Genome analysis through image processing with deep learning models.

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

An enterococcal phage-derived enzyme suppresses graft-versus-host disease.

Changes in the gut microbiome have pivotal roles in the pathogenesis of acute graft-versus-host disease (aGVHD) after allogenic haematopoietic cell transplantation (allo-HCT)1-6. However, effective methods for safely resolving gut dysbiosis have not yet been established. An expansion of the pathogen Enterococcus faecalis in the intestine, associated with dysbiosis, has been shown to be a risk factor for aGVHD7-10. Here we analyse the intestinal microbiome of patients with allo-HCT, and find that E. faecalis escapes elimination and proliferates in the intestine by forming biofilms, rather than by acquiring drug-resistance genes. We isolated cytolysin-positive highly pathogenic E. faecalis from faecal samples and identified an anti-E. faecalis enzyme derived from E. faecalis-specific bacteriophages by analysing bacterial whole-genome sequencing data. The antibacterial enzyme had lytic activity against the biofilm of E. faecalis in vitro and in vivo. Furthermore, in aGVHD-induced gnotobiotic mice that were colonized with E. faecalis or with patient faecal samples characterized by the domination of Enterococcus, levels of intestinal cytolysin-positive E. faecalis were decreased and survival was significantly increased in the group that was treated with the E. faecalis-specific enzyme, compared with controls. Thus, administration of a phage-derived antibacterial enzyme that is specific to biofilm-forming pathogenic E. faecalis-which is difficult to eliminate with existing antibiotics-might provide an approach to protect against aGVHD.

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT.

HLA somatic mutations can alter the expression and function of HLA molecules, which in turn affect the ability of the immune system to recognize and respond to cancer cells. Therefore, it is crucial to accurately identify HLA somatic mutations to enhance our understanding of the interaction between cancer and the immune system and improve cancer treatment strategies. ALPHLARD-NT is a reliable tool that can accurately identify HLA somatic mutations as well as HLA genotypes from whole genome sequencing data of paired normal and tumor samples. Here, we provide a comprehensive guide on how to use ALPHLARD-NT and interpret the results.

biotextgraph: graphical summarization of functional similarities from textual information.

SUMMARY: Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. AVAILABILITY AND IMPLEMENTATION: The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

Pathogenicity Prediction of Gene Fusion in Structural Variations: A Knowledge Graph-Infused Explainable Artificial Intelligence (XAI) Framework.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Prognostic significance of chronic kidney disease and impaired renal function in Japanese patients with COVID-19.

BACKGROUND: Renal impairment is a predictor of coronavirus disease (COVID-19) severity. No studies have compared COVID-19 outcomes in patients with chronic kidney disease (CKD) and patients with impaired renal function without a prior diagnosis of CKD. This study aimed to identify the impact of pre-existing impaired renal function without CKD on COVID-19 outcomes. METHODS: This retrospective study included 3,637 patients with COVID-19 classified into three groups by CKD history and estimated glomerular filtration rate (eGFR) on referral: Group 1 (n = 2,460), normal renal function without a CKD history; Group 2 (n = 905), impaired renal function without a CKD history; and Group 3 (n = 272), history of CKD. We compared the clinical characteristics of these groups and assessed the effect of CKD and impaired renal function on critical outcomes (requirement for respiratory support with high-flow oxygen devices, invasive mechanical ventilation, or extracorporeal membrane oxygen, and death during hospitalization) using multivariable logistic regression. RESULTS: The prevalence of comorbidities (hypertension, diabetes, and cardiovascular disease) and incidence of inflammatory responses (white blood counts, and C-reactive protein, procalcitonin, and D-dimer levels) and complications (bacterial infection and heart failure) were higher in Groups 2 and 3 than that in Group 1. The incidence of critical outcomes was 10.8%, 17.7%, and 26.8% in Groups 1, 2, and 3, respectively. The mortality rate and the rate of requiring IMV support was lowest in Group 1 and highest in Group 3. Compared with Group 1, the risk of critical outcomes was higher in Group 2 (adjusted odds ratio [aOR]: 1.32, 95% confidence interval [CI]: 1.03-1.70, P = 0.030) and Group 3 (aOR: 1.94, 95% CI: 1.36-2.78, P < 0.001). Additionally, the eGFR was significantly associated with critical outcomes in Groups 2 (odds ratio [OR]: 2.89, 95% CI: 1.64-4.98, P < 0.001) and 3 (OR: 1.87, 95% CI: 1.08-3.23, P = 0.025) only. CONCLUSIONS: Clinicians should consider pre-existing CKD and impaired renal function at the time of COVID-19 diagnosis for the management of COVID-19.

CT-derived vertebral bone mineral density is a useful biomarker to predict COVID-19 outcome.

The low vertebral bone computed tomography (CT) Hounsfield unit values measured on CT scans reflect low bone mineral density (BMD) and are known as diagnostic indicators for osteoporosis. The potential prognostic significance of low BMD defined by vertebral bone CT values for the coronavirus disease 2019 (COVID-19) remains unclear. This study aimed to assess the impact of BMD on the clinical outcome in Japanese patients with COVID-19 and evaluate the association between BMD and critical outcomes, such as high-flow nasal cannula, non-invasive and invasive positive pressure ventilation, extracorporeal membrane oxygenation, or death. We examined the effects of COVID-19 severity on the change of BMD over time. This multicenter retrospective cohort study enrolled 1132 inpatients with COVID-19 from the Japan COVID-19 Task Force database between February 2020 and September 2022. The bone CT values of the 4th, 7th, and 10th thoracic vertebrae were measured from chest CT images. The average of these values was defined as BMD. Furthermore, a comparative analysis was conducted between the BMD on admission and its value 3 months later. The low BMD group had a higher proportion of critical outcomes than did the high BMD group. In a subanalysis stratifying patients by epidemic wave according to onset time, critical outcomes were higher in the low BMD group in the 1st-4th waves. Multivariable logistic analysis of previously reported factors associated with COVID-19 severity revealed that low BMD, chronic kidney disease, and diabetes were independently associated with critical outcomes. At 3 months post-infection, patients with oxygen demand during hospitalization showed markedly decreased BMD than did those on admission. Low BMD in patients with COVID-19 may help predict severe disease after the disease onset. BMD may decrease over time in patients with severe COVID-19, and the impact on sequelae symptoms should be investigated in the future.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan.

OBJECTIVE: This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients. METHODS: This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months. RESULTS: The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months. CONCLUSION: Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

M2 macrophage-derived TGF-ß induces age-associated loss of adipogenesis through progenitor cell senescence.

OBJECTIVES: Adipose tissue is an endocrine and energy storage organ composed of several different cell types, including mature adipocytes, stromal cells, endothelial cells, and a variety of immune cells. Adipose tissue aging contributes to the pathogenesis of metabolic dysfunction and is likely induced by crosstalk between adipose progenitor cells (APCs) and immune cells, but the underlying molecular mechanisms remain largely unknown. In this study, we revealed the biological role of p16high senescent APCs, and investigated the crosstalk between each cell type in the aged white adipose tissue. METHODS: We performed the single-cell RNA sequencing (scRNA-seq) analysis on the p16high adipose cells sorted from aged p16-CreERT2/Rosa26-LSL-tdTomato mice. We also performed the time serial analysis on the age-dependent bulk RNA-seq datasets of human and mouse white adipose tissues to infer the transcriptome alteration of adipogenic potential within aging. RESULTS: We show that M2 macrophage-derived TGF-ß induces APCs senescence which impairs adipogenesis in vivo. p16high senescent APCs increase with age and show loss of adipogenic potential. The ligand-receptor interaction analysis reveals that M2 macrophages are the donors for TGF-ß and the senescent APCs are the recipients. Indeed, treatment of APCs with TGF-ß1 induces senescent phenotypes through mitochondrial ROS-mediated DNA damage in vitro. TGF-ß1 injection into gonadal white adipose tissue (gWAT) suppresses adipogenic potential and induces fibrotic genes as well as p16 in APCs. A gWAT atrophy is observed in cancer cachexia by APCs senescence, whose induction appeared to be independent of TGF-ß induction. CONCLUSIONS: Our results suggest that M2 macrophage-derived TGF-ß induces age-related lipodystrophy by APCs senescence. The TGF-ß treatment induced DNA damage, mitochondrial ROS, and finally cellular senescence in APCs.

Machine Learning Prediction Model for Neutrophil Recovery after Unrelated Cord Blood Transplantation.

Delayed neutrophil recovery is an important limitation to the administration of cord blood transplantation (CBT) and leaves the recipient vulnerable to life-threatening infection and increases the risk of other complications. A predictive model for neutrophil recovery after single-unit CBT was developed by using a machine learning method, which can handle large and complex datasets, allowing for the analysis of massive amounts of information to uncover patterns and make accurate predictions. Japanese registry data, the largest real-world dataset of CBT, was selected as the data source. Ninety-eight variables with observed values for >80% of the subjects known at the time of CBT were selected. Model building was performed with a competing risk regression model with lasso penalty. Prediction accuracy of the models was evaluated by calculating the area under the receiver operating characteristic curve (AUC) using a test dataset. The primary outcome was neutrophil recovery at day (D) 28, with recovery at D14 and D42 analyzed as secondary outcomes. The final cord blood engraftment prediction (CBEP) models included 2991 single-unit CBT recipients with acute leukemia. The median AUC of a D28-CBEP lasso regression model run 100 times was .74, and those for D14 and D42 were .88 and .68, respectively. The predictivity of the D28-CBEP model was higher than that of 4 different legacy models constructed separately. A highly predictive model for neutrophil recovery by 28 days after CBT was constructed using machine learning techniques; however, identification of significant risk factors was insufficient for outcome prediction for an individual patient, which is necessary for improving therapeutic outcomes. Notably, the prediction accuracy for post-transplantation D14, D28, and D42 decreased, and the model became more complex with more associated factors with increased time after transplantation.

Integrated assessment of computed tomography density in pectoralis and erector spinae muscles as a prognostic biomarker for coronavirus disease 2019.

BACKGROUND ＆ AIMS: Muscle quantification using chest computed tomography (CT) is a useful prognostic biomarker for coronavirus disease 2019 (COVID-19). However, no studies have evaluated the clinical course through comprehensive assessment of the pectoralis and erector spinae muscles. Therefore, we compared the impact of the areas and densities of these muscles on COVID-19 infection outcome. METHODS: This multicenter retrospective cohort study was conducted by the COVID-19 Task Force. A total of 1410 patients with COVID-19 were included, and data on the area and density of the pectoralis and erector spinae muscles on chest CT were collected. The impact of each muscle parameter on the clinical outcome of COVID-19 was stratified according to sex. The primary outcome was the percentage of patients with severe disease, including those requiring oxygen supplementation and those who died. Additionally, 167 patients were followed up for changes in muscle parameters at three months and for the clinical characteristics in case of reduced CT density. RESULTS: For both muscles, low density rather than muscle area was associated with COVID-19 severity. Regardless of sex, lower erector spinae muscle density was associated with more severe disease than pectoralis muscle density. The muscles were divided into two groups using the receiver operating characteristic curve of CT density, and the population was classified into four (Group A: high CT density for both muscles, Group B: low CT density for pectoralis and high for erector spinae muscle. Group C: high CT density for pectoralis and low for erector spinae muscle, Group D: low CT density for both muscles). In univariate analysis, Group D patients exhibited worse outcomes than Group A (OR: 2.96, 95% CI: 2.03-4.34 in men; OR: 3.02, 95% CI: 2.66-10.4 in women). Multivariate analysis revealed that men in Group D had a significantly more severe prognosis than those in Group A (OR: 1.82, 95% CI: 1.16-2.87). Moreover, Group D patients tended to have the highest incidence of other complications due to secondary infections and acute kidney injury during the clinical course. Longitudinal analysis of both muscle densities over three months revealed that patients with decreased muscle density over time were more likely to have severe cases than those who did not. CONCLUSIONS: Muscle density, rather than muscle area, predicts the clinical outcomes of COVID-19. Integrated assessment of pectoralis and erector spinae muscle densities demonstrated higher accuracy in predicting the clinical course of COVID-19 than individual assessments.

[Durable remission of T-cell prolymphocytic leukemia with CLEC16A::IL2 after allogeneic hematopoietic stem cell transplantation].

A 64-year-old woman presented with fine motor impairment in both hands. MRI revealed a contrast-enhanced lesion in the medulla oblongata. Lymphoid cells with abnormal blebs were observed and a CD4+/CD8+ double positive (DP) T cell population was detected by flow cytometry (FCM) in the bone marrow (BM) and the peripheral blood (PB). CLEC16A::IL2 fusion gene was identified by whole exome sequencing with DNA prepared from DP T cells. Clonal rearrangement of the T-cell receptor gene and expression of TCL1A protein were detected. This led to a diagnosis of T-cell prolymphocytic leukemia (T-PLL) with central nervous system (CNS) infiltration. Abnormal cells in BM and PB became undetectable on microscopy and FCM, and the CNS lesion disappeared on MRI after second-line therapy with alemtuzumab. Meanwhile, the CLEC16A::IL2 fusion mRNA remained detectable in PB. Allogeneic hematopoietic stem-cell transplantation was performed, and the fusion mRNA has now been undetectable for more than 5 years since transplantation. This is the first report of a T-PLL case with a CLEC16A::IL2 fusion gene.

Predicting cell types with supervised contrastive learning on cells and their types.

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

Gastrointestinal symptoms in COVID-19 and disease severity: a Japanese registry-based retrospective cohort study.

BACKGROUND: Research on whether gastrointestinal symptoms correlate with the severity of Coronavirus Disease 2019 (COVID-19) has been inconclusive. This study aimed to clarify any associations between gastrointestinal symptoms and the prognosis of COVID-19. METHODS: We collected data from the Japanese nationwide registry for COVID-19 to conduct a retrospective cohort study. Data from 3498 Japanese COVID-19 patients, diagnosed at 74 facilities between February 2020 and August 2022, were analyzed in this study. Hospitalized patients were followed up until discharge or transfer to another hospital. Outpatients were observed until the end of treatment. Associations between gastrointestinal symptoms and clinical outcomes were investigated using multivariable-adjusted logistic regression models. RESULTS: The prevalence of diarrhea, nausea/vomiting, abdominal pain, and melena were 16.6% (581/3498), 8.9% (311/3498), 3.5% (121/3498), and 0.7% (23/3498), respectively. In the univariable analysis, admission to intensive care unit (ICU) and requirement for mechanical ventilation were less common in patients with diarrhea than those without (ICU, 15.7% vs. 20.6% (p = 0.006); mechanical ventilation, 7.9% vs. 11.4% (p = 0.013)). In the multivariable-adjusted analysis, diarrhea was associated with lower likelihood of ICU admission (adjusted odds ratio (aOR), 0.70; 95% confidence interval (CI), 0.53-0.92) and mechanical ventilation (aOR, 0.61; 95% CI, 0.42-0.89). Similar results were obtained in a sensitivity analysis with another logistic regression model that adjusted for 14 possible covariates with diarrhea (ICU; aOR, 0.70; 95% CI, 0.53-0.93; mechanical ventilation; aOR 0.62; 95% CI, 0.42-0.92). CONCLUSIONS: Diarrhea was associated with better clinical outcomes in COVID-19 patients.

COVID-19 infection risk assessment and management at the Tokyo 2020 Olympic and Paralympic Games: A scoping review.

The Tokyo 2020 Olympic and Paralympic Games was one of the largest international mass-gathering events held after the beginning of coronavirus disease 2019 (COVID-19) pandemic. In this scoping review, we extracted papers discussing COVID-19 risk assessment or management at the Tokyo 2020 Games to determine the nature of studies that were conducted. Among the 75 papers obtained from two search engines (PubMed and ScienceDirect) and four papers collected from hand-searches, 30 papers were extracted. Only eight papers performed both COVID-19 prior risk assessment and quantitative evaluation of effectiveness measures, highlighting the importance of rapid, solution-focused risk assessment. Furthermore, this review revealed that the findings regarding the spread of COVID-19 infection to citizens in the host country were inconsistent depending on the assessment methods and that assessments of the spread of infection outside the host country were lacking.

Prognostic significance of hypertension history and blood pressure on admission in Japanese patients with coronavirus disease 2019: integrative analysis from the Japan COVID-19 Task Force.

The effect of preexisting hypertension on coronavirus disease 2019 (COVID-19) prognosis remains controversial. Additionally, no studies have compared the association between blood pressure (BP) indices on admission and COVID-19 outcomes using preexisting hypertension status. Therefore, this study aimed to investigate the association between preexisting hypertension and COVID-19 outcomes in Japanese patients with COVID-19 and assess the impact of BP indices on admission on clinical outcomes in patients with and without preexisting hypertension. Preexisting hypertension presence was confirmed based on the patient's clinical history. Critical outcomes were defined as high-flow oxygen use, non-invasive and invasive positive-pressure ventilation, extracorporeal membrane oxygenation, or death during hospitalization. Preexisting hypertension was observed in 64.6% of the patients. Multivariable logistic regression analysis of severe COVID-19 risk factors indicated that preexisting hypertension was independently associated with critical outcomes [adjusted odds ratio (OR): 1.35; 95% confidence interval (CI): 1.05-1.73]. Low or high BP and high pulse pressure on admission were associated with critical outcomes in patients without preexisting hypertension [OR for systolic BP < 100 mmHg: 2.13, 95% CI: 1.21-3.75; OR for high BP stage 2 (160-179 systolic and/or 100-109 mmHg diastolic BP): 2.13, 95% CI: 1.27-3.58; OR for pulse pressure ≥60 mmHg: 1.68, 95% CI: 1.14-2.48]. Preexisting hypertension is a risk factor for critical outcomes in Japanese patients with COVID-19. BP indices are useful biomarkers for predicting COVID-19 outcomes, particularly in patients without preexisting hypertension. Thus, hypertension history, systolic BP, and pulse pressure should be assessed to predict severe COVID-19 outcomes.

