Michal Gordon

STRESS RESPONSE SUPPRESSOR1 and STRESS RESPONSE SUPPRESSOR2, Two DEAD-Box RNA Helicases That Attenuate Arabidopsis Responses to Multiple Abiotic Stresses

Two genes encoding Arabidopsis (Arabidopsis thaliana) DEAD-box RNA helicases were identified in a functional genomics screen as being down-regulated by multiple abiotic stresses. Mutations in either gene caused increased tolerance to salt, osmotic, and heat stresses, suggesting that the helicases suppress responses to abiotic stress. The genes were therefore designated STRESS RESPONSE SUPPRESSOR1 (STRS1; At1g31970) and STRS2 (At5g08620). In the strs mutants, salt, osmotic, and cold stresses induced enhanced expression of genes encoding the transcriptional activators DREB1A/CBF3 and DREB2A and a downstream DREB target gene, RD29A. Under heat stress, the strs mutants exhibited enhanced expression of the heat shock transcription factor genes, HSF4 and HSF7, and the downstream gene HEAT SHOCK PROTEIN101. Germination of mutant seed was hyposensitive to the phytohormone abscisic acid (ABA), but mutants showed up-regulated expression of genes encoding ABA-dependent stress-responsive transcriptional activators and their downstream targets. In wild-type plants, STRS1 and STRS2 expression was rapidly down-regulated by salt, osmotic, and heat stress, but not cold stress. STRS expression was also reduced by ABA, but salt stress led to reduced STRS expression in both wild-type and ABA-deficient mutant plants. Taken together, our results suggest that STRS1 and STRS2 attenuate the expression of stress-responsive transcriptional activators and function in ABA-dependent and ABA-independent abiotic stress signaling networks.

A combination of gene expression ranking and co‐expression network analysis increases discovery rate in large‐scale mutant screens for novel Arabidopsis thaliana abiotic stress genes

As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

Comparative genomic analysis of clinical and environmental Vibrio vulnificus isolates revealed biotype 3 evolutionary relationships

In 1996 a common-source outbreak of severe soft tissue and bloodstream infections erupted among Israeli fish farmers and fish consumers due to changes in fish marketing policies. The causative pathogen was a new strain of Vibrio vulnificus, named biotype 3, which displayed a unique biochemical and genotypic profile. Initial observations suggested that the pathogen erupted as a result of genetic recombination between two distinct populations. We applied a whole genome shotgun sequencing approach using several V. vulnificus strains from Israel in order to study the pan genome of V. vulnificus and determine the phylogenetic relationship of biotype 3 with existing populations. The core genome of V. vulnificus based on 16 draft and complete genomes consisted of 3068 genes, representing between 59 and 78% of the whole genome of 16 strains. The accessory genome varied in size from 781 to 2044 kbp. Phylogenetic analysis based on whole, core, and accessory genomes displayed similar clustering patterns with two main clusters, clinical (C) and environmental (E), all biotype 3 strains formed a distinct group within the E cluster. Annotation of accessory genomic regions found in biotype 3 strains and absent from the core genome yielded 1732 genes, of which the vast majority encoded hypothetical proteins, phage-related proteins, and mobile element proteins. A total of 1916 proteins (including 713 hypothetical proteins) were present in all human pathogenic strains (both biotype 3 and non-biotype 3) and absent from the environmental strains. Clustering analysis of the non-hypothetical proteins revealed 148 protein clusters shared by all human pathogenic strains; these included transcriptional regulators, arylsulfatases, methyl-accepting chemotaxis proteins, acetyltransferases, GGDEF family proteins, transposases, type IV secretory system (T4SS) proteins, and integrases. Our study showed that V. vulnificus biotype 3 evolved from environmental populations and formed a genetically distinct group within the E-cluster. The unique epidemiological circumstances facilitated disease outbreak and brought this genotype to the attention of the scientific community.

Unsupervised analysis of classical biomedical markers: robustness and medical relevance of patient clustering using bioinformatics tools.

Motivation It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. Method Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES). The 1999–2002 survey was used for training, while data from the 2003–2006 survey was tested as a validation set. Twelve combinations of pre-processing and clustering algorithms were applied to the training set. The quality of the resulting clusters was evaluated both by considering their properties and by comparative enrichment analysis. Cluster assignments were projected to the validation set (using an artificial neural network) and enrichment in health/life style traits in the resulting clusters was compared to the clusters generated from the original training set. Results The clusters obtained with different pre-processing and clustering combinations differed both in terms of cluster quality measures and in terms of reproducibility of enrichment with health/life style properties. Z-score normalization, for example, dramatically improved cluster quality and enrichments, as compared to unprocessed data, regardless of the clustering algorithm used. Clustering diabetes patients revealed a group of patients enriched with retinopathies. This could indicate that routine laboratory tests can be used to detect patients suffering from complications of diabetes, although other explanations for this observation should also be considered. Conclusions Clustering according to classical clinical biomarkers is a robust process, which may help in patient stratification. However, optimization of the pre-processing and clustering process may be still required.

