Systematic determination of genetic network architecture

Identification of the PCA29 gene signature as a predictor in prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer death among men worldwide. About 70% of PCa patients were diagnosed at later stage, and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. The identification of biomarkers for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we developed a novel scoring strategy, including cluster score (CS) and predicting score (PS), to identify 29 PCa genes (called PCa29) for early diagnostic biomarkers from two datasets in Gene Expression Omnibus. The result indicates that PCa29 can discriminate between normal and tumor tissues and are specific for prostate cancer. To validate PCa29, we found that 97% of PCa29 were consistently significant with these gene expressions in The Cancer Genome Atlas; furthermore, ∼ 70% of PCa29 are consensus to the protein expression in The Human Protein Atlas. Finally, we examined 10 genes in PCa29 on three PCa cell lines by real-time quantitative polymerase chain reaction. The experimental results show that the trend of the differential PCa29 expression is consistent with the analyzed results from our novel scoring method. We believe that our method is useful and PCa29 are potential biomarkers that provide the clues to develop targeting therapy for PCa.

The EXPANDER Integrated Platform for Transcriptome Analysis

Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mainstay of current biological and biomedical research. EXPANDER (EXPression ANalyzer and DisplayER) is a comprehensive software package for analysis of expression data, with built-in support for 18 different organisms. It is designed as a "one-stop shop" platform for transcriptomic analysis, allowing for execution of all analysis steps starting with gene expression data matrix. Analyses offered include low-level preprocessing and normalization, differential expression analysis, clustering, bi-clustering, supervised grouping, high-level functional and pathway enrichment tests, and networks and motif analyses. A variety of options is offered for each step, using established algorithms, including many developed and published by our laboratory. EXPANDER has been continuously developed since 2003, having to date over 18,000 downloads and 540 citations. One of the innovations in the recent version is support for combined analysis of gene expression and ChIP-seq data to enhance the inference of transcriptional networks and their functional interpretation. EXPANDER implements cutting-edge algorithms and makes them accessible to users through user-friendly interface and intuitive visualizations. It is freely available to users at http://acgt.cs.tau.ac.il/expander/.

I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment

Crohn’s disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions.

We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment.

We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn’s disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn’s disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.

Patients exhibiting similar phenotypical characteristics, diagnosed with the same disease, exhibit variable response to therapeutics. This is a major health care issue, due to the increased patient suffering and the socioeconomical burden that occurs. Crohn’s disease and ulcerative colitis constitute good examples of inflammatory conditions, with sufferers responding differentially to existent therapeutics. Here, we identified disease-driving pathways and classified individuals into subpopulations that differ in their pathobiology and response to treatment. We utilized gene set variation analysis and transcriptomic data from inflammatory bowel disease sufferers to stratify patients at baseline or after anti-TNF treatment in clinical responders and non-responders. We explored gene signatures obtained from the literature, relevant to immune processes, which were significantly enriched in disease compared to healthy controls, as well as before and after treatment. Using these signatures, we established an inflammatory bowel disease molecular activity score, which allowed us to separate clinical responders and non-responders at baseline with high specificity and sensitivity. We validated the proposed approach in an independent data set, demonstrating comparable classification. This methodological approach may lead to better targeted design of clinical studies, allowing the selection of patient sharing similar underlying pathobiology, thus increasing the likelihood of clinical response to treatment.

Network-Based Approaches for Pathway Level Analysis

Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.

Tethering soluble meprin α in an enzyme complex to the cell surface affects IBD-associated genes

Biologic activity of proteases is mainly characterized by the substrate specificity, tissue distribution, and cellular localization. The human metalloproteases meprin α and meprin β share 41% sequence identity and exhibit a similar cleavage specificity with a preference for negatively charged amino acids. However, shedding of meprin α by furin on the secretory pathway makes it a secreted enzyme in comparison with the membrane-bound meprin β. In this study, we identified human meprin α and meprin β as forming covalently linked membrane-tethered heterodimers in the early endoplasmic reticulum, thereby preventing furin-mediated secretion of meprin α. Within this newly formed enzyme complex, meprin α was able to be activated on the cell surface and detected by cleavage of a novel specific fluorogenic peptide substrate. However, the known meprin β substrates amyloid precursor protein and CD99 were not shed by membrane-tethered meprin α. On the other hand, being linked to meprin α, activation of or substrate cleavage by meprin β on the cell surface was not altered. Interestingly, proteolytic activity of both proteases was increased in the heteromeric complex, indicating an increased proteolytic potential at the plasma membrane. Because meprins are susceptibility genes for inflammatory bowel disease (IBD), and to investigate the physiologic impact of the enzyme complex, we performed transcriptome analyses of intestinal mucosa from meprin-knockout mice. Comparison of the transcriptional gene analysis data with gene analyses of IBD patients revealed that different gene subsets were dysregulated if meprin α was expressed alone or in the enzyme complex, demonstrating the physiologic and pathophysiological relevance of the meprin heterodimer formation.-Peters, F., Scharfenberg, F., Colmorgen, C., Armbrust, F., Wichert, R., Arnold, P., Potempa, B., Potempa, J., Pietrzik, C. U., Häsler, R., Rosenstiel, P., Becker-Pauly, C. Tethering soluble meprin α in an enzyme complex to the cell surface affects IBD-associated genes.

