Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Hippocampal tandem mass tag (TMT) proteomics analysis during kindling epileptogenesis in rat

Epilepsy is a neurological disorder that remains difficult to treat due to the lack of a clear molecular mechanism and incomplete understanding of involved proteins. To identify potential therapeutic targets, it is important to gain insight into changes in protein expression patterns related to epileptogenesis. One promising approach is to analyze proteomic data, which can provide valuable information about these changes. In this study, to evaluate the changes in gene expression during epileptogenesis, LC-MC2 analysis was carried out on hippocampus during stages of electrical kindling in rat models. Subsequently, progressive changes in the expression of proteins were detected as a result of epileptogenesis development. In line with behavioral kindled seizure stages and according to the proteomics data, we described epileptogenesis phases by comparing Stage3 versus Control (S3/C0), Stage5 versus Stage3 (S5/S3), and Stage5 versus Control group (S5/C0). Gene ontology analysis on differentially expressed proteins (DEPs) showed significant changes of proteins involved in immune responses like Csf1R, Aif1 and Stat1 during S3/C0, regulation of synaptic plasticity like Bdnf, Rac1, CaMK, Cdc42 and P38 during S5/S3, and nervous system development throughout S5/C0 like Bdnd, Kcc2 and Slc1a3.There were also proteins like Cox2, which were altered commonly among all three phases. The pathway enrichment analysis of DEPs was also done to discover molecular connections between phases and we have found that the targets like Csf1R, Bdnf and Cox2 were analyzed throughout all three phases were highly involved in the PPI network analysis as hub nodes. Additionally, these same targets underwent changes which were confirmed through Western blotting. Our results have identified proteomic patterns that could shed light on the molecular mechanisms underlying epileptogenesis which may allow for novel targeted therapeutic strategies.

Data-driven networking of global transcriptomics and male sexual development in the main malaria vector, Anopheles funestus

Deaths from malaria remain staggering despite global support that drives research into new territories. One major gap is our understanding of the sexual biological aspects of the male mosquito, which maintain the vector population solidity. Although Anopheles funestus s.s. is an extremely efficient African vector, little is known about the network between its sexual physiology and gene expression. The Culicidae male's sexual maturity involves a suite of physiological changes, such as genitalia rotation that is necessary for successful mating to occur. We show that mating success is guided by genes and physiological plasticity. Transcriptome analysis between newly emerged males (immature) versus males with rotating genitalia (maturing) provides insight into possible molecular mechanisms regulating male sexual behaviour. Putative transcripts that were associated with male sexual maturation were identified and validated. The discovery of the functions of these transcripts could lead to identifying potential targets for innovative vector control interventions, and mosquito population suppression.

Extracting Potential New Targets for Treatment of Adenoid Cystic Carcinoma using Bioinformatic Methods

Background

Adenoid cystic carcinoma is a slow-growing malignancy that most often occurs in the salivary glands. Currently, no FDA-approved therapeutic target or diagnostic biomarker has been identified for this cancer. The aim of this study was to find new therapeutic and diagnostic targets using bioinformatics methods.

Methods

We extracted the gene expression information from two GEO datasets (including GSE59701 and GSE88804). Different expression genes between adenoid cystic carcinoma (ACC) and normal samples were extracted using R software. The biochemical pathways involved in ACC were obtained by using the Enrichr database. PPI network was drawn by STRING, and important genes were extracted by Cytoscape. Real-time PCR and immunohistochemistry were used for biomarker verification.

Results

After analyzing the PPI network, 20 hub genes were introduced to have potential as diagnostic and therapeutic targets. Among these genes, PLCG1 was presented as new biomarker in ACC. Furthermore, by studying the function of the hub genes in the enriched biochemical pathways, we found that insulin-like growth factor type 1 receptor and PPARG pathways most likely play a critical role in tumorigenesis and drug resistance in ACC and have a high potential for selection as therapeutic targets in future studies.

Conclusion

In this study, we achieved the recognition of the pathways involving in ACC pathogenesis and also found potential targets for treatment and diagnosis of ACC. Further experimental studies are required to confirm the results of this study.

Extracting Potential New Targets for Treatment of Adenoid Cystic Carcinoma using Bioinformatic Methods

Background

Adenoid cystic carcinoma is a slow-growing malignancy that most often occurs in the salivary glands. Currently, no FDA-approved therapeutic target or diagnostic biomarker has been identified for this cancer. The aim of this study was to find new therapeutic and diagnostic targets using bioinformatics methods.

Methods

We extracted the gene expression information from two GEO datasets (including GSE59701 and GSE88804). Different expression genes between adenoid cystic carcinoma (ACC) and normal samples were extracted using R software. The biochemical pathways involved in ACC were obtained by using the Enrichr database. PPI network was drawn by STRING, and important genes were extracted by Cytoscape. Real-time PCR and immunohistochemistry were used for biomarker verification.

Results

After analyzing the PPI network, 20 hub genes were introduced to have potential as diagnostic and therapeutic targets. Among these genes, PLCG1 was presented as new biomarker in ACC. Furthermore, by studying the function of the hub genes in the enriched biochemical pathways, we found that insulin-like growth factor type 1 receptor and PPARG pathways most likely play a critical role in tumorigenesis and drug resistance in ACC and have a high potential for selection as therapeutic targets in future studies.

Conclusion

In this study, we achieved the recognition of the pathways involving in ACC pathogenesis and also found potential targets for treatment and diagnosis of ACC. Further experimental studies are required to confirm the results of this study.

Role of Surface Electrocardiography in Differentiation between Obstructive and Non-Obstructive Hypertrophic Cardiomyopathy

Background

Electrocardiography (ECG), as an easily accessible modality, is usually helpful in hypertrophic cardiomyopathy (HCM) diagnosis. The purpose of this study was to evaluate the role of ECG in differentiating between obstructive (OHCM) and non-obstructive (NOHCM) HCM.

Methods

The present study is a cross-sectional analysis of HCM patients referred to our center between 2008 and 2017. The study variables included age, sex, clinical presentation, medications, and ECG characteristics including PR interval, QRS width, QTc duration, Tpeak-Tend interval, QRS axis, QRS transition, ventricular hypertrophies, atrial abnormalities, ST-T abnormalities, and abnormal Q waves.

Results

The HCM sample consisted of 200 patients (55% males; age 45.60±15.50 y) from our HCM database. We compared the clinical and ECG characteristics of 143 NOHCM patients with those of 57 OHCM patients. The OHCM group was significantly younger than the NOHCM group (age =41.7 vs 47.0 y; P=0.016). The initial clinical presentation was similar between the 2 forms (P>0.05), and palpitations were the dominant symptom. Baseline ECG intervals, including PR (155.6 vs 157.9 ms), QRS (82.5 vs 82.0 ms), and QTc (430.5 vs 433.0 ms), were similar (all Ps>0.050). There were no differences regarding baseline rhythm, atrial abnormalities, QRS transition, ventricular hypertrophies, axis changes, ST-T changes, and abnormal Q waves between the HCM groups (all Ps>0.05).

Conclusion

The present study showed that standard 12-lead ECG had no role in distinguishing patients with the obstructive and non-obstructive forms of HCM.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

Nkx3-1 and Fech genes might be switch genes involved in pituitary non-functioning adenoma invasiveness

Non-functioning pituitary adenomas (NFPAs) are typical pituitary macroadenomas in adults associated with increased mortality and morbidity. Although pituitary adenomas are commonly considered slow-growing benign brain tumors, numerous of them possess an invasive nature. Such tumors destroy sella turcica and invade the adjacent tissues such as the cavernous sinus and sphenoid sinus. In these cases, the most critical obstacle for complete surgical removal is the high risk of damaging adjacent vital structures. Therefore, the development of novel therapeutic strategies for either early diagnosis through biomarkers or medical therapies to reduce the recurrence rate of NFPAs is imperative. Identification of gene interactions has paved the way for decoding complex molecular mechanisms, including disease-related pathways, and identifying the most momentous genes involved in a specific disease. Currently, our knowledge of the invasion of the pituitary adenoma at the molecular level is not sufficient. The current study aimed to identify critical biomarkers and biological pathways associated with invasiveness in the NFPAs using a three-way interaction model for the first time. In the current study, the Liquid association method was applied to capture the statistically significant triplets involved in NFPAs invasiveness. Subsequently, Random Forest analysis was applied to select the most important switch genes. Finally, gene set enrichment (GSE) and gene regulatory network (GRN) analyses were applied to trace the biological relevance of the statistically significant triplets. The results of this study suggest that "mRNA processing" and "spindle organization" biological processes are important in NFAPs invasiveness. Specifically, our results suggest Nkx3-1 and Fech as two switch genes in NFAPs invasiveness that may be potential biomarkers or target genes in this pathology.

Metabolomic signature of amino acids in plasma of patients with non-segmental Vitiligo

INTRODUCTION

Vitiligo pathogenesis is complicated, and several possibilities were suggested. However, it is well-known that the metabolism of pigments plays a significant role in the pathogenicity of the disease.

OBJECTIVES

We explored the role of amino acids in vitiligo using targeted metabolomics.

METHODS

The amino acid profile was studied in plasma using liquid chromatography. First, 22 amino acids were derivatized and precisely determined. Next, the concentrations of the amino acids and the molar ratios were calculated in 31 patients and 34 healthy individuals.

RESULTS

The differential concentrations of amino acids were analyzed and eight amino acids, i.e., cysteine, arginine, lysine, ornithine, proline, glutamic acid, histidine, and glycine were observed differentially. The ratios of cysteine, glutamic acid, and proline increased significantly in Vitiligo patients, whereas arginine, lysine, ornithine, glycine, and histidine decreased significantly compared to healthy individuals. Considering the percentage of skin area, we also showed that glutamic acid significantly has a higher amount in patients with less than 25% involvement compared to others. Finally, cysteine and lysine are considered promising candidates for diagnosing and developing the disorder with high accuracy (0.96).

CONCLUSION

The findings are consistent with the previously illustrated mechanism of Vitiligo, such as production deficiency in melanin and an increase in immune activity and oxidative stress. Furthermore, new evidence was provided by using amino acids profile toward the pathogenicity of the disorder.

Serum metabolomics study of women with different annual decline rates of anti-Müllerian hormone: an untargeted gas chromatography-mass spectrometry-based study

STUDY QUESTION

Which metabolites are associated with varying rates of ovarian aging, measured as annual decline rates of anti-Müllerian hormone (AMH) concentrations?

SUMMARY ANSWER

Higher serum concentrations of metabolites of phosphate, N-acetyl-d-glucosamine, branched chained amino acids (BCAAs), proline, urea and pyroglutamic acid were associated with higher odds of fast annual decline rate of AMH.

WHAT IS KNOWN ALREADY

Age-related rate of ovarian follicular loss varies among women, and the factors underlying such inter-individual variations are mainly unknown. The rate of ovarian aging is clinically important due to its effects on both reproduction and health of women. Metabolomics, a global investigation of metabolites in biological samples, provides an opportunity to study metabolites or metabolic pathways in relation to a physiological/pathophysiological condition. To date, no metabolomics study has been conducted regarding the differences in the rates of ovarian follicular loss.

STUDY DESIGN, SIZE, DURATION

This prospective study was conducted on 186 reproductive-aged women with regular menstrual cycles and history of natural fertility, randomly selected using random case selection option in SPSS from the Tehran Lipid and Glucose Study.

PARTICIPANTS/MATERIALS, SETTING, METHODS

AMH concentrations were measured at baseline (1999-2001) and the fifth follow-up examination (2014-2017), after a median follow-up of 16 years, by immunoassay using Gen II kit. The annual decline rate of AMH was calculated by dividing the AMH decline rate by the follow-up duration (percent/year). The women were categorized based on the tertiles of the annual decline rates. Untargeted metabolomics analysis of the fasting-serum samples collected during the second follow-up examination cycle (2005-2008) was performed using gas chromatography-mass spectrometry. A combination of univariate and multivariate approaches was used to investigate the associations between metabolites and the annual decline rates of AMH.

MAIN RESULTS AND THE ROLE OF CHANCE

After adjusting the baseline values of age, AMH and BMI, 29 metabolites were positively correlated with the annual AMH decline rates. The comparisons among the tertiles of the annual decline rate of AMH revealed an increase in the relative abundance of 15 metabolites in the women with a fast decline (tertile 3), compared to those with a slow decline (tertile 1). There was no distinct separation between women with slow and fast decline rates while considering 41 metabolites simultaneously using the principal component analysis and the partial least-squares discriminant analysis models. The odds of fast AMH decline was increased with higher serum metabolites of phosphate, N-acetyl-d-glucosamine, BCAAs, proline, urea and pyroglutamic acid. Amino sugar and nucleotide sugar metabolism, BCAAs metabolism and aminoacyl tRNA biosynthesis were among the most significant pathways associated with the fast decline rate of AMH.

LIMITATIONS, REASONS FOR CAUTION

Estimating the annual decline rates of AMH using the only two measures of AMH is the main limitation of the study which assumes a linear fixed reduction in AMH during the study. Since using the two-time points did not account for the variability in the decline rate of AMH, the annual decline rates estimated in this study may not accurately show the trend of the reduction in AMH. In addition, despite the longitudinal nature of the study and statistical adjustment of the participants' ages, it is difficult to distinguish the AMH-related metabolites observed in this study can accelerate ovarian aging or they are reflections of different rates of the aging process.

WIDER IMPLICATIONS OF THE FINDINGS

Some metabolite features related to the decline rates of AMH have been suggested in this study; further prospective studies with multiple measurements of AMH are needed to confirm the findings of this study and to better understand the molecular process underlying variations in ovarian aging.

STUDY FUNDING/COMPETING INTEREST(S)

This study, as a part of PhD thesis of Ms Nazanin Moslehi, was supported by Shahid Beheshti University of Medical Sciences (10522-4). There were no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

IrGO: Iranian traditional medicine General Ontology and knowledge base

Background

Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.

Construction and content

Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry.

Utility and discussion

The ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica.

Conclusion

We constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.

Proteomics Analysis of Trastuzumab Toxicity in the H9c2 Cardiomyoblast Cell Line and its Inhibition by Carvedilol

OBJECTIVE

Heart dysfunctions are the major complications of trastuzumab in patients with Human Epidermal growth factor Receptor-2 (HER2)-positive breast cancers.

METHODS

In this study, the cytotoxicity of trastuzumab on H9c2 cardiomyoblasts was demonstrated, and the proteome changes of cells were investigated by a tandem mass tagging quantitative approach. The Differentially Abundant Proteins (DAPs) were identified and functionally enriched.

RESULTS

We determined that carvedilol, a non-selective beta-blocker, could effectively inhibit trastuzumab toxicity when administrated in a proper dose and at the same time. The proteomics analysis of carvedilol co-treated cardiomyoblasts showed complete or partial reversion in expressional levels of trastuzumab-induced DAPs.

CONCLUSION

Downregulation of proteins involved in the translation biological process is one of the most important changes induced by trastuzumab and reversed by carvedilol. These findings provide novel insights to develop new strategies for the cardiotoxicity of trastuzumab.

Optimal control and differential game solutions for social distancing in response to epidemics of infectious diseases on networks

In this paper, the problem of social distancing in the spread of infectious diseases in the human network is extended by optimal control and differential game approaches. Hear, SEAIR model on simulation network is used. Total costs for both approaches are formulated as objective functions. SEAIR dynamics for group k that contacts with k individuals including susceptible, exposed, asymptomatically infected, symptomatically infected and improved or safe individuals is modeled. A novel random model including the concept of social distancing and relative risk of infection using Markov process is proposed. For each group, an aggregate investment is derived and computed using adjoint equations and maximum principle. Results show that for each group, investments in the differential game are less than investments in an optimal control approach. Although individuals' participation in investment for social distancing causes to reduce the epidemic cost, the epidemic cost according to the second approach is too much less than the first approach.

Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory

Background and Purpose

Recently, many researchers from different fields of science have been used networks to analyze complex relational big data. The identification of which nodes are more important than the others, known as centrality analysis, is a key issue in biological network analysis. Although, several centralities have been introduced degree, closeness, and betweenness centralities are the most popular. These centralities are based on the individual position of each node and the cooperation and synergies between nodes have been ignored.

Objectives

Since in many cases, the network function is a consequence of cooperation and interaction between nodes, classical centralities were extended to a group of nodes instead of only individual nodes using cooperative game theory concepts. In this study, we analyze the protein interaction network inferred in rabies disease and rank gene products based on group centrality measurements to identify the novel gene candidates.

Materials and Methods

For this purpose, we used a game-theoretic approach at three scenarios, where the power of a coalition of genes assessed using different criteria including the neighbors of genes in the network, and predefined importance of the genes in its neighborhood. The Shapley value of such a game was considered as a new centrality. In this study, we analyze the network of gene products implicates rabies. The network has 1059 nodes and 8844 edges and centrality analysis was performed using CINNA package in R software.

Results

Based on three scenarios, we selected genes among the highest Shapley value that had low ranking from classical centralities. The enrichment analysis among the selected genes in scenario 1 indicates important pathways in rabies pathogenesis. Pair-wise correlation analysis reveals that changing the weights of nodes at different scenarios can significantly affect the results of ranking genes in the network.

Conclusion

A prior knowledge about the disease and the topology of the network, enable us to design an appropriate game and consequently infer some biological important nodes (genes) in the network. Obviously, a single centrality cannot capture all significant features embedded in the network.

Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

UNaProd: A Universal Natural Product Database for Materia Medica of Iranian Traditional Medicine

BACKGROUND

Iranian traditional medicine (ITM) is a holistic medical system that uses a wide range of medicinal substances to treat disease. Reorganization and standardization of the data on ITM concepts is a necessity for optimal use of this rich source. In an initial step towards this goal, we created a database of ITM materia medica. Main Body. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, a database of natural medicinal substances was created using both text mining methods and manual editing. UNaProd, a Universal Natural Product database for materia medica of ITM, is currently host to 2696 monographs, from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. Currently, systems biology, and more precisely systems medicine and pharmacology, can be an aid in providing rationalizations for many traditional medicines and elucidating a great deal of knowledge they can offer to guide future research in medicine.

CONCLUSIONS

A database of materia medica is a stepping stone in creating a systems pharmacology platform of ITM that encompasses the relationships between the drugs, their targets, and diseases. UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features, respectively, and it is freely available at http://jafarilab.com/unaprod/.

CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis

MOTIVATION

Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods.

METHODS

The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance.

AVAILABILITY AND IMPLEMENTATION

CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

The Effect of ACE Inhibitor on the Quality of Life amongst Patients with Cancer Cachexia

Background:

ACEI (Angiotensin Converting Enzyme Inhibitors) inhibits tumor growth and development. Different mechanisms have been proposed for this matter, including the inhibition of enzymes that are involved in extracellular matrix degradation, matrix metalloproteinase (MMP) and etc. The present study was designed with the aim to investigate the effects of low dose ACEI on the Quality of Life (QoL) of non-hospitalized gastric cancer patients with cachexia.

Materials and Methods:

This study was a single-blinded randomized controlled clinical trial conducted in clinics affiliated with Shiraz University of Medical Sciences (SUMS). All participants were patients with gastric cancer in cancer cachexia step aged 40-80 years old who had referred to our clinics from October 2013 to April 2014. In the intervention group, patients were assigned to receive ACEI (Captopril) and the placebo group served as control and received placebo during the same time course. They were asked questions in order to fill out QLQ-C30 (Persian Version) questionnaire 3 times; baseline, 1 and 2 months after their first visit.

Results:

The mean age of patients was 60.55 ± 12.07 (range 31-80) years and the mean BMI of the patients was 17.21 ± 2.31. In the ACEI group, physical functioning and fatigue score changes were significant 1 and 2 months after treatment. The mean of fatigue score decreased significantly in the placebo group. Overall, global health status scores significantly increased in both groups, but other items of QoL did not change significantly.

Conclusion:

Overall, our results showed that Captopril does not have a significant positive effect on QoL of patients with cancer cachexia.

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

BACKGROUND

Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP.

RESULTS

High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12).

CONCLUSIONS

High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

A mathematical model of honey bee colony dynamics to predict the effect of pollen on colony failure

The decline in colony populations of the honey bee, known as the Colony Collapse Disorder (CCD), is a global concern. Numerous studies have reported possible causes, including pesticides, parasites, and nutritional stress. Poor nutrition affects the immune system at both the individual and colony level, amplifying effects of other stress factors. Pollen is the only source of ten amino acids that are essential to honey bee development, brood rearing and reproduction. This paper presents a new mathematical model to explore the effect of pollen on honey bee colony dynamics. In this model, we considered pollen and nectar as the required food for the colony. The effect of pollen and nectar collected by foragers was evaluated at different mortality rates of pupa, pollen and nectar foragers.

Identification of native protein structures captured by principal interactions

Background

Evaluation of protein structure is based on trustworthy potential function. The total potential of a protein structure is approximated as the summation of all pair-wise interaction potentials. Knowledge-based potentials (KBP) are one type of potential functions derived by known experimentally determined protein structures. Although several KBP functions with different methods have been introduced, the key interactions that capture the total potential have not studied yet.

Results

In this study, we seek the interaction types that preserve as much of the total potential as possible. We employ a procedure based on the principal component analysis (PCA) to extract the significant and key interactions in native protein structures. We call these interactions as principal interactions and show that the results of the model that considers only these interactions are very close to the full interaction model that considers all interactions in protein fold recognition. In fact, the principal interactions maintain the discriminative power of the full interaction model. This method was evaluated on 3 KBPs with different contact definitions and thresholds of distance and revealed that their corresponding principal interactions are very similar and have a lot in common. Additionally, the principal interactions consisted of 20 % of the full interactions on average, and they are between residues, which are considered important in protein folding.

