Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
BMC Systems Biology volume 9, Article number: S4 (2015)
Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-stroke gene expressions and the relationships between neurodegenerative diseases and neural function recovery processes after a stroke.
Differential protein-protein interaction networks (PPINs) were constructed at 3 post-stroke time points, and proteins with a significant stroke relevance value (SRV) were discovered. Genes, including UBC, CUL3, APP, NEDD8, JUP, and SIRT7, showed high associations with time after a stroke, and Ingenuity Pathway Analysis results showed that these post-stroke time series-associated genes were related to molecular and cellular functions of cell death, cell survival, the cell cycle, cellular development, cellular movement, and cell-to-cell signaling and interactions. These biomarkers may be helpful for the early detection, diagnosis, and prognosis of ischemic stroke.
This is our first attempt to use our theory of a systems biology framework on strokes. We focused on 3 key post-stroke time points. We identified the network and corresponding network biomarkers for the 3 time points, further studies are needed to experimentally confirm the findings and compare them with the causes of ischemic stroke. Our findings showed that stroke-associated biomarker genes at different time points were significantly involved in cell cycle processing, including G2-M, G1-S and meiosis, which contributes to the current understanding of the etiology of stroke. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes.
Stroke is the third leading cause of mortality and the primary cause of permanent disability worldwide; 87% of all strokes are ischemic . Ischemic strokes are classified into cardioembolic, large-vessel, small-vessel lacunar, cryptogenic, and other causes based on stroke etiology. Cardiogenic embolisms account for ~20% of ischemic strokes each year . Cardioembolic strokes are largely preventable through efforts at primary prevention for major-risk cardioembolic sources, e.g. high blood pressure, hyperlipidemia, etc. Once a cardioembolic stroke occurrs, the likelihood of recurrence is relatively high; therefore, the following prevention is also important. When known causes of strokes are identified, etiologic classification can guide treatments. Not knowing the etiology of a stroke restricts optimal therapy implementation and limits stroke research . Several studies offered evidence of significant genetic implications in ischemic stroke . We attempted to examine whether gene expression features in the blood can distinguish the causes of stroke, and determine whether these gene expression profiles can predict the stroke etiology and its outcomes.
Although no existing valid clinical criteria for diagnosing cardioembolic stroke, a diagnosis of cardioembolism can be based on the triad of (1) identification of a potential source of cardiogenic embolisms, (2) exclusion of other potential sources of cerebral ischemia, and (3) consideration of clinical neurologic features. Cardioembolism can be predicted on clinical grounds but is difficult to document . Magnetic resonance imaging (MRI), echocardiography, Holter monitoring, transcranial Doppler, and electrophysiological studies increase the ability to identify the origin of cardioembolisms. In general, cardioembolic strokes have much worse prognosis and produce larger and more-disabling symptoms than other stroke subtypes. A recurrent embolism occurs in 30%~60% of patients with a history of a previous embolic event . Cardioembolic strokes are a heterogeneous, complex disease resulting from interactions between genetic and environmental risk factors . To understand contributions of various genetic risk factors to the etiology of stroke, the genetic risk factor must be analyzed and integrated in terms of biological functions and pathways . With advances in affordable, high-throughput technologies, a systems biology study of diagnoses and treatments of cardioembolic strokes can shed light on applications of systems biology to the diagnosis, prognosis, and therapy of cardioembolic strokes.
In this study, we compared molecular interaction networks of 3 stages of cardioembolic strokes to reveal the underlying cellular mechanisms of cardioembolic strokes. As to different etiologies and heterogenic genomic alterations of cardioembolic stroke, the systems biology methodology integrated with Omics data is suitable to develop accurate diagnoses, novel therapeutic targets, and efficient targeted therapies. In this study, microarray data were applied to build the protein-protein interaction (PPI) networks (PPINs) of 3 stages of cardioembolic strokes. Network structures and protein association abilities in different stages of cardioembolic strokes were compared to obtain a set of significant proteins which can serve as important network biomarkers in the progressive process of cardioembolic strokes. In the future, significant proteins including UBC, CUL3, APP, NEDD8, JUP, SIRT7, etc., can be potent drug targets for first aid and emergency treatment within 24 h post-stroke. The complex behaviors of strokes differ from those of cancer and other complex diseases. We hope that this work can help scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes.
