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While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement certain functions that would have been difficult or even impossible otherwise. In this article, we review the role and impact of noise in systems and synthetic biology, with a particular emphasis on its role in the genetic control of biological systems, an area we refer to as cybergenetics. The main modeling paradigm is that of stochastic reaction networks, whose applicability goes beyond biology, as these networks can represent any population dynamics system, including ecological, epidemiological, and opinion dynamics networks. We review different ways to mathematically represent these systems, and we notably argue that the concept of ergodicity presents a particularly suitable way to characterize their stability. We then discuss noise-induced properties and show that noise can be both an asset and a nuisance in this setting. Finally, we discuss recent results on (stochastic) cybergenetics and explore their relationships to noise. Along the way, we detail the different technical and biological constraints that need to be respected when designing synthetic biological circuits. Finally, we discuss the concepts, problems, and solutions exposed in the article; raise criticisms and concerns about current ideas and approaches; suggest current (open) problems with potential solutions; and provide some ideas for future research directions., Annual Review of Control, Robotics, and Autonomous Systems, 6 (1), ISSN:2573-5144

Aspergillus niger is a robust microbial cell factory for organic acid production. However, the regulation of many industrially important pathways is still poorly understood. The regulation of the glucose oxidase (Gox) expression system, involved in the biosynthesis of gluconic acid, has recently been uncovered. The results of that study show hydrogen peroxide, a by-product of the extracellular conversion of glucose to gluconate, has a pivotal role as a signaling molecule in the induction of this system. In this study, the facilitated diffusion of hydrogen peroxide via aquaporin water channels (AQPs) was studied. AQPs are transmembrane proteins of the major intrinsic proteins (MIPs) superfamily. In addition to water and glycerol, they may also transport small solutes such as hydrogen peroxide. The genome sequence of A. niger N402 was screened for putative AQPs. Seven AQPs were found and could be classified into three main groups. One protein (AQPA) belonged to orthodox AQP, three (AQPB, AQPD, and AQPE) were grouped in aquaglyceroporins (AQGP), two (AQPC and AQPF) were in X-intrinsic proteins (XIPs), and the other (AQPG) could not be classified. Their ability to facilitate diffusion of hydrogen peroxide was identified using yeast phenotypic growth assays and by studying AQP gene knock-outs in A. niger. The X-intrinsic protein AQPF appears to play roles in facilitating hydrogen peroxide transport across the cellular membrane in both Saccharomyces cerevisiae and A. niger experiments.

Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite–metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.

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

We used a single foraminifera enabled, holistic hydroclimate-to-sediment transient modelling approach to fundamentally evaluate the efficacy of discrete-depth individual foraminifera analysis (IFA) for reconstructing past sea surface temperature (SST) variability from deep-sea sediment archives, a method that has been used, amongst other applications, for reconstructing El Niño-Southern Oscillation (ENSO). The computer model environment allows us to strictly control for variables such as SST, foraminifera species abundance response to SST, as well as depositional processes such as sediment accumulation rate (SAR) and bioturbation depth (BD) and subsequent laboratory processes such as sample size and machine error. Examining a number of best-case scenarios, we find that IFA-derived reconstructions of past SST variability are sensitive to all of the aforementioned variables. Running 100 ensembles for each scenario, we find that the influence of bioturbation upon IFA-derived SST reconstructions, combined with typical samples sizes employed in the field, produces noisy SST reconstructions with poor correlation to the original SST distribution in the water. This noise is especially apparent for values near the tails of the SST distribution, which is the distribution region of particular interest in the case of, e.g. ENSO. The noise is further increased in the case of increasing machine error, decreasing SAR and decreasing sample size. We also find poor agreement between ensembles, underscoring the need for replication studies in the field to confirm findings at particular sites and time periods. Furthermore, we show that a species abundance response to SST could in theory bias IFA-derived SST reconstructions, which can have consequences when comparing IFA-derived SST distributions from markedly different mean climate states. We provide a number of idealised simulations spanning a number of SAR, sample size, machine error and species abundance scenarios, which can help assist researchers in the field to determine under which conditions they could expect to retrieve significant results.

This is a repository of the scripts used for a manuscript where we evaluated genome-scale modeling tools for microbial communities.

BackgroundThe life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers.FindingsWe developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the “form generation module” creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the “validation module.” Finally, the “resource module” can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive–compatible XML metadata file.ConclusionsTurning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

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.

