Screening for antibiotic resistance genes (ARGs) in especially environmental sampleswith (meta)genomic sequencing is associated with false-positive predictions ofphenotypic resistance. This stems from the fact that most acquired ARGs require beingoverexpressed before conferring resistance, which is often caused by decontextualizationof putative ARGs by mobile genetic elements (MGEs). Consequent overexpression of ARGscan be caused by strong promoters often present in insertion sequence (IS) elements andintegrons and the copy number effect of plasmids, which may contribute to highexpression of accessory genes.Here, we screen all complete bacterial RefSeq genomes for ARGs. The genetic contexts ofdetected ARGs are investigated for IS elements, integrons, plasmids, and phylogeneticdispersion. The ARG-MOB scale is proposed, which indicates how mobilized detected ARGsare in bacterial genomes. It is concluded that antibiotic efflux genes are rarelymobilized and even 80% of β-lactamases have never, or very rarely, been mobilized in the15,790 studied genomes. However, some ARGs are indeed mobilized and co-occur with ISelements, plasmids, and integrons.In this study, ARGs in all complete bacterial genomes are classified by theirassociation with MGEs, using the proposed ARG-MOB scale. These results have consequencesfor the design and interpretation of studies screening for resistance determinants, asmobilized ARGs pose a more concrete risk to human health. An interactive table of allresults is provided for future studies targeting highly mobilized ARGs. Pathogenic bacteria resistant to antibiotics pose an enormous threat to human health,resulting in up to 10 million annual deaths in 2050 if we do not address the issue now, asestimated by the UN Interagency Coordination Group on Antimicrobial Resistance [Antibiotic resistance remains a significant global issue despite numerous studies intounderstanding the spread of genes via mobile genetic elements , 3 and dMycobacterium tuberculosis with aspecificity and sensitivity ranging from 82% to 92.7% [Salmonella with an accuracy of 95% [Escherichia coli with anaverage accuracy of 91% [When coupling (meta)genomic predictions with culture-based detection of resistant strains,it is often found that the two approaches do not agree , 29–32, to 92.7% , in nonty of 95% , and in y of 91% . These eAcinetobacter baumannii serves as a great example of bacteria that host awide range of efflux pumps, of which some confer intrinsic resistance and others requireoverexpression. Many of the native A. baumannii pumps are involved innonresistance functions such as membrane composition and stability, opaque/translucentcolony phase variation, various stress reliefs, biofilm formation, plasmid transfer rates,natural transformation, quorum sensing, and efflux of dyes, disinfectants, metals, and othernonantibiotic compounds [Although low specificity of resistance prediction from efflux-based ARGs has been reported, it shouompounds . Expressompounds .BesidesMost ARGs likely have native roles unrelated to resistance to clinical concentrations ofantibiotics . Many anFunctional (meta)genomic approaches, essentially cloning fragmented DNA into expressionvectors followed by screening for antibiotic resistance , 43, 44,The associations between ARGs and mobile genetic elements (MGEs) are important , 52, 53 Decontextualization of ARGs is explored by examining their association with (i) plasmids,(ii) IS elements, (iii) integrons, and (iv) their dispersal across distinct genera. Wecollect this information per class of ARG in the unifying ARG-MOB scale for mobilization ofresistance genes in all complete RefSeq bacterial genomes. Among other results, we concludethat most classes of antibiotic efflux genes are rarely mobilized from their original,chromosomal location and that even 80% of classes of β-lactamases have never or very rarelybeen mobilized. This necessitates both increased awareness of the genetic context of ARGsbut also more critical choosing of ARG targets for future, especially environmental,studies.n = 15,790). Then, 12,170 bp up- and downstream of predicted ARGswere analyzed for IS elements and integrons, while replicon type (plasmid or chromosome) wasalso considered. For more details, see n = 176,888) were clustered to 53,895 clustered resistance loci (CRLs),representing 1,176 Antibiotic Resistance Ontology (ARO) terms from CARD  mechanism is the most abundant category and its CRL count ismore than two times more numerous than the second largest category, antibioticinactivation (inactivation), althoughinactivation has over three times as many AROs asefflux , ARO3003836 (qacHsubunit of fluoroquinolone exporter), and ARO3000165 (tetracycline efflux pumptetA). Many efflux AROs contain a large number ofunique CRLs, as is also reflected by efflux CRL count in Fig. efflux ARGs are rarelyassociated with either IS elements or plasmids. Distances, in terms of nucleotides,between ARGs and IS elements are larger for efflux AROs than for othermechanisms, indicating that efflux ARGs are more “loosely” associatedwith IS elements than other mechanisms  AROs have low IS and replicon ratios while also exhibiting a lownumber of unique CRLs, indicating that target alteration CRLs areconserved and often not decontextualized. On the other hand, antibiotic targetreplacement (target replacement) AROs are more mobilized by ISelements and plasmids  are associated with IS elements and plasmids tovarying degrees Fig.  and werePadj < 0.01). Within Proteobacteria,Enterobacteriaceae have a higher median IS ratio thanCampylobacteriaceae, and Burkholderiaceae but lowerthan Aeromonadaceae, Pasterurellaceae, andMorganellaceae , showing that many ARGs inEnterobacteriaceae are highly mobilized by both IS elements andplasmids. Within Enterobacteriaceae, many ARG loci have been mobilizedboth by IS elements and plasmids, especially within the genera Shigella,Escherichia, Salmonella, Klebsiella, Enterobacter, and, to a lesser degree,Citrobacter . Within Firmicutes,whose IS ratio is not different from that of Proteobacteria, some families are associatedwith human activities such as Enterococcaceae andStaphylococcaceae. These harbor highly mobilized ARGs, whileenvironmentally associated Firmicutes, such as Bacillaceae, have manyARGs barely mobilized by either IS elements or plasmids . The most abundant major mechanismwas antibiotic inactivation with 2,684 unique CRL occurrences. Mechanismsantibiotic target replacement and antibiotic effluxwere found in association with integrons in 694 and 310 CRLs, respectively  in 1,393 out of 1,527 unclustered regions, supporting the well-describedassociation between class 1 integrons and sul1 [dfr genes associatedwith class 2 integrons and Tn7 transposon [inactivation” ant(3′′) category,whose genes encode aminoglycoside nucleotidylylating enzymes, with 1,009 CRLs associatedwith integrons. The ant genes are often found in association withintegrons [antAROs, aadA, aadA2, aadA5, ant(3'')-IIa, and ant(2'')-Ia,all display integron associations in at least 54.24% of their total CRL occurrences (aac(6′) AROs(aac(6′)-Ib-cr, aac(6′′)-Ib10, aac(6′′)-Ib7, andaac(6′)-Ib9) are here mostly found in integrons, agreeing with previousdescription of this class of ARGs [Under selective pressure for resistance, ARGs may be decontextualized into integrons,where a strong promoter confers overexpression of said ARGs, which may result inphenotypic resistance . Using Iectively . At firsntegrons , sul1–4,and sul1 . Trimethansposon  were herntegrons . Here, turrences . Similar of ARGs . GeneseFrom these results, it is evident that some target ARGs are highly associated withintegrons and are thus more relevant to screen for in an environment with, for example,PCR or sequencing, if the aim is to predict phenotypic resistance. This again emphasizesthe importance of considering the genetic contexts of ARGs.Inspired by previous work , we calcantibiotic targetreplacement (Padj < 0.0001), whileantibiotic efflux has a low median ARG-MOB but not significantlydifferent from antibiotic target alteration and antibioticinactivation groups. Antibiotic target protection, antibioticinactivation,and antibiotic target alteration groups are notsignificantly different , which is reflected by median replicon, IS, and integron ratiosthat are lower than most other groups  a low-ARG-MOB branch mostly populated by efflux AROs.The four MOB parameters all correlate significantly with each other, showing that theycovary and are appropriate for calculating the ARG-MOB scale Fig. ,B. HieraThe highest correlation coefficient is seen for IS/replicon ratios, showing that ARGsplaced on plasmids are likely mobilized by IS elements prior to insertion on plasmidsFig. ,B. The sNot surprisingly, the Simpson diversity index correlates positively with IS, replicon,and integron ratios Fig. , showingoqxAB (ARO3003922-3) [Klebsiella pneumoniae chromosomes, where they do not confer resistanceunless overexpressed [oqx AROs show high spread across their mean IS andreplicon ratios , showing that their mean ratios are low due to Klebsiellachromosomes but that there are many outliers due to variants inEscherichia and Salmonella that are only foundmobilized by IS elements and usually on plasmids  ,66 , Low (0 < ARG-MOB <0.182), Medium (0.182 < ARG-MOB < 0.378), High(0.378 < ARG-MOB < 0.681), and Very high (ARG-MOB > 0.681).These definitions are largely the same when estimating per mechanism individually , whiletarget replacement has the highest percentage of Highand Very high ARG-MOB AROs with these categories representing 64% oftarget replacement AROs . Inactivation has many AROs withHigh and Very high ARG-MOB, which include infamousβ-lactamases, aminoglycoside nucleotidyltransferases (ANTs), and others . It is dispersed across 6 unique genera for aSimpson index of 0.75 . The VIM-1 gene was found in amultiresistant E. coli from a patient. It was inserted in a class 1integron and found on a conjugative plasmid [Enterobacteriaceae, typically inassociation with MGEs, and is globally spread [For all major mechanisms, many AROs are classified as MOB Fig.  andantiers Fig. . Aminoglobilized . Likewisobilized . Witha  plasmid .It has y spread .target replacement AROs belong to thetrimethoprim-resistant dihydrofolate reductase dfr submechanism. TheARO3003013 within this submechanism has the highest ARG-MOB of any ARO at 0.95. A class 1integron with dfrA15 is widespread in Vibrio choleraisolates in Africa and was found on a conjugative plasmid [Vibrio,Salmonella, Enterobacter, Leclercia, Klebsiella, andEscherichia). Generally, ARGs conferring diaminopyrimidine (includingtrimethoprim) resistance by antibiotic target replacement mechanism arehighly mobilized and numerous in the complete genomes studied here  [dfrA15 [aac(6′) [arr-2 [murA [norB [bla(CME-1) [Based on examples of high and low ARG-MOB AROs , a pattebla(VIM) ,dfrA15  [dfrA15 ,aac(6′)[aac(6′) , andarr) [arr-2 , were or-2 [murA ,norB [4rA [norB , andblaa(CME-1)  (Supplema(CME-1) , will ala(CME-1) .Based on the results presented here and as discussed elsewhere , 23, 57,All code for data processing was written in BASH scripts, and statistics and plottingwere primarily done in RStudio.RRID:SCR_011936)was used to predict genes from nucleotide sequences and write corresponding amino acidsequences from all RefSeq genomes. Since Prodigal first trains itself based on the inputsequence, gene prediction was performed on subsets of each genus present in RefSeqgenomes. Per genus, two rounds of Prodigal were performed with the -meta flag enabled inthe second run, as it predicts some genes that are missed in single genome mode and viceversa. Results from the “single” and “meta” gene predictions were consolidated to omitredundancy.All complete bacterial genomes  were downloaded from RefSeq on 12 December 2019 using the ncbi-genome-downloadtool v0.2.11 [antibiotic