Subnetwork Analysis Research Articles

BIOS-04. THERAPIES FOR LEPTOMENINGEAL DISEASE: A NETWORK META-ANALYSIS

Abstract INTRODUCTION Leptomeningeal disease (LMD) has limited head-to-head comparison of available therapies, including systemic chemotherapy (SysCT), intrathecal (IT) or intraventricular therapy (ITV), whole-brain radiotherapy (WBRT), or cranio-spinal irradiation (CSI). No network meta-analysis (NMA) of randomized controlled trials (RCTs) of LMD has been reported so far. METHODS NMA was conducted using published LMD RCTs and reported following PRISMA-NMA guidelines. Primary outcome was survival, assessed using hazard ratios (HRs) and 95% confidence intervals (95%CI). Survival times were converted to HRs under exponential distribution assumption. LogHR standard errors were calculated using deaths per RCT arm. P-scores were used to estimate probability of treatment being superior. Analysis was performed in R using frequentist approach, followed by back-transforming logHRs for indirect and direct comparisons. Incremental effects of individual treatment components were estimated using additive model. Heterogeneity and inconsistency were assessed using tau-squared (τ²), I-squared (I²), and Q-statistics. Analyses were performed separately for main subnetwork with disconnected subnetworks accommodated in additive model. RESULTS Seven studies were included, incorporating 7 pairwise comparisons and 9 treatments. 3 subnetworks were identified. Primary subnetwork analysis included 5 studies. Compared to SysCT±RT (reference), logHRs (95%CI) under random effects model were: IT/ITV DepoCyt±SysCT±RT -0.094(-0.468,0.281); IT/ITV methotrexate(MTX)±cytarabine(AraC)±SysCT±RT 0.8567(0.066,1.647). IT/ITV MTX±SysCT±RT 0.318(-0.163,0.798); ITV Thiotepa±SysCT±RT 0.438(-0.294,1.169). Heterogeneity and inconsistency were not detected (τ²=0,I²=0%,Q-statistic=0.57,P=0.45). P-scores were highest for IT/ITV DepoCyt±SysCT±RT (0.894), followed by SysCT±RT (0.769). Disconnected-component-NMA demonstrated incremental logHR: AraC 0.539(-0.347,1.425); IT/ITV DepoCyt 0.052(-0.566,0.670); IT/ITV MTX 0.179(-0.484,0.840); ITV thiotepa 0.298(-0.767,1.362); proton CSI -0.250(-0.726,0.225). No component demonstrated statistically significant incremental benefit, with moderate heterogeneity and adequate fit present (τ²=0.1017,I²=57%,Q-statistic=4.65,P=0.098). CONCLUSIONS IT/ITV DepoCyt±SysCT±RT and SysCT±RT were found most effective. Lack of significant incremental benefit warrants evaluating older therapies in contemporary RCTs.

Large-scale proteomic profiling reveals characteristic HIV-specific druggable protein signatures associated with incident heart failure

