Gene network inference and visualization tools for biologists: application to new human transcriptome datasets.

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis.

BACKGROUND: Apoptosis is a critical process in endothelial cell (EC) biology and pathology, which has been extensively studied at protein level. Numerous gene expression studies of EC apoptosis have also been performed, however few attempts have been made to use gene expression data to identify the molecular relationships and master regulators that underlie EC apoptosis. Therefore, we sought to understand these relationships by generating a Bayesian gene regulatory network (GRN) model. RESULTS: ECs were induced to undergo apoptosis using serum withdrawal and followed over a time course in triplicate, using microarrays. When generating the GRN, this EC time course data was supplemented by a library of microarray data from EC treated with siRNAs targeting over 350 signalling molecules.The GRN model proposed Vasohibin-1 (VASH1) as one of the candidate master-regulators of EC apoptosis with numerous downstream mRNAs. To evaluate the role played by VASH1 in EC, we used siRNA to reduce the expression of VASH1. Of 10 mRNAs downstream of VASH1 in the GRN that were examined, 7 were significantly up- or down-regulated in the direction predicted by the GRN.Further supporting an important biological role of VASH1 in EC, targeted reduction of VASH1 mRNA abundance conferred resistance to serum withdrawal-induced EC death. CONCLUSION: We have utilised Bayesian GRN modelling to identify a novel candidate master regulator of EC apoptosis. This study demonstrates how GRN technology can complement traditional methods to hypothesise the regulatory relationships that underlie important biological processes.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery.

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.

Optimizing human coronavirus OC43 growth and titration.

Coronaviruses have been at the forefront of the news for the last 2 years. Unfortunately, SARS-CoV-2, the etiologic agent for the COVID-19 pandemic, must be manipulated in biosecurity level 3 settings, which significantly limits research. Meanwhile, several less pathogenic human coronaviruses (HCoV) exist and can be studied in much more common biosafety level 2 laboratories. Among them, HCoV-OC43 is a good surrogate candidate for SARS-CoV-2 since both are phylogenetically related human Betacoronaviruses. However, one issue has been the lack of standardized means among laboratories to propagate and titer this less virulent coronavirus. The present study probes the optimal parameters to propagate HCoV-OC43. First, testing of five different cell lines (MRC-5, Huh7.5, Vero, HCT-8, HRT-18) indicated that the physiologically relevant MRC-5 human lung cell line produced among the highest viral titers. HRT-18 may however be an interesting alternative as they are quick growing cells that also led to higher viral titers and a better tropism for various HCoV-OC43 variants. We also probed the impact of serum and temperature during viral expansion and confirmed that the normal temperature of the upper respiratory track (33 °C) improves viral yields over the typical 37 °C used to grow many other viruses. Meanwhile, we did not notice any evidence that serum concentrations significantly affected the virus but interestingly noted that the virus grew quite efficiently in a serum-free media formulation. Meanwhile sonication of viral stocks somewhat improved viral titers. Four titration methods (plaque assays, TCID50-CPE, TCID50-IFA and TCID50-IPA) were also probed using two cell lines (VeroE6 and HRT-18). In our hands, plaque assays proved unreliable and quantification of the virus by scoring CPE positive wells was significantly less sensitive than antibody-based assays (IFA and IPA). While the latter methods were equally sensitive, we favor the TCID50-IPA method since simpler, faster and cheaper than the IFA protocol. Moreover, the HRT-18 cells appeared more sensitive to quantify the virus. Perhaps most importantly, these optimized protocols routinely led to high titer viral stocks in the order of 108 TCID50/ml magnitude, which should fulfill the requirements of most experimental settings.

Synthesis and structure-activity studies of benzyl ester meperidine and normeperidine derivatives as selective serotonin transporter ligands.

A series of benzyl esters of meperidine and normeperidine were synthesized and evaluated for binding affinity at serotonin, dopamine and norepinephrine transporters. The 4-methoxybenzyl ester 8b and 4-nitrobenzyl ester 8c in the meperidine series and 4-methoxybenzyl ester 14a in the normeperidine series exhibited low nanomolar binding affinities at the SERT (K(i) values <2nM) and high SERT selectivity (DAT/SERT >1500 and NET/SERT >1500).

