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Air pollution and the urban heat island effect are consistently linked to numerous respiratory and heat-related illnesses. Additionally, these stressors disproportionately impact low-income and historically marginalized communities due to their proximity to emissions sources, lack of access to green space, and exposure to other adverse environmental conditions. Here, we use relatively low-cost stationary sensors to analyze PM2.5 and temperature data throughout the city of Richmond, Virginia, on the ten hottest days of 2019. For both hourly means within the ten hottest days of 2019 and daily means for the entire record for the year, the temperature was found to exhibit a positive correlation with PM2.5. Analysis of hourly means on the ten hottest days yielded a diurnal pattern in which PM2.5 levels peaked in the early morning and reached their minima in the mid-afternoon. Spatially, sites exhibiting higher temperatures consistently had higher PM2.5 readings, with vulnerable communities in the east end and more intensely developed parts of the city experiencing significantly higher temperatures and PM2.5 concentrations than the suburban neighborhoods in the west end. These findings suggest an uneven distribution of air pollution in Richmond during extreme heat events that are similar in pattern but less pronounced than the temperature differences during these events, although further investigation is required to verify the extent of this relationship. As other studies have found both of these environmental stressors to correlate with the distribution of green space and other land-use factors in cities, innovative and sustainable planning decisions are crucial to the mitigation of these issues of inequity going forward.
Andre Eanes; Todd Lookingbill; Jeremy Hoffman; Kelly Saverino; Stephen Fong. Assessing Inequitable Urban Heat Islands and Air Pollution Disparities with Low-Cost Sensors in Richmond, Virginia. Sustainability 2020, 12, 10089 .
AMA StyleAndre Eanes, Todd Lookingbill, Jeremy Hoffman, Kelly Saverino, Stephen Fong. Assessing Inequitable Urban Heat Islands and Air Pollution Disparities with Low-Cost Sensors in Richmond, Virginia. Sustainability. 2020; 12 (23):10089.
Chicago/Turabian StyleAndre Eanes; Todd Lookingbill; Jeremy Hoffman; Kelly Saverino; Stephen Fong. 2020. "Assessing Inequitable Urban Heat Islands and Air Pollution Disparities with Low-Cost Sensors in Richmond, Virginia." Sustainability 12, no. 23: 10089.
Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.
Shomeek Chowdhury; Stephen S Fong. Leveraging genome-scale metabolic models for human health applications. Current Opinion in Biotechnology 2020, 66, 267 -276.
AMA StyleShomeek Chowdhury, Stephen S Fong. Leveraging genome-scale metabolic models for human health applications. Current Opinion in Biotechnology. 2020; 66 ():267-276.
Chicago/Turabian StyleShomeek Chowdhury; Stephen S Fong. 2020. "Leveraging genome-scale metabolic models for human health applications." Current Opinion in Biotechnology 66, no. : 267-276.
The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
Shomeek Chowdhury; Stephen S. Fong. Computational Modeling of the Human Microbiome. Microorganisms 2020, 8, 197 .
AMA StyleShomeek Chowdhury, Stephen S. Fong. Computational Modeling of the Human Microbiome. Microorganisms. 2020; 8 (2):197.
Chicago/Turabian StyleShomeek Chowdhury; Stephen S. Fong. 2020. "Computational Modeling of the Human Microbiome." Microorganisms 8, no. 2: 197.
Research that meaningfully integrates constraint-based modeling with machine learning is at its infancy but holds much promise. Here, we consider where machine learning has been implemented within the constraint-based modeling reconstruction framework and highlight the need to develop approaches that can identify meaningful features from large-scale data and connect them to biological mechanisms to establish causality to connect genotype to phenotype. We motivate the construction of iterative integrative schemes where machine learning can fine-tune the input constraints in a constraint-based model or contrarily, constraint-based model simulation results are analyzed by machine learning and reconciled with experimental data. This can iteratively refine a constraint-based model until there is consistency between experimental data, machine learning results, and constraint-based model simulations.
Pratip Rana; Carter Berry; Preetam Ghosh; Stephen S Fong. Recent advances on constraint-based models by integrating machine learning. Current Opinion in Biotechnology 2019, 64, 85 -91.
AMA StylePratip Rana, Carter Berry, Preetam Ghosh, Stephen S Fong. Recent advances on constraint-based models by integrating machine learning. Current Opinion in Biotechnology. 2019; 64 ():85-91.
Chicago/Turabian StylePratip Rana; Carter Berry; Preetam Ghosh; Stephen S Fong. 2019. "Recent advances on constraint-based models by integrating machine learning." Current Opinion in Biotechnology 64, no. : 85-91.
Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.
Qiang Yan; Seth Robert; J. Paul Brooks; Stephen S. Fong. Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model. BMC Bioinformatics 2019, 20, 227 .
AMA StyleQiang Yan, Seth Robert, J. Paul Brooks, Stephen S. Fong. Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model. BMC Bioinformatics. 2019; 20 (1):227.
Chicago/Turabian StyleQiang Yan; Seth Robert; J. Paul Brooks; Stephen S. Fong. 2019. "Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model." BMC Bioinformatics 20, no. 1: 227.
During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and 13C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.
Garrett W. Roell; Jian Zha; Rhiannon R. Carr; Mattheos A. Koffas; Stephen S. Fong; Yinjie J. Tang. Engineering microbial consortia by division of labor. Microbial Cell Factories 2019, 18, 1 -11.
AMA StyleGarrett W. Roell, Jian Zha, Rhiannon R. Carr, Mattheos A. Koffas, Stephen S. Fong, Yinjie J. Tang. Engineering microbial consortia by division of labor. Microbial Cell Factories. 2019; 18 (1):1-11.
Chicago/Turabian StyleGarrett W. Roell; Jian Zha; Rhiannon R. Carr; Mattheos A. Koffas; Stephen S. Fong; Yinjie J. Tang. 2019. "Engineering microbial consortia by division of labor." Microbial Cell Factories 18, no. 1: 1-11.
The photoelectrochemical electrode has been intensively studied in recent years as a means of generating electricity from light through the use of intact thylakoid membranes or highly purified photosystem II. PSII-enriched thylakoid membrane fragments (PSII-BBY), also have the potential to construct the photoelectrochemical anode. In this study, we examined the feasibility of utilizing PSII-BBY preparations to construct a relatively inexpensive photoelectrochemical anode with a comparable current density and a reasonable stability. Intact thylakoid membrane based photoelectrochemical electrode was also constructed to compare with the PSII-BBY based photoelectrochemical electrode with respect to the protein activity and current density. In addition, the practicability of replacing the popular gold nanoparticle modified gold slide with multi-walled carbon nanotube modified indium tin oxide coated slides was tested. In order to understand the surface change during slide surface modification, an atomic force microscope (AFM) was used to image the topography of the slide. Above all, we observed a current density of 20.44 ± 1.58 μA/cm2 when PSII-BBY was used to construct the photoelectrochemical anode. Moreover, the PSII-BBY based photoelectrochemical anode showed high stability over time with the current decreasing at a rate of 0.78%/h.
Yang Liu; John Daye; David Jenson; Stephen Fong. Evaluating the efficiency of a photoelectrochemical electrode constructed with photosystem II-enriched thylakoid membrane fragments. Bioelectrochemistry 2018, 124, 22 -27.
AMA StyleYang Liu, John Daye, David Jenson, Stephen Fong. Evaluating the efficiency of a photoelectrochemical electrode constructed with photosystem II-enriched thylakoid membrane fragments. Bioelectrochemistry. 2018; 124 ():22-27.
Chicago/Turabian StyleYang Liu; John Daye; David Jenson; Stephen Fong. 2018. "Evaluating the efficiency of a photoelectrochemical electrode constructed with photosystem II-enriched thylakoid membrane fragments." Bioelectrochemistry 124, no. : 22-27.
Being capable of hydrolyzing chitin, chitinases have various applications such as production of N-acetylchitooligosaccharides (COSs) and N-acetylglucosamine (GlcNAc), degrading chitin as a consolidated bioprocessing, and bio-control of fungal phytopathogens. Here, a putative chitinase in Thermobifida fusca, Tfu_0580, is characterized. Tfu_0580 was purified by homogeneity with a molecular weight of 44.9 kDa by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis. Tfu_0580 displayed a clear activity against colloidal chitin, which is comparable to a commercial Streptomyces griseus chitinase. Enzyme activities against p-nitrophenyl β-D-N,N′,N′′-triacetylchitotriose (p-NP-(GlcNAc)3), N,N′-diacetyl-β-D-chitobioside (p-NP-(GlcNAc)2) and p-nitrophenyl N-acetyl-β-D-glucosaminide (p-NP-(GlcNAc)) showed that Tfu_0580 exhibited highest activity against p-NP-(GlcNAc)3. Further optimization of the enzyme activity conditions showed: 1) an optimum catalytic activity at pH 6.0 and 30 °C; 2) activity over broad pH (4.8–7.5) and temperature (20–55 °C); 3) stimulation of activity by the metallic ions Ca2+ and Mn2+.
Qiang Yan; Stephen S. Fong. Cloning and characterization of a chitinase from Thermobifida fusca reveals Tfu_0580 as a thermostable and acidic endochitinase. Biotechnology Reports 2018, 19, e00274 .
AMA StyleQiang Yan, Stephen S. Fong. Cloning and characterization of a chitinase from Thermobifida fusca reveals Tfu_0580 as a thermostable and acidic endochitinase. Biotechnology Reports. 2018; 19 ():e00274.
Chicago/Turabian StyleQiang Yan; Stephen S. Fong. 2018. "Cloning and characterization of a chitinase from Thermobifida fusca reveals Tfu_0580 as a thermostable and acidic endochitinase." Biotechnology Reports 19, no. : e00274.
Carbon source uptake rates limit chemical productivity in cellular systems. Increasing cellular carbon source uptake rates can improve chemical productivity. Various strategies to modify carbon source uptake rates are presented. Considerations for commercialization of cell-based chemical production are discussed. Metabolic engineering seeks to convert low-cost input material into value-added products by using biochemical reactions. Increasing carbon source uptake rates (input) can be beneficial for improving the biochemical productivity of engineered systems. Here, we discuss recent advances associated with in vivo and in vitro approaches to improve carbon source uptake rates for metabolic engineering applications. More specifically, topics covered include: first, developing fast-growing organisms with high basal growth and carbon source uptake rates; second, maximizing growth rates by adaptive evolution under carbon limited conditions; third, rewiring endogenous/exogenous transporter systems to maximize uptake of single and/or multiple substrates; fourth, decoupling cell growth from biochemical production; and fifth, implementing cell-free biochemical production. Download high-res image (113KB)Download full-size image
Qiang Yan; Stephen S Fong. Increasing carbon source uptake rates to improve chemical productivity in metabolic engineering. Current Opinion in Biotechnology 2018, 53, 254 -263.
AMA StyleQiang Yan, Stephen S Fong. Increasing carbon source uptake rates to improve chemical productivity in metabolic engineering. Current Opinion in Biotechnology. 2018; 53 ():254-263.
Chicago/Turabian StyleQiang Yan; Stephen S Fong. 2018. "Increasing carbon source uptake rates to improve chemical productivity in metabolic engineering." Current Opinion in Biotechnology 53, no. : 254-263.
Chitin is an abundant, biorenewable, nitrogen‐rich biomass feedstock that can be potentially developed for biochemical production, however, efficient bioprocesses have yet to be established. Here, we demonstrate an engineered bioprocess to produce N‐acetylneuraminic acid (Neu5Ac) directly from chitin using the chitinolytic bacterium, Serratia marcescens by selecting and characterizing promoters, characterization of heterologous enzyme activity, and optimization of pathway fluxes. By generating RNASeq data for S. marcescens growth in different carbon‐limited conditions (glucose, N‐acetylglucosamine and glycerol), 12 promoters with varying strength were identified and characterized to implement for transcriptional control. Neu5Ac production was initially engineered into S. marcescens through heterologous expression of N‐acetylglucosamine 2‐epimerase (slr1975) and N‐acetylneuraminic acid aldolase (nanA). Activity of both genes was characterized in vitro for kinetics and in vivo expression using promoters identified in this study. Optimization of Neu5Ac production was accomplished by balancing pathways fluxes through promoter swapping and replacing the reversible nanA with the irreversible gene neuB. The optimized recombinant strain PT5‐slr1975‐PrplJ‐neuB was able to produce 0.48 g/L Neu5Ac from 20 g/L N‐acetylglucosamine, and 0.30 g/L Neu5Ac from 5 g/L crystal chitin. These results represent the first demonstration of direct conversion of crystal chitin to N‐acetylneuraminic acid.
Qiang Yan; Stephen S. Fong. Design and modularized optimization of one‐step production of N‐ acetylneuraminic acid from chitin in Serratia marcescens. Biotechnology and Bioengineering 2018, 115, 2255 -2267.
AMA StyleQiang Yan, Stephen S. Fong. Design and modularized optimization of one‐step production of N‐ acetylneuraminic acid from chitin in Serratia marcescens. Biotechnology and Bioengineering. 2018; 115 (9):2255-2267.
Chicago/Turabian StyleQiang Yan; Stephen S. Fong. 2018. "Design and modularized optimization of one‐step production of N‐ acetylneuraminic acid from chitin in Serratia marcescens." Biotechnology and Bioengineering 115, no. 9: 2255-2267.
Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.
Niti VanEe; J. Paul Brooks; Stephen S. Fong. Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca. Metabolites 2017, 7, 57 .
AMA StyleNiti VanEe, J. Paul Brooks, Stephen S. Fong. Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca. Metabolites. 2017; 7 (4):57.
Chicago/Turabian StyleNiti VanEe; J. Paul Brooks; Stephen S. Fong. 2017. "Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca." Metabolites 7, no. 4: 57.
UP elements (upstream element) are DNA sequences upstream of a promoter that interact with the α-subunit of RNA polymerase (RNAP) and can affect transcription by altering the binding RNAP to DNA. However, details of UP element and binding affinity effects on transcriptional strength are unclear. Here, we investigated the effects of UP element sequences on gene transcription, binding affinity, and gene expression noise. Addition of UP elements resulted in increased gene expression (maximum 95.7-fold increase) and reduced gene expression noise (8.51-fold reduction). Half UP element sequences at the proximal subsite has little effect on transcriptional strength despite increasing binding affinity by 2.28-fold. In vitro binding assays were used to determine dissociation constants (Kd) and in the in vitro system, the full range of gene expression occurs in a small range of dissociation constants (25 nM < Kd < 45 nM) indicating that transcriptional strength is highly sensitive to small changes in binding affinity. These results demonstrate the utility of UP elements and provide mechanistic insight into the functional relationship between binding affinity and transcription. Given the centrality of gene expression via transcription to biology, additional insight into transcriptional mechanisms can foster both fundamental and applied research. In particular, knowledge of the DNA sequence-specific effects on expression strength can aid in promoter engineering for different organisms and for metabolic engineering to balance pathway fluxes.
Qiang Yan; Stephen S. Fong. Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli. Journal of Biological Engineering 2017, 11, 33 .
AMA StyleQiang Yan, Stephen S. Fong. Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli. Journal of Biological Engineering. 2017; 11 (1):33.
Chicago/Turabian StyleQiang Yan; Stephen S. Fong. 2017. "Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli." Journal of Biological Engineering 11, no. 1: 33.
Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: 1) current techniques in controlling gene expression (transcriptional/translational level), 2) advances in site-specific genome engineering tools (homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)), and 3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects.
Qiang Yan; Stephen S. Fong. Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing. Frontiers in Microbiology 2017, 8, 2060 .
AMA StyleQiang Yan, Stephen S. Fong. Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing. Frontiers in Microbiology. 2017; 8 ():2060.
Chicago/Turabian StyleQiang Yan; Stephen S. Fong. 2017. "Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing." Frontiers in Microbiology 8, no. : 2060.
Microbial utilization of chitin, a potential renewable biomass feedstock, is being pursued as a means of developing novel consolidated bioprocessing for the production of chemicals. Serratia marcescens is a gram-negative bacterium that is known for its chitinolytic capability and as a native 2,3-butanediol producer. In S. marcescens, ChiR has been suggested to be a positive regulator of chitinase production. In this study, we aim to understand the effect of ChiR in regulating nine chitinase-related genes in S. marcescens Db11 and demonstrate manipulation of chiR as a useful and efficient genetic target to enhance chitin utilization. First, a chiR overexpression (chiROE) strain and a chiR deletion (ΔchiR) strain were generated and characterized in terms of cellular growth, chitinase activity, and total secreted protein. Compared to the wild-type Db11 strain, the S. marcescens chiROE strain showed an increase in chitinase activity (2.14- to 6.31-fold increase). Increased transcriptional expression of chitinase-related genes was measured using real-time PCR, showing 2.12- to 10.93-fold increases. The S. marcescens ΔchiR strain showed decreases in chitinase activity (4.5- to 25-fold decrease), confirming ChiR’s role as a positive regulator of chitinase expression. Finally, chiR overexpression was investigated as a means of increasing biochemical production (2,3-butanediol) from crystal chitin. The chiROE strain produced 1.13 ± 0.08 g/L 2,3-butanediol from 2% crystal chitin, a 2.83-fold improvement from the wild-type strain, indicating ChiR is an important and useful genetic engineering target for enhancing chitin utilization in S. marcescens.
Qiang Yan; Eunsoo Hong; Stephen S. Fong. Study of ChiR function in Serratia marcescens and its application for improving 2,3-butanediol from crystal chitin. Applied Microbiology and Biotechnology 2017, 101, 7567 -7578.
AMA StyleQiang Yan, Eunsoo Hong, Stephen S. Fong. Study of ChiR function in Serratia marcescens and its application for improving 2,3-butanediol from crystal chitin. Applied Microbiology and Biotechnology. 2017; 101 (20):7567-7578.
Chicago/Turabian StyleQiang Yan; Eunsoo Hong; Stephen S. Fong. 2017. "Study of ChiR function in Serratia marcescens and its application for improving 2,3-butanediol from crystal chitin." Applied Microbiology and Biotechnology 101, no. 20: 7567-7578.
With the advent of synthetic biology, it is possible to accelerate metabolic engineering research due to lower cost gene synthesis. In particular, genetically encoded biosensors can be used in synthetic circuits to dynamically respond to metabolites to actuate desired metabolic engineering functions. Biosensors can be employed to target high-producing strains by high-throughput screening, to sense desirable products in selective conditions, and to dynamically control metabolic fluxes. In this chapter, we first describe different types of biosensors (transcription factor-based biosensors, RNA-based biosensors, protein activity-based biosensors, and whole cell biosensors) that have been developed. Next, we describe the application of developed biosensors well-developed methods that are used to develop novel biosensors. Finally, we provide some perspectives on biosensor utilization in metabolic engineering and some potential problems that face this field.
Qiang Yan; Stephen S. Fong. Biosensors for Metabolic Engineering. Systems Biology Application in Synthetic Biology 2016, 53 -70.
AMA StyleQiang Yan, Stephen S. Fong. Biosensors for Metabolic Engineering. Systems Biology Application in Synthetic Biology. 2016; ():53-70.
