Although there is now an extensive understanding of the diversity of microbial life on earth through culture-independent metagenomic DNA sequence analyses, the isolation and cultivation of microbes remains critical to directly study them and confirm their metabolic and physiological functions, and their ecological roles. The majority of environmental microbes are as yet uncultured however; therefore, bringing these rare or poorly characterized groups into culture is a priority to further understand microbiome functions. Moreover, cultivated isolates may find utility in a range of applications, such as new probiotics, biocontrol agents, and agents for industrial processes. The growing abundance of metagenomic and meta-transcriptomic sequence information from a wide range of environments provides more opportunities to guide the isolation and cultivation of microbes of interest. In this paper, we discuss a range of successful methodologies and applications that have underpinned recent metagenome-guided isolation and cultivation of microbe efforts. These approaches include determining specific culture conditions to enrich for taxa of interest, to more complex strategies that specifically target the capture of microbial species through antibody engineering and genome editing strategies. With the greater degree of genomic information now available from uncultivated members, such as via metagenome-assembled genomes, the theoretical understanding of their cultivation requirements will enable greater possibilities to capture these and ultimately gain a more comprehensive understanding of the microbiomes.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01272-5. Microbes are the earliest known life forms on earth, and fossil evidence for their existence dates to over 3 billion years ago. Subsequently, microbial life has diversified and adapted, and is found in almost all environments examined –3. TheseEfforts to bringing uncultivated microbial “dark matter” into cultivation have been growing in popularity in the past decade as the value of cultures in the post-‘omics’ era is increasingly recognized. Traditional untargeted and new high-throughput methods of cultivation, such as culturomics platforms that rely on the use of ranges of culture media and high-throughput screening approaches, have resulted in the cultivation of many new and previously uncultured lineages being brought into culture , 19. SucBacteroidetes, Proteobacteria, Firmicutes, and Actinobacteria) [29 (81%) and 2.2 × 1029 (25%) of microbial cells across earth’s microbiomes, respectively [Much controversy has been associated with attempts to estimate the global diversity of microbes on earth , 21–25. acteria) –29. Thesacteria) . Moreoveectively . Across ectively , 31. Micectively , which nectively , 34. Theectively , which rStable pure cultures of microbes are valuable resources that can be used to experimentally investigate microbial traits , confirmCultures enable microbial metabolism to be studied at the biochemical level, and reveal as-yet unknown physiological traits under different growth conditions . These fvia experimental data will considerably aid the extent and accuracy of annotations, which could further enhance the ability to bring more uncultured microbes into cultivation. Moreover, classifying organisms is central to improving taxonomic frameworks to describe biodiversity. Presently, the International Code of Nomenclature of Prokaryotes recognizes only cultures as ‘type material’, thus uncultured organisms cannot be formally named. While community efforts to change this position are underway to recognize DNA sequences as type material [The availability of cultures will contribute to the richness of reference databases and taxonomic frameworks that will further aid biological interpretation of microbial function in ecosystems. Currently in genetic databases, such as KEGG , almost material , new spematerial .It is not always possible of obtain stable pure cultures of microbes, and enrichment cultures and stable defined co-cultures are also valuable to gain biological insights into the uncultured organisms. Enrichment cultures  enhance Metagenomics is the study of the collective genomes of the members of a microbial community, and can provide valuable insights for environmental uncultured microbes . MetagenShort-read  next generation sequencing (NGS) technologies, such as Illumina sequencing, are popular due to their high-throughput  and costWhile not based on metagenome sequencing, single-cell genomics, the recovery of genome information amplified from single cells from environmental samples , 62, is The functional annotation of MAGs and SAGs is critical for metabolic pathway reconstruction and understanding the functional potential of organisms that can be used to aid their cultivation. In general, metagenome functional annotation involves two steps: gene prediction and gene annotation . Coding Based on reference pathways which are available in public repositories, protein sequences can be mapped by sequence homology, which can facilitate the prediction and reconstruction the metabolic pathways. Present methods to reconstruct metabolic pathways include BlastKOALA , KAAS 7, GhostKOBased on other culture-independent genomic information, a number of strategies can be pursued to bring uncultured organisms into cultivation in a targeted fashion, as described below. These strategies contrast and complement many untargeted and high throughput methods to bring organisms into cultivation, such as Culturomics , 83, in An understanding of nutritional requirements and metabolic characteristics is important for the isolation and stable cultivation of uncultured microbes. Metagenomic sequence data provides valuable information on traits such as primary metabolism, substrate utilization, oxygen requirements, resistance to antibiotics, and even potential interactions with the host through eukaryotic-like proteins , which pLeptospirillum strain involved in nitrogen fixation was successfully isolated through improved culture media design informed by metagenomic data from an acid mine drainage biofilm [Leptospirillum had been divided into three groups—I, II, and III, based on 16S rRNA gene analyses. However, prior to this study, representatives of groups I and II were identified [nif gene operon in the genomic data from the Leptospirillum group III population, which was lacking in group II. They inferred that Leptospirillum group II, also present in the biofilm, lacked nitrogen-fixing genes. So, the growth medium was modified to lack nitrogen and was used to successfully isolate the first cultivated representative of Leptospirillum group III based on the ability to carry out nitrogen fixation.In a pioneering study by Tyson et al, a  biofilm . The genentified , but no entified  detectedMacropus eugenii, produces significantly less methane than ruminants per unit of energy intake, and harbors a unique gut microbiota with, then uncultured, OTUs of the Succinovibrionaceae being the dominant members of the Proteobacteria. Pope et al. [Succinovibrionaceae group, WG-1. WG-1 was predicted to use starch as a carbohydrate source and the assembly included a urease gene cluster encoding all 13 genes required for urea transport and catabolism. Using this knowledge, they developed a defined medium, which contained starch and urea as the sole carbohydrate and nitrogen sources, respectively. The antibiotic bacitracin was also supplied in the medium because a putative bacitracin resistance gene was identified in the WG-1 assembly. Adopting this strategy, WG-1 was successfully enriched for from the wallaby digesta samples, and these were used to generate axenic cultures through dilution and plating on the enrichment medium. This work afforded a detailed characterization of WG-1 substrate utilization and fermentation in culture to further understand its contributions to lower methane emissions.The foregut of the herbivorous Tammar wallaby, e et al.  used binBrevinematales. Firstly, a novel MAG was identified from metagenomes of the deep subsurface aquifers. This genomic information suggested the presence of genes that encode enzymes that enable the utilization of starch, and maltose/maltodextrin ABC transport system for uptake of extracellular starch hydrolyzed products. Finally, enrichment and cultivation experiments were performed using modified spirochete medium with maltose and starch, and BY33 was isolated successfully. In the study by Renesto et al. [Tropheryma whipplei revealed metabolic deficiencies in amino acid biosynthesis. Nine amino acid synthetic pathways  were absent in the genome, which suggested that T. whipplei acquires amino acids or their precursors from the external environment. Using this information, a comprehensive cell culture medium was developed that provided the missing amino acids and resulted in the isolation of three T. whipplei strains, previously cultivated from human cells and one new strain from a clinical sample. While this example was derived from genome sequence data, this approach can be similarly applied to SAG and MAG data derived from uncultivated samples.Metagenome sequencing permits the identification of specific substrates of putative novel species, then employed these to isolate new species through a cultivation method. Lugli et al.  utilizedo et al. , the genAn understanding of the optimal conditions for growth is also an important factor for the isolation and cultivation of bacteria. David et al.  reportedOscillibacter. In the example by Pope et al. [The antibiotic resistance phenotypes in microbiota can be directly linked to specific taxa and provide useful phenotypic information  that cane et al.  above, b2O). D2O is an effective DIP probe for Raman detection of substrate shifted from the C-H band at 2000-2300 cm−1. When samples incubated in the designed medium, which contained the specific metabolic substrate and D2O, metabolic activated bacteria will generate C–D band in the single-cell Raman spectra (SCRS). Then, RACS helps for the isolation of these targeted bacteria according to SCRS [Another isolation approach of new microbial cells is stable-isotope probing guided Raman-activated microbial cell sorting (RACS) , which y to SCRS .in situ from human gut microbiota. Fecal samples were cultivated in PBS supplemented with 40% D2O (v/v) and antibiotics  for 24 h at 37 °C. They successfully sorted six samples including two amoxicillin-resistant bacteria (AmoxR), two cephalexin-resistant (CephR), and two cephalosporins-sensitive (CephS) bacteria. Kang et al. [2O-containing PBS and porcine stomach mucin and incubated anaerobically. RACS identified a diverse consortium of bacteria, including several members of the underexplored family Muribaculaceae.Yi et al.  used RACg et al.  applied 13C and 15N. These studies highlight the potential of Raman-activated cell sorting for identifying and isolation key players in targeted processes, in the age of omics.The RACS platform can be readily customized to sort cells according to other parameters, such as storage compounds , pigmentCross et al. described a genome-informed antibody engineering approach to capture previously uncultured microbes of interest from complex communities, which they have described as reverse genomics . The useSaccharibacteria (TM7), a phylum belonging to the bacterial candidate phyla radiation [Saccharibacteria. SR1/Absconditabacteria, found in low abundance in human oral samples, were also brought into cultivation for the first time using the reverse genomics method [Cross et al. used this method to culture adiation , from hus method . These sRuminococcaceae family. This approach overcomes sampling bias from differential microbial growth kinetics, and the small size of the chip enables the use of growth stimulants available only in small quantities, and use in anaerobic environments to isolate strict anaerobes, such as those from gut environments. The difficulty of small volume cultivation methods is the supply of gaseous substrates. However, the new methods of enhancing and providing a homogeneous oxygen supply for droplet microfluidics could be applied to microplates [Metagenomic sequence data can be used to identify genes for the targeted isolation of microbes. Such genes may include functional genes of interest, for which specific variants may be revealed through metagenomic sequencing. Shotgun metagenomic sequence reads also have the potential to reveal rare 16S ribosomal RNA gene sequences, that may not be readily detected in amplicon sequencing datasets due to quality filtering steps that remove low abundance reads, or are derived from artefacts during the amplification step . Ma et aroplates .Fig. 3OvOne method that shows promise to capture target cells of interest using probe sequences derived from metagenomic sequence data is fluorescent in situ hybridization (FISH)-labelled samples that can be sorted with fluorescence-activated cell sorting (FACS) to enrich cells belonging to selected taxonomic groups . MetagenSome antibiotics function by directly binding to a specific bacterial surface structure, thus providing the opportunity to use these drugs as probes for bacteria . FluoropIn addition, genome editing also can be used in isolation of new targeted microbes. The editing of specific genes based on MAGs sequence information can facilitate the design of growth medium such as that containing selective antibiotics or substrates, which enables positive selection of edited microbial cells with negative selection of unedited cells in the community Fig. .pyrF genes in Klebsiella michiganensis and Pseudomonas simiae by VcDART transposon. The mutations contained transposons carried two antibiotic resistance markers conferring resistance to streptomycin, spectinomycin, and carbenicillin. The organisms were successfully enriched to an abundance of > 99%. In addition, gene editing of lactose assimilation genes of target bacteria and provision of lactose as a sole carbon source in growth medium successfully enriched the targeted bacteria to the abundance of ~ 95%. The methodology of gene editing guided enrichment also had a high resolution that could distinguish different strains in the same species, such as E. coli subsp. 2 and 3 [Rubin et al.  characte 2 and 3 .While there have been numerous studies that have successfully used metagenomic sequence data to guide the isolation and cultivation of microbes, there are still some challenges and limitations that are yet to be overcome to enable more widespread success.Genome-informed antibody engineering provides an exciting new opportunity for the targeted cultivation of microbes, several limitations are recognized. Firstly, it may be not possible to identify suitable surface exposed epitopes as structural data on homologs from related species may improve the prediction of such regions, but are not always available. Next, expression levels of the target membrane protein in situ might also be too low for antibody capture. However, gene expression or proteomics data, if available, could be used to identify membrane proteins that are highly expressed. Finally, post-translational modification of the target epitope, such as glycosylation, may sterically hinder antibody recognition of their epitopes . To overThe targeted isolation of anaerobic microbes  may posePoribacteria sp. WGA-4E. Although metabolic properties of this phylum, such as possible utilization of urea as nitrogen source and assimilatory sulfate reduction metabolism, were deduced from genomic data [Poribacteria in culture. Potentially the concentrations of medium components were suboptimal for Poribacteria, or the addition of signal molecules may have been required [Once target organisms have been isolated, to maintain their growth continuously, a suitable growth medium and physicochemical conditions must be found. However, genome sequences alone often provide insufficient information for accurately determining all necessary requirements to grow a particular microorganism successfully, since the chemical composition of natural environments is often unknown. For example, Lavy et al.  designedmic data , this sprequired . Complemrequired , 83, in required , 85, sinrequired , 87, by The recovery of genomic data from target organisms of interest to gain insight into cultivation strategies, largely relies on their representation and effectiveness of DNA extraction methods, which common problems are incomplete cell extraction, cell lysis, or DNA degradation . These mThe rapid development of DNA sequencing technologies has unveiled the enormous variety of as-yet-uncultured microbes in nature. Now, the challenge is to use these data to support cultivation and explore ecological questions about the roles of microbes and microbiomes in their natural habitats. Metagenome sequencing has greater potential than providing new insights into microbiome function, as it also brings new opportunities for microbial isolation and cultivation. In particular, the long-read DNA sequencing technologies, greater depths of sequencing and advanced bioinformatic methods can improve the quality of genomic data from individual organisms and species via MAGs, enabling more accurate metabolic interpretation. These data support new culture medium development, genome-informed antibody engineering and gene-targeted cultivation, and a variety of microbes have been successfully isolated from different environments. The possibilities for microbial isolation guided by metagenomic and culture-independent sequence data are plentiful and can significantly increase the chances of successful cultivation for target organisms of interest. However, it is recognized that the processes for targeted cultiation are complex and currently require good comprehension of genomic data to predict cultivation requirements, or advanced technologies that are not readily available to all microbiology laboratories. However, such directed methods can decrease the overall time to isolate specific target microbes and bring them into culture. The challenge is to now use the abundance of culture-independent genetic data for high throughput targeted cultivation, which, combined with advances in cultivation methodologies, may lead to new breakthroughs in the capturing of the uncultured majority.


More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO Re. ReArthrfections  67]. Fu. FuArthrminophen  68]. Th. ThArthrFremyella diplosiphon is a freshwater cyanobacterium isolated from a Connecticut lake in 1952, which possesses one of the largest genomes amongst bacteria  and most abundant photosynthetic organism in the ocean . . cpcG2 n Pcpc560 . T7 RNA  Pcpc560 , Syn7942 Pcpc560  and Syn2 Pcpc560  for stroE. coli under anaerobic conditions . A. A230]. −1 of culture  from mouse, jojoba and Arabidopsis, leading to a low initial product titer of around 10 µg L−1 [Liu et al. engineered Syn6803 to produce and secrete fatty acids to circumvent the biomass separation and extracting steps needed in other cases, producing a majority of 43% of C16:0 and a secretion yield of 197 mg L culture  , C18:3, production in Syn7942 via the introduction of two desaturases, deletion of bacteria , which e profile . Thus, tvipp1. The latter manipulation increases the formation of thylakoid membrane to which the desaturase reactions are localized [More recently, a higher amount of polyunsaturated fatty acids (PUFA) was produced in two engineered cyanobacteria strains: Lep0902, known to naturally produce a high amount of FA and Syn7002. Both strains produced ALA, but also stearidonic acid (SDA), C18:4 and eicosatetraenoic acid, C20:4, up to 40% of total FAs . To achiocalized .Despite these advancements, maximal titers of various fatty acids are typically 10–50 times lower than those obtained with heterotrophs , a limitCyanobacteria naturally keep a high carbohydrate content as part of their osmotic regulatory system and accumulate them under stress conditions. Engineering high carbohydrate-producing strains has, therefore, the potential for enhancement via metabolic engineering. The primary use for high carbohydrate-producing cyanobacteria strains is as feedstock for growing industrially relevant microorganisms .cscB and knocked out invertase and an ADP-glucose pyro-phosphorylase [−1 h−1. The same phenotype was created in the UTEX 2973 strain  and a maltooligosyl trehalose trehalo-hydrolase (MTH). They also expressed the insect trehalose transporter 1 TRET1 to secrete the sugar and engineered the glycogen metabolism to reach a titer of 5.7 g L−1  [−1 of secreted EPS  as ingredients for the cosmetics and food industry as adjuvants and texture modifiers. With that aim in mind, Arias and Uggetti fully characterized a polysaccharide produced by healing) ,241 and eted EPS .Although not inherently highly valuable, cyanobacterial carbohydrate production has clear potential for various industrial applications, both for biofuel production and as a cosmetic ingredient and pharmaceutical additive production.Unicellular, photosynthetic organisms are in the spotlight of microbial terpenoid production because these organisms possess traits that might benefit terpenoid production. Cyanobacteria synthesize terpenoids via the MEP pathway, of which a complete set of homologous genes has been identified. The flux through the terpenoid synthesis pathways is assumed to be higher, as in heterotrophs, because of the demand for chlorophyll and carotenoids to sustain photosynthesis and combat oxidative stress. Microalgae deliver the reducing equivalents strongly demanded by terpenoid production via photosynthesis while having a simpler cellular architecture and being more straightforwardly genetically manipulated than higher plants . Despite−1 of isoprene within three weeks in Syn7942 through upregulation of the native methylerythritol 4-phosphate (MEP) pathway . However, single overexpression of ispG did not result in higher isoprene production. Likely, the HMBPP synthase activity is not the primary limiting step of the Synechococcus MEP pathway and this strategy only becomes effective when other flux-constraining enzyme activities are upregulated [Following these results, Englund et al.  observedegulated . Productegulated ,253,261.cpcB. In the engineered strain, the abundance of the synthase was significantly increased and, in combination with the expression of a heterologous isopentenyl isomerase, resulted in a 62-fold improvement of the isoprene titer [Chaves and Melis addressed low productivity by fusing the isoprene synthase characterized by low catalytic efficiency to the β-subunit of the highly expressed photosynthesis pigment phycocyanin encoded by ne titer . The grone titer .Besides the canonical MEP Pathway, cyanobacteria are assumed to operate a shunt that channels intermediates of the pentose phosphate pathway into the lower MEP pathway under phototrophic conditions . This hyrpi and rpe, which pushed the limonene titers to more than 6 mg L−1 when combined with the simultaneous upregulation of geranyl pyrophosphate synthase (GPPS) activity. It remains to be elucidated if this increase is caused by a higher flux through the proposed PPP-MEP shunt (This shunt might fuel the MEP pathway with intermediates from the pentose phosphate pathway (PPP), thereby bypassing the rate-limiting Dxs step and may have contributed to the increased limonene synthesis observed in Lin et al. . The autEP shunt .E. coli, the recombinant expression of a mevalonate pathway likewise resulted in increased terpenoid production [As described above for oduction .Bacterial nano-compartments (BNC) are intracellular structures composed of a selective proteinaceous scaffold that enclose a segment of a metabolic pathway . SimilarExploring and gaining knowledge about cyanobacterial nano-compartments and their roles, is an interesting avenue for enhancing stress resistance and engineering novel metabolic pathways in nano-compartments.2 to obtain the highest possible activity of the RuBisCO enzyme, which is essential for carbon fixation [One of the most commonly studied cyanobacterial nano-compartments are carboxysomes. Caroboxysomes were discovered in cyanobacteria in 1956 by Drew and Niklowitz; these protein compartments concentrate the COfixation . Plant sfixation . Deliverfixation ,267.E. coli using 19 genes split onto two synthetic plasmids [Stable integration of carboxysomes genes in the chloroplast is notoriously quite challenging . Fang etplasmids . Carboxyplasmids .Besides carboxysomes, other nano-compartments exist in prokaryotic organisms, such as encapsulins . Many diS. cerevisiae.The direct use of cyanobacteria as feedstock involves mixing the cells with other microorganisms, allowing them to either feed on the biomass and/or ferment the carbohydrates. Another strategy is integrating both cultures into a synthetic ecosystem to achieve production of the final compound of interest. Ideally the organisms of such a consortium would interact with and be mutually dependent on each other. Such a symbiotic culture could, for instance, be realized using cyanobacteria as the photosynthetic producer of carbon food sources used by an established industrial biotechnology workhorse such as Bacillus, E. coli and Saccharomyces strains. The stability of each consortium over extended periods was assessed, as well as their response to various stresses and, finally, their ability to produce alpha-amylase and PHB. Challenges encountered were that heterotrophs needed a higher amount of sucrose than what was produced by the cyanobacteria to sustain growth after 24 h and the fact that a high optical density (OD) of cyanobacteria led to inhibition of the heterotrophs independently from the level of sucrose [Recent work has explored the use of cyanobacteria in fully characterized synthetic consortia of microorganisms symbiotically interacting to produce various compounds of interest, including the biopolymer polyhydroxy-butyrate (PHB) alpha-amylase  and has  sucrose . Potenti sucrose , which ccscB, of E. coli) and the carotenogenic yeast Rhodotorula glutinis was able to achieve a higher rate of linoleic acid and palmitoleic acids compared to the yeast alone. Similarly, a positive impact of the heterotroph on the cyanobacteria growth was noticed [In another consortium involving a sucrose-producing cyanobacterium and three yeast strains, a similar issue of growth inhibition due to the presence of reactive oxygen species (ROS) was observed and alleviated by using a catalase enzyme . The co- noticed .Other systems have been studied involving a semi-controlled ecosystem growing cyanobacteria on wastewater, leading to high production of the biopolymer poly-hydroxy-alkanoate (PHA), a natural product made by some cyanobacteria when under stress, reviewed by Arias and Uggetti .Although this consortium approach appears as a seducing alternative offering a sustainable and integrated system, harnessing various microorganisms’ strengths and combining them needs careful optimization. Optimizing such complex systems will probably require advanced metabolic engineering to design perfect partnerships. The potential for a photoproduction system involving cyanobacteria associated with one or multiple heterotroph microorganisms is vast and ecosystem engineering could allow tuning and controlling metabolite production at another level. Nevertheless, studies exploring the potential of these ecosystem approaches highlighted that, rather than focusing purely on metabolic engineering, product yield can also significantly be optimized by intentionally applying stress or other external factors; sometimes aiming for slowing growth purposefully or tuning down the expression to balance metabolic activities can lead to the best production.This review covered the current state of cyanobacterial biotechnology, including cyanobacteria with industrially relevant traits, the synthetic biology tools available to control gene expression and a description of several metabolic engineering approaches applied for photosynthetic bioproduction.E. coli, to improve understanding of cyanobacterial metabolism and physiology and to develop advanced photobioreactors, these capabilities will enable the full potential of cyanobacteria and sustainable and photosynthetic bioproduction to be realized.We anticipate that more synthetic biology tools for the efficient development of cyanobacterial cell factories will be rapidly developed. Combined with large-scale efforts to generate genetic libraries (overexpression and deletion mutants), such as those available for


Medulloblastoma (MB), occurring in the cerebellum, is the most common childhood brain tumor. Because conventional methods decline life quality and endanger children with detrimental side effects, computer models are needed to imitate the characteristics of cancer cells and uncover effective therapeutic targets with minimum toxic effects on healthy cells. In this study, metabolic changes specific to MB were captured by the genome-scale metabolic brain model integrated with transcriptome data. To determine the roles of sphingolipid metabolism in proliferation and metastasis in the cancer cell, 79 reactions were incorporated into the MB model. The pathways employed by MB without a carbon source and the link between metastasis and the Warburg effect were examined in detail. To reveal therapeutic targets for MB, biomass-coupled reactions, the essential genes/gene products, and the antimetabolites, which might deplete the use of metabolites in cells by triggering competitive inhibition, were determined. As a result, interfering with the enzymes associated with fatty acid synthesis (FAs) and the mevalonate pathway in cholesterol synthesis, suppressing cardiolipin production, and tumor-supporting sphingolipid metabolites might be effective therapeutic approaches for MB. Moreover, decreasing the activity of succinate synthesis and GABA-catalyzing enzymes concurrently might be a promising strategy for metastatic MB. Medulloblastoma (MB) is the most common brain tumor in children with an incidence of five per million ,2. MB isMetabolic changes stemming from medulloblastoma include the over-expression of enzymes related to lipid production and the Warburg effect in which abnormal glucose consumption and overproduction of lactate are observed regardless of oxygen presence ,11. ReduFurthermore, MB cells were found to upregulate the amino acid transporter SLC1A5, which takes up glutamine in cells . High acIn the present work, the genome-scale brain metabolic model iMS570 reflects healthy brain metabolism and was constructed previously by our group  and thenThe genome-scale metabolic brain models are reconstructed for healthy and MB cases (nine MB models) and integrated with transcriptomic data (three GSE data incorporated with Gene Inactivity Moderated by Metabolism and Expression (GIMME)). Two MB models were reconstructed by using the average of all gene expression levels in GSE62600 and GSE37418 in order to compare the results with each other and with the healthy results. Four MB-specific models that reflect subtypes of MB were created and named WNT, SHH, GR3, and GR4 using the GSE37418, including gene expression levels of these subtypes. Lastly, the non-metastatic (M0) model, metastatic (M2), and (M4) MB models were developed employing GSE10327 to find the relation between the Warburg effect and metastasis. The models and simulation results were validated by comparing the FBA results with the data obtained experimentally in the literature. The main reactions carried out in the cell are presented in The substantial metabolic differences between the MB and healthy cases and the experimental findings in the literature are summarized in Despite low TCA flux rates in the MB models, acetyl-CoA formation (R28 + R72) in the TCA cycle, which is necessary for lipid synthesis, is higher compared to the healthy brain model. Acetyl-CoA is produced in the pyruvate dehydrogenase reaction (R13 + R57) and threonine metabolism (R197 + R200). The glutaminase reaction (R96) showed higher activity in all MB models than the healthy model, which is consistent with the literature where the low expression levels of glutamate uptake proteins, EAAT1 and EAAT2-4 were observed . GlutamiThe ratio of glutamate over glutamine accumulation in the cerebellum predicted by the MB model was found to be very close to the concentration ratio determined experimentally  Table 1Table 1. When glucose is available, the MB cells mainly use aerobic glycolysis because of the Warburg effect. Even though OXPHOS was also utilized by MB cells, the importance of glycolysis for both energy production and cell proliferation is undeniable. Additionally, due to the heterogeneity of the tumor environment, tumor cells may require different metabolic needs. Using FBA, the glucose uptake rate was restricted to zero to be able to observe the impacts of glucose deficiency on energy production in the MB. MB, which utilizes glycolysis regardless of oxygen availability, was observed to be almost entirely reliant on OXPHOS when glucose is not available. In this case, whole energy generation was reduced by 82% a and theThen, to simulate the effects of a glutamine deficiency, two consecutive adjustments to the MB model, glutamine uptake and both glutamine and glucose uptake rates, were done where these uptake rates were reduced to zero.In the absence of glutamine, energy generation in the TCA cycle decreased to zero. In both glutamine and glucose deficiencies, energy generated in the TCA cycle declined from 6% to 4% b. In botThen, biomass rate (R25) was also examined in cancer cells to investigate the impact of carbon source deficiency on the growth of MB cells c. It wasHexokinase 2 (HK2), Phosphofructokinase (PFK), and Pyruvate Kinase (PK) genes in metastatic samples, the ratio of ATP produced in glycolysis over ATP produced in OXPHOS (ATPG/ATPOP) was detected to be almost seven times higher in MB-M2 than the ATPG/ATPOP found in MB-M0  procedure, 205 reactions out of 594 were estimated to be significantly different from one another. In the MB neuron cells, 73% of reactions (11 out of 15 reactions) in the glycolysis pathway showed statistically important deviation from the healthy neuron cells because of the increased glucose uptake and excessive lactate production. As was observed in the FBA result, lactate production was determined to be statistically higher relative to the outcome for the healthy model  and gene expression data values (ZG) that were higher than 1.96 or lower than −1.96 were categorized as transcriptionally regulated reactions and reactions that are not regulated at the transcriptional level (NTR). The reactions where both flux and gene expression alter are called transcriptionally regulated (TR) . As a reIn glycolysis, most of the reactions are not regulated at the transcriptional level . This stIt was detected that lactate dehydrogenase in the glycolysis pathway has an increased ZF score, although its ZG score is remarkably low. A high level of pyruvate might be causing an increase in lactate formation.Fatty acid synthesis had the highest TR ratio with more than one-third of reactions being transcriptionally regulated . ACOT7 gene controls the stearate production from stearoyl-CoA and FADS2, and SCD, the genes that regulate oleoyl-CoA production from stearoyl-CoA. Due to significantly low ZF and ZG scores for the ACOT7, FADS2, and SCD genes, it is pertinent to point out that the oleoyl-CoA production is downregulated in MB and there is a decrease in oleic acid quantity in MB relative to the healthy brain. Oleoyl-CoA, which is the metabolically active form of oleic acid, has anti-cancer features [The features ,26. Basefeatures . Therefoasparagine synthetase(ASNS) gene regulating this reaction was upregulated in some cancer types such as pancreatic, ovarian, and prostate [ASNS, which was found by the MB model in both analyses, could be a potential therapeutic target for MB.The reactions related to biomass reactions that do not affect the vital functions of healthy cells are counted as potential drug targets to suppress tumor growth. The reactions controlling the uptake of essential amino acids including phenylalanine, lysine, and histidine  were fulprostate . Reducedprostate . Thus, AThe reactions associated with the production of cardiolipin, which is used in the structure of the cell membrane , are dirConsistent with other analyses performed in this work, the reaction where cholesterol produced in an astrocyte is transferred to the neuron was also found to be directly coupled to the growth reaction (R25).Single/double gene and reaction deletion analyses were performed to be able to find potential therapeutic targets for MB. In essentiality analysis, eight genes out of forty-two were removed as they significantly affected the main functions of the healthy cells see . When thThe essential reactions were found in pathways such as phosphatidylethanolamine, cholesterol, sphingomyelin, glycosphingolipid, phosphatidyl-inositol, and fatty acid metabolism. In agreement with the experimental findings in which lipid was found abundantly in MB , lipid pASNS, which is associated with several malignancies [PGS and CRLS1, and the glycerol-3-phosphate syntheses genes GPD1 and GPD2 were found as essential genes. Glycerol-3-phosphate is utilized in cardiolipin metabolism to produce cardiolipin, making up 20% of the mitochondrial membrane [GPD2 was observed to be excessively active in numerous cancers [GPD2 inhibition led to anti-cancer effects in a prostate cancer cell line [GPD1 and GPD2 genes and the reactions controlled by them were found in both flux coupling and single-gene deletion analyses by the MB model. Furthermore, inhibition of these genes did not cause severe changes in the vital functions of the healthy model.Many of the controlling genes/gene products related to the reactions detected in flux coupling analysis were also found in essentiality analyses. For instance, gnancies , was idemembrane . GPD2 wa cancers . Furtherell line . ConsistST3GAL5 gene regulating GM3 synthesis was identified as essential with the MB model and GM3 was found abundantly in MB by Chang et al. [ST3GAL5 gene was highly active in comparison to the control group and other subtypes [The g et al. . Even thg et al. ,39. Partsubtypes . Hence, B4GALNT1 gene that controls GM2, GD2, and GT2 syntheses using GM3, GD3, and GT3 gangliosides, respectively [Another essential gene found by the MB model was the ectively . The mosectively ,41,42. Tectively . Only MBectively .In double gene deletion analysis, 26 synthetic lethal gene pairs were determined once the genes found in single deletion analysis were removed . HoweverATP citrate lyase (ACL) and Fatty Acid Synthase (FASN) genes was captured by the MB model in double gene deletion analysis. In agreement with our simulations, simultaneous inhibition of the ACL and FAS genes hindered cancer cell division [ACL, which is responsible for the production of acetyl CoA in the TCA cycle, reduced the growth of lung cancer cells [FASN also caused a decrease in the hallmarks of tumors such as lipid production in various cancers such as pancreatic, ovarian, and lung tumors [The combination of the division . Moreoveer cells . Suppresg tumors . AlthougABAT gene with succinate-CoA ligase ADP-forming subunit beta (SUCLA2) and succinate-CoA ligase GDP/ADP-forming subunit alpha (SUCLG1) genes that regulate succinate synthesis (R34 + R77) from succinyl-CoA in the TCA cycle were also determined by the MB model (GABA transaminase (ABAT), which controls SuccinateSAL and glutamate productions (R99 + R101) from alpha-ketoglutarate and GABA, is relatively lower in primary MB than in healthy cerebellum since overexpression of ABAT in MB decelerates proliferation and increases the activity of OXPHOS [ABAT was found to be less active than SHH and WNT. Furthermore, activation of ABAT was observed to trigger apoptosis and diminish cancer cell division in rats with MB [ABAT expression was found to be enhanced in metastatic MB in comparison to primary MB in order to meet its energy needs by utilizing GABA [ABAT gene was found to be crucial for metastatic cells because ABAT recharges the metabolites necessary for the TCA cycle and OXPHOS by contributing to succinate and NADH productions.Combinations of the MB model . The actf OXPHOS ,47. In fSDH), which converts succinate to fumarate, were found in some malignancies as high succinate levels cause hypoxia-inducible factor 1-alpha  stabilization and HIF-alpha controls the genes triggering cell proliferation [Succinate upregulation is also associated with cancer . Abnormaferation . HoweverSGPL1 gene, which regulates reversible phosphoryl-ethanolamine synthesis from sphingosine 1-phosphate (SP1), was identified with the KDSR and DEGS2 genes .Single gene essentiality was repeated for lactate and energy productions as the objective functions to capture the genes that are crucial for three parameters  since interfering with these genes was considered to improve the therapeutic strategy. In total, 32 common essential genes were found in the three analyses. Then, these 32 essential genes were silenced one at a time from the normal brain model to identify the ones whose knockout does not affect the necessary functions of the healthy brain cells. The genes that decrease MB growth by more than 40% were determined. Taking these criteria into consideration, the essential gene list was narrowed down to eight. By using the DrugBank and GeneCard databases, 54 drugs/compounds were found for eight essential genes  was studied in numerous tumors such as colon cancer, lung cancer, ovarian cancer, melanoma, and GBM . StudiesFASN was determined to be overexpressed [Antimetabolites are chemically similar substances to natural metabolites and they are often used for cancer and viral diseases to inhibit the use of natural metabolites . To idenxpressed .HMGCR, was found to be considerably similar to mevalonate with a 0.91 Tanimoto score; however, it crosses the blood–brain barrier with poor transport capacity [Metabolites of cholesterol metabolism were found to make up 10% of the total detected substrates. These metabolites are mostly intermediates of the mevalonate pathway, which is stimulated by malicious cells to support cancer-associated features such as uncontrollable growth . In addicapacity .Fifteen percent of natural metabolites consumed in reactions whose catalyzing enzymes are potential targets are linked to sphingolipid metabolism. While 8% of these metabolites were determined to be associated with the sphingomyelin pathway, 7% of them are related to the glycosphingolipid pathway. Indeed, enzymes and intermediates associated with sphingolipid metabolism were determined to play vital roles in cell signaling mechanisms. One of the antimetabolite candidates detected similar to sphinganine metabolite is phytosphingosine with a 0.95 Tanimoto score. Sphinganine is used in the production of sphinganine-1-phosphate and dihydroceramide . PhytospDespite its anti-cancer features, its delivery might be challenging owing to its low solubility and permeability through the blood–brain barrier (BBB) ,75. The Considering the lack of studies on phytosphingosine delivery, further research using nano-engineering techniques including micelles, nano-capsules, liposomes, and emulsions seems promising.Among pediatric brain tumors, medulloblastoma (MB) is the most prevalent kind  with an annual rate of ~5 cases per one million population ,2. The tCRLS1, PGS1, GPD1, and GPD2) were determined as essential in the essentiality analyses. Notably, the CRLS1 gene was also identified as one of eight essential genes required for ATP and lactate production besides growth. In the essentiality analyses, 45 genes and 312 reactions were identified as essential. A total of 42 of them were detected in both analyses and almost 60% of the essential genes were associated with lipid production. In sphingolipid metabolism, the B4GALNT1 and ST3GAL5 genes were determined as essential by the MB model. The fact that inhibition of GM3 synthase led to anti-cancer effects in MB, and GM3 was found abundantly in MB, renders the ST3GAL5 gene a desirable target for MB treatment. The essentiality analyses for biomass, energy and lactate productions resulted in eight common genes , regarding their toxic effects on the healthy model and their potential as therapeutic targets for MB. The antimetabolites that result in competitive inhibition to natural substrates in reactions were discovered, and it was found that 32%, 15%, and 10% of natural metabolites in reactions whose enzymes can be used as therapeutic targets were related to fatty acid synthesis, sphingolipid metabolism, and cholesterol synthesis, respectively. Mevalonate–meglutol and sphinganine–phytosphingosine were identified to be very similar to each other with 0.91 and 0.95 Tanimoto scores, respectively. Phytosphingosine has anti-cancer properties and has the potential to act as an antimetabolite, but further experimental study is needed to fully understand how it enters the BBB.In an attempt to find the drug targets for MB, 158 reactions were estimated to be coupled with the growth reaction. Interfering with these reactions may be a potential therapeutic strategy for MB. Because various cancer agents are interfering with nucleotide metabolism and considering how the MB model captured nucleotide exchange reactions coupled to growth reaction, the MB model made the right predictions about cancer metabolism. In addition, reactions associated with the production of CDP diacylglycerol and glycerol-3-phosphate and main cardiolipin production reactions stand out in the flux coupling analysis. Moreover, cardiolipin and glycerol-3-phosphate synthesis reactions and the genes regulating them , FASN, Acetyl-CoA Carboxylase 1 (ACACA), Glutaminase (GLS) were the upper limit for the threshold value since these genes are upregulated in MB [FASN expression value and high enough to create a model which reflects the MB characteristics.The expression levels of ed in MB ,85,90,91To generate specific models for MB subtypes, four different thresholds were created by using averages of gene expression of patients in different subgroups. Taking into consideration the criteria determined, thresholds were chosen as 72.5%, 74.7 %, 90.24%, and 76.34% of the average of WNT, SHH, GR3, and GR4 gene expressions, respectively. GIMME deleted 7, 7, 9, 13, and 8 reactions from MB, WNT, SHH, GR3, and GR4 models, respectively.GLS expression value. As in GSE37418, the expression levels of upregulated genes in MB were determined as an upper bound. Consequently, 30% of the transcriptome data values average was selected, and 38 reactions were erased by GIMME from the MB model for GSE62600.The differences between gene expression values in GSE62600 were higher compared to GSE37418. Thus, small variations in the threshold did not influence the results remarkably. The thresholds that are more than 50% of the transcriptome data average of MB were eliminated because these values were exceeding the To investigate the correlation between metastasis and the Warburg effect, the expression values of M0, M2, and M4 medulloblastoma metastasis stages that are present in GSE10327 were utilized to construct MB models that reflect metastasis-specific properties. As thresholds, 45%, 50%, and 55% of the gene expression averages were selected for the non-metastatic (M0), M2, and M4 stages, respectively. GIMME eliminated 31, 31, and 26 reactions from the non-metastatic (M0) model, metastatic (M2), and (M4) MB models, respectively.The context-specific constraints imposed on the system include the uptake of glucose, glutamine, oxygen, glycogen, ketone bodies, leucine, tyrosine, tryptophan, methionine, and ammonia. Since similar proliferative propensities were observed for both glioblastoma (GBM) and medulloblastoma (MB) cell lines, in 5 mL of glutamine for 10 days , 0.080 mThe tryptophan and tyrosine uptake rates of MB over the ones in the healthy cerebellum were determined as 3.5–3.7 and 2.8, respectively ,94. UsinAll simulations were carried out using CPLEX ILOG optimization algorithms provided by IBM’s Academic Initiative Program on the COBRA toolbox under MATLAB 2017b. The objective function was set as maximization of biomass reaction if not otherwise stated, and the flux balance analysis was conducted after the integration of transcriptome data. Next, the minimization of the metabolic adjustment method (MOMA) was performed to predict closer suboptimal results to the healthy case in order to improve the flux results acquired with FBA ,96. The The objective function for MOMA was determined as the minimization of the distance (D) between medulloblastoma (m) and healthy (h) cases. For medulloblastoma (m) and healthy (h) cases, flux rates obtained from FBA in MB and healthy (iMS570) models were utilized. Since this expression includes quadratic terms, quadprog function found in optimization toolbox of MATLAB was employed.Flux sampling method was conducted by employing the gpSampler function of the COBRA Toolbox. This function uses Artificially Centered Hit-and-Run (ACHR) algorithm that estimates the random flux profiles . In totaFlux sampling results were also used for the regulation analysis that incorporates flux data with gene expression data by transforming flux and DNA microarray data into statistical scores. Z scores for metabolic fluxes (ZF) and gene expression data values (ZG) were estimated to find the significance of change between MB and healthy conditions.The reactions whose flux values and/or expression levels of the genes controlling these reactions were higher/lower than 1.96 and −1.96 which were computed with respect to a 5% significance level, were categorized regarding whether they are metabolically or transcriptionally, or post-transcriptionally controlled. ZG scores of some reactions whose controlling genes are more than one were estimated by summing up all ZG scores designated for each gene.Flux coupling analysis (FCA) was executed with the F2C2 function to uncover reactions that have a direct impact on biomass reactions. Hence, the reactions that can be intervened to hinder growth in MB were revealed. Then, these potential therapeutic targets, which have no destructive impact on healthy cells, were detected.The essentiality analyses were conducted where all reactions or genes are erased one at a time from the model. If the removal of components leads the flux rate of growth reaction to decline less than the specified flux rate (40%), they are acknowledged as essential. The genes required for energy generation and lactate production along with biomass production rate for the survival of tumor cells were uncovered. Then, the reactions regulated commonly by these essential genes identified in three analyses  were deleted one at a time from the healthy brain and MB models and the genes controlling eliminated reactions that do not influence the basic functions of the healthy cells and decrease the MB growth rate by more than 40% were estimated.To predict the compounds targeting metabolic enzymes and their impact on both medulloblastoma and healthy cells, the antimetabolites  were explored by using Tanimoto scores and performing FBA. Antimetabolites might diminish the usage of substrates in cells by engendering competitive inhibition; therefore, all the compounds similar to the metabolites present in the medulloblastoma brain model were determined from DrugBank using Simplified molecular-input line-entry system (SMILES) of these metabolites and the compounds with Tanimoto scores higher than 90% were identified . Then, t


Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol—key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the “one transporter-one organ” concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal “drug” transporters regulate metabolism across the organelle , cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk  and inter-organismal communication .In vitro and in vivo studies have established the organic anion transporters OAT1  and OAT3 (SLC22A8) among the main multi-specific “drug” transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from  In particular, so-called drug transporters are understudied from a systems biology perspective. Along with mono-specific and oligo-specific transporters, these multi-specific SLC and ABC drug transporters are now known to have important roles in endogenous metabolism. Thus, transporters are increasingly considered to be therapeutic targets for treatment of metabolic diseases. For example, the targeting by gliflozins of the sodium-glucose transporter SGLT2 (SLC5A2) is becoming a widely utilized therapy for Type 2 diabetes4. The Food and Drug Administration (FDA) has emphasized the clinical importance of understanding whether newly approved drugs are transported by these and a few other multi-specific SLC and ABC transporters6. Although they have immense pharmacokinetic importance, there is a growing appreciation of their endogenous roles in the transport of key metabolites and signaling molecules5. Many of the metabolites handled by “drug” transporters originate in the gut microbiome in physiological and pathophysiological circumstances7. Therefore, understanding the local and systemic metabolic consequences of disruption of multi-specific drug transporters (as well as oligo-specific and mono-specific SLC and ABC transporters)—including metabolism dependent upon small organic molecules originating in the gut microbes—is of tremendous practical import.Multi-specific transporters  handle a remarkably wide range of drugs4. From the organ and inter-organ perspective, there is now considerable evidence regarding the key roles of OATs in kidney proximal tubule regulation of plasma levels as well as renal and urine levels of fatty acids, uric acid, vitamins, bile acids, and prostaglandins, as well wide range of other metabolites and signaling molecules8. Intracellularly, OATs help regulate cytosolic levels of “classic” Krebs Cycle metabolites such as alpha-ketoglutarate9. On an inter-organismal scale, OATs appear to be one of the major systemic routes for elimination of numerous gut microbiome-derived metabolites; once inside the cells of the kidney, some of these metabolites also activate nuclear receptor signaling, leading to the expression of other transporters and enzymes12. Given this coming together of recent in vitro and in vivo experimental evidence from several groups, suggesting the impact of OAT functioning upon multiple biological scales , Oat1 and Oat3 seem an ideal set of genes for condition-specific genome-scale metabolic reconstruction at multiple scales.OAT1 and OAT3 are particularly attractive for integrating metabolism across multiple drug transporters, multiple organs, the gut microbiome, and multiple scales14. Gene-protein-reaction relationships provide the means to map transcriptomic data onto the metabolic network. Data integration algorithms provide the means to generate context-specific models using transcriptomic data (as well as any available proteomic or metabolomic data)16. Beginning over a decade ago, physiologically-relevant results were obtained with this general approach using earlier versions of human genome annotation and incomplete representation of transporters to integrate transcriptomics and very limited metabolomics data in a "one transporter-one organ" approach. Recent approaches have enabled the generation of useful condition-specific models capable of simulating the effects of drug-induced, as well as genetic, perturbations with experimental validation21. The models in turn can be used to test current notions and generate new, testable hypotheses25.Genome-scale metabolic network reconstructions provide a means to carry out multi-omic, data-driven model construction, that enable network flux estimations to provide an assessment of the metabolic capabilities of the cells Fig. . Most coOat1 and Oat3 knockouts (KO) in conjunction with the current iteration of the human metabolic network reconstruction—and with the incorporation of multiple microbial models—we are now able expand the scope of the systemic alterations resulting from the loss of function of these transporters. For the first time, the reconstruction explores inter-organ interactions as well as the role of the gut microbiome in OAT-regulated metabolism. The results, while consistent with previous "one transporter-one organ" analysis, reveal many novel interactions across multiple organs and multiple scales. Furthermore, because of the intimate connection between gut microbe-derived metabolites and renal OAT1 and OAT3 function in the setting of kidney disease3, this strategy could set the stage for a much more formal and transparent understanding of the systems biology of chronic kidney disease (CKD).Due to limitations in scope of the metabolic reconstructions as well as relatively limited throughput of metabolomics, many complex inter-organ and gut-microbiome interactions were not detectable a decade ago. However, through the use of much more expansive plasma metabolomic profiling of 26, in conjunction with a representative microbial model  to construct multiple host organ-microbiome models. The resulting OAT1 and OAT3 wildtype (WT) and KO models are then used to evaluate aspects of function within the whole organism in relation to: (1) kidney-specific functions; (2) multi-organ and microbiome interactions; (3) intracellular reaction fluxes and organelle-associated functions; and (4) metabolic pathways . The results indicate how the changes in function of a single transporter, as well as multiple transporters, manifest in specific organelle, cellular, organ, inter-organ, systemic, and inter-organismal (host-gut microbe) alterations in metabolism. While the centrality of the transport function OAT1 and OAT3 in the gut (including microbiome)-liver-kidney axis is reaffirmed, previously unrecognized connections to reactions in other tissues, such as those predominantly occurring in skin, are revealed. The results support the Remote Sensing and Signaling Theory (RSST) of inter-organ and inter-organismal communication via small organic molecules that interact with "drug" transporters, "drug" metabolizing enzymes and nuclear receptors12.Here we use a multi-omics-based systems biology approach to interpret organ-specific as well as systemic responses (including gut microbe responses) to the organic anion transporters, OAT1 and OAT3 in KO mice. Our strategy reconciles intra-cellular (including inter-organelle) metabolism with multi-organ (e.g. kidney-liver) metabolism as well as inter-organismal (e.g. host-gut microbe) metabolism using a human metabolic network reconstruction, Recon3DOat1 and Oat3 WT and KO mice were analyzed using an integrated human-microbiome genome-scale metabolic network model, starting with Recon3D merged with a representative multi-microbial model  functional comparison between WT and KO for Oat1 and Oat3, respectively, based on context specific network reconstruction content and steady state flux comparisons.To briefly summarize the reconstruction strategy that follows, renal transcriptomic and untargeted plasma metabolomic data from del Fig. . A two-tdel Fig. , resultiOat1 and Oat3 WT and KO mice.. Purine metabolism intermediates, including inosine, inosine monophosphate (IMP), and adenine, that are also found to have increased plasma concentrations in Oat1 knockouts, are highlighted. The Bile Acid Synthesis sub-system changes in the Oat3 knockout are slightly more nuanced but appear consistent with the well-established role of Oat3 in in vivo bile acid regulation31. The increased plasma choline concentration in the Oat3 knockouts leaves less intracellular choline use for bile acid metabolism, leading to increased metabolism of other, multi-ring intermediates, such as 3 Two sub-systems that increased in number for 34. Comparisons between the WT and KO models for the number of different metabolites for each of the 9 compartments  were statistically significant for the knockouts of both Oat1 and Oat3 with Chi-square values of 103.9 (corresponding to p < 0.001) and 20.1 (corresponding to p < 0.01), respectively. Many of these metabolic processes are related to the endoplasmic reticulum (ER), a key site of metabolism of lipids, steroids, and xenobiotics  in Golgi, lysosome, and peroxisome metabolic processes  Cycle, and indeed, counter-transport of a TCA Cycle intermediate  is necessary for uptake of metabolites and drugs into the kidney proximal tubule cellFig. , Tier 2:24. For the present analysis, given the much broader and deeper metabolomic coverage that has recently been achieved in the Oat1 and Oat3 knockouts, the interest was to assess more global alterations resulting from the OAT KO, including direct and indirect consequences from loss of Oat1 and Oat3. Differentially active reactions achieving significance were identified by randomized sampling of the steady state flux solution space .In order to further constrain the models and also expand the scope of the models by accounting for the plasma metabolomic measurements (i.e. the circulating metabolome), the metabolomic data from the four conditions  was mapped onto the model Fig. . PreviouOat1 knockouts32. Both the Oat1 and Oat3 knockouts have increased plasma pyridoxal concentrations. However, the Oat1 knockouts have increases in numerous other vitamins including biotin, pantothenate, pyridoxate, and salicylate  were driven by reactions related to production of microbial biomass. However, uniformly across all of the models, the second largest co-set involved central metabolites in host and microbial metabolism  from the two transporter KO mice and two WT mice, we found many reactions relating to tissue-specific functions of other organs. This suggested that the new metabolomics-constrained host-microbe models, which already revealed intracellular and intra-organelle reactions, also included reactions spanning multiple organs.41 was used as an independently validated approach for identifying tissue-specific subsets of genes for kidney, liver, brain, skin, adipose, and colon lon see “”. The neOat1 or Oat3 knockout mouse. For each of the different organs and microbiome, the metabolic demand for each tissue was optimized while setting a non-zero demand for a different organ or microbiome (see “We considered pairwise interactions among the six different organs plus the microbiome in order to determine whether an increased metabolic demand for one tissue affects (or doesn’t affect) the metabolic capabilities of a different organ. The relative interactions for the WT and KO mice were then compared in order to provide an assessment of the changes (if any) between different organs that result from transporter loss of function in the ome see “”. Cruciaome see “.Figure 1Oat3 WT and KO, respectively) provide a visual depiction of the metabolic demands of one organ on another. The width of each ribbon relative to itself (e.g. red brain to green brain or red kidney to green kidney) represents the ‘reference’ metabolic demand for that organ. The width of the self-organ ribbons in comparison to the inter-organ ribbons  represents the competitive metabolic demand of the other (green) organ. Differences in the weightings were then compared (as percentages) between the wildtype versus knockout models.The Circos plots of the optimized metabolic organ objectives , whereas for the Oat3 the largest alterations occurred with the liver and skin (followed by the colon). Finally, since these interactions are occurring simultaneously and dynamically, the conceptual model of a hierarchical axis gives way to a structured web of interactions.Interestingly, the organ-organ interaction difference maps are asymmetric for the ons Fig. . This su44.Collectively, we see the consequences of OAT influence on systemic metabolism extending beyond the kidney-microbiome axis and involving multiple organs. Our results suggest that interactions between the liver, colon, and skin with the other organs are most prominently affected by loss of OAT function Fig. . The int12. The RSST is discussed further below.This reconstruction provides the first multi-scale representation of how multiple drug transporters regulate metabolism across organelles, cells, organs, multi-organ axes (e.g. gut-liver-kidney), and organisms (e.g. gut microbes-body). Many interesting insights emerge. For instance, to compensate for the loss of transport by a plasma membrane OAT, cells in the kidney alter a large set of reactions occurring in certain organelles. More generally, the reconstruction supports the RSST, which seeks to explain the role of "drug" transporters and other proteins—many of which are also involved in the drug absorption, distribution, metabolism and elimination (ADME) of small molecule drugs—in inter-organ and inter-organismal communication across multiple scales (organelle to multi-organism)Considering both OAT1 and OAT3 together is particularly interesting and important from a physiological perspective. These two "drug" transporters are, together, the main route of renal handling of a very large class of clinically-relevant small molecules: organic anions. One of the main roles of the kidney is the elimination of small molecule drugs and toxins (exogenous and endogenous), as well as numerous by-products of metabolism, including uremic toxins of CKD originating from gut microbes. Many of these gut-microbe small molecules are organic anions with molecular weights on the order of 100–1000 Da known to be transported by OATs in the kidney proximal tubule.46, a growing amount of evidence indicates an essential role for OATs in many aspects of endogenous physiology47. They and other closely related SLC22 family transporters are expressed early in organ development40, and OATs and other SLC22 transporters are evolutionarily conserved, with orthologous and/or highly homologous genes found in mouse, zebrafish, fly, and worm48. Indeed, many family members in fly are essential for development of the embryo, or in adults, recovery from oxidative stress33. In zebrafish, OATs appear to have endogenous functions similar to mammals49. There continues to be growing evidence for the direct relevance of mouse OATs and humans51, further supporting the important role of murine experimental models. While the murine knockouts of Oat1 and Oat3 have the predicted pharmacological and toxicological phenotypes, the application of metabolomics has radically changed our understanding of OAT function53. Much of this wet lab experimental data comes from our research group.While OAT1 and OAT3 are among the seven drug transporters initially focused upon by regulatory agencies because their immense pharmaceutical and pharmacokinetic importancep-cresol sulfate56, intracellular molecules such as TCA cycle intermediates9, and links between Phase 1 and Phase 2 enzymatic modifications of endogenous molecules by the liver  and secretion by the proximal tubule of the kidney57. Co-expression networks including SLC and ABC transporters support the existence of endogenous networks linking the physiology of the gut, liver, and kidney to many multi-specific, oligo-specific and mono-specific transporters with SLC22 transporters  forming key hubs in the transporter network by several network metrics29.In addition, numerous in vitro and in vivo experiments carried out by multiple groups, including ours, have revealed connections of OAT1 and OAT3 to the transport of gut microbe-derived products such as hippurate and Oat1 and Oat3 knockouts using a host-microbiome metabolic reconstruction, we have shown, to our knowledge for the first time, alterations in biochemical reactions involving two major multi-specific “drug” transporters affecting intra-cellular, inter-organ, and inter-organismal metabolic capabilities. These endogenous biochemical reactions are, at various scales, dependent upon OAT1, OAT3, or both.Nevertheless, a formal analysis of these connections across multiple scales  has been lacking until now, leaving much of the aforementioned experimental data piecemeal and unconnected. Through analysis of multiple omics datasets from the Oat1 and Oat3 KO mice, new insight is provided into the local and systemic effects of OATs, including connections to relevant aspects of host metabolism dependent upon the metabolism in the gut microbiome.We used TSEA as a reconstruction-independent means to define tissue-specific functions and then analyzed the metabolomic data with different organ objectives. Indeed, it is interesting to note how one or the other transporter impacts certain non-renal organs similarly and differently Fig.  as well The implications for understanding the scope of drug transporter biology, as opposed to more narrowly-defined pharmacokinetics analyses, are far-ranging and raises many new questions for research in this field. The metabolomic and transcriptomic measurements in conjunction with the systems analysis implicating altered interactions of multiple organs in the context of loss of OAT functions may have significant implications for understanding triggers for multi-organ failure when patients are administered multiple therapeutic drugs including probenecid, antivirals, and penicillin-based antibiotics, for example, in the setting of compromised renal or hepatic function.58. Its role in systemic metabolism is only beginning to be explored. Another interesting and unexpected finding was the shift in glutathione metabolism in OAT KO models. Interestingly, ophthalmate has been directly linked to biochemical aging of cells with associated decreased glutathione availability59. The ability to maintain redox pools and to respond to exogenous and endogenous redox loads is a core, essential function of cellular metabolism. An impaired ability to tolerate redox stresses, as suggested by the OAT KO models, may have practical implications in our understanding of ‘off-target’ effects of drugs interacting with these transporters. Future studies to further evaluate these mechanisms, as well as understanding regulation of different sub-pathways in Oat1 versus Oat3 KO  may expand our understanding of metabolic regulation of redox states.One of the most unexpected findings of this study is the connection between kidney OATs and the skin. We emphasize again a significant and largely unappreciated role of skin metabolism relating to endogenous metabolites and drugs. A growing number of experimental and clinical studies, however, indicate a significant role of skin as a drug metabolizing organA critical challenge with analyzing OATs and other multi-specific  and oligo-specific transporters (e.g. organic cation/carnitine transporter 2 (OCTN2 or SLC22A5), multidrug and toxin extrusion protein 2 (MATE2 or SLC47A2)), most of which are usually considered drug transporters, is the spectrum of their transport capabilities not only for drugs but also endogenous molecules –resulting in local and systemic effects secondary to their loss of function.60 posits that key multi-, oligo-, and monospecific transporters, "drug" metabolizing enzymes, and nuclear receptors mediate communication via small molecules  between organs and organisms (e.g. host-microbiome). This ultimately coordinates the shifts in the availability of small molecules with "high informational content" across different tissues and fluid compartments in the body3. By and large consistent with the RSST, the systems approach employed here using transcriptomic and broad plasma metabolomic coverage takes steps towards analyzing how metabolic signals are transmitted across many scales including organelles, cells, organs, the mammalian organism (whole body), and the interfacing microbiome. More complete formulations of the RSST—as well as the experimental evidence behind the theory and applications to human disease—have been presented elsewhere61.The Remote Sensing and Signaling Theory (RSST)61. The SLC22 transporter family, of which OAT1 and OAT3 are among the best-known members, is a key hub in the Remote Sensing and Signaling protein network45. It would be particularly interesting, provided similar omics data becomes available, to perform metabolic reconstructions of members of other transporter, DME, and nuclear receptor families that are hubs in the Remote Sensing and Signaling protein network . This would give a more complete portrait of how these multi-, oligo- and monospecific transporters, enzymes and regulatory proteins affect different aspects of metabolism across multiple scales.OAT1 and OAT3 are but two of the > 500 proteins  that appear to be part of a Remote Sensing and Signaling protein network regulating small molecule homeostasisOat1 and Oat3 knockout mice; (2) connecting them to bacterial metabolic reconstructions; and then 3) constraining by knockout metabolomics (as done here). The resulting reconstruction provides key and novel insights into how the system has responded, in terms of metabolic capabilities, to the loss of function of one or both OATs. However, one challenge with high-throughput profiling of complex systems with dynamically interacting organelles, cells, organs, and organisms, is that the direction of causality cannot be immediately inferred. We acknowledge that this multi-scale model remains incomplete and simplified. Currently, it is difficult to perform rigorously controlled ex vivo multi-organ plus microbiome experiments that authentically reproduce in vivo inter-organ and inter-organismal cross-talk with simultaneous omics profiling at the many scales necessary to fully evaluate the model. However, this should be possible within a few years. Such experiments should be able to help refine the model as well as add complexity in a step-by-step fashion that is not easily done with in vivo experimentation even with multi-omics readouts. Then predictions of such multi-scale metabolic reconstructions can be further refined based on dynamic data based on ex vivo model perturbations in light of comparable in vivo perturbations in well-established physiological and pathophysiological animal models.The context-specific genome-scale reconstructions that we present here were based on: (1) the transcriptomic alterations in the 62. As such, the work also represents a foundational approach to understanding transporter-based drug-metabolite interactions beyond a single transporter, beyond a single organ, and—given that it considers the microbiome—beyond a single organism. The implications are far-ranging in that they both provide a basis for contextualizing previous in vivo studies66 and also suggest the types of detailed analyses that will need to be done in future studies in model organisms and humans to more clearly define the multi-scale  impact of small molecule drugs and drug transporter perturbations on metabolism and physiology.Importantly, through the use of organ-specific transcriptomics and plasma (systemic) metabolomics, the resulting models are a step towards reconciling genome-scale metabolic networks with ADME models. Thus, they provide a novel, rigorous, and quantitative avenue for relating the systems biology of metabolism to quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetics56—have 90–95 percent of their total expression in a single organ, the kidney. A major function of the kidney is to mediate, in large part via the activity of OAT1 and OAT3, the elimination of small organic anions. Thus, the quantitative data on levels of bacterial-derived metabolites made it possible to integrate a microbiome model into the multi-scale reconstruction. While we were able to incorporate basic interactions, future iterations may further build upon this as more extensive data becomes available.Ideally, we would like to integrate human metabolism with many more species of gut microbes; we would also like to have omics data at many scales from multiple organs and cell types for genome-scale metabolic reconstructions. We have used to advantage the fact that OAT1 and OAT3—while experimentally demonstrated to regulate or modulate many local and systemic metabolic pathways and shown to be key determinants of plasma levels of gut microbe-derived moleculesThis overwhelming expression of the OATs, the main drug transporters in the single organ responsible for the elimination of many common drugs , is in marked contrast to many other multi-specific drug transporters such as p-glycoprotein (ABCB1), breast cancer resistance protein , multidrug resistance related protein 2 , which are expressed in a number of organs involved in drug handling such as the gut, liver, and kidney. OAT1 and OAT3 are, however, not expressed in the adult gut and adult liver.It is important to re-emphasize that the vast preponderance of expression of the OATs in a single organ seems to have been an advantage for our reconstruction in that we did not have to reconcile transcriptomics data from multiple organs, as might be necessary in the case of well-known multi-specific ABC drug transporters expressed broadly across many organs; in this latter case of major expression in many organs, the appropriate methodological approach is also far less clear. Given the aforementioned expression patterns, OAT knockout transcriptomics data from the kidney, constrained by plasma metabolomics, seems likely to yield a useful multiscale reconstruction of drug transporter-mediated metabolism. This view is strongly supported by the considerable in vitro and in vivo wet lab data already discussed. As more datasets are generated, we anticipate that the model predictions will improve with greater specificity, although it is expected that the general observations from the current simulations will be refined rather than be radically altered.In the future, exposing and exploring the potential to identify organ-specific side-effects of renal eliminated drugs with multi-organ metabolic reconstructions (MOMR) may become critical for understanding the extent of direct and indirect drug-metabolite interactions. This is likely to be particularly important in the care of patients, who have pre-existing organ compromise (e.g. CKD) and who receive multiple medications, as these can conceivably lead to iatrogenic precipitation of multi-organ dysfunction.Oat1 KO, and Oat3 KO males were housed separately under a 12-h light–dark cycle and were provided ad libitum access to food and water. Serum was extracted from the whole blood. These animals have been described in previous publications28.All experimental protocols involving the use of animals were approved by the UC San Diego Institutional Animal Care and Use Committee (IACUC) and consistent with ARRIVE guidelines (arriveguidelines.org). All animals were handled in accordance with the Institutional Guidelines on the Use of Live Animals for Research. Adult WT, 61. Two-way Analysis of Variance (ANOVA) testing was used to calculate the p-values and the metabolites that were selected for display in figures and tables had either: (1) a fold change ≥ 1.2 with a p-value ≤ 0.05; or (2) a fold change ≥ 2.0 with a p-value ≤ 0.1 in at least one of the various comparisons  . Samples were prepared and subjected to ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) utilizing an ACQUITY ultra-performance liquid chromatography (UPLC)  and a Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution . Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and softwareBacillus subtilis (iYO844)67 metabolic network reconstruction with incorporation of pathways from Lactococcus lactis MG1363 (iNF517)68, Escherichia coli K-12 MG1655 (iAF1260)69, Escherichia coli ETEC H10407 (iETEC_1333)70, and Escherichia coli (iML1515)71. In particular, 2-hydroxy-3-methyl pentanoate metabolism including biosynthesis from S-3-Methyl-2-oxopentanoate and transport were incorporated from iNF517, secretion of S-methyl-l-methionine from iAF1260, glutarate metabolism pathway from iML1515, and tyrosine metabolism from iETEC_1333. The role of the microbiome is to represent a distinct ‘organ’ that interacts with the kidney and other organs, and most importantly, accounts for some of the metabolites that are not endogenously produced by murine organs.Multiple genome-scale metabolic reconstructions were used to construct a scaffold for data mapping and simulation including Recon3D and multiple microbial reconstructions. The “representative” microbiome microbe model (MB) was constructed starting with the 15. These different networks are then compared to calculate a ‘consistency score’ that is minimized to optimize alignment of the resultant network with the gene expression data.The GIMME algorithm maps gene expression data as reaction weightings and calculates context-specific subnetworks to achieve particular metabolic objectives using FBA15 and implemented in72.For the metabolic network with m metabolites, n reactions and corresponding stoichiometric matrix, 22. A minimum of 3 microarray datasets were included for each condition (WT and KO) and were analyzed separately; for a gene to be considered present, it had to be present in at least 2 of 3 sets of data.The mouse transcriptomic data were mapped to human orthologs using the NCBI Homologene database . Since transcript expression levels and fluxes are qualitatively associated, but generally not quantitatively correlated, the GIMME algorithm was used to apply context-specification with the transcriptomic data for WT and KO model construction. As described previously, transcriptomic data were incorporated based on present/absent (P/A) calls using Affymetrix Microarray Suite Version 5.073 to classify metabolites into three groups, those that can only be secreted, those that can only be taken up, and those that can be secreted or taken up. Interpretation of the metabolomic fold change measurements (KO relative to WT) were as follows:If KO/WT > 1 and the metabolite could only be secreted, then the metabolite exchange was constrained with a non-zero lower bound (10% of maximum secretion)If KO/WT > 1 and the metabolite could only be taken up, then the metabolite exchange was constrained with a lower bound that was greater than the minimumIf KO/WT < 1 and the metabolite could only be secreted, then the metabolite exchange was constrained with an upper bound that was less than the maximumIf KO/WT < 1 and the metabolite could only be taken up, then the metabolite exchange was constrained with upper bound that was less than zero (90% of maximum uptake).We are ultimately interested in the response to the transporter knockouts and the absence of condition specific measurements, substrate uptake was refined to only account for metabolites that were mapped from Recon3D to the experimental data. Metabolomic data constraints test with flux variability analysis (FVA)Since the comparison is targeted at looking at alterations, an arbitrary reference uptake of 0.25 mM/gDW/hour was specified for the OAT WT models. The metabolomic constraints for the set of metabolites that were significantly altered in either OAT1 or OAT3 knockouts were applied as described above. Additionally ‘free’ uptake was permitted for oxygen, sodium, potassium, iron, magnesium, bicarbonate, protons, and water.74. An advantage of GIMME is that it does not assume that flux is correlated with levels of gene expression; this is generally more biologically appropriate in comparison to other approaches that may assume a quantitative (linear) correlation between transcription and enzymatic flux. A limitation of GIMME , is that frequently non-tissue specific genes may be included when tissue profiling is performed across an entire organ . We leveraged this potential weakness as a strength for the current study, in order to enable detection of potential interactions via the plasma metabolome with different organs (see sub-section “Tissue specific reactions (TSEA) and analysis”).Cell specificity for metabolic genome reconstructions remains a challenge in terms of achieving high sensitivity and specificityGIMME models were evaluated for variety of constraining objective functions, including biomass, urea, and ATP production. Model content did not vary significantly (fewer than 10 reactions) in reaction content when using the other objectives. ATP production was used, given that the transcriptomic data analysis was performed on the organ level (as opposed to single-cell) transcriptomics. Following construction of four GIMME models (WT and KO for OAT1 and OAT3) and merging with the microbiome model, MB, the feasible solution space of each model was sampled. In order to ensure microbiome viability, a small non-zero lower bound was applied for the microbial biomass vector.75, requiring the following conditions: p < 0.001 for Kolmogorov–Smirnov test as well as one-way ANOVA F-test, with at least ≥ twofold change (or ≤ 0.5 fold change).Differentially active reactions between WT and KO were than computed from the normalized sampled feasible flux states77. These co-sets are of particular interest since they can be used to understand complex metabolic pathway dependencies, such as host-microbiome interactions. Reaction co-sets were calculated from these points with a 0.95 Pearson correlation coefficient cutoff. Model modifications and simulations were carried out with using CobraPy78 and the CobraToolbox v2.072 with the Gurobi Optimizer (v 8.0). Network pathway maps were created using Escher Maps79.The gpSampler in the CobraToolbox was used to characterize the steady state solution space with 2n sampled points (for n reactions in the model). FBA can be used to define functional, context-specific subsystems, or “reaction co-sets”—groups of reactions with tightly correlated fluxes80. Here we specifically refer to organs rather than tissues in order to highlight the more general nature of the metabolic objectives. For each model, TSEA was applied to the gene set and the genes corresponding to kidney, liver, colon, adipose tissue, skin, and brain were selected following Benjamini–Hochberg correction with p < 0.05. The corresponding reactions were then selected (using the rxnGeneMat matrix field in the model structure in Matlab) and the uniformly weighted linear sum of the reactions was used to define metabolic demand for each organ.Human metabolic reconstructions are built upon the known/established biochemical functions across all cells; thus, the construction of cell, tissue, and organ specific models can be performed through the specification of appropriate demands on metabolites. A recognized limitation of interpretation of genome-scale models has been the inability to assign causality in plasma metabolome sampling, due to the ability to interact with multiple organs. Since most tissues express the large majority of metabolic enzymes, we felt it was important to use a reconstruction independent methodology and dataset in order to define tissue specific subsets of genes for the purpose of subsequent genome-scale metabolic network analysis. In order to approximate the cellular metabolic objectives of different tissues, we used Tissue-Specific Enrichment Analysis (TSEA)i, vj, vk} 3D phase planes for metabolic organ objectives of interest were constructed. For a triplet of reactions of interest, {vi, vj, vk) was then plotted as a 3D flux phase plane.The resulting matrix  for i = j, and max(ci) for i jl = α*max(cj) for j subset of the reaction list. For each vector, a set is calculated covering a range of α ∈ , c ∈ {0,1}n with Circos plotsorgan to organ_lb ribbon can be considered the reference point. So, for example, if the ribbon width of organ1_lb to organ2 is the same as the width of the ribbon from organ1_lb to organ1, then it can be stated that organ1 and organ2 have essentially independent metabolic objectives. Conversely, if the width of the ribbon from organ1_lb to organ3 is very narrow (< 10%), then it can be concluded that they are metabolically competing objectives. Constrained organs (“_lb” post-fix) that have large ribbons to some tissues and small ribbons to others indicated organ-organ interactions that are independent of one another versus competitive with one another, respectively. Comparison of the WT and KO organ-dependency Circos plots identifies changes in the ‘normal’ pairwise tissue interactions that result as a consequent Oat1 or Oat3 knockout.Interpretation of Circos plots: Each plot should be compared with respect to a ‘reference’ organ that has a fixed lower bound . The lower bound minimum was set to 80% of the corresponding organ objective. Pairwise organ-organ optimizations were linear from 10 to 90%, so the selection of the lower bound is relatively independent of qualitative interpretations of results. For example, in the OAT1WT model for skin_lb, there are essentially equally sized ribbons extending to the different tissues  and microbiome. The 81. Data plots and phase planes were generated with Matlab R2016b . All other figures were generated in Adobe Illustrator CC 2015 .Circos plots were generated with Circos version 0.69–6 as cited aboveSupplementary Figures.Supplementary Tables.