Biology subjects

Martins dos Santos, V. A. P.

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

30 records · Page 2Linked to original sources

bioRxiv2022

Comparative genome-scale constraint-based metabolic modeling reveals key lifestyle features of plant-associated Pseudomonas spp.

Plant Growth Promoting Rhizobacteria (PGPR) dwell in the rhizosphere, the area surrounding the root of plants, and enhance growth of the host through different mechanisms: they can protect plants against pathogens, assist in nutrient gathering, and in increasing stress tolerance. Hence, developing strategies to enhance their performance is important to increase crop productivity. Specific solutions are necessary to enhance the performance of the beneficials while simultaneously avoiding nurturing of pathogens. This requires insights into the mechanisms underlying these microbials interactions. Pseudomonas is one of the most studied genera and contains both beneficials and pathogenic species. Hence, we used comparative genome-scale constraint-based metabolic modeling to reveal key features of both classes of Pseudomonads and which can provide leads for the possible interventions regarding these solutions. Models of 75 plant-growth promoting rhizosphere and 33 epiphytic pathogenic Pseudomonas strains were automatically reconstructed and validated using phenotype microarray (Biolog) data. The models were used for compositional analysis and 12 representative strains, 6 of each group, were further selected for extensive simulation. The analyses reveal differences in the potential for metabolite uptake and transport between these two distinct classes that suggest their nutrient preferences and their differences in, among other, D-ornithine acquisition mechanisms. The models enable simulation of metabolic state of root exudates. Simulations highlighted and summarized the differences in pathway utilization and intracellular states between two groups. The insights obtained will be very valuable to broader such studies of rhizobiome and to possibly develop strategies to improve crop productivity by supporting the beneficial microbiome while reducing pathogen activities.

systems biology↗

bioRxiv2022

Paving the way for synthetic C1- metabolism in Pseudomonas putida through the reductive glycine pathway

One-carbon (C1) compounds such as methanol, formate, and CO2 are alternative, sustainable microbial feedstocks for the biobased production of chemicals and fuels. In this study, we engineered the carbon metabolism of the industrially important bacterium Pseudomonas putida to assimilate these three substrates through the reductive glycine pathway. First, we demonstrated the functionality of the C1-assimilation module by coupling the growth of auxotrophic strains to formate assimilation. Next, we extended the module from formate to methanol using both NAD and PQQ - dependent methanol dehydrogenases. Finally, we demonstrated CO2-dependent growth through CO2 reduction to formate by the native formate dehydrogenase, which required short-term evolution to rebalance the cellular NADH/NAD+ ratio. This research paves the way to engineer P. putida towards growth on formate, methanol, and CO2 as sole feedstocks, thereby substantially expanding its potential as a sustainable and versatile cell factory.

synthetic biology↗

bioRxiv2022

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

Computational approaches for sub-organelle protein localisation and identification are often neglected while general methods, not suitable for specific use cases, are promoted instead. In particular, organelle-specific research lacks user-friendly and easily accessible computational tools that allow researchers to perform computational analysis before starting time-consuming and expensive wet-lab experiments. We present the Organelx e-Science Web Server which hosts three sequence localisation predictive algorithms: In-Pero and In-Mito for classifying sub-peroxisomal and sub-mitochondrial protein localisations given their FASTA sequences, as well as the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal. These tools can be used for a fast and accurate screening while looking for new peroxisomal and mitochondrial proteins. To our knowledge, this is the only service that provides these functionalities and can fasten the daily research of the peroxisomal science community.

bioinformatics↗

bioRxiv2022

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

BackgroundMicrobial 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. ResultsOur 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. ConclusionsThe 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.

systems biology↗

bioRxiv2021

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models

bioengineering↗

bioRxiv2021

Hyperinflammation evokes different antiviral TMPRSS2 and ADAM17 expression responses in human gut xenograft versus host mouse gut although overall genomic responses are similar

The global spread of the newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the pandemic outbreak of coronavirus disease 2019 (COVID-19), an inflammatory disease that is primarily affecting the respiratory system. However, gastrointestinal symptoms in COVID-19 patients suggests that the gut may present another viral target organ. Disease development and severity is dependent on viral interaction with two cell surface human proteins, ACE2 and TMPRSS2, and on antiviral response which may lead to systemic hyperinflammatory syndrome and multiorgan dysfunction. Understanding the host response to SARS-CoV-2 infection and the pathology of the disease will be greatly enhanced by the development of appropriate animal models. Laboratory mice have been the mainstay of therapeutic and vaccine development, however, the virus does not grow in wild type mice and only induced mild disease in transgenic animals expressing human ACE2. As there are known differences between immune response in laboratory mice and humans we evaluated the response of human gut developed as xenografts and host mouse gut following systemic LPS injections as a hyperinflammation model system. The orthologous gene expression levels in the mouse and human gut were highly correlated (Spearmans rank correlation coefficient: 0.28-0.76) and gene set enrichment analysis of significantly upregulated human and mouse genes revealed that a number of inflammatory and immune response pathways are commonly regulated in the two species. However, species differences were also observed, most importantly, in the inflamed human gut but not in the mouse gut, there was clear upregulation of mRNAs coding for TMPRSS2, ADAM17 and for RIG-I-like receptors, which are involved in the recognition of viruses and in antiviral innate immune response. Moreover, using species-specific immunofluorescence microscopy, we demonstrated the expression and localization of human ACE2 and TMPRSS2 proteins, which are essential elements of the molecular machinery that enables SARS-CoV-2 to infect and replicate in human gut cells. Our findings demonstrate that the intestinal immune response to inflammation in humans and mice are generally very similar. However, certain human-specific diseases, such as COVID-19, can only be successfully studied in an experimental model of human tissue, such as the gut xenograft.

immunology↗

bioRxiv2021

In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools. All data sets and codes are available at https://github.com/MarcoAnteghini and at www.systemsbiology.nl

bioinformatics↗

bioRxiv2020

A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of novel growth-associated genes

The black tiger shrimp (Penaeus monodon) is one of the most prominent farmed crustacean species with an average annual global production of 0.5 million tons in the last decade. To ensure sustainable and profitable production through genetic selective breeding programs, several research groups have attempted to generate a reference genome using short-read sequencing technology. However, the currently available assemblies lack the contiguity and completeness required for accurate genome annotation due to the highly repetitive nature of the genome and technical difficulty in extracting high-quality, high-molecular weight DNA in this species. Here, we report the first chromosome-level whole-genome assembly of P. monodon. The combination of long-read Pacific Biosciences (PacBio) and long-range Chicago and Hi-C technologies enabled a successful assembly of this first high-quality genome sequence. The final assembly covered 2.39 Gb (92.3% of the estimated genome size) and contained 44 pseudomolecules, corresponding to the haploid chromosome number. Repetitive elements occupied a substantial portion of the assembly (62.5%), highest of the figures reported among crustacean species. The availability of this high-quality genome assembly enabled the identification of novel genes associated with rapid growth in the black tiger shrimp through the comparison of hepatopancreas transcriptome of slow-growing and fast-growing shrimps. The results highlighted several gene groups involved in nutrient metabolism pathways and revealed 67 newly identified growth-associated genes. Our high-quality genome assembly provides an invaluable resource for accelerating the development of improved shrimp strain in breeding programs and future studies on gene regulations and comparative genomics.

genomics↗

bioRxiv2020

Navigating the Valley of Death: Perceptions of Industry and Academia on Production Platforms and Opportunities

Rational lifestyle engineering using computational methods and synthetic biology has made it possible to genetically improve industrial performance of microbial cell factories for the production of a range of biobased chemicals. However, only an estimated 1 in 5,000 to 10,000 innovations make it through the Valley of Death to market implementation.To gain in-depth insights into the views of industry and academia on key bottlenecks and opportunities to reach market implementation, a qualitative and exploratory study was performed by conducting 12 in depth interviews with 8 industrial and 4 academic participants. The characteristics that any cell factory must have were schematically listed, and commonly recognised opportunities were identified.We found that academics are limited by only technical factors in their research, while industry is restricted in their research choices and flexibility by a series of technical, sector dependent and social factors. This leads to a misalignment of interest of academics and funding industrial partners, often resulting in miscommunication. Although both are of the opinion that academia must perform curiosity-driven research to find innovative solutions, there is a certain pressure to aim for short-term industrial applications. All these factors add up to the Valley of Death; the gap between development and market implementation.A third party, in the form of start-up companies, could be the answer to bridging the Valley of Death.View Full Text

scientific communication and education↗

bioRxiv2019

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective, and vaccination is required. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation for vaccine production challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict and validate in vitro two serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach enabled us to optimize medium composition, capacitated growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive vaccine production.

systems biology↗

bioRxiv2018

Cofactors revisited - predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.

systems biology↗

bioRxiv2018

The Rumen Metatranscriptome Landscape Reflects Dietary Adaptation and Methanogenesis in Lactating Dairy Cows

Methane eructed by ruminant animals is a main contributor to greenhouse gas emissions and is solely produced by members of the phylum Euryarchaeota within the domain Archaea. Methanogenesis depends on the availability of hydrogen, carbon dioxide, methanol and acetate produced, which are metabolic products of anaerobic microbial degradation of feed-derived fibers. Changing the feed composition of the ruminants has been proposed as a strategy to mitigate methanogenesis of the rumen microbiota.\n\nWe investigated the impact of corn silage enhanced diets on the rumen microbiota of rumen-fistulated dairy cows, with a special focus on carbohydrate breakdown and methanogenesis. Metatranscriptome analysis of rumen samples taken from animals fed corn silage enhanced diets revealed that genes involved in starch metabolism were significantly more expressed while archaeal genes involved in methanogenesis showed lower expression values. The nutritional intervention also influenced the cross-feeding between Archaea and Bacteria.\n\nThe results indicate that the ruminant diet is important in methanogenesis. The diet-induced changes resulted in a reduced methane emission. The metatranscriptomic analysis provided insights into key underlying mechanisms and opens the way for new rational methods to further reduce methane output of ruminant animals.

microbiology↗