Abstract Background Compared with the general population, persons with HIV suffer from an augmented risk of both systolic and diastolic heart failure despite effective antiretroviral therapy [1]. While inflammatory and coagulation markers are thought to be involved in this increased risk, other contributing mechanisms, such as pro-inflammatory protein networks, remain to be elucidated. Purpose To identify novel protein predictors of incident heart failure events in the longitudinal Veterans Aging Cohort Study Biomarker Cohort of United States Veterans with HIV (PLWH; n = 1525) and without HIV (PWoH; n = 853) and to assess the functional relatedness of these proteins in PLWH. Methods We characterized the plasma proteome of PLWH and PWoH using an aptamer-based platform and assessed univariable associations with incident heart failure. Druggable proteins were identified using the Therapeutic Target Database [2]. We built a protein interaction network querying significant proteins (q-value <0.05) using Cytoscape for the Retrieval of Interacting Genes/Proteins (STRING) database [3]. To focus on densely connected core proteins, we used the mcl clustering method and identified a subnetwork of approximately 77 proteins. Over-representation analysis was performed for the core network by identifying enriched gene sets using Gene Ontology biological processes and WikiPathways. Final results are filtered based on a false discovery rate (FDR) threshold of 0.05. Results Of 4926 plasma proteins, 441 proteins in all PLWH and 706 in those with well-controlled HIV (viral load <200 copies/mL) were significantly associated with incident heart failure, compared with only 142 among PWoH (Figure 1A). Of the top 50 proteins most significantly associated with heart failure, 21 were druggable in PLWH and 14 in PWoH; only 5 druggable markers were shared, while 16 were unique to HIV (Table 1). Pleiotrophin, a pro-angiogenic, pro-inflammatory cytokine that induces tumor necrosis factor- (TNF) α, interleukin- (IL) 1β, and IL-6 expression and has been implicated in cardiomyocyte apoptosis [4,5], had the highest hazard ratio (HR) for incident heart failure (HR = 4.7; p = 1.6E-20; q = 1.7E-18) in PLWH. The most significant enriched protein pathway included 36 genes involved in chemotaxis (FDR = 1.3E-30); 3 of the 21 druggable targets (pleiotrophin, ephrin-A5, and ephrin type-A receptor 2) were represented in this pathway (Figure 1B). Ephrin signaling pathways, which are involved in cell adhesion, differentiation, and proliferation [6], have not previously been implicated in heart failure, nor in HIV. Subnetwork analysis of druggable proteins demonstrated that ephrin-signaling, IL-15, and TNF pathways were functionally interconnected (Figure 1C). Conclusions Our findings indicate that there may be unique pro-inflammatory pathways characteristic of HIV-associated cardiomyopathy that merit further study as dedicated pharmacologic targets and/or risk markers in this population.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation.

Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets. Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed. MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction. This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

Possible Role of Correlation Coefficients and Network Analysis of Multiple Intracellular Proteins in Blood Cells of Patients with Bipolar Disorder in Studying the Mechanism of Lithium Responsiveness: A Proof-Concept Study.

Background: The mechanism of lithium treatment responsiveness in bipolar disorder (BD) remains unclear. The aim of this study was to explore the utility of correlation coefficients and protein-to-protein interaction (PPI) network analyses of intracellular proteins in monocytes and CD4+ lymphocytes of patients with BD in studying the potential mechanism of lithium treatment responsiveness. Methods: Patients with bipolar I or II disorder who were diagnosed with the MINI for DSM-5 and at any phase of the illness with at least mild symptom severity and received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks were divided into two groups, responders (≥50% improvement in Montgomery-Asberg Depression Rating Scale and/or Young Mania Rating Scale scores from baseline) and non-responders. Twenty-eight intracellular proteins/analytes in CD4+ lymphocytes and monocytes were analyzed with a tyramine-based signal-amplified flow cytometry procedure. Correlation coefficients between analytes at baseline were estimated in both responders and non-responders and before and after lithium treatment in responders. PPI network, subnetwork, and pathway analyses were generated based on fold change/difference in studied proteins/analytes between responders and non-responders. Results: Of the 28 analytes from 12 lithium-responders and 11 lithium-non-responders, there were more significant correlations between analytes in responders than in non-responders at baseline. Of the nine lithium responders with pre- and post-lithium blood samples available, the correlations between most analytes were weakened after lithium treatment with cell-type specific patterns in CD4+ lymphocytes and monocytes. PPI network/subnetwork and pathway analyses showed that lithium response was involved in four pathways, including prolactin, leptin, neurotrophin, and brain-derived neurotrophic factor pathways. Glycogen synthase kinase 3 beta and nuclear factor NF-kappa-B p65 subunit genes were found in all four pathways. Conclusions: Using correlation coefficients, PPI network/subnetwork, and pathway analysis with multiple intracellular proteins appears to be a workable concept for studying the mechanism of lithium responsiveness in BD. Larger sample size studies are necessary to determine its utility.

Bisphenol A exposure affects specific gut taxa and drives microbiota dynamics in childhood obesity.

