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5: Metabolic Activities of Bacteria - Biology


5: Metabolic Activities of Bacteria

Systems biology of lactic acid bacteria: For food and thought

Lactic acid bacteria (LAB) are champions in nutrient-rich environments such as foods.

Their adaptations raise inspiring questions for systems biology.

Nutrient-rich environments introduce auxotrophies but how and why is not yet fully understood.

Ecosystems dynamics and diversity are key to application but poorly understood.

Lactate as overflow product still needs proper mechanistic and evolutionary explanations.


Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling

Background: Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.

Results: In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris (bean plant). Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1) extended the metabolic reconstruction reported for R. etli 2) simulated the metabolic activity during symbiotic nitrogen fixation and 3) evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced different effects in nitrogen fixation, all of these in qualitative agreement with observations made in R. etli and other Rhizobiaceas.

Conclusions: In this work we present a genome scale study of the metabolic activity in bacterial nitrogen fixation. This approach leads us to construct a computational model that serves as a guide for 1) integrating high-throughput data, 2) describing and predicting metabolic activity, and 3) designing experiments to explore the genotype-phenotype relationship in bacterial nitrogen fixation.


Effects of antibiotics on bacterial species composition and metabolic activities in chemostats containing defined populations of human gut microorganisms

The composition and metabolic activities of the human colonic microbiota are modulated by a number of external factors, including diet and antibiotic therapy. Changes in the structure and metabolism of the gut microbiota may have long-term consequences for host health. The large intestine harbors a complex microbial ecosystem comprising several hundreds of different bacterial species, which complicates investigations on intestinal physiology and ecology. To facilitate such studies, a highly simplified microbiota consisting of 14 anaerobic and facultatively anaerobic organisms (Bacteroides thetaiotaomicron, Bacteroides vulgatus, Bifidobacterium longum, Bifidobacterium infantis, Bifidobacterium pseudolongum, Bifidobacterium adolescentis, Clostridium butyricum, C. perfringens, C. bifermentans, C. innocuum, Escherichia coli, Enterococcus faecalis, Enterococcus faecium, Lactobacillus acidophilus) was used in this investigation. Ampicillin [9.2 μg (ml culture)(-1)] was added to two chemostats operated at different dilution rates (D 0.10 h(-1) and 0.21 h(-1)), and metronidazole [76.9 μg (ml culture)(-1)] was added to a third vessel (D = 0.21 h(-1)). Perturbations in bacterial physiology and metabolism were sampled over a 48-h period. Lactobacillus acidophilus and C. bifermentans populations did not establish in the fermentors under the imposed growth conditions. Ampicillin resulted in substantial reductions in bacteroides and C. perfringens populations at both dilution rates. Metronidazole strongly affected bacteroides communities but had no effect on bifidobacterial communities. The bacteriostatic effect of ampicillin on bifidobacterial species was growth rate dependent. Several metabolic activities were affected by antibiotic addition, including fermentation product formation and enzyme synthesis. The growth of antibiotic-resistant bifidobacteria in the large bowel may enable them to occupy ecological niches left vacant after antibiotic administration, preventing colonization by pathogenic species.

Figures

Effects of antibiotics on populations…

Effects of antibiotics on populations of bifidobacteria grown in continuous culture. (A) Ampicillin…

Effects of antibiotics on populations…

Effects of antibiotics on populations of bacteroides grown in continuous culture. (A) Ampicillin…

Effects of antibiotics on clostridial…

Effects of antibiotics on clostridial populations grown in continuous culture. (A) Ampicillin addition…

Effects of antibiotics on populations…

Effects of antibiotics on populations of facultatively anaerobic bacteria grown in continuous culture.…


5: Metabolic Activities of Bacteria - Biology

Regulation and Control of Metabolism in Bacteria (page 1)

Bacterial Adaptation to the Nutritional and Physical Environment

Unlike plant and animal cells, most bacteria are exposed to a constantly changing physical and chemical environment. Within limits, bacteria can react to changes in their environment through changes in patterns of structural proteins, transport proteins, toxins, enzymes, etc., which adapt them to a particular ecological situation. For example, E. coli does not produce fimbriae for colonization purposes when living in a planktonic (free-floating or swimming) environment. Vibrio cholerae does not produce the cholera toxin that causes diarrhea unless it is in the human intestinal tract. Bacillus subtilis does not make the enzymes for tryptophan biosynthesis if it can find preexisting tryptophan in its environment. If E. coli is fed glucose and lactose together, it will use the glucose first because it takes two less enzymes to use glucose than it does to use lactose. The bacterium Neisseria gonorrhoeae will develop a sophisticated iron gathering and transport system if it senses that iron is in short supply in its environment.

Bacteria have developed sophisticated mechanisms for the regulation of both catabolic and anabolic pathways. Generally, bacteria do not synthesize degradative (catabolic) enzymes unless the substrates for these enzymes are present in their environment. For example, synthesis of enzymes that degrade lactose would be wasteful unless the substrate for these enzymes (lactose) is available in the environment. Similarly, bacteria have developed diverse mechanisms for the control of biosynthetic (anabolic) pathways. Bacterial cells shut down biosynthetic pathways when the end product of the pathway is not needed or is readily obtained by uptake from the environment. For example, if a bacterium could find a preformed amino acid like tryptophan in its environment, it would make sense to shut down its own pathway of tryptophan biosynthesis, and thereby conserve energy. However, in real bacterial life, the control mechanisms for all these metabolic pathways must be reversible, since the environment can change quickly and drastically.

Some of the common mechanisms by which bacterial cells regulate and control their metabolic activities are discussed in this chapter It is important for the reader to realize that most of these mechanisms have been observed or described in the bacterium, Escherichia coli, and they are mostly untested and unproved to exist in many other bacteria or procaryotes (although, whenever they are looked for, they are often found). The perceptive reader will appreciate that the origins of the modern science of molecular biology are found in the experiments that explained these regulatory processes in E. coli.

Conditions Affecting Enzyme Formation in Bacteria

As stated above, bacterial cells can change patterns of enzymes, in order to adapt them to their specific environment. Often the concentration of an enzyme in a bacterial cell depends on the presence of the substrate for the enzyme. Constitutive enzymes are always produced by cells independently of the composition of the medium in which the cells are grown. The enzymes that operate during glycolysis and the TCA cycle are generally constitutive: they are present at more or less the same concentration in cells at all times. Inducible enzymes are produced ("turned on") in cells in response to a particular substrate they are produced only when needed. In the process of induction, the substrate, or a compound structurally similar to the substrate, evokes formation of the enzyme and is sometimes called an inducer. A repressible enzyme is one whose synthesis is downregulated or "turned off" by the presence of (for example) the end product of a pathway that the enzyme normally participates in. In this case, the end product is called a corepressor of the enzyme.

Regulation of Enzyme Reactions

Not all enzymatic reactions occur in a cell to the same extent. Some substances are needed in large amounts and the reactions involved in their synthesis must therefore occur in large amounts. Other substances are needed in small amounts and the corresponding reactions involved in their synthesis need only occur in small amounts.

In bacterial cells, enzymatic reactions may be regulated by two unrelated modes: (1) control or regulation of enzyme activity (feedback inhibition or end product inhibition ), which mainly operates to regulate biosynthetic pathways and (2) control or regulation of enzyme synthesis, including end-product repression, which functions in the regulation of biosynthetic pathways, and enzyme induction and catabolite repression, which regulate mainly degradative pathways. The process of feedback inhibition regulates the activity of preexisting enzymes in the cells. The processes of end-product repression, enzyme induction and catabolite repression are involved in the control of synthesis of enzymes. The processes which regulate the synthesis of enzymes may be either a form of positive control or negative control. End-product repression and enzyme induction are mechanisms of negative control because they lead to a decrease in the rate of transcription of proteins. Catabolite repression is considered a form of positive control because it affects an increase in rates of transcription of proteins.

Table 1. Points for regulation of various metabolic processes. Bacteria exert control over their metabolism at every possible stage starting at the level of the gene that encodes for a protein and ending with alteration or modifications in the protein after it is produced. For example, variation in gene structure can vary the activity or production of a protein, just as modifications of a protein after it is produced can alter or change its activity. One of the most important sites for control of metabolism at the genetic level is regulation of transcription. At this level, in positive control mechanisms (e.g. catabolite repression), a regulatory protein has an effect to increase the rate of transcription of a gene, while in negative control mechanisms (e.g. enzyme induction or end product repression), a regulatory protein has the effect to decrease the rate of transcription of a gene. Sometimes this nomenclature may seem counter-intuitive, but molecular biologists have stuck us with it.

Although there are examples of regulatory processes that occur at all stages in molecular biology of bacterial cells (see Table 1 above), the most common points of regulation are at the level of transcription (e.g. enzyme induction and enzyme repression) and changing the activity of preexisting proteins. In turn, these levels of control are usually modulated by proteins with the property of allostery.

An allosteric protein is one which has an active (catalytic) site and an allosteric (effector) site. In an allosteric enzyme, the active site binds to the substrate of the enzyme and converts it to a product. The allosteric site is occupied by some small molecule which is not a substrate. However, when the allosteric site is occupied by the effector molecule, the configuration of the active site is changed so that it is now unable to recognize and bind to its substrate (Figure 1). If the protein is an enzyme, when the allosteric site is occupied, the enzyme is inactive, i.e., the effector molecule decreases the activity of the enzyme. There is an alternative situation, however. The effector molecule of certain allosteric enzymes binds to its allosteric site and consequently transforms the enzyme from an inactive to an active state (Figure 2). Some multicomponent allosteric enzymes have several sites occupied by various effector molecules that modulate enzyme activity over a range of conditions.

Figure 1. Example of an allosteric enzyme with a negative effector site. When the effector molecule binds to the allosteric site, substrate binding and catalytic activity of the enzyme are inactivated. When the effector is detached from the allosteric site the enzyme is active.


Figure 2. Example of an allosteric enzyme with a positive effector site. The effector molecule binds to the allosteric site resulting in alteration of the active site that stimulates substrate binding and catalytic activity.

Some allosteric proteins are not enzymes, but nonetheless have an active site and an allosteric site. The regulatory proteins that control metabolic pathways involving end product repression, enzyme induction and catabolite repression are allosteric proteins. In their case, the active site is a DNA binding site, which, when active, binds to a specific sequence of DNA, and which, when inactive, does not bind to DNA. The allosteric or effector molecule is a small molecule which can occupy the allosteric site and affect the active site. In the case of enzyme repression, a positive effector molecule (called a corepressor) binds to the allosteric regulatory protein and activates its ability to bind to DNA. In the case of enzyme induction a negative effector molecule (called an inducer) binds to the allosteric site, causing the active site to change conformation thereby detaching the protein from its DNA binding site.


Applications of CRISPR/Cas System to Bacterial Metabolic Engineering

The clustered regularly interspaced short palindromic repeats/CRISPR-associated (CRISPR/Cas) adaptive immune system has been extensively used for gene editing, including gene deletion, insertion, and replacement in bacterial and eukaryotic cells owing to its simple, rapid, and efficient activities in unprecedented resolution. Furthermore, the CRISPR interference (CRISPRi) system including deactivated Cas9 (dCas9) with inactivated endonuclease activity has been further investigated for regulation of the target gene transiently or constitutively, avoiding cell death by disruption of genome. This review discusses the applications of CRISPR/Cas for genome editing in various bacterial systems and their applications. In particular, CRISPR technology has been used for the production of metabolites of high industrial significance, including biochemical, biofuel, and pharmaceutical products/precursors in bacteria. Here, we focus on methods to increase the productivity and yield/titer scan by controlling metabolic flux through individual or combinatorial use of CRISPR/Cas and CRISPRi systems with introduction of synthetic pathway in industrially common bacteria including Escherichia coli. Further, we discuss additional useful applications of the CRISPR/Cas system, including its use in functional genomics.

Keywords: CRISPR/Cas CRISPRa CRISPRi gene regulation genome editing metabolic engineering.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

The CRISPR, CRISPRi, and CRISPRa…

The CRISPR, CRISPRi, and CRISPRa systems. ( A ) CRISPR/Cas system consists of…

A metabolic engineering strategy for…

A metabolic engineering strategy for GABA production in wild-type C. glutamicum . (…

Regulation of actinorhodin biosynthetic pathway…

Regulation of actinorhodin biosynthetic pathway in S. coelicolor A3. The organization of the…

Identification of novel metabolites through…

Identification of novel metabolites through insertion of synthetic promoter in the upstream of…

For high-yield pinosylvin synthesis, three…

For high-yield pinosylvin synthesis, three different modules are selected and redirected. Genes selected…


Light and Primary Production Shape Bacterial Activity and Community Composition of Aerobic Anoxygenic Phototrophic Bacteria in a Microcosm Experiment

Phytoplankton is a key component of aquatic microbial communities, and metabolic coupling between phytoplankton and bacteria determines the fate of dissolved organic carbon (DOC). Yet, the impact of primary production on bacterial activity and community composition remains largely unknown, as, for example, in the case of aerobic anoxygenic phototrophic (AAP) bacteria that utilize both phytoplankton-derived DOC and light as energy sources. Here, we studied how reduction of primary production in a natural freshwater community affects the bacterial community composition and its activity, focusing primarily on AAP bacteria. The bacterial respiration rate was the lowest when photosynthesis was reduced by direct inhibition of photosystem II and the highest in ambient light condition with no photosynthesis inhibition, suggesting that it was limited by carbon availability. However, bacterial assimilation rates of leucine and glucose were unaffected, indicating that increased bacterial growth efficiency (e.g., due to photoheterotrophy) can help to maintain overall bacterial production when low primary production limits DOC availability. Bacterial community composition was tightly linked to light intensity, mainly due to the increased relative abundance of light-dependent AAP bacteria. This notion shows that changes in bacterial community composition are not necessarily reflected by changes in bacterial production or growth and vice versa. Moreover, we demonstrated for the first time that light can directly affect bacterial community composition, a topic which has been neglected in studies of phytoplankton-bacteria interactions.IMPORTANCE Metabolic coupling between phytoplankton and bacteria determines the fate of dissolved organic carbon in aquatic environments, and yet how changes in the rate of primary production affect the bacterial activity and community composition remains understudied. Here, we experimentally limited the rate of primary production either by lowering light intensity or by adding a photosynthesis inhibitor. The induced decrease had a greater influence on bacterial respiration than on bacterial production and growth rate, especially at an optimal light intensity. This suggests that changes in primary production drive bacterial activity, but the effect on carbon flow may be mitigated by increased bacterial growth efficiencies, especially of light-dependent AAP bacteria. Bacterial activities were independent of changes in bacterial community composition, which were driven by light availability and AAP bacteria. This direct effect of light on composition of bacterial communities has not been documented previously.

Keywords: AAP community composition aerobic anoxygenic phototrophic bacteria bacterial community composition phytoplankton-bacteria coupling.

Copyright © 2020 Piwosz et al.

Figures

Phytoplankton activity and dynamics in…

Phytoplankton activity and dynamics in the experimental treatments. (A) Rate of CO 2…

Bacterial activity and dynamics in…

Bacterial activity and dynamics in the experimental treatments. (A) Abundance of all bacteria…

Redundancy analysis (RDA) correlation biplot…

Redundancy analysis (RDA) correlation biplot using the biotic factors as response variables (blue…

Changes in total bacterial communities…

Changes in total bacterial communities during the experiment based on the 16S rRNA…

Changes in AAP bacterial communities…

Changes in AAP bacterial communities during the experiment based on the pufM amplicons.…


Mapping human microbiome drug metabolism by gut bacteria and their genes

Individuals vary widely in their responses to medicinal drugs, which can be dangerous and expensive owing to treatment delays and adverse effects. Although increasing evidence implicates the gut microbiome in this variability, the molecular mechanisms involved remain largely unknown. Here we show, by measuring the ability of 76 human gut bacteria from diverse clades to metabolize 271 orally administered drugs, that many drugs are chemically modified by microorganisms. We combined high-throughput genetic analyses with mass spectrometry to systematically identify microbial gene products that metabolize drugs. These microbiome-encoded enzymes can directly and substantially affect intestinal and systemic drug metabolism in mice, and can explain the drug-metabolizing activities of human gut bacteria and communities on the basis of their genomic contents. These causal links between the gene content and metabolic activities of the microbiota connect interpersonal variability in microbiomes to interpersonal differences in drug metabolism, which has implications for medical therapy and drug development across multiple disease indications.

Figures

Extended Data Figure 1.. Setup of drug…

Extended Data Figure 1.. Setup of drug assay, characterization of tested drugs, and summary of…

Extended Data Figure 2.. Metabolism of drugs…

Extended Data Figure 2.. Metabolism of drugs previously reported to be transformed by bacteria and…

Extended Data Figure 3.. Hierarchical clustering of…

Extended Data Figure 3.. Hierarchical clustering of bacterial strains/species and drugs according to microbial drug…

Extended Data Figure 4.. Structural drug features…

Extended Data Figure 4.. Structural drug features targeted for biotransformation and microbiome metabolism of dexamethasone.