Regulation of the innate immune response and gut microbiome by p53.

p53 is a key tumor suppressor mutated in half of human cancers. In recent years, p53 was shown to regulate a wide variety of functions. From the transcriptome analysis of 24 tissues of irradiated mice, we identified 553 genes markedly induced by p53. Gene Ontology (GO) enrichment analysis found that the most associated biological process was innate immunity. 16S rRNA-seq analysis revealed that Akkermansia, which has anti-inflammatory properties and is involved in the regulation of intestinal barrier integrity, was decreased in p53-knockout (p53-/- ) mice after radiation. p53-/- mice were susceptible to radiation-induced GI toxicity and had a significantly shorter survival time than p53-wild-type (p53+/+ ) mice following radiation. However, administration of antibiotics resulted in a significant improvement in survival and protection against GI toxicity. Mbl2 and Lcn2, which have antimicrobial activity, were identified to be directly transactivated by p53 and secreted by liver into the circulatory system. We also found the expression of MBL2 and LCN2 was decreased in liver cancer tissues with p53 mutations compared with those without p53 mutations. These results indicate that p53 is involved in shaping the gut microbiome through its downstream targets related to the innate immune system, thus protecting the intestinal barrier.

Epicardial adipose tissue measured from analysis of adipose tissue area using chest CT imaging is the best potential predictor of COVID-19 severity.

BACKGROUND: Computed tomography (CT) imaging is widely used for diagnosing and determining the severity of coronavirus disease 2019 (COVID-19). Chest CT imaging can be used to calculate the epicardial adipose tissue (EAT) and upper abdominal visceral adipose tissue (Abd-VAT) areas. The EAT is the main source of inflammatory cytokines involved in chest inflammatory diseases; thus, the EAT area might be a more useful severity predictor than the Abd-VAT area for COVID-19. However, to the best of our knowledge, there are no large-scale reports that sufficiently consider this issue. In addition, there are no reports on the characteristics of patients with normal body mass index (BMI) and high adipose tissue. AIM: The purpose of this study was to analyze whether the EAT area, among various adipose tissues, was the most associated factor with COVID-19 severity. Using a multicenter COVID-19 patient database, we analyzed the associations of chest subcutaneous, chest visceral, abdominal subcutaneous, and Abd-VAT areas with COVID-19 outcomes. In addition, the clinical significance of central obesity, commonly disregarded by BMI, was examined. METHODS: This retrospective cohort study evaluated patients with COVID-19 aged ≥18 years In Japan. Data including from chest CT images collected between February 2020 and October 2022 in four hospitals of the Japan COVID-19 Task Force were analyzed. Patient characteristics and COVID-19 severity were compared according to the adipose tissue areas (chest and abdominal subcutaneous adipose tissue [Chest-SAT and Abd-SAT], EAT, and Abd-VAT) calculated from chest CT images. RESULTS: We included 1077 patients in the analysis. Patients with risk factors of severe COVID-19 such as old age, male sex, and comorbidities had significantly higher areas of EAT and Abd-VAT. High EAT area but not high Abd-VAT area was significantly associated with COVID-19 severity (adjusted odds ratio (aOR): 2.66, 95 % confidence interval [CI]: 1.19-5.93). There was no strong correlation between BMI and VAT. Patients with high VAT area accounted for 40.7 % of the non-obesity population (BMI < 25 kg/m2). High EAT area was also significantly associated with COVID-19 severity in the non-obesity population (aOR: 2.50, 95 % CI: 1.17-5.34). CONCLUSIONS: Our study indicated that VAT is significantly associated with COVID-19 severity and that EAT is the best potential predictor for risk stratification in COVID-19 among adipose tissue areas. Body composition assessment using EAT is an appropriate marker for identifying obesity patients overlooked by BMI. Considering the next pandemic of the global health crisis, our findings open new avenues for implementing appropriate body composition assessments based on CT imaging.