Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002

Engineering regulatory networks of cyanobacteria

Engineering a cell's regulatory networks to dynamically control gene expression has been considered a new frontier in biological engineering. In cyanobacteria, the lack of well-characterized, modular gene regulatory elements makes regulatory network engineering challenging. Here, we suggest potential tools to modify various gene expression steps in cyanobacterial regulatory networks.

The globins of cyanobacteria and green algae: An update

The globin superfamily of proteins is ancient and diverse. Regular assessments based on the increasing number of available genome sequences have elaborated on a complex evolutionary history. In this review, we present a summary of a decade of advances in characterising the globins of cyanobacteria and green algae. The focus is on haem-containing globins with an emphasis on recent experimental developments, which reinforce links to nitrogen metabolism and nitrosative stress response in addition to dioxygen management. Mention is made of globins that do not bind haem to provide an encompassing view of the superfamily and perspective on the field. It is reiterated that an effort toward phenotypical and in-vivo characterisation is needed to elucidate the many roles that these versatile proteins fulfil in oxygenic photosynthetic microbes. It is also proposed that globins from oxygenic organisms are promising proteins for applications in the biotechnology arena.

Bioactive exometabolites drive maintenance competition in simple bacterial communities

During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532, and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure.

IMPORTANCE

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.

The need for an integrated multi-OMICs approach in microbiome science in the food system

Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop "Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research" gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.

Gene co-expression network analysis in zebrafish reveals chemical class specific modules

BACKGROUND

Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response.

RESULTS

Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure.

CONCLUSIONS

Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

Enhancing photosynthetic production of glycogen-rich biomass for use as a fermentation feedstock

Current sources of fermentation feedstocks, i.e. corn, sugar cane, or plant biomass, fall short of demand for liquid transportation fuels and commodity chemicals in the United States. Aquatic phototrophs including cyanobacteria have the potential to supplement the supply of current fermentable feedstocks. In this strategy, cells are engineered to accumulate storage molecules including glycogen, cellulose, and/or lipid oils that can be extracted from harvested biomass and fed to heterotrophic organisms engineered to produce desired chemical products. In this manuscript, we examine the production of glycogen in the model cyanobacteria, Synechococcus sp. strain PCC 7002, and subsequent conversion of cyanobacterial biomass by an engineered Escherichia coli to octanoic acid as a model product. In effort to maximize glycogen production, we explored the deletion of catabolic enzymes and overexpression of GlgC, an enzyme that catalyzes the first committed step towards glycogen synthesis. We found that deletion of glgP increased final glycogen titers when cells were grown in diurnal light. Overexpression of GlgC led to a temporal increase in glycogen content but not in an overall increase in final titer or content. The best strains were grown, harvested, and used to formulate media for growth of E. coli. The cyanobacterial media was able to support the growth of an engineered E. coli and produce octanoic acid at the same titer as common laboratory media.

Global Network Analysis of Neisseria gonorrhoeae Identifies Coordination between Pathways, Processes, and Regulators Expressed during Human Infection

Neisseria gonorrhoeae is a Gram-negative diplococcus that is responsible for the sexually transmitted infection gonorrhea, a high-morbidity disease in the United States and worldwide. Over the past several years, N. gonorrhoeae strains resistant to antibiotics used to treat this infection have begun to emerge across the globe. Thus, new treatment strategies are needed to combat this organism. Here, we utilized N. gonorrhoeae transcriptomic data sets, including those obtained from natural infection of the human genital tract, to infer the first global gene coexpression network of this pathogen. Interrogation of this network revealed genes central to the network that are likely critical for gonococcal growth, metabolism, and virulence, including genes encoding hypothetical proteins expressed during mucosal infection. In addition, network analysis revealed overlap in the response of N. gonorrhoeae to incubation with neutrophils and exposure to hydrogen peroxide stress in vitro Network analysis also identified new targets of the gonococcal global regulatory protein Fur, while examination of the network neighborhood of genes allowed us to assign additional putative categories to several proteins. Collectively, the characterization of the first gene coexpression network for N. gonorrhoeae described here has revealed new regulatory pathways and new categories for proteins and has shown how processes important to gonococcal infection in both men and women are linked. This information fills a critical gap in our understanding of virulence strategies of this obligate human pathogen and will aid in the development of new treatment strategies for gonorrhea.IMPORTANCE Neisseria gonorrhoeae is the causative agent of the sexually transmitted infection (STI) gonorrhea, a disease with high morbidity worldwide with an estimated 87 million cases annually. Current therapeutic and pharmacologic approaches to treat gonorrhea have been compromised by increased antibiotic resistance worldwide, including to the most recent FDA-approved antibiotic. New treatment strategies are urgently needed to combat this organism. In this study, we used network analysis to interrogate and define the coordination of pathways and processes in N. gonorrhoeae An analysis of the gonococcal network was also used to assign categories to genes and to expand our understanding of regulatory strategies. Network analysis provides important insights into pathogenic mechanisms of this organism that will guide the design of new strategies for disease treatment.

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Toward a Better Understanding of Species Interactions through Network Biology

Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance.

Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance. These developments prompt two important questions. What can be done with network approaches to better understand microbial species interactions? What challenges remain in applying network approaches to query the more complex systems of natural settings? Here, I briefly describe what has been done and what questions still need to be answered. Over the next 5 to 10 years, we will be in a strong position to infer networks that contain multiple kinds of omic data and describe systems with multiple species. These applications will open the door for a better understanding and use of microbiomes across a variety of fields.

Effect of CO2 Concentration on Uptake and Assimilation of Inorganic Carbon in the Extreme Acidophile Acidithiobacillus ferrooxidans

This study was motivated by surprising gaps in the current knowledge of microbial inorganic carbon (Ci) uptake and assimilation at acidic pH values (pH < 3). Particularly striking is the limited understanding of the differences between Ci uptake mechanisms in acidic versus circumneutral environments where the Ci predominantly occurs either as a dissolved gas (CO2) or as bicarbonate (HCO3 -), respectively. In order to gain initial traction on the problem, the relative abundance of transcripts encoding proteins involved in Ci uptake and assimilation was studied in the autotrophic, polyextreme acidophile Acidithiobacillus ferrooxidans whose optimum pH for growth is 2.5 using ferrous iron as an energy source, although they are able to grow at pH 5 when using sulfur as an energy source. The relative abundance of transcripts of five operons (cbb1-5) and one gene cluster (can-sulP) was monitored by RT-qPCR and, in selected cases, at the protein level by Western blotting, when cells were grown under different regimens of CO2 concentration in elemental sulfur. Of particular note was the absence of a classical bicarbonate uptake system in A. ferrooxidans. However, bioinformatic approaches predict that sulP, previously annotated as a sulfate transporter, is a novel type of bicarbonate transporter. A conceptual model of CO2 fixation was constructed from combined bioinformatic and experimental approaches that suggests strategies for providing ecological flexibility under changing concentrations of CO2 and provides a portal to elucidating Ci uptake and regulation in acidic conditions. The results could advance the understanding of industrial bioleaching processes to recover metals such as copper at acidic pH. In addition, they may also shed light on how chemolithoautotrophic acidophiles influence the nutrient and energy balance in naturally occurring low pH environments.

Systematic identification of a neutral site on chromosome of Synechococcus sp. PCC7002, a promising photosynthetic chassis strain

Cyanobacteria based photosynthetic biomanufacturing is supposed to be one of the alternative routes for sustainable production of biofuels and biochemicals. Synechococcus sp. PCC 7002 is a promising cyanobacterial chassis strain possessing desired properties for scaled cultivation in future. Development of cyanobacterial cell factories requires modifications of PCC7002 chromosome to expand the photosynthesis-based metabolism networks. Therefore, mining genomic neutral sites for stable integration of heterologous genes and pathways would be of great significance for expanding the toolbox for PCC7002 genome engineering. Here we demonstrate a paradigm for identification of potential neutral sites from chromosome of PCC7002 based on genomic and transcriptomics data. By refining the massive omics information from database, 51 putative sites with no significant physiological or metabolism effects were extracted as candidates. Combining genomic context analysis, a locus termed as NS0027 between two neighboring putative neutral genes was selected and evaluated as a neutral site for genetic integration. In addition, an ethanol synthesis pathway was introduced into the NS0027 site to assess the functionality of this site. The sites we identified and the strategy we adopted in this work would benefit the development of effective genetic toolbox and efficient photosynthetic cell factories based on PCC7002.

Regulatory Tools for Controlling Gene Expression in Cyanobacteria

Cyanobacteria are attractive hosts for converting carbon dioxide and sunlight into desirable chemical products. To engineer these organisms and manipulate their metabolic pathways, the biotechnology community has developed genetic tools to control gene expression. Many native cyanobacterial promoters and related sequence elements have been used to regulate genes of interest, and heterologous tools that use non-native small molecules to induce gene expression have been demonstrated. Overall, IPTG-based induction systems seem to be leaky and initially demonstrate small dynamic ranges in cyanobacteria. Consequently, a variety of other induction systems have been optimized to enable tighter control of gene expression. Tools require significant optimization because they function quite differently in cyanobacteria when compared to analogous use in model heterotrophs. We hypothesize that these differences are due to fundamental differences in physiology between organisms. This review is not intended to summarize all known products made in cyanobacteria nor the performance (titer, rate, yield) of individual strains, but instead will focus on the genetic tools and the inherent aspects of cellular physiology that influence gene expression in cyanobacteria.

Improving network inference algorithms using resampling methods

Background

Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications.

Results

We evaluated the effect of bootstrap aggregation on inferred networks using commonly applied network inference methods in terms of stability, or resilience to perturbations in the underlying expression data, a metric for accuracy, and functional enrichment of edge interactions.

Conclusion

Bootstrap aggregation results in improved stability and, depending on the size of the input dataset, a marginal improvement to accuracy assessed by each method’s ability to link genes in the same functional pathway.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2402-0) contains supplementary material, which is available to authorized users.

Species-specific transcriptomic network inference of interspecies interactions

The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.

