Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation

Bacterial proteome adaptation during fermentation in dairy environments

The enzymatic repertoire of starter cultures belonging to the Lactococcus genus determines various important characteristics of fermented dairy products but might change in response to the substantial environmental changes in the manufacturing process. Assessing bacterial proteome adaptation in dairy and other food environments is challenging due to the high matrix-protein concentration and is even further complicated in particularly cheese by the high fat concentrations, the semi-solid state of that matrix, and the non-growing state of the bacteria. Here, we present bacterial harvesting and processing procedures that enable reproducible, high-resolution proteome determination in lactococcal cultures harvested from laboratory media, milk, and miniature Gouda cheese. Comparative proteome analysis of Lactococcus cremoris NCDO712 grown in laboratory medium and milk revealed proteome adaptations that predominantly reflect the differential (micro-)nutrient availability in these two environments. Additionally, the drastic environmental changes during cheese manufacturing only elicited subtle changes in the L. cremoris NCDO712 proteome, including modified expression levels of enzymes involved in flavour formation. The technical advances we describe offer novel opportunities to evaluate bacterial proteomes in relation to their performance in complex, protein- and/or fat-rich food matrices and highlight the potential of steering starter culture performance by preculture condition adjustments.

Promoter recruitment drives the emergence of proto-genes in a long-term evolution experiment with Escherichia coli

The phenomenon of de novo gene birth-the emergence of genes from non-genic sequences-has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli long-term evolution experiment (LTEE) for changes indicative of "proto-genic" phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, with levels of transcription across low-expressed regions increasing in later generations of the experiment. Proto-genes formed downstream of new mutations result either from insertion element activity or chromosomal translocations that fused preexisting regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter, although such cases were rare compared to those caused by recruitment of preexisting promoters. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, can persist stably, and can serve as potential substrates for new gene formation.

Promoter capture drives the emergence of proto-genes in Escherichia coli

The phenomenon of de novo gene birth-the emergence of genes from non-genic sequences-has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli Long-Term Evolution Experiment (LTEE) for changes indicative of "proto-genic" phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time-span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, thereby serving as raw material for new gene emergence. Most proto-genes result either from insertion element activity or chromosomal translocations that fused pre-existing regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, persist stably, and can serve as potential substrates for new gene formation.

Growth-dependent Gene Expression Variation Influences the Strength of Codon Usage Biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

Growth-dependent gene expression variation influences the strength of codon usage biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

This protocol describes CAROM, a computational tool that combines genome-scale metabolic networks (GEMs) and machine learning to identify enzyme targets of post-translational modifications (PTMs). Condition-specific enzyme and reaction properties are used to predict targets of phosphorylation and acetylation in multiple organisms. CAROM is influenced by the accuracy of GEMs and associated flux-balance analysis (FBA), which generate the inputs of the model. We demonstrate the protocol using multi-omics data from E. coli. For complete details on the use and execution of this protocol, please refer to Smith et al. (2022).

Analysis of proteome adaptation reveals a key role of the bacterial envelope in starvation survival

Bacteria reorganize their physiology upon entry to stationary phase. What part of this reorganization improves starvation survival is a difficult question because the change in physiology includes a global reorganization of the proteome, envelope, and metabolism of the cell. In this work, we used several trade-offs between fast growth and long survival to statistically score over 2,000 Escherichia coli proteins for their global correlation with death rate. The combined ranking allowed us to narrow down the set of proteins that positively correlate with survival and validate the causal role of a subset of proteins. Remarkably, we found that important survival genes are related to the cell envelope, i.e., periplasm and outer membrane, because the maintenance of envelope integrity of E. coli plays a crucial role during starvation. Our results uncover a new protective feature of the outer membrane that adds to the growing evidence that the outer membrane is not only a barrier that prevents abiotic substances from reaching the cytoplasm but also essential for bacterial proliferation and survival.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.

Peptidoglycan maturation controls outer membrane protein assembly

Linkages between the outer membrane of Gram-negative bacteria and the peptidoglycan layer are crucial for the maintenance of cellular integrity and enable survival in challenging environments^1–5. The function of the outer membrane is dependent on outer membrane proteins (OMPs), which are inserted into the membrane by the β-barrel assembly machine^6,7 (BAM). Growing Escherichia coli cells segregate old OMPs towards the poles by a process known as binary partitioning, the basis of which is unknown⁸. Here we demonstrate that peptidoglycan underpins the spatiotemporal organization of OMPs. Mature, tetrapeptide-rich peptidoglycan binds to BAM components and suppresses OMP foldase activity. Nascent peptidoglycan, which is enriched in pentapeptides and concentrated at septa⁹, associates with BAM poorly and has little effect on its activity, leading to preferential insertion of OMPs at division sites. The synchronization of OMP biogenesis with cell wall growth results in the binary partitioning of OMPs as cells divide. Our study reveals that Gram-negative bacteria coordinate the assembly of two major cell envelope layers by rendering OMP biogenesis responsive to peptidoglycan maturation, a potential vulnerability that could be exploited in future antibiotic design.

Peptidoglycan stem peptides in the Gram-negative bacterial cell wall regulate the insertion of essential outer membrane proteins, thus representing a potential target for antibiotic design.

PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Biochemical and functional characterization of Mycobacterium tuberculosis ketol-acid reductoisomerase

Branched-chain amino acids (BCAAs) are essential amino acids, but their biosynthetic pathway is absent in mammals. Ketol-acid reductoisomerase (IlvC) is a BCAA biosynthetic enzyme that is coded by Rv3001c in Mycobacterium tuberculosis H37Rv (Mtb-Rv) and MRA_3031 in M. tuberculosis H37Ra (Mtb-Ra). IlvCs are essential in Mtb-Rv as well as in Escherichia coli. Compared to wild-type and IlvC-complemented Mtb-Ra strains, IlvC knockdown strain showed reduced survival at low pH and under low pH+starvation stress conditions. Further, increased expression of IlvC was observed under low pH and starvation stress conditions. Confirmation of a role for IlvC in pH and starvation stress was achieved by developing E. coli BL21(DE3) IlvC knockout, which was defective for growth in M9 minimal medium, but growth could be rescued by isoleucine and valine supplementation. Growth was also restored by complementing with over-expressing constructs of Mtb-Ra and E. coli IlvCs. The E. coli knockout also had a survival deficit at pH=5.5 and 4.5 and was more susceptible to killing at pH=3.0. The biochemical characterization of Mtb-Ra and E. coli IlvCs confirmed that both have NADPH-dependent activity. In conclusion, this study demonstrates the functional complementation of E. coli IlvC by Mtb-Ra IlvC and also suggests that IlvC has a role in tolerance to low pH and starvation stress.

ANALYSIS OF CARBOHYDRATES AND GLYCOCONJUGATES BY MATRIX-ASSISTED LASER DESORPTION/IONIZATION MASS SPECTROMETRY: AN UPDATE FOR 2015-2016

This review is the ninth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2016. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation and arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals. Much of this material is presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions and applications to chemical synthesis. The reported work shows increasing use of combined new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented over 30 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show no sign of deminishing. © 2020 Wiley Periodicals, Inc.

SCORE: Smart Consensus Of RNA Expression-a consensus tool for detecting differentially expressed genes in bacteria

SUMMARY

RNA-sequencing (RNA-Seq) is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq data, but no gold-standard has been widely accepted. We present the Snakemake-based tool Smart Consensus Of RNA Expression (SCORE) which uses a consensus approach founded on a selection of well-established tools for differential gene expression analysis. This allows SCORE to increase the overall prediction accuracy and to merge varying results into a single, human-readable output. SCORE performs all steps for the analysis of bacterial RNA-Seq data, from read preprocessing to the overrepresentation analysis of significantly associated ontologies. Development of consensus approaches like SCORE will help to streamline future RNA-Seq workflows and will fundamentally contribute to the creation of new gold-standards for the analysis of these types of data.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SiWolf/SCORE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolites Potentiate Nitrofurans in Nongrowing Escherichia coli

Nitrofurantoin (NIT) is a broad-spectrum bactericidal antibiotic used in the treatment of urinary tract infections. It is a prodrug that once activated by nitroreductases goes on to inhibit bacterial DNA, RNA, cell wall, and protein synthesis. Previous work has suggested that NIT retains considerable activity against nongrowing bacteria. Here, we have found that Escherichia coli grown to stationary phase in minimal or artificial urine medium is not susceptible to NIT. Supplementation with glucose under conditions where cells remained nongrowing (other essential nutrients were absent) sensitized cultures to NIT. We conceptualized NIT sensitivity as a multi-input AND gate and lack of susceptibility as an insufficiency in one or more of those inputs. The inputs considered were an activating enzyme, cytoplasmic abundance of NIT, and reducing equivalents required for NIT activation. We systematically assessed the contribution of each of these inputs and found that NIT import and the level of activating enzyme were not contributing factors to the lack of susceptibility. Rather, evidence suggested that the low abundance of reducing equivalents is why stationary-phase E. coli are not killed by NIT and catabolites can resensitize those cells. We found that this phenomenon also occurred when using nitrofurazone, which established generality to the nitrofuran antibiotic class. In addition, we observed that NIT activity against stationary-phase uropathogenic E. coli (UPEC) could also be potentiated through metabolite supplementation. These findings suggest that the combination of nitrofurans with specific metabolites could improve the outcome of uncomplicated urinary tract infections.

