Identification of Gene Networks and Pathways Associated with Guillain-Barré Syndrome

BACKGROUND: The underlying change of gene network expression of Guillain-Barré syndrome (GBS) remains elusive. We sought to identify GBS-associated gene networks and signaling pathways by analyzing the transcriptional profile of leukocytes in the patients with GBS. METHODS AND FINDINGS: Quantitative global gene expression microarray analysis of peripheral blood leukocytes was performed on 7 patients with GBS and 7 healthy controls. Gene expression profiles were compared between patients and controls after standardization. The set of genes that significantly correlated with GBS was further analyzed by Ingenuity Pathways Analyses. 256 genes and 18 gene networks were significantly associated with GBS (fold change ≥2, P<0.05). FOS, PTGS2, HMGB2 and MMP9 are the top four of 246 significantly up-regulated genes. The most significant disease and altered biological function genes associated with GBS were those involved in inflammatory response, infectious disease, and respiratory disease. Cell death, cellular development and cellular movement were the top significant molecular and cellular functions involved in GBS. Hematological system development and function, immune cell trafficking and organismal survival were the most significant GBS-associated function in physiological development and system category. Several hub genes, such as MMP9, PTGS2 and CREB1 were identified in the associated gene networks. Canonical pathway analysis showed that GnRH, corticotrophin-releasing hormone and ERK/MAPK signaling were the most significant pathways in the up-regulated gene set in GBS. CONCLUSIONS: This study reveals the gene networks and canonical pathways associated with GBS. These data provide not only networks between the genes for understanding the pathogenic properties of GBS but also map significant pathways for the future development of novel therapeutic strategies

Multiscale digital rock analysis for complex rocks

Many properties of complex porous media such as reservoir rocks are strongly affected by heterogeneity at different scales. Complex depositional and diagenetic processes have a strong control on the pore structures, leading to systems with a wide range of pore sizes covering many orders of magnitude in length scales. This poses a significant challenge for digital rock analysis since a single resolution image and associated simulation model cannot capture all the relevant length scales in sufficient detail due to limitations in computer memory and speed. The scale-transgressive effects of heterogeneity must therefore be accounted for through a multiscale digital rock workflow. We introduce a generalized multiscale imaging and pore-scale modelling workflow to derive transport properties of complex rocks having broad pore size distributions. A dry/wet micro-CT imaging sequence is used to spatially map the porosity and the connectivity of resolved and unresolved porous regions. The unresolved porosity regions are classified into different porosity classes or rock types. The resulting 3D rock-type map and the porosity map are combined and transformed into a multiscale pore network model. Resolved pores are treated in a conventional pore network manner while unresolved network elements are treated as a continuum Darcy-type porous medium. Similar to conventional continuum models, each Darcy pore is populated with single and multiphase flow properties. These properties are derived from high-resolution rock-type models constructed from backscatter SEM images and/or high-resolution micro-CT images of sub-samples. The multiscale digital rock workflow is applied to two heterogeneous rock samples: a mixed wet thinly laminated reservoir sandstone and an oil wet reservoir carbonate. Experimentally measured mercury-air primary drainage and oil-water imbibition capillary pressure curves (after ageing to restore wettability) are used to anchor the multiscale pore network model. Waterflood relative permeability is calculated in a blind test and compared with high-quality experimental data. A very encouraging agreement between computed and measured properties is found