Biology subjects

Martins dos Santos, V. A. P.

Publications and source records attributed to Martins dos Santos, V. A. P..

At least 19 recordsLinked to original sources

bioRxiv2025

From Light to Lipids: Constraint-Based Metabolic Modeling of Nannochloropsis oceanica Under Light Acclimation Conditions

Oleaginous microalgae, such as Nannochloropsis oceanica, hold strong promise for sustainable lipid bioproduction, but fully realizing this potential requires systems-level insight into their complex metabolism. One of the central challenges in optimizing lipid productivity is the resolution of the highly heterogeneous and incompletely annotated metabolic networks, which respond dynamically to strain-specific traits and cultivation conditions. Here we present iSO1949_N.oceanica, the first genome-scale constraint-based metabolic model (GEM) for this species. Constructed through an orthology-based approach using curated models of related microalgae, the GEM integrates core photosynthetic metabolism and lipid biosynthesis pathways with extensive subcellular localization predictions. To capture environmental dynamics, we introduce two light-acclimation modes derived from continuous cyclostat cultivations, incorporating biomass composition, oxygen exchange, and maintenance rates based on photosynthesis-irradiance curves. Simulations reproduce carbon assimilation under variable light conditions and differentiate acclimated phenotypes. iSO1949_N.oceanica provides a comprehensive framework for exploring photosynthetic metabolism and guiding engineering strategies under photobioreactor-relevant conditions. This resource advances the use of N. oceanica as a chassis for sustainable lipid production and establishes a foundation for systems-level analysis of stramenopile microalgae.

systems biology↗

bioRxiv2025

Rhodo-Box: a Synthetic Biology Toolbox to Facilitate Metabolic Engineering of Rhodobacter sphaeroides

Rhodobacter sphaeroides is a purple non-sulphur alphaproteobacterium with a highly versatile metabolism. This microorganism holds promise as a chassis for sustainable biomanufacturing of numerous chemicals. Yet, its potential is constrained by a lack of standardized, well-characterized genetic elements to tune gene expression such as transcriptional promoters and ribosome binding sites (RBSs). In this study, we present Rhodo-Box, a comprehensive toolkit for R. sphaeroides created by adapting and extending the Zymo-Parts modular cloning framework. Using Rhodo-Box we built and characterized: (a) three broad-host origins of replication (pBBR1, RK2 and RSF1010), (b) a set of 13 promoters, (c) four inducible expression systems (NahR-PsalTTC, LacI-PlacT7A1_O3O4, VanR-PvanCC, and XylS-Pm), (d) 11 RBSs, and (e) four transcriptional terminators. Furthermore, we present a semi-automated, user-friendly cloning approach which enables rapid construction of R. sphaeroides strains. The Rhodo-Box toolkit equips R. sphaeroides with a standardized, automation-compatible collection of parts and workflows essential for efficient design-build-test-learn cycles and advanced metabolic engineering. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/685836v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@fcb172org.highwire.dtl.DTLVardef@1e5eb44org.highwire.dtl.DTLVardef@1b8df28org.highwire.dtl.DTLVardef@42eb2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Steering fatty acid composition of yeast microbial oil via genetic modification and bioprocess adjustment

The increasing demand for palm oil has drastic effects on the ecosystem as its production is not sustainable. To that end, developing a sustainable alternative to fatty acids and oils is urgent and of utmost interest. Oils produced by oleaginous yeasts present a promising solution, particularly because the fatty acid profile of the oil produced by these yeasts is comparable to that of plant-based oils and fats. The fatty acid composition of the oil determines its physiological properties, thereby determining its potential applications. Accordingly, the production of microbial oil with an optimal composition profile for a specific application is of great importance. In this study, we evaluated the variation that occurred in fatty acid composition due to different cultivation parameters (temperature, C/N ratio, carbon, and nitrogen sources) and applied genetic modifications to improve the lipid accumulation of Cutaneotrichosporon oleaginosus and Yarrowia lipolytica. We showed that specific fatty acid profiles associated with a particular application can be obtained by carefully selecting the microorganism and cultivation conditions.

microbiology↗

bioRxiv2024

Combining metabolic engineering and fermentation optimization to achieve cost-effective oil production by Cutaneotrichosporon oleaginosus

