Analysis and identification of toxin targets by topological properties in protein–protein interaction network

DeepSG2PPI: A Protein-Protein Interaction Prediction Method Based on Deep Learning

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. First, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Second, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

CAF-Associated Paracrine Signaling Worsens Outcome and Potentially Contributes to Chemoresistance in Epithelial Ovarian Cancer

Background

High-grade serous ovarian cancer (HGSOC) is the predominant and deadliest form of ovarian cancer. Some of its histological subtypes can be distinguished by frequent occurrence of cancer-associated myofibroblasts (CAFs) and desmoplastic stroma reaction (DSR). In this study, we want to explore the relationship between therapy outcome and the activity of CAF-associated signaling pathways in a homogeneous HGSOC patient collective. Furthermore, we want to validate these findings in a general Epithelial ovarian cancer (EOC) cohort.

Methods

The investigation cohort consists of 24 HGSOC patients. All of them were treated with platinum-based components and clinical follow-up was available. The validation cohort was comprised of 303 patients. Sequencing data (whole transcriptome) and clinical data were extracted from The Cancer Genome Atlas (TCGA). RNA of HGSOC patients was isolated using a Maxwell RSC instrument and the appropriate RNA isolation kit. For digital expression analysis a custom-designed gene panel was employed. All genes were linked to various DSR- and CAF- associated pathways. Expression analysis was performed on the NanoString nCounter platform. Finally, data were explored using the R programming environment (v. 4.0.3).

Result

In total, 15 CAF-associated genes were associated with patients’ survival. More specifically, 6 genes (MMP13, CGA, EPHA3, PSMD9, PITX2, PHLPP1) were linked to poor therapy outcome. Though a variety of different pathways appeared to be associated with therapy failure, many were related to CAF paracrine signaling, including MAPK, Ras and TGF-β pathways. Similar results were obtained from the validation cohort.

Discussion

In this study, we could successfully link CAF-associated pathways, as shown by increased Ras, MAPK and PI3K-Akt signaling to therapy failure (chemotherapy) in HGSOC and EOCs in general. As platinum-based chemotherapy has been the state-of-the-art therapy to treat HGSOC for decades, it is necessary to unveil the reasons behind resistance developments and poor outcome. In this work, CAF-associated signaling is shown to compromise therapy response. In the validation cohort, CAF-associated signaling is also associated with therapy failure in general EOC, possibly hinting towards a conserved mechanism. Therefore, it may be helpful to stratify HGSOC patients for CAF activity and consider alternative treatment options.

Digital Immune-Related Gene Expression Signatures In High-Grade Serous Ovarian Carcinoma: Developing Prediction Models For Platinum Response

Purpose

Response to platinum-based therapy is a major prognostic factor in high-grade serous ovarian cancer (HGSOC). While the exact mechanisms of platinum-resistance remain unclear, evidence is accumulating for a connection between the organism’s immune-response and response to platinum. However, predictive tools are missing. This study was performed to examine the putative role of the genetic tumor immune-microenvironment in mediating differential chemotherapy response in HGSOC patients.

Patients and methods

Expression profiling of 770 immune-related genes was performed in tumor tissues from 23 HGSOC cases. Tumors were screened for prognostic and predictive biomarkers using the NanoString nCounter platform for digital gene expression analysis with the appurtenant PanCancer Immune Profiling panel. As validation cohort, gene expression data (RNA Seq) of 303 patients with epithelial ovarian carcinoma (EOC) were retrieved from the The Cancer Genome Atlas (TCGA) database. Different scoring-systems were computed for prediction of risk-of-resistance to cisplatin, disease-free survival (DFS) and overall survival (OS).

Results

Validated on the TCGA-dataset, the developed scores identified 11 significantly differentially expressed genes (p <0.01**) associated with platinum response. HSD11B1 was highly significantly associated with lower risk of recurrence and 7 targets were found highly significantly influencing OS time (p <0.01**).

Conclusion

Our results suggest that response to platinum-based therapy and DFS in ovarian HGSOC is associated with distinct gene-expression patterns related to the tumor immune-system. We generated predictive scoring systems which proved valid when applied to a set of 303 EOC patients.

Characterization of human proteins with different subcellular localizations by topological and biological properties

Knowing the protein localization can provide valuable information resource for elucidating protein function. In recent years, with the advances of human genomics and proteomics, it is possible to characterize human proteins that are located in different subcellular localizations. In this study, we used the topological properties and biological properties to characterize human proteins with six subcellular localizations. Almost all of these properties were found to be significantly different among six protein categories. Network topology analysis indicated that several significant topological properties, including the degree and k-core, were higher for the mitochondrial proteins. Biological property analysis showed that the nuclear proteins appeared to be correlated with important biological function. We hope these findings may provide some important help for comprehensive understanding the biological function of proteins, and prediction of protein subcellular localizations in human.

iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties

Motivation

DNA N4-methylcytosine (4mC) is an epigenetic modification. The knowledge about the distribution of 4mC is helpful for understanding its biological functions. Although experimental methods have been proposed to detect 4mC sites, they are expensive for performing genome-wide detections. Thus, it is necessary to develop computational methods for predicting 4mC sites.

