Metabolic modeling-based drug repurposing in Glioblastoma

The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of ‘omics’ data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network’s topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar

Base editing enables duplex point mutagenesis in Clostridium autoethanogenum at the price of numerous off-target mutations

Base editors are recent multiplex gene editing tools derived from the Cas9 nuclease of Streptomyces pyogenes. They can target and modify a single nucleotide in the genome without inducing double-strand breaks (DSB) of the DNA helix. As such, they hold great potential for the engineering of microbes that lack effective DSB repair pathways such as homologous recombination (HR) or non-homologous end-joining (NHEJ). However, few applications of base editors have been reported in prokaryotes to date, and their advantages and drawbacks have not been systematically reported. Here, we used the base editors Target-AID and Target-AID-NG to introduce nonsense mutations into four different coding sequences of the industrially relevant Gram-positive bacterium Clostridium autoethanogenum. While up to two loci could be edited simultaneously using a variety of multiplexing strategies, most colonies exhibited mixed genotypes and most available protospacers led to undesired mutations within the targeted editing window. Additionally, fifteen off-target mutations were detected by sequencing the genome of the resulting strain, among them seven single-nucleotide polymorphisms (SNP) in or near loci bearing some similarity with the targeted protospacers, one 15 nt duplication, and one 12 kb deletion which removed uracil DNA glycosylase (UDG), a key DNA repair enzyme thought to be an obstacle to base editing mutagenesis. A strategy to process prokaryotic single-guide RNA arrays by exploiting tRNA maturation mechanisms is also illustrated

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

© 2021 Tomi-Andrino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies

From bacteria to cancer exploiting metabolic networks for industrial and therapeutic purposes

Metabolic modelling has been used to facilitate industrial microbiology projects and identify potential drug targets for the last 15 years. Although originally modest, the reliability of simulations has greatly improved by the progressive inclusion of chemical, biological and medical information in the models. While the multidisciplinary nature of systems biology entails rich and novel perspectives to solve problems, it is accompanied by a general lack of standardisation and consensus on methodology. Therefore, establishing robust workflows requires (i) assessing and comparing the predictive capabilities of current approaches against experimental data, and (ii) combining the strong points of different strategies to minimize their limitations and generate useful data in a timely manner. In this study, metabolic networks for a bacterium, a parasite and a type of cancer were inspected by means of flux balance analysis and network analysis. Firstly, a readily available toolbox integrating thermodynamics information into flux balance analysis was modified to increase the number of physicochemical parameters including more suitable equations to physiological conditions. Thermodynamics constraints are closely related to the quantitation of metabolites in the cell. However, the lack of a “one size fits all” extraction and quantitation method prevents from performing a systems-wide analysis, forcing the researcher to select ca. 50 compounds for a targeted method. Exploring the topology of the network allowed identifying a set of important metabolites with a high constraining power, thus providing a rationale to such selection. Secondly, metabolic modelling was used to facilitate drug repurposing efforts. There is a plethora of neglected diseases or conditions yet to be efficiently treated, so screening readily available drugs for novel uses has been deemed a cheaper and faster option than developing drugs from scratch. Therefore, systems biology has been exploited to reduce the list of potential candidates to be tested, as well as to identify novel potential drug targets. Specifically, a novel topological feature was introduced and combined with multiple metabolic states predicted by flux balance analysis in a model for the parasite causing sleeping sickness. Comparing against the literature validated the predictive capabilities of such approach and identified an antiviral whose potential lethality was tested in vitro. Thirdly, a more information-rich workflow was developed based on the previous results. Transcriptomics data for a brain tumour was exploited to generate contextualised human metabolic models. Network analyses pinpointed important reactions and topological features of interest, greatly reducing the list of potential drug targets to be considered. Consequently, an automated search of chemical gene interactions and published results allowed identifying five compounds that had proven anti-proliferative and anti invasion effects on other cancer types. Finally, in vitro testing on patient-derived cancer cell lines showed their potential for further studies and generated new research questions. This study provides considerations and approaches to increase the reliability of metabolic modelling predictions, as well as a novel workflow to identify relevant potential drug targets yet to be explored and prioritise chemicals to assess their suitability as drugs in a timely manner

From bacteria to cancer exploiting metabolic networks for industrial and therapeutic purposes