Evolution and dissemination of the Klebsiella pneumoniae clonal group 258 throughout Israeli post-acute care hospitals, 2008–13

Objectives The KPC-producing Klebsiella pneumoniae (KPC-KP) clonal group (CG) 258 has disseminated throughout Israeli post-acute care hospitals (PACHs). The objectives of the study were (i) to describe the evolution and (ii) to understand the dissemination modes of CG 258 in the PACH system in Israel. Methods KPC-KP surveillance cultures isolates were collected in Israeli PACHs in three national point-prevalence surveys: 2008, 2011 and 2013. CG 258 was identified by pilv-l PCR. WGS was performed for CG 258 isolates from 9 of 14 PACHs and data extracted for core-genome MLST (cgMLST) and for capsule polysaccharide gene cluster analysis. Results The proportional representation of CG 258 among the KPC-KP isolates increased from 72 of 104 isolates (69.2%) in 2008 to 113 of 133 isolates (85%) in 2011 ( P  =   0.004 for 2008 versus 2011) and remained high in 2013 [56 of 67 isolates (83.6%)]. All isolates were related to CG 258 clade 2. cgMLST phylogenetic analysis showed relative convergence in the 2008 survey, with increasing diversification in the subsequent surveys. A predominantly institutional dissemination pattern was observed only in centre F from southern Israel. A predominantly regional dissemination pattern was observed in the two PACHs in Jerusalem. The other PACHs were characterized by a combined institutional and generalized pattern, with the majority of isolates clustering within the same PACH and survey. Conclusions CG 258 clade 2 has retained its predominance despite increased diversification. Although interchanging of CG 258 strains occurred between most PACHs, local spread is the leading cause of its dissemination.

Draft Genome Sequence of Fish Pathogenic Vibrio vulnificus Biotype 2

ABSTRACT Vibrio vulnificus is a marine pathogen capable of causing severe soft tissue infections and septicemia in humans. V. vulnificus biotype 2 is the etiological agent of fish vibriosis. We describe here the first draft genome sequence of V. vulnificus biotype 2, strain ES-7601, isolated from an infected eel in Japan.

NeatSeq-Flow: A Lightweight High Throughput Sequencing Workflow Platform for Non-Programmers and Programmers alike

Nowadays, it has become almost a necessity for many biologists to execute bioinformatics workflows (WFs) as part of their research. However, most WF-management software packages require for their operation at least some programming expertise. Here we describe NeatSeq-Flow, a platform that enables users with no programming knowledge to design and execute complex high throughput sequencing WFs. This is achieved by using a compendium of pre-built modules as well as a generic module, both do not require programming expertise. Nonetheless, NeatSeq-Flow retains the flexibility to generate sophisticated WF modules using templates and only basic Python programming abilities. NeatSeq-Flow is designed to enable easy sharing of WFs and modules by conceptually separating modules, WF design, sample information and execution. Moreover, NeatSeq-Flow works hand in hand with CONDA environments for easy installation of the WF’s analysis programs in one go. NeatSeq-Flow enables efficient WF execution on computer clusters by parallelizing on both samples and WF steps. NeatSeq-Flow operates by shell-script generation; thus it allows full transparency of the WF process. NeatSeq-Flow offers real-time WF execution monitoring, detailed documentation and self-sustaining WF backups for reproducibility. All of these features make NeatSeq-Flow an easy-to-use WF platform while not compromising for flexibility, reproducibility, transparency and efficiency. Availability http://neatseq-flow.readthedocs.io/en/latest/ Contact sklarz@bgu.ac.ilBioinformatics workflows (WFs) in general, and those involving High Throughput Sequencing data in particular, typically involve executing a sequence of programs on raw sequence files from as many as thousands of samples. Management of these WFs is laborious and error-prone. We have developed NeatSeq-Flow, a python package that manages WF creation for execution on computer clusters. NeatSeq-Flow creates shell scripts as well as a directory structure for storing analysis results, error messages, and execution logs. The user maintains full control over the execution of the WF, while the computer cluster enforces sequential execution and parallelization. NeatSeq-Flow also supplies tools for version tracking, documentation and execution logging.