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In December 2019, SARS-CoV-2 emerged causing the COVID-19 pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilizes ACE2 and TMPRSS2 host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147, and GRP78 may also function as receptors for SARS-CoV-2.To determine the expression and in situ localization of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analyzed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples.We demonstrate absent to low ACE2 promoter activity in a variety of lung epithelial cell samples and low ACE2 gene expression in both microarray and scRNAseq datasets of epithelial cell populations. Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung. We present confirmatory evidence for the presence of TMPRSS2, CD147, and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 in the respiratory mucosa.Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression in human lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternate receptors for SARS-CoV-2 exist to facilitate initial host cell infection.

In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged, causing the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilises angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147 and GRP78 may also function as receptors for SARS-CoV-2.To determine the expression and in situ localisation of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analysed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, proteomic datasets, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples.We demonstrate absent to low ACE2 promoter activity in a variety of lung epithelial cell samples and low ACE2 gene expression in both microarray and scRNAseq datasets of epithelial cell populations. Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung, confirmed with proteomics. We present confirmatory evidence for the presence of TMPRSS2, CD147 and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 and GRP78 in the respiratory mucosa.Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression in human lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternative receptors for SARS-CoV-2 exist to facilitate initial host cell infection.

Mitogen-Activated Protein Kinase (MAPK) cascades play central roles in innate immune signaling networks in plants and animals1,2. In plants, however, the molecular mechanisms of how signal perception is transduced to MAPK activation remain elusive1. We report that pathogen-secreted proteases activate a previously unknown signaling pathway in Arabidopsis thaliana involving the Gα, Gβ and Gγ subunits of heterotrimeric G-protein complexes, which function upstream of a MAPK cascade. In this pathway, Receptor for Activated C Kinase 1 (RACK1) functions as a novel scaffold that binds to the Gβ subunit as well as to all three tiers of the MAPK cascade, thereby linking upstream G protein signaling to downstream activation of a MAPK cascade. The protease-G protein-RACK1-MAPK cascade modules identified in these studies are distinct from previously described plant immune signaling pathways such as the one elicited by bacterial flagellin, in which G proteins function downstream of or in parallel to a MAPK cascade without the involvement of the RACK1 scaffolding protein. The discovery of the novel protease-mediated immune signaling pathway described here was facilitated by the use of the broad host range, opportunistic bacterial pathogen Pseudomonas aeruginosa. The ability of P. aeruginosa to infect both plants and animals makes it an excellent model to identify novel types of immunoregulatory strategies that account for its niche adaptation to diverse host tissues and immune systems.

The influences of two strains of Pseudomonas putida UW4, a plant growth-promoting bacterium (PGPB) on the shoot and root proteomic profiles of Brassica napus (canola) were examined under salinity stress using two-dimensional difference gel electrophoresis. The two strains (wild-type UW4 and an AcdS minus mutant strain) differ in the availability of a functional 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, a bacterial protein known to play a role in host plant's stress responses. In total, ninety protein spots (representing 76 different proteins) with significantly altered expression levels in the presence of salt and/or bacteria were identified by mass spectrometry. Many of the identified proteins are involved in photosynthesis, anti-oxidative processes, transportation across membranes, and pathogenesis-related responses. The effects of salt and bacteria on the canola proteome were shown to be quite diverse, with salinity stress causing more dramatic plant protein expression changes than bacteria. In addition, bacteria were demonstrated to moderate some of the salt effects on plant protein differential expression. This work contributes to the understanding of how plant protein expression is affected by various environmental signals.

