Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages

Background: Endometriosis (EMS) is an estrogen-dependent disease in which endometrial glands and stroma arise outside the uterus. Current studies have suggested that the number and function of immune cells are abnormal in the abdominal fluid and ectopic lesion tissues of patients with EMS. The developed CIBERSORT method allows immune cell profiling by the deconvolution of gene expression microarray data.Methods: By applying CIBERSORT, we assessed the relative proportions of immune cells in 68 normal endometrial tissues (NO), 112 eutopic endometrial tissues (EU) and 24 ectopic endometrial tissues (EC). The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. We obtained associations between the immune cell environment and EMS r-AFS stages. Macrophages were evaluated by immunohistochemistry (IHC) in 60 patients with ovarian endometriomas.Results: Total natural killer (NK) cells were significantly decreased in EC, while plasma cells and resting CD4 memory T cells were increased in EC. Total macrophages in EC were significantly increased compared to those of EU and NO, and M2 macrophages were the primary macrophages in EC. Compared to those of EC from patients with r-AFS stage I ~ II, M2 macrophages in EC from patients with stage III ~ IV were significantly increased. IHC experiments showed that total macrophages were increased in EC, with M2 macrophages being the primary subtype.Conclusions: Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition in EMS.

CSNK2B contributes to colorectal cancer cell proliferation by activating the mTOR signaling

The function of Casein kinase 2 beta (CSNK2B) in human malignancies has drawn increasing attention in recent years. However, its role in colorectal cancer (CRC) remains unclear. In the present study, we aimed to explore the expression and biological functions of CSNK2B in CRC. Public gene expression microarray data from online database and immunohistochemistry analysis demonstrated that CSNK2B was highly expressed in CRC tissues than in normal tissues. In vitro and in vivo cellular functional experiments showed that increased CSNK2B expression promoted CRC cell viability and tumorigenesis of CRC. Further western blots and rescue experiments confirmed that CSNK2B promoted CRC cell proliferation mainly by activating the mTOR signaling pathway. These findings identified CSNK2B as a novel oncogene contributing to the development of CRC.

Exploring the limits of ComBat method for multi-site diffusion MRI harmonization

The findings from diffusion-weighted magnetic resonance imaging (dMRI) studies often show inconsistent and sometimes contradictory results due to small sample sizes as well as differences in acquisition parameters and pre-/post-processing methods. To address these challenges, collaborative multi-site initiatives have provided an opportunity to collect larger and more diverse groups of subjects, including those with neuropsychiatric disorders, leading to increased power and findings that may be more representative at the group and individual level. With the availability of these datasets openly, the ability of joint analysis of multi-site dMRI data has become more important than ever. However, intrinsic- or acquisition-related variability in scanner models, acquisition protocols, and reconstruction settings hinder pooling multi-site dMRI directly. One powerful and fast statistical harmonization method called ComBat (https://github.com/Jfortin1/ComBatHarmonization) was developed to mitigate the “batch effect” in gene expression microarray data and was adapted for multi-site dMRI harmonization to reduce scanner/site effect. Our goal is to evaluate this commonly used harmonization approach using a large diffusion MRI dataset involving 542 individuals from 5 sites. We investigated two important aspects of using ComBat for harmonization of fractional anisotropy (FA) across sites: First, we assessed how well ComBat preserves the inter-subject biological variability (measured by the effect sizes of between-group FA differences) after harmonization. Second, we evaluated the effect of minor differences in pre-processing on ComBat’s performance. While the majority of effect sizes are mostly preserved in some sites after harmonization, they are not well-preserved at other sites where non-linear scanner contributions exist. Further, even minor differences in pre-processing can yield unwanted effects during ComBat harmonization. Thus, our findings suggest paying careful attention to the data being harmonized as well as using the same processing pipeline while using ComBat for data harmonization.

Comparative functional genomic analysis of Alzheimer’s affected and naturally aging brains

Background Alzheimer’s disease (AD) is a prevalent progressive neurodegenerative human disease whose cause remains unclear. Numerous initially highly hopeful anti-AD drugs based on the amyloid-β (Aβ) hypothesis of AD have failed recent late-phase tests. Natural aging (AG) is a high-risk factor for AD. Here, we aim to gain insights in AD that may lead to its novel therapeutic treatment through conducting meta-analyses of gene expression microarray data from AG and AD-affected brain. Methods Five sets of gene expression microarray data from different regions of AD (hereafter, ALZ when referring to data)-affected brain, and one set from AG, were analyzed by means of the application of the methods of differentially expressed genes and differentially co-expressed gene pairs for the identification of putatively disrupted biological pathways and associated abnormal molecular contents. Results Brain-region specificity among ALZ cases and AG-ALZ differences in gene expression and in KEGG pathway disruption were identified. Strong heterogeneity in AD signatures among the five brain regions was observed: HC/PC/SFG showed clear and pronounced AD signatures, MTG moderately so, and EC showed essentially none. There were stark differences between ALZ and AG. OXPHOS and Proteasome were the most disrupted pathways in HC/PC/SFG, while AG showed no OXPHOS disruption and relatively weak Proteasome disruption in AG. Metabolic related pathways including TCA cycle and Pyruvate metabolism were disrupted in ALZ but not in AG. Three pathogenic infection related pathways were disrupted in ALZ. Many cancer and signaling related pathways were shown to be disrupted AG but far less so in ALZ, and not at all in HC. We identified 54 “ALZ-only” differentially expressed genes, all down-regulated and which, when used to augment the gene list of the KEGG AD pathway, made it significantly more AD-specific.