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.

Heterogeneous osteoimmune profiles via single-cell transcriptomics in osteoporotic patients who fail bisphosphonate treatment.

Postmenopausal osteoporosis arises from imbalanced osteoclast and osteoblast activity, and mounting evidence suggests a role for the osteoimmune system in bone homeostasis. Bisphosphonate (BP) is an antiresorptive agent, but its treatment failure rate can be as high as 40%. Here, we performed single-cell RNA sequencing on peripheral immune cells from carefully selected postmenopausal women: non-osteoporotic, osteoporosis improved after BP treatment, and BP-failed cases. We found an increase in myeloid cells in patients with osteoporosis (specifically, T cell receptor+ macrophages). Furthermore, lymphoid lineage cells varied significantly, notably elevated natural killer cells (NKs) in the BP-failed group. Moreover, we provide fruitful lists of biomarkers within the immune cells that exhibit condition-dependent differences. The existence of osteoporotic- and BP-failure-specific cellular information flows was revealed by cell-cell interaction analysis. These findings deepen our insight of the osteoporosis pathology enhancing comprehension of the role of immune heterogeneity in postmenopausal osteoporosis and BP treatment failure.

OASIS portable: User-friendly offline suite for secure survival analysis.

Online application for survival analysis (OASIS) and its update, OASIS 2, have been widely used for survival analysis in biological and medical sciences. Here, we provide a portable version of OASIS, an all-in-one offline suite, to facilitate secure survival analysis without uploading the data to online servers. OASIS portable provides a virtualized and isolated instance of the OASIS 2 webserver, operating on the users' personal computers, and enables user-friendly survival analysis without internet connection and security issues.

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.

Identification of novel candidate genes associated with non-syndromic tooth agenesis in Mongolian families.

OBJECTIVES: This study aimed to identify genetic variants associated with non-syndromic tooth agenesis (TA) in nine families from Mongolia using whole-exome sequencing (WES) and bioinformatics analysis. MATERIAL AND METHODS: The study enrolled 41 participants, including three inherited and six non-inherited families. WES analysis was performed on 14 saliva samples from individuals with non-syndromic TA. The potential candidate genes were identified through variant filtering and segregation analysis. The filtered variants were then analyzed in silico mutation impact analysis. RESULTS: WES analysis identified 21 variants associated with TA, and 5 of these variants met all filtering criteria. These variants were located in the exome region of MAST4, ITGA6, PITX2, CACNA1S, and CDON genes. The variant in PITX2 was found in eight participants from inherited and non-inherited families, while the MAST4 variant was identified in 6 participants from inherited families. CONCLUSIONS: The study identified various genetic variant candidates associated with TA in different family groups, with PITX2 being the most commonly identified. Our findings suggest that MAST4 may also be a novel candidate gene for TA due to its association with the Wnt signaling pathway. Additionally, we found that five candidate genes related to focal adhesion and calcium channel complex were significant and essential in tooth development. CLINICAL RELEVANCE: Identifying new pathogenic genes associated with TA can improve our understanding of the molecular mechanisms underlying the disease, leading to better diagnosis, prevention, and treatment. Early detection of TA based on biomarkers can improve dental management and facilitate orthodontic and prosthetic treatment.

Identification of potential key variants in mandibular premolar hypodontia through whole-exome sequencing.

Determining genotype-phenotype correlations in patients with hypodontia is important for understanding disease pathogenesis, although only a few studies have elucidated it. We aimed to identify genetic variants linked to non-syndromic bilateral mandibular second premolar hypodontia in a Korean population for the first time by specifying the phenotype of hypodontia. Twenty unrelated individuals with non-syndromic bilateral mandibular second premolar hypodontia were enrolled for whole-exome sequencing. Using a tooth agenesis gene set panel consisting of 112 genes based on literature, potential candidate variants were screened through variant filtering and prioritization. We identified 13 candidate variants in 12 genes, including a stop-gain variant (c.4750C>T) in LAMA3. Through the functional enrichment analysis of the prioritized genes, several terms related to tooth development were enriched in a protein-protein interaction network of candidate genes for mandibular premolar hypodontia. The hypodontia group also had approximately 2-fold as many mutated variants in all four genes related to these key terms, which are CDH1, ITGB4, LAMA3, LAMB3, as those in the 100 healthy control group individuals. The relationship between enriched terms and pathways and mandibular premolar hypodontia was also investigated. In addition, we identified some known oligodontia variants in patients with hypodontia, strengthening the possibility of synergistic effects in other genes. This genetic investigation may be a worthwhile preliminary attempt to reveal the pathogenesis of tooth agenesis and sets a background for future studies.

Comparison of treatment outcomes and complications of coil embolization in elderly and very elderly patients with cerebral aneurysms: a propensity score matching analysis.

PURPOSE: This single center study aims to compare the treatment outcomes and procedure-related complications of coil embolization in elderly patients (60-79 years) and very elderly patients (aged 80 years or older) with cerebral aneurysms. METHODS: Data was collected from 504 elderly patients aged 60 years or older who underwent coil embolization for intracranial aneurysms from 2018 to 2021. The study evaluated patient-related and anatomical factors and assessed various outcomes, comparing results between groups using statistical analysis and propensity score matching. RESULTS: A total of 503 cerebral aneurysms were analyzed from individuals aged 60-79 years (n = 472) and those aged 80 years or older (n = 31). The majority of the aneurysms were unruptured with an average size of 3.5 mm in height and 3.4 mm in width. The patients were compared using 1:1 propensity score matching, and no significant differences were found in factors other than age and aortic elongation. Logistic analysis revealed that being over 80 years old and having a severe aortic arch elongation were identified as risk factors for procedure-related events in both total and unruptured cases. CONCLUSIONS: The study compared coil embolization treatment for cerebral aneurysms in patients aged 60-79 and over 80, finding no significant difference in treatment outcomes except for procedure-related events. Procedure-related events were associated with severe aortic arch elongation and being over 80 years old. Coil embolization can be considered safe and effective for patients over 80, but further trials are needed for accurate conclusions.

Analysis of Factors Affecting Good Neovascularization After Indirect Bypass Surgery: A Two-center Retrospective Study.

OBJECTIVE: The purpose of this study was to analyze factors affecting good neovascularization after indirect bypass surgery. METHODS: From August 2000 to July 2020, postoperative image results and medical records of 132 patients (159 hemispheres) who underwent EDAS of indirect bypass surgery at two institutions were reviewed retrospectively. Based on DSA results, angiogenesis after indirect bypass was divided into "good" or "poor" according to the Matsushima criteria. STA flap length affecting GPN were analyzed in the entire group (n = 159) and a MMD group (n = 134). RESULTS: In the entire group, GPN after EDAS was observed in 94 (59.1%) hemispheres. Age, MMD, hypertension, and bone flap size were identified as significant factors in univariate analysis. Also, in the MMD group, 86 (64.2%) hemispheres showed GPN. Hypertension and bone flap size were significant factors in both univariate and multivariate analyses. Cutoff values of bone flap size and GPN were 47.91 cm2 in the entire group and the MMD group. CONCLUSIONS: In all patients who received EDAS, good postoperative neovascularization was significant in those with a young age, MMD, without hypertension, and large bone flap size. No hypertension and large bone flap size were meaningful factors in the MMD group. AUROC showed that an appropriate bone flap size was 47.91 cm2. However, a further controlled prospective study is needed.

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.

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).

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.

Development of bioinformatics and multi-omics analyses in organoids.

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.

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.

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.

The implication of holocytochrome c synthase mutation in Korean familial hypoplastic amelogenesis imperfecta.

OBJECTIVES: This study aimed to comprehensively characterise genetic variants of amelogenesis imperfecta in a single Korean family through whole-exome sequencing and bioinformatics analysis. MATERIAL AND METHODS: Thirty-one individuals of a Korean family, 9 of whom were affected and 22 unaffected by amelogenesis imperfecta, were enrolled. Whole-exome sequencing was performed on 12 saliva samples, including samples from 8 affected and 4 unaffected individuals. The possible candidate genes associated with the disease were screened by segregation analysis and variant filtering. In silico mutation impact analysis was then performed on the filtered variants based on sequence conservation and protein structure. RESULTS: Whole-exome sequencing data revealed an X-linked dominant, heterozygous genomic missense mutation in the mitochondrial gene holocytochrome c synthase (HCCS). We also found that HCCS is potentially related to the role of mitochondria in amelogenesis. The HCCS variant was expected to be deleterious in both evolution-based and large population-based analyses. Further, the variant was predicted to have a negative effect on catalytic function of HCCS by in silico analysis of protein structure. In addition, HCCS had significant association with amelogenesis in literature mining analysis. CONCLUSIONS: These findings suggest new evidence for the relationship between amelogenesis and mitochondria function, which could be implicated in the pathogenesis of amelogenesis imperfecta. CLINICAL RELEVANCE: The discovery of HCCS mutations and a deeper understanding of the pathogenesis of amelogenesis imperfecta could lead to finding solutions for the fundamental treatment of this disease. Furthermore, it enables dental practitioners to establish predictable prosthetic treatment plans at an early stage by early detection of amelogenesis imperfecta through personalised medicine.

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).

MON-2, a Golgi protein, mediates autophagy-dependent longevity in Caenorhabditis elegans.

The Golgi apparatus plays a central role in trafficking cargoes such as proteins and lipids. Defects in the Golgi apparatus lead to various diseases, but its role in organismal longevity is largely unknown. Using a quantitative proteomic approach, we found that a Golgi protein, MON-2, was up-regulated in long-lived Caenorhabditis elegans mutants with mitochondrial respiration defects and was required for their longevity. Similarly, we showed that DOP1/PAD-1, which acts with MON-2 to traffic macromolecules between the Golgi and endosome, contributed to the longevity of respiration mutants. Furthermore, we demonstrated that MON-2 was required for up-regulation of autophagy, a longevity-associated recycling process, by activating the Atg8 ortholog GABARAP/LGG-1 in C. elegans. Consistently, we showed that mammalian MON2 activated GABARAPL2 through physical interaction, which increased autophagic flux in mammalian cells. Thus, the evolutionarily conserved role of MON2 in trafficking between the Golgi and endosome is an integral part of autophagy-mediated longevity.

Is a small coil more effective as a finishing coil for the embolization of intracranial aneurysms? Outcomes using GALAXY G3™ MINI micro-coils as finishing coils in a single center.

OBJECTIVE: The purpose of this study was to compare the outcomes of coil embolization using a 0.009 inches primary outer diameter coil as finishing coil (FC) to that of 0.01 inches. METHODS: From February and August 2020, 131 aneurysms that performed coil embolization using FC with a second loop diameter of 1 mm, were reviewed retrospectively, conducting propensity score matching and logistic regression analysis. Angiographic results such as, occlusion grade, packing density, failure and event were compared between 0.009 inches coil of GALAXY G3™ MINI microcoil (n = 54) and 0.01 inches coils (n = 77). RESULTS: There were no statistically significant differences between two groups, but more events occurred in the 0.009 group. (Odds ratio, 3.65; 95% CI, 1.06-12.55; P = 0.031) In the results of coil embolization, successful occlusion occlusion (complete occlusion and residual neck) was identified more in the 0.01 group. After propensity score matching, the variables in each group were similar, but the successful occlusion was higher in the 0.01 group as in the total population. Events tended to occur more frequently in the 0.009 inch group, and logistic regression analysis showed slightly higher events in the angled microcatheter. (48.3% versus 76.9%., P = 0.075), Also, the 0.009 inch FC is an independent risk factor. (Odds ratio, 3.84; 95% CI, 1.07-13.80; P = 0.039). CONCLUSIONS: Using 0.01 inches coils as FC increased the packing density after the procedure, and showed more successful occlusion than using a 0.009 inches coil. The probability of unexpected events was observed more than three times in the 0.009 inch group.

Phylogenetic analysis of ABCG subfamily proteins in plants: functional clustering and coevolution with ABCGs of pathogens.

ABCG subfamily proteins are highly enriched in terrestrial plants. Many of these proteins secrete secondary metabolites that repel or inhibit pathogens. To establish why the ABCG subfamily proteins proliferated extensively during evolution, we constructed phylogenetic trees from a broad range of eukaryotic organisms. ABCG proteins were massively duplicated in land plants and in oomycetes, a group of agronomically important plant pathogens, which prompted us to hypothesize that plant and pathogen ABCGs coevolved. Supporting this hypothesis, full-size ABCGs in host plants (Arabidopsis thaliana and Glycine max) and their pathogens (Hyaloperonospora arabidopsidis and Phytophthora sojae, respectively) had similar divergence times and patterns. Furthermore, generalist pathogens with broad ranges of host plants have diversified more ABCGs than their specialist counterparts. The hypothesis was further tested using an example pair of ABCGs that first diverged during multiplication in a host plant and its pathogen: AtABCG31 of A. thaliana and HpaP802307 of H. arabidopsidis. AtABCG31 expression was activated following infection with H. arabidopsidis, and disrupting AtABCG31 led to increased susceptibility to H. arabidopsidis. Together, our results suggest that ABCG genes in plants and their oomycete pathogens coevolved in an arms race, to extrude secondary metabolites involved in the plant's defense response against pathogens.