Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow

Lysine Phoshoglycerylation Is Widespread in Bacteria and Overlaps with Acylation

Phosphoglycerylation is a non-enzymatic protein modification in which a phosphoglyceryl moiety is covalently bound to the ε-amino group of lysine. It is enriched in glycolytic enzymes from humans and mice and is thought to provide a feedback mechanism for regulating glycolytic flux. We report the first proteomic analysis of this post-translational modification in bacteria by profiling phosphoglyceryl-lysine during the growth of Streptococcus pyogenes in different culture media. The identity of phosphoglyceryl-lysine was confirmed by a previously unknown diagnostic cyclic immonium ion generated during MS/MS. We identified 370 lysine phosphoglycerylation sites in 123 proteins of S. pyogenes. Growth in a defined medium on 1% fructose caused a significant accumulation of phosphoglycerylation compared to growth in a rich medium containing 0.2% glucose. Re-analysis of phosphoproteomes from 14 bacterial species revealed that phosphoglycerylation is generally widespread in bacteria. Many phosphoglycerylation sites were conserved in several bacteria, including S. pyogenes. There was considerable overlap between phosphoglycerylation, acetylation, succinylation, and other acylations on the same lysine residues. Despite some exceptions, most lysine phosphoglycerylations in S. pyogenes occurred with low stoichiometry. Such modifications may be meaningless, but it is also conceivable that phosphoglycerylation, acetylation, and other acylations jointly contribute to the overall regulation of metabolism.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Posttranslational modifications in bacteria during phage infection

During phage infection, both virus and bacteria attempt to gain and/or maintain control over critical bacterial functions, through a plethora of strategies. These strategies include posttranslational modifications (PTMs, including phosphorylation, ribosylation, and acetylation), as rapid and dynamic regulators of protein behavior. However, to date, knowledge on the topic remains scarce and fragmented, while a more systematic investigation lies within reach. The release of AlphaFold, which advances PTM enzyme discovery and functional elucidation, and the increasing inclusivity and scale of mass spectrometry applications to new PTM types, could significantly accelerate research in the field. In this review, we highlight the current knowledge on PTMs during phage infection, and conceive a possible pipeline for future research, following an enzyme-target-function scheme.

Evaluation of the impact of polypeptide-p on diabetic rats upon its cloning, expression, and secretion in Saccharomyces boulardii

This study was designed to evaluate the effectiveness of recombinant polypeptide-p derived from Momordica charantia on diabetic rats. In this research, the optimized sequence of polypeptide-p gene fused to a secretion signal tag was cloned into the expression vector and transformed into probiotic Saccharomyces boulardii. The production of recombinant secretion protein was verified by western blotting, HPLC, and mass spectrometry. To assay recombinant yeast bioactivity in the gut, diabetic rats were orally fed wild-type and recombinant S. boulardii, in short SB and rSB, respectively, at two low and high doses as well as glibenclamide as a reference drug. In untreated diabetic and treated diabetic + SB rats (low and high doses), the blood glucose increased from 461, 481, and 455 (mg/dl), respectively, to higher than 600 mg/dl on the 21st day. Whereas glibenclamide and rSB treatments showed a significant reduction in the blood glucose level. The result of this study promised a safe plant-source supplement for diabetes through probiotic orchestration.

Mycobacterium tuberculosis H37Rv enolase (Rv1023)- expression, characterization and effect of host dependent modifications on protein functionality

