The role of protein interaction networks in systems biomedicine

Molecular docking and antimicrobial activities of photoexcited inhibitors in antimicrobial photodynamic therapy against Enterococcus faecalis biofilms in endodontic infections

Antimicrobial photodynamic therapy (aPDT) is a promising approach to combat antibiotic resistance in endodontic infections. It eliminates residual bacteria from the root canal space and reduces the need for antibiotics. To enhance its effectiveness, an in silico and in vitro study was performed to investigate the potential of targeted aPDT using natural photosensitizers, Kojic acid and Parietin. This approach aims to inhibit the biofilm formation of Enterococcus faecalis, a frequent cause of endodontic infections, by targeting the Ace and Esp proteins. After determining the physicochemical characteristics of Ace and Esp proteins and model quality assessment, the molecular dynamic simulation was performed to recognize the structural variations. The stability and physical movement of the protein-ligand complexes were evaluated. In silico molecular docking was conducted, followed by ADME/Tox profiling, pharmacokinetics characteristics, and assessment of drug-likeness properties of the natural photosensitizers. The study also investigated the changes in the expression of genes (esp and ace) involved in E. faecalis biofilm formation. The results showed that both Kojic acid and Parietin complied with Lipinski's rule of five and exhibited drug-like properties. In silico analysis indicated stable complexes between Ace and Esp proteins and the natural photosensitizers. The molecular docking studies demonstrated good binding affinity. Additionally, the expression of the ace and esp genes was significantly downregulated in aPDT using Kojic acid and Parietin with blue light compared to the control group. This investigation concluded that Kojic acid and Parietin with drug-likeness could efficiently interact with Ace and Esp proteins with a strong binding affinity. Hence, natural photosensitizers-mediated aPDT can be considered a promising adjunctive treatment against endodontic infections.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Combining Chemical Protein Synthesis and Random Nonstandard Peptides Integrated Discovery for Modulating Biological Processes

Chemical manipulation of naturally occurring peptides offers a convenient route for generating analogs to screen against different therapeutic targets. However, the limited success of the conventional chemical libraries has urged chemical biologists to adopt alternative methods such as phage and mRNA displays and create libraries of a large number of variants for the screening and selection of novel peptides. Messenger RNA (mRNA) display provides great advantages in terms of the library size and the straightforward recovery of the selected polypeptide sequences. Importantly, the integration of the flexible in vitro translation (FIT) system with the mRNA display provides the basis of the random nonstandard peptides integrated discovery (RaPID) approach for the introduction of diverse nonstandard motifs, such as unnatural side chains and backbone modifications. This platform allows the discovery of functionalized peptides with tight binding against virtually any protein of interest (POI) and therefore shows great potential in the pharmaceutical industry. However, this method has been limited to targets generated by recombinant expression, excluding its applications to uniquely modified proteins, particularly those with post-translational modifications.Chemical protein synthesis allows a wide range of changes to the protein's chemical composition to be performed, including side chain and backbone modifications and access to post-translationally modified proteins, which are often inaccessible or difficult to achieve via recombinant expression methods. Notably, d-proteins can be prepared via chemical synthesis, which has been used in mirror image phase display for the discovery of nonproteolytic d-peptide binders.Combining chemical protein synthesis with the RaPID system allows the production of a library of trillions of cyclic peptides and subsequent selection for novel cyclic peptide binders targeting a uniquely modified protein to assist in studying its unexplored biology and possibly the discovery of new drug candidates.Interestingly, the small post-translational modifier protein ubiquitin (Ub), with its various polymeric forms, regulates directly or indirectly many biochemical processes, e.g., proteasomal degradation, DNA damage repair, cell cycle regulation, etc. In this Account, we discuss combining the RaPID approach against various synthetic Ub chains for selecting effective and specific macrocyclic peptide binders. This offers an advancement in modulating central Ub pathways and provides opportunities in drug discovery areas associated with Ub signaling. We highlight experimental approaches and conceptual adaptations required to design and modulate the activity of Lys48- and Lys63-linked Ub chains by macrocyclic peptides. We also present the applications of these approaches to shed light on related biological activities and ultimately their activity against cancer. Finally, we contemplate future developments still pending in this exciting multidisciplinary field.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

The neuroprotective effects of estrogen and estrogenic compounds in spinal cord injury

Spinal cord injury (SCI) occurs when the spinal cord is damaged from either a traumatic event or disease. SCI is characterised by multiple injury phases that affect the transmission of sensory and motor signals and lead to temporary or long-term functional deficits. There are few treatments for SCI. Estrogens and estrogenic compounds, however, may effectively mitigate the effects of SCI and therefore represent viable treatment options. This review systematically examines the pre-clinical literature on estrogen and estrogenic compound neuroprotection after SCI. Several estrogens were examined by the included studies: estrogen, estradiol benzoate, Premarin, isopsoralen, genistein, and selective estrogen receptor modulators. Across these pharmacotherapies, we find significant evidence that estrogens indeed offer protection against myriad pathophysiological effects of SCI and lead to improvements in functional outcomes, including locomotion. A STRING functional network analysis of proteins modulated by estrogen after SCI demonstrated that estrogen simultaneously upregulates known neuroprotective pathways, such as HIF-1, and downregulates pro-inflammatory pathways, including IL-17. These findings highlight the strong therapeutic potential of estrogen and estrogenic compounds after SCI.

Deciphering sperm functions using biological networks

The global human population is exponentially increasing, which requires the production of quality food through efficient reproduction as well as sustainable production of livestock. Lack of knowledge and technology for assessing semen quality and predicting bull fertility is hindering advances in animal science and food animal production and causing millions of dollars of economic losses annually. The intent of this systemic review is to summarize methods from computational biology for analysis of gene, metabolite, and protein networks to identify potential markers that can be applied to improve livestock reproduction, with a focus on bull fertility. We provide examples of available gene, metabolic, and protein networks and computational biology methods to show how the interactions between genes, proteins, and metabolites together drive the complex process of spermatogenesis and regulate fertility in animals. We demonstrate the use of the National Center for Biotechnology Information (NCBI) and Ensembl for finding gene sequences, and then use them to create and understand gene, protein and metabolite networks for sperm associated factors to elucidate global cellular processes in sperm. This study highlights the value of mapping complex biological pathways among livestock and potential for conducting studies on promoting livestock improvement for global food security.

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks

The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the-art approaches for protein family classification while being much more general than other architectures. Further, our method outperforms other approaches for protein interaction prediction for two out of three different scenarios that we generated. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.

NetSHy: network summarization via a hybrid approach leveraging topological properties

MOTIVATION

Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information.

RESULTS

In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation.

AVAILABILITY AND IMPLEMENTATION

R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Cellular and molecular level host-pathogen interactions in Francisella tularensis: A microbial gene network study

Due to the high infectivity and fatal effect on human population, Francisella tularensis (F. tularensis) is classified as a potential biological warfare agent. The interaction between host and pathogen behind the successful establishment of F. tularensis infection within the human host is largely unknown. In our present work, we have studied the molecular level interactions between the host cellular components and F. tularensis genes to understand the interplay between the host and pathogen. Interestingly, we have identified the pathways associated with the pathogen offensive strategies that help in invasion of host defensive systems. The F. tularensis genes purL, katG, proS, rpoB and fusA have displayed high number of interactions with the host genes and thus play a crucial role in vital pathogen pathways. The pathways identified were involved in adaptation to different stress conditions within the host and might be crucial for designing new therapeutic interventions against tularemia.

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

A glimpse on the architecture of hnRNP C1/C2 interaction network in cultured oligodendrocytes

hnRNP represent a large family of RNA-binding proteins related to regulation of transcriptional and translational processes. More specifically, hnRNPs play pivotal roles in the myelination of the central nervous system. The regulation of these proteins are associated with neurodegenerative and psychiatric disorders, including schizophrenia. hnRNPs were shown differentially regulated on schizophrenia postmortem brain tissue as well as in cultured oligodendrocytes treated with clozapine, a common antipsychotic used in schizophrenia treatment. Here we employed co-immunoprecipitation of hnRNP C1/C2 to investigate for the first time in a large-scale manner its interaction partners on cultured oligodendrocytes (MO3.13). Even preliminarily, results bring a more comprehensive description of hnRNP C1/C2 interaction network, and therefore insights regarding the potential role of this protein in the central nervous system in health and disease, warranting further investigation.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

Retinoschisis and Norrie disease: a missing link

OBJECTIVE

Retinoschisis and Norrie disease are X-linked recessive retinal disorders caused by mutations in RS1 and NDP genes respectively. Both are likely to be monogenic and no locus heterogeneity has been reported. However, there are reports showing overlapping features of Norrie disease and retinoschisis in a NDP knock-out mouse model and also the involvement of both the genes in retinoschisis patients. Yet, the exact molecular relationships between the two disorders have still not been understood. The study investigated the association between retinoschisin (RS1) and norrin (NDP) using in vitro and in silico approaches. Specific protein-protein interaction between RS1 and NDP was analyzed in human retina by co-immunoprecipitation assay and MALDI-TOF mass spectrometry. STRING database was used to explore the functional relationship.

RESULT

Co-immunoprecipitation demonstrated lack of a direct interaction between RS1 and NDP and was further substantiated by mass spectrometry. However, STRING revealed a potential indirect functional association between the two proteins. Progressively, our analyses indicate that FZD4 protein interactome via PLIN2 as well as the MAP kinase signaling pathway to be a likely link bridging the functional relationship between retinoschisis and Norrie disease.

Evolution of biophysical tools for quantitative protein interactions and drug discovery

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer

Tumor stroma interaction is known to take a crucial role in cancer growth and progression. In the present study, it was performed gene expression analysis of stroma samples with ovarian and breast cancer through an integrative analysis framework to identify common critical biomolecules at multiomics levels. Gene expression datasets were statistically analyzed to identify common differentially expressed genes (DEGs) by comparing tumor stroma and normal stroma samples. The integrative analyses of DEGs indicated that there were 59 common core genes, which might be feasible to be potential marks for cancer stroma targeted strategies. Reporter molecules (i.e. receptor, transcription factors and miRNAs) were determined through a statistical test employing the hypergeometric probability density function. Afterward, the tumor microenvironment protein-protein interaction and the generic network were reconstructed by using identified reporter molecules and common core DEGs. Through a systems medicine approach, it was determined that hub biomolecules, AR, GATA2, miR-124, TOR1AIP1, ESR1, EGFR, STAT1, miR-192, GATA3, COL1A1, in tumor microenvironment generic network. These molecules were also identified as prognostic signatures in breast and ovarian tumor samples via survival analysis. According to literature searching, GATA2 and TORYAIP1 might represent potential biomarkers and candidate drug targets for the stroma targeted cancer therapy applications.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Background

Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states.

Results

In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis.

Conclusions

The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

Supplementary Information

The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2).

From classical to new generation approaches: An excursus of -omics methods for investigation of protein-protein interaction networks

Functional Proteomics aims to the identification of in vivo protein-protein interaction (PPI) in order to piece together protein complexes, and therefore, cell pathways involved in biological processes of interest. Over the years, proteomic approaches used for protein-protein interaction investigation have relied on classical biochemical protocols adapted to a global overview of protein-protein interactions, within so-called "interactomics" investigation. In particular, their coupling with advanced mass spectrometry instruments and innovative analytical methods led to make great strides in the PPIs investigation in proteomics. In this review, an overview of protein complexes purification strategies, from affinity purification approaches, including proximity-dependent labeling techniques and cross-linking strategy for the identification of transient interactions, to Blue Native Gel Electrophoresis (BN-PAGE) and Size Exclusion Chromatography (SEC) employed in the "complexome profiling", has been reported, giving a look to their developments, strengths and weakness and providing to readers several recent applications of each strategy. Moreover, a section dedicated to bioinformatic databases and platforms employed for protein networks analyses was also included.

NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information

BACKGROUND

Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition.

RESULTS

In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors.

CONCLUSIONS

NAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds.

REVIEWERS

This article was reviewed by Zoltán Hegedüs and Endre Barta.

Signatures of co-deregulated genes and their transcriptional regulators in colorectal cancer

The deregulated genes in colorectal cancer (CRC) vary significantly across different studies. Thus, a systems biology approach is needed to identify the co-deregulated genes (co-DEGs), explore their molecular networks, and spot the major hub proteins within these networks. We reanalyzed 19 GEO gene expression profiles to identify and annotate CRC versus normal signatures, single-gene perturbation, and single-drug perturbation signatures. We identified the co-DEGs across different studies, their upstream regulating kinases and transcription factors (TFs). Connectivity Map was used to identify likely repurposing drugs against CRC within each group. The functional changes of the co-upregulated genes in the first category were mainly associated with negative regulation of transforming growth factor β production and glomerular epithelial cell differentiation; whereas the co-downregulated genes were enriched in cotranslational protein targeting to the membrane. We identified 17 hub proteins across the co-upregulated genes and 18 hub proteins across the co-downregulated genes, composed of well-known TFs (MYC, TCF3, PML) and kinases (CSNK2A1, CDK1/4, MAPK14), and validated most of them using GEPIA2 and HPA, but also through two signature gene lists composed of the co-up and co-downregulated genes. We further identified a list of repurposing drugs that can potentially target the co-DEGs in CRC, including camptothecin, neostigmine bromide, emetine, remoxipride, cephaeline, thioridazine, and omeprazole. Similar analyses were performed in the co-DEG signatures in single-gene or drug perturbation experiments in CRC. MYC, PML, CDKs, CSNK2A1, and MAPKs were common hub proteins among all studies. Overall, we identified the critical genes in CRC and we propose repurposing drugs that could be used against them.

Information Theory in Computational Biology: Where We Stand Today

"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

A Genetic Analysis of Tumor Progression in Drosophila Identifies the Cohesin Complex as a Suppressor of Individual and Collective Cell Invasion

Metastasis is the leading cause of death for patients with cancer. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumor growth toward malignancy. Advances in genome characterization technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. However, the difficulty in evaluating whether these candidates drive tumor progression remains a major challenge. Using the genetic amenability of Drosophila melanogaster we generated tumors with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify conserved genes that enhance or suppress epithelial tumor progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved invasion suppressors. This includes constituents of the cohesin complex, whose loss of function either promotes individual or collective cell invasion, depending on the severity of effect on cohesin complex function.

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Screen identifies genes that affect tumor behavior in a wide variety of ways

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A functionally validated network of invasion-suppressor genes was generated

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Loss of cohesin complex function can promote individual or collective cell invasion

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The fly pupal notum is an excellent in vivo system to study tumor progression

Gene Prioritization in Parkinson's Disease Using Human Protein-Protein Interaction Network

Parkinson's disease (PD) is the second-most common neurodegenerative disorder, and the actual cause of this disease is still unknown. Identifying the target genes that are associated with disease plays an essential role in the treatment of PD. Various genetic studies have determined the significant target genes for disease progression, although this continues to be challenging in the field of drug designing. In this study, we proposed a network-based approach to identify target genes for PD using gene mutation, gene expression, and gene deletion analysis. The subnetwork of PD genes was constructed from human protein-protein interaction network, and the potential genes were identified using network centrality measures. Two genes, PARK1 and PARK2, were identified as target genes by integrating gene mutation and expression data into the subnetwork. Gene deletion analysis was carried out to determine the significant target, and results revealed that VDAC1 and ATP5C1 genes were crucial for the Parkinson's subnetwork. Thus, findings from the network-based approach will provide additional insight for understanding the disease mechanism of PD. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing.

In-depth proteome analysis of more than 12,500 proteins in buffalo mammary epithelial cell line identifies protein signatures for active proliferation and lactation

The mature mammary gland is made up of a network of ducts that terminates in alveoli. The innermost layer of alveoli is surrounded by the differentiated mammary epithelial cells (MECs), which are responsible for milk synthesis and secretion during lactation. However, the MECs are in a state of active proliferation during pregnancy, when they give rise to network like structures in the mammary gland. Buffalo (Bubalus bubalis) constitute a major source of milk for human consumption, and the MECs are the major precursor cells which are mainly responsible for their lactation potential. The proteome of MECs defines their functional state and suggests their role in various cellular activities such as proliferation and lactation. To date, the proteome profile of MECs from buffalo origin is not available. In the present study, we have profiled in-depth proteome of in vitro cultured buffalo MECs (BuMECs) during active proliferation using high throughput tandem mass spectrometry (MS). MS analysis identified a total of 8330, 5970, 5289, 4818 proteins in four sub-cellular fractions (SCFs) that included cytosolic (SCF-I), membranous and membranous organelle’s (SCF-II), nuclear (SCF-III), and cytoskeletal (SCF-IV). However, 792 proteins were identified in the conditioned media, which represented the secretome. Altogether, combined analysis of all the five fractions (SCFs- I to IV, and secretome) revealed a total of 12,609 non-redundant proteins. The KEGG analysis suggested that these proteins were associated with 325 molecular pathways. Some of the highly enriched molecular pathways observed were metabolic, MAPK, PI3-AKT, insulin, estrogen, and cGMP-PKG signalling pathway. The newly identified proteins in this study are reported to be involved in NOTCH signalling, transport and secretion processes.

Pan-cancer mapping of differential protein-protein interactions

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

High Confidence Identification of Cross-Linked Peptides by an Enrichment-Based Dual Cleavable Cross-Linking Technology and Data Analysis tool Cleave-XL

Cleavable cross-linking technology requires further MS/MS of the cleavable fragments for unambiguous identification of cross-linked peptides. These spectra are sometimes very ambiguous due to the sensitivity and complex fragmentation pattern of the peptides with the cross-linked residues. We recently reported a dual cleavable cross-linking technology (DUCCT), which can enhance the confidence in the identification of cross-linked peptides. The heart of this strategy is a novel dual mass spectrometry cleavable cross linker that can be cleaved preferentially by two differential tandem mass spectrometry methods, collision induced dissociation and electron transfer dissociation (CID and ETD). Different signature ions from two different mass spectra for the same cross-linked peptide helped identify the cross-linked peptides with high confidence. In this study, we developed an enrichment-based photocleavable DUCCT (PC-DUCCT-biotin), where cross-linked products were enriched from biological samples using affinity purification, and subsequently, two sequential tandem (CID and ETD) mass spectrometry processes were utilized. Furthermore, we developed a prototype software called Cleave-XL to analyze cross-linked products generated by DUCCT. Photocleavable DUCCT was demonstrated in standard peptides and proteins. Efficiency of the software tools to search and compare CID and ETD data of photocleavable DUCCT biotin in standard peptides and proteins as well as regular DUCCT in protein complexes from immune cells were tested. The software is efficient in pinpointing cross-linked sites using CID and ETD cross-linking data. We believe this new DUCCT and associated software tool Cleave-XL will advance high confidence identification of protein cross-linking sites and automated identification of low-resolution protein structures.

The role of hippocampal estradiol in synaptic plasticity and memory: A systematic review

The consolidation of long-term memory is influenced by various neuromodulators. One of these is estradiol, a steroid hormone that is synthesized both in peripheral endocrine tissue and in the brain, including the hippocampus. Here, we examine the evidence regarding the role of estradiol in the hippocampus, specifically, in memory formation and its effects on the molecular mechanisms underlying synaptic plasticity. We conclude that estradiol improves memory consolidation and, thereby, long-term memory. Previous studies have shown that it does this in three, interconnected ways: (1) via functional changes in excitatory activity, (2) signaling changes in calcium dynamics, protein phosphorylation and protein expression, and (3) structural changes to synaptic morphology. Through a functional network analysis of proteins affected by estradiol, we identify potential protein-protein interactions that further support a role for estradiol in modulating synaptic plasticity as well as highlight signaling pathways that may be involved in these changes within the hippocampus.

DCARS: differential correlation across ranked samples

MOTIVATION

Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest.

RESULTS

When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data.

AVAILABILITY AND IMPLEMENTATION

DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

METHODS

Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

RESULTS

The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

CONCLUSIONS

These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user-specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).

