Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood^1–3. Here, the Molecular Transducers of Physical Activity Consortium⁴ profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository (https://motrpac-data.org/).

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine–lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Retrograde signals emanate from the DNA-containing cell organelles (plastids and mitochondria) and control the expression of a large number of nuclear genes in response to environmental and developmental cues. Previous studies on retrograde signaling have mainly analyzed the regulation of nuclear gene expression at the transcript level. To determine the contribution of translational and posttranslational regulation to plastid retrograde signaling, we combined label-free proteomics with transcriptomic analysis of Arabidopsis (Arabidopsis thaliana) seedlings and studied their response to interference with the plastid gene expression pathway of retrograde signaling. By comparing the proteomes of the genomes uncoupled1 (gun1) and gun5 mutants with the wild type, we show that GUN1 is critical in the maintenance of plastid protein homeostasis (proteostasis) when plastid translation is blocked. Combining transcriptomic and proteomic analyses of the wild type and gun1, we identified 181 highly translationally or posttranslationally regulated (HiToP) genes. We demonstrate that HiToP photosynthesis-associated nuclear genes (PhANGs) are largely regulated by translational repression, while HiToP ribosomal protein genes are regulated posttranslationally, likely at the level of protein stability without the involvement of GUN1. Our findings suggest distinct posttranscriptional control mechanisms of nuclear gene expression in response to plastid-derived retrograde signals. They also reveal a role for GUN1 in the translational regulation of several PhANGs and highlight extensive posttranslational regulation that does not necessitate GUN1. This study advances our understanding of the molecular mechanisms underlying intracellular communication and provides new insight into cellular responses to impaired plastid protein biosynthesis.

Postmenopausal osteoporosis (PMOP) and sarcopenia are common diseases that predominantly affect postmenopausal women. In the occurrence and development of these two diseases, they are potentially pathologically connected with each other at various molecular levels. However, the application of metabolomics in sarco-osteoporosis and the metabolic rewiring happening throughout the estrogen loss-replenish process have not been reported. To investigate the metabolic alteration of sarco-osteoporosis and the possible therapeutical effects of estradiol, 24 mice were randomly divided into sham surgery, ovariectomy (OVX), and estradiol-treated groups. Three-dimensional reconstructions and histopathology examination showed significant bone loss after ovariectomy. Estrogen can well protect against OVX-induced bone loss deterioration. UHPLC-Q-TOF/MS was preformed to profile semi- polar metabolites of skeletal muscle samples from all groups. Metabolomics analysis revealed metabolic rewiring occurred in OVX group, most of which can be reversed by estrogen supplementation. In total, 65 differential metabolites were identified, and pathway analysis revealed that sarco-osteoporosis was related to the alterations in purine metabolism, glycerophospholipid metabolism, arginine biosynthesis, tryptophan metabolism, histidine metabolism, oxidative phosphorylation, and thermogenesis, which provided possible explanations for the metabolic mechanism of sarco-osteoporosis. This study indicates that an UHPLC-Q-TOF/MS-based metabolomics approach can elucidate the metabolic reprogramming mechanisms of sarco-osteoporosis and provide biological evidence of the therapeutical effects of estrogen on sarco-osteoporosis.

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.