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

Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites

BACKGROUND

Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery.

RESULTS

We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated.

SIGNIFICANCE

Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

Active metabolomics identify potential functional metabolites for preeclampsia prevention

BACKGROUND

Preeclampsia (PE) is a leading cause of maternal and fetal morbidity and mortality, with limited effective clinical treatment options. Active metabolomics offers a promising approach to uncover metabolic changes in PE and identify potential biomarkers or therapeutic targets. This study performed untargeted metabolomics using LC-MS to compare serum samples from preeclampsia and normal pregnancies.

METHODS

We performed untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) to compare serum samples from PE patients and normal pregnancies. We analyzed the alterations in metabolites and conducted functional experiments to assess the effects of LysoPE(16:0) on trophoblast cell invasion and migration. Mechanistic studies were performed to explore the potential targeting of GSK-3β by LysoPE(16:0).

RESULTS

Our metabolomics analysis revealed significant alterations in several metabolites, including lysophosphatidylcholines and organic acids. Notably, LysoPE(16:0) was found to be downregulated in the serum of PE patients. Functional experiments demonstrated that LysoPE(16:0) could promote trophoblast cell invasion and migration. Mechanistic studies suggest that the protective effect of LysoPE(16:0) against PE might be mediated through the modulation of the GSK-3β/β-Catenin pathway, with LysoPE(16:0) potentially targeting the GSK-3β protein.

CONCLUSIONS

Our findings highlight the potential role of LysoPE(16:0) in the pathophysiology of PE and its ability to modulate the GSK-3β/β-Catenin pathway. These results provide new insights into the metabolic changes associated with PE and suggest that LysoPE(16:0) could serve as a promising biomarker or therapeutic target for the prevention and treatment of PE.

Immunomodulation in Endometriosis: Investigating the interrelationship between VISTA expression and Escherichia.Shigella-Associated metabolites

AIMS

Endometriosis is characterized by an abnormal immune microenvironment. Despite the extensive use of immune therapies, the application of immune checkpoint inhibitors in endometriosis lacks confidence due to the instability of preclinical research data. This study aims to elucidate the regulation of the immune inhibitory checkpoint VISTA and its effects on T cells from the perspective of microbiota and metabolism.

MAIN METHODS

We divided endometriosis patients into high and low groups based on the expression levels of VISTA in lesion tissues. We collected peritoneal fluid samples from these two groups and performed 16 s RNA sequencing and metabolomics analysis to investigate microbial diversity and differential metabolites. Through combined analysis, we identified microbial-associated metabolites and validated their correlation with VISTA and CD8 + T cells using ELISA and immunofluorescence. In vitro experiments were conducted to confirm the regulatory relationship among these factors.

KEY FINDINGS

Our findings revealed a distinct correlation between VISTA expression and the microbial colony Escherichia.Shigella. Moreover, we identified the metabolites LTD4-d5 and 2-n-Propylthiazolidine-4-carboxylic acid as being associated with both Escherichia.Shigella and VISTA expression. In vitro experiments confirmed the inhibitory effects of these metabolites on VISTA expression, while they demonstrated a positive regulation of CD8 + T cell infiltration into endometriotic lesions.

SIGNIFICANCE

This study reveals the connection between microbial diversity, metabolites, and VISTA expression in the immune microenvironment of endometriosis, providing potential targets for therapeutic interventions.

Discovery of metal-binding proteins by thermal proteome profiling

Metal-binding proteins (MBPs) have various and important biological roles in all living species and many human diseases are intricately linked to dysfunctional MBPs. Here, we report a chemoproteomic method named 'metal extraction-triggered agitation logged by thermal proteome profiling' (METAL-TPP) to globally profile MBPs in proteomes. The method involves the extraction of metals from MBPs using chelators and monitoring the resulting protein stability changes through thermal proteome profiling. Applying METAL-TPP to the human proteome with a broad-spectrum chelator, EDTA, revealed a group of proteins with reduced thermal stability that contained both previously known MBPs and currently unannotated MBP candidates. Biochemical characterization of one potential target, glutamine-fructose-6-phosphate transaminase 2 (GFPT2), showed that zinc bound the protein, inhibited its enzymatic activity and modulated the hexosamine biosynthesis pathway. METAL-TPP profiling with another chelator, TPEN, uncovered additional MBPs in proteomes. Collectively, this study developed a robust tool for proteomic discovery of MBPs and provides a rich resource for functional studies of metals in cell biology.

Systematic identification of structure-specific protein-protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Cobalamins Function as Allosteric Activators of an Angelman Syndrome-Associated UBE3A/E6AP Variant

Genetic aberrations of the maternal UBE3A allele, which encodes the E3 ubiquitin ligase E6AP, are the cause of Angelman syndrome (AS), an imprinting disorder. In most cases, the maternal UBE3A allele is not expressed. Yet, approximately 10 percent of AS individuals harbor distinct point mutations in the maternal allele resulting in the expression of full-length E6AP variants that frequently display compromised ligase activity. In a high-throughput screen, we identified cyanocobalamin, a vitamin B12-derivative, and several alloxazine derivatives as activators of the AS-linked E6AP-F583S variant. Furthermore, we show by cross-linking coupled to mass spectrometry that cobalamins affect the structural dynamics of E6AP-F583S and apply limited proteolysis coupled to mass spectrometry to obtain information about the regions of E6AP that are involved in, or are affected by binding cobalamins and alloxazine derivatives. Our data suggest that dietary supplementation with vitamin B12 can be beneficial for AS individuals.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Phytohormones in a universe of regulatory metabolites: lessons from jasmonate

Small-molecule phytohormones exert control over plant growth, development, and stress responses by coordinating the patterns of gene expression within and between cells. Increasing evidence indicates that currently recognized plant hormones are part of a larger group of regulatory metabolites that have acquired signaling properties during the evolution of land plants. This rich assortment of chemical signals reflects the tremendous diversity of plant secondary metabolism, which offers evolutionary solutions to the daunting challenges of sessility and other unique aspects of plant biology. A major gap in our current understanding of plant regulatory metabolites is the lack of insight into the direct targets of these compounds. Here, we illustrate the blurred distinction between classical phytohormones and other bioactive metabolites by highlighting the major scientific advances that transformed the view of jasmonate from an interesting floral scent to a potent transcriptional regulator. Lessons from jasmonate research generally apply to other phytohormones and thus may help provide a broad understanding of regulatory metabolite-protein interactions. In providing a framework that links small-molecule diversity to transcriptional plasticity, we hope to stimulate future research to explore the evolution, functions, and mechanisms of perception of a broad range of plant regulatory metabolites.

Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as an antimalarial strategy

New antimalarial drug candidates that act via novel mechanisms are urgently needed to combat malaria drug resistance. Here, we describe the multi-omic chemical validation of Plasmodium M1 alanyl metalloaminopeptidase as an attractive drug target using the selective inhibitor, MIPS2673. MIPS2673 demonstrated potent inhibition of recombinant Plasmodium falciparum ( Pf A-M1) and Plasmodium vivax ( Pv A-M1) M1 metalloaminopeptidases, with selectivity over other Plasmodium and human aminopeptidases, and displayed excellent in vitro antimalarial activity with no significant host cytotoxicity. Orthogonal label-free chemoproteomic methods based on thermal stability and limited proteolysis of whole parasite lysates revealed that MIPS2673 solely targets Pf A-M1 in parasites, with limited proteolysis also enabling estimation of the binding site on Pf A-M1 to within ~5 Å of that determined by X-ray crystallography. Finally, functional investigation by untargeted metabolomics demonstrated that MIPS2673 inhibits the key role of Pf A-M1 in haemoglobin digestion. Combined, our unbiased multi-omic target deconvolution methods confirmed the on-target activity of MIPS2673, and validated selective inhibition of M1 alanyl metalloaminopeptidase as a promising antimalarial strategy.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Phenazine biosynthesis protein MoPhzF regulates appressorium formation and host infection through canonical metabolic and noncanonical signaling function in Magnaporthe oryzae

Microbe-produced secondary metabolite phenazine-1-carboxylic acid (PCA) facilitates pathogen virulence and defense mechanisms against competitors. Magnaporthe oryzae, a causal agent of the devastating rice blast disease, needs to compete with other phyllosphere microbes and overcome host immunity for successful colonization and infection. However, whether M. oryzae produces PCA or it has any other functions remains unknown. Here, we found that the MoPHZF gene encodes the phenazine biosynthesis protein MoPhzF, synthesizes PCA in M. oryzae, and regulates appressorium formation and host virulence. MoPhzF is likely acquired through an ancient horizontal gene transfer event and has a canonical function in PCA synthesis. In addition, we found that PCA has a role in suppressing the accumulation of host-derived reactive oxygen species (ROS) during infection. Further examination indicated that MoPhzF recruits both the endoplasmic reticulum membrane protein MoEmc2 and the regulator of G-protein signaling MoRgs1 to the plasma membrane (PM) for MoRgs1 phosphorylation, which is a critical regulatory mechanism in appressorium formation and pathogenicity. Collectively, our studies unveiled a canonical function of MoPhzF in PCA synthesis and a noncanonical signaling function in promoting appressorium formation and host infection.