Mycobacterium tuberculosis enolase is an essential glycolytic enzyme that catalyzes the conversion of 2, phosphoglycerate (PGA) to phosphoenol pyruvate (PEP). It is also a crucial link between glycolysis and the tricarboxylic acid (TCA) pathway. The depletion of PEP has recently been associated with the emergence of non-replicating drug resistant bacteria. Enolase is also known to exhibit multiple alternate functions, such as promoting tissue invasion via its role as a plasminogen (Plg) receptor. In addition, proteomic studies have identified the presence of enolase in the Mtb degradosome and in biofilms. However, the precise role in these processes has not been elaborated. The enzyme was recently identified as a target for 2-amino thiazoles - a novel class of anti-mycobacterials. In vitro assays and characterization of this enzyme were unsuccessful due to the inability to obtain functional recombinant protein. In the present study, we report the expression and characterization of enolase using Mtb H37Ra as a host strain. Our study demonstrates that the enzyme activity and alternate functions of this protein are significantly impacted by the choice of expression host (Mtb H37Ra or E. coli). Detailed analysis of the protein from each source revealed subtle differences in the post-translational modifications. Lastly, our study confirms the role of enolase in Mtb biofilm formation and describes the potential for inhibiting this process.

Detection of Cysteine Sulfenic Acid on E. coli Proteins with a Biotin-Benzoboroxole Probe

S-sulfenylation of cysteine residues on proteins can effectively change protein structures and accordingly regulate their functions in vivo. Investigation of S-sulfenylation in different biological environments is thus vital for a systematic understanding of cellular redox regulation. In this work, a functional probe, biotin-benzoboroxole (Bio-ben), was designed for the detection of cysteine sulfenic acid (Cys-SOH). The performance of Bio-ben was characterized by small-molecule sulfenic acid, protein models, and proteome tests via mass spectra and western blotting. The results showed that Bio-ben was validated for cysteine sulfenic acid on proteins with good capture efficiency even at low concentrations. Compared with commonly used probes such as dimedone, the current probe has significantly shortened labeling time and exhibited comparable sensitivity. The proposed method provides a new approach for exploring S-sulfenylation in the oxidative modification of proteins and is helpful for related biological and clinical applications.

The impact of metabolism on the adaptation of organisms to environmental change

Since Jacob and Monod's discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as "metabolic reprogramming." The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.

Improving therapeutic protein secretion in the probiotic yeast Saccharomyces boulardii using a multifactorial engineering approach

The probiotic yeast Saccharomyces boulardii (Sb) is a promising chassis to deliver therapeutic proteins to the gut due to Sb's innate therapeutic properties, resistance to phage and antibiotics, and high protein secretion capacity. To maintain therapeutic efficacy in the context of challenges such as washout, low rates of diffusion, weak target binding, and/or high rates of proteolysis, it is desirable to engineer Sb strains with enhanced levels of protein secretion. In this work, we explored genetic modifications in both cis- (i.e. to the expression cassette of the secreted protein) and trans- (i.e. to the Sb genome) that enhance Sb's ability to secrete proteins, taking a Clostridioides difficile Toxin A neutralizing peptide (NPA) as our model therapeutic. First, by modulating the copy number of the NPA expression cassette, we found NPA concentrations in the supernatant could be varied by sixfold (76-458 mg/L) in microbioreactor fermentations. In the context of high NPA copy number, we found a previously-developed collection of native and synthetic secretion signals could further tune NPA secretion between 121 and 463 mg/L. Then, guided by prior knowledge of S. cerevisiae's secretion mechanisms, we generated a library of homozygous single gene deletion strains, the most productive of which achieved 2297 mg/L secretory production of NPA. We then expanded on this library by performing combinatorial gene deletions, supplemented by proteomics experiments. We ultimately constructed a quadruple protease-deficient Sb strain that produces 5045 mg/L secretory NPA, an improvement of > tenfold over wild-type Sb. Overall, this work systematically explores a broad collection of engineering strategies to improve protein secretion in Sb and highlights the ability of proteomics to highlight under-explored mediators of this process. In doing so, we created a set of probiotic strains that are capable of delivering a wide range of protein titers and therefore furthers the ability of Sb to deliver therapeutics to the gut and other settings to which it is adapted.