Metabolic modelling has been used to facilitate industrial microbiology projects and identify potential drug targets for the last 15 years. Although originally modest, the reliability of simulations has greatly improved by the progressive inclusion of chemical, biological and medical information in the models. While the multidisciplinary nature of systems biology entails rich and novel perspectives to solve problems, it is accompanied by a general lack of standardisation and consensus on methodology. Therefore, establishing robust workflows requires (i) assessing and comparing the predictive capabilities of current approaches against experimental data, and (ii) combining the strong points of different strategies to minimize their limitations and generate useful data in a timely manner. In this study, metabolic networks for a bacterium, a parasite and a type of cancer were inspected by means of flux balance analysis and network analysis. Firstly, a readily available toolbox integrating thermodynamics information into flux balance analysis was modified to increase the number of physicochemical parameters including more suitable equations to physiological conditions. Thermodynamics constraints are closely related to the quantitation of metabolites in the cell. However, the lack of a “one size fits all” extraction and quantitation method prevents from performing a systems-wide analysis, forcing the researcher to select ca. 50 compounds for a targeted method. Exploring the topology of the network allowed identifying a set of important metabolites with a high constraining power, thus providing a rationale to such selection. Secondly, metabolic modelling was used to facilitate drug repurposing efforts. There is a plethora of neglected diseases or conditions yet to be efficiently treated, so screening readily available drugs for novel uses has been deemed a cheaper and faster option than developing drugs from scratch. Therefore, systems biology has been exploited to reduce the list of potential candidates to be tested, as well as to identify novel potential drug targets. Specifically, a novel topological feature was introduced and combined with multiple metabolic states predicted by flux balance analysis in a model for the parasite causing sleeping sickness. Comparing against the literature validated the predictive capabilities of such approach and identified an antiviral whose potential lethality was tested in vitro. Thirdly, a more information-rich workflow was developed based on the previous results. Transcriptomics data for a brain tumour was exploited to generate contextualised human metabolic models. Network analyses pinpointed important reactions and topological features of interest, greatly reducing the list of potential drug targets to be considered. Consequently, an automated search of chemical gene interactions and published results allowed identifying five compounds that had proven anti-proliferative and anti invasion effects on other cancer types. Finally, in vitro testing on patient-derived cancer cell lines showed their potential for further studies and generated new research questions. This study provides considerations and approaches to increase the reliability of metabolic modelling predictions, as well as a novel workflow to identify relevant potential drug targets yet to be explored and prioritise chemicals to assess their suitability as drugs in a timely manner

Required Gene Set for Autotrophic Growth of Clostridium autoethanogenum

The majority of the genes present in bacterial genomes remain poorly characterized, with up to one-third of those that are protein encoding having no definitive function. Transposon insertion sequencing represents a high-throughput technique that can help rectify this deficiency. The technology, however, can only be realistically applied to those species in which high rates of DNA transfer can be achieved. Here, we have developed a number of approaches that overcome this barrier in the autotrophic species Clostridium autoethanogenum by using a mariner-based transposon system. The inherent instability of such systems in the Escherichia coli conjugation donor due to transposition events was counteracted through the incorporation of a conditionally lethal codA marker on the plasmid backbone. Relatively low frequencies of transformation of the plasmid into C. autoethanogenum were circumvented through the use of a plasmid that is conditional for replication coupled with the routine implementation of an Illumina library preparation protocol that eliminates plasmid-based reads. A transposon library was then used to determine the essential genes needed for growth using carbon monoxide as the sole carbon and energy source.IMPORTANCE Although microbial genome sequences are relatively easily determined, assigning gene function remains a bottleneck. Consequently, relatively few genes are well characterized, leaving the function of many as either hypothetical or entirely unknown. High-throughput transposon sequencing can help remedy this deficiency, but is generally only applicable to microbes with efficient DNA transfer procedures. These exclude many microorganisms of importance to humankind either as agents of disease or as industrial process organisms. Here, we developed approaches to facilitate transposon insertion sequencing in the acetogen Clostridium autoethanogenum, a chassis being exploited to convert single-carbon waste gases CO and CO2 into chemicals and fuels at an industrial scale. This allowed the determination of gene essentiality under heterotrophic and autotrophic growth, providing insights into the utilization of CO as a sole carbon and energy source. The strategies implemented are translatable and will allow others to apply transposon insertion sequencing to other microbes where DNA transfer has until now represented a barrier to progress

Assessing the impact of physicochemical parameters in the predictive capabilities of thermodynamics-based stoichiometric approaches under mesophilic and thermophilic conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed