The importance of age and high degree, in protein-protein interaction networks

Roles and mechanisms of action of HNF‑4α in the hepatic differentiation of WB‑F344 cells

Hepatocyte nuclear factor 4 α (HNF‑4α) is a nuclear receptor and mediates hepatic genes. WB‑F344 liver epithelial cells can differentiate into hepatocytes. The present study aimed to examine the roles and mechanisms of action of HNF‑4α on the hepatic differentiation of WB‑F344 cells. WB‑F344 cells were divided into a normal cell group (WB‑F344), empty vector group (PLKO), and gene silencing group (PLKO‑SH). The expression levels of HNF‑4α were measured using reverse transcription‑quantitative polymerase chain reaction analysis. Proliferation of the cells was determined using a Cell Counting kit‑8 assay. Based on western blot analysis, the protein levels of α‑fetoprotein (AFP), albumin (ALB) and cytokeratin 19 (CK19) were determined. The positive cell rates of the three groups were assessed using periodic acid‑Schiff (PAS) staining. Following construction of an RNA‑sequencing library, differentially expressed genes (DEGs) between the HNF‑4α‑silenced and normal samples were screened using the limma package and enrichment analysis was conducted using the DAVID tool. Protein‑protein interaction (PPI) and microRNA‑targeted regulatory networks were constructed in Cytoscape software. The PLKO‑SH group exhibited a lower mRNA level of HNF‑4α, higher protein level of AFP, lower protein levels of ALB and CK19, increased cell proliferation, and a lower PAS‑positive cell rate. The HNF‑4α‑silenced and normal samples differed in 499 DEGs. In the PPI network, matrix metallopeptidase 9 (MMP9), early growth response 1 (EGR1), SMAD family member 2 (SMAD2), and RAS‑related C3 botulinum substrate 2 (RAC2) were key nodes. HNF‑4α may promote the differentiation of WB‑F344 cells into hepatocytes by targeting MMP9, EGR1, SMAD2 and RAC2.

Genes and transcription factors related to the adverse effects of maternal type I diabetes mellitus on fetal development

PURPOSE

Maternal type I diabetes mellitus (T1DM) increases the risk of adverse pregnancy outcomes, but the corresponding mechanism is unclear. This study aims to investigate the mechanism underlying the adverse pregnancy outcomes of maternal T1DM.

METHODS

Gene expression microarray (GSE51546) was down-loaded from the Gene Expression Omnibus. This dataset included 12 umbilical cord samples from the newborns of T1DM mothers (T1DM group, N = six) and non-diabetic mothers (control group, N = six).

RESULTS

Consequently, 1051 differentially expressed genes (DEGs) were found between the two groups. The up-regulated DEGs enriched in 30 KEGG pathways. HLA-DPA1, HLA-DMA, HLA-DMB, HLA-DQA1, HLA-DQA2 and HLA-DRA enriched in "Type I diabetes mellitus". This pathway was strongly related to 14 pathways, most of which were associated with diseases. Then, a protein-protein interaction network was constructed, and 45 potential key DEGs were identified. The 45 DEGs enriched in pathways such as "Rheumatoid arthritis", "Chemokine signaling pathway" and "Cytokine-cytokine receptor interaction" (e.g. CXCL12 and CCL5). Transcription factors (TFs) of key DEGs were predicted, and a TF-DEG regulatory network was constructed.

CONCLUSIONS

Some genes (e.g. CXCL12 and CCL5) and their TFs were significantly and abnormally regulated in the umbilical cord tissue from the pregnancies of T1DM mothers compared to that from non-T1DM mothers.

