GenSensor Suite: A Web-Based Tool for the Analysis of Gene and Protein Interactions, Pathways, and Regulation

The GenSensor Suite consists of four web tools for elucidating relationships among genes and proteins. GenPath results show which biochemical, regulatory, or other gene set categories are over- or under-represented in an input list compared to a background list. All common gene sets are available for searching in GenPath, plus some specialized sets. Users can add custom background lists. GenInteract builds an interaction gene list from a single gene input and then analyzes this in GenPath. GenPubMed uses a PubMed query to identify a list of PubMed IDs, from which a gene list is extracted and queried in GenPath. GenViewer allows the user to query one gene set against another in GenPath. GenPath results are presented with relevant P- and q-values in an uncluttered, fully linked, and integrated table. Users can easily copy this table and paste it directly into a spreadsheet or document

Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. Taking advantage of these opportunities requires identifying which effectors should be specifically activated or avoided so as to promote desired clinical responses and avoid side effects. However, identifying signaling profiles that support desired clinical outcomes remains challenging. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts. It then uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. The ability of the classification method to associate specific in vitro signaling profiles to clinically relevant responses was corroborated using β2-adrenergic receptor ligands.This research was supported by a research contract from Pfizer Inc. and grants from the Natural Sciences and Engineering Research Council of Canada (Grant 311997 to G.P.) and the Canadian Institutes of Health Research MOP 324876 (to G.P.), MOP 102630 (to M.B. and O.L.) and Foundation grant (FDN-148431) to MB. MB holds a Canada Research Chair in Signal Transduction and Molecular Pharmacology. Dr Lichtarge’s research was supported by National Institutes of Health (NIH 2R01 GM066099; NIH 5R01 GM079656). B.B. was supported by a studentship from Fonds de Recherche en Santé du Québec. P.D. was supported by a MITACS fellowship

Structure of the native chemotaxis core signaling unit from phage E-protein lysed E. coli cells

Motile bacteria employ conserved chemotaxis networks to detect chemical gradients in their surroundings and effectively regulate their locomotion, enabling the location of essential nutrients and other important biological niches. The sensory apparatus of the chemotaxis pathway is an array of core-signaling units (CSUs) composed of transmembrane chemoreceptors, the histidine kinase CheA and an adaptor protein, CheW. Although chemotaxis pathways represent the best understood signaling systems, a detailed mechanistic understanding of signal transduction has been hindered by the lack of a complete structural picture of the CSU and extended array. In this study, we present the structure of the complete CSU from phage φX174 E protein lysed Escherichia coli cells, determined using cryo-electron tomography and sub-tomogram averaging to 12-Å resolution. Using AlphaFold2, we further predict the atomic structures of the CSU’s constituent proteins as well as key protein-protein interfaces, enabling the assembly an all-atom CSU model, which we conformationally refine using our cryo-electron tomography map. Molecular dynamics simulations of the resulting model provide new insight into the periplasmic organization of the complex, including novel interactions between neighboring receptor ligand-binding domains. Our results further elucidate previously unresolved interactions between individual CheA domains, including an anti-parallel P1 dimer and non-productive binding mode between P1 and P4, enhancing our understanding of the structural mechanisms underlying CheA signaling and regulation. IMPORTANCE Bacterial chemotaxis is a ubiquitous behavior that enables cell movement toward or away from specific chemicals. It serves as an important model for understanding cell sensory signal transduction and motility. Characterization of the molecular mechanisms underlying chemotaxis is of fundamental interest and requires a high-resolution structural picture of the sensing machinery, the chemosensory array. In this study, we combine cryo-electron tomography and molecular simulation to present the complete structure of the core signaling unit, the basic building block of chemosensory arrays, from Escherichia coli . Our results provide new insight into previously poorly-resolved regions of the complex and offer a structural basis for designing new experiments to test mechanistic hypotheses