Materials and methods
Overview of the construction process of stroke network marker
We successfully used our methods to find the core and specific network markers of 4 different cancers and the evolution of network markers from the early to late stages of bladder cancer [9, 10]. A similar theoretical framework was employed in this study to find the evolution of network biomarkers of stroke at 3 time points which represent 3 important stages after a stroke has occurred. The theoretical systematic method in this paper was developed from a previous study. Figure 1 shows the flowchart to identify network biomarkers of stroke at 3 time points. Due to the theoretical framework have been successfully applied by us on various cancers and have been published on many journals, so we do not repeat it in detail in the main text. We only highlighted the significant key points of it and put the detailed description in the Additional file 1.
At first, two kinds of data sources were combined to build the network, they are microarray gene expression data and the protein-protein interaction data. We used them to construct the stroke PPINs (SPPINs, stroke protein-protein interaction networks) and normal PPIN (NPPINs). We calculated the stroke relevance value (SRV) for each protein in the network, and choose the proteins with top significant SRVs to be the network biomarkers. Detailed please refer to Additional file 1.
Data sets selection and pre-processing
The stroke microarray dataset GSE58294  and its corresponding platform, GPL570, were obtained from the NCBI GEO . It contains gene expression data following a cardioembolic stroke. The dataset contained 3 time points of 23 stroke patients' samples and 23 control samples from non-disease subjects (totally 23*4 = 92 samples)(Table 1). We built 3 SPPINs for 3, 5, and 24 h post-stroke in this study and the NPPIN. We extract the PPI data for Homo sapiens from the online interaction repository with data compiled through comprehensive curation efforts, Biological General Repository for Interaction Database (BioGRID). It was used to delete false-positive PPIs for pruning PPINs. These PPINs of 3, 5, and 24 h post-stroke (3 SPPINs), and normal stage (NPPIN) were then compared mathematically to get SRVs and corresponding network markers (top SRVs). Detailed please refer to Additional file 1[13–15].
Protein pool selection and the PPINs identification for stroke and normal samples
We collect a protein pool of those proteins with differential expressions to construct the corresponding SPPINs and NPPIN. A one-way analysis of variance (ANOVA) was used to screen out the differential proteins. We used the following protein association model to describe the PPI relationship:
where x i (n) is the target protein i's expression level for each sample n (stroke or normal); x j (n) is the j-th protein's expression level interacting with target protein i for each sample n; α ij means the ability of association interaction (combination strength) between the i-th target protein and its corresponding j-th interaction protein; M i is the number of proteins that interacting with their i-th target protein; and finally ω i (n) means stochastic noise caused by other factors in the biological systems or uncertainty of our model.
The second step is to use the maximum-likelihood (ML) estimation method  to determine associated parameters (combination strength) in (1) by the microarray expression data as follows (see Additional file 2):
where was determined by using microarray expression data and the ML estimation method.
To do the model order selection and determine the significant protein interactions in, finally we use the Akaike information criterion (AIC)  and a Student's t-test  method (see Additional file 3). Please refer to details in Additional file 1.
Determination of the network structures and their corresponding significant proteins at 3, 5, and 24 h post-stroke and normal stage
After pruning away the spurious false-positive PPIs, only significant PPIs are remained:
where M i '≤M i is the number of significant PPIs in the total PPIN, with the i-th target protein. The refined PPIN is:
, , and
The interaction matrix A of refined PPINs in equation (4) for 3, 5, and 24 h post-stroke and normal cells was constructed, respectively, as follows:
where k = 3, 5, and 24 h post-stroke; and A N are the interaction matrices of the refined PPINs of 3, 5, and 24 h post-stroke, respectively; and M denotes the proteins number in the refined PPIN. The two protein association (combination strength) models for both SPPINs and the NPPIN for 3, 5, and 24 h post-stroke and normal stage are:
where k = 3, 5, and 24 h post-stroke and and x N (n)=[x1Nx2N··· x MN ]T are vectors of proteins expression levels.