Patients' and health care providers' knowledge of wheelchair transportation issues.

The objective of this study was to determine the level of knowledge about wheelchair transportation safety practices among wheelchair users (WCUs) and caregivers, therapists, physicians, and other professionals (CTPs). Two 10-question surveys were designed. One was administered to a convenience sample of 107 WCUs, average age 31 years (range: 4 to 86). Diagnoses included cerebral palsy (39%), spinal cord injury (19%), and others. The second was given to a convenience sample of 87 caregivers (33%), therapists (27%), physicians (18%), and others. Results indicated that most WCUs (67%) and about half of the CTPs had not received education about best transportation practices. Less than 20% in either group had heard of the transit option or a transit wheelchair, and less than 7% were aware of SAE J2249 and ANSI-RESNA WC/19 standards for wheelchairs and transportation. Sixty-seven percent of WCUs and 46% of CTPs felt lack of knowledge restricted their use of best transportation practices, followed by cost and vehicle space. Education about best practices was related to increased knowledge as well as increased use of occupant and wheelchair restraints. We concluded that there is a poor level of knowledge about wheelchair transportation safety practices among WCUs, caregivers, and professionals who prescribe wheelchairs. This knowledge deficit leads to a lack of standardized transport and a potential compromise of safety. Education can be beneficial, and should be pursued.

Analysis of gene networks for drug target discovery and validation.

Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.

Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades.

We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains, each with a single-gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent griseofulvin, whose exact molecular targets were previously unknown, we used Boolean and Bayesian network discovery techniques to determine the gene expression regulatory cascades affected directly by this drug. Using this method we identified CIK1 as an important affected target gene related to the functional phenotype induced by griseofulvin. Cellular functional analysis of griseofulvin showed similar tubulin-specific morphological effects on mitotic spindle formation to those of the drug, in agreement with the known function of CIK1p. Further, using the nonparametric, nonlinear Bayesian gene networks we were able to identify alternative ligand-dependant transcription factors and G protein homologues upstream of CIK1 that regulate CIK1 expression and might therefore serve as alternative molecular targets to induce the same molecular response as griseofulvin.

Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries.

Gene regulatory networks elucidated from strategic, genome-wide experimental data can aid in the discovery of novel gene function information and expression regulation events from observation of transcriptional regulation among genes of known and unknown biological function. To create a reliable and comprehensive data set for the elucidation of transcription regulation networks, we conducted systematic genome-wide disruption expression experiments of yeast on 118 genes with known involvement in transcription regulation. We report several novel regulatory relationships between known transcription factors and other genes with previously unknown biological function discovered with this expression library. Here we report the downstream regulatory subnetworks for UME6 and MET28. The elucidated network topology among these genes demonstrates MET28's role as a nodal point between genes involved in cell division and those involved in DNA repair mechanisms.

Use of gene networks for identifying and validating drug targets.

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal?

Endothelial cell (EC) apoptosis may play an important role in blood vessel development, homeostasis and remodelling. In support of this concept, EC apoptosis has been detected within remodelling vessels in vivo, and inactivation of EC apoptosis regulators has caused dramatic vascular phenotypes. EC apoptosis has also been associated with cardiovascular pathologies. Therefore, understanding the regulation of EC apoptosis, with the goal of intervening in this process, has become a current research focus. The protein-based signalling and cleavage cascades that regulate EC apoptosis are well known. However, the possibility that programmed transcriptome and glycome changes contribute to EC apoptosis has only recently been explored. Traditional bioinformatic techniques have allowed simultaneous study of thousands of molecular signals during the process of EC apoptosis. However, to progress further, we now need to understand the complex cause and effect relationships among these signals. In this article, we will first review current knowledge about the function and regulation of EC apoptosis including the roles of the proteome transcriptome and glycome. Then, we assess the potential for further bioinformatic analysis to advance our understanding of EC apoptosis, including the limitations of current technologies and the potential of emerging technologies such as gene regulatory networks.

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles.

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.