Engineering the plant metabolic system by exploiting metabolic regulation

Plants are the most sophisticated biofactories and sources of food and biofuels present in nature. By engineering plant metabolism, the production of desired compounds can be increased and the nutritional or commercial value of the plant species can be improved. However, this can be challenging because of the complexity of the regulation of multiple genes and the involvement of different protein interactions. To improve metabolic engineering (ME) capabilities, different tools and strategies for rerouting the metabolic pathways have been developed, including genome editing and transcriptional regulation approaches. In addition, cutting-edge technologies have provided new methods for understanding uncharacterized biosynthetic pathways, protein degradation mechanisms, protein-protein interactions, or allosteric feedback, enabling the design of novel ME approaches.

An emerging field: Post-translational modification in microbiome

Post-translational modifications (PTMs) play an essential role in most biological processes. PTMs on human proteins have been extensively studied. Studies on bacterial PTMs are emerging, which demonstrate that bacterial PTMs are different from human PTMs in their types, mechanisms and functions. Few PTM studies have been done on the microbiome. Here, we reviewed several studied PTMs in bacteria including phosphorylation, acetylation, succinylation, glycosylation, and proteases. We discussed the enzymes responsible for each PTM and their functions. We also summarized the current methods used to study microbiome PTMs and the observations demonstrating the roles of PTM in the microbe-microbe interactions within the microbiome and their interactions with the environment or host. Although new methods and tools for PTM studies are still needed, the existing technologies have made great progress enabling a deeper understanding of the functional regulation of the microbiome. Large-scale application of these microbiome-wide PTM studies will provide a better understanding of the microbiome and its roles in the development of human diseases.

Non-enzymatic acetylation inhibits glycolytic enzymes in Escherichia coli

Advanced mass spectrometry methods have detected thousands of post-translational phosphorylation and acetylation sites in bacteria, but their functional role and the enzymes catalyzing these modifications remain largely unknown. In addition to enzymatic acetylation, lysine residues can also be chemically acetylated by the metabolite acetyl phosphate. In Escherichia coli, acetylation at over 3,000 sites has been linked to acetyl phosphate, but the functionality of this widespread non-enzymatic acetylation is even less clear than the enzyme-catalyzed one. Here, we investigate the role of acetyl-phosphate-mediated acetylation in E. coli central metabolism. Out of 19 enzymes investigated, only GapA and GpmA are acetylated at high stoichiometry, which inhibits their activity by interfering with substrate binding, effectively reducing glycolysis when flux to or from acetate is high. Extrapolating our results to the whole proteome, maximally 10% of the reported non-enzymatically acetylated proteins are expected to reach a stoichiometry that could inhibit their activity.

The evolution of post-translational modifications

Post-translational modifications (PTMs) are chemical modifications that can regulate the activity and function of proteins. From an evolutionary perspective, they also represent a fast mechanism for the generation of phenotypic diversity and divergence. Advances in mass spectrometry have now enabled the identification of over 600 distinct PTM classes collectively spanning an order of 10⁶ unique sites. However, the chemical detection of PTMs has lagged far behind their functional characterisation, and relatively little is still known about the selective constraints that govern PTM evolution. In particular, the true fraction of PTM sites that are functional - and thus subject to selection - remains an open question. Here, I review advances made in the past two years towards understanding the evolution of PTMs and their associated enzymes.

Pyruvate kinase, a metabolic sensor powering glycolysis, drives the metabolic control of DNA replication

Background

In all living organisms, DNA replication is exquisitely regulated in a wide range of growth conditions to achieve timely and accurate genome duplication prior to cell division. Failures in this regulation cause DNA damage with potentially disastrous consequences for cell viability and human health, including cancer. To cope with these threats, cells tightly control replication initiation using well-known mechanisms. They also couple DNA synthesis to nutrient richness and growth rate through a poorly understood process thought to involve central carbon metabolism. One such process may involve the cross-species conserved pyruvate kinase (PykA) which catalyzes the last reaction of glycolysis. Here we have investigated the role of PykA in regulating DNA replication in the model system Bacillus subtilis.

