Multi-tissue network analysis for drug prioritization in knee osteoarthritis

Repopulated spinal cord microglia exhibit a unique transcriptome and contribute to pain resolution

Microglia are implicated as primarily detrimental in pain models; however, they exist across a continuum of states that contribute to homeostasis or pathology depending on timing and context. To clarify the specific contribution of microglia to pain progression, we take advantage of a temporally controlled transgenic approach to transiently deplete microglia. Unexpectedly, we observe complete resolution of pain coinciding with microglial repopulation rather than depletion. We find that repopulated mouse spinal cord microglia are morphologically distinct from control microglia and exhibit a unique transcriptome. Repopulated microglia from males and females express overlapping networks of genes related to phagocytosis and response to stress. We intersect the identified mouse genes with a single-nuclei microglial dataset from human spinal cord to identify human-relevant genes that may ultimately promote pain resolution after injury. This work presents a comprehensive approach to gene discovery in pain and provides datasets for the development of future microglial-targeted therapeutics.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Network-based modelling of mechano-inflammatory chondrocyte regulation in early osteoarthritis

Osteoarthritis (OA) is a debilitating joint disease characterized by articular cartilage degradation, inflammation and pain. An extensive range of in vivo and in vitro studies evidences that mechanical loads induce changes in chondrocyte gene expression, through a process known as mechanotransduction. It involves cascades of complex molecular interactions that convert physical signals into cellular response(s) that favor either chondroprotection or cartilage destruction. Systematic representations of those interactions can positively inform early strategies for OA management, and dynamic modelling allows semi-quantitative representations of the steady states of complex biological system according to imposed initial conditions. Yet, mechanotransduction is rarely integrated. Hence, a novel mechano-sensitive network-based model is proposed, in the form of a continuous dynamical system: an interactome of a set of 118 nodes, i.e., mechano-sensitive cellular receptors, second messengers, transcription factors and proteins, related among each other through a specific topology of 358 directed edges is developed. Results show that under physio-osmotic initial conditions, an anabolic state is reached, whereas initial perturbations caused by pro-inflammatory and injurious mechanical loads leads to a catabolic profile of node expression. More specifically, healthy chondrocyte markers (Sox9 and CITED2) are fully expressed under physio-osmotic conditions, and reduced under inflammation, or injurious loadings. In contrast, NF-κB and Runx₂, characteristic of an osteoarthritic chondrocyte, become activated under inflammation or excessive loading regimes. A literature-based evaluation shows that the model can replicate 94% of the experiments tested. Sensitivity analysis based on a factorial design of a treatment shows that inflammation has the strongest influence on chondrocyte metabolism, along with a significant deleterious effect of static compressive loads. At the same time, anti-inflammatory therapies appear as the most promising ones, though the restoration of structural protein production seems to remain a major challenge even in beneficial mechanical environments. The newly developed mechano-sensitive network model for chondrocyte activity reveals a unique potential to reflect load-induced chondroprotection or articular cartilage degradation in different mechano-chemical-environments.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Apolipoprotein D as a Potential Biomarker and Construction of a Transcriptional Regulatory-Immune Network Associated with Osteoarthritis by Weighted Gene Coexpression Network Analysis

OBJECTIVE

Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA).

DESIGN

A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments.

RESULTS

The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell.

CONCLUSIONS

Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Background

Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.

Methods

GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.

Results

Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946.

Conclusions

These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

Co-expression analysis of pancreatic cancer proteome reveals biology and prognostic biomarkers

Purpose

Despite extensive biological and clinical studies, including comprehensive genomic and transcriptomic profiling efforts, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with a poor survival and limited therapeutic options. The goal of this study was to assess co-expressed PDAC proteins and their associations with biological pathways and clinical parameters.

Methods

Correlation network analysis is emerging as a powerful approach to infer tumor biology from omics data and to prioritize candidate genes as biomarkers or drug targets. In this study, we applied a weighted gene co-expression network analysis (WGCNA) to the proteome of 20 surgically resected PDAC specimens (PXD015744) and confirmed its clinical value in 82 independent primary cases.

Results

Using WGCNA, we obtained twelve co-expressed clusters with a distinct biology. Notably, we found that one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p = 0.01) and disease-free survival (p = 0.03). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 independent resected patients. Risk score evaluation of the prognostic signature confirmed its association with overall survival in multivariate analyses. Finally, immunofluorescence analysis confirmed co-expression of SPTBN1 and KHSRP in Hs766t PDAC cells.

Conclusions

Our WGCNA analysis revealed a PDAC module enriched for metabolic and EMT-associated processes. In addition, we found that three of the proteins involved were associated with PDAC survival.

Electronic supplementary material

The online version of this article (10.1007/s13402-020-00548-y) contains supplementary material, which is available to authorized users.

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BACKGROUND

The underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).

MATERIAL AND METHODS

We obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the "limma" package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.

RESULTS

The preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.

CONCLUSIONS

We identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.