Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome

<p>Abstract</p> <p>Background</p> <p>Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.</p> <p>Results</p> <p>We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.</p> <p>Conclusion</p> <p>We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.</p

Evaluation of key genes and biological pathways that play a role in primary Sjogren syndrome by using a systems biology approach

Primary Sjogren syndrome (PSS) is one of the most common systemic autoimmune diseases. Lymphocytic infiltration of exocrine glands, especially lacrimal and salivary in PSS, causes ocular and oral dryness. Dry mouth may lead to difficulty in speaking, chewing, and swallowing and result in reduced quality. The pathogenesis of PSS involves multiple factors, such as genetic, environmental, and immunological factors. Despite extensive research over the last few decades, the exact etiology and progression of PSS and its inflammatory lesions is still unknown. Gene co-expression network analysis (WGCNA) is a system biology method that can be used to describe the correlation between different genes and find modules of highly correlated genes and key genes. Also, by using these modules, we can get gene ontology information and biological pathways. In this study, we used WGCNA to analyze the GSE40611 dataset, which consists of 17 PSS patients and 18 healthy controls. We construct a co-expression network for mRNA expression data of patients and control groups and then find the most significant module and hub genes that play important roles in PSS. We also identify biological pathways and related miRNA for hub genes. Among all the modules, turquoise had the most correlation with PSS and some of the hub genes, including GPR18, FCRL1, VNN2 and etc. Also, a large number of pathways were identified in the turquoise module, most of them related to immune system activity, like T-cell activation, lymphocyte differentiation, leukocyte and lymphocyte activation, regulation of immune system processes, regulation of immune response, and cell-cell adhesion. External validation using bulk RNA sequencing data also confirmed the presence of selected hub-genes in pathogenicity of PSS. Finally, these results can lead to finding key players in treatment of PSS

Connectivity differences between Gulf War Illness (GWI) phenotypes during a test of attention

One quarter of veterans returning from the 1990–1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention in cognitive dysfunction was assessed by seed region correlations during a simple 0-back stimulus matching task (“see a letter, push a button”) performed before exercise. Analysis was analogous to resting state, but different from psychophysiological interactions (PPI). The patterns of correlations between nodes in task and default networks were significantly different for START (n = 9), STOPP (n = 18) and control (n = 8) subjects. Edges shared by the 3 groups may represent co-activation caused by the 0-back task. Controls had a task network of right dorsolateral and left ventrolateral prefrontal cortex, dorsal anterior cingulate cortex, posterior insulae and frontal eye fields (dorsal attention network). START had a large task module centered on the dorsal anterior cingulate cortex with direct links to basal ganglia, anterior insulae, and right dorsolateral prefrontal cortex nodes, and through dorsal attention network (intraparietal sulci and frontal eye fields) nodes to a default module. STOPP had 2 task submodules of basal ganglia–anterior insulae, and dorsolateral prefrontal executive control regions. Dorsal attention and posterior insulae nodes were embedded in the default module and were distant from the task networks. These three unique connectivity patterns during an attention task support the concept of Gulf War Disease with recognizable, objective patterns of cognitive dysfunction

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Glycomic phenotyping of twin patients with low back pain

Križobolja je sveprisutan mišićno-skeletni sindrom koji se javlja u svim dobnim skupinama. Detaljne informacije o dijagnozi ovog sindroma potrebne su zbog njegove kliničke i socijalne važnosti. Trenutno postoje ograničeni biomarkeri (uglavnom su to snimke dobivene magnetskom rezonancijom) zbog čega je potrebno razviti nove, jednostavnije metode rane detekcije križobolje. Jedan od glavnih uzroka kronične križobolje jest lokalizirana upala u epiduralnom prostoru. Kako je svaki upalni proces povezan s brojnim promjenama na mjestu upale, tako su uočene i promjene u glikanima vezanim za Fc regiju imunoglobulina G (IgG). U ovom radu nastojala sam odrediti postoje li različiti profili količine glikana kod blizanca iz TwinsUK baze podataka s križoboljom i zdravih pojedinaca. Da bih to ostvarila, analizirala sam N-glikom IgG-a 4416 blizanaca koji su odgovorili na upitnike o povijesti boli, čiji je glikanski profil određen i koji su podvrgnuti snimanju kralježnice magnetskom rezonancijom. S dobivenim podatcima sam, koristeći statističke metode za analizu blizanaca, tražila asocijaciju N-glikana s fenotipovima boli. Dodatno sam koristila i metode koje se koriste u genomici za određivanje razlike u ekspresiji gena kod pacijenata i kontrola te tako dobila module različitih količina N-glikana. Dobivenim modulima kreirala sam prediktivne algoritme koji govore o vjerojatnosti razvoja križobolje kod novih ispitanika.Lumbago is a common musculoskeletal condition in all ages. Owing to its clinical and social impact, a clear diagnosis of this syndrome is needed in order to define besides pain intensity also the interference of pain with daily and work activity. Currently, there are limited biomarkers (mostly imaging), but there is a need for novel and simpler detection methods. One of the major determinants of pain in persistent lumbago is localised inflammation in epidural space. As every inflammatory process is linked with changes on the inflamed region, so are altered N-glycan IgG compositions. In this study, using classical twin design conducted between twins from TwinsUK registry, I assessed whether twins reporting episodes of lumbago had detectable levels of altered IgG glycosylation. I analysed IgG N-glycome of 4416 twins having completed pain history questionnaires, whose glycan profile was determined and who underwent magnetic-resonance spine scan. With this data, using statistical methods for twin studies, I looked for association between glycan quantities and pain phenotypes. Additionally, I used methods applied in genomics and obtained modules of glycans that show different patterns of glycan quantities. With these modules I built prediction algorithms that are giving likelihood of person reporting episodes of lumbago