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Mice are some of the widely used experimental animal models for studying human diseases. Defining the compositions of immune cell populations in various tissues from experimental mouse models is critical to understanding the involvement of immune responses in various physiological and patho-physiological conditions. However, non-lymphoid tissues are normally composed of vast and diverse cellular components, which make it difficult to quantify the relative proportions of immune cell types. Here we report the development of a computational algorithm, ImmuCC, to infer the relative compositions of 25 immune cell types in mouse tissues using microarray-based mRNA expression data. The ImmuCC algorithm showed good performance and robustness in many simulated datasets. Remarkable concordances were observed when ImmuCC was used on three public datasets, one including enriched immune cells, one with normal single positive T cells, and one with leukemia cell samples. To validate the performance of ImmuCC objectively, thorough cross-comparison of ImmuCC predicted compositions and flow cytometry results was done with in-house generated datasets collected from four distinct mouse lymphoid tissues and three different types of tumor tissues. The good correlation and biologically meaningful results demonstrate the broad utility of ImmuCC for assessing immune cell composition in diverse mouse tissues under various conditions.

A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein–protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), the most common type of arthritis, is a complex biological response caused by cartilage wear and synovial inflammation that links biomechanics and inflammation. The progression of OA correlates with a rise in the number of senescent cells in multiple joint tissues. However, the mechanisms by which senescent cells and their involvement with immune infiltration promote OA progression are not fully understood. The gene expression profiles and clinical information of OA and healthy control synovial tissue samples were retrieved from the Gene Expression Omnibus database, and then differential analysis of senescence regulators between OA and normal samples was performed. The random forest (RF) was used to screen candidate senescence regulators to predict the occurrence of OA. The reverse transcription quantitative real-time PCR experiments at tissue's level was performed to confirm these biomarkers. Moreover, two distinct senescence patterns were identified and systematic correlation between these senescence patterns and immune cell infiltration was analyzed. The senescence score and senescence gene clusters were constructed to quantify senescence patterns together with immune infiltration of individual OA patient. 73 senescence differentially expressed genes were identified between OA patients and normal controls. The RF method was utilized to build an OA risk model based on two senescence related genes: BCL6 and VEGFA. Next, two distinct aging patterns were determined in OA synovial samples. Most patients from senescence cluster A were further classified into gene cluster B and high senescence score group correlated with a non-inflamed phenotype, whereas senescence cluster B were classified into gene cluster A and low senescence score group correlated with an inflamed phenotype. Our study revealed that senescence played an important role in in OA synovial inflammation. Evaluating the senescence patterns of individuals with OA will contribute to enhancing our cognition of immune infiltration characterization, providing novel diagnostic and prognostic biomarkers, and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]