efflux” (efflux),“antibiotic inactivation” (inactivation),“antibiotic target alteration” ,“antibiotic target protection” (target protection),“antibiotic target replacement” (target replacement),and the less abundant “reduced permeability to antibiotic”(reduced permeability). A few additional categories exist that arehybrids of two of the above mechanisms, but there are very few entries of these in CARDand are for most of the analyses not considered.Several ARG databases and tools for predicting ARGs have been produced, including CARD, ARDB 8, MEGAResThe CARD database (v3.0.7) was downloaded, and only the protein homolog model was used inthis study, excluding resistance determinants related to sequence variants . DIAMOND blastp .Data tables were imported into R for statistics and visualization using the packagesggplot2 , dplyr , tidyr , gridExtra, ggpubr , ggExtra, reshape2, knitr, kableExtra, vegan ,PerformanceAnalytics, ComplexHeatmap , RColorBrewer, DT, rstatix , tidyverse, broom, and plotly . Prior topairwise Four main mobilization (MOB) metrics Fig. , orratiFor each ARO category, the number of CRLs with and without identified IS elements wascounted and the IS ratio was derived where an IS ratio of 1 indicates that all CRLsbelonging to a given ARO have an IS element within 12,170 bp either up- or downstream ofthe ARG. Vice versa, an IS ratio of 0 indicates that none of the CRLs in an ARO have ISelements in proximity. Similarly, the replicon ratio was calculated per ARO based on theCRLs’ location on either plasmids or chromosomes. A replicon ratio of 1 means that allCRLs in a given ARO are of plasmid origin and a 0 means that all CRLs are fromchromosomes. The integron ratio indicates how many CRLs are inserted in integrons per ARO.For measuring the taxonomic distribution of each ARO category, the Simpson diversity index(range 0 to 1) was calculated per ARO using unclustered sequences and the genera they wereidentified in.Very low. Valleys in the density distribution ofARG-MOB scores were used to computationally pinpoint thresholds between ARG-MOBcategories. The ARG-MOB score in the Low group ranges from 0.0 to 0.182,the Medium group ranges from 0.182 to 0.378, High rangesfrom 0.378 to 0.685, and Very high ranges from 0.685 to 1.0. For theLow-Medium and Medium-High cutoffs, the low point invalleys was used to define values but no apparent valley is present betweenHigh and Very high. Instead, a linear model was fittedto the right-side slope of the High peak and another fitted to theapproximately linear data range starting at ARG-MOB score of 0.7. The intersection betweenthe two linear models (0.685) was used as the cutoff between the High andVery high groups  represents the mean of the four MOB metrics described above and serves as aranking scheme to evaluate the degree to which members of an AROs have been mobilized.Based on the smoothed kernel density estimates of all ARG-MOB scores, groupings were madeto categorize AROs by their ARG-MOB score. An ARG-MOB score of 0 indicates that ARGs ofthe given ARO were not once found to be mobilized in the RefSeq genomes and a score of 0is thus categorized as h groups .Additional File 1. Dataset1.html. An interactive table summarizing the resultspresented in the study. This file can be opened in a web browser.The file “ARGMOB_Supplementary Material.pdf” contains the following supplementarymaterials:Supplementary Text 1. Additional details about filtering of DIAMOND blastp hitsagainst CARD database.Supplementary Fig. 1. Distribution of DIAMOND blastp hits plotted as % IDagainst the bitratio. The defined filters are shown.Supplementary Fig. 2. Major bacterial orders of ARG blastp hits passing eitherof the defined filters. This plot shows the taxonomic distribution of passing hits on theOrder level. Only orders that constitute >0.2% of the total hits are shown here.Supplementary Fig. 3. Minor bacterial orders of ARG blastp hits passing eitherof the defined filters. This plot shows the taxonomic distribution of passing hits on theOrder level. Only orders that constitute <0.2% of the total hits are shown here.Supplementary Fig. 4. Violin plot of the lengths of unit- and compositetransposons in The Transposon Registry. The mean length of transposons from this curateddatabase is used to find the length cutoff for investigating the proximity of ARGs.Supplementary Table 1. Summary of filters and cutoffs for including ARG and ISelement blastp hits.Supplementary Text 2. Investigation of the taxonomic biases and skews of theRefSeq complete genome database and the CARD database. These two databases are both biased,but they are not biased towards the same genera.Supplementary Fig. 5. Biased composition of the RefSeq complete genome databaseinvestigated at Order level.Supplementary Fig. 6. Comparison of the relative representation of genera inthe RefSeq complete genome database and the CARD database. The databases are not skewedtowards the same genera. The biggest differences are from the genera Acinetobacter,Klebsiella, Escherichia, Salmonella, Streptococcus, andBordetella.Supplementary Text 3. By clustering 176,688 genetic loci with ARGs to 53,895Clustered Resistance Loci, we reduce the differential biases between the databases.Furthermore, we reduce the compositional database biases stemming from the presence of manyalmost identical genomes (e.g. E. coli genomes) in RefSeq.Supplementary Fig. 7. Effect of clustering ARG loci to CRLs. Several of themost abundant genera in RefSeq are reduced in their relative abundance by clustering ofalmost identical ARG loci. Loci with antibiotic efflux ARGs are compressed more byclustering than other resistance mechanisms.Supplementary Table 2. Overview of blastp hits passing filters, number of CRLsformed, and total number of AROs represented.Supplementary Fig. 8. Smoothed kernel density estimates of all AROs and theirARG-MOB values. In this density plot, the cutoffs between ARG-MOB groupings  are shown. For the cutoff betweenHigh and Very high groups, no local minimum could be found. Instead, linear models werefitted in two approximately linear ARG-MOB ranges. The intersection of the two linear modelswere chosen as the cutoff.Supplementary Text 4 and Supplementary Figure 9. 16S rRNA genes were identifiedin the studied complete genomes and IS elements were identified within the same proximity asfor ARGs. This serves as a control, in that 16S rRNA is rarely expected to be associatedwith IS elements. Within 12.17 kbp on either side of 16S rRNA genes, IS elements wereidentified in 5.59% of cases. We consider this an indication of a false-positive rate ofapprox. 5% of association of ARGs with IS elements.Supplementary Text 5. Investigation of which IS element families co-occur withindividual resistance mechanisms.Supplementary Fig. 10. Boxplots of abundance of IS families in proximity toARGs, shown by major resistance mechanism. Significance values for Mann-Whitney tests areshown above boxplots.Supplementary Text 6. The distance, in terms of bases, between IS elements andARGs are discussed here. Efflux ARGs have significantly larger distance to IS elements thanother mechanisms.Supplementary Fig. 11. a: Density plots of IS ratio against distance to nearestIS element per ARO. b: boxplot of the ARG-IS distance per resistance mechanism withMann-Whitney tests.Supplementary Fig. 12. Replicon ratios of the 10 most abundant Proteobacteriafamilies. ARO points are colored by their Replicon ratio and sized by how many unique CRLsbelong to them.Supplementary Fig. 13. IS ratios of Firmicutes families. ARO points are coloredby their Replicon ratio and sized by how many unique CRLs belong to them. Many Firmicutesfamilies are associated with the environment but some are associated with human activites,such as Enterococcaceae and Staphylococcaceae. This plotillustrates well the differences in ARG mobilization in environmental and human-associatedbacteria.Supplementary Table 3. Pairwise Mann-Whitney tests for IS and Replicon ratiosin Enterobacteriaceae genera.Supplementary Fig. 14. a: Overview of major mechanisms associated withintegrons. b: submechanisms associated with integrons. ANT(3’’) is the mostintegron-associated submechanism.Supplementary Table 4. Table of the top 20 AROs associated with integrons withtheir % integron association.Supplementary Text 7 and Supplementary Figures 15-19. Pearson correlationcoefficient analyses for major mechanisms. For each of the five major mechanisms, pairwisePearson correlation coefficients are calculated and shown, similar to main figure 5a.Supplementary Fig. 20. Bar charts of co-occurrence of IS elements,chromosome/plasmid location, and integrons per major mechanism. This shows that e.g. effluxARGs are most often located on chromosomes and not in association with integrons or ISelements. On the other hand, inactivation ARGs are often found on plasmids and inassociation with IS elements and integrons.Supplementary Text 8, Supplementary Figures 21-22, and Supplementary Table 5.Some AROs are highly divergent in mobilization. It is here discussed that some AROs have alarge spread from their mean IS and Replicon ratios. As discussed in the main text, theoqxAB efflux pump genes are good examples of AROs with low IS and Replicon ratio means,since they are found un-mobilized on almost all Klebsiella genomes, buthave been mobilized in many Escherichia and Salmonellastrains, resulting in large spreads from the mean IS and Replicon ratios. It is alsodiscussed that Proteus, Pseudomonas, Morganella, andAcinetobacter may act as reservoirs of yet unmobilized potentialARGs.Supplementary Fig. 23. Count density of ARG-MOB per mechanism. Similar to mainfigure 6a, the count density of ARG-MOB categories are shown per major resistancemechanism.Supplementary Figure 24. Antimicrobial compounds to which there are ARGs withVery high or High ARG-MOB scores.Supplementary Text 9. Some examples Additional information and analyses forintegron-ARG associations. Some examples of low and high ARG-MOB AROs are discussed withreferences. These examples illustrate that low ARG-MOB scoring AROs have generally only beenshown to confer resistance in broad screenings where genes have been randomly cloned intostrong expression vectors. On the other hand, high ARG-MOB scoring AROs were identified inclinical isolates that were experimentally verified to be resistant.Project name: ARG-MOBhttps://github.com/tueknielsen/ARG-MOBProject home page: Operating system: Linux—UbuntuProgramming language: bash, Rhttps://github.com/tueknielsen/ARG-MOB.Other requirements: For a full list of software and databases, please consult the scriptARG_MOB_v0_5.sh at License: MIT licenseGigaScience GigaDBdatabase [A searchable table of all results is available online as Dataset S1 .All supporting data and materials are available in the database .attC siteslacking integron–integrases; CRL: clustered resistance locus; ICE: integrative conjugativeelement; IS: insertion sequence; MGE: mobile genetic element; MOB: mobilization parameter;MWU: Mann–Whitney U; PCR: polymerase chain reaction; rRNA: ribosomal RNA;VIM: Verone integron-encoded metallo-β-lactamase.ANT: aminoglycoside nucleotidyltransferases; ARG: antibiotic resistance gene; ARO:antibiotic resistance ontology; bp: base pair; CALIN: cluster of Conceptualization: TKN, LHH; Methodology: TKN, PDB, LHH; Investigation: TKN, PDB;Visualization: TKN; Supervision: LHH; Writing—original draft: TKN, PDB, LHH; Writing—review& editing: TKN, PDB, LHH.The authors declare that they have no competing interests.giac072_GIGA-D-22-00081_Original_SubmissionClick here for additional data file.giac072_Reviewer_1_Report_Original_SubmissionQing-Lin Chen -- 5/29/2022 ReviewedClick here for additional data file.giac072_Reviewer_2_Report_Original_SubmissionTimothy Ghaly -- 5/31/2022 ReviewedClick here for additional data file.giac072_Supplemental_FilesClick here for additional data file.


