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Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation. The authors report a systematic analyses of blood biomarkers for metabolism against the whole spectrum of diseases in 100,000 individuals and reveals a prominent role of numerous metabolic biomarkers as risk markers beyond heart disease and diabetes.

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant ( K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Molecular communication in biology is mediated by protein interactions. According to the current paradigm, the specificity and affinity required for these interactions are encoded in the precise complementarity of binding interfaces. Even proteins that are disordered under physiological conditions or that contain large unstructured regions commonly interact with well-structured binding sites on other biomolecules. Here we demonstrate the existence of an unexpected interaction mechanism: the two intrinsically disordered human proteins histone H1 and its nuclear chaperone prothymosin-α associate in a complex with picomolar affinity, but fully retain their structural disorder, long-range flexibility and highly dynamic character. On the basis of closely integrated experiments and molecular simulations, we show that the interaction can be explained by the large opposite net charge of the two proteins, without requiring defined binding sites or interactions between specific individual residues. Proteome-wide sequence analysis suggests that this interaction mechanism may be abundant in eukaryotes. A high-affinity complex of histone H1 and prothymosin-α reveals an unexpected interaction mechanism, where the large opposite net charge enables the two proteins to remain highly disordered even in the complex. High-affinity complexes of disordered proteins Disordered protein regions have been increasingly implicated in high-affinity protein–protein interactions. However, once the resulting protein complexes have formed, at least one interacting protein partner has been found to be stably folded. Using a suite of independent biophysical approaches, Ben Schuler and colleagues reveal a ultrahigh-affinity (picomolar) complex between two proteins (histone H1 and its nuclear chaperone prothymosin-α) that both remain fully disordered when bound to each other. High-affinity binding results from numerous, dynamic and non-specific electrostatic interactions along the extended chains of the highly charged polypeptides. This structural feature is prevalent among signalling molecules in eukaryotes, including in humans.

Cross-linking between lignin and polysaccharide in plant cell-wall determines physical, chemical, and biological features of lignocellulosic biomass. Since Erdmann’s first report in 1866, numerous studies have suggested the presence of a bond between hemicelluloses and lignin; however, no clear evidence for this interaction has been reported. We describe the first direct proof of covalent bonding between plant cell-wall polysaccharides and lignin. Nuclear magnetic resonance spectroscopy was used to observe the long-range correlations through an α-ether bond between lignin and the primary hydroxyl group of a mannose residue in glucomannan. Complete signal assignment of the cognate structural units was also achieved. Thus, we identified lignin–carbohydrate bonds by complete connectivity analysis from the phenylpropane unit to the carbohydrate moiety.

While acetylated, RNA-binding-deficient TDP-43 reversibly phase separates within nuclei into complex droplets (anisosomes) comprised of TDP-43-containing liquid outer shells and liquid centres of HSP70-family chaperones, cytoplasmic aggregates of TDP-43 are hallmarks of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Here we show that transient oxidative stress, proteasome inhibition or inhibition of the ATP-dependent chaperone activity of HSP70 provokes reversible cytoplasmic TDP-43 de-mixing and transition from liquid to gel/solid, independently of RNA binding or stress granules. Isotope labelling mass spectrometry was used to identify that phase-separated cytoplasmic TDP-43 is bound by the small heat-shock protein HSPB1. Binding is direct, mediated through TDP-43’s RNA binding and low-complexity domains. HSPB1 partitions into TDP-43 droplets, inhibits TDP-43 assembly into fibrils, and is essential for disassembly of stress-induced TDP-43 droplets. A decrease in HSPB1 promotes cytoplasmic TDP-43 de-mixing and mislocalization. HSPB1 depletion was identified in spinal motor neurons of patients with ALS containing aggregated TDP-43. These findings identify HSPB1 to be a regulator of cytoplasmic TDP-43 phase separation and aggregation. Lu et al. report that biomolecular condensation of cytoplasmic TDP-43 is regulated by HSPB1 to maintain its droplets in liquid and not gel/solid structures and that HSPB1 is decreased in spinal motor neurons with TDP-43 pathology in patients with amyotrophic lateral sclerosis.

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gastrointestinal tract of uncertain origin, which includes ulcerative colitis (UC) and Crohn’s disease (CD). The composition of gut microbiota may change in IBD affected individuals, but whether dysbiosis is the cause or the consequence of inflammatory processes in the intestinal tissue is still unclear. Here, the composition of the microbiota and the metabolites in stool of 183 subjects (82 UC, 50 CD, and 51 healthy controls) were determined. The metabolites content and the microbiological profiles were significantly different between IBD and healthy subjects. In the IBD group, Firmicutes, Proteobacteria, Verrucomicrobia, and Fusobacteria were significantly increased, whereas Bacteroidetes and Cyanobacteria were decreased. At genus level Escherichia , Faecalibacterium , Streptococcus , Sutterella and Veillonella were increased, whereas Bacteroides , Flavobacterium , and Oscillospira decreased. Various metabolites including biogenic amines, amino acids, lipids, were significantly increased in IBD, while others, such as two B group vitamins, were decreased in IBD compared to healthy subjects. This study underlines the potential role of an inter-omics approach in understanding the metabolic pathways involved in IBD. The combined evaluation of metabolites and fecal microbiome can be useful to discriminate between healthy subjects and patients with IBD.