AmyCo: the amyloidoses collection

Amyloid fibrils are formed when soluble proteins misfold into highly ordered insoluble fibrillar aggregates and affect various organs and tissues. The deposition of amyloid fibrils is the main hallmark of a group of disorders, called amyloidoses. Curiously, fibril deposition has been also recorded as a complication in a number of other pathological conditions, including well-known neurodegenerative or endocrine diseases. To date, amyloidoses are roughly classified, owing to their tremendous heterogeneity. In this work, we introduce AmyCo, a freely available collection of amyloidoses and clinical disorders related to amyloid deposition. AmyCo classifies 75 diseases associated with amyloid deposition into two distinct categories, namely 1) amyloidosis and 2) clinical conditions associated with amyloidosis. Each database entry is annotated with the major protein component (causative protein), other components of amyloid deposits and affected tissues or organs. Database entries are also supplemented with appropriate detailed annotation and are referenced to ICD-10, MeSH, OMIM, PubMed, AmyPro and UniProtKB databases. To our knowledge, AmyCo is the first attempt towards the creation of a complete and an up-to-date repository, containing information about amyloidoses and diseases related to amyloid deposition. The AmyCo web interface is available at http://bioinformatics.biol.uoa.gr/amyco .

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

Caenorhabditis elegans hub genes that respond to amyloid beta are homologs of genes involved in human Alzheimer's disease

The prominent characteristic of Alzheimer’s disease (AD) is the accumulation of amyloid beta (Abeta) proteins in the form of plaques that cause molecular and cellular alterations in the brain. Due to the paucity of brain samples of early-stage Abeta aggregation, animal models have been developed to study early events in AD. Caenorhabditis elegans is a genetically tractable animal model for AD. Here, we used transcriptomic data, network-based protein-protein interactions and weighted gene co-expression network analysis (WGCNA), to detect modules and their gene ontology in response to Abeta aggregation in C. elegans. Additionally, hub genes and their orthologues in human and mouse were identified to study their relation to AD. We also found several transcription factors (TFs) responding to Abeta accumulation. Our results show that Abeta expression in C. elegans relates to general processes such as molting cycle, locomotion, and larval development plus AD-associated processes, including protein phosphorylation, and G-protein coupled receptor-regulated pathways. We reveal that many hub genes and TFs including ttbk-2, daf-16, and unc-49 have human and mouse orthologues that are directly or potentially associated with AD and neural development. In conclusion, using systems biology we identified important genes and biological processes in C. elegans that respond to Abeta aggregation, which could be used as potential diagnostic or therapeutic targets. In addition, because of evolutionary relationship to AD in human, we suggest that C. elegans is a useful model for studying early molecular events in AD.

Genome-wide functional association networks: background, data & state-of-the-art resources

The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.

Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network

Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.

Drug Targeting and Biomarkers in Head and Neck Cancers: Insights from Systems Biology Analyses

The head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, but robust biomarkers and diagnostics are still not available. This study provides in-depth insights from systems biology analyses to identify molecular biomarker signatures to inform systematic drug targeting in HNSCC. Gene expression profiles from tumors and normal tissues of 22 patients with histological confirmation of nonmetastatic HNSCC were subjected to integrative analyses with genome-scale biomolecular networks (i.e., protein-protein interaction and transcriptional and post-transcriptional regulatory networks). We aimed to discover molecular signatures at RNA and protein levels, which could serve as potential drug targets for therapeutic innovation in the future. Eleven proteins, 5 transcription factors, and 20 microRNAs (miRNAs) came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq datasets, and risk discrimination performance of the reporter biomolecules, BLNK, CCL2, E4F1, FOSL1, ISG15, MMP9, MYCN, MYH11, miR-1252, miR-29b, miR-29c, miR-3610, miR-431, and miR-523, was also evaluated. Using the transcriptome guided drug repositioning tool, geneXpharma, several candidate drugs were repurposed, including antineoplastic agents (e.g., gemcitabine and irinotecan), antidiabetics (e.g., rosiglitazone), dermatological agents (e.g., clocortolone and acitretin), and antipsychotics (e.g., risperidone), and binding affinities of the drugs to their potential targets were assessed using molecular docking analyses. The molecular signatures and repurposed drugs presented in this study warrant further attention for experimental studies since they offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of HNSCC.