Microbial oils, produced by oleaginous microorganisms, offer a sustainable alternative to plant-derived oils. Among these microorganisms, Cutaneotrichosporon oleaginosus attracts attention as a promising microbial cell factory for sustainable oil production due to its capacity to accumulate lipids with a similar composition to palm oil. Although C. oleaginosus can reach higher lipid contents than other oleaginous yeasts, suboptimal lipid yields and productivity limit economic feasibility. Enhanced productivity is necessary to have a feasible microbial oil production process via C. oleaginosus. In this study, we followed a combinatorial approach for strain design and bioprocess development to improve the lipid content and lipid yield. Initially, we deployed a full factorial design with genetic factors (ATP-citrate lyase (ACL), acetyl-CoA carboxylase (ACC), threonine synthase (TS)) and carbon-to-nitrogen ratio (C/N) in the medium. The C/N ratio appeared to have the most impact on oil accumulation. Combined with genetic modifications, lipid content and lipid yield increased by 1.6-fold. In a two-stage fermentation approach at a 2L scale, the triple transformant overexpressing ACL, ACC, and TS outperformed the wild-type by achieving a lipid content of 75.4% (w/w) with lipid productivity of 0.40 g L-1 h-1 and around 0.30 g lipids/g glycerol. In all, we established a cultivation strategy and strain that reached almost the theoretical maximum yield, and highest lipid content reported for a medium containing glycerol as a carbon source. These results strengthen the basis of using C. oleaginous as a platform for microbial oil production, thereby facilitating the development of processes substituting palm oil with a sustainable alternative. HighlightsO_LIFull factorial design allowed selection of the best performer transformant. C_LIO_LIOverexpression of ACL1, ACC, and TS in C. oleaginosus provided higher lipid yield. C_LIO_LITwo-stage fermentation enhanced the lipid content of wild-type and transformant. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/624102v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1371a7aorg.highwire.dtl.DTLVardef@ab5ae1org.highwire.dtl.DTLVardef@1db84caorg.highwire.dtl.DTLVardef@eb19e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Model-driven engineering of Yarrowia lipolytica for improved microbial oil production

Extensive usage of plant-based oils, especially palm oil, has led to environmental and social issues, such as deforestation and loss of biodiversity, thus sustainable alternatives are required. Microbial oils, especially from Yarrowia lipolytica, offer a promising solution due to their similar composition to palm oil, low carbon footprint, and ability to utilize low-cost substrates. In this study, we employed the Design-Build-Test-Learn (DBTL) approach to enhance lipid production in Y. lipolytica. We systematically evaluated predictions from the genome-scale metabolic model to identify and overcome bottlenecks in lipid biosynthesis. We tested the effect of predicted medium supplements and genetic intervention targets, including the overexpression of ATP-citrate lyase (ACL), acetyl-CoA carboxylase (ACC), threonine synthase (TS), diacylglycerol acyltransferase(DGA1), the deletion of citrate exporter gene (CEX1) and disruption of {beta}-oxidation pathway (MFE1). Combining TS and DGA1 overexpression in the{Delta} mfe_{Delta}cex background achieved a remarkable 200% increase in lipid content (56 % w/w) and a 230% increase in lipid yield on glycerol. These findings underscore the potential of Y. lipolytica as an efficient microbial cell factory for fatty acid production. Our study advances the understanding of lipid metabolism in Y. lipolytica and demonstrates a viable approach for developing sustainable and economically feasible alternatives to palm oil. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/606002v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1394cf6org.highwire.dtl.DTLVardef@ebdd5eorg.highwire.dtl.DTLVardef@1126ab2org.highwire.dtl.DTLVardef@1ae028_HPS_FORMAT_FIGEXP M_FIG C_FIG We followed the Design-Build-Test-Learn approach to identify and overcome bottlenecks in lipid biosynthesis in Y. lipolytica. DBTL intertwined the predictions from the metabolic model with addressed bottlenecks, investigated the effect of genetic interventions and medium supplements on lipid content, and ultimately defined an efficient strain design strategy.

molecular biology↗

bioRxiv2024

Shikimate pathway-Dependent Catabolism:enabling near-to-maximum production yield of aromatics

Catabolism is a complex network of tightly regulated metabolic reactions that provides energy and carbon to fuel anabolism in all living organisms. Rewiring catabolism is essential for harnessing industrial biotechnology but remains a substantial metabolic engineering challenge due to its high genetic stability and tight regulation acquired through evolution. In this study, by combining metabolic modeling, rational engineering, and adaptive laboratory evolution, we fundamentally redesigned bacterial catabolism. We created a new-to-nature shikimate pathway-dependent catabolism (SDC) in Pseudomonas putida by reprogramming the shikimate pathway as the primary catabolic route. SDC supports growth by supplying the glycerol catabolic end-product pyruvate, enabling superior production of shikimate pathway-derived molecules. Through SDC, aromatics production reached over 89% of the pathways maximum theoretical yield, setting a new benchmark for their microbial synthesis. Our study successfully repurposed an anabolic pathway for catabolism, exemplifying the high metabolic plasticity of microbes and providing a bacterial chassis for the efficient production of high-added value compounds.