Objective Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets. Methods Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with β-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed. Results Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1β, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome. Conclusion Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes. publishedVersion

Synthetic biologists design and engineer organisms for a better and more sustainable future. While the manifold prospects are encouraging, concerns about the uncertain risks of genome editing affect public opinion as well as local regulations. As a consequence, biosafety and associated concepts, such as the Safe-by-design framework and genetic safeguard technologies, have gained notoriety and occupy a central position in the conversation about genetically modified organisms. Yet, as regulatory interest and academic research in genetic safeguard technologies advance, the implementation in industrial biotechnology, a sector that is already employing engineered microorganisms, lags behind. The main goal of this work is to explore the utilization of genetic safeguard technologies for designing biosafety in industrial biotechnology. Based on our results, we posit that biosafety is a case of a changing value, by means of further specification of how to realize biosafety. Our investigation is inspired by the Value Sensitive Design framework, to investigate scientific and technological choices in their appropriate social context. Our findings discuss stakeholder norms for biosafety, reasonings about genetic safeguards, and how these impact the practice of designing for biosafety. We show that tensions between stakeholders occur at the level of norms, and that prior stakeholder alignment is crucial for value specification to happen in practice. Finally, we elaborate in different reasonings about genetic safeguards for biosafety and conclude that, in absence of a common multi-stakeholder effort, the differences in informal biosafety norms and the disparity in biosafety thinking could end up leading to design requirements for compliance instead of for safety.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities.This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

We present the OrganelX e-Science Web Server that provides a user-friendly implementation of the In-Pero and In-Mito classifiers for sub-peroxisomal and sub-mitochondrial localization of peroxisomal and mitochondrial proteins and the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal sequence. The OrganelX e-Science Web Server is available at https://organelx.hpc.rug.nl/fasta/.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities.This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

Background Pseudomonas putida has received increasing interest as a cell factory due to its remarkable features such as fast growth, a versatile and robust metabolism, an extensive genetic toolbox and its high tolerance to oxidative stress and toxic compounds. This interest is driven by the need to improve microbial performance to a level that enables biologically possible processes to become economically feasible, thereby fostering the transition from an oil-based economy to a more sustainable bio-based one. To this end, one of the current strategies is to maximize the product-substrate yield of an aerobic biocatalyst such as P. putida during growth on glycolytic carbon sources, such as glycerol and xylose. We demonstrate that this can be achieved by implementing the phosphoketolase shunt, through which pyruvate decarboxylation is prevented, and thus carbon loss is minimized. Results In this study, we introduced the phosphoketolase shunt in the metabolism of P. putida KT2440. To maximize the effect of this pathway, we first tested and selected a phosphoketolase (Xfpk) enzyme with high activity in P. putida. Results of the enzymatic assays revealed that the most efficient Xfpk was the one isolated from Bifidobacterium breve. Using this enzyme, we improved the P. putida growth rate on glycerol and xylose by 44 and 167%, respectively, as well as the biomass yield quantified by OD600 by 50 and 30%, respectively. Finally, we demonstrated the impact on product formation and achieved a 38.5% increase in mevalonate and a 25.9% increase in flaviolin yield from glycerol. A similar effect was observed on the mevalonate-xylose and flaviolin-xylose yields, which increased by 48.7 and 49.4%, respectively. Conclusions Pseudomonas putida with the implemented Xfpk shunt grew faster, reached a higher final OD600nm and provided better product-substrate yields than the wild type. By reducing the pyruvate decarboxylation flux, we significantly improved the performance of this important workhorse for industrial applications. This work encompasses the first steps towards full implementation of the non-oxidative glycolysis (NOG) or the glycolysis alternative high carbon yield cycle (GATCHYC), in which a substrate is converted into products without CO2 loss These enhanced properties of P. putida will be crucial for its subsequent use in a range of industrial processes.

Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (, Journal of Cleaner Production, 384, ISSN:0959-6526

One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted into valuable products while reducing its atmospheric levels. Acetogens are microorganisms that can grow on CO2/H2 gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species 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-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modeling 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 batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling 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 modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

Introduction: Necrotizing Soft Tissue Infections (NSTI) are severe infections with high mortality affecting a heterogeneous patient population. There is a need for a clinical decision support system which predicts outcomes and provides treatment recommendations early in the disease course. Methods: To identify relevant clinical needs, interviews with eight medical professionals (surgeons, intensivists, general practitioner, emergency department physician) were conducted. This resulted in 24 unique questions. Mortality was selected as first endpoint to develop a machine learning (Random Forest) based prediction model. For this purpose, data from the prospective, international INFECT cohort (N = 409) was used. Results: Applying a feature selection procedure based on an unsupervised algorithm (Boruta) to the > 1000 variables available in INFECT, including baseline, and both NSTI specific and NSTI non-specific clinical data yielded sixteen predictive parameters available on or prior to the first day on the intensive care unit (ICU). Using these sixteen variables 30-day mortality could be accurately predicted (AUC = 0.91, 95% CI 0.88–0.96). Except for age, all variables were related to sepsis (e.g. lactate, urine production, systole). No NSTI-specific variables were identified. Predictions significantly outperformed the SOFA score(p < 0.001, AUC = 0.77, 95% CI 0.69–0.84) and exceeded but did not significantly differ from the SAPS II score (p = 0.07, AUC = 0.88, 95% CI 0.83–0.92). The developed model proved to be stable with AUC > 0.8 in case of high rates of missing data (50% missing) or when only using very early (

Background Akkermansia muciniphila is a member of the human gut microbiota where it resides in the mucus layer and uses mucin as the sole carbon, nitrogen and energy source. A. muciniphila is the only representative of the Verrucomicrobia phylum in the human gut. However, A. muciniphila 16S rRNA gene sequences have also been found in the intestines of many vertebrates. Results We detected A. muciniphila-like bacteria in the intestines of animals belonging to 15 out of 16 mammalian orders. In addition, other species belonging to the Verrucomicrobia phylum were detected in fecal samples. We isolated 10 new A. muciniphila strains from the feces of chimpanzee, siamang, mouse, pig, reindeer, horse and elephant. The physiology and genome of these strains were highly similar in comparison to the type strain A. muciniphila MucT. Overall, the genomes of the new strains showed high average nucleotide identity (93.9 to 99.7%). In these genomes, we detected considerable conservation of at least 75 of the 78 mucin degradation genes that were previously detected in the genome of the type strain MucT. Conclusions The low genomic divergence observed in the new strains may indicate that A. muciniphila favors mucosal colonization independent of the differences in hosts. In addition, the conserved mucus degradation capability points towards a similar beneficial role of the new strains in regulating host metabolic health.

The high-value carotenoid astaxanthin (3,3′-dihydroxy-β,β-carotene-4,4′-dione) is one of the most potent antioxidants in nature. In addition to its large-scale use in fish farming, the pigment has applications as a food supplement and an active ingredient in cosmetics and in pharmaceuticals for the treatment of diseases linked to reactive oxygen species. The biochemical pathway for astaxanthin synthesis has been introduced into seed plants, which do not naturally synthesize this pigment, by nuclear and plastid engineering. The highest accumulation rates have been achieved in transplastomic plants, but massive production of astaxanthin has resulted in severe growth retardation. What limits astaxanthin accumulation levels and what causes the mutant phenotype is unknown. Here, we addressed these questions by making astaxanthin synthesis in tobacco (Nicotiana tabacum) plastids inducible by a synthetic riboswitch. We show that, already in the uninduced state, astaxanthin accumulates to similarly high levels as in transplastomic plants expressing the pathway constitutively. Importantly, the inducible plants displayed wild-type–like growth properties and riboswitch induction resulted in a further increase in astaxanthin accumulation. Our data suggest that the mutant phenotype associated with constitutive astaxanthin synthesis is due to massive metabolite turnover, and indicate that astaxanthin accumulation is limited by the sequestration capacity of the plastid., Inducible expression of a synthetic astaxanthin operon in plastids alleviates the growth phenotype of constitutive pathway expression and provides insights into carotenoid biosynthesis bottlenecks.

This study investigated the associations between the levels of 27 plasma metabolites, 114 lipoprotein parameters, determined using nuclear magnetic resonance spectroscopy, and the ABO blood groups and the Rhesus (Rh) blood system in a cohort of n = 840 Italian healthy blood donors of both sexes. We observed good multivariate discrimination between the metabolomic and lipoproteomic profiles of subjects with positive and negative Rh. In contrast, we did not observe significant discrimination for the ABO blood group pairwise comparisons, suggesting only slight metabolic differences between these group-specific metabolic profiles. We report univariate associations (P-value < 0.05) between the subfraction HDL1 related to Apo A1, the subfraction HDL2 related to cholesterol and phospholipids, and the particle number of LDL2 related to free cholesterol, cholesterol, phospholipids, and Apo B and the ABO blood groups; we observed association of the lipid main fraction LDL4 related to free cholesterol, triglycerides, and Apo B; creatine; the particle number of LDL5; the subfraction LDL5 related to Apo B; the particle number of LDL4; and the subfraction LDL4 related to Apo B with Rh blood factors. These results suggest blood group-dependent (re)shaping of lipoprotein metabolism in healthy subjects, which may provide relevant information to explain the differential susceptibility to certain diseases observed in different blood groups.

Systems and Synthetic Biology are complementary fields emerging side-by-side into mainstream scientific research. Whilst systems biologists focus on understanding natural systems, synthetic biologists wish to modify, adapt and re-purpose biological systems towards certain desired goals, for example enhancing efficiency and robustness of desired biological traits. In both fields, data analysis, predictive mathematical modelling, experimental design, and controlled experimentation are crucial to obtain reproducible results and understand how applications can be scaled to larger systems and processes. As such, students from Life Sciences, Engineering, and Mathematics backgrounds must be taught fundamentals in biological systems, experimental techniques, mathematics, and data analysis/statistics. In addition, students must be trained for future multidisciplinary careers, where the interaction and communication between experimental and modelling researchers is fundamental. With the acceleration of technological developments (both computational and experimental) continuing unabated, educators need to bridge the increasing gap between fundamentally-required knowledge and skills that students need to pursue future academic or industrial research projects. In this paper, we will discuss how we have re-designed an introductory course in Systems and Synthetic Biology at Wageningen University and Research (Netherlands) that is targeted simultaneously to mathematical/computational students with an interest in biology and experimental methods, and to Life Science students interested in learning how biological systems can be mathematically analysed and modelled. The course highlights the links between fundamental methodologies and recently developed technologies within the Systems and Synthetic Biology fields. The course was re-designed for the 2021/22 academic year, we report that students from biology and biotechnology programmes graded their satisfaction of the course as 4.4 out of 5. We discuss how the course can act as a gateway to advanced courses in Systems Biology-oriented curricula (comprising: data infrastructure, modelling, and experimental synthetic biology), and towards future research projects.

