To see the other types of publications on this topic, follow the link: Network pathway.
Journal articles on the topic 'Network pathway'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Network pathway.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Milano, Marianna, Giuseppe Agapito, and Mario Cannataro. "Challenges and Limitations of Biological Network Analysis." BioTech 11, no. 3 (July 7, 2022): 24. http://dx.doi.org/10.3390/biotech11030024.
Full textAbstract:
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
2
Zheng, Fang, Le Wei, Liang Zhao, and FuChuan Ni. "Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks." BioMed Research International 2018 (July 30, 2018): 1–12. http://dx.doi.org/10.1155/2018/5670210.
Full textAbstract:
Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.
3
Wong, Yung-Hao, Chia-Chou Wu, Chih-Lung Lin, Ting-Shou Chen, Tzu-Hao Chang, and Bor-Sen Chen. "Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma." BioMed Research International 2015 (2015): 1–27. http://dx.doi.org/10.1155/2015/391475.
Full textAbstract:
Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.
4
Villeneuve, Daniel L., Michelle M. Angrish, Marie C. Fortin, Ioanna Katsiadaki, Marc Leonard, Luigi Margiotta-Casaluci, Sharon Munn, et al. "Adverse outcome pathway networks II: Network analytics." Environmental Toxicology and Chemistry 37, no. 6 (May 7, 2018): 1734–48. http://dx.doi.org/10.1002/etc.4124.
Full text
5
Loney, Fred, and Guanming Wu. "Automation of ReactomeFIViz via CyREST API." F1000Research 7 (May 2, 2018): 531. http://dx.doi.org/10.12688/f1000research.14776.1.
Full textAbstract:
Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.
6
Loney, Fred, and Guanming Wu. "Automation of ReactomeFIViz via CyREST API." F1000Research 7 (May 23, 2018): 531. http://dx.doi.org/10.12688/f1000research.14776.2.
Full textAbstract:
Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.
7
Ellershaw, John, and Deborah Murphy. "The National Pathway Network of Palliative Care Pathways." Journal of integrated Care Pathways 7, no. 1 (April 2003): 11–13. http://dx.doi.org/10.1177/147322970300700104.
Full text
8
Cocco, Nicoletta, Mercè Llabrés, Mariana Reyes-Prieto, and Marta Simeoni. "MetNet: A two-level approach to reconstructing and comparing metabolic networks." PLOS ONE 16, no. 2 (February 12, 2021): e0246962. http://dx.doi.org/10.1371/journal.pone.0246962.
Full textAbstract:
Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.
9
Li, Chaoxing, Li Liu, and Valentin Dinu. "Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma." PeerJ 6 (April 9, 2018): e4571. http://dx.doi.org/10.7717/peerj.4571.
Full textAbstract:
Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.
10
Li, Xiao, Xu Feng, Chunkang Chang, Qi He, and Wu Lingyun. "Identification of microRNA-Regulated Pathways through a Integration of Mcrorna-mRNA Microarray and Bioinformatics Analysis in CD34+ Cells of Myelodysplastic Syndromes." Blood 124, no. 21 (December 6, 2014): 3238. http://dx.doi.org/10.1182/blood.v124.21.3238.3238.
Full textAbstract:
Abstract Background MicroRNAs (miRNAs) are considered to play a key role in the pathogenesis of myelodysplastic syndromes (MDS). However, the effect of miRNA and targeted mRNA on signal transduction is not fully understood in MDS. Objective The objective of this study is to identify the miRNAs-regulated pathways. Methods Affymetrix GeneChip microRNA and PrimeView Array were used to analyze miRNAs and gene expression profile of CD34+ cells in 12 MDS patients and 6 healthy controls. Comprehensive bioinformatics analysis of the coordinate expression of miRNAs and mRNAs including Difference, Go, Pathway, Pathway-network, miRNA-Gene-Network and miRNA-Go-Network analysis was performed to identify the miRNAs-regulated networks. Results 1. 34 differentially expressed miRNAs (5 up- and 29 down-regulated miRNAs) and 1783 mRNAs (405 up- and 1378 down-regulated mRNAs) in CD34+ cells from MDS and Healthy controls were identified by miRNA and mRNA microarray, respectively (Fig.1). 2. 25 dysregulated miRNAs and 234 targeted mRNAs were identified by a combination of Pearson's correlation analysis and prediction by TargetScan; 394 target relationship of miRNAs was established (Fig.2). 3. Go analysis revealed that these miRNA-mRNAs pairs were involved in signal transduction, apoptotic process, DNA-dependent transcription regulation, protein phosphophoration, etc. Pathway analysis showed that MAPK, JAK/STAT and PI3K/Akt signaling pathways might be regulated by these miRNA-mRNAs pairs (Fig.3). 4. The pathway-network analysis revealed that MAPK signaling pathway, Jak-Stat signaling pathway and apoptosis signaling pathway (displayed by red cycle) located in the downstream of signal networks (Fig. 3E). Dysregulation of These pathways may be more meaningful for explaining the pathogenesis of MDS. 5. Through a combination of Pathway, miRNA-Gene-Network and miRNA-Go- Network analysis, 29 miRNA-mRNA-regulated pathways were identified such as miR-148a/TEK/PI3K-Akt signaling pathway, miR-195/BDNF/MAPK signaling pathway, miR-195/DLL1/Notch signaling pathway, miR-145/CCND2/ JAK-STAT signaling pathway, etc. (Table 1). Conclusion Alteration expression of several miRNAs and targeted mRNAs might have an important impact on cancer-related cellular pathways including MAPK, PI3K/Akt, JAK/STAT, etc. The role of these miRNAs-mediated pathways in pathogenesis of MDS merit further investigation. Fig. 1 Affymetrix mcroRNA and mRNA microarray in MDS Fig. 1. Affymetrix mcroRNA and mRNA microarray in MDS Fig. 2 Significant miRNA-mRNA pairs identified through a integration of mcroRNA-mRNA microarray Fig. 2. Significant miRNA-mRNA pairs identified through a integration of mcroRNA-mRNA microarray Table 1. Parts of dysregulated miRNAs, genes and targeted pathway in MDS MicroRNA Style Gene_synbol Pathway miR-148a Down TEK PI3K-Akt signaling pathway ITGA9 PI3K-Akt signaling pathway KIT PI3K-Akt signaling pathway HMGA2 Transcriptional misregulation in cancer miR-145 Down HHEX Transcriptional misregulation in cancer MEIS1 Transcriptional misregulation in cancer miR-200c Down EFNA1 PI3K-Akt signaling pathway KLF3 Transcriptional misregulation in cancer miR-195 Up BDNF MAPK signaling pathway CDC25B MAPK signaling pathway DLL1 Notch signaling pathway MRAS MAPK signaling pathway miR-17 Up CAMK2D Calcium signaling pathway miR-19a Up MAML1 Notch signaling pathway SLC8A1 Calcium signaling pathway THBS1 Proteoglycans in cancer TNF MAPK signaling pathway TNFRSF1B Adipocytokine signaling pathway ACSL1 Adipocytokine signaling pathway EDNRB Calcium signaling pathway miR-19b Up CALM1 Calcium signaling pathway TNF Proteoglycans in cancer Fig. 3 Go and pathway analysis Fig. 3. Go and pathway analysis Disclosures No relevant conflicts of interest to declare.
11
Pita-Juárez, Yered, Gabriel Altschuler, Sokratis Kariotis, Wenbin Wei, Katjuša Koler, Claire Green, Rudolph E. Tanzi, and Winston Hide. "The Pathway Coexpression Network: Revealing pathway relationships." PLOS Computational Biology 14, no. 3 (March 19, 2018): e1006042. http://dx.doi.org/10.1371/journal.pcbi.1006042.
Full text
12
Kim, Kyung Soo, Dong Wook Jekarl, Jaeeun Yoo, Seungok Lee, Myungshin Kim, and Yonggoo Kim. "Immune gene expression networks in sepsis: A network biology approach." PLOS ONE 16, no. 3 (March 5, 2021): e0247669. http://dx.doi.org/10.1371/journal.pone.0247669.
Full textAbstract:
To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.
13
Cheng, Qiong, and Alexander Zelikovsky. "Combinatorial Optimization Algorithms for Metabolic Networks Alignments and Their Applications." International Journal of Knowledge Discovery in Bioinformatics 2, no. 1 (January 2011): 1–23. http://dx.doi.org/10.4018/jkdb.2011010101.
Full textAbstract:
The accumulation of high-throughput genomic and proteomic data allows for reconstruction of large and complex metabolic networks. To analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks; finding similar networks is computationally challenging. Based on gene duplication and function sharing in biological networks, a network alignment problem is formulated that asks the optimal vertex-to-vertex mapping allowing path contraction, different types of vertex deletion, and vertex insertions. This paper presents fixed parameter tractable combinatorial optimization algorithms, which take into account the similarity of both the enzymes’ functions arbitrary network topologies. Results are evaluated by the randomized P-Value computation. The authors perform pairwise alignments of all pathways for four organisms and find a set of statistically significant pathway similarities. The network alignment is used to identify pathway holes that are the result of inconsistencies and missing enzymes. The authors propose a framework of filling pathway holes by including database searches for missing enzymes and proteins with the matching prosites and further finding potential candidates with high sequence similarity.
14
Xie, Fuda, Mingxiang Xie, Yibing Yang, Miaomiao Zhang, Xiaojie Xu, Na Liu, Wei Xiao, and Jiangyong Gu. "Assessing the Anti-inflammatory Mechanism of Reduning Injection by Network Pharmacology." BioMed Research International 2020 (December 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/6134098.
Full textAbstract:
Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets (Bcl-2, eNOS, PTGS2, PPARA, and MMPs), and four key pathways (estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway) which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease.
15
Shuey, Megan M., Rachel R. Xiang, M. Elizabeth Moss, Brigett V. Carvajal, Yihua Wang, Nicholas Camarda, Daniel Fabbri, et al. "Systems Approach to Integrating Preclinical Apolipoprotein E-Knockout Investigations Reveals Novel Etiologic Pathways and Master Atherosclerosis Network in Humans." Arteriosclerosis, Thrombosis, and Vascular Biology 42, no. 1 (January 2022): 35–48. http://dx.doi.org/10.1161/atvbaha.121.317071.
