Bifidobacterium animalis subsp. lactis BB-12® and B. longum subsp. longum BB-46, and subjected to iterative cycles of manual curation and experimental validation. A constraint-based modeling framework was used to probe the metabolic landscape of the strains and identify their essential nutritional requirements. Both strains showed an absolute requirement for pantethine as a precursor for coenzyme A biosynthesis. Menaquinone-4 was found to be essential only for BB-46 growth, whereas nicotinic acid was only required by BB-12®. The model-generated insights were used to formulate a chemically defined medium that supports the growth of both strains to the same extent as a complex culture medium. Carbohydrate utilization profiles predicted by the models were experimentally validated. Furthermore, model predictions were quantitatively validated in the newly formulated medium in lab-scale batch fermentations. The models and the formulated medium represent valuable tools to further explore the metabolism and physiology of the two species, investigate the mechanisms underlying their health-promoting effects and guide the optimization of their industrial production processes.Although bifidobacteria are widely used as probiotics, their metabolism and physiology remain to be explored in depth. In this work, strain-specific genome-scale metabolic models were developed for two industrially and clinically relevant bifidobacteria,  Based on their beneficial impact on health, several Bifidobacterium strains, such as Bifidobacterium animalis subsp. lactis BB-12®, are commercially applied as probiotics in food and pharmaceutical products4.Bifidobacteria are Gram-positive, obligate anaerobic prokaryotes that commonly inhabit the gastrointestinal tract of humans and animals. The presence of bifidobacteria is associated with a healthy gastrointestinal tract and a strong immune function of the host. Health-promoting effects of bifidobacteria include the production of antimicrobial agents and vitaminsBifidobacterium species and strains6. However, all bifidobacteria use a characteristic hexose fermentation pathway, known as the bifid shunt, for carbon dissimilation7, due to the lack of 6-phosphofructokinase (EC 2.7.1.11), a key enzyme of the canonical Embden-Meyerhof-Parnas pathway. This heterofermentative pathway relies on the key enzyme fructose-6-phosphate phosphoketolase (Xfp)7. Xfp has a dual substrate specificity8 and can catalyze the conversion of both fructose-6-phosphate and xylulose-5-phosphate to acetyl phosphate and the corresponding aldose phosphate , which are further catabolized towards different fermentation products  spectroscopy17, and examination of catalytic activities of individual enzymes17. However, systems-level metabolic and physiological characteristics of bifidobacteria remain yet to be explored in greater depth. These include the essential nutritional requirements of bifidobacteria and the underlying genetic basis of their biosynthetic capabilities. As bifidobacteria are rather fastidious microorganisms, most previous studies used either complex undefined media20 or semi-defined media supplemented with complex additives, such as (hydrolyzed) casein or the permeate of dialyzed complex medium22, to sustain growth. While such media allow for high growth rates and high final biomass concentrations, they are not suitable for detailed physiological studies since complex medium components with undefined composition may hide the effect of a metabolite and hamper the quantification of product yields and growth energetics.The central carbon metabolism of bifidobacteria has been investigated by quantitative analysis of fermentation end products23. Converting a network reconstruction into a GEM enables the quantitative and qualitative analysis of the reconstructed metabolic network using constraint-based methods. Although GEMs for some Bifidobacterium strains have been previously developed, their value is confined by their quality and scope, as they have been subjected to a limited amount of manual curation and/or experimental validation26.Currently, industrial production processes of probiotics, including bifidobacteria, are typically optimized based on empirical design approaches. While it is desirable to adopt more rational, knowledge-driven approaches for identifying the essential nutritional requirements and advancing industrial-scale production, necessary tools such as genome-scale metabolic models (GEMs) are yet to be developed. The reconstruction of high-quality strain-specific genome-scale metabolic networks is a powerful approach in microbial systems biology, since it leverages the value of the exponentially growing genomics data, enabling its integration with other biological knowledge in a structured formatB. animalis subsp. lactis BB-12® (hereafter abbreviated BB-12) and B. longum subsp. longum BB-46 (hereafter abbreviated BB-46). The GEMs are capable of accurately describing and predicting the metabolic capabilities of these strains. We present the general features of these manually curated GEMs and describe how they were employed to probe the metabolic landscape and identify the individual nutritional requirements of the two strains. Model-generated insights were used to guide the formulation of a chemically defined medium (CDM) that supports the growth of both strains in the same manner as complex medium. Finally, the models were quantitatively validated in the newly developed medium in lab-scale batch fermentations.In this work, we constructed and manually curated strain-specific GEMs for two industrially important probiotic strains, B. longum BB-46 genome resulted in a single 2.385.558-bp circular chromosome with an average G + C content of 60.33%. A total of 1870 predicted protein-encoding sequences, 77 tRNA genes, and 4 rRNA operons were identified. No plasmids could be detected in the strain. The complete genome sequence was deposited in NCBI GenBank (Accession no. CP065209). Sequencing statistics and other relevant characteristics of the genome sequence are summarized in Supplementary Table The hybrid assembly of B. animalis BB-12 (hereafter referred to as iAZ480) contained 731 reactions and 480 genes, whereas that of B. longum BB-46 (hereafter referred to as iMS520) contained 771 reactions and 520 genes 24 covers a lower percentage of the protein-encoding genes in the strain’s genome and includes a significantly smaller number of non-gene assigned reactions . Some variations in the reconstruction of the pathways for central carbon and amino acid metabolism in iBif452 from iAZ480 and iMS520 are discussed in later sections.In comparison to ons Fig. . The throns Fig. , most ofThe draft network reconstruction of BB-12 generated by Model SEED originally included 727 reactions, in addition to a generic biomass formation reaction. During the refinement of the reconstruction, a total of 287 reactions were added to the network based on biochemical, physiological, or genomic evidence, whereas 339 reactions were removed, as their presence in the strain could not be verified. Many of the removed reactions where non-specific reactions that are not known to occur in anaerobic bacteria in general or bifidobacteria in particular. Other reactions were removed, since their associated genes were assigned to different reactions during manual curation. This clearly highlights the importance of curating automated network reconstructions for incorporating organism-specific knowledge and eliminating unvalidated generic reactions.To create a network reconstruction for BB-46, 526 gene-associated and non-gene-associated reactions and 79 exchange reactions were carried over from the metabolic network of BB-12. Based on various evidence, new reactions were added to BB-46 reconstruction, genes could be assigned to non-gene associated reactions and the gene-protein-reaction (GPR) associations were generally updated. A complete overview of reactions that were added, removed, or changed in the draft reconstruction of BB-12 and BB-46 is provided in Supplementary Data 28.Reactions of the bifid shunt, the pentose-phosphate pathway (PPP) and the tricarboxylic acid cycle (TCA) cycle included in the refined reconstructions of both strains Fig.  were inf13C-NMR pathway analysis in BB-1217, the pyruvate formate-lyase (EC 2.3.1.54) reaction (PFL) was made reversible, allowing both the formation of acetyl-CoA and its condensation with formate to generate pyruvate. The alternative route of generating acetyl-CoA via pyruvate dehydrogenase was not included in the GEMs, since both strains lack the genes encoding the complete pyruvate dehydrogenase enzyme complex.Based on a previous 29 showed that an incomplete TCA cycle is a common feature in these bacteria. All sequenced bifidobacteria seem to lack the same set of genes, except for Bifidobacterium dentium Bd1, which possesses a gene for malate dehydrogenase. In contrast to iAZ480 and iMS520, iBif452 includes pyruvate dehydrogenase and 2-oxoglutarate dehydrogenase reactions, which are both assigned to the dihydrolipoamide dehydrogenase gene24. This highlights an inherent issue in automated network reconstructions based on KEGG pathways, since the KEGG database considers the presence of the genes for any of the enzymes in a multienzyme complex as an indication of the presence of the corresponding reaction.BB-12 and BB-46 possess an incomplete TCA cycle, as both strains lack genes for malate dehydrogenase (EC 1.1.1.37), fumarase (EC 4.2.1.2), and the 2-oxoglutarate dehydrogenase enzyme complex. Examining the presence of TCA cycle enzymes in other bifidobacteria in the KEGG databaseBifidobacterium-specific BOF was formulated