We defined the difference matrix of the DPPIN between SPPINs and NPPIN as follows:
where k = 3, 5, and 24 h post-stroke; is the protein association (combination strength) ability difference between SPPINs and NPPIN at k = 3, 5, and 24 h post-stroke and normal samples; and matrix Dk is the difference in network structures between SPPINs and the NPPIN for k = 3, 5, and 24 h post-stroke and normal samples.
Then we defined a stroke relevance value (SRV) to show the difference summation of SPPIN and NPPIN as follows :
where , and k = 3, 5, and 24 h post-stroke. Detailed please refer to Additional file 1.
Pathway analysis by many on-line freeware and powerful commercial software
We mapped the network biomarkers found to several on-line freeware of pathway analysis, such as KEEG (Kyoto Encyclopedia of Genes and Genomes) , NOA (network ontology analysis) [19, 20] and the DAVID bioinformatics database [21, 22]. They can help to investigate critical pathways related to these network markers and explore the relationships between these pathways and stroke. They also can illustrate the biological processes, cellular components and molecular functions. They also interpret the pathways involved in stoke etiology and repair processes. To complete our research results, we used the well-known commercial software, Ingenuity® Pathway Analysis (IPA) and Metacore, to do multiple functional and pathway analyses. IPA® is from QIAGEN (Redwood City, CA, http://www.qiagen.com/ingenuity). MetaCore™ is an integrated software suite from GeneGo for functional analysis of microarray, metabolic, SAGE, proteomics, siRNA, microRNA, and screening data. Please refer to details in Additional file 1.
Results and discussion
Evolution of network biomarkers at 3 post-stroke time points
We built DPPINs for the 3 post-stroke time points (3, 5, and 24 h) (Figure 2). The SRVs of each protein in the 3 PPINs were calculated. One can find more information than SRVs in this figure, such as the edges and nodes of these PPINs. Screened by the p value of the SRV, we found significant proteins of network markers for these 3 stroke stages. Similar to our previous experience with bladder cancer , we wanted to reveal the repair mechanism of stroke at these 3 time points.
Network markers at the 3 time points
After p value (≤0.01) screening, we found that there were 5, 9, and 4 significant proteins at 3, 5, and 24 h, respectively, post-stroke (Table 2). In addition, their corresponding SRVs respectively ranged 1.7~6.1, 2.1~11.7, and 1.7~26. These significant top SRV proteins and their corresponding PPIs were used to construct network markers at 3 post-stroke time points. We found that SRVs of stroke were much smaller than SRVs of our pervious cancer results [9, 10], and also the cancer networks were much more complex than the stroke network. To compare the overall stroke process, we also combined samples at 3 time points into a total one (69 samples), and used it with normal data to build the DPPIN. This is not the main topic of this research, so we only put the total DPPIN in the results of Metacore. We do not discuss UBC in this paper, because it is another complex problem. It is a house keeping gene for many different kinds of diseases. We will extend our research on this target in the future.
Pathway analysis of network biomarkers at 3 h post-stroke
After SRV screening with our systems biology approach, the complete and complex functional and pathway analyses fundamentally revealed the evolutionary process of repair mechanisms of stroke. Because the number of significant proteins was very small compared to results for cancers, the KEGG results could not give us as much information as in cancer cases.
The IPA gave us the clearest information on the disease, so we first show the IPA results (Table 3). We then show additional information given by NOA (Table 4). From Figure 3, one can see that the 2 key moduli of Tx_Cardiac-Hypertrophy and ML_Cardiovascular-Disease were related to our significant proteins (Figure 2(A)). We found that CUL3 appeared at all 3 stages, which implies that this time stationary network marker would be a significant target for therapy. It is easily seen that CUL3 is a key hub of the network. Functions and behaviors of CUL3 are very complex. Salinas et al. discussed how actinfilin acts as a CUL3 substrate adaptor, linking gluR6 kainate receptor subunits to the ubiquitin-proteasome pathway. They said that kainate receptors were implicated in excitotoxic neuronal death induced by stroke . We list the disease functional analyses in a Additional file 3. The IPA results are shown in Table 3. NOA results are shown in Table 4. Results of Metacore are shown in Figure 7 to 14, for 3, 5, and 24 h, and the total (the sum of all samples).
