A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Metabolome and proteome analyses reveal transcriptional misregulation in glycolysis of engineered E. coli

Synthetic metabolic pathways are a burden for engineered bacteria, but the underlying mechanisms often remain elusive. Here we show that the misregulated activity of the transcription factor Cra is responsible for the growth burden of glycerol overproducing E. coli. Glycerol production decreases the concentration of fructose-1,6-bisphoshate (FBP), which then activates Cra resulting in the downregulation of glycolytic enzymes and upregulation of gluconeogenesis enzymes. Because cells grow on glucose, the improper activation of gluconeogenesis and the concomitant inhibition of glycolysis likely impairs growth at higher induction of the glycerol pathway. We solve this misregulation by engineering a Cra-binding site in the promoter controlling the expression of the rate limiting enzyme of the glycerol pathway to maintain FBP levels sufficiently high. We show the broad applicability of this approach by engineering Cra-dependent regulation into a set of constitutive and inducible promoters, and use one of them to overproduce carotenoids in E. coli.

Synthetic pathways represent a metabolic burden on host cells. Here the authors engineer Cra-binding sites to prevent misregulation in glycerol and carotenoid overproducing E. coli strains.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

Fuzzy protein theory for disordered proteins

Why proteins are fuzzy? Constant adaptation to the cellular environment requires a wide range of changes in protein structure and interactions. Conformational ensembles of disordered proteins in particular exhibit large shifts to activate or inhibit alternative pathways. Fuzziness is critical for liquid–liquid phase separation and conversion of biomolecular condensates into fibrils. Interpretation of these phenomena presents a challenge for the classical structure-function paradigm. Here I discuss a multi-valued formalism, based on fuzzy logic, which can be applied to describe complex cellular behavior of proteins.

Nonrefoldability is Pervasive Across the E. coli Proteome

Decades of research on protein folding have primarily focused on a subset of small proteins that can reversibly refold from a denatured state. However, these studies have generally not been representative of the complexity of natural proteomes, which consist of many proteins with complex architectures and domain organizations. Here, we introduce an experimental approach to probe protein refolding kinetics for whole proteomes using mass spectrometry-based proteomics. Our study covers the majority of the soluble E. coli proteome expressed during log-phase growth, and among this group, we find that one-third of the E. coli proteome is not intrinsically refoldable on physiological time scales, a cohort that is enriched with certain fold-types, domain organizations, and other biophysical features. We also identify several properties and fold-types that are correlated with slow refolding on the minute time scale. Hence, these results illuminate when exogenous factors and processes, such as chaperones or cotranslational folding, might be required for efficient protein folding.

Coupling proteomics and metabolomics for the unsupervised identification of protein-metabolite interactions in Chaetomium thermophilum

Protein-metabolite interactions play an important role in the cell's metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.

In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology

Biological systems have evolved to utilize proteins to accomplish nearly all functional roles needed to sustain life. A majority of biological functions occur within the crowded environment inside cells and subcellular compartments where proteins exist in a densely packed complex network of protein-protein interactions. The structural biology field has experienced a renaissance with recent advances in crystallography, NMR, and CryoEM that now produce stunning models of large and complex structures previously unimaginable. Nevertheless, measurements of such structural detail within cellular environments remain elusive. This review will highlight how advances in mass spectrometry, chemical labeling, and informatics capabilities are merging to provide structural insights on proteins, complexes, and networks that exist inside cells. Because of the molecular detection specificity provided by mass spectrometry and proteomics, these approaches provide systems-level information that not only benefits from conventional structural analysis, but also is highly complementary. Although far from comprehensive in their current form, these approaches are currently providing systems structural biology information that can uniquely reveal how conformations and interactions involving many proteins change inside cells with perturbations such as disease, drug treatment, or phenotypic differences. With continued advancements and more widespread adaptation, systems structural biology based on in-cell labeling and mass spectrometry will provide an even greater wealth of structural knowledge.

Quantitative Chemical Proteomics Reveals Interspecies Variations on Binding Schemes of L-FABP with Perfluorooctanesulfonate

Evaluating interspecies toxicity variation is a long-standing challenge for chemical hazard assessment. This study developed a quantitative interspecies thermal shift assay (QITSA) for in situ, quantitative, and modest-throughput investigation of chemical-protein interactions in cell and tissue samples across species. By using liver fatty acid binding protein (L-FABP) as a case study, the QITSA method was benchmarked with six per- and polyfluoroalkyl substances, and thermal shifts (ΔT_m) were inversely related to their dissociation constants (R² = 0.98). The QITSA can also distinguish binding modes of chemicals exemplified by palmitic acid. The QITSA was applied to determine the interactions between perfluorooctanesulfonate (PFOS) and L-FABP in liver cells or tissues from humans, mice, rats, and zebrafish. The largest thermal stability enhancement by PFOS was observed for human L-FABP followed by the mouse, rat, and zebrafish. While endogenous ligands were revealed to partially contribute to the large interspecies variation, recombinant proteins were employed to confirm the high binding affinity of PFOS to human L-FABP, compared to the rat and mouse. This study implemented an experimental strategy to characterize chemical-protein interactions across species, and future application of QITSA to other chemical contaminants is of great interest.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