based on the macromolecular composition of BB-12 determined in this study, as well as data from literature on bifidobacteria and closely related species. The protein content of BB-12  as well as its DNA content  is higher than the corresponding fractions in other Gram-positive bacteria, e.g., Lactococcus lactis30, Lactobacillus plantarum31, and Streptococcus thermophilus32, while its RNA content  is comparatively low. However, the deviations might be explained by the use of different growth conditions, which are known to affect the macromolecular composition of bacterial cells33. For BB-46, the peptidoglycan composition in the BOF was adjusted to reflect its cell wall type. The resulting BOF was incorporated into the metabolic networks of BB-12 and BB-46. Based on the BOF, the elemental composition of the cells is CH1.57N0.23O0.43P0.01, corresponding to a molecular weight of 24.7 g C-mol−1. Further detailed information on the cellular composition and the formulation of the BOF is provided in Supplementary Note A 34. None of the GEMs produced matter or energy from nothing when optimizing for the BOF while constraining all exchange rates. FBA revealed the presence of a number of internal loops (type III extreme cycles35) among the reactions, listed in Supplementary Note The extensive refinement of the reconstructed networks in a pathway-by-pathway manner resulted in immediately functional GEMs that could predict non-zero flux through the biomass reaction when performing flux balance analysis (FBA)iAZ480 and iMS520, were further evaluated by comparing FBA predictions to known metabolic characteristics of bifidobacteria. Sucrose was used as the carbon source in the simulations with its uptake rate constrained to 10 mmol gCDW−1h−1, where CDW is the cell dry weight, whereas the uptake rates of all amino acids, vitamins, and nucleobases were unconstrained. When maximizing for biomass formation, the GEMs predicted unlikely high uptake rates of some amino acids, resulting in unrealistically high growth rates. The use of amino acids as additional carbon source in iAZ480 and iMS520 could be attributed to the activity of serine deaminase (EC 4.3.1.17) or serine-specific threonine-ammonia lyase (EC 4.3.1.19) as well as alanine transaminases (EC 2.6.1.2), converting the corresponding amino acids into intermediates in the central carbon metabolism and further to acetate for ATP synthesis. When the sucrose uptake was constrained to zero, no growth on amino acids as the sole carbon and energy source was predicted, which is in agreement with previous knowledge on bifidobacteria36. When constraining the uptake rates of all amino acids to 1 mmol gCDW−1 h−1, iAZ480 and iMS520 predicted the formation of 1.8 mol acetate, 1.0 mol formate, 0.4 mol ethanol, and 3 mol ATP per mol hexose consumed, but no lactate secretion was observed. The absence of lactate formation and a higher acetate:lactate ratio than the theoretical ratio of 3:2 have been previously observed in bifidobacteria17, indicating that acetate formation is preferred under certain conditions, since it is linked to additional ATP generation18. When the acetate:lactate ratio was constrained to the theoretical ratio of 3:2, both models predicted the secretion of 0.9 mol lactate, 1.3 mol acetate, and minor amounts of formate per mol of hexose, while the ATP yield slightly dropped to 2.9 mol per mol hexose. A similar end-product profile was obtained by constraining the secretion of formate to zero, which restricts the flux through PFL. With the same constraint on formate secretion for growth on pentoses, iAZ480 and iMS520 predicted an acetate:lactate ratio of 1:1. The lower acetate production is attributed to pentoses entering the bifid shunt directly via xylulose-5-phosphate, eliminating the possibility of additional acetyl–phosphate formation through the fructose-6-phosphate phosphoketolase (F6PPK/PK) reaction .iAZ480 and iMS520 predicted the secretion of small amounts of succinate, which has been previously observed in bifidobacteria and was suggested to occur from phosphoenolpyruvate via the reduction of oxaloacetate, malate and fumarate12. Due to the absence of malate dehydrogenase and fumarase, iAZ480 and iMS520 predicted succinate production from fumarate either through fumarate reductase (EC 1.3.5.4) in the TCA cycle or via fumarate-dependent dihydroorotic acid dehydrogenase (EC 1.3.98.1) in pyrimidine biosynthesis. According to model predictions, the required fumarate may be formed in the adenylosuccinate lyase (EC 4.3.2.2) reaction in the purine metabolism. Apart from being secreted, succinate also serves as the direct precursor for succinyl-CoA formation by succinyl-CoA synthetase (EC 6.2.1.5)  around 5 in the enriched CDM, which exceeded the maximum cell density in complex Man-Rogosa-Sharpe (MRS) medium under the same fermentation conditions. In the first cultivation passage in CDM, which was inoculated from a working stock in complex medium, BB-46 started growing after a couple of hours, while BB-12 spent around 30 h in the lag phase. To identify which of the added nutrients are essential for each strain, in vitro omission experiments were performed in the fortified CDM, omitting one of the 15 nutrients at a time. In addition, in silico predictions regarding the strains’ prototrophy for vitamins and amino acids already included in the medium were also validated experimentally  was the only precursor in the generic CDM for the biosynthesis of coenzyme A (CoA), which is a crucial coenzyme in sugar catabolism and fatty acid biosynthesis. The GEMs revealed that BB-12 and BB-46 share an incomplete pathway for CoA biosynthesis from pantothenate  and phosphopantothenoyl cysteine decarboxylase (EC 4.1.1.36). Initially, the reactions of PPNCL3 and PPCDC were added to iAZ480 and iMS520 as non-gene associated reactions, since their removal abolishes in silico growth  was included in the model without gene association. A pantethine reductase reaction (PTTR) was also included without gene association, whereas the pantetheine kinase reaction (PTTK) was associated to the gene encoding pantothenate kinase (EC 2.7.1.33), previously reported to show a broad substrate specificity including pantetheine50.ate Fig. . As descons Fig. . The preins Fig. . Our expins Fig. . Based oine Fig. . The preVitamin B3: Nicotinic acid and nicotinamide are two forms of vitamin B3 that can serve as precursors for the biosynthesis of NAD(P)+. iMS520 predicted that BB-46 can synthesize NAD(P)+ from L-aspartate and fumarate and that it does not require any form of vitamin B3, although it possesses all necessary genes for utilization of nicotinic acid or nicotinamide as precursors , the active form of folate (vitamin B9), is an essential cofactor for nucleic acid synthesis. The ability of Bifidobacterium strains to produce folate was previously reported to be strain-dependent53 and in some strains promoted by the supply of para-aminobenzoic acid54. In bacterial folate biosynthesis, para-aminobenzoic acid is required for the formation of dihydropteroate in the dihydropteroate synthase (EC 2.5.1.15) catalyzed reaction  is known as an electron carrier in bacteria55. Previous studies indicated that some forms of vitamin K, such as menaquinone, act as growth factors for bifidobacteria57 but cannot be synthesized by representatives of the genus58. iAZ480 and iMS520 lack a pathway for the biosynthesis of any of the vitamin K homologs, while they suggest its potential requirement as a cofactor in some redox reactions, such as the dihydroorotic acid dehydrogenase (EC 1.3.5.2) reaction (DHORD5). Moreover, the genome sequences of BB-12 and BB-46 indicate the presence of a gene encoding NADPH-quinone oxidoreductase (EC 1.6.5.5), which requires a quinone as redox mediator. Based on these findings, commercially available menaquinone-4 was tested as one of the potentially essential nutrients missing in the generic CDM  nor for the vitamin-like nutrient lipoic acid. These predictions were confirmed experimentally , biotin (vitamin B7), thiamine (vitamin B1), and cobalamin (vitamin B12), the GEMs predicted an absolute requirement of both strains for all four vitamins. The requirement of these vitamins was, however, not validated experimentally , methanethiol, or glutathione, may function as the sole sulfur source in bifidobacteria69. iAZ480 and iMS520 predicted that BB-12 and BB-46 can assimilate H2S through its incorporation into O-succinyl-L-homoserine to generate homocysteine by an O-succinyl-L-homoserine succinate-lyase , which lacks gene association and requires an additional non-gene-associated reaction to produce acetyl-serine24. Contradictory conclusions have been drawn about the presence of cysteine synthase in bifidobacteria67. Much of the ambiguity is caused by inconclusive gene annotation; the same gene is assigned to the function of cystathionine beta-synthase (EC 4.2.1.22) or cysteine synthase (EC 2.4.1.47) depending on the functional annotation source. Due to the tight safety restrictions around the laboratory use of H2S, we have tested the ability of an H2S-releasing compound, GYY4137, to serve as the sole sulfur source in the CDM. However, no growth of BB-12 or BB-46 was observed under this condition.Previous studies suggested that sulfur-containing metabolites, such as hydrogen sulfide  both strains have an absolute requirement for pantethine; (ii) BB-12 requires nicotinic acid, but not nicotinamide; and (iii) menaquinone-4 is essential for growth of BB-46 and has a growth-promoting effect on BB-12. All three essential nutrients were identified based on the network reconstruction and in silico predictions Fig. , emphasiiMS520, BB-46 