Publications by authors named "Inhae Kim"

Article Synopsis
  • - Innate immune cells play a crucial role in fighting tumors, and their function can be influenced by beneficial bacteria found in food.
  • - The specific bacterial strain Lactiplantibacillus plantarum IMB19 enhances antitumor immunity in mouse models, primarily through its capsular heteropolysaccharide, which activates immune pathways.
  • - This strain reprograms tumor-related macrophages to promote robust T cell responses while also capturing iron in the tumor environment, potentially leading to cancer cell death and improved treatment strategies using "oncobiotics."
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Article Synopsis
  • - The gut microbiota composition affects the immune system and is linked to diseases like allergies and inflammation; however, more research is needed on microbial therapies.
  • - The study compared the bacterial consortium MPRO (HY7712, HY8002, HY2782) with its individual strains, finding that MPRO was more effective in treating atopic dermatitis and inflammatory colitis.
  • - Administration of MPRO reduced inflammation and changed the gut microbiome, resulting in increased immune cells that help suppress inflammation, suggesting that combined bacterial treatments may be more beneficial than single strains.
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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.

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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.

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Article Synopsis
  • Predicting cancer recurrence in colorectal cancer (CRC) is crucial for enhancing patient outcomes, as different patients at the same tumor stage can have varying clinical results.
  • The study presents a new method called NIMO, which integrates multiomics data to identify transcriptome signatures for better predicting CRC recurrence by analyzing immune cell methylation patterns.
  • The effectiveness of this prediction approach was validated using data from two separate patient groups, showing that combining immune cell composition with traditional TNM staging significantly improves the accuracy of CRC recurrence predictions.
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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.

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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.

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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.

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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.

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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.

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Docosahexaenoic acid (DHA) is a long-chain polyunsaturated fatty acid mainly found in fish oil. Although several studies have suggested that it can alleviate allergy symptoms, its mechanism of action remains to be elucidated. In the present study, we found that docosahexaenoyl ethanolamide (DHEA), a metabolite of DHA produced in the human body, exerts the anti-allergic activity in vitro and in vivo.

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A series of unsaturated fatty acids in fish oil and their corresponding ethanolamide metabolites were explored to find active fish oil components of antiallergic activity in vitro. Ethanolamides of omega-3 fatty acids (α-linolenic acid, EPA, and DHA) were found to possess promising antiallergic activity, whereas free fatty acids and ethanolamides of other fatty acids exhibited no or weak potency. Based on this finding, structure-activity relationships of DHA-ethanolamide (DHEA) derivatives were investigated to yield better fatty acid derivatives with enhanced antiallergic activity in vitro and in vivo.

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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.

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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.

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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.

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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.

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N,N'-disubstituted urea-based soluble epoxide hydrolase (sEH) inhibitors are promising therapeutics for hypertension, inflammation, and pain in multiple animal models. The drug absorption and pharmacological efficacy of these inhibitors have been reported extensively. However, the drug metabolism of these inhibitors is not well described.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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Oysters are one of foodstuffs containing a relatively high amount of cadmium. Here we report on establishment of an immunochromatographic assay (ICA) method of cadmium levels in oysters. Cadmium was extracted with 0.

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