Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease.
ABSTRACT: Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a "Core" Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An "Expanded" network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.
Project description:AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori (H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins. A network of protein interactions was constructed by searching the primary interactions of selected proteins. The constructed network was mathematically analyzed and its biological function was examined. In addition, the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed. Mathematical analysis showed hub and bottleneck proteins, and these proteins were mostly related to immune response. The network contained pathways and proteins related to H pylori infection, such as the JAK-STAT pathway triggered by interleukins. Activation of nuclear factor (NF)-kappaB, TLR4, and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle, cell maintenance and proliferation, and transcription regulators such as BRCA1, FOS, REL, and zinc finger proteins. The extension of nodes showed interactions of the immune proteins with cancer-related proteins. One extended network, the core network, a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins. The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.
Project description:Gene profiling studies provide important information for key molecules relevant to a disease but are less informative of protein-protein interactions, post-translational modifications and regulation by targeted subcellular localization. Integration of genomic data and construction of functional gene networks may provide additional insights into complex diseases such as systemic lupus erythematosus (SLE).We analyzed gene expression microarray data of bone marrow mononuclear cells (BMMCs) from 20 SLE patients (11 with active disease) and 10 controls. Gene networks were constructed using the bioinformatic tool Ingenuity Gene Network Analysis. In SLE patients, comparative analysis of BMMCs genes revealed a network with 19 central nodes as major gene regulators including ERK, JNK, and p38 MAP kinases, insulin, Ca(2+) and STAT3. Comparison between active versus inactive SLE identified 30 central nodes associated with immune response, protein synthesis, and post-transcriptional modification. A high degree of identity between networks in active SLE and non-Hodgkin's lymphoma (NHL) patients was found, with overlapping central nodes including kinases (MAPK, ERK, JNK, PKC), transcription factors (NF-kappaB, STAT3), and insulin. In validation studies, western blot analysis in splenic B cells from 5-month-old NZB/NZW F1 lupus mice showed activation of STAT3, ITGB2, HSPB1, ERK, JNK, p38, and p32 kinases, and downregulation of FOXO3 and VDR compared to normal C57Bl/6 mice.Gene network analysis of lupus BMMCs identified central gene regulators implicated in disease pathogenesis which could represent targets of novel therapies in human SLE. The high similarity between active SLE and NHL networks provides a molecular basis for the reported association of the former with lymphoid malignancies.
Project description:MAPK pathways regulate different responses yet can share common components. Although core regulators of MAPK pathways are well known, new pathway regulators continue to be identified. Overexpression screens can uncover new roles for genes in biological processes and are well suited to identify essential genes that cannot be evaluated by gene deletion analysis. In this study, a genome-wide screen was performed to identify genes that, when overexpressed, induce a reporter (FUS1-HIS3) that responds to ERK-type pathways (Mating and filamentous growth or fMAPK) but not p38-type pathways (HOG) in yeast. Approximately 4500 plasmids overexpressing individual yeast genes were introduced into strains containing the reporter by high-throughput transformation. Candidate genes were identified by measuring growth as a readout of reporter activity. Fourteen genes were identified and validated by re-testing: two were metabolic controls (HIS3, ATR1), five had established roles in regulating ERK-type pathways (STE4, STE7, BMH1, BMH2, MIG2) and seven represent potentially new regulators of MAPK signaling (RRN6, CIN5, MRS6, KAR2, TFA1, RSC3, RGT2). MRS6 encodes a Rab escort protein and effector of the TOR pathway that plays a role in nutrient signaling. MRS6 overexpression stimulated invasive growth and phosphorylation of the ERK-type fMAPK, Kss1. Overexpression of MRS6 reduced the osmotolerance of cells and phosphorylation of the p38/HOG MAPK, Hog1. Mrs6 interacted with the PAK kinase Ste20 and MAPKK Ste7 by two-hybrid analysis. Based on these results, Mrs6 may selectively propagate an ERK-dependent signal. Identifying new regulators of MAPK pathways may provide new insights into signal integration among core cellular processes and the execution of pathway-specific responses.
Project description:Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research.
Project description:We previously reported that the peroxisome proliferator-activated receptor ? (PPAR?) agonist rosiglitazone (RSG) improved hippocampus-dependent cognition in the Alzheimer's disease (AD) mouse model, Tg2576. RSG had no effect on wild-type littermate cognitive performance. Since extracellular signal-regulated protein kinase mitogen-activated protein kinase (ERK MAPK) is required for many forms of learning and memory that are affected in AD, and since both PPAR? and ERK MAPK are key mediators of insulin signaling, the current study tested the hypothesis that RSG-mediated cognitive improvement induces a hippocampal PPAR? pattern of gene and protein expression that converges with the ERK MAPK signaling axis in Tg2576 AD mice. In the hippocampal PPAR? transcriptome, we found significant overlap between peroxisome proliferator response element-containing PPAR? target genes and ERK-regulated, cAMP response element-containing target genes. Within the Tg2576 dentate gyrus proteome, RSG induced proteins with structural, energy, biosynthesis and plasticity functions. Several of these proteins are known to be important for cognitive function and are also regulated by ERK MAPK. In addition, we found the RSG-mediated augmentation of PPAR? and ERK2 activity during Tg2576 cognitive enhancement was reversed when hippocampal PPAR? was pharmacologically antagonized, revealing a coordinate relationship between PPAR? transcriptional competency and phosphorylated ERK that is reciprocally affected in response to chronic activation, compared with acute inhibition, of PPAR?. We conclude that the hippocampal transcriptome and proteome induced by cognitive enhancement with RSG harnesses a dysregulated ERK MAPK signal transduction pathway to overcome AD-like cognitive deficits in Tg2576 mice. Thus, PPAR? represents a signaling system that is not crucial for normal cognition yet can intercede to restore neural networks compromised by AD.
