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Mahadevan, R.

Publications and source records attributed to Mahadevan, R..

At least 37 records · Page 2Linked to original sources

In vitro quantification of metal ion specificity of CcNikZ-II in the presence of competing metal ions

Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite binding affinities for divalent transition metal ions being predominantly dictated by the Irving-Williams series for wild-type proteins, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the proteins metal binding affinities. A goal of metalloprotein engineering is to modulate these affinities to improve a proteins specificity towards a particular metal; however, the quantitative relationship between the affinities and the equilibrium metal-bound protein fractions depends on the underlying binding kinetics. Here we demonstrate a high-throughput intrinsic tryptophan fluorescence quenching method to validate kinetic models in multi-metal solutions for CcNikZ-II, a nickel-binding protein from Clostridium carboxidivorans. Using our validated models, we quantify the relationship between binding affinity and specificity in different classes of metal-binding models for CcNikZ-II. We further demonstrate that principles for improving specificity through changes in binding affinity are qualitatively different depending on the competing metals, highlighting the power of mechanistic models to guide metalloprotein engineering targets.

biochemistry↗

Ni(II) binding affinity and specificity of solute binding proteins: the importance of the double His motif and variable loop revealed by structural and mutational studies

Extracytoplasmic solute binding proteins (SBPs) are molecular shuttles involved in the cellular uptake of various small molecules and metal ions including Ni(II). Our previous study with the Ni(II) binding proteins (NiBPs) CjNikZ from Campylobacter jejuni and CcNikZ-II from Clostridium carboxidivorans demonstrated they were able to bind Ni(II) at low micromolar affinity without the need for additional chelators. Here, we determined the crystal structure of apo CcNikZ-II, which revealed a Ni(II) binding site comprised of the highly conserved double His (HH-)prong (His511, His512) and a short variable (v-)loop nearby (Thr59-Thr64, TEDKYT). Alanine scanning mutagenesis of the CcNikZ-II Ni(II) binding site identified Glu60 and His511 as essential for high affinity binding to Ni(II). Phylogenetic analysis of >4000 SBP sequences demonstrated the presence of two clusters of proteins containing the HH-prong with CcNikZ-II and CjNikZ. To provide insights into the role of the double His-prong and v-loop sequence in Ni(II) binding of NiBPs, nine purified CcNikZ-II homologues containing the HH-prong and v-loop were screened using an automated screening workflow. Metal binding assays with purified homologous NiBPs revealed high Ni(II) binding affinity without requirement for chelators indicating that the double His prong represents a signature motif for the presence of Ni(II) binding activity in SBPs. The engineered CcNikZ-II variants with the wild type v-loop (TEDKYT) replaced with v-loops from NiBPs with higher affinity showed up to an order of magnitude higher affinity for Ni(II). In addition, the v-loop appears to play a role in metal ion specificity as purified wild type and engineered NiBPs with different v-loop sequences showed distinct metal profiles. This work paves way for metalloprotein engineering of NiBPs towards biocatalytic and metal recovery applications.

biochemistry↗

Engineered nickel bioaccumulation in Escherichia coli by NikABCDE transporter and metallothionein overexpression

Mine wastewater often contains dissolved metals at concentrations too low to be economically extracted by existing technologies, yet too high for environmental discharge. The most common treatment is chemical precipitation of the dissolved metals using limestone and subsequent disposal of the sludge in tailing impoundments. While it is a cost-effective solution to meet regulatory standards, it represents a lost opportunity. In this study, we engineered Escherichia coli to overexpress its native NikABCDE transporter and a heterologous metallothionein to capture nickel at concentrations in local effluent streams. We found the engineered strain had a 7-fold improvement in the bioaccumulation performance for nickel compared to controls, but also observed a drastic decrease in cell viability due to metabolic burden or inducer (IPTG) toxicity. Growth kinetic analysis revealed the IPTG concentrations used based on past studies lead to growth inhibition, thus delineating future avenues for optimization of the engineered strain and its growth conditions to perform in more complex environments.

bioengineering↗

Increasing evenness and stability in synthetic microbial consortia

Construction of successful synthetic microbial consortia will harbour a new era in the field of agriculture, bioremediation, and human health. Engineering communities is a complex, multi-dimensional problem with several considerations ranging from the choice of consortia members and spatial factors to genetic circuit performances. There has been a growing number of computational strategies to aid in synthetic microbial consortia design, but a framework to optimize communities for two essential properties, evenness and stability, is missing. We investigated how the structure of different social interactions (cooperation, competition, and predation) in quorum-sensing based circuits impacts robustness of synthetic microbial communities and specifically affected evenness and stability. Our proposed work predicts engineering targets and computes their operating ranges to maximize the probability of synthetic microbial consortia to have high evenness and high stability. Our exhaustive pipeline for rapid and thorough analysis of large and complex parametric spaces further allowed us to dissect the relationship between evenness and stability for different social interactions. Our results showed that in cooperation, the speed at which species stabilizes is unrelated to evenness, however the region of stability increases with evenness. The opposite effect was noted for competition, where evenness and stable regions are negatively correlated. In both competition and predation, the system takes significantly longer to stabilize following a perturbation in uneven microbial conditions. We believe our study takes us one step closer to resolving the pivotal debate of evenness-stability relationship in ecology and has contributed to computational design of synthetic microbial communities by optimizing for previously unaddressed properties allowing for more accurate and streamlined ecological engineering.

systems biology↗

Model guided design of enhanced bi-stable controllers to effectively switch cellular states

Bi-stable gene regulatory motifs are found in a wide variety of natural gene regulatory networks and effect transcriptional switching between stable phenotypic states in cells. In synthetic gene regulatory circuits, these architectures can be leveraged to dynamically switch between distinct metabolic states for metabolic engineering and therapeutic applications. However, the lack of modularity and predictability of these motifs in varying environments has limited widespread application, especially since the factors that affect switching characteristics are still unclear. Using a genome-scale metabolic model, we first show that the productivity of a two-stage bioprocess declines as switching is delayed, establishing the value of fast-switching motifs. We then use a mathematical model, along with a newly developed dynamical modeling and continuity analysis framework, to analyze the dynamics and robustness of bi-stable switches over a range of biologically relevant parameter values, and identify a trade-off between the robustness of the motif - the parameter range over which it retains bi-stable function - and the speed at which it effects a phenotypic change. Crucially, by extending our model to incorporate host growth rate through a proteome-partitioning framework, we find that growth rate reshapes the achievable speed-robustness frontier, with the most robust switches attainable at an intermediate growth rate, and that the optimal balance of repressor degradation rates is set by both their relative binding affinities and the growth rate of the host. Using E. coli as a model host, we then constructed a library of 100 transcriptional switches spanning a wide range of switching speeds, experimentally confirming this trade-off and revealing host growth rate as a third design axis - alongside robustness and switching speed - that shapes switch behavior. Together, these results establish design principles for building bi-stable switches with defined switching characteristics, and we anticipate that our library of experimentally validated bi-stable switches will be valuable for effecting phenotypic changes with differing switching-speed requirements in metabolic engineering applications.

synthetic biology↗

Genetic engineering of Acidithiobacillus ferridurans with CRISPR-Cas9/dCas9 systems

