Genome-scale co-expression network comparison across Escherichia coli and Salmonella enterica serovar Typhimurium reveals significant conservation at the regulon level of local regulators despite their dissimilar lifestyles

Evolution of gene networks underlying adaptation to drought stress in the wild tomato Solanum chilense

Drought stress is a key limitation for plant growth and colonization of arid habitats. We study the evolution of gene expression response to drought stress in a wild tomato, Solanum chilense, naturally occurring in dry habitats in South America. We conduct a transcriptome analysis under standard and drought experimental conditions to identify drought-responsive gene networks and estimate the age of the involved genes. We identify two main regulatory networks corresponding to two typical drought-responsive strategies: cell cycle and fundamental metabolic processes. The metabolic network exhibits a more recent evolutionary origin and a more variable transcriptome response than the cell cycle network (with ancestral origin and higher conservation of the transcriptional response). We also integrate population genomics analyses to reveal positive selection signals acting at the genes of both networks, revealing that genes exhibiting selective sweeps of older age also exhibit greater connectivity in the networks. These findings suggest that adaptive changes first occur at core genes of drought response networks, driving significant network re-wiring, which likely underpins species divergence and further spread into drier habitats. Combining transcriptomics and population genomics approaches, we decipher the timing of gene network evolution for drought stress response in arid habitats.

A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria

Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options.

Purifying selection enduringly acts on the sequence evolution of highly expressed proteins in Escherichia coli

The evolutionary speed of a protein sequence is constrained by its expression level, with highly expressed proteins evolving relatively slowly. This negative correlation between expression levels and evolutionary rates (known as the E–R anticorrelation) has already been widely observed in past macroevolution between species from bacteria to animals. However, it remains unclear whether this seemingly general law also governs recent evolution, including past and de novo, within a species. However, the advent of genomic sequencing and high-throughput phenotyping, particularly for bacteria, has revealed fundamental gaps between the 2 evolutionary processes and has provided empirical data opposing the possible underlying mechanisms which are widely believed. These conflicts raise questions about the generalization of the E–R anticorrelation and the relevance of plausible mechanisms. To explore the ubiquitous impact of expression levels on molecular evolution and test the relevance of the possible underlying mechanisms, we analyzed the genome sequences of 99 strains of Escherichia coli for evolution within species in nature. We also analyzed genomic mutations accumulated under laboratory conditions as a model of de novo evolution within species. Here, we show that E–R anticorrelation is significant in both past and de novo evolution within species in E. coli. Our data also confirmed ongoing purifying selection on highly expressed genes. Ongoing selection included codon-level purifying selection, supporting the relevance of the underlying mechanisms. However, the impact of codon-level purifying selection on the constraints in evolution within species might be smaller than previously expected from evolution between species.

Salmonella Regulator STM0347 Mediates Flagellar Phase Variation via Hin Invertase

Salmonella enterica is one of the most important food-borne pathogens, whose motility and virulence are highly related to flagella. Flagella alternatively express two kinds of surface antigen flagellin, FliC and FljB, in a phenomenon known as flagellar phase variation. The molecular mechanisms by which the switching orientation of the Hin-composed DNA segment mediates the expression of the fljBA promoter have been thoroughly illustrated. However, the precise regulators that control DNA strand exchange are barely understood. In this study, we found that a putative response regulator, STM0347, contributed to the phase variation of flagellin in S. Typhimurium. With quantitative proteomics and secretome profiling, a lack of STM0347 was confirmed to induce the transformation of flagellin from FliC to FljB. Real-time PCR and in vitro incubation of SMT0347 with the hin DNA segment suggested that STM0347 disturbed Hin-catalyzed DNA reversion via hin degradation, and the overexpression of Hin was sufficient to elicit flagellin variation. Subsequently, the Δstm0347 strain was outcompeted by its parental strain in HeLa cell invasion. Collectively, our results reveal the crucial role of STM0347 in Salmonella virulence and flagellar phase variation and highlight the complexity of the regulatory network of Hin-modulated flagellum phase variation in Salmonella.

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

CoNekT: an open-source framework for comparative genomic and transcriptomic network analyses

The recent accumulation of gene expression data in the form of RNA sequencing creates unprecedented opportunities to study gene regulation and function. Furthermore, comparative analysis of the expression data from multiple species can elucidate which functional gene modules are conserved across species, allowing the study of the evolution of these modules. However, performing such comparative analyses on raw data is not feasible for many biologists. Here, we present CoNekT (Co-expression Network Toolkit), an open source web server, that contains user-friendly tools and interactive visualizations for comparative analyses of gene expression data and co-expression networks. These tools allow analysis and cross-species comparison of (i) gene expression profiles; (ii) co-expression networks; (iii) co-expressed clusters involved in specific biological processes; (iv) tissue-specific gene expression; and (v) expression profiles of gene families. To demonstrate these features, we constructed CoNekT-Plants for green alga, seed plants and flowering plants (Picea abies, Chlamydomonas reinhardtii, Vitis vinifera, Arabidopsis thaliana, Oryza sativa, Zea mays and Solanum lycopersicum) and thus provide a web-tool with the broadest available collection of plant phyla. CoNekT-Plants is freely available from http://conekt.plant.tools, while the CoNekT source code and documentation can be found at https://github.molgen.mpg.de/proost/CoNekT/.

PhytoNet: comparative co-expression network analyses across phytoplankton and land plants

Phytoplankton consists of autotrophic, photosynthesizing microorganisms that are a crucial component of freshwater and ocean ecosystems. However, despite being the major primary producers of organic compounds, accounting for half of the photosynthetic activity worldwide and serving as the entry point to the food chain, functions of most of the genes of the model phytoplankton organisms remain unknown. To remedy this, we have gathered publicly available expression data for one chlorophyte, one rhodophyte, one haptophyte, two heterokonts and four cyanobacteria and integrated it into our PlaNet (Plant Networks) database, which now allows mining gene expression profiles and identification of co-expressed genes of 19 species. We exemplify how the co-expressed gene networks can be used to reveal functionally related genes and how the comparative features of PhytoNet allow detection of conserved transcriptional programs between cyanobacteria, green algae, and land plants. Additionally, we illustrate how the database allows detection of duplicated transcriptional programs within an organism, as exemplified by two putative DNA repair programs within Chlamydomonas reinhardtii. PhytoNet is available from www.gene2function.de.

Expression atlas and comparative coexpression network analyses reveal important genes involved in the formation of lignified cell wall in Brachypodium distachyon

While Brachypodium distachyon (Brachypodium) is an emerging model for grasses, no expression atlas or gene coexpression network is available. Such tools are of high importance to provide insights into the function of Brachypodium genes. We present a detailed Brachypodium expression atlas, capturing gene expression in its major organs at different developmental stages. The data were integrated into a large-scale coexpression database ( www.gene2function.de), enabling identification of duplicated pathways and conserved processes across 10 plant species, thus allowing genome-wide inference of gene function. We highlight the importance of the atlas and the platform through the identification of duplicated cell wall modules, and show that a lignin biosynthesis module is conserved across angiosperms. We identified and functionally characterised a putative ferulate 5-hydroxylase gene through overexpression of it in Brachypodium, which resulted in an increase in lignin syringyl units and reduced lignin content of mature stems, and led to improved saccharification of the stem biomass. Our Brachypodium expression atlas thus provides a powerful resource to reveal functionally related genes, which may advance our understanding of important biological processes in grasses.

Phylogenomic analysis of gene co-expression networks reveals the evolution of functional modules

Molecular evolutionary studies correlate genomic and phylogenetic information with the emergence of new traits of organisms. These traits are, however, the consequence of dynamic gene networks composed of functional modules, which might not be captured by genomic analyses. Here, we established a method that combines large-scale genomic and phylogenetic data with gene co-expression networks to extensively study the evolutionary make-up of modules in the moss Physcomitrella patens, and in the angiosperms Arabidopsis thaliana and Oryza sativa (rice). We first show that younger genes are less annotated than older genes. By mapping genomic data onto the co-expression networks, we found that genes from the same evolutionary period tend to be connected, whereas old and young genes tend to be disconnected. Consequently, the analysis revealed modules that emerged at a specific time in plant evolution. To uncover the evolutionary relationships of the modules that are conserved across the plant kingdom, we added phylogenetic information that revealed duplication and speciation events on the module level. This combined analysis revealed an independent duplication of cell wall modules in bryophytes and angiosperms, suggesting a parallel evolution of cell wall pathways in land plants. We provide an online tool allowing plant researchers to perform these analyses at http://www.gene2function.de.

Beyond Genomics: Studying Evolution with Gene Coexpression Networks

Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.

PlaNet: Comparative Co-Expression Network Analyses for Plants

Functional relations between genes can be represented as networks. These networks have been successfully used to infer gene function and to mediate transfer of functional knowledge between species. Transcriptionally coordinated or co-expressed genes tend to be functionally related, which combined with availability of transcriptomic data for multiple plant species make the co-expression networks a useful resource for the plant community. In this chapter, we describe PlaNet ( www.gene2function.de ), a database that includes comparative analyses for co-expression networks of 11 plant species. We exemplify how the tools included in PlaNet can be used to predict gene function, transfer knowledge, and discover conserved and multiplied gene modules.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data. In the present study we applied cross-species co-expressional analysis on a module of biofilm and stress response associated genes. We addressed different kinds of stresses in three most intensively studied members of Gammaproteobacteria including Escherichia coli K12, Pseudomonas aeruginosa PAO1 and Salmonella enterica for which large sets of gene expression data are available. Our aim was to evaluate the presence of common stress response strategies adopted by these microorganisms that may be assigned to the other members of Gammaproteobacteria. Results of functional annotation analysis revealed distinct categories among co-expressed genes, most of which concerned biological processes associated with virulence and stress response. Transcriptional regulatory analysis of genes present in co-expressed modules showed that the global stress sigma factor, RpoS, besides several local transcription factors accounts for the observed co-expressional response, and that several cases of feed-forward loops exist between global regulators, local transcription factors and their targets. Our results lend partial support to our underlying assumption of the conservation of core biological processes and regulatory interactions among these related Gammaproteobacteria members. This has led to the implementation of transferring gene function annotations from well-studied Gammaproteobacterial species to less-characterized members. These findings can shed light on the discovery of new drug targets capable of controlling severe infections caused by these groups of bacteria.

Analysis of Genome-scale Expression Network in Four Major Bacterial Residents of Cystic Fibrosis Lung

In polymicrobial communities where several species co-exist in a certain niche and consequently the possibility of interactions among species is very high, gene expression data sources can give better insights in to underlying adaptation mechanisms assumed by bacteria. Furthermore, several possible synergistic or antagonistic interactions among species can be investigated through gene expression comparisons. Lung is one of the habitats harboring several distinct pathogens during severe pulmonary disorders such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Expression data analysis of these lung residents can help to gain a better understanding on how these species interact with each other within the host cells. The first part of this paper deals with introducing available data sources for the major bacteria responsible for causing lung diseases and their genomic relations. In the second part, the main focus is on the studies concerning gene expression analyses of these species.