With growing concerns about environmental issues and sustainable economy, bioproduction of chemicals utilizing microbial cell factories provides an eco-friendly alternative to current petro-based processes. Creating high-performance strains  through metabolic engineering strategies is critical for cost-competitive production. Commonly, it is inevitable to fine-tuning or rewire the endogenous or heterologous pathways in such processes. As an important pathway involved in the synthesis of many kinds of chemicals, the potential of the glyoxylate cycle in metabolic engineering has been studied extensively these years. Here, we review the metabolic regulation of the glyoxylate cycle and summarize recent achievements in microbial production of chemicals through tuning of the glyoxylate cycle, with a focus on studies implemented in model microorganisms. Also, future prospects for bioproduction of glyoxylate cycle-related chemicals are discussed. The glyoxylate cycle, also known as glyoxylate shunt (GS), was identified by Kornberg and Krebs in 1957, explaining how organisms could grow on acetate as the sole carbon source . For subEscherichia coli and the pathogenic bacterium Mycobacterium tuberculosis . Thus, tight regulation of the glyoxylate cycle is necessary.E. coli, ICL is a tetramer encoded by aceA. Its affinity for isocitrate is much lower than that of the NADP-dependent isocitrate dehydrogenase . As a result, the control of ICD activity plays an important role in determining the flux of GS (aceK) (In ux of GS . The actS (aceK) . When baS (aceK) . It is wE. coli (aceB) shows high affinities for the substrates glyoxylate and acetyl-CoA, and thus inhibition or activation of MS plays only a minor role in controlling the glyoxylate cycle  . In addiE. coli . On the te cycle .E. coli, the open reading frames (ORFs) of aceB, aceA, and aceK form a tricistronic operon (aceBAK), which is positively controlled by a pleiotropic transcriptional regulator Cra and the integration host factor (IHF) and negatively controlled by IclR (isocitrate lyase regulator)    . The expetA gene . GlyoxyaetA gene . ExpressetA gene . The cooE. coli, the homologue (named IDH) is monomeric in C. glutamicum. The ORFs of aceA and aceB are transcribed divergently in C. glutamicum and a homologue of AceK is absent . The induction of glyoxylate cycle genes by acetate occurs independently of the presence or absence of glucose and other carbon source, which is different from E. coli.Unlike the dimeric ICD enzyme from s absent . This ors absent . In the aceB in C. glutamicum, which was designated as GlxR (ramA expression independent of the carbon source used (rneG) could cleave the aceA mRNA at the 3’ untranslated region, and the level of aceA mRNA was approximately 3-fold higher in the rneG mutant than in the wild type , two isoforms of ICL (E. coli-like ICL-1 and a less-studied ICL-2), and lacks an AceK homologue . Acetylation at position 392 increased ICL activity, whereas acetylation of K322 reduced its activity . In ICD,P. fluorescens, RccR, a homologue of the Entner–Doudoroff pathway regulator HexR, plays the key role in the GS control via reciprocal regulation. In the presence of glucose, RccR binds 2-keto-3-deoxy-6-phosphogluconate (KDPG) and represses the expression of aceA and glcB. In the absence of KDPG, it represses the expression of aceEF genes. In this way, strain can modulate pyruvate metabolism and GS/gluconeogenesis in response to carbon source availability  was overexpressed then to fix CO2 and increase the flux into the reductive TCA branch. Most importantly, conserved NADH and acetyl-CoA were channeled into the glyoxylate cycle by deletion of the repressor IclR. The engineered E. coli strain produced succinate at a yield of 1.6 mol/mol glucose under anaerobic conditions, which was close to the theoretical maximum (1.7 mol/mol glucose).However, the required NADH is typically generated by glucose heterofermentation, which reduces the theoretical yield to 1 mol/mol glucose . Ailen e+-reducing pyruvate dehydrogenase complex (aceEF-lpdA) to enhance the flux toward the glyoxylate cycle while supplying additional NADH at the same time  to drive acetyl-CoA formation solely by the overexpressed pyruvate dehydrogenase under anaerobic conditions. Further deletion of IclR with a concomitant heterologous pyruvate carboxylase (pyc) overexpression enabled a succinate yield of 1.69 mol/mol at tube-scale experiments with NaHCO3 addition.To achieve the theoretical maximum, the distribution of metabolic flux between carboxylation of PEP to OAA and decarboxylation of PEP to acetyl-CoA should be 5:2 . Skorokhame time . In an EEfforts were also put into the aerobic succinate production due to the faster cell growth and higher productivity compared with the anaerobic production. Recruitment of the glyoxylate cycle improved theoretical yield to 1.33 mol/mol from 1.0 mol/mol using the oxidative TCA part solely . Under aE. coli  was overexpressed, then arcA  and ptsG (one of the phosphotransferase system genes) were deleted to reinforce the oxidative TCA cycle. Additionally, fumarate consumption was blocked through fumABC and aspA gene deletion. Finally, glucose uptake was increased by substituting the galP promoter. The engineered E. coli strain produced 28.2 g/L fumarate in 63 h under aerobic conditions , citrate synthase (gltA), aconitase (acnB), isocitrate lyase (aceA), and succinate dehydrogenase (sdh) overexpression on fumarate production, and found that pyruvate carboxylase and isocitrate lyase played crucial roles. The expression level of these two enzymes varied to balance the glyoxylate cycle and oxiditive TCA flux. Through pathway optimization, the fumarate titer was increased from 8.7 g/L to 16.2 g/L.In another study, the decreased activity of ICD and activated GS was demonstrated to be effective to reduce acetate accumulation and increase the fumarate production. However, CA cycle . Chen et pathway . They exE. coli by the same research group , citrate synthase (CS), aconitase (ACN), isocitrate lyase (ICL), and malate synthase (MS), and explored the optimal level for each enzyme. Then, CRISPRi was employed for enzyme modular coordination in vivo. Through the moderate inhibition of PC and CS expression, and fixing the level of ACN: ICL: MS at 4: 5: 4, malate titer was increased by two folds with a yield of 0.85 mol/mol glucose. In addition, iclR and arcA were inactivated to enhance the flux toward glyoxylate cycle and oxiditive TCA cycle.The glyoxylate pathway was also engineered for malate production in ch group . First, E. coli strain for malate production through deletion of the iclR and arcA genes, block of malate consuming, and overexpression of malate-insensitive PEP carboxylase PpcK620S and NADH-insensitive citrate synthase GltAR164L. A malate yield of 0.82 mol/mol was obtained. The metabolic flux analysis indicated that the engineered strains had a very high flux over the glyoxylate shunt with almost no flux passing through the isocitrate dehydrogenase reaction , a high added-value polyester composed of L-malic acid monomers, can be produced via the glyoxylate cycle naturally by Aureobasidium pullulans. A few studies engineered PMA production through enhancement of the glyoxylate cycle  from A. terreus, flux toward the itaconate production was enhanced. Together with isocitrate dehydrogenase deletion, the titer of itaconate reached to 43 g/L in 32 h from glycerol under high aeration. Elimination of the glyoxylate shunt demonstrated harmful impact on the itaconate production, although this pathway pulled out the carbon flux via the isocitrate node. This may be due to its positive role in regeneration of oxaloacetate, which was important for itaconate synthesis in turn  was produced from acetate.In another study, an acid-tolerant  acetate . By overvia several synthetic pathways including the modified GS pathway  overexpression. Consumption of glyoxylate and GA was blocked through inactivation of MS, glyoxylate carboligase (gcl), 2-keto 3-deoxygluconate 6-phosphate aldolase (eda), glycolate oxidase (glcDEFGB) and glycolaldehyde dehydrogenase . To increase the availability of NADPH, which is needed by YcdW, edd encoding the 6-phosphogluconate dehydratase was deleted. Finally, 52.2 g/L GA was produced from glucose  is a small two-carbon α-hydroxy acid used in multiple daily applications . There a pathway . In an a glucose .icd deletion may cause growth retardation. Deng et al. tried to weaken the flux to α-ketoglutarate (α-KG) through overexpression of AceK, which could repress the ICD activity. In this way, the ICD activity was decreased by 83.03%, but poor growth of strain was incurred concomitantly. Then, adaptive evolution was performed to increase the growth rate. Nevertheless, the evolved strain still grew much more slowly than the wild type, which suggested the importance of balance between the TCA cycle and GS reactions. In fed-batch fermentation, the engineered strain produced 56.4 g/L GA (ldhA), which competed for the carbon flux to glycolate and AldA involved in GA consumption were deleted. The following optimization of fermentation, 65.5 g/L GA was produced with a yield of 0.765 g/g glucose .Sometimes complete block of the TCA cycle through 4 g/L GA . Subsequ4 g/L GA . CitrateE. coli strain to produce GA from xylose, the most abundant pentose , although the titer did nott improve obviously. Cam et al. engineered GA production in E. coli from xylose via a synthetic xylulose-1 phosphate (X1P) pathway  were co-overexpressed. As a result, the glycolate titer was improved to 2.75 g/L with the pH control in shake flasks. The GA production pathway can be integrated with other metabolic routes to produce value-added chemicals including 3-hydroxy-γ-butyrolactone (3HBL) and GA-containing polymer directly from glucose or/and xylose  was deleted to conserve OAA. Subsequently, pyruvate kinase (pykA and pykF) was deleted to drive more anaplerotic flux into OAA. Together with disrupting the competitive and degradative pathways, 35.8 g/L L-homoserine was produced in the fed-batch fermentation. Conversely, simultaneous activation of the glyoxylate cycle and overexpression of gltA (rather than weakening its expression) improved AFAAs production in some other studies, which suggested the complexity of metabolism  by the trc promoter in the chromosome and plasmid overexpression of thrA* (a mutated thrA), thrB, thrC, and asd . The final strain TWF006/pFW01-thrA*BC-asd produced 15.85 g/l L-threonine with a yield of 0.53 g/g glucose. In a subsequent study, the same group found that increase of the acetyl-CoA pool positively affected L-threonine production (acs) to convert acetate into acetyl-CoA; and overexpression of fadBA to facilitate fatty acid degradation. The fatty acid degradation and L-threonine biosynthesis pathway were coupled via the glyoxylate shunt, and 103.89 g/l L-threonine was produced after 48-h fed-batch fermentation.In another study, L-threonine, which can be synthesized from L-homoserine, was produced in E. coli . To consoduction . The aceectABC gene cluster from Halomonas elongata) into E. coli to produce this valuable chemical (iclR was deleted to enhance the glyoxylate shunt and the expression level of ppc (encoding the phosphoenolpyruvate carboxylase) was improved through promoter change. Then, the synthetic pathway of L-lysine and L-threonine was blocked, and a feedback resistant LysC from C. glutamicum (encoding the aspartate kinase) was introduced to enhance the flux to ectoine. Together with the overexpression of ectABC, 25.1 g/L ectoine was produced by fed-batch fermentation. Increase of the OAA pool via deletion of iclR was also proved to be effective in a study to produce