Detection of Metabolite-Protein Interactions in Complex Biological Samples by High-Resolution Relaxometry: Toward Interactomics by NMR

Metabolomics, the systematic investigation of metabolites in biological fluids, cells, or tissues, reveals essential information about metabolism and diseases. Metabolites have functional roles in a myriad of biological processes, as substrates and products of enzymatic reactions but also as cofactors and regulators of large numbers of biochemical mechanisms. These functions involve interactions of metabolites with macromolecules. Yet, methods to systematically investigate these interactions are still scarce to date. In particular, there is a need for techniques suited to identify and characterize weak metabolite-macromolecule interactions directly in complex media such as biological fluids. Here, we introduce a method to investigate weak interactions between metabolites and macromolecules in biological fluids. Our approach is based on high-resolution NMR relaxometry and does not require any invasive procedure or separation step. We show that we can detect interactions between small and large molecules in human blood serum and quantify the size of the complex. Our work opens the way for investigations of metabolite (or other small molecules)-protein interactions in biological fluids for interactomics or pharmaceutical applications.

Proteomic and metabolomic characterization of amygdala in chronic social defeat stress rats

BACKGROUND

Depression is a leading cause of disability worldwide. There is increasing evidence showing that depression is associated with the pathophysiology in amygdala; however, the underlying mechanism remains poorly understood.

METHOD

We established a rat model of chronic social defeat stress (CSDS) and conducted a series of behavior tests to observe behavioral changes. Then liquid chromatography mass spectrometry (LC-MS)-based metabolomics and isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics were employed to detect metabolomes and proteomes in the amygdala, respectively. Ingenuity pathway analysis (IPA) and other bioinformatic analyses were used to analyze differentially expressed metabolites and proteins.

RESULTS

The significantly lower sucrose preference index in the sucrose preference test and longer immobile time in the forced swim test were observed in the CSDS rats compared with control rats. In the multi-omics analysis, thirty-seven significantly differentially expressed metabolites and 123 significant proteins were identified. Integrated analysis of differentially expressed metabolites and proteins by IPA revealed molecular changes mainly associated with synaptic plasticity, phospholipase c signaling, and glutamine degradation I. We compared the metabolites in the amygdala with those in the hippocampus and prefrontal cortex from our previous studies and found two common metabolites: arachidonic acid and N-acetyl-l-aspartic acid among these three brain regions.

CONCLUSION

Our study revealed the presence of depressive-like behaviors and molecular changes of amygdala in the CSDS rat model, which may provide further insights into the pathogenesis of depression, and help to identify potential targets for antidepressants.

New applications of advanced instrumental techniques for the characterization of food allergenic proteins

Current approaches based on electrophoretic, chromatographic or immunochemical principles have allowed characterizing multiple allergens, mapping their epitopes, studying their mechanisms of action, developing detection and diagnostic methods and therapeutic strategies for the food and pharmaceutical industry. However, some of the common structural features related to the allergenic potential of food proteins remain unknown, or the pathological mechanism of food allergy is not yet fully understood. In addition, it is also necessary to evaluate new allergens from novel protein sources that may pose a new risk for consumers. Technological development has allowed the expansion of advanced technologies for which their whole potential has not been entirely exploited and could provide novel contributions to still unexplored molecular traits underlying both the structure of food allergens and the mechanisms through which they sensitize or elicit adverse responses in human subjects, as well as improving analytical techniques for their detection. This review presents cutting-edge instrumental techniques recently applied when studying structural and functional aspects of proteins, mechanism of action and interaction between biomolecules. We also exemplify their role in the food allergy research and discuss their new possible applications in several areas of the food allergy field.

The Synergistic Effects of 5-Aminosalicylic Acid and Vorinostat in the Treatment of Ulcerative Colitis

Background: The drug 5-aminosalicylic acid (5-ASA) is the first-line therapy for the treatment of patients with mild-to-moderate ulcerative colitis (UC). However, in some cases, 5-ASA cannot achieve the desired therapeutic effects. Therefore, patients have to undergo therapies that include corticosteroids, monoclonal antibodies or immunosuppressants, which are expensive and may be accompanied by significant side effects. Synergistic drug combinations can achieve greater therapeutic effects than individual drugs while contributing to combating drug resistance and lessening toxic side effects. Thus, in this study, we sought to identify synergistic drugs that can act synergistically with 5-ASA. Methods: We started our study with protein-metabolite analysis based on peroxisome proliferator-activated receptor gamma (PPARG), the therapeutic target of 5-ASA, to identify more additional potential drug targets. Then, we further evaluated the possibility of their synergy with PPARG by integrating Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, pathway-pathway interaction analysis, and semantic similarity analysis. Finally, we validated the synergistic effects with in vitro and in vivo experiments. Results: The combination of 5-ASA and vorinostat (SAHA) showed lower toxicity and mRNA expression of p65 in human colonic epithelial cell lines (Caco-2 and HCT-116), and more efficiently alleviated the symptoms of dextran sulfate sodium (DSS)-induced colitis than treatment with 5-ASA and SAHA alone. Conclusion: SAHA can exert effective synergistic effects with 5-ASA in the treatment of UC. One possible mechanism of synergism may be synergistic inhibition of the nuclear factor kappa B (NF-kB) signaling pathway. Moreover, the metabolite-butyric acid may be involved.

