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.

Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as “statistically significant” in the study. Such metabolic “dark matter” means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function.

Anti-microbial signaling pathways are normally triggered by innate immune receptors when detecting pathogenic microbes to provide protective immunity. Here we show that the inflammasome sensor Nlrp1 aggravates DSS-induced experimental mouse colitis by limiting beneficial, butyrate-producingClostridialesin the gut. The colitis-protective effects ofNlrp1deficiency are thus reversed by vancomycin treatment, but recapitulated with butyrate supplementation in wild-type mice. Moreover, an activating mutation inNlrp1aincreases IL-18 and IFNγ production, and decreases colonic butyrate to exacerbate colitis. We also show that, in patients with ulcerative colitis, increasedNLRP1in inflamed regions of the colon is associated with increasedIFN-γ. In this context,NLRP1,IL-18orIFN-γexpression negatively correlates with the abundance ofClostridialesin human rectal mucosal biopsies. Our data identify the NLRP1 inflammasome to be a key negative regulator of protective, butyrate-producing commensals, which therefore promotes inflammatory bowel disease.

The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions.

Considering the short shelf-life of certain food products such as red meat, there is a need for rapid and cost-effective methods for pathogen detection. Routine pathogen testing in food laboratories mostly relies on conventional microbiological methods which involve the use of multiple selective culture media and long incubation periods, often taking up to 7 days for confirmed identifications. The current study investigated the application of omics-based approaches, proteomics using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) and metabolomics using gas chromatography-mass spectrometry (GC-MS), for detection of three red meat pathogens – Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7. Species-level identification was achieved within 18 h for S. enterica and E. coli O157:H7 and 30 h for L. monocytogenes using MALDI-ToF MS analysis. For the metabolomics approach, metabolites were extracted directly from selective enrichment broth samples containing spiked meat samples (obviating the need for culturing on solid media) and data obtained using GC-MS were analyzed using chemometric methods. Putative biomarkers relating to L. monocytogenes, S. enterica and E. coli O157:H7 were observed within 24, 18, and 12 h, respectively, of inoculating meat samples. Many of the identified metabolites were sugars, fatty acids, amino acids, nucleosides and organic acids. Secondary metabolites such as cadaverine, hydroxymelatonin and 3,4-dihydroxymadelic acid were also observed. The results obtained in this study will assist in the future development of rapid diagnostic tests for these important foodborne pathogens.