Akkermansia muciniphila is a champion of mucin degradation in the human gastrointestinal tract. Here, we report the isolation of six novel strains from healthy human donors and their genomic, proteomic and physiological characterization in comparison to the type-strains A. muciniphila Muc and A. glycaniphila Pyt. Complete genome sequencing revealed that, despite their large genomic similarity (>97.6%), the novel isolates clustered into two distinct subspecies of A. muciniphila: Amuc1, which includes the type-strain Muc, and AmucU, a cluster of unassigned strains that have not yet been well characterized. CRISPR analysis showed all strains to be unique and confirmed that single healthy subjects can carry more than one A. muciniphila strain. Mucin degradation pathways were strongly conserved amongst all isolates, illustrating the exemplary niche adaptation of A. muciniphila to the mucin interface. This was confirmed by analysis of the predicted glycoside hydrolase profiles and supported by comparing the proteomes of A. muciniphila strain H2, belonging to the AmucU cluster, to Muc and A. glycaniphila Pyt (including 610 and 727 proteins, respectively). While some intrinsic resistance was observed among the A. muciniphila straind, none of these seem to pose strain-specific risks in terms of their antibiotic resistance patterns nor a significant risk for the horizontal transfer of antibiotic resistance determinants, opening the way to apply the type-strain Muc or these new A. muciniphila strains as next generation beneficial microbes., M-SHIME operation was supported by UGent project BOF12-GOA-008. JPO and CB were funded by the ERC Advanced Grand 250172—Microbes Inside awarded to WMdV. The antibiotic testing was partly supported by A-mansia Biotech SA. Further work atWageningen University by JE and HPLT was supported by the Spinoza Award and SIAM Gravity Grant 024.002.002 of the Netherlands Organization for Scientific Research (NWO) of WMdV. PDC is research director at FRS-FNRS (Fonds de la Recherche Scientifique) and recipient of grants from FNRS (FRFS-WELBIO: WELBIO-CR-2022A-02).

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes., Specific metabolic pathways (especially those from amino acid and carbohydrate metabolism) underlie heterosis of six agronomic traits in rice.

Ca. Neoehrlichia mikurensis is widely prevalent in I. ricinus across Europe and has been associated with human disease. However, diagnostic modalities are limited, and much is still unknown about its biology. Here, we present the first complete Ca. Neoehrlichia mikurensis genomes directly derived from wildlife reservoir host tissues, using both long- and short-read sequencing technologies. This pragmatic approach provides an alternative to obtaining sufficient material from clinical cases, a difficult task for emerging infectious diseases, and to expensive and challenging bacterial isolation and culture methods. Both genomes exhibit a larger chromosome than the currently available Ca. Neoehrlichia mikurensis genomes and expand the ability to find new targets for the development of supportive laboratory diagnostics in the future. Moreover, this method could be utilized for other tick-borne pathogens that are difficult to culture.

Optimizing bioreactor performance for organic matter removal can achieve sustainable and energy-efficient micropollutant removal in subsequent tertiary treatment. Bioreactor performance heavily depends on its resident microbial community; hence, a deeper understanding of community dynamics is essential. The microbial communities of three different bioreactors (biological activated carbon, moving bed biofilm reactor, sand filter), used for organic matter removal from wastewater treatment effluent, were characterized by 16S rRNA gene amplicon sequence analysis. An interdependency between bioreactor performance and microbial community profile was observed. Overall, Proteobacteria was the most predominant phylum, and Comamonadaceae was the most predominant family in all bioreactors. The relative abundance of the genus Roseococcus was positively correlated with organic matter removal. A generalized Lotka-Volterra (gLV) model was established to understand the interactions in the microbial community. By identifying microbial dynamics and their role in bioreactors, a strategy can be developed to improve bioreactor performance.

To understand the host-gut microbial relationship, we used multidisciplinary platforms, metagenome, transcriptome, and metabolome analyses, to determine shrimp and intestinal microbial interactions upon 0, 6, 12, 24, and 48 h exposure to 107 CFU/mL of Vibrio harveyi, a shrimp pathogen. The bacterial communities in the intestine of shrimp were disrupted during exposure to V. harveyi. The abundance of Vibrio ASVs in the Harveyi and Vulnificus clades was significantly increased after exposure to the pathogen (6, 12, and 24 h) and decreased later after 48 h. Conversely, Pseudoalteromonas was found in lower abundance (6, 12, and 24 h) but significantly increased after 48 h of the bacterial challenge. Gene expression analysis revealed that genes belonging to several immune-related pathways, including the Toll pathway, the immune deficiency (IMD) pathway, and pattern recognition proteins (PRPs), were significantly upregulated. Early responses in the first 6 h after exposure were the genes involved in phagocytosis, pattern recognition proteins (PRPs), and signal transduction (spätzle and ankyrin). Late responses (12–48 h) were genes related to proteinases and proteinase inhibitors (PPIs), antimicrobial peptides (AMPs), and oxidative stress. Genes related to lipid metabolisms such as fatty acid metabolism and choline metabolism were also upregulated. Metabolomics analysis also showed an increase in phospholipids, including phosphocholine groups, in the intestine of shrimp exposed to the pathogen. Taken together, the gene expression and metabolomics analyses suggest the importance of lipid metabolism in the defense mechanism against bacterial invasion. Moreover, our metabolomic analysis showed a decrease in tryptophan and indole-3-acrylic acid, metabolites related to intestinal immune homeostasis, after bacterial infection. Our observations suggest that pathogenic Vibrio disrupted biological processes in the shrimp intestine, resulting in a decrease in indole-3-acrylic acid and its derivatives which in turn compromised intestinal immunity. In addition, shrimp responded to the bacterial invasion by activating metabolites related to eicosanoid and phospholipid biosynthesis. Our findings on the interactions between shrimp and intestinal microbiota will be an indispensable gateway to systems biology to better understand the function of shrimp gut microbiota and its influence on shrimp innate immunity.

BackgroundPandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons.” Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions.ResultsAs a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates.ConclusionsAlthough this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

BACKGROUND: Clostridium difficile Infection (CDI) can lead to diarrhea and fulminant colitis. C. difficile infects the host using toxins. Recent studies report prevalence of CDI in the small intestine. Berries are known to contain antioxidants and phenolic compounds that might mitigate bacterial infection. OBJECTIVE: We explored the impact of C. difficile toxins on the small intestine using an in vitro approach and used systems biology techniques together with data integration to identify food compounds that can reduce their cytopathic impact. METHODS: Differentiated Caco-2 cells were exposed to C. difficile toxins and the transcriptomic changes were studied. To identify foods with potential beneficial counteracting effects, the transcriptomic profiles were integrated with transcriptomics data from Caco-2 cells exposed to various food compounds and analyzed using multivariate analysis. RESULTS: Beneficial food candidates, selected by multivariate analysis, such as blackcurrant, strawberry and yellow onion were further examined for their potential to counteract the effect of the toxin-induced disruption of cell integrity and toxin translocation. Our results confirmed effects of food compounds, on the cytopathic effects of toxins in the small intestine. CONCLUSION: Blackcurrant, strawberry and yellow onion can counteract C. difficile toxins induced effects.

Natural microbial communities are composed of a large diversity of interacting microorganisms, each with a specific role in the functional properties of the ecosystem. The objectives in microbial ecology research are related to identifying, understanding and exploring the role of these different microorganisms. Because of the rapidly increasing power of DNA sequencing and the rapid increase of genomic data, main attention of microbial ecology research shifted from cultivation-oriented studies towards metagenomic studies. Despite these efforts, the direct link between the molecular properties and the measurable changes in the functional performance of the ecosystem is often poorly documented. A quantitative understanding of functional properties in relation to the molecular changes requires effective integration, standardization, and parallelization of experiments. High-resolution functional characterization is a prerequisite for interpretation of changes in metagenomic properties, and will improve our understanding of microbial communities and facilitate their exploration for health and circular economy related objectives.

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite-metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Safety-by-design (SbD) is paramount for risk management in synthetic biology, with genetic safeguards being a key technology for its implementation. While attractive in theory, the integration of genetic safeguards into SbD strategies is rarely exercised in practice, despite technological advances. Here we question why and what might be done about it.

Geobacter sulfurreducens is a model bacterium to study the degradation of organic compounds coupled to the reduction of Fe(III). The response of G. sulfurreducens to the electron donors acetate, formate, hydrogen and a mixture of all three with Fe(III) citrate as electron acceptor was studied using comparative physiological and proteomic approaches. Variations in the supplied electron donors resulted in differential abundance of proteins involved in the citric acid cycle (CAC), gluconeogenesis, electron transport, and hydrogenases and formate dehydrogenase. Our results provided new insights into the electron donor metabolism of G. sulfurreducens. Remarkably, formate was the preferred electron donor compared to acetate, hydrogen, or acetate plus hydrogen. When hydrogen was the electron donor, formate was formed, which was associated with a high abundance of formate dehydrogenase. Notably, abundant proteins of two CO2 fixation pathways (acetyl-CoA pathway and the reversed oxidative CAC) corroborated chemolithoautotrophic growth of G. sulfurreducens with formate or hydrogen and CO2, and provided novel insight into chemolithoautotrophic growth of G. sulfurreducens. This article is protected by copyright. All rights reserved., This work was performed in the TTIW-cooperation framework of Wetsus, European Centre of Excellence for Sustain able Water Technology (www.wetsus.nl). Wetsus is funded by the Dutch Ministry of Economic Affairs, the European Union Regional Development Fund, the Province of Fryslân, the City of Leeuwarden and the EZ/Kompas program of the “Samenwerkingsverband Noord-Nederland”. The authors like to thank the participants of the research theme ‘Resource Recovery’ for fruitful discussions and their financial support. Research of AJMS is financed by an advanced grant of the European Research Council under the European Union’s Seventh Framework Programme (FP/2007e2013)/ERC Grant Agreement (project 323009). Research of AJMS and PHAT is supported by a Gravitation grant (project 024.002.002) of the Netherlands Ministry of Education, Culture and Science., info:eu-repo/semantics/publishedVersion