The Role of Hypoxia in 2-Butoxyethanol–Induced Hemangiosarcoma

To understand the molecular mechanisms underlying compound-induced hemangiosarcomas in mice, and therefore, their human relevance, a systems biology approach was undertaken using transcriptomics and Causal Network Modeling from mice treated with 2-butoxyethanol (2-BE). 2-BE is a hemolytic agent that induces hemangiosarcomas in mice. We hypothesized that the hemolysis induced by 2-BE would result in local tissue hypoxia, a well-documented trigger for endothelial cell proliferation leading to hemangiosarcoma. Gene expression data from bone marrow (BM), liver, and spleen of mice exposed to a single dose (4 h) or seven daily doses of 2-BE were used to develop a mechanistic model of hemangiosarcoma. The resulting mechanistic model confirms previous work proposing that 2-BE induces macrophage activation and inflammation in the liver. In addition, the model supports local tissue hypoxia in the liver and spleen, coupled with increased erythropoeitin signaling and erythropoiesis in the spleen and BM, and suppression of mechanisms that contribute to genomic stability, events that could be contributing factors to hemangiosarcoma formation. Finally, an immunohistochemistry method (Hypoxyprobe) demonstrated that tissue hypoxia was present in the spleen and BM. Together, the results of this study identify molecular mechanisms that initiate hemangiosarcoma, a key step in understanding safety concerns that can impact drug decision processes, and identified hypoxia as a possible contributing factor for 2-BE–induced hemangiosarcoma in mice

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

SAG/ROC2/Rbx2/Hrt2, a Component of SCF E3 Ubiquitin Ligase: Genomic Structure, a Splicing Variant, and Two Family Pseudogenes

We have recently cloned and characterized an evolutionarily conserved gene, Sensitive to Apoptosis Gene (SAG), which encodes a redox-sensitive antioxidant protein that protects cells from apoptosis induced by redox agents. The SAG protein was later found to be the second family member of ROC/Rbx/Hrt, a component of the Skp1-cullin-F box protein (SCF) E3 ubiquitin ligase, being required for yeast growth and capable of promoting cell growth during serum starvation. Here, we report the genomic structure of the SAG gene that consists of four exons and three introns. We also report the characterization of a SAG splicing variant (SAG-v), that contains an additional exon (exon 2; 264 bp) not present in wildtype SAG. The inclusion of exon 2 disrupts the SAG ORF and gives rise to a protein of 108 amino acids that contains the first 59 amino acids identical to SAG and a 49-amino acid novel sequence at the C terminus. The entire RING-finger domain of SAG was not translated because of several inframe stop codons within the exon 2. The SAG-v protein was expressed in multiple human tissues as well as cell lines, but at a much lower level than wildtype SAG. Unlike SAG, SAG-v was not able to rescue yeast cells from lethality in a ySAG knockout, nor did it bind to cullin-1 or have ligase activity, probably because of the lack of the RING-finger domain. Finally, we report the identification of two SAG family pseudogenes, SAGP1 and SAGP2, that share 36% or 47% sequence identity with ROC1/Rbx1/Hrt1 and 30% or 88% with SAG, respectively. Both genes are intronless with two inframe stop codons.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63441/1/104454901750361488.pd

Electronically subtracting expression patterns from a mixed cell population

Motivation: Biological samples frequently contain multiple cell-types that each can play a crucial role in the development and/or regulation of adjacent cells or tissues. The search for biomarkers, or expression patterns of, one cell-type in those samples can be a complex and time-consuming process. Ordinarily, extensive laboratory bench work must be performed to separate the mixed cell population into its subcomponents, such that each can be accurately characterized. Results: We have developed a methodology to electronically subtract gene expression in one or more components of a mixed cell population from a mixture, to reveal the expression patterns of other minor or difficult to isolate components. Examination of simulated data indicates that this procedure can reliably determine the expression patterns in cell-types that contribute as little as 5% of the total expression in a mixed cell population. We re-analyzed microarray expression data from the viral infection of macrophages and from the T-cells of wild type and Foxp3 deletion mice. Using our subtraction methodology, we were able to substantially improve the identification of genes involved in processes of subcomponent portions of these samples. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Feature Identification and Extraction in Function Fields

We present interactive techniques for identifying and extracting features in function fields. Function fields map points in $n$-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is accomplished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset

Interactive Visualization of Function Fields by Range-Space Segmentation

We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are one-dimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations