Recently, there has been a resurgence of interest in metabolic rewiring of tumors to identify clinically relevant genes. However, most of these studies have had either focused on individual tumors, or are too general, providing a broad outlook on overall changes. In this study, we have first curated an extensive list of genes encoding metabolic enzymes and metabolite transporters relevant to carbohydrate, fatty acid and amino acid oxidation and biosynthesis. Next, we have used publicly available transcriptomic data for 20 different tumor types from The Cancer Genome Atlas Network (TCGA) and focused on differential expression of these genes between tumor and adjacent normal tissue. Our study revealed major transcriptional alterations in genes that are involved in central metabolism. Most tumors exhibit upregulation in carbohydrate and amino acid transporters, increased glycolysis and pentose phosphate pathway, and decreased fatty acid and amino acid oxidation. On the other hand, the expression of genes of the tricarboxylic acid cycle, anaplerotic reactions and electron transport chain differed between tumors. Although most transcriptomic alterations were conserved across many tumor types suggesting the initiation of common regulatory programs, expression changes unique to specific tumors were also identified, which can provide gene expression fingerprints as potential biomarkers or drug targets. Our study also emphasizes the value of transcriptomic data in the deeper understanding of metabolic changes in diseases. All tumors, irrespective of their origin show increased levels of glycolysis despite the presence of oxygen. The increased utilization of glucose in tumor cells is a feature that is routinely exploited to detect and visualize them by fluorodeoxyglucose positron emission tomography (FDG-PET) scans2. Metabolic changes in cancer were first recognized by Otto Warburg in a seminal study in 1926 in which he proposed that in contrast to non-transformed cells, cancer cells rely on aerobic glycolysis (Warburg effect) for their energy needs as a result of impaired mitochondrial function3. Altered metabolism in cancer cells gained attention after Warburg’s proposal; however, interest in the field waned after his death aided by a subsequent rejection of the mitochondrial dysfunction hypothesis4. Additionally, the discovery of oncogenes backed by an increasing interest in sequencing technologies brought forth the somatic mutation theory in full force. Scientists aimed to identify recurring mutations across all cancer types and develop drugs to target them5. However, despite the massive amount of genomic data generated across different tumor types, the benefit of this approach was limited due to the inherent heterogeneity of tumors and the broad spectrum of somatic mutations7. Thus, the need for new approaches became apparent and a modified version of Warburg’s theory gained attention according to which metabolic reconstitution under the control of oncogenes is an essential process for cancer growth and survival8. Currently, about 250 oncogenes and 700 tumor suppressor genes have been identified, the majority of them playing important roles in controlling key metabolic pathways in tumors9. Additionally, widespread transcriptional dysregulation of metabolic genes in cancer have been reported10 with major metabolic enzymes regulating oncogenic signaling11.Rewiring of cellular metabolism to aid the initiation and progression of tumors has come to be recognized as a hallmark of cancers10. Hu et al., also studied the changes in expression between tumors and their adjacent normal samples across 22 cancers and found each tumor’s metabolic programs to be more similar to its parental tissues than to other tumors. In addition, these authors also reported tumor-specific expression differences for multiple isoforms of the same enzyme, especially those involved in carbohydrate metabolism12. More recently, RNA sequencing from TCGA was used to study the changes in metabolic genes across 33 tumor types and classify them according to 7 major metabolic pathways. The study showed that tumors with upregulated carbohydrate, nucleotide and vitamin metabolism were associated with poor prognosis, while those with upregulated beta oxidation had better prognosis. Amino acid metabolism and energy integration pathways on the other hand showed mixed clinical results13. In another study, 26 tumors and their matched normal samples were compared for changes across 114 metabolic pathways. This pipeline was able to identify metabolic pathways that were broadly dysregulated across tumors as well as within the same tumor. In addition, the authors reported that Master Metabolic Transcriptional Regulators (MMTR) can serve as biomarkers that can predict the outcome of therapeutics directed against metabolism-related genes14. All the above-mentioned studies take a systemic approach at comparing the differences between tumors and their adjacent normal tissues or across multiple tumors. However, systemic approaches in understanding metabolism may suffer from at least two drawbacks; the high pathway inter-connectivity, which leads to over-representation of a number of genes, and the current indiscriminate nature of the approach of assigning equal weights to all genes in the network. Some authors have used various statistical approaches to deal with the problem of interconnectivity10 but the second drawback still persists, although it is a well-known fact that the flux through metabolic pathways is heavily determined by the expression and/or activity of rate-limiting enzymes.Several large scale studies have used transcriptomics to compare metabolic pathways between cancer and adjacent normal tissues or between different cancer types. Gaude and Frezza reported a general deregulation of mitochondrial function with widespread downregulation of genes involved in oxidative phosphorylation (OXPHOS) across multiple tumor types, a characteristic that was correlated with the upregulation of genes associated with epithelial-to-mesenchymal transition (EMT) and poor patient prognosisIn the current study, we analyzed RNA sequencing data from TCGA to compare differential gene expression (DGE) for 20 tumors and their adjacent normal tissues Fig. . In tota15. Enhanced expression of hexose transporters is implicit in increased sugar utilization by cancer cells. DGE for a total of 26 genes covering the three main families of hexose transporters, namely SLC2, SLC5 and SLC4517 were evaluated  when patients were separated according to its median expression in LIHC , LUAD  and PAAD . These data suggest that GLUT1 could be a major driver of Warburg effect; therefore, developing drugs that can exclusively target the channel expressed in tumor cells, or reducing the amount of carbohydrate intake in the diet may improve cancer management and therapy. On the other hand, SLC2A2 (GLUT2) is the major sugar transporter in hepatic tissues and has a relatively low affinity for glucose. Our data show that it was downregulated in LIHC, a state that is associated with poor prognosis . SLC2A3 (GLUT3) showed a mixed pattern of up- and downregulation in various tumors. SLC2A5 (GLUT 5) is the main transporter of fructose, and its expression was significantly upregulated in 7 tumors. LUAD is one such tumor where the upregulation of SLC2A5 was shown to enhance tumorigenicity; inhibition of SLC2A5 in lung cancer cell lines using the fructose analog 2,5-anhydro-d-mannitol sensitized the cells to paclitaxel20. Some members of the SLC5 and SLC45 families showed significant upregulation in more than half of the tumors, with SLC45A2 showing a very strong upregulation in PRAD  is shown in Fig. 22. These proteins form a part of the larger SLC16 family of carrier proteins. MCT-4 is considered to be particularly suited for the export of lactate from highly glycolytic cells; additionally, MCT-4 has low affinity for pyruvate, suggesting this transporter to be particularly suited for cancer cells that are known to exhibit high NAD+/NADH ratio23. Indeed, we observed that the expression of MCT4 was the highest among the four lactate transporters  1–415. HK is known to be expressed as four isoforms, HK1-4. We observed that HK2 was significantly overexpressed in 16 out of the 20 tumors, and only significantly downregulated in COAD. In addition, its higher expression was associated with poor OS in LIHC  and CESC . HK4 is known to be mostly expressed in the liver and kinetic studies have shown that it has a comparatively lower affinity for glucose25. On the other hand, HK2 has about 250-fold higher affinity for glucose compared to HK426 and it has been shown to directly interact with a voltage-dependent anion channel (VDAC1) on the outer mitochondrial membrane of tumor cells. This interaction gives HK2 preferential access to ATP for glucose phosphorylation and at the same time competitively prevents the interaction of VDAC1 with pro-apoptotic proteins, preventing cellular apoptosis29. We found HK3 to be upregulated in half of the tumors and showed significant correlation with poor OS in KIRC , while HK1 was significantly upregulated in only 3 tumors .We next analyzed the expression of glycolysis, pentose phosphate pathway (PPP) and the hexosamine biosynthesis pathway (HBP)  and fructose 2,6-bisphosphate (activation)31. We observed increased expression of PFK-L and PFK-P several different cancer types with particularly high expression of PFK-P in LUAD, LUSC, KIRC, LIHC and CHOL , PFK-P (found in the plasma) and PFK-L (expressed in the liver)HOL Fig. . Pyruvat33. Though the high expression of HKDC1 in LIHC was associated with poor overall survival (OS), this has not been addressed mechanistically yet34. Another recent study showed that high expression of HKDC1 was also associated with poor prognosis in LUAD by inducing EMT and activation of mTOR pathway35. Future studies are needed to evaluate the role and importance of this gene in tumorigenesis. The expression of the remaining glycolytic enzymes was increased in all tumors, with different isoforms showing different patterns of expression  in nearly half of the tumors Fig. C. Kinetiion