Lore Cloots

Stress response regulators identified through genome-wide transcriptome analysis of the (p)ppGpp-dependent response in Rhizobium etli

BackgroundThe alarmone (p)ppGpp mediates a global reprogramming of gene expression upon nutrient limitation and other stresses to cope with these unfavorable conditions. Synthesis of (p)ppGpp is, in most bacteria, controlled by RelA/SpoT (Rsh) proteins. The role of (p)ppGpp has been characterized primarily in Escherichia coli and several Gram-positive bacteria. Here, we report the first in-depth analysis of the (p)ppGpp-regulon in an α-proteobacterium using a high-resolution tiling array to better understand the pleiotropic stress phenotype of a relA/rsh mutant.ResultsWe compared gene expression of the Rhizobium etli wild type and rsh (previously rel) mutant during exponential and stationary phase, identifying numerous (p)ppGpp targets, including small non-coding RNAs. The majority of the 834 (p)ppGpp-dependent genes were detected during stationary phase. Unexpectedly, 223 genes were expressed (p)ppGpp-dependently during early exponential phase, indicating the hitherto unrecognized importance of (p)ppGpp during active growth. Furthermore, we identified two (p)ppGpp-dependent key regulators for survival during heat and oxidative stress and one regulator putatively involved in metabolic adaptation, namely extracytoplasmic function sigma factor EcfG2/PF00052, transcription factor CH00371, and serine protein kinase PrkA.ConclusionsThe regulatory role of (p)ppGpp in R. etli stress adaptation is far-reaching in redirecting gene expression during all growth phases. Genome-wide transcriptome analysis of a strain deficient in a global regulator, and exhibiting a pleiotropic phenotype, enables the identification of more specific regulators that control genes associated with a subset of stress phenotypes. This work is an important step toward a full understanding of the regulatory network underlying stress responses in α-proteobacteria.

Transcriptome Analysis of the Rhizosphere Bacterium Azospirillum brasilense Reveals an Extensive Auxin Response

The rhizosphere bacterium Azospirillum brasilense produces the auxin indole-3-acetic acid (IAA) through the indole-3-pyruvate pathway. As we previously demonstrated that transcription of the indole-3-pyruvate decarboxylase (ipdC) gene is positively regulated by IAA, produced by A. brasilense itself or added exogenously, we performed a microarray analysis to study the overall effects of IAA on the transcriptome of A. brasilense. The transcriptomes of A. brasilense wild-type and the ipdC knockout mutant, both cultured in the absence and presence of exogenously added IAA, were compared.Interfering with the IAA biosynthesis/homeostasis in A. brasilense through inactivation of the ipdC gene or IAA addition results in much broader transcriptional changes than anticipated. Based on the multitude of changes observed by comparing the different transcriptomes, we can conclude that IAA is a signaling molecule in A. brasilense. It appears that the bacterium, when exposed to IAA, adapts itself to the plant rhizosphere, by changing its arsenal of transport proteins and cell surface proteins. A striking example of adaptation to IAA exposure, as happens in the rhizosphere, is the upregulation of a type VI secretion system (T6SS) in the presence of IAA. The T6SS is described as specifically involved in bacterium–eukaryotic host interactions. Additionally, many transcription factors show an altered regulation as well, indicating that the regulatory machinery of the bacterium is changing.

Identification of a complex genetic network underlying Saccharomyces cerevisiae colony morphology

When grown on solid substrates, different microorganisms often form colonies with very specific morphologies. Whereas the pioneers of microbiology often used colony morphology to discriminate between species and strains, the phenomenon has not received much attention recently. In this study, we use a genome‐wide assay in the model yeast Saccharomyces cerevisiae to identify all genes that affect colony morphology. We show that several major signalling cascades, including the MAPK, TORC, SNF1 and RIM101 pathways play a role, indicating that morphological changes are a reaction to changing environments. Other genes that affect colony morphology are involved in protein sorting and epigenetic regulation. Interestingly, the screen reveals only few genes that are likely to play a direct role in establishing colony morphology, with one notable example being FLO11, a gene encoding a cell‐surface adhesin that has already been implicated in colony morphology, biofilm formation, and invasive and pseudohyphal growth. Using a series of modified promoters for fine‐tuning FLO11 expression, we confirm the central role of Flo11 and show that differences in FLO11 expression result in distinct colony morphologies. Together, our results provide a first comprehensive look at the complex genetic network that underlies the diversity in the morphologies of yeast colonies.

Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria

Molecular entities present in a cell (mRNA, proteins, metabolites,…) do not act in isolation, but rather in cooperation with each other to define an organisms form and function. Their concerted action can be viewed as networks of interacting entities that are active under certain conditions within the cell or upon certain environmental signals. A main challenge in systems biology is to model these networks, or in other words studying which entities interact to form cellular systems or accomplish similar functions. On the contrary, viewing a single entity or an experimental dataset in the light of an interaction network can reveal previous unknown insights in biological processes. In this review we give an overview of how integrated networks can be reconstructed from multiple omics data and how they can subsequently be used for network-based modeling of cellular function in bacteria.

A Comparative Transcriptome Analysis of Rhizobium etli Bacteroids: Specific Gene Expression During Symbiotic Nongrowth

Rhizobium etli occurs either in a nitrogen-fixing symbiosis with its host plant, Phaseolus vulgaris, or free-living in the soil. During both conditions, the bacterium has been suggested to reside primarily in a nongrowing state. Using genome-wide transcriptome profiles, we here examine the molecular basis of the physiological adaptations of rhizobia to nongrowth inside and outside of the host. Compared with exponentially growing cells, we found an extensive overlap of downregulated growth-associated genes during both symbiosis and stationary phase, confirming the essentially nongrowing state of nitrogen-fixing bacteroids in determinate nodules that are not terminally differentiated. In contrast, the overlap of upregulated genes was limited. Generally, actively growing cells have hitherto been used as reference to analyze symbiosis-specific expression. However, this prevents the distinction between differential expression arising specifically from adaptation to a symbiotic lifestyle and features associated with nongrowth in general. Using stationary phase as the reference condition, we report a distinct transcriptome profile for bacteroids, containing 203 induced and 354 repressed genes. Certain previously described symbiosis-specific characteristics, such as the downregulation of amino acid metabolism genes, were no longer observed, indicating that these features are more likely due to the nongrowing state of bacteroids rather than representing bacteroid-specific physiological adaptations.

Query-based biclustering of gene expression data using Probabilistic Relational Models

BackgroundWith the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developed Pro Bic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set.ResultsWe applied Pro Bic on a large scale Escherichia coli compendium to extend partially described regulons with potentially novel members. We compared Pro Bics performance with previously published query-based biclustering algorithms, namely ISA and QDB, from the perspective of bicluster expression quality, robustness of the outcome against noisy seed sets and biological relevance.This comparison learns that Pro Bic is able to retrieve biologically relevant, high quality biclusters that retain their seed genes and that it is particularly strong in handling noisy seeds.ConclusionsPro Bic is a query-based biclustering algorithm developed in a flexible framework, designed to detect biologically relevant, high quality biclusters that retain relevant seed genes even in the presence of noise or when dealing with low quality seed sets.

PheNetic: Network-based interpretation of unstructured gene lists in E. coli

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .

EPSILON: an eQTL prioritization framework using similarity measures derived from local networks

MOTIVATION When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene. RESULTS We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5)). AVAILABILITY The physical interaction network and the source code (Matlab/C++) of our implementation can be downloaded from http://bioinformatics.intec.ugent.be/epsilon. CONTACT lieven.verbeke@intec.ugent.be, kamar@psb.ugent.be, jan.fostier@intec.ugent.be SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Omics Derived Networks in Bacteria

Understanding the cellular behavior from a systems perspective requires the identification of functional and physical interactions among diverse molecular entities in a cell (i.e. DNA/RNA, proteins and metabolites). Powerful and scalable technologies enabled the generation of genome-wide datasets that describe cellular systems by capturing the interactions of their building blocks under different environmental stimuli. The most straightforward way to represent such datasets is by means of molecular networks of which nodes correspond to molecular entities and edges to the interactions amongst those entities. In this review we give an overview of the different functional and physical interaction networks in bacteria that have been or potentially can be built by the integration of diverse omics datasets.

Path Finding in Biological Networks

Understanding the cellular behavior from a systems perspective requires the identification of functional and physical interactions among diverse molecular entities in a cell (i.e., DNA/RNA, proteins, and metabolites). The most straightforward way to represent such datasets is by means of molecular networks of which nodes correspond to molecular entities and edges to the interactions amongst those entities. Nowadays with large amounts of interaction data being generated, genome-wide networks can be created for an increasing number of organisms. These networks can be exploited to study a molecular entity like a protein in a wider context than just in isolation and provide a way of representing our knowledge of the system as a whole. On the other hand, viewing a single entity or an experimental dataset in the light of an interaction network can reveal previous unknown insights in biological processes.