Synthetic biologists design and engineer organisms for a better and more sustainable future based on a currently transitioning global economy grounded in biomanufacturing. While the prospects are encouraging, concerns about uncertainty and risks associated to genome editing, including uncontrolled proliferation of transgenic microorganisms, need to be considered in safe-by-design bioengineering strategies. This dissertation delves into different types of genetic safeguards to conditionally restrict cell host viability to defined environments using the popular metabolic engineering hostPseudomonas putidaKT2440. Specifically, this thesis encompasses the generation of an industrially appealing synthetic phosphite auxotroph attained via metabolic rewiring, and a CRISPR-Cas9-based kill switch based on different genetic circuits that react to external signals for the activation of a highly genotoxic response. To also prevent horizontal gene transfer, we developed ReScribe, a highly optimized recombineering tool enhanced by CRISPR-ScCas9-mediated counterselection that was used to generate a minimally recodedP. putidastrain of essential metabolic genes. To further improve our genome editing capabilities, we employed a serial enrichment workflow followed by a next-generation sequencing analysis to screen in a high-throughput manner a library of potential recombineering systems in differentPseudomonasspecies, eventually finding new variants for enhanced mutagenesis. While all these genetic safeguards and tools are attractive in theory, their implementation in industrial biotechnology and real-world applications is rarely exercised in practice, despite technological advances. In this dissertation we question why and what could be done about it and claim that an explicit strategy of contextualization, that is, an early emphasis on potential applications, can assist the development of genetic safeguards. Moreover, the conduction of a series of interviews and analysis inspired by social science methodologies resulted in different explorations of research and ethical questions. These included the learning of safety and safe-by-design approaches in the life sciences programmes at Wageningen University, the impact of tensions in stakeholder norms on designing for biosafety in the industrial biotechnology context, and the perceptions of industry and academia on production platforms and opportunities in the biotechnology field. Lastly, this thesis ends providing a snapshot of the state of biomanufacturing and proposing the idea of meta-workflows. These are unifying ecosystems amongst research infrastructures that emphasize the importance of interoperability, harmonization, and sustainability in making a successful transition to global manufacturing that utilizes bio-based and safe production platforms. &nbsp

Pseudoclostridium thermosuccinogenes is a thermophilic bacterium capable of producing succinate from lignocellulosic-derived sugars and has the potential to be exploited as a platform organism. However, exploitation of P. thermosuccinogenes has been limited partly due to the genetic inaccessibility and lack of genome engineering tools. In this study, we established the genetic accessibility for P. thermosuccinogenes DSM 5809. By overcoming restriction barriers, transformation efficiencies of 102 CFU/µg plasmid DNA were achieved. To this end, the plasmid DNA was methylated in vivo when transformed into an engineered E. coli HST04 strain expressing three native methylation systems of the thermophile. This protocol was used to introduce a ThermodCas9-based CRISPRi tool targeting the gene encoding malic enzyme in P. thermosuccinogenes, demonstrating the principle of gene silencing. This resulted in 75% downregulation of its expression and had an impact on the strain’s fermentation profile. Although the details of the functioning of the restriction modification systems require further study, in vivo methylation can already be applied to improve transformation efficiency of P. thermosuccinogenes. Making use of the ThermodCas9-based CRISPRi, this is the first example demonstrating that genetic engineering in P. thermosuccinogenes is feasible and establishing the way for metabolic engineering of this bacterium.