Screening of genes associated with inflammatory responses in the endolymphatic sac reveals underlying mechanisms for autoimmune inner ear diseases

The current study analyzed gene expression profiles of the endolymphatic sac (ES) in rats and identified expressed genes, present in the human and rat ES, to reveal key hubs for inflammatory responses. Microarray data (accession no. E-MEXP-3022) were obtained from the European Bioinformatics Institute database, including three biological replicates of ES plus dura tissues and three replicates of pure dura tissues form rats. Differentially expressed genes (DEGs) were screened using the Linear Model for Microarray data method and a protein-protein interaction (PPI) network was constructed using data from the Search Tool for the Retrieval of Interacting Genes/Proteins database followed by a module analysis via Clustering with Overlapping Neighborhood Expansion. Function enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery online tool. A total of 612 DEGs were identified, including 396 upregulated and 216 downregulated genes. Gene ontology term enrichment analysis indicated DEGs were associated with cell adhesion, including α5-integrin (Itga1) and secreted phosphoprotein 1 (Spp1); T cell co-stimulation, including C-C chemokine ligand (Ccl)21 and Ccl19; and the toll-like receptor signaling pathway, including toll-like receptor (Tlr)2, Tlr7 and Tlr8. These conclusions were supported by Kyoto Encyclopedia of Genes and Genomes pathway analyses revealing extracellular matrix-receptor interaction, including Itga1 and Spp1; leukocyte transendothelial migration, includingclaudin-4 (Cldn4); and malaria, including Tlr2. The hub roles of Itga1, Cd24 and Spp1 were revealed by calculating three topological properties of the PPI network. Ccl21, Ccl19 and Cldn4 were demonstrated to be crucial following significant module analysis according to the corresponding threshold, which revealed they were enriched in inflammation pathways. Tlr7, Tlr2, granzyme m and Tlr8 were common genes associated with inflammatory responses in rat and human ES. In conclusion, abnormal expression of the aforementioned inflammation-associated genes may be associated with the development of autoimmune inner ear diseases.

RNA Sequencing Uncovers Molecular Mechanisms Underlying Pathological Complete Response to Chemotherapy in Patients with Operable Breast Cancer

Background

This study aimed to identify key genes contributing to pathological complete response (pCR) to chemotherapy by mRNA sequencing (RNA-seq).

Material/Methods

RNA was extracted from the frozen biopsy tissue of patients with pathological complete response and patients with non-pathological complete response. Sequencing was performed on the HiSeq2000 platform. Differentially expressed genes (DEGs) were identified between the pCR group and non-pCR (NpCR) group. Pathway enrichment analysis of DEGs was performed. A protein-protein interaction network was constructed, then module analysis was performed to identify a subnetwork. Finally, transcription factors were predicted.

Results

A total of 673 DEGs were identified, including 419 upregulated ones and 254 downregulated ones. The PPI network constructed consisted of 276 proteins forming 471 PPI pairs, and a subnetwork containing 18 protein nodes was obtained. Pathway enrichment analysis revealed that PLCB4 and ADCY6 were enriched in pathways renin secretion, gastric acid secretion, gap junction, inflammatory mediator regulation of TRP channels, retrograde endocannabinoid signaling, melanogenesis, cGMP-PKG signaling pathway, calcium signaling pathway, chemokine signaling pathway, cAMP signaling pathway, and rap1 signaling pathway. CNR1 was enriched in the neuroactive ligand-receptor interaction pathway, retrograde endocannabinoid signaling pathway, and rap1 signaling pathway. The transcription factor-gene network consists of 15 transcription factors and 16 targeted genes, of which 5 were downregulated and 10 were upregulated.

Conclusions

We found key genes that may contribute to pCR to chemotherapy, such as PLCB4, ADCY6, and CNR1, as well as some transcription factors.

Functional prediction of miR-3144-5p in human cardiac myocytes based on transcriptome sequencing and bioinformatics

BACKGROUND

RAN guanine nucleotide release factor (RANGRF) encoding protein MOG1 plays an important role in cardiac arrhythmia, so we intended to investigate the regulatory miRNA of RANGRF and explore its potential regulatory mechanism in arrhythmogenesis.

