DetSpace: a web server for engineering detectable pathways for bio-based chemical production.

Tackling climate change challenges requires replacing current chemical industrial processes through the rational and sustainable use of biodiversity resources. To that end, production routes to key bio-based chemicals for the bioeconomy have been identified. However, their production still remains inefficient in terms of titers, rates, and yields; because of the hurdles found when scaling up. In order to make production more efficient, strategies like automated screening and dynamic pathway regulation through biosensors have been applied as part of strain optimization. However, to date, no systematic way exists to design a genetic circuit that is responsive to concentrations of a given target compound. Here, the DetSpace web server provides a set of integrated tools that allows a user to select and design a biological circuit that performs the sensing of a molecule of interest by its enzymatic conversion to a detectable molecule through a transcription factor. In that way, the DetSpace web server allows synthetic biologists to easily design biosensing routes for the dynamic regulation of metabolic pathways in applications ranging from genetic circuits design, screening, production, and bioremediation of bio-based chemicals, to diagnostics and drug delivery.

SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions.

Selenzyme is a retrobiosynthesis tool that suggests candidate enzymes for user query reactions. Enzyme suggestions are based on identical reactions, as well as similar reactions, since enzymes are often capable of promiscuous substrate binding. Selenzyme is a user-friendly, widely used web-tool for ranking enzymes based on reaction similarity and additional features, including the phylogenetic distance between the source species of the enzyme and the intended host. While Selenzyme has proved invaluable in assisting with enzyme selection for known reactions, as well as many novel or orphan reactions, weaknesses have been exposed in its ability to rank functionally related enzymes. Within this update, we introduce a new reaction similarity scoring algorithm, which is used in conjunction with the previous similarity calculation, to improve the accuracy of enzyme suggestions based on non-identical similar reactions, across a range of EC reaction classes. This allows enzymes to be suggested for reactions not found within the database, even if the reaction is unbalanced. A database update was also carried out, to ensure that reaction and enzyme knowledge remains current. This update can be accessed at http://selenzymeRF.synbiochem.co.uk/.

Sensbio: an online server for biosensor design.

Allosteric transcription factor (aTF) based biosensors can be used to engineer genetic circuits for a wide range of applications. The literature and online databases contain hundreds of experimentally validated molecule-TF pairs; however, the knowledge is scattered and often incomplete. Additionally, compared to the number of compounds that can be produced in living systems, those with known associated TF-compound interactions are low. For these reasons, new tools that help researchers find new possible TF-ligand pairs are called for. In this work, we present Sensbio, a computational tool that through similarity comparison against a TF-ligand reference database, is able to identify putative transcription factors that can be activated by a given input molecule. In addition to the collection of algorithms, an online application has also been developed, together with a predictive model created to find new possible matches based on machine learning.

Transcription factor-based biosensors for screening and dynamic regulation.

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Reconstrucción biológica de grandes defectos óseos con autoinjerto de peroné vascularizado en huesos largos / Biological reconstruction of large bone defects with vascularized fibular autograft

Introducción: El autoinjerto vascular de peroné se presenta como una muy buena opción en la reconstrucción de grandes defectos óseos en huesos largos gracias a sus características estructurales y propiedades biológicas. Materiales y Métodos: Se realizó un estudio observacional descriptivo y retrospectivo que incluyó a todos los pacientes operados con un injerto vascular de peroné aislado o asociado a injerto estructural (técnica de Capanna) desde el 1 de enero de 2014 hasta el 1 de enero de 2021 en nuestro hospital. Resultados: Se realizaron 26 cirugías mediante un injerto vascular de peroné; en 8 de ellas, se utilizó el colgajo vascularizado de peroné para la reconstrucción del defecto óseo en hueso largo. El tamaño medio del defecto era de 7,7 cm. El origen del defecto era postraumático en 5 casos y tumoral en el resto. Se consiguió la consolidación completa en todos los pacientes. Los resultados clínicos y funcionales en las escalas de valoración fueron mejores en pacientes operados en el miembro inferior. Conclusiones: El uso de un colgajo vascularizado de peroné asociado o no a aloinjerto estructural es una estrategia útil en la reconstrucción de grandes defectos óseos (≥5 cm), independientemente de la causa de la lesión; la supervivencia del injerto y la función son buenas, con una tasa de complicaciones aceptable. Nivel de Evidencia: IV

Background: Given its biological and structural qualities, vascular fibular autograft is a good option for the reconstruction of large defects in long bones. Materials and Methods: A descriptive and retrospective observational study was conducted. We included all cases of patients who underwent surgery in our hospital between January 1, 2014, and January 1, 2021, and who had a vascular fibula autograft either standalone or in combination with a structural graft (Capanna technique). Results:There were 26 documented vascular fibula autograft procedures. Eight of the procedures involved the reconstruction of a long bone defect. The bone defect was an average of 7.7 cm in length. In five of the cases, the origin of the bone defect was post-traumatic, and in the remaining cases, it was tumoral. In all cases, complete consolidation was achieved. Surgical procedures performed on the lower extremities yielded better clinical and functional outcomes. Conclusions:Vascular fibula autograft either on its own or in combination with a structural graft, as described in the Capanna technique, is an excellent alternative for the reconstruction of bone defects ≥ 5 cm. Radiological, clinical and functional outcomes are good, with an acceptable rate of complications. Level of Evidence: IV

The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering.

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.

Considerations on diagnosis and surveillance measures of PTEN hamartoma tumor syndrome: clinical and genetic study in a series of Spanish patients.

BACKGROUND: The limited knowledge about the PTEN hamartoma tumor syndrome (PHTS) makes its diagnosis a challenging task. We aimed to define the clinical and genetic characteristics of this syndrome in the Spanish population and to identify new genes potentially associated with the disease. RESULTS: We reviewed the clinical data collected through a specific questionnaire in a series of 145 Spanish patients with a phenotypic features compatible with PHTS and performed molecular characterization through several approaches including next generation sequencing and whole exome sequencing (WES). Macrocephaly, mucocutaneous lesions, gastrointestinal polyposis and obesity are prevalent phenotypic features in PHTS and help predict the presence of a PTEN germline variant in our population. We also find that PHTS patients are at risk to develop cancer in childhood or adolescence. Furthermore, we observe a high frequency of variants in exon 1 of PTEN, which are associated with renal cancer and overexpression of KLLN and PTEN. Moreover, WES revealed variants in genes like NEDD4 that merit further research. CONCLUSIONS: This study expands previously reported findings in other PHTS population studies and makes new contributions regarding clinical and molecular aspects of PHTS, which are useful for translation to the clinic and for new research lines.

Fast biofoundries: coping with the challenges of biomanufacturing.

Biofoundries are highly automated facilities that enable the rapid and efficient design, build, test, and learn cycle of biomanufacturing and engineering biology, which is applicable to both research and industrial production. However, developing a biofoundry platform can be expensive and time consuming. A biofoundry should grow organically, starting from a basic platform but with a vision for automation, equipment interoperability, and efficiency. By thinking about strategies early in the process through process planning, simulation, and optimization, bottlenecks can be identified and resolved. Here, we provide a survey of technological solutions in biofoundries and their advantages and limitations. We explore possible pathways towards the creation of a functional, early-phase biofoundry, and strategies towards long-term sustainability.

Blood Glucose Estimation From Voice: First Review of Successes and Challenges.

The possibility to estimate glucose value from voice would make a breakthrough in diabetes treatment: namely, remove the delay in the nonintrusive instantaneous blood glucose estimation, relieve medical budgets and significantly improve wellbeing of diabetics. In this review, different approaches have been described and systematized, in order to provide an objective snapshot of the state of the art. Since nonintrusive glucose estimation is notoriously difficult, we included a Transparence and Reproducibility Score aimed at revealing the biases in the primary research articles. The review is completed with the discussion on future research pathways.

Global Methylome Scores Correlate with Histological Subtypes of Colorectal Carcinoma and Show Different Associations with Common Clinical and Molecular Features.

