Prog-Plot - a visual method to determine functional relationships for false discovery rate regression methods.

Multiple test corrections are a fundamental step in the analysis of differentially expressed genes, as the number of tests performed would otherwise inflate the false discovery rate (FDR). Recent methods for P-value correction involve a regression model in order to include covariates that are informative of the power of the test. Here, we present Progressive proportions plot (Prog-Plot), a visual tool to identify the functional relationship between the covariate and the proportion of P-values consistent with the null hypothesis. The relationship between the proportion of P-values and the covariate to be included is needed, but there are no available tools to verify it. The approach presented here aims at having an objective way to specify regression models instead of relying on prior knowledge.

Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer.

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Long Noncoding RNA TALAM1 Is a Transcriptional Target of the RUNX2 Transcription Factor in Lung Adenocarcinoma.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. It has been reported that genetic and epigenetic factors play a crucial role in the onset and evolution of lung cancer. Previous reports have shown that essential transcription factors in embryonic development contribute to this pathology. Runt-related transcription factor (RUNX) proteins belong to a family of master regulators of embryonic developmental programs. Specifically, RUNX2 is the master transcription factor (TF) of osteoblastic differentiation, and it can be involved in pathological conditions such as prostate, thyroid, and lung cancer by regulating apoptosis and mesenchymal-epithelial transition processes. In this paper, we identified TALAM1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) as a genetic target of the RUNX2 TF in lung cancer and then performed functional validation of the main findings. METHODS: We performed ChIP-seq analysis of tumor samples from a patient diagnosed with lung adenocarcinoma to evaluate the target genes of the RUNX2 TF. In addition, we performed shRNA-mediated knockdown of RUNX2 in this lung adenocarcinoma cell line to confirm the regulatory role of RUNX2 in TALAM1 expression. RESULTS: We observed RUNX2 overexpression in cell lines and primary cultured lung cancer cells. Interestingly, we found that lncRNA TALAM1 was a target of RUNX2 and that RUNX2 exerted a negative regulatory effect on TALAM1 transcription.

Temporal Changes of the Epiphytic Bacteria Community From the Marine Macroalga Ulva lactuca (Santa Marta, Colombian-Caribbean).

Microbial communities live on macroalgal surfaces. The identity and abundance of the bacteria making these epiphytic communities depend on the macroalgal host and the environmental conditions. Macroalgae rely on epiphytic bacteria for basic functions (spore settlement, morphogenesis, growth, and protection against pathogens). However, these marine bacterial-macroalgal associations are still poorly understood for macroalgae inhabiting the Colombian Caribbean. This study aimed at characterizing the epiphytic bacterial community from macroalgae of the species Ulva lactuca growing in La Punta de la Loma (Santa Marta, Colombia). We conducted a 16S rRNA gene sequencing-based study of these microbial communities sampled twice a year between 2014 and 2016. Within these communities, the Proteobacteria, Bacterioidetes, Cyanobacteria, Deinococcus-Thermus and Actinobacteria were the most abundant phyla. At low taxonomic levels, we found high variability among epiphytic bacteria from U. lactuca and bacterial communities associated with macroalgae from Germany and Australia. We observed differences in the bacterial community composition across years driven by abundance shifts of Rhodobacteraceae Hyphomonadaceae, and Flavobacteriaceae, probably caused by an increase of seawater temperature. Our results support the need for functional studies of the microbiota associated with U. lactuca, a common macroalga in the Colombian Caribbean Sea.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Co-expressed miRNAs in gastric adenocarcinoma.

Co-expression networks may provide insights into the patterns of molecular interactions that underlie cellular processes. To obtain a better understanding of miRNA expression patterns in gastric adenocarcinoma and to provide markers that can be associated with histopathological findings, we performed weighted gene correlation network analysis (WGCNA) and compare it with a supervised analysis. Integrative analysis of target predictions and miRNA expression profiles in gastric cancer samples was also performed. WGCNA identified a module of co-expressed miRNAs that were associated with histological traits and tumor condition. Hub genes were identified based on statistical analysis and network centrality. The miRNAs 100, let-7c, 125b and 99a stood out for their association with the diffuse histological subtype. The 181 miRNA family and miRNA 21 highlighted for their association with the tumoral phenotype. The integrated analysis of miRNA and gene expression profiles showed the let-7 miRNA family playing a central role in the regulatory relationships.

Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia.

