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CHEN, JAKE Y., ZHONG YAN, CHANGYU SHEN, DAWN P. G. FITZPATRICK, and MU WANG. "A SYSTEMS BIOLOGY APPROACH TO THE STUDY OF CISPLATIN DRUG RESISTANCE IN OVARIAN CANCERS." Journal of Bioinformatics and Computational Biology 05, no. 02a (April 2007): 383–405. http://dx.doi.org/10.1142/s0219720007002606.
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Cisplatin-induced drug resistance is known to involve a complex set of cellular changes whose molecular mechanism details remain unclear. In this study, we developed a systems biology approach to examine proteomics- and network-level changes between cisplatin-resistant and cisplatin-sensitive cell lines. This approach involves experimental investigation of differential proteomics profiles and computational study of activated enriched proteins, protein interactions, and protein interaction networks. Our experimental platform is based on a Label-free liquid Chromatography/mass spectrometry proteomics platform. Our computational methods start with an initial list of 119 differentially expressed proteins. We expanded these proteins into a cisplatin-resistant activated sub-network using a database of human protein-protein interactions. An examination of network topology features revealed the activated responses in the network are closely coupled. By examining sub-network proteins using gene ontology categories, we found significant enrichment of proton-transporting ATPase and ATP synthase complexes activities in cisplatin-resistant cells in the form of cooperative down-regulations. Using two-dimensional visualization matrixes, we further found significant cascading of endogenous, abiotic, and stress-related signals. Using a visual representation of activated protein categorical sub-networks, we showed that molecular regulation of cell differentiation and development caused by responses to proteome-wide stress as a key signature to the acquired drug resistance.
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DESAI, KAUSHAL, DAVID BROTT, XIAOHUA HU, and ANASTASIA CHRISTIANSON. "A SYSTEMS BIOLOGY APPROACH FOR DETECTING TOXICITY-RELATED HOTSPOTS INSIDE PROTEIN INTERACTION NETWORKS." Journal of Bioinformatics and Computational Biology 09, no. 05 (October 2011): 647–62. http://dx.doi.org/10.1142/s0219720011005707.
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Drug-induced neutropenia can be fatal when severe and therefore requires an improved understanding of its mechanism(s) of toxicity. Systems biology provides an opportunity to understand adverse events after drug administration using analysis of biomolecular networks. In this study, a human protein interaction network was analyzed to identify proteins that are most central to topological paths connecting a drug's target proteins to hematopoiesis-related proteins. For a set of non-immune neutropenia inducing drugs, 9 proteins were found to be common to putative signaling paths across all drugs evaluated. All 9 proteins showed relevance to neutrophil biology. Geneset enrichment analysis showed that proteins associated with cancer-related processes such as apoptosis provide topological linkages between drug targets and proteins involved in neutrophil production. The algorithm can be applied towards analysis of any toxicity where the drugs and the physiological processes involved in the toxic mechanism are known.
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Matsubara, Teppei, Tomoshiro Ochiai, Morihiro Hayashida, Tatsuya Akutsu, and Jose C. Nacher. "Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles." Journal of Bioinformatics and Computational Biology 17, no. 03 (June 2019): 1940007. http://dx.doi.org/10.1142/s0219720019400079.
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Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to “omics” data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis
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WU, YONGHUI, and STEFANO LONARDI. "A LINEAR-TIME ALGORITHM FOR PREDICTING FUNCTIONAL ANNOTATIONS FROM PPI NETWORKS." Journal of Bioinformatics and Computational Biology 06, no. 06 (December 2008): 1049–65. http://dx.doi.org/10.1142/s0219720008003916.
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Recent proteome-wide screening efforts have made available genome-wide, high-throughput protein–protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the systems biology community. Here, we address the problem of predicting the functional classes of proteins (i.e. GO annotations) based solely on the structure of the PPI network. We present a maximum likelihood formulation of the problem and the corresponding learning and inference algorithms. The time complexity of both algorithms is linear in the size of the PPI network, and our experimental results show that their accuracy in functional prediction outperforms current existing methods.
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Patra, Sabyasachi, and Anjali Mohapatra. "Motif discovery in biological network using expansion tree." Journal of Bioinformatics and Computational Biology 16, no. 06 (December 2018): 1850024. http://dx.doi.org/10.1142/s0219720018500245.
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Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.
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Chang, Shen, Jian-You Chen, Yung-Jen Chuang, and Bor-Sen Chen. "Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications." International Journal of Molecular Sciences 22, no. 1 (December 26, 2020): 166. http://dx.doi.org/10.3390/ijms22010166.
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In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment. We first integrated databases to construct the genome-wide genetic and epigenetic networks (GWGENs), which consist of protein–protein interaction networks (PPINs) and gene regulatory networks (GRNs) for T2D and non-T2D (health), respectively. Second, the relevant “real GWGENs” are identified by system identification and system order detection methods performed on the T2D and non-T2D RNA-seq data. To simplify network analysis, principal network projection (PNP) was thereby exploited to extract core GWGENs from real GWGENs. Then, with the help of KEGG pathway annotation, core signaling pathways were constructed to identify significant biomarkers. Furthermore, in order to discover potential drugs for the selected pathogenic biomarkers (i.e., drug targets) from the core signaling pathways, not only did we train a deep neural network (DNN)-based drug–target interaction (DTI) model to predict candidate drug’s binding with the identified biomarkers but also considered a set of design specifications, including drug regulation ability, toxicity, sensitivity, and side effects to sieve out promising drugs suitable for T2D.
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ZHU, ZHENGWEI, ANDREY TOVCHIGRECHKO, TATIANA BARONOVA, YING GAO, DOMINIQUE DOUGUET, NICHOLAS O'TOOLE, and ILYA A. VAKSER. "LARGE-SCALE STRUCTURAL MODELING OF PROTEIN COMPLEXES AT LOW RESOLUTION." Journal of Bioinformatics and Computational Biology 06, no. 04 (August 2008): 789–810. http://dx.doi.org/10.1142/s0219720008003679.
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Structural aspects of protein–protein interactions provided by large-scale, genome-wide studies are essential for the description of life processes at the molecular level. A methodology is developed that applies the protein docking approach (GRAMM), based on the knowledge of experimentally determined protein–protein structures (DOCKGROUND resource) and properties of intermolecular energy landscapes, to genome-wide systems of protein interactions. The full sequence-to-structure-of-complex modeling pipeline is implemented in the Genome Wide Docking Database (GWIDD) resource. Protein interaction data are imported to GWIDD from external datasets of experimentally determined interaction networks. Essential information is extracted and unified to form the GWIDD database. Structures of individual interacting proteins in the database are retrieved (if available) or modeled, and protein complex structures are predicted by the docking program. All protein sequence, structure, and docking information is conveniently accessible through a Web interface.
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Fulnečková, Jana, Ladislav Dokládal, Karolína Kolářová, Martina Nešpor Dadejová, Klára Procházková, Sabina Gomelská, Martin Sivčák, et al. "Telomerase Interaction Partners–Insight from Plants." International Journal of Molecular Sciences 23, no. 1 (December 29, 2021): 368. http://dx.doi.org/10.3390/ijms23010368.
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Telomerase, an essential enzyme that maintains chromosome ends, is important for genome integrity and organism development. Various hypotheses have been proposed in human, ciliate and yeast systems to explain the coordination of telomerase holoenzyme assembly and the timing of telomerase performance at telomeres during DNA replication or repair. However, a general model is still unclear, especially pathways connecting telomerase with proposed non-telomeric functions. To strengthen our understanding of telomerase function during its intracellular life, we report on interactions of several groups of proteins with the Arabidopsis telomerase protein subunit (AtTERT) and/or a component of telomerase holoenzyme, POT1a protein. Among these are the nucleosome assembly proteins (NAP) and the minichromosome maintenance (MCM) system, which reveal new insights into the telomerase interaction network with links to telomere chromatin assembly and replication. A targeted investigation of 176 candidate proteins demonstrated numerous interactions with nucleolar, transport and ribosomal proteins, as well as molecular chaperones, shedding light on interactions during telomerase biogenesis. We further identified protein domains responsible for binding and analyzed the subcellular localization of these interactions. Moreover, additional interaction networks of NAP proteins and the DOMINO1 protein were identified. Our data support an image of functional telomerase contacts with multiprotein complexes including chromatin remodeling and cell differentiation pathways.
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Kohutyuk, Oksana, Fadi Towfic, M. Heather West Greenlee, and Vasant Honavar. "BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks." Bioinformatics and Biology Insights 6 (January 2012): BBI.S9728. http://dx.doi.org/10.4137/bbi.s9728.
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Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/ Background The emergence of high-throughput technologies has allowed many biological investigators to collect a great deal of information about the behavior of genes and gene products over time or during a particular disease state. Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from such high-throughput analyses. There are a growing number of public databases, as well as tools for visualization and analysis of networks. However, such databases and tools have yet to be widely utilized by bench scientists, as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by biological scientists with limited computational expertise. Results We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. BioNetwork Bench currently supports a broad class of gene and protein network models (eg, weighted and un-weighted, undirected graphs, multi-graphs). It enables biologists to analyze public as well as private gene expression, macromolecular interaction and annotation data; interactively query gene expression datasets; integrate data from multiple networks; query multiple networks for interactions of interest; store and selectively share the data as well as results of analyses. BioNetwork Bench is implemented as a plug-in for, and hence is fully interoperable with, Cytoscape, a popular open-source software suite for visualizing macromolecular interaction networks. Finally, we describe an application of BioNetwork Bench to the problem of assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. Conclusions BioNetwork Bench provides a suite of open source software for construction, querying, and selective sharing of gene and protein networks. Although initially aimed at a community of biologists interested in retinal development, the tool can be adapted easily to work with other biological systems simply by populating the associated database with the relevant datasets.
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Caetano-Anollés, Gustavo, M. Fayez Aziz, Fizza Mughal, Frauke Gräter, Ibrahim Koç, Kelsey Caetano-Anollés, and Derek Caetano-Anollés. "Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis." Evolutionary Bioinformatics 15 (January 2019): 117693431987298. http://dx.doi.org/10.1177/1176934319872980.
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Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies and the other that diversifies. Here, we propose a biphasic (bow-tie) theory of module emergence. In the first phase, parts are at first weakly linked and associate variously. As they diversify, they compete with each other and are often selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In the second phase, variants of the modules diversify and become new parts for a new generative cycle of higher level organization. The paradigm predicts the rise of hierarchical modularity in evolving networks at different timescales and complexity levels. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving networks of metabolism, elementary functionomes, and protein domain organization.
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Rubanova, Natalia, and Nadya Morozova. "Centrality and the shortest path approach in the human interactome." Journal of Bioinformatics and Computational Biology 17, no. 04 (August 2019): 1950027. http://dx.doi.org/10.1142/s0219720019500276.
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Many notions and concepts for network analysis, including the shortest path approach, came to systems biology from the theory of graphs — the field of mathematics that studies graphs. We studied the relationship between the shortest paths and a biologically meaningful molecular path between vertices in human molecular interaction networks. We analyzed the sets of the shortest paths in the human interactome derived from HPRD and HIPPIE databases between all possible combinations of start and end proteins in eight signaling pathways in the KEGG database — NF-kappa B, MAPK, Jak-STAT, mTOR, ErbB, Wnt, TGF-beta, and the signaling part of the apoptotic process. We investigated whether the shortest paths match the canonical paths. We studied whether centrality of vertices and paths in the subnetworks induced by the shortest paths can highlight vertices and paths that are part of meaningful molecular paths. We found that the shortest paths match canonical counterparts only for canonical paths of length 2 or 3 interactions. The shortest paths match longer canonical counterparts with shortcuts or substitutions by protein complex members. We found that high centrality vertices are part of the canonical paths for up to 80% of the canonical paths depending on the database and the length.
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Suratanee, Apichat, and Kitiporn Plaimas. "Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins." International Journal of Molecular Sciences 21, no. 4 (February 15, 2020): 1310. http://dx.doi.org/10.3390/ijms21041310.
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Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.
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Naseem, Muhammad, Meik Kunz, and Thomas Dandekar. "Probing the Unknowns in Cytokinin-Mediated Immune Defense in Arabidopsis with Systems Biology Approaches." Bioinformatics and Biology Insights 8 (January 2014): BBI.S13462. http://dx.doi.org/10.4137/bbi.s13462.
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Plant hormones involving salicylic acid (SA), jasmonic acid (JA), ethylene (Et), and auxin, gibberellins, and abscisic acid (ABA) are known to regulate host immune responses. However, plant hormone cytokinin has the potential to modulate defense signaling including SA and JA. It promotes plant pathogen and herbivore resistance; underlying mechanisms are still unknown. Using systems biology approaches, we unravel hub points of immune interaction mediated by cytokinin signaling in Arabidopsis. High-confidence Arabidopsis protein—protein interactions (PPI) are coupled to changes in cytokinin-mediated gene expression. Nodes of the cellular interactome that are enriched in immune functions also reconstitute sub-networks. Topological analyses and their specific immunological relevance lead to the identification of functional hubs in cellular interactome. We discuss our identified immune hubs in light of an emerging model of cytokinin-mediated immune defense against pathogen infection in plants.
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Chen, Muhao, Chelsea J. T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang. "Multifaceted protein–protein interaction prediction based on Siamese residual RCNN." Bioinformatics 35, no. 14 (July 2019): i305—i314. http://dx.doi.org/10.1093/bioinformatics/btz328.
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AbstractMotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.
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Salladini, Edoardo, Maria L. M. Jørgensen, Frederik F. Theisen, and Karen Skriver. "Intrinsic Disorder in Plant Transcription Factor Systems: Functional Implications." International Journal of Molecular Sciences 21, no. 24 (December 21, 2020): 9755. http://dx.doi.org/10.3390/ijms21249755.
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Eukaryotic cells are complex biological systems that depend on highly connected molecular interaction networks with intrinsically disordered proteins as essential components. Through specific examples, we relate the conformational ensemble nature of intrinsic disorder (ID) in transcription factors to functions in plants. Transcription factors contain large regulatory ID-regions with numerous orphan sequence motifs, representing potential important interaction sites. ID-regions may affect DNA-binding through electrostatic interactions or allosterically as for the bZIP transcription factors, in which the DNA-binding domains also populate ensembles of dynamic transient structures. The flexibility of ID is well-suited for interaction networks requiring efficient molecular adjustments. For example, Radical Induced Cell Death1 depends on ID in transcription factors for its numerous, structurally heterogeneous interactions, and the JAZ:MYC:MED15 regulatory unit depends on protein dynamics, including binding-associated unfolding, for regulation of jasmonate-signaling. Flexibility makes ID-regions excellent targets of posttranslational modifications. For example, the extent of phosphorylation of the NAC transcription factor SOG1 regulates target gene expression and the DNA-damage response, and phosphorylation of the AP2/ERF transcription factor DREB2A acts as a switch enabling heat-regulated degradation. ID-related phase separation is emerging as being important to transcriptional regulation with condensates functioning in storage and inactivation of transcription factors. The applicative potential of ID-regions is apparent, as removal of an ID-region of the AP2/ERF transcription factor WRI1 affects its stability and consequently oil biosynthesis. The highlighted examples show that ID plays essential functional roles in plant biology and has a promising potential in engineering.
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Sinha, Indu, Rachel L. Fogle, Gizem Gulfidan, Anne E. Stanley, Vonn Walter, Christopher S. Hollenbeak, Kazim Y. Arga, and Raghu Sinha. "Potential Early Markers for Breast Cancer: A Proteomic Approach Comparing Saliva and Serum Samples in a Pilot Study." International Journal of Molecular Sciences 24, no. 4 (February 19, 2023): 4164. http://dx.doi.org/10.3390/ijms24044164.
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Breast cancer is the second leading cause of death for women in the United States, and early detection could offer patients the opportunity to receive early intervention. The current methods of diagnosis rely on mammograms and have relatively high rates of false positivity, causing anxiety in patients. We sought to identify protein markers in saliva and serum for early detection of breast cancer. A rigorous analysis was performed for individual saliva and serum samples from women without breast disease, and women diagnosed with benign or malignant breast disease, using isobaric tags for relative and absolute quantitation (iTRAQ) technique, and employing a random effects model. A total of 591 and 371 proteins were identified in saliva and serum samples from the same individuals, respectively. The differentially expressed proteins were mainly involved in exocytosis, secretion, immune response, neutrophil-mediated immunity and cytokine-mediated signaling pathway. Using a network biology approach, significantly expressed proteins in both biological fluids were evaluated for protein–protein interaction networks and further analyzed for these being potential biomarkers in breast cancer diagnosis and prognosis. Our systems approach illustrates a feasible platform for investigating the responsive proteomic profile in benign and malignant breast disease using saliva and serum from the same women.
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Verma, Archana, Shweta Singh Chauhan, Vaishali Pankaj, Neha Srivastva, and Prachi Srivastava. "Network Biology Approaches to Identify the Drug Lead Molecule for Neurodevelopmental Disorders in Human." Open Bioinformatics Journal 13, no. 1 (March 20, 2020): 15–24. http://dx.doi.org/10.2174/1875036202013010015.
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Aims: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches Background: Neurodevelopmental disorders (NNDs) are disabilities associated chiefly with the functioning of the neurological system and brain. Children with neurodevelopmental disorders have difficulties with speech, behaviour, learning and other neurological functions. Systems biology is a holistic approach to enciphering the complexity of biological systems and their interactions. It opens the way to a more successful discovery of novel therapeutics. Objective: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches. Methods: A list of genes was collected from NCBI database for Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases. STRING database and Cytoscape software was used for construction and interpreting molecular interaction in the network. 3D structure of target protein, was build and validated.The phytochemicals were identified through various research articles and filtered out by virtual screening through Molinspiration. Molecular docking analyses of known phytochemical with target proteins were performed usingAutoDock tool. Result: AKT1 for Autism, SNAP25 for Intellectual Disability (ID) and DRD4 for Attention Deficit Hyperactivity Disorder (ADHD) were identified as most potential drug target through network study. further the modelled structure of obtained target were undergo molecular docking study with kown phytochemicals. Based on lowest binding energy, Huperzine A for Autism and ID, Valerenic acid for ADHD found to be the most potential therapeutic molecules. Conclusion: Huperzine A against Autism and ID, Valerenic acid against ADHD found to be the most potential therapeutic molecules and expected to be effective in the treatment of NNDs. Phytochemicals do not have side effects so extract of these can be taken in preventive form too as these disorders occur during developmental stages of the child. Further the obtained molecule if experimentally validated would play promising role for the treatment of NDDs in human.
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Luscombe, N. M., D. Greenbaum, and M. Gerstein. "What is Bioinformatics? A Proposed Definition and Overview of the Field." Methods of Information in Medicine 40, no. 04 (2001): 346–58. http://dx.doi.org/10.1055/s-0038-1634431.
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Summary Background: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Objectives: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Methods: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Results and Conclusions: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data). Additional information includes the text of scientific papers and “relationship data” from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.
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Kerbler, Sandra M., Roberto Natale, Alisdair R. Fernie, and Youjun Zhang. "From Affinity to Proximity Techniques to Investigate Protein Complexes in Plants." International Journal of Molecular Sciences 22, no. 13 (July 1, 2021): 7101. http://dx.doi.org/10.3390/ijms22137101.
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The study of protein–protein interactions (PPIs) is fundamental in understanding the unique role of proteins within cells and their contribution to complex biological systems. While the toolkit to study PPIs has grown immensely in mammalian and unicellular eukaryote systems over recent years, application of these techniques in plants remains under-utilized. Affinity purification coupled to mass spectrometry (AP-MS) and proximity labeling coupled to mass spectrometry (PL-MS) are two powerful techniques that have significantly enhanced our understanding of PPIs. Relying on the specific binding properties of a protein to an immobilized ligand, AP is a fast, sensitive and targeted approach used to detect interactions between bait (protein of interest) and prey (interacting partners) under near-physiological conditions. Similarly, PL, which utilizes the close proximity of proteins to identify potential interacting partners, has the ability to detect transient or hydrophobic interactions under native conditions. Combined, these techniques have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. In this review, we summarize the advantages and disadvantages of two increasingly common PPI determination techniques: AP-MS and PL-MS and discuss their important application to plant systems.
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McEvoy, Malgorzata J., Emilia Sinderewicz, Leo Creedon, Marion McAfee, Agnieszka W. Jonczyk, Katarzyna K. Piotrowska-Tomala, and Dariusz J. Skarzynski. "Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach." International Journal of Molecular Sciences 22, no. 9 (May 5, 2021): 4888. http://dx.doi.org/10.3390/ijms22094888.
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In this paper, newly discovered mechanisms of atresia and cell death processes in bovine ovarian follicles are investigated. For this purpose the mRNA expression of receptor interacting protein kinases 1 and 3 (RIPK1 and RIPK3) of the granulosa and theca cells derived from healthy and atretic follicles are studied. The follicles were assigned as either healthy or atretic based on the estradiol to progesterone ratio. A statistically significant difference was recorded for the mRNA expression of a RIPK1 and RIPK3 between granulosa cells from healthy and atretic follicles. To further investigate this result a systems biology approach was used. The genes playing roles in necroptosis, apoptosis and atresia were chosen and a network was created based on human genes annotated by the IMEx database in Cytoscape to identify hubs and bottle-necks. Moreover, correlation networks were built in the Cluepedia plug-in. The networks were created separately for terms describing apoptosis and programmed cell death. We demonstrate that necroptosis (RIPK—dependent cell death pathway) is an alternative mechanism responsible for death of bovine granulosa and theca cells. We conclude that both apoptosis and necroptosis occur in the granulosa cells of dominant follicles undergoing luteinisation and in the theca cells from newly selected follicles.
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Huo, Ruxue, Zhenning Liu, Xiaolin Yu, and Zongyun Li. "The Interaction Network and Signaling Specificity of Two-Component System in Arabidopsis." International Journal of Molecular Sciences 21, no. 14 (July 11, 2020): 4898. http://dx.doi.org/10.3390/ijms21144898.
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Two-component systems (TCS) in plants have evolved into a more complicated multi-step phosphorelay (MSP) pathway, which employs histidine kinases (HKs), histidine-containing phosphotransfer proteins (HPts), and response regulators (RRs) to regulate various aspects of plant growth and development. How plants perceive the external signals, then integrate and transduce the secondary signals specifically to the desired destination, is a fundamental characteristic of the MSP signaling network. The TCS elements involved in the MSP pathway and molecular mechanisms of signal transduction have been best understood in the model plant Arabidopsis thaliana. In this review, we focus on updated knowledge on TCS signal transduction in Arabidopsis. We first present a brief description of the TCS elements; then, the protein–protein interaction network is established. Finally, we discuss the possible molecular mechanisms involved in the specificity of the MSP signaling at the mRNA and protein levels.
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Suratanee, Apichat, and Kitiporn Plaimas. "DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations." Bioinformatics and Biology Insights 9 (January 2015): BBI.S35237. http://dx.doi.org/10.4137/bbi.s35237.
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Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein-protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease-disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.
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Mredul, Md Bazlur Rahman, Umama Khan, Humayan Kabir Rana, Tahera Mahnaz Meem, Md Abdul Awal, Md Habibur Rahman, and Md Salauddin Khan. "Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder." Bioinformatics and Biology Insights 16 (January 2022): 117793222211453. http://dx.doi.org/10.1177/11779322221145373.
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Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein–protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.
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Yeh, Shan-Ju, Chien-Yu Lin, Cheng-Wei Li, and Bor-Sen Chen. "Systems Biology Approaches to Investigate Genetic and Epigenetic Molecular Progression Mechanisms for Identifying Gene Expression Signatures in Papillary Thyroid Cancer." International Journal of Molecular Sciences 20, no. 10 (May 23, 2019): 2536. http://dx.doi.org/10.3390/ijms20102536.
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Thyroid cancer is the most common endocrine cancer. Particularly, papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. Up to now, there are few researches discussing the pathogenesis and progression mechanisms of PTC from the viewpoint of systems biology approaches. In this study, first we constructed the candidate genetic and epigenetic network (GEN) consisting of candidate protein–protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big database mining. Secondly, system identification and system order detection methods were applied to prune candidate GEN via next-generation sequencing (NGS) and DNA methylation profiles to obtain the real GEN. After that, we extracted core GENs from real GENs by the principal network projection (PNP) method. To investigate the pathogenic and progression mechanisms in each stage of PTC, core GEN was denoted in respect of KEGG pathways. Finally, by comparing two successive core signaling pathways of PTC, we not only shed light on the causes of PTC progression, but also identified essential biomarkers with specific gene expression signature. Moreover, based on the identified gene expression signature, we suggested potential candidate drugs to prevent the progression of PTC with querying Connectivity Map (CMap).
