Effective Biomarkers and Therapeutic Targets of Nerve-Immunity Interaction in The Treatment of Depression: A Integrated Analysis of miRNA-mRNA Regulatory Networks

Background: Major depressive disorder (MDD) is an emotional disorder that has a negative effect on patients' studies and daily lives. A great number of studies have found that miRNAs play an important role in the development of MDD and that they can be used as a biomarker for the diagnosis and treatment of MDD. However, there have been few investigations on nerve-immunity interaction therapy for MMD patients' brains.Methods: We attempted to evaluate MDD in the gene expression matrix database and miRNAs in plasma samples from healthy controls using bioinformatics methods. Four plasma miRNAs (DE-miRNAs) samples were found from MDD patients. Funrich planned the transcription factors and target genes of miRNAs, and the enrichment of TF and GO was examined. The intersecting mRNAs were discovered by comparing the various expressions of the projected target genes and 5 mRNAs (DE-mRNAs) samples. In the end, 34 DE-miRNAs, 386 DE-mRNAs, and 17 intersecting mRNAs were detected. Intersecting core genes were then investigated using GO and KEGG enrichment analysis to find the intersecting mRNA. Identify particular candidate genes and pathways in neurology and immunology that may be associated with MDD for further investigation.Results: We discovered 17 important HUB genes by the advance of a miRNA-mRNA network, and 5 HUB DE-MRNAs were derived following CytoNCA topology.Conclusion: Our findings from a comprehensive bioinformatics analysis of miRNAs and mRNAs in MDD show that DE-miRNAs like miR-338-3P and miR-206 may be excellent biomarkers and potential therapeutic targets for the treatment of MDD via nerve-immunity interaction.

Infer disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data

Unveiling disease-associated microbial biomarkers (e.g., key species, genes, and pathways) is an efficient strategy for the diagnosis and therapy of diseases. However, the heterogeneity and large size of microbial data bring tremendous challenges for fundamental characteristics discovery. We present IDAM, a novel method for disease-associated biomarker identification from metagenomic and metatranscriptomic data, without requiring prior metadata. It integrates gene context conservation and regulatory mechanism through a mathematical model for maximizing the number of connected components between local-low rank submatrices of a gene expression matrix and known uber-operon structures. We applied IDAM to 813 inflammatory bowel disease-associated datasets and showed IDAM outperformed existing methods in microbial biomarker identification. In addition, the identified biomarkers successfully distinguished disease subtypes and showcased their power in discovering novel disease subtypes/states. IDAM is freely available at https://github.com/OSU-BMBL/IDAM.

Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes

Background Fat deposition is an important economic consideration in pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers’ choice of pork. Weighted gene co-expression network analysis (WGCNA) is effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low) and to identify the core genes in each of these modules. Results We used RNA sequences generated in different pig tissues to construct a gene expression matrix consisting of 12,862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition. Conclusions RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq Data

Single-cell RNA sequencing (scRNA-seq) can reveal accurate and sensitive RNA abundance in a single sample, but robust integration of multiple samples remains challenging. Large-scale scRNA-seq data generated by different workflows or laboratories can contain batch-specific systemic variation. Such variation challenges data integration by confounding sample-specific biology with undesirable batch-specific systemic effects. Therefore, there is a need for guidance in selecting computational and experimental approaches to minimize batch-specific impacts on data interpretation and a need to empirically evaluate the sources of systemic variation in a given dataset. To uncover the contributions of experimental variables to systemic variation, we intentionally perturb four potential sources of batch-effect in five human peripheral blood samples. We investigate sequencing replicate, sequencing depth, sample replicate, and the effects of pooling libraries for concurrent sequencing. To quantify the downstream effects of these variables on data interpretation, we introduced a new scoring metric, the Cell Misclassification Statistic (CMS), which identifies losses to cell type fidelity that occur when merging datasets of different batches. CMS reveals an undesirable overcorrection by popular batch-effect correction and data integration methods. We show that optimizing gene expression matrix normalization and merging can reduce the need for batch-effect correction and minimize the risk of overcorrecting true biological differences between samples.