Fig. C. An int37. Classically, the PPP is one of the primary sources of cellular NADPH with malic enzyme and serine-driven one-carbon metabolism mediated by tetrahydrofolate also contributing towards to cellular NADPH pool38. NADPH serves as the reducing currency for most biosynthetic reactions and protects cells from oxidative stress by reducing oxidized glutathione39. The activity of this pathway is therefore expected to be high in cancer cells. PPP is divided into two branches, the oxidative and the non-oxidative branch. The oxidative branch oxidizes glucose 6-phosphate via the rate-limiting enzyme glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate dehydrogenase (PGD), with both reactions generating NADPH. The non-oxidative branch is catalyzed by TKT and TALDO1, and it reconnects the oxidative branch to glycolysis ), a state that was associated with poor OS in 3 tumors; KIRC , KIRP  and LIHC . None of the tumors showed any significant downregulation of G6PD , the end product of this pathway, is used for N- and O-glycosylation of proteins, a process that is deregulated in many tumors40. The rate limiting step of HBP is catalyzed by glutamine-fructose-6-phosphate transaminase 1/2 (GFPT1/2) by using glutamine as a co-substrate while the reverse reaction is catalyzed by glucosamine-6-phosphate deaminase 1/2 (GNPDA1/2). Our results show an overall pattern of increased expression of the HBP enzymes in many tumors; however, none of the genes were upregulated as strongly as the enzymes of glycolysis and most of the upregulations did not reach statistical significance  that can oxidize alcohols such as ethanol into acetaldehydes; the latter are further oxidized into ketones by aldehyde dehydrogenases (ALDHs). The ADH and ALDH family of enzymes have 7 and 19 known members, respectively, and some of them are functional in the cytosol while others in the mitochondriaDHs Fig. C. Elevat46. Gluconeogenesis takes place mostly in the liver, which provides glucose to organs such as the brain during long periods of fasting. Kidneys, muscle and intestine are some of the other organs where gluconeogenesis also takes place47. DGE in the current study showed that the pathway was significantly downregulated in all tumors  by pyruvate carboxylase (PC); conversion of OAA into Phosphoenolpyruvate (PEP) by PCK1/2, conversion of Fructose 1,6-Bisphosphate (F16BP) into Fructose 6-Phosphate (F6P) by FBP1/2, and dephosphorylation of Glucose 6-Phosphate (G6P) to Glucose by G6PC , incorporating β-oxidation and ketone body metabolism, and fatty acid synthesis (FAS), incorporating cellular FAS (FAS-I) and mitochondrial FAS (FAS-II).The importance of fatty acid (FA) metabolism in cancer progression, survival and metastasis has received increasing attention in recent years2 in the TCA cycle. β-oxidation is separated between the cytosol and the mitochondria  and very long (FATPs) ACSs with oxidative enzymes.β-oxidation is the process by which FAs are broken down into acetyl-CoA to be further oxidized into COria Fig. A. In the52. Other studies have shown ACSL1 to have a higher impact on β-oxidation in highly oxidative tissues such as cardiac muscle and white/brown adipose tissues. The expression of ACSL1 was decreased by more than 1 LFC in CHOL, LUSC, BRCA, HNSC and LIHC  in 5 tumor types and strong downregulation in 9 others  into β-oxidation, but these functions are not universal and may vary in a tissue-dependent mannerIHC Fig. B. In LIHers Fig. B. Howeve57. The expression of FATP5 was suppressed in LIHC, which was associated with poor prognosis . The most dysregulated FA transporter among tumors was FATP2, showing significant up- and downregulation in many tumor types  are mostly involved in cellular uptake and activation of very long FAs, with the exception of FATP5 which activates bile acids entering the liverpes Fig. B. Low FA60. Our data show that CPT1A was strongly downregulated (> 1 LFC) in COAD and KIRP and did not show any significant changes in other tumors. On the other hand, CPT1B was strongly upregulated in 8 tumors. Although it would be tempting to speculate that this enhanced expression leads to increased β-oxidation, the expression of other enzymes involved in β-oxidation did not support this view with only modest changes in expression. Peroxisomes use ABCD channels for the transfer of lipids. We observed increased expression of ABCD1 and decreased expression of ABCD2 in different tumor types  system on the mitochondrial membrane. This step is deemed as the rate-limiting step of FAO as any acyl-CoA entering the mitochondria is funneled into the TCA cycle to be completely oxidized. CP consists of four main components: (1) CPT1 (CPT1A/B) on the outer mitochondrial membrane which conjugates acyl-CoAs to carnitine forming acylcarnitine; (2) CACT, which transfers acylcarnitine to the mitochondrial matrix from the cytosol and carnitine in the opposite direction; 3) CPT2, which exchanges carnitine for CoA, re-forming acyl-CoA inside the matrix; and (4) CRAT (known as CROT in peroxisomes), which re-conjugates carnitine to an acetyl-CoA moiety forming acetyl-carnitine to be transported back to cytosol and completing the carnitine cycle CPT2, wh+, which in turn transfers them to the electron transport chain (ETC) for ATP production. On the other hand, ACADs involved in oxidation of amino acid acyl chains , with few exceptions were generally either downregulated or showed no significant changes. A similar picture emerged for peroxisomal FAO which shortens very long-chained FAs and then transfers them to the mitochondria for final degradation. The pathway is similar to β-oxidation, except that the first and second steps are catalyzed by Acyl-CoA Oxidases (ACOXs) and the peroxisomal bifunctional enzyme enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase (EHHADH), respectively. These enzymes were either strongly suppressed (e.g. ACOX2) or showed no significant change. An exception was ACOXL, which showed a mixed pattern of increased and decreased expression in different tumors. Interestingly, ACOXL, a protein of unknown function, was reported as a potential biomarker of benign prostate cancer61  enzymes for short (ACADS), medium (ACADM), long (ACADL) and very long fatty acids (ACADVL) was observed in almost all tumor types except KICH. These enzymes catalyze the first reaction of FAO in the mitochondrial matrix by transferring electrons to FAD62  . These need to be better evaluated in future in vivo studies.Ketone bodies are generated in the liver and serve as an important energy source for the brain, as well as skeletal and cardiac muscles, especially during long periods of exercise or fasting62 Fig. A. The ra2 to form malonyl-CoA by acetyl-CoA carboxylase 1 (ACACA). The expression of ACLY was significantly increased in 15/20 tumors and only decreased significantly in KICH. The expression of ACACA was also observed to increase in some but not in all tumors. The malonyl-CoA generated has two functions: it serves as the basic unit for fatty acid synthase (FASN) to increase the nascent FA chain by two carbons per cycle; it also inhibits CPT1, thereby preventing a futile cycle of simultaneous FAS and FAO. After the chain length reaches 16 carbon units , it is released from FASN to be further processed by elongase and desaturase enzymes into more complex lipids65  ACACB showed increased β-oxidation and did not accumulate fat in their livers67. This is in contrast to our observations which suggest that β-oxidation is suppressed in most tumors despite an overall downregulation in the expression of ACACB (except for KICH), potentially refuting the idea that lower ACACB leads to increased β-oxidation. This discrepancy may have arisen because the levels of malonyl-CoA were shown to remain unchanged in the liver of mutant ACACB mice, and were reduced only in the heart and muscles, two organs that are already known to be highly oxidative and relying on β-oxidation. The expression pattern of enzymes involved in the elongation and desaturation of FA showed remarkable differences among tumors, which most likely contributes to major differences in their lipid profiles with potentially different outcomes in cellular signaling, membrane dynamics and cellular behavior  and acetyl-CoA by ATP-Acyl Lyase (ACLY) Fig. A. The cys65 Fig. A. ACACB ior Fig. C. Memberior Fig. C, which 69. Based on their molecular weight, these enzymes are divided into two groups, Type I (ACOT1/2/4/6) and Type II (the rest), and despite being characterized a long time ago, their precise roles are still unclear. However, it is generally assumed that Type I enzymes are mostly involved in FAO, while Type II enzymes play roles in regulating the channeling of fatty acids between FAS and FAO70. Our data show that ACOT1/2 were downregulated in most of the tumors except KICH, while ACOT4/6 showed more nuanced patterns. On the other hand, ACOT7 and ACOT11 were significantly upregulated in almost half of the tumors  family which consists of 12 members and carries out the opposite function of ACSs, namely removing CoA groups from Acyl-CoAs to produce FFAsors Fig. C. Recent73  in three tumor types  and showed decreased expression in most of the others. ACSM1 has been implicated in the activation of lipoic acid in the mitochondria, but whether the changes observed here have any impact on mitochondrial function cannot be determined solely from transcriptomic data. Additionally, the expression of genes involved in cardiolipin synthesis or modification remained mostly unchanged except GPAT2 and THEM5, which were significantly overexpressed in eight tumors  was also evaluated in different tumors because the end-product of this pathway is necessary for the production of lipoic acid, an essential component of the enzymes pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH)Overall, our analyses suggest that most of the tumors showed downregulated fatty acid metabolism, especially the oxidative branch Fig. D. The on76.As building blocks of proteins and the source of amino groups in the cell, amino acids (AAs) are essential for cellular growth and survival. Among the 20 AAs used for protein