β-alanine, another AFAA in E. coli . As a result, α-KG production was increased by 16-fold to 12.4 g/L in flask culture and 47.5 g/L in 5-L fermentor  of the TCA cycle and ICL (aceA) of the glyoxylate cycle were actively expressed, while turned off in the GABA production mode. On the contrary, pyruvate carboxylase (pyc), glutamate dehydrogenase (gdhA), glutamate decarboxylase (gadB), and the GABA transporter (gadC) involved in GABA synthesis were turned on only during the production mode. This strategy balanced the competition for isocitrate and α-KG between cell growth and GABA production, resulting in a 3-fold improvement in the total GABA production titer and yield.In biofermentation, it is essential to address the conflict between cell growth and target chemical production, as the overexpressed production pathways often lead to metabolic burden for the cell. In a study to produce γ-aminobutyric acid (GABA), a genetic switch was designed to balance the GABA production and bacterial cell growth . The celaceA) to increase one of the substrates α-KG  and mqo  were coexpressed with ido to draw more flux into the Ile and 4-HIL biosynthetic pathways, resulting in another 31.8% increase of the titer. Further expression of the Vitreoscilla hemoglobin (vgb) and optimization of the fermentation medium led to a final 4-HIL titer of 17.2 g/L.In another study to produce 4-hydroxyisoleucine (4-HIL), a potential medicine for diabetes, the glyoxylate cycle was blocked   and yield (0.28 g/g) for ALA production from glucose as a sole carbon source was achieved.5-Aminolevulinic acid (ALA) is an α-KG derived non-proteinogenic amino acid with multiple applications in medical, agricultural, and food industries. There are two distinct routes to produce this chemical biologically, the Coxylase) . Althoug4 pathway, ALA was synthesized from succinyl-CoA and glycine. To reduce the conversion of ALA to downstream tetrapyrrole/porphyrin, Miscevic et al. applied CRISPRi to repress hemB expression  and the strain relied on 1-butanol formation as the sole electron sink to regenerate NAD+. 1-Butanol was produced using a heterologous Clostridial CoA-dependent pathway. Then, formate dehydrogenase (fdh) from Candida boidinii was introduced to supply more NADH. Also, phosphate acetyltransferase (pta) was deleted to prevent formation of by-product acetate. In addition, the expression of AdhE2, the key enzyme for 1-butanol synthesis was optimized, which resulted in a 1-butanol titer of 18.3 g/L. However, considerable amounts of acetate accumulated  increased, which resulted in a 10% increase of the cell growth. Finally, 1-butanol production was improved by 39%  and 3-hydroxypropionate (3-HP) in E. coli BL21  improved the titer of 3-HP. Using fed-batch fermentation, 7.3 g/L 3-HP was produced with a yield of 0.26 mol/mol acetate. Notably, the yield was still low compared with the theoretical maximum of 0.5 mol/mol acetate, which may reflect the operational cost of GS and subsequent gluconeogenesis.To balance the cell growth and 3-HP synthesis, a two-stage strategy was adopted by Lama et al., whereby glucose is used for the cell growth and acetate for 3-HP synthesis . To incrP. denitrificans as the producer, activation of GS by deleting iclR or enhancement of the oxidative TCA branch by aceK deletion did not improve 3-HP significantly, when a similar two-stage production strategy was adopted  or malic acid synthase (MLS1 in the cytoplasm) was knocked out in a study to enhance the supply of precursor acetyl-CoA for production of crocetin, a potential drug in revisiae . The CITrevisiae . Using Crevisiae . Howeverrevisiae .E. coli, the oxidative TCA cycle was blocked via deletion of sdhA and the glyoxylate cycle was deregulated by iclR deletion to improve the succinyl-CoA level. Through this modification, propionate titer was increased to 3.68 g/L from 1.1 g/L. Finally, 30.9 g/L of propionate was produced with an overall yield of 49.7% under optimized aeration condition using fed-batch fermentation  pathway in entation . Subsequn source . The effn source .E. coli engineered for both aerobic and microaerobic conditions were constructed by Choi et al. based on a genome-scale metabolic model  nor block of GS (via deletion of aceBA) were effective in enhancing 4-HB production under the microaerobic condition, and there seemed to exist an optimal expression level of GS. In addition, glycerol was proved to be effective only for the microaerobic producer. Finally, 103.4 g/L of 4-HB was produced by microaerobic fed-batch fermentation from glycerol.4-HB is another chemical derived from succinyl-CoA that can be converted into various industrial chemicals and polymers. Producer strains of ic model . Under tAcinetobacter baylyi ADP1 from acetate and gluconate  and two potential pathogenic microorganisms (M. tuberculosis and P. fluorescens). Although applications in biosynthesis by M. tuberculosis and P. fluorescens are limited due to the pathogenicity, the GS control mechanisms may be instructive. Production of target chemicals, divided into several categories, including organic acids, amino acids and fatty acid-related chemicals were also briefly introduced.As outlined in this review, the biochemistry and regulation of glyoxylate cycle was demonstrated in two industrial microorganisms . On theostridia . In addi4 carboxylates into two molecules of acetyl-CoA without loss of CO2 -based models, elementary mode analysis-based ones and kinetic models . Then, f


Ultrafiltration experiments were conducted using porous polymeric membranes (Ultracel® 10 kDa). The hybrid process of adsorption-ultrafiltration was modeled and optimized using three computational tools: (1) response surface methodology (RSM), (2) artificial neural network (ANN), and (3) support vector machine (SVM). The optimal conditions provided by machine learning (SVM) were found to be the best, revealing a rejection efficiency of 86.3% and an initial flux of permeate of 185 LMH for a moderate dose of the nanoclay (0.674% w/v). Likewise, a new and more retentive membrane  inorganic nanotubes) was produced by the phase-inversion method, characterized by SEM, EDX, AFM, and FTIR techniques, and then tested under optimal conditions. This new composite membrane (PVDF-HFP/HS) with a thickness of 112 μm and a porosity of 75.32% unveiled an enhanced rejection efficiency (95.0%) and a lower initial flux of permeate (28 LMH). Moreover, molecular docking simulations disclosed the intermolecular interactions between nitrite ions and the functional moiety of the organonanoclay.Herein, we report the results of a study on combining adsorption and ultrafiltration in a single-stage process to remove nitrite ions from contaminated water. As adsorbent, a surface-modified nanoclay was employed (i.e., Nanomer However, high nitrite and nitrate concentrations in natural waters are very toxic to human health . Th. Thxi) i(tansig) ,28.The placement, organization, and interconnection of artificial neurons in the network define the architecture (topology) of the network, which can be represented by a support graph. The network architecture involves several layers, namely: the inputs, the hidden layer (or hidden layers), and the output layer. The computation nodes  are placed in hidden and output layers.To address the multiple-regression problem by using the ANN concept, a multilayer neural network with unidirectional architecture (feed-forward) is most often applied. In a feed-forward ANN topology , the information flows in a single direction (input → output). For this type of unidirectional architecture, the support graph does not contain cycles (neurons are placed on consecutive levels), and the output vector is determined by direct calculation from the input vector. The estimation of the response function by a feed-forward ANN (with one hidden layer and one output layer) can be written by using the following vector-matrix expression:lied see .In the course of the machine learning procedure, the feed-forward artificial neural network (ANN) is trained by applying an algorithm involving the back-propagation of errors. This algorithm allows the adjustment of the parameters of the neural network  in order to minimize the residual error between the network output , which can be employed for response function estimation. Hence, the support vector machine (SVM) represents a powerful methodology for solving problems in machine learning, such as function estimation by regression analysis, nonlinear classification, and pattern recognition ,41,42,43unctions ,36,41:. The Gaussian RBF kernel is widely used in SVM owing to its flexibility; it represents the square of the Euclidean distance between the two vectors, that is ,39,41:Y^In this study, the least squares support vector machine (LS-SVM) was applied to address the function estimation problem by machine learning. Hence, we employed the LS-SVMlab Toolbox  developed by Suykens and co-workers ,44. As d2−) from aqueous solutions, were performed in accordance with a central composite experimental plan of rotatable type  sorbent dose SD (% w/v) and (2) pH of feed solution. As sorbent, we employed the organoclay adsorbent (TmSA-MMT), representing the montmorillonite (MMT) clay modified with trimethyl stearyl ammonium TmSA (C21H46N+). The idea here was that the positively charged moiety of TmSA (C21H46N+) from organoclay can interact with negatively charged nitrite anions (NO2−) by electrostatic forces. This assumption was supported by molecular docking simulation reported in SD (0.12–0.58% w/v) and pH (4.9–9.1), as given in The adsorption-ultrafiltration experiments, designed to remove nitrite ions  was transferred to the UF stirred cell, where the ultrafiltration process was performed at room temperature (24 ± 2 °C) under three bars (operating pressure). To minimize the concentration polarization effect, the colloidal suspension was gently stirred during ultrafiltration. Thus, the separation process was performed by retaining the particles of the adsorbent loaded with nitrite ions (TmSA-MMT/NO2−) onto the membrane surface and collecting the purified water (permeate flux). The performance of separation of the hybrid process (adsorption-ultrafiltration) was evaluated by determining experimentally the removal (rejection) efficiency  of nitrite ions, which can be expressed as:The ultrafiltration experiments were carried out in dead-end mode, and the experimental set-up is depicted in t dosage . Then, tt dosage , and thet dosage . After aY, %) determined for each run. In addition, the initial permeate flux iJ (recorded in the first three minutes of ultrafiltration) was determined experimentally. For the conditions given in iJ varied into the narrow interval of 176–189  with an average value of 183 ± 5 (L∙m−2∙h−1).The experimental design reported in First, starting from the experimental data summarized in After applying the mathematical substitution technique, the RSM model with actual factors can be written asThe mathematical RSM model was validated statistically by using the analysis of variance (ANOVA), which is detailed in analyzed . The par = 0.939 a. FigureSD and pH factors on the estimated rejection efficiency SD factor (sorbent dose) is greater than the main effect of the pH factor. Generally, the greater the SD factor is, the higher the estimated response SD and pH. According to this interaction effect, the increment of pH value at a low sorbent dose conducts gentle increases of the rejection efficiency SD > 0.55% w/v and pH 5.3–8.3. More precise localization of the optimal point by RSM is detailed in the next section, where the numerical optimization is discussed.The estimated response surface by the conventional multiple-regression model (RSM) is depicted in analysis , this opSecond, regarding machine learning by using the artificial neural network (ANN), the experimental plan reported in SD and pH); (2) one hidden layer with three artificial neurons activated by logsig nonlinear function; and (3) one output layer with a single artificial neuron activated by purelin linear function  was performed highlighting the significance of the ANN model see . As deta8.2.