Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Conference talk at BioSB 2015 in Lunteren, The Netherlands
* Date: 20 May 2015
* Title: "Integrative network based analysis of mRNA and miRNA expression in vitamin D3-treated cancer cells"
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Illuminating the Druggable Genome with Knowledge Engineering and Machine Lear...Jeremy Yang
Talk given at 14th Annual New Mexico BioInformatics, Science and Technology (NMBIST) Symposium, entitled Integrative Omics, on March 14-15, 2019. Most slides c/o IDG KMC PI Tudor Oprea, MD, PhD.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Phone Us ❤85270-49040❤ #ℂall #gIRLS In Surat By Surat @ℂall @Girls Hotel With...
NetBioSIG2013-Talk Thomas Kelder
1. Network signatures link hepatic effects of
anti-diabetic interventions with systemic
disease parameters
Thomas Kelder
Microbiology and Systems Biology, TNO, The Netherlands
Network Biology SIG, ISMB 2013, Berlin
9. WGCNA
• Weighted Gene Co-expression Analysis*
• Identify co-expressed network modules
• Correlate modules to disease parameters based on their “eigengene” (1st
Principal Component)
9*Langfelder et al. BMC Bioinformatics, 2008
Disease parameter
Disease parameter
Disease parameter
?
?
?
10. Modules to disease parameters
• 14 coherent co-expression modules
• 10 modules with GO annotation
• 4 modules correlated with disease parameter(s)
• All correlating endpoints related to dyslipidemia rather than dysglycemia
despite improvement of dysglycemia by all interventions
10
15. Random walks algorithm
15
[1] Dupont, et al. "Relevant subgraph extraction from random walks in a graph." Machine Learning (2006)
[2] Faust, et al. "Pathway discovery in metabolic networks by subgraph extraction." Bioinformatics (2010)
Randomwalks
Intervention
Nodes and edges scored by probability of being
visited by the random walker
Intervention
Disease
parameter
Disease
parameter
20. Conclusions
Network signatures underlying effects of interventions on
dyslipidemia-related disease parameters
– Template for successful intervention or response to circumvent
– Improves selection of genes relevant to disease parameters
– Underlying interaction help interpretation
20
21. Acknowledgements
• Marijana Radonjic
• Lars Verschuren
• Alain van Gool
• Ben van Ommen
• Ivana Bobeldijk
Check out our poster at ISMB on Sunday
Network Biology of Systems Flexibility
21
R scripts and data for this analysis available at:
https://github.com/thomaskelder/ADT-liver-network
igraph
23. 23
High fat diet “diseased” control group
Chow diet “healthy” control group
High fat diet DLI (switch to chow)
Fenofibrate
T0901317
wk 9wk 16wk
LDLR-/-
MICE
HEPATIC
TRANSCRIPTOME
24. 24
DLI Fenofibrate T0901317
Hepatic transcriptome dataset:
- Chow control
- Dietary lifestyle intervention (DLI)
- Fenofibrate
- T0901317
Compared to high fat diet (HFD) at 16
Co-expression network modules ide
by Weighted Gene Co-expression Ne
Analysis (WGCNA) [2]. Provides high
overview of relevant processes.
WGCNA
26. 26
en modules that could be annotated to a biological proce
correlated significantly with disease parameters. All s
ns were with dyslipidemia related disease parameters, de
mprovement of glycemic status by the interventions.
NO. GENES GO TERMS SIGNIFICANT CORRE
198
Lipid biosynthetic process,
Oxidoreductase activity
Liver weight (-0.91), Triglycerid
Atherosclerosis (-0.79), Choles
161
Cell activation, Immune system
process, Inflammatory response
Atherosclerosis (0.80), Cholest
Liver weight (0.75)
142
Lipid metabolic process,
Oxidation-reduction process
Liver weight (0.88); Cholestero
27. WGCNA
• Weighted co-expression network analysis*
• Correlate modules to other measurements (clinical, plasma proteins,
microbiome)
*Langfelder et al. BMC Bioinformatics, 2008
28. glucose
C
how
H
F
16
w
eeks
Lifesty
le
R
osig
lita
zone
T0901317
0
5
10
15
20
** **
*
glucose(mM)
Omics, genetics, physiological data, prior knowledge
Molecular signatures
of metabolic health
and disease
Mechanistic insight:
Biological context of
molecular signatures
Prognostic /
diagnostics molecular
signatures
Coexpression networks (WGCNA)
Prior-knowledge networks
Causality networks
Variable selection methods
Subgraph ID/ (K-walks)
topology/ network clustering
Network signatures for improved diagnostics & interventions
Link to pathological
endpoint
Subgroup-specific
molecular signatures
prioritization and
refinement
Editor's Notes
This analysis is like finding the right pebbles on a huge pebble beach -> can’t take them all, but want to find a representative sample to take a part of your vacation home. Molecular network underlying disease is huge, we can’t focus on everything at once, but need to find the most relevant parts that tells us about specific aspects of disease.
Big network underlying diseaseDrug often targets single pathwayBased on what we think (single signaling cascade)Leads to both good and bad effectsIneffective in improving health systems-wideHow should network look like for effective treatment? -> marker nodesWhat should interventions target for optimal treatment -> target nodes
DLI as template for good intervention16 disease parameters. These include plasma glucose and insulin, QUICKI index, body and organ weights (adipose depots, kidney, liver, heart, and total body weight), atherosclerotic lesion area, plasma cholesterol, and plasma and liver triglycerides
Nodes in the network with key role in linking intervention target to dyslipidemia parameters. Circumvent responses like drug signatures, since all link to disease parameters that get worse.
(Fasn, Axl, Fgf21, Gpd2, Cyp17a1, Pkm, Fastkd5), may point to putative targets for improved interventions mimicking the mechanisms underlying DLI. Notably, the gene products of two of these genes are already under investigation as therapeutic targets. Fgf21, encoding for Fibroblast growth factor 21, is currently being investigated as novel therapeutic agent for T2DM [29, 30], and the anti-diabetic properties of thefatty acid synthase (Fasn) inhibitor platensimycin have recently been demonstrated in a mouse model[31]. Interestingly, Axl, encoding for the AXL receptor tyrosine kinase, was found to induce T2DM afteroverexpression in transgenic mice [32].
Sets of 25 genes of more, outperforms DEGs. DEG finds top of iceberg, but network based method seems better when going deeper.Also complete signature has significantly higher enrichment with known disease genes than same number of genes ranked by DEG.To next slide: Signatures are not just lists of genes, but networks that provide biological context. You can study the underlying interactions that cause the genes to have a high score, this facilitates interpretation and identifying biological mechanisms. Example in next slide.
Network visualization of underlying interactions:See both expression, relevance and interaction together -> biological contextTopologyRed module, inflammation and consistently opposite regulation DLI vs T09. Ccnd1 top score in both DLI and T09 networks, but different neighbors. Direct regulation by 5TFs (4 inflammation related), versus indirect links and more concentrated along single path for T09. Perhaps tighter, more balanced regulation required for good effect?This module also shows a clear opposite pattern of regulation between these interventions where the majority of genes were downregulated by DLI, while upregulated by T0901317 intervention. Several nodes receive a non-zero relevance score for both interventions (Ccnd1, Lgals3, Gja1) while the network visualization provides insight in difference in their regulation by the interventions. For example, Ccnd1 has a high relevance score in both signatures, but is downregulated by DLI and upregulated by T0901317. In the DLI network, Ccnd1 is directly regulated by 5 transcription factors affected by DLI, of which 4 could be related to inflammation or immune response pathways (Nr3c1, Nr4a1, Rxra, Smarcb1; based on annotations in Gene Ontology, Ingenuity Pathway Analysis, and WikiPathways). In contrast, Ccnd1 is connected to T0901317 through a single indirect association involving multiple intermediate interactions. This difference can be observed throughout the network, as the average shortest path length from intervention to the module nodes is twice as long in the T0901317 subnetwork compared to the DLI subnetwork. In addition, the edge relevance scores for the DLI network are more equally distributed across nodes, while the scores in the T090137 network are mainly concentrated in the path through Mmp9. This may indicate a more direct and balanced activation of repression of a combination of multiple transcription factors by DLI, while the indirect regulation by T0901317 intervention leads to a less controlled mechanism.
WGCNA mainly applied on genetically perturbed datasets (e.g. F2 crosses)We applied to datasets where variation is induced by intervention(s) -> 10OAD, WUR-DR- Generate network from data- Identify new relations- Link to physiology or other external measurements
Network signatures: Utilize known network information, different datasets (transcripomic like shown before, but also genetic for causal links).Identify parts of network that are linked / determine specific disease endpoints or phenotypes -> network signaturesMarkers: Could be used as markers to distinguish subgroups (i.e. develops NASH or not), prognostic for complications or diagnostic to determine which part of the system is diseased.Specific interventions: Networks provide biological context, mechanistic insights may lead to ways to design interventions that push that specific part of the network in the right direction to cure disease.