Biology subjects

Hamilton, R.

Publications and source records attributed to Hamilton, R..

5 recordsLinked to original sources

bioRxiv2018

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Integrated mining of public transcriptomic and ChIP-Seq datasets has the potential to illuminate facets of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates stable community classifications of the four major categories of signaling pathway node (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules (BSMs). We then mapped over 10,000 public transcriptomic or cistromic experiments to their relevant signaling pathway node, BSM or biosample of study. To provide for prediction of pathway node-target transcriptional regulatory relationships, we generated consensus omics signatures, or consensomes, based on measures of significant differential expression of genomic targets across all underlying transcriptomic experiments. To expose the SPP knowledgebase to researchers, a web browser interface accommodates a variety of routine data mining strategies. Consensomes were validated using alignment with literature-based knowledge, gene target-level integration of transcriptomic and ChIP-Seq data points, and in bench experiments that confirmed previously uncharacterized node-gene target regulatory relationships. SPP is freely accessible at https://beta.signalingpathways.org.

bioinformatics↗

bioRxiv2018

Algorithm for Reliable Detection of Beat Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 {+/-} 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 ms and 30 ms, respectively, of the true onsets. The algorithms performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

bioinformatics↗

bioRxiv2018

Tristetraprolin/ZFP36 regulates the turnover of autoimmune-associated HLA-DQ mRNAs

We have previously demonstrated that the expression of HLA class II genes is regulated by the binding of a ribonucleoprotein complex that affects the mRNA processing. We identified protein components of a complex binding transcripts encoding the HLA-DR molecule. Here we investigate whether the same RNA binding proteins interact with 3UTR of mRNAs encoding the HLA-DQ isotype. Specifically, we focused on the HLA-DQ2.5 molecule, expressed on the surface of antigen presenting cells, and representing the main susceptibility factor for celiac disease (CD). This molecule, encoded by HLA-DQA1*05 and HLA-DQB1*02 alleles, presents the antigenic gluten peptides to CD4+ T lymphocytes, activating the autoimmune response.\n\nHere, we identified an additional component of the RNP complex, Tristetraprolin (TTP) or ZFP36, a zinc-finger protein, widely described as a factor modulating mRNA stability. TTP shows high affinity binding to 3UTR of CD-associated HLA-DQA1*05 and HLA-DQB1*02 alleles, in contrast to lower affinity binding to HLA-DQA1*01 and HLA-DQB1*05 non-CD associated alleles. Our in silico analysis, confirmed by molecular experiments, demonstrates that TTP specifically modulates the stability of the transcripts associated with celiac disease.

molecular biology↗

bioRxiv2018

Molecular definition of group 1 innate lymphoid cells in the mouse uterus

Determining the function of uterine lymphocytes is challenging because of the rapidly changing nature of the organ in response to sex hormones and, during pregnancy, to the invading fetal trophoblast cells. Here we provide the first genome-wide transcriptome atlas of mouse uterine group 1 innate lymphoid cells (g1 ILCs) at mid-gestation. The composition of g1 ILCs fluctuates throughout reproductive life, with Eomes-veCD49a+ ILC1s dominating before puberty and specifically expanding in second pregnancies, when the expression of CXCR6, a marker of memory cells, is upregulated. Tissue-resident Eomes+CD49a+ NK cells (trNK), which resemble human uterine NK cells, are most abundant during early pregnancy, and showcase gene signatures of responsiveness to TGF-{beta}, connections with trophoblast, epithelial, endothelial and smooth muscle cells, leucocytes, as well as extracellular matrix. Unexpectedly, trNK cells express genes involved in anaerobic glycolysis, lipid metabolism, iron transport, protein ubiquitination, and recognition of microbial molecular patterns. Conventional NK cells expand late in gestation and may engage in crosstalk with trNK cells involving IL-18 and IFN-{gamma}. These results identify trNK cells as the cellular hub of uterine g1 ILCs at mid-gestation and mark CXCR6+ ILC1s as potential memory cells of pregnancy.

immunology↗

bioRxiv2017

Estimating the footprint of pollution on coral reefs using models of species turn-over

Ecological communities typically change along gradients of human impact, though it is difficult to estimate the footprint of impacts for diffuse threats like pollution. Here we develop a joint model of benthic habitats on lagoonal coral reefs and use it to infer change in benthic composition along a gradient of distance from logging operations. The model estimates both changes in abundances of benthic groups and their compositional turn-over, a type of beta-diversity. We detect compositional turnover across the gradient and use the model to predict the footprint of turbidity impacts from logging. We then apply the model to predict impacts of recent logging activities, finding recent impacts to be small, because recent logging has occurred far from lagoonal reefs. Our model can be used more generally to estimate the footprint of human impacts on ecosystems and evaluate the benefits of conservation actions for ecosystems.

ecology↗