Results

On analysing mutants of the catalytic (Cat) and C-terminal (PEPut) domains of B. subtilis PykA we found replication phenotypes in conditions where PykA is dispensable for growth. These phenotypes are independent from the effect of mutations on PykA catalytic activity and are not associated with significant changes in the metabolome. PEPut operates as a nutrient-dependent inhibitor of initiation while Cat acts as a stimulator of replication fork speed. Disruption of either PEPut or Cat replication function dramatically impacted the cell cycle and replication timing even in cells fully proficient in known replication control functions. In vitro, PykA modulates activities of enzymes essential for replication initiation and elongation via functional interactions. Additional experiments showed that PEPut regulates PykA activity and that Cat and PEPut determinants important for PykA catalytic activity regulation are also important for PykA-driven replication functions.

Conclusions

We infer from our findings that PykA typifies a new family of cross-species replication control regulators that drive the metabolic control of replication through a mechanism involving regulatory determinants of PykA catalytic activity. As disruption of PykA replication functions causes dramatic replication defects, we suggest that dysfunctions in this new family of universal replication regulators may pave the path to genetic instability and carcinogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01278-3.

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.

Extensive regulation of enzyme activity by phosphorylation in Escherichia coli

Protein serine/threonine/tyrosine (S/T/Y) phosphorylation is an essential and frequent post-translational modification in eukaryotes, but historically has been considered less prevalent in bacteria because fewer proteins were found to be phosphorylated and most proteins were modified to a lower degree. Recent proteomics studies greatly expanded the phosphoproteome of Escherichia coli to more than 2000 phosphorylation sites (phosphosites), yet mechanisms of action were proposed for only six phosphosites and fitness effects were described for 38 phosphosites upon perturbation. By systematically characterizing functional relevance of S/T/Y phosphorylation in E. coli metabolism, we found 44 of the 52 mutated phosphosites to be functional based on growth phenotypes and intracellular metabolome profiles. By effectively doubling the number of known functional phosphosites, we provide evidence that protein phosphorylation is a major regulation process in bacterial metabolism. Combining in vitro and in vivo experiments, we demonstrate how single phosphosites modulate enzymatic activity and regulate metabolic fluxes in glycolysis, methylglyoxal bypass, acetate metabolism and the split between pentose phosphate and Entner-Doudoroff pathways through mechanisms that include shielding the substrate binding site, limiting structural dynamics, and disrupting interactions relevant for activity in vivo.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Non-enzymatic Covalent Modifications as a New Chapter in the Histone Code

The interior of the cell abounds with reactive species that can accumulate as non-enzymatic covalent modifications (NECMs) on biological macromolecules. These adducts interfere with many cellular processes, for example, by altering proteins' surface topology, enzymatic activity, or interactomes. Here, we discuss dynamic NECMs on chromatin, which serves as the cellular blueprint. We first outline the chemistry of NECM formation and then focus on the recently identified effects of their accumulation on chromatin structure and transcriptional output. We next describe the known cellular regulatory mechanisms that prevent or reverse NECM formation. Finally, we discuss recently developed chemical biology platforms for probing and manipulating these NECMs in vitro and in vivo.

Challenges and opportunities in biological funneling of heterogeneous and toxic substrates beyond lignin

Significant developments in the understanding and manipulation of microbial metabolism have enabled the use of engineered biological systems toward a more sustainable energy and materials economy. While developments in metabolic engineering have primarily focused on the conversion of carbohydrates, substantial opportunities exist for using these same principles to extract value from more heterogeneous and toxic waste streams, such as those derived from lignin, biomass pyrolysis, or industrial waste. Funneling heterogeneous substrates from these streams toward valuable products, termed biological funneling, presents new challenges in balancing multiple catabolic pathways competing for shared cellular resources and engineering against perturbation from toxic substrates. Solutions to many of these challenges have been explored within the field of lignin valorization. This perspective aims to extend beyond lignin to highlight the challenges and discuss opportunities for use of biological systems to upgrade previously inaccessible waste streams.