synthetic biology↗

bioRxiv2024

Combinatorial engineering reveals shikimate pathway bottlenecks in para-aminobenzoic acid production in Pseudomonas putida

Combinatorial approaches in metabolic engineering enable the optimization of multigene pathways, thereby improving product titers. However, the optimization of complex metabolic pathways is hindered by their multiple interactions. Testing all possible combinations of suitable genetic parts is often prevented by the large number of possible variants. A valuable alternative to this is to use statistical design of experiments and linear modeling to collect important information for optimization without testing every possible combination. The shikimate pathway is an example of a complex metabolic pathway involved in the production of aromatic compounds, which are prevalent in industry. In this study, we explore the impact of the modulation of the expression levels of all the genes in the shikimate and para-aminobenzoic acid (pABA) biosynthesis pathways for pABA production (a widely used industrial intermediate) in Pseudomonas putida. We used this approach to select 14 representative strains from a total of 512 possible combinations. We obtained a range of product titers from 2 to 186.2 mg/l. This information was used to guide a second round of strain construction to further increase the production to 232.1 mg/l. Using this strategy, we demonstrate that aroB expression, encoding 3-dehydroquinate synthase, is a significant limiting factor in the production of pABA.

synthetic biology↗

bioRxiv2024

Model-driven engineering of Cutaneotrichosporon oleaginosus ATCC 20509 for improved microbial oil production

Consumption of plant-based oils, especially palm oil, is increasing at an alarming rate. This boosted demand for palm oil has drastic effects on the ecosystem as its production is not sustainable. C. oleaginosus is an oleaginous yeast with great potential as a source for microbial-based oil production which is a sustainable alternative to palm oil. However, microbial processes are not yet economically feasible to replace palm oil, unto a large extent due to limited lipid accumulation in the microbe, which limits titers and productivity. Therefore, obtaining enhanced lipid accumulation is essential to render this process commercially viable. Herein we deployed a systematic, iterative Design-Build-Test-Learn (DBTL) approach to establish C. oleaginosus as an efficient fatty acid production platform. In the design step, we identified genes and medium supplements that improved lipid content. To this end, we compared its transcriptional landscape in conditions with high and low amounts of lipid production. A metabolic map was reconstructed and integrated with the expression data. Finally, the genome-scale metabolic model of C. oleaginosus was used to explore metabolism under maximal growth and maximal production conditions. The combination of these four analyses led to the selection of four overexpression targets (ATP-citrate lyase (ACL1), acetyl-CoA carboxylase (ACC), threonine synthase (TS), and hydroxymethylglutaryl-CoA synthase (HMGS)) and five media supplements (biotin, thiamine, threonine, serine, and aspartate). We established an electroporation-based co-transformation method to implement selected genetic interventions. These findings were experimentally validated in the build and test steps of the DBTL approach by adding supplements into the medium and overexpressing the identified genes. Characterization of ACL, ACC, and TS at various C/N ratios, and the addition of medium supplements provided up to 56% (w/w) lipid content, and a 2.5-fold increase in total lipid in the glycerol and urea-based defined medium. In the learn step, quadratic models identified the optimum C/N ratios shifted towards around C/N240. These results firmly confirm C. oleaginous as a sustainable alternative to replace palm as an oil source. HighlightsO_LITranscriptional profile and metabolic model analyzed, predicting genetic targets and medium supplements. C_LIO_LIGenetic targets and medium supplements for improved oil production. C_LIO_LIThe genetic toolbox for C. oleaginosus was expanded (co-transformation method, promoters, genes, and terminators). C_LIO_LIExperimental validations showed that biotin, and threonine increased lipid content. C_LIO_LIOverexpression of ACL1, ACC, and TS in C. oleaginosus provided higher oil content. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/585731v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@e3b6org.highwire.dtl.DTLVardef@65d016org.highwire.dtl.DTLVardef@40952eorg.highwire.dtl.DTLVardef@22724_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyp- ing and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders - external factors unrelated to the condition, e.g. batch effect or age - on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include: i) removal of latent features correlated with confounders ii) a conditional variational autoencoder, iii) adversarial training, and iv) adding a regularization term to the loss function. Using real-life multi-omics data from TCGA, we simulated various confounding effects (linear, non-linear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically-driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine.

genomics↗

bioRxiv2023

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organisms genotype and its environment. This study employs statistical Design of Experiments (DoE) to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted on a 168-fold improvement on pCA titer. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bio-process design efforts to unlock the full potential of microbial cell factories.

bioengineering↗

bioRxiv2023

Machine learning-guided optimization of p-coumaric acid production in yeast

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a bio-based economy. To use them effectively, appropriate connections between each phase of the cycle are crucial. Using p-coumaric acid production in Saccharomyces cerevisiea as case study, we propose the use of one-pot library generation, random screening, targeted sequencing and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of ML models strongly enable pathway optimization, and propose feature importance and SHAP values as a guide to expand the design space of original libraries. This approach led to a 68% increased production of p-coumaric acid within two DBTL cycles.

bioengineering↗

bioRxiv2023

mEthAE: an Explainable AutoEncoder for methylation data

1In the quest to unravel the mysteries of our epigenetic landscape, researchers are continually challenged by the relationships among CpG sites. Traditional approaches are often limited by the immense complexity and high dimensionality of DNA methylation data. To address this problem, deep learning algorithms, such as autoencoders, are increasingly applied to capture the complex patterns and reduce dimensionality into latent space. In this pioneering study, we introduce an innovative chromosome-wise autoencoder, termed mEthAE, specifically designed for the interpretive reduction of methylation data. mEthAE achieves an impressive 400-fold reduction in data dimensions without compromising on reconstruction accuracy or predictive power in the latent space. In attempt to go beyond mere data compression, we developed a perturbation-based method for interpretation of latent dimensions. Through our approach we identified clusters of CpG sites that exhibit strong connections across all latent dimensions, which we refer to as global CpGs. Remarkably, these global CpGs are more frequently highlighted in epigenome-wide association studies (EWAS), suggesting our methods ability to pinpoint biologically significant CpG sites. Our findings reveal a surprising lack of correlation patterns, or even physical proximity on the chromosome among these connected CpGs. This leads us to propose an intriguing hypothesis: our autoencoder may be detecting complex, long-range, non-linear interaction patterns among CpGs. These patterns, largely uncharacterised in current epigenetic research, hold the potential to shed new light on our understanding of epigenetics. In conclusion, this study not only showcases the power of autoencoders in untangling the complexities of epigenetic data but also opens up new avenues for understanding the hidden connections within CpGs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/549496v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@16c5076org.highwire.dtl.DTLVardef@16b455org.highwire.dtl.DTLVardef@9948e8org.highwire.dtl.DTLVardef@181914b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

P-PPI: accurate prediction of peroxisomal protein-protein interactions (P-PPI) using deep learning-based protein sequence embeddings

Protein-protein interactions (PPIs) are crucial for various biological processes, and their prediction is typically accomplished through experimental methods, which can be time-consuming and costly. Computational methods provide a faster and more cost-effective approach, leveraging protein sequences and other data sources to infer PPIs. Deep learning (DL) approaches have shown promising results in various protein-related tasks, including PPI prediction. However, DL-based embeddings are often not thoroughly compared or evaluated against state-of-the-art tools. Additionally, existing PPI predictors incorporate different types of information beyond protein sequence representation, making it important to assess the effectiveness of DL-based embeddings solely relying on protein sequences. In this work, we benchmark and compare commonly used DL-based embeddings for PPI prediction based solely on protein sequence information. We utilize high-quality training data, including experimentally validated negative interactions from the Negatome database. The best model, obtained through double cross-validation and hyperparameter optimization, is selected and evaluated to predict peroxisomal PPIs. The resulting tool, P-PPI, is further enhanced by combining AlphaFold2-Multimer predictions with the P-PPI model, leveraging DL-based embeddings and protein structure predictions for a comprehensive analysis of peroxisomal PPIs. This integrated approach holds significant potential to advance our understanding of complex protein networks and their functions.

bioinformatics↗

bioRxiv2023

Regulating microbiome metabolic stability for stable indigenous liquor fermentation

