Bacterial Metabolic Networks Research Articles

The marine environmental microbiome mediates physiological outcomes in host nematodes.

Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown. RESULTS: Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5'-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe-nematodes framework for microbiome mediated effects on host animal fitness.

Dynamic fluctuations in a bacterial metabolic network

The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.

Bacterial species metabolic interaction network for deciphering the lignocellulolytic system in fungal cultivating termite gut microbiota

Fungus-cultivating termite Odontotermes badius developed a mutualistic association with Termitomyces fungi for the plant material decomposition and providing a food source for the host survival. The mutualistic relationship sifted the microbiome composition of the termite gut and Termitomyces fungal comb. Symbiotic bacterial communities in the O. badius gut and fungal comb have been studied extensively to identify abundant bacteria and their lignocellulose degradation capabilities. Despite several metagenomic studies, the species-wide metabolic interaction patterns of bacterial communities in termite gut and fungal comb remains unclear. The bacterial species metabolic interaction network (BSMIN) has been constructed with 230 bacteria identified from the O. badius gut and fungal comb microbiota. The network portrayed the metabolic map of the entire microbiota and highlighted several inter-species biochemical interactions like cross-feeding, metabolic interdependency, and competition. Further, the reconstruction and analysis of the bacterial influence network (BIN) quantified the positive and negative pairwise influences in the termite gut and fungal comb microbial communities. Several key macromolecule degraders and fermentative microbial entities have been identified by analyzing the BIN. The mechanistic interplay between these influential microbial groups and the crucial glycoside hydrolases (GH) enzymes produced by the macromolecule degraders execute the community-wide functionality of lignocellulose degradation and subsequent fermentation. The metabolic interaction pattern between the nine influential microbial species has been determined by considering them growing in a synthetic microbial community. Competition (30%), parasitism (47%), and mutualism (17%) were predicted to be the major mode of metabolic interaction in this synthetic microbial community. Further, the antagonistic metabolic effect was found to be very high in the metabolic-deprived condition, which may disrupt the community functionality. Thus, metabolic interactions of the crucial bacterial species and their GH enzyme cocktail identified from the O. badius gut and fungal comb microbiota may provide essential knowledge for developing a synthetic microcosm with efficient lignocellulolytic machinery.

Horizontal gene transfer drives the evolution of dependencies in bacteria

SummaryMany naturally occurring bacteria lead a lifestyle of metabolic dependency for crucial resources. We do not understand what factors drive bacteria toward this lifestyle and how. Here, we systematically show the crucial role of horizontal gene transfer (HGT) in dependency evolution in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree and assess the impact of HGT and gene loss on metabolic networks. Our analyses suggest that HGT-enabled gene gains can affect which genes are later lost. HGT typically adds new catabolic routes to bacterial metabolic networks, leading to new metabolic interactions between bacteria. We also find that gaining new routes can promote the loss of ancestral routes (”coupled gains and losses”, CGLs). Phylogenetic patterns indicate that both dependencies—mediated by CGLs and those purely by gene loss—are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.

Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks

Information fusion (IF), perturbation theory (PT), and machine learning (ML) algorithm (IFPTML) allows for nanomaterial design by predicting the interactions of drug–nanoparticle systems vs. metabolic networks of bacteria.

Informational cost and networks navigability

Understanding how information navigates through nodes of a complex network has become an increasingly pressing problem across scientific disciplines. Several approaches have been proposed on the basis of shortest paths or diffusive navigation. However, no existing approaches have tackled the challenges of efficient communication in networks without full knowledge of their global topology under external noise. Here, we develop a first principles approach and mathematical formalism to determine the informational cost of navigating a network under different levels of external noise. Using this approach we discover the existence of a trade-off between the ways in which networks route information through shortest paths, their entropies and stability, which define three classes of real-world networks. This approach reveals that environmental pressure has shaped the ways in which information is transferred in bacterial metabolic networks and allowed us to determine the levels of noise at which a protein–protein interaction network seems to work in normal conditions in a cell.

Horizontal Gene Transfer Drives the Evolution of Dependencies in Bacteria

Many naturally-occurring bacteria lead a lifestyle of metabolic dependency, i.e., they depend on others for crucial resources. We do not understand what factors drive bacteria towards this lifestyle, and how. Here, we systematically show that horizontal gene transfer (HGT) plays a crucial role in the evolution of dependencies in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree, and assess the impact of HGT and gene loss on bacterial metabolic networks. Our analyses suggest that genes acquired by HGT can affect which genes are later lost. Dependency evolution is contingent on earlier HGT because of two reasons. First, we find that HGT typically adds new catabolic routes to bacterial metabolic networks. This increases the chance of new metabolic interactions between bacteria, which is a prerequisite for dependency evolution. Second, we show that gaining new routes can promote the loss of specific ancestral routes (a mechanism we call coupled gains and losses, CGLs). Phylogenetic patterns indicate that both types of dependencies — those mediated by CGLs and those purely by gene loss — are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.

Conserved Virulence-Linked Metabolic Reprogramming in <i>Clostridioides Difficile</i> Identified Through Genome-Scale Metabolic Network Analysis

The bacterial pathogen Clostridioides difficile causes a toxin-mediated diarrheal illness and is now the leading cause of hospital-acquired infection in the US. Due to growing threats of antibiotic resistance and recurrent infection, targeting components of metabolism presents a novel approach to combat this infection. Analyses of bacterial genome-scale metabolic network reconstructions (GENREs) have identified new therapeutic targets and helped uncover properties that drive cellular behaviors. We sought to leverage this approach and thus constructed highly-curated C. difficile GENREs for a hyper-virulent isolate (R20291) as well as a historic strain (630). Growth simulations of carbon source usage revealed significant correlations between in silico and experimentally measured values ( p -values ≤ 0.002, PPV ≈ 95%), and single-gene deletion analysis showed accuracies of >89% compared with transposon mutant libraries. Contextualizing these models with in situ omics datasets revealed conserved patterns of elevated proline, leucine, and valine fermentation that corresponded with significant increases in expression of multiple virulence factors during infection. Collectively, our results support that C. difficile utilizes distinct metabolic programs as infection progresses and highlights that GENREs can reveal the underpinnings of bacterial pathogenesis.

Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism.

The processes involved in cell growth are extremely complicated even for a single cell organism such as Escherichia coli, while the relationship between growth rate and cell size is simple. We aimed to reveal the systematic link between them from the aspect of the genome-scale metabolic network. Since the growth rate reflects metabolic rates of bacteria and the cell size relates to phospholipid synthesis, a part of bacterial metabolic networks, we calculated the cell length from the cardiolipin synthesis rate, where the cardiolipin synthesis reaction is able to represent the phospholipid metabolism of Escherichia coli in the exponential growth phase. Combined with the flux balance analysis, it enables us to predict cell length and to examine the quantitative relationship between cell length and growth rate. By simulating bacteria growing in various nutrient media with the flux balance analysis and calculating the corresponding cell length, we found that the increase of the synthesis rate of phospholipid, the cell width, and the protein fraction in membranes caused the increase of cell length with growth rate. Different tendencies of phospholipid synthesis rate changing with growth rate result in different relationships between cell length and growth rate. The effects of gene deletions on cell size and growth rate are also examined. Knocking out the genes, such as tktA, tktB, yqaB, pgm, and cysQ, affects growth rate largely while affecting cell length slightly. Results of this method are in good agreement with experiments.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities.

SummaryGenome‐scale reconstructions of metabolism are computational species‐specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock‐out library and Bar‐seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Evolution of hierarchy in bacterial metabolic networks

Flow hierarchy is a useful way to characterize the movement of information and matter throughout a network. Hierarchical network organizations are shown to arise when there is a cost of maintaining links in the network. A similar constraint exists in metabolic networks, where costs come from reduced efficiency of nonspecific enzymes or from producing unnecessary enzymes. Previous analyses of bacterial metabolic networks have been used to predict the minimal nutrients that a bacterium needs to grow, its mutualistic relationships with other bacteria, and its major ecological niche.We use metabolic network inference to obtain metabolite flow graphs of 2935 bacterial metabolic networks and find that flow hierarchy evolves independently of modularity and other network properties. By inferring the ancestral metabolic networks and estimating the hierarchical character of the inferred network, we show that hierarchical structure first increased and later decreased over evolutionary history. Furthermore, hierarchical structure in the network is associated with slower growth rates; bacteria with hierarchy scores above the median grow on average 2.25 times faster than those with hierarchy scores below the median.

Exploration of Nicotine Metabolism in Paenarthrobacter nicotinovorans pAO1 by Microbial Proteomics.

Proteomics, or the large-scale study of proteins, is a post-genomics field that, together with transcriptomics and metabolomics, has moved the study of bacteria to a new era based on system-wide understanding of bacterial metabolic and regulatory networks. The study of bacterial proteins or microbial proteomics has found a wide array of applications in many fields of microbiology, from food, clinical, and industrial microbiology to microbial ecology and physiology. The current chapter makes a brief technical introduction into the available approaches for the large-scale study of bacterial proteins using mass-spectrometry. Furthermore, the advantages and disadvantages of using bacteria for proteomics studies are indicated as well as several example studies where MS-based bacterial proteomics had a fundamental role in deciphering the scientific question. Finally, the proteomics study of nicotine catabolism in Paenarthrobacter nicotinovorans pAO1 using nanoLC-MS/MS is given as an in-depth example for possible applications of microbial proteomics.The nicotine degradation pathway functioning in Paenarthrobacter nicotinovorans is encoded by the catabolic megaplasmid pAO1 that contains about 40 nicotine-related genes making out the nic-gens cluster. Despite the promising biotechnological potential for the production of green-chemicals, only half of the nic-genes have been experimentally linked to nicotine. In an attempt to systematically identify all the proteins involved in nicotine degradation, a gel-based proteomics approach was used to identify a total of 801 proteins when Paenarthrobacter nicotinovorans was grown on three carbon sources: citrate, nicotine and nicotine and citrate. The differences in protein abundance showed that the bacterium is able to switch between deamination and demethylation in the lower nicotine pathway based on the available C source. Several pAO1 putative genes including a hypothetical polyketide cyclase have been shown to have a nicotine-dependent expression and we hypothesize that the polyketide cyclase would hydrolyze the N1-C6 bond from the pyridine ring with the formation of alpha-keto-glutaramate. Two chromosomal proteins, a malate dehydrogenase, and a D-3-phosphoglycerate dehydrogenase were shown to be strongly upregulated when nicotine was the sole carbon source and could be related to the production of the alpha-keto-glutaramate by the polyketide cyclase.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network.

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux—high substrate to product conversion rates—evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme’s role in the function of a metabolic network affects its evolution more than its place in the network’s structure. They highlight the value of a system-level perspective for studies of molecular evolution.

The antibiotic resistome: gene flow in environments, animals and human beings.

The antibiotic resistance is natural in bacteria and predates the human use of antibiotics. Numerous antibiotic resistance genes (ARGs) have been discovered to confer resistance to a wide range of antibiotics. The ARGs in natural environments are highly integrated and tightly regulated in specific bacterial metabolic networks. However, the antibiotic selection pressure conferred by the use of antibiotics in both human medicine and agriculture practice leads to a significant increase of antibiotic resistance and a steady accumulation of ARGs in bacteria. In this review, we summarized, with an emphasis on an ecological point of view, the important research progress regarding the collective ARGs (antibiotic resistome) in bacterial communities of natural environments, human and animals, i.e., in the one health settings.We propose that the resistance gene flow in nature is "from the natural environments" and "to the natural environments"; human and animals, as intermediate recipients and disseminators, contribute greatly to such a resistance gene "circulation."

Complexity and robustness in hypernetwork models of metabolism

Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity.

Marine systems biology.

EDITORIAL article Front. Genet., 13 May 2015Sec. Systems Biology Archive Volume 6 - 2015 | https://doi.org/10.3389/fgene.2015.00181

The Crystal Structures of Apo and cAMP-Bound GlxR from Corynebacterium glutamicum Reveal Structural and Dynamic Changes upon cAMP Binding in CRP/FNR Family Transcription Factors

The cyclic AMP-dependent transcriptional regulator GlxR from Corynebacterium glutamicum is a member of the super-family of CRP/FNR (cyclic AMP receptor protein/fumarate and nitrate reduction regulator) transcriptional regulators that play central roles in bacterial metabolic regulatory networks. In C. glutamicum, which is widely used for the industrial production of amino acids and serves as a non-pathogenic model organism for members of the Corynebacteriales including Mycobacterium tuberculosis, the GlxR homodimer controls the transcription of a large number of genes involved in carbon metabolism. GlxR therefore represents a key target for understanding the regulation and coordination of C. glutamicum metabolism. Here we investigate cylic AMP and DNA binding of GlxR from C. glutamicum and describe the crystal structures of apo GlxR determined at a resolution of 2.5 Å, and two crystal forms of holo GlxR at resolutions of 2.38 and 1.82 Å, respectively. The detailed structural analysis and comparison of GlxR with CRP reveals that the protein undergoes a distinctive conformational change upon cyclic AMP binding leading to a dimer structure more compatible to DNA-binding. As the two binding sites in the GlxR homodimer are structurally identical dynamic changes upon binding of the first ligand are responsible for the allosteric behavior. The results presented here show how dynamic and structural changes in GlxR lead to optimization of orientation and distance of its two DNA-binding helices for optimal DNA recognition.

Horizontal transfer beyond genes.

Horizontal gene transfer (HGT) is a major process in the evolution of bacteria and archaea (1⇓–3) that is thought to be the principal source of functional innovation in microbial evolution (4, 5). The signal of vertical, tree-like evolution is detectable primarily among genes encoding components of information processing systems, whereas evolution of the operational genes (primarily, metabolic enzymes, transporters, and regulators of these processes) effectively shows network-type evolutionary dynamics (6, 7). Quantification of HGT across the diversity of bacteria reveals extremely high characteristic rates of gene transfer in most groups of bacteria, with multiple HGT events detectable per nucleotide substitution per gene, indicating that HGT is indeed the dominant mode of microbial evolution (8). Many HGT events involve individual genes, but transfer of larger portions of genomes, in particular operons, is common as well, leading to the selfish operon hypothesis under which operons represent self-contained and hence readily transferable units of evolution (9, 10). Accordingly, evolution of bacterial metabolic networks has been modeled as a process of acquisition of operons encoding distinct metabolic pathways, complete with the cognate regulatory elements (11). In PNAS, Oren et al. (12) add an unexpected, potentially important facet to the already extensive evidence of the dominance of …

Carbon catabolite repression correlates with the maintenance of near invariant molecular crowding in proliferating E. coli cells

BackgroundCarbon catabolite repression (CCR) is critical for optimal bacterial growth, and in bacterial (and yeast) cells it leads to their selective consumption of a single substrate from a complex environment. However, the root cause(s) for the development of this regulatory mechanism is unknown. Previously, a flux balance model (FBAwMC) of Escherichia coli metabolism that takes into account the crowded intracellular milieu of the bacterial cell correctly predicted selective glucose uptake in a medium containing five different carbon sources, suggesting that CCR may be an adaptive mechanism that ensures optimal bacterial metabolic network activity for growth.ResultsHere, we show that slowly growing E. coli cells do not display CCR in a mixed substrate culture and gradual activation of CCR correlates with an increasing rate of E. coli cell growth and proliferation. In contrast, CCR mutant cells do not achieve fast growth in mixed substrate culture, and display differences in their cell volume and density compared to wild-type cells. Analyses of transcriptome data from wt E. coli cells indicate the expected regulation of substrate uptake and metabolic pathway utilization upon growth rate change. We also find that forced transient increase of intracellular crowding or transient perturbation of CCR delay cell growth, the latter leading to associated cell density-and volume alterations.ConclusionsCCR is activated at an increased bacterial cell growth rate when it is required for optimal cell growth while intracellular macromolecular density is maintained within a narrow physiological range. In addition to CCR, there are likely to be other regulatory mechanisms of cell metabolism that have evolved to ensure optimal cell growth in the context of the fundamental biophysical constraint imposed by intracellular molecular crowding.

Genetic diversity of bacterial β-glucosidase-encoding genes as a function of soil management

This study is the first approach to evaluate the diversity of bacterial β-glucosidase-encoding gene sequences, aiming to identify the main environmental factors structuring bacterial β-glucosidase genetic diversity in semiarid soils. Two agricultural management systems, soils under spontaneous cover vegetation vs. noncovered herbicide-treated soils, were tested. The weed biomass generated in the former was estimated around 2,600 kg ha−1 year−1, whereas leaves and root exudates from olive trees were the only input of C biomass in the latter. Dendrograms generated from polymerase chain reaction–denaturing gradient gel electrophoresis profiles of bacterial β-glucosidase-encoding genes revealed two clusters determined by soil treatment and sharing <20 % similarity. The sequences of a total of 59 DNA fragments, representing 39 operational taxonomic units, were successfully determined. The Proteobacteria phylum clearly dominated all the soil samples, but representatives of Chloroflexi, Deinococci, Actinobacteria, Thermotogae, and Firmicutes class were also detected. Management strategies favoring the presence of spontaneous vegetation determined a higher genetic diversity of β-glucosidase-encoding genes of soil bacteria. However, since there is little information of β-glucosidase gene sequences available in databases, it is difficult to establish particular relationships between bacterial networks for C degradation and land use. Results from canonical correspondence analysis indicated that bacterial metabolic networks for oligomeric C substrates utilization were affected by the physicochemical properties of the soil; the uppermost 10 cm of covered soil clustered together and were positively correlated with some chemical properties related to soil fertility, whereas less influence of soil texture was observed for the deeper layers of bare soils.