–8.4 , which dThird, the machine learning technique based on the support vector machine (SVM) was also employed for building a predictive model for the same region of experimentation. In this respect, the experimental design given in VM model . The ploSD and pH factors. Predictions estimated by the SVM model are also plotted in both formats, i.e., 3D graph ; (2) the first local maximum ; (3) the second local maximum . Hence, the SVM model showed a different ability to describe the nonlinear effects than the ANN and RSM models, providing a more complex response surface with more than one extreme point inside the region of experimentation. This might be attributed to the radial distribution function (RDF) implied in the SVM algorithm.The estimated response by the SVM model is represented in 3D graph a and 2D 3D graph b. ComparThe built data-driven models  offer functional relationships between the input and output variables of the process, without giving insights into the interaction mechanism at the molecular level. To address this aspect, we employed molecular docking simulations, which revealed the importance of the electrostatic interactions in the retention of nitrite ions, as detailed in 2R) were found to be the best for the SVM model, followed by the ANN model, and then by the RSM model. Thus, according to the residual analysis, the prediction performance of the models slightly diminished in the following order: SVM > ANN > RSM. The superiority of SVM over ANN in response estimation with lower residual errors might be attributed to the fact that SVM possesses a high generalization capability in solving practical problems related to nonlinearity, small samples, and over-fitting 0 = 5 mg/L) was estimated as 0J = 28.0 LMH. This value was somewhat lower than the pure water flux (PWF0 = 31.1 ± 1.5 LMH) determined for the composite membrane. According to As one can see from tudy see a, the paDJ, EJ, and τ) can be distinguished from t < τ) the permeate flux decline is very fast, and afterward (for t > τ) the dynamics of the flux slows gradually until it reaches steady state at a permeate flux equal to EJ.The meaning of the model parameters , the removal efficiency of 95% means about 0.25 mg/L of remaining nitrite ions are in the aqueous solution. This value is two times below the maximum allowed concentration. By comparing the performance of the commercial membrane  and the produced composite membrane , it is evident that establishing the trade-off between the rejection efficiency and the permeate flux is essential for real applications. In our study, the focus was placed on the environmental protection aspect ; thereby, the enhanced rejection was preferred instead of productive water flux. In this respect, the produced composite membrane (PVDF-HFP/HS) was more suitable for achieving this objective. It is important to note that the pristine montmorillonite (MMT) was also tested , disclosing a low rejection efficiency of 22.1%, compared to the rejection of 95.0% indicated by the organoclay (MMT-TmSA). Hence, the major role in the retention of nitrite ions can be attributed to the functional groups TmSA (C21H46N+) from the organoclay.It is worth mentioning here that the nitrite removal efficiency by the more retentive membrane (PVDF-HFP/HS) was equal to 95.0%, which represents a greater value than the one recorded for the commercial membrane (~86%). Note that a separation yield of 95% is a significant result. For a feed solution of [NOIn ® I.28E) to adsorb the nitrite ions (NO2−) and the efficiency of ultrafiltration to retain the formed colloids in a one-step hybrid process. The proposed mechanism of adsorption envisaged the interaction between organic moiety (C21H46N+) from organoclay and nitrite ion (NO2−). This interaction relied on electrostatic forces (Coulomb) as molecular docking simulation suggested. The UF experiments were conducted using a commercial porous membrane (Ultracel®) of 10 kDa MWCO. The hybrid adsorption-ultrafiltration process was modeled mathematically using three different computational tools, for comparison. Thus, the classical RSM modeling tool was compared with machine learning tools such as ANN and SVM. The residual analysis disclosed that machine learning by the SVM model provided the most accurate predictions compared to ANN and RSM models. For this case study, we believe that the true response surface was better estimated by the SVM model compared to the ANN and RSM models. The optimal conditions for the adsorption-ultrafiltration hybrid process were established by numerical optimization, disclosing an adsorbent dose of 0.674% w/v and pH 7.0. Under these optimal conditions (indicated by the SVM model), the nitrite removal efficiency was found to be 86.28% and the initial permeate flux was 185 LMH on the Ultracel® commercial membrane. In addition, these optimal conditions of adsorption-ultrafiltration were tested on a more retentive membrane prepared in the laboratory by the phase-inversion method. This new composite membrane with 75.32% porosity  inorganic nanotubes) revealed a higher nitrite removal efficiency of 95.0%, but a lower initial permeate flux of 28.0 LMH. Moreover, the kinetics of permeate flux decline was modeled by using a hyperbolic regression equation with three parameters. This hyperbolic regression model enabled the extrapolation of the permeate flux at time zero (t = 0).In summary, we demonstrated the capability of organoclay (Nanomer


The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth.IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen’s metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth. In 1942, the French scientist Jacques Monod introduced a single mathematical expression,μmax) and half-saturation constant (KM), to describe his discovery that specific rate (μ) of microbial growth, the relative rate of biomass increase (s−1), responds hyperbolically to the concentration (C) of limiting substrate  . Taken together, a total of 21 reactions consume and produce 35 metabolites.We built a kinetic metabolic model of  source) . This mothe cell . The modequation , the modM. acetivorans   , 39. TheM. barkeri under typical laboratory conditions. The results are shown in ad hoc constraints on methane production or methanogen growth, and allow us to simulate methanogen metabolism limited by energy sources, a condition prevailing in both bioreactors and natural environments.Last, we estimated the concentrations of membrane enzymes by using optimization. Enzyme concentrations are required to compute the velocities of enzyme reactions, but the abundances of most of the membrane enzymes have yet to be determined experimentally. As an alternative, we estimated their concentrations by maximizing the growth rate of M. barkeri growing under typical laboratory conditions and compared the simulation results at steady-state to the independent experimental observations that had been excluded from the model construction. For example, the simulated membrane potential is 135 mV, close to the experimentally determined value of 130 mV   . In additentials , which hining 2% , consistining 2% , 44.E. coli   , 47. Thi.64 CH4) .M. barkeri laboratory cultures varies hyperbolically with the external methanol concentration. This prediction was obtained by simulating the growth of the laboratory cultures at external methanol concentrations ranging from 1 μM to 100 mM. The results fit to the laboratory observations of Daußmann et al. (R2 of 0.96 (μmax), half-saturation constant (KM), and specific affinity (α). Following the phenomenological interpretation of these parameters, we approximated μmax with the growth rate at 1 M methanol, determined KM as the methanol concentration that drives growth at half of μmax, and estimated α as the slope of the rate increase at methanol concentrations <10 μM. The results are 1.0 d−1 for μmax, 0.4 mM for KM, and 1.6 ± 0.0 × 103 M−1 · d−1 for α. The values of μmax and KM are close to those obtained from the laboratory, determined to be 1.0 ± 0.5 d−1 and 0.4 ± 0.2 mM, respectively , but the specific affinity has yet to be analyzed experimentally. Combining these tests, we concluded that our model reproduces previous laboratory observations across different scales and can be applied to investigate the kinetics of methanogen growth.The model also predicts that the growth rate of n et al. , with an of 0.96 . Three pM. barkeri, we performed sensitivity analysis on the metabolic model using MCA. We computed both the scaled flux control coefficients of enzymes and the scaled flux response coefficients of metabolites  of methanol is relatively large, i.e., ∼50 mM methanol , ϕMCR is the proteome fraction of MCR, and kMCR,app is the apparent catalytic constant (mol · g−1 · s−1), the catalytic constant kMCR adjusted with the concentrations of methyl-coenzyme M and coenzyme B at >15 mM methanol. This is determined by the following formula:TCoB is the total concentration of coenzyme B moiety. Because coenzyme B moiety has a concentration of 1.7 mM, much larger than the Michaelis constant Km,CoB of 59 μM  with the ratio of maximum growth rate (μmax) to specific affinity (α), and recast the Monod equation in an alternative form:εMCR) and MTA (εMTA) can be calculated according to the following formulae:The Monod equation bears a limited link to the metabolic network of methanogen growth. To illustrate this point, we followed Button  and Healrameters  and 5, tWe calculated the flux response coefficient of coenzyme M moiety by substituting M. barkeri growth. μmax and α values, the relative difference between the alternative Monod equation and the simulation results remains <5% at <30 μM and >5 mM methanol, and reaches a maximum value of 23% at 0.4 mM methanol. By applying the simulation-derived μmax and the KM values, the Monod equation reproduces well the simulation results at >0.2 mM methanol, with relative differences of <5%. Below 0.2 mM methanol, the differences become larger at smaller concentrations, reaching a maximum value of 58% at 1 μM methanol. Consistent with these results, the Monod equation fits well with observations of laboratory experiments carried out at relatively large substrate concentrations. For example, Daußmann et al. . Each ODE describes the rate at which a metabolite concentration changes over time (t), and is constructed according to the principle of mass balance. Specifically, the ODE of metabolite j isCj is the concentration (mol · L−1) of the metabolite, Jj is the diffusive flux (mol · s−1) of methanol, CO2, or CH4, νi is the reaction velocity of enzyme i (mol · s−1), cj,i is the stoichiometric coefficient of metabolite j in the reaction (negative for metabolite consumption), V is the volume of the compartment, which is either the cytoplasm volume (Vcyto) or the membrane volume . In addition, for coenzyme M, ferredoxin, and other chemical moieties, their concentrations are subject to the law of moiety conservation, as given inCM and CM,i are the concentrations of total moiety M and its form, i. A special metabolite is the charges, or protons and sodium cations, translocated across the membrane, which contribute to the membrane potential Δψ,F is the Faraday constant , Cm is the membrane capacitance , and cC,i is the stoichiometric coefficient of protons or