Publications by authors named "Cristal Zuniga"

24 Publications

  • Page 1 of 1

Host DNA Depletion in Saliva Samples for Improved Shotgun Metagenomics.

Methods Mol Biol 2021 ;2327:87-92

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Host DNA makes up the majority of DNA in a saliva sample. Therefore, shotgun metagenomics can be an inefficient way to evaluate the microbial populations of saliva since often <10% of the sequencing reads are microbial. In this chapter, we describe a method to deplete human DNA from fresh or frozen saliva samples, allowing for more efficient shotgun metagenomic sequencing of the salivary microbial community.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-0716-1518-8_6DOI Listing
January 2021

Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities.

Curr Opin Biotechnol 2021 10 20;71:91-97. Epub 2021 Jul 20.

Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.

Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. This model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.copbio.2021.06.024DOI Listing
October 2021

Biotechnology for secure biocontainment designs in an emerging bioeconomy.

Curr Opin Biotechnol 2021 10 3;71:25-31. Epub 2021 Jun 3.

National Renewable Energy Laboratory, Golden, CO, United States. Electronic address:

Genetically modified organisms (GMOs) have emerged as an integral component of a sustainable bioeconomy, with an array of applications in agriculture, bioenergy, and biomedicine. However, the rapid development of GMOs and associated synthetic biology approaches raises a number of biosecurity concerns related to environmental escape of GMOs, detection thereof, and impact upon native ecosystems. A myriad of genetic safeguards have been deployed in diverse microbial hosts, ranging from classical auxotrophies to global genome recoding. However, to realize the full potential of microbes as biocatalytic platforms in the bioeconomy, a deeper understanding of the fundamental principles governing microbial responsiveness to biocontainment constraints, and interactivity of GMOs with the environment, is required. Herein, we review recent analytical biotechnological advances and strategies to assess biocontainment and microbial bioproductivity, as well as opportunities for predictive systems biodesigns towards securing a viable bioeconomy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.copbio.2021.05.004DOI Listing
October 2021

The sum is greater than the parts: exploiting microbial communities to achieve complex functions.

Curr Opin Biotechnol 2021 02 6;67:149-157. Epub 2021 Feb 6.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. Electronic address:

Multi-species microbial communities are ubiquitous in nature. The widespread prevalence of these communities is due to highly elaborated interactions among their members thereby accomplishing metabolic functions that are unattainable by individual members alone. Harnessing these communal capabilities is an emerging field in biotechnology. The rational intervention of microbial communities for the purpose of improved function has been facilitated in part by developments in multi-omics approaches, synthetic biology, and computational methods. Recent studies have demonstrated the benefits of rational interventions to human and animal health as well as agricultural productivity. Emergent technologies, such as in situ modification of complex microbial community and community metabolic modeling, represent an avenue to engineer sustainable microbial communities. In this opinion, we review relevant computational and experimental approaches to study and engineer microbial communities and discuss their potential for biotechnological applications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.copbio.2021.01.013DOI Listing
February 2021

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts.

NPJ Syst Biol Appl 2020 08 4;6(1):24. Epub 2020 Aug 4.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41540-020-00142-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403731PMC
August 2020

Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth.

Nat Commun 2020 07 30;11(1):3803. Epub 2020 Jul 30.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-17612-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393147PMC
July 2020

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph DJ.

Metab Eng Commun 2020 Dec 30;11:e00132. Epub 2020 May 30.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium DJ (DT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. DT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.mec.2020.e00132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292883PMC
December 2020

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes.

NPJ Syst Biol Appl 2020 05 15;6(1):14. Epub 2020 May 15.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41540-020-0135-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229059PMC
May 2020

Author Correction: Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities.

Nat Microbiol 2019 Dec;4(12):2578

Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41564-019-0621-4DOI Listing
December 2019

Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities.

Nat Microbiol 2019 12 7;4(12):2184-2191. Epub 2019 Oct 7.

Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood. Here, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Furthermore, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework provides insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41564-019-0567-6DOI Listing
December 2019

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity.

NPJ Syst Biol Appl 2019 24;5:33. Epub 2019 Sep 24.

1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 USA.

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga (CZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41540-019-0110-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760154PMC
April 2020

Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates.

Nat Microbiol 2019 12 23;4(12):2082-2089. Epub 2019 Sep 23.

Department of Pediatrics, University of California, San Diego, CA, USA.

Dietary habits have been associated with alterations of the human gut resident microorganisms contributing to obesity, diabetes and cancer. In Western diets, red meat is a frequently eaten food, but long-term consumption has been associated with increased risk of disease. Red meat is enriched in N-glycolylneuraminic acid (Neu5Gc) that cannot be synthesized by humans. However, consumption can cause Neu5Gc incorporation into cell surface glycans, especially in carcinomas. As a consequence, an inflammatory response is triggered when Neu5Gc-containing glycans encounter circulating anti-Neu5Gc antibodies. Although bacteria can use free sialic acids as a nutrient source, it is currently unknown if gut microorganisms contribute to releasing Neu5Gc from food. We found that a Neu5Gc-rich diet induces changes in the gut microbiota, with Bacteroidales and Clostridiales responding the most. Genome assembling of mouse and human shotgun metagenomic sequencing identified bacterial sialidases with previously unobserved substrate preference for Neu5Gc-containing glycans. X-ray crystallography revealed key amino acids potentially contributing to substrate preference. Additionally, we verified that mouse and human sialidases were able to release Neu5Gc from red meat. The release of Neu5Gc from red meat using bacterial sialidases could reduce the risk of inflammatory diseases associated with red meat consumption, including colorectal cancer and atherosclerosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41564-019-0564-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879853PMC
December 2019

Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks.

PLoS Comput Biol 2019 07 25;15(7):e1007007. Epub 2019 Jul 25.

Data Science Hub, San Diego Supercomputer Center, UC San Diego, La Jolla, California, United States of America.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1007007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657818PMC
July 2019

A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types.

PLoS Comput Biol 2019 01 9;15(1):e1006644. Epub 2019 Jan 9.

Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.

S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus' metabolic response to its environment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1006644DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326480PMC
January 2019

Optimization of carbon and energy utilization through differential translational efficiency.

Nat Commun 2018 10 26;9(1):4474. Epub 2018 Oct 26.

Department of Pediatrics, Division of Host-Microbe Systems and Therapeutics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.

Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-06993-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203783PMC
October 2018

Advances in metabolic modeling of oleaginous microalgae.

Biotechnol Biofuels 2018 5;11:241. Epub 2018 Sep 5.

2Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760 USA.

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13068-018-1244-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124020PMC
September 2018

A systematic comparison of two empirical gas-liquid mass transfer determination methodologies to characterize methane biodegradation in stirred tank bioreactors.

J Environ Manage 2018 Jul 5;217:247-252. Epub 2018 Apr 5.

Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Unidad Cuajimalpa, Avenida Vasco de Quiroga 4871, Col. Santa Fe Cuajimalpa. Delegación Cuajimalpa de Morelos, Ciudad de México, Mexico. Electronic address:

This study aimed at systematically comparing the potential of two empirical methods for the estimation of the volumetric CH mass transfer coefficient (ka), namely gassing-out and oxygen transfer rate (OTR), to describe CH biodegradation in a fermenter operated with a methanotrophic consortium at 400, 600 and 800 rpm. The ka estimated from the OTR methodology accurately predicted the CH elimination capacity (EC) under CH mass transfer limiting conditions regardless of the stirring rate (∼9% of average error between empirical and estimated ECs). Thus, empirical CH-ECs of 37.8 ± 5.8, 42.5 ± 5.4 and 62.3 ± 5.2 g CH m hvs predicted CH-ECs of 35.6 ± 2.2, 50.1 ± 2.3 and 59.6 ± 3.4 g CH m h were recorded at 400, 600 and 800 rpm, respectively. The rapid Co-catalyzed reaction of O with SO in the vicinity of the gas-liquid interphase during OTR determinations, mimicking microbial CH uptake in the biotic experiments, was central to accurately describe the ka.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jenvman.2018.03.097DOI Listing
July 2018

Improving saliva shotgun metagenomics by chemical host DNA depletion.

Microbiome 2018 02 27;6(1):42. Epub 2018 Feb 27.

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.

Background: Shotgun sequencing of microbial communities provides in-depth knowledge of the microbiome by cataloging bacterial, fungal, and viral gene content within a sample, providing an advantage over amplicon sequencing approaches that assess taxonomy but not function and are taxonomically limited. However, mammalian DNA can dominate host-derived samples, obscuring changes in microbial populations because few DNA sequence reads are from the microbial component. We developed and optimized a novel method for enriching microbial DNA from human oral samples and compared its efficiency and potential taxonomic bias with commercially available kits.

Results: Three commercially available host depletion kits were directly compared with size filtration and a novel method involving osmotic lysis and treatment with propidium monoazide (lyPMA) in human saliva samples. We evaluated the percentage of shotgun metagenomic sequencing reads aligning to the human genome, and taxonomic biases of those not aligning, compared to untreated samples. lyPMA was the most efficient method of removing host-derived sequencing reads compared to untreated sample (8.53 ± 0.10% versus 89.29 ± 0.03%). Furthermore, lyPMA-treated samples exhibit the lowest taxonomic bias compared to untreated samples.