Pathway analysis of network biomarkers at 5 h post-stroke
IPA results (Figure 4) show that there were 5 modules of ML_Cardiovascular-Disease, ML_Cell-Death-Brain, Tx_Increases-Heart-Failure, Tx_Cardiac Necrosis/Cell Death, and BM_Unspecified-Application/Actute-Coronary Syndrome related to our significant proteins (Figure 2(B)). We found that caspase was related to 4 modules. Aries et el. discussed caspase-1 cleavage of transcription factor GATA4 and regulation of cardiac cell fate. They showed that GATA4 is cleaved by caspase-1 in cardiomyocytes, and their data identified a target for caspase-1 in nuclei and a pathway to explain its related cardiac actions . The amyloid precursor protein (APP) is part of a binding-protein-dependent transport system. It is probably responsible for translocation of substrate across membranes, and it belongs to the permease family of the binding-protein-dependent transport system. It is also known as the β-amyloid (Aβ) precursor protein. From , we know that APP is a key gene related to Alzheimer disease (AD), and it implicates the relationship between neurodegenerative diseases and stroke. A lot of research has discussed this gene [26–29]. It could possibly be an efficient therapy target at this time point. We list the disease functional analyses in a Additional file 3. IPA results are shown in Table 5. NOA results are shown in Table 6.
Pathway analysis of network biomarkers at 24 h post-stroke
IPA results (Figure 5) (Figure 6 shows the detailed legend of IPA in Figure 3, 4, 5) show that there were 6 modules of ML_Cell-Cycle-Brain, ML_Cell-Death-Brain, Tx_Cardiac-Necrosis/Cell Death, Tx_Cardiac-Fibrosis, Tx_Cardiac-Hypertrophy, and ML_Cardiovascular-Disease related to our 4 significant proteins (Figure 2(C)). Another key protein, SIRT7, was found at this time point. We found that SIRT7 was related to 4 modules. Vakhrusheva et al. discussed how "SIRT7 increases stress resistance of cardiomyocytes and prevents apoptosis and inflammatory cardiomyopathy in mice." It is a member of the mammalian sirtuin family that consists of 7 genes, SIRT1~7. Its deficiency can cause the development of heart hypertrophy and inflammatory cardiomyopathy . SIRT7 was discovered to be highly associated with ischemic stroke in our analytical results. Previous studies showed the roles of sirtuins in cell death. Increasing evidence has suggested that sirtuins play fundamental roles in a variety of biological processes, including cell death, inflammation, and energy metabolism. In addition, SIRT7 increases the stress resistance of cardiomyocytes and prevents apoptosis and inflammatory cardiomyopathy in mice. We list the disease functional analyses in Additional file 3. IPA results are shown in Table 7. NOA results are shown in Table 8. Results of Metacore are shown in Figure 7 to 14, for 3, 5, and 24 h and the total (the sum of all samples).
Network biomarkers and the evolution of network biomarkers of stroke etiology and repair processes
Our stroke PPI model was constructed from differential expressions of stroke and normal microarray data and data mining of PPI information from the BioGRID database. So, the 3 SPPINs and NPPIN were the results of our systems biology model using the original microarray data and PPI databases. There are 3 key factors which affected the final results.
(i) The effect of different microarray data: We know that microarray data have the drawback of being irreproducible. That means even in the same case, microarray data might not produce the same results as previous ones. Also, for the same diseases, patients of different ethnicities, different ages, or different genders will produce different microarray data. This is the first factor that affected the final results.
