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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.

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.

Safety failures in clinical trials increase the cost of drug development. Appropriate drug target selection with minimal toxicity is critical for successful drug development. However, the discrepancy in drug target perturbation effects between cells and humans results in safety failures of drugs with poor translatability between preclinical and clinical research. To address this issue, we developed a machine learning framework to predict the probability of drug approval in clinical trials based on the discrepancy between effects on cells/humans. We hypothesized that genes with tolerance and intolerance to perturbation effects on cells and humans, respectively, were associated with drugs having safety problems, such as drugs that failed clinical trials and were withdrawn from markets. For the first time, we demonstrated that drug approval can be predicted based on the discrepancy between gene perturbation effects in cells and humans, which explains the safety failure of drugs during preclinical to clinical translation.

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.

Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29–47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.

Intestinal commensals are being increasingly appreciated for their role in modulating the therapeutic efficacy of cancer therapies. Human-associated bacterial strains with demonstrable modulation of anti-tumor immune responses have great potential to be developed as biotherapeutics.We developed the AVATIOME࣪ model, a screening prototype based on murine primary immune cells, rationally targeting the innate and adaptive immune system to identify immuno-stimulating commensals.Our lead Lactobacillus plantarum IMB19 (Lp IMB19), suppresses tumor progression in pre-clinical models of melanoma, renal cancer, and NSCLC. LpIMB19 efficiently potentiates the therapeutic effect of anti-PD-L1 in murine tumor models. A capsular component of LpIMB19, structurally defined as rhamnose-rich hetero-polysaccharide (RHP) recapitulates the anti-tumor efficacy of live bacteria.The TLR2 agonist specifically targets macrophages towards an inflammatory phenotype followed by an increase in the activity of CD8+ IFNy+ T-cells. LpIMB19 and RHP enhance iron sequestration by activated macrophages in the tumor microenvironment through a defined mechanism. Thus, depriving tumor cells of iron and suppressing tumor cell proliferation. Translational investigations revealed the efficacy of LpIMB19 in urinary bladder xenograft studies.LpIMB19 mediated iron sequestration by inflammatory tumor macrophages, a byproduct of innate immune activation along with systemic CD8+ T-cells activation leads to enhancement of anti-tumor immunity. Our data on human cancers suggests the applicability of LpIMB19 as a potential onco-microbiotic. Currently, LpIMB19 is being progressed for clinical investigation as a live bio-therapeutic product (LBP) in combination with immune check-point inhibitors. Citation Format: Garima Sharma, Amit Sharma, Inhae Kim, SooWoong Lee, Hae-Na Lee, ChangWook Park, Sin-Hyeog Im. Lactobacillus plantarum IMB19 enhances anti-tumor immune response by modulating multi-layered innate and adaptive immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3525.

e15047 Background: Numerous studies indicate that selective microbiota could enhance anti-tumor immunity with checkpoint inhibitors. However, it is arduous to identify the best candidate bacteria strains and understand their underlying action mechanisms. Methods: We have developed Avatiome platform technology, an innovative pre-clinical model system that mimics the human body's immune system and microbiome. Avatiome incorporates up-to-date technologies such as immunophenotyping, single-cell transcriptomics, and an Artificial Intelligence system to develop novel therapeutics targeting neuronal, systemic inflammatory diseases, and cancers. Results: We have identified Lp IMB19 as an LBP candidate that can enhance anti-cancer immunity through this technology. Lp IMB19 suppresses tumor progression in pre-clinical models of melanoma, renal cancer, and NSCLC. Lp IMB19 efficiently potentiates the therapeutic effect of anti-PD-L1 in murine tumor models by enhancing the effector function of CD8+ T cells via altering the phenotypes of tumor-infiltrating macrophages with inflammatory M1 type. We have identified rhamnose-rich heteropolysaccharides (RHP) as key effector molecules to induce M1 macrophage in a TLR2 dependent manner. Lp IMB19 also limits the amount of iron availability via Lipocalin 2 (LCN2) to capture trace iron from the microenvironment and switch to an iron sequestration phenotype, subsequently inhibiting tumor growth. No side effect or toxicity is observed in pre-clinical toxicity studies. Conclusions: We are currently developing Lp IMB19 as an onco-biotics for clinical investigation as a live biotherapeutic product (LBP) combined with immune checkpoint inhibitors.

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.

Essential genes (EGs) often form central nodes in protein-protein interaction (PPI) networks. However, many reports have shown that numerous EGs are non-central, suggesting that another principle governs gene essentiality. We propose link clustering as a distinct indicator of the essentiality for non-central nodes. Specifically, in various human and yeast PPI networks, we found that 29 to 47% of EGs were better characterized by link clustering than by centrality. Such non-central EGs with clustered links have significant impacts on communities at lower hierarchical levels, suggesting that their essentiality derives from functional dependency among relevant local neighbors, rather than their implication on global connectivity. Moreover, these non-central EGs exhibited several distinct characteristics: they tend to be younger and fast-evolving, and likely change their essentiality across different human cell lines and between human and mouse than central-EGs. Author summary The centrality in network structure was thought to be the cause of gene essentiality, as it conveys nodes’ importance in given networks. However, many essential genes are also found to be non-central, which leads us to ask whether a principle other than centrality may govern gene essentiality. Here we demonstrate that link clustering conveying functional dependency between nodes is the other principle characterizing gene essentiality. The link clustering explains numerous essential genes that are non-central, which are distinct from central ones regarding their molecular function and evolution. Our results clearly establish a two-dimensional principle governing gene essentiality.

