an increasing burden worldwide. Although the neuropathologic diagnosis of AD has been established for many years, the metabolic changes in neuropathologic diagnosed AD samples have not been fully investigated.

OBJECTIVE

To elucidate the potential metabolism dysregulation in the postmortem human brain samples assessed by AD related pathological examination.

METHODS

We performed untargeted and targeted metabolomics in 44 postmortem human brain tissues. The metabolic differences in the hippocampus between AD group and control (NC) group were compared.

RESULTS

The results show that a pervasive metabolic dysregulation including phenylalanine metabolism, valine, leucine, and isoleucine biosynthesis, biotin metabolism, and purine metabolism are associated with AD pathology. Targeted metabolomics reveal that phenylalanine, phenylpyruvic acid, and N-acetyl-L-phenylalanine are upregulated in AD samples. In addition, the enzyme IL-4I1 catalyzing transformation from phenylalanine to phenylpyruvic acid is also upregulated in AD samples.

CONCLUSION

There is a pervasive metabolic dysregulation in hippocampus with AD-related pathological changes. Our study suggests that the dysregulation of phenylalanine metabolism in hippocampus may be an important pathogenesis for AD pathology formation.

Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models

BACKGROUND

For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now.

RESULTS

A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry.

CONCLUSIONS

We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

A CRISPR knockout negative screen reveals synergy between CDKs inhibitor and metformin in the treatment of human cancer in vitro and in vivo

Laboratory research and pharmacoepidemiology provide support for metformin as a potential antitumor agent. However, the lack of a clear understanding of the indications of metformin limits its efficacy. Here, we performed a genome-wide CRISPR knockout negative screen to identify potential targets that might synergize with metformin. Next-generation sequencing of pooled genomic DNAs isolated from surviving cells after 18 days of metformin treatment (T18) compared to those of the untreated cells at day 0 (T0) yielded candidate genes. Knockdown of a group of cyclin-dependent kinases (CDKs), including CDK1, CDK4, and CDK6, confirmed the results of the screen. Combination treatment of the CDKs inhibitor abemaciclib with metformin profoundly inhibited tumor viability in vitro and in vivo. Although cell cycle parameters were not further altered under the combination treatment, investigation of the metabolome revealed significant changes in cell metabolism, especially with regard to fatty acid oxidation, the tricarboxylic acid cycle and aspartate metabolism. Such changes appeared to be mediated through inhibition of the mTOR pathway. Collectively, our study suggests that the combination of CDKs inhibitor with metformin could be recognized as a potential therapy in future clinical applications.

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.

Joint and unique multiblock analysis of biological data - multiomics malaria study

Modern profiling technologies enable us to obtain large amounts of data which can be used later for a comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be carried out by bare analysis of separate data sets. Integrated approaches are necessary, because only data integration allows us to find correlation trends common for all studied data sets and reveal hidden structures not known a priori. This improves the understanding and interpretation of complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variations shared with other connected matrices and variations that are unique for each single matrix. Mapping unique variations is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for the integrated analysis of lipidomic, metabolomic and oxylipins data sets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, which makes the development of new diagnostic and prognostic tools, as well as a better understanding of the disease, of utmost importance. In the presented work, JUMBA made it possible to detect already known trends related to the disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced the complexity of the analysis and facilitated the detection of samples and variables corresponding to specific structures across multiple data sets, and by doing this enabled fast interpretation of the studied system. All of this makes JUMBA a perfect choice for multiblock analysis of systems biology data.

Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks

INTRODUCTION

Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.

OBJECTIVES

We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN.

METHODS

We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub.

RESULTS

The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach.

CONCLUSION

We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.

The application of artificial neural networks in metabolomics: a historical perspective

BACKGROUND

Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods.

AIM OF REVIEW

We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics.

KEY SCIENTIFIC CONCEPT OF REVIEW

Is metabolomics ready for the return of artificial neural networks?

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

BACKGROUND

A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike.

AIM OF REVIEW

To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Does exercise impact gut microbiota composition in men receiving androgen deprivation therapy for prostate cancer? A single-blinded, two-armed, randomised controlled trial

INTRODUCTION

A potential link exists between prostate cancer (PCa) disease and treatment and increased inflammatory levels from gut dysbiosis. This study aims to examine if exercise favourably alters gut microbiota in men receiving androgen deprivation therapy (ADT) for PCa. Specifically, this study will explore whether: (1) exercise improves the composition of gut microbiota and increases the abundance of bacteria associated with health promotion and (2) whether gut health correlates with favourable inflammatory status, bowel function, continence and nausea among patients participating in the exercise intervention.

METHODS AND ANALYSIS

A single-blinded, two-armed, randomised controlled trial will explore the influence of a 3-month exercise programme (3 days/week) for men with high-risk localised PCa receiving ADT. Sixty patients will be randomly assigned to either exercise intervention or usual care. The primary endpoint (gut health and function assessed via feacal samples) and secondary endpoints (self-reported quality of life via standardised questionnaires, blood biomarkers, body composition and physical fitness) will be measured at baseline and following the intervention. A variety of statistical methods will be used to understand the covariance between microbial diversity and metabolomics profile across time and intervention. An intention-to-treat approach will be utilised for the analyses with multiple imputations followed by a secondary sensitivity analysis to ensure data robustness using a complete cases approach.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Human Research Ethics Committee of Edith Cowan University (ID: 19827 NEWTON). Findings will be reported in peer-reviewed publications and scientific conferences in addition to working with national support groups to translate findings for the broader community. If exercise is shown to result in favourable changes in gut microbial diversity, composition and metabolic profile, and reduce gastrointestinal complications in PCa patients receiving ADT, this study will form the basis of a future phase III trial.

TRIAL REGISTRATION NUMBER

ANZCTR12618000280202.