sodium cations translocated out of the cytoplasm in the metabolic reaction of enzyme i.The kinetic model defines the metabolic state of Jj (mol · s−1) into a cell can be calculated by the following formula:Dj is the diffusion coefficient (m2 · s−1), r is cell radius and its value is 1 μm  , ϕi is the mass fraction of enzyme i in the proteome, CS and CP are the concentrations of substrate and product, respectively, Km,S and Km,P are the respective Michaelis constants, ΔGi is the Gibbs free energy change of the reaction (J · mol−1), χi is the stoichiometric number of electrons transferred or charges translocated per reaction, R is the gas constant (8.3145 J · mol−1 · K−1), and T is the temperature in Kelvin. The free energy change is calculated according to the formulaQi is the quotient and iK is the equilibrium constant of the reaction.We applied a generalized reversible multiplicative Michaelis-Menten equation to calculate reaction velocity l · s−1) , 37, of the membrane enzymes, as given byϕM at 10%.We take the proteome fractions rvations , 77, we M. barkeri optimizes flux distribution through its metabolic network, including the metabolite fluxes from the methanogenesis pathway to biomass synthesis, in order to maximize growth rate. Accordingly, we analyzed the updated iMG746 genome-scale metabolic model of M. barkeri using FBA  . We perfεi, calculated byμ) is divided by a fractional change in the cellular level of the component. For enzymes, ϕi is the mass fraction in the proteome and coefficient εi is the scaled flux control coefficient; for metabolites, ϕi is the concentration and coefficient εi is the scaled flux response coefficient. We numerically calculated the coefficients by changing ϕi by 1%. Coefficients for diffusion reactions were computed by changing diffusion coefficients by 1%.According to MCA theory , 25, thehttps://github.com/geomicrobiology/Methanosarcina). The model components, including the ODEs and initial concentrations of metabolites, the kinetic expressions of metabolic reactions, and the respective thermodynamic and kinetic parameters, are available in the supplementary material (Data Set S1).The kinetic model in SBML and COPASI formats and the MATLAB program for running FBA are available from GitHub (


Escherichia coli (E. coli) to generate a metabolic platform that redirects metabolic flux. Poly-β-hydroxybutyric acid (PHB) production was taken as an example to demonstrate the performance of this platform. A two-to-three-fold increase in PHB content was observed under green light when compared with the production of PHB under red light, confirming the regulatory activity of this platform and its potential to redirect metabolic flux to synthesize target products.‘Metabolic burden,’ which arises when introducing exogenic synthesizing pathways into a host strain, remains a challenging issue in metabolic engineering. Redirecting metabolic flux from cell growth to product synthesis at an appropriate culture timepoint is ideal for resolving this issue. In this report, we introduce optogenetics—which is capable of precise temporal and spatial control—as a genetic switch, accompanied by the endogenous type I-E CRISPRi system in  Metabolic engineering is a promising research field that can meet the demands of target compound production while meeting social and environmental obligations . The priSeveral gene expression systems can redirect metabolic flux from mainstream to introduced pathways. Generally, exogenous chemical inducers are combined with soluble transcription factors to achieve the artificial control of gene expression , e.g., LE. coli fall into the categories of phytochromes and the light-oxygen-voltage (LOV) family of proteins. Plant pigments share a photosensitive core, including the PAS (Per-Arnt-Sim) domain; GAF  domain; and PHY (photochrome-specific GAF-associated) domain [In contrast, the application of optogenetic switches appears to be suitable for precise and complex regulation in various fields. These switches are inexpensive and compatible with any carbon source or nutrient composition . In addi) domain . The cor) domain . LOV dom) domain . The div) domain  and in r) domain . Previou) domain . Recentl) domain . Evan et) domain . The preE. coli with a 130% increase in production [dCas9, respectively, to induce the transcription of endogenous genes in the presence of blue light [dCas9 (4 kb) should be constructed. In contrast, the endogenous type I-E CRISPR-Cas system only requires the expression of the CRISPR array and, thus, can be easily used for internal regulation without causing a metabolic burden. Our group applied IPTG-induced type I-E CRISPR-Cas system to the metabolic engineering of E. coli for the first time [The optical control gene circuit can be used in metabolic engineering to redistribute metabolic flux to synthesize desired products, but metabolic engineering usually involves complex metabolic pathways, which require the activation or inhibition of multiple genes. Currently, most studies use light-controlled gene circuits in combination with the widely used CRISPR type II system to achieve the redirection of metabolic flux. For example, researchers have proposed an optogenetic CRISPR interference (opto-CRISPRi) technique that allocates metabolic resources based on different optical signal frequencies to allow bacteria to be controlled during growth and production stages . These soduction . Furtherue light . When usrst time .By taking advantage of the green/red light-sensitive CcaS-CcaR system and the endogenous type I-E CRISPRi system , we presE. coli DH5α cells with the sequence of EE-E15 . The strain was attained by knocking out cas3 and substituting the native promoter of the cascade operon with J23119. CcaS consists of an N-terminal transmembrane helix; a GAF domain, which acts as the sensor domain; a linker region (L1); two PAS domains; a second linker region (L2); and a C-terminal histidine kinase (HK) domain. By removing L1 and the two PAS domains and fusing the GAF and HK domains with the truncated linker region, a truncated version of the photoreceptor can be constructed [phbCAB were assembled by Gibson to obtain the plasmid pHZ-PHBcon. The plasmids Paracr58.6, pHZ-crRNA, 43.6-crRNA and pHZ-PHBcon were transformed into E. coli EE-E15 by chemical transformation to obtain the strains Ara58.6, SphZ, S43.6 and SlcrPHB.The strains, plasmids and primers used in this work are listed in structed . The pla600. The cultures were diluted to an OD600 = 0.1 with M9 culture medium (with chloramphenicol and streptomycin) to a final volume of 1 mL. Three parallel repeats were prepared for each sample. The new plates were wrapped with aluminum foil to prevent natural light reaching the cells, and the lighting device was placed in the aluminum foil to ensure that the cells can be illuminated by green/red light. The plate was cultured at 37 °C and 500 rpm. The containers were placed into a microplate reader  every hour to measure the cell density and fluorescence. A negative control was used to measure and subtract intrinsic cellular fluorescence.Bacteria were inoculated in 2 mL of LB culture medium with chloramphenicol and streptomycin in 24-well plates. The plates were placed in a shaker and cultured at 37 °C and 500 rpm for 12 h. The cells were then placed on ice to stop bacteria growth, and 100 μL of the culture was transferred to wells of a 96-well plate to measure OD600 of 0.06–0.07 using 50 mL of M9 medium (containing 1% glycerol and appropriate antibiotics), and these cultures were placed in Erlenmeyer flasks. Three parallel repeats were prepared for each sample. The cultures in the Erlenmeyer flasks were cultured at 37 °C and 220 rpm under the red-light condition and then changed to green light at 0, 5 and 9 h. Then, 200 μL of the samples were placed into 96-well cell culture clusters using the microplate reader to measure the absorbance every 2 h.Single colonies were inoculated into 50 mL of LB medium with appropriate antibiotics (spectinomycin and chloramphenicol) and cultured at 37 °C and 220 rpm in darkness overnight for 13 h. The samples were diluted to an ODE. coli colonies were each inoculated into 6 mL of LB culture medium containing chloramphenicol and streptomycin, and these seeds were cultured for 12 h. Subsequently, 1 mL of the first seed was inoculated into 50 mL of LB culture medium containing chloramphenicol and streptomycin and cultured at 37 °C, 220 rpm for 8 h. Approximately 1 mL of the 50 mL second seed culture was inoculated into 100 mL of M9 culture medium (OD600 = 0.05) containing chloramphenicol and streptomycin for fermentation. Glucose was the only carbon resource. Fermentation cultures were grown at 37 °C, 180 rpm for 64 h. The OD and glucose consumption were measured every 8 h. OD was detected with an Ultraviolet-visible spectrophotometer and 15 μL of fermentation culture supernatant was used to analyze glucose consumption in a biosensor analyzer. The PHB content was determined by gas chromatography (GC).Single After fermentation, the cells were harvested by centrifugation at 7000 rpm for 15 min. The bacteria sample was lyophilized for 6 h, and the cell sample was weighed. The bacteria were ground carefully, and 15 mg of the bacteria sample was accurately weighed into a well-sealed small bottle. The weight of the bottle with the bacteria sample was weighed. Concentrated sulfuric acid (150 μL), 850 μL of methanol and 1 mL of trichloromethane were added in that order under a fume hood. The bottles were sealed and placed into a 100 °C oil bath for 1 h. The bottles were then removed, and 1 mL of ultrapure water was added. The bottles were sealed, and the samples were thoroughly mixed by vortex oscillation. The samples were left standing for at least an hour until the liquid separated. The top water phase and lower organic phase were stored. PHB dissolved into the organic solvent. The organic phase (~600 μL) was carefully transferred to a 1 mL vial for a GC analysis to determine the content of PHB.The chromatographic column of the gas chromatograph is an Rtx-5 capillary column, and the detector is a hydrogen flame ionization detector (FID). The column temperature was kept at 80 °C for 1 min, then the column temperature was raised to 120 °C at the rate of 10 °C/min, and then to 160 °C at the rate of 45 °C/min. The column temperature was kept for 5 min, with a total time of 10.89 min. The FID temperature was 300 °C, the hydrogen flow rate was 40 mL/min, and the air flow rate was 400 mL/min. The injection port temperature was 250 °C, and helium was used as the carrier for a 1:15 split injection.CcaS-CcaR, a green/red photo-reversible two-component signal transduction system (TCS) first discovered in cyanobacteria , is usedE. coli TOP10 to explore and improve its regulatory performance. We constructed three variant strains, #3/#4/#10, by removing different lengths of PAS domain and linker DNA [In this study, we expressed the CcaS/CcaR system in nker DNA ,39  [cas3 and activating the expression of the CRISPR-associated complex for antiviral defense (Cascade), the DNA-cutting function of the CRISPR system is inactivated, whereas the DNA-binding function is maintained, which can inhibit the expression of target genes  . In thiset genes . After eE. coli TOP10 with the Cas3 protein knocked out, and the promoter of Cascade substituted by the promoter J23119 was constructed and the strain named EE-E15 [E. coli TOP10Δcas3, and a series of strains SGFP-Y  were constructed to test the function of crRNA targeting different GFP sites  [E. coli was applied to convert the growth phase to the production phase without causing extra metabolic burden and interference. Therefore, we used the type-I-E CRISPR system for regulating gltA expression to control metabolic flux  ,45, an elic flux a.Cas3 knocked out. As demonstrated in other studies, targeting different sites of gene can lead to a different regulation effect for gltA, and because the spacer targeting the latter of the two promoters of the gltA gene showed the greatest effect [gltA to inhibit the expression of gltA  and high-copy plasmid (pHZ-crRNA) with a specific crRNA spacer and transformed both plasmids into Top10Δcas3 cells to gain the strains S43.6 and SphZ, respectively. Upon green or red illumination, the two groups showed variance in growth ; experimental group B (red light converted to green light irradiation at 5 h inhibition of gltA); experimental group C (red light converted to green light irradiation at 13.5 h inhibition of gltA); and control group D . To provide different corresponding lighting for PHB fermentation, the inner wall of each light box was surrounded by a circle of LEDs, and the maximum changes in radiance were related to the number of LEDs.We used our system to regulate the PHB synthetic pathway as a proof of concept for the practical application of the light-mediated metabolic flux redirection system. The PHB production pathway competes with the TCA cycle for the common substrate acetyl-CoA. Thus, green light was used to activate the I-E CRISPRi system. This system targeted the R system . crRNA w600 values between SlcrPHB growing under different light conditions (gltA did not affect the growth of E. coli, which was probably because PHB had a positive effect on cell growth and offset the detrimental impact of gltA knockdown [During fermentation, there was no significant difference in the ODnditions a. This onockdown .gltA was essential for improving the content of PHB. In contrast, the PHB production of experimental group B was 1.12 and 1.35 times that of experimental group A and experimental group C, respectively, possessing the highest productivity and content (gltA expression during the lag phase causes detrimental effects on intrinsic metabolism. In contrast, the inhibition of gltA during the stationary phase exerts weaker effects on cell growth, whereas a lower level of the carbon source is available for PHB synthesis. This observation indicated that suppressing gltA expression during the exponential phase may be a feasible option to optimize PHB production, which is consistent with the work by Soma et al. [Moreover, compared with that of control group D under red light irradiation, the PHB contents for experimental group A and experimental group B increased by 2.6- and 2.7-fold, respectively b. This r content c. This ra et al. , where lE. coli growth after knocking out gltA, which can be attributed to the inhibition of the TCA cycle [gltA serves as a promising strategy to redirect metabolic flux from the TCA cycle to desired pathways.We demonstrated that optogenetic tools are suitable for the metabolic flux regulation of a prokaryotic system. The effect of multi-light channels applied in metabolic engineering to inhibit the expression of acetate-producing genes has been demonstrated previously , indicatCA cycle . HoweverE. coli. After characterizing the effects of different CcaS variants in E. coli, a platform that dynamically regulates metabolic flux was constructed by combining the CcaS #10 variant with the endogenous I-E CRISPRi system, and PHB production was taken as an example to illustrate the performance of this metabolic platform. PHB productivity was found to differ by 2–3-fold from green light to red light irradiation, indicating the successful use of the platform. Moreover, we showed that the time point for induction gave different target product yields. Therefore, inhibiting gltA at three different time points, the lag phase, logarithmic phase and stationary phase, can significantly improve the effectiveness and universality of light-controlled CRISPR switches. We demonstrated the possibility of combining light-controlled elements with the endogenous I-E CRISPRi system for dynamic regulation studies, providing new research ideas and tools for the precise allocation of metabolic flux.In this report, we demonstrated that a light-controlled gene expression system can be used for the metabolic flux regulation in


The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use. Microorganism synthetic biology has been exploited to produce a variety of useful compounds ,2,3. In iAF1260 model of Escherichia coli [Flux balance analysis (FBA) is often used to modify cellular metabolism for the production of target compounds based on a GEM . Concrethia coli  paired whia coli . This enhia coli . Neverthhia coli , Gurobi hia coli ). In anyEscherichia coli GEM) was used to evaluate the effectiveness of the proposed algorithm for the production of various useful compounds.In this study, we developed an algorithm to identify candidate reaction deletions that can potentially result in the production of large amounts of a target compound by simply iterating single-level linear programming problems. Furthermore, the proposed algorithm can be easily implemented using freely licensed solvers such as glpkmex. The iJO1366 GEM , after which the next candidate for the deletion reaction was selected. A set of deletion reactions in which the production flux of the target compound increased and approached the theoretical maximum value (Tmaxv) was thus obtained by repeating the simple linear program described above. The above-described iterative algorithm using single-level LP is shown in A reaction minOUR values are summarized in L-alanine were mainly produced, and sometimes no reaction-deficient combinations were proposed for the production of the target compounds. These compounds are not naturally produced by E. coli, i.e., they can only be produced by artificially enhancing genes related to their biosynthetic pathways [E. coli, such as succinate, ethanol, formate, acetate, D-lactate [r value was decreased from 0.95 to 0.90  and those involved in redox reactions  were also frequently selected as candidates for deletion, indicating that these reactions contribute to the supply of ATP and reducing power (NADH and NADPH), the latter of which acts as a cofactor for the production of target compounds. Although the OptKnock-based metabolic design method requires narrowing down the candidate reactions to be deleted [To evaluate the AERITH algorithm proposed herein, calculations were performed for each of the 81 compounds whose production could be confirmed using the iJO1366 model. These compounds were those for which FBA was performed using the compounds described as exchange fluxes in the model as the objective function, after which positive values were confirmed. The 81 compounds, their theoretical maximum production flux values, and the pathways ,29,30,31-lactate ,33,34, u-lactate , hydroge-lactate , the upp to 0.90 . The mornditions . Effectinditions . The maxnditions . Additio deleted , the incE. coli were compared with those of GDBB   in succinate-producing strains shown in real experiments [pflB, LDH_D to ldhA, and POX to poxB, respectively. PDH deletion stops the supply of pyruvate to acetyl-CoA, which is considered to be equivalent to a defect in acetic acid (ackA) and ethanol (adhE), which are synthesized from acetyl-CoA. Although the algorithm did not predict the possibility of lactic acid production bypassed by the mgsA reaction, it could be said that the algorithm was able to propose a combination of reaction deletions similar to an actual high-producing strain. A comparison of the deletion reactions performed for the construction of 2,3-butanediol-producing yeast [It is important to state both the reduction in computation time and the validity of the deletion sets obtained from the execution of the algorithms in this study for real production. In the production of succinic acid in eld) see . This preriments . PFL corng yeast  with theng yeast  construcCompared with the OptKnock-based method, the method described in this study showed a stepwise decrease in growth by selecting one deletion reaction. Moreover, target compound yields could also be improved. Therefore, researchers can select the appropriate combination of reaction deletions that satisfy the desired growth and productivity, after which bacterial strains can be developed for downstream experimental phases. This method tends to have a relatively large number of candidate deletion reactions because it is a stepwise method to increase the production of the target compound. When constructing the strain, the number of reaction deletions required may be reduced by evaluating relevant phenotypes via gene expression analysis ,42, prot


Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of ~50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected previously from in vitro characterization of both laboratory and clinical isolates which revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and genome-scale metabolic network reconstructions (GENREs) have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased catabolism of L-valine in clinical isolate-specific growth simulations. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is  This recent surge in CRE-associated infections has been driven primarily by the emergence and dissemination of carbapenemases, a specific type of β-lactamase that has the ability to hydrolyze carbapenems, rendering even carbapenem-class antibiotics ineffective2. A large proportion of these CRE-related infections are due to the Gram-negative bacterium Klebsiella pneumoniae3, with over 50% of K. pneumoniae infections now being resistant to carbapenems in parts of the Eastern Mediterranean and Europe3. As K. pneumoniae has been rapidly acquiring antibiotic resistance and rendering almost all available treatments ineffective, the discovery of new treatment strategies for this bacterial pathogen are critical3.Carbapenem-resistant Enterobacteriaceae (CRE) have emerged as a growing and urgent issue in healthcare facilities around the world, posing a significant threat to public health. Carbapenem antibiotics, currently considered to be the most potent and highly effective class of antimicrobial agents, are often considered a last-resort, reserved specifically for the treatment of severe multidrug-resistant (MDR) bacterial infections4. By identifying those characteristics lost during evolution toward sustained laboratory culture, while remaining conserved across infections, it becomes possible to gain insight into important phenotypes that contribute to successful infection. Furthermore, it has been shown that clinical and laboratory isolates of other bacterial pathogens may also be easily differentiated by distinct metabolic capacities5. Employing this approach for K. pneumoniae, we may highlight “core” metabolic pathways in clinical isolates that may present ideal therapeutic target candidates. Consistent with this strategy, certain elements of metabolism have already been successfully identified as drug targets in bacterial pathogens including other Enterobacteriaceae9.One strategy that has emerged recently is the targeting of elements of virulence or core metabolism that may be too costly for the organism to accumulate mutations in or diminish the ability to manifest disease10. While RNA-seq has become a relatively standard method for characterizing transcription, technical variability, small sample sizes, and sample heterogeneity still exist and may influence study-specific results12. Additional differences in data processing criteria also introduce variability into downstream interpretations14. To account for these factors, meta-analyses of transcriptomic datasets across multiple studies can be performed using a unified curation and analysis pipeline. As such, we assembled 56 publicly available transcriptomes of K. pneumoniae isolates from both the laboratory and clinical profiles during growth in similar media conditions at multiple institutions18. Overall, certain transcriptional patterns varied consistently between clinical and laboratory isolates, and differential expression analysis revealed increased transcription of aminoglycoside degradation and key regulators for histidine utilization among clinical isolates.Changes in bacterial transcription have been used to assess differences in active metabolism with higher resolution than metabolomics screens, as shifts can be traced to specific pathways and gene productsK. pneumoniae (iYL1228)19. GENREs are computational formalisms of the biochemical reactions encoded for in an organism’s genome21. Previously, GENRE-based growth simulations in other pathogens have successfully highlighted novel enzyme targets which were subsequently validated in the laboratory, effectively accelerating research efforts21. Additionally, GENREs can also be utilized to provide improved context for omics data as the network architecture can reveal additive effects of small changes in activity across interconnected pathways22. These network-based analyses enable greater insight into metabolic patterns that correspond with growth under specific conditions, such as during active infection21. We continued the transcriptomic meta-analysis through integration with metabolic network-based investigation which allowed us to discern novel conserved components of K. pneumoniae’s metabolic strategy specific to active infection. Most prominent among these predictions was significantly elevated uptake and utilization of environmental L-valine through the increased activity of an Enterobacteriaceae-specific valine transaminase. This elevated uptake of L-valine was observed across >89% of clinical isolate context-specific models, while nearly entirely absent from models of laboratory strains, supporting the hypothesis of increased importance for survival in vivo. These results also agreed with previous findings that macrophages respond to high concentrations of exogenous valine in order to upregulate phagocytosis and the killing of K. pneumoniae during infection23. Our study highlights the utility of well-curated GENREs integrated with transcriptomic data to accelerate molecular target identification.To further explore possible targets within infection-associated metabolic pathways, we integrated our transcriptomic meta-analysis with a previously published genome-scale metabolic network reconstruction (GENRE) of 24 characterizing either laboratory or clinical isolates of K. pneumoniae. Datasets were considered for meta-analysis if isolates were grown to exponential phase in vitro using LB growth medium at 37 °C, at which point transcriptomic samples were collected and sequenced. This selected standard for transcriptomic samples was still anticipated to be reflective of emergent properties within both laboratory strains and clinical isolates25. Strains that undergo serial passaging, including strains that originated from the clinic, are understood to have many different characteristics when compared to strains which were more recently isolated from a clinical setting27. Therefore, the strains represented throughout this study were classified as laboratory strains if they had undergone serial passaging (defined within this study as having undergone greater than four passages29), whereas strains were classified as clinical isolates if they were recently isolated (having undergone a maximum of four passages) from a clinical setting. As properties characteristic of laboratory strains versus clinical isolates are typically understood to be developed over an extended period of time30. These criteria resulted in 56 total RNA-Seq datasets across four distinct studies; of these datasets, 17 represented laboratory strains17 and 39 represented clinical isolates18 37. Briefly, the transcriptomic data integration method identifies the most cost-effective usage of metabolism to achieve growth that best reflects the cell’s investments into transcription and further prunes inactive reactions36. Using this approach, we generated unique isolate-type-specific models of K. pneumoniae metabolism in rich medium for each of the 56 collected transcriptomes, and assessed the emergent differences in active metabolism , indicating that clinical isolates have a higher potential ability to grow more rapidly. This observation may be explained by the fact that colonizing a host organism presents substantial environmental pressure and encourages rapid growth to ensure the highest probability of colonization. Though it was observed that laboratory strains had potentially evolved metabolic machinery towards faster growth in culture medium, the growth simulations performed here demonstrated that clinical isolates may be able to grow more effectively due to their optimization around environmental pressures.After observing consistent differences conserved across the metabolic models with individually integrated transcriptomes, we then performed a unified analysis within each group  to incorporate all possible strain-level variation into single models of metabolism for each isolate type. We first specified the objective function as biomass for the subsequent analysis. We then iterated through a wide range of minimum objective flux values, assessing the correlation between the transcript abundances and the median reaction flux values during each iteration. From this analysis, the optimum biomass flux threshold value corresponding to the highest transcript-to-model correlation value was selected for both conditions. The two resultant models, being one laboratory strain model and one clinical isolate model, are able to account for the variation captured within each isolate type and the optimal flux distributions that most fit with the transcriptomic investments made by the bacterium. From the corresponding flux distributions, we first measured differences in sampled biomass reaction flux, which is analogous to the growth rate and accounts for biosynthesis of major cellular components Fig. . Strikinp-value = 0.001). This finding was intriguing as it indicated the strain groups are likely adapted for growth in distinct metabolic environments despite simulated growth in the same media conditions. Within-group variation was also significantly greater in clinical isolates . To accomplish this goal, flux sampling of the optimal metabolic solution space was performed exhaustively which resulted in possible activity levels for all reactions in the network that satisfies not only robust growth but also the integrated transcriptome. To focus specifically on differential patterns of core metabolism, we temporarily excluded biomass synthesis reaction components as they remained largely consistent across conditions. Using the reaction flux distributions from this subset of the overall metabolic networks, we performed unsupervised machine learning through non-metric multidimensional scaling (NMDS) of Bray–Curtis dissimilarities Fig. , which ip-value ≪ 0.001). Closer investigation revealed the increased L-valine consumption was due to activity of valine transaminase, which mediates the conversion of L-valine to L-alanine and results in the downstream production and export of acetate  is more highly transcribed in laboratory isolates, downstream enzymes in the valine degradation pathway are more highly transcribed in clinical isolates  was curated using K. pneumoniae strain MGH 78578, the diversity of strains the transcriptomic datasets were gathered from may result in a differing ability for the selected model to fully represent the metabolic activity of each of these strains. Furthermore, while the discordant relationship with higher valine transaminase transcription in laboratory strains yet lower reaction activity is most likely due to increased transcription of functionally related enzymes in clinical isolates, inaccuracies in construction of the GENRE may also be a contributing factor that requires additional curation. Despite these considerations, our analyses demonstrate the strength of systems biology approaches to identify potential metabolic targets against bacterial pathogens.While this study presents several novel insights into the relationship between the metabolism of K. pneumoniae. Future studies may build on the targets identified in this study to investigate the role of L-valine in K. pneumoniae colonization and virulence or amino acid release and utilization by immune cells. Finally, the methods described here may be applied to other recalcitrant bacterial pathogens in the future as a platform for accelerated drug target discovery.Our results indicate that increased valine catabolism is a metabolic phenotype more closely associated with clinical isolates of 45 to ≥Q25, a tool that uses quality and length thresholds to determine the appropriate location to trim the 5’- and 3’- end of reads (Supplementary Table K. pneumoniae MGH 78578 genes (GenBank accession number: CP000647.1) using Bowtie246, a tool for aligning sequencing reads to a designated reference sequence, and screened for optical/PCR duplicate reads with Picard MarkDuplicates47, an algorithm for locating and tagging duplicate reads originating from a single fragment of DNA. Mapping files were converted to human-readable format using SAMtools48, a tool designed to facilitate alignment manipulation in the common Sequence Alignment/Map (SAM) format. Transcript abundances were normalized to both read and target gene lengths then evenly subsampled for equal comparison across conditions.All transcriptomic datasets were obtained from the Sequence Read Archive (SRA) in FASTQ format using the SRA Toolkit. Raw reads were quality trimmed using SickleKlebsiella pneumoniae strain MGH 78578, iYL122837, was obtained from the BiGG Model database49 on 5/21/20. Flux analyses performed in this study utilized cobrapy (v0.22.1)50. Growth simulations were performed using a previously published rich medium in silico formulation51. Gene and reaction essentiality screens were both performed with a minimum objective flux threshold of 1.0% of the optimal value, the commonly accepted threshold for these simulations39. Replicate GENRE transcriptome integration was performed with RIPTiDe (v3.2.3)36 with 0.75 minimum objective flux fraction. RIPTiDe is a tool developed to use both transcriptomic abundances and overall flux parsimony to identify the most cost-effective usage of metabolism while still accurately reflecting the inputted transcriptomic data. To prune the model so that it represents the most focused and biologically feasible metabolic solution space possible, RIPTiDe first sets the objective function to carry near optimal flux. Transcriptomic abundance values are then used to assign linear coefficients to each reaction, prior to performing an optimization of the minimum sum of fluxes. Flux balance analysis is then used to identify the reactions that no longer carry flux, and these reactions are subsequently pruned from the model. Maximum fit RIPTiDe analysis, where all minimum objective flux fractions are iteratively tested to return the model with the best correlation between the context-specific flux reactions and the inputted transcriptomic values, was performed with all transcriptome replicates on the default settings.The GENRE of 52.Statistical analyses were performed in R (v3.2.0). Ordination analysis was accomplished using the vegan package (v2.5.7)Supplemental MaterialSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6