grew on all tested carbohydrates but mannose. The predictions of iAZ480 agreed with the experimental results for nine out of the twelve tested carbohydrates, which is further discussed below.The newly formulated CDM was further used for the experimental validation of the capabilities of the GEMs to simulate growth on twelve carbohydrates, including monosaccharides (pentoses and hexoses) and oligosaccharides, as the sole carbon source Fig. . Both thArabinose and xylose: In contrast to iAZ480 predictions, batch fermentation experiments showed that BB-12 does not grow on xylose or arabinose, whereas growth was observed on melibiose -linked glucosides and galactosides, including melibiose70. Based on this finding, a melibiose transporter was added to the same genes as the raffinose ABC transporter in iAZ480 and iMS520.ose Fig. . AccordiMannose: Both strains are unable to grow on mannose  classification75) reaction (SUCR) or converted to fructose and glucose-1-phosphate through the sucrose phosphorylase  reaction (SUCPHOS) using free phosphate as a phosphate donor. A higher biomass yield can be achieved via the SUCPHOS route, as it bypasses the initial ATP consumption in the bifid shunt4. Raffinose galactohydrolase  catalyzes the hydrolysis of raffinose into galactose and sucrose74, while raffinose fructohydrolase  catalyzed the hydrolysis of raffinose to fructose and melibiose. A higher biomass yield is predicted with the former route. In addition, high-performance liquid chromatography (HPLC) analysis of the growth supernatant revealed the presence of galactose and sucrose, which confirms the activity of raffinose galactohydrolase in BB-46.ose Fig. . GlycosyLacto-N-biose and galacto-N-biose: BB-46 possesses all enzymes to convert lacto-N-biose and galacto-N-biose into intermediates of the bifid shunt. Previous studies confirmed the growth on lacto-N-biose in other Bifidobacterium strains possessing a homologous gene cluster to the one found in BB-4677. This gene cluster is, however, absent in the genome of BB-12, preventing its growth on these substrates.78, whereas that of BB-46 agrees with the carbohydrate utilization profile of B. longum NCC270571.Overall, the carbohydrate utilization profile of BB-12 matches that observed in a previous study using the strainiAZ480 and iMS520, growth, sugar uptake, and fermentation end-product secretion rates by BB-12 and BB-46 were studied in the newly formulated CDM, with 10 g L−1 sucrose as a carbon source, in lab-scale batch fermentations. BB-12 and BB-46 showed a maximum specific growth rate of 0.45 h−1 and 0.35 h−1 . Under these conditions, model predictions largely agreed with the experimental data, except for formate secretion rate, which was higher than the experimentally measured value  before sterilization, and cysteine-HCl was added to the medium as a reducing agent at a final concentration of 0.5 g L−1 prior to inoculation. Temperature was maintained at 37 °C and agitation (magnetic stirring) was applied at 400–500 rpm to keep the cells in suspension. Exponentially growing cells of the first passage were used to initiate a second cultivation at an OD600 of 0.05. The cells were grown until exponential growth phase (OD600 about 1) and stored in 20% (v/v) glycerol at −80 °C.BB-12 and BB-46 were routinely maintained as glycerol stocks prepared as follows. Strains were cultivated by two consecutive passages in 50 mL MRS medium containing 10 g L2 and 20% (v/v) CO2 for 20 min under agitation (magnetic stirring) before medium sterilization. In addition, cysteine-HCl (0.5 g L−1) was added to the medium as reducing agent prior to inoculation. Temperature and agitation (magnetic stirring) were maintained at 37 °C and 400–500 rpm, respectively. Batch cultivations were initiated at an OD600 of 0.05 by addition of a preculture in the exponential growth phase (OD600 about 1). To adjust bacteria to the culture medium and decrease culture variability, exponentially growing cells were transferred at least twice in the same cultivation medium before the main culture. Fermentation experiments with quantitative analysis of external metabolite concentrations were performed in triplicates. Nutrient omission experiments were run in duplicates.Batch fermentations of BB-46 and BB-12 were performed under anaerobic conditions in crimp-top serum bottles with a working volume of 25–200 mL using media indicated in the individual experiments, without pH control . Anaerobiosis was attained by flushing the headspace of serum bottles with a gas mixture of 80% (v/v) N−1 sucrose, 1 g L− 1 NH4Cl, 0.6 g L−1 citrate, 0.42 g L−1 NaHCO3, 6.5 mg L−1 FeCl2· 4H2O, 23.1 mg L−1 MnCl2· 4H2O, 0.07 mg L−1 ZnCl2, 0.006 mg L−1 H3BO3, 0.19 mg L−1 CoCl2· 6H2O, 0.002 mg L−1 CuCl2· 2H2O, 0.024 mg L−1 NiCl2· 6H2O, 400 mg L−1 MgCl2· 6H2O, 50 mg L−1 CaCl2· 2H2O, 500 mg L−1 cysteine-HCl, 40 mg L−1 of L-alanine, L-arginine, L-asparagine, L-aspartic acid, L-glutamine, L-glutamic acid, glycine, L-histidine, L-isoleucine, L-leucine, L-lysine, L-methionine, L-phenylalanine, L-proline, L-serine, DL-threonine, L-tryptophan, L-tyrosine and L-valine, 8.7 g L−1 K2HPO4, 6.1 g L−1 KH2PO4, 0.5 mg L−1para-aminobenzoic acid, 0.2 mg L−1 biotin, 4 mg L−1 calcium pantothenate, 0.2 mg L−1 folic acid, 0.5 mg L−1 nicotinamide, 2 mg L−1 pyridoxal-HCl, 0.5 mg L−1 cobalamin, 0.5 mg L−1 riboflavin, 0.5 mg L−1 thiamine-HCl, 0.5 mg L−1 dl-6,8-thioctic acid, and 1 mL L−1 Tween® 80. Following the recommendations of the supplier, stock solutions of individual and sets of medium components were either sterile filtered using 0.22 µm filter or autoclaved for 10–15 min at 121 °C before being combined to the final concentrations. For the determination of the macromolecular composition of the cells, a semi-chemically defined medium was used, whose composition was similar to that of the generic CDM and supplemented with 0.5 g L−1 yeast extract . The CDW was determined in triplicates using 10 mL aliquots of cell suspension harvested in the exponential growth phase. The cell suspension was filtered through a pre-dried membrane filter (0.22 µm) and washed three times with equal amount of Milli-Q water and allowed to dry to a constant weight, before determining the CDW. A linear correlation between OD600 and CDW was established, with one unit of OD600 corresponding to 0.30 gCDW L−1 for BB-12 and 0.32 gCDW L−1 for BB-46. The concentrations of fermentation end products in the culture broth were determined on a HPLC system  equipped with both a UV (210 nm) and RI detectors using a Rezex ROA H+ (8%) ion-exclusion column  at 80 °C, with 5 mM H2SO4 as mobile phase and a flow rate of 0.6 mL min−1. Sucrose concentrations were determined by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAE-PAD) on an Ultimate 5000 system  using Dionex CarboPac PA1 guard and analytical columns . The separation was performed under isocratic conditions at 30 °C with 100 mM NaOH as eluent at a flow rate of 1 mL min−1.To monitor cellular growth throughout the fermentations, the OD79 and was retrieved from NCBI GenBank (Accession no. CP001853.2). The genome of BB-46 was sequenced in this study, using both Illumina short-read and Oxford Nanopore Technologies (ONT) long-read sequencing platforms. For short-read sequencing, genomic DNA (gDNA) was extracted from an exponentially growing culture (OD600 = 1) with the DNeasy Blood and Tissue kit on a QiaCube system , following the manufacturer’s protocol. The DNA was fragmented using a BioRuptor sonication device  with a target insert size of 800 bp. A gDNA library was generated using the KAPA HyperPlus Kit , according to the manufacturer’s protocol. Sequencing was performed on an Illumina MiSeq system using a MiSeq Reagent Kit v3 , with a paired-end protocol and a read length of 2 × 300 bp. For long-read sequencing, gDNA extraction, library preparation, and sequencing were performed at GenXone Sequencing . Briefly, bacterial cell pellets were harvested by centrifugation from overnight cultures (OD600 = 1.5). gDNA was extracted by Genomic Micro AX Bacteria Gravity Kit , following the manufacture’s protocol. The sequencing library was prepared using a Rapid Barcoding Kit  and was subjected to an additional purification and concentration step using AMPure XP beads . Sequencing was performed on a MinION flow cell . A combination of the short and long sequencing reads was used to create a hybrid genome assembly for BB-46 with Unicycler80. The assembly was run under Unicycler’s ‘conservative’ mode in order to minimize sequence misassembly. Open reading frames (ORFs) in the assembled genome were identified and functionally annotated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP).The complete genome of BB-12 was sequenced in a previous study81. The Model SEED reaction identifiers were converted to BiGG reaction identifiers82 using the MetaNetX/MNXref namespace83 and the corresponding reaction descriptions and reaction formulas were retrieved from the BiGG database. Metabolite identifiers, charged formulas and charges were also retrieved from the BiGG database. The draft reconstruction was then subjected to manual curation in a pathway-by-pathway manner. In this process, an improved functional genome annotation of BB-12 that integrates information from multiple databases was retrieved from the Integrated Microbial Genomes system (https://img.jgi.doe.gov/)84 and was used to refine the RAST annotations85, originally used for the draft reconstruction by Model SEED. Additional reactions were included in the draft reconstruction from the BiGG database82 when biochemical, physiological, or genomic evidence on their presence in BB-12 existed. Besides, reactions originally included in