Genus Acidithiobacillus includes a group of Gram-negative Fe/S-oxidizing acidophilic chemolithotrophic bacteria that are extensively studied and used for biomining processes. Synthetic biology approaches are key means to study and improve their biomining performance. However, efficient genetic manipulations in Acidithiobacillus are still major bottlenecks. In this study, we report a simple and efficient pAFi system (CRISPR-dCas9) and a scarless pAF system (CRISPR-Cas9) for genetic manipulations in A. ferridurans JAGS. The pAFi system harboring both dCas9 and sgRNA was constructed based on pBBR1MCS-2 to knockdown HdrA and TusA genes, separately, of which the transcription levels were significantly downregulated by 48% and 93%, separately. The pAF system carrying pCas9-sgRNA-homology arms was constructed based on pJRD215 to delete HdrB3 gene and overexpress Rus gene. Our results demonstrated that the pAF system is a fast and efficient genome editing method with an average rate of 15-20% per transconjugant in one recombination event, compared to 10-3 and then 10-2 in two recombination events by traditional markerless engineering strategy. Moreover, with these two systems, we successfully regulated iron and sulfur metabolisms in A. ferridurans JAGS: the deletion of HdrB3 reduced 48% of sulfate production, and substitution overexpression of Rus promoter showed 8.82-fold of mRNA level and enhanced iron oxidation rate. With these high-efficient genetic tools for A. ferridurans, we will be able to study gene functions and create useful recombinants for biomining applications. Moreover, these systems could be extended to other Acidithiobacillus strains and promote the development of synthetic biology-assisted biomining. HighlightsO_LITwo shuttle vectors were constructed for Acidithiobacillus ferridurans C_LIO_LIAll-in-one pAFi (CRISPR-dCas9) and pAF (CRISPR-Cas9) systems were built up for gene knockdown and genome editing, separately C_LIO_LIThe transcription levels of HdrA and TusA were reduced 48% and 93% using pAFi system and thus suppressed sulfur oxidation C_LIO_LIHdrB3 deletion and Rus overexpression were achieved using pAF system and showed significant effects on sulfur and iron oxidation respectively C_LIO_LIOur pAF system facilitated genome editing in Acidithiobacillus ferridurans with high efficiency (15-20%) in less than 4 weeks C_LI

bioengineering↗

REVOLVER: a low-cost automated protein purifier based on parallel preparative gravity column workflows

Protein purification is a ubiquitous operation in biochemistry and life sciences and represents a key step to producing purified proteins for research (understanding how proteins work) and various applications. The need for scalable and parallel protein purification systems keeps growing due to the increase in throughput in the production of recombinant proteins and in the ever-growing scale of biochemistry research. Therefore, automating the process to handle multiple samples in parallel with minimal human intervention is highly desirable; yet only a handful of such tools have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable and automatic protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER completes a full protein purification process with almost no human intervention and yields results equivalent to those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems will be useful to accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering. Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@13fff66org.highwire.dtl.DTLVardef@98aa2forg.highwire.dtl.DTLVardef@cd2e70org.highwire.dtl.DTLVardef@a3bd86org.highwire.dtl.DTLVardef@1efc67d_HPS_FORMAT_FIGEXP M_TBL C_TBL

biochemistry↗

Context-Dependent Flux Coupling via Conserved Small-Molecule Regulatory Structures

Small-molecule regulation modulates enzyme activity and is widespread in metabolic networks. However, the organization of small-molecule regulatory networks and its generalized role is not well understood. We analyze the structure of the genome-wide Escherichia coli small-molecule regulatory network (SMRN) to reveal that it optimizes controllability in the metabolic network. This is achieved by conserved, highly overabundant incoherent feedforward loops. Using multi-omics data, we characterize loop examples in central carbon metabolism. These use signals from hypothesized flux-sensing metabolites phosphoenolpyruvate, -ketoglutarate, citrate, and malate to distinguish between glycolysis, gluconeogenesis, and glyoxylate shunt activity to differentially couple fluxes across these major modes of metabolism. Our results suggest that coupling of fluxes by direct modulation of enzyme activity is an emergent property of the SMRN that depends heavily on both regulatory structure and metabolic context via the metabolome, and further that flux sensing and coupling may be a global property of the metabolic network.

bioengineering↗

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities. Author summaryMicrobes compose the most abundant form of life on our planet and they are almost never found in isolation, as they live in close association with one another and with other organisms. The metabolic capacity of individual microbial species dictates their ways of interaction with other species as well as with their environment. The metabolic interactions among microorganisms have been recognised as the driving force for the emergence of the properties of microbial communities. For this reason, understanding the effect of microbial metabolism on interspecies metabolic interactions is detrimental for the study of microbial communities. This study can be benefited by metabolic modeling and the insights offered by constraint-based methods which have been developed for interrogating metabolic models. In this paper, we present an algorithm that predicts cooperative and competitive metabolic interactions between species, while it resolves metabolic gaps in their metabolic models in a computationally efficient way. We use our community gap-filling algorithm to study microbial communities with interesting environmental and health-related applications.

systems biology↗

Automation assisted anaerobic phenotyping for metabolic engineering

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for a high-throughput, laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies - an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. Further, we propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding in conjunction with our phenotyping platform to serve as an effective scale-down model for bioreactor phenotypes. By integrating an in-house data-analysis pipeline, we were able to accelerate the test phase of the design-build-test-learn cycle of metabolic engineering.

bioengineering↗

SARS-CoV-2 receptor binding mutations and antibody mediated immunity.

SARS-CoV-2 mutations can impact infectivity, viral load, and overall morbidity/mortality during infection. In this analysis, we look at the mutational landscape of the SARS-CoV-2 receptor binding domain, a structure that is antigenic and allows for viral binding to the host. We analyze 104193 GISAID sequences acquired on October 15th, 2020 with a majority of sequences (96%) containing point mutations. We report high frequency mutations with improved binding affinity to ACE2 including S477N, N439K, V367F, and N501Y and address the potential impact of RBD mutations on antibody binding. The high frequency S477N mutation is present in 6.7% of all SARS-CoV-2 sequences, co-occurs with D614G, and is currently present in 14 countries. To address RBD-antibody interactions we take a subset of human derived antibodies and define their interacting residues using PDBsum. Our analysis shows that adaptive immunity against SARS-CoV-2 enlists broad coverage of the RBD suggesting that antibody mediated immunity should be sufficient to resolve infection in the presence of RBD point mutations that conserve structure.

bioinformatics↗

Challenges in Undergraduate Synthetic Biology Training: Insights from a Canadian iGEM Student Perspective

The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of synthetic biology technologies. However, pedagogical research on synthetic biology is scarce, especially when compared to some scientific and engineering disciplines. Within Canada, there are only three universities that formally teach synthetic biology programs; two of which are at the undergraduate level. Many Canadian undergraduate students are instead introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition where they work in design teams to conceive of and execute a synthetic biology project that they present at an international jamboree. We surveyed the Canadian landscape of synthetic biology education through the experience of students from the Canadian iGEM teams of 2019. Using a thematic codebook analysis, we gathered insights to generate recommendations that could empower future iGEM team operations and inform educators about best practices in teaching undergraduate synthetic biology.

scientific communication and education↗

Thermodynamic constraints are sufficient for the emergence of flux sensors in metabolism

Metabolism is a precisely coordinated phenomenon, the apparent goal of which is to balance fluxes to maintain robust growth. However, coordinating fluxes requires information about rates, which is not obviously reconcilable with known regulatory mechanisms in which concentrations are sensed through metabolite binding. While flux sensor examples have been characterized, the fundamental principles underlying the phenomenon in general are not well understood. Specifically, the questions of which fluxes can be sensed, and the mechanism by which they are remain open. We address this by showing that the concentrations of substrates of thermodynamically constrained reactions reflect upstream flux and therefore carry information about rates which can be propagated through regulatory interactions to control other fluxes in the network. Using fluxomic, metabolomic, and thermodynamic data in E coli, we show that the concentrations of a few metabolites in central carbon metabolism reflect their producing fluxes and demonstrate that they can transmit information about these rates because of their positions in the network and their roles as effectors.