(ii) The effect of different original PPI databases: We know that PPI databases, such as BioGRID and MIPS, are constructed from putative information and then validated by wet-lab experiments. Due to advances in many high-throughput experimental skills, the original PPI databases have evolved with time. Newly updated original PPI databases were the second factor that affected the final results.
(iii) The effect of the systems biology model: Our mathematical model combined with many biological databases to be a novel one that we have successfully applied it on various cancer researches [9, 10]. We used AIC and Student's t-test methods to construct the DPPIN of SPPIN and NPPIN, and get the SRV for three time points post stroke. The significance and the novelty of our model please refer to our previous work . Although we described the novelty of our systems biology method, we have validated our results through a literature survey in the research. In the future, our results should be validated by other researchers' wet-lab experiments, and we will repeatedly modify our mathematical model. This is the third key factor that affected the results. Although not directly, it also had an influence on the protein interaction networks.
We also know that bio-systems evolve with time. It is obvious that different-stage patients have very different symptoms; these are key features for us to classify stroke stages. Since patients of different stages have greatly different symptoms, there is no doubt that the microarray data of these stage patients will be quite different. As described above, protein expressions from microarray data are one of the key factors of our systems biology model used to produce the final SPPINs and NPPIN. And the SPPINs and NPPIN yielded the final network biomarkers from our systems biology method. So, the most important thing for the evolution of network biomarkers is the evolution of microarray data at different stroke stages, which is inherent in the exhibition of stroke-related genes due to DNA mutations in the stroke process. The main purpose of this research was to discuss the network evolutionary process of stroke at 3 time points, and we hope it can provide clues for therapy and medical recovery processes. We found that CUL3 appeared at all 3 time points, and may be a target we should pay more attention to. At the second time point of 5 h, we found that the APP and caspase both played significant roles. At the last time point of 24 h, we found another important one, SIRT7. A lot of research has discussed these key proteins (Table 2).
Results in Figure 13 show that stroke-associated biomarker genes among different time points were significantly involved in cell cycle processing, including G2-M, G1-S and meiosis. Both in vitro and in vivo evidences for involvement of cell cycle elements in stroke was reported in a previous study . The activity level of key regulators of the cell cycle are downregulated in differentiated neurons, and there is increasing evidence that activation of cell cycle machinery leads to death of neurons following stroke insults [32, 33]. Our finding also shows the involvement of multiple cell cycle-regulatory signals in ischemic injury, and this may contribute to our current understanding of the etiology of stroke .
Comparison with our previous results of traumatic brain injury in Danio rerio
We compared the results with our previous study, "On the Crucial Cerebellar Wound Healing-Related Pathways and Their Cross-Talks after Traumatic Brain Injury in Danio rerio ". We found that there were no intersections between these 2 results. To discuss core and specific network biomarkers of cardiac and brain injury between humans and other species is important work, and we will extend this work in the future. It is difficult to obtain datasets for stroke patients. The original reason we wanted to compare the results with traumatic brain injury in D. rerio was to determine if any intersection existed between these 2 results. Then maybe it would be possible to use D. rerio as a model organism to model human stroke. However, we found nothing at this stage, and we will try to develop other methods to model human stroke.
Summary of results and discussion
Due to the help of high-throughput data and the power of our systems biology model, we determined total different network structures and biomarkers at 3 significant time points. Besides the original results of our model of SRV and network structure, we offer an abundant pathway analysis by various powerful commercial software and free web-servers. The entire work should be very valuable for experts (doctors and researchers) in developing novel strategies of recovery, therapy and prevention for stroke patients. Take for example, if you are only interested on SRVs, you can refer to Table 2 to choose the top SRV for drug targets. If you want to separate the PPIN by multiple drug targets, you can refer to Figure 1 to focus on elements of the network and select some of them to be drug targets. If you want to break down the network by destroying the regulatory relationship, you can refer to Figure 3 to 5, the IPA results, to choose some regulatory elements for your drug targets. If you want to break down the network by the complex modules given by Metacore, you can refer to Figure 7 to 14. You can use your medical knowledge combined with the complex modules to develop novel strategies. Additionally, the diseases and functional annotation given by IPA was shown in Additional file 4. And we also extended our research to examine relationships between significant genes determined by our models and many other diseases. This can give clues for new clinical application of old drug.