Distinct and redundant functions of three homologs of RNase III in the cyanobacterium Synechococcus sp. strain PCC 7002

RNase III is a ribonuclease that recognizes and cleaves double-stranded RNA. Across bacteria, RNase III is involved in rRNA maturation, CRISPR RNA maturation, controlling gene expression, and turnover of messenger RNAs. Many organisms have only one RNase III while others have both a full-length RNase III and another version that lacks a double-stranded RNA binding domain (mini-III). The genome of the cyanobacterium Synechococcus sp. strain PCC 7002 (PCC 7002) encodes three homologs of RNase III, two full-length and one mini-III, that are not essential even when deleted in combination. To discern if each enzyme had distinct responsibilities, we collected and sequenced global RNA samples from the wild type strain, the single, double, and triple RNase III mutants. Approximately 20% of genes were differentially expressed in various mutants with some operons and regulons showing complex changes in expression levels between mutants. Two RNase III's had a role in 23S rRNA maturation and the third was involved in copy number regulation one of six native plasmids. In vitro, purified RNase III enzymes were capable of cleaving some of the known Escherichia coli RNase III target sequences, highlighting the remarkably conserved substrate specificity between organisms yet complex regulation of gene expression.

Zn2+-Inducible Expression Platform for Synechococcus sp. Strain PCC 7002 Based on the smtA Promoter/Operator and smtB Repressor

Synechococcus sp. strain PCC 7002 has been gaining significance as both a model system for photosynthesis research and for industrial applications. Until recently, the genetic toolbox for this model cyanobacterium was rather limited and relied primarily on tools that only allowed constitutive gene expression. This work describes a two-plasmid, Zn²⁺-inducible expression platform that is coupled with a zurA mutation, providing enhanced Zn²⁺ uptake. The control elements are based on the metal homeostasis system of a class II metallothionein gene (smtA₇₉₄₂) and its cognate SmtB₇₉₄₂ repressor from Synechococcus elongatus strain PCC 7942. Under optimal induction conditions, yellow fluorescent protein (YFP) levels were about half of those obtained with the strong, constitutive phycocyanin (cpcBA₆₈₀₃) promoter of Synechocystis sp. strain PCC 6803. This metal-inducible expression system in Synechococcus sp. strain PCC 7002 allowed the titratable gene expression of YFP that was up to 19-fold greater than the background level. This system was utilized successfully to control the expression of the Drosophila melanogaster β-carotene 15,15'-dioxygenase, NinaB, which is toxic when constitutively expressed from a strong promoter in Synechococcus sp. strain PCC 7002. Together, these properties establish this metal-inducible system as an additional useful tool that is capable of controlling gene expression for applications ranging from basic research to synthetic biology in Synechococcus sp. strain PCC 7002.

IMPORTANCE

This is the first metal-responsive expression system in cyanobacteria, to our knowledge, that does not exhibit low sensitivity for induction, which is one of the major hurdles for utilizing this class of genetic tools. In addition, high levels of expression can be generated that approximate those of established constitutive systems, with the added advantage of titratable control. Together, these properties establish this Zn²⁺-inducible system, which is based on the smtA₇₉₄₂ operator/promoter and smtB₇₉₄₂ repressor, as a versatile gene expression platform that expands the genetic toolbox of Synechococcus sp. strain PCC 7002.

Small proteins in cyanobacteria provide a paradigm for the functional analysis of the bacterial micro-proteome

Background

Despite their versatile functions in multimeric protein complexes, in the modification of enzymatic activities, intercellular communication or regulatory processes, proteins shorter than 80 amino acids (μ-proteins) are a systematically underestimated class of gene products in bacteria. Photosynthetic cyanobacteria provide a paradigm for small protein functions due to extensive work on the photosynthetic apparatus that led to the functional characterization of 19 small proteins of less than 50 amino acids. In analogy, previously unstudied small ORFs with similar degrees of conservation might encode small proteins of high relevance also in other functional contexts.

Results

Here we used comparative transcriptomic information available for two model cyanobacteria, Synechocystis sp. PCC 6803 and Synechocystis sp. PCC 6714 for the prediction of small ORFs. We found 293 transcriptional units containing candidate small ORFs ≤80 codons in Synechocystis sp. PCC 6803, also including the known mRNAs encoding small proteins of the photosynthetic apparatus. From these transcriptional units, 146 are shared between the two strains, 42 are shared with the higher plant Arabidopsis thaliana and 25 with E. coli. To verify the existence of the respective μ-proteins in vivo, we selected five genes as examples to which a FLAG tag sequence was added and re-introduced them into Synechocystis sp. PCC 6803. These were the previously annotated gene ssr1169, two newly defined genes norf1 and norf4, as well as nsiR6(nitrogen stress-induced RNA 6) and hliR1(high light-inducible RNA 1) , which originally were considered non-coding. Upon activation of expression via the Cu^2+.responsive petE promoter or from the native promoters, all five proteins were detected in Western blot experiments.

Conclusions

The distribution and conservation of these five genes as well as their regulation of expression and the physico-chemical properties of the encoded proteins underline the likely great bandwidth of small protein functions in bacteria and makes them attractive candidates for functional studies.