In Situ Biomanufacturing of Small Molecules in the Mammalian Gut by Probiotic Saccharomyces boulardii

Saccharomyces boulardii is a probiotic yeast that exhibits rapid growth at 37 °C, is easy to transform, and can produce therapeutic proteins in the gut. To establish its ability to produce small molecules encoded by multigene pathways, we measured the amount and variance in protein expression enabled by promoters, terminators, selective markers, and copy number control elements. We next demonstrated efficient (>95%) CRISPR-mediated genome editing in this strain, allowing us to probe engineered gene expression across different genomic sites. We leveraged these strategies to assemble pathways enabling a wide range of vitamin precursor (β-carotene) and drug (violacein) titers. We found that S. boulardii colonizes germ-free mice stably for over 30 days and competes for niche space with commensal microbes, exhibiting short (1-2 day) gut residence times in conventional and antibiotic-treated mice. Using these tools, we enabled β-carotene synthesis (194 μg total) in the germ-free mouse gut over 14 days, estimating that the total mass of additional β-carotene recovered in feces was 56-fold higher than the β-carotene present in the initial probiotic dose. This work quantifies heterologous small molecule production titers by S. boulardii living in the mammalian gut and provides a set of tools for modulating these titers.

The landscape of metabolic pathway dependencies in cancer cell lines

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Phosphoproteome Study of Escherichia coli Devoid of Ser/Thr Kinase YeaG During the Metabolic Shift From Glucose to Malate

Understanding phosphorylation-mediated regulation of metabolic enzymes, pathways, and cell phenotypes under metabolic shifts represents a major challenge. The kinases associated with most phosphorylation sites and the link between phosphorylation and enzyme activity remain unknown. In this study, we performed stable isotope labeling by amino acids in cell culture (SILAC)-based proteome and phosphoproteome analysis of Escherichia coli ΔyeaG, a strain lacking a poorly characterized serine/threonine kinase YeaG, to decipher kinase-substrate interactions and the effects on metabolic phenotype during shifts from glucose to malate. The starting point of our analysis was the identification of physiological conditions under which ΔyeaG exhibits a clear phenotype. By metabolic profiling, we discovered that ΔyeaG strain has a significantly shorter lag phase than the wild type during metabolic shift from glucose to malate. Under those conditions, our SILAC analysis revealed several proteins that were differentially phosphorylated in the ΔyeaG strain. By focusing on metabolic enzymes potentially involved in central carbon metabolism, we narrowed down our search for putative YeaG substrates and identified isocitrate lyase AceA as the direct substrate of YeaG. YeaG was capable of phosphorylating AceA in vitro only in the presence of malate, suggesting that this phosphorylation event is indeed relevant for glucose to malate shift. There is currently not enough evidence to firmly establish the exact mechanism of this newly observed regulatory phenomenon. However, our study clearly exemplifies the usefulness of SILAC-based approaches in identifying proteins kinase substrates, when applied in physiological conditions relevant for the activity of the protein kinase in question.

Discovering the landscape of protein modifications

Protein modifications modulate nearly every aspect of cell biology in organisms, ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass-spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with more than 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress in discovering new protein modifications and determining their function. In this review, we take an opportunity to revisit the historical discovery of key post-translational modifications, quantify the current landscape of covalent protein adducts, and assess the role that new computational tools will play in the future of this field.

A predictive coarse-grained model for position-specific effects of post-translational modifications

Biomolecules undergo liquid-liquid phase separation (LLPS), resulting in the formation of multicomponent protein-RNA membraneless organelles in cells. However, the physiological and pathological role of post-translational modifications (PTMs) on the biophysics of phase behavior is only beginning to be probed. To study the effect of PTMs on LLPS in silico, we extend our transferable coarse-grained model of intrinsically disordered proteins to include phosphorylated and acetylated amino acids. Using the parameters for modified amino acids available for fixed-charge atomistic force fields, we parameterize the size and atomistic hydropathy of the coarse-grained-modified amino acid beads and, hence, the interactions between the modified and natural amino acids. We then elucidate how the number and position of phosphorylated and acetylated residues alter the protein's single-chain compactness and its propensity to phase separate. We show that both the number and the position of phosphorylated threonines/serines or acetylated lysines can serve as a molecular on/off switch for phase separation in the well-studied disordered regions of Fused in Sarcoma (FUS) and DDX3X, respectively. We also compare modified residues to their commonly used PTM mimics for their impact on chain properties. Importantly, we show that the model can predict and capture experimentally measured differences in the phase behavior for position-specific modifications, showing that the position of modifications can dictate phase separation. In sum, this model will be useful for studying LLPS of post-translationally modified intrinsically disordered proteins and predicting how modifications control phase behavior with position-specific resolution.

Independent component analysis recovers consistent regulatory signals from disparate datasets

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

Cells adapt to diverse environments by regulating gene expression. Genome-wide measurements of gene expression levels have exponentially increased in recent years, but successful integration and analysis of these datasets are limited. Recently, we showed that independent component analysis (ICA), a signal deconvolution algorithm, can separate a large bacterial gene expression dataset into groups of co-regulated genes. This previous study focused on data generated by a standardized pipeline and did not address whether ICA extracts the same quantitative co-expression signals across expression profiling platforms. In this study, we show that ICA finds similar co-regulation patterns underlying multiple gene expression datasets and can be used as a tool to integrate and interpret diverse datasets. Using a dataset containing over 3,000 expression profiles, we predicted three new regulons and characterized their activities. Since large, standardized expression datasets only exist for a few bacterial strains, these results broaden the possible applications of this tool to better understand transcriptional regulation across a wide range of microbes.

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

CRAF Methylation by PRMT6 Regulates Aerobic Glycolysis-Driven Hepatocarcinogenesis via ERK-Dependent PKM2 Nuclear Relocalization and Activation

BACKGROUND AND AIMS

Most tumor cells use aerobic glycolysis (the Warburg effect) to support anabolic growth and promote tumorigenicity and drug resistance. Intriguingly, the molecular mechanisms underlying this phenomenon are not well understood. In this work, using gain-of-function and loss-of-function in vitro studies in patient-derived organoid and cell cultures as well as in vivo positron emission tomography-magnetic resonance imaging animal models, we showed that protein arginine N-methyltransferase 6 (PRMT6) regulates aerobic glycolysis in human hepatocellular carcinoma (HCC) through nuclear relocalization of pyruvate kinase M2 isoform (PKM2), a key regulator of the Warburg effect.

APPROACH AND RESULTS

We found PRMT6 to methylate CRAF at arginine 100, interfering with its RAS/RAF binding potential, and therefore altering extracellular signal-regulated kinase (ERK)-mediated PKM2 translocation into the nucleus. This altered PRMT6-ERK-PKM2 signaling axis was further confirmed in both a HCC mouse model with endogenous knockout of PRMT6 as well as in HCC clinical samples. We also identified PRMT6 as a target of hypoxia through the transcriptional repressor element 1-silencing transcription factor, linking PRMT6 with hypoxia in driving glycolytic events. Finally, we showed as a proof of concept the therapeutic potential of using 2-deoxyglucose, a glycolysis inhibitor, to reverse tumorigenicity and sorafenib resistance mediated by PRMT6 deficiency in HCC.

CONCLUSIONS

Our findings indicate that the PRMT6-ERK-PKM2 regulatory axis is an important determinant of the Warburg effect in tumor cells, and provide a mechanistic link among tumorigenicity, sorafenib resistance, and glucose metabolism.

The Impact of ackA, pta, and ackA-pta Mutations on Growth, Gene Expression and Protein Acetylation in Escherichia coli K-12

