Unraveling the Significance of Nanog in the Generation of Embryonic Stem-like Cells from Spermatogonia Stem Cells: A Combined In Silico Analysis and In Vitro Experimental Approach.

Embryonic stem-like cells (ES-like cells) are promising for medical research and clinical applications. Traditional methods involve "Yamanaka" transcription (OSKM) to derive these cells from somatic cells in vitro. Recently, a novel approach has emerged, obtaining ES-like cells from spermatogonia stem cells (SSCs) in a time-related process without adding artificial additives to cell cultures, like transcription factors or small molecules such as pten or p53 inhibitors. This study aims to investigate the role of the Nanog in the conversion of SSCs to pluripotent stem cells through both in silico analysis and in vitro experiments. We used bioinformatic methods and microarray data to find significant genes connected to this derivation path, to construct PPI networks, using enrichment analysis, and to construct miRNA-lncRNA networks, as well as in vitro experiments, immunostaining, and Fluidigm qPCR analysis to connect the dots of Nanog significance. We concluded that Nanog is one of the most crucial differentially expressed genes during SSC conversion, collaborating with critical regulators such as Sox2, Dazl, Pou5f1, Dnmt3, and Cdh1. This intricate protein network positions Nanog as a pivotal factor in pathway enrichment for generating ES-like cells, including Wnt signaling, focal adhesion, and PI3K-Akt-mTOR signaling. Nanog expression is presumed to play a vital role in deriving ES-like cells from SSCs in vitro. Finding its pivotal role in this path illuminates future research and clinical applications.

Differential Proteome Profiling Analysis under Pesticide Stress by the Use of a Nano-UHPLC-MS/MS Untargeted Proteomic-Based Approach on a 3D-Developed Neurospheroid Model: Identification of Protein Interactions, Prognostic Biomarkers, and Potential Therapeutic Targets in Human IDH Mutant High-Grade Gliomas.

High-grade gliomas represent the most common group of infiltrative primary brain tumors in adults associated with high invasiveness, agressivity, and resistance to therapy, which highlights the need to develop potent drugs with novel mechanisms of action. The aim of this study is to reveal changes in proteome profiles under stressful conditions to identify prognostic biomarkers and altered apoptogenic pathways involved in the anticancer action of human isocitrate dehydrogenase (IDH) mutant high-grade gliomas. Our protocol consists first of a 3D in vitro developing neurospheroid model and then treatment by a pesticide mixture at relevant concentrations. Furthermore, we adopted an untargeted proteomic-based approach with high-resolution mass spectrometry for a comparative analysis of the differentially expressed proteins between treated and nontreated spheroids. Our analysis revealed that the majority of altered proteins were key members in glioma pathogenesis, implicated in the cellular metabolism, biological regulation, binding, and catalytic and structural activity and linked to many cascading regulatory pathways. Our finding revealed that grade-IV astrocytomas promote the downstream of the mitogen-activated-protein-kinases/extracellular-signal-regulated kinase (MAPK1/ERK2) pathway involving massive calcium influx. The gonadotrophin-releasing-hormone signaling enhances MAKP activity and may serve as a negative feedback compensating regulator. Thus, our study can pave the way for effective new therapeutic and diagnostic strategies to improve the overall survival.

Prospective Analysis of Proteins Carried in Extracellular Vesicles with Clinical Outcome in Hepatocellular Carcinoma.

Background: Extracellular vehicles (EVs) contain different proteins that relay information between tumor cells, thus promoting tumorigenesis. Therefore, EVs can serve as an ideal marker for tumor pathogenesis and clinical application. Objective: Here, we characterised EV-specific proteins in hepatocellular carcinoma (HCC) samples and established their potential protein-protein interaction (PPI) networks. Materials and Methods: We used multi-dimensional bioinformatics methods to mine a network module to use as a prognostic signature and validated the model's prediction using additional datasets. The relationship between the prognostic model and tumor immune cells or the tumor microenvironment status was also examined. Results: 1134 proteins from 316 HCC samples were mapped to the exoRBase database. HCC-specific EVs specifically expressed a total of 437 proteins. The PPI network revealed 321 proteins and 938 interaction pathways, which were mined to identify a three network module (3NM) with significant prognostic prediction ability. Validation of the 3NM in two more datasets demonstrated that the model outperformed the other signatures in prognostic prediction ability. Functional analysis revealed that the network proteins were involved in various tumor-related pathways. Additionally, these findings demonstrated a favorable association between the 3NM signature and macrophages, dendritic, and mast cells. Besides, the 3NM revealed the tumor microenvironment status, including hypoxia and inflammation. Conclusion: These findings demonstrate that the 3NM signature reliably predicts HCC pathogenesis. Therefore, the model may be used as an effective prognostic biomarker in managing patients with HCC.

A novel essential protein identification method based on PPI networks and gene expression data.

BACKGROUND: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. RESULTS: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. CONCLUSIONS: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort.

BACKGROUND: Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results. MATERIALS AND METHODS: Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis. RESULTS: A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database. CONCLUSIONS: Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.

BACKGROUND: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks. RESULTS: As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers. CONCLUSION: Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins.

Functional modules can be predicted using genome-wide protein-protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

Identification of protein complexes and functional modules in E. coli PPI networks.

BACKGROUND: Escherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less. RESULTS: In this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes. CONCLUSIONS: In this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.

A novel algorithm for alignment of multiple PPI networks based on simulated annealing.

Proteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.

BACKGROUND: Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging. RESULTS: We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments. CONCLUSION: The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

The prognostic signature of the somatic mutations in Ewing sarcoma: from a network view.

BACKGROUND: Ewing sarcoma is a malignant bone tumor mainly affecting teenagers and young adults. Its main driver mutation, the EWS-FLI1 fusion gene, has been identified more than 20 years ago, whereas its other somatic mutations have been just recently reported. METHODS: In this study, we organized the somatic mutations from 216 Ewing sarcoma cases into 216 individual protein-protein interaction networks by using interactome information. These mutation networks were then classified into five different clusters based on their structural similarities. The prognostic effect of mutation genes was evaluated according to their network features. RESULTS: The cases in cluster two exhibited remarkably high metastasis and mortality rates, and STAG2, TP53 and TTN were the three most significantly mutated genes in this cluster. Microarray data demonstrate that the expression of STAG2, TP53 and TTN are down-regulated in the EWS-FLI1-knockdown Ewing sarcoma cells. However, the mutation effect analysis shows that the somatic mutations in TTN are less damaging than those in STAG2 and TP53. The analyses of functional network modules further revealed that STAG2, TP53 and their interacting gene partners participate in the oncogenic-related biological modules such as cell cycle and regulation of transcription from RNA polymerase II promoter while TTN, TP53 and their interacting gene partners constitute the modules less relevant to oncogenesis. The results of Gene Ontology analyses demonstrated that the uniquely mutated genes associated with poor prognosis in Clusters 1, 4 and 5 were distinctively enriched in epidermal growth factor-related functions and phosphoproteins. CONCLUSIONS: Our study identified the highly lethal mutation combination cases and characterized the possible prognostic genes in Ewing sarcoma from a network perceptive.

WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks.

BACKGROUND: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users' test datasets. RESULTS: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses. CONCLUSION: WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users' test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks. AVAILABILITY: http://www.nwpu-bioinformatics.com/WebNetCoffee.

Genome-Wide Investigation of Genes Regulated by ERα in Breast Cancer Cells.

Estrogen receptor alpha (ERα), which has been detected in over 70% of breast cancer cases, is a driving factor for breast cancer growth. For investigating the underlying genes and networks regulated by ERα in breast cancer, RNA-seq was performed between ERα transgenic MDA-MB-231 cells and wild type MDA-MB-231 cells. A total of 267 differentially expressed genes (DEGs) were identified. Then bioinformatics analyses were performed to illustrate the mechanism of ERα. Besides, by comparison of RNA-seq data obtained from MDA-MB-231 cells and microarray dataset obtained from estrogen (E2) stimulated MCF-7 cells, an overlap of 126 DEGs was screened. The expression level of ERα was negatively associated with metastasis and EMT in breast cancer. We further verified that ERα might inhibit metastasis by regulating of VCL and TNFRSF12A, and suppress EMT by the regulating of JUNB and ID3. And the relationship between ERα and these genes were validated by RT-PCR and correlation analysis based on TCGA database. By PPI network analysis, we identified TOP5 hub genes, FOS, SP1, CDKN1A, CALCR and JUNB, which were involved in cell proliferation and invasion. Taken together, the whole-genome insights carried in this work can help fully understanding biological roles of ERα in breast cancer.

A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

Chameleon proteins are proteins which include sequences that can adopt α-helix-ß-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or ß-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

Protein complex prediction for large protein protein interaction networks with the Core&Peel method.

BACKGROUND: Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. RESULTS AND DISCUSSION: We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. CONCLUSIONS: Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Identification of genes and pathways associated with sex in Non-smoking lung cancer population.

INTRODUCTION: Women represent a higher proportion than men among those with lung cancer in nonsmokers compared to smokers. The reason for this abnormally higher proportion is not yet clear, but sex differences suggest there may be a genetic component at play. MATERIALS AND METHODS: The gene expression determined by Illumina RNA Sequencing and the relevant clinical information of lung cancer patients was download from TCGA. The differentially expressed genes (DEGs) were screened between males and females in both nonsmoking and smoking populations. The top 50 validated DEGs are represented with heatmaps. Based on the DEGs, GO functional and KEGG pathway enrichment analyses were performed. PPI networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect lung cancer patient prognosis. RESULTS: In non-smoking patients, there were significantly more females than males (female 73.0% vs male 27.0%, P < 0.001). Such difference was not found in smoking patients (female 50.7% vs male 49.3%, P = 0.770). A total of 898 DEGs were identified in the non-smoking population, while a total of 992 DEGs were identified in the smoking population. Of these, only 122 genes were shared by both populations. Some pathways were enriched specifical in non-smoking population, such as cAMP signaling pathway and ovarian steroidogenesis. Several proteins related to estrogen function and MAPK/PI3K signaling, such as KRT16, ERBB4 and NTF4, showed differential effects on the lung adenocarcinoma progression in non-smoking males or females. CONCLUSIONS: Some genetic differences between male and female in non-smoking lung adenocarcinoma patients have been identified. Potentially, ER signaling and MAPK/PI3K signaling partially participated in this discrepancy.

Brassica juncea (L.) Czern. leaves alleviate adjuvant-induced rheumatoid arthritis in rats via modulating the finest disease targets - IL2RA, IL18 and VEGFA.

Brassica juncea (BJ) is a familiar edible crop, which has been used as a dietary ingredient and to prepare anti-inflammatory/anti-arthritic formulations in Ayurveda. But, the scientific validation or confirmation of its therapeutic properties is very limited. This study was performed to determine the efficiency of BJ leaves for the treatment of Rheumatoid arthritis using in vivo and in silico systems. Standard in vitro procedures was followed to study the total phenolic, flavonoid contents and free radical scavenging ability of the extracts of BJ. The effective extract was screened and the presence of bioactive chemicals was studied using HPLC. Further, the possible therapeutic actions of the BJ active principles against the disease targets were studied using PPI networking and docking analysis. IL2RA, IL18 and VEGFA are found to be the potential RA target and the compounds detected from BJ extract have shown great binding efficiency towards the target from molecular docking study. The resulting complexes were then subject to 100 ns molecular dynamics simulation studies with the GROMACS package to analyze the stability of docked protein-ligand complexes and to assess the fluctuation and conformational changes during protein-ligand interactions. To confirm the anti-arthritic activity of BJ, the extract was tested in CFA-induced arthritic Wistar rats. The test groups administered with BJ extract showed retrieval of altered hematological parameters and substantial recovery from inflammation and degeneration of rat hind paw.Communicated by Ramaswamy H. Sarma.

Extracting functional insights from loss-of-function screens using deep link prediction.

We present deep link prediction (DLP), a method for the interpretation of loss-of-function screens. Our approach uses representation-based link prediction to reprioritize phenotypic readouts by integrating screening experiments with gene-gene interaction networks. We validate on 2 different loss-of-function technologies, RNAi and CRISPR, using datasets obtained from DepMap. Extensive benchmarking shows that DLP-DeepWalk outperforms other methods in recovering cell-specific dependencies, achieving an average precision well above 90% across 7 different cancer types and on both RNAi and CRISPR data. We show that the genes ranked highest by DLP-DeepWalk are appreciably more enriched in drug targets compared to the ranking based on original screening scores. Interestingly, this enrichment is more pronounced on RNAi data compared to CRISPR data, consistent with the greater inherent noise of RNAi screens. Finally, we demonstrate how DLP-DeepWalk can infer the molecular mechanism through which putative targets trigger cell line mortality.

Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks.

BACKGROUND: Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug. METHOD: In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method. RESULTS: The proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer. INTERPRETATION & CONCLUSION: The new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results.