the draft reconstruction were removed if their presence in the strain could not be verified. During the curation process, we conveyed available literature on the strain and closely related microorganisms as well as data compiled from the online databases KEGG29, TransportDB86, and BioCyc87. GPR associations for all reactions were verified and adjusted, when necessary; some reactions were incorporated in the reconstruction without GPR when experimental evidence on the presence of the corresponding functions in BB-12 existed.The genome-scale metabolic networks of BB-12 and BB-46 were reconstructed following a semi-automated approach  was performed against the translated ORFs in the BB-12 genome. If a homolog was found, the corresponding gene was included in the BB-12 reconstruction, resulting in the incorporation of additional reactions or the assignment of additional genes to previously incorporated reactions in the BB-12 GEM.Following the conversion of the network reconstruction to a mathematical model and the process of model debugging and refinement, which are described in detail below, the curated metabolic network of BB-12 was used as a template to automatically generate a draft network reconstruction for BB-46. This draft reconstruction was created based on protein sequence homology of translated ORFs, using MetaDraft v0.8.195.To visualize different pathways or subsystems in the metabolic network, the Omix Visualization software version 1.8 was usedBifidobacterium-specific BOF97, CDW of BB-12 was divided into the main categories of protein, DNA, RNA, carbohydrates, cell wall components , lipids, and inorganic ions and metabolites. The total protein content of the BB-12 cells was determined by the Biuret method, with bovine serum albumin as a standard98. Total genomic DNA was quantitatively extracted using the Easy-DNA kit , according to the manufacturer’s protocol and DNA concentration was measured spectrophotometrically at 260 nm using a NanoDrop 2000 (Thermo Scientific). The total cellular RNA content was quantified by the KOH/UV method99. Total carbohydrate content of BB-12 cells (capsular polysaccharide and free sugars in addition to sugar residues in peptidoglycan and lipoteichoic acids) was determined using the phenol-sulfuric acid method98. The remaining biomass fraction was assigned to cell wall components, lipids, inorganic ions, and soluble metabolites based on average ratios in other bacteria.For the formulation of a B. animalis subsp. lactis LKM512100. The structure of the peptidoglycan subunit in each strain was deduced from their cell wall type, which is A3α for B. animalis subsp. lactis101 and A3β for B. longum102. The cellular fatty acid profile was adapted from that of Bifidobacterium bifidum var. pennsylvanicus43. Phospholipids were divided into diphosphatidylglycerol (cardiolipin), phosphatidylglycerol, compound 15, and compound 17, as previously described43. Teichoic acids were considered to be mainly glycerol-type teichoic acid associated with membrane lipids, i.e., lipoteichoic acid, as was previously reported for B. bifidum var. pennsylvanicus43. The concentrations of different inorganic ions as well as metabolites and cofactors in the soluble pool were adapted from the iJO136689 and iAF1260 GEMs of E. coli103.The average amino acid composition of the protein fraction of the CDW was estimated from the codon usage frequency for each amino acid, as calculated from all protein-encoding genes in BB-12 genome. The structure of the capsular polysaccharide subunit was considered to be the same as in 104. FBA34, which seeks an optimal solution by optimizing an objective function through the use of linear programming (LP) methods, was employed for network evaluation. The FBA-based function GapFind105 in the COBRA Toolbox was used for evaluating and debugging the genome-scale metabolic networks, with Gurobi  as an LP solver. The mass and charge balances of all reactions were checked, and unbalanced reactions were adjusted by consulting biochemical reaction databases. The quality of the final models were evaluated using MEMOTE, a software that runs a standardized set of metabolic model test27. The GEMs were further evaluated by comparing simulation results with known growth characteristics of bifidobacteria using FBA.The refined metabolic network reconstructions of BB-12 and BB-46 were converted to a mathematical format by parsing the stoichiometric coefficients of the curated reactions using the COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox version 2.17.1 within MATLAB Bifidobacterium strains was consulted. Following this approach, several candidate nutrients, which could be essential for growth of the strains but were missing in the generic CDM, were identified. For the formulation of a CDM supporting growth of BB-12 and BB-46, all missing nutrients were added simultaneously to the generic CDM. The formulated CDM was tested for its ability to sustain growth of BB-12 and BB-46 in batch fermentations for three successive subcultures. Growth in complex MRS medium with 10 g L−1 sucrose as carbon source and 0.5 g L−1 cysteine as reducing agent was used as a positive control. To identify which of the added nutrients are essential for each strain, in vitro omission experiments were performed in the fortified CDM, omitting one nutrient at a time.Following the initial model debugging and refinements, the individual GEMs were used to identify the essential nutritional requirements of each strain through in silico single omission experiments. For this, the uptake rate was set to zero for one nutrient a time before optimizing for biomass formation using FBA. If no growth was predicted, the nutrient was considered essential. In addition, literature on nutritional requirements of closely related 600 over two passages as in the newly formulated CDM containing all amino acids and vitamins (positive control). When removing cysteine from the medium, 0.5 g L−1 L-ascorbic acid was added as a reducing agent, and 0.4 g L−1 methionine was added as a potential alternative sulfur source.In silico predictions regarding the strains’ prototrophy for vitamins and amino acids already included in the generic CDM were also validated experimentally. An omitted nutrient was considered to be dispensable if the strain reached a similar final OD−1. The inoculum was grown for two passages in the CDM with sucrose as carbon source. Cellular growth was assessed by measuring OD600 after incubation for a maximum of 70 h. Knowledge gained throughout the study was used to further refine the genome-scale metabolic networks in an iterative approach.The GEMs were used to study the ability of both strains to use different carbohydrates as the sole carbon and energy source. FBA results were validated against experimental data from batch fermentations in the newly formulated CDM containing different carbohydrates at a concentration of 10 g LATP gCDW−1 and 5 mmolATP gCDW−1 h−1, respectively, based on experimentally determined values in the Gram-positive, anaerobic bacterium Caldicellulosiruptor saccharolyticus106.The ATP requirements for growth- and non-growth associated maintenance were initially set to 39.31 mmol−1) and the regression slope for the relationship of metabolite concentration (in mmol L−1) versus the biomass concentration (in gCDW L−1) in the exponential growth phase. Growth and non-growth-associated maintenance requirements in each GEM were adjusted by fitting the predicted maximum specific rate to the experimentally determined value under the same condition.Following the model-guided development of a CDM, the GEM predictions were validated against data from batch fermentations in the newly developed medium. Nutrient uptake rates in the GEMs were constrained based on the composition of the CDM and the specific uptake and secretions rates calculated from measurements in the exponential growth phase of the fermentations. The specific rate of a metabolite was determined as the product of the specific growth rate (hSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques  for developing FA-producing yeast cell factories.Metabolic engineering involves the sustainable production of high-value products. Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast  Metabolic engineering is the scientific discipline that is used to study the systematic analysis of biological pathways with molecular biology techniques to modify the metabolic potential and genetics of microorganisms in order to produce desired products. It deals with metabolic fluxes and their reactions to determine the metabolic functions in systems biology .Metabolic engineering took its first leap when genes were introduced into bacteria for the first time in 1973 . Soon afS. cerevisiae.This review discusses the technological advancement for fatty acid (FA) production in yeast and other microbes, ranging from classical techniques such as random mutagenesis to cutting-edge technologies such as CRISPR and synthetic biology. We also discuss different sources of FA production and gene manipulation techniques such as recombinant DNA technology, synthetic biology, and CRISPR–Cas, with a particular focus on The sustainable production of biomolecules requires the engineering of microbial cell factories using metabolic engineering principles. They can convert inexpensive feedstocks into chemicals and fuels . The traPreferably, a microbial cell factory should possess specific characteristics that enable it to produce desired products from the desired feedstock. There are three main features such as (i) enough basic knowledge of the microbe, modeling of media cultures, and bioprocesses, and (ii) the