Acetate is a characteristic by-product of Escherichia coli K-12 growing in batch cultures with glucose, both under aerobic as well as anaerobic conditions. While the reason underlying aerobic acetate production is still under discussion, during anaerobic growth acetate production is important for ATP generation by substrate level phosphorylation. Under both conditions, acetate is produced by a pathway consisting of the enzyme phosphate acetyltransferase (Pta) producing acetyl-phosphate from acetyl-coenzyme A, and of the enzyme acetate kinase (AckA) producing acetate from acetyl-phosphate, a reaction that is coupled to the production of ATP. Mutants in the AckA-Pta pathway differ from each other in the potential to produce and accumulate acetyl-phosphate. In the publication at hand, we investigated different mutants in the acetate pathway, both under aerobic as well as anaerobic conditions. While under aerobic conditions only small changes in growth rate were observed, all acetate mutants showed severe reduction in growth rate and changes in the by-product pattern during anaerobic growth. The AckA- mutant showed the most severe growth defect. The glucose uptake rate and the ATP concentration were strongly reduced in this strain. This mutant exhibited also changes in gene expression. In this strain, the atoDAEB operon was significantly upregulated under anaerobic conditions hinting to the production of acetoacetate. During anaerobic growth, protein acetylation increased significantly in the ackA mutant. Acetylation of several enzymes of glycolysis and central metabolism, of aspartate carbamoyl transferase, methionine synthase, catalase and of proteins involved in translation was increased. Supplementation of methionine and uracil eliminated the additional growth defect of the ackA mutant. The data show that anaerobic, fermentative growth of mutants in the AckA-Pta pathway is reduced but still possible. Growth reduction can be explained by the lack of an important ATP generating pathway of mixed acid fermentation. An ackA deletion mutant is more severely impaired than pta or ackA-pta deletion mutants. This is most probably due to the production of acetyl-P in the ackA mutant, leading to increased protein acetylation.

Protein post-translational modifications in bacteria

Over the past decade the number and variety of protein post-translational modifications that have been detected and characterized in bacteria have rapidly increased. Most post-translational protein modifications occur in a relatively low number of bacterial proteins in comparison with eukaryotic proteins, and most of the modified proteins carry low, substoichiometric levels of modification; therefore, their structural and functional analysis is particularly challenging. The number of modifying enzymes differs greatly among bacterial species, and the extent of the modified proteome strongly depends on environmental conditions. Nevertheless, evidence is rapidly accumulating that protein post-translational modifications have vital roles in various cellular processes such as protein synthesis and turnover, nitrogen metabolism, the cell cycle, dormancy, sporulation, spore germination, persistence and virulence. Further research of protein post-translational modifications will fill current gaps in the understanding of bacterial physiology and open new avenues for treatment of infectious diseases.

Global Lysine Acetylation in Escherichia coli Results from Growth Conditions That Favor Acetate Fermentation

Lysine acetylation is thought to provide a mechanism for regulating metabolism in diverse bacteria. Indeed, many studies have shown that the majority of enzymes involved in central metabolism are acetylated and that acetylation can alter enzyme activity. However, the details regarding this regulatory mechanism are still unclear, specifically with regard to the signals that induce lysine acetylation. To better understand this global regulatory mechanism, we profiled changes in lysine acetylation during growth of Escherichia coli on the hexose glucose or the pentose xylose at both high and low sugar concentrations using label-free mass spectrometry. The goal was to see whether lysine acetylation differed during growth on these two different sugars. No significant differences, however, were observed. Rather, the initial sugar concentration was the principal factor governing changes in lysine acetylation, with higher sugar concentrations causing more acetylation. These results suggest that acetylation does not target specific metabolic pathways but rather simply targets accessible lysines, which may or may not alter enzyme activity. They further suggest that lysine acetylation principally results from conditions that favor accumulation of acetyl phosphate, the principal acetate donor in E. coli IMPORTANCE Bacteria alter their metabolism in response to nutrient availability, growth conditions, and environmental stresses using a number of different mechanisms. One is lysine acetylation, a posttranslational modification known to target many metabolic enzymes. However, little is known about this regulatory mode. We investigated the factors inducing changes in lysine acetylation by comparing growth on glucose and xylose. We found that the specific sugar used for growth did not alter the pattern of acetylation; rather, the amount of sugar did, with more sugar causing more acetylation. These results imply that lysine acetylation is a global regulatory mechanism that is responsive not to the specific carbon source per se but rather to the accumulation of downstream metabolites.