Results

In this work, we developed iDNA4mC, the first webserver to identify 4mC sites, in which DNA sequences are encoded with both nucleotide chemical properties and nucleotide frequency. The predictive results of the rigorous jackknife test and cross species test demonstrated that the performance of iDNA4mC is quite promising and holds high potential to become a useful tool for identifying 4mC sites.

Availability and implementation

The user-friendly web-server, iDNA4mC, is freely accessible at http://lin.uestc.edu.cn/server/iDNA4mC.

Contact

chenweiimu@gmail.com or hlin@uestc.edu.cn.

Characterization of proteins in different subcellular localizations for Escherichia coli K12

Knowing the comprehensive knowledge about the protein subcellular localization is an important step to understand the function of the proteins. Recent advances in system biology have allowed us to develop more accurate methods for characterizing the proteins at subcellular localization level. In this study, the analysis method was developed to characterize the topological properties and biological properties of the cytoplasmic proteins, inner membrane proteins, outer membrane proteins and periplasmic proteins in Escherichia coli (E. coli). Statistical significant differences were found in all topological properties and biological properties among proteins in different subcellular localizations. In addition, investigation was carried out to analyze the differences in 20 amino acid compositions for four protein categories. We also found that there were significant differences in all of the 20 amino acid compositions. These findings may be helpful for understanding the comprehensive relationship between protein subcellular localization and biological function.

A systems-level approach for investigating organophosphorus pesticide toxicity

The full understanding of the single and joint toxicity of a variety of organophosphorus (OP) pesticides is still unavailable, because of the extreme complex mechanism of action. This study established a systems-level approach based on systems toxicology to investigate OP pesticide toxicity by incorporating ADME/T properties, protein prediction, and network and pathway analysis. The results showed that most OP pesticides are highly toxic according to the ADME/T parameters, and can interact with significant receptor proteins to cooperatively lead to various diseases by the established OP pesticide -protein and protein-disease networks. Furthermore, the studies that multiple OP pesticides potentially act on the same receptor proteins and/or the functionally diverse proteins explained that multiple OP pesticides could mutually enhance toxicological synergy or additive on a molecular/systematic level. To the end, the integrated pathways revealed the mechanism of toxicity of the interaction of OP pesticides and elucidated the pathogenesis induced by OP pesticides. This study demonstrates a systems-level approach for investigating OP pesticide toxicity that can be further applied to risk assessments of various toxins, which is of significant interest to food security and environmental protection.

Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties

Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms.

Genome-wide characterization of essential, toxicity-modulating and no-phenotype genes in S. cerevisiae

Based on the requirements for an organism's viability, genes can be classified into essential genes and non-essential genes. Non-essential genes can be further classified into toxicity-modulating genes and no-phenotype genes based on the fitness phenotype of yeast cells when the gene is deleted under DNA-damaging conditions. In this study, graph theoretical approaches were used to characterize essential, toxicity-modulating and no-phenotype genes for S. cerevisiae in the physical interaction (PI) network and the perturbation sensitivity (PS) network. We also gained previously published biological datasets to gain a more complete understanding of the differences and relationships between essential, toxicity-modulating genes and no-phenotype genes. The analysis results indicate that toxicity-modulating genes have similar properties as essential genes, and toxicity-modulating genes might represent a middle ground between essential genes and no-phenotype genes, suggesting that cells initiate highly coordinated responses to damage that are similar to those needed for vital cellular functions. These findings may elucidate the mechanisms for understanding toxicity-modulating processes relevant to certain diseases.

Characterization of proteins in S. cerevisiae with subcellular localizations

Acquiring comprehensive knowledge of protein in various subcellular localizations is one of the fundamental goals in cell biology and proteomics.

System analysis of LWDH related genes based on text mining in biological networks

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM.

Human proteins characterization with subcellular localizations

Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.

Characterization of essential genes by topological properties in the perturbation sensitivity network

Genes that are indispensable for survival are called essential genes. In recent years, the analysis of essential genes has become extremely important for understanding the way a cell functions. With the advent of large-scale gene expression profiling technologies, it is now possible to profile transcriptional changes in the entire genome of Saccharomyces cerevisiae. Notwithstanding the accumulation of gene expression profiling in recent years, only a few studies have used these data to construct the network for S. cerevisiae. In this paper, based on the transcriptional profiling of the S. cerevisiae genome in hundreds of different gene disruptions, the perturbation sensitivity (PS) network is constructed. A scale-free topology with node degree following a power-law distribution is shown in the PS network. Twelve topological properties are used to investigate the characteristics of essential and non-essential genes in the PS network. Most of the properties are found to be statistically discriminative between essential and non-essential genes. In addition, the F-score is used to estimate the essentiality of each property, and the core number demonstrates the highest F-score among all properties.