Exceeding planetary boundaries, and in particular climate change, is quickly forcing the global economy to align with the principles of sustainability. In this context, there is a strong call from policy makers, scientists, and citizens to step out of the oil-based era and relentlessly pursue a biobased, sustainable economy. The industrial exploitation of microbes for the sustainable production of bio-based chemicals and fuels has contributed significantly to this effort. Nevertheless, it is becoming clear that industrial biotechnology requires a wider repertoire of more effective microbial hosts to expand and enhance its capabilities. At the same time, advancements in synthetic biology allow the rapid and efficient domestication of several unconventional microbes with appealing industrial characteristics. One bacterial host that has gained increasing attention as an efficient microbial cell factory is Pseudomonas putida KT2440. However, despite its many attractive inherent features, additional efforts must be made to elucidate and engineer its complex metabolic and regulatory architecture. Hence, this thesis aimed at contributing to the industrial domestication of P. putida by developing and applying cutting-edge synthetic biology technologies for engineering its cellular metabolism.The first part of the research herein was focused on the development of genetic tools, namely: i) the SEVA 3.1 vector library, optimized for Gram-negative bacteria, built upon the principles of the SEVA, BioBricks, and Type IIS restriction enzyme standards; and ii) the first tunable CRISPRi system for gene repression in P. putida. The second part aimed at constructing three P. putida strains tailored to overproduce the key precursor metabolites pyruvate, malonyl-CoA, and chorismate. Each strain was approached from a different metabolic engineering perspective dealing with the concepts of dynamic growth control, high-throughput optimization, and directed adaptive laboratory evolution. The work performed follows the principles of the DBTL cycle using in combination innovative computational and genetic methods that can be readily applied to further expand the industrial potential of P. putida.

BackgroundSince sequencing techniques have become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observe dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data were used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota datasets and the effect on the sample size.ResultsOur data showed that beta diversity metrics are the most sensitive to observe differences as compared with alpha diversity metrics. The structure of the data influenced which alpha metrics are the most sensitive. Regarding beta diversity, the Bray–Curtis metric is in general the most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias.ConclusionWe recommend performing power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies, awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: because of this, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e., p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish a statistical plan before experiments are initiated, describing the outcomes of interest and the corresponding statistical analyses to be performed.

Summary Hungateiclostridium thermocellum DSM 1313 has biotechnological potential as a whole‐cell biocatalyst for ethanol production using lignocellulosic renewable sources. The full exploitation of H. thermocellum has been hampered due to the lack of simple and high‐throughput genome engineering tools. Recently in our research group, a thermophilic bacterial CRISPR–Cas9‐based system has been developed as a transcriptional suppression tool for regulation of gene expression. We applied ThermoCas9‐based CRISPR interference (CRISPRi) to repress the H. thermocellum central metabolic lactate dehydrogenase (ldh) and phosphotransacetylase (pta) genes. The effects of repression on target genes were studied based on transcriptional expression and product formation. Single‐guide RNA (sgRNA) under the control of native intergenic 16S/23S rRNA promoter from H. thermocellum directing the ThermodCas9 to the promoter region of both pta and ldh silencing transformants reduced expression up to 67% and 62% respectively. This resulted in 24% and 17% decrease in lactate and acetate production, correspondingly. Hence, the CRISPRi approach for H. thermocellum to downregulate metabolic genes can be used for remodelling of metabolic pathways without the requisite for genome engineering. These data established for the first time the feasibility of employing CRISPRi‐mediated gene repression of metabolic genes in H. thermocellum DSM 1313., This work describes the development of CRISPRi for the thermophilic Hungateiclostridium thermocellum and its application to silence two genes in central metabolism. These data established for the first time the feasibility of employing CRISPRi‐mediated gene repression in H. thermocellum DSM 1313.

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

Background Necrotizing soft tissue infections (NSTIs) caused by group A Streptococcus (GAS) and occasionally by Staphylococcus aureus (SA) frequently involve the deep fascia and often lead to muscle necrosis. Methods To assess the pathogenicity of GAS and S. aureus for muscles in comparison to keratinocytes, adhesion and invasion of NSTI-GAS and NSTI-SA isolates were assessed in these cells. Bloodstream infections (BSI-SA) and noninvasive coagulase-negative staphylococci (CNS) isolates were used as controls. Results NSTI-SA and BSI-SA exhibited stronger internalization into human keratinocytes and myoblasts than NSTI-GAS or CNS. S. aureus internalization reached over 30% in human myoblasts due to a higher percentage of infected myoblasts (>11%) as compared to keratinocytes ( Conclusions Our findings suggest a factor in NSTI-SA severity is the strong invasiveness of S. aureus in muscle cells, a property not shared by NSTI-GAS isolates.

Proteins are key to many different processes in and around living cells, they are the molecular mechanisms that facilitate life. Therefore, protein function and structure have been of great interest to researchers over the past decades. A better understanding of how proteins work is important, as it will help us understand the various diseases caused by their malfunction and help us apply proteins in various applications, such as industrial processes. The research on proteins has resulted in an ever-increasing amount of data related to proteins.Protein information systems were developed to make sense out of all the data available for specific protein classes. The work described in this thesis makes use of the 3DM platform to store and order the enormous amount of protein data that is nowadays available. The 3DM platform integrates heterogeneous datasets for complete protein superfamilies to generate molecular class specific information systems (3DM systems).The main objective of this thesis is to facilitate a better understanding of protein function through data integration- and analysis-tools to support both DNA diagnostics and protein engineering efforts. To that end, chapter 2 and chapter 3 focus on the collection and integration of mutational data from scientific literature, followed by protein superfamily analysis to reveal predictive patterns. In chapter 4, mutational data mined for whole families are combined with a superfamily alignment analysis to assign function to networks of residues that appear to have co-evolved. And in chapter 5, the data of a 3DM system is used to train a machine learning model for the prediction of pathogenicity of novel genetic variants.In chapter 2 a method for the automated collection and integration of mutation data from scientific literature is described. This text-mining tool, Mutator, was designed to automatically identify and retrieve relevant literature for a complete protein family.Mutator was tested on the alpha-amylase protein family which contains a protein that, when mutated, can lead to Fabry’s disease. The Fabry disease mutation database was constructed which contained 30% more mutation data when compared to the manually curated HGMD. To differentiate between natural variation and pathogenic mutations, alignment patterns in the alpha-amylase superfamily were investigated and Validator was developed, a tool to evaluate the potential pathogenicity of individual mutations.In a follow up study, chapter 3, nine disease-related proteins across two different protein families are investigated. An improved version of Mutator was developed and used to collect mutational data for these protein families. A comparison to the manually curated HGMD showed that Mutator extracts far more mutation data, both in terms of unique mutations extracted, as well as number of references per mutation.These mutation datasets were analyzed in the context of the protein families to identify patterns to assess pathogenicity for novel variants. This analysis showed that pathogenic mutations would more often be observed in the structurally conserved core of the family than in structurally variable regions. A significant overlap was observed of protein positions with pathogenic mutations between very distant homologs, indicating that data can be transferred between equivalent residue positions even across large phylogenetic distances. Protein superfamily information systems that use structural alignments, such as 3DM, can facilitate the transfer of data across equivalent residues of structurally conserved positions.Chapter 4 describes a web-based tool for the analysis of co-evolving residues in protein super-family alignments. This tool, CorNet, combines networks of co-evolving positions with literature data to shed light on the function of these residues. When keywords, such as “specificity”, are mentioned in the sentences that contain mutational data for an alignment position, these were used to annotate the alignment position of a network.For six different protein families, a list of literature keywords was used to annotate network positions and to calculate an enrichment score of any keyword to be specifically associated with a network of co-evolving positions. Enriched keywords indicate the function behind evolutionary pressures that caused networks of residues to co-evolve. It was shown that an assigned function can be transferred from annotated to non-annotated positions in a network with a mutagenesis study that targeted only the non-annotated positions of a network enriched for the keyword “enantioselectivity”.In chapter 5, the integrated data of 3DM systems is utilized to train a model to predict variant pathogenicity with machine learning techniques. For three genes related to Long QT syndrome and Brugada syndrome, both cardiac arrhythmia syndromes, datasets of both benign and pathogenic variants were collected. 3DM was used to generate much heterogeneous data to describe each variant. Using this data, a pathogenicity predictor was generated to separate disease-causing from non-disease causing variants. This predictor outperformed frequently used genome-wide prediction tools and even gene-family specific predictors.In the general discussion an outlook is given on the scale-up of protein information systems to cover the complete structural space and even the whole human exome. This thesis provides a variety of avenues to apply integrated data to gain insight in proteins and support protein related research and development.

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

Synthetic biology, as a research field, brings together molecular life scientists, computational biologists, and social scientists to (re)engineer biological systems toward societally desired goals. Given the field’s broad multidisciplinarity and relatively young age, innovative educational methods are required to provide students with the needed background knowledge to push the field forward in the future. The international Genetically Engineered Machine (iGEM) competition is such an example where education and high-level research merge, providing the synthetic biology field with trained students, new ideas, and novel results. In the 2021 edition alone, 343 teams from across the world completed the competition to tackle societal problems with synthetic biology. As Wageningen University & Research (WUR) celebrates 10 years of participation in iGEM, we share our thoughts and experiences on supervising iGEM teams, which is especially relevant to organizers of current and new teams, and also others interested in the iGEM competition.

The inclusion of biosafety strategies into strain engineering pipelines is crucial for safe-by-design biobased processes. This in turn might enable a more rapid regulatory acceptance of bioengineered organisms in both industrial and environmental applications. For this reason, we equipped the industrially relevant microbial chassis Pseudomonas putida KT2440 with an effective biocontainment strategy based on a synthetic dependency on phosphite, which is generally not readily available in the environment. The produced PSAG-9 strain was first engineered to assimilate phosphite through the genome-integration of a phosphite dehydrogenase and a phosphite-specific transport complex. Subsequently, to deter the strain from growing on naturally assimilated phosphate, all native genes related to its transport were identified and deleted generating a strain unable to grow on media containing any phosphorous source other than phosphite. PSAG-9 exhibited fitness levels with phosphite similar to those of the wild type with phosphate, and low levels of escape frequency. Beyond biosafety, this strategy endowed P. putida with the capacity to be cultured under non-sterile conditions using phosphite as the sole phosphorous source with a reduced risk of contamination by other microbes, while displaying enhanced NADH regenerative capacity. These industrially beneficial features complement the metabolic advantages for which this species is known for, thereby strengthening it as a synthetic biology chassis with potential uses in industry, with suitability towards environmental release.

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.