Despite the advances in synthetic biology and metabolic engineering during the last years, it is clear that still further improvements are required if cyanobacteria are to be competitive with heterotrophic microorganisms for the bioproduction of added-value compounds.Cyanobacteria are promising microorganisms for sustainable biotechnologies, yet unlocking their potential requires radical re-engineering and application of cutting-edge synthetic biology techniques. In recent years, the available devices and strategies for modifying cyanobacteria have been increasing, including advances in the design of genetic promoters, ribosome binding sites, riboswitches, reporter proteins, modular vector systems, and markerless selection systems. Because of these new toolkits, cyanobacteria have been successfully engineered to express heterologous pathways for the production of a wide variety of valuable compounds. Cyanobacterial strains with the potential to be used in real-world applications will require the refinement of genetic circuits used to express the heterologous pathways and development of accurate models that predict how these pathways can be best integrated into the larger cellular metabolic network. Herein, we review advances that have been made to translate synthetic biology tools into cyanobacterial model organisms and summarize experimental and  As fast-growing, and relatively simple bacteria, cyanobacteria hold the promise of being an ideal chassis for ambitious metabolic engineering projects. In recent years, cyanobacterial potential is being unlocked through the development of an increasing number of molecular tools , the first reported strain to be transformed through natural DNA uptake pathways , originally isolated in 1968  is a filamentous freshwater cyanobacterial strain which has been used extensively as a model to investigate cellular differentiation   in recent years, a unicellular cyanobacterium with a faster doubling time of ~2.6 h    as responsible for rapid growth in S. elongatus UTEX 2973  was also recently identified  in S. elongatus PCC 7942 or Synechocystis PCC 6803  that has long been a staple induction system in E. coli has recently been introduced in S. elongatus PCC 7942  and its strength resides in the presence of multiple transcription factor binding sites. In another study, Psca3−2, a variant of Ptac of E. coli was found to act as constitutive promoter with high levels of expression   and assembly schemes  were originally designed for organisms such as E. coli, B. subtilis, or yeast in order to increase the modular assembly of many different parts. By contrast, most standard genetic tools and vectors were developed for a specific cyanobacterial strain and generally, these tools have not been designed to be modular. In recent years, a few vector systems have been designed specifically to work in diverse strains and/or to contain a modular organization, which could facilitate standardization and characterization of component parts.Vector systems based on the use of standard biological parts . This creates hybrid Cas proteins that bind to endogenous sequences, but which can perform other functions, such as regulating gene expression. Recently, these approaches have been used to create synthetic transcription factors able to modulate the expression of essential genes and key metabolic pathways in cyanobacteria. For a more comprehensive review of CRISPR-based applications in cyanobacteria, see  diffusion rates of CO2 in water compared to air  relative to other phototrophs such as algae and plants  cycle to improve its total metabolic flux. Liang and Lindblad have demonstrated that overexpression of any of four separate enzymes in the CBB cycle  can improve total carbon fixation rates in While it is frequently assumed that the rate limiting metabolic step of carbon fixation is directly related to the slow catalytic activity of RuBisCO, photosynthetic activity can be limited by other metabolic steps. Utilization of the primary products of the CBB cycle can limit step photosynthesis rather than the fixation of carbon dioxide itself. This is concept was first explored in plant models, where the mechanisms underlying triose phosphate utilization (TPU) limitation are more deeply characterized. In plants, there is a physical separation between where primary products of the CBB are generated  and where much of these products will ultimately be utilized to support metabolic activity and growth . Efficient operation of photosynthetic metabolism requires the balance of light energy (“source”) that can be highly dynamic in the environment with an equivalent capacity to utilize/dissipate this energy using anabolic metabolism or quenching mechanisms (“sinks”). Without absorption of adequate photons , a photosynthetic organism will starve; and without sufficient pathways to process or dissipate absorbed energy (low “sink”), end products of photosynthesis can accumulate leading to feedback inhibition and overreduction of the electron transport chain (ETC)  in S. elongatus UTEX 2973 and S. elongatus PCC 11801) will also exhibit similar increases in photosynthetic flux when engineered to export bioproducts. Current evidence suggests that a greater flux of carbon is allocated away from storage products  and instead invested in cell growth and light-harvesting/carbon-fixation machinery in fast-growing cyanobacteria  are large-scale stoichiometric models that describe metabolic pathways as stoichiometric coefficients and mass balances of participating metabolites, and are simulated using numerical optimization  is very useful for validating and refining metabolic network reconstructions  has been developed specifically to address this problem   (Wang et al., Cyanobacteria possess desirable characteristics as a chassis for biotechnological production and have demonstrated capacities to produce high-value bioproducts. However, tools from heterotrophs are not always transferrable to these microorganisms, leading to the delay in their advancement as industrial hosts. Accelerating the development of sophisticated genomic tools is likely to greatly assist in making scaled cyanobacterial bioproduction a commercially-viable endeavor. Many recent efforts have been focused on the characterization of new parts and development of libraries and standardized modular parts in cyanobacteria. However, characterization of such parts could likely be significantly accelerated by the development of robust and modular expression libraries, like those that have been established in in silico models can be validated and improved from experimental outcomes in vivo. Recent efforts have focused on the establishment of GPRs to improve the quality of GSMs, and thus, their phenotypic predictions. However, there are still some challenges that need to be addressed to ensure the accuracy of GSM predictions. Improving the scope and accuracy of existing GSMs will likely involve the integration of -omic datasets in to improve in silico representation of metabolism and identify additional biological unknowns (The increasing availability of GSMs for different cyanobacterial species have allowed for reiterative design-build-test cycles whereby the predictions from in silico hypothesis generation (Srinivasan et al., Although existing GSMs have been successfully applied to inform genetic engineering, it is clear that future efforts are being redirected to genome-scale kinetic models, which have still-greater promise. These new models overcome many of GSM shortcomings, such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Moreover, they enable dynamic analysis of biological systems for enhanced Most efforts in the genetic and metabolic engineering of cyanobacteria for the production of compounds with biotechnological applications have been focused on increasing product yield under laboratory conditions. A limited number of studies has been performed with the aim to optimize cyanobacterial cells to be more compatible with downstream scaled cultivation and processing (Singh et al., MS-M, AS, and DD contributed to the writing and editing of the manuscript and generation of figures and tables.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PKD1 or PKD2. Increased glycolysis is a prominent feature of the disease, but how it impacts on other metabolic pathways is unknown. Here, we present an analysis of mouse Pkd1 mutant cells and kidneys to investigate the metabolic reprogramming of this pathology. We show that loss of Pkd1 leads to profound metabolic changes that affect glycolysis, mitochondrial metabolism, and fatty acid synthesis (FAS). We find that Pkd1-mutant cells preferentially use glutamine to fuel the TCA cycle and to sustain FAS. Interfering with either glutamine uptake or FAS retards cell growth and survival. We also find that glutamine is diverted to asparagine via asparagine synthetase (ASNS). Transcriptional profiling of PKD1-mutant human kidneys confirmed these alterations. We find that silencing of Asns is lethal in Pkd1-mutant cells when combined with glucose deprivation, suggesting therapeutic approaches for ADPKD.Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder caused by loss-of-function mutations in  Pkd1 mutant mouse cells and kidneys, providing new insight into autosomal dominant polycystic kidney disease (ADPKD). They find that Pkd1 loss leads to profound metabolic changes, including asparagine synthase-driven glutamine anaplerosis.Christine Podrini et al. present a comprehensive analysis of  PKD1 (in ∼85% of cases) or PKD2 (in the remaining ∼15%) genes3. The two proteins encoded by these genes, Polycystin-1 (PC-1) and Polycystin-2 (PC-2), are assembled into a functional complex at primary cilia, whose activity is defective in the disease. Additionally, PC-1 can be cleaved at several proteolytic sites4 resulting in products that can translocate either into the nucleus5, or into mitochondria6 or be localized at mitochondrial-associated membrane contacts8. Cysts are epithelial outpouches of clonal origin increasing in number and size along the life of affected individuals. Inheriting one mutant allele is not sufficient for cysts to arise, requiring a second event causing the function of the polycystins to drop below a critical threshold of activity2. Loss of heterozygosity has been reported in a subset of cysts suggesting that this might be one of the mechanisms9.Autosomal dominant Polycystic Kidney Disease (ADPKD) is a monogenic disorder, caused by loss-of-function mutations in either the 12. Among these, defective glucose metabolism was shown to be a feature of the disease12 in a process resembling the Warburg effect observed in cancer. This finding prompted investigators to hypothesize that metabolic reprogramming might be a general feature of the disease14. Indeed, increased aerobic glycolysis, impaired beta-oxidation, reduced mitochondrial activity were reported in cellular and animal models lacking the Pkd1 gene19, while altered glutamine usage was reported in a non-orthologous animal model of recessive polycystic kidney disease20. Likewise, inhibitors of glutamine usage proved effective in retarding disease progression in some, but not in other, models of the disease22. However, an overview of these metabolic alterations and their interconnections is still lacking. Metabolic profiling was carried out in non-orthologous models of the disease (i.e. cystogenesis caused by mutations in other genes)24, while a single study has attempted at profiling metabolites in the kidneys of a Pkd1 orthologous mouse model15 reporting only a minimal metabolic change in murine kidneys derived from a ubiquitous, inducible inactivation of the Pkd1 gene.Together with the deregulation of several signalling cascades, ADPKD exhibits metabolic alterationsPkd1 gene. Our data indicate a broad metabolic rewiring that involves several pathways including central carbon metabolism and glutamine utilization. Finally, we show that glutamine metabolism is interlinked with asparagine synthesis in ADPKD and we identify the Asparagine Synthase (Asns) gene as an essential component of the process. Of note, targeting this enzyme by siRNA becomes lethal to ADPKD when associated with inhibition of glycolysis. Our data provide evidence of a broad and coordinated metabolic reprogramming in ADPKD, suggesting potential therapeutic strategies.Here, we present a comprehensive metabolomics characterization of cells and renal tissues from a mouse model carrying the kidney-specific inactivation of the 25 to an orthologous mouse model of ADPKD carrying inactivation of the Pkd1 gene exclusively in the kidney as to avoid confounding effects derived from extra-renal inactivation. To this end we employed KspCre;Pkd1flox/− kidneys carrying inactivation of the Pkd1 gene in the distal tubules and collecting ducts of the kidney. To minimize phenotype variability in the experimental design we used a pure C57BL/6N background (i.e. >10 backcrosses) and performed the study upon precise timing of the day of birth of the animals (see methods). Furthermore, samples were collected at P4, when the kidneys are already cystic, but not yet functionally or structurally severely compromised  and 2 control littermates  of each gender  , but very similar in males and females at this neonatal stage (P4), possibly because sexual maturation is not yet achieved , and 384 significantly different metabolites when considering both P-value and fold change  between cystic and control samples. A volcano plot of the data shows that 213 of these metabolites are downregulated, while 171 are upregulated, and that the main alterations are related to amino acids, carbohydrates, and lipids  Fig.  to maximved Fig. . FurtherP < 0.05, absolute fold change > 2) , aconitate (P = 0.0003), and α-ketoglutarate (α-KG) (P = 0.0079)   Fig. . In linePkd1 in the mouse kidney leads to profound metabolic changes, broader than what previously appreciated.These data indicate that the loss of Pkd1+/+ and Pkd1−/− Mouse Embryonic Fibroblasts (MEFs)26 to further investigate the metabolic changes in Pkd1-mutant cells. A metabolomic profiling revealed that Pkd1+/+ and Pkd1−/− MEFs are well separated by PCA with many metabolites being significantly different between the two genotypes (adjusted P < 0.05)     Fig.  and defe01) Fig.  as compaPkd1 affects central carbon metabolism. To track the contribution of glucose, one of the major carbon sources for the cells, to the TCA cycle in the Pkd1−/− cells, we incubated cells with uniformly labelled 13C-glucose and followed the incorporation of 13C in downstream metabolites  and converted it into lactate (P = 0.0001), which is released into the culture medium . Importantly, the data showed that the glucose-derived isotopologues of succinate (M + 2) (P = 0.0025), fumarate (M + 2) (P = 0.0001), and malate (M + 2) (P = 0.0023) were all significantly decreased in Pkd1−/− MEFs, indicating a reduced contribution of glucose to the TCA cycle    (P < 0.0001), fumarate (M + 4) (P < 0.0001), and malate (M + 4) (P < 0.0001) in Pkd1−/− as compared with Pkd1+/+ cells . This assay measures the OCR of Pkd1+/+ and Pkd1−/− cells in the presence of glucose and glutamine followed by their blockade using specific inhibitors of the two metabolic pathways . Data showed that Pkd1−/− cells are dependent on glutamine for their OCR production (P = 0.0007), while the glucose-driven OCR is overall reduced (P < 0.0001)    and on apoptosis (P = 0.0032)  Fig. , and glu01) Fig. . In addills Fig. . Next, w01) Fig. . These dly) Fig.  and incrly) Fig.  relative32) Fig. .Fig. 3Me15N2-labelled glutamine we noticed that there was a significant increase (P = 0.0006) of 15N-asparagine in the Pkd1−/− cells suggesting an increased activity of asparagine synthase    and murine kidneys (P = 0.0095)    (P < 0.0001) showing that Asns plays a central role in glutamine fuelling of the TCA cycle in Pkd1−/− cells , but not in controls   Fig. , while a60) Fig. . We therate Fig. , was siglls Fig. . As expe001)Fig. . Furtherted Fig.  and impo01) Fig. . Furtherlls Fig. . Importaols Fig. . Further01) Fig. . We concAsns in conjunction with glycolysis might offer a novel therapeutic opportunity.Thus, our data show that increased glutaminolysis, interlinked with asparagine metabolism is an important feature of ADPKD and targeting Pkd1−/− cells. Indeed, the increased level of citrate (M + 5) in the 13C5–15N2 glutamine labelling experiment (P < 0.0001)  , although higher isotopologues could not be detected in this assay. Next, we tested whether Pkd1−/− cells showed increased expression of fatty acids synthase (Fasn), a key enzyme involved in FAS. We found that Fasn is highly expressed in Pkd1−/− cells (P = 0.044) and kidneys of KspCre;Pkd1flox/− animals (P = 0.038)  and enhanced cell apoptosis (P < 0.05) in Pkd1−/− cells , triacyglycerols (TAG) (P = 0.03), and sterol esters (P = 0.002) in cystic kidneys compared to controls  Fig.  suggeste 2) Fig.  in Pkd1−38) Fig. , e. FurtPkd1−/− cells to proliferate and survive.Overall, enhanced de novo fatty acids biosynthesis is a feature of ADPKD observed both in cells and in murine tissues and this process is necessary for 28. After removing all liver-specific functions, we used DFA to simulate in silico an increase in glucose uptake by imposing a constrain of enhanced glucose input driven by two transporters   and kidneys lacking functional Pkd1 (P = 0.009) ols Fig. . Next, wols Fig. . ImportaThe analysis of the in silico fluxes generated by the model revealed that most of the alterations in the discussed metabolic pathways is coordinated and likely causally linked, to the point that a single change in the increased uptake of glucose recapitulates a broad alteration in the other metabolic pathways Fig. .TM) applied to a KspCre;Pkd1flox/− kidneys analysed at the same time point in which metabolomics was performed (P4). Although the qPCR-based arrays provided a rather limited number of targets, the data showed a trend of alterations to key enzymes involved in GLY, PPP, FAS, and FAO, despite only a few displaying significant changes between the cystic and the control samples (P in the range 0.0002–0.03), , which results in a milder polycystic kidney disease phenotype29. First, data were analysed by PCA and hierarchical clustering analysis, showing a very good separation of the samples in both assays   . In contrast, both the OXPHOS and FAO enzymes are markedly downregulated (FDR = 0)  Fig. . Next, ton) Fig. 31. The rKD Figs.  and by tKD Figs.  are all  0) Fig. .These data taken together show that a general metabolic reprogramming of bioenergetic pathways is a hallmark of ADPKD and that likely most alterations are highly coordinated and occur simultaneously, opening unique opportunities for targeting them all with a few interventions.Asns. This is particularly intriguing because we found that targeting Asns is lethal in ADPKD cells, particularly when associated with glucose deprivation, thus opening new therapeutic perspectives for a combination therapy in this disease.In this study we performed a thorough analysis of the metabolic derangements observed in ADPKD using studies that range from global profiling in orthologous animal models to in silico flux analysis, in vitro carbon and nitrogen tracing, and finally validation in murine and human microarray data sets. The main conclusion of our studies is that a global metabolic reprogramming occurs in ADPKD, involving several pathways. Of all pathways involved, rewiring of central carbon metabolism is the most prominent and includes interlinked alterations of increased GLY, PPP, and FAS along with decreased OXPHOS and FAO. Of interest, we found that glutaminolysis is enhanced as a compensatory mechanism and used for both energy yielding purposes as well as for anabolic needs. Finally, we found that the usage of glutamine in ADPKD is interlinked with the asparagine metabolism and it involves upregulation of Based on previous studies it appeared that minimal changes in a few individual metabolic pathways might occur in ADPKD. Here we show instead that the metabolic alteration in ADPKD tissues is rather robust and occurs in multiple pathways. In addition to GLY and FAO, we find here alterations in the PPP, in FAO, in glutaminolysis and in OXPHOS. Furthermore, we show here that not only multiple metabolic pathways are occurring simultaneously, but that they are interconnected and likely causally linked.Pkd1-mutant kidneys. The authors used a Pkd1 orthologous mouse model carrying an inducible ubiquitous Cre15. Inactivation was induced after weaning, leading to a slowly progressive disease model with great variability in the renal phenotype15. This may explain why the metabolomics profiling data failed to separate in PCA based on the genotype. In the current study, we have used a kidney-specific Cre line to inactivate the Pkd1 gene exclusively in the distal tubules and in collecting ducts . This animal model develops a rapidly progressive phenotype with manifestation in the neonatal life. The different time points at which samples were analysed might in part explain the discrepancies with previous work15. More importantly, the animal model used in the current study shows reduced variability in the renal phenotype allowing for a good separation of the sample by PCA analysis and hierarchical clustering. Our data on the reduced FAO agree with those previously reported15, but we propose here that a much broader metabolic derangement than previously appreciated is present in ADPKD.A previous study has performed profiling of metabolites in an orthologous model of ADPKD, reporting only minimal changes in a few metabolites in 32. Here we have excluded that at the time of analysis (P4) the animals are reaching CKD. Indeed, we observed a minimal initial increase in circulating urea, but absence of prominent inflammatory infiltrate or fibrosis. In addition, most of the metabolic alterations can be observed in isolated cells. Likewise, we have excluded that a prominent hypoxic state is present in the kidneys at the stages analysed. All these pieces of evidence suggest that the observed metabolic derangements are not secondary to CKD or hypoxia. However, based on the current knowledge we cannot exclude that, as disease progresses, additional alterations such as hypoxia and/or CKD can further contribute to disease worsening through additional metabolic stress.Importantly, we analysed renal samples at the cystic stage. Therefore, we cannot exclude that the metabolic alterations are secondary to cyst expansion. Relevant to this is the fact that recent investigations have unveiled an important correlation between chronic kidney disease (CKD) and metabolic changes16. Of interest, the authors were able to rescue the phenotype of Pkd1 mutant mice by using fenofibrate, a natural compound acting as an agonist of Pparα and achieving enhancement of β-oxidation and OXPHOS33. Notably, the authors showed that the oncogene Myc is the main driver of miRNA17 expression16. It is important to note here that Myc is indeed considered a master regulator of metabolism in several types of cancer, with glycolysis and glutaminolysis both being regulated by this oncogene34.In a recent elegant study, Hajarnis et al. has shown that the reduced FAO is secondary to the upregulation of microRNA-17, which in turn downregulates the expression levels of Pparα, ultimately reducing FAOKspCre;Pkd1flox/−, including lactate. This is likely due to a rapid degradation or excretion of lactate in vivo. Indeed, by metabolic tracing with 13C6-glucose we previously showed detection of a large increase in lactate production in vivo12 when kidneys are collected after 40 minutes.In the current study we have used both LC-MS and GC-MS studies to perform metabolic profiling. While this type of analysis is informative, it also has the limitation that it provides a static snapshot of the metabolites detected, without a precise information on whether a given metabolic pathway is upregulated or downregulated. Indeed, we found decreased levels of most glycolytic intermediates in the static analysis of Pkd1−/− cells uptake large amounts of glucose used in the glycolytic pathway to generate lactate and that only minimal amounts of glucose are funnelled into the TCA cycle in mitochondria. Furthermore, we found that an increased uptake of glutamine is occurring in Pkd1−/− cells as compared to the controls and it is oxidized into the TCA cycle, likely compensating for the reduced usage of glucose. Since the overall respiration of the cells is diminished, the likely explanation is that glutamine is used by these cells to preserve the mitochondrial membrane potential and avoid undergoing apoptosis. Indeed, in a previous study we demonstrated that the mitochondrial membrane potential was not majorly affected in the mutant cells11, despite the negligible contribution of glucose to mitochondria-generated ATP11. Our data are in disagreement with previous work reporting lack of enhanced glycolysis, and presence of defective mitochondrial membrane potential in Pkd1−/− cells6. While differences in the cell type employed, in the immortalization procedure or in the culture conditions might account for part of the discrepancies, this cannot be the sole explanation for the controversial results. The glycolysis measurements in the previous studies are affected by wild-type cells being highly glycolytic, suggesting a minimal mitochondrial activity in the control cells and possibly limiting the capability to detect any increase in mutant cells15. Further studies will be required to reconcile the findings in the various different cell types analysed.Metabolic tracing is a much more powerful tool to study the dynamic regulation of metabolic pathways. Our current studies show that Pkd1−/− cells to generate citrate, which is transported into the cytosol and converted to acetyl-CoA, an essential substrate for fatty acids biosynthesis. The last process is upregulated in ADPKD likely to generate the membranes required for the proliferation of these cells. Indeed, silencing of Fasn greatly impacts cell proliferation and survival. Thus, our studies demonstrate a critical role for glutamine as a compensatory mechanism for the reduced usage of glucose in polycystic kidney disease. Our data provide the mechanistic explanation for two recent studies demonstrating that targeting glutaminolysis via the inhibition of the enzyme glutaminase (GLS) retards the progression of the disease. However, this effect was observed in some, but not in other animals carrying mutations in Pkd122. Our study here provides a potential explanation for this discrepant observation. In our analyses we did not detect changes in the expression levels of GLS. This is in line with the results of Soomro et al., that showed no differences in vitro in the response of polycystic kidney disease or normal cells to inhibitors of GLS22. It should be noted, however, that despite this lack of increased expression we would expect that interfering with this enzyme should reduce glutamine uptake at least in vivo, because GLS is certainly the most commonly used enzyme to convert glutamine to glutamate in most tissues under physiological conditions. Indeed, treatment with the GLS inhibitors CB-839 resulted in a certain degree of reduction of cyst burden in two models of Pkd1 mutants22, while it failed to improve the phenotype in a third one22. However, we have shown here that the utilization of glutamine in ADPKD is interlinked with the synthesis of asparagine via asparagine synthetase, ASNS, a transamidase that converts aspartate into asparagine while deaminating glutamine to form glutamate35. A recent study has shown that this reaction is used in physiological conditions by endothelial cells to utilize glutamine as a carbon source36. Our study shows that a similar mechanism is likely employed in ADPKD to consume glutamine. Indeed, silencing of Asns resulted in a complete rescue of the accumulation of α-KG, specifically by reducing the glutamine contribution to α-KG generation. Based on this, we propose that inhibiting ASNS would be a much more specific way to reduce glutamine usage in polycystic kidney disease, opening an important opportunity for a more targeted approach in ADPKD treatment. In line with this, we have found that silencing Asns impacts the growth of Pkd1−/− cells and this is more prominent when cells are also deprived from glucose. The data indicates that indeed glutamine usage compensates for the lack of glucose utilization in the TCA cycle by the Pkd1−/− cells and that targeting both processes at once is more effective than either one alone. In line with this, the starvation from both glucose and glutamine drastically enhances cell death in cells lacking Pkd1. Thus, inhibitors of Asns along with the glycolytic inhibitor 2-deoxy-d-glucose12 might offer a good therapeutic strategy in ADPKD and further studies should be devoted to test this possibility.In the current study, we show that in addition to being oxidized in the TCA, glutamine is also used reductively in the 2+ uptake in mitochondrial-associated membranes7 or by direct translocation of a short fragment of the protein into the matrix of mitochondria6. Future efforts should be devoted to the understanding of the origin of the metabolic alterations in ADPKD, including the role of the Polycystins in regulation of mitochondrial function.Ultimately, it should be noted that our results do not show the precise mechanism through which PC-1 regulates cellular and mitochondrial metabolism. Recent studies have reported a possible role for PC-1 in regulation of mitochondrial function either through regulation of mitochondrial CaIn conclusion, we report here the first broad overview of the metabolic derangement observed in ADPKD. Importantly, the altered pathways that we report in the current study expand our view on the potential use of inhibitors able to tackle the metabolic alterations to retard disease progression. The highly coordinated alterations observed offer a unique opportunity for targeting the process at multiple levels to block at once the capability of ADPKD cells to produce energy and to synthesize the building blocks needed for proliferation and survival.Pkd1flox/−: Ksp-Cre is a mouse model for ADPKD that develops massive enlarged kidney cysts within few days after birth and was generated by crossing Pkd1flox/flox and Pkd1+/−:Ksp-Cre mice38. The age of the pups was accurately assessed by daily control of birth combined with the follow up of the variation of coat colours as described by Jackson Laboratories (https://www.jax.org). All mice used in these experiments were in a pure C57/BL6N genetic background (i.e. >10 backcrosses) and were maintained in specific pathogen free colonies handled by a service company provided at the San Raffaele Scientific Institute (Charles River). Mice received a sterilized (vacuum packed and irradiated) chow diet . All mice had ad libitum access to water and food. All experiments involving animals were performed under a protocol approved by an institutional ethical committee and, subsequently, by the Italian Ministry of Health (IACUC number: 736).n scans using dynamic exclusion. The scan range varied slighted between methods but covered 70–1000 m/z. Metabolite concentrations were determined by automated ion detection, manual visual curation and were analysed in-line using software developed by Metabolon®39.The untargeted metabolomics in kidneys Fig.  and MEFs40. Briefly, samples were treated as per the description of the company Lypotype GmbH: samples were spiked with internal lipid standard mixture containing: cardiolipin16:1/15:0/15:0/15:0 (CL), ceramide 18:1;2/17:0 (Cer), diacylglycerol 17:0/17:0 (DAG), hexosylceramide18:1;2/12:0 (HexCer), lyso-phosphatidate 17:0 (LPA), lyso-phosphatidylcholine 12:0 (LPC), lysophosphatidylethanolamine 17:1 (LPE), lyso-phosphatidylglycerol 17:1 (LPG), lyso-phosphatidylinositol 17:1 (LPI), lyso-phosphatidylserine 17:1 (LPS), phosphatidate 17:0/17:0 (PA), phosphatidylcholine 17:0/17:0 (PC), phosphatidylethanolamine 17:0/17:0 (PE), phosphatidylglycerol 17:0/17:0 (PG), phosphatidylinositol 16:0/16:0 (PI), phosphatidylserine 17:0/17:0 (PS), cholesterol ester 20:0 (CE), sphingomyelin 18:1;2/12:0;0 (SM), triacylglycerol 17:0/17:0/17:0 (TAG), and cholesterol D6 (Chol). After extraction, the organic phase was transferred to an infusion plate and dried in a speed vacuum concentrator. First step dry extract was re-suspended in 7.5 mM ammonium acetate in chloroform/methanol/propanol  and second step dry extract in 33% ethanol solution of methylamine in chloroform/methanol . All liquid handling steps were performed using Hamilton Robotics STARlet robotic platform with the Anti Droplet Control feature for organic solvents pipetting.Mass spectrometry-based lipid analysis was performed at Lipotype GmbH  as previously described and lipids were extracted using a two-step chloroform/methanol procedurePkd1+/+ and Pkd1−/− MEFs26 and siAsns Pkd1−/− were plated at 150,000 cells/well onto a 6-well plate (n = 6) and cultured in standard conditions. 24 h before sampling for 13C-labelling experiments, MEFs were washed twice with PBS and supplemented with media (DMEM (Gibco) supplemented with 10% dialysed foetal bovine serum (Gibco), 1% Penicillin Streptomycin , Sodium Bicarbonate  containing either uniformly labelled 13C6-glucose (25 mM) (Cortecnet), 13C5-15N2-glutamine (4 mM) (Cortecnet), 15N2-Glutamine (Cambridge Isotope Laboratories), or 13C5-Glutamine (Cambridge Isotope Laboratories). Metabolites were extracted from cell pellets (intracellular) with 1 ml of extraction solution (methanol for highly pure liquid chromatography (Sigma Aldrich): acetonitrile gradient grade for liquid chromatography (Merck): ultrapure water (Sigma Aldrich), 50:30:20 with 100 ng ml−1 of HEPES (Sigma Aldrich) per million cells. The cell culture medium (extracellular) extracts were prepared by adding 750 µl of extraction solution to 50 µl of centrifuged cell culture medium. Samples were incubated at 4 °C for 15 min at 700 r.p.m., before centrifugation at 13,000 r.p.m. The supernatant was transferred into autosampler vials and stored at −80 °C prior to analysis by LC-MS.Immortalized −1 with the gradient described previously41. To expand on the range of metabolites covered in the analysis, the sample extracts were then run on a ZIC-HILIC column (150 mm × 4.6 mm) fitted with a guard column (20 mm × 2.1 mm) (both Merck Millipore). The aqueous mobile phase solvent used was 0.1% formic acid in water, and the organic mobile phase was 0.1% formic acid in acetonitrile. The flow rate was set at 300 μl min−1 and the column oven set to 30 °C, as described previously41. The mass spectrometer was operated in full MS mode with polarity switching, and samples were randomized in order to avoid bias due to machine drift and processed blindly. The acquired spectra were analysed using XCalibur Qual Browser and XCalibur Quan Browser software (Thermo Fisher Scientific).LC-MS analysis was performed using a Q Exactive Orbitrap mass spectrometer coupled to a Dionex U3000 UHPLC system (Thermo Fisher Scientific). The liquid chromatography system was fitted with a Sequant ZIC-pHILIC column (150×2.1 mm) and guard column (20×2.1 mm) from Merck Millipore and temperature maintained at 45 °C. The mobile phase was composed of 20 mM ammonium carbonate and 0.1% ammonium hydroxide in water, and acetonitrile. The flow rate was set at 200 µl minTM IsoCorrect tool from the cloud-based platform Elucidata (https://polly.elucidata.io).Mass isotopologue distribution of metabolites was determined by integration of the corresponding peaks, and correction for natural abundance was performed using the PollyConsumption and release of metabolites was assessed by subtracting each metabolite total pool in the fresh medium (incubated in the absence of cells) from the pool found in the spent medium samples. The resulting value was then adjusted to the amount of protein generated during the incubation of the cells with the labelled substrate. For this purpose, cells were seeded in parallel plates and protein content was determined by the Bradford method at 0 and 24 h post medium change. Percentage of intracellular pool from each isotopologue was calculated respective of the control (for each metabolite).2 incubator at 37 °C overnight. The following day, 1 h before the test, culture media was replaced with pH-adjusted (pH = 7.4 ± 0.1) bicarbonate-free DMEM (Agilent) with 10 mM glucose (Sigma Aldrich), 1 mM sodium pyruvate (Gibco), and 2 mM l-glutamine (Gibco) for Mito Flex Test (Agilent) and Mito Stress Test or with 2 mM l-glutamine only for Glycolysis Stress Test (Agilent). The plate was then incubated at 37 °C for 1 h in a non-CO2 incubator. For the Mito Fuel Flex test and Mito Stress Test, OCR were measured using the Seahorse XF Mito Fuel Flex Test Kit XF and Mito Stress Test Kit (Agilent). Extracellular Acidification Rate (ECAR) was measured using XF Glycolysis Stress Test Kit (Agilent) on an XFe96 Analyzer (Agilent) following the manufacturer’s instructions. Cell numbers were normalized using CyQuant (Thermo Fisher).Cells were plated at a density of 20,000 or 30,000 cells per well in a 96-wells Seahorse cell culture microplates and incubated in a 5% COn = 4) and Pkd1V/V (n = 4) mice at P10 and homogenized in PBS buffer using mini handheld homogenizer. The homogenate was centrifuged and supernatant was discarded. Total RNAs were extracted from the pellet using an RNeasy mini kit (Qiagen) and the quality of total RNA samples was verified by a 260/280 ratio in NanoDrop and agarose gel electrophoresis. Further sample processing for microarray analysis was performed by the Genomics Core of Cleveland Clinic’s Lerner Research Institute, following the facility’s protocols, and hybridized to Illumina’s MouseRef-8 v2.0 BeadChip expression arrays.Kidneys were removed from wild-type (15 primers (Promega) and ImProm-II Reverse Transcriptase (Promega) were used. Quantitative real-time PCR analysis was performed on duplicate using SYBR Green I master mix (Roche) on LightCycler 480 Instrument (Roche). For primers sequence see supplementary methods, Supplementary Table RNA was isolated from plated cells or snap-frozen kidneys using the RNAspin Mini kit . Total RNA was isolated using the RNA-Isol lysis reagent according to the manufacturer’s instructions. For reverse transcription of RNA, Oligo(dT)Fasn and siAsns 20 nM (Ambion) pre-designed mRNA with the target sequences: FASN 5′-GGGAUCAUAAAGAUAACUUtt-3′ and 5′-AAGUUAUCUUUAUGAUCCCtc ASNS 5′-GGCCCUUGUUUAAAGCCAUtt-3′ and 5′-AUGGUUUAAACAAGGGCCtg-3’ along with control scrambled siRNA , were used following the manufacturer’s instructions and transfection control. For siRNA transfection cells were seeded into a 6-well plate, 100,000/well in 10% FBS (Gibco) in DMEM (Gibco) without antibiotics. The transfections were performed two times over 2 days at a final concentration of 30 nM using Lipofectamine 3000® following the manufacturer’s protocol. Total RNA was prepared from the cells 72 h after the first transfection and qRT-PCR was performed.For transient knockdown of siFasn or after 48 h of starvation experiments . The protocols and quantifications were previously optimized11.Cell death detection kit  was performed according to the manufacturer instructions after transient knockdown of siPkd1+/+ and Pkd1−/− MEFs were plated at a density of 150,000 cells/well in DMEM (Gibco) supplemented with 0.5% FBS (Gibco) and 1% Pen/Strep (Gibco). After 16 h, medium was changed to control medium , supplemented with 10% FBS (Gibco), 1% Pen/Strep (Gibco), sodium pyruvate , sodium bicarbonate , 25 mM glucose (Sigma Aldrich), and 4 mM glutamine (Gibco); glucose starvation medium ; glutamine starvation medium  or glucose and glutamine starvation medium . After 24 and 48 h, cells were trypsinized and counted with an automated cell counter . Pictures from each sample at both time points were taken with a white field microscope, using a ×10 objective.Immortalized 42 which tests whether compounds involved in predefined pathways