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Vilella-Figuerola, Alba, Alex Gallinat, Rafael Escate, Sònia Mirabet, Teresa Padró, and Lina Badimon. "Systems Biology in Chronic Heart Failure—Identification of Potential miRNA Regulators." International Journal of Molecular Sciences 23, no. 23 (December 3, 2022): 15226. http://dx.doi.org/10.3390/ijms232315226.
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Heart failure (HF) is a complex disease entity with high clinical impact, poorly understood pathophysiology and scantly known miRNA-mediated epigenetic regulation. We have analysed miRNA patterns in patients with chronic HF (cHF) and a sex- and age-matched reference group and pursued an in silico system biology analysis to discern pathways involved in cHF pathophysiology. Twenty-eight miRNAs were identified in cHF that were up-regulated in the reference group, and eight of them were validated by RT-qPCR. In silico analysis of predicted targets by STRING protein-protein interaction networks revealed eight cluster networks (involving seven of the identified miRNAs) enriched in pathways related to cell cycle, Ras, chemokine, PI3K-AKT and TGF-β signaling. By ROC curve analysis, combined probabilities of these seven miRNAs (let-7a-5p, miR-107, miR-125a-5p, miR-139-5p, miR-150-5p, miR-30b-5p and miR-342-3p; clusters 1–4 [C:1–4]), discriminated between HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), and ischaemic and non-ischaemic aetiology. A combination of miR-107, miR-139-5p and miR-150-5p, involved in clusters 5 and 7 (C:5+7), discriminated HFpEF from HFrEF. Pathway enrichment analysis of miRNAs present in C:1–4 (let-7a-5p, miR-125a-5p, miR-30b-5p and miR-342-3p) revealed pathways related to HF pathogenesis. In conclusion, we have identified a differential signature of down-regulated miRNAs in the plasma of HF patients and propose novel cellular mechanisms involved in cHF pathogenesis.
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Lin, Dongtao, Yudan Zeng, Deyu Tang, and Yongming Cai. "Study on the Mechanism of Liuwei Dihuang Pills in Treating Parkinson’s Disease Based on Network Pharmacology." BioMed Research International 2021 (October 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/4490081.
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Background. Parkinson’s disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks. Methods. The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein–protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity. Results. After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets. Conclusion. LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.
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Lin, Yi-Chung, and Bor-Sen Chen. "Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications." International Journal of Molecular Sciences 23, no. 18 (September 8, 2022): 10409. http://dx.doi.org/10.3390/ijms231810409.
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In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a deep neural network (DNN)-based drug–target interaction (DTI) model and drug design specifications is proposed to design a potential multiple-molecule drug for the medical treatment of OSCC before clinical trials. First, we use big database mining to construct the candidate genome-wide genetic and epigenetic network (GWGEN) including a protein–protein interaction network (PPIN) and a gene regulatory network (GRN) for OSCC and non-OSCC. In the next step, real GWGENs are identified for OSCC and non-OSCC by system identification and system order detection methods based on the OSCC and non-OSCC microarray data, respectively. Then, the principal network projection (PNP) method was used to extract core GWGENs of OSCC and non-OSCC from real GWGENs of OSCC and non-OSCC, respectively. Afterward, core signaling pathways were constructed through the annotation of KEGG pathways, and then the carcinogenic mechanism of OSCC was investigated by comparing the core signal pathways and their downstream abnormal cellular functions of OSCC and non-OSCC. Consequently, HES1, TCF, NF-κB and SP1 are identified as significant biomarkers of OSCC. In order to discover multiple molecular drugs for these significant biomarkers (drug targets) of the carcinogenic mechanism of OSCC, we trained a DNN-based drug–target interaction (DTI) model by DTI databases to predict candidate drugs for these significant biomarkers. Finally, drug design specifications such as adequate drug regulation ability, low toxicity and high sensitivity are employed to filter out the appropriate molecular drugs metformin, gefitinib and gallic-acid to combine as a potential multiple-molecule drug for the therapeutic treatment of OSCC.
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Tran, Van Dinh, Alessandro Sperduti, Rolf Backofen, and Fabrizio Costa. "Heterogeneous networks integration for disease–gene prioritization with node kernels." Bioinformatics 36, no. 9 (January 28, 2020): 2649–56. http://dx.doi.org/10.1093/bioinformatics/btaa008.
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Abstract Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.
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Termine, Andrea, Carlo Fabrizio, Juliette Gimenez, Anna Panuccio, Francesca Balsamo, Noemi Passarello, Silvia Caioli, et al. "Transcriptomic and Network Analyses Reveal Immune Modulation by Endocannabinoids in Approach/Avoidance Traits." International Journal of Molecular Sciences 23, no. 5 (February 25, 2022): 2538. http://dx.doi.org/10.3390/ijms23052538.
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Approach and avoidance (A/A) tendencies are stable behavioral traits in responding to rewarding and fearful stimuli. They represent the superordinate division of emotion, and individual differences in such traits are associated with disease susceptibility. The neural circuitry underlying A/A traits is retained to be the cortico-limbic pathway including the amygdala, the central hub for the emotional processing. Furthermore, A/A-specific individual differences are associated with the activity of the endocannabinoid system (ECS) and especially of CB1 receptors whose density and functionality in amygdala differ according to A/A traits. ECS markedly interacts with the immune system (IS). However, how the interplay between ECS and IS is associated with A/A individual differences is still ill-defined. To fill this gap, here we analyzed the interaction between the gene expression of ECS and immune system (IS) in relation to individual differences. To unveil the deep architecture of ECS-IS interaction, we performed cell-specific transcriptomics analysis. Differential gene expression profiling, functional enrichment, and protein–protein interaction network analyses were performed in amygdala pyramidal neurons of mice showing different A/A behavioral tendencies. Several altered pro-inflammatory pathways were identified as associated with individual differences in A/A traits, indicating the chronic activation of the adaptive immune response sustained by the interplay between endocannabinoids and the IS. Furthermore, results showed that the interaction between the two systems modulates synaptic plasticity and neuronal metabolism in individual difference-specific manner. Deepening our knowledge about ECS/IS interaction may provide useful targets for treatment and prevention of psychopathology associated with A/A traits.
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Nie, Xiner, Jinyi Wei, Youjin Hao, Jingxin Tao, Yinghong Li, Mingwei Liu, Boying Xu, and Bo Li. "Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach." International Journal of Molecular Sciences 20, no. 16 (August 19, 2019): 4037. http://dx.doi.org/10.3390/ijms20164037.
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Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies.
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Su, Po-Wei, and Bor-Sen Chen. "Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications." International Journal of Molecular Sciences 23, no. 22 (November 10, 2022): 13869. http://dx.doi.org/10.3390/ijms232213869.
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Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.
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Hadwen, Jeremiah, Sarah Schock, Faraz Farooq, Alex MacKenzie, and Julio Plaza-Diaz. "Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets." International Journal of Molecular Sciences 22, no. 12 (June 11, 2021): 6295. http://dx.doi.org/10.3390/ijms22126295.
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The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood–brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug–gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.
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De Luca, Ciro, Assunta Virtuoso, Nicola Maggio, Sara Izzo, Michele Papa, and Anna Maria Colangelo. "Roadmap for Stroke: Challenging the Role of the Neuronal Extracellular Matrix." International Journal of Molecular Sciences 21, no. 20 (October 13, 2020): 7554. http://dx.doi.org/10.3390/ijms21207554.
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Stroke is a major challenge in modern medicine and understanding the role of the neuronal extracellular matrix (NECM) in its pathophysiology is fundamental for promoting brain repair. Currently, stroke research is focused on the neurovascular unit (NVU). Impairment of the NVU leads to neuronal loss through post-ischemic and reperfusion injuries, as well as coagulatory and inflammatory processes. The ictal core is produced in a few minutes by the high metabolic demand of the central nervous system. Uncontrolled or prolonged inflammatory response is characterized by leukocyte infiltration of the injured site that is limited by astroglial reaction. The metabolic failure reshapes the NECM through matrix metalloproteinases (MMPs) and novel deposition of structural proteins continues within months of the acute event. These maladaptive reparative processes are responsible for the neurological clinical phenotype. In this review, we aim to provide a systems biology approach to stroke pathophysiology, relating the injury to the NVU with the pervasive metabolic failure, inflammatory response and modifications of the NECM. The available data will be used to build a protein–protein interaction (PPI) map starting with 38 proteins involved in stroke pathophysiology, taking into account the timeline of damage and the co-expression scores of their RNA patterns The application of the proposed network could lead to a more accurate design of translational experiments aiming at improving both the therapy and the rehabilitation processes.
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Ang’ang’o, Lilian Mbaisi, Jeremy Keith Herren, and Özlem Tastan Bishop. "Structural and Functional Annotation of Hypothetical Proteins from the Microsporidia Species Vittaforma corneae ATCC 50505 Using in silico Approaches." International Journal of Molecular Sciences 24, no. 4 (February 9, 2023): 3507. http://dx.doi.org/10.3390/ijms24043507.
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Microsporidia are spore-forming eukaryotes that are related to fungi but have unique traits that set them apart. They have compact genomes as a result of evolutionary gene loss associated with their complete dependency on hosts for survival. Despite having a relatively small number of genes, a disproportionately high percentage of the genes in microsporidia genomes code for proteins whose functions remain unknown (hypothetical proteins—HPs). Computational annotation of HPs has become a more efficient and cost-effective alternative to experimental investigation. This research developed a robust bioinformatics annotation pipeline of HPs from Vittaforma corneae, a clinically important microsporidian that causes ocular infections in immunocompromised individuals. Here, we describe various steps to retrieve sequences and homologs and to carry out physicochemical characterization, protein family classification, identification of motifs and domains, protein–protein interaction network analysis, and homology modelling using a variety of online resources. Classification of protein families produced consistent findings across platforms, demonstrating the accuracy of annotation utilizing in silico methods. A total of 162 out of 2034 HPs were fully annotated, with the bulk of them categorized as binding proteins, enzymes, or regulatory proteins. The protein functions of several HPs from Vittaforma corneae were accurately inferred. This improved our understanding of microsporidian HPs despite challenges related to the obligate nature of microsporidia, the absence of fully characterized genes, and the lack of homologous genes in other systems.
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Uldry, Anne-Christine, Anabel Maciel-Dominguez, Maïwenn Jornod, Natasha Buchs, Sophie Braga-Lagache, Justine Brodard, Jovana Jankovic, Nicolas Bonadies, and Manfred Heller. "Effect of Sample Transportation on the Proteome of Human Circulating Blood Extracellular Vesicles." International Journal of Molecular Sciences 23, no. 9 (April 19, 2022): 4515. http://dx.doi.org/10.3390/ijms23094515.
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Circulating extracellular vesicles (cEV) are released by many kinds of cells and play an important role in cellular communication, signaling, inflammation modulation, coagulation, and tumor growth. cEV are of growing interest, not only as biomarkers, but also as potential treatment targets. However, very little is known about the effect of transporting biological samples from the clinical ward to the diagnostic laboratory, notably on the protein composition. Pneumatic tube systems (PTS) and human carriers (C) are both routinely used for transport, subjecting the samples to different ranges of mechanical forces. We therefore investigated qualitatively and quantitatively the effect of transport by C and PTS on the human cEV proteome and particle size distribution. We found that samples transported by PTS were subjected to intense, irregular, and multidirectional shocks, while those that were transported by C mostly underwent oscillations at a ground frequency of approximately 4 Hz. PTS resulted in the broadening of nanoparticle size distribution in platelet-free (PFP) but not in platelet-poor plasma (PPP). Cell-type specific cEV-associated protein abundances remained largely unaffected by the transport type. Since residual material of lymphocytes, monocytes, and platelets seemed to dominate cEV proteomes in PPP, it was concluded that PFP should be preferred for any further analyses. Differential expression showed that the impact of the transport method on cEV-associated protein composition was heterogeneous and likely donor-specific. Correlation analysis was nonetheless able to detect that vibration dose, shocks, and imparted energy were associated with different terms depending on the transport, namely in C with cytoskeleton-regulated cell organization activity, and in PTS with a release of extracellular vesicles, mainly from organelle origin, and specifically from mitochondrial structures. Feature selection algorithm identified proteins which, when considered together with the correlated protein-protein interaction network, could be viewed as surrogates of network clusters.
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Ma, Jun-Xiao, Yi Yang, Guang Li, and Bin-Guang Ma. "Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation." International Journal of Molecular Sciences 22, no. 21 (November 2, 2021): 11907. http://dx.doi.org/10.3390/ijms222111907.
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Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.
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Lin, Kai-Jung, Kai-Lieh Lin, Shang-Der Chen, Chia-Wei Liou, Yao-Chung Chuang, Hung-Yu Lin, and Tsu-Kung Lin. "The Overcrowded Crossroads: Mitochondria, Alpha-Synuclein, and the Endo-Lysosomal System Interaction in Parkinson’s Disease." International Journal of Molecular Sciences 20, no. 21 (October 25, 2019): 5312. http://dx.doi.org/10.3390/ijms20215312.
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Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, mainly affecting the elderly. The disease progresses gradually, with core motor presentations and a multitude of non-motor manifestations. There are two neuropathological hallmarks of PD, the dopaminergic neuronal loss and the alpha-synuclein-containing Lewy body inclusions in the substantia nigra. While the exact pathomechanisms of PD remain unclear, genetic investigations have revealed evidence of the involvement of mitochondrial function, alpha-synuclein (α-syn) aggregation, and the endo-lysosomal system, in disease pathogenesis. Due to the high energy demand of dopaminergic neurons, mitochondria are of special importance acting as the cellular powerhouse. Mitochondrial dynamic fusion and fission, and autophagy quality control keep the mitochondrial network in a healthy state. Should defects of the organelle occur, a variety of reactions would ensue at the cellular level, including disrupted mitochondrial respiratory network and perturbed calcium homeostasis, possibly resulting in cellular death. Meanwhile, α-syn is a presynaptic protein that helps regulate synaptic vesicle transportation and endocytosis. Its misfolding into oligomeric sheets and fibrillation is toxic to the mitochondria and neurons. Increased cellular oxidative stress leads to α-syn accumulation, causing mitochondrial dysfunction. The proteasome and endo-lysosomal systems function to regulate damage and unwanted waste management within the cell while facilitating the quality control of mitochondria and α-syn. This review will analyze the biological functions and interactions between mitochondria, α-syn, and the endo-lysosomal system in the pathogenesis of PD.
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Fan, Jason, Xuan Cindy Li, Mark Crovella, and Mark D. M. Leiserson. "Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information." Bioinformatics 36, Supplement_2 (December 2020): i866—i874. http://dx.doi.org/10.1093/bioinformatics/btaa818.
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Abstract Motivation Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker’s yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce. Results In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein–protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker’s and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost. Availability Implementations of models and experiments are available at: https://github.com/lrgr/EMF. Supplementary information Supplementary data are available at Bioinformatics online.
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Khalid, Rana, Abdul Siddiqi, Efstratios Mylonas, Arooma Maryam, and Michael Kokkinidis. "Dynamic Characterization of the Human Heme Nitric Oxide/Oxygen (HNOX) Domain under the Influence of Diatomic Gaseous Ligands." International Journal of Molecular Sciences 20, no. 3 (February 6, 2019): 698. http://dx.doi.org/10.3390/ijms20030698.
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Soluble guanylate cyclase (sGC) regulates numerous physiological processes. The β subunit Heme Nitric Oxide/Oxygen (HNOX) domain makes this protein sensitive to small gaseous ligands. The structural basis of the activation mechanism of sGC under the influence of ligands (NO, O2, CO) is poorly understood. We examine the effect of different ligands on the human sGC HNOX domain. HNOX systems with gaseous ligands were generated and explored using Molecular Dynamics (MD). The distance between heme Fe2+ and histidine in the NO-ligated HNOX (NO-HNOX) system is larger compared to the O2, CO systems. NO-HNOX rapidly adopts the conformation of the five-group metal coordination system. Loops α, β, γ and helix-f exhibit increased mobility and different hydrogen bond networks in NO-HNOX compared to the other systems. The removal of His from the Fe coordination sphere in NO-HNOX is assisted by interaction of the imidazole ring with the surrounding residues which in turn leads to the release of signaling helix-f and activation of the sGC enzyme. Insights into the conformational dynamics of a human sGC HNOX domain, especially for regions which are functionally critical for signal transduction, are valuable in the understanding of cardiovascular diseases.
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Contreras-Riquelme, Sebastián, Jose-Antonio Garate, Tomas Perez-Acle, and Alberto J. M. Martin. "RIP-MD: a tool to study residue interaction networks in protein molecular dynamics." PeerJ 6 (December 7, 2018): e5998. http://dx.doi.org/10.7717/peerj.5998.
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Protein structure is not static; residues undergo conformational rearrangements and, in doing so, create, stabilize or break non-covalent interactions. Molecular dynamics (MD) is a technique used to simulate these movements with atomic resolution. However, given the data-intensive nature of the technique, gathering relevant information from MD simulations is a complex and time consuming process requiring several computational tools to perform these analyses. Among different approaches, the study of residue interaction networks (RINs) has proven to facilitate the study of protein structures. In a RIN, nodes represent amino-acid residues and the connections between them depict non-covalent interactions. Here, we describe residue interaction networks in protein molecular dynamics (RIP-MD), a visual molecular dynamics (VMD) plugin to facilitate the study of RINs using trajectories obtained from MD simulations of proteins. Our software generates RINs from MD trajectory files. The non-covalent interactions defined by RIP-MD include H-bonds, salt bridges, VdWs, cation-π, π–π, Arginine–Arginine, and Coulomb interactions. In addition, RIP-MD also computes interactions based on distances between Cαs and disulfide bridges. The results of the analysis are shown in an user friendly interface. Moreover, the user can take advantage of the VMD visualization capacities, whereby through some effortless steps, it is possible to select and visualize interactions described for a single, several or all residues in a MD trajectory. Network and descriptive table files are also generated, allowing their further study in other specialized platforms. Our method was written in python in a parallelized fashion. This characteristic allows the analysis of large systems impossible to handle otherwise. RIP-MD is available at http://www.dlab.cl/ripmd.
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Timsit, Youri, and Sergeant-Perthuis Grégoire. "Towards the Idea of Molecular Brains." International Journal of Molecular Sciences 22, no. 21 (November 1, 2021): 11868. http://dx.doi.org/10.3390/ijms222111868.
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How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.
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Zeng, Xiangxiang, Siyi Zhu, Yuan Hou, Pengyue Zhang, Lang Li, Jing Li, L. Frank Huang, Stephen J. Lewis, Ruth Nussinov, and Feixiong Cheng. "Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest." Bioinformatics 36, no. 9 (January 23, 2020): 2805–12. http://dx.doi.org/10.1093/bioinformatics/btaa010.