information of dietary requirements. Typically, microbes that can feed on simpler carbon and nitrogen sources are preferred; (iii) resistance to various stresses is crucial since it can affect the overall productivity. Other factors, such as tolerance to harsh conditions, such as high temperature and pH in the fermenter, are also considered . IdeallyE. coli and S. cerevisiae are mostly being utilized as microbial chassis for the synthesis of FAs and are discussed in detail in the reviews [Rhodococcus opacus, a chemolithotrophic oleaginous bacterium, has recently been engineered by the deletion of acyl-coenzyme synthetases and the increased exprssion of three lipase enzymes which had lipase-specific foldase to produce 50.2 gL−1 of Free fatty acids (FFAs) [Moreover, heterogeneous enzymes should also be implemented to functionalize fatty acyl-CoAs into desired derivatives . Current reviews ,14,15. Hs (FFAs) .S. cerevisiae to ethanol, propanol, and butanol.Robustness is a major requirement while selecting microbes as a cell factory. However, additional challenges are faced during the production of novel compounds using engineered pathways . ClassicWith the development of next-generation DNA assembly and synthesis tools, microbes’ development and characterization as chassis has been in focus. However, not all microbes can be cloned. For instance, more than 99% of the environmental microbes cannot be cultured and studied using the current technologies . FurtherFAs are essential components of cellular processes and are involved in cell signaling and building the phospholipid bilayer of the cell membrane . GeneralJatropha curcas), karanja (Pongamia pinnata), and mahua (Madhuca indica) are much more cost-effective as compared to edible oil crops since they can be easily harvested in lands where edible oil crops cannot grow [Oleochemicals are primarily derived from plant-based and animal fat sources, which have increasingly concerned sustainability and adverse effects on the environment . Becausenot grow . Some otnot grow . AdditioChlamydomonas reinhardtii and Microchloropsis gaditana, which can produce lipids up to 60% of its dry weight biomass, have been intensively studied due to their high oleogenic capability [C. cryptica and Navicula saprophila did not show any increased oil production [P. tricornutum enhanced the production of NADPH, resulting in the increased production of lipids up to 55.7% of its dry weight [Recent concerns regarding climate change and crude oil depilation have increased the attention to cleaner biofuels’ production using carbohydrates as a feed source ,33. Withpability . Bioengioduction , the ovey weight . Despitey weight , there ay weight ,39. Algay weight . Moreovey weight .FA production in microbes serves as an excellent alternative for the production of biofulels. Initial studies of the microbial production of FAs took place in the 20th century when evidence of similarities between microbial oil and FAs was found in animals and plants . MoreoveRecent advancements in the field of metabolic engineering have made it possible to industrially synthesize hydrocarbons in microbes. Linear hydrocarbons such as alkanes, alkenes, and esters, particularly hydrocarbons for diesel and jet fuels, can be synthesized by microorganisms using the FA biosynthetic pathway ,46. TheyEscherichia coli by utilizing CO2 as a substrate to produce all of its biomass [E. coli, the group co-expressed formate dehydrogenase, phosphoribulokinase, and RubisCO. These three enzymes are involved in CO2-fixing during the Calvin–Benson–Bassham cycle. However, the study did not only rely on bioengineering, and adaptive laboratory evolution was also applied to achieved autotrophic growth. This is very significant since the low-cost feed source for producing highly valued products is highly demandable [2 as an abundant, low-cost feedstock for biochemical production have been conducted [E. coli and yeast, these advancements have led to the production of artemisinic acid [Rhodococcus opacus was able to produce higher FAs as well as long-chain hydrocarbons as compared to the wild-type [Another aspect of using microbes is the potential for diversifying the substrate range. This is usually performed by introducing new genes or a pathway enabling the host organism to utilize a new substrate. Recently, Gleizer et al. showed a proof-of-concept study in  biomass . In ordemandable . Similaronducted ,50,51. Anic acid , resveranic acid , FAs [25nic acid ,56,57,58nic acid . Recentlild-type . HoweverE. coli to produce FAs. Plant-based, renewable feedstocks, for example biomass-derived carbohydrates, have been utilized to produce FAs in E. coli. Steen et al. [E. coli by expressing the native E. coli thioesterase, tesA (a “leaderless” version of TesA), and also eliminated FadD and FadE, the first two competing enzymes which are involved in b-oxidation. Similarly, about 11% more FAs were produced in an engineered E. coli than the control through the over-expression of fabD from four different sources. The well-studied and characterized E. coli fabD gene encodes malonyl CoA-acyl carrier protein transacylase, which catalyzes malonyl-CoA to malonyl-ACP [fabD gene from E. coli, Streptomyces avermitilis MA-4680, or Streptomyces coelicolor A3(2). However, the strain carrying the fabD gene from Clostridium acetobutylicum ATCC 824 was found to produce the same amount of FFAs as the control strain. Notably, the overexpression of the fabD gene was performed by an optimized gene construct. The fabD gene was cloned to form an operon with the TE gene, and hence achieving the overexpression of the gene. The study suggested that FFA production improvements can be brought through the overexpression of the fabD gene to increase malonyl-ACP activity [The majority of the genetic manipulations in microorganisms have targeted n et al.  synthesionyl-ACP . An incractivity .−1 h−1, 1.4-fold higher than the wild type. In the study, Chu et al. increased FA production by identifying and cloning novel aldehyde dehydrogenase, GabD4 from Cupriavidus necator. Upon investigation, GabD4_E209Q/E269Q showed the highest enzymatic activity. Metabolic enhancement in E. coli through fatty acyl-ACP reductase from Synechococcus elongatus resulted in improvement of both productivity and yield [E. coli AdhP plays a vital role in reducing fatty aldehydes to fatty alcohols, and thus, the study established an encouraging synthetic route for industrial microbial synthesis of fatty acid in method E. coli [Similarly, the level of an omega-hydroxy fatty acid, 3-hydroxy propionic acid production, has shown an increased titer up to 1.8 gLnd yield . The end E. coli .E. coli, other organisms have also been utilized. S. cerevisiae has also been a subject of many genetic manipulations for metabolic engineered products, and FAs are not an exception. In yeast, the majority of metabolic fluxes are involved in ethanol production during the fermentation process. Therefore, most enhanced FA production strategies are devised to redirect the fluxes to go through the ethanol pathway. To increase microbial FA production, the main target is to increase the amount of acetyl-CoA, malonyl-CoA, and fatty acyl-CoA. Thus, genetic manipulations for the enhanced synthesis of FAs often target the genes involved in these molecules’ production. Since yeast (S. cerevisiae) does not have enough cytosolic acetyl-CoA production, improving the supply of acetyl-CoA, several genetic manipulations in S. cerevisiae have been conducted. One way to increase the amount of acetyl-CoA production is to inactivate its consumption pathways. The glycolytic flux was redirected towards acetyl-CoA by inactivating the genes for ethanol formation and glycerol production. The inactivation was performed using the widely used marker-based homologous recombination, the loxP–KanMX–loxP method [ADH1, ADH4, GPD1, and GPD2 genes resulted in the production of 100 mg/L FA-derived advanced biofuel, n-butanol [Although most of the work for the microbial production of FAs has been performed using P method . The ina-butanol .IDH1 and IDH2 genes, which play a role in citrate turnover in the TCA cycle through the marker-based homologous recombination. The strategy yielded a 3–4-fold increase in the citrate levels. The excess of citrate was dealt with by the overexpression of a heterologous ATP-citrate lyase (ACL), and hence a significant amount of FAs was recorded in the study [−1 of FFAs, a 20% increase compared to the previously recorded amount, in engineered S. cerevisiae by optimizing a synthetic citrate lyase pathway. This was done by expressing the ATP citrate lyase and malic enzyme [Y. lipolytica, and eliminating the expression of cytoplasmic glycerol-3-phosphate dehydrogenase. Altogether, due to these genetic manipulations, 0.8 g/L of FFAs in shake flasks was recorded.Similarly, in another study, citrate levels were increased by deleting he study . Moreovec enzyme . Alongsic enzyme , by downS. cerevisiae, is catalyzed by acetyl-CoA carboxylase (ACC) from acetyl-CoA. Increasing the supply of malonyl-CoA is vital for increasing FAs in yeast. The increased expression of the ACC1 gene (encodes ACC) and FAS gene yields a higher level of malonyl-CoA. In S. cerevisiae, the overexpression of ACC1, FAS1, and FAS2 enhanced the titer of FAs [S. cerevisiae resulted in producing 10,498 μg FAEE/g CDW of fatty acid ethyl ester, an increase of about 6.2-fold compared to the wild type [AAE13, a plant malonyl-CoA synthetase gene overexpression in S. cerevisiae, led to the increased accumulation of lipid and resveratrol of 1.6-times and 2.4-times, respectively [Malonyl-CoA, another important precursor for the biosynthesis of FAs in r of FAs . Moreoveild type . In anotectively . More reectively .On the other hand, another way to increase the oleaginous microbes’ efficiency by increasing NADPH supply is to reduce the acetyl groups (CH3–CO–) in the growing acyl chain of FAs (–CH2– CH2–). In most oleaginous species, the malic enzyme provides most of the NADPH for FA biosynthesis . AdditioS. cerevisiae, transposon insertion mutagenesis has been utilized for enhanced FA production. In the study, five open reading frames (ORFs), SNF2, IRA2, PRE9, PHO90, and SPT21, through transposon-based insertion, were found to be associated with increased lipid content [M. alpine. They were able to silence the 12-desaturase gene involved in FA synthesis and obtain a higher amount of FAs. More importantly, the RNAi-based silencing did not compromise the FA composition [With the recent advancements in the field of metabolic engineering and other biological tools, it has become easier to manipulate organisms for the desired purposes. The construction of living cells using scalable genetic parts can be performed in the modern age by using various genome editing tools, DNA assembly and chemical DNA synthesis tools to introduce the desired functional properties required in microbes . Beyond  content . Althoug content ,78. Usinposition .Yarrowia lipolytica has been applied to produce medium-chain FAs by introducing point mutations identified through computer-aided engineering in the ketoacyl synthase domain of FAS. The domain is directly linked with chain length specificity [On the other hand, targeted nucleases that induce double-stranded breaks are of particular interest. These nucleases can create a double-stranded cut, which is repaired by the cell either through a non-homology end-joining repair (NHEJ) or if a repair fragment is available; the path of homology-directed repair (HDR) is taken, which is helpful for the insertion of genes in the genome a. Transccificity . Furthercificity . The genAlthough CRISPR/Cas9 is widely applied in metabolic engineering, other CRISPR proteins are also being utilized. For instance, CRISPR Cas12 , which offers certain advantages compared to Cas9, has also been utilized for genome engineering . The CRIS. cerevisiae, many CRISPR-mediated genetic manipulations have been conducted to produce desired bioproducts. For instance, it has been applied for the engineering of marker-free xylose-fermenting yeast [S. cerevisiae compared to the wild type, deleting genes resposible for the synthesis of storage lipids, FA oxidation, and the conversion of FFAs to fatty acyl-CoA [FAA1 and FAA4, the acyl-CoA synthetase-encoding genes responsible for the deregulation of fatty acid biosynthesis, were removed as a result, and the modified strain was able to produce 35 mg·gDCW−1 of FFAs, about 10-fold more as compared to the control strain. Similarly, POX1, which encodes fatty acyl-CoA oxidase, was also deleted in the previously modified strain to prevent FFA and acyl-CoA degradation. The deletion resulted in a 58% increase in the production of FFAs, to 53 mg·gDCW−1. Furthermore, the deletion of PAH1, LPP1 and DPP1 resulted in an increase in FFAs to 102 mg·gDCW−1, a 98% increase. Phosphatidic acid (PA) is characterized as an important signaling molecule for the regulation of lipid metabolism, but its dephosphorylation results in the formation of diacylglycerols. High levels of PA are linked with the upregulation of the fatty acid biosynthesis machinery. The deletion of genes PAH1, LPP1, and DPP1 involved in the dephosphorylation of PA resulted in a 98% increase in total FFAs, 102 mg·gDCW−1 [S. cerevisiae to disrupt the isocitrate dehydrogenase gene of the TCA cycle and, at the same time, HDR-based insertion of the ATP-citrate lyase gene [CRISPR-based editing of microbial genes has been applied in bacteria  and yeasng yeast . In anotng yeast . More spacyl-CoA  In the sg·gDCW−1 . Similarase gene . CRISPR-ase gene .Moreover, CRISPR can also be repurposed for gene activation and silencing without inducing a double-stranded break . CatalytAnother CRISPR tool is base editing, which is achieved through the fusion of dCas9 with base editors . Two typS. cerevisiae, fatty acid biosensors have been reported [S. cerevisiae has been reported [3-hydroxypropionic acid in S. cerevisiae.Although DNA manipulating techniques have played a significant role in advancing metabolic engineering, many problems such as low titer, yield, and tolerance still await enhancement. Traditional DNA manipulation techniques are tedious, time-consuming, and require several restriction digestions, such as ligation-based cloning. On the other hand, synthetic biology provides more sophisticated de novo synthesis and assembly methods of desired DNA that can be used for the reconstruction of desired pathways for different metabolic engineering applications. Furthermore, metabolite sensors are useful tool for metabolic engineering applications. They can be used both for high-throughput screening of high-producers and also for pathway regulation in response to specific metabolic stimuli. These biosensors sense the signals and give an output, which can be fluorescent molecules or regulatory switches. They can be transcription factor (TF)-based, RNA-based, and enzyme-coupled biosensors. In reported . The bacreported . Similarreported . The bioE. coli [S. cerevisiae [With the advancements in synthetic biology tools, the applications of metabolic engineering can be extended, and the bottlenecks such as product tolerance and low supply of key precursors can be solved. For instance, synthetic biology has been used to enhance FA-derived biofuels in E. coli  and imprrevisiae .Yeast is an important organism that has been used extensively as a model organism for molecular biology studies. It is the first eukaryotic organism whose genome has been sequenced . CompareSaccharomyces Genome Deletion Project [Saccharomyces cerevisiae strains were constructed, knocking out almost 96% of the yeast genome. The project provided unique knowledge of over 6000 gene disruption mutants and proved to be of great importance for future metabolic engineers. Apart from metabolic engineering uses, yeast has also become a reliable model organism for studying various fundamental studies such as the cell cycle [A large breadth of genetic information is available about the yeast genome. Various specific databases are available, containing genetics, proteomics, and interactomics information . The inf Project . During ll cycle , numeroull cycle , and virll cycle . Generalll cycle . Since tS. cerevisiae are catalyzed by the type I FAS system and have two functional subunits: the α-subunit and β-subunit encoded by FAS2 and FAS1, respectively [S. cerevisiae are synthesized by the type I FAS system. Principally, there are two ways of generating acetyl-CoA in yeast cells. First, CoA is generated from the glycolysis of fermentable sugars through the pyruvate–acetaldehyde–acetate pathway through cytoplasmic acetyl-CoA synthases activity. The second source for acetyl-CoA synthesis in S. cerevisiae is the excess of citrate  to FAs using malonyl-CoA as the extender unit. Each elongation of two-carbon units in FA biosynthesis requires two NADPH. FAs in the cytosol of ectively ,117. Alt citrate . This exS. cerevisiae is citrate catabolism. During the TCA cycle, citrate catabolism is performed by an enzyme of mitochondrial origin, isocitrate dehydrogenase. The enzyme is coded by two distinct genes, IDH1 and IDH2 [IDH1 and IDH2 separately, and then the effect on citrate accumulation is measured compared to the wild-type strains. The double gene disturbance resultantly yielded a 4 to 5-fold enhancement in citrate level in the media. This suggested that since two genes and those genes code, the enzyme that catalyzes citrate was disturbed in the process, resulting in the net increase in citrate accumulation in the media [The possible way for the metabolic improvement of acetyl-CoA production, as proposed by , is firsand IDH2 . The acche media .ACL). Several studies regarding the effect of ACL on the cytoplasmic acetyl-CoA generation or FA accumulation have been conducted [ACL action and acetyl-CoA precursor synthesis for FA production. However, despite being present in various organisms, including human cells, animal cells, plant cells, and in certain species of yeasts, the enzyme is absent in the Saccharomycotina. Therefore, an obvious target for improving acetyl-CoA production and eventually increasing FA synthesis is the insertion of genes that code for the enzymes responsible for catalyzing the accumulated citrate to acetyl-CoA in the cytosol [IDH1 and expressing ACL from Aspergillus nidulans for production mevalonate [ACL pathway by increasing citrated supply and the expression of active ACL can be applied to synthesize acetyl-CoA-derived molecules such as FAs.Furthermore, the production of a surplus amount of citrate is not only required; instead, the accumulated citrate should also be converted into cytosolic acetyl-CoA. This is performed by the enzyme ATP-citrate lyase  and pathways responsible for producing natural products ,127. A n strains . Furtheroters by . In E. cof genes . With suHowever, there are still many hurdles, such as poor predictive models due to the inherent non-linearity of biological systems, the lack of widely applicable and scalable part libraries, and low-throughput characterization techniques.S. cerevisiae, efforts are being made to use their beneficial traits [Kluyveromyces marxianus has been studied due to its thermotolerance [K. marxianus and Ogataea for their resistance towards hydrolysate-derived inhibitors [Y. lipolytica, is of particular importance due to its oleaginous properties and is becoming a competitor of S. cerevisiae due to the rapid development of its assembly tools using synthetic biology and CRISPR–Cas [Y. lipolytica an increase titer of the lipid has been achieved through the inter-convertsion of excess NADH into NADPH [Non-conventional yeasts are also gaining much attention and are being characterized due to their beneficial traits, such as tolerance towards high temperatures and pentose sugar conversion. Although these non-conventional yeasts lack genetic tools compared to l traits ,133. Forolerance  and K. mhibitors . AnotherISPR–Cas ,140,141.to NADPH , reducinto NADPH , and engto NADPH . Due theFapR. The biosensor achieved an 82% repression ratio when the malonyl-CoA was at a high level [As new metabolic engineering, synthetic biology, and genome-editing tools and technologies become available, it will become quicker and cheaper to engineer yeast strains. Altogether, combinatorial approaches utilizing traditional and modern tools see  to produgh level . Principgh level . Furthergh level .Classical metabolic engineering methods were based on random modifications through different mutagens such as UV and certain mutagenic chemicals, followed by screening the modified strains for overproduction. Molecular biology techniques such as recombinant DNA technology rely on restriction enzymes and different cloning methods to enhance strains for the overproduction of a particular molecule. Targeted gene-editing technologies such as different CRISPR–Cas proteins with various functions have made the process of targeted gene editing fast, efficient, and cost-effective. The CRISPR-Cas tool kit has been expanding ever since its discovery. Modifications such as the fusion of Cas proteins with other effector domains and abolishing the activity of the catalytic domain of Cas proteins are also being applied to achieve desired metabolic engineering goals. System and synthetic biology have been used to create genome-scale metabolic models and predict and construct indigenous and novel/non-natural pathways.S. cerevisiae has remained an important player in the field; like many others, it has produced diverse chemicals, including FAs, which are the critical starting materials for biodiesel production. This has only been possible due to the tremendous amount of work that has been performed on studying the yeast genome. The introduction of non-native pathways into microorganisms and manipulating them to produce the desired product has shown promising results because of the continuous advancements in gene manipulation and analysis techniques. Precise gene-editing technologies such as CRISPR/Cas9 are proving a handful in this regard. FA production in yeast has also been enhanced through several gene insertions and mutations. However, there are still many problems, such as tolerance to the end product, which at high concentrations is usually toxic, thus affecting the cell growth; solving it is not a straightforward task. The comparative cost of yeast-based FAs with animal and plant sources is another limiting factor. With significant technological advancements in the field and an exponential reduction in the cost of sequencing and other such technologies, the field is hoping to offer much more in terms of chemical synthesis from low-cost feedstock and with improving resistance against fermenter conditions. CRISPR/Cas-based genome engineering in yeast has been applied, and it has been extensively used since its discovery for gene manipulations for metabolic engineering applications. Several CRISPR techniques such as base editing, CRISPR inactivation, and silencing have been applied in yeast. More efficient Cas proteins and engineered variants such as Cas12, Cas13, dCpf1, and dCas9-Fok1 have broader applications, such as multiplex targeting, transcriptional regulation, and less off-target effects, which prove more valuable for genetic manipulation in yeast for FA production. Moreover, synthetic biology has the potentials to play a significant role in the synthesis of non-native products in yeast utilizing cheap feedstock, and thus in the future, it could solve the problem of global warming associated with fossil fuels and prove cost-effective.Metabolic engineering has been able to engineer microbes for chemical production and continues to do so. Yeast


Snowmelt dynamics are a significant determinant of microbial metabolism in soil and regulate global biogeochemical cycles of carbon and nutrients by creating seasonal variations in soil redox and nutrient pools. With an increasing concern that climate change accelerates both snowmelt timing and rate, obtaining an accurate characterization of microbial response to snowmelt is important for understanding biogeochemical cycles intertwined with soil. However, observing microbial metabolism and its dynamics non-destructively remains a major challenge for systems such as soil. Microbial volatile compounds (mVCs) emitted from soil represent information-dense signatures and when assayed non-destructively using state-of-the-art instrumentation such as Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-TOF-MS) provide time resolved insights into the metabolism of active microbiomes. In this study, we used PTR-TOF-MS to investigate the metabolic trajectory of microbiomes from a subalpine forest soil, and their response to a simulated wet-up event akin to snowmelt. Using an information theory approach based on the partitioning of mutual information, we identified mVC metabolite pairs with robust interactions, including those that were non-linear and with time lags. The biological context for these mVC interactions was evaluated by projecting the connections onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) network of known metabolic pathways. Simulated snowmelt resulted in a rapid increase in the production of trimethylamine (TMA) suggesting that anaerobic degradation of quaternary amine osmo/cryoprotectants, such as glycine betaine, may be important contributors to this resource pulse. Unique and synergistic connections between intermediates of methylotrophic pathways such as dimethylamine, formaldehyde and methanol were observed upon wet-up and indicate that the initial pulse of TMA was likely transformed into these intermediates by methylotrophs. Increases in ammonia oxidation signatures (transformation of hydroxylamine to nitrite) were observed in parallel, and while the relative role of nitrifiers or methylotrophs cannot be confirmed, the inferred connection to TMA oxidation suggests either a direct or indirect coupling between these processes. Overall, it appears that such mVC time-series from PTR-TOF-MS combined with causal inference represents an attractive approach to non-destructively observe soil microbial metabolism and its response to environmental perturbation. Soil is a major compartment of the Earth system and its constituent organisms are central contributors to global biogeochemical cycles. Accurate characterization of the activity of soil microbiomes is key to understanding how the soil compartment functions and responds to chronic and acute perturbations . SnowpacAlthough the characterization of soil microbial metabolism has become much more convenient and effective due to the development of sequence-based and mass spectrometry approaches to interrogate biomolecular processes, most approaches require destructive sample retrieval and processing. This limits analyses to sequential snapshots rather than more continuous time-series and results in important information loss. Non-destructive approaches such as the continuous monitoring of trace gases can be considered “information-rich,” although not “information-dense,” and therefore provide more limited insight into mechanistic processes underlying their fluxes. While sequence-based or metabolomics approaches requiring extraction excel at delivering broad snapshots of soil microbiome function, alternative and non-destructive approaches to monitor microbial metabolic dynamics are needed.Microbial volatile compounds (mVCs) represent a range of organic and inorganic compounds that can exist in both aqueous and gaseous phases under standard conditions. Together with recent advances in measurement techniques, mVCs show great promise in non-destructive representation of microbial metabolism across multiple disciplines, including soil microbiology and epidemiology . mVCs caThis combination of information-rich time series and information-dense measurements may provide opportunities to explore mVC interactions in a metabolic network context. These metabolic networks are dynamic in terms of interaction strength and connectivity including both linear and non-linear relationships . Approacvia PTR-TOF-MS and used these mVC time-series to establish directed connections between various mVCs. Using an information theory approach, we inferred the directed connections as causal  in parallel with snow excavation for snow water equivalent analyses. Snow depth was 163 cm at the time of sampling and soil cores were taken with a hand corer (two-inch diameter) following snow excavation to a depth of 15 cm from the soil surface. Snow was in amorphous state and had accumulated over 5 months prior to excavation. Dissolved inorganic nitrogen pools were determined colorimetrically according to protocols as described previously . NO3– co-100 kPa . This ma-100 kPa . More deIn order to investigate the effects of wet-up on the soil mVC profile, soil microcosms were prepared in 300 mL VC-grade glass jars with Teflon covers. Each jar contained 20 g soil wet weight taken from a homogenized core with an empty jar used to control for any background VC fluxes . Microco2 during microcosm incubations  of 60.021 but will be detected at m/z 61.028 due to the added proton from the hydronium ion. The following protonation reaction will take place if the proton affinity of the volatile compound X is higher than that of water (Proton-transfer-reaction time-of-flight mass spectrometers use soft-ionization (protonation) of water .Despite being a soft-ionization method, PTR-TOF-MS can still induce fragmentation and such fragments can sometimes be used as identifiers for the mVCs of interest. For example, m/z 43.018 can be considered as an acetyl fragment originating from volatiles containing an acetyl functional group.A PTR-TOF-MS 8000  was operated at a drift-tube pressure of 2.3 mbar, a drift-tube temperature of 75°C, a field density ratio (E/N) of 120 Td, and an inlet temperature of 70°C.All time-series data of raw PTR-TOF-MS ion counts were normalized using the following equationto account for the primary ion abundance. A time-series gap-filling protocol was followed using “dplyr” and “zoo” packages  in R x64Dry-Phase; and purple for post-wet-up, or Wet-Phase) together with gradients representing time are used to show the temporal dynamics of mVCs. Vectors representing loading scores were plotted to represent specific mVCs that contributed most to separation across the PCA axes, in addition to others that were of particular biological interest based on prior literature.Principal components analysis (PCA) was performed on the processed time-series dataset to explore mVCs that differentiated the post wet-up phase from the preceding dry phase. R packages “factoextra,” “ggplot2,” “ggnewscale” were used for PCA calculation and processing . Each poU), synergistic (S), and redundant (R) information to establish directionality as well as inferred causality in the relationships between variables, termed source-target relationships. This was assessed by simultaneously considering the relationships between two to three m/z’s. U shows the strength of a causal relationship between two nodes (mVCs). S is a measure of new information that is obtained by using two source nodes to explain a single target node -based information partitioning was performed to go beyond synchronous metabolic connections suggested by PCA . The calget node . For exan = 2,500) were obtained from TIPNet calculations of dry and wet phases. Shuffled surrogates test for statistical significance by randomly shuffling time-series data of source time-series n number of times (X(t-1) and target node Y, X(t-1) data will undergo random shuffling for “n” number of times. Each U connection would imply a causal relationship between two mVCs. Each S connection would imply a metabolic relationship between the three mVCs in question .To explore this, we used the KEGG reaction network and projected the top 20%  of mVC pairs or triads showing significant U connections. For S connections, every source-target pair had 129 candidate mVCs to qualify as the second source contributing to the amount of explained information. Only the top mVC with the highest S information for a given source-target pair was considered as the second pair to form an S connection that later was projected onto the KEGG reaction network. KEGG pathways that included at least two of the inferred metabolic connections were extracted using “MetaboSignal” and “gdata” packages  in R andape 3.72 . Sub-netmVC profiles of the soil microcosms were initially characterized prior to and following wet-up. Time-series measurements of mVCs in the range of m/z 22–150 were obtained by PTR-TOF-MS and are included in U) and synergistic (S) information to infer causal relationships between mVCs. Unique and synergistic connections were projected onto the KEGG reaction network and yielded six sub-networks . This approach allowed us to identify metabolic connections between pairs or triads of mVCs (with consideration of timelags) indicative of directional source-target relationships. In this way, each LMI between two or three mVCs was considered as a proxy for a metabolic connection and was partitioned into unique , for example is a quaternary amine known to be produced during the anaerobic degradation of glycine betaine and choline , formed abolites . With thmddA methyltransferase pathway in KEGG (EC 2.1.1.334). Hydrogen sulfide was uniquely connected to acetate and the connection was projected onto a CysO cysteine synthase pathway with acetate as its target mVC (EC 2.5.1.47). Time-series data of both methanethiol and DMS differed from those of pure carbon mVCs as they showed a more gradual increase and peaked at a later time point with patterns showing an apparent co-variance. This co-variance that was exclusive to sulfur-containing mVCs suggested not only the metabolic connectivity of the two compounds but also the metabolic progression of the soil microbial community that shows evidence of mostly oxidative and some fermentative carbon and nitrogen metabolism followed by a reduced sulfur-based one, possibly suggesting a change in redox conditions.Metabolic connections in sulfur-containing mVCs were projected onto the KEGG reaction network and formed a distinct cluster containing pathways related to methylotrophy and sulfur cycling. Formaldehyde once again emerged as an important component of the sub-network being identified as a target mVC with sources including dimethyl sulfide (DMS) and methanethiol , projectvia pathways likely driven by alcohol dehydrogenase (EC 1.1.1.1). A unique connection from isoprene to acetate was also included  based on the hypothesis from previous studies that isoprene is oxidized through beta-oxidation pathway with an unknown enzyme  have been used as metabolic fingerprints in multiple disciplines including epidemiology, microbial ecology, and environmental microbiology  and haveRe-wetting of soil could result in mVC pulses by purely abiotic factors such as chemo-desorption caused by water addition . This unvia trimethylamine dehydrogenase often found in methylotrophs (via PTR-TOF-MS due to high background from molecular oxygen (m/z 32), the rapid consumption of TMA via methylotrophic pathway suggests an equivalent release of ammonium ions as a result of methylamine oxidation. This hypothesis aligns with the results of another study from our group on nearby soils that showed a rapid increase in microbial biomass N early upon snowmelt initialization followed by a population crash, a pulse of nitrate in parallel with an increase in nitrifier abundance , showed a gradual increase in emission as TMA emission disappeared . Both mess soils . Regardlss soils . It appevia beta-oxidation pathway to supply carbon to the central metabolism (As presented, we propose that information partitioning of time-series data from PTR-TOF-MS can be used to build a metabolic narrative of soil microbiome metabolism and offers a practical means of diagnosing the activation of major metabolic pathways following perturbations. This approach is amenable to other diagnostic purposes such as evaluating soil redox transitions and estimating the significance of anaerobic microsites. In this regard, diacetyl, 2,3-butanediol and other mVCs that are known fermentation byproducts are useful markers and metabolic connections, such as pyruvate to 2,3-butanediol, and acetaldehyde to ethanol can be computed to further verify the biological pathways suggested by the presence of the mVCs detected. Further, this approach can also be used to generate hypotheses regarding unexplored pathways. For example, in our study, a unique connection, methanol to 2-butanone, suggests that this metabolic transformation is occurring, and while soil methanol-utilizers have been shown to produce 2-butanone, the responsible enzymes have yet to be identified . Similartabolism . Such hyIn this proof-of-principle study, we explored an information theory approach where mVC time-series are analyzed to discover unique and synergistic metabolite-to-metabolite connections that are projectable to KEGG reaction networks. We showed that this approach can be useful in providing a metabolic narrative that coincides with the results from other studies. A number of sophisticated metabolomic approaches exist with higher fidelity, however, a necessity to destructively sample makes the assessment of microbial dynamics and metabolic couplings difficult to interpret and precludes true time-series measurements. Continuous or semi-continuous time-series data also allows directional connections to be computed from LMI and its partitioned constituents such as unique and synergistic information. Several m/zs explored in this study can have contributions from multiple compounds and cause challenges in unambiguous identification without gas standards. However, our approach of inferring strong causal relationships that map to biological pathways relevant for soil under snowmelt provides a compelling evidence for their identities. In future studies, we plan to facilitate the results and framework of this approach by coupling measurements with GC-MS and other instruments that may complement data provided by PTR-TOF-MS. Overall, we propose that mVCs, especially when combined with online measurement such as PTR-TOF-MS, represent an attractive approach to “remotely sense” soil microbiome metabolism.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.JK, AG, KJ, RW, PM, and EB designed the research. JK, RW, and KJ collected the data. JK wrote the manuscript with contributions from all authors. All authors performed parts of the data analysis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.