Signal Recognition Particle RNA Contributes to Oxidative Stress Response in Deinococcus radiodurans by Modulating Catalase Localization

The proper functioning of many proteins requires their transport to the correct cellular compartment or their secretion. Signal recognition particle (SRP) is a major protein transport pathway responsible for the co-translational movement of integral membrane proteins as well as periplasmic proteins. Deinococcus radiodurans is a ubiquitous bacterium that expresses a complex phenotype of extreme oxidative stress resistance, which depends on proteins involved in DNA repair, metabolism, gene regulation, and antioxidant defense. These proteins are located extracellularly or subcellularly, but the molecular mechanism of protein localization in D. radiodurans to manage oxidative stress response remains unexplored. In this study, we characterized the SRP complex in D. radiodurans R1 and showed that the knockdown (KD) of the SRP RNA (Qpr6) reduced bacterial survival under hydrogen peroxide and growth under chronic ionizing radiation. Through LC-mass spectrometry (MS/MS) analysis, we detected 162 proteins in the periplasm of wild-type D. radiodurans, of which the transport of 65 of these proteins to the periplasm was significantly reduced in the Qpr6 KD strain. Through Western blotting, we further demonstrated the localization of the catalases in D. radiodurans, DR_1998 (KatE1) and DR_A0259 (KatE2), in both the cytoplasm and periplasm, respectively, and showed that the accumulation of KatE1 and KatE2 in the periplasm was reduced in the SRP-defective strains. Collectively, this study establishes the importance of the SRP pathway in the survival and the transport of antioxidant proteins in D. radiodurans under oxidative stress.

Arming the troops: Post-translational modification of extracellular bacterial proteins

Protein secretion is almost universally employed by bacteria. Some proteins are retained on the cell surface, whereas others are released into the extracellular milieu, often playing a key role in virulence. In this review, we discuss the diverse types and potential functions of post-translational modifications (PTMs) occurring to extracellular bacterial proteins.

Slower growth of Escherichia coli leads to longer survival in carbon starvation due to a decrease in the maintenance rate

Fitness of bacteria is determined both by how fast cells grow when nutrients are abundant and by how well they survive when conditions worsen. Here, we study how prior growth conditions affect the death rate of Escherichia coli during carbon starvation. We control the growth rate prior to starvation either via the carbon source or via a carbon-limited chemostat. We find a consistent dependence where death rate depends on the prior growth conditions only via the growth rate, with slower growth leading to exponentially slower death. Breaking down the observed death rate into two factors, maintenance rate and recycling yield, reveals that slower growing cells display a decreased maintenance rate per cell volume during starvation, thereby decreasing their death rate. In contrast, the ability to scavenge nutrients from carcasses of dead cells (recycling yield) remains constant. Our results suggest a physiological trade-off between rapid proliferation and long survival. We explore the implications of this trade-off within a mathematical model, which can rationalize the observation that bacteria outside of lab environments are not optimized for fast growth.

Optimizing the dynamics of protein expression

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

Predicting bacterial growth conditions from mRNA and protein abundances

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

Distinct mechanisms coordinate transcription and translation under carbon and nitrogen starvation in Escherichia coli

Bacteria adapt to environmental stress by producing proteins that provide stress protection. However, stress can severely perturb the kinetics of gene expression, disrupting protein production. Here, we characterized how Escherichia coli mitigates such perturbations under nutrient stress through the kinetic coordination of transcription and translation. We observed that, when translation became limiting under nitrogen starvation, transcription elongation slowed accordingly. This slowdown was mediated by (p)ppGpp, the alarmone whose primary role is thought to be promoter regulation. This kinetic coordination by (p)ppGpp was critical for the robust synthesis of gene products. Surprisingly, under carbon starvation, (p)ppGpp was dispensable for robust synthesis. Characterization of the underlying kinetics revealed that under carbon starvation, transcription became limiting, and translation aided transcription elongation. This mechanism naturally coordinated transcription with translation, alleviating the need for (p)ppGpp as a mediator. These contrasting mechanisms for coordination resulted in the condition-dependent effects of (p)ppGpp on global protein synthesis and starvation survival. Our findings reveal a kinetic aspect of gene expression plasticity, establishing (p)ppGpp as a condition-dependent global effector of gene expression.