RNA-sequencing in ophthalmology research: considerations for experimental design and analysis

High-throughput, massively parallel sequence analysis has revolutionized the way that researchers design and execute scientific investigations. Vast amounts of sequence data can be generated in short periods of time. Regarding ophthalmology and vision research, extensive interrogation of patient samples for underlying causative DNA mutations has resulted in the discovery of many new genes relevant to eye disease. However, such analysis remains functionally limited. RNA-sequencing accurately snapshots thousands of genes, capturing many subtypes of RNA molecules, and has become the gold standard for transcriptome gene expression quantification. RNA-sequencing has the potential to advance our understanding of eye development and disease; it can reveal new candidates to improve our molecular diagnosis rates and highlight therapeutic targets for intervention. But with a wide range of applications, the design of such experiments can be problematic, no single optimal pipeline exists, and therefore, several considerations must be undertaken for optimal study design. We review the key steps involved in RNA-sequencing experimental design and the downstream bioinformatic pipelines used for differential gene expression. We provide guidance on the application of RNA-sequencing to ophthalmology and sources of open-access eye-related data sets.

Selection for novel metabolic capabilities in Salmonella enterica

Bacteria are known to display extensive metabolic diversity and many studies have shown that they can use an extensive repertoire of small molecules as carbon‐ and energy sources. However, it is less clear to what extent a bacterium can expand its existing metabolic capabilities by acquiring mutations that, for example, rewire its metabolic pathways. To investigate this capability and potential for evolution of novel phenotypes, we sampled large populations of mutagenized Salmonella enterica to select very rare mutants that can grow on minimal media containing 124 low molecular weight compounds as sole carbon sources. We found mutants growing on 18 of these novel carbon sources, and identified the causal mutations that allowed growth for four of them. Mutations that relieve physiological constraints or increase expression of existing pathways were found to be important contributors to the novel phenotypes. For the remaining 14 novel phenotypes, whole genome sequencing of independent mutants and genetic analysis suggested that these novel metabolic phenotypes result from a combination of multiple mutations. This work, by virtue of identifying the genetic and mechanistic basis for new metabolic capabilities, sheds light on the properties of adaptive landscapes underlying the evolution of novel phenotypes.

Identification of key lncRNAs contributing to diabetic nephropathy by gene co-expression network analysis

LncRNA is reported to have important role in diabetic nephropathy (DN). Here, we aim to identify key lncRNAs of DN using bioinformatics and systems biological methods. Method: Five microarray data sets from Gene Expression Omnibus (GEO) database were included. Probe sets were re-annotated. In the training set, differential expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was constructed to screen diabetic-related hub genes and reveal their potential biological function. Two more human data sets and mouse data sets were used as validation sets. Results: A total of 424 DEGs, including 10 lncRNAs, were filtered in the training data set. WGCNA and enrichment analysis of hub genes showed that inflammation and metabolic disorders are prominent in DN. Three key lncRNAs (NR_130134.1, NR_029395.1 and NR_038335.1) were identified. These lncRNAs are also differently expressed in another two human data sets. Functional enrichment of the mouse data sets showed consistent changes with that in human, indicating similar changes in gene expression pattern of DN and confirmed confidence of our analysis. Human podocytes and mesangial cells were culture in vitro. QPCR and fluorescence in situ hybridization were taken out to validate the expression and relationship of key lncRNAs and their related mRNAs. Results were also consistent with our analysis. Conclusions: Inflammation and metabolic disorders are prominent in DN. We identify three lncRNAs that are involved in these processes possibly by interacting with co-expressed mRNAs.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.

IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models of drug sensitivity. The previously proposed methods of enrichment analysis could achieve comparable levels of performance in certain tests. However, only our method could discover predictors of both in vitro response and patient survival given administration of the same drug.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition

Development of maternal blood transcriptomic markers to monitor placental function and risk of obstetrical complications throughout pregnancy requires accurate quantification of gene expression. Herein, we benchmark three state-of-the-art expression profiling techniques to assess in maternal circulation the expression of cell type-specific gene sets previously discovered by single-cell genomics studies of the placenta. We compared Affymetrix Human Transcriptome Arrays, Illumina RNA-Seq, and sequencing-based targeted expression profiling (DriverMap^TM) to assess transcriptomic changes with gestational age and labor status at term, and tested 86 candidate genes by qRT-PCR. DriverMap identified twice as many significant genes (q < 0.1) than RNA-Seq and five times more than microarrays. The gap in the number of significant genes remained when testing only protein-coding genes detected by all platforms. qRT-PCR validation statistics (PPV and AUC) were high and similar among platforms, yet dynamic ranges were higher for sequencing based platforms than microarrays. DriverMap provided the strongest evidence for the association of B-cell and T-cell gene signatures with gestational age, while the T-cell expression was increased with spontaneous labor at term according to all three platforms. We concluded that sequencing-based techniques are more suitable to quantify whole-blood gene expression compared to microarrays, as they have an expanded dynamic range and identify more true positives. Targeted expression profiling achieved higher coverage of protein-coding genes with fewer total sequenced reads, and it is especially suited to track cell type-specific signatures discovered in the placenta. The T-cell gene expression signature was increased in women who underwent spontaneous labor at term, mimicking immunological processes at the maternal-fetal interface and placenta.

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.

Systematic analysis of genes involved in oral cancer metastasis to lymph nodes

Oral cancer remains a deadly disease worldwide. Lymph node metastasis and invasion is one of the causes of death from oral cancer. Elucidating the mechanism of oral cancer lymph node metastasis and identifying critical regulatory genes are important for the treatment of this disease. This study aimed to identify differentially expressed genes (gene signature) and pathways that contribute to oral cancer metastasis to lymph nodes. The GSE70604-associated study compared gene profiles in lymph nodes with metastasis of oral cancer to those of normal lymph nodes. The GSE2280-associated study compared gene profiles in primary tumor of oral cancer with lymph node metastasis to those in tumors without lymph node metastasis. There are 28 common differentially expressed genes (DEGs) showing consistent changes in both datasets in overlapping analysis. GO biological process and KEGG pathway analysis of these 28 DEGs identified the gene signature CCND1, JUN and SPP1, which are categorized as key regulatory genes involved in the focal adhesion pathway. Silencing expression of CCND1, JUN and SPP1 in the human oral cancer cell line OECM-1 confirmed that those genes play essential roles in oral cancer cell invasion. Analysis of clinical samples of oral cancer found a strong correlation of these genes with short survival, especially JUN expression associated with metastasis. Our study identified a unique gene signature - CCND1, JUN and SPP1 - which may be involved in oral cancer lymph node metastasis.

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R²  = -0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.

A simple null model for inferences from network enrichment analysis

A prevailing technique to infer function from lists of identifications, from molecular biological high-throughput experiments, is over-representation analysis, where the identifications are compared to predefined sets of related genes often referred to as pathways. As at least some pathways are known to be incomplete in their annotation, algorithmic efforts have been made to complement them with information from functional association networks. While the terminology varies in the literature, we will here refer to such methods as Network Enrichment Analysis (NEA). Traditionally, the significance of inferences from NEA has been assigned using a null model constructed from randomizations of the network. Here we instead argue for a null model that more directly relates to the set of genes being studied, and have designed one dynamic programming algorithm that calculates the score distribution of NEA scores that makes it possible to assign unbiased mid p values to inferences. We also implemented a random sampling method, carrying out the same task. We demonstrate that our method obtains a superior statistical calibration as compared to the popular NEA inference engine, BinoX, while also providing statistics that are easier to interpret.

Knockdown of FOXP1 promotes the development of lung adenocarcinoma

Lung cancer is one of the most common cancers in the world, which accounts for about 27% of all cancer deaths. However, the mechanisms underlying the pathogenesis of lung cancer cells remain largely elusive. In this study, we examined the role of the Forkhead box protein P1 (FOXP1) in lung cancer development. Our Oncomine analysis shows that FOXP1 is downregulated in lung adenocarcinoma compared with normal lung tissue. Knockdown of FOXP1 promotes the growth and invasion of PC9 and A549 cells by regulating genes of chemokine signaling molecules, including CCR1, ADCY5, GNG7, VAV3, and PLCB1. Simultaneous knockdown of CCR1 and FOXP1 attenuated FOXP1 knockdown-induced increase of lung cancer cell growth. Finally, knockdown of FOXP1 in PC9 cells promotes the tumorigenesis via CCR1 signaling in xenograft mouse model. Taken together, our data suggest that FOXP1 plays important roles in preventing lung adenocarcinoma development via suppressing chemokine signaling pathways.

Advances in the Regulation of In Vitro Paclitaxel Production: Methylation of a Y-Patch Promoter Region Alters BAPT Gene Expression in Taxus Cell Cultures

Plant cell biofactories represent a promising solution to the increasing demand for plant-derived compounds, but there are still limiting factors that prevent optimal production, including the loss of yield during in vitro maintenance. Our results reveal a clear correlation between genomic methylation levels and a progressive decline in taxane production in Taxus spp. cell cultures. A comparative study of two cell lines, one 10 years old and low productive and the other new and high productive, revealed important differences in appearance, growth, taxane accumulation and expression levels of several taxane biosynthetic genes. Differences in taxane content and gene expression profile indicate an altered pathway regulation and that the BAPT gene, located in the center of the expression network of taxane biosynthetic genes, is active in a potentially flux-limiting step. The methylation patterns of the BAPT gene were studied in both cell lines by bisulfite sequencing, which revealed high rates of CHH methylated cytosines on the core promoter. Using a bioinformatics approach, this hotspot was identified as a Y-patch promoter element. The Y-patch may play a key role in the epigenetic regulation of the taxane biosynthetic pathway, which would open up novel genetic engineering strategies toward stable and high productivity.