Stroke is a complex disease, and its complex cellular behaviors differ from those of cancers. We found a lot of research work that focused on cancer systems biology, and not as much work on stroke systems biology. Our systems biology method applied to cancers helped us successfully identify network biomarkers. This is our first attempt to apply a similar framework of systematic theory to the stroke process. We focused on a systematic analysis of 3 key post-stroke time points, and our findings showed that stroke-associated biomarker genes among different time points were significantly involved in cell cycle processing, including G2-M, G1-S and meiosis, which contributes to our current understanding of the etiology of strokes. We identified a significant PPIN and the corresponding network biomarkers for 3 time points. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and recovery processes. In future work, we will try to integrate more data samples and more critical time points of data, and design new methods of model organisms to unearth more deeply the mechanisms and processes.
Center TIS: Stroke Statistics. 2015, Available from: http://www.strokecenter.org/patients/about-stroke/stroke-statistics/.
Wessler BS, Kent DM: Controversies in cardioembolic stroke. Curr Treat Options Cardiovasc Med. 2015, 17 (1): 358-
Jickling GC, Stamova B, Ander BP, Zhan X, Liu D, Sison SM, et al: Prediction of Cardioembolic, Arterial, and Lacunar Causes of Cryptogenic Stroke by Gene Expression and Infarct Location. Stroke. 2012, 43 (8): 2036-2041.
Giralt D, Domingues-Montanari S, Mendioroz M, Ortega L, Maisterra O, Perea-Gainza M, et al: The gender gap in stroke: a meta-analysis. Acta Neurologica Scandinavica. 2012, 125 (2): 83-90.
Ferro JM: Cardioembolic stroke: an update. Lancet Neurology. 2003, 2 (3): 177-188.
Leary MC, Caplan LR: Cardioembolic stroke: An update on etiology, diagnosis and management. Annals of Indian Academy of Neurology. 2008, 11 (5): S52-S63.
Silverman EK, Loscalzo J: Network Medicine Approaches to the Genetics of Complex Diseases. Discov Med. 2012, 14 (75): 143-152.
Park YK, Bang OS, Cha MH, Kim J, Cole JW, Lee YJ, et al: SigCS base: an integrated genetic information resource for human cerebral stroke. Bmc Systems Biology. 2011, 5 Suppl 2: S10-
Wong YH, Chen RH, Chen BS: Core and specific network markers of carcinogenesis from multiple cancer samples. J Theor Biol. 2014, 362: 17-34.
Wong YH, Li CW, Chen BS: Evolution of network biomarkers from early to late stage bladder cancer samples. Biomed Res Int. 2014, 2014: 159078-
Stamova B, Jickling GC, Ander BP, Zhan X, Liu D, Turner R, et al: Gene expression in peripheral immune cells following cardioembolic stroke is sexually dimorphic. PLoS One. 2014, 9 (7): e102550-
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al: NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013, 41 (Database issue): D991-D995.
Wang YC, Chen BS: A network-based biomarker approach for molecular investigation and diagnosis of lung cancer. BMC Med Genomics. 2011, 4: 2-
Liu KQ, Liu ZP, Hao JK, Chen L, Zhao XM: Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics. 2012, 13: 126-
Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, et al: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015, 43 (Database issue): D470-D478.
Johansson R: System Modeling and Identification. 1993
Pagano M, Gauvreau K: Principles of biostatistics. 2000
Kanehisa M: Molecular network analysis of diseases and drugs in KEGG. Methods Mol Biol. 2013, 939: 263-75.
Wang J, Huang Q, Liu ZP, Wang Y, Wu L, Chen XS, et al: NOA: a novel Network Ontology Analysis method. Nucleic Acids Res. 2011, 39 (13): e87-
Zhang C, Wang J, Hanspers K, Xu D, Chen L, Pico AR: NOA: a cytoscape plugin for network ontology analysis. Bioinformatics. 2013, 29 (16): 2066-2067.
Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.
Huang da W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37 (1): 1-13.
Salinas GD, Blair LA, Needleman KA, Gonzales JD, Chen Y, Li M, et al: Actinfilin is a Cul3 substrate adaptor, linking GluR6 kainate receptor subunits to the ubiquitin-proteasome pathway. J Biol Chem. 2006, 281 (52): 40164-40173.
Aries A, Whitcomb J, Shao W, Komati H, Saleh M, Nemer M: Caspase-1 cleavage of transcription factor GATA4 and regulation of cardiac cell fate. Cell Death and Disease. 2014, 5: e1566-
Harel A, et al: GIFtS: annotation landscape analysis with GeneCards. BMC Bioinformatics. 2009, 10: 348-
Bobylev AG, Shatalin IuV, Vikhliantsev IM, Bobyleva LG, Gudkov SV, Podlubnaia ZA: [Interaction of C60 fullerene-polyvinylpyrrolidone complex and brain Abeta(1-42)-peptide in vitro]. Biofizika. 2014, 59 (5): 843-847.
Wang LS, Naj AC, Graham RR, Crane PK, Kunkle BW, Cruchaga C, et al: Rarity of the Alzheimer disease-protective APP A673T variant in the United States. JAMA Neurol. 2015, 72 (2): 209-216.
Hoefgen S, Dahms SO, Oertwig K, Than ME: The Amyloid Precursor Protein Shows a pH-Dependent Conformational Switch in Its E1 Domain. J Mol Biol. 2015, 427 (2): 433-442.
Xu W, et al: Early hyperactivity in lateral entorhinal cortex is associated with elevated levels of AβPP metabolites in the Tg2576 mouse model of Alzheimer's disease. Exp Neurol. 2015, 264: 82-91.
Vakhrusheva O, Smolka C, Gajawada P, Kostin S, Boettger T, Kubin T, et al: Sirt7 increases stress resistance of cardiomyocytes and prevents apoptosis and inflammatory cardiomyopathy in mice. Circ Res. 2008, 102 (6): 703-710.
Rashidian J, Iyirhiaro GO, Park DS: Cell cycle machinery and stroke. Biochim Biophys Acta. 2007, 1772 (4): 484-493.
Kranenburg O, Scharnhorst V, Van der Eb AJ, Zantema A, et al: Inhibition of cyclin-dependent kinase activity triggers neuronal differentiation of mouse neuroblastoma cells. J Cell Biol. 1995, 131 (1): 227-234.
Sumrejkanchanakij P, Tamamori-Adachi M, Matsunaga Y, Eto K, Ikeda MA, et al: Role of cyclin D1 cytoplasmic sequestration in the survival of postmitotic neurons. Oncogene. 2003, 22 (54): 8723-8730.
Song B, Tang X, Wang X, Huang X, Ye Y, Lu X, et al: Bererine induces peripheral lymphocytes immune regulations to realize its neuroprotective effects in the cerebral ischemia/reperfusion mice. Cell Immunol. 2012, 276 (1-2): 91-100.
Wu CC, Tsai TH, Chang C, Lee TT, Lin C, Cheng IH, et al: On the crucial cerebellar wound healing-related pathways and their cross-talks after traumatic brain injury in danio rerio. PLoS One. 2014, 9 (6): e97902-
Chou SHY, Robertson CS, Consensus IM: Monitoring Biomarkers of Cellular Injury and Death in Acute Brain Injury. Neurocrit Care. 2014, 21 Suppl 2: S187-S214.
Moreau M, Tian MY, Klessig DF: Salicylic acid binds NPR3 and NPR4 to regulate NPR1-dependent defense responses. Cell Research. 2012, 22 (12): 1631-1633.
Miyawaki S, Imai H, Takayanagi S, Mukasa A, Nakatomi H, Saito N: Identification of a Genetic Variant Common to Moyamoya Disease and Intracranial Major Artery Stenosis/Occlusion. Stroke. 2012, 43 (12): 3371-3374.
Zhou J, Li J, Rosenbaum DM, Barone FC: Thrombopoietin protects the brain and improves sensorimotor functions: reduction of stroke-induced MMP-9 upregulation and blood-brain barrier injury. J Cereb Blood Flow Metab. 2011, 31 (3): 924-933.
Hahn CD, Manlhiot C, Schmidt MR, Nielsen TT, Redington AN: Remote Ischemic Per-Conditioning A Novel Therapy for Acute Stroke?. Stroke. 2011, 42 (10): 2960-2962.
Shi J, Yang SH, Stubley L, Day AL, Simpkins JW: Hypoperfusion induces overexpression of beta-amyloid precursor protein mRNA in a focal ischemic rodent model. Brain Research. 2000, 853 (1): 1-4.
Wojcik C, Di Napoli M: Ubiquitin-proteasome system and proteasome inhibition: New strategies in stroke therapy. Stroke. 2004, 35 (6): 1506-1518.
Bahls M, Bidwell CA, Hu J, Tellez A, Kaluza GL, JF Granada, et al: Gene expression differences during the heterogeneous progression of peripheral atherosclerosis in familial hypercholesterolemic swine. BMC Genomics. 2013, 14: 443-
Ben-Haim MS, Moshitch-Moshkovitz S, Rechavi G: FTO: linking m6A demethylation to adipogenesis. Cell Res. 2015, 25 (1): 3-4.
Tseveleki V, Rubio R, Vamvakas SS, White J, Taoufik E, Petit E, et al: Comparative gene expression analysis in mouse models for multiple sclerosis, Alzheimer's disease and stroke for identifying commonly regulated and disease-specific gene changes. Genomics. 2010, 96 (2): 82-91.
Stark LA, Taliansky M: Old and new faces of the nucleolus Workshop on the Nucleolus and Disease. EMBO Rep. 2009, 10 (1): 35-40.
D'Amico D, Moschiano F, Leone M, Ariano C, Ciusani E, Erba N, et al: Genetic abnormalities of the protein C system: shared risk factors in young adults with migraine with aura and with ischemic stroke?. Cephalalgia. 1998, 18 (9): 618-621.
Wang ZY, Qin W, Yi F: Targeting histone deacetylases: perspectives for epigenetic-based therapy in cardio-cerebrovascular disease. J Geriatr Cardiol. 2015, 12 (2): 153-164.
The authors are grateful for the support provided by the Ministry of Science and Technology through grant nos. MOST-103-2745-E-007-001-ASP, MOST-103-2221-E-038 -013-MY2 and MOST 104-2218-E-007-021.
Publication costs for this article were funded by the Ministry of Science and Technology through grant nos. MOST-103-2745-E-007-001-ASP, MOST-103-2221-E-038 -013-MY2 and MOST 104-2218-E-007-021.
This article has been published as part of BMC Systems Biology Volume 9 Supplement 6, 2015: Joint 26th Genome Informatics Workshop and 14th International Conference on Bioinformatics: Systems biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/9/S6.
The authors declare that they have no competing interests.
BSC and THC directed the research project. THC accumulated and organized the source data. YHW and CCW performed the experiments. YHW, CCW, and THC drafted different sections of the manuscript. HYL and HYW contributed viewpoints of clinical doctors to this research. BRJ helped to revise the manuscript. All of the authors approved publication of the manuscript.
Yung-Hao Wong, Chia-Chou Wu contributed equally to this work.
Electronic supplementary material
Additional file 2: Parameter identification of the regression model in equation (1) by the maximum-likelihood method (*.pdf). (PDF 153 KB)
Additional file 3: Determination of significant protein associations by the Akaike information criterion and Student's t-test (*.pdf). (PDF 121 KB)
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Wong, YH., Wu, CC., Lai, HY. et al. Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data. BMC Syst Biol 9 (Suppl 6), S4 (2015). https://doi.org/10.1186/1752-0509-9-S6-S4