synthesis, eukaryotic cells cannot synthesize nine; these are therefore essential amino acids (EAAs) that need to be obtained from the diet. Seven amino acids are synthesized as byproducts of the EAAs, and only 4 can be synthesized by simple transamination reactions, so they are known as non-essential amino acids (NEAA)78. AAs are also essential for the mitigation of oxidative stress via the formation of glutathione from three AAs , in addition to their role in nucleotide biosynthesis. Glutamine (Gln) comprises the largest pool of FAAs in the human body reaching up to ~ 500 μM in the circulation79. Until recently, glutamine [together with alanine (Ala)] was thought to act solely as an amino group carrier from other organs to the liver where excess amino groups were removed in the urea cycle (UC). However, once it was shown to act as major source of energy for rapidly-dividing cells, Gln metabolism attracted a lot of attention in cancer research80. AA metabolism is very complex and is highly interconnected with many other cellular processes. To better evaluate their role in the bioenergetics of cancer, we have divided the amino acid related pathways into cellular transport, oxidation and biosynthesis; the latter including the polyamine (PA) pathway, UC and folate, otherwise known as one-carbon cycle (OCC)  supports anabolism and inhibits catabolism, increases protein synthesis, mRNA abundance and its stability via the mTOR pathway, while lower concentrations of FAAs have the opposite effectCC) Fig. A.Figure 81. Since AA concentrations are higher inside the cell compared to the extracellular environment, their influx has to be coupled to the transport of ions such as K+, Na+, H+, and Cl+, or to the efflux of another amino acid, generally Gln or Glu82. In addition, mutations of certain AATs have been implicated in number of inborn AA metabolic disorders such as cystinuria, cystiniosis and iminoglycinuria83. The expression of AATs varies drastically between different organs, making data interpretation very challenging84. Nonetheless, it is obvious from Supplementary Fig. 85, reported that this system was essential for the influx of EAAs, in particular Leu into the cells. AA influx can activate the mTOR signaling pathway, leading to cell proliferation; therefore, inhibition of AA transporters could lead to the deactivation of the mTOR pathway, inhibiting cell proliferation and initiating autophagy. Two additional studies showed that Gln and Ser transporters SLC1A5 and SLC1A4 were essential for progression of PRAD and the knockdown of SLC1A5 significantly decreased tumor progression in vivo87. The influx of Cys, a component of glutathione, is coupled to the efflux of Glu and its two known transporters are SLC7A11 and SLC3A2. It has been shown recently that SLC7A11 (but not SLC3A2) was essential for the survival of TP53 mutant cells in vitro and in vivo, and its blockage caused cell death by significantly increasing oxidative stress88. Our data show that SLC7A11 was strongly upregulated in all tumors except THCA  in eukaryotic cells belonging to 11 solute carrier (SLC) families76. For clarity, we have grouped AAs according to their common oxidation pathways. As seen in Fig. 89. The role of Pro catabolism is controversial; some studies have shown Pro oxidation to be involved in the mitigation of oxidative stress, while others have shown that increased expression of proline dehydrogenase (PRODH) was involved in apoptosis and reduced DNA synthesis90. In the current study, we observed that PRODH was significantly dysregulated in 14 tumors (7 upregulated and 7 downregulated)  is highly complex, with some pathways having as many as 20 steps, but the end products are all channeled into the TCA to be oxidized and produce energy91. Ser can be synthesized from 3-phosphoglycerate in glycolysis by the rate-limiting enzyme PHGDH or in OCC by SHMT1/2 when ample amounts of Gly are available. Our data show that enzymes involved in Ser biosynthesis through glycolysis  were significantly upregulated in CESC, LUAD, LUSC, PCPG, READ, COAD and UCEC. In addition, PHGDH was associated with poor prognosis in BLCA , COAD  and UCEC . Corroborating this, Maddocks et al.92, showed that colon cancer cells rely heavily on Ser available in cell culture, and Ser deprivation slowed down proliferation and increased de novo Ser synthesis in a p53 dependent manner. On the other hand, Ser biosynthesis was downregulated in CHOL, KIRC, KIRP and LIHC, indicating that these tumors may rely on transport of Ser into the cell. Of note, Ser biosynthesis was recently shown to activate mitogenic signaling via mTORC1 in lung metastasis of breast cancer but not in the primary breast tumor. Expression of PHGDH in the lung metastatic tissue was necessary for the sensitivity of mTORC1 to rapamycin93. Cys is conditionally essential because it can be synthesized from Homocysteine and Ser by cystathionine gamma-lyase (CTH). We observed that CTH was either downregulated or showed no significant change in expression in most tumor types except LUAD , KIRC  and LIHC .Ser is a conditionally