Co-expression network analysis identified CDH11 in association with progression and prognosis in gastric cancer

Background and aims

Gastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.

Methods

Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein-protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein-protein interaction (PPI) network as "real" hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.

Results

In the significant purple module (R ²=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015-2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.

Conclusion

CDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.

Using predictive specificity to determine when gene set analysis is biologically meaningful

Gene set analysis, which translates gene lists into enriched functions, is among the most common bioinformatic methods. Yet few would advocate taking the results at face value. Not only is there no agreement on the algorithms themselves, there is no agreement on how to benchmark them. In this paper, we evaluate the robustness and uniqueness of enrichment results as a means of assessing methods even where correctness is unknown. We show that heavily annotated (‘multifunctional’) genes are likely to appear in genomics study results and drive the generation of biologically non-specific enrichment results as well as highly fragile significances. By providing a means of determining where enrichment analyses report non-specific and non-robust findings, we are able to assess where we can be confident in their use. We find significant progress in recent bias correction methods for enrichment and provide our own software implementation. Our approach can be readily adapted to any pre-existing package.

The kinome of pineapple: catalog and insights into functions in crassulacean acid metabolism plants

BACKGROUND

Crassulacean acid metabolism (CAM) plants use water 20-80% more efficiently by shifting stomata opening and primary CO₂ uptake and fixation to the nighttime. Protein kinases (PKs) play pivotal roles in this biological process. However, few PKs have been functionally analyzed precisely due to their abundance and potential functional redundancy (caused by numerous gene duplications).

RESULTS

In this study, we systematically identified a total of 758 predicted PK genes in the genome of a CAM plant, pineapple (Ananas comosus). The pineapple kinome was classified into 20 groups and 116 families based on the kinase domain sequences. The RLK was the largest group, containing 480 members, and over half of them were predicted to locate at the plasma membrane. Both segmental and tandem duplications make important contributions to the expansion of pineapple kinome based on the synteny analysis. Ka/Ks ratios showed all of the duplication events were under purifying selection. The global expression analysis revealed that pineapple PKs exhibit different tissue-specific and diurnal expression patterns. Forty PK genes in a cluster performed higher expression levels in green leaf tip than in white leaf base, and fourteen of them had strong differential expression patterns between the photosynthetic green leaf tip and the non-photosynthetic white leaf base tissues.

CONCLUSIONS

Our findings provide insights into the evolution and biological function of pineapple PKs and a foundation for further functional analysis of PKs in CAM plants. The gene duplication, expression, and coexpression analysis helped us to rapidly identify the key candidates in pineapple kinome, which may play roles in the carbon fixation process in pineapple and help engineering CAM pathway into C3 crops for improved drought tolerance.

Synergistic Interaction of HOXB13 and CIP2A Predisposes to Aggressive Prostate Cancer

PURPOSE

Distinguishing aggressive prostate cancer from indolent disease improves personalized treatment. Although only few genetic variants are known to predispose to aggressive prostate cancer, synergistic interactions of HOXB13 G84E high-risk prostate cancer susceptibility mutation with other genetic loci remain unknown. The purpose of this study was to examine the interplay of HOXB13 rs138213197 (G84E) and CIP2A rs2278911 (R229Q) germline variants on prostate cancer risk.

EXPERIMENTAL DESIGN

Genotyping was done in Finnish discovery cohort (n = 2,738) and validated in Swedish (n = 3,132) and independent Finnish (n = 1,155) prostate cancer cohorts. Expression pattern analysis was followed by functional studies in prostate cancer cell models.

RESULTS

Interplay of HOXB13 (G84E) and CIP2A (R229Q) variants results in highest observed inherited prostate cancer risk (OR, 21.1; P = 0.000024). In addition, this synergism indicates a significant association of HOXB13 T and CIP2A T dual carriers with elevated risk for high Gleason score (OR, 2.3; P = 0.025) and worse prostate cancer-specific life expectancy (HR, 3.9; P = 0.048), and it is linked with high PSA at diagnosis (OR, 3.30; P = 0.028). Furthermore, combined high expression of HOXB13-CIP2A correlates with earlier biochemical recurrence. Finally, functional experiments showed that ectopic expression of variants stimulates prostate cancer cell growth and migration. In addition, we observed strong chromatin binding of HOXB13 at CIP2A locus and revealed that HOXB13 functionally promotes CIP2A transcription. The study is limited to retrospective Nordic cohorts.

CONCLUSIONS

Simultaneous presence of HOXB13 T and CIP2A T alleles confers for high prostate cancer risk and aggressiveness of disease, earlier biochemical relapse, and lower disease-specific life expectancy. HOXB13 protein binds to CIP2A gene and functionally promotes CIP2A transcription.