Chicago/Turabian StyleQiang Yan; Stephen S. Fong. 2016. "Biosensors for Metabolic Engineering." Systems Biology Application in Synthetic Biology , no. : 53-70.
Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.
Gang Wu; Qiang Yan; J. Andrew Jones; Yinjie J. Tang; Stephen S. Fong; Mattheos A.G. Koffas. Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications. Trends in Biotechnology 2016, 34, 652 -664.
AMA StyleGang Wu, Qiang Yan, J. Andrew Jones, Yinjie J. Tang, Stephen S. Fong, Mattheos A.G. Koffas. Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications. Trends in Biotechnology. 2016; 34 (8):652-664.
Chicago/Turabian StyleGang Wu; Qiang Yan; J. Andrew Jones; Yinjie J. Tang; Stephen S. Fong; Mattheos A.G. Koffas. 2016. "Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications." Trends in Biotechnology 34, no. 8: 652-664.
Thermobifida fusca is a moderately thermophilic actinobacterium naturally capable of utilizing lignocellulosic biomass. The B6 strain of T. fusca was previously engineered to produce 1-propanol directly on lignocellulosic biomass by expressing a bifunctional butyraldehyde/alcohol dehydrogenase (adhE2). To characterize the intracellular mechanisms related to the accumulation of 1-propanol, the engineered B6 and wild-type (WT) strains were systematically compared by analysis of the transcriptome and intracellular metabolome during exponential growth on glucose, cellobiose, and Avicel. Of the 18 known cellulases in T. fusca, 10 cellulase genes were transcriptionally expressed on all three substrates along with three hemicellulases. Transcriptomic analysis of cellodextrin and cellulose transport revealed that Tfu_0936 (multiple sugar transport system permease) was the key enzyme regulating the uptake of sugars in T. fusca. For both WT and B6 strains, it was found that growth in oxygen-limited conditions resulted in a blocked tricarboxylic acid (TCA) cycle caused by repressed expression of Tfu_1925 (aconitate hydratase). Further, the transcriptome suggested a pathway for synthesizing succinyl-CoA: oxaloacetate to malate (by malate dehydrogenase), malate to fumarate (by fumarate hydratase), and fumarate to succinate (by succinate dehydrogenase/fumarate reductase) which was ultimately converted to succinyl-CoA by succinyl-CoA synthetase. Both the transcriptome and the intracellular metabolome confirmed that 1-propanol was produced through succinyl-CoA, L-methylmalonyl-CoA, D-methylmalonyl-CoA, and propionyl-CoA in the B6 strain.
Yu Deng; Adam B. Fisher; Stephen S. Fong. Systematic analysis of intracellular mechanisms of propanol production in the engineered Thermobifida fusca B6 strain. Applied Microbiology and Biotechnology 2015, 99, 8089 -8100.
AMA StyleYu Deng, Adam B. Fisher, Stephen S. Fong. Systematic analysis of intracellular mechanisms of propanol production in the engineered Thermobifida fusca B6 strain. Applied Microbiology and Biotechnology. 2015; 99 (19):8089-8100.
Chicago/Turabian StyleYu Deng; Adam B. Fisher; Stephen S. Fong. 2015. "Systematic analysis of intracellular mechanisms of propanol production in the engineered Thermobifida fusca B6 strain." Applied Microbiology and Biotechnology 99, no. 19: 8089-8100.
Concurrent advances in a number of fields have fostered the development of bioprocesses for biochemical production. Ideally, future bioprocesses will meet the demands of commercial chemical markets in an economical fashion while being sustainable through the use of renewable starting materials. A number of different renewable and abundant biopolymers (e.g., cellulose, hemicelluloses, lignin, and chitin) are potential starting material for sustainable bioprocesses, but a broad challenge remains on how to efficiently depolymerize these biopolymers to generate monomeric sugars that can be metabolized by industrial microorganisms or other useful building block chemicals. Indeed, a variety of specialty chemicals may be able to be generated from these various monomers. This review focuses on the biopolymer chitin and discusses research and knowledge relevant to chitin degradation and potential chemical products that can be made from chitin degradation products.
Qiang Yan; Stephen S Fong. Bacterial chitinase: nature and perspectives for sustainable bioproduction. Bioresources and Bioprocessing 2015, 2, 31 .
AMA StyleQiang Yan, Stephen S Fong. Bacterial chitinase: nature and perspectives for sustainable bioproduction. Bioresources and Bioprocessing. 2015; 2 (1):31.
Chicago/Turabian StyleQiang Yan; Stephen S Fong. 2015. "Bacterial chitinase: nature and perspectives for sustainable bioproduction." Bioresources and Bioprocessing 2, no. 1: 31.
Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum. The online version of this article (doi:10.1186/s12918-015-0159-x) contains supplementary material, which is available to authorized users.
Devin H Currie; Babu Raman; Christopher M Gowen; Timothy J Tschaplinski; Miriam L Land; Steven D Brown; Sean F Covalla; Dawn M Klingeman; Zamin K Yang; Nancy L Engle; Courtney M Johnson; Miguel Rodriguez; A Joe Shaw; William R Kenealy; Lee R Lynd; Stephen S Fong; Jonathan R Mielenz; Brian H Davison; David A Hogsett; Christopher D Herring. Genome-scale resources for Thermoanaerobacterium saccharolyticum. BMC Systems Biology 2015, 9, 30 .
AMA StyleDevin H Currie, Babu Raman, Christopher M Gowen, Timothy J Tschaplinski, Miriam L Land, Steven D Brown, Sean F Covalla, Dawn M Klingeman, Zamin K Yang, Nancy L Engle, Courtney M Johnson, Miguel Rodriguez, A Joe Shaw, William R Kenealy, Lee R Lynd, Stephen S Fong, Jonathan R Mielenz, Brian H Davison, David A Hogsett, Christopher D Herring. Genome-scale resources for Thermoanaerobacterium saccharolyticum. BMC Systems Biology. 2015; 9 (1):30.
Chicago/Turabian StyleDevin H Currie; Babu Raman; Christopher M Gowen; Timothy J Tschaplinski; Miriam L Land; Steven D Brown; Sean F Covalla; Dawn M Klingeman; Zamin K Yang; Nancy L Engle; Courtney M Johnson; Miguel Rodriguez; A Joe Shaw; William R Kenealy; Lee R Lynd; Stephen S Fong; Jonathan R Mielenz; Brian H Davison; David A Hogsett; Christopher D Herring. 2015. "Genome-scale resources for Thermoanaerobacterium saccharolyticum." BMC Systems Biology 9, no. 1: 30.
Recent advances in experimental and computational synthetic biology are extremely useful for achieving metabolic engineering objectives. The integration of synthetic biology and metabolic engineering within an iterative design-build-test framework will improve the practice of metabolic engineering by relying more on efficient design strategies. Computational tools that aid in the design and in silico simulation of metabolic pathways are especially useful. However, software helpful for constructing, implementing, measuring and characterizing engineered pathways and networks should not be overlooked. In this review, we highlight computational synthetic biology tools relevant to metabolic engineering, organized in the context of the design-build-test cycle
George H McArthur IV; Pooja P Nanjannavar; Emily H Miller; Stephen S Fong. Integrative metabolic engineering. AIMS Bioengineering 2015, 2, 93 -103.
AMA StyleGeorge H McArthur IV, Pooja P Nanjannavar, Emily H Miller, Stephen S Fong. Integrative metabolic engineering. AIMS Bioengineering. 2015; 2 (3):93-103.
Chicago/Turabian StyleGeorge H McArthur IV; Pooja P Nanjannavar; Emily H Miller; Stephen S Fong. 2015. "Integrative metabolic engineering." AIMS Bioengineering 2, no. 3: 93-103.