Progress of depression mechanism based on Omics method

Depression is a very common disabling mental disorder, which is typically characterized by high rates of disability and mortality. Although research into the various mechanisms of depression was still underway, its physiopathology remains uncertain. The rapid developments in new technologies and the combined use of a variety of techniques will help to understand the pathogenesis of depression and explore effective treatment methods. In this review, we focus on the combination of proteomic and metabolomic approaches to analyze metabolites and proteins in animal models of depression induced by different modeling approaches, with the aim of broadening the understanding of the physiopathological mechanisms of depression using complementary "omics" strategy.

The Calvin Benson cycle in bacteria: New insights from systems biology

The Calvin Benson cycle in phototrophic and chemolithoautotrophic bacteria has ecological and biotechnological importance, which has motivated study of its regulation. I review recent advances in our understanding of how the Calvin Benson cycle is regulated in bacteria and the technologies used to elucidate regulation and modify it, and highlight differences between and photoautotrophic and chemolithoautotrophic models. Systems biology studies have shown that in oxygenic phototrophic bacteria, Calvin Benson cycle enzymes are extensively regulated at post-transcriptional and post-translational levels, with multiple enzyme activities connected to cellular redox status through thioredoxin. In chemolithoautotrophic bacteria, regulation is primarily at the transcriptional level, with effector metabolites transducing cell status, though new methods should now allow facile, proteome-wide exploration of biochemical regulation in these models. A biotechnological objective is to enhance CO2 fixation in the cycle and partition that carbon to a product of interest. Flux control of CO2 fixation is distributed over multiple enzymes, and attempts to modulate gene Calvin cycle gene expression show a robust homeostatic regulation of growth rate, though the synthesis rates of products can be significantly increased. Therefore, de-regulation of cycle enzymes through protein engineering may be necessary to increase fluxes. Non-canonical Calvin Benson cycles, if implemented with synthetic biology, could have reduced energy demand and enzyme loading, thus increasing the attractiveness of these bacteria for industrial applications.

NNMT/1-MNA Promote Cell-Cycle Progression of Breast Cancer by Targeting UBC12/Cullin-1-Mediated Degradation of P27 Proteins

Cell cycle dysregulation is a defining feature of breast cancer. Here, 1-methyl-nicotinamide (1-MNA), metabolite of nicotinamide N-methyltransferase(NNMT) is identified, as a novel driver of cell-cycle progression in breast cancer. NNMT, highly expressed in breast cancer tissues, positively correlates with tumor grade, TNM stage, Ki-67 index, and tumor size. Ablation of NNMT expression dramatically suppresses cell proliferation and causes cell-cycle arrest in G0/G1 phase. This phenomenon predominantly stems from the targeted action of 1-MNA, resulting in a specific down-regulation of p27 protein expression. Mechanistically, 1-MNA expedites the degradation of p27 proteins by enhancing cullin-1 neddylation, crucial for the activation of Cullin-1-RING E3 ubiquitin ligase(CRL1)-an E3 ubiquitin ligase targeting p27 proteins.  NNMT/1-MNA specifically up-regulates the expression of UBC12, an E2 NEDD8-conjugating enzyme required for cullin-1 neddylation. 1-MNA showes high binding affinity to UBC12, extending the half-life of UBC12 proteins via preventing their localization to lysosome for degradation. Therefore, 1-MNA is a bioactive metabolite that promotes breast cancer progression by reinforcing neddylation pathway-mediated p27 degradation. The study unveils the link between NNMT enzymatic activity with cell-cycle progression, indicating that 1-MNA may be involved in the remodeling of tumor microenvironment.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

Benzoylaconitine: A promising ACE2-targeted agonist for enhancing cardiac function in heart failure

Benzoylaconitine is a natural product in the treatment of cardiovascular disease. However, its pharmacological effect, direct target protein, and molecular mechanisms for the treatment of heart failure are unclear. In this study, benzoylaconitine inhibited Ang II-induced cell hypertrophy and fibrosis in rat primary cardiomyocytes and rat fibroblasts, while attenuating cardiac function and cardiac remodeling in TAC mice. Using the limited proteolysis-mass spectrometry (LiP-MS) method, the angiotensin-converting enzyme 2 (ACE2) was confirmed as a direct binding target of benzoylaconitine for the treatment of heart failure. In ACE2-knockdown cells and ACE2-/- mice, benzoylaconitine failed to ameliorate cardiomyocyte hypertrophy, fibrosis, and heart failure. Online RNA-sequence analysis indicated p38/ERK-mediated mitochondrial reactive oxygen species (ROS) and nuclear factor kappa B (NF-κB) activation are the possible downstream molecular mechanisms for the effect of BAC-ACE2 interaction. Further studies in ACE2-knockdown cells and ACE2-/- mice suggested that benzoylaconitine targeted ACE2 to suppress p38/ERK-mediated mitochondrial ROS and NF-κB pathway activation. Our findings suggest that benzoylaconitine is a promising ACE2 agonist in regulating mitochondrial ROS release and inflammation activation to improve cardiac function in the treatment of heart failure.

Model Agnostic Semi-Supervised Meta-Learning Elucidates Understudied Out-of-distribution Molecular Interactions

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We developed a semi-supervised meta learning framework - Meta Model Agnostic Pseudo Label Learning (MMAPLE) - to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when transfer learning fails. The power of MMAPLE is demonstrated in multiple applications: predicting OOD drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions, where chemicals or proteins in unseen data are dramatically different from those in training data. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over baseline models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

In situ analysis of osmolyte mechanisms of proteome thermal stabilization

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

Arachidonic acid inhibit granulosa cell function by affecting metabolic function of liver in brown adipose transplantation rats

BACKGROUND

Polycystic ovary syndrome (PCOS) is a gynecological endocrine disease and could be considered a metabolic disease because it is often accompanied by obesity and insulin resistance. Brown adipose tissue (BAT) transplantation has been shown to be effective in treating PCOS rats.

RESULTS

The study demonstrated that BAT successfully recovered the reproductive and metabolic phenotype of PCOS rats. The disorder estrous cycle, abnormal hyperglycemia and the expression of liver factors were improved. Differentially expressed metabolites were analyzed, among them, arachidonic acid may play a role in inhibiting cell proliferation, enhancing oxidative stress reaction, promoting estrogen expression, and reducing progesterone level in KGN cells.

CONCLUSION

Our findings suggest that BAT transplantation may be a therapeutic strategy for PCOS by changing the expression of some cytokines and metabolites. Differentially expressed metabolites might be crucially important for the pathogenesis of PCOS.

Role of Hemigraphis alternata in wound healing: metabolomic profiling and molecular insights into mechanisms

Hemigraphis alternata (H. alternata), commonly known as Red Flame Ivy, is widely recognized for its wound healing capabilities. However, the pharmacologically active plant components and their mechanisms of action in wound healing are yet to be determined. This study presents the mass spectrometry-based global metabolite profiling of aqueous and ethanolic extract of H. alternata leaves. The analysis identified 2285 metabolites from 24,203 spectra obtained in both positive and negative polarities. The identified metabolites were classified under ketones, carboxylic acids, primary aliphatic amines, steroids and steroid derivatives. We performed network pharmacology analysis to explore metabolite-protein interactions and identified 124 human proteins as targets for H. alternata metabolites. Among these, several of them were implicated in wound healing including prothrombin (F2), alpha-2A adrenergic receptor (ADRA2A) and fibroblast growth factor receptor 1 (FGFR1). Gene ontology analysis of target proteins enriched cellular functions related to glucose metabolic process, platelet activation, membrane organization and response to wounding. Additionally, pathway enrichment analysis revealed potential molecular network involved in wound healing. Moreover, in-silico docking analysis showed strong binding energy between H. alternata metabolites with identified protein targets (F2 and PTPN11). Furthermore, the key metabolites involved in wound healing were further validated by multiple reaction monitoring-based targeted analysis.

The genetic and dietary landscape of the muscle insulin signalling network

Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway's organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasising the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.

Open questions in the NF-κB field

A variety of stress signals leads to activation of the inducible transcription factor NF-κB, one of the master regulators of the innate immune response. Despite a wealth of information available on the NF-κB core components and its control by different activation pathways and negative feedback loops, several levels of complexity hamper our understanding of the system. This has also contributed to the limited success of NF-κB inhibitors in the clinic and explains some of their unexpected effects. Here we consider the molecular and cellular events generating this complexity at all levels and point to a number of unresolved questions in the field. We also discuss potential future experimental and computational strategies to provide a deeper understanding of NF-κB and its coregulatory signaling networks.

Chemoproteomics, A Broad Avenue to Target Deconvolution

As a vital project of forward chemical genetic research, target deconvolution aims to identify the molecular targets of an active hit compound. Chemoproteomics, either with chemical probe-facilitated target enrichment or probe-free, provides a straightforward and effective approach to profile the target landscape and unravel the mechanisms of action. Canonical methods rely on chemical probes to enable target engagement, enrichment, and identification, whereas click chemistry and photoaffinity labeling techniques improve the efficiency, sensitivity, and spatial accuracy of target recognition. In comparison, recently developed probe-free methods detect protein-ligand interactions without the need to modify the ligand molecule. This review provides a comprehensive overview of different approaches and recent advancements for target identification and highlights the significance of chemoproteomics in investigating biological processes and advancing drug discovery processes.

