Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

BACKGROUND

Microbial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO₂ to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate.

RESULTS

Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP⁺ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium.

CONCLUSIONS

The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

BpForms and BcForms: a toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks

Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our limited formats for describing macromolecules. To overcome this barrier, we develop BpForms and BcForms, a toolkit for representing the primary structure of macromolecules as combinations of residues, caps, crosslinks, and nicks. The toolkit can help omics researchers perform quality control and exchange information about macromolecules, help systems biologists assemble global models of cells that encompass processes such as post-translational modification, and help bioengineers design cells.

Improvement of l-arginine production by in silico genome-scale metabolic network model guided genetic engineering

Genome-scale metabolic network model (GSMM) is an important in silico tool that can efficiently predict the target genes to be modulated. A Corynebacterium crenatum argB-M4 Cc_iKK446_arginine model was constructed on the basis of the GSMM of Corynebacterium glutamicum ATCC 13032 Cg_iKK446. Sixty-four gene deletion sites, twenty-four gene enhancement sites, and seven gene attenuation sites were determined for the improvement of l-arginine production in engineered C. crenatum. Among these sites, the effects of disrupting putP, cgl2310, pta, and Ncgl1221 and overexpressing lysE on l-arginine production were investigated. Moreover, the strain CCM007 with deleted putP, cgl2310, pta, and Ncgl1221 and overexpressed lysE produced 24.85 g/L l-arginine. This finding indicated a 106.8% improvement in l-arginine production compared with that in CCM01. GSMM is an excellent tool for identifying target genes to promote l-arginine accumulation in engineered C. crenatum.

A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models

Genome-scale metabolic network models have been widely used over the last decade and have been shown to successfully predict the metabolic behavior of many organisms. Yet the complexity of metabolic regulation often limits the accuracy of these models. Integrative modeling approaches have recently been developed that combine metabolic and regulatory networks, thereby expanding the capabilities and accuracy of genome-scale modeling. This chapter provides a guide to reconstruct and curate such integrated network models. Specifically, this protocol describes the PROM (Probabilistic Regulation of Metabolism) and GEMINI (Gene Expression and Metabolism Integrated for Network Inference) approaches. PROM is an automated method for the construction of integrated metabolic and transcriptional regulatory network models, while the GEMINI approach curates the integrated network models using transcriptomics and phenomics data. GEMINI represents the first attempt at applying well-established curation tools that exist for metabolic networks to be applied for curating regulatory networks. The integrated network models generated by these approaches enable the mechanistic integration of diverse biological data and can identify novel strategies to engineer cellular metabolism.

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph Methylococcus capsulatus str. Bath

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

Epigenetic marks of prenatal air pollution exposure found in multiple tissues relevant for child health

BACKGROUND

Prenatal air pollution exposure has been linked to many adverse health conditions in the offspring. However, little is known about the mechanisms underlying these associations. Epigenetics may be one plausible biologic link. Here, we sought to identify site-specific and global DNA methylation (DNAm) changes, in developmentally relevant tissues, associated with prenatal exposure to nitrogen dioxide (NO₂) and ozone (O₃). Additionally, we assessed whether sex-specific changes in methylation exist and whether DNAm changes are consistently observed across tissues.

METHODS

Genome-scale DNAm measurements were obtained using the Infinium HumanMethylation450k platform for 133 placenta and 175 cord blood specimens from Early Autism Risk Longitudinal Investigation (EARLI) neonates. Ambient NO₂ and O₃ exposure levels were based on prenatal address locations of EARLI mothers and the Environmental Protection Agency's AirNOW monitoring network using inverse distance weighting. We computed sample-level aggregate methylation measures for each of 5 types of genomic regions including genome-wide, open sea, shelf, shore, and island regions. Linear regression was performed for each genomic region; per-sample aggregate methylation measures were modeled as a function of quantitative exposure level with covariate adjustment. In addition, bumphunting was performed to identify differentially methylated regions (DMRs) associated with prenatal O₃ and NO₂ exposures in each tissue and by sex, with adjustment for technical and biological sources of variation.