Conclusion: Osmotic lysis followed by PMA treatment is a cost-effective, rapid, and robust method for enriching microbial sequence data in shotgun metagenomics from fresh and frozen saliva samples and may be extensible to other host-derived sample types.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40168-018-0426-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5827986PMC
February 2018

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote .

Plant Physiol 2018 01 26;176(1):450-462. Epub 2017 Sep 26.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0760

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of , CZ946, thus increasing our knowledgebase by 10% for this model green alga.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1104/pp.17.00605DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761767PMC
January 2018

Elucidation of complexity and prediction of interactions in microbial communities.

Microb Biotechnol 2017 11 19;10(6):1500-1522. Epub 2017 Sep 19.

Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/1751-7915.12855DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658597PMC
November 2017

Draft Genome Sequence of sp. CZ-UAM, Isolated from a Methanotrophic Consortium.

Genome Announc 2017 Aug 17;5(33). Epub 2017 Aug 17.

Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico

sp. CZ-UAM was isolated from a methanotrophic consortium in mineral medium using methane as the only carbon source. A draft genome of 5.84 Mb with a 40.77% G+C content is reported here. This genome sequence will allow the investigation of potential methanotrophy in this isolated strain.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/genomeA.00792-17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604772PMC
August 2017

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

Plant Physiol 2016 09 2;172(1):589-602. Epub 2016 Jul 2.

Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412 (C.Z., T.H., J.L., D.C.Z., K.Z.);Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218 (C.-T.L., M.J.B.);Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, Delaware 19716 (B.O.M., C.P.L., M.R.A.); andNational Bioenergy Center, National Renewable Energy Laboratory, Golden, Colorado 80401 (E.P.K., M.T.G.)

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1104/pp.16.00593DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5074608PMC
September 2016

Unraveling interactions in microbial communities - from co-cultures to microbiomes.

J Microbiol 2015 May 3;53(5):295-305. Epub 2015 May 3.

Department of Bioengineering, University of California, San Diego, CA, 92093, USA.

Microorganisms do not exist in isolation in the environment. Instead, they form complex communities among themselves as well as with their hosts. Different forms of interactions not only shape the composition of these communities but also define how these communities are established and maintained. The kinds of interaction a bacterium can employ are largely encoded in its genome. This allows us to deploy a genomescale modeling approach to understand, and ultimately predict, the complex and intertwined relationships in which microorganisms engage. So far, most studies on microbial communities have been focused on synthetic co-cultures and simple communities. However, recent advances in molecular and computational biology now enable bottom up methods to be deployed for complex microbial communities from the environment to provide insight into the intricate and dynamic interactions in which microorganisms are engaged. These methods will be applicable for a wide range of microbial communities involved in industrial processes, as well as understanding, preserving and reconditioning natural microbial communities present in soil, water, and the human microbiome.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s12275-015-5060-1DOI Listing
May 2015

Polyhydroxyalkanoates accumulation by Methylobacterium organophilum CZ-2 during methane degradation using citrate or propionate as cosubstrates.

Bioresour Technol 2013 Feb 6;129:686-9. Epub 2012 Dec 6.

Posgrado de Biotecnología, Universidad Autónoma Metropolitana-Iztapalapa, Mexico.

Methylobacterium organophilum CZ-2 synthesized polyhydroxyalkanoates (PHAs) under nitrogen limitation with CH4 as carbon source and when either citrate or propionate was added as cosubstrates. The highest PHAs content (yPHA) in closed flasks was obtained in the CH4-citrate and CH4-propionate experiments attaining values of 0.82 and 0.68, respectively. M. organophilum CZ-2 cultivated in bioreactors with citrate and continuous CH4 addition yielded a final PHAs concentration of 143 gm(-3) containing hydroxybutyrate (HB), hydroxyvalerate (HV) and hydroxyoctanoate (HO), in a 55:35:10 ratio, with, yPHA of 0.88 and a CH4 elimination capacity (EC) of 20 gm(-3) h(-1). With propionate, the yPHA was 0.3 and the EC around 8 gm(-3) h(-1). From 1H and 13C NMR experiments it was found that the polymer produced with CH4-citrate contained six different monomers: 3HB, 3HV, 4HV, 4-hydroxyheptanoate (4HH), 3HO and 4HO, showing the great versatility of this PHAs producing bacterium.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biortech.2012.11.120DOI Listing
February 2013
-->