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Abstract Motivation Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug–target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. Results In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF. Supplementary information Supplementary data are available at Bioinformatics online.
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Nakayoshi, Tomoki, Yusuke Ohnishi, Hideaki Tanaka, Genji Kurisu, Hiroko X. Kondo, and Yu Takano. "Effects of Active-Center Reduction of Plant-Type Ferredoxin on Its Structure and Dynamics: Computational Analysis Using Molecular Dynamics Simulations." International Journal of Molecular Sciences 23, no. 24 (December 14, 2022): 15913. http://dx.doi.org/10.3390/ijms232415913.
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“Plant-type” ferredoxins (Fds) in the thylakoid membranes of plants, algae, and cyanobacteria possess a single [2Fe‒2S] cluster in active sites and mediate light-induced electron transfer from Photosystem I reaction centers to various Fd-dependent enzymes. Structural knowledge of plant-type Fds is relatively limited to static structures, and the detailed behavior of oxidized and reduced Fds has not been fully elucidated. It is important that the investigations of the effects of active-center reduction on the structures and dynamics for elucidating electron-transfer mechanisms. In this study, model systems of oxidized and reduced Fds were constructed from the high-resolution crystal structure of Chlamydomonas reinhardtii Fd1, and three 200 ns molecular dynamics simulations were performed for each system. The force field parameters of the oxidized and reduced active centers were independently obtained using quantum chemical calculations. There were no substantial differences in the global conformations of the oxidized and reduced forms. In contrast, active-center reduction affected the hydrogen-bond network and compactness of the surrounding residues, leading to the increased flexibility of the side chain of Phe61, which is essential for the interaction between Fd and the target protein. These computational results will provide insight into the electron-transfer mechanisms in the Fds.
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Alshalalfa, Mohammed. "MicroRNA Response Elements-Mediated miRNA-miRNA Interactions in Prostate Cancer." Advances in Bioinformatics 2012 (November 4, 2012): 1–10. http://dx.doi.org/10.1155/2012/839837.
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The cell is a highly organized system of interacting molecules including proteins, mRNAs, and miRNAs. Analyzing the cell from a systems perspective by integrating different types of data helps revealing the complexity of diseases. Although there is emerging evidence that microRNAs have a functional role in cancer, the role of microRNAs in mediating cancer progression and metastasis remains not fully explored. As the amount of available miRNA and mRNA gene expression data grows, more systematic methods combining gene expression and biological networks become necessary to explore miRNA function. In this work I integrated functional miRNA-target interactions with mRNA and miRNA expression to infer mRNA-mediated miRNA-miRNA interactions. The inferred network represents miRNA modulation through common targets. The network is used to characterize the functional role of microRNA response element (MRE) to mediate interactions between miRNAs targeting the MRE. Results revealed that miRNA-1 is a key player in regulating prostate cancer progression. 11 miRNAs were identified as diagnostic and prognostic biomarkers that act as tumor suppressor miRNAs. This work demonstrates the utility of a network analysis as opposed to differential expression to find important miRNAs that regulate prostate cancer.
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FUJITA, ANDRÉ, JOÃO RICARDO SATO, HUMBERTO MIGUEL GARAY-MALPARTIDA, MARI CLEIDE SOGAYAR, CARLOS EDUARDO FERREIRA, and SATORU MIYANO. "MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 06, no. 05 (October 2008): 961–79. http://dx.doi.org/10.1142/s0219720008003746.
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In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-κB, and c-Myc) for HeLa cells.
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CHUA, HON NIAN, KANG NING, WING-KIN SUNG, HON WAI LEONG, and LIMSOON WONG. "USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.
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Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.
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MATSUZAKI, YURI, YUSUKE MATSUZAKI, TOSHIYUKI SATO, and YUTAKA AKIYAMA. "IN SILICO SCREENING OF PROTEIN–PROTEIN INTERACTIONS WITH ALL-TO-ALL RIGID DOCKING AND CLUSTERING: AN APPLICATION TO PATHWAY ANALYSIS." Journal of Bioinformatics and Computational Biology 07, no. 06 (December 2009): 991–1012. http://dx.doi.org/10.1142/s0219720009004461.
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We propose a computational screening system of protein–protein interactions using tertiary structure data. Our system combines all-to-all protein docking and clustering to find interacting protein pairs. We tuned our prediction system by applying various parameters and clustering algorithms and succeeded in outperforming previous methods. This method was also applied to a biological pathway estimation problem to show its use in network level analysis. The structural data were collected from the Protein Data Bank, PDB. Then all-to-all docking among target protein structures was conducted using a conventional protein–protein docking software package, ZDOCK. The highest-ranked 2000 decoys were clustered based on structural similarity among the predicted docking forms. The features of generated clusters were analyzed to estimate the biological relevance of protein–protein interactions. Our system achieves a best F-measure value of 0.43 when applied to a subset of general protein–protein docking benchmark data. The same system was applied to protein data in a bacterial chemotaxis pathway, utilizing essentially the same parameter set as the benchmark data. We obtained 0.45 for the F-measure value. The proposed approach to computational PPI detection is a promising methodology for mediating between structural studies and systems biology by utilizing cumulative protein structure data for pathway analysis.
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Alwadi, Diaaidden, Quentin Felty, Changwon Yoo, Deodutta Roy, and Alok Deoraj. "Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer." International Journal of Molecular Sciences 24, no. 4 (February 6, 2023): 3191. http://dx.doi.org/10.3390/ijms24043191.
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Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein–protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60–80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.
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Wang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.
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Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%–80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.
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GAO, LIN, PENG-GANG SUN, and JIA SONG. "CLUSTERING ALGORITHMS FOR DETECTING FUNCTIONAL MODULES IN PROTEIN INTERACTION NETWORKS." Journal of Bioinformatics and Computational Biology 07, no. 01 (February 2009): 217–42. http://dx.doi.org/10.1142/s0219720009004023.
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Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPI networks. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitate a fast, accurate approach to biological complex identification. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPI networks. In this paper, we review some representative algorithms, focusing on the algorithms underlying the approaches and how the algorithms relate to each other. In particular, a comparison is given based on the property of the algorithms. Since the PPI network is noisy and still incomplete, some methods which consider other additional properties for preprocessing and purifying of PPI data are presented. We also give a discussion about the functional annotation and validation of protein complexes. Finally, new progress and future research directions are discussed from the computational viewpoint.