A novel method for predicting cell abundance based on single-cell RNA-seq data

Background It is important to understand the composition of cell type and its proportion in intact tissues, as changes in certain cell types are the underlying cause of disease in humans. Although compositions of cell type and ratios can be obtained by single-cell sequencing, single-cell sequencing is currently expensive and cannot be applied in clinical studies involving a large number of subjects. Therefore, it is useful to apply the bulk RNA-Seq dataset and the single-cell RNA dataset to deconvolute and obtain the cell type composition in the tissue. Results By analyzing the existing cell population prediction methods, we found that most of the existing methods need the cell-type-specific gene expression profile as the input of the signature matrix. However, in real applications, it is not always possible to find an available signature matrix. To solve this problem, we proposed a novel method, named DCap, to predict cell abundance. DCap is a deconvolution method based on non-negative least squares. DCap considers the weight resulting from measurement noise of bulk RNA-seq and calculation error of single-cell RNA-seq data, during the calculation process of non-negative least squares and performs the weighted iterative calculation based on least squares. By weighting the bulk tissue gene expression matrix and single-cell gene expression matrix, DCap minimizes the measurement error of bulk RNA-Seq and also reduces errors resulting from differences in the number of expressed genes in the same type of cells in different samples. Evaluation test shows that DCap performs better in cell type abundance prediction than existing methods. Conclusion DCap solves the deconvolution problem using weighted non-negative least squares to predict cell type abundance in tissues. DCap has better prediction results and does not need to prepare a signature matrix that gives the cell-type-specific gene expression profile in advance. By using DCap, we can better study the changes in cell proportion in diseased tissues and provide more information on the follow-up treatment of diseases.

WASP: a versatile, web-accessible single cell RNA-Seq processing platform

Background The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. Results Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. Conclusions With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP’s post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update

Bone is a specialized tissue formed by different cell types and a multiscale, complex mineralized matrix. The architecture and the surface chemistry of this microenvironment can be factors of considerable influence on cell biology, and can affect cell proliferation, commitment to differentiation, gene expression, matrix production and/or composition. It has been shown that osteoblasts encounter natural motifs in vivo, with various topographies (shapes, sizes, organization), and that cell cultures on flat surfaces do not reflect the total potential of the tissue. Therefore, studies investigating the role of topographies on cell behavior are important in order to better understand the interaction between cells and surfaces, to improve osseointegration processes in vivo between tissues and biomaterials, and to find a better topographic surface to enhance bone repair. In this review, we evaluate the main available data about surface topographies, techniques for topographies’ production, mechanical signal transduction from surfaces to cells and the impact of cell–surface interactions on osteoblasts or preosteoblasts’ behavior.

Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

Single-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

Identification of Key Genes Affecting Porcine Fat Deposition Based on Co-Expression Network Analysis of Weighted Genes

Background: Fat deposition is an important economic consideration for pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) has been shown to be effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low), and to identify the central genes in each of these modules. Results: We used RNA-seq of different pig tissues to construct a gene expression matrix consisting of 12 862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA; the number of genes in these modules ranged from 39 to 3363. We found four co-expression modules were significantly correlated with backfat thickness. A total of 14 genes ( RAD9A , IGF2R , SCAP , TCAP , DGAT1 , GPS2 , IGF1 , MAPK8 , FABP , FABP5 , LEPR , UCP3 , APOF , and FASN ) were found to be related to fat deposition. Conclusions: RAD9A , TCAP , GPS2 , and APOF were found to be the key genes in the four modules according to the degree of gene connectivity. Combining the results of differential gene analysis, APOF was proposed as a strong candidate gene for body size traits. This study explores the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms behind porcine fat deposition.

A biomimetic engineered bone platform for advanced testing of prosthetic implants

Existing methods for testing prosthetic implants suffer from critical limitations, creating an urgent need for new strategies that facilitate research and development of implants with enhanced osseointegration potential. Herein, we describe a novel, biomimetic, human bone platform for advanced testing of implants in vitro, and demonstrate the scientific validity and predictive value of this approach using an assortment of complementary evaluation methods. We anchored titanium (Ti) and stainless steel (SS) implants into biomimetic scaffolds, seeded with human induced mesenchymal stem cells, to recapitulate the osseointegration process in vitro. We show distinct patterns of gene expression, matrix deposition, and mineralization in response to the two materials, with Ti implants ultimately resulting in stronger integration strength, as seen in other preclinical and clinical studies. Interestingly, RNAseq analysis reveals that the TGF-beta and the FGF2 pathways are overexpressed in response to Ti implants, while the Wnt, BMP, and IGF pathways are overexpressed in response to SS implants. High-resolution imaging shows significantly increased tissue mineralization and calcium deposition at the tissue-implant interface in response to Ti implants, contributing to a twofold increase in pullout strength compared to SS implants. Our technology creates unprecedented research opportunities towards the design of implants and biomaterials that can be personalized, and exhibit enhanced osseointegration potential, with reduced need for animal testing.