New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

<p>Abstract</p> <p>Background</p> <p>Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.</p> <p>Results</p> <p>Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of <it>Dictyostelium discoideum </it>development. The PKA network is well studied in <it>D. discoideum </it>but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in <it>D. discoideum</it>, and a histidine kinase, a candidate upstream regulator of PKA activity.</p> <p>Conclusions</p> <p>The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets.</p

Correlation of MR Perfusion Imaging and Vessel Tortuosity Parameters in Assessment of Intracranial Neoplasms

Advances in noninvasive imaging techniques such as magnetic resonance perfusion imaging have been found useful in grading cerebral neoplasms and have potential for significant clinical benefit. The purpose of this study was to determine the correlation between tumor vessel tortuosity as measured from vessels extracted from magnetic resonance angiograms (MRA) and perfusion parameters of cerebral blood flow (CBF) and cerebral blood volume (CBV) in intracranial neoplasms. We hypothesized that tumor blood vessel tortuosity measures and perfusion measures would be correlated, since both are increased by tumor angiogenesis. 18 patients with 19 cerebral neoplasms were evaluated with conventional MR imaging and dynamic contrast-enhanced T2-weighted perfusion MR imaging (PWI). Both benign and malignant lesions were included, as were hyper- and hypovascular tumors. Regions of interest were plotted within the tumor area to locate foci of maximum CBV and CBF. CBV and CBF measurements were also recorded in contralateral normal appearing white matter to calculate relative CBV (rCBV) and relative CBF (rCBF). Vessel tortuosity analyses were conducted upon vessels segmented from MRA images of the same patients using two tortuosity descriptors (SOAM and ICM), which have previously been demonstrated to have efficacy in separating benign from malignant disease. Linear regression analyses were conducted to determine if correlations exist between CBV or CBF and the two tortuosity measurements. For the overall set of tumors, no significant correlations were found between flow or volume measures and the tortuosity measures. However, when the 7 glioblastoma multiforme tumors were examined as a subgroup, the following significant correlations were found: rCBV and SOAM (R2=0.799), rCBV and ICM (R2=0.214). Our results demonstrate that MR perfusion imaging data do not correlate significantly with vessel tortuosity parameters as determined from the larger vessels seen by MRA. However, for subgroups of a particular tumor type such as GBM, there may be significant correlations. It appears that perfusion and tortuosity data may provide independently useful data in the assessment of cerebral neoplasms

Conserved developmental transcriptomes in evolutionarily divergent species

Transcriptional profiling of Dictyostelium development reveals significant conservation of transcriptional profiles between evolutionarily divergent species

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

BACKGROUND. Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. RESULTS. We have developed dictyExpress, a database that consists of more than 1000 Dictyostelium discoideum gene expression experiments and features a graphical, highly-interactive, explorative web interface. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, and perform analyses of Gene Ontology term enrichment. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. CONCLUSION. dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms

Bacterial discrimination by dictyostelid amoebae reveals the complexity of ancient interspecies interactions

Background Amoebae and bacteria interact within predator-prey and host-pathogen relationships, but the general response of amoeba to bacteria is not well understood. The amoeba Dictyostelium discoideum feeds on, and is colonized by, diverse bacterial species, including Gram-positive [Gram(+)] and Gram-negative [Gram(–)] bacteria, two major groups of bacteria that differ in structure and macromolecular composition. Results Transcriptional profiling of D. discoideum revealed sets of genes whose expression is enriched in amoebae interacting with different species of bacteria, including sets that appear specific to amoebae interacting with Gram(+) or with Gram(–) bacteria. In a genetic screen utilizing the growth of mutant amoebae on a variety of bacteria as a phenotypic readout, we identified amoebal genes that are only required for growth on Gram(+) bacteria, including one that encodes the cell-surface protein gp130, as well as several genes that are only required for growth on Gram(–) bacteria, including one that encodes a putative lysozyme, AlyL. These genes are required for parts of the transcriptional response of wild-type amoebae, and this allowed their classification into potential response pathways. Conclusions We have defined genes that are critical for amoebal survival during feeding on Gram(+), or Gram(–), bacteria that we propose form part of a regulatory network that allows D. discoideum to elicit specific cellular responses to different species of bacteria in order to optimize survival

Finding Waldo: The Evolving Paradigm of Circulating Tumor DNA (ctDNA)—Guided Minimal Residual Disease (MRD) Assessment in Colorectal Cancer (CRC)

Circulating tumor DNA (ctDNA), the tumor-derived cell-free DNA fragments in the bloodstream carrying tumor-specific genetic and epigenetic alterations, represents an emerging novel tool for minimal residual disease (MRD) assessment in patients with resected colorectal cancer (CRC). For many decades, precise risk-stratification following curative-intent colorectal surgery has remained an enduring challenge. The current risk stratification strategy relies on clinicopathologic characteristics of the tumors that lacks precision and results in over-and undertreatment in a significant proportion of patients. Consequently, a biomarker that can reliably identify patients harboring MRD would be of critical importance in refining patient selection for adjuvant therapy. Several prospective cohort studies have provided compelling data suggesting that ctDNA could be a robust biomarker for MRD that outperforms all existing clinicopathologic criteria. Numerous clinical trials are currently underway to validate the ctDNA-guided MRD assessment and adjuvant treatment strategies. Once validated, the ctDNA technology will likely transform the adjuvant therapy paradigm of colorectal cancer, supporting ctDNA-guided treatment escalation and de-escalation. The current article presents a comprehensive overview of the published studies supporting the utility of ctDNA for MRD assessment in patients with CRC. We also discuss ongoing ctDNA-guided adjuvant clinical trials that will likely shape future adjuvant therapy strategies for patients with CRC