BackgroundRegulating microbial metabolic stability is an ever-challenging goal in the food industry to ensure the productivity and quality of fermented foods. The microbiome underlying traditional Chinese liquor fermentation is such a representative microbiome metabolism that is affected by many dynamic abiotic/biotic factors. The complex microbial activities bring beneficial qualities (complex and rich aroma profiles, etc.) to the fermented product, but can also cause unstable fermentation outcomes. Here, we designed a three-step experiment (abiotic regulation; biotic regulation; lab-scale validation) to explore which factors cause unstable fermentation outcomes and how to regulate microbiome metabolic functional stability accordingly. ResultsWe found that 30.5% industrial fermentation of traditional Chinese liquor outcomes could be precisely predicted by initial abiotic factors. We could ensure the stability of partial fermentation batches by regulating the initial ratio of acidity to reducing sugar, moisture, and starch. Furthermore, in two representative unpredictable fermentation batches (named batch A and batch B), we found that unstable fermentation outcomes occurred even with similar initial abiotic factors after a dynamic three-phase fermentation. Unstable fermentation batches showed fluctuations in microbial community assembly that affected fermentation stability by altering the beneficial distribution (metabolic flux) of redundant metabolic pathways between yeasts and Lactobacilli. The metabolism of batch B was more stable than that of batch A due to the consistent overexpression of a specific set of bacterial metabolic genes. In repeated feed-batch fermentation processes, the difference in metabolic functional stability between the two batches was amplified 9.02 times. Batch B had significantly lower microbiome metabolic fluctuations than batch A, with higher robustness and lower complexity of the metabolic functional network. Moreover, we found that adjusting the initial microbial inoculation ratio could regulate both the metabolic beneficial distribution and temporal metabolic fluctuations of the microbiome to appropriately reduce the instability caused by biotic factors. ConclusionsThis study demonstrates that rationally regulating initial parameters and microbial inoculation ratio is a practical strategy to optimize indigenous liquor fermentation. The stable microbial beneficial distribution and high metabolic robustness are essential to obtain the ideal microbiome metabolic stability. Our study provides insights and shows the feasibility of enhancing metabolic functional stability through initial conditions in dynamic microbial ecosystems.

systems biology↗

bioRxiv2023

PortPred: exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

bioinformatics↗

bioRxiv2022

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii.

One-carbon (C1) compounds are promising feedstocks for sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted to valuable products while reducing its atmospheric levels. Acetogens are known microorganisms that can grow on CO2/H2 and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with microbes that can further process such products can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone, butanol and ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of a solventogen and acetogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modelling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in serum bottles and subsequently in pH-controlled bioreactors. Community modelling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modelling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

microbiology↗

bioRxiv2022

Exploring the adaptability and robustness of the central carbon metabolism of Mycoplasma pneumoniae

In this study we explored the adaptability and robustness of glycolysis and pyruvate metabolism of Mycoplasma pneumoniae (MPN). We used a dual approach, we analysed metabolomics data collected for a large number of OE and KO mutants and perturbation samples. Furthermore, we trained a dynamic model of central carbon metabolism and tested the models capacity to predict these mutants and perturbation samples as well as identify key controlling factors in central carbon metabolism. Our analysis of metabolite data as well as our model analysis indicate MPN metabolism is inherently robust against perturbations due to its network structure. Two key control hubs of central carbon metabolism were identified.

systems biology↗

bioRxiv2022

Predicting Mycoplasma tissue and host specificity from genome sequences

To gain insights into the genotype-phenotype relationships in Mycoplasmas, we set to investigate which Mycoplasma proteins are most predictive of tissue and host trophism and to which functional groups of proteins they belong. We retrieved and annotated 430 Mycoplasma genomes and combined their genome information with data on which host and tissue these Mycoplasmas were isolated from. We assessed clustering of Mycoplasma strains from a wide range of hosts and tissues based on different functional groups of proteins. Additionally, we assessed clustering using only a subset of M. pneumoniae strains based on different functional groups of proteins. We found that proteins belonging to the Gene Ontology (GO) Biological process group Interspecies interaction between organisms proteins are most important for predicting the pathogenesis of Mycoplasma strains whereas for M. pneumoniae, those belonging to Quorum sensing and Biofilm formation proteins are most important for predicting pathogenesis. Two Random Forest Classifiers were trained to accurately predicts host and tissue specificity based on only 12 proteins. For Mycoplasma host specificity CTP synthase complex, magnesium transporter MgtE, and glycine cleavage system are most important for correctly classifying Mycoplasma strains that infect humans, including opportunistic zoonotic strains. For tissue specificity, we found that a) known virulence and adhesions factor Methionine sulphate reductase MetA is predictive of urinary tract infecting Mycoplasmas; b) an extra cytoplasmic thiamine binding lipoprotein is most predictive of gastro-intestinal infecting Mycoplasmas; c) a type I restriction endonuclease is most predictive of respiratory infecting Mycoplasmas, and; d) a branched-chain amino acid transport system is most predictive for blood infecting Mycoplasmas. These findings can aid in predicting host and tissue specific pathogenicity of Mycoplasmas as well as provide insight in which proteins are important for specific host and tissue adaptations. Furthermore, these results underscore the usefulness of deploying genome-wide methodologies for gaining insights into pathogenicity from genome sequences.

systems biology↗