Gene duplication has long been recognized as a powerful mechanism facilitating development and evolution in genomes. Duplication events produce additional copies of genomic information, perhaps including one or more genes. While in some cases these duplicated elements may be of immediate benefit (i.e. increasing availability and effective dosage of a desired gene product), often they are initially at least somewhat redundant, and either neutral or mildly detrimental to the fitness of the organism. It is perhaps no surprise, then, that the majority of duplicated genes are quickly deactivated by mutations abolishing transcription or translation. Some duplicated genes, however, survive and persist, suggesting that their retention has some benefit. Many of these genes seem to have acquired properties that distinguish them from their progenitors – they may be expressed in a novel tissue type, for example, or differ in their functional specificity. In these cases, it appears as though duplication has facilitated evolution, either by allowing specialization and refinement or, perhaps most intriguingly, generating genes free to mutate and acquire ‘novel’ functions. These retained duplicates form a family of genes related through common ancestry. As a result of their common origin, gene sequences within gene families are often quite similar, complicating the task of assigning them unique and specific functions. As such, there has been a significant effort to study and characterize the evolution of function in the aftermath of a duplication event. This chapter will briefly cover the various modes of gene duplication, and then will focus on the various functional outcomes of duplication. The theoretical models for functional specialization following a duplication event are discussed, as are practical techniques for applying these models to observed gene duplicates.

A proteome reference map containing 326 2-D gel spots representing 275 different proteins was constructed for the plant growth-promoting bacterium Pseudomonas putida UW4. Protein identifications were obtained using Q-TOF MS/MS spectra matching to homologous proteins from other Pseudomonas strains and confirmed by PMF analysis. This data set is accessible at http://world-2dpage.expasy.org/repository/ and will aid in further characterization of Pseudomonas strains and interactions of plant growth-promoting bacterium with the plant rhizosphere environment.

Background DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data. Results Three Empirical Bayes methods (CyberT, BRB, and limma t-statistics) were the most effective statistical tests across simulated and both 2-colour cDNA and Affymetrix experimental data. The CyberT regularized t-statistic in particular was able to maintain expected false positive rates with simulated data showing high variances at low gene intensities, although at the cost of low true positive rates. The Local Pooled Error (LPE) test introduced a bias that lowered false positive rates below theoretically expected values and had lower power relative to the top performers. The standard two-sample t-test and fold change were also found to be sub-optimal for detecting differentially expressed genes. The generalized log transformation was shown to be beneficial in improving results with certain data sets, in particular high variance cDNA data. Conclusion Pre-processing of data influences performance and the proper combination of pre-processing and statistical testing is necessary for obtaining the best results. All three Empirical Bayes methods assessed in our study are good choices for statistical tests for small n microarray studies for both Affymetrix and cDNA data. Choice of method for a particular study will depend on software and normalization preferences.

Background High-throughput systems are powerful tools for the life science research community. The complexity and volume of data from these systems, however, demand special treatment. Graphical tools are needed to evaluate many aspects of the data throughout the analysis process because plots can provide quality assessments for thousands of values simultaneously. The utility of a plot, in turn, is contingent on both its interpretability and its efficiency. Results The shivplot, a graphical technique motivated by microarrays but applicable to any replicated high-throughput data set, is described. The plot capitalizes on the strengths of three well-established plotting graphics – a boxplot, a distribution density plot, and a variability vs intensity plot – by effectively combining them into a single representation. Conclusion The utility of the new display is illustrated with microarray data sets. The proposed graph, retaining all the information of its precursors, conserves space and minimizes redundancy, but also highlights features of the data that would be difficult to appreciate from the individual display components. We recommend the use of the shivplot both for exploratory data analysis and for the communication of experimental data in publications.

Background DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies. Results We present an approach for the global validation of DNA microarray experiments that will allow researchers to evaluate the general quality of their experiment and to extrapolate validation results of a subset of genes to the remaining non-validated genes. We illustrate why the popular strategy of selecting only the most differentially expressed genes for validation generally fails as a global validation strategy and propose random-stratified sampling as a better gene selection method. We also illustrate shortcomings of often-used validation indices such as overlap of significant effects and the correlation coefficient and recommend the concordance correlation coefficient (CCC) as an alternative. Conclusion We provide recommendations that will enhance validity checks of microarray experiments while minimizing the need to run a large number of labour-intensive individual validation assays.