Full textAbstract:
Objective: Animal models of atherosclerosis are used extensively to interrogate molecular mechanisms in serial fashion. We tested whether a novel systems biology approach to integration of preclinical data identifies novel pathways and regulators in human disease. Approach and Results: Of 716 articles published in ATVB from 1995 to 2019 using the apolipoprotein E knockout mouse to study atherosclerosis, data were extracted from 360 unique studies in which a gene was experimentally perturbed to impact plaque size or composition and analyzed using Ingenuity Pathway Analysis software. TREM1 (triggering receptor expressed on myeloid cells) signaling and LXR/RXR (liver X receptor/retinoid X receptor) activation were identified as the top atherosclerosis-associated pathways in mice (both P <1.93×10 − 4 , TREM1 implicated early and LXR/RXR in late atherogenesis). The top upstream regulatory network in mice (sc-58125, a COX2 inhibitor) linked 64.0% of the genes into a single network. The pathways and networks identified in mice were interrogated by testing for associations between the genetically predicted gene expression of each mouse pathway-identified human homolog with clinical atherosclerosis in a cohort of 88 660 human subjects. Homologous human pathways and networks were significantly enriched for gene-atherosclerosis associations (empirical P <0.01 for TREM1 and LXR/RXR pathways and COX2 network). This included 12(60.0%) TREM1 pathway genes, 15(53.6%) LXR/RXR pathway genes, and 67(49.3%) COX2 network genes. Mouse analyses predicted, and human study validated, the strong association of COX2 expression ( PTGS2 ) with increased likelihood of atherosclerosis (odds ratio, 1.68 per SD of genetically predicted gene expression; P =1.07×10 − 6 ). Conclusions: PRESCIANT (Preclinical Science Integration and Translation) leverages published preclinical investigations to identify high-confidence pathways, networks, and regulators of human disease.
16
Jaeger, Savina, Junxia Min, Florian Nigsch, Miguel Camargo, Janna Hutz, Allen Cornett, Stephen Cleaver, Alan Buckler, and Jeremy L. Jenkins. "Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer." Journal of Biomolecular Screening 19, no. 5 (February 11, 2014): 791–802. http://dx.doi.org/10.1177/1087057114522690.
Full textAbstract:
Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than “upstream” nodes that are potentially causal of “downstream” changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2–phosphatidylinositide 3-kinase–AKT–MAPK growth pathway and ATR–p53–BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF–WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR–IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
17
Kubal, Timothy Edward, Douglas D. Letson, Karen K. Fields, Richard M. Levine, Charles F. Andrews, John Turner Hamm, Diana Lachica, Riti Shimkhada, and John W. Peabody. "Building a provider network based on quality: The Moffitt Oncology Network initiative." Journal of Clinical Oncology 32, no. 30_suppl (October 20, 2014): 49. http://dx.doi.org/10.1200/jco.2014.32.30_suppl.49.
Full textAbstract:
49 Background: Before entering into risk bearing contracts with payors, ACOs are challenged to find a basis for forming partnerships. Specialty ACO networks, in particular, must find ways to provide a common, high standard of care among a typically varied set of partners. The Moffitt Oncology Network (MON) Initiative demonstrates a possible solution to forming a value based ACO network across a broad geographical area that is based upon using clinical pathways. Methods: Moffitt Cancer Center (MCC) has developed more than 24 different disease specific pathways. The MCC pathways translate evidence-based guidelines into personalized cancer care throughout the continuum of care from evaluation to treatment. MCC is using these pathways with other hospital systems and physician groups throughout the MON. To enhance the use of pathways in the MON, MCC uses Clinical Performance and Value (CPV) Vignettes. CPV’s, are virtual patient cases related to the specific clinical pathways. The report herein is on pathway implementation in several disease areas (breast, lung and gastrointestinal (GI) cancers) across multiple sites: Lehigh Valley Hospital (Pennsylvania), Norton Cancer Institute (Kentucky), and Space Coast Cancer Center (Florida). Results: Pathway based clinical care was measured at baseline using CPVs across disease and site (Table). A total of 67 breast cancer providers took 131 breast cancer vignettes; 35 lung cancer providers took 104 lung cancer vignettes; and to date 27 GI cancer providers have taken 54 GI vignettes. There is statistically significant variation in performance among providers and between sites. This is manifest in pathway-specified areas of work-up, diagnosis, and treatment. Conclusions: Fostering adoption of clinical pathways is a practical objective that can help guide the formation of an ACO oncology network. This may be useful for forming specialty ACOs that establish a standard of care and set the stage for adopting new payment models with payors. [Table: see text]
18
Alcaraz, Nicolas, Markus List, Martin Dissing-Hansen, Marc Rehmsmeier, Qihua Tan, Jan Mollenhauer, Henrik J. Ditzel, and Jan Baumbach. "Robust de novo pathway enrichment with KeyPathwayMiner 5." F1000Research 5 (June 28, 2016): 1531. http://dx.doi.org/10.12688/f1000research.9054.1.
Full textAbstract:
Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.
19
Wong, Yung-Hao, Cheng-Wei Li, and Bor-Sen Chen. "Evolution of Network Biomarkers from Early to Late Stage Bladder Cancer Samples." BioMed Research International 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/159078.
Full textAbstract:
We use a systems biology approach to construct protein-protein interaction networks (PPINs) for early and late stage bladder cancer. By comparing the networks of these two stages, we find that both networks showed very significantly different mechanisms. To obtain the differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two bladder cancer stages using microarray data from cancer cells and their adjacent noncancer cells, respectively. With their carcinogenesis relevance values (CRVs), we identified 152 and 50 significant proteins and their PPI networks (network markers) for early and late stage bladder cancer by statistical assessment. To investigate the evolution of network biomarkers in the carcinogenesis process, primary pathway analysis showed that the significant pathways of early stage bladder cancer are related to ordinary cancer mechanisms, while the ribosome pathway and spliceosome pathway are most important for late stage bladder cancer. Their only intersection is the ubiquitin mediated proteolysis pathway in the whole stage of bladder cancer. The evolution of network biomarkers from early to late stage can reveal the carcinogenesis of bladder cancer. The findings in this study are new clues specific to this study and give us a direction for targeted cancer therapy, and it should be validated in vivo or in vitro in the future.
20
Fan, Wufeng, Yuhan Zhou, and Hao Li. "Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes." Computational and Mathematical Methods in Medicine 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/3195348.
Full textAbstract:
In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.