Major depressive disorder (MDD) is a serious mental illness, characterized by high morbidity, which has increased in recent decades. However, the molecular mechanisms underlying MDD remain unclear. Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD. Using curated metabolic characterization data from a large sample of MDD patients, we meta-analyzed the results of metabolites in peripheral blood. Pathway and network analyses were then performed to elucidate the biological themes within these altered metabolites. We identified 23 differentially expressed metabolites between MDD patients and controls from 46 studies. MDD patients were characterized by higher levels of asymmetric dimethylarginine, tyramine, 2-hydroxybutyric acid, phosphatidylcholine (32:1), and taurochenodesoxycholic acid and lower levels of l-acetylcarnitine, creatinine, l-asparagine, l-glutamine, linoleic acid, pyruvic acid, palmitoleic acid, l-serine, oleic acid, myo-inositol, dodecanoic acid, l-methionine, hypoxanthine, palmitic acid, l-tryptophan, kynurenic acid, taurine, and 25-hydroxyvitamin D compared with controls. l-tryptophan and kynurenic acid were consistently downregulated in MDD patients, regardless of antidepressant exposure. Depression rating scores were negatively associated with decreased levels of l-tryptophan. Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism, especially for the tryptophan–kynurenine pathway and fatty acid metabolism, in the peripheral system of MDD patients. Taken together, our integrated results revealed that metabolic changes in the peripheral blood were associated with MDD, particularly decreased l-tryptophan and kynurenic acid levels, and alterations in the tryptophan–kynurenine and fatty acid metabolism pathways. Our findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.

BCAT1, a cytosolic aminotransferase for branched-chain amino acids (BCAAs), is aberrantly activated and functionally required for disease progression in chronic myeloid leukaemia. Role of metabolic changes in cancer progression Cellular metabolic changes are commonly observed in various cancers. How they directly influence cancer development is under investigation. Ayuna Hattori et al . show that the metabolic enzyme BCAT1 is upregulated in chronic myeloid leukemia, mediated by Musashi2. BCAT1 is shown to function by aminating branch-chain keto acids to BCAAs. Inhibition of BCAT1 leads to differentiation and impaired propagation of chronic myeloid leukaemia in mice. In humans, elevated BCAT1 expression is also associated with poorer outcome of the illness and therefore might be used alongside other biomarkers to help predict disease outcome in patients. Reprogrammed cellular metabolism is a common characteristic observed in various cancers 1 , 2 . However, whether metabolic changes directly regulate cancer development and progression remains poorly understood. Here we show that BCAT1, a cytosolic aminotransferase for branched-chain amino acids (BCAAs), is aberrantly activated and functionally required for chronic myeloid leukaemia (CML) in humans and in mouse models of CML. BCAT1 is upregulated during progression of CML and promotes BCAA production in leukaemia cells by aminating the branched-chain keto acids. Blocking BCAT1 gene expression or enzymatic activity induces cellular differentiation and impairs the propagation of blast crisis CML both in vitro and in vivo . Stable-isotope tracer experiments combined with nuclear magnetic resonance-based metabolic analysis demonstrate the intracellular production of BCAAs by BCAT1. Direct supplementation with BCAAs ameliorates the defects caused by BCAT1 knockdown, indicating that BCAT1 exerts its oncogenic function through BCAA production in blast crisis CML cells. Importantly, BCAT1 expression not only is activated in human blast crisis CML and de novo acute myeloid leukaemia, but also predicts disease outcome in patients. As an upstream regulator of BCAT1 expression, we identified Musashi2 (MSI2), an oncogenic RNA binding protein that is required for blast crisis CML. MSI2 is physically associated with the BCAT1 transcript and positively regulates its protein expression in leukaemia. Taken together, this work reveals that altered BCAA metabolism activated through the MSI2–BCAT1 axis drives cancer progression in myeloid leukaemia.

Nucleotide-binding domain, leucine-rich repeat receptors (NLRs) mediate innate immunity by forming inflammasomes. Activation of the NLR protein NLRP1 requires autocleavage within its function-to-find domain (FIIND) 1 – 7 . In resting cells, the dipeptidyl peptidases DPP8 and DPP9 interact with the FIIND of NLRP1 and suppress spontaneous NLRP1 activation 8 , 9 ; however, the mechanisms through which this occurs remain unknown. Here we present structural and biochemical evidence that full-length rat NLRP1 (rNLRP1) and rat DPP9 (rDPP9) form a 2:1 complex that contains an autoinhibited rNLRP1 molecule and an active UPA–CARD fragment of rNLRP1. The ZU5 domain is required not only for autoinhibition of rNLRP1 but also for assembly of the 2:1 complex. Formation of the complex prevents UPA-mediated higher-order oligomerization of UPA–CARD fragments and strengthens ZU5-mediated NLRP1 autoinhibition. Structure-guided biochemical and functional assays show that both NLRP1 binding and enzymatic activity are required for DPP9 to suppress NLRP1 in human cells. Together, our data reveal the mechanism of DPP9-mediated inhibition of NLRP1 and shed light on the activation of the NLRP1 inflammasome. The 2:1 complex between the inflammatory mediator NLRP1 and the dipeptidyl peptidase DPP9 functions to sequester an inflammasome-forming fragment of NLRP1 and enhance NLRP1 autoinhibition.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics 1 . Here we examine a large cohort (the INTERVAL study 2 ; n  = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n  = 3,175; Olink, n  = 4,822), plasma metabolomics (Metabolon HD4, n  = 8,153), serum metabolomics (Nightingale, n  = 37,359) and whole-blood Illumina RNA sequencing ( n  = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank 3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK–STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores. A machine learning approach is used to analyse multi-omics (proteomics, metabolomics and transcriptomics) data, producing genetic scores for more than 17,000 biomolecular traits in human blood, and identifying possible associations with disease.