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Biology subjects

Alter, T. B.

Publications and source records attributed to Alter, T. B..

6 recordsLinked to original sources

Protein without farms: What comparative genomics reveals about ''Power-to-Food'' microbes

Conventional agriculture is increasingly incompatible with planetary boundaries, such as land and water demand, greenhouse-gas emissions, and disruption of the nitrogen cycle. Hydrogen-oxidizing bacteria (HOB) enable a scalable "power-to-food" approach in which aerobic gas fermentation turns CO2 and renewable H2, along with N2 in some strains, into protein-rich biomass, largely decoupling protein production from arable land and climate variability. The same chemistry is attractive for closed-loop space life support, where crew CO2 and electrolysis-derived H2 can be recycled into edible biomass. Here, we compare two leading HOB chassis strains, Cupriavidus necator H16 and Xanthobacter sp. SoF1, using standardized re-annotation, orthology-based comparison, pathway reconstruction, and safety-oriented genome screening. Importantly, SoF1 is the production strain for Solar Foods Solein(R), a dried microbial biomass ingredient, which is approved as a novel food in Singapore and has a self-affirmed GRAS status in the United States. H16 has a larger, multipartite genome of 7.41 Mb split across two chromosomes and the pHG1 megaplasmid, whereas SoF1 is more compact at 4.91 Mb and encoded on a single replicon. Both encode Calvin-Benson-Bassham CO2 fixation and multiple [NiFe]-hydrogenase systems supporting growth on CO2/H2, but nitrogen economy differentiates the hosts. SoF1 encodes a complete nitrogen-fixation module (nifHDK) and nitrate-assimilation genes, whereas H16 lacks nif and instead encodes nitrate/nitrite respiration for oxygen-limited flexibility. Safety screening revealed no evidence of canonical virulence determinants, integron or plasmid-linked antimicrobial resistant (AMR) cassettes, or high-confidence foodborne exotoxins under strict thresholds. These results convert genome-level features into actionable design constraints for selecting and engineering food-grade HOB, strengthening robust air-to-protein bioprocesses on Earth and informing a blueprint for closed-loop, space-compatible protein production. HighlightsO_LIHydrogen-oxidizing bacteria enable power-to-protein from CO2, H2 with minimal land use. C_LIO_LIHead-to-head genomics defines design rules for food-grade "air-to-protein" bioprocesses. C_LIO_LIContrasting nitrogen routes guide media design, nutrient inputs, and closed-loop operation. C_LIO_LICO2 fixation and hydrogenase gene sets reveal complementary robustness and control features. C_LIO_LIGenome architecture and COG shifts inform safety, stability, and regulatory-ready strain choice. C_LI

genomics↗

Growth-coupled microbial biosynthesis of the animal pigment xanthommatin

The mining of genomes across life has unearthed a bounty of biosynthetic potential to diverse molecules key to a biobased future. While the heterologous expression of metabolic pathways has achieved broad success, most approaches suffer a similar fate in low initial production levels that require extensive, resource-heavy iterative strain engineering refinement. Herein we introduce a growth-coupled biosynthetic (GrowBio) strategy that irrevocably connects microbial growth with specialized compound production. We demonstrate the plug-and-play versatility of GrowBio in the production of the structurally complex animal biopigment xanthommatin, a color-changing ommochrome with material and cosmetic potential. Xanthommatin biosynthesis directly fuels growth of a newly designed Pseudomonas putida 5,10-methylenetetrahydrofolate auxotroph (PUMA). Aided by genome-scale metabolic modeling, PUMA was designed and built to be controlled by endogenous formate co-produced as a coupled biosynthetic byproduct in the multistep conversion of tryptophan to xanthommatin. Adaptive laboratory evolution was utilized to streamline xanthommatins gram-scale bioproduction via growth rate selection, establishing GrowBio as a promising biotechnological approach for establishing and optimizing the microbial production of value-added molecules.

synthetic biology↗

Metabolic growth coupling strategies for in vivo enzyme selection systems

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. Model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. Furthermore, the use of currently available genome-reduced strains or one preeminent carbon source does not significantly enable or improve growth-coupling of target enzymes. Most importantly, observed trade-offs between the predicted viability of ESSs, the design-inflicted metabolic perturbations, and the coupling strength suggest that a suboptimal coupling has benefits regarding the experimental implementation of ESSs and growth-coupling in general. Overall, the computed design database, which is accessible through https://biosustain.github.io/ESS-Designs/, and its analysis lay the foundation for generating valuable in vivo ESSs for a range of biotechnological applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/613188v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@15d5a10org.highwire.dtl.DTLVardef@1c4067aorg.highwire.dtl.DTLVardef@856ce9org.highwire.dtl.DTLVardef@1b14cdb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIEnzyme selection system designs link enzyme activities to global metabolic processes. C_LIO_LIEnzymes can be categorized based on growth-couplable reaction stoichiometries. C_LIO_LIA database of strain designs for building enzyme selection systems was created. C_LIO_LIEnzyme selection systems enable optimization of a range product synthesis pathways. C_LIO_LIApplication recommendations: weakly coupled enzyme activities can have practical advantages. C_LI

systems biology↗

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes. One challenge in the development of a highly productive microbe is the high demand for reducing power. Here, we engineered Pseudomonas taiwanensis VLB120 for methyl ketone production as this microbe has been shown to sustain exceptionally high NAD(P)H regeneration rates. The implementation of published strategies resulted in 2.1 g Laq-1 methyl ketones in fed-batch fermentation. We further increased the production by eliminating competing reactions suggested by metabolic analyses. These efforts resulted in the production of 9.8 g Laq-1 methyl ketones (corresponding to 69.3 g Lorg-1 in the in situ extraction phase) at 53 % of the maximum theoretical yield. This represents a 4-fold improvement in product titer compared to the initial production strain and the highest titer of recombinantly produced methyl ketones reported to date. Accordingly, this study underlines the high potential of P. taiwanensis VLB120 to produce methyl ketones and emphasizes model-driven metabolic engineering to rationalize and accelerate strain optimization efforts.Competing Interest StatementJay D. Keasling has a financial interest in Amyris, Lygos, Demetrix, Napigen, Maple Bio, Apertor Labs, Ansa Biotechnologies, and Berkeley Brewing Sciences.Abbreviationsaqaqueous phaseCDWcell dry weightDOdissolved oxygenGCgas chromatographyGLCglucoseHPLChigh-performance liquid chromatographyMiMBlMinimization of Metabolites BalancesMKmethyl ketoneMSmass spectrometryOD600nmoptical density determined at 600 nmorgorganic phase(p)FBA(parsimonious) flux balance analysisView Full Text

synthetic biology↗

Protein allocation and enzymatic constraints explain Escherichia coli wildtype and mutant phenotypes

Proteins have generally been recognized to constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. Regulatory patterns of protein allocation were utilized to account for the condition-dependent proteome in a genome-scale metabolic reconstruction of Escherichia coli by linearly linking mass concentrations of protein sectors and single metabolic enzymes to flux variables. The resulting protein allocation model (PAM) correctly approximates wildtype phenotypes and flux distributions for various substrates, even under data scarcity. Moreover, we showed the ability of the PAM to predict metabolic responses of single gene deletion mutants by additionally assuming growth-limiting, transcriptional restrictions. Thus, we promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and metabolic engineering purposes.

systems biology↗

Determination of growth-coupling strategies and their underlying principles

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. We have developed the computational tool gcOpt, which identifies knockout strategies leading to the best possible GC by maximizing the minimally guaranteed product yield. gcOpt implicitly favors solutions resulting in strict coupling of product synthesis to growth and metabolic activity while avoiding solutions inferring weak, conditional coupling.\n\nGC intervention strategies identified by gcOpt were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. Thus, generating a dependency between supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC.\n\nAbbreviations

systems biology↗