Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP ...analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.
Although chronic inflammation increases many cancers’ risk, how inflammation facilitates cancer development is still not well studied. Recognizing whether and when inflamed tissues transition to ...cancerous tissues is of utmost importance. To unbiasedly infer molecular events, immune cell types, and secreted factors contributing to the inflammation-to-cancer (I2C) transition, we develop a computational package called “SwitchDetector” based on liver, gastric, and colon cancer I2C data. Using it, we identify angiogenesis associated with a common critical transition stage for multiple I2C events. Furthermore, we infer infiltrated immune cell type composition and their secreted or suppressed extracellular proteins to predict expression of important transition stage genes. This identifies extracellular proteins that may serve as early-detection biomarkers for pre-cancer and early-cancer stages. They alone or together with I2C hallmark angiogenesis genes are significantly related to cancer prognosis and can predict immune therapy response. The SwitchDetector and I2C database are publicly available at www.inflammation2cancer.org.
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•Angiogenesis is common during the inflammation-to-cancer (I2C) transition•Data suggest link between immune cells, cytokines, and angiogenesis for I2C in the liver•I2C cytokines and angiogenesis predict cancer survival and immune therapy response•SwitchDetector package and I2C database are freely available
Chen et al. develop the SwitchDetector package for transcriptome module detection during inflammation-to-cancer (I2C) stage transitions. They show that angiogenesis is a common critical event for I2C in multiple cancers. The data also suggest that immune cells and secreted cytokines contribute to the I2C transition.
Calculable capacitor (CC) is outstanding in its linear relationship between capacitance and its guard electrodes' displacement. However, the relationship is no longer linear for capacitance below 0.1 ...pF level because the electrostatic field on guard electrode's tip gets disordered, which is known as close approach effect. To rescue the linearity, we assemble an auxiliary electrode on the end of the movable guard electrode and load a scaled ac voltage with the same frequency as capacitance bridge by a ratio transformer to make the electrostatic field regular. The scaled ratio is carefully selected by simulation and experiments. We show that some specified ratios can perfectly compensate the close approach effect. As a result, the range of the linearity of the capacitance-distance curve is extended by 10 mm on our prototype by adopting optimal positive compensation voltage. Furthermore, the nonlinearity can be totally compensated even with capacitance less than 100 aF when adopting negative compensation voltage.
Glioblastoma arises from complex interactions between a variety of genetic alterations and environmental perturbations. Little attention has been paid to understanding how genetic variations, altered ...gene expression and microRNA (miRNA) expression are integrated into networks which act together to alter regulation and finally lead to the emergence of complex phenotypes and glioblastoma.
We identified association of somatic mutations in 14 genes with glioblastoma, of which 8 genes are newly identified, and association of loss of heterozygosity (LOH) is identified in 11 genes with glioblastoma, of which 9 genes are newly discovered. By gene coexpression network analysis, we identified 15 genes essential to the function of the network, most of which are cancer related genes. We also constructed miRNA coexpression networks and found 19 important miRNAs of which 3 were significantly related to glioblastoma patients' survival. We identified 3,953 predicted miRNA-mRNA pairs, of which 14 were previously verified by experiments in other groups. Using pathway enrichment analysis we also found that the genes in the target network of the top 19 important miRNAs were mainly involved in cancer related signaling pathways, synaptic transmission and nervous systems processes. Finally, we developed new methods to decipher the pathway connecting mutations, expression information and glioblastoma. We identified 4 cis-expression quantitative trait locus (eQTL): TP53, EGFR, NF1 and PIK3C2G; 262 trans eQTL and 26 trans miRNA eQTL for somatic mutation; 2 cis-eQTL: NRAP and EGFR; 409 trans- eQTL and 27 trans- miRNA eQTL for lost of heterozygosity (LOH) mutation.
Our results demonstrate that integrated analysis of multi-dimensional data has the potential to unravel the mechanism of tumor initiation and progression.
Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling ...mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels.
Abstract
Sleep problems are related to the elevated levels of the Alzheimer’s disease (AD) biomarker β-amyloid (Aβ). Hypotheses about the causes of this relationship can be generated from molecular ...markers of sleep problems identified in rodents. A major marker of sleep deprivation is Homer1a, a neural protein coded by the HOMER1 gene, which has also been implicated in brain Aβ accumulation. Here, we tested whether the relationship between cortical Aβ accumulation and self-reported sleep quality, as well as changes in sleep quality over 3 years, was stronger in cortical regions with high HOMER1 mRNA expression levels. In a sample of 154 cognitively healthy older adults, Aβ correlated with poorer sleep quality cross-sectionally and longitudinally (n = 62), but more strongly in the younger than in older individuals. Effects were mainly found in regions with high expression of HOMER1. The anatomical distribution of the sleep-Aβ relationship followed closely the Aβ accumulation pattern in 69 patients with mild cognitive impairment or AD. Thus, the results indicate that the relationship between sleep problems and Aβ accumulation may involve Homer1 activity in the cortical regions, where harbor Aβ deposits in AD. The findings may advance our understanding of the relationship between sleep problems and AD risk.
Abstract
Background
The National Institute on Aging‐Alzheimer’s Association (NIA‐AA) proposed the ATN framework as a classification system for Alzheimer’s disease. The ATN framework helps to inform ...participant inclusion and potentially trial outcomes as clinical trials are increasingly targeting a range of pathologies. However, it is limited by biomarkers that are either not yet fully qualified or are relatively invasive and where access can be difficult. A blood‐based version of the ATN framework would be of considerable value and recent progress suggests such an objective is realizable.
Method
To identify blood‐based biomarkers predicting different ATN profiles, we used SOMAscan assay platform to measure 4001 proteins in 785 subjects selected from the European Medical Information Framework for Alzheimer’s disease Multimodal Biomarker Discovery study (EMIF‐AD MBD) study, all of whom had measures of amyloid, CSF total tau (T‐tau) and phosphorylated tau (P‐tau). We firstly performed linear regression to identify single proteins associated with the ATN framework. Then we constructed protein co‐expression network to identify co‐expressed protein modules. We further rank‐ordered modules based upon their relevance to ATN. Using the proteins within the module with the highest relevance, we performed machine learning to differentiate different ATN profiles from non‐pathological controls (NPC).
Result
The proteins identified from linear regression were enriched with AD related pathways. Seven modules were identified from co‐expression analysis, among which blue module was highly associated with the ATN framework (Figure 1). Using machine learning, we identified a subset of proteins within the blue module, along with age and
apolipoprotein E
ε4, that discriminated NPC from amyloid pathology dementia including A+T‐N‐, A+T+N‐, A+T‐N+ and A+T+N+ profile with high area under the curve (AUC, 0.72, 0.80, 0.84, 0.84 respectively) (Figure 2). However, these proteins could not differentiate NPC from Suspected Non‐Alzheimer Pathology (SNAP), or non‐amyloid dementia (A‐T‐N+, A‐T+N‐ and A‐T+N+).
Conclusion
The results suggest that high‐dimensional plasma protein testing could be a useful and reproducible approach for discriminating NPC from amyloid pathology dementia. A minimally invasive and cost‐effective blood biomarker of the ATN framework could facilitate clinical trials by contributing to rapid and effective selection of participants.
Background
The National Institute on Aging‐Alzheimer’s Association (NIA‐AA) proposed the ATN framework as a classification system for Alzheimer’s disease. The ATN framework helps to inform ...participant inclusion and potentially trial outcomes as clinical trials are increasingly targeting a range of pathologies. However, it is limited by biomarkers that are either not yet fully qualified or are relatively invasive and where access can be difficult. A blood‐based version of the ATN framework would be of considerable value and recent progress suggests such an objective is realizable.
Method
To identify blood‐based biomarkers predicting different ATN profiles, we used SOMAscan assay platform to measure 4001 proteins in 785 subjects selected from the European Medical Information Framework for Alzheimer’s disease Multimodal Biomarker Discovery study (EMIF‐AD MBD) study, all of whom had measures of amyloid, CSF total tau (T‐tau) and phosphorylated tau (P‐tau). We firstly performed linear regression to identify single proteins associated with the ATN framework. Then we constructed protein co‐expression network to identify co‐expressed protein modules. We further rank‐ordered modules based upon their relevance to ATN. Using the proteins within the module with the highest relevance, we performed machine learning to differentiate different ATN profiles from non‐pathological controls (NPC).
Result
The proteins identified from linear regression were enriched with AD related pathways. Seven modules were identified from co‐expression analysis, among which blue module was highly associated with the ATN framework (Figure 1). Using machine learning, we identified a subset of proteins within the blue module, along with age and apolipoprotein E ε4, that discriminated NPC from amyloid pathology dementia including A+T‐N‐, A+T+N‐, A+T‐N+ and A+T+N+ profile with high area under the curve (AUC, 0.72, 0.80, 0.84, 0.84 respectively) (Figure 2). However, these proteins could not differentiate NPC from Suspected Non‐Alzheimer Pathology (SNAP), or non‐amyloid dementia (A‐T‐N+, A‐T+N‐ and A‐T+N+).
Conclusion
The results suggest that high‐dimensional plasma protein testing could be a useful and reproducible approach for discriminating NPC from amyloid pathology dementia. A minimally invasive and cost‐effective blood biomarker of the ATN framework could facilitate clinical trials by contributing to rapid and effective selection of participants.
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