Conclusions

This work shows that all interaction types are not equally important in discrimination of native structure. The results of the reduced model based on principal interactions that were very close to the full interaction model suggest that a new strategy is needed to capture the role of remaining interactions (non-principal interactions) to improve the power of knowledge-based potential functions.

Lipid and β-Carotene Production by Rhodosporidium diobovatum Cultured with Different Carbon to Nitrogen Ratios

Background: In food industry, carotenoids are used as food colorants conferring yellow to red color. This research was designed to study on lipid and β-carotene production by Rhodosporidium diobovatum cultured with different Carbon to Nitrogen (C/N) ratios. Methods: R. diobovatum was cultured in a medium containing 40 g/l glucose (as the carbon source) and different C/N ratios (20, 50, and 80), which were established by adding different amounts of (NH4)2SO4 (3.78, 1.51, and 0.94 g/l) as the source of nitrogen. High performance liquid chromatography, gas chromatography, and microplate reader were used to determine the glucose concentration, lipid production, and β-carotene concentration, respectively. Data were analyzed using IBM SPSS statistics (v. 24). Results: Cultures with a C/N ratio of 50 produced the greatest amount of lipids during 120 h pi. However, lipid synthesis in the first 48 h pi was very low for all three C/N ratios. Analyses of the lipid composition revealed that oleic acid and linoleic acid were the dominant (60%) fatty acids. Cultures with a C/N ratio of 50 also produced the greatest amount of β-carotene. Conclusion: R. diobovatum in the C/N of 50 culture medium resulted in greater concentrations of lipid and β-carotene. Defining the optimum C/N ratio will enable development of optimized bioprocess engineering parameters

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis

Background

Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.

Results

Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks.

Conclusions

IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2659-y) contains supplementary material, which is available to authorized users.

Protein complex prediction: A survey

Protein complexes are one of the most important functional units for deriving biological processes within the cell. Experimental methods have provided valuable data to infer protein complexes. However, these methods have inherent limitations. Considering these limitations, many computational methods have been proposed to predict protein complexes, in the last decade. Almost all of these in-silico methods predict protein complexes from the ever-increasing protein-protein interaction (PPI) data. These computational approaches usually use the PPI data in the format of a huge protein-protein interaction network (PPIN) as input and output various sub-networks of the given PPIN as the predicted protein complexes. Some of these methods have already reached a promising efficiency in protein complex detection. Nonetheless, there are challenges in prediction of other types of protein complexes, specially sparse and small ones. New methods should further incorporate the knowledge of biological properties of proteins to improve the performance. Additionally, there are several challenges that should be considered more effectively in designing the new complex prediction algorithms in the future. This article not only reviews the history of computational protein complex prediction but also provides new insight for improvement of new methodologies. In this article, most important computational methods for protein complex prediction are evaluated and compared. In addition, some of the challenges in the reconstruction of the protein complexes are discussed. Finally, various tools for protein complex prediction and PPIN analysis as well as the current high-throughput databases are reviewed.

Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes

Introduction: Identification of functionally-related genes is an important step in understanding biological systems. The most popular strategy to infer functional dependence is to study pairwise correlations between gene expression levels. However, certain functionally-related genes may have a low expression correlation due to their nonlinear interactions. The use of a three-way interaction (3WI) model with switching mechanism (SM) is a relatively new strategy to trace functionally-related genes. The 3WI model traces the dynamic and nonlinear nature of the co-expression relationship of two genes by introducing their link to the expression level of a third gene. Areas covered: In this paper, we reviewed a variety of existing methods for tracing the 3WIs. Furthermore, we provide a comprehensive review of the previous biological studies based on 3WI models. Expert commentary: Comparison of features of these methods indicates that the modified liquid association algorithm has the best efficiency for tracing 3WI between others. The limited number of biological studies based on the 3WI suggests that high computational demand of the available algorithms is a major challenge to apply this approach for analyzing high-throughput omics data.

Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology

Interaction between tumor and stromal cells is beginning to be decoded as a contributor to chemotherapy resistance. Here, we aim to take a system-level approach to explore a mechanism by which stromal cells induce chemoresistance in cancer cells and subsequently identify a drug that can inhibit such interaction. Using a proteomic dataset containing quantitative data on secretome of stromal cells, we performed multivariate analyses and found that bone-marrow mesenchymal stem cells (BM-MSCs) play the most protective role against chemotherapeutics. Pathway enrichment tests showed that secreted cytokines from BM-MSCs activated 4 signaling pathways including Janus kinase-signal transducer and activator of transcription, phosphatidylinositol 3-kinase-protein kinase B, and mitogen-activated protein kinase, transforming growth factor-β in cancer cells collectively leading to nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) transcription factor activation. Based on the data from integrated Library of Integrated Network-Based Cellular Signatures (iLINCs) program, we found that among different drugs, valproic acid (VA) affected the expression of 34 genes within the identified pathways that are activated by stromal cells. Our in vitro experiments confirmed that VA inhibits NF-kB activation in cancer cells. In addition, analyzing gene expression data in patients taking oral VA showed that this drug decreased expression of antioxidant enzymes culminating in increased oxidative stress in tumor cells. These results suggest that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stromal cells. Since VA is an already prescribed drug manifesting anticancer effects, this study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting.

Human T-lymphotropic virus 1 (HTLV-1) pathogenesis: A systems virology study

The main mechanisms of interaction between Human T-lymphotropic virus type 1 (HTLV-1) and its hosts in the manifestation of the related disease including HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and Adult T-cell leukemia/lymphoma (ATLL) are yet to be determined. It is pivotal to find out the changes in the genes expression toward an asymptomatic or symptomatic states. To this end, the systems virology analysis was performed. Firstly, the differentially expressed genes (DEGs) were taken pairwise among the four sample sets of Normal, Asymptomatic Carriers (ACs), ATLL, and HAM/TSP. Afterwards, the protein-protein interaction networks were reconstructed utilizing the hub genes. In conclusion, the pathways of cells proliferation and transformation were identified in the ACs state. In addition to immune pathways in ATLL, the inflammation and cancer pathways were discened in both diseases of ATLL and HAM/TSP. The outcomes can specify the genes involved in the pathogenesis and help to design the drugs in the future.

Three-way interaction model to trace the mechanisms involved in Alzheimer's disease transgenic mice

Alzheimer's disease (AD) is the most common cause for dementia in human. Currently, more than 46 million people in the world suffer from AD and it is estimated that by 2050 this number increases to more than 131 million. AD is considered as a complex disease. Therefore, understanding the mechanism of AD is a universal challenge. Nowadays, a huge number of disease-related high-throughput “omics” datasets are freely available. Such datasets contain valuable information about disease-related pathways and their corresponding gene interactions. In the present work, a three-way interaction model is used as a novel approach to understand AD-related mechanisms. This model can trace the dynamic nature of co-expression relationship between two genes by introducing their link to a third gene. Apparently, such relationships cannot be traced by the classical two-way interaction model. Liquid association method was applied to capture the statistically significant triplets which are involved in three-way interaction. Subsequently, gene set enrichment analysis (GSEA) and gene regulatory network (GRN) inference were applied to analyze the biological relevance of the statistically significant triplets. The results of this study suggest that the innate immunity processes are important in AD. Specifically, our results suggest that H2-Ob as the switching gene and the gene pair {Csf1r, Milr1} form a statistically significant and biologically relevant triplet, which may play an important role in AD. We propose that the homeostasis-related link between mast cells and microglia is presumably controlled with H2-Ob expression levels as a switching gene.

Trauma pneumonectomy: A narrative review

PURPOSE

Thoracic injuries are common in both blunt and penetrating trauma. Most thoracic injuries are managed non-operatively, approximately 7-20% undergo thoracotomy. Of the injuries requiring thoracotomy, 1-6% ultimately require pulmonary resection. Wedge resection and lobectomies are well-studied in the literature; however, there is a paucity regarding reports on total pneumonectomy in the setting of trauma. Our objectives were to summarize the evidence supporting the role of trauma pneumonectomy (TP) in the current era and reiterate that despite the associated morbidity and mortality TP is justified in selective cases.

METHODS

A review of the world's literature was conducted following standard guidelines. Inclusion criteria included those studies reviewing blunt and penetrating trauma to the lungs in adults (age greater than 15 year) that reported mortality rates and outcome measures.

RESULTS

The PubMed search yielded 713 studies. Of these, 14 studies included pertinent information on TP. Studies included in this review were published from 1985 to 2017 and involved patient data that was collected from 1972 to 2014. Mortality ranged from 50% to 100% (median 63%; mean 68%).