non-essential amino acid of major importance for normal cell function because it serves as precursor for the synthesis of Cys and Gly, is incorporated in phospholipids, and donates carbon units to OCC, methionine cycle and de novo nucleotide synthesis Fig. A91. Ser UAD Fig. . Asparag90, this AA appears to be essential for tumor progression. Survival analysis showed that increased expression of ALDH18A1 and PYCR1/2 was associated with poor prognosis in KIRP , KIRC  and LIHC , indicating that Pro biosynthesis may be an important factor in survival and development of these three tumors. Alanine (GPT/GPT2) and Aspartate (GOT1/GOT2) transaminases are involved in transferring amino groups from Ala and Asp respectively, to pyruvate and αKG , which interconverts αKG into Glu was upregulated in a number of tumors. On the other hand, GOT1/2 which are involved in the Malate-Aspartate shuttle showed mild, mostly non-significant changes between tumors and their normal adjacent tissues. Based on transcriptome data, this suggests that the transfer of electrons generated from glycolysis into the mitochondria for OXPHOS was not prioritized in tumor cells; however, further regulation at the protein or metabolome level are likely to occur.Enzymes for the biosynthesis of Pro (ALDH18A1/PYCR1/2/3) were also upregulated in almost all tumors Fig. C. Despit98. Our data show that the two rate-limiting enzymes of the UC, namely carbamoyl phosphate synthetase 1 (CPS1) and ornithine transcarbamoylase (OTC) were downregulated in LIHC . These observations are in line with recent findings that tumors do not discard ammonia; rather it is utilized for nucleotide, polyamine and amino acid biosynthesis98.The urea cycle (UC) mostly takes place in the liver where ammonia is collected from other parts of the body in the form of Gln and Ala to be converted to urea and excreted Fig. A. TherefIHC Fig. C, and noIHC Fig. C. Ammoni101. Our data show that ODC1 was significantly downregulated in BLCA, KICH and PAAD and significantly upregulated in 10 other tumors  are a group of highly charged molecules synthesized by the multistep polyamine pathway (PAA). PAs are involved in multiple cellular processes such as cell signaling, chromatin remodeling and enhancement of protein translation, as well as tumorigenesis. The most important step in PAA is the formation of putrescine from ornithine in presence of the enzyme ornithine decarboxylase 1 (ODC1), and subsequently the formation of spermine and spermidine. These three compounds comprise the majority of PAs found in the cellors Fig. C. A simiOverall, our study has shown a significant overexpression of genes involved in AA transportation through the plasma membrane and activation of pathways utilizing glutamine which may serve as an amino group donor for nucleotide biosynthesis, for transamination reactions, or enter TCA either to be used for energy generation or to be funneled to FAS. In addition, UC was shown to be suppressed while PA was activated, especially in LIHC, a state associated with poor overall patient survival. These pathways and their significance, especially in liver cancer will need more elaborate studies in the future because urea metabolism is one of the major functions of this organ. The deregulation of genes (either up or down regulation) was evenly balanced across tumor types Fig. D.The tricarboxylic acid (TCA) cycle, besides being the main source of cellular ATP, is also essential for anaplerotic reactions since its intermediates can be inter-converted to amino acids, channeled into FAS and used for transferring reducing electrons by means of NADH/NADPH via various mitochondrial shuttles Fig. A. Overal102. On the other hand, under hypoxia-induced suppression of PDH, cells rely more on IDH1-driven reductive carboxylation to generate citrate for FAS103. PDH also did not show any marked decrease, but PDK1 and PDK3 were upregulated in some of the tumors, pointing at additional mechanisms for suppressing the normal functions of PDH  and upregulation of pyruvate kinases (PDKs), but also by a downregulation of its transporters, especially MPC1 which showed at least 1 LFC downregulation in 9 tumors, while MPC2 was mildly upregulated (< 1 LFC) in only four of them, with the rest having non significant changes Fig. B. StudiePDH Fig. B.104. Overall, our data show that changes in expression of genes involved in TCA and anaplerosis were not as drastic as in other pathways, however almost half of the tumors had significantly more downregulated genes as compared to normal tissues. On the other hand, KICH, LUSC, LUAD and UCEC showed the opposite trend  and neurodegeneration (UCP4 and UCP5)111, but the real significance of these enzymes in tumorigenesis remains to be established.Electron Transport Chain (ETC) complexes are composed of multiple protein subunits, the majority of which are still not well characterized or have no direct role in electron transfer; therefore, based on the available literature we evaluated the expression of only the core subunits encoded by mitochondrial and nuclear genomes112. Another member of this shuttle is GPD1L, which is thought to have similar roles with GPD1. GPD1 and GPD1L were downregulated in most tumor types; low GPD1L was associated with poor prognosis in COAD , LUAD  and KIRC , while higher expression was associated with poor prognosis in LIHC . The mitochondrial isoform did not show significant changes in expression except in CHOL, PAAD and CESC, and studies have shown that increased expression of this enzyme is associated with increased glycolysis and ROS levels in prostate tumor cell lines113. Overall, the cohorts can be divided into three major groups based on DGE of ETC genes; those that had a high number of downregulated genes, those that showed the opposite trend, and those that showed not much change  and are known to be metabolically very heterogeneous as well119. These recent findings are in line with a model proposed by Costello & Franklin according to which glycolysis is essential for tumors to produce acetyl-CoA which is used for lipogenesis and cholesterologenesis. Pyruvate can be used to produce acetyl-CoA directly, as well as replenish OAA by means of anaplerosis because OAA is depleted when there is a high demand for FAS. Biochemical calculations also show that this is an optimal choice for cancers to produce the required energy in the form of ATP for anabolism and secure the necessary amounts of acetyl-CoA115. Very detailed biochemical studies gathered by Pedersen120 in the past have shown that tumor cells derive 60–85% of their ATP from mitochondria, which may come from OCC, ETC or substrate-level phosphorylation by succinate dehydrogenase121. These studies also showed that the more aggressive the tumor cells, the higher their glycolytic rate and the lower their mitochondrial content as compared to normal cells120.One overlooked aspect of glycolysis is its role in providing acetyl-CoA for FAS, partially because a major portion of current research assumes that all the pyruvate produced by glycolysis is converted into lactate due to the decrease in expression or activity of PDH, and also because of heavy focus of clinical research on approaches that interfere with DNA replication. However, only a fraction of pyruvate is converted into lactate, while the rest enters mitochondria and TCA. Moreover, there are also other alternative ways for pyruvate to enter mitochondria through anaplerosis. In addition, it is also generally assumed that lactate is a waste byproduct of the Warburg effect to be excreted by the cell, but recent metabolomics studies in mice have shown that tumors are able to metabolize lactate for energy production122. The use of transcriptomics as a surrogate for metabolomics relies on strong correlation between transcript levels of metabolic enzymes and their corresponding metabolites. This was confirmed in a breast cancer cohort where metabolite levels were found to be correlated with RNA-Seq data123. Therefore, with some caution, we can claim that the data presented here can be a good representation of the real state of the metabolome in tumors.The best way to understand the flow of metabolic pathways is to quantify the metabolites in a particular tissue. However, despite all the advancements in mass spectrometric approaches and software development, isolation, quantification, characterization and identification of metabolites is currently a daunting task124. In addition, we have determined the correlation of certain key enzymes with patient survival and found several significant associations.In the current study, we have shown that tumors show enhanced uptake of carbohydrate and amino acid by overexpressing certain families and isoforms of their transporters. This, to our knowledge is the first systematic study to analyze the mRNA expression of this important group of proteins and evaluate their relevance in tumor metabolism. Although the genes evaluated in this study were manually curated, potentially opening up possibilities of a bias in selection, we ensured to the best of our knowledge and data available in the literature that the genes represented all relevant pathways. Supporting the extensive data available in the literature, we have shown that glucose is used in glycolysis, while AAs, with the exception of glutamine are not catabolized for energy generation, but are funneled into protein synthesis. Our data also suggest that all tumors showed elevated FAS and decreased FAO, a state that is in agreement with the anabolic nature of cancers. Another important and common feature of tumors is a decrease in the utilization of ketone bodies for the purpose of energy generation. These data support findings from several pre-clinical studies showing better response to cancer chemotherapy of patients who underwent intermittent fasting or were kept in ketogenic diet. Our data also corroborates recent suggestions that cancers can be managed by means of dietary changes, however the data on this area are scattered and there is no consistent dietary regime for cancer patients125, these tissues are known to be different from healthy tissues, expressing a