The maximum performance of microbial fuel cells (MFCs) is significantly affected by the reduction reactions in the cathode, but their optimum condition is not fully understood yet. The air-cathode MFC operations with different separators (Nafion 117 and polypropylene (PP80) were evaluated at various relative humidity (RH) at the cathode chamber. Air cathode MFCs with a Nafion 117 separator at RH of 90 &plusmn, 2% produced the highest cell voltage of 0.35 V (600 &Omega, ) and power density of 116 mW/m2. With a PP80 separator, the maximum power generation of 381 mW/m2 was obtained at a relatively lower RH of 30 &plusmn, 2%. The cyclic voltammogram and Tafel analysis indicated that the best performance of cathodic oxygen reduction reactions could be observed at 90% RH for Nafion and 50% RH for the PP80 separator. Additionally, the RH conditions also affected the anodic reactions and oxygen mass transfer rates to the anode chamber through the cathode and separators. This study suggests that the optimum RH condition at the cathode is important in order to obtain a high performance of MFC operations and needs to be controlled at different optimum levels depending on the characteristics of the separators.

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.

A central question in animal evolution is how multicellular animals evolved from unicellular ancestors. We hypothesize that membrane proteins must be key players in the development of multicellularity because they are well positioned to form the cell-cell contacts and to provide the intercellular communication required for the creation of complex organisms. Here we find that a major mechanism for the necessary increase in membrane protein complexity in the transition from non-metazoan to metazoan life was the new incorporation of domains from soluble proteins. The membrane proteins that have incorporated soluble domains in metazoans are enriched in many of the functions unique to multicellular organisms such as cell-cell adhesion, signaling, immune defense and developmental processes. They also show enhanced protein-protein interaction (PPI) network complexity and centrality, suggesting an important role in the cellular diversification found in complex organisms. Our results expose an evolutionary mechanism that contributed to the development of higher life forms.

Recent advances in genome sequencing techniques have improved our understanding of the genotype-phenotype relationship between genetic variants and human diseases. However, genetic variations uncovered from patient populations do not provide enough information to understand the mechanisms underlying the progression and clinical severity of human diseases. Moreover, building a high-resolution genotype-phenotype map is difficult due to the diverse genetic backgrounds of the human population. We built a cross-species genotype-phenotype map to explain the clinical severity of human genetic diseases. We developed a data-integrative framework to investigate network modules composed of human diseases mapped with gene essentiality measured from a model organism. Essential and nonessential genes connect diseases of different types which form clusters in the human disease network. In a large patient population study, we found that disease classes enriched with essential genes tended to show a higher mortality rate than disease classes enriched with nonessential genes. Moreover, high disease mortality rates are explained by the multiple comorbid relationships and the high pleiotropy of disease genes found in the essential gene-enriched diseases. Our results reveal that the genotype-phenotype map of a model organism can facilitate the identification of human disease-gene associations and predict human disease progression.

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., Author Summary Modular architecture is important for the evolution of cellular systems. Modular rearrangements facilitate functional innovations and modular insulations provide robustness to perturbations. However, molecular-level understanding of the mechanisms underlying modular network evolution is currently not well understood. Here we show that strong domain-domain interactions (DDIs) and weak domain-linear motif interactions (DLIs) made different contributions to the evolution of the modular architecture of PPI networks. Especially, DLIs mediate between-module interactions, and that their relative abundance has dramatically increased in metazoan species. Linear motifs have been identified as evolutionary interaction switches since subtle amino acid changes can cause the short sequences in linear motifs to appear and disappear. Our results suggest that subtle changes in linear motifs have contributed to the rewiring of functional modules and, consequently, to functional innovations in metazoan species.

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.

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., Author Summary Rewiring of interactions is a powerful tool for the evolution of organism complexity. Rewiring among preexisting proteins provides a simple mechanism for the development of new signaling circuits by redirecting information flows without a gain or loss of genes. Particularly, interactions mediated by short linear motifs can be easily changed by mutations during evolution, resulting in a rewiring of interactions. However, how interaction rewiring of linear motif interactions facilitates the emergence of new protein function during evolution is poorly understood. Here, we systematically investigated the rewiring of interactions mediated by PDZ domains, which are one of the most commonly found peptide recognition modules. We found that PDZ domain-ligand interactions are frequently rewired by C-terminal sequence mutations in PDZ ligands during evolution. Especially, rewiring of PDZ domain-ligand interactions was involved in neuronal function development, occurring concurrently with the emergence of vertebrates and suggesting that reorganization of signaling pathways by rewiring PDZ domain-ligand interactions significantly contributed to the evolution of nervous systems in vertebrates. Our findings highlight the rewiring of interactions as an effective means for functional innovation, providing new insight into eukaryotic evolution, which has not been fully explained by only the expansion of protein families.

Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality.