GSAR: Bioconductor package for Gene Set analysis in R

Construction of a pathway-level model for preeclampsia based on gene expression data

Preeclampsia (PE) is a heterogeneous disease that seriously affects the health of mothers and fetuses. Lack of detection assays, its diagnosis and intervention are often delayed when the clinical symptoms are atypical. Using personalized pathway-based analysis and machine learning algorithms, we built a PE diagnosis model consisting of nine core pathways using multiple cohorts from the Gene Expression Omnibus database. The model showed an area under the receiver operating characteristic (AUROC) curve of 0.959 with the data from the placental tissue samples in the development cohort. In the two validation cohorts, the AUROCs were 0.898 and 0.876, respectively. The model also performed well with the maternal plasma data in another validation cohort (AUROC: 0.815). Moreover, we identified tyrosine-protein kinase Lck (LCK) as the hub gene in this model and found that LCK and pLCK proteins were downregulated in placentas from PE patients. The pathway-level model for PE can provide a novel direction to develop molecular diagnostic assay and investigate potential mechanisms of PE in future studies.

Enhancing the Initial Acceleration Performance of Elite Rugby Backs. Part II: Insights From Multiple Longitudinal Individual-Specific Case-Study Interventions

PURPOSE

This study implemented 18-week individual-specific sprint acceleration training interventions in elite male rugby backs based on their predetermined individual technical needs and evaluated the effectiveness of these interventions.

METHODS

Individual-specific interventions were prescribed to 5 elite rugby backs over an 18-week period. Interventions were informed by the relationships between individual technique strategies and initial acceleration performance, and their strength-based capabilities. Individual-specific changes in technique and initial acceleration performance were measured at multiple time points across the intervention period and compared with 3 control participants who underwent their normal training.

RESULTS

Of the technique variables intentionally targeted during the intervention period, moderate to very large (|d| = 0.93-3.99) meaningful changes were observed in the participants who received an individual-specific intervention but not in control participants. Resultant changes to the intervention participants' whole-body kinematic strategies were broadly consistent with the intended changes. Moderate to very large (|d| = 1.11-2.82) improvements in initial acceleration performance were observed in participants receiving individual-specific technical interventions but not in the control participants or the participant who received an individual-specific strength intervention.

CONCLUSIONS

Individual-specific technical interventions were more effective in manipulating aspects of acceleration technique and performance compared with the traditional "one-size-fits-all" approach adopted by the control participants. This study provides a novel, evidence-based approach for applied practitioners working to individualize sprint-based practices to enhance acceleration performance.

Group B Streptococcus Drives Major Transcriptomic Changes in the Colonic Epithelium

Group B Streptococcus (GBS) is a leading cause of infant sepsis worldwide. Colonization of the gastrointestinal tract is a critical precursor to late-onset disease in exposed newborns. Neonatal susceptibility to GBS intestinal translocation stems from intestinal immaturity; however, the mechanisms by which GBS exploits the immature host remain unclear. β-hemolysin/cytolysin (βH/C) is a highly conserved toxin produced by GBS capable of disrupting epithelial barriers. However, its role in the pathogenesis of late-onset GBS disease is unknown. Our aim was to determine the contribution of βH/C to intestinal colonization and translocation to extraintestinal tissues. Using our established mouse model of late-onset GBS disease, we exposed animals to GBS COH-1 (WT), a βH/C-deficient mutant (KO), or vehicle control (phosphate-buffered saline [PBS]) via gavage. Blood, spleen, brain, and intestines were harvested 4 days post-exposure for determination of bacterial burden and isolation of intestinal epithelial cells. RNA sequencing was used to examine the transcriptomes of host cells followed by gene ontology enrichment and KEGG pathway analysis. A separate cohort of animals was followed longitudinally to compare colonization kinetics and mortality between WT and KO groups. We demonstrate that dissemination to extraintestinal tissues occurred only in the WT exposed animals. We observed major transcriptomic changes in the colons of colonized animals, but not in the small intestines. We noted differential expression of genes that indicated the role of βH/C in altering epithelial barrier structure and immune response signaling. Overall, our results demonstrate an important role of βH/C in the pathogenesis of late-onset GBS disease.

Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease

BACKGROUND

No study has compared pharmacologic properties of ticagrelor and clopidogrel in non-dialysis patients with stage 4-5 chronic kidney disease (CKD).

METHODS

We conducted a double-blind RCT to compare effects of ticagrelor and clopidogrel in 48 CKD, with the primary outcome of ADP-induced platelet aggregation (WBPA) after 2 weeks of DAPT. In a parallel arm, we compared effects of 2 weeks of ticagrelor plus aspirin on mean changes in WBPA and markers of thromboinflammation among non-CKD controls (n = 26) with that of CKD in the ticagrelor-arm.

RESULTS

Average age of CKD was 53.7 years, with 62% women, 54% African American, and 42% with stage 5 CKD. Ticagrelor generated statistically lower WBPA values post treatment [median 0 Ω (IQR 0, 2)] vs. clopidogrel [median 0 Ω (IQR 0, 5)] (P = 0.002); percent inhibition of WBPA was greater (87 ± 22% vs. 63 ± 50%; P = 0.04; and plasma IL-6 levels were much lower (8.42 ± 1.73 pg/ml vs. 18.48 ± 26.56 pg/ml; P = 0.04). No differences in mean changes in WBPA between CKD-ticagrelor and control groups were observed. Ticagrelor- DAPT reduced levels of IL-1α and IL-1β in CKD-ticagrelor and control groups, attenuated lowering of TNFα and TRAIL levels in CKD-ticagrelor (vs controls), and had global changes in correlation between various cytokines in a subgroup of CKD-ticagrelor subjects not on statins (n = 10). Peak/trough levels of ticagrelor/metabolite were not different between CKD-ticagrelor and control groups.

CONCLUSIONS

We report significant differences in platelet aggregation and anti-inflammatory properties between ticagrelor- and clopidogrel-based DAPT in non-dialysis people with stage 4-5 CKD. These notable inflammatory responses suggest ticagrelor-based DAPT might lower inflammatory burden of asymptomatic patients with stage 4 or 5 CKD. (clinicaltrials.gov # NCT03649711).

Transcriptomic Analysis of the Acute Skeletal Muscle Effects after Intramuscular DNA Electroporation Reveals Inflammatory Signaling

Skeletal muscle is a promising tissue for therapeutic gene delivery because it is highly vascularized, accessible, and capable of synthesizing protein for therapies or vaccines. The application of electric pulses (electroporation) enhances plasmid DNA delivery and expression by increasing membrane permeability. Four hours after plasmid electroporation, we evaluated acute gene and protein expression changes in mouse skeletal muscle to identify regulated genes and genetic pathways. RNA sequencing followed by functional annotation was used to evaluate differentially expressed mRNAs. Our data highlighted immune signaling pathways that may influence the effectiveness of DNA electroporation. Cytokine and chemokine protein levels in muscle lysates revealed the upregulation of a subset of inflammatory proteins and confirmed the RNA sequencing analysis. Several regulated DNA-specific pattern recognition receptor mRNAs were also detected. Identifying unique molecular changes in the muscle will facilitate a better understanding of the underlying molecular mechanisms and the development of safety biomarkers and novel strategies to improve skeletal muscle targeted gene therapy.

Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study

Background

Chronic kidney disease (CKD) is characterized by dysregulated inflammation that worsens with CKD severity. The role of platelets in modulating inflammation in stage 4 or 5 CKD remains unexplored. We investigated whether there are changes in platelet-derived thromboinflammatory markers in CKD with dual antiplatelet therapy (DAPT; aspirin 81 mg/d plus P2Y12 inhibitor).

Methods

In a mechanistic clinical trial, we compared platelet activation markers (aggregation and surface receptor expression), circulating platelet-leukocyte aggregates, leukocyte composition (monocyte subtypes and CD11b surface expression), and plasma cytokine profile (45 analytes) of non-CKD controls (n=26) and CKD outpatients (n=48) with a glomerular filtration rate (GFR) <30 ml/min per 1.73 m² on 2 weeks of DAPT.

Results

Patients with CKD demonstrated a reduced mean platelet count, elevated mean platelet volume, reduced platelet-leukocyte aggregates, reduced platelet-bound monocytes, higher total non-classic monocytes in the circulation, and higher levels of IL-1RA, VEGF, and fractalkine (all P<0.05). There were no differences in platelet activation markers between CKD and controls. Although DAPT reduced platelet aggregation in both groups, it had multifaceted effects on thromboinflammatory markers in CKD, including a reduction in PDGF levels in all CKD individuals, reductions in IL-1β and TNF-α levels in select CKD individuals, and no change in a number of other cytokines. Significant positive correlations existed for baseline IL-1β, PDGF, and TNF-α levels with older age, and for baseline TNF-α levels with presence of diabetes mellitus and worse albuminuria. Mean change in IL-1β and PDGF levels on DAPT positively correlated with younger age, mean change in TNF-α levels with higher GFR, and mean changes in PDGF, and TRAIL levels correlated with worse albuminuria. Minimum spanning trees plot of cytokines showed platelet-derived CD40L had a large reduction in weight factor after DAPT in CKD. Additionally, platelet-derived IL-1β and PDGF were tightly correlated with other cytokines, with IL-1β as the hub cytokine.

Conclusions

Attenuated interactions between platelets and leukocytes in the CKD state coincided with no change in platelet activation status, an altered differentiation state of monocytes, and heightened inflammatory markers. Platelet-derived cytokines were one of the central cytokines in patients with CKD that were tightly correlated with others. DAPT had multifaceted effects on thromboinflammation, suggesting that there is platelet-dependent and -independent inflammation in stage 4 or 5 CKD.

GSA Central—A web platform to perform, learn, and discuss gene set analysis

Gene Set Analysis (GSA) is one of the most commonly used strategies to analyze omics data. Hundreds of GSA-related papers have been published, giving birth to a GSA field in Bioinformatics studies. However, as the field grows, it is becoming more difficult to obtain a clear view of all available methods, resources, and their quality. In this paper, we introduce a web platform called “GSA Central” which, as its name indicates, acts as a focal point to centralize GSA information and tools useful to beginners, average users, and experts in the GSA field. “GSA Central” contains five different resources: A Galaxy instance containing GSA tools (“Galaxy-GSA”), a portal to educational material (“GSA Classroom”), a comprehensive database of articles (“GSARefDB”), a set of benchmarking tools (“GSA BenchmarKING”), and a blog (“GSA Blog”). We expect that “GSA Central” will become a useful resource for users looking for introductory learning, state-of-the-art updates, method/tool selection guidelines and insights, tool usage, tool integration under a Galaxy environment, tool design, and tool validation/benchmarking. Moreover, we expect this kind of platform to become an example of a “thematic platform” containing all the resources that people in the field might need, an approach that could be extended to other bioinformatics topics or scientific fields.

FUNAGE-Pro: comprehensive web server for gene set enrichment analysis of prokaryotes

Recent advances in the field of high throughput (meta-)transcriptomics and proteomics call for easy and rapid methods enabling to explore not only single genes or proteins but also extended biological systems. Gene set enrichment analysis is commonly used to find relations in a set of genes and helps to uncover the biological meaning in results derived from high-throughput data. The basis for gene set enrichment analysis is a solid functional classification of genes. Here, we describe a comprehensive database containing multiple functional classifications of genes of all (>55 000) publicly available complete bacterial genomes. In addition to the most common functional classes such as COG and GO, also KEGG, InterPro, PFAM, eggnog and operon classes are supported. As classification data for features is often not available, we offer fast annotation and classification of proteins in any newly sequenced bacterial genome. The web server FUNAGE-Pro enables fast functional analysis on single gene sets, multiple experiments, time series data, clusters, and gene network modules for any prokaryote species or strain. FUNAGE-Pro is freely available at http://funagepro.molgenrug.nl.

Genetic Programming for Instance Transfer Learning in Symbolic Regression

Transfer learning has attracted more attention in the machine-learning community recently. It aims to improve the learning performance on the domain of interest with the help of the knowledge acquired from a similar domain(s). However, there is only a limited number of research on tackling transfer learning in genetic programming for symbolic regression. This article attempts to fill this gap by proposing a new instance weighting framework for transfer learning in genetic programming-based symbolic regression. In the new framework, differential evolution is employed to search for optimal weights for source-domain instances, which helps genetic programming to identify more useful source-domain instances and learn from them. Meanwhile, a density estimation method is used to provide good starting points to help the search for the optimal weights while discarding some irrelevant or less important source-domain instances before learning regression models. The experimental results show that compared with genetic programming and support vector regression that learn only from the target instances, and learning from a mixture of instances from the source and target domains without any transfer learning component, the proposed method can evolve regression models which not only achieve notably better cross-domain generalization performance in stability but also reduce the trend of overfitting effectively. Meanwhile, these models are generally much simpler than those generated by the other GP methods.

The misuse of distributional assumptions in functional class scoring gene-set and pathway analysis

Gene-set analysis (GSA) is a standard procedure for exploring potential biological functions of a group of genes. The development of its methodology has been an active research topic in recent decades. Many GSA methods, when newly proposed, rely on simulation studies to evaluate their performance with an implicit assumption that the multivariate expression values are normally distributed. This assumption is commonly adopted in GSAs, particularly those in the group of functional class scoring (FCS) methods. The validity of the normality assumption, however, has been disputed in several studies, yet no systematic analysis has been carried out to assess the effect of this distributional assumption. Our goal in this study is not to propose a new GSA method but to first examine if the multi-dimensional gene expression data in gene sets follow a multivariate normal (MVN) distribution. Six statistical methods in three categories of MVN tests were considered and applied to a total of 24 RNA data sets. These RNA values were collected from cancer patients as well as normal subjects, and the values were derived from microarray experiments, RNA sequencing, and single-cell RNA sequencing. Our first finding suggests that the MVN assumption is not always satisfied. This assumption does not hold true in many applications tested here. In the second part of this research, we evaluated the influence of non-normality on the statistical power of current FCS methods, both parametric and nonparametric ones. Specifically, the scenario of mixture distributions representing more than one population for the RNA values was considered. This second investigation demonstrates that the non-normality distribution of the RNA values causes a loss in the statistical power of these GSA tests, especially when subtypes exist. Among the FCS GSA tools examined here and among the scenarios studied in this research, the N-statistics outperform the others. Based on the results from these two investigations, we conclude that the assumption of MVN should be used with caution when evaluating new GSA tools, since this assumption cannot be guaranteed and violation may lead to spurious results, loss of power, and incorrect comparison between methods. If a newly proposed GSA tool is to be evaluated, we recommend the incorporation of a wide range of multivariate non-normal distributions or sampling from large databases if available.

Resistant starch slows the progression of CKD in the 5/6 nephrectomy mouse model

Background

Resistant Starch (RS) improves CKD outcomes. In this report, we study how RS modulates host‐microbiome interactions in CKD by measuring changes in the abundance of proteins and bacteria in the gut. In addition, we demonstrate RS‐mediated reduction in CKD‐induced kidney damage.

Methods

Eight mice underwent 5/6 nephrectomy to induce CKD and eight served as healthy controls. CKD and Healthy (H) groups were further split into those receiving RS (CKDRS, n = 4; HRS, n = 4) and those on normal diet (CKD, n = 4, H, n = 4). Kidney injury was evaluated by measuring BUN/creatinine and by histopathological evaluation. Cecal contents were analyzed using mass spectrometry‐based metaproteomics and de novo sequencing using PEAKS. All the data were analyzed using R/Bioconductor packages.

Results

The 5/6 nephrectomy compromised kidney function as seen by an increase in BUN/creatinine compared to healthy groups. Histopathology of kidney sections showed reduced tubulointerstitial injury in the CKDRS versus CKD group; while no significant difference in BUN/creatinine was observed between the two CKD groups. Identified proteins point toward a higher population of butyrate‐producing bacteria, reduced abundance of mucin‐degrading bacteria in the RS fed groups, and to the downregulation of indole metabolism in CKD groups.

Conclusion

RS slows the progression of chronic kidney disease. Resistant starch supplementation leads to active bacterial proliferation and the reduction of harmful bacterial metabolites.

Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data

Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this paper, we propose a new statistical method to identify differentially co-expressed genes from microarray gene expression data. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods to detect co-expressed genes through extensive simulation studies. Also, we applied it to various microarray gene expression datasets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer.

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods

Lung adenocarcinoma (LUAD) is one of the most common forms of lung cancer, with a very high mortality rate. Although the treatments available for LUAD have become more effective in recent years, significant improvement is still needed. Advances in sequencing technologies and bioinformatics analysis have enabled new approaches to be developed for identifying drug targets. In this work we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify hub genes related to LUAD through Weighted Gene Correlation Network Analysis (WGCNA) and other bioinformatics methods, with the goal of identifying new drug targets for cancer treatment.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.

L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository

The LINCS L1000 data repository contains almost two million gene expression profiles for thousands of small molecules and drugs. However, due to the complexity and the size of the data repository and a lack of an interoperable interface, the creation of pharmacologically meaningful workflows utilizing these data is severely hampered. In order to overcome this limitation, we developed the L1000 Viewer, a search engine and graphical web interface for the LINCS data repository. The web interface serves as an interactive platform allowing the user to select different forms of perturbation profiles, e.g., for specific cell lines, drugs, dosages, time points and combinations thereof. At its core, our method has a database we created from inferring and utilizing the intricate dependency graph structure among the data files. The L1000 Viewer is accessible via http://L1000viewer.bio-complexity.com/.

RNA-Sequencing of Umbilical Cord Blood to Investigate Spontaneous Preterm Birth: A Pilot Study

Objective  To analyze the transcriptomic gene expression of umbilical cord blood leukocytes using RNA-sequencing from preterm birth (PTB) and term birth (TB). Study Design  Eight women with spontaneous PTB (sPTB) and eight women with unlabored TB were enrolled prospectively. The sPTB and TB cohorts were matched for maternal age, race, mode of delivery, and fetal sex. Cord blood RNA was extracted and a globin depletion protocol was applied, then sequenced on the Illumina HiSeq 4000. Raw read counts were analyzed with DESeq2 to test for gene expression differences between sPTB and TB. Results  148 genes had significant differential expression ( q  < 0.01). Cell cycle/metabolism gene expression was significantly higher and immune/inflammatory signaling gene expression significantly lower in the sPTB cohort compared with term. In African American (AA) infants, 18 genes specific to cell signaling, neutrophil activity, and major histocompatibility complex type 1 had lower expression in preterm compared with term cohort; the opposite pattern was seen in non-Hispanic Whites (NHWs). Conclusion  Compared with term, preterm fetuses have higher cell cycle/metabolism gene expression, suggesting metabolic focus on growth and development. Immune function gene expression in this pilot study is lower in the sPTB group compared with term and differs in AA compared with NHW infants.

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

Altered expression of K13 disrupts DNA replication and repair in Plasmodium falciparum

BACKGROUND

Plasmodium falciparum exhibits resistance to the artemisinin component of the frontline antimalarial treatment Artemisinin-based Combination Therapy in South East Asia. Millions of lives will be at risk if artemisinin resistance (ART-R) spreads to Africa. Single non-synonymous mutations in the propeller region of PF3D7_1343700,"K13" are implicated in resistance. In this work, we use transcriptional profiling to characterize a laboratory-generated k13 insertional mutant previously demonstrated to have increased sensitivity to artemisinins to explore the functional role of k13.

RESULTS

A set of RNA-seq and microarray experiments confirmed that the expression profile of k13 is specifically altered during the early ring and early trophozoite stages of the mutant intraerythrocytic development cycle. The down-regulation of k13 transcripts in this mutant during the early ring stage is associated with a transcriptome advance towards a more trophozoite-like state. To discover the specific downstream effect of k13 dysregulation, we developed a new computational method to search for differential gene expression while accounting for the temporal sequence of transcription. We found that the strongest biological signature of the transcriptome shift is an up-regulation of DNA replication and repair genes during the early ring developmental stage and a down-regulation of DNA replication and repair genes during the early trophozoite stage; by contrast, the expressions of housekeeping genes are unchanged. This effect, due to k13 dysregulation, is antagonistic, such that k13 levels are negatively correlated with DNA replication and repair gene expression.

CONCLUSION

Our results support a role for k13 as a stress response regulator consistent with the hypothesis that artemisinins mode of action is oxidative stress and k13 as a functional homolog of Keap1 which in humans regulates DNA replication and repair genes in response to oxidative stress.

