Pathogen-secreted proteases activate a novel plant immune pathway.

Mitogen-activated protein kinase (MAPK) cascades play central roles in innate immune signalling networks in plants and animals. In plants, however, the molecular mechanisms of how signal perception is transduced to MAPK activation remain elusive. Here we report that pathogen-secreted proteases activate a previously unknown signalling pathway in Arabidopsis thaliana involving the Gα, Gß, and Gγ subunits of heterotrimeric G-protein complexes, which function upstream of an MAPK cascade. In this pathway, receptor for activated C kinase 1 (RACK1) functions as a novel scaffold that binds to the Gß subunit as well as to all three tiers of the MAPK cascade, thereby linking upstream G-protein signalling to downstream activation of an MAPK cascade. The protease-G-protein-RACK1-MAPK cascade modules identified in these studies are distinct from previously described plant immune signalling pathways such as that elicited by bacterial flagellin, in which G proteins function downstream of or in parallel to an MAPK cascade without the involvement of the RACK1 scaffolding protein. The discovery of the new protease-mediated immune signalling 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 immunoregulatory strategies that account for its niche adaptation to diverse host tissues and immune systems.

Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue.

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.

Effects of bacterial ACC deaminase on Brassica napus gene expression.

Plants in association with plant growth-promoting rhizobacteria can benefit from lower plant ethylene levels through the action of the bacterial enzyme 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase. This enzyme cleaves the immediate biosynthetic precursor of ethylene, ACC. Ethylene is responsible for many aspects of plant growth and development but, under stressful conditions, it exacerbates stress symptoms. The ACC deaminase-containing bacterium Pseudomonas putida UW4 is a potent plant growth-promoting strain and, as such, was used to elaborate the detailed role of bacterial ACC deaminase in Brassica napus (canola) plant growth promotion. Transcriptional changes in bacterially treated canola plants were investigated with the use of an Arabidopsis thaliana oligonucleotide microarray. A heterologous approach was necessary because there are few tools available at present to measure global expression changes in nonmodel organisms, specifically with the sensitivity of microarrays. The results indicate that the transcription of genes involved in plant hormone regulation, secondary metabolism, and stress response was altered in plants by the presence of the bacterium, whereas the upregulation of genes for auxin response factors and the downregulation of stress response genes was observed only in the presence of bacterial ACC deaminase. These results support the suggestion that there is a direct link between ethylene and the auxin response, which has been suggested from physiological studies, and provide more evidence for the stress-reducing benefits of ACC deaminase-expressing plant growth-promoting bacteria.

Proteome reference map for the plant growth-promoting bacterium Pseudomonas putida UW4.

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.

Comparison of small n statistical tests of differential expression applied to microarrays.

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.

A methodology for global validation of microarray experiments.

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.

Assessing the evolution of gene expression using microarray data.

Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies have revealed an equally important role for gene regulation in the retention and evolution of duplicate genes. Here we review approaches to assessing the evolution of gene expression using microarray data, and discuss potential influences on expression divergence. Currently, there are no established standards on how best to identify and quantify instances of expression divergence. There have also been few efforts to date that incorporate suspected influences into mathematical models of expression divergence. Such developments will be crucial to a comprehensive understanding of the role gene duplications and expression evolution play in the emergence of complex traits and functional diversity. An integrative approach to gene family evolution, including both orthologous and paralogous genes, has the potential to bring strong predictive power both to the functional annotation of extant proteins and to the inference of functional characteristics of ancestral gene family members.

The Shivplot: a graphical display for trend elucidation and exploratory analysis of microarray data.

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.