Extended Data Figure 5.. Microbial corticosteroid metabolism…

Extended Data Figure 5.. Microbial corticosteroid metabolism in vivo and in human gut communities.

Extended Data Figure 6.. Gain-of-function approach to…

Extended Data Figure 6.. Gain-of-function approach to identify microbial drug-metabolizing gene products: B. thetaiotaomicron diltiazem…

Extended Data Figure 7.. In vivo diltiazem…

Extended Data Figure 7.. In vivo diltiazem metabolism and tandem mass spectrometry to validate metabolite…

Extended Data Figure 8.. bt4096-depended in vivo…

Extended Data Figure 8.. bt4096-depended in vivo diltiazem metabolism.

Extended Data Figure 9.. Validation of identified…

Extended Data Figure 9.. Validation of identified drug-metabolizing gene products.

Extended Data Figure 10.. Identified drug-metabolizing gene…

Extended Data Figure 10.. Identified drug-metabolizing gene products explain observed drug metabolism of gut bacteria.

37,000 clones. b, Network of enzyme-substrate-product drug metabolic interactions for B. dorei and C. aerofaciens. Each node represents an enzyme (rectangles), a drug substrate (hexagons) or a metabolite product (circles), and each edge represents a validated metabolic interaction (targeted cloning of the gene into E. coli results in metabolism of a given drug or production of a specific drug metabolite). c, Comparison between maximal BD03091 and CA01707 identity of a given bacterial strain and its metabolism of norethindrone acetate and tinidazole, respectively. d, e, Reciprocal BLAST analysis of identified drug-metabolizing proteins. Line-width depicts the % of length (d) and identity (e) of mutual protein sequence alignment. e, Specific drug metabolism rates of 67 genome sequenced gut bacteria and presence of homologs to respective drug-metabolizing gene products. Notably, roxatidine acetate, famciclovir, diacetamate and diltiazem (Fig. 5b) all undergo the same chemical transformation (deacetylation), yet distinct sets of gene products explain their microbial metabolism. Bars and error bars represent mean and STD of n=4 assay replicates. Gene locus tag abbreviations: BD: BACDOR CA: COLAER.

Extended Data Figure 11.. Identified drug-metabolizing gene…

Extended Data Figure 11.. Identified drug-metabolizing gene products explain observed drug metabolism of bacterial gut…

Fig. 1.. Drug-metabolizing activities of human gut…

Fig. 1.. Drug-metabolizing activities of human gut bacteria.

Fig. 2.. Bacteria-derived drug metabolites.

Fig. 2.. Bacteria-derived drug metabolites.

Fig. 3.. Identification and in vivo characterization…

Fig. 3.. Identification and in vivo characterization of microbial drug-metabolizing gene products: B. thetaiotaomicron diltiazem…

Fig. 4.. Genome-wide identification of drug-metabolizing gene…

Fig. 4.. Genome-wide identification of drug-metabolizing gene products in B. thetaiotaomicron.

Fig. 5.. Microbiome-encoded drug-metabolizing gene products explain…

Fig. 5.. Microbiome-encoded drug-metabolizing gene products explain drug metabolism of gut bacterial strains and communities.


5: Metabolic Activities of Bacteria - Biology

To commercialize recombinant organisms for renewable chemical production, it is essential to characterize the cost and benefit of metabolic burden using metabolic flux analysis tools.

Genome-scale modeling can incorporate 13 C-fluxome information and machine learning to predict the metabolic burden of synthetic biology modules.

Modularized expression of native or recombinant pathways using a variety of experimental tools for controlling expression can substantially reduce the metabolic burden introduced by these pathways.

The development of a standard synthetic-biology publication database may allow the use of machine learning or artificial intelligence to harness past knowledge for future rational design.

Detailed computational methods have been developed to model macromolecule synthesis (DNA, RNA, proteins) to account for the maintenance costs associated with basal cellular function.

Systems-level dynamic simulations and design algorithms can inform new approaches to engineering microbial production strains.


Watch the video: The beneficial bacteria that make delicious food - Erez Garty (January 2022).