BACKGROUND: The clinical course of chronic lymphocytic leukemia (CLL) is highly variable; some patients follow an indolent course, but others progress to a more advanced stage. The mutational status of rearranged immunoglobulin heavy chain variable (IGVH) genes in CLL is a feature that is widely recognized for dividing patients into groups that are related to their prognoses. However, the regulatory programs associated with the IGVH statuses are poorly understood, and markers that can precisely predict survival outcomes have yet to be identified. METHODS: In this study, (i) we reconstructed gene regulatory networks in CLL by applying an information-theoretic approach to the expression profiles of 5 cohorts. (ii) We applied master regulator analysis (MRA) to these networks to identify transcription factors (TFs) that regulate an IGVH mutational status signature. The IGVH mutational status signature was developed by searching for differentially expressed genes between the IGVH mutational statuses in numerous CLL cohorts. (iii) To evaluate the biological implication of the inferred regulators, prognostic values were determined using time to treatment (TTT) and overall survival (OS) in two different cohorts. RESULTS: A robust IGVH expression signature was obtained, and various TFs emerged as regulators of the signature in most of the reconstructed networks. The TF targets expression profiles exhibited significant differences with respect to survival, which allowed the definition of a reduced profile with a high value for OS. TCF7 and its targets stood out for their roles in progression. CONCLUSION: TFs and their targets, which were obtained merely from inferred regulatory associations, have prognostic implications and reflect a regulatory context for prognosis.

Algorithm for the Construction of a Global Enzymatic Network to be Used for Gene Network Reconstruction.

Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.

Chromosome 10 in the tomato plant carries clusters of genes responsible for field resistance/defence to Phytophthora infestans.

The main objective of the present study was to reanalyse tomato expression data that was previously submitted to the Tomato Expression Database to dissect the resistance/defence genomic and metabolic responses of tomato to Phytophthora infestans under field conditions. Overrepresented gene sets belonging to chromosome 10 were identified using the Gene Set Enrichment Analysis, and we found that these genes tend to be located towards the end of the chromosome 10. An analysis of syntenic regions between Arabidopsis thaliana chromosomes and the tomato chromosome 10 allowed us to identify conserved regions in the two genomes. In addition to allowing for the identification of tomato candidate genes participating in resistance/defence in the field, this approach allowed us to investigate the relationships of the candidate genes with chromosomal position and participation in metabolic functions, thus offering more insight into the phenomena occurring during the infection process.

MIR4435-2HG as a possible novel predictive biomarker of chemotherapy response and death in pediatric B-cell ALL.

Introduction: Although B-cell acute lymphoblastic leukemia (B-cell ALL) survival rates have improved in recent years, Hispanic children continue to have poorer survival rates. There are few tools available to identify at the time of diagnosis whether the patient will respond to induction therapy. Our goal was to identify predictive biomarkers of treatment response, which could also serve as prognostic biomarkers of death, by identifying methylated and differentially expressed genes between patients with positive minimal residual disease (MRD+) and negative minimal residual disease (MRD-). Methods: DNA and RNA were extracted from tumor blasts separated by immunomagnetic columns. Illumina MethlationEPIC and mRNA sequencing assays were performed on 13 bone marrows from Hispanic children with B-cell ALL. Partek Flow was used for transcript mapping and quantification, followed by differential expression analysis using DEseq2. DNA methylation analyses were performed with Partek Genomic Suite and Genome Studio. Gene expression and differential methylation were compared between patients with MRD-/- and MRD+/+ at the end of induction chemotherapy. Overexpressed and hypomethylated genes were selected and validated by RT-qPCR in samples of an independent validation cohort. The predictive ability of the genes was assessed by logistic regression. Survival and Cox regression analyses were performed to determine the association of genes with death. Results: DAPK1, BOC, CNKSR3, MIR4435-2HG, CTHRC1, NPDC1, SLC45A3, ITGA6, and ASCL2 were overexpressed and hypomethylated in MRD+/+ patients. Overexpression was also validated by RT-qPCR. DAPK1, BOC, ASCL2, and CNKSR3 can predict refractoriness, but MIR4435-2HG is the best predictor. Additionally, higher expression of MIR4435-2HG increases the probability of non-response, death, and the risk of death. Finally, MIR4435-2HG overexpression, together with MRD+, are associated with poorer survival, and together with overexpression of DAPK1 and ASCL2, it could improve the risk classification of patients with normal karyotype. Conclusion: MIR4435-2HG is a potential predictive biomarker of treatment response and death in children with B-cell ALL.