Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

Proteomic profiling of prostate cancer reveals molecular signatures under antiandrogen treatment

BACKGROUND

Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for patients with advanced PCa. However, the molecular characteristics of PCa with antiandrogen intervention have not yet been fully uncovered.

METHODS

We first performed proteome analysis with 32 PCa tumor samples and 10 adjacent tissues using data-independent acquisition (DIA)- parallel accumulation serial fragmentation (PASEF) proteomics. Then label-free quantification (LFQ) mass spectrometry was employed to analyze protein profiles in LNCaP and PC3 cells.

RESULTS

M-type creatine kinase CKM and cartilage oligomeric matrix protein COMP were demonstrated to have the potential to be diagnostic biomarkers for PCa at both mRNA and protein levels. Several E3 ubiquitin ligases and deubiquitinating enzymes (DUBs) were significantly altered in PCa and PCa cells under enzalutamide treatment, and these proteins might reprogram proteostasis at protein levels in PCa. Finally, we discovered 127 significantly varied proteins in PCa samples with antiandrogen therapy and further uncovered 4 proteins in LNCaP cells upon enzalutamide treatment.

CONCLUSIONS

Our research reveals new potential diagnostic biomarkers for prostate cancer and might help resensitize resistance to antiandrogen therapy.

cAMP-phosphodiesterase 4D7 (PDE4D7) forms a cAMP signalosome complex with DHX9 and is implicated in prostate cancer progression

A robust body of work has demonstrated that a reduction in cAMP-specific 3',5'-cyclic phosphodiesterase 4D isoform 7 (PDE4D7) is linked with negative prostate cancer outcomes; however, the exact molecular mechanism that underpins this relationship is unknown. Epigenetic profiling has shown that the PDE4D gene can be hyper-methylated in transmembrane serine protease 2 (TMPRSS2)-ETS transcriptional regulator ERG (ERG) gene-fusion-positive prostate cancer (PCa) tumours, and this inhibits messenger RNA (mRNA) expression, leading to a paucity of cellular PDE4D7 protein. In an attempt to understand how the resulting aberrant cAMP signalling drives PCa growth, we immunopurified PDE4D7 and identified binding proteins by mass spectrometry. We used peptide array technology and proximity ligation assay to confirm binding between PDE4D7 and ATP-dependent RNA helicase A (DHX9), and in the design of a novel cell-permeable disruptor peptide that mimics the DHX9-binding region on PDE4D7. We discovered that PDE4D7 forms a signalling complex with the DExD/H-box RNA helicase DHX9. Importantly, disruption of the PDE4D7-DHX9 complex reduced proliferation of LNCaP cells, suggesting the complex is pro-tumorigenic. Additionally, we have identified a novel protein kinase A (PKA) phosphorylation site on DHX9 that is regulated by PDE4D7 association. In summary, we report the existence of a newly identified PDE4D7-DHX9 signalling complex that may be crucial in PCa pathogenesis and could represent a potential therapeutic target.

Genome analysis of clinical genotype Vibrio vulnificus isolated from seafood in Mangaluru Coast, India provides insights into its pathogenicity

Vibrio vulnificus an opportunistic human pathogen native to marine/estuarine environment, is one of the leading causes of death due to seafood consumption and exposure of wounds to seawater worldwide. The present study involves the whole genome sequence analysis of an environmental strain of V. vulnificus (clinical genotype) isolated from seafood along the Mangaluru coast of India. The sequenced genome data was subjected to in-silico analysis of phylogeny, virulence genes, antimicrobial resistance determinants, and secretary proteins using suitable bioinformatics tools. The sequenced isolate had an overall genome length of 4.8 Mb and GC content of 46% with 4400 coding DNA sequences. The sequenced strain belongs to a new sequence type (Multilocus sequence typing) and was also found to branch with a phylogenetic lineage that groups the most infectious strains of V. vulnificus. The seafood isolate had complete genes involved in conferring serum resistance yet showed limited serum resistance. The study identified several genes against the antibiotics that are commonly used in their treatment, highlighting the need for alternative treatments. Also, the secretory protein analysis revealed genes associated with major pathways like ABC transporters, two-component systems, quorum sensing, biofilm formation, cationic antimicrobial peptide (CAMP) resistance, and others that play a critical role in the pathogenesis of the V. vulnificus. To the best of our knowledge, this is the first report of a detailed analysis of the genomic information of a V. vulnificus isolated from the Indian subcontinent and provides evidence that raises public health concerns about the safety of seafood.

Dissection of paracrine/autocrine interplay in lung tumor microenvironment mimicking cancer cell-monocyte co-culture models reveals proteins that promote inflammation and metastasis

BACKGROUND

Tumor cell-monocyte interactions play crucial roles in shaping up the pro-tumorigenic phenotype and functional output of tumor-associated macrophages. Within the tumor microenvironment, such heterotypic cell-cell interactions are known to occur via secretory proteins. Secretory proteins establish a diabolic liaison between tumor cells and monocytes, leading to their recruitment, subsequent polarization and consequent tumor progression.

METHODS

We co-cultured model lung adenocarcinoma cell line A549 with model monocytes, THP-1 to delineate the interactions between them. The levels of prototypical pro-inflammatory cytokines like TNF-𝛼, IL-6 and anti-inflammatory cytokines like IL-10 were measured by ELISA. Migration, invasion and attachment independence of lung cancer cells was assessed by wound healing, transwell invasion and colony formation assays respectively. The status of EMT was evaluated by immunofluorescence. Identification of secretory proteins differentially expressed in monocultures and co-culture was carried out using SILAC LC-MS/MS. Various insilico tools like Cytoscape, Reacfoam, CHAT and Kaplan-Meier plotter were utilized for association studies, pathway analysis, functional classification, cancer hallmark relevance and predicting the prognostic potential of the candidate secretory proteins respectively.

RESULTS

Co-culture of A549 and THP-1 cells in 1:10 ratio showed early release of prototypical pro-inflammatory cytokines TNF-𝛼 and IL-6, however anti-inflammatory cytokine, IL-10 was observed to be released at the highest time point. The conditioned medium obtained from this co-culture ratio promoted the migration, invasion and colony formation as well as the EMT of A549 cells. Co-culturing of A549 with THP-1 cells modulated the secretion of proteins involved in cell proliferation, migration, invasion, EMT, inflammation, angiogenesis and inhibition of apoptosis. Among these proteins Versican, Tetranectin, IGFBP2, TUBB4B, C2 and IFI30 were found to correlate with the inflammatory and pro-metastatic milieu observed in our experimental setup. Furthermore, dysregulated expression of these proteins was found to be associated with poor prognosis and negative disease outcomes in lung adenocarcinoma compared to other cancer types. Pharmacological interventions targeting these proteins may serve as useful therapeutic approaches in lung adenocarcinoma.

CONCLUSION

In this study, we have demonstrated that the lung cancer cell-monocyte cross-talk modulates the secretion of IFI30, RNH1, CLEC3B, VCAN, IGFBP2, C2 and TUBB4B favoring tumor growth and metastasis.

Gene expression profile analysis unravelled the systems level association of renal cell carcinoma with diabetic nephropathy and Matrix-metalloproteinase-9 as a potential therapeutic target

Type 2 diabetes (T2D) and cancer share many common risk factors. However, the potential biological link that connects the two at the molecular level is still unclear. The experimental evidence suggests that several genes and their pathways may be involved in developing cancerous conditions associated with diabetes. In this study, we identified the protein-protein interaction (PPI) networks and the hub protein(s) that interlink T2D and cancer using genome-scale differential gene expression profiles. Further, the PPI network of AMP-activated protein kinase (AMPK) in cancer was analyzed to explore novel insights into the molecular association between the two conditions. The densely connected regions were analyzed by constructing the backbone and subnetworks with key nodes and shortest pathways, respectively. The PPI network studies identified Matrix-metalloproteinase-9 (MMP-9) as a hub protein playing a vital role in glomerulonephritis tubular diseases and some genetic kidney diseases. MMP-9 was also associated with different growth factors, like tumor necrosis factor (TNF-α), transforming growth factor 1 (TGF-1), and pathways like chemokine signaling, NOD-like receptor signaling, etc. Further, the molecular docking and molecular dynamic simulation studies supported the druggability of MMP-9, suggesting it as a potential therapeutic target in treating renal cell carcinoma linked with diabetic kidney disease.Communicated by Ramaswamy H. Sarma.

Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification

Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desmoplakin, Hexokinase-3 and Desmocollin-1 decreased, while that of BPI fold-containing family A member 2 increased in AIHp compared to HCs; the abundance of Gelsolin, CD14, Tumor-associated calcium signal transducer 2, Clusterin, Heterogeneous nuclear ribonucleoproteins A2/B1, Cofilin-1 and BPI fold-containing family B member 2 increased in PBCp compared to HCs. The abundance of Hornerin decreased in both AIHp and PBCp with respect to HCs and provided an area under the ROC curve of 0.939. Machine learning analysis confirmed the feasibility of the salivary proteome to discriminate groups of subjects based on AIH or PBC occurrence as previously suggested by our group. The topology-based functional enrichment analysis performed on these potential salivary biomarkers highlights an enrichment of terms mostly related to the immune system, but also with a strong involvement in liver fibrosis process and with antimicrobial activity.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues

BACKGROUND

P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data.

RESULTS

We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors).

CONCLUSIONS

Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Transcriptome profiling reveals transcriptional regulation of VISTA in T cell activation

PURPOSE

V-domain immunoglobulin suppressor of T-cell activation (VISTA) is a novel type of immune checkpoint. This study was performed to explore the potential mechanism by which different domains of VISTA affect T-cell activation and search for potential interacting proteins.

METHODS

Stably transfected Jurkat cell lines were constructed to overexpress human VISTA (VISTA-FL), cytoplasmic domain deletion mutants (VISTA-ΔECD) and extracellular domain deletion mutants (VISTA- ΔCD). Empty vector (EV) control cell lines were constructed. Four stable cell lines were subjected to transcriptome sequencing after stimulation with PMA and PHA. The differentially expressed genes (DEGs) were analysed to explore the potential pathway by which VISTA inhibits T-cell activation. Proteinprotein interaction (PPI) network analysis was used to search for potential interacting proteins of VISTA.

RESULTS

In this study, 1256 DEGs were identified in Jurkat-VISTA-FL cells, 740 DEGs in Jurkat-VISTA-ΔCD cells, and 5605 DEGs in Jurkat-VISTA-ΔECD cells compared with Jurkat-EV cells. DEGs were mainly enriched in pathways related to T-cell differentiation, T-cell receptor signalling pathway and T-cell migration in Jurkat-VISTA-ΔECD cells; with cholesterol biosynthesis in Jurkat-VISTA-ΔCD cells; and with the inflammatory response in Jurkat-VISTA-FL cells. HHLA2 and CTH were identified as potential partners that interact directly with VISTA. The results also show an indirect interaction between VISTA and PSGL-1.

CONCLUSIONS

This study revealed the pathways by which VISTA is involved in T-cell activation and identified the potential binding partners of VISTA through RNA-seq, providing valuable resources for developing in-depth studies of the action mechanisms of VISTA as a potential target for cancer and inflammatory diseases.

Topological analysis as a tool for detection of abnormalities in protein-protein interaction data

MOTIVATION

Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis.

RESULTS

We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences.

AVAILABILITY AND IMPLEMENTATION

A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb.

CONTACT

alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

An integrated network representation of multiple cancer-specific data for graph-based machine learning

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

β-Tubulin Isotype, TUBB4B, Regulates The Maintenance of Cancer Stem Cells

Recent advancements in cancer research have shown that cancer stem cell (CSC) niche is a crucial factor modulating tumor progression and treatment outcomes. It sustains CSCs by orchestrated regulation of several cytokines, growth factors, and signaling pathways. Although the features defining adult stem cell niches are well-explored, the CSC niche is poorly characterized. Since membrane trafficking proteins have been shown to be essential for the localization of critical proteins supporting CSCs, we investigated the role of TUBB4B, a probable membrane trafficking protein that was found to be overexpressed in the membranes of stem cell enriched cultures, in sustaining CSCs in oral cancer. Here, we show that the knockdown of TUBB4B downregulates the expression of pluripotency markers, depletes ALDH1A1+ population, decreases in vitro sphere formation, and diminishes the tumor initiation potential in vivo. As TUBB4B is not known to have any role in transcriptional regulation nor cell signaling, we suspected that its membrane trafficking function plays a role in constituting a CSC niche. The pattern of its expression in tissue sections, forming a gradient in and around the CSCs, reinforced the notion. Later, we explored its possible cooperation with a signaling protein, Ephrin-B1, the abrogation of which reduces the self-renewal of oral cancer stem cells. Expression and survival analyses based on the TCGA dataset of head and neck squamous cell carcinoma (HNSCC) samples indicated that the functional cooperation of TUBB4 and EFNB1 results in a poor prognosis. We also show that TUBB4B and Ephrin-B1 cohabit in the CSC niche. Moreover, depletion of TUBB4B downregulates the membrane expression of Ephrin-B1 and reduces the CSC population. Our results imply that the dynamics of TUBB4B is decisive for the surface localization of proteins, like Ephrin-B1, that sustain CSCs by their concerted signaling.

Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets

The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.

Mapping Transcriptome Data to Protein-Protein Interaction Networks of Inflammatory Bowel Diseases Reveals Disease-Specific Subnetworks

Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn’s disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn’s disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein–protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer’s disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines—special types of cytokines—and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

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

Non-alcoholic fatty liver disease (NAFLD) is a disease with multidimensional complexities. Many attempts have been made over the years to treat this disease but its incidence is rising. For this reason, the need to identify and study new candidate proteins that may be associated with NAFLD is of utmost importance. Systems-based approaches such as the analysis of protein-protein interaction (PPI) network could lead to the discovery of new proteins associated with a disease that can then be translated into clinical practice. The aim of this study is to analyze the interaction network of human proteins associated with NAFLD as well as their experimentally verified interactors and to identify novel associations with other human proteins that may be involved in this disease. Computational analysis made it feasible to detect 77 candidate proteins associated with NAFLD, having high network scores. Furthermore, clustering analysis was performed to identify densely connected regions with biological significance in this network. Additionally, gene expression analysis was conducted to validate part of the findings of this research work. We believe that our research will be helpful in extending experimental efforts to address the pathogenesis and progression of NAFLD.

NGPINT: a next-generation protein-protein interaction software

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein-protein interactions in model and non-model organisms.

GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth.. [DOI: 10.1101/2020.05.20.107458]

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.

Design, synthesis, and anticancer evaluation of novel quinoline derivatives of ursolic acid with hydrazide, oxadiazole, and thiadiazole moieties as potent MEK inhibitors

In this article, a series of novel quinoline derivatives of ursolic acid (UA) bearing hydrazide, oxadiazole, or thiadiazole moieties were designed, synthesised, and screened for their in vitro antiproliferative activities against three cancer cell lines (MDA-MB-231, HeLa, and SMMC-7721). A number of compounds showed significant activity against at least one cell line. Among them, compound 4d exhibited the most potent activity against three cancer cell lines with IC50 values of 0.12 ± 0.01, 0.08 ± 0.01, and 0.34 ± 0.03 μM, respectively. In particular, compound 4d could induce the apoptosis of HeLa cells, arrest cell cycle at the G0/G1 phase, elevate intracellular reactive oxygen species level, and decrease mitochondrial membrane potential. In addition, compound 4d could significantly inhibit MEK1 kinase activity and impede Ras/Raf/MEK/ERK transduction pathway. Therefore, compound 4d may be a potential anticancer agent and a promising lead worthy of further investigation.

Circulating miRNA Profiling in Plasma Samples of Ovarian Cancer Patients

Ovarian cancer is one of the most common cancer types in women characterized by a high mortality rate due to lack of early diagnosis. Circulating miRNAs besides being important regulators of cancer development could be potential biomarkers to aid diagnosis. We performed the circulating miRNA expression analysis in plasma samples obtained from ovarian cancer patients stratified into FIGO I, FIGO III, and FIGO IV stages and from healthy females using the NanoString quantitative assay. Forty-five miRNAs were differentially expressed, out of these 17 miRNAs showed significantly different expression between controls and patients, 28 were expressed only in patients, among them 19 were expressed only in FIGO I patients. Differentially expressed miRNAs were ranked by the network-based analysis to assess their importance. Target genes of the differentially expressed miRNAs were identified then functional annotation of the target genes by the GO and KEGG-based enrichment analysis was carried out. A general and an ovary-specific protein–protein interaction network was constructed from target genes. Results of our network and the functional enrichment analysis suggest that besides HSP90AA1, MYC, SP1, BRCA1, RB1, CFTR, STAT3, E2F1, ERBB2, EZH2, and MET genes, additional genes which are enriched in cell cycle regulation, FOXO, TP53, PI-3AKT, AMPK, TGFβ, ERBB signaling pathways and in the regulation of gene expression, proliferation, cellular response to hypoxia, and negative regulation of the apoptotic process, the GO terms have central importance in ovarian cancer development. The aberrantly expressed miRNAs might be considered as potential biomarkers for the diagnosis of ovarian cancer after validation of these results in a larger cohort of ovarian cancer patients.

Tubulin βII and βIII Isoforms as the Regulators of VDAC Channel Permeability in Health and Disease

In recent decades, there have been several models describing the relationships between the cytoskeleton and the bioenergetic function of the cell. The main player in these models is the voltage-dependent anion channel (VDAC), located in the mitochondrial outer membrane. Most metabolites including respiratory substrates, ADP, and Pi enter mitochondria only through VDAC. At the same time, high-energy phosphates are channeled out and directed to cellular energy transfer networks. Regulation of these energy fluxes is controlled by β-tubulin, bound to VDAC. It is also thought that β-tubulin‒VDAC interaction modulates cellular energy metabolism in cancer, e.g., switching from oxidative phosphorylation to glycolysis. In this review we focus on the described roles of unpolymerized αβ-tubulin heterodimers in regulating VDAC permeability for adenine nucleotides and cellular bioenergetics. We introduce the Mitochondrial Interactosome model and the function of the βII-tubulin subunit in this model in muscle cells and brain synaptosomes, and also consider the role of βIII-tubulin in cancer cells.

Evaluation of 12 mouse marker genes in rat toxicogenomics public data, Open TG-GATEs: Discrimination of genotoxic from non-genotoxic hepatocarcinogens

Previously, we proposed 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb4b) to discriminate mouse genotoxic hepatocarcinogens (GTHC) from non-genotoxic hepatocarcinogens (NGTHC). This was determined by qPCR and principal component analysis (PCA), as the aim of an in vivo short-term screening for genotoxic hepatocarcinogens. For this paper, we conducted an application study of the 12 mouse marker genes to rat data, Open TG-GATEs (public data). We analyzed five typical rat GTHC (2-acetamodofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine), and not only seven typical rat NGTHC (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital and WY-14643) but also 11 non-genotoxic non-hepatocarcinogens (NGTNHC; allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline and tolbutamide) from Open TG-GATEs. The analysis was performed at 3, 6, 9 and 24 h after a single administration and 4, 8, 15 and 29 days after repeated administrations. We transferred Open TG-GATEs DNA microarray data into log₂ data using the "R Project for Statistical Computing". GTHC-specific dose-dependent gene expression changes were observed and significance assessed with the Williams test. Similar significant changes were observed during 3-24 h and 4-29 days, assessed with Welch's t-test, except not for NGTHC or NGTNHC. Significant differential changes in gene expression were observed between GTHC and NGTHC in 11 genes (except not Tubb4b) and between GTHC and NGTNHC in all 12 genes at 24 h and 10 genes (except Ccnf and Mbd1) at 29 days, per Tukey's test. PCA successfully discriminated GTHC from NGTHC and NGTNHC at 24 h and 29 days. The results demonstrate that 12 previously proposed mouse marker genes are useful for discriminating rat GTHC from NGTHC and NGTNHC from Open TG-GATEs.

Using RNA-Seq with 11 marker genes to evaluate 1,4-dioxane compared with typical genotoxic and non-genotoxic rat hepatocarcinogens

It has long been unclear whether 1,4-dioxane (DO) is a genotoxic hepatocarcinogen (GTHC). Therefore, the present study aimed to evaluate rat GTHCs and non-genotoxic hepatocarcinogens (NGTHCs) via selected gene expression patterns in the liver, as determined by next generation sequencing-targeted mRNA sequencing (RNA-Seq) and principal component analysis (PCA). Previously, we selected 11 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) to discriminate GTHCs and NGTHCs. In the present study, we quantified changes in the expression of these genes following DO treatment, and compared them with treatment with two typical rat GTHCs, N-nitrosodiethylamine (DEN) and 3,3'-dimethylbenzidine·2HCl (DMB), and a typical rat NGTHC, di(2-ethylhexyl)phthalate (DEHP). RNA-Seq was conducted on liver samples from groups of five male, 10-week-old F344 rats after 4 weeks' feeding of chemicals in the water or the food. Rats in the control group were given water and a basal diet. Significant changes in gene expression in experimental groups compared with the control group were observed in eight genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Phlda3 and Plk2), as shown by Tukey's test. Gene expression profiles of the 11 genes under DO treatment differed significantly from those with DEN and DMB, as well as DEHP. Gene expression profiles with DO treatment differed partially from those with typical GTHCs for five genes (Bax, Btg2, Cdkn1a, Lrp1 and Plk2) and were substantially different from treatment with a typical NGTHC (DEHP) for nine genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Mbd1, Phlda3 and Tubb4b) as determined by Tukey's test. Finally, PCA successfully differentiated GTHCs from DEHP and DO with the 11 genes. The present results suggest that RNA-Seq and PCA are useful to evaluate rat typical GTHCs and typical NGTHCs. DO was suggested to result in a different intermediate gene expression profile from typical GTHCs and NGTHC.

Structural Studies of Predicted Ligand Binding Sites and Molecular Docking Analysis of Slc2a4 as a Therapeutic Target for the Treatment of Cancer

Presently, many studies have focused on exploring in silico approaches in the identification and development of alternative therapy for the treatment and management of cancer. Solute carrier family-2-member-4-gene (Slc2a4) which encodes glucose transporter 4 protein (GLUT4), has been identified as a promising therapeutic target for cancer. Though Slc2a4 is known to play a major regulatory role in the pathophysiology of type 2 diabetes, emerging evidence suggests that successful pharmacological inhibition of this protein may lead to the development of a novel drug candidate for the treatment of cancer. In this study, Slc2a4 protein sequence was retrieved and analysed using in silico approaches, and we identified seven putative antimicrobial peptides (AMPs; RAB1-RAB7) as anti-cancer. The structures of the protein and AMPs were modelled using I-TASSER server, and the overall quality of the Slc2a4 model was validated using PROCHECK. Subsequently, the probable motifs and active site of the protein were forecasted. Also, the molecular interaction between the AMPs and Slc2a4 was ascertained using PatchDock. The result revealed that, all the AMPs are good Slc2a4 inhibitors with RAB1 having the highest binding affinity of 12,392 and binding energy of −39.13 kcal/mol. Hence, this study reveals that all the generated AMPs can serve as therapeutic drug in treating cancer by inhibiting Slc2a4 which is responsible for the production of energy for cancer cells during angiogenesis. This is the first report on AMPs as inhibitors of Slc2a4 for the treatment of cancer.

Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives

INTRODUCTION

Although prostate cancer constitutes one of the most important, death-related diseases in the male population, there is still a need for identification of sensitive biomarkers that could precociously detect the disease and differentiate aggressive from indolent cancers, in order to decrease overtreatment. Proteomics research has improved understanding on mechanisms underlying tumorigenesis, cancer cells migration and invasion potential, and castration resistance. This review has focused on proteomic studies of prostate cancer published in the recent years, with a special emphasis on determination of biomarkers for cancer progression and diagnosis. Areas covered: Shotgun and targeted-proteomic studies of prostate cancer in different matrices are reviewed, i.e., prostate tissue, prostate cell lines, blood (serum and plasma), urine, seminal plasma, and exosomes. The most important biomarkers for cancer diagnosis and aggressiveness characterization are highlighted. Expert commentary: In general, results demonstrate alteration in cell cycle control, DNA repair, proteasomal degradation, and metabolic activity. However, these studies suffer from low reproducibility due to heterogeneity of the cancer itself, as well as to techniques utilized for protein identification/quantification. Downstream confirmatory studies in separate cohorts are warranted in order to demonstrate accuracy of these results.

Molecular Genetic Analysis of Human Endometrial Mesenchymal Stem Cells That Survived Sublethal Heat Shock

High temperature is a critical environmental and personal factor. Although heat shock is a well-studied biological phenomenon, hyperthermia response of stem cells is poorly understood. Previously, we demonstrated that sublethal heat shock induced premature senescence in human endometrial mesenchymal stem cells (eMSC). This study aimed to investigate the fate of eMSC-survived sublethal heat shock (SHS) with special emphasis on their genetic stability and possible malignant transformation using methods of classic and molecular karyotyping, next-generation sequencing, and transcriptome functional analysis. G-banding revealed random chromosome breakages and aneuploidy in the SHS-treated eMSC. Molecular karyotyping found no genomic imbalance in these cells. Gene module and protein interaction network analysis of mRNA sequencing data showed that compared to untreated cells, SHS-survived progeny revealed some difference in gene expression. However, no hallmarks of cancer were found. Our data identified downregulation of oncogenic signaling, upregulation of tumor-suppressing and prosenescence signaling, induction of mismatch, and excision DNA repair. The common feature of heated eMSC is the silence of MYC, AKT1/PKB oncogenes, and hTERT telomerase. Overall, our data indicate that despite genetic instability, SHS-survived eMSC do not undergo transformation. After long-term cultivation, these cells like their unheated counterparts enter replicative senescence and die.