systems biology↗

A microplate screen for metal-binding activity based on a nickel-binding protein from Clostridium carboxidivorans

Solute-binding proteins (SBPs) from ATP-binding cassette (ABC) transporters play crucial roles across all forms of life in transporting compounds against chemical gradients. Some SBPs have evolved to scavenge metal substrates from the environment with nanomolar and micromolar affinities (KD). There exist well established techniques like isothermal titration calorimetry for thoroughly studying these metalloprotein interactions with metal ions, but they are low-throughput. For protein libraries comprised of many metalloprotein homologues and mutants, and for collections of buffer conditions and potential ligands, the throughput of these techniques is paramount. In this study, we describe an improved method termed the microITFQ-LTA and validated it using CjNikZ, a well-characterized nickel-specific SBP (Ni-BP) from Campylobacter jejuni. We then demonstrated how the microITFQ-LTA can be designed to screen through a small collection of buffers and ligands to elucidate the binding profile of a putative Ni-BP from Clostridium carboxidivorans that we call CcSBPII. Through this study, we showed CcSBPII can bind to various metal ions with KD ranged over 3 orders of magnitude. In the presence of L-histidine, CcSBPII could bind to Ni2+ over 2000-fold more tightly, which was 11.6-fold tighter than CjNikZ given the same ligand. HighlightsO_LIan improved version of a high-throughput screen (microITFQ-LTA) is described for multiplexed elucidation of metalloprotein binding profiles C_LIO_LIvalidation was accomplished with the previously characterized CjNikZ; testing was accomplished with an uncharacterized homologue herein named CcSBPII C_LIO_LICcSBPII is shown to bind to multiple transition metal ions with a large range of affinities, and potentially overcome mismetallation using a simple histidine metallophore C_LI

biochemistry↗

Phenotypic design choices for enhanced productivity in microbial chemical production processes

Microbial metabolism can be harnessed to produce a broad range of industrially important chemicals. Often, three key process variables: Titer, Rate and Yield (TRY) are the target of metabolic engineering efforts to improve microbial hosts toward industrial production. Previous research into improving the TRY metrics have examined the efficacy of having distinct growth and production stages to achieve enhanced productivity. However, these studies assumed a switch from a maximum growth to a maximum production phenotype. Hence, phenotypes with intermediate growth and chemical production for the growth and production stages of two-stage processes are yet to be explored. The impact of reduced growth rates on substrate uptake adds to the need for intelligent choice of operating points while designing two-stage processes. In this work, we develop a computational framework that scans the phenotypic space of microbial metabolism to identify ideal growth and production phenotypic targets, to achieve optimal TRY targets. Using this framework, with Escherichia coli as a model organism, we compare two-stage processes that use dynamic pathway regulation, with one-stage processes that use static intervention strategies, for different bioprocess objectives. Our results indicate that two-stage processes with intermediate growth during the production stage always result in optimal TRY values even in cases where substrate uptake is limited due to reduced growth during chemical production. By analyzing the flux distributions for the production enhancing strategies, we identify key reactions and reaction subsystems that require perturbation to achieve a production phenotype for a wide range of metabolites in E. coli. Interestingly, flux perturbations that increase phosphoenolpyruvate and NADPH availability are enriched among these production phenotypes. Furthermore, reactions in the pentose phosphate pathway emerge as key control nodes that function together to increase the availability of precursors to most products in E. coli. The inherently modular nature of microbial metabolism results in common reactions and reaction subsystems that need to be regulated to modify microbes from their target of growth to the production of a diverse range of metabolites. Due to the presence of these common patterns in the flux perturbations, we propose the possibility of a universal production strain.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

A Multi-Scale Modelling Framework of the Whole Human Body Based on Multi-Objective Functions

Constraint-based multi-scale metabolic models are powerful tools to study complex biological systems like the human body, and to develop new treatment strategies for human diseases. They capture different scales of the system under study and provide the opportunity of understanding the interaction between multiple scales. In this paper, we have used our previously developed multi-scale whole body metabolism framework to establish a new approach to include multiple objectives of the human cells in computational analysis. The model has 3555 ordinary differential equations (ODEs) integrated with a genome-scale model of the hepatocyte, including 1826 biochemical reactions. The model has been solved for 74 objective functions simultaneously. Simulation results show that the integration algorithm has promise with respect to convergence, computational efficiency and response to perturbation. The results suggest that this method holds significant potential for the simulation of the range of metabolic phenotypes for mammalian cells where there are multiple objectives.

systems biology↗

Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life

A genome-scale network reconstruction (GENRE) represents the knowledgebase of an organism and can be used in a variety of applications. The drop in genome sequencing costs has led to an increase in sequenced genomes, but the number of curated GENRE s has not kept pace. This gap hinders our ability to study physiology across the tree of life. Furthermore, our analysis of yeast GENRE s has found they contain significant commission and omission errors, especially in central metabolism. To address these quantity and quality issues for GENRE s, we propose open and transparent curation of the pan-genome, pan-reactome, pan-metabolome, and pan-phenome for taxons by research communities, rather than for a single species. We outline our approach with a Fungi pan-GENRE by integrating AYbRAH, our ortholog database, and AYbRAHAM, our new fungal reaction database. This pan-GENRE was used to compile 33 yeast/fungi GENRE s in the Dikarya subkingdom, spanning 600 million years. The fungal pan-GENRE contains 1547 orthologs, 2726 reactions, 2226 metabolites, and 10 compartments. The strain GENRE s have a wider genomic and metabolic than previous yeast and fungi GENRE s. Metabolic simulations show the amino acid yields from glucose differs between yeast lineages, indicating metabolic networks have evolved in yeasts. Curating ortholog and reaction databases for a taxon can be used to increase the quantity and quality of strain GENRE s. This pan-GENRE framework provides the ability to scale high-quality GENRE s to more branches in the tree of life.

bioinformatics↗

Flux balance analysis predicts NADP phosphatase and NADH kinase are critical to balancing redox during xylose fermentation in Scheffersomyces stipitis

Xylose is the second most abundant sugar in lignocellulose and can be used as a feedstock for next-generation biofuels by industry. Saccharomyces cerevisiae, one of the main workhorses in biotechnology, is unable to metabolize xylose natively but has been engineered to ferment xylose to ethanol with the xylose reductase (XR) and xylitol dehydrogenase (XDH) genes from Scheffersoymces stipitis. In the scientific literature, the yield and volumetric productivity of xylose fermentation to ethanol in engineered S. cerevisiae still lags S. stipitis, despite expressing of the same XR-XDH genes. These contrasting phenotypes can be due to differences in S. cerevisiaes redox metabolism that hinders xylose fermentation, differences in S. stipitis redox metabolism that promotes xylose fermentation, or both. To help elucidate how S. stipitis ferments xylose, we used flux balance analysis to test various redox balancing mechanisms, reviewed published omics datasets, and studied the phylogeny of key genes in xylose fermentation. In vivo and in silico xylose fermentation cannot be reconciled without NADP phosphatase (NADPase) and NADH kinase. We identified eight candidate genes for NADPase. PHO3.2 was the sole candidate showing evidence of expression during xylose fermentation. Pho3.2p and Pho3p, a recent paralog, were purified and characterized for their substrate preferences. Only Pho3.2p was found to have NADPase activity. Both NADPase and NAD(P)H-dependent XR emerged from recent duplications in a common ancestor of Scheffersoymces and Spathaspora to enable efficient xylose fermentation to ethanol. This study demonstrates the advantages of using metabolic simulations, omics data, bioinformatics, and enzymology to reverse engineer metabolism.

microbiology↗