21
Nazha, Aziz, Bartlomiej Przychodzen, Hideki Makishima, Hetty E. Carraway, Bhumika J. Patel, Cassandra M. Hirsch, Michael Clemente, et al. "Network-Based Analysis of Exome Sequencing Mutations Identifies Molecular Subtypes of Myelodysplastic Syndromes." Blood 126, no. 23 (December 3, 2015): 611. http://dx.doi.org/10.1182/blood.v126.23.611.611.
Full textAbstract:
Abstract Myelodysplastic syndromes (MDS) are a complex, heterogeneous group of disorders characterized by the accumulation of somatic mutations in combinations that vary between patients. While individual mutations have been identified that can risk stratify patients or identify targets for therapies, these findings have been relevant to only a minority of patients, as MDS is a disease not of individual mutations but of combinations of genes acting in molecular networks corresponding to several functional and biological pathways. Further, although two patients with MDS may not have mutations in common, they may share a network affected by similar genes. We performed exome sequencing of 201 samples from the bone marrow and peripheral blood of patients with MDS, MDS/MPN, and secondary AML (sAML). Network interactions were retrieved from several publically available databases (InACT, MINT, STRING, etc) and uploaded into cytoscape (an open source software platform for visualizing molecular interaction networks and biological pathways). Functional interactions and pathways were uploaded from Reactome and visualized in cytoscape using Reactome Functional Interaction (FI) network function (Reactome WIKI). Survival analyses were calculated from time of diagnosis to last follow up or death on samples with clinical data. Overall, 3452 mutations were detected with a median of 25 mutations per sample. Network-based analyses identified 745 genes with 293 interactions. Pathway enrichment analysis of the network identified novel pathways that have not been described previously in MDS including: Robo receptor signaling pathway, EphB-Abl signaling pathway, amb2 integrin signaling pathway and NOD-like receptor pathway. Standard clustering analysis (networks with high connections between nodes within the cluster but sparse connections with nodes in different clusters) identified 6 molecular subtypes of MDS, Figure 1. Pathway enrichment analysis of each subtype identified distinct pathways for each: subtype 1 was enriched mainly in immune mediated pathways, RAS/RAF/MAP kinase signaling pathway, EGFR signaling pathway, VEGF signaling pathway, and ERBB signaling pathway; subtype 2 enriched in spliceosome and RNA polymerase transcription pathways; subtype 3 enriched in mitosis and cell cycle pathways; subtype 4 enriched in cadherin and Wnt signaling pathways; subtype 5 enriched in DNA and histone methylation pathways; and subtype 6 with TP53 and DNA damage pathways. To determine the biological importance of the identified subtypes on outcome, we investigated whether each subtype affected clinical characteristics and overall survival. Overall, clinical data was available for 126 patients. Median age was 70 years, 66% have MDS, 17% MDS/MPN, and 17% s AML, 53% have low risk, 21% intermediate, and 26% high risk by the Revised International Prognostic Scoring System (IPSS-R). Clinical characteristics correlated with molecular subtypes: subtype 6 patients were older (median age 76) with higher blasts percentage (median 7%), 50% had sAML, and 20% RAEB-2 (higher risk by IPSS-R), whereas subtype 3 patients were younger (median age 65), has lower blasts percentage (median 2%) and 83% of them had lower risk MDS by IPSS-R. Excluding samples with overlapping subtypes, the median overall survival for patients with subtype 1,2,3,4,5,6 was 33.0, 24.6, 46.6, 22.9, 25.7, 6.6 months, respectively, p= 0.002. Given similar survival for subtypes 2,4, and 5, these were combined in one group, Figure 1. To further identify potential genes in our network for targeted therapies, we searched the publically available targeted therapies databases (TARGET and Therapeutic Target Database). We found 30 potential compounds either in clinical trials or under development that could be explored in MDS. In conclusion: network-based analyses defined molecular subtypes of MDS that were predictive of survival. It also identified potential targets for novel therapies that are in clinical trials or under development. These subtypes may be useful in the development of precision medicine strategies that are specifically directed at the pathways that are enriched in each subtype. Figure 1. Network-based analysis subtypes of MDS and overall survival Figure 1. Network-based analysis subtypes of MDS and overall survival Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.
22
Gerlee, P., T. Lundh, B. Zhang, and A. R. A. Anderson. "Gene divergence and pathway duplication in the metabolic network of yeast and digital organisms." Journal of The Royal Society Interface 6, no. 41 (March 18, 2009): 1233–45. http://dx.doi.org/10.1098/rsif.2008.0514.
Full textAbstract:
We have studied the metabolic gene–function network in yeast and digital organisms evolved in the artificial life platform A vida . The gene–function network is a bipartite network in which a link exists between a gene and a function (pathway) if that function depends on that gene, and can also be viewed as a decomposition of the more traditional functional gene networks, where two genes are linked if they share any function. We show that the gene–function network exhibits two distinct degree distributions: the gene degree distribution is scale-free while the pathway distribution is exponential. This is true for both yeast and digital organisms, which suggests that this is a general property of evolving systems, and we propose that the scale-free gene degree distribution is due to pathway duplication, i.e. the development of a new pathway where the original function is still retained. Pathway duplication would serve as preferential attachment for the genes, and the experiments with A vida revealed precisely this; genes involved in many pathways are more likely to increase their connectivity. Measuring the overlap between different pathways, in terms of the genes that constitute them, showed that pathway duplication also is a likely mechanism in yeast evolution. This analysis sheds new light on the evolution of genes and functionality, and suggests that function duplication could be an important mechanism in evolution.
23
Hicks, Chindo, Lucio Miele, Tejaswi Koganti, and Srinivasan Vijayakumar. "Comprehensive Assessment and Network Analysis of the Emerging Genetic Susceptibility Landscape of Prostate Cancer." Cancer Informatics 12 (January 2013): CIN.S12128. http://dx.doi.org/10.4137/cin.s12128.
