Construction and analysis of protein-protein interaction network of non-alcoholic fatty liver disease

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

Transcriptomic analysis of the liver in aged laying hens with different eggshell strength

Eggshell is composed of a very ordered and mineralized structure and is important for egg quality. Eggshell strength is particularly important because of its direct association with economic outcomes and egg safety. Various factors related to laying hens and their environment affects eggshell strength. However, the molecular mechanisms of liver functions related to decreased eggshell strength of aged laying hens are largely unknown. Therefore, this study aimed to identify potential factors affecting eggshell strength in aged laying hens at the hepatic transcriptomic level. A total of five hundred 92-wk-old Hy-line Brown laying hens were screened to select those exhibiting the greatest variation in eggshell strength. Based on the final eggshell strength, 12 hens producing eggs with strong eggshell strength (SES) and weak eggshell strength (WES) were finally selected (n = 6) for liver tissue sampling. The RNA-sequencing was performed to identify differentially expressed genes (DEGs) between the 2 groups. We identified a total of 2,084 DEGs, of which 1,358 genes were upregulated and 726 genes were downregulated in the WES group compared with SES group. According to the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, the DEGs indicated the mammalian target of rapamycin signaling pathway, the Janus kinase-signal transducer and activator of transcription pathway, the mitogen‑activated protein kinase signaling pathway, and the insulin resistance pathways. Genes related to fatty liver disease were upregulated in WES group compared with SES group. In addition, expression of several genes associated with oxidative stress and bone resorption activity was altered in aged laying hens with different eggshell strength. Overall, these findings contribute to the identification of genes involved in different intensity of eggshell strength, enabling more understanding of the hepatic molecular mechanism underlying in decreased eggshell strength of aged laying hens.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Identification of transcriptomic signatures and crucial pathways involved in non-alcoholic steatohepatitis

PURPOSE

Our study aimed to uncover the crucial genes and functional pathways involved in the development of non-alcoholic steatohepatitis (NASH).

METHODS

Liver transcriptome datasets were integrated with Robust rank aggregation (RRA) method, and transcriptomic signatures for NASH progression and fibrosis severity in NAFLD were developed. The functions of transcriptomic signatures were explored by multiple bioinformatic analyses, and their diagnostic role was also evaluated.

RESULTS

RRA analyses of 12 transcriptome datasets comparing NASH with non-alcoholic fatty liver (NAFL) identified 116 abnormally up-regulated genes in NASH patients. RRA analyses of five transcriptome datasets focusing fibrosis severity identified 78 abnormally up-regulated genes in NAFLD patients with advanced fibrosis. The functions of those transcriptomic signatures of NASH development or fibrosis progression were similar, and were both characterized by extracellular matrix (ECM)-related pathways (Adjusted P < 0.05). The transcriptomic signatures could effectively differentiate NASH from NAFL, and could help to identify NAFLD patients with advanced fibrosis. Gene set enrichment analysis and weighted gene co-expression network analysis further validated the key role of ECM-related pathways in NASH development. The top 10 up-regulated genes in NASH patients were SPP1, FBLN5, CHI3L1, CCL20, CD24, FABP4, GPNMB, VCAN, EFEMP1, and CXCL10, and their functions were mainly related to either ECM-related pathways or immunity-related pathways. Single cell RNA-sequencing analyses revealed that those crucial genes were expressed by distinct cells such as hepatocytes, macrophages, and hepatic stellate cells.

CONCLUSIONS

Transcriptomic signatures related to NASH development and fibrosis severity of NAFLD patients are both characterized by ECM-related pathways, and fibrosis is a main player during NASH progression. This study uncovers some novel key genes involved in NASH progression, which may be promising therapeutic targets.

Cellular Mechanisms of Liver Fibrosis

The liver is a central organ in the human body, coordinating several key metabolic roles. The structure of the liver which consists of the distinctive arrangement of hepatocytes, hepatic sinusoids, the hepatic artery, portal vein and the central vein, is critical for its function. Due to its unique position in the human body, the liver interacts with components of circulation targeted for the rest of the body and in the process, it is exposed to a vast array of external agents such as dietary metabolites and compounds absorbed through the intestine, including alcohol and drugs, as well as pathogens. Some of these agents may result in injury to the cellular components of liver leading to the activation of the natural wound healing response of the body or fibrogenesis. Long-term injury to liver cells and consistent activation of the fibrogenic response can lead to liver fibrosis such as that seen in chronic alcoholics or clinically obese individuals. Unidentified fibrosis can evolve into more severe consequences over a period of time such as cirrhosis and hepatocellular carcinoma. It is well recognized now that in addition to external agents, genetic predisposition also plays a role in the development of liver fibrosis. An improved understanding of the cellular pathways of fibrosis can illuminate our understanding of this process, and uncover potential therapeutic targets. Here we summarized recent aspects in the understanding of relevant pathways, cellular and molecular drivers of hepatic fibrosis and discuss how this knowledge impact the therapy of respective disease.