DiffGRN: differential gene regulatory network analysis

Identification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementation, the approach lacks in the representation of causal effects and multivariate effects between genes. In this paper, we propose an approach named Differential Gene Regulatory Network (DiffGRN) that infers differential gene regulation between two groups. We infer gene regulatory networks of two groups using Random LASSO, and then we identify differential gene regulations by the proposed significance test. The advantages of DiffGRN are to capture multivariate effects of genes that regulate a gene simultaneously, to identify causality of gene regulations, and to discover differential gene regulators between regression-based gene regulatory networks. We assessed DiffGRN by simulation experiments and showed its outstanding performance than the current state-of-the-art correlation-based method, DINGO. DiffGRN is applied to gene expression data in asthma. The DiNA with asthma data showed a number of gene regulations, such as ADAM12 and RELB, reported in biological literature.

Platform-independent gene expression signature differentiates sessile serrated adenomas/polyps and hyperplastic polyps of the colon

Background

Sessile serrated adenomas/polyps are distinguished from hyperplastic colonic polyps subjectively by their endoscopic appearance and histological morphology. However, hyperplastic and sessile serrated polyps can have overlapping morphological features resulting in sessile serrated polyps diagnosed as hyperplastic. While sessile serrated polyps can progress into colon cancer, hyperplastic polyps have virtually no risk for colon cancer. Objective measures, differentiating these types of polyps would improve cancer prevention and treatment outcome.

Methods

RNA-seq training data set and Affimetrix, Illumina testing data sets were obtained from Gene Expression Omnibus (GEO). RNA-seq single-end reads were filtered with FastX toolkit. Read mapping to the human genome, gene abundance estimation, and differential expression analysis were performed with Tophat-Cufflinks pipeline. Background correction, normalization, and probe summarization steps for Affimetrix arrays were performed using the robust multi-array method (RMA). For Illumina arrays, log₂-scale expression data was obtained from GEO. Pathway analysis was implemented using Bioconductor package GSAR. To build a platform-independent molecular classifier that accurately differentiates sessile serrated and hyperplastic polyps we developed a new feature selection step. We also developed a simple procedure to classify new samples as either sessile serrated or hyperplastic with a class probability assigned to the decision, estimated using Cantelli’s inequality.

Results

The classifier trained on RNA-seq data and tested on two independent microarray data sets resulted in zero and three errors. The classifier was further tested using quantitative real-time PCR expression levels of 45 blinded independent formalin-fixed paraffin-embedded specimens and was highly accurate. Pathway analyses have shown that sessile serrated polyps are distinguished from hyperplastic polyps and normal controls by: up-regulation of pathways implicated in proliferation, inflammation, cell-cell adhesion and down-regulation of serine threonine kinase signaling pathway; differential co-expression of pathways regulating cell division, protein trafficking and kinase activities.

Conclusions

Most of the differentially expressed pathways are known as hallmarks of cancer and likely to explain why sessile serrated polyps are more prone to neoplastic transformation than hyperplastic. The new molecular classifier includes 13 genes and may facilitate objective differentiation between two polyps.

Electronic supplementary material

The online version of this article (10.1186/s12920-017-0317-7) contains supplementary material, which is available to authorized users.

Psychiatric polygenic risk associates with cortical morphology and functional organization in aging

Common brain abnormalities in cortical morphology and functional organization are observed in psychiatric disorders and aging, reflecting shared genetic influences. This preliminary study aimed to examine the contribution of a polygenetic risk for psychiatric disorders (PRS_cross) to aging brain and to identify molecular mechanisms through the use of multimodal brain images, genotypes, and transcriptome data. We showed age-related cortical thinning in bilateral inferior frontal cortex (IFC) and superior temporal gyrus and alterations in the functional connectivity between bilateral IFC and between right IFC and right inferior parietal lobe as a function of PRS_cross. Interestingly, the genes in PRS_cross, that contributed most to aging neurodegeneration, were expressed in the functioanlly connected cortical regions. Especially, genes identified through the genotype-functional connectivity association analysis were commonly expressed in both cortical regions and formed strong gene networks with biological processes related to neural plasticity and synaptogenesis, regulated by glutamatergic and GABAergic transmission, neurotrophin signaling, and metabolism. This study suggested integrating genotype and transcriptome with neuroimage data sheds new light on the mechanisms of aging brain.