Full textAbstract:
Background Recent advances in high-throughput genotyping have made possible identification of genetic variants associated with increased risk of developing prostate cancer using genome-wide associations studies (GWAS). However, the broader context in which the identified genetic variants operate is poorly understood. Here we present a comprehensive assessment, network, and pathway analysis of the emerging genetic susceptibility landscape of prostate cancer. Methods We created a comprehensive catalog of genetic variants and associated genes by mining published reports and accompanying websites hosting supplementary data on GWAS. We then performed network and pathway analysis using single nucleotide polymorphism (SNP)-containing genes to identify gene regulatory networks and pathways enriched for genetic variants. Results We identified multiple gene networks and pathways enriched for genetic variants including IGF-1, androgen biosynthesis and androgen signaling pathways, and the molecular mechanisms of cancer. The results provide putative functional bridges between GWAS findings and gene regulatory networks and biological pathways.
24
Windels, Sam F. L., Noël Malod-Dognin, and Nataša Pržulj. "Graphlet eigencentralities capture novel central roles of genes in pathways." PLOS ONE 17, no. 1 (January 25, 2022): e0261676. http://dx.doi.org/10.1371/journal.pone.0261676.
Full textAbstract:
Motivation Graphlet adjacency extends regular node adjacency in a network by considering a pair of nodes being adjacent if they participate in a given graphlet (small, connected, induced subgraph). Graphlet adjacencies captured by different graphlets were shown to contain complementary biological functions and cancer mechanisms. To further investigate the relationships between the topological features of genes participating in molecular networks, as captured by graphlet adjacencies, and their biological functions, we build more descriptive pathway-based approaches. Contribution We introduce a new graphlet-based definition of eigencentrality of genes in a pathway, graphlet eigencentrality, to identify pathways and cancer mechanisms described by a given graphlet adjacency. We compute the centrality of genes in a pathway either from the local perspective of the pathway or from the global perspective of the entire network. Results We show that in molecular networks of human and yeast, different local graphlet adjacencies describe different pathways (i.e., all the genes that are functionally important in a pathway are also considered topologically important by their local graphlet eigencentrality). Pathways described by the same graphlet adjacency are functionally similar, suggesting that each graphlet adjacency captures different pathway topology and function relationships. Additionally, we show that different graphlet eigencentralities describe different cancer driver genes that play central roles in pathways, or in the crosstalk between them (i.e. we can predict cancer driver genes participating in a pathway by their local or global graphlet eigencentrality). This result suggests that by considering different graphlet eigencentralities, we can capture different functional roles of genes in and between pathways.
25
Shao, Min. "Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis." Computational and Mathematical Methods in Medicine 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9426280.
Full textAbstract:
We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP) via establishing an microRNAs- (miRNA-) regulated pathway network (MRPN). Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS). Significant pathways based on differentially expressed genes (DEGs) using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC), TargetScan context score (TSCS), and probabilities of conserved targeting (PCT)). An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR) < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and logFC≥2, 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.
26
Tanabe, Shihori, Sabina Quader, Ryuichi Ono, Horacio Cabral, Kazuhiko Aoyagi, Akihiko Hirose, Hiroshi Yokozaki, and Hiroki Sasaki. "Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer." Cancers 12, no. 12 (December 18, 2020): 3833. http://dx.doi.org/10.3390/cancers12123833.
Full textAbstract:
Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC) and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric cancer. Network pathway analysis was performed by Ingenuity Pathway Analysis (IPA). A total of 2815 probe set IDs were significantly different between intestinal- and diffuse-type GC data in cBioPortal Cancer Genomics. Our analysis uncovered 10 genes including male-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP), which have differences in gene expression between intestinal- and diffuse-type GC. A total of 463 direct relationships with three molecules (MYC, NTRK1, UBE2M) were found in the biomarker-filtered network generated by network pathway analysis. The networks and features in intestinal- and diffuse-type GC have been investigated and profiled in bioinformatics. Our results revealed the signaling pathway networks in intestinal- and diffuse-type GC, bringing new light for the elucidation of drug resistance mechanisms in CSCs.
27
Vandermeulen, Matthew D., and Paul J. Cullen. "Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity." PLOS Genetics 18, no. 1 (January 4, 2022): e1009988. http://dx.doi.org/10.1371/journal.pgen.1009988.
Full textAbstract:
Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways’ roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.
28
Liu, Chuan, Fang-Fang Fan, Xuan-Hao Li, Wen-Xiang Wang, Ya Tu, and Yi Zhang. "Elucidation of the mechanism of action of the anticholecystitis effect of the Tibetan medicine “Dida” using network pharmacology." Tropical Journal of Pharmaceutical Research 19, no. 7 (November 17, 2020): 1449–57. http://dx.doi.org/10.4314/tjpr.v19i7.17.
Full textAbstract:
Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein–protein interaction (PPI) network of cholecystitis targets, and compound–target– pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida has been successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical practice.
Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism, Network pharmacology, Molecular docking simulation
29
Liu, Chuan, Fang-Fang Fan, Xuan-Hao Li, Wen-Xiang Wang, Ya Tu, and Yi Zhang. "Elucidation of the mechanisms underlying the anticholecystitis effect of the Tibetan medicine “Dida” using Network pharmacology." Tropical Journal of Pharmaceutical Research 19, no. 9 (November 24, 2020): 1953–61. http://dx.doi.org/10.4314/tjpr.v19i9.22.
Full textAbstract:
Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein-protein interaction (PPI) network of cholecystitis targets, and compound-target-pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida was successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical medication.
Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism of effect, Network pharmacology, Molecular docking simulation
30
Blucher, Aurora S., Shannon K. McWeeney, Lincoln Stein, and Guanming Wu. "Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz." F1000Research 8 (June 20, 2019): 908. http://dx.doi.org/10.12688/f1000research.19592.1.
Full textAbstract:
The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.
31
Wu, Xiaogang, Christoph Reinhard, Shuyu D. Li, Hui Huang, Tao Wei, Ragini Pandey, and Jake Y. Chen. "Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays." Journal of Integrative Bioinformatics 9, no. 2 (June 1, 2012): 113–25. http://dx.doi.org/10.1515/jib-2012-213.
Full textAbstract:
Summary In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.
32
Gogoi, Barbi, and S. P. Saikia. "Virtual Screening and Network Pharmacology-Based Study to Explore the Pharmacological Mechanism of Clerodendrum Species for Anticancer Treatment." Evidence-Based Complementary and Alternative Medicine 2022 (November 2, 2022): 1–17. http://dx.doi.org/10.1155/2022/3106363.
Full textAbstract:
Background. Cancer is a second leading cause of death in the world, killing approximately 3500 per million people each year. Therefore, the drugs with multitarget pharmacology based on biological networks are crucial to investigate the molecular mechanisms of cancer drugs and repurpose the existing drugs to reduce adverse effects. Clerodendrum is a diversified genus with a wide range of economic and pharmacological properties. Limited studies were conducted on the genus’s putative anticancer properties and the mechanisms of action based on biological networks remains unknown. This study was aimed to construct the possible compound/target/pathway biological networks for anticancer effect of Clerodendrum sp. using docking weighted network pharmacological approach and to investigate its potential mechanism of action. Methods. A total of 194 natural Clerodendrum sp. Compounds were retrieved from public databases and screened using eight molecular descriptors. The cancer-associated gene targets were retrieved from databases and the function of the target genes with related pathways were examined. Cytoscape v3.7.2 was used to build three major networks: compound-target network, target-target pathway network, and compound-target-pathway network. Results. Our finding indicates that the anticancer activity of Clerodendrum sp. involves 6 compounds, 9 targets, and 63 signaling pathways, resulting in multicompounds, multitargets, and multipathways networks. Additionally, molecular dynamics (MD) simulations were used to estimate the binding affinity of the best hit protein-ligand complexes. Conclusion. This study suggests the potential anticancer activity of Clerodendrum sp. which could further contribute to scavenger novel compounds for the development of new alternative anticancer drugs.
33
Kaniak, Aneta, Zhixiong Xue, Daniel Macool, Jeong-Ho Kim, and Mark Johnston. "Regulatory Network Connecting Two Glucose Signal Transduction Pathways in Saccharomyces cerevisiae." Eukaryotic Cell 3, no. 1 (February 2004): 221–31. http://dx.doi.org/10.1128/ec.3.1.221-231.2004.
Full textAbstract:
ABSTRACT The yeast Saccharomyces cerevisiae senses glucose, its preferred carbon source, through multiple signal transduction pathways. In one pathway, glucose represses the expression of many genes through the Mig1 transcriptional repressor, which is regulated by the Snf1 protein kinase. In another pathway, glucose induces the expression of HXT genes encoding glucose transporters through two glucose sensors on the cell surface that generate an intracellular signal that affects function of the Rgt1 transcription factor. We profiled the yeast transcriptome to determine the range of genes targeted by this second pathway. Candidate target genes were verified by testing for Rgt1 binding to their promoters by chromatin immunoprecipitation and by measuring the regulation of the expression of promoter lacZ fusions. Relatively few genes could be validated as targets of this pathway, suggesting that this pathway is primarily dedicated to regulating the expression of HXT genes. Among the genes regulated by this glucose signaling pathway are several genes involved in the glucose induction and glucose repression pathways. The Snf3/Rgt2-Rgt1 glucose induction pathway contributes to glucose repression by inducing the transcription of MIG2, which encodes a repressor of glucose-repressed genes, and regulates itself by inducing the expression of STD1, which encodes a regulator of the Rgt1 transcription factor. The Snf1-Mig1 glucose repression pathway contributes to glucose induction by repressing the expression of SNF3 and MTH1, which encodes another regulator of Rgt1, and also regulates itself by repressing the transcription of MIG1. Thus, these two glucose signaling pathways are intertwined in a regulatory network that serves to integrate the different glucose signals operating in these two pathways.
34
Ogris, Christoph, Thomas Helleday, and Erik L. L. Sonnhammer. "PathwAX: a web server for network crosstalk based pathway annotation." Nucleic Acids Research 44, W1 (May 5, 2016): W105—W109. http://dx.doi.org/10.1093/nar/gkw356.
Full text
35
Shang, R. P., and W. Wang. "Investigating Dysregulated Pathways in Dilated Cardiomyopathy from Pathway Interaction Network." Russian Journal of Genetics 54, no. 2 (February 2018): 244–49. http://dx.doi.org/10.1134/s1022795418020151.
Full text
36
Yu, Liang, and Lin Gao. "Human Pathway-Based Disease Network." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 4 (July 1, 2019): 1240–49. http://dx.doi.org/10.1109/tcbb.2017.2774802.
Full text
37
Arias, Alfonso Martinez, Anthony MC Browntand, and Keith Brennan. "Wnt signalling: pathway or network?" Current Opinion in Genetics & Development 9, no. 4 (August 1999): 447–54. http://dx.doi.org/10.1016/s0959-437x(99)80068-9.
Full text
38
Li, Yong, Pankaj Agarwal, and Dilip Rajagopalan. "A global pathway crosstalk network." Bioinformatics 24, no. 12 (April 23, 2008): 1442–47. http://dx.doi.org/10.1093/bioinformatics/btn200.
Full text
39
Meng, Ziqi, Xinkui Liu, Jiarui Wu, Wei Zhou, Kaihuan Wang, Zhiwei Jing, Shuyu Liu, Mengwei Ni, and Xiaomeng Zhang. "Mechanisms of Compound Kushen Injection for the Treatment of Lung Cancer Based on Network Pharmacology." Evidence-Based Complementary and Alternative Medicine 2019 (May 28, 2019): 1–15. http://dx.doi.org/10.1155/2019/4637839.
Full textAbstract:
Background. Compound Kushen Injection (CKI) is a Chinese patent drug that shows good efficacy in treating lung cancer (LC). However, its underlying mechanisms need to be further clarified.Methods. In this study, we adopted a network pharmacology method to gather compounds, predict targets, construct networks, and analyze biological functions and pathways. Moreover, molecular docking simulation was employed to assess the binding potential of selected target-compound pairs.Results. Four networks were established, including the compound-putative target network, protein-protein interaction (PPI) network of LC targets, compound-LC target network, and herb-compound-target-pathway network. Network analysis showed that 8 targets (CHRNA3, DRD2, PRKCA, CDK1, CDK2, CHRNA5, MMP1, and MMP9) may be the therapeutic targets of CKI in LC. In addition, molecular docking simulation indicated that CHRNA3, DRD2, PRKCA, CDK1, CDK2, MMP1, and MMP9 had good binding activity with the corresponding compounds. Furthermore, enrichment analysis indicated that CKI might exert a therapeutic role in LC by regulating some important pathways, namely, pathways in cancer, proteoglycans in cancer, PI3K-Akt signaling pathway, non-small-cell lung cancer, and small cell lung cancer.Conclusions. This study validated and predicted the mechanism of CKI in treating LC. Additionally, this study provides a good foundation for further experimental studies and promotes the reasonable application of CKI in the clinical treatment of LC.
40
Abedi, Maryam, and Yousof Gheisari. "Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy." PeerJ 3 (October 1, 2015): e1284. http://dx.doi.org/10.7717/peerj.1284.
Full textAbstract:
In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.
41
Ghulam, Ali, Xiujuan Lei, Min Guo, and Chen Bian. "Comprehensive Analysis of Features and Annotations of Pathway Databases." Current Bioinformatics 15, no. 8 (January 1, 2021): 803–20. http://dx.doi.org/10.2174/1574893615999200413123352.
Full textAbstract:
This study focused on describing the necessary information related to pathway mechanisms, characteristics, and databases feature annotations. Various difficulties related to data storage and retrieval in biological pathway databases are discussed. These focus on different techniques for retrieving annotations, features, and methods of digital pathway databases for biological pathway analysis. Furthermore, many pathway databases annotations, features, and search databases were also examined (which are reasonable for the integration into microarray examination). The investigation was performed on the databases, which contain human pathways to understand the hidden components of cells applied in this process. Three different domain-specific pathways were selected for this study and the information of pathway databases was extracted from the existing literature. The research compared different pathways and performed molecular level relations. Moreover, the associations between pathway networks were also evaluated. The study involved datasets for gene pathway matrices and pathway scoring techniques. Additionally, different pathways techniques, such as metabolomics and biochemical pathways, translation, control, and signaling pathways and signal transduction, were also considered. We also analyzed the list of gene sets and constructed a gene pathway network. This article will serve as a useful manual for storing a repository of specific biological data and disease pathways.
42
Jusufi, Ilir, Christian Klukas, Andreas Kerren, and Falk Schreiber. "Guiding the interactive exploration of metabolic pathway interconnections." Information Visualization 11, no. 2 (September 19, 2011): 136–50. http://dx.doi.org/10.1177/1473871611405677.
Full textAbstract:
Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas, especially Systems Biology. Biochemical networks as representations of processes are very important for a comprehensive understanding of living beings. Drawings of these networks are often visually overloaded and do not scale. A common solution to deal with this complexity is to divide the complete network, for example, the metabolism, into a large set of single pathways that are hierarchically structured. If those pathways are visualized, this strategy generates additional navigation and exploration problems as the user loses the context within the complete network. In this article, we present a general solution to this problem of visualizing interconnected pathways and discuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtaining an overview to related pathways if they are working within a particular pathway of interest. By using glyphs, brushing, and topological information of the related pathways, our interactive visualization is able to intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal with real data and current networks, our tool has been implemented as a plugin for the VANTED system.
43
Medhora, Meetha, Anuradha Dhanasekaran, Phillip F. Pratt, Craig R. Cook, Laurel K. Dunn, Stephanie K. Gruenloh, and Elizabeth R. Jacobs. "Role of JNK in network formation of human lung microvascular endothelial cells." American Journal of Physiology-Lung Cellular and Molecular Physiology 294, no. 4 (April 2008): L676—L685. http://dx.doi.org/10.1152/ajplung.00496.2007.
