Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data

Abstract

BackgroundMolecular 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.ResultsDifferential 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.ConclusionsThis 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.

Serum Neuroinflammatory Disease-Induced Central Nervous System Proteins Predict Clinical Onset of Experimental Autoimmune Encephalomyelitis

“…High-confidence serum NDICP expression waves and protein biomarker candidates for the prediction of disease onset were selected based on the following prioritization criteria: (i) Statistically significant difference ( P  ≤ 0.05) in expression compared to control during the preclinical (pre-onset) stage at either day 5 or day 7 post-immunization ( P  = 0.05 to 1.10E−43); proteins which did not achieve statistical significance during either one of these time points (day 5 or day 7) were excluded. (ii) Biological/functional relevance for neuroinflammatory and demyelinating diseases based on bioinformatic analysis of the pre-onset M2 proteomics datasets (Figure S1 in Supplementary Material), in accordance to previous studies (22). (iii) CNS tissue-specific or primary expression (i.e., NDICPs) based on bioinformatic analysis with tools provided by the EMBL-EBI expression atlas (23), MOPED (24), and ProteomicsDB (25).…”

Systems multiple molecule drug design with less side-effects via drug data mining and genome-wide data identification

“…The reason underlying the attack efficiency of multiple target is easier to understand from the systematic point of view. [29][30][31] In general multiple target attacks are much better because they affect the GEN at more sites if they are distributed in the entire network. 21,23,24 In order to gain much better multiple target attacks, a structure-based and ligand-based drug design has been further discussed in.…”

“…In order to investigate pathogenetic molecule mechanism, we need to construct the GENs of different stages of disease by big data mining and system identification method via genomewide high throughput data. [13][14][15][16] The systematic design precedence of multiple molecule drugs in Figure 1 will be discussed in detail in the following.…”

“…Based on systems biology method via host/pathogen two-sided microarray data, system identification, and principal network projection method in Figure 1, we could obtain core cross-talk GENs of early and late CDI, which could be projected to KEGG pathways to find the core cross-talk pathways for infection and defense mechanism of host and pathogen in the early and late CDI. From the infectious and defensive mechanism investigated by two core cross-talk pathways in the early and late stage of CDI, we found the following two cell wall proteins CD2787 and CD0237 (7) play an important role in cell adhesion and pathogen defence mechanism; the following crucial proteins 13 CD1214, CD2629, and CD2643 (8) are employed by C. difficile for sporulation.…”

“…11 Further, drug-target interactions are discussed in more detail from the perspective of cellular networking. 10,[12][13][14] Based on the above analyses, an integration of computational network-based approaches for multiple drug targets with drug data mining for multiple molecule drugs could not only reduce the time and expenses of pre-clinical stages, but also could lead to more precise medicines with less side-effects that will eventually be translated into lower attrition rates due to multiple drug targets and multiple molecule drugs. From the flowchart of systems drug discovery in Figure 1, the first step is to construct a candidate genetic and epigenetic network (GEN) for diseases by big database mining (if an infectious disease is considered, interspecies candidate cross-talk genetic and epigenetic network should be constructed).…”