An Automated, High-Throughput Method for Interpreting the Tandem Mass Spectra of Glycosaminoglycans

The biological interactions between glycosaminoglycans (GAGs) and other biomolecules are heavily influenced by structural features of the glycan. The structure of GAGs can be assigned using tandem mass spectrometry (MS2), but analysis of these data, to date, requires manually interpretation, a slow process that presents a bottleneck to the broader deployment of this approach to solving biologically relevant problems. Automated interpretation remains a challenge, as GAG biosynthesis is not template-driven, and therefore, one cannot predict structures from genomic data, as is done with proteins. The lack of a structure database, a consequence of the non-template biosynthesis, requires a de novo approach to interpretation of the mass spectral data. We propose a model for rapid, high-throughput GAG analysis by using an approach in which candidate structures are scored for the likelihood that they would produce the features observed in the mass spectrum. To make this approach tractable, a genetic algorithm is used to greatly reduce the search-space of isomeric structures that are considered. The time required for analysis is significantly reduced compared to an approach in which every possible isomer is considered and scored. The model is coded in a software package using the MATLAB environment. This approach was tested on tandem mass spectrometry data for long-chain, moderately sulfated chondroitin sulfate oligomers that were derived from the proteoglycan bikunin. The bikunin data was previously interpreted manually. Our approach examines glycosidic fragments to localize SO3 modifications to specific residues and yields the same structures reported in literature, only much more quickly. Graphical Abstract ᅟ.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes

Motivation

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions.

Results

We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully.

Availability and Implementation

The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/.

Contact

sunkim.bioinfo@snu.ac.kr.

Supplementary information

Supplementary data are available at Bioinformatics online.

In vitro activity of collinin isolated from the leaves of Zanthoxylum schinifolium against multidrug- and extensively drug-resistant Mycobacterium tuberculosis

BACKGROUND

Tuberculosis is a very serious infectious disease that threatens humanity, and the emergence of multidrug-resistant (MDR), extensively drug-resistant (XDR) strains resistant to drugs suggests that new drug development is urgent. In order to develop new tuberculosis drug, we have conducted in vitro anti-tubercular tests on thousands of plant-derived substances and finally found collinin extracted from the leaves of Zanthoxylum schinifolium, which has an excellent anti-tuberculosis effect.

PURPOSE

To isolate an anti-tubercular bioactive compound from the leaves of Z. schinifolium and evaluate whether this agent demonstrates any potential in vitro characteristics suitable for the development of future anti-tubercular drugs to treat MDR and XDR Mycobacterium tuberculosis.

METHODS

The methanolic extracts of the leaves of Z. schinifolium were subjected to bioassay-guided fractionation against M. tuberculosis using a microbial cell viability assay. In addition, following cell cytotoxicity assay, an intracellular anti-mycobacterial activity of the most active anti-tubercular compound was investigated after it was purified.

RESULTS

The active compound with anti-tubercular activity isolated from leaves of Z. schinifolium was identified as a collinin. The extracted collinin showed anti-tubercular activity against both drug-susceptible and -resistant strains of M. tuberculosis at 50% minimum inhibitory concentrations (MIC₅₀s) of 3.13-6.25 µg/ml in culture broth and MIC₅₀s of 6.25-12.50 µg/ml inside Raw264.7 and A549 cells. Collinin had no cytotoxicity against human lung pneumocytes up to a concentration of 100 µg/ml (selectivity index > 16-32).

CONCLUSIONS

Collinin extracted from the leaves of Z. schinifolium significantly inhibits the growth of MDR and XDR M. tuberculosis in the culture broth. In addition, it also inhibits the growth of intracellular drug-susceptible and drug-resistant tuberculosis in Raw264.7 and A549 cells. To our knowledge, this is the first report on the in vitro anti-tubercular activity of collinin, and our data suggest collinin as a potential drug to treat drug-resistant tuberculosis. Further studies are warranted to assess the in vivo efficacy and therapeutic potential of collinin.

Euclidean distance can identify the mannitol level that produces the most remarkable integral effect on sugarcane micropropagation in temporary immersion bioreactors

Plant scientists usually record several indicators in their abiotic factor experiments. The common statistical management involves univariate analyses. Such analyses generally create a split picture of the effects of experimental treatments since each indicator is addressed independently. The Euclidean distance combined with the information of the control treatment could have potential as an integrating indicator. The Euclidean distance has demonstrated its usefulness in many scientific fields but, as far as we know, it has not yet been employed for plant experimental analyses. To exemplify the use of the Euclidean distance in this field, we performed an experiment focused on the effects of mannitol on sugarcane micropropagation in temporary immersion bioreactors. Five mannitol concentrations were compared: 0, 50, 100, 150 and 200 mM. As dependent variables we recorded shoot multiplication rate, fresh weight, and levels of aldehydes, chlorophylls, carotenoids and phenolics. The statistical protocol which we then carried out integrated all dependent variables to easily identify the mannitol concentration that produced the most remarkable integral effect. Results provided by the Euclidean distance demonstrate a gradually increasing distance from the control in function of increasing mannitol concentrations. 200 mM mannitol caused the most significant alteration of sugarcane biochemistry and physiology under the experimental conditions described here. This treatment showed the longest statistically significant Euclidean distance to the control treatment (2.38). In contrast, 50 and 100 mM mannitol showed the lowest Euclidean distances (0.61 and 0.84, respectively) and thus poor integrated effects of mannitol. The analysis shown here indicates that the use of the Euclidean distance can contribute to establishing a more integrated evaluation of the contrasting mannitol treatments.

GEsture: an online hand-drawing tool for gene expression pattern search

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

Essential genes of the macrophage response to Staphylococcus aureus exposure

Background

Although significant advances have been made in understanding the mechanisms of macrophage response to Staphylococcus aureus infection, the molecular details are still elusive. Identification of the essential genes and biological processes of macrophages that are specifically changed at different durations of S. aureus exposure is of great clinical significance.

Methods

We aimed to identify the significantly changed genes and biological processes of S. aureus-exposed macrophages. We systematically analyzed the macrophage gene expression profile GSE 13670 database with 8 h, 24 h or 48 h S. aureus infection. The results were further confirmed by western blot and quantitative polymerase chain reaction (qPCR) analyses.

Results

After 8 h of S. aureus infection, the expression of 624 genes was significantly changed. Six hundred thirteen differentially expressed genes (DEGs) were identified after 24 h of S. aureus infection. Two hundred fifty-three genes were significantly changed after 48 h of S. aureus infection. STAT1 was consistently up-regulated in these three treatments. TP53, JAK2, CEBPA, STAT3, MYC, CTNNB1 and PRKCA were only identified in the 8 h or 24 h S. aureus infection groups. CTNNB1 and PRKCA were for the first time identified as potential essential genes in S. aureus infection of macrophages. In the Gene Ontology (GO) term analysis, the defense response was shown to be the most significantly changed biological process among all processes; KEGG pathway analysis identified the JAK-STAT signaling pathway involved in early infection.

Conclusions

Our systematic analysis identified unique gene expression profiles and specifically changed biological processes of the macrophage response to different S. aureus exposure times.

Detecting Differential Transcription Factor Activity from ATAC-Seq Data

Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data (specifically ATAC-seq), that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases.

Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, and its carcinogenesis and progression are influenced by a complex network of gene interactions. A weighted gene co-expression network was constructed to identify gene modules associated with the clinical traits in HCC (n = 214). Among the 13 modules, high correlation was only found between the red module and metastasis risk (classified by the HCC metastasis gene signature) (R² = −0.74). Moreover, in the red module, 34 network hub genes for metastasis risk were identified, six of which (ABAT, AGXT, ALDH6A1, CYP4A11, DAO and EHHADH) were also hub nodes in the protein-protein interaction network of the module genes. Thus, a total of six hub genes were identified. In validation, all hub genes showed a negative correlation with the four-stage HCC progression (P for trend < 0.05) in the test set. Furthermore, in the training set, HCC samples with any hub gene lowly expressed demonstrated a higher recurrence rate and poorer survival rate (hazard ratios with 95% confidence intervals > 1). RNA-sequencing data of 142 HCC samples showed consistent results in the prognosis. Gene set enrichment analysis (GSEA) demonstrated that in the samples with any hub gene highly expressed, a total of 24 functional gene sets were enriched, most of which focused on amino acid metabolism and oxidation. In conclusion, co-expression network analysis identified six hub genes in association with HCC metastasis risk and prognosis, which might improve the prognosis by influencing amino acid metabolism and oxidation.

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.

Identification of genomic differences among peripheral arterial beds in atherosclerotic and healthy arteries

Calcification is independently associated with cardiovascular events and morbidity. The calcification burden in atherosclerotic lesions quantitatively and qualitatively differs between arterial beds. Cardiovascular risk factors (CVRF) differentially affect plaque development between arterial beds. The aim of this study was to evaluate the impact of CVRF on atherosclerotic plaque calcification and to further study the molecular arterial heterogeneity that could account for these differences. Histological analysis was performed on atherosclerotic plaques from 153 carotid, 97 femoral and 28 infrapopliteal arteries. CVRF showed minor associations with plaque calcification: age and hypertension affected only the overall presence of calcification but not the type of the calcification, which significantly differed between arterial beds. Transcriptome analysis revealed distinct gene expression profiles associated with each territory in atherosclerotic and healthy arteries. Canonical pathway analysis showed the preferential involvement of immune system-related processes in both atherosclerotic and healthy carotid arteries. Bone development-related genes were among those mostly enriched in atherosclerotic and healthy femoral arteries, which are more prone to developing endochondral calcification. This study highlights the heterogeneous nature of arteries from different peripheral vascular beds and contributes to a better understanding of atherosclerosis formation and evolution.

Characterization of moso bamboo (Phyllostachys edulis) Dof transcription factors in floral development and abiotic stress responses

The Dof transcription factor (TF) family belongs to a class of plant-specific TFs and is involved in plant growth, development, and response to abiotic stresses. However, there are only very limited reports on the characterization of Dof TFs in moso bamboo (Phyllostachys edulis). In the present research, PheDof TFs showed specific expression profiles based on RNA-seq data analyses. The co-expression network indicated that PheDof12, PheDof14, and PheDof16 might play vital roles during flower development. Cis-regulatory element analysis of these PheDof genes suggested diverse functions. Expression patterns of 12 selected genes from seven different classes under three abiotic stresses (cold, salt, and drought) are further investigated by quantitative real-time PCR. This work will provide useful information for functional analysis and regulation mechanisms of Dof TFs in moso bamboo.

PRKAR2B plays an oncogenic role in the castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) is an advanced form of prostate cancer. Despite some progresses have been made, the mechanism of CRPC development is still largely unknown, including the genes involved in its development have not been well defined. Here, we identifiedPRKAR2B to be a gene over-expressingin castration-resistant prostate cancer by analyzing the different online databases. Followed functional validation experiments showed that PRKAR2B promoted CRPC cell proliferation and invasion, and inhibited CRPC cell apoptosis. Whole genome transcriptome and GO enrichment analyses of the knock-down of PRKAR2B in CRPC cells showed that PRKAR2B mainly accelerated cell cycle biological process and modulated multiple cell cycle genes, such as CCNB1, MCM2, PLK1 and AURKB. Our study firstly identified PRKAR2B as a novel oncogenic gene involved in CRPC development and suggested it is a promising target for the future investigation and the treatment of CRPC.

Dynamic genome wide expression profiling of Drosophila head development reveals a novel role of Hunchback in retinal glia cell development and blood-brain barrier integrity

Drosophila melanogaster head development represents a valuable process to study the developmental control of various organs, such as the antennae, the dorsal ocelli and the compound eyes from a common precursor, the eye-antennal imaginal disc. While the gene regulatory network underlying compound eye development has been extensively studied, the key transcription factors regulating the formation of other head structures from the same imaginal disc are largely unknown. We obtained the developmental transcriptome of the eye-antennal discs covering late patterning processes at the late 2^nd larval instar stage to the onset and progression of differentiation at the end of larval development. We revealed the expression profiles of all genes expressed during eye-antennal disc development and we determined temporally co-expressed genes by hierarchical clustering. Since co-expressed genes may be regulated by common transcriptional regulators, we combined our transcriptome dataset with publicly available ChIP-seq data to identify central transcription factors that co-regulate genes during head development. Besides the identification of already known and well-described transcription factors, we show that the transcription factor Hunchback (Hb) regulates a significant number of genes that are expressed during late differentiation stages. We confirm that hb is expressed in two polyploid subperineurial glia cells (carpet cells) and a thorough functional analysis shows that loss of Hb function results in a loss of carpet cells in the eye-antennal disc. Additionally, we provide for the first time functional data indicating that carpet cells are an integral part of the blood-brain barrier. Eventually, we combined our expression data with a de novo Hb motif search to reveal stage specific putative target genes of which we find a significant number indeed expressed in carpet cells.

The development of different cell types must be tightly coordinated, and the eye-antennal imaginal discs of Drosophila melanogaster represent an excellent model to study the molecular mechanisms underlying this coordination. These imaginal discs contain the anlagen of nearly all adult head structures, such as the antennae, the head cuticle, the ocelli and the compound eyes. While large scale screens have been performed to unravel the gene regulatory network underlying compound eye development, a comprehensive understanding of genome wide expression dynamics throughout head development is still missing to date. We studied the genome wide gene expression dynamics during eye-antennal disc development in D. melanogaster to identify new central regulators of the underlying gene regulatory network. Expression based gene clustering and transcription factor motif enrichment analyses revealed a central regulatory role of the transcription factor Hunchback (Hb). We confirmed that hb is expressed in two polyploid retinal subperineurial glia cells (carpet cells). Our functional analysis shows that Hb is necessary for carpet cell development and we show for the first time that the carpet cells are an integral part of the blood-brain barrier.

Contribution of transposable elements and distal enhancers to evolution of human-specific features of interphase chromatin architecture in embryonic stem cells

Transposable elements have made major evolutionary impacts on creation of primate-specific and human-specific genomic regulatory loci and species-specific genomic regulatory networks (GRNs). Molecular and genetic definitions of human-specific changes to GRNs contributing to development of unique to human phenotypes remain a highly significant challenge. Genome-wide proximity placement analysis of diverse families of human-specific genomic regulatory loci (HSGRL) identified topologically associating domains (TADs) that are significantly enriched for HSGRL and designated rapidly evolving in human TADs. Here, the analysis of HSGRL, hESC-enriched enhancers, super-enhancers (SEs), and specific sub-TAD structures termed super-enhancer domains (SEDs) has been performed. In the hESC genome, 331 of 504 (66%) of SED-harboring TADs contain HSGRL and 68% of SEDs co-localize with HSGRL, suggesting that emergence of HSGRL may have rewired SED-associated GRNs within specific TADs by inserting novel and/or erasing existing non-coding regulatory sequences. Consequently, markedly distinct features of the principal regulatory structures of interphase chromatin evolved in the hESC genome compared to mouse: the SED quantity is 3-fold higher and the median SED size is significantly larger. Concomitantly, the overall TAD quantity is increased by 42% while the median TAD size is significantly decreased (p = 9.11E-37) in the hESC genome. Present analyses illustrate a putative global role for transposable elements and HSGRL in shaping the human-specific features of the interphase chromatin organization and functions, which are facilitated by accelerated creation of novel transcription factor binding sites and new enhancers driven by targeted placement of HSGRL at defined genomic coordinates. A trend toward the convergence of TAD and SED architectures of interphase chromatin in the hESC genome may reflect changes of 3D-folding patterns of linear chromatin fibers designed to enhance both regulatory complexity and functional precision of GRNs by creating predominantly a single gene (or a set of functionally linked genes) per regulatory domain structures. Collectively, present analyses reveal critical evolutionary contributions of transposable elements and distal enhancers to creation of thousands primate- and human-specific elements of a chromatin folding code, which defines the 3D context of interphase chromatin both restricting and facilitating biological functions of GRNs.

