Systematic analysis for clinical characteristics and outcomes of IgG4-related disease patients during the COVID-19 pandemic.

BACKGROUND: To systematically describe clinical characteristics and investigate factors associated with COVID-19-related infection, hospital admission, and IgG4-related disease relapse in IgG4-RD patients. METHODS: Physician-reported IgG4-RD patients were included in this retrospective study. Using multivariable logistic regression analysis to determine factors for primary outcome (COVID-19-related IgG4-RD relapse) and secondary outcome (COVID-19-related infection and hospital admission). Covariates included age, sex, body mass index, smoking status, comorbidities, IgG4-RD clinical features, and treatment strategies. RESULTS: Among 649 patients, 530 had a diagnosis of COVID-19, 25 had COVID-19-related hospital admission, and 69 had COVID-19-related IgG4-RD relapse. Independent factors associated with COVID-19 infection were age (OR, 0.98; 95% CI, 0.96-1.00), body mass index (1.10, 1.03-1.18), and tofacitinib (0.34, 0.14-0.79). Further analysis indicated that age (1.10, 1.03-1.16), coronary heart disease (24.38, 3.33-178.33), COVID-19-related dyspnea (7.11, 1.85-27.34), pulmonary infection (73.63, 16.22-4615.34), and methotrexate (17.15, 1.93-157.79) were associated with a higher risk of COVID-19-related hospital admission. Importantly, age (0.93, 0.89-0.98), male sex (0.16, 0.03-0.80), ever/current smoking (19.23, 3.78-97.80), COVID-19-related headache (2.98, 1.09-8.17) and psychiatric symptoms (3.12, 1.07-9.10), disease activity before COVID-19 (1.89, 1.02-3.51), number of involved organs (1.38, 1.08-1.76), glucocorticoid dosage (1.08, 1.03-1.13), and methotrexate (5.56, 1.40-22.08) were strong factors for COVID-19-related IgG4-RD relapse. CONCLUSIONS: Our data add to evidence that smoking and disease-specific factors (disease activity, number of involved organs, and specific medications) were risk factors of COVID-19-related IgG4-RD relapse. The results highlight the importance of adequate disease control with b/tsDMARDs, preferably without using methotrexate and increasing glucocorticoid dosages in the COVID-19 era. Key Points • COVID-19-related infection or hospital admission were associated with known general factors (age, body mass index, specific comorbidities and methotrexate) among IgG4-RD patients. • Smoking and disease-specific factors (disease activity, number of involved organs and specific medications) were associated with higher odds of COVID-19-related IgG4-RD relapse. • The results highlight the importance of adequate disease control with b/tsDMARDs, preferably without using methotrexate or increasing glucocorticoid dosages.

The miR-7/EGFR axis controls the epithelial cell immunomodulation and regeneration and orchestrates the pathology in inflammatory bowel disease.

INTRODUCTION: The epithelial immunomodulation and regeneration are intrinsic critical events against inflammatory bowel disease (IBD). MiR-7 is well documented as a promising regulator in the development of various diseases including inflammatory diseases. OBJECTIVES: This study aimed to assess the effect of miR-7 in intestinal epithelial cells (IECs) in IBD. METHODS: MiR-7def mice were given dextran sulfate sodium (DSS) to induce enteritis model. The infiltration of inflammatory cells was measured by FCM and immunofluorescence assay. 5'deletion assay and EMSA assays were performed to study the regulatory mechanism of miR-7 expression in IECs. The inflammatory signals and the targets of miR-7 were analyzed by RNA-seq and FISH assay. IECs were isolated from miR-7def, miR-7oe and WT mice to identify the immunomodulation and regeneration capacity. IEC-specific miR-7 silencing expression vector was designed and administered by the tail vein into murine DSS-induced enteritis model to evaluate the pathological lesions of IBD. RESULTS: We found miR-7 deficiency improved the pathological lesions of DSS-induced murine enteritis model, accompanied by elevated proliferation and enhanced transduction of NF-κB/AKT/ERK signals in colonic IECs, as well as decreased local infiltration of inflammatory cells. MiR-7 was dominantly upregulated in colonic IECs in colitis. Moreover, the transcription of pre-miR-7a-1, orchestrated by transcription factor C/EBPα, was a main resource of mature miR-7 in IECs. As for the mechanism, EGFR, a miR-7 target gene, was downregulated in colonic IECs in colitis model and Crohn's disease patients. Furthermore, miR-7 also controlled the proliferation and inflammatory-cytokine secretion of IECs in response to inflammatory-signals through EGFR/NF-κB/AKT/ERK pathway. Finally, IEC-specific miR-7 silencing promoted the proliferation and transduction of NF-κB pathway in IECs and alleviated the pathological damage of colitis. CONCLUSION: Our results present the unknown role of miR-7/EGFR axis in IEC immunomodulation and regeneration in IBD and might provide clues for the application of miRNA-based therapeutic strategies in colonic diseases.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Investigation of the anti-cancer potential of epoxyazadiradione in neuroblastoma: experimental assays and molecular analysis.

Neuroblastoma, the most common childhood solid tumor, originates from primitive sympathetic nervous system cells. Epoxyazadiradione (EAD) is a limonoid derived from Azadirachta indica, belonging to the family Meliaceae. In this study, we isolated the EAD from Azadirachta indica seed and studied the anti-cancer potential against neuroblastoma. Herein, EAD demonstrated significant efficacy against neuroblastoma by suppressing cell proliferation, enhancing the rate of apoptosis and cycle arrest at the SubG0 and G2/M phases. EAD enhanced the pro-apoptotic Caspase 3 and Caspase 9 and inhibited the NF-kß translocation in a dose-dependent manner. In order to identify the specific EAD target, a gel-free quantitative proteomics study on SH-SY5Y cells using Liquid Chromatography with tandem mass spectrometry was done in a dose-dependent manner, followed by detailed bioinformatics analysis to identify effects on protein. Proteomics data identified that Enolase1 and HSP90 were up-regulated in neuroblastoma. EAD inhibited the expression of Enolase1 and HSP90, validated by mRNA expression, immunoblotting, Enolase1 and HSP90 kit and flow-cytometry based bioassay. Molecular docking study, Molecular dynamic simulation, and along with molecular mechanics/Poisson-Boltzmann surface area analysis also suggested that EAD binds at the active site of the proteins and were stable throughout the 100 ns Molecular dynamic simulation study. Overall, this study suggested EAD exhibited anti-cancer activity against neuroblastoma by targeting Enolase1 and HSP90 pathways.Communicated by Ramaswamy H. Sarma.

New inequalities for p(n) and logp(n).

Let p(n) denote the number of partitions of n. A new infinite family of inequalities for p(n) is presented. This generalizes a result by William Chen et al. From this infinite family, another infinite family of inequalities for logp(n) is derived. As an application of the latter family one, for instance obtains that for n≥120, p(n)2>(1+π24n3/2-1n2)p(n-1)p(n+1).

DIAMOND + MEGAN Microbiome Analysis.

Metagenomics is the study of microbiomes using DNA sequencing technologies. Basic computational tasks are to determine the taxonomic composition (who is out there?), the functional composition (what can they do?), and also to correlate changes of composition to changes in external parameters (how do they compare?). One approach to address these issues is to first align all sequences against a protein reference database such as NCBI-nr and to then perform taxonomic and functional binning of all sequences based on their alignments. The resulting classifications can then be interactively analyzed and compared. Here we illustrate how to pursue this approach using the DIAMOND+MEGAN pipeline, on two different publicly available datasets, one containing short-read samples and other containing long-read samples.

MeganServer: facilitating interactive access to metagenomic data on a server.

MOTIVATION: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. AVAILABILITY AND IMPLEMENTATION: MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Expression of antisense of microRNA-26a-5p in mesenchymal stem cells increases their therapeutic effects against cirrhosis.

Hepatocyte growth factor (HGF) is a potent mitogen for mature hepatocytes, and has been shown to prevent cirrhosis during liver regeneration. Transplantation of mesenchymal stem cells (MSCs) reduces the development of cirrhosis after liver injury. However, the production and secretion of transplanted MSCs in liver were not studied yet. Here we found that the MSCs expressed low levels of HGF protein, but surprisingly high levels of HGF mRNA. Further investigation using bioinformatics analyses and luciferase reporter assay showed that MSCs expressed high levels of microRNA-26a-5p (miR-26a-5p), which targeted 3'-UTR of HGF mRNA to inhibit its protein translation. In vivo, miR-26a-5p-depleted MSCs were transplanted into mice with carbon tetrachloride (CCl4)-induced cirrhosis. We found that suppression of miR-26a-5p in MSCs further ameliorated the severity of liver fibrosis, reduced the portal hypertension and sodium retention, compared to transplantation of control MSCs. Hence, our study suggests that suppression of miR-26a-5p in MSCs may improve their therapeutic effects against cirrhosis through increasing HGF production.