Deciphering the signaling network of breast cancer improves drug sensitivity prediction

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.

Conservation of metabolic regulation by phosphorylation and non-covalent small-molecule interactions

Here, we review extant observations of protein phosphorylation and small-molecule interactions in metabolism and ask which of their specific regulatory functions are conserved in Escherichia coli and Homo sapiens. While the number of phosphosites is dramatically higher in humans, the number of metabolite-protein interactions remains largely constant. Moreover, we found the regulatory logic of metabolite-protein interactions, and in many cases also the effector molecules, to be conserved. Post-translational regulation through phosphorylation does not appear to replace this regulation in human but rather seems to add additional opportunities for fine-tuning and more complex responses. The abundance of metabolite-protein interactions in metabolism, their conserved cross-species abundance, and the apparent conservation of regulatory logic across enormous phylogenetic distance demonstrate their relevance for maintaining cellular homeostasis in these ancient biological processes.

Why and How to Dig into Plant Metabolite-Protein Interactions

Interaction between metabolites and proteins drives cellular regulatory processes within and between organisms. Recent reports highlight that numerous plant metabolites embrace multiple biological activities, beyond a sole role as substrates, products, or cofactors of enzymes, or as defense or growth-regulatory compounds. Though several technologies have been developed to identify and characterize metabolite-protein interactions, the systematic implementation of such methods in the plant field remains limited. Here, we discuss the plant metabolic space, with a specific focus on specialized metabolites and their roles, and review the technologies to study their interaction with proteins. We approach it both from a plant's perspective, to increase our understanding of plant metabolite-dependent regulatory networks, and from a human perspective, to empower agrochemical and drug discoveries.

Combination of network and molecule structure accurately predicts competitive inhibitory interactions

Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metabolite-protein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Administration of isoliquiritigenin prevents nonalcoholic fatty liver disease through a novel IQGAP2-CREB-SIRT1 axis

Isoliquiritigenin (ISO) is a flavonoid extracted from the root of licorice, which serves various biological and pharmacological functions including antiinflammatory, antioxidation, liver protection, and heart protection. However, the mechanism of its action remains elusive and the direct target proteins of ISO have not been identified so far. Through cell-based screening, we identified ISO as a potent lipid-lowering compound. ISO treatment successfully ameliorated fatty acid-induced cellular lipid accumulation and improved nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) by increasing PPARα-dependent lipid oxidation and decreasing SREBPs-dependent lipid synthesis. Both these signaling required the activation of SIRT1. Knockdown of SIRT1 resulted in the reversal of ISO beneficiary effects suggesting that the lipid-lowering activity of ISO was regulated by SIRT1 expression. To identify the direct target of ISO, limited proteolysis combined with mass spectrometry (LiP-SMap) strategy was applied and IQGAP2 was identified as the direct target for ISO in regulating lipid homeostasis. In the presence of ISO, both mRNA and protein levels of SIRT1 were increased; however, this effect was abolished by blocking IQGAP2 expression using siRNA. To explore how IQGAP2 regulated the expression level of SIRT1, proteome profiler human phospho-kinase array kit was used to reveal possible phosphorylated kinases and signaling nodes that ISO affected. We found that through phosphorylation of CREB, ISO transduced signals from IQGAP2 to upregulate SIRT1 expression. Thus, we not only demonstrated the molecular basis of ISO in regulating lipid metabolism but also exhibited for the first time a novel IQGAP2-CREB-SIRT1 axis in treating NAFLD/NASH.

Fe-MOGs-based enzyme mimetic and its mediated electrochemiluminescence for in situ detection of H2O2 released from Hela cells

Enzyme mimetics have attracted wide interest due to their inherent enzyme-like activity and unique physicochemical properties, as well as promising applications in disease diagnosis, treatment and monitoring. Inspired by the attributes of nonheme iron enzymes, synthetic models were designed to mimic their capability and investigate the catalytic mechanisms. Herein, metal-organic gels (Fe-MOGs) with horseradish peroxidase (HRP) like Fe-N_X structure were successfully synthesized though the coordination between iron and 1,10-phenanthroline-2,9-dicarboxylic acid (PDA) and exhibited excellent peroxidase-like activity. Its structure-activity relationship and the in-situ electrochemiluminescence (ECL) detection of H₂O₂ secreted by Hela cells were further investigated. The highly dispersed Fe-N_X active sites inside Fe-MOGs were able to catalyze the decomposition of H₂O₂ into large amounts of reactive oxygen species (ROS) via a Fenton-like reaction under a low overpotential. Due to the accumulation of ROS free radicals, the luminol ECL emission was significantly amplified. A proof-of-concept biosensor was constructed with a detection limit as low as 2.2 nM and a wide linear range from 0.01 to 40 μM. As a novel metal organic gels based enzyme mimetic, Fe-MOGs show great promises in early cancer detection and pathological process monitoring.

