A dietary commensal microbe enhances antitumor immunity by activating tumor macrophages to sequester iron.

Innate immune cells generate a multifaceted antitumor immune response, including the conservation of essential nutrients such as iron. These cells can be modulated by commensal bacteria; however, identifying and understanding how this occurs is a challenge. Here we show that the food commensal Lactiplantibacillus plantarum IMB19 augments antitumor immunity in syngeneic and xenograft mouse tumor models. Its capsular heteropolysaccharide is the major effector molecule, functioning as a ligand for TLR2. In a two-pronged manner, it skews tumor-associated macrophages to a classically active phenotype, leading to generation of a sustained CD8+ T cell response, and triggers macrophage 'nutritional immunity' to deploy the high-affinity iron transporter lipocalin-2 for capturing and sequestering iron in the tumor microenvironment. This process induces a cycle of tumor cell death, epitope expansion and subsequent tumor clearance. Together these data indicate that food commensals might be identified and developed into 'oncobiotics' for a multi-layered approach to cancer therapy.

An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes.

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN).

Enzyme activity engineering based on sequence co-evolution analysis.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3'-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.

Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.

Divergence of Noncoding Regulatory Elements Explains Gene-Phenotype Differences between Human and Mouse Orthologous Genes.

Mice have been widely used as a model organism to investigate human gene-phenotype relationships based on a conjecture that orthologous genes generally perform similar functions and are associated with similar phenotypes. However, phenotypes associated with orthologous genes often turn out to be quite different between human and mouse. Herein, we devised a method to quantitatively compare phenotypes annotations associated with mouse models and human. Using semantic similarity comparisons, we identified orthologous genes with different phenotype annotations, of which the similarity score is on a par with that of random gene pairs. Analysis of sequence evolution and transcriptomic changes revealed that orthologous genes with phenotypic differences are correlated with changes in noncoding regulatory elements and tissue-specific expression profiles rather than changes in protein-coding sequences. To map accurate gene-phenotype relationships using model organisms, we propose that careful consideration of the evolutionary divergence of noncoding regulatory elements and transcriptomic profiles is essential.

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Structure-activity relationships of amide-phosphonate derivatives as inhibitors of the human soluble epoxide hydrolase.

Structure-activity relationships of amide-phosphonate derivatives as inhibitors of the human soluble epoxide hydrolase (sEH) were investigated. First, a series of alkyl or aryl groups were substituted on the carbon alpha to the phosphonate function in amide compounds to see whether substituted phosphonates can act as a secondary pharmacophore. A tert-butyl group (16) on the alpha carbon was found to yield most potent inhibition on the target enzyme. A 4-50-fold drop in inhibition was induced by other substituents such as aryls, substituted aryls, cycloalkyls, and alkyls. Then, the modification of the O-substituents on the phosphonate function revealed that diethyl groups (16 and 23) were preferable for inhibition to other longer alkyls or substituted alkyls. In amide compounds with the optimized diethylphosphonate moiety and an alkyl substitution such as adamantane (16), tetrahydronaphthalene (31), or adamantanemethane (36), highly potent inhibitions were gained. In addition, the resulting potent amide-phosphonate compounds had reasonable water solubility, suggesting that substituted phosphonates in amide inhibitors are effective for both inhibition potency on the human sEH and water solubility as a secondary pharmacophore.

Rewiring of PDZ domain-ligand interaction network contributed to eukaryotic evolution.

PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome.

Structure-activity relationships of substituted oxyoxalamides as inhibitors of the human soluble epoxide hydrolase.

We explored both structure-activity relationships among substituted oxyoxalamides used as the primary pharmacophore of inhibitors of the human sEH and as a secondary pharmacophore to improve water solubility of inhibitors. When the oxyoxalamide function was modified with a variety of alkyls or substituted alkyls, compound 6 with a 2-adamantyl group and a benzyl group was found to be a potent sEH inhibitor, suggesting that the substituted oxyoxalamide function is a promising primary pharmacophore for the human sEH, and compound 6 can be a novel lead structure for the development of further improved oxyoxalamide or other related derivatives. In addition, introduction of substituted oxyoxalamide to inhibitors with an amide or urea primary pharmacophore produced significant improvements in inhibition potency and water solubility. In particular, the N,N,O-trimethyloxyoxalamide group in amide or urea inhibitors (26 and 31) was most effective among those tested for both inhibition and solubility. The results indicate that substituted oxyoxalamide function incorporated into amide or urea inhibitors is a useful secondary pharmacophore, and the resulting structures will be an important basis for the development of bioavailable sEH inhibitors.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Probiotic Consortium Confers Synergistic Anti-Inflammatory Effects in Inflammatory Disorders.