Clustering gene expression time series data using an infinite Gaussian process mixture model

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

A novel correlation between ATP5A1 gene expression and progression of human clear cell renal cell carcinoma identified by co‑expression analysis

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidneys, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, a weighted gene co-expression network was constructed to identify gene modules associated with the progression of ccRCC (n=35). In the significant module (R² = −0.53), a total of 13 network hub genes were identified, and 2 of them were hub nodes in the protein-protein interaction network as well. In validation, ATP5A1 showed a higher correlation with the disease progression than any other hub gene in the hub module (P=0.001219). In the test set (n=202), ATP5A1 was also highly expressed in normal kidney than ccRCC tissues of each grade (P<0.001). Functional and pathway enrichment analysis demonstrated that ATP5A1 is overrepresented in pathway of oxidative phosphorylation, which associated with tumorigenesis and tumor progression. Gene set enrichment analysis (GSEA) also demonstrated that the gene set of ‘oxidative phosphorylation’ and metabolic pathways were enriched in ccRCC samples with ATP5A1 highly expressed (P<0.05). In conclusion, based on the co-expression analysis, ATP5A1 was validated to be associated with progression of ccRCC, probably by regulating tumor-related phosphorylation.

Easy and efficient ensemble gene set testing with EGSEA

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

Multiple-cumulative probabilities used to cluster and visualize transcriptomes

Analysis of gene expression data by clustering and visualizing played a central role in obtaining biological knowledge. Here, we used Pearson's correlation coefficient of multiple-cumulative probabilities (PCC-MCP) of genes to define the similarity of gene expression behaviors. To answer the challenge of the high-dimensional MCPs, we used icc-cluster, a clustering algorithm that obtained solutions by iterating clustering centers, with PCC-MCP to group genes. We then used t-statistic stochastic neighbor embedding (t-SNE) of KC-data to generate optimal maps for clusters of MCP (t-SNE-MCP-O maps). From the analysis of several transcriptome data sets, we demonstrated clear advantages for using icc-cluster with PCC-MCP over commonly used clustering methods. t-SNE-MCP-O was also shown to give clearly projecting boundaries for clusters of PCC-MCP, which made the relationships between clusters easy to visualize and understand.

Incorporating gene ontology into fuzzy relational clustering of microarray gene expression data

The product of gene expression works together in the cell for each living organism in order to achieve different biological processes. Many proteins are involved in different roles depending on the environment of the organism for the functioning of the cell. In this paper, we propose gene ontology (GO) annotations based semi-supervised clustering algorithm called GO fuzzy relational clustering (GO-FRC) where one gene is allowed to be assigned to multiple clusters which are the most biologically relevant behavior of genes. In the clustering process, GO-FRC utilizes useful biological knowledge which is available in the form of a gene ontology, as a prior knowledge along with the gene expression data. The prior knowledge helps to improve the coherence of the groups concerning the knowledge field. The proposed GO-FRC has been tested on the two yeast (Saccharomyces cerevisiae) expression profiles datasets (Eisen and Dream5 yeast datasets) and compared with other state-of-the-art clustering algorithms. Experimental results imply that GO-FRC is able to produce more biologically relevant clusters with the use of the small amount of GO annotations.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In current study, the microarray data GSE66272 containing ccRCC and adjacent normal tissues was analyzed to identify 4042 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 12 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = -0.77) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on inflammatory response, immune response, chemotaxis (all p < 1e-10). In the significant module, a total of 38 network hub genes were identified, FCER1G exhibited the highest correlation (r = 0.95) with ccRCC progression. In addition, FCER1G was hub node in the protein-protein interaction network of the genes in blue module as well. Thus, FCER1G was subsequently selected for validation. In the test set GSE53757 and RNA-sequencing data, FCER1G expression was also positively correlated with four stages of ccRCC progression (p < 0.001). Receiver operating characteristic (ROC) curve indicated that FCER1G could distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) ccRCC (AUC=0.74, p < 0.001). Besides, FCER1G could be a prognostic gene in clinical practice as well, revealed by survival analysis based on RNA-sequencing data (p < 0.05). In conclusion, using weighted gene co-expression analysis, FCER1G was identified and validated in association with ccRCC progression and prognosis, which might improve the prognosis by influencing immune-related pathways.