METHODS

Based on bioinformatic analysis, miR-3144-5p was predicted to be a regulatory miRNA of RANGRF, which were then validated through a dual-luciferase reporter plasmid assay. Subsequently, the expression level of miR-3144-5p in human cardiac myocytes (HCMs) was detected, followed by cell transfection with miR-3144-5p mimics. Transcriptome sequencing was then performed in HCMs with or without transfection. The sequencing results were subjected to bioinformatic analyses, including differentially expressed gene (DEG) analysis, functional enrichment analysis, protein-protein interaction (PPI) network analysis, miRNA-target gene analysis, and miRNA-transcription factor (TF)-target gene coregulatory network analysis.

RESULTS

There really existed a regulatory relation between miR-3144-5p and RANGRF. The expression level of miR-3144-5p was low in HCMs. After cell transfection, miR-3144-5p expression level significantly increased in HCMs. Bioinformatic analyses of the transcriptome sequencing results identified 300 upregulated and 271 downregulated DEGs between miR-3144-5p mimic and control group. The upregulated genes ISL1 and neuregulin 1 (NRG1) were significantly enriched in cardiac muscle cell myoblast differentiation (GO:0060379). CCL21 was one of the hub genes in the PPI network and also a target gene of miR-3144-5p. Moreover, the TF of v-Myc avian myelocytomatosis viral oncogene neuroblastoma-derived homolog (MYCN) was involved in the miR-3144-5p-TF-target gene coregulatory network and interacted with the target genes of miR-3144-5p.

CONCLUSION

ISL1, NRG1, CCL21, and MYCN were differentially expressed in the miR-3144-5p mimic group, suggesting that miR-3144-5p overexpression plays a role in HCMs by regulating these genes and TF. This study may provide new insight into the mechanisms behind the progression of cardiac arrhythmia.

Alignment-free protein interaction network comparison

MOTIVATION

Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.

RESULTS

We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type.

AVAILABILITY AND IMPLEMENTATION

The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring fold space preferences of new-born and ancient protein superfamilies

The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today. While we tend to define protein evolution in terms of sequence level mutations, insertions and deletions, it is hard to translate these processes to a more complete picture incorporating a polypeptide's structure and function. By considering how protein structures change over time we can gain an entirely new appreciation of their long-term evolutionary dynamics. In this work we seek to identify how populations of proteins at different stages of evolution explore their possible structure space. We use an annotation of superfamily age to this space and explore the relationship between these ages and a diverse set of properties pertaining to a superfamily's sequence, structure and function. We note several marked differences between the populations of newly evolved and ancient structures, such as in their length distributions, secondary structure content and tertiary packing arrangements. In particular, many of these differences suggest a less elaborate structure for newly evolved superfamilies when compared with their ancient counterparts. We show that the structural preferences we report are not a residual effect of a more fundamental relationship with function. Furthermore, we demonstrate the robustness of our results, using significant variation in the algorithm used to estimate the ages. We present these age estimates as a useful tool to analyse protein populations. In particularly, we apply this in a comparison of domains containing greek key or jelly roll motifs.

Role of antisense RNAs in evolution of yeast regulatory complexity

Antisense RNAs (asRNAs) are known to regulate gene expression. However, a genome-wide mechanism of asRNA regulation is unclear, and there is no good explanation why partial asRNAs are not functional. To explore its regulatory role, we investigated asRNAs using an evolutionary approach, as genome-wide experimental data are limited. We found that the percentage of genes coupling with asRNAs in Saccharomyces cerevisiae is negatively associated with regulatory complexity and evolutionary age. Nevertheless, asRNAs evolve more slowly when their sense genes are under more complex regulation. Older genes coupling with asRNAs are more likely to demonstrate inverse expression, reflecting the role of these asRNAs as repressors. Our analyses provide novel evidence, suggesting a minor contribution of asRNAs in developing regulatory complexity. Although our results support the leaky hypothesis for asRNA transcription, our evidence also suggests that partial asRNAs may have evolved as repressors. Our study deepens the understanding of asRNA regulatory evolution.