CONCLUSION

In the setting of severe thoracic trauma, pulmonary resection may be necessary. Less aggressive techniques are options in a stable patient; however, in the setting of ongoing hemorrhage, TP should be considered and expediently conducted. The role of damage control thoracic surgery and related techniques is vitally important in these patients to improve the significant mortality of trauma pneumonectomy.

Trauma to the bladder and ureter: a review of diagnosis, management, and prognosis

BACKGROUND

Injuries to the ureter or bladder are relatively rare. Therefore, a high level of clinical suspicion and knowledge of operative anatomy is of utmost importance for their management. Herein, a review of the literature related to the modern diagnosis, management, and prognosis for bladder and ureteral injuries is presented.

METHODS

A literature search was conducted through PubMed. A thorough search of the world's literature published in English was completed. Search terms included "injury, diagnosis, prognosis, and management for ureter and bladder". All years, both genders, as well as penetrating, blunt, and iatrogenic mechanisms were evaluated for inclusion. Following PRISMA guidelines, studies were selected based on relevance and then categorized.

RESULTS

172 potentially relevant studies were identified. Given our focus on modern diagnosis and treatment, we then narrowed the studies in each category to those published within the last 30 years, resulting in a total of 26 studies largely consisting of Level IV retrospective case series. Our review found that bladder ruptures occur from penetrating, blunt, or iatrogenic mechanisms, and most are extraperitoneal (63%). Ureteral injuries are incurred from penetrating mechanisms in 77% of cases. The overall mortality rates for bladder rupture and ureteral injury were 8 and 7%, respectively.

LIMITATIONS

Limitations of this article are similar to all PRISMA-guided review articles: the dependence on previously published research and availability of references.

CONCLUSION

The bladder is injured far more often than the ureter but ureteral injuries have higher injury severity. Both of these organs can be damaged by penetrating, blunt, or iatrogenic mechanisms and surgical intervention is often required for severe ureter or bladder injuries. Since symptoms of these injuries may not always be apparent, a high level of suspicion is required for appropriate diagnosis and treatment.

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein–protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

Resonantly Enhanced Betatron Hard X-rays from Ionization Injected Electrons in a Laser Plasma Accelerator

Ultrafast betatron x-ray emission from electron oscillations in laser wakefield acceleration (LWFA) has been widely investigated as a promising source. Betatron x-rays are usually produced via self-injected electron beams, which are not controllable and are not optimized for x-ray yields. Here, we present a new method for bright hard x-ray emission via ionization injection from the K-shell electrons of nitrogen into the accelerating bucket. A total photon yield of 8 × 10(8)/shot and 10(8 )photons with energy greater than 110 keV is obtained. The yield is 10 times higher than that achieved with self-injection mode in helium under similar laser parameters. The simulation suggests that ionization-injected electrons are quickly accelerated to the driving laser region and are subsequently driven into betatron resonance. The present scheme enables the single-stage betatron radiation from LWFA to be extended to bright γ-ray radiation, which is beyond the capability of 3(rd) generation synchrotrons.

Polarity-based fractionation in proteomics: hydrophilic interaction vs reversed-phase liquid chromatography

During recent decades, hydrophilic interaction liquid chromatography (HILIC) ahs been introduced to fractionate or purify especially polar solutes such as peptides and proteins while reversed-phase liquid chromatography (RPLC) is also a common strategy. RPLC is also a common dimension in multidimensional chromatography. In this study, the potential of HILIC vs RPLC chromatography was compared for proteome mapping of human peripheral blood mononuclear cell extract. In HILIC a silica-based stationary phase and for RPLC a C18 column were applied. Then separated proteins were eluted to an ion trap mass spectrometry system. Our results showed that the HILIC leads to more proteins being identified in comparison to RPLC. Among the total 181 identified proteins, 56 and 38 proteins were fractionated specifically by HILIC and RPLC, respectively. In order to demonstrate this, the physicochemical properties of identified proteins such as polarity and hydrophobicity were considered. This analysis indicated that polarity may play a major role in the HILIC separation of proteins vs RPLC. Using gene ontology enrichment analysis, it was also observed that differences in physicochemical properties conform to the cellular compartment and biological features. Finally, this study highlighted the potential of HILIC and the great orthogonality of RPLC in gel-free proteomic studies. Copyright © 2015 John Wiley & Sons, Ltd.

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms

BACKGROUND

In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary.

METHODS

Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs).

RESULTS

The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome.

CONCLUSION

In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.