BACKGROUND: The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have not been associated with histological colorectal carcinomas (CRC) subsets. Using Illumina's 450 k Infinium Human Methylation beadchip, the methylome of 82 CRCs were analyzed, comprising different histological subtypes: 40 serrated adenocarcinomas (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing histological and molecular features of microsatellite instability (hmMSI-H), and, additionally, 35 normal adjacent mucosae. Scores reflecting the overall methylation at 250 bp, 1 kb and 2 kb from the transcription starting site (TSS) were studied. RESULTS: SAC has an intermediate methylation pattern between CC and hmMSI-H for the three genome locations. In addition, the shift from promoter hypermethylation to genomic hypomethylation occurs at a small sequence between 250 bp and 1 Kb from the gene TSS, and an asymmetric distribution of methylation was observed between both sides of the CpG islands (N vs. S shores). CONCLUSION: These findings show that different histological subtypes of CRC have a particular global methylation pattern depending on sequence distance to TSS and highlight the so far underestimated importance of CpGs aberrantly hypomethylated in the clinical phenotype of CRCs.

Prototyping of microbial chassis for the biomanufacturing of high-value chemical targets.

Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.

Malignant prediction in paragangliomas: analysis for clinical risk factors.

INTRODUCTION: Paragangliomas are infrequent neuroendocrine tumours whose only criterion for malignancy is presence of metastases; thus, all paragangliomas show malignant potential. Actually, different risk factors have been analyzed to predict metastases but they remain unclear. PURPOSE: To analyze clinical, histological, and genetic factors to predict the occurrence of metastasis. PATIENTS AND METHOD: A multicentre retrospective observational analysis was performed between January 1990 and July 2019. Patients diagnosed with paraganglioma were selected. Clinical, histological, and genetic features were analyzed for the prediction of malignancy. RESULTS: A total of 83 patients diagnosed with paraganglioma were included, of which nine (10.8%) had malignant paraganglioma. Tumour size was greater in malignant tumours than in benign (6 cm vs. 4 cm, respectively; p = 0.027). The most frequent location of malignancy was the thorax-abdomen-pelvis area observed in six cases (p = 0.024). No differences were observed in histological differentiation, age, symptoms, and catecholaminergic production. The most frequent genetic mutation was SDHD followed by SDHB but no differences were observed between benign and malignant tumours. In the univariate analysis for predictive factors for malignancy, location, tumour size, and histological differentiation showed statistical significance (p = 0.025, p = 0.014, and p = 0.046, respectively); however, they were not confirmed as predictive factors for malignancy in the multivariate analysis. CONCLUSION: In this study, no risk factors for malignancy have been established; therefore, we recommend follow-up of all patients diagnosed with paraganglioma.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing.

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

In silico design and automated learning to boost next-generation smart biomanufacturing.

The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by in silico optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.

Engineering Escherichia coli towards de novo production of gatekeeper (2S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol.

Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2S)-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories.

Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers.

Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.

Pathways to cellular supremacy in biocomputing.

Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the "genetic circuit" metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of "cellular supremacy" to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.

Identification of major malate export systems in an engineered malate-producing Escherichia coli aided by substrate similarity search.

Optimization of export mechanisms for valuable extracellular products is important for the development of efficient microbial production processes. Identification of the relevant export mechanism is the prerequisite step for product export optimization. In this work, we identified transporters involved in malate export in an engineered L-malate-producing Escherichia coli strain using cheminformatics-guided genetics tests. Among all short-chain di- or tricarboxylates with known transporters in E. coli, citrate, tartrate, and succinate are most chemically similar to malate as estimated by their molecular signatures. Inactivation of three previously reported transporters for succinate, tartrate, and citrate, DcuA, TtdT, and CitT, respectively, dramatically decreased malate production and fermentative growth, suggesting that these transporters have substrate promiscuity for different short-chain organic acids and constitute the major malate export system in E. coli. Malate export deficiency led to an increase in cell sizes and accumulation of intracellular metabolites related to malate metabolism.

Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation.

Our inability to predict the behavior of biological systems severely hampers progress in bioengineering and biomedical applications. We cannot predict the effect of genotype changes on phenotype, nor extrapolate the large-scale behavior from small-scale experiments. Machine learning techniques recently reached a new level of maturity, and are capable of providing the needed predictive power without a detailed mechanistic understanding. However, they require large amounts of data to be trained. The amount and quality of data required can only be produced through a combination of synthetic biology and automation, so as to generate a large diversity of biological systems with high reproducibility. A sustained investment in the intersection of synthetic biology, machine learning, and automation will drive forward predictive biology, and produce improved machine learning algorithms.