Burden-driven feedback control of gene expression

Cells use feedback regulation to ensure robust growth despite fluctuating demands for resources and differing environmental conditions. However, the expression of foreign proteins from engineered constructs is an unnatural burden that cells are not adapted for. Here we combined RNA-seq with an in vivo assay to identify the major transcriptional changes that occur in Escherichia coli when inducible synthetic constructs are expressed. We observed that native promoters related to the heat-shock response activated expression rapidly in response to synthetic expression, regardless of the construct. Using these promoters, we built a dCas9-based feedback-regulation system that automatically adjusts the expression of a synthetic construct in response to burden. Cells equipped with this general-use controller maintained their capacity for native gene expression to ensure robust growth and thus outperformed unregulated cells in terms of protein yield in batch production. This engineered feedback is to our knowledge the first example of a universal, burden-based biomolecular control system and is modular, tunable and portable.

Metabolic crosstalk regulates Porphyromonas gingivalis colonization and virulence during oral polymicrobial infection

Many human infections are polymicrobial in origin, and interactions among community inhabitants shape colonization patterns and pathogenic potential 1 . Periodontitis, which is the sixth most prevalent infectious disease worldwide 2 , ensues from the action of dysbiotic polymicrobial communities 3 . The keystone pathogen Porphyromonas gingivalis and the accessory pathogen Streptococcus gordonii interact to form communities in vitro and exhibit increased fitness in vivo 3,4 . The mechanistic basis of this polymicrobial synergy, however, has not been fully elucidated. Here we show that streptococcal 4-aminobenzoate/para-amino benzoic acid (pABA) is required for maximal accumulation of P. gingivalis in dual-species communities. Metabolomic and proteomic data showed that exogenous pABA is used for folate biosynthesis, and leads to decreased stress and elevated expression of fimbrial adhesins. Moreover, pABA increased the colonization and survival of P. gingivalis in a murine oral infection model. However, pABA also caused a reduction in virulence in vivo and suppressed extracellular polysaccharide production by P. gingivalis. Collectively, these data reveal a multidimensional aspect to P. gingivalis-S. gordonii interactions and establish pABA as a critical cue produced by a partner species that enhances the fitness of P. gingivalis while diminishing its virulence.

Integrative FourD omics approach profiles the target network of the carbon storage regulatory system

Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets.

Reduced Protein Expression in a Virus Attenuated by Codon Deoptimization

A general means of viral attenuation involves the extensive recoding of synonymous codons in the viral genome. The mechanistic underpinnings of this approach remain unclear, however. Using quantitative proteomics and RNA sequencing, we explore the molecular basis of attenuation in a strain of bacteriophage T7 whose major capsid gene was engineered to carry 182 suboptimal codons. We do not detect transcriptional effects from recoding. Proteomic observations reveal that translation is halved for the recoded major capsid gene, and a more modest reduction applies to several coexpressed downstream genes. We observe no changes in protein abundances of other coexpressed genes that are encoded upstream. Viral burst size, like capsid protein abundance, is also decreased by half. Together, these observations suggest that, in this virus, reduced translation of an essential polycistronic transcript and diminished virion assembly form the molecular basis of attenuation.

The E. coli molecular phenotype under different growth conditions

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. Additionally, we provide measurements of doubling times and in-vivo metabolic fluxes through the central carbon metabolism. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions

Background

Post-translational modification (PTM) of proteins is central to many cellular processes across all domains of life, but despite decades of study and a wealth of genomic and proteomic data the biological function of many PTMs remains unknown. This is especially true for prokaryotic PTM systems, many of which have only recently been recognized and studied in depth. It is increasingly apparent that a deep sampling of abundance across a wide range of environmental stresses, growth conditions, and PTM types, rather than simply cataloging targets for a handful of modifications, is critical to understanding the complex pathways that govern PTM deposition and downstream effects.

Results

We utilized a deeply-sampled dataset of MS/MS proteomic analysis covering 9 timepoints spanning the Escherichia coli growth cycle and an unbiased PTM search strategy to construct a temporal map of abundance for all PTMs within a 400 Da window of mass shifts. Using this map, we are able to identify novel targets and temporal patterns for N-terminal N α acetylation, C-terminal glutamylation, and asparagine deamidation. Furthermore, we identify a possible relationship between N-terminal N α acetylation and regulation of protein degradation in stationary phase, pointing to a previously unrecognized biological function for this poorly-understood PTM.

Conclusions

Unbiased detection of PTM in MS/MS proteomics data facilitates the discovery of novel modification types and previously unobserved dynamic changes in modification across growth timepoints.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3676-8) contains supplementary material, which is available to authorized users.

Elucidation of the sequential transcriptional activity in Escherichia coli using time-series RNA-seq data

Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. Here, we present a new method to augment regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. We apply our method on a time-series RNA-Seq data set of Escherichia coli as it transitions from growth to stationary phase over five hours and investigate the various activity in gene regulation process by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. We analyse the changes in metabolic activity of the pagP gene and associated transcription factors during phase transition, and visualize the sequential transcriptional activity to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. We observe a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.