Cumulative xenobiotic exposure has an environmental and human health impact which is currently assessed under the One Health approach. Bisphenol A (BPA) exposure and its potential link with childhood obesity that has parallelly increased during the last decades deserve special attention. It stands during prenatal or early life and could trigger comorbidities and non-communicable diseases along life. Accumulation in the nature of synthetic chemicals supports the "environmental obesogen" hypothesis, such as BPA. This estrogen-mimicking xenobiotic has shown endocrine disruptive and obesogenic effects accompanied by gut microbiota misbalance that is not yet well elucidated. This study aimed to investigate specific microbiota taxa isolated and selected by direct BPA exposure and reveal its role on the overall children microbiota community and dynamics, driving toward specific obesity dysbiosis. A total of 333 BPA-resistant isolated species obtained through culturing after several exposure conditions were evaluated for their role and interplay with the global microbial community. The selected BPA-cultured taxa biomarkers showed a significant impact on alpha diversity. Specifically, Clostridium and Romboutsia were positively associated promoting the richness of microbiota communities, while Intestinibacter, Escherichia-Shigella, Bifidobacterium, and Lactobacillus were negatively associated. Microbial community dynamics and networks analyses showed differences according to the study groups. The normal-weight children group exhibited a more enriched, structured, and connected taxa network compared to overweight and obese groups, which could represent a more resilient community to xenobiotic substances. In this sense, subnetwork analysis generated with the BPA-cultured genera showed a correlation between taxa connectivity and more diverse potential enzymatic BPA degradation capacities.IMPORTANCEOur findings indicate how gut microbiota taxa with the capacity to grow in BPA were differentially represented within differential body mass index children study groups and how these taxa affected the overall dynamics toward patterns of diversity generally recognized in dysbiosis. Community network and subnetwork analyses corroborated the better connectedness and stability profiles for normal-weight group compared to the overweight and obese groups.

The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective

BackgroundThere are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective. MethodsA total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested. Results“Failure”, “External control”, and “Lack of activity” were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were “Failure”, “Guilty”, and “No future”. Positive PLE “Odd looks” and negative PLE “Unable to terminate” are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network. ConclusionIn this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms.Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.

언어 네트워크 분석기법을 통한 생태(환경)교육 기본계획 문서 분석

Objectives The main purpose of this study is to determine what the key words are in the basic plans for ecological(environmental) education created by provincial offices of education and what the relationships are between those words. In addition, this study was conducted to determine whether the documents of the provincial office of education contain the value of ecological transformation in education.
 Methods The research method used in this study is semantic network analysis. The words included in the basic plans for ecological(environmental) education created by the provincial offices of education in 13 regions were corrected, controlled, and removed to extract key words, and the frequencies of the words were analyzed. Additionally, basic attribute analysis, centrality analysis, sub-network analysis, and ego network analysis were performed using Gephi 0.10.1, a network analysis program.
 Results The results of this study are as follows. First, as a result of analyzing the frequency of use and centrality of each key words, ‘practice’ and ‘carbon neutrality’ appeared most frequently and have the highest influence in the documents. Second, as a result of clustering key words according to their similarity, these words formed four sub-networks. Third, as a result of examining the connection structure between ‘ecological transformation education’ and ‘ecological environment education’ through ego network analysis, the two words shared a lot of related words.
 Conclusions The conclusions of this study are as follows. First, The use of ‘practice’ and ‘carbon neutrality’ as key words in the basic plans for ecological (environment) education should be maintained in the future. At this time, educational activities at the cognitive and affective levels should also be treated with great importance in the document. Second, since ecological transformation education and ecological environment education form different clusters, each documents must establish a mutually cooperative and complementary relationship. Third, many words are shared between the ecological transformation ego network and the ecological environment education ego network, and since there is ongoing discussion about what term to replace the existing environmental education with, so provincial offices of education should be cautious in using the terms.

Proteomic analysis of human aqueous humor from fuchs uveitis syndrome

Fuchs uveitis syndrome (FUS) is a commonly misdiagnosed uveitis syndrome often presenting as an asymptomatic mild inflammatory condition until complications arise. The diagnosis of this disease remains clinical because of the lack of specific laboratory tests. The aqueous humor (AH) is a complex fluid containing nutrients and metabolic wastes from the eye. Changes in the AH protein provide important information for diagnosing intraocular diseases. This study aimed to analyze the proteomic profile of AH in individuals diagnosed with FUS and to identify potential biomarkers of the disease. We used liquid chromatography-tandem mass spectrometry-based proteomic methods to evaluate the AH protein profiles of all 37 samples, comprising 15 patients with FUS, six patients with Posner-Schlossman syndrome (PSS), and 16 patients with age-related cataract. A total of 538 proteins were identified from a comprehensive spectral library of 634 proteins. Subsequent differential expression analysis, enrichment analysis, and construction of key sub-networks revealed that the inflammatory response, complement activation and hypoxia might be crucial in mediating the process of FUS. The hypoxia inducible factor-1 may serve as a key regulator and therapeutic target. Additionally, the innate and adaptive immune responses are considered dominant in the patients with FUS. A diagnostic model was constructed using machine-learning algorithm to classify FUS, PSS, and normal controls. Two proteins, complement C1q subcomponent subunit B and secretogranin-1, were found to have the highest scores by the Extreme Gradient Boosting, suggesting their potential utility as a biomarker panel. Furthermore, these two proteins as biomarkers were validated in a cohort of 18 patients using high resolution multiple reaction monitoring assays. Therefore, this study contributes to advancing of the current knowledge of FUS pathogenesis and promotes the development of effective diagnostic strategies.

Identification of diagnostic markers of pancreatic ductal adenocarcinoma using transcriptomic tumour and blood sample data

AbstractBackgroundPancreatic ductal adenocarcinoma (PDAC) is the most frequently diagnosed form of pancreatic cancer worldwide. PDAC is associated with a poor survival rate mainly due to the disease being usually diagnosed at late stages.MethodsPublicly available gene expression data from 10 studies with tumour tissue (448 samples) and/or blood samples (128 samples) from PDAC patients were pooled together and analyzed for the identification of stage‐specific and global diagnostic markers using differential gene expression analysis. The list of statistically significant () differentially expressed genes were used to carry out enrichment analysis via active subnetworks and miRNA enrichment analysis. We then used the results from these analyses to identify the most significant genes and pathways and map these to marketed drugs’ pharmacological targets. The same process was replicated for studies with blood samples and results were compared to those from the tissue analysis. A set of consistently deregulated genes (pancreatic tumour signature, PTS) in both tissue and blood samples was derived and validated in external cohorts and The Cancer Genome Atlas (TCGA) data.ResultsNotable gene expression deregulation was found in all tumour stages with significant overlap. We identified 820 consistently deregulated genes (PTS) in tissue samples of all stages and blood samples. Active subnetwork analysis revealed enriched ribosome, proteasome, adherens junction and cell cycle pathways across all stages and blood samples. Our findings suggest that microRNA (miRNA) contribution to PDAC pathology plays a significant role and is probably mediated by distinct miRNAs across stages of PDAC. Stage‐specific enriched miRNAs with diagnostic potential included miR‐21, miR‐29, miR‐124 and miR‐30, for stages 1–4, respectively. By investigating the pharmacogenetic interactions of the identified targets with clinically approved drugs, we outline potential paths for personalized interventions. Importantly, the PTS showed a significant association with survival in TCGA data.ConclusionThus, we present a compilation of protein‐coding markers and miRNAs that hold potential as a diagnostic tool for the early detection of PDAC, as well as for designing novel therapeutic strategies aimed at improving patient outcomes.

Identification of hub genes associated with somatic cell score in dairy cow.

Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study.

Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection.

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures.

Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs. Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis. A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses. This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.

Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis.

Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.

Meta-analysis of RNA-Seq datasets highlights novel genes/pathways involved in fat deposition in fat-tail of sheep.