Project description:Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at <i>k</i>-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.
Project description:Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.
Project description:?-arrestins are critical regulator and transducer proteins for G-protein-coupled receptors (GPCRs). ?-arrestin is widely believed to be activated by forming a stable and stoichiometric GPCR-?-arrestin scaffold complex, which requires and is driven by the phosphorylated tail of the GPCR. Here we demonstrate a distinct and additional mechanism of ?-arrestin activation that does not require stable GPCR-?-arrestin scaffolding or the GPCR tail. Instead, it occurs through transient engagement of the GPCR core, which destabilizes a conserved inter-domain charge network in ?-arrestin. This promotes capture of ?-arrestin at the plasma membrane and its accumulation in clathrin-coated endocytic structures (CCSs) after dissociation from the GPCR, requiring a series of interactions with membrane phosphoinositides and CCS-lattice proteins. ?-arrestin clustering in CCSs in the absence of the upstream activating GPCR is associated with a ?-arrestin-dependent component of the cellular ERK (extracellular signal-regulated kinase) response. These results delineate a discrete mechanism of cellular ?-arrestin function that is activated catalytically by GPCRs.
Project description:AIMS:The molecular pathogenesis of COVID-19 is similar to other coronavirus (CoV) infections viz. severe acute respiratory syndrome (SARS) in human. Due to scarcity of the suitable treatment strategy, the present study was undertaken to explore host protein(s) targeted by potent repurposed drug(s) in COVID-19. MATERIALS AND METHODS:The differentially expressed genes (DEGs) were identified from microarray data repository of SARS-CoV patient blood. The repurposed drugs for COVID-19 were selected from available literature. Using DEGs and drugs, the protein-protein interaction (PPI) and chemo-protein interaction (CPI) networks were constructed and combined to develop an interactome model of PPI-CPI network. The top-ranked sub-network with its hub-bottleneck nodes were evaluated with their functional annotations. KEY FINDINGS:A total of 120 DEGs and 65 drugs were identified. The PPI-CPI network (118 nodes and 293 edges) exhibited a top-ranked sub-network (35 nodes and 174 connectivities) with 12 hub-bottleneck nodes having two drugs chloroquine and melatonin in association with 10 proteins corresponding to six upregulated and four downregulated genes. Two drugs interacted directly with the hub-bottleneck node i.e. matrix metallopeptidase 9 (MMP9), a host protein corresponding to its upregulated gene. MMP9 showed functional annotations associated with neutrophil mediated immunoinflammation. Moreover, literature survey revealed that angiotensin converting enzyme 2, a membrane receptor of SARS-CoV-2 virus, might have functional cooperativity with MMP9 and a possible interaction with both drugs. SIGNIFICANCE:The present study reveals that between chloroquine and melatonin, melatonin appears to be more promising repurposed drug against MMP9 for better immunocompromisation in COVID-19.
This submission contains networks and code used for the s-core+ decomposition
analyses presented in the manuscript "Comparative analysis of weighted gene
co-expression networks in human and mouse", published in PLoS One, 2017.
An example of how to use the perl script `s-core_plus.pl` is given below,
under `s-core+`. In short:
`perl s-core_plus.pl <input_network> <output_results_file>`
It should be noted that the code is not optimized for speed or memory
efficiency. It is provided to make the s-core / s-core+ method more
transparent to the community.
Weighted topological overlap (wTO) networks
There are 4 networks, 2 from mouse data, and 2 from human data. And one
denoted by `all`, and one by `cns`, for both species. `all` means that
the network is generated comparing gene-expression profiles across all
tissue types in the data. `cns` means that only the central nervous
system (CNS) and brain tissue types are included in the network.
The `all` networks are a lot larger than the `cns` networks, since they
contain >10,000 nodes, while the `cns` only contain transcription factors,
and thus around 1,000 nodes.
The networks are very dense undirected, weighted networks given as link
lists. The naming convention is:
`hs`: Homo Sapiens (Human)
`mm`: Mus Musculus (Mouse)
`all`: Network constructed from all tissue types
`cns`: Network constructed from brain and CNS tissue types
`cutX`: Cutoff value (`cut4`: cutoff = 0.4, `cut3`: cutoff = 0.3)
The s-core method works by peeling off peripheral nodes in an undirected,
weighted network. For unweighted networks, the s-core is equal to the k-core
network decomposition method.
s-core eventually peels the network down to an innermost core, and s-core+
commences. s-core+ works by removing the weakest links in the network until
a new, stable innermost s-core can be found.
In general, s-core is a relatively quick method, while s-core+ is slow.
For instance, `s-core_plus.pl` decomposes the entire `all` networks into its
s-cores in <30 minutes, while s-core+ takes a few days to complete.
The `cns` networks are quick to run, both for s-core and s-core+, due to
their smaller size and denseness.
To, for instance, obtain the results for the human `cns` network, run:
`perl s-core_plus.pl hs_wTO_cns_cut4.txt results.txt`
The first argument of `s-core_plus.pl` is the <input_network>.
The second argument of `s-core_plus.pl` is the <output_results_file>.
The output is in the shape of 4 tab-delimited columns:
`#Node HighestCore StrengthThreshold CoreSize`
`Node`: Node ID (gene name)
`HighestCore`: The innermost core `Node` is a member of
`StrengthThreshold`: The strength threshold value for the given s-core(+)
`CoreSize`: Number of nodes in core number `HighestCore`
If only the s-core results are needed, and not the s-core+ results, adjust
the parameter `$networkSizeForFallback` so that it is smaller than the
innermost s-core (e.g. by setting it to 1).
There is also an option for verbosity. Set `$ifVerbose = "yes"` if you want
insight into the progress for every s-core iteration.