The kynurenine pathway: towards metabolic equilibrium

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Coombs Hill: A Late Devonian fossil locality in the Witpoort Formation (Witteberg Group, South Africa)

Coombs Hill, a new fossil locality in the Witpoort Formation (Witteberg Group) of South Africa, preserves a record of Famennian (Late Devonian) life in Gondwana. Fossil plants collected at Coombs Hill are preliminarily assigned to several classes. Shelly invertebrates include a variety of bivalve mollusc forms, some of which appear to be preserved in life position. Biodiversity at Coombs Hill is comparable to that of the well-known Waterloo Farm lagerstätte in ordinal diversity, but exhibits differences in species composition. Ongoing taxonomic analysis will provide a rare window into the ecology of high-latitude environments during this pivotal stage of Earth history, which immediately preceded the end-Devonian extinction. Sandstone dominated sedimentary facies at Coombs Hill suggest a high-energy coastal marine setting, with brackish back-barrier estuarine/lagoonally derived fossiliferous mudstones. Exact stratigraphic placement within the Witpoort Formation is hampered by structural deformation, and precise age comparisons with Waterloo Farm are currently tenuous.Significance: A new fossil locality at Coombs Hill comprises the second known site with a suite of well preserved continental and marginal marine fossils from the Witpoort Formation, providing an exceptionally rare example of high-latitude life during the critical latest Devonian Famennian age. Several new plant taxa will be diagnosed from this locality, which also gives important insights into the morphology of Archaeopteris notosaria, South Africa’s earliest known tree. Discovery of a second palaeontologically significant site in the Witpoort Formation provides impetus for further structural and sedimentary facies analyses to align the unit with datable global eustatic events, and to clarify its internal chronology

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

<p>Abstract</p> <p>Background</p> <p>Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.</p> <p>Results</p> <p>The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.</p> <p>Conclusion</p> <p>The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (<it>e.g</it>., LOOCV) and biologically (<it>e.g</it>., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.</p

Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation

Probiotic \u3ci\u3eBifidobacterium\u3c/i\u3e strains and galactooligosaccharides improve intestinal barrier function in obese adults but show no synergism when used together as synbiotics

Background: One way to improve both the ecological performance and functionality of probiotic bacteria is by combining them with a prebiotic in the form of a synbiotic. However, the degree to which such synbiotic formulations improve probiotic strain functionality in humans has not been tested systematically. Our goal was to use a randomized, double-blind, placebo-controlled, parallel-arm clinical trial in obese humans to compare the ecological and physiological impact of the prebiotic galactooligosaccharides (GOS) and the probiotic strains Bifidobacterium adolescentis IVS-1 (autochthonous and selected via in vivo selection) and Bifidobacterium lactis BB-12 (commercial probiotic allochthonous to the human gut) when used on their own or as synbiotic combinations. After 3 weeks of consumption, strain-specific quantitative real-time PCR and 16S rRNA gene sequencing were performed on fecal samples to assess changes in the microbiota. Intestinal permeability was determined by measuring sugar recovery in urine by GC after consumption of a sugar mixture. Serum-based endotoxin exposure was also assessed. Results: IVS-1 reached significantly higher cell numbers in fecal samples than BB-12 (P \u3c 0.01) and, remarkably, its administration induced an increase in total bifidobacteria that was comparable to that of GOS. Although GOS showed a clear bifidogenic effect on the resident gut microbiota, both probiotic strains showed only a non-significant trend of higher fecal cell numbers when administered with GOS. Post-aspirin sucralose:lactulose ratios were reduced in groups IVS-1 (P = 0.050), IVS-1 + GOS (P = 0.022), and GOS (P = 0.010), while sucralose excretion was reduced with BB-12 (P = 0.002) and GOS (P = 0.020), indicating improvements in colonic permeability but no synergistic effects. No changes in markers of endotoxemia were observed. Conclusion: This study demonstrated that “autochthony” of the probiotic strain has a larger effect on ecological performance than the provision of a prebiotic substrate, likely due to competitive interactions with members of the resident microbiota. Although the synbiotic combinations tested in this study did not demonstrate functional synergism, our findings clearly showed that the pro- and prebiotic components by themselves improved markers of colonic permeability, providing a rational for their use in pathologies with an underlying leakiness of the gut

Radio Astronomy in LSST Era

A community meeting on the topic of "Radio Astronomy in the LSST Era" was hosted by the National Radio Astronomy Observatory in Charlottesville, VA (2013 May 6--8). The focus of the workshop was on time domain radio astronomy and sky surveys. For the time domain, the extent to which radio and visible wavelength observations are required to understand several classes of transients was stressed, but there are also classes of radio transients for which no visible wavelength counterpart is yet known, providing an opportunity for discovery. From the LSST perspective, the LSST is expected to generate as many as 1 million alerts nightly, which will require even more selective specification and identification of the classes and characteristics of transients that can warrant follow up, at radio or any wavelength. The LSST will also conduct a deep survey of the sky, producing a catalog expected to contain over 38 billion objects in it. Deep radio wavelength sky surveys will also be conducted on a comparable time scale, and radio and visible wavelength observations are part of the multi-wavelength approach needed to classify and understand these objects. Radio wavelengths are valuable because they are unaffected by dust obscuration and, for galaxies, contain contributions both from star formation and from active galactic nuclei. The workshop touched on several other topics, on which there was consensus including the placement of other LSST "Deep Drilling Fields," inter-operability of software tools, and the challenge of filtering and exploiting the LSST data stream. There were also topics for which there was insufficient time for full discussion or for which no consensus was reached, which included the procedures for following up on LSST observations and the nature for future support of researchers desiring to use LSST data products.Comment: Conference summary, 29 pages, 1 figure; to be published in the Publ. Astron. Soc. Pacific; full science program and presentations available at http://science.nrao.edu/science/event/RALSST201