occur towards the top or bottom of the ranked query list. The method identifies altered pathways of the Kyoto Encyclopaedia of Genes and Genomes (KEGG)43 using metabolite data. An enrichment score and a statistical significance  have been computed for each pathway having at least one metabolite captured in the list of metabolites ranked, in descending order, according to a specific metric. Particularly, a fold change was used to rank the metabolites deriving from non-targeted global metabolomics, while the Differential Flux balance Analysis algorithm was used to rank the compounds resulting from the in silico simulations. In order to avoid ambiguous identification and to obtain reliable results only metabolites having a KEGG-identifier have been considered. The list of ranked metabolites named as L, and S for the set of compounds of a particular pathway. Analysis is performed to test whether the elements of S are randomly distributed through L or primarily found at the top or bottom of the list. The enrichment score reflects the degree to which S is over-represented at the extremes of L, and it is computed by walking down L, increasing a running-sum statistic when a metabolite in S is found and decreasing it when a compound not in S is encountered. The obtained score corresponds to a weighted Kolmogorov-Smirnov-like statistic43 and represents the maximum deviation from zero associated with a random walk. The P-value associated with an enrichment score has been computed as the fraction of 1000 random permutations of the elements of L that are at least as extreme as the original enrichment score, that has been derived from non-permuted elements. A Matlab® implementation of the method is available upon request.Analysis of the untargeted metabolomics studies was performed by implementing a KEGG-Pathways Based Enrichment Analysis (PBEA) system based on a similar concept than the Gene Set Enrichment Analysis (GSEA)P, the Differential Abundance (DAp) score is defined as:ur is the delta of in silico flux of reaction r computed by DFA. This score captures the tendency for a pathway to be upregulated or downregulated and varies from −1 to 1. A score of −1 indicates that all the metabolic fluxes associated with reactions in pathway P decreased respect to wild-type, while a score of 1 indicates that all fluxes increased when comparing with the wild-type simulation.For a particular pathway 28. The model comprises 785 metabolites and 2589 enzymatic and transport reactions in eight compartments. In order to have more physiological results, the following additional constrains have been imposed: citrate cannot move from cytosol to mitochondria; pyruvate cannot move from mitochondria to cytosol; cytosolic enzyme LDH can only convert pyruvate to lactate and not vice versa. Then, Differential Flux balance Analysis was applied28 to simulate either the wild-type condition or an increase of glucose entering the cytosol (GLY). DFA is based on solving a linear optimization problem across 442 metabolic objectives for both the wild-type model and the perturbed model. For the current study, the liver-specific metabolic functions were removed. The average flux carried by each reaction across the different metabolic objectives for each of the two models is computed. For each reaction, the difference of the average flux in the wild-type model minus its value in the modified model are then considered. These differential fluxes are then used to rank the reactions from the ones that change the most in the modified model to the ones that change the least or do not change at all. Metabolites are then ranked according to the sum of the absolute values of the differential fluxes. In order to simulate an increase of the glucose uptake the results of the 13C-glucose labelled experiments showing an increase of 1.6 fold change in the uptake of glucose has been used. Therefore, the fluxes through the reactions Glucose(s) + Na + (s) − > Glucose(c) + Na + (c) (TCDB:2.A.21.3.6) and Glucose(s) < = > Glucose(c) (TCDB:2.A.1.1.29) has been forced, to be equal or greater than a specific threshold set to the their average value obtained in the wild-type simulations increased of 60%.For the in silico studies the Genome-Scale Metabolic Model described in Pagliarini et al. was appliedPkd1V/V animal model at P10, a Matlab® implementation the SAM algorithm with delta = 1.0 was applied.In order to infer differentially expressed genes in microarrays data from 31 have been considered. First, affymetrix probe sets have been collapsed to one gene level by using the maximum expression value of the probe set in each gene. Then, the SAM algorithm has been applied, with delta = 2.4, to identify differentially expressed genes between transcripts in renal cysts of different sizes, and minimal cystic tissue plus normal renal cortical samples. The Matlab® implementation of SAM can be found in . In both microarrays analysis, after the computation of all the differentially expressed genes, those belonging to glycolysis, pentose phosphate pathway, TCA cycle/OXPHOS, fatty acid synthesis, and fatty acid oxidation were identified (Supplementary Data For human microarrays, data from Song et al.t-test was used for all analysis of two groups. ANOVA statistical analysis followed by Bonferroni’s multiple comparison test was performed in all analysis where more than two groups were present. The P-values for each condition are indicated in the legends.For statistical analysis the Prism 5, GraphPad Software, and Matlab® were used using statistical analysis tool. http://smart.servier.com/. All other images were generated by the authors.Figures Supplementary InformationDescription of Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11