Fat-tail in sheep is considered as an important energy reservoir to provide energy as a survival buffer during harsh challenges. However, fat-tail is losing its importance in modern sheep industry systems and thin-tailed breeds are more desirable. Using comparative transcriptome analysis to compare fat-tail tissue between fat- and thin-tailed sheep breeds provides a valuable approach to study the complex genetic factors associated with fat-tail development. However, transcriptomic studies often suffer from issues with reproducibility, which can be improved by integrating multiple studies based on a meta-analysis. Hence, for the first time, an RNA-Seq meta-analysis on sheep fat-tail transcriptomes was performed using six publicly available datasets. A total of 500 genes (221 up-regulated, 279 down-regulated) were identified as differentially expressed genes (DEGs). A jackknife sensitivity analysis confirmed the robustness of the DEGs. Moreover, QTL and functional enrichment analysis reinforced the importance of the DEGs in the underlying molecular mechanisms of fat deposition. Protein-protein interactions (PPIs) network analysis revealed the functional interactions among the DEGs and the subsequent sub-network analysis led to identify six functional sub-networks. According to the results of the network analysis, down-regulated DEGs in green and pink sub-networks (like collagen subunits IV, V, and VI, integrins 1 and 2, SCD, SCD5, ELOVL6, ACLY, SLC27A2, and LPIN1) may impair lipolysis or fatty acid oxidation and cause fat accumulation in tail. On the other hand, up-regulated DEGs, especially those are presented in green and pink sub-networks (like IL6, RBP4, LEPR, PAI-1, EPHX1, HSD11B1, and FMO2), might contribute to a network controlling fat accumulation in the tail of sheep breed through mediating adipogenesis and fatty acid biosynthesis. Our results highlighted a set of known and novel genes/pathways associated with fat-tail development, which could improve the understanding of molecular mechanisms behind fat deposition in sheep fat-tail.

OpenXGR: a web-server update for genomic summary data interpretation.

How to effectively convert genomic summary data into downstream knowledge discovery represents a major challenge in human genomics research. To address this challenge, we have developed efficient and effective approaches and tools. Extending our previously established software tools, we here introduce OpenXGR (http://www.openxgr.com), a newly designed web server that offers almost real-time enrichment and subnetwork analyses for a user-input list of genes, SNPsor genomic regions. It achieves so through leveraging ontologies, networks, and functional genomic datasets (such as promoter capture Hi-C, e/pQTLand enhancer-gene maps for linking SNPs or genomic regions to candidate genes). Six analysers are provided, each doing specific interpretations tailored to genomic summary data at various levels. Three enrichment analysers are designed to identify ontology terms enriched for input genes, as well as genes linked from input SNPs or genomic regions. Three subnetwork analysers allow users to identify gene subnetworks from input gene-, SNP-or genomic region-level summary data. With a step-by-step user manual, OpenXGR provides a user-friendly and all-in-one platform for interpreting summary data on the human genome, enabling more integrated and effective knowledge discovery.

OA03 Harnessing quantitative proteomics to identify biomarkers of treatment response to etanercept in patients with rheumatoid arthritis

Abstract Background/Aims Tumour necrosis factor inhibitors (TNFi) are commonly prescribed to treat rheumatoid arthritis (RA), but response to TNFi is not universal and there are no validated pre-treatment biomarkers of treatment response at present. Proteins have many features that make them ideal potential biomarkers, as they carry out diverse biological processes, interact with drugs and can reflect post-translational modifications. This study aimed to identify protein biomarkers associated with response to TNFi in patients with RA. Methods Participants were recruited from a UK-based prospective multi-centre study of patients fulfilling either the 1987 ACR or 2010 ACR/EULAR classification for RA starting biologic treatment for the first time. Quantitative proteomics was performed using Sequential Window Acquisition of all THeoretical fragment ion spectra mass spectrometry (SWATH-MS) in sera from study participants before and after three months of treatment with the TNFi etanercept. Linear regression models were used to study the association between protein levels and the composite 28-joint count Disease Activity Score (DAS28) and its sub-components, adjusting for baseline covariates including age, biological sex disease duration and baseline DAS28. Sub-network analysis was carried out using the DIAMOnD algorithm, followed by functional enrichment analysis. Results Of the total 180 participants recruited, 134 (74.44%) were female, with a median age of 56.90 years and median pre-treatment DAS28 of 5.9. Eight individual proteins were found to be significantly associated with RA clinical outcome measures (see Table). Sub-network analysis identified the ontological theme with the strongest association as being positive regulation of response to endoplasmic reticulum (ER) stress (-log10(P) ∼ 7). Conclusion This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response and identified ER stress as an underlying pathway. The ER stress pathway has been previously reported to contribute to RA pathogenesis via synoviocyte proliferation and stimulation of pro-inflammatory cytokine production. Disclosure S.F. Ling: None. C. Yap: None. N. Nair: None. J. Bluett: None. A.W. Morgan: None. J.D. Isaacs: None. A.G. Wilson: None. K.L. Hyrich: None. A. Barton: None. D. Plant: None.

BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions.

The BioPlex project has created two proteome scale, cell-line-specific protein-protein interaction (PPI) networks: the first in 293T cells, including 120k interactions among 15k proteins; and the second in HCT116 cells, including 70k interactions between 10k proteins. Here, we describe programmatic access to the BioPlex PPI networks and integration with related resources from within R and Python. Besides PPI networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, PFAM protein domain data, PDB protein structures, and transcriptome and proteome data for the two cell lines. The implemented functionality serves as a basis for integrative downstream analysis of BioPlex PPI data with domain-specific R and Python packages, including efficient execution of maximum scoring sub-network analysis, protein domain-domain association analysis, mapping of PPIs onto 3D protein structures and analysis of BioPlex PPIs at the interface of transcriptomic and proteomic data. The BioPlex R package is available from Bioconductor (bioconductor.org/packages/BioPlex), and the BioPlex Python package is available from PyPI (pypi.org/project/bioplexpy). Applications and downstream analyses are available from GitHub (github.com/ccb-hms/BioPlexAnalysis).

Bioinformatics Identification of ZNFs/LINC00520/miR-181d/BCL2 Axis as a Novel Network in Cisplatin-Resistant Lung Adenocarcinoma Cells

Background: Resistance to cisplatin (DDP) leads to poor prognosis in patients with Lung Adenocarcinoma (LUAD) and limits its clinical application. It has been confirmed that autophagy promotes chemoresistance and, therefore, novel strategies to reverse chemoresistance by regulating autophagy are desperately needed. Methods: The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between A549 and A549/DDP cell lines were identified using the limma package in R, after gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. By combining Autophagy-Related Genes (ARGs) from Human Autophagy Database (HADb), the interactions lncRNA-miRNAs and the interactions miRNAs-mRNAs respectively predicted by miRcode and miRDB/Targetscan database, the autophagy-related ceRNA network was constructed. Then, extraction of ceRNA subnetwork and Cox regression analyses were performed. A prognosis-related ceRNA subnetwork was constructed, and the upstream Transcription Factors (TFs) regulating lncRNAs were predicted by the JASPAR database. Finally, the expression patterns of candidate genes were further verified by quantitative real-time polymerase chain reaction (qRT-PCR) experiments. Results: A total of 3179 DEmRNAs, 180 DEmiRNAs, and 160 DElncRNAs were identified, and 35 DEmRNAs were contained in the HADb. Based on the ceRNA hypothesis, we established a ceRNA network, including 10 autophagy-related DEmRNAs, 9 DEmiRNAs, and 14 DElncRNAs. Then, LINC00520, miR-181d, and BCL2 were identified to construct a risk score model, which was confirmed to be a well-predicting prognostic factor. Furthermore, 5 TF ZNF family members were predicted to regulate LINC00520, whereas the RT-PCR results showed that the 5 ZNFs were consistent with the bioinformatics analysis. Finally, a ZNF regulatory LINC00520/miR-181d/BCL2 ceRNA subnetwork was constructed. Conclusions: An ZNFs/LINC00520/miR-181d/BCL2 axis as a novel network in DDP-resistant LUAD has been constructed successfully, which may provide potential therapeutic targets for LUAD.