The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

Cooperative Adaptation to Establishment of a Synthetic Bacterial Mutualism

To understand how two organisms that have not previously been in contact can establish mutualism, it is first necessary to examine temporal changes in their phenotypes during the establishment of mutualism. Instead of tracing back the history of known, well-established, natural mutualisms, we experimentally simulated the development of mutualism using two genetically-engineered auxotrophic strains of Escherichia coli, which mimic two organisms that have never met before but later establish mutualism. In the development of this synthetic mutualism, one strain, approximately 10 hours after meeting the partner strain, started oversupplying a metabolite essential for the partner's growth, eventually leading to the successive growth of both strains. This cooperative phenotype adaptively appeared only after encountering the partner strain but before the growth of the strain itself. By transcriptome analysis, we found that the cooperative phenotype of the strain was not accompanied by the local activation of the biosynthesis and transport of the oversupplied metabolite but rather by the global activation of anabolic metabolism. This study demonstrates that an organism has the potential to adapt its phenotype after the first encounter with another organism to establish mutualism before its extinction. As diverse organisms inevitably encounter each other in nature, this potential would play an important role in the establishment of a nascent mutualism in nature

Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass.

UNLABELLED: Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia

Computational design and designability of gene regulatory networks

Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1417

Synthetic Genomics

The current advances in sequencing, data mining, DNA synthesis, cloning, in silico modeling, and genome editing have opened a new field of research known as Synthetic Genomics. The main goal of this emerging area is to engineer entire synthetic genomes from scratch using pre-designed building blocks obtained by chemical synthesis and rational design. This has opened the possibility to further improve our understanding of genome fundamentals by considering the effect of the whole biological system on biological function. Moreover, the construction of non-natural biological systems has allowed us to explore novel biological functions so far not discovered in nature. This book summarizes the current state of Synthetic Genomics, providing relevant examples in this emerging field

From Molecular Recognition to the “Vehicles” of Evolutionary Complexity: An Informational Approach

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química

Strategies for engineering microbial communities

Understanding how microbes assemble into communities is a fundamental open question in biology, with applications to human health, environmental sustainability, and metabolic engineering. Although it is known that the competition and exchange of nutrients (i.e., metabolic interactions) shape microbial community structure and dynamics, the ability to reliably predict the metabolic interactions and their effect on microbial communities is still being studied. This dissertation investigates how metabolism and environment shape microbial communities through the use of mathematical models, based on linear programming (LP) and mixed integer linear programming (MILP) methods. The first system I studied is a synthetic microbial consortium composed of two species, Cellulomonas fimi and Yarrowia lipolytica, hypothesized to be able to jointly transform cellulose into biofuel precursors. I combined experimental data and flux balance analysis (FBA) to test our capacity to predict metabolic interactions between the two organisms, and explored a proof-of-concept method to monitor the growth dynamics of this coculture. I next explored the possibility of generalizing the design of synthetic communities through the implementation of a computational method that can design division of labor strategies. The algorithm finds consortia of engineered bacterial strains that can survive by exchanging with each other specific nutrients. By distributing functions, microbial consortia can perform tasks that are impossible for individual species to accomplish alone. In addition to highlighting the trade-off between metabolic self-reliance and mutualistic exchange, this approach suggests how division of labor may arise in Escherichia coli monocultures. While mechanistic models are helpful for studying metabolism in microbes and microbial communities, it is interesting to ask whether increasingly cheaper high-throughput phenotypic data, can help achieve similar goals. To address this question, I developed a computational approach to investigate the relationship between growth profiles and microbial species, based on the identification of growth conditions that can best represent the whole dataset. This approach can help engineer microbial communities by identifying microbes that are more likely to engage in cross-feeding, rather than competition, based on their phenotypic profiles. In general, this dissertation demonstrates how different types of metabolic modeling approaches, both mechanism-based and data-driven, can be used to predict metabolic interactions between members of microbial consortia, and to help engineer novel synthetic communities