Full textAbstract:
The signaling mechanisms in vasculogenesis and/or angiogenesis remain poorly understood, limiting the ability to regulate growth of new blood vessels in vitro and in vivo. Cultured human lung microvascular endothelial cells align into tubular networks in the three-dimensional matrix, Matrigel. Overexpression of MAPK phosphatase-1 (MKP-1), an enzyme that inactivates the ERK, JNK, and p38 pathways, inhibited network formation of these cells. Adenoviral-mediated overexpression of recombinant MKP-3 (a dual specificity phosphatase that specifically inactivates the ERK pathway) and dominant negative or constitutively active MEK did not attenuate network formation in Matrigel compared with negative controls. This result suggested that the ERK pathway may not be essential for tube assembly, a conclusion which was supported by the action of specific MEK inhibitor PD 184352, which also did not alter network formation. Inhibition of the JNK pathway using SP-600125 or l-stereoisomer (l-JNKI-1) blocked network formation, whereas the p38 MAPK blocker SB-203580 slightly enhanced it. Inhibition of JNK also attenuated the number of small vessel branches in the developing chick chorioallantoic membrane. Our results demonstrate a specific role for the JNK pathway in network formation of human lung endothelial cells in vitro while confirming that it is essential for the formation of new vessels in vivo.
44
Lee, Sangseon, Sangsoo Lim, Taeheon Lee, Inyoung Sung, and Sun Kim. "Cancer subtype classification and modeling by pathway attention and propagation." Bioinformatics 36, no. 12 (March 24, 2020): 3818–24. http://dx.doi.org/10.1093/bioinformatics/btaa203.
Full textAbstract:
Abstract Motivation Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification. Results We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway–gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions. Availability and implementation The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE. Supplementary information Supplementary data are available at Bioinformatics online.
45
Makolo, Angela U., and Temitayo A. Olagunju. "Computational identification of signaling pathways in protein interaction networks." F1000Research 4 (December 30, 2015): 1522. http://dx.doi.org/10.12688/f1000research.7591.1.
Full textAbstract:
The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.
46
Tian, Haoyi, and Yun Tian. "The Study on the Action Mechanism of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) Couplet Herbs on Membranous Nephropathy Based on Network Pharmacology." Chinese medicine and natural products 02, no. 03 (September 2022): e158-e168. http://dx.doi.org/10.1055/s-0042-1757458.
Full textAbstract:
Abstract Objective Our objective was to explore the action mechanism of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs in the treatment of membranous nephropathy (MN) based on network pharmacology. Methods The active ingredients and targets of Jinyingzi (Rosae Laevigatae Fructus) and Qianshi (Euryales Semen) were screened by systematic pharmacology database and analysis platform. Online Human Mendelian Genetic database and GeneCards database were used to retrieve MN-related targets. The active ingredient-related targets and MN disease targets were introduced into Venny 2.1, and Wayne diagram was drawn. The intersection targets were the potential targets of the Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs in the treatment of MN. The protein interaction network of potential targets was constructed, and the core targets were screened with String platform. Metascape platform was used for functional enrichment analysis of gene ontology (GO) and pathway enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG). The “herb-active ingredient-target-pathway” networks were drawn by using Cytoscape software, and the key components, targets, and signaling pathways were screened. Results A total of 8 active ingredients and 193 related targets in Jinyingzi (Rosae Laevigatae Fructus) and Qianshi (Euryales Semen) were screened out; a total of 1,621 targets of MN disease and 105 potential targets for the treatment of MN were obtained in the treatment with Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs; 40 core targets were screened by protein–protein interaction network topology analysis; a total of 1,978 results were obtained by GO function enrichment analysis, and 206 signal pathways were obtained by KEGG pathway enrichment analysis and screening. The network topology analysis of “herb-active ingredient-target-pathway” showed that the key components included quercetin, kaempferol, β-sitosterol, etc.; the key targets included protein kinase Bα (AKT), mitogen-activated protein kinase 1 (MAPK1), B-cell lymphoma-2 (BCL2), prostaglandin-endoperoxide synthase 2 (PTGS2), etc.; the key pathways included advanced glycation end product/receptor of AGE signaling pathway, phosphatidyl inositol 3-kinase (PI3K)/AKT signaling pathway, MAPK signaling pathway, hypoxia-inducible factor-1 signaling pathway, Ras signaling pathway, nuclear factor kappa-B (NF-κB) signaling pathway, Toll-like receptors signaling pathway, p53 signaling pathway and vascular endothelial growth factor signaling pathway in the late stage of diabetic complications. Conclusion The Jinyingzi (Rosae Laevigatae Fructus)–Qianshi (Euryales Semen) couplet herbs can regulate PI3K/AKT, MAPK, NF-κB signaling pathways in MN by targeting proteins of AKT1, MAPK8, PTGS2 through key components of quercetin, β-sitosterol, and kaempferol, so as to inhibit the overexpression of inflammatory factors in renal tissues, regulate inflammatory response, and improve renal function.
47
Wu, Xian-Jun, Xin-Bin Zhou, Chen Chen, and Wei Mao. "Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology." Combinatorial Chemistry & High Throughput Screening 22, no. 6 (September 5, 2019): 411–20. http://dx.doi.org/10.2174/1386207322666190717124507.
Full textAbstract:
Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.
48
Paley, Suzanne, Richard Billington, James Herson, Markus Krummenacker, and Peter D. Karp. "Pathway Tools Visualization of Organism-Scale Metabolic Networks." Metabolites 11, no. 2 (January 22, 2021): 64. http://dx.doi.org/10.3390/metabo11020064.
Full textAbstract:
Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.
49
Du, Bin, Daniel C. Zielinski, Jonathan M. Monk, and Bernhard O. Palsson. "Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice." Proceedings of the National Academy of Sciences 115, no. 44 (October 11, 2018): 11339–44. http://dx.doi.org/10.1073/pnas.1805367115.
Full textAbstract:
The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use–efficient pathways exist widely among organisms; (iv) cofactor-use–efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.
50
Xu, Bing, and Chunlei Zheng. "Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network." Genetics Research 2022 (December 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/8548804.
Full textAbstract:
Objective. This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD). Methods. Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied. Results. A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1. Conclusion. The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.