The global challenge of methane emissions from enteric fermentation is critical, as it contributes significantly to atmospheric greenhouse gases and represents a loss of energy that could otherwise be utilized by ruminants. With the increasing demand for dairy and meat products, finding effective methods to reduce methane production is essential. This review explores the use of advanced meta-omics techniques – including metagenomics, metatranscriptomics, metaproteomics, and metabolomics – to deepen our understanding of ruminal methane production and identify potential strategies for its mitigation. These high-throughput technologies provide comprehensive insights into the rumen microbial communities and their metabolic functions by analyzing DNA, RNA, proteins, and metabolites directly from environmental samples. Metagenomics and metatranscriptomics offer a detailed view of microbial diversity and gene expression, while metaproteomics can identify specific enzymes and proteins directly involved in methane production pathways, revealing potential targets for mitigation strategies. Integrating these meta-omics approaches allows for a holistic understanding of the microbial processes that drive methane emissions, enabling the development of more precise interventions, such as tailored dietary modifications and the use of specific inhibitors. This review underscores the importance of a multi-omics strategy in characterizing microbial roles and interactions within the rumen, which is crucial for devising effective and sustainable methods to reduce methane emissions without compromising livestock productivity.

Humans operating in extreme environments often conduct their operations at the edges of the limits of human performance. Sometimes, they are required to push these limits to previously unattained levels. As a result, their margins for error in execution are much smaller than that found in the general public. These same small margins for error that impact execution may also impact risk, safety, health, and even survival. Thus, humans operating in extreme environments have a need for greater refinement in their preparation, training, fitness, and medical care. Precision medicine (PM) is uniquely suited to address the needs of those engaged in these extreme operations because of its depth of molecular analysis, derived precision countermeasures, and ability to match each individual (and his or her specific molecular phenotype) with any given operating context (environment). Herein, we present an overview of a systems approach to PM in extreme environments, which affords clinicians one method to contextualize the inputs, processes, and outputs that can form the basis of a formal practice. For the sake of brevity, this overview is focused on molecular dynamics, while providing only a brief introduction to the also important physiologic and behavioral phenotypes in PM. Moreover, rather than a full review, it highlights important concepts, while using only selected citations to illustrate those concepts. It further explores, by demonstration, the basic principles of using functionally characterized molecular networks to guide the practical application of PM in extreme environments. At its core, PM in extreme environments is about attention to incremental gains and losses in molecular network efficiency that can scale to produce notable changes in health and performance. The aim of this overview is to provide a conceptual overview of one approach to PM in extreme environments, coupled with a selected suite of practical considerations for molecular profiling and countermeasures.

The overall aim of precision nutrition is to replace the ‘one size fits all’ approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more ‘precision’ dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene–diet or microbiota–diet interactions. Third, precision nutrition can be used for ‘personalised nutrition’ advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet–disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Preterm birth (PTB) is one of the leading causes of deaths in infants under the age of five. Known risk factors of PTB include genetic factors, lifestyle choices or infection. Identification of omic biomarkers associated with PTB could aid clinical management of women at high risk of early labour and thereby reduce neonatal morbidity. This systematic literature review aimed to identify and summarise maternal omic and multi-omic (genomics, transcriptomics, proteomics and metabolites) biomarker studies of PTB. Original research articles were retrieved from three databases: PubMed, Web of Science and Science Direct, using specified search terms for each omic discipline. PTB studies investigating genomics, transcriptomics, proteomics or metabolomics biomarkers prior to onset of labour were included. Data were collected and reviewed independently. Pathway analyses were completed on the biomarkers from non-targeted omic studies using Reactome pathway analysis tool. A total of 149 omic studies were identified; most of the literature investigated proteomic biomarkers. Pathway analysis identified several cellular processes associated with the omic biomarkers reported in the literature. Study heterogeneity was observed across the research articles, including the use of different gestation cut-offs to define PTB. Infection/inflammatory biomarkers were identified across majority of papers using a range of targeted and non-targeted approaches.

Mate is a traditional drink obtained from the leaves of yerba mate and rich in a diversity of plant bioactive compounds including polyphenols, particularly chlorogenic acids. Studies, even though limited, suggest that consumption of mate is associated with health effects, including prevention of cardiometabolic disorders. Molecular mechanisms underlying the potential health properties are still largely unknown, especially in humans. The aim of this study was to investigate nutrigenomic effects of mate consumption and identify regulatory networks potentially mediating cardiometabolic health benefits. Healthy middle-aged men at risk for CVD consumed a standardised mate extract or placebo for 4 weeks. Global gene expression, including protein coding and non-coding RNA profiles, was determined using microarrays. Biological function analyses were performed using integrated bioinformatic tools. Comparison of global gene expression profiles showed significant change following mate consumption with 2635 significantly differentially expressed genes, among which six are miRNA and 244 are lncRNA. Functional analyses showed that these genes are involved in regulation of cell interactions and motility, inflammation or cell signalling. Transcription factors, such as MEF2A, MYB or HNF1A, could have their activity modulated by mate consumption either by direct interaction with polyphenol metabolites or by interactions of metabolites with cell signalling proteins, like p38 or ERK1/2, that could modulate transcription factor activity and regulate expression of genes observed. Correlation analysis suggests that expression profile is inversely associated with gene expression profiles of patients with cardiometabolic disorders. Therefore, mate consumption may exert cardiometabolic protective effects by modulating gene expression towards a protective profile.

Antigenic variation in malaria was discovered in Plasmodium knowlesi studies involving longitudinal infections of rhesus macaques (M. mulatta). The variant proteins, known as the P. knowlesi Schizont Infected Cell Agglutination (SICA) antigens and the P. falciparum Erythrocyte Membrane Protein 1 (PfEMP1) antigens, expressed by the SICAvar and var multigene families, respectively, have been studied for over 30 years. Expression of the SICA antigens in P. knowlesi requires a splenic component, and specific antibodies are necessary for variant antigen switch events in vivo. Outstanding questions revolve around the role of the spleen and the mechanisms by which the expression of these variant antigen families are regulated. Importantly, the longitudinal dynamics and molecular mechanisms that govern variant antigen expression can be studied with P. knowlesi infection of its mammalian and vector hosts. Synchronous infections can be initiated with established clones and studied at multi-omic levels, with the benefit of computational tools from systems biology that permit the integration of datasets and the design of explanatory, predictive mathematical models. Here we provide an historical account of this topic, while highlighting the potential for maximizing the use of P. knowlesi – macaque model systems and summarizing exciting new progress in this area of research.