RESULTS

We identified global and locus-specific changes in DNA methylation related to prenatal exposure to NO₂ and O₃ in 2 developmentally relevant tissues. Neonates with increased prenatal O₃ exposure had lower aggregate levels of DNAm at CpGs located in open sea and shelf regions of the genome. We identified 6 DMRs associated with prenatal NO₂ exposure, including 3 sex-specific. An additional 3 sex-specific DMRs were associated with prenatal O₃ exposure levels. DMRs initially detected in cord blood samples (n = 4) showed consistent exposure-related changes in DNAm in placenta. However, the DMRs initially detected in placenta (n = 5) did not show DNAm differences in cord blood and, thus, they appear to be tissue-specific.

CONCLUSIONS

We observed global, locus, and sex-specific methylation changes associated with prenatal NO₂ and O₃ exposures. Our findings support DNAm is a biologic target of prenatal air pollutant exposures and highlight epigenetic involvement in sex-specific differential susceptibility to environmental exposure effects in 2 developmentally relevant tissues.

Link between short tandem repeats and translation initiation site selection

BACKGROUND

Despite their vast biological implication, the relevance of short tandem repeats (STRs)/microsatellites to the protein-coding gene translation initiation sites (TISs) remains largely unknown.

METHODS

We performed an Ensembl-based comparative genomics study of all annotated orthologous TIS-flanking sequences in human and 46 other species across vertebrates, on the genomic DNA and cDNA platforms (755,956 TISs), aimed at identifying human-specific STRs in this interval. The collected data were used to examine the hypothesis of a link between STRs and TISs. BLAST was used to compare the initial five amino acids (excluding the initial methionine), codons of which were flanked by STRs in human, with the initial five amino acids of all annotated proteins for the orthologous genes in other vertebrates (total of 5,314,979 pair-wise TIS comparisons on the genomic DNA and cDNA platforms) in order to compare the number of events in which human-specific and non-specific STRs occurred with homologous and non-homologous TISs (i.e., ≥ 50% and < 50% similarity of the five amino acids).

RESULTS

We detected differential distribution of the human-specific STRs in comparison to the overall distribution of STRs on the genomic DNA and cDNA platforms (Mann Whitney U test p = 1.4 × 10-11 and p < 7.9 × 10-11, respectively). We also found excess occurrence of non-homologous TISs with human-specific STRs and excess occurrence of homologous TISs with non-specific STRs on both platforms (p < 0.00001).

CONCLUSION

We propose a link between STRs and TIS selection, based on the differential co-occurrence rate of human-specific STRs with non-homologous TISs and non-specific STRs with homologous TISs.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems

Background

Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed.

Results

In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics.

Conclusion

The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0584-8) contains supplementary material, which is available to authorized users.

Allosteric pathways in nuclear receptors - Potential targets for drug design

The nuclear receptor family of transcription factor proteins mediates endocrine function and plays critical roles in the development, physiology and pharmacology. Malfunctioning nuclear receptors are associated with several disease states. The functional activity of nuclear receptors is regulated by small molecular hormonal and synthetic molecules. Multiple sources of evidence have identified and distinguished between the different allosteric pathways initiated by ligands, DNA and cofactors such as co-activators and co-repressors. Also, these biophysical studies are attempting to determine how these pathways that regulate co-activator and DNA recognition can control gene transcription. Thus, there is a growing interest in determining the genome-scale impact of allostery in nuclear receptors. Today, it is accepted that a detailed understanding of the allosteric regulatory pathways within the nuclear receptor molecular complex will enable the development of efficient drug therapies in the long term.

Designing Optimized Production Hosts by Metabolic Modeling

Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis (EFMA) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two EFMA-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.

13C metabolic flux analysis at a genome-scale

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified.