To reduce emissions from petrochemical refinement, bio-production has been heralded as a way to create economically valuable compounds with fewer harmful effects. For example, gaseous alkenes are precursor molecules that can be polymerized into a variety of industrially significant compounds and have biological production pathways. Production levels, however, remain low, thus enhancing bio-production of gaseous petrochemicals for chemical precursors is critical. This review covers the metabolic pathways and production levels of the gaseous alkenes ethylene, isoprene, and isobutene. Techniques needed to drive production to higher levels are also discussed. In today’s world, the impact of fossil fuels is inescapable. In addition to energy, fossil fuel products are used to create high-value industrial chemicals and polymers that permeate our society. Currently, petrochemicals are produced via steam cracking of crude petroleum products, which requires high temperature and pressure, and anoxic conditions Fig. . This prMuch of the effort to date has been in production of liquid biofuels or biodiesels . Ethanol S-adenosyl-methionine (SAM) pathway, (2) 4-(methylsulfanyl)-2-oxobutanoate (KMBA) pathway, and (3) 2-oxoglutarate pathway [Three pathways for biological ethylene production have been identified (1) pathway , 7. Plan pathway .In addition to plants, a variety of bacteria and fungi also naturally produce ethylene using the KMBA pathway. KMBA is produced through a NADH:Fe(III)EDTA oxidoreductase-mitigated reaction with methionine. The pathway produces ethylene in trace amounts, but has been observed to be enhanced under ammonia limitations . It is hPseudomonas and Penicillium genus. Ethylene is produced from 2-oxoglutarate using ethylene-forming enzyme (EFE)  into the active site [The 2-oxoglutarate pathway is used by several microbes within the Reaction  e analog . Additioive site , 13. Futive site , 15. Thud-xylulose-5-phosphate (DXP) is made through a condensation reaction with pyruvate and glyceraldehyde-3-phosphate. This is eventually converted to hydroxy-2-methyl-2-butenyl-4-diphosphate (HMBPP), which is the precursor for IPP and DMAPP. An IPP isomerase (IDI) catalyzes the reversible reaction of IPP to DMAPP. In this pathway, 1-deoxy-d-xylulose-5-phosphate synthase (DXS), 1-deoxy-d-xylulose-5-phosphate reductoisomerase (DXR), and IDI have been identified as the rate-limiting enzymes [The two main isoprenoid pathways that can lead to isobutene are the methylerythritol-phosphate (MEP) pathway and mevalonate (MVA) pathway Fig. . Both pa enzymes .The MVA pathway starts with two molecules of acetyl-CoA to form 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA). The rate-limiting step in this pathway is the reduction of HMG-CoA to mevalonate by the HMG-CoA reductase . MevalonSaccharomyces cerevisiae and Lactococcus lactis [E. coli that produce isobutanol via acetolactate and 2-oxoisovalerate [R)-10-hydroxystearate with narrow substrate specificity. A patent by Marliere searched 165 homologs of oleate hydratase and found several that can produce isobutene [Isobutene can be produced from three different intermediates—isobutanol, isovalerate, and 3-hydroxyisovalerate Fig. . There as lactis . There avalerate  Fig. 2)Saccharomvalerate . The reasobutene . No infoRhodotorula minuta with a rate of 0.45 mg L−1 h−1 and 41 μg g−1 h−1 [R. minuta produces isobutene by the decarboxylation of isovalerate using a microsomal cytochrome P450  [−1 g cells−1) and acts through catalyzing the phosphorylation of 3-HIV into an unstable 3-phosphate intermediate that undergoes spontaneous decarboxylation to form isobutene  . This neReaction .3\documeefe gene can produce ethylene from various forms of carbon , Quercus petraea (oak), Eucalyptus globulus , Salix discolor (willow), and Pueraria montana (kudzu vine) [Km) [Km values from DMAPP range from 18.3 to 0.03 mM with Eucalyptus globulus having the lowest value [ispS for expression in Synechocystis sp. PCC6803 [S. cerevisiae IDI and P. alba ISPS was generated. Mutants expressing this fusion protein were shown to have higher production, up to 4.6 mg h−1 L−1, of isoprene than in mutant strains overexpression either of the two enzymes [ispS gene in the Ipomoea batata, a member of the asterids clade. Compared to the most similar ISPS protein (Quercus petraea), the I. batata ISPS protein is only 55% similar. Ilimen et al. went further and compared the isoprene production rates of ISPS proteins from different plants that were cloned into E. coli, thus allowing for a direct comparison of the unmodified proteins. The highest isoprene production rate was 40 μg L−1 h−1 by the I. batata ISPS. This was two times more than the second highest production rate produced by the poplar ISPS. Other studies that have expressed ISPS from kudzu and poplar in E. coli without further optimization found slightly higher production values than found in Ilimen et al. but the highest was 33 μg L−1 h−1 [Km of the I. batata ISPS has not been determined, the faster rates of isoprene production warrant further investigation. Furthermore, a comparative analysis may identify further residues for site mutagenesis.Isoprene synthase has been characterized in several plants, mainly in the rosids clade such as zu vine) –50. Biocne) [Km) , 51, 52.st value . This re PCC6803 , removed PCC6803 , 54, sit PCC6803 , and per PCC6803 . Additio enzymes . A recen enzymes . Ilmen e L−1 h−1 , 59. WhiS. cerevisiae, that was previously engineered to have increased precursor supply, performed a combinatorial approach that included overexpression and deletion of competing promoters, increased the transcriptional activator Gal4p, performed directed evolution of ispS, while growing in a fed-batch fermentation system with dextrose as the carbon source [−1 h−1, which is the highest reported values thus far. However, production levels could be pushed with continued combinatorial approaches that further removed competing pathways or in new organisms that have higher efficiencies. For example, a methanogen has been genetically engineered to produce isoprene increasing efficiency by alleviating the need for oxygen [In addition to identifying alternative isoprene synthases, research has been performed to optimize pathways that produce isoprene. For example, pathways that produce DMAPP can be optimized through gene overexpression, different promoters, and alternate control methods , 60, 61.n source . The appr oxygen . IdentifE. coli using plasmid-based systems [S. cerevisiae into E. coli. When they used error prone PCR on the MVD, one of the variants increased the production rates by 38-fold  protein engineering or directed evolution, (2) pathway construction, and (3) regulation of key enzymes or transcription factors. Each of these have helped to enhance production values, but, in general, it is the combination of multiple steps that have seen the greatest advancement in production values. Moving forward, bioinformatics will be integral to combining each of these steps to enhance production levels . AdvanceE. coli has been the typical model organism for ethylene, isoprene, and isobutene production [E. coli is not appropriate for all feedstocks and the majority of microbes are uncultured or ‘biological dark matter’ and may hold clues for faster and more efficient production strains. As our knowledge of microbial genomes grows incorporating more of the typically “non-model” organisms will become more feasible. In addition, if these non-model organisms are extremophiles, it opens up the gate for wider industrial conditions to be used.A variety of organisms have been used for bio-production of gaseous alkenes –76, but oduction , 78. In oduction . HoweverNot all hurdles for bio-production are biological. One of the hurdles in bringing biofuels to market is effectively scaling up the process from benchtop, to industrial scale bioreactors. This is due in large part to the large differences in reaction conditions common for benchtop biogas production monitoring vs industrial production, i.e., sealed headspace vessels vs. bioreactors. To our knowledge, there have not been any reports on attempts to scale-up production for any of these gaseous alkenes. It is outside the scope of this review to detail all the types of bioreactors and the modes of operation, for more detailed information see –82; howeA multifaceted approach is needed to overcome these barriers. The ability to rapidly create optimized production strains will require further genetic engineering as well as advances in bioreactor design, bioinformatics pipelines, and identification of novel microbes and enzymes combined with the ability to accurately integrate novel pathways into both model and non-model organisms. Optimization of every stage of the process must occur, which will require multidisciplinary teams. For example, teams of engineers, modelers, and synthetic biologists can work together to design a complete optimized system from the bioreactor, to streamlining genetic engineering through deriving mathematical models of metabolic networks to predict energetic favorability . Powerfu


Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.We investigate in silico the metabolic capabilities of P. putida.We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida toward maximization of biosurfactant production.We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of  Pseudomonas aeruginosa  dimerizes R-3-hydroxy FAs to form R-3-oxy)alkanoic acids (HAA)  ; subsequds (HAA) . By contications . AlthougThe applications of bacterial surfactants are diverse and rapidly growing in demand. One of the reasons rhamnolipids have become such an attractive area for biochemical research is the scope of their applications. Rhamnolipids could replace petrochemical derived surfactants used in many cleaning products detergents . RhamnolSalmonella typhimurium, Escherichia coli, Enterobacter aerogenes, Serratia marcescens and Klebsiella pneumoniae), Gram-positive bacteria  and fungi   . In addi grisea) . The usePseudomonas spp.  synthase  with dimensions of m × n, where the m metabolites are represented a rows and the n reactions are represented as columns. The stoichiometric matrix is a numerical matrix of stoichiometric coefficients for each metabolite participating in a reaction. The stoichiometric coefficient for every metabolite consumed and produced in the system has a negative and positive coefficient respectively. A zero stoichiometric coefficient is given for every metabolite that does not take part in a given reaction. It is assumed that the system is at a pseudo-steady state S · v = 0 that holds for internal metabolites, i.e. those reactants and products of the chemical reactions constituting the model that cannot be imported or exported directly. The vector v represents the flux distribution of the n reactions. Exchange metabolites can be imported and exported from the system, so they do not satisfy the steady state assumption. This flux distribution v therefore represents a feasible flux of metabolites through the reaction network, where under the principle of mass conservation the total amounts of internal metabolite consumed and internal metabolite produced are the same, and the derivative of their concentration is therefore equal to zero.Flux balance analysis is a widely used mathematical approach for modelling large-scale metabolic networks . BecauseVmax) and lower (Vmin) bounds on the range of values that the flux of each reaction can hold (Vmin ≤ v ≤ Vmax). These constraints define the space of allowable flux distributions at which every metabolite is consumed or produced by each reaction in the system. Despite these constraints, the system is still underdetermined (there are more unknowns than equations), and therefore infinite possible solutions exist. A flux distribution can be obtained by defining an objective function that is a scalar product of the vector of flux rates v, and a vector of weights c, measuring how each component in the network contributes to the production of a biologically desirable phenotype. The set of all possible solutions to the FBA problem is given by the equation and constraints:c allows to select either the biomass or rhamnolipids as the objective function.Constraints such as directionality and capacity  are placed on individual reactions by defining the upper  as a control growth condition. To determine the best carbon source for optimal rhamnolipids synthesis, we investigated alternative carbon sources separately: fructose, sucrose, glycerol, benzoate and myristic acid. We simulated the growth medium with a single carbon source by setting to 10 mmol/gDW/h the uptake of the carbon source under investigation and to zero the uptake of the other sources.Consistent with the reference model P. putida model was optimized to maximize the production and export of rhamnolipids. Hence, we used maximum rhamnolipids production as the objective function in our engineered model. Geometric FBA was used to calculate the optimal flux distribution that maximizes the objective function. Simulations were carried out in MATLAB (version R2018a) using the COBRA toolbox . The constant γ sets the weight of the gene set expression level as an indicator of the rate of production of the associated enzyme (GEMsplice  was usedd enzyme . We ran P. putida. We downloaded the expression data of P. putida from GEO (accession number: GSE28257). The dataset provides the expression levels θ of 5,547 genes for 40 samples of the P. putida wild type and 40 samples of P. putida Tn5 mutants. For each sample, a condition specific model was created by using We used the integrated model to investigate the relation between gene-expression data and rhamnolipids production in v) of the two groups (wild type and Tn5 mutant) to identify a set of differentially active reactions (DARs), that is, reactions with an adjusted p-value < 0.05. The identified reactions belong to disrupted metabolic pathways that carry a significantly different flux between the wild type samples and the Tn5 mutant samples when the rhamnolipids production is maximized. To further investigate those disrupted metabolic pathways and identify the genes contributing to the flux rates of the DARs in the two groups, we applied the variable selection regression method described below. The idea is to identify the genes that are highly predictive of the flux rate of each DAR when optimizing the rhamnolipids production rate.After running the condition-specific models, we compared the predicted flux rates  with p predictors (genes). Let y = (y1t)T,…, y be the response (the FBA solution vector) and X = (x1|…|px) be the model matrix (gene expression matrix), where jx = T, j = 1,…, p are the predictors. For any fixed non-negative λ1 and λ2, we use the elastic-net regularization criterion  is the vector of coefficients to be estimated, 2/(λ1 + λ2) and λ = λ1 + λ2; then solving Pα,λ(β) is the elastic-net penalty function defined asLet riterion , namely X was set equal to the normalized gene expression matrix, with t = 80 observations (expression profiles) and p = 5,547 predictors (genes). The response variable y was set equal to the vector of flux rates of the DAR to be analyzed (y is a vector with dimension t × 1). Hence, y is the vector with the flux rates of a given DAR resulting from running each of the t = 80 FBA condition-specific models. For each DAR, we set the regularization parameter α = 0.5 to achieve a balance between lasso and ridge regression. We used a 10-fold cross validation to identify the optimal λ. Simulations were carried out in R version 3.5.1 using the glmnet package 2.0-16 . We found that biomass synthesis and rhamnolipids production increased linearly with the rate of metabolite uptake. Our simulation-based predictive results are in keeping with our lab-based fermentation work previously carried out with Pseudomonas strains  to drive the biosynthetic pathway through two distinct routes: the rhamnose pathway and FA pathway . GlucoseP. putida model was optimized for biomass and rhamnolipids production, the amount of rhamnolipids production increased with the uptake of each metabolite. Metabolism of myristic acid (C-14), followed by fructose and sucrose/glucose, provided the best condition for optimal rhamnolipids synthesis. As expected, rhamnolipids synthesis increased in a linear relationship with the increased uptake of various carbon sources , a form of unsupervised machine learning, identifies data similarities from multidimensional biological datasets . More spWe investigated the individual reactions and identified the key components that drive change in the growth and rhamnolipids production in the engineered model. We applied PCA on our observed flux dataset generated under different growth media; glucose, fructose, sucrose, glycerol, benzoate and myristic acid. To characterize the unique features of individual reactions and variables in the observed flux datasets, we plotted the first two singular vectors of PCA  and 2B. 2O and H2O transport , scored highly with the first component, indicating the energy demand for growth and rhamnolipids synthesis  has also been previously linked to the rhamnolipids pathways (P. putida metabolic model). As a consequence, these results are complementary to the outcomes obtained through metabolic network analysis alone, which does not take into account specific transcriptomic profiles.Purine metabolism and FA biosynthesis are the two pathways associated with the top-2 DARs with adjusted oduction  and bactoduction . The benpathways . Indeed,P. putida samples.Regression analysis  was then applied to identify the key genes contributing to the flux rate of the DARs. The growing demand for biosurfactants requires rapid, efficient and innovative approaches for its synthesis, including the use of microorganisms. However, native bacterial cells are very inefficient at maximizing the production of industrially-relevant products. Bioengineering of such cells can improve the yield, but the number of potential metabolic and genetic interventions is enormous in practice . At the P. putida KT2440, offcially classified as a “generally recognized as safe” strain and used in the production of diverse natural products, including rhamnolipids (P. putida was observed to have resistance to higher rhamnolipid concentrations (90 g/L) in the production medium when compared to other microbial hosts of industrial importance like E. coli, B. subtilis and C. glutamicum  and P. putida (0.6 g/L). But no rhamnolipids were produced by recombinant E. coli and P. oleovorans, despite the detection of an active rhamnosyltransferase. Recombinant E. coli strains were also used by P. aeruginosa rhlAB genes. P. putida (7.3 g/L) and recombinant E. coli HB101 (52 mg/L) with soybean oil and oleic acid as substrates, respectively. As the production of high yields of rhamnolipids is dependent upon precursors provided by the metabolic flux within the bacterium, it is unlikely that simply implanting the necessary genes in a bacterium will be sufficient to make that organism produce rhamnolipids in higher concentrations  supported optimal growth rate and rhamnolipids production compared to the other carbon sources. When the P. putida model was optimized for biomass and rhamnolipids production, the amount of rhamnolipids production increased with the uptake of each metabolite. Quorum sensing, namely the mechanism by which bacteria engage in cell-to-cell signaling communication using diffusible molecules based on a critical cell density, might be one of the reasons why rhamnolipid synthesis is associated with exponential stage of the biomass , multi-target optimization algorithms have been developed and applied to genome-scale metabolic models of microorganisms to optimize multiple cellular functions  If more than one omic-level information is available, methods from network theory  Multi-step optimization algorithms can be used to maximize the growth rate and rhamnolipids synthesis, and simultaneously minimize byproduct formation in a multi-target fashion . The prok theory  can be aPseudomonas to environmental perturbations and in the production of rhamnolipids (P. putida for rhamnolipids production and provide a framework and a working model for further studies involving optimization of biosurfactant production.The ability to adapt to such conditions across multiple omic levels can for instance be assessed by evaluating the changes in the proteins of the outer membrane, key players in the adaptation of nolipids . Taken t10.7717/peerj.6046/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.6046/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.6046/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.6046/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.6046/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.6046/supp-6Supplemental Information 6Click here for additional data file.


Escherichia coli and Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes in these fast-growing microbes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same metabolic enzyme-encoding gene with increasingly reduced enzymatic function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts of metabolic genes will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for  A classic example is phenylketonuria, a human disease that is caused by a single gene defect but which affects multiple systems, with symptoms ranging from lighter skin color to mental disorders3. Pleiotropic effects can cause alleles to affect fitness differentially at different ages, a phenomenon believed to be a major cause of aging6; indeed, alleles contributing to increased longevity often show reduced fertility and stress tolerance7. Similar antagonistic epistasis may underlie other important biological phenomena such as speciation8 and cooperation9. Understanding the factors that contribute to pleiotropy is of fundamental importance in genetics12, evolution16, development18, as well as in disease20 and ageing4. In comparison to its fundamental importance, empirical knowledge of the prevalence and especially on the causal mechanisms of pleiotropy is scarce21.A gene is pleiotropic if it affects more than one phenotypic trait22. Molecular gene pleiotropy refers to the number of functions of a gene and its products, e.g., the number of reactions catalyzed by a single enzyme. Developmental pleiotropy describes the genetic and evolutionary interdependence of phenotypic aspects. Finally, selectional pleiotropy refers to the number of separate fitness components affected by mutations to a gene. In this study, we focus on the latter type of pleiotropy.Pleiotropy may be classified according to the types of traits considered25. The degree of pleiotropy is then defined as the number of traits affected when a gene becomes fully non-functional. Wang et al.25 analyzed phenotypes of large numbers of yeast, nematode, and mouse mutants. They found that pleiotropy is widespread: on average, yeast gene knockouts affect 8% of the examined traits; for the nematode, the corresponding number is 10%, for the mouse 3% . The distributions of the degree of pleiotropy appear rather similar across very different study systems, from the skeletal features of mice25 to metabolic systems28. Moreover, pleiotropy was found to be modular, such that sets of genes tend to affect the same sets of traits25.Experimental studies generally assess pleiotropy through the analysis of gene knockoutsE. coli, 36% of metabolic reactions are catalyzed by enzymes also involved in other reactions; the same is true for 27% of metabolic reactions in the yeast Saccharomyces cerevisiae28. Pleiotropic effects of mutations that affect enzyme activity can be simulated from genome-scale metabolic models using constraint-based modeling techniques such as flux balance analysis (FBA)30. The functional pleiotropy of a metabolic gene can then be defined as the number of biomass components whose maximal production is affected by the gene’s knockout26. Previous studies using this definition found that a metabolic gene’s functional pleiotropy is related to its propensity to form negative epistatic interactions with other metabolic genes27.In 31. Thus, it is important to distinguish between the pleiotropy of the gene and the pleiotropy of individual mutations, especially in evolutionary and clinical contexts. For example, while 4.6% of human SNPs implicated in complex non-Mendelian phenotypes show pleiotropic effects, most of these do not fully abolish protein function32. Experimental studies indicate that mutational pleiotropy tends to be smaller than gene pleiotropy31.While full gene knockouts are easily examined experimentally, they may not be representative of the effects of deleterious alleles segregating in natural populations: individual mutations may affect only a subset of all traits influenced by the genei.e., are the same number of functions affected when protein function or expression is reduced only partially? How modular is metabolic pleiotropy? Currency metabolites, such as ATP and NADPH, are used as cofactors in many otherwise unrelated reactions; it thus appears highly likely that a substantial fraction of metabolic pleiotropy is due to effects on the production of currency metabolites. Is such an effect of currency metabolites on patterns of pleiotropy confirmed by simulated data?Genome-scale metabolic models allow us to dissect the relationship between gene and mutational pleiotropy in quantitative detail, without being hampered by the detection limits of experimental assays. Does the degree of pleiotropy depend on how severely a given allele of a metabolic gene reduces protein activity, Escherichia coli, and a corresponding eukaryotic system, the baker’s yeast Saccharomyces cerevisiae. We find that most gene knockouts that impact fitness do so by affecting the production of multiple biomass components, and that the number of affected biomass components typically increases with increasing mutation severity. Pleiotropy is rarely a consequence of multiple molecular gene functions, but is an emergent property of the metabolic network. For many genes, pleiotropy is indeed mediated through their involvement in the generation of currency metabolites.Below, we address these questions by analyzing the metabolic networks of a representative bacterial model system, E. coli33 and the yeast S. cerevisiae34 (obtained from https://sourceforge.net/projects/yeast/files/). The maximal biomass production rates were estimated using flux balance analysis (FBA)30. For both model systems, we identified the flux distribution compatible with maximal biomass production that had the smallest sum of absolute fluxes, a strategy often termed parsimonious FBA (pFBA), which approximates optimal utilization of limited cellular protein resources35.We first estimated wildtype flux distributions in the default growth condition for the genome-scale metabolic model of 36, starting from 100% (the wildtype) down to 0% (a full gene knockout) in steps of 0.5%. For each flux reduction, we defined the degree of pleiotropy (referred to simply as “pleiotropy” below) as the number of biomass components whose production was reduced by at least 0.01% compared to the maximal (wildtype) production. Note that with this definition, only genes with pleiotropy ≥2 are pleiotropic, while genes with pleiotropy 0 (no affected biomass component) or pleiotropy 1 (one affected biomass component) are non-pleiotropic.To simulate mutations that cause different reductions of protein function or expression and correspond to different deleterious alleles of a metabolic gene, we restricted the maximal flux through all reactions requiring this gene to a fixed percentage of the estimated wildtype flux35, and so in many cases a flux restriction through one reaction may be compensated by a rerouting of fluxes through alternative pathways. Such rerouting would require the upregulation of the corresponding genes. While it has been observed experimentally that cells can survive many gene deletions in central metabolism without drastic changes in gene expression37, the necessary upregulation of protein expression will not occur spontaneously at least for some pathways38. More importantly, if we are interested in the de facto contribution of a given gene to the production of biomass components, then it is of no consequence if alternative pathways could take over part of this functionality. Thus, when calculating the maximal (wildtype) production rate of individual biomass components as well as when simulating the effects of mutations to a given metabolic gene, we did not allow the redistribution of fluxes to alternative pathways: we allowed only decreases, not increases, of the absolute value of any flux compared to the wildtype flux distribution obtained with pFBA.Flux distributions at maximal biomass production rate are usually not uniqueNote that experimental studies often employ a pragmatic working definition of pleiotropy that lies somewhere between the definitions of pleiotropy proposed here based on the wild type flux distribution on the one hand and a quantitative measure of essentiality based on an analogous calculation that allows the free redistribution of fluxes. In these studies, pleiotropy is typically estimated as the number of traits with observable phenotypic changes after the gene knockout, but before allowing the strain to adapt to its new genotype. In this case, some fluxes may be rerouted due to enzymes and transporters that are expressed either spuriously or because of other roles they play in wildtype physiology, while other fluxes that require the upregulation of the corresponding enzymes and transporters will not yet be active. Thus, our definition of pleiotropy describes a “worst case scenario”, providing an upper limit on experimentally measured pleiotropy.E. coli: 1,067 genes or 78.1%; S. cerevisiae: 687 genes or 75.6%). Among genes contributing to biomass production—and thus fitness—in the wildtype, non-pleiotropic cases are rare: in E. coli, only 54 full-gene knockouts  affect exactly one biomass component, while the same is true for only 12 knockouts  in S. cerevisiae. Conversely, knockouts of 32 genes in E. coli (10.7% of knockouts with fitness contributions) and 40 genes in S. cerevisiae (18.1% of knockouts with fitness contributions) affect the production of all biomass components. Many of the remaining genes show low degrees of pleiotropy, affecting the production of only a few biomass components; on average, full gene knockouts of fitness-relevant genes affect the production of 20% of biomass components in E. coli and 34% of biomass components in S. cerevisiae . These numbers drop only marginally when we consider only gene products that are essential for multiple reactions, to 92.2% in E. coli and to 94.5% in yeast  rather than random sets of traits. Supplementary Figure 41, resulting in raw modularities of Q = 0.235 for E. coli and Q = 0.197 for S. cerevisiae. Both networks show highly statistically significant modularity: in each case, the modularity of 10,000 randomly rewired networks was always lower than observed for the real pleiotropy network  can be represented as a bipartite graph, with links connecting genes with affected biomass components. Modules are defined as sets of genes and traits with significantly more within-module than between-module links25, we then defined a z-score for modularity 42 as the difference between the observed modularity and the mean modularity of randomly rewired networks, measured in number of standard deviations. The E. coli pleiotropy network exhibits a scaled modularity of 9.1, while the S. cerevisiae network has a scaled modularity of 4.9, i.e., the modularity of metabolic pleiotropy is about 9 and 5 standard deviations higher than for corresponding random gene-trait networks. These values are surprisingly low: for five different experimental study systems and trait definitions, Wang et al. found a median scaled modularity of 37 (range 34–238). Thus, metabolic pleiotropy networks are less modular than other pleiotropy networks, suggesting that the underlying metabolic network shows more interconnections between the pathways producing different sets of biomass components than the genetic networks underlying other types of traits. Our findings on modularity may be related to the role of currency metabolites, which crosslink the diverse metabolic pathways (see below).Following ref.i.e., mutations that reduce enzyme capacity without fully abolishing enzyme function. About 20% of E. coli genes with fitness contributions have constant pleiotropy: small-effect mutations of these genes affect the same number of biomass components as full gene knockouts. In comparison, only 7.7% of yeast genes contributing to fitness exhibit constant pleiotropy.We next examined the pleiotropy of alleles with small-effect mutations, Lipoamide dehydrogenase .All other genes contributing to fitness affect an increasing number of biomass components for increasingly deleterious alleles. Figure ρ = 0.926 (E. coli) and ρ = 0.986 (S. cerevisiae), P < 10−6 in each case from randomizations; see Methods). Thus, genes whose full knockout showed higher metabolic pleiotropy also showed more stepwise increases in pleiotropy for increasingly debilitating mutations.The pleiotropy of the full gene knockout constitutes an upper limit to the number of stepwise increases in pleiotropy. If there was otherwise no systematic relationship between maximal pleiotropy and the number of steps, we would expect the numbers of steps to be uniformly distributed between zero and the pleiotropy of the full knockout. However, the correlation between the number of steps and pleiotropy at full knockout was much stronger than expected from such a relationship . The reverse is not true: a mutation to a gene may affect the maximal production of biomass, but not the production of any individual biomass component. This is a consequence of the algorithm employed to estimate production capabilities for individual biomass components. If we maximize the production of a single compound, then pathways usually concerned with the production of other biomass components can be diverted to the production of this compound. While we find no such genes for S. cerevisiae, this is indeed the case for 3 essential E. coli genes, which encode transporters for acetate (b4067), magnesium/nickel/cobalt (b3816), and calcium/sodium .All genes whose mutations affect the production of at least one biomass component must also affect the overall production of biomass  in E. coli and 310 out of 3,324 reactions (9.3%) in S. cerevisiae involved at least one of these metabolites.We can conceptually partition internal metabolites into currency metabolites—those involved in many reactions, E. coli: here, 55.3% of pleiotropic genes are affected, with NADH making the biggest contribution (over 40%) , but is an emergent property of the metabolic network (type II). Pleiotropy is modular, but to a lower degree than estimated experimentally for non-metabolic systems25.Using constraint-based simulations of the metabolic