Background: Recently, the association between inflammatory bowel disease (including ulcerative colitis and Crohn’s disease) and BMD has attracted great interest in the research community. However, the results of the published epidemiological observational studies on the relationship between inflammatory bowel disease and BMD are still inconclusive. Here, we performed a two-sample Mendelian randomization analysis to investigate the causal link between inflammatory bowel disease and level of BMD using publically available GWAS summary statistics.Methods: A series of quality control steps were taken in our analysis to select eligible instrumental SNPs which were strongly associated with exposure. To make the conclusions more robust and reliable, we utilized several robust analytical methods (inverse-variance weighting, MR-PRESSO method, mode-based estimate method, weighted median, MR-Egger regression, and MR.RAPS method) that are based on different assumptions of two-sample MR analysis. The MR-Egger intercept test, Cochran’sQtest, and “leave-one-out” sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of these genetic variants on BMD. Outlier variants identified by the MR-PRESSO outlier test were removed step-by-step to reduce heterogeneity and the effect of horizontal pleiotropy.Results: Our two-sample Mendelian randomization analysis with two groups of exposure GWAS summary statistics and four groups of outcome GWAS summary statistics suggested a definitively causal effect of genetically predicted ulcerative colitis on TB-BMD and FA-BMD but not on FN-BMD or LS-BMD (after Bonferroni correction), and we merely determined a causal effect of Crohn’s disease on FN-BMD but not on the others, which was somewhat inconsistent with many published observational researches. The causal effect of inflammatory bowel disease on TB-BMD was significant and robust but not on FA-BMD, FN-BMD, and LS-BMD, which might result from the cumulative effect of ulcerative colitis and Crohn’s disease on BMDs.Conclusions: Our Mendelian randomization analysis supported the causal effect of ulcerative colitis on TB-BMD and FA-BMD. As to Crohn’s disease, only the definitively causal effect of it on decreased FN-BMD was observed. Updated MR analysis is warranted to confirm our findings when a more advanced method to get less biased estimates and better precision or GWAS summary data with more ulcerative colitis and Crohn’s disease patients was available.

Single-cell technologies provide us new ways to profile transcriptomic landscape, chromatin accessibility, spatial expression patterns in heterogeneous tissues at the resolution of single cell. With enormous generated single-cell datasets, a key analytic challenge is to integrate these datasets to gain biological insights into cellular compositions. Here, we developed a domain-adversarial and variational approximation, DAVAE, which can integrate multiple single-cell datasets across samples, technologies and modalities with a single strategy. Besides, DAVAE can also integrate paired data of ATAC profile and transcriptome profile that are simultaneously measured from a same cell. With a mini-batch stochastic gradient descent strategy, it is scalable for large-scale data and can be accelerated by GPUs. Results on seven real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning and cell-type predictions for multiple single-cell datasets across samples, technologies and modalities. Availability: DAVAE has been implemented in a toolkit package “scbean” in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean. All our data and source code for reproducing the results of this paper can be accessible at https://github.com/jhu99/davae_paper.

Hypoxic exposure makes plateau migrators susceptible to high altitude polycythemia (HAPC). Astragalus membranaceus (AM) is an edible and medicinal plant with remarkable immunomodulatory activities. The purpose of this study was to discover if AM could be a candidate for the prevention of HAPC and its mechanism. Here, network pharmacology was applied to screen active compounds, key targets, and enriched pathways of AM in the treatment of HAPC. Molecular docking evaluated the affinity between compounds and core targets. Subsequently, the mechanisms of AM were further verified using the hypoxia exposure-induced mice model of HAPC. The network pharmacology analysis and molecular docking results identified 14 core targets of AM on HAPC, which were predominantly mainly enriched in the HIF-1 pathway. In the HAPC animal models, we found that AM inhibited the differentiation of hematopoietic stem cells into the erythroid lineage. It also suppressed the production of erythrocytes and hemoglobin in peripheral blood by reducing the expression of HIF-1α, EPO, VEGFA, and Gata-1 mRNA. Furthermore, AM downregulated the expression of IL-6, TNF-α, and IFN-γ mRNA, thereby alleviating organ inflammation. In conclusion, AM supplementation alleviates hypoxia-induced HAPC in mice, and TNF-α, AKT1, HIF-1α, VEGFA, IL-6, and IL-1B may be the key targets.

Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development, and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with ten existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. The VIMCCA algorithm has been implemented in our toolkit package scbean (≥ 0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license. Supplementary data are available at Bioinformatics online.

Backgrounds: There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm.Results: We developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such asα,β,landr, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures.Conclusions: In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors’ interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity.Availability: https://github.com/screamer/BiwalkLDA

Network motifs are patterns of complex networks occurring significantly more frequently than those in random networks. They have been considered as fundamental building blocks of complex networks. Therefore, the detection of network motifs in transcriptional regulation networks is a crucial step in understanding the mechanism of transcriptional regulation and network evolution. The search for network motifs is similar to solving subgraph searching problems, which has proven to be NP-complete. To quickly and effectively count subgraphs of a large biological network, we propose a novel graph canonization algorithm based on resolving sets. This method has been implemented in a command line interface (CLI) program sgip using the SeqAn library. Comparing to Babai’s algorithm, this approach has a tighter complexity bound, $o(\exp (\sqrt{n}{log}^{2}n+4logn))$ , on strongly regular graphs. Results on several simulated datasets and transcriptional regulation networks indicate that sgip outperforms nauty on many graph cases. The source code of sgip is freely accessible in https://github.com/seqan/seqan/tree/master/apps/sgip and the binary code in http://packages.seqan.de/sgip/.