The composition and diversity of gut microbiota significantly influence the immune system and are linked to various diseases, including inflammatory and allergy disorders. While considerable research has focused on exploring single bacterial species or consortia, the optimal strategies for microbiota-based therapeutics remain underexplored. Specifically, the comparative effectiveness of bacterial consortia versus individual species warrants further investigation. In our study, we assessed the impact of the bacterial consortium MPRO, comprising Lactiplantibacillus plantarum HY7712, Bifidobacterium animalis ssp. lactis HY8002, and Lacticaseibacillus casei HY2782, in comparison to its individual components. The administration of MPRO demonstrated enhanced therapeutic efficacy in experimental models of atopic dermatitis and inflammatory colitis when compared to single strains. MPRO exhibited the ability to dampen inflammatory responses and alter the gut microbial landscape significantly. Notably, MPRO administration led to an increase in intestinal CD103+CD11b+ dendritic cells, promoting the induction of regulatory T cells and the robust suppression of inflammation in experimental disease settings. Our findings advocate the preference for bacterial consortia over single strains in the treatment of inflammatory disorders, carrying potential clinical relevance.

Predictive design of mRNA translation initiation region to control prokaryotic translation efficiency.

Precise prediction of prokaryotic translation efficiency can provide valuable information for optimizing bacterial host for the production of biochemical compounds or recombinant proteins. However, dynamic changes in mRNA folding throughout translation make it difficult to assess translation efficiency. Here, we systematically determined the universal folding regions that significantly affect the efficiency of translation in Escherichia coli. By assessing the specific regions for mRNA folding, we could construct a predictive design method, UTR Designer, and demonstrate that proper codon optimization around the 5'-proximal coding sequence is necessary to achieve a broad range of expression levels. Finally, we applied our method to control the threshold value of input signals switching on a genetic circuit. This should increase our understanding of the processes underlying gene expression and provide an efficient design principle for optimizing various biological systems, thereby facilitating future efforts in metabolic engineering and synthetic biology.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans.

BACKGROUND: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. METHODS: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. FINDINGS: Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. INTERPRETATION: The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. FUNDING: S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

Biologically active ester derivatives as potent inhibitors of the soluble epoxide hydrolase.

Substituted ureas with a carboxylic acid ester as a secondary pharmacophore are potent soluble epoxide hydrolase (sEH) inhibitors. Although the ester substituent imparts better physical properties, such compounds are quickly metabolized to the corresponding less potent acids. Toward producing biologically active ester compounds, a series of esters were prepared and evaluated for potency on the human enzyme, stability in human liver microsomes, and physical properties. Modifications around the ester function enhanced in vitro metabolic stability of the ester inhibitors up to 32-fold without a decrease in inhibition potency. Further, several compounds had improved physical properties.

Screening a library of 1600 adamantyl ureas for anti-Mycobacterium tuberculosis activity in vitro and for better physical chemical properties for bioavailability.

Adamantyl ureas were previously identified as a group of compounds active against Mycobacterium tuberculosis in culture with minimum inhibitor concentrations (MICs) below 0.1 µg/ml. These compounds have been shown to target MmpL3, a protein involved in secretion of trehalose mono-mycolate. They also inhibit both human soluble epoxide hydrolase (hsEH) and M. tuberculosis epoxide hydrolases. However, active compounds to date have high cLogP's and are poorly soluble, leading to low bioavailability and thus limiting any therapeutic application. In this study, a library of 1600 ureas (mostly adamantyl ureas), which were synthesized for the purpose of increasing the bioavailability of inhibitors of hsEH, was screened for activity against M. tuberculosis. 1-Adamantyl-3-phenyl ureas with a polar para substituent were found to retain moderate activity against M. tuberculosis and one of these compounds was shown to be present in serum after oral administration to mice. However, neither it, nor a closely related analog, reduced M. tuberculosis infection in mice. No correlation between in vitro potency against M. tuberculosis and the hsEH inhibition were found supporting the concept that activity against hsEH and M. tuberculosis can be separated. Also there was a lack of correlation with cLogP and inhibition of the growth of M. tuberculosis. Finally, members of two classes of adamantyl ureas that contained polar components to increase their bioavailability, but lacked efficacy against growing M. tuberculosis, were found to taken up by the bacterium as effectively as a highly active apolar urea suggesting that these modifications to increase bioavailability affected the interaction of the urea against its target rather than making them unable to enter the bacterium.

Network-based machine learning approach to predict immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Expression of the human soluble epoxide hydrolase in Escherichia coli by auto-induction for the study of high-throughput inhibition assays.

Soluble epoxide hydrolase (sEH) is a key enzyme involved in the metabolism of epoxy fatty acid mediators such as epoxyeicosatrienoic acids with emerging roles in the regulations of hypertension and inflammation. Inhibitors of human sEH (hsEH) are effective drug candidates for the treatment of cardiovascular diseases. Preparation of hsEH for enzyme inhibition studies has been carried out by using baculovirus expression system. We herein explored the feasibility of expression of hsEH in Escherichia coli cells for the study of high-throughput screening assays of enzyme inhibitors, because the bacterial expression system is easier to handle and more cost-effective than the baculovirus expression system. The functional target enzyme was successfully produced in prokaryotic expression system by an auto-induction method and exhibited comparable enzyme activity to that yielded in baculovirus expression system. The bacterial-hsEH showed similar sensitivity to the baculovirus-hsEH against six reported inhibitors. Overalls indicate that bacterial expression of hsEH employed in the present study is useful for preparing enzymatically active hsEH, leading to effective performance of high-throughput screening assay of hsEH inhibitors and to rapid identification of novel drug candidates for the treatment of cardiovascular diseases.

Domain-mediated interactions for protein subfamily identification.

Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.