Global Profiling of Lysine Accessibility to Evaluate Protein Structure Changes in Alzheimer's Disease

The linear sequence of amino acids in a protein folds into a 3D structure to execute protein activity and function, but it is still challenging to profile the 3D structure at the proteome scale. Here, we present a method of native protein tandem mass tag (TMT) profiling of Lys accessibility and its application to investigate structural alterations in human brain specimens of Alzheimer's disease (AD). In this method, proteins are extracted under a native condition, labeled by TMT reagents, followed by trypsin digestion and peptide analysis using two-dimensional liquid chromatography and tandem mass spectrometry (LC/LC-MS/MS). The method quantifies Lys labeling efficiency to evaluate its accessibility on the protein surface, which may be affected by protein conformations, protein modifications, and/or other molecular interactions. We systematically optimized the amount of TMT reagents, reaction time, and temperature and then analyzed protein samples under multiple conditions, including different labeling time (5 and 30 min), heat treatment, AD and normal human cases. The experiment profiled 15370 TMT-labeled peptides in 4475 proteins. As expected, the heat treatment led to extensive changes in protein conformations, with 17% of the detected proteome displaying differential labeling. Compared to the normal controls, AD brain showed different Lys accessibility of tau and RNA splicing complexes, which are the hallmarks of AD pathology, as well as proteins involved in transcription, mitochondrial, and synaptic functions. To eliminate the possibility that the observed differential Lys labeling was caused by protein level change, the whole proteome was quantified with standard TMT-LC/LC-MS/MS for normalization. Thus, this native protein TMT method enables the proteome-wide measurement of Lys accessibility, representing a straightforward strategy to explore protein structure in any biological system.

Leukocyte proteomics coupled with serum metabolomics identifies novel biomarkers and abnormal amino acid metabolism in Kawasaki disease

Kawasaki disease (KD) is a systemic vasculitis that can lead to severe cardiovascular complications, whereas the development and clinical usage of specific biomarkers might help diagnose KD and avoid certain complications. To this end, the molecular profiles of acute KD patients with coronary artery lesions (CAL) were first investigated through leukocyte proteomics and serum metabolomics assays. A total of 269 differentially abundant proteins and 35 differentially abundant metabolites with the top fold-changed levels were identified in acute KD patients compared to those in the healthy controls. Among them, several highly promising candidate marker proteins and metabolites indicative of KD progression were further analysed, such as the increased proteins ALPL, NAMPT, and S100P, as well as the decreased proteins C1QB and apolipoprotein family members. Moreover, metabolites, including succinic acid, dGMP, hyaluronic acid, L-tryptophan, propionylcarnitine, inosine, and phosphorylcholine, were found to be highly accurate at distinguishing between KD patients and healthy controls. Interestingly, the abnormal expression levels of a distinct set of proteins and metabolites in acute KD patients can be restored to normal levels upon intravenous immunoglobulin (IVIG) treatment. Overall, this work has revealed novel biomarkers and abnormal amino-acid metabolism as a prominent feature involved in KD patients with CAL. SIGNIFICANCE: KD is frequently concomitant with the development of life-threatening coronary vasculitis. Here, the profiles of leukocyte proteomics and serum metabolomics in acute KD patients with CALs were first investigated, and several hub molecules identified here could be used as supplemental biomarkers for KD diagnosis. Moreover, the metabolomic abnormalities especially the amino acids are particularly prominent in KD patients. Interestingly, the abnormal expression levels of a distinct set of proteins and metabolites in acute KD patients can be restored to normal levels upon IVIG treatment. Therefore, these findings might help understand the IVIG activities and also the underlying mechanisms of IVIG-resistant patients, thereby providing a new perspective for the exploration of mechanisms related to KD.

The phytochemical hyperforin triggers thermogenesis in adipose tissue via a Dlat-AMPK signaling axis to curb obesity

Stimulation of adipose tissue thermogenesis is regarded as a promising avenue in the treatment of obesity. However, pharmacologic engagement of this process has proven difficult. Using the Connectivity Map (CMap) approach, we identified the phytochemical hyperforin (HPF) as an anti-obesity agent. We found that HPF efficiently promoted thermogenesis by stimulating AMPK and PGC-1α via a Ucp1-dependent pathway. Using LiP-SMap (limited proteolysis-mass spectrometry) combined with a microscale thermophoresis assay and molecular docking analysis, we confirmed dihydrolipoamide S-acetyltransferase (Dlat) as a direct molecular target of HPF. Ablation of Dlat significantly attenuated HPF-mediated adipose tissue browning both in vitro and in vivo. Furthermore, genome-wide association study analysis indicated that a variation in DLAT is significantly associated with obesity in humans. These findings suggest that HPF is a promising lead compound in the pursuit of a pharmacological approach to promote energy expenditure in the treatment of obesity.

System-wide identification and prioritization of enzyme substrates by thermal analysis

Despite the immense importance of enzyme-substrate reactions, there is a lack of general and unbiased tools for identifying and prioritizing substrate proteins that are modified by the enzyme on the structural level. Here we describe a high-throughput unbiased proteomics method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that the enzymatic post-translational modification of substrate proteins is likely to change their thermal stability. In our proof-of-concept studies, SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, opening opportunities to investigate the effect of post-translational modifications on signal transduction and facilitate drug discovery.

Global mapping of protein-metabolite interactions in Saccharomyces cerevisiae reveals that Ser-Leu dipeptide regulates phosphoglycerate kinase activity

Protein-metabolite interactions are of crucial importance for all cellular processes but remain understudied. Here, we applied a biochemical approach named PROMIS, to address the complexity of the protein-small molecule interactome in the model yeast Saccharomyces cerevisiae. By doing so, we provide a unique dataset, which can be queried for interactions between 74 small molecules and 3982 proteins using a user-friendly interface available at https://promis.mpimp-golm.mpg.de/yeastpmi/ . By interpolating PROMIS with the list of predicted protein-metabolite interactions, we provided experimental validation for 225 binding events. Remarkably, of the 74 small molecules co-eluting with proteins, 36 were proteogenic dipeptides. Targeted analysis of a representative dipeptide, Ser-Leu, revealed numerous protein interactors comprising chaperones, proteasomal subunits, and metabolic enzymes. We could further demonstrate that Ser-Leu binding increases activity of a glycolytic enzyme phosphoglycerate kinase (Pgk1). Consistent with the binding analysis, Ser-Leu supplementation leads to the acute metabolic changes and delays timing of a diauxic shift. Supported by the dipeptide accumulation analysis our work attests to the role of Ser-Leu as a metabolic regulator at the interface of protein degradation and central metabolism.

An Improved Calibration Approach for Traveling Wave Ion Mobility Spectrometry: Robust, High-Precision Collision Cross Sections

The combination of ion-mobility (IM) separation with mass spectrometry (MS) has impacted global measurement efforts in areas ranging from food analysis to drug discovery. Reasons for the broad adoption of IM-MS include its significantly increased peak capacity, duty-cycle, and ability to reconstruct fragmentation data in parallel, all of which greatly enable the analyses of complex mixtures. More fundamentally, however, measurements of ion-gas molecule collision cross sections (CCSs) are used to support compound identification and quantitation efforts as well as study the structures of large biomolecules. As the first commercialized form of IM-MS, Traveling Wave Ion Mobility (TWIM) devices are operated at low pressures (∼3 mbar) and voltages, are relatively short (∼25 cm), and separate ions on a timescale of tens of milliseconds. These qualities make TWIM ideally suited for hybridization with MS. Owing to the complicated motion of ions in TWIM devices, however, IM transit times must be calibrated to enable CCS measurements. Applicability of these calibrations has hitherto been restricted to primarily singly charged small molecules and some classes of large, multiply charged ions under a significantly narrower range of instrument conditions. Here, we introduce and extensively characterize a dramatically improved TWIM calibration methodology. Using over 2500 experimental TWIM data sets, covering ions that span over 3.5 orders of magnitude of molecular mass, we demonstrate robust calibrations for a significantly expanded range of instrument conditions, thereby opening up new analytical application areas and enabling the expansion of high-precision CCS measurements for both existing and next-generation TWIM instrumentation.

Prediction and collection of protein-metabolite interactions

Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein-metabolite interactions (PMIs). Compared with the rich knowledge of protein-protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.

Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a pandemic infection in March 2020. As of December 2020, two COVID-19 vaccines have been authorized for emergency use by the U.S. Food and Drug Administration, but there are no effective drugs to treat COVID-19, and pandemic mitigation efforts like physical distancing have had acute social and economic consequences. In this perspective, we discuss how the proteomic research community can leverage technologies and expertise to address the pandemic by investigating four key areas of study in SARS-CoV-2 biology. Specifically, we discuss how (1) mass spectrometry-based structural techniques can overcome limitations and complement traditional structural approaches to inform the dynamic structure of SARS-CoV-2 proteins, complexes, and virions; (2) virus-host protein-protein interaction mapping can identify the cellular machinery required for SARS-CoV-2 replication; (3) global protein abundance and post-translational modification profiling can characterize signaling pathways that are rewired during infection; and (4) proteomic technologies can aid in biomarker identification, diagnostics, and drug development in order to monitor COVID-19 pathology and investigate treatment strategies. Systems-level high-throughput capabilities of proteomic technologies can yield important insights into SARS-CoV-2 biology that are urgently needed during the pandemic, and more broadly, can inform coronavirus virology and host biology.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