Ligand Modification-Free Methods for the Profiling of Protein-Environmental Chemical Interactions

Adverse health outcomes caused by environmental chemicals are often initiated via their interactions with proteins. Essentially, one environmental chemical may interact with a number of proteins and/or a protein may interact with a multitude of environmental chemicals, forming an intricate interaction network. Omics-wide protein-environmental chemical interaction profiling (PECI) is of prominent importance for comprehensive understanding of these interaction networks, including the toxicity mechanisms of action (MoA), and for providing systematic chemical safety assessment. However, such information remains unknown for most environmental chemicals, partly due to their vast chemical diversity. In recent years, with the continuous efforts afforded, especially in mass spectrometry (MS) based omics technologies, several ligand modification-free methods have been developed, and new attention for systematic PECI profiling was gained. In this Review, we provide a comprehensive overview on these methodologies for the identification of ligand-protein interactions, including affinity interaction-based methods of affinity-driven purification, covalent modification profiling, and activity-based protein profiling (ABPP) in a competitive mode, physicochemical property changes assessment methods of ligand-directed nuclear magnetic resonance (ligand-directed NMR), MS integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS), thermal proteome profiling (TPP), limited proteolysis-coupled mass spectrometry (LiP-MS), stability of proteins from rates of oxidation (SPROX), and several intracellular downstream response characterization methods. We expect that the applications of these ligand modification-free technologies will drive a considerable increase in the number of PECI identified, facilitate unveiling the toxicological mechanisms, and ultimately contribute to systematic health risk assessment of environmental chemicals.

Chemoproteomic Profiling of Erastin-Interacting Proteins

Ferroptosis is an iron-related cell death caused by irregular lipid peroxidation that has been implicated with a variety of disease. Erastin is a canonical ferroptosis inducer that is known to function by inhibiting system Xc- and cystine transport; however, the global interactome of erastin in cells remains unexplored. In this work, we employed a quantitative chemoproteomic approach to profile direct interacting proteins of erastin in living cells using a multifunctional photo-cross-linking probe. A number of novel erastin-interacting proteins were identified, including a serine hydrolase, ABHD6, whose overexpression showed a potentiating impact on ferroptosis. Further biochemical experiments revealed that erastin can allosterically activate ABHD6's activity to produce more arachidonic acids and elevate the level of lipid reactive oxygen species. Collectively, our work provided a global portrait of erastin-interacting proteins and discovered ABHD6 as a new ferroptosis regulator.

MACC: a visual interactive knowledgebase of metabolite-associated cell communications

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

Elovanoid-N34 modulates TXNRD1 key in protection against oxidative stress-related diseases

The thioredoxin (TXN) system is an NADPH + H+/FAD redox-triggered effector that sustains homeostasis, bioenergetics, detoxifying drug networks, and cell survival in oxidative stress-related diseases. Elovanoid (ELV)-N34 is an endogenously formed lipid mediator in neural cells from omega-3 fatty acid precursors that modulate neuroinflammation and senescence gene programming when reduction-oxidation (redox) homeostasis is disrupted, enhancing cell survival. Limited proteolysis (LiP) screening of human retinal pigment epithelial (RPE) cells identified TXNRD1 isoforms 2, 3, or 5, the reductase of the TXN system, as an intracellular target of ELV-N34. TXNRD1 silencing confirmed that the ELV-N34 target was isoform 2 or 3. This lipid mediator induces TXNRD1 structure changes that modify the FAD interface domain, leading to its activity modulation. The addition of ELV-N34 decreased membrane and cytosolic TXNRD1 activity, suggesting localizations for the targeted reductase. These results show for the first time that the lipid mediator ELV-N34 directly modulates TXNRD1 activity, underling its protection in several pathologies when uncompensated oxidative stress (UOS) evolves.

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage the fast, sensitive, and accurate search and label-free quantification algorithms in FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a new data merging scheme (from ions to cut-sites) and a protein-centric multiple hypothesis correction scheme, collectively enabling processed LiP-MS datasets to be more robust and less redundant. These improvements substantially strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. As a final feature, FLiPPR facilitates the collection of structural metadata to identify correlations between experiments and structural features. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Identification of α-amanitin effector proteins in hepatocytes by limited proteolysis-coupled mass spectrometry

The misuse of poisonous mushrooms containing amatoxins causes acute liver failure (ALF) in patients and is a cause of significant mortality. Although the toxic mechanisms of α-amanitin (α-AMA) and its interactions with RNA polymerase II (RNAP II) have been studied, α-AMA effector proteins that can interact with α-AMA in hepatocytes have not been systematically studied. Limited proteolysis-coupled mass spectrometry (LiP-MS) is an advanced technology that can quickly identify protein-ligand interactions based on global comparative proteomics. This study identified the α-AMA effector proteins found in human hepatocytes, following the detection of conformotypic peptides using LiP-MS coupled with tandem mass tag (TMT) technology. Proteins that are classified into protein processing in the endoplasmic reticulum and the ribosome during the KEGG pathway can be identified through affinity evaluation, according to α-AMA concentration-dependent LiP-MS and LiP-MS in hepatocytes derived from humans and mice, respectively. The possibility of interaction between α-AMA and proteins containing conformotypic peptides was evaluated through molecular docking studies. The results of this study suggest a novel path for α-AMA to induce hepatotoxicity through interactions with various proteins involved in protein synthesis, as well as with RNAP II.

Chemoproteomic mapping of the glycolytic targetome in cancer cells

Hyperactivated glycolysis is a metabolic hallmark of most cancer cells. Although sporadic information has revealed that glycolytic metabolites possess nonmetabolic functions as signaling molecules, how these metabolites interact with and functionally regulate their binding targets remains largely elusive. Here, we introduce a target-responsive accessibility profiling (TRAP) approach that measures changes in ligand binding-induced accessibility for target identification by globally labeling reactive proteinaceous lysines. With TRAP, we mapped 913 responsive target candidates and 2,487 interactions for 10 major glycolytic metabolites in a model cancer cell line. The wide targetome depicted by TRAP unveils diverse regulatory modalities of glycolytic metabolites, and these modalities involve direct perturbation of enzymes in carbohydrate metabolism, intervention of an orphan transcriptional protein's activity and modulation of targetome-level acetylation. These results further our knowledge of how glycolysis orchestrates signaling pathways in cancer cells to support their survival, and inspire exploitation of the glycolytic targetome for cancer therapy.

Scutellarin Rescued Mitochondrial Damage through Ameliorating Mitochondrial Glucose Oxidation via the Pdk-Pdc Axis

Mitochondrial bioenergetic deficits and their resulting glucose hypometabolism are the key pathophysiological modulators that promote neurodegeneration. However, there are no specific potential molecules that have been identified to treat neurological diseases by regulating energy metabolism and repairing mitochondrial damage. Pyruvate dehydrogenase (PDH) complex (PDC), which can be phosphorylated by pyruvate dehydrogenase kinase (PDK), is the gate-keeping enzyme for mitochondrial glucose oxidation. In this study, a small-molecule scutellarin (SG) is discovered that can significantly alleviate the neuropathological changes in hippocampal CA1 of cerebral hypoperfusion model rats, rescued the morphological changes of abnormal mitochondria, and restored mitochondrial homeostasis. Mitochondrial proteomics, energy metabolism monitoring, and 13 C-metabolic flux analysis targeted SG activity on PDK2, thus regulating PDK-PDC-mediated glycolytic metabolism to TCA cycle during mitochondrial OXPHOS damage. The knockdown of PDK2 in the SK-N-SH cells validated that SG could rescue mitochondrial damage via the PDK-PDC axis, promote the MMP level and reduce the mitochondria-dependent apoptosis. Collectively, this study explored the novel therapeutic approach: the PDK-PDC axis for neurological injury and cognitive impairment and uncovered the effect of SG on mitochondrial protection via the PDK-PDC axis and mitochondrial glucose oxidation. The findings indicate that active components ameliorating mitochondrial bioenergetic deficits could be of significant value for neurological disease therapy.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

Synergistic investigation of natural and synthetic C1-trophic microorganisms to foster a circular carbon economy

A true circular carbon economy must upgrade waste greenhouse gases. C1-based biomanufacturing is an attractive solution, in which one carbon (C1) molecules (e.g. CO2, formate, methanol, etc.) are converted by microbial cell factories into value-added goods (i.e. food, feed, and chemicals). To render C1-based biomanufacturing cost-competitive, we must adapt microbial metabolism to perform chemical conversions at high rates and yields. To this end, the biotechnology community has undertaken two (seemingly opposing) paths: optimizing natural C1-trophic microorganisms versus engineering synthetic C1-assimilation de novo in model microorganisms. Here, we pose how these approaches can instead create synergies for strengthening the competitiveness of C1-based biomanufacturing as a whole.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

Acidic Methanol Treatment Facilitates Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging of Energy Metabolism

Detection of small molecule metabolites (SMM), particularly those involved in energy metabolism using MALDI-mass spectrometry imaging (MSI), is challenging due to factors including ion suppression from other analytes present (e.g., proteins and lipids). One potential solution to enhance SMM detection is to remove analytes that cause ion suppression from tissue sections before matrix deposition through solvent washes. Here, we systematically investigated solvent treatment conditions to improve SMM signal and preserve metabolite localization. Washing with acidic methanol significantly enhances the detection of phosphate-containing metabolites involved in energy metabolism. The improved detection is due to removing lipids and highly polar metabolites that cause ion suppression and denaturing proteins that release bound phosphate-containing metabolites. Stable isotope infusions of [13C6]nicotinamide coupled to MALDI-MSI ("Iso-imaging") in the kidney reveal patterns that indicate blood vessels, medulla, outer stripe, and cortex. We also observed different ATP:ADP raw signals across mouse kidney regions, consistent with regional differences in glucose metabolism favoring either gluconeogenesis or glycolysis. In mouse muscle, Iso-imaging using [13C6]glucose shows high glycolytic flux from infused circulating glucose in type 1 and 2a fibers (soleus) and relatively lower glycolytic flux in type 2b fiber type (gastrocnemius). Thus, improved detection of phosphate-containing metabolites due to acidic methanol treatment combined with isotope tracing provides an improved way to probe energy metabolism with spatial resolution in vivo.

The potential of cross-linking mass spectrometry in the development of protein-protein interaction modulators

Cross-linking mass spectrometry (XL-MS) can provide a wealth of information on endogenous protein-protein interaction (PPI) networks and protein binding interfaces. These features make XL-MS an attractive tool to support the development of PPI-targeting drugs. Though not yet widely used, applications of XL-MS to drug characterization are beginning to emerge. Here, we compare XL-MS to established structural proteomics methods in drug research, discuss the current state and remaining challenges of XL-MS technology, and provide a perspective on the future role XL-MS can play in drug development, with a particular emphasis on PPI modulators.

Genomic signaling of vitamin D

It took several hundred million years of evolution, in order to develop the endocrine vitamin D signaling system, which is formed by a nuclear receptor, the transcription factor VDR (vitamin D receptor), its ligand, the vitamin D3 metabolite 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) and several metabolizing enzymes and transport proteins. Even within the nuclear receptor superfamily the affinity of VDR for 1,25(OH)2D3 is outstandingly high (KD = 0.1 nM). The activation of VDR by 1,25(OH)2D3 is the core mechanism of genomic signaling of vitamin D3, which results in the modulation of the epigenome at thousands of promoter and enhancer regions as well as finally in the activation or repression of hundreds of target gene transcription. In addition, rapid non-genomic actions of vitamin D are described, which are mechanistically far less understood. The main function of vitamin D is to keep the human body in homeostasis. This implies the control of calcium levels, which is essential for bone mineralization, as well as for pushing of innate immunity to react sufficiently strong to microbe infection and preventing overreactions of adaptive immunity, i.e., not to cause autoimmune diseases. This review will discuss whether genomic signaling is sufficient for explaining all physiological functions of vitamin D3.

Novel Approaches to the Establishment of Local Microenvironment from Resorbable Biomaterials in the Brain In Vitro Models

The development of brain in vitro models requires the application of novel biocompatible materials and biopolymers as scaffolds for controllable and effective cell growth and functioning. The "ideal" brain in vitro model should demonstrate the principal features of brain plasticity like synaptic transmission and remodeling, neurogenesis and angiogenesis, and changes in the metabolism associated with the establishment of new intercellular connections. Therefore, the extracellular scaffolds that are helpful in the establishment and maintenance of local microenvironments supporting brain plasticity mechanisms are of critical importance. In this review, we will focus on some carbohydrate metabolites-lactate, pyruvate, oxaloacetate, malate-that greatly contribute to the regulation of cell-to-cell communications and metabolic plasticity of brain cells and on some resorbable biopolymers that may reproduce the local microenvironment enriched in particular cell metabolites.

Small-molecule MHC-II inducers promote immune detection and anti-cancer immunity via editing cancer metabolism

Lack of MHC-II is emerging as a causal factor in cancer immune evasion, and the development of small-molecule MHC-II inducers is an unmet clinical need. Here, we identified three MHC-II inducers, including pristane and its two superior derivatives, that potently induce MHC-II expression in breast cancer cells and effectively inhibit the development of breast cancer. Our data suggest that MHC-II is central in promoting the immune detection of cancer to increase the tumor infiltration of T cells and enhance anti-cancer immunity. By discovering the malonyl/acetyltransferase (MAT) domain in fatty acid synthase (FASN) as the direct binding target of MHC-II inducers, we demonstrate that evasion of immune detection and cancer metabolic reprogramming are directly linked by fatty acid-mediated MHC-II silencing. Collectively, we identified three MHC-II inducers and illustrated that lack of MHC-II caused by hyper-activated fatty acid synthesis to limit immune detection is a potentially widespread mechanism underlying the development of cancer.

Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation

Metabolite-level regulation of enzyme activity is important for microbes to cope with environmental shifts. Knowledge of such regulations can also guide strain engineering for biotechnology. Here we apply limited proteolysis-small molecule mapping (LiP-SMap) to identify and compare metabolite-protein interactions in the proteomes of two cyanobacteria and two lithoautotrophic bacteria that fix CO2 using the Calvin cycle. Clustering analysis of the hundreds of detected interactions shows that some metabolites interact in a species-specific manner. We estimate that approximately 35% of interacting metabolites affect enzyme activity in vitro, and the effect is often minor. Using LiP-SMap data as a guide, we find that the Calvin cycle intermediate glyceraldehyde-3-phosphate enhances activity of fructose-1,6/sedoheptulose-1,7-bisphosphatase (F/SBPase) from Synechocystis sp. PCC 6803 and Cupriavidus necator in reducing conditions, suggesting a convergent feed-forward activation of the cycle. In oxidizing conditions, glyceraldehyde-3-phosphate inhibits Synechocystis F/SBPase by promoting enzyme aggregation. In contrast, the glycolytic intermediate glucose-6-phosphate activates F/SBPase from Cupriavidus necator but not F/SBPase from Synechocystis. Thus, metabolite-level regulation of the Calvin cycle is more prevalent than previously appreciated.

Local flux coordination and global gene expression regulation in metabolic modeling

Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Fluctuating pH for efficient photomixotrophic succinate production

Cyanobacteria are attracting increasing attention as a photosynthetic chassis organism for diverse biochemical production, however, photoautotrophic production remains inefficient. Photomixotrophy, a method where sugar is used to supplement baseline autotrophic metabolism in photosynthetic hosts, is becoming increasingly popular for enhancing sustainable bioproduction with multiple input energy streams. In this study, the commercially relevant diacid, succinate, was produced photomixotrophically. Succinate is an important industrial chemical that can be used for the production of a wide array of products, from pharmaceuticals to biopolymers. In this system, the substrate, glucose, is transported by a proton symporter and the product, succinate, is hypothesized to be transported by another proton symporter, but in the opposite direction. Thus, low pH is required for the import of glucose and high pH is required for the export of succinate. Succinate production was initiated in a pH 7 medium containing bicarbonate. Glucose was efficiently imported at around neutral pH. Utilization of bicarbonate by CO2 fixation raised the pH of the medium. As succinate, a diacid, was produced, the pH of the medium dropped. By repeating this cycle with additional pH adjustment, those contradictory requirements for transport were overcome. pH affects a variety of biological factors and by cycling from high pH to neutral pH processes such as CO2 fixation rates and CO2 solubility can vary. In this study the engineered strains produced succinate during fluctuating pH conditions, achieving a titer of 5.0 g L-1 after 10 days under shake flask conditions. These results demonstrate the potential for photomixotrophic production as a viable option for the large-scale production of succinate.

Biochemical evidence that the whole compartment activity behavior of GAPDH differs between the cytoplasm and nucleus

Some metabolic enzymes normally occur in the nucleus and cytoplasm. These compartments differ in molecular composition. Since post-translational modification and interaction with allosteric effectors can tune enzyme activity, it follows that the behavior of an enzyme as a catalyst may differ between the cytoplasm and nucleus. We explored this possibility for the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Homogenates of pristine nuclei and cytoplasms isolated from Xenopus laevis oocytes were used for whole compartment activity profiling in a near-physiological buffer. Titrations of NAD+ revealed similar whole compartment activity profiles for GAPDH in nuclear and cytoplasmic homogenates. Surprisingly however GAPDH in these compartments did not have the same behavior in assays of the dependence of initial velocity (v0) on G3P concentration. First, the peak v0 for nuclear GAPDH was up to 2.5-fold higher than the peak for cytoplasmic GAPDH. Second, while Michaelis Menten-like behavior was observed in all assays of cytoplasm, the v0 versus [G3P] plots for nuclear GAPDH typically exhibited a non-Michaelis Menten (sigmoidal) profile. Apparent Km and Vmax (G3P) values for nuclear GAPDH activity were highly variable, even between replicates of the same sample. Possible sources of this variability include in vitro processing of a metabolite that allosterically regulates GAPDH, turnover of a post-translational modification of the enzyme, and fluctuation of the state of interaction of GAPDH with other proteins. Collectively these findings are consistent with the hypothesis that the environment of the nucleus is distinct from the environment of the cytoplasm with regard to GAPDH activity and its modulation. This finding warrants further comparison of the regulation of nuclear and cytoplasmic GAPDH, as well as whole compartment activity profiling of other enzymes of metabolism with cytosolic and nuclear pools.