models for 31. Alleles that only knock down protein activity (by reducing enzyme/transporter function or expression level) often affect only a subset of phenotypic traits, with additional traits affected progressively as alleles become more deleterious. Thus, the physiological effect of the full gene knockout will in most cases not be representative for the effects of deleterious alleles that retain some level of enzyme function. This type of effect is also evident from individual medical observations of pleiotropy. For example, some small-effect mutations affecting human SOX9 expression lead to minor skeletal malformations, while the consequences of large-effect mutations can include sex reversals44.For most pleiotropic genes, pleiotropy increases strongly for alleles with increasingly debilitating effects. Thus, standard measures of pleiotropy based on gene knockout studies are more likely to reflect the maximal degree of mutational pleiotropy of a given gene27, we found that the pleiotropy of many genes is mediated through the generation of currency metabolites such as ATP, NADPH, or FADH2. This is true for more than half of the pleiotropic genes in E. coli, and for 87% of pleiotropic genes in yeast.How can we understand the dependence of pleiotropy on the degree to which an allele reduces protein activity? For increasingly deleterious alleles, more and more metabolic resources must be channeled into the compensation of the compromised pathway; as a consequence of this increasing drain of resources, more and more other pathways are affected. Not surprisinglyE. coli and yeast, we found a number of quantitative differences. Compared to E. coli genes, yeast genes (i) showed generally higher pleiotropy and were rarely of pleiotropy 1; (ii) were less likely to have constant pleiotropy; and (iii) were more likely to show reduced pleiotropy when supplied with currency metabolites. Moreover, (iv) the yeast pleiotropy network exhibited lower modularity. In part, these differences may be related to network size. The E. coli metabolic network encompasses substantially more genes overall than the yeast network. However, we constrained network usage to reactions active in the wildtype. In contrast to total network sizes, the active metabolic network of yeast (755 reactions and 184 metabolites) is substantially larger than the active metabolic network of E. coli (462 reactions and 103 metabolites); this difference is consistent with the notion that yeast metabolism is more complex, yet less flexible than E. coli metabolism. While the average number of reactions per metabolite is similar between E. coli (4.49) and yeast (4.10), the lower yeast modularity indicates that reactions more often connect otherwise distant network parts. A role of currency metabolites in such connecting reactions would be consistent with the larger effect of currency metabolite supply on pleiotropy in yeast. In sum, the higher interconnectedness of the yeast pleiotropic network, combined with the larger active metabolic network size, appears to provide more potential for pleiotropic effects.While the overall patterns of pleiotropy appear qualitatively similar between 45.Pleiotropy is a complex phenomenon: it is not constant, but varies between different alleles of the same gene, and its causes are often indirect. Thus, experimental as well as computational analyses of pleiotropy should move away from focusing on full gene knockouts, and instead consider explicitly the degree to which mutations reduce protein activity. The necessity of a corresponding nuanced view of pleiotropy may be particularly evident in studies of medically relevant mutations, where full knockouts are often lethal, while small-effect mutations may segregate at appreciable frequencies in the human populationEscherichia coli metabolism, we used the metabolic reconstruction iJO136633, encompassing 1,366 metabolic genes associated with 2,251 reactions. For the yeast S. cerevisiae, we used the yeast7.6 model (https://sourceforge.net/projects/yeast)34, accounting for 909 metabolic genes associated with 3,324 reactions. The published models were used without any modifications. The E. coli model contains a growth-independent maintenance energy consumption term (the ATPM reaction), which enforcies a minimal ATPase activity of 3.15 mmol/gDW/h. We utilized the default biomass reactions for E. coli (Ec_biomass_iJO1366_core_53p95M), which comprises 50 essential biomass components  in the nutritional environment studied, and fluxes should not exceed these wildtype fluxes. We thus first estimate the wildtype flux distribution vWT, by running a flux balance analysis (FBA) with the biomass reaction as the objective function, followed by a minimization of the sum of absolute fluxes at the previously determined maximal biomass production rate (parsimonious FBA35).In order to approximate the vi to values between zero and the wildtype flux viWT for this reaction, i.e.,When simulating the production of individual biomass components, we constrained all fluxes 46. As some biomass components may be coupled through the biomass reaction, we allowed the free excretion of all other biomass components when maximizing the production of one selected biomass component .For each essential biomass component  mapping contained the affected gene either alone or only in an “AND” relationship ; if the GPR listed the affected gene in an “OR” relationship , the reaction was not affected.We then calculated the maximum production of each biomass component by maximizing its exchange reaction flux while enforcing the wildtype flux distribution constraints Eq. . For eac26. Thus, an allele not involved in the maximal production of any essential biomass component is considered to have pleiotropy 0; an allele that affects the production of exactly one essential biomass component has pleiotropy 1.We defined pleiotropy as the number of biomass components whose maximal production was reduced by at least 0.01% compared to the unperturbed state (WT) for the allele consideredafter the mutant strain has been allowed to adapt its physiology to the gene deletion, a different algorithm is more appropriate. In this case, one needs to allow the free redistribution of fluxes after the simulated activity reduction of the protein encoded by the gene in question.Our estimate of pleiotropy reflects the actual contribution of a gene product to biomass formation, based on estimated enzyme and transporter activities in the wildtype. If instead, one is interested in a quantitative measure of essentiality, defined as the number of biomass components affected by a deleterious allele i.e., the null hypothesis assumes that apart from the upper limit, there is no systematic relationship between maximal pleiotropy and number of steps. We tested this through a randomization protocol, where we constructed 106 datasets with the same maximal pleiotropies, but with step numbers drawn from the corresponding uniform distributions. All random datasets for both the E. coli and the yeast data had Spearman rank correlation coefficients between pleiotropy at full knockout and number of steps that were lower than the observed correlation coefficients. Thus, the empirical P-value was <10−6 for both data sets.The full knockout  pleiotropy sets an upper limit to the possible number of pleiotropy steps at decreasing enzyme activity. The null hypothesis is that the number of steps is uniformly distributed between zero and maximal pleiotropy; In additional analyses, we made several cofactors freely available to study how pleiotropy is associated with the generation of currency metabolites. We did this by adding a balanced biochemical reaction that interconverts the activated and inactivated versions of the cofactor and allowing unlimited flux of this reaction in both directions. For example, to simulate free NADPH, we added the following reversible reaction:43. Supplementary Table A list of currency metabolites was obtained from ref.47 using sybil, a computer library optimized for efficient constraint-based modeling of metabolic networks48. We used IBM ILOG CPLEX as the linear solver, connected to sybil via the cplexAPI R package.All simulations were performed in R49.To calculate network modularities, we used the LP&BRIM algorithm (Label Propagation with Bipartite Recursively Induced Modules) implemented in MatlabSupplementary Information


Mucor circinelloides WJ11 was developed. The model (iNI1159) contained 1,159 genes, 648 EC numbers, 1,537 metabolites, and 1,355 metabolic reactions, which were localized in different compartments of the cell. Using flux balance analysis (FBA), the iNI1159 model was validated by predicting the specific growth rate. The metabolic traits investigated by phenotypic phase plane analysis (PhPP) showed a relationship between the nutrient uptake rate, cell growth, and the triacylglycerol production rate, demonstrating the strength of the model. A putative set of metabolic reactions affecting the lipid-accumulation process was identified when the metabolic flux distributions under nitrogen-limited conditions were altered by performing fast flux variability analysis (fastFVA) and relative flux change. Comparative analysis of the metabolic models of the lipid-overproducing strain WJ11 (iNI1159) and the reference strain CBS277.49 (iWV1213) using both fastFVA and coordinate hit-and-run with rounding (CHRR) showed that the flux distributions between these two models were significantly different. Notably, a higher flux distribution through lipid metabolisms such as lanosterol, zymosterol, glycerolipid and fatty acids biosynthesis in iNI1159 was observed, leading to an increased lipid production when compared to iWV1213. In contrast, iWV1213 exhibited a higher flux distribution across carbohydrate and amino acid metabolisms and thus generated a high flux for biomass production. This study demonstrated that iNI1159 is an effective predictive tool for the pathway engineering of oleaginous strains for the production of diversified oleochemicals with industrial relevance.The genome-scale metabolic model of a lipid-overproducing strain of  Mucor circinelloides is a well-known oleaginous fungus that accumulates lipids at high levels, particularly under certain culture conditions (M. circinelloides are lipid bodies (LBs) or lipid particles (LPs), in which triacylglycerol (TAGLY) is predominant. Moreover, M. circinelloides is capable of synthesizing the nutritionally important polyunsaturated fatty acid (PUFA), γ-linolenic acid , which has beneficial effects on human and animal health , M. alpina (iCY1106) (Y. lipolytica (iYL619_PCP)  is one of the computational tools used to predict metabolic behaviors and it is usually employed in systems biology in conjunction with other modern technologies, such as gene editing and synthetic biology . Of the products . In oleanic acid . FurtheriCY1106)  and Y. l619_PCP) .M. circinelloides strain WJ11 has been identified as a promising strain for the overproduction of lipid-derived products based on its ability to accumulate lipids up to 36% of its dry cell weight (DCW) under nitrogen-deficiency conditions, which is higher than that has been reported in the reference strain CBS 277.49 (15% lipid of DCW)  . As a co of DCW) . Recentlynthesis . To gaindes WJ11  was initM. circinelloides WJ11 , eggNOG (genome.jgi-psf.org/Mucci2/Mucci2.home.html) were used for annotation of genes and protein functions. Concerning the expanding GEM development of M. circinelloides WJ11, compartmentalization information of all possible reactions was afterwards determined by a subcellular localization prediction tool, such as CELLO (http://www.genome.jp/kegg/pathway.html). Moreover, transport and exchange reactions were then added or deleted through network connectivity. Subsequently, GEM was converted to the form of the stoichiometric model of M. circinelloides WJ11. The biomass composition reaction of M. circinelloides WJ11 used in the model was calculated from different sources of biochemical literature and genome database. The contents of nucleotides, lipids, proteins and carbohydrates were adopted from the published research of the M. circinelloides strain WJ11  (ATP) and the operational P/O ratio were considered. These parameters could not be determined independently, but if one of the parameters is known the others can be estimated from experimental data. The operational P/O ratio was assumed to be 1.5 (−1 and the ATP requirement for biomass formation (KATP) was estimated by fitting model simulations with experimental data obtained at a specific growth rate of 0.1671 h−1  . For eneo be 1.5 , the mai1671 h−1  with gluFBA is a mathematical approach that is widely used for studying and identifying flux distribution through a metabolic network to generate an optimal flux towards the objective function . Accordi3) and glucose uptake rates in a range of 0 to 10 mmol gDW−1 h−1 and iteratively calculating the specific growth rates. In contrast, the second simulation aimed to characterize the lipid production behavior by constraining the specific growth rate and setting the boundaries of nitrogen  and glucose uptake rates in a range of 0 to 10 mmol gDW−1 h−1 and iteratively calculating the specific TAGLY production rates. In addition, fastFVA (−1 h−1). At every specified nitrogen uptake rate, the flux distribution was determined as follows. First, FBA was applied to simulate the optimal flux distribution for lipid production by constraining the glucose uptake rate and growth rate at 10 mmol gDW−1 h −1 and0.1671 h−1, respectively, and setting the specific TAGLY production rate as the objective function. Second, a function namely, fastFVA  and fast flux variability analysis (fastFVA) were used. PhPP is a useful way to extend the study of genotype-phenotype relationships based on FBA . This ap fastFVA  was empl fastFVA , which i fastFVA , was empM. circinelloides was performed. Here, the GEM of the reference strain CBS 277.49 in the form of SBML was retrieved from the previous publication by −1 h−1) (T-test was used to compare the flux distributions between the lipid-overproducing and reference strains.A comparison between the models of the lipid-overproducing strain WJ11 and the reference strain CBS277.49 of  −1 h−1) , while t −1 h−1)  was usedthe M. circinelloides WJ11 strain (iNI1159) was achieved. It consisted of 1,159 genes , 648 EC numbers, 1,537 metabolites and 1,355 metabolic reactions, which were distributed into five compartments of the cell, namely, the mitochondria, extracellular space, cytoplasm, plasma membrane and peroxisome. It was observed that iNI1159 had a higher number of genes and EC numbers than iWV1122 due to improved gene annotation , energy (18 reactions), amino acids (24 reactions), nucleotides (six reactions), lipids (13 reactions), cofactors (16 reactions), and terpenoids and polyketides (six reactions) metabolisms  and reference strain CBS 277.49 (iWV1213) of M. circinelloides is shown in iNI1159 model is shown in iNI1159 is also available in As shown in notation . Noticea numbers . The genabolisms . Apart fstructed . ConsideiNI1159 on a glucose-containing medium were predicted. When the glucose uptake rates were fixed at 2.518 , the specific growth rates of at 2.518  and 1.80at 2.518  mmol gDWescribed . This suescribed .iNI1159 when nitrogen-limited conditions are given in To identify major metabolic reactions possibly responsible for increased lipid production behavior under nitrogen-limited conditions, fastFVA followed by relative flux change analysis was applied . iNI1159 and iWV1213 undoubtedly shared almost common metabolic reactions in their metabolic networks as shown in iNI1159 and iWV1213 were different. To gain a better understanding of the metabolic behaviors between the WJ11 and reference CBS277.49 models, the flux distributions in both models were determined and compared based on fastFVA and CHRR. Upon setting the glucose uptake rate to 2.518 mmol gDW −1h−1 and biomass growth as the objective function, FBA was performed and showed that the specific growth rates of iNI1159 and iWV1213 were 0.1671 h −1, 152 sometimes-active reactions , and 951 never-active reactions . For iWV1213, 240 always-active reactions , 175 sometimes- active reactions , and 912 never-active reactions  were observed. Detailed information is provided in iNI1159 and iWV1213 were the reactions catalyzed by hexokinase (EC: 2.7.1.1) through pyruvate kinase (EC: 2.7.1.40), and some reactions in pentose phosphate pathway catalyzed by ribulose-phosphate 3-epimerase (EC: 5.1.3.1) through phosphogluconate dehydrogenase (EC: 1.1.1.44). The most common sometimes-active reactions were involved in the tricarboxylic acid cycle. However, the metabolic fluxes between these two strains were different, particularly in lipid metabolism, including a set of reactions catalyzed by the enzymes involved in glycerophospholipid and glycerolipid metabolism, such as lysophosphatidic acid acyltransferase (EC: 2.3.1.51), phosphatidate phosphatase (EC: 3.1.3.4), and the enzymes involved fatty acids biosynthesis (acetyl-CoA carboxylase (EC: 6.4.1.2), fatty-acyl-CoA synthase (EC: 2.3.1.86), and fatty acid elongase (EC: 2.3.1.199), as shown in iNI1159 had the unique always-active reactions in lipid metabolism, whereas iWV1213 had the unique sometimes-active reactions in lipid metabolism (iNI1159 and iWV1213 are different indicated by dotted arrows (Jaccard index = 0) as shown in iNI1159 and bottom figures representing fluxes of iWV1213  and amino acids , which were relevant to cell growth or biomass production. The detailed information of Jaccard index, average fluxes and their standard deviations as well as statistical differences  for the entire networks of the two models are provided in Because of the colinear evolution between the genomes of the lipid-overproducing and reference strains , iNI1159 h −1see  and 0.25e −1 see , respecttabolism . Moreove iWV1213 . The flu (t-test . It was M. circinelloides WJ11 expanded the metabolic information in the lipid biosynthetic pathway by incorporating the TAGLY production and exchange reaction (iNI1159 model could predict the simulated growth rate with low percent deviation (iNI1159 (−1 h−1) and nitrogen uptake rate (2–10 mmol gDW−1 h−1)  in lipid biosynthesis and current metabolites   or lipid particle (LP). Recently, the efficient strategy for rational improvement of SQL production in Y. lipolytica by engineering the acetyl-CoA metabolism based on genome-scale metabolic model has also been reported  see . Of themd NADPH) . Particuus yeast . These rhaustion . As a rei NI1159 , the increported .Y. lipolytica during nitrogen limitation by integrative analysis of multilevel omics data through the iYali4 GEM  and reference strain CBS277.49 (iWV1213), enabling analysis of metabolic control involved in the oleaginicity. Thus, this iNI1159 model offers a promising scaffold for metabolic engineering at precise targets to achieve an optimized strain for industrial purpose.The developed 10.7717/peerj.7015/supp-1File S1M. circinelloides WJ11 (iNI1159).Table S1.1: The GEM of iNI1159 model.Table S1.2: List of metabolite name, description and compartment of the i NI1159 model. Table S1.4: Table S1.4: List of added/updated EC numbers in the iNI1159 model.Table S1.3: List of improved annotated genes in the Click here for additional data file.10.7717/peerj.7015/supp-2File S2M. circinelloides (iNI1159).Table S2.1: The unique metabolic reactions of M. circinelloides (iWV1213).Table S2.2: The unique metabolic reactions of Click here for additional data file.10.7717/peerj.7015/supp-3File S3M. circinelloides WJ11.Table S3.1: Biomass composition of M. circinelloides CBS277.49 modified from  at glucose uptake rate of 10 mmol gDW −1 h −1.Note: Click here for additional data file.10.7717/peerj.7015/supp-7File S7iNI1159 model.Table S7.1: fastFVA distribution of iWV1213 model.Table S7.2: fastFVA distribution of Click here for additional data file.10.7717/peerj.7015/supp-8File S8Table S8.1: fastFVA results provide always-active reactions sets.Table S8.2: fastFVA results provide sometimes-active reactions sets.Table S8.3: fastFVA results provide never-active reactions sets.Click here for additional data file.10.7717/peerj.7015/supp-9File S9Table S9.1: fastFVA results provide always-active reactions sets.Table S9.2: fastFVA results provide sometimes-active reactions sets.Table S9.3: fastFVA results provide never-active reactions sets.Click here for additional data file.10.7717/peerj.7015/supp-10File S10i NI1159.Table S10.1: Unique always-active reactions in i WV1213.Table S10.2: Unique sometimes-active reactions in Click here for additional data file.10.7717/peerj.7015/supp-11File S11 i NI1159 and iWV1213 model simulations under glucose uptake rate of 2.518 mmol gDW −1 h −1, and all exchange fluxes were constrained using the values at optimal growth conditions.Note:Click here for additional data file.


We investigated the photoluminescence property of carbon dots (CDs) in the presence of Fe3O4-capped Au (Fe3O4@Au). Firstly, we designed two dedicated probes for unique long sequence region of human T-lymphotropic virus type 1 genome. One of the probes was covalently bound to the CDs. In the absence of target, CDs-probe was adsorbed on the surface of Fe3O4@Au through two possible mechanisms, leading to quenching the fluorescence emission of CDs. The fluorescence emission of CDs was recovered in the presence of target since double-stranded DNA cannot adsorb on the Fe3O4@Au. Also, Fe3O4@Au can adsorb the unhybridized oligonucleotides and improves the accuracy of detection. The specificity of the proposed biosensor was confirmed by BLAST search and assessed by exposing the biosensor to other virus targets. The experimental detection limit of the biosensor was below 10 nM with linear range from 10 to 320 nM.Carbon dots and Fe One of the main challenges to reach this achievement is utilizing simple methods and environmentally-friendly materials. Carbon nanostructures are known as beneficial biomaterials due to their optical and electrochemical characterizations3. Moreover, it has been reported that functionalization of carbon nanostructures with biomolecules can significantly ameliorate their performance. Therefore, carbon nanostructures have extensively employed in various biomedical applications including biosensors, drug and gene delivery systems, bioimaging, and tissue scaffold reinforcement4.Nanomaterial-based detection methods have been developed to open novel and simple routes toward improving point-of-care diagnoses5. Different techniques accompany with using signal amplification labels have been used to develop the selective and sensitive biosensors. Among them, biosensors relying on the fluorescence emission measurement and hybridization between target DNA and labelled probe oligonucleotides facilitate rapid and sensitive detection of biomolecules. Although, organic fluorophores have good features of photostability and high efficiency, they suffer from high cost and photobleaching effect7. One of the proper substitution to the traditional fluorophores is quantum dot nanoparticles (QDs) due to their unique traits, like high quantum yields and photostability, broad excitation, narrow emission, and excellent resonance energy transfer9. However, the application of QDs confronts with problems such as intricate and costly synthesis steps, and toxicity10. Thus, fluorescent carbon dots have attracted researchers due to their photo- and chemical stability, low toxicity and cost, and biocompatibility11.In the recent decades, biosensors have attracted numerous attentions in pathogen diagnosis, disease progression, point-of-care monitoring of treatment, and drug discovery12. However, their magnetization should be conserved after surface modification. The coating of MNPs with gold shell causes the surface stabilization, biocompatibility, and magnetic property preservation13.The other useful and applicable nanoparticles in biomedical research are magnetic nanoparticles (MNPs), which usually coated by polymers or other metals to increase their stability in various physiological pHset al. presented a biosensor utilizing methylene blue (MB) as a quencher of CDs through adsorption on the surface of CDs14. With addition of DNA, the intensity of CDs fluorescence were restored, since MB bound DNA and removed it from the CDs. Huang et al. developed a radiometric nanosensor based on the quenching fluorescence of CDs in the presence of ethidium bromide (EB)15. Upon the addition of DNA, the fluorescence of EB was significantly increased but the fluorescence intensity of CDs remained constant. Qadarre et al. introduced a HIV-1 gene sensor based on higher association tendency of the CDs-labeled oligonucleotides to the target rather than AuNPs/graphene oxide nanocomposite, which caused the recovery of the quenched fluorescence of CDs16. However, the mentioned biosensors either used organic compounds that were not specific for the oligonucleotide sequences or designed for detection of single-stranded oligonucleotide target.According to the advantage of inimitable properties of carbon dots (CDs) and also their fluorescence quenching in the proximity of quenchers, several biosensors have designed for oligonucleotides detection. Bai 17. The detection of HTLV-1 can be performed by polymerase chain reaction (PCR), serological methods, and western blot18. The early detection of HTLV-1 is momentous, as it can escape from the host defense mechanisms. The aforementioned methods need sample preparation, high cost and tied with the false positive results21. To best of our knowledge, the biosensor based on quenching the fluorescence emission of CDs in proximity of iron magnetic nanoparticles capped-Au and its different affinity to single-stranded DNA and double-stranded DNA has not been reported.Human T-lymphotropic virus type 1 (HTLV-1) is only known retrovirus, which can cause cancer in human and develop two diseases including adult T-cell leukemia/lymphoma (ATLL) and HTLV-1-associatedmyelopathy/tropical spastic paraparesis (HAM/TSP)3O4@Au). Afterwards, we present an inexpensive, versatile, and sensitive method for detection of oligonucleotides that is part of a special region of HTLV-1. To this end, we designed two specific probes to diagnosis target DNA. One of the probes was functionalized with CDs. In the absence of target, CDs-probe was adsorbed on the surface of Fe3O4@Au, resulted in quenching the fluorescence emission of CDs which was retrieved in the presence of target.In this study, we develop a simple method for the synthesis of carbon dots (denoted as CDs). Furthermore, we survey the fluorescence property of the prepared CDs and their fluorescence quenching in presence of the synthesized nanoparticles coated by a gold layer  containing CDs-probe A, target, and probe B constitutes. Finally, the unhybridized probes and targets that adsorbed on the Fe3O4@Au were separated using a magnet. Therefore, the fluorescence emission of C-dots was recovered, since dsDNA does not adsorbed on the surface of Fe3O4@Au.Figure 3O4@Au. The presence of carboxylic group on Fe3O4@Au is confirmed according to the distinctive band at 1640 cm−1 while the band from 3000 to 3500 cm−1 can be assigned to O–H stretching vibration. The FT-IR spectrum of CDs is shown in Fig. −1. Also, the O–H stretching vibration band is around 3000 to 3600 cm−1. The stretch vibrational band at 3555 cm−1 is related to the N–H, which is overlapped with O–H stretching vibration and become wider. Hence, the FT-IR spectrum of CDs confirms the presence of carboxyl and amine functional groups present on the surface of CDs.FT-IR spectrometry was employed to identify types of functionality of ligands attached to the nanoparticles. Figure 3O4@Au were of spherical shape and around 80–90 nm in dimeter , amino N (401.3 eV), and pyrrolic N (401.9 eV).X-ray photoelectron spectroscopy (XPS) was used for element and surface composition analyses of carbon dots. The full spectra Fig.  indicate3O4@Au confirms the fluorescence quenching of CDs , where FDA denotes the integrated fluorescence intensity of the donor in the presence of the acceptor and FD shows the integrated fluorescence intensity of the donor alone25.The emission intensity of CDs was measured under 380 nm excitation before and after conjugation with probe A. To measure the fluorescence emission, the peak intensity in the range of 400–700 nm was followed. Figure CDs Fig. . The pri@Au Fig. . The proThe efficiency of the biosensor for target HTLV-1 gene detection was surveyed. To this end, different concentrations of target from 0 to 544 nM were prepared and analyzed according to the mentioned principle of sensing. As shown in Fig. To explore the specificity of the biosensor toward HTLV1 target, the genes of hepatitis B virus (HBV) and human immunodeficiency virus (HIV) were also considered as the target. However, the probes were designed for a specific region of HTLV1 gene and their alignment with the complete genome of the virus revealed its specificity for HTLV1. Nevertheless, Fig. 3O4@Au. The sensing signal was upon changing the fluorescence emission of CDs-probe. In the absence of target, CDs-probe were adsorbed on the Fe3O4@Au surface, which caused their fluorescence quenching. However, the fluorescence emission of CDs were recovered in the presence of the target which is because of desorption from the surface of Fe3O4@Au. The major novelty of our proposed biosensor is first applying Fe3O4@Au with high surface area as an efficient quencher of CDs. Also, it was employed to adsorption of excessive probes and second strand of dsDNA target, which leads to decreasing unwanted signals to improve the accuracy of detection. In fact, in our proposed biosensor setup, the presence of complementary sequence may interfere with oligonucleotide probes which can decrease the detectable signals. Also, the Fe3O4@Au nanoparticles have some advantages that make them good candidate in biosensor applications such as ease-synthesis, high biocompatibility, cost-effectivity, large surface area, and strong adsorption ability26. Moreover, selecting the specific region of genome as the target sequences is important in development of a biosensor for detection of partial pathogen genome sequence. The long specific sequences can increase the efficiency of detection, Herein, we chose the 122-base fragment of the tax region of the HTLV-1 genome. Also, we designed proper complementary probes to detect it. In addition, we used the nucleotide BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to ensure about the specificity of designed probes toward target.In this study, a novel and cost-effective genosensor was introduced based on fluorescence quenching of CDs in the proximity of Fe28 and fluorescence 29 techniques. However, these biosensors need complex sample preparations and also multiple steps to final detection. The present simple method can be modified in future studies to detect wide concentration ranges of other genomic biomolecules.In addition to the common methods of PCR and ELISA, only a few biosensors have been designed for HTLV-1 DNA detection including based on electrochemical 3O4 nanoparticles were synthesized by co-precipitation of ferric and ferrous salts31. In brief, 0.1 g FeCl3 g and 0.04 g FeCl2 were dissolved into 20 mL of deoxygenated deionized water under N2 gas. After stirring for 10 minutes at 50 °C, 5 mL of NaOH solution (0.3 M) were added gradually while vigorously stirring until its color changed from orange to black. Then, the mixture was stirred for an additional 1 hour and gradually cooled down to room temperature. After separation of the black product with a permanent magnet, the precipitate was washed 3 times with 70 mL of deionized water. To avoid Fe3O4 aggregation, 0.02 g of tetraethylammonium perchlorate was added to the synthesized nanoparticles. For synthesis of Fe3O4@Au, 4 mL of HAuCl4 (14 mM) was added into prepared Fe3O4 nanoparticles solution and then heated to boiling under stirring. Then, 6 mL of sodium citrate (%1) was added into the reaction mixture. The mixture was boiled under stirring until the color of solution changed from black to burgundy. The reaction mixture was boiled under stirring for 5 min32.FeCarbon dots were easily synthesized through one step hydrothermal method. Briefly, 0.5 g of o-phenylenediamine was dissolved in 20 ml of ethanol at room temperature under vigorous stirring. Then, 50 ml of deionized water was added to the mixture and poured into a 100 ml Teflon container. The reactor was subsequently put in an oven at 200 °C for 24 h. After that, the reactor was naturally cooled down to room temperature and the product was dialyzed using 500 KDa dialysis bag for 3 days. The final product was kept in 4 °C for future use.https://blast.ncbi.nlm.nih.gov/Blast.cgi) was utilized to confirm the specificity of the probes. The designed probes were completely specified for the 122-base fragment of the tax region of the HTLV-1 genome. The DNA oligonucleotides (Probe A: 5′-CAGCCATCTTTAGTACTACAGTCCTCCTCC-(T)10-NH2-3′) and Probe B: 5′-TTCCGTTCCACTCAACCCTCAC-3′ were purchased from Takapouzist Biotech Company (Iran). DNA target sequences was as follow:Two probes A and B, which were complementary to two specific region of HTLV-1, were designed by Gene Runner software (version 6.5.48). The nucleotide BLAST tool  and 2 µL NHS (320 µg/mL) were added and incubated for 1 hr. After that, 50 µL probe A was added and again incubated for 2 hr.UV-Vis absorption spectra were recorded using a Varian Cary Bio 100 spectrophotometer. Fourier transforms infrared (FTIR) spectral analyses of nanoparticle in KBr disc were recorded using a Perkin-Elmer 343 spectrometer (USA). The images of nanoparticles were recorded by transmission electron microscopy electron microscope . XPS analysis was performed using a hemispherical analyser supplied by an Al Ka X-ray source (operating at energy of 1486.6 eV in a vacuum higher than 10_7 Pa) and the deconvolution of signals was done by Gaussian components. Dynamic light scattering  was used for size and charge determinations. Zeta potential measurements were attained by a ZetaPALS analyzer (Brookhaven Instruments).The fluorescence measurements were performed on a fluorescence microplate reader  at room temperature. The excitation was set at 380 nm and the emission spectra was recorded from 400 to 700 nm with both excitation and emission slits of 5 nm.3O4@Au and deionized water and in the presence of target as deionized water and then subtracted from the corresponding sample to correct the fluorescence background.The blank in the absence of target was considered as Fe3O4@Au were mixed and incubated for about 30 min at room temperature. After separation with magnet, the supernatant was removed. Then, the fluorescence emission of CDs in equivalent volume of deionized water was measured. Afterwards, 20 µL probe B and different concentrations of target were added. The Fe3O4@Au was collected by magnet and upper supernatant was extracted to further fluorescence emission measurement.In order to target detection, 20 µL CDs-probe A and 20 µL Fe