Omics in Systems Biology: Current Progress and Future Outlook

Biological research has undergone tremendous changes over the past three decades. Research used to almost exclusively focus on a single aspect of a single molecule per experiment. Modern technologies have enabled thousands of molecules to be simultaneously analyzed and the way that these molecules influence each other to be discerned. The change is so dramatic that it has given rise to a whole new descriptive suffix (i.e., omics) to describe these fields of study. While genomics was arguably the initial driver of this new trend, it quickly spread to other biological entities resulting in the creation of transcriptomics, proteomics, metabolomics, etc. The development of these "big four omics" created a wave of other omic fields, such as epigenomics, glycomics, lipidomics, microbiomics, and even foodomics; all with the purpose of comprehensively studying all the molecular entities or processes within their respective domain. The large number of omic fields that are invented even led to the term "panomics" as a way to classify them all under one category. Ultimately, all of these omic fields are setting the foundation for developing systems biology; in which the focus will be on determining the complex interactions that occur within biological systems.

The Metabolic Reprogramming of Frem2 Mutant Mice Embryos in Cryptophthalmos Development

Background

Cryptophthalmos is characterized by congenital ocular dysplasia with eyelid malformation. The pathogenicity of mutations in genes encoding components of the FRAS1/FREM protein complex is well established, but the underlying pathomechanisms of this disease are still unclear. In the previous study, we generated mice carrying Frem2^R725X/R2156W compound heterozygous mutations using CRISPR/Cas9 and showed that these mice recapitulated the human cryptophthalmos phenotype.

Methods

In this study, we tracked changes in the metabolic profile of embryos and expression of metabolism-related genes in Frem2 mutant mice on E13.5 compared with wild-type mice. RNA sequencing (RNA-seq) was utilized to decipher the differentiated expression of genes associated with metabolism. Untargeted metabolomics and targeted metabolomics analyses were performed to detect and verify the shifts in the composition of the embryonic metabolome.

Results

Differentially expressed genes participating in amino acid metabolism and energy metabolism were observed by RNA-seq. Transcriptomic analysis suggests that 821 (39.89%) up-regulated genes and 320 (32.99%) down-regulated genes were involved in the metabolic process in the enriched GO terms. A total of 92 significantly different metabolites were identified including creatine, guanosine 5′-monophosphate, cytosine, cytidine 5′-monophosphate, adenine, and L-serine. Interestingly, major shifts related to ATP binding cassette transporters (ABC transporters) and the biosynthesis of amino acids in the composition of the embryonic metabolome were observed by KEGG metabolic analysis, indicating that these pathways could also be involved in the pathogenesis of cryptophthalmos.

Conclusion

We demonstrate that Frem2 mutant fetal mice have increased susceptibility to the disruption of eye morphogenesis in association with distinct transcriptomic and metabolomic signatures. Our findings suggest that the metabolomic signature established before birth may play a role in mediating cryptophthalmos in Frem2 mutant mice, which may have important implications for the pathogenesis of cryptophthalmos.

Anti-allergic drug azelastine suppresses colon tumorigenesis by directly targeting ARF1 to inhibit IQGAP1-ERK-Drp1-mediated mitochondrial fission

This study aimed to screen novel anticancer strategies from FDA-approved non-cancer drugs and identify potential biomarkers and therapeutic targets for colorectal cancer (CRC).

Methods: A library consisting of 1056 FDA-approved drugs was screened for anticancer agents. WST-1, colony-formation, flow cytometry, and tumor xenograft assays were used to determine the anticancer effect of azelastine. Quantitative proteomics, confocal imaging, Western blotting and JC-1 assays were performed to examine the effects on mitochondrial pathways. The target protein of azelastine was analyzed and confirmed by DARTS, WST-1, Biacore and tumor xenograft assays. Immunohistochemistry, gain- and loss-of-function experiments, WST-1, colony-formation, immunoprecipitation, and tumor xenograft assays were used to examine the functional and clinical significance of ARF1 in colon tumorigenesis.

Results: Azelastine, a current anti-allergic drug, was found to exert a significant inhibitory effect on CRC cell proliferation in vitro and in vivo, but not on ARF1-deficient or ARF1-T48S mutant cells. ARF1 was identified as a direct target of azelastine. High ARF1 expression was associated with advanced stages and poor survival of CRC. ARF1 promoted colon tumorigenesis through its interaction with IQGAP1 and subsequent activation of ERK signaling and mitochondrial fission by enhancing the interaction of IQGAP1 with MEK and ERK. Mechanistically, azelastine bound to Thr-48 in ARF1 and repressed its activity, decreasing Drp1 phosphorylation. This, in turn, inhibited mitochondrial fission and suppressed colon tumorigenesis by blocking IQGAP1-ERK signaling.

Conclusions: This study provides the first evidence that azelastine may be novel therapeutics for CRC treatment. ARF1 promotes colon tumorigenesis, representing a promising biomarker and therapeutic target in CRC.

Crosstalk between mechanotransduction and metabolism

Mechanical forces shape cells and tissues during development and adult homeostasis. In addition, they also signal to cells via mechanotransduction pathways to control cell proliferation, differentiation and death. These processes require metabolism of nutrients for both energy generation and biosynthesis of macromolecules. However, how cellular mechanics and metabolism are connected is still poorly understood. Here, we discuss recent evidence indicating how the mechanical cues exerted by the extracellular matrix (ECM), cell-ECM and cell-cell adhesion complexes influence metabolic pathways. Moreover, we explore the energy and metabolic requirements associated with cell mechanics and ECM remodelling, implicating a reciprocal crosstalk between cell mechanics and metabolism.

Metabolomic changes in animal models of depression: a systematic analysis

Extensive research has been carried out on the metabolomic changes in animal models of depression; however, there is no general agreement about which metabolites exhibit constant changes. Therefore, the aim of this study was to identify consistently altered metabolites in large-scale metabolomics studies of depression models. We performed vote counting analyses to identify consistently upregulated or downregulated metabolites in the brain, blood, and urine of animal models of depression based on 3743 differential metabolites from 241 animal metabolomics studies. We found that serotonin, dopamine, gamma-aminobutyric acid, norepinephrine, N-acetyl-L-aspartic acid, anandamide, and tryptophan were downregulated in the brain, while kynurenine, myo-inositol, hydroxykynurenine, and the kynurenine to tryptophan ratio were upregulated. Regarding blood metabolites, tryptophan, leucine, tyrosine, valine, trimethylamine N-oxide, proline, oleamide, pyruvic acid, and serotonin were downregulated, while N-acetyl glycoprotein, corticosterone, and glutamine were upregulated. Moreover, citric acid, oxoglutaric acid, proline, tryptophan, creatine, betaine, L-dopa, palmitic acid, and pimelic acid were downregulated, and hippuric acid was upregulated in urine. We also identified consistently altered metabolites in the hippocampus, prefrontal cortex, serum, and plasma. These findings suggested that metabolomic changes in depression models are characterized by decreased neurotransmitter and increased kynurenine metabolite levels in the brain, decreased amino acid and increased corticosterone levels in blood, and imbalanced energy metabolism and microbial metabolites in urine. This study contributes to existing knowledge of metabolomic changes in depression and revealed that the reproducibility of candidate metabolites was inadequate in previous studies.

Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ

Biological processes are regulated by intermolecular interactions and chemical modifications that do not affect protein levels, thus escaping detection in classical proteomic screens. We demonstrate here that a global protein structural readout based on limited proteolysis-mass spectrometry (LiP-MS) detects many such functional alterations, simultaneously and in situ, in bacteria undergoing nutrient adaptation and in yeast responding to acute stress. The structural readout, visualized as structural barcodes, captured enzyme activity changes, phosphorylation, protein aggregation, and complex formation, with the resolution of individual regulated functional sites such as binding and active sites. Comparison with prior knowledge, including other ‘omics data, showed that LiP-MS detects many known functional alterations within well-studied pathways. It suggested distinct metabolite-protein interactions and enabled identification of a fructose-1,6-bisphosphate-based regulatory mechanism of glucose uptake in E. coli. The structural readout dramatically increases classical proteomics coverage, generates mechanistic hypotheses, and paves the way for in situ structural systems biology.

•

Dynamic structural proteomic screens detect functional changes at high resolution

•

Detect enzyme activity, phosphorylation, and molecular interactions in situ

•

Generate new molecular hypotheses and increase functional proteomics coverage

•

Enabled discovery of a regulatory mechanism of glucose uptake in E. coli

Recent advances in identifying protein targets in drug discovery

Phenotype-based screening has emerged as an alternative route for discovering new chemical entities toward first-in-class therapeutics. However, clarifying their mode of action has been a significant bottleneck for drug discovery. For target protein identification, conventionally bioactive small molecules are conjugated onto solid supports and then applied to isolate target proteins from whole proteome. This approach requires a high binding affinity between bioactive small molecules and their target proteins. Besides, the binding affinity can be significantly hampered after structural modifications of bioactive molecules with linkers. To overcome these limitations, two major strategies have recently been pursued: (1) the covalent conjugation between small molecules and target proteins using photoactivatable moieties or electrophiles, and (2) label-free target identification through monitoring target engagement by tracking the thermal, proteolytic, or chemical stability of target proteins. This review focuses on recent advancements in target identification from covalent capturing to label-free strategies.

Nanomolar Protein-Protein Interaction Monitoring with a Label-Free Protein-Probe Technique

Protein-protein interactions (PPIs) are an essential part of correct cellular functionality, making them increasingly interesting drug targets. While Förster resonance energy transfer-based methods have traditionally been widely used for PPI studies, label-free techniques have recently drawn significant attention. These methods are ideal for studying PPIs, most importantly as there is no need for labeling of either interaction partner, reducing potential interferences and overall costs. Already, several different label-free methods are available, such as differential scanning calorimetry and surface plasmon resonance, but these biophysical methods suffer from low to medium throughput, which reduces suitability for high-throughput screening (HTS) of PPI inhibitors. Differential scanning fluorimetry, utilizing external fluorescent probes, is an HTS compatible technique, but high protein concentration is needed for experiments. To improve the current concepts, we have developed a method based on time-resolved luminescence, enabling PPI monitoring even at low nanomolar protein concentrations. This method, called the protein probe technique, is based on a peptide conjugated with Eu3+ chelate, and it has already been applied to monitor protein structural changes and small molecule interactions at elevated temperatures. Here, the applicability of the protein probe technique was demonstrated by monitoring single-protein pairing and multiprotein complexes at room and elevated temperatures. The concept functionality was proven by using both artificial and multiple natural protein pairs, such as KRAS and eIF4A together with their binding partners, and C-reactive protein in a complex with its antibody.

A Novel Mechanism of Monoethylhexyl Phthalate in Lipid Accumulation via Inhibiting Fatty Acid Beta-Oxidation on Hepatic Cells

Monoethylhexyl phthalate (MEHP) is one of the main active metabolites of the plasticizer di(2-ethylhexyl) phthalate. It has been known that MEHP has an impact on lipolysis; however, its mechanism on the cellular lipid metabolism remains largely unclear. Here, we first utilized global lipid profiling to fully characterize the lipid synthesis and degradation pathways upon MEHP treatment on hepatic cells. Meanwhile, we further identified the possible MEHP-targeted proteins in living cells using the cellular thermal shift assay (CETSA) method. The lipidomics results showed that there was a significant accumulation of fatty acids and other lipids in the cell. The CETSA identified 18 proteins and fatty acid β-oxidation inhibition pathways that were significantly perturbed. MEHP's binding with selected proteins HADH and HSD17B10 was further evaluated using molecule docking, and results showed that MEHP has higher affinities as compared to endogenous substrates, which was further experimentally confirmed in the surface plasma resonance interaction assay. In summary, we found a novel mechanism for MEHP-induced lipid accumulation, which was probably due to its inhibitive effects on the enzymes in fatty acid β-oxidation. This mechanism substantiates the public concerns on the high exposure level to plasticizers and their possible role as an obesogen.

Plant Biosystems Design Research Roadmap 1.0

Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.

Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.

A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor (https://bioconductor.org/packages/TPP2D). We hope that our method will facilitate prioritizing targets from thermal profiling experiments.

2D-thermal proteome profiling (2D-TPP) is a powerful assay for probing interactions of proteins with small molecules in their native context. Here the authors provide a statistical method for false discovery rate controlled analysis for 2D-TPP applications.

Target identification and validation of natural products with label-free methodology: A critical review from 2005 to 2020

Natural products (NPs) have been an important source of therapeutic drugs in clinic use and contributed many chemical probes for research. The usefulness of NPs is however often marred by the incomplete understanding of their direct cellular targets. A number of experimental methods for drug target identification have been developed over the years. One class of methods, termed "label-free" methodology, exploits the energetic and biophysical features accompanying the association of macromolecules with drugs and other compounds in their native forms. Herein we review the working principles, assay implementations, and key applications of the most important approaches, and also give examples where they have been applied to NPs. We also assess the key advantages and limitations of each method. Furthermore, we address when and how the label-free methodology can be particularly useful considering some of the unique features of NP chemistry and bioactivation.

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.

Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.

Systems level profiling of arginine starvation reveals MYC and ERK adaptive metabolic reprogramming

Arginine auxotrophy due to the silencing of argininosuccinate synthetase 1 (ASS1) occurs in many carcinomas and in the majority of sarcomas. Arginine deiminase (ADI-PEG20) therapy exploits this metabolic vulnerability by depleting extracellular arginine, causing arginine starvation. ASS1-negative cells develop resistance to ADI-PEG20 through a metabolic adaptation that includes re-expressing ASS1. As arginine-based multiagent therapies are being developed, further characterization of the changes induced by arginine starvation is needed. In order to develop a systems-level understanding of these changes, activity-based proteomic profiling (ABPP) and phosphoproteomic profiling were performed before and after ADI-PEG20 treatment in ADI-PEG20-sensitive and resistant sarcoma cells. When integrated with metabolomic profiling, this multi-omic analysis reveals that cellular response to arginine starvation is mediated by adaptive ERK signaling and activation of the Myc–Max transcriptional network. Concomitantly, these data elucidate proteomic changes that facilitate oxaloacetate production by enhancing glutamine and pyruvate anaplerosis and altering lipid metabolism to recycle citrate for oxidative glutaminolysis. Based on the complexity of metabolic and cellular signaling interactions, these multi-omic approaches could provide valuable tools for evaluating response to metabolically targeted therapies.