pro-inflammatory gene signature126 and contributing to patient survival across cancer types127. Transcriptome data from healthy tissues that are collected from a distance greater than 2 mm are not available for the TCGA cohort and the use of expression data from healthy tissues that are independently collected and processed may lead to significant disparities that may make data interpretation difficult.To minimize confounding effects, we have chosen exclusively treatment-naive tumor samples that also had data for matched normal tissues. The last factor was important to mitigate variations coming from different genetic/epigenetic backgrounds, as well as the variations coming from the organs themselves. However, it needs to be emphasized that although the matched normal samples were collected > 2 cm away from the tumor and were histopathologically free of tumor cellsAlthough this study is not the first to use pan cancer transcriptomics to understand metabolism, it has its own unique features. First, instead of using ready-available pathways, we used a more targeted approach of the most important and relevant enzymes and transporters. Although systemic approaches are more appealing, they are sometimes less informative because metabolic pathways are highly interconnected, and all the enzymes in the network are given the same weight, missing the more important ones. Second and most important, systemic studies rely heavily on statistics and often do not explain the relevant biochemical pathways in a language that can be understood by most of the biological research community. Our study focuses on a manually curated extensive list of genes that are known to be highly relevant to metabolism in cancers and clearly shows the importance of gene expression changes while providing simplified descriptions of the alterations in metabolic pathways and interconnections between those pathways.https://www.genome.jp/kegg/)128 while the rest were retrieved from the Molecular Signature Database of the Gene Set Enrichment repository (https://www.gsea-msigdb.org/). They were then manually curated from literature, after which some genes irrelevant for our study were removed and a number of others were added in order to make the pathways more complete and less redundant. In addition, all the enzymes were classified according to their function, and when necessary, their cellular localization. In this way, we created a database of genes involved in bioenergetics representing 5 major pathways: (1) carbohydrate metabolism comprising genes involved in carbohydrate transport, glycolysis, pentose phosphate pathway and hexosamine biosynthesis pathway; (2) fatty acid metabolism, comprising genes involved in fatty acid oxidation, transport through mitochondrial membrane, ketone body oxidation and biosynthesis, cellular fatty acid biosynthesis and modification (FAS-I), and mitochondrial fatty acid biosynthesis (FAS-II); (3) amino acid metabolism comprising of genes taking part in amino acid transportation through the plasma membrane, amino acid oxidation, amino acid biosynthesis, polyamine pathway (PA), urea cycle (UC) and once-carbon cycle (OCC); (4) tricarboxylic acid cycle (TCA) and anaplerotic pathways in mitochondria; and (5) electron transport chain (ETC). All the gene names are available in Supplementary Tables The enzymes and transporters used in this study were gathered through a combination of database querying and manual curation. More specifically, glycolysis/gluconeogenesis and amino acid and nucleotide sugar metabolism (including genes of the hexosamine pathway) related genes were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG)  was built for each tumor using the samples status  as variable and all probes having no read counts were filtered out for easier computation. Finally, differential gene expression dataframes for log-fold change differences of tumor-normal were obtained to be used for further analysis. Each gene’s significance was determined by Wald Test’s p-value adjusted for multiple testing by Benjamini and Hochberg procedure false discovery rate (FDR)131 < 0.05 and log-fold change (LFC) higher than 0.5. Genes that were upregulated, downregulated or did not show any statistically significant difference in expression according to the conditions mentioned above are listed in Supplementary Tables pheatmap package with euclidean distance and ward.D2 linkage method. Radar plots were generated with ggplot2 package and the number of genes up- or down-regulated for each cohort were determined according to the criteria mentioned above. Additionally, we validated sample separation  for each cohort by PCA using vst-transformed read counts.For each cohort, read count normalization and differential gene expression (DGE) analysis for tumor versus normal sample was carried out separately by http://www.oncolnc.org/). After normalization, the expression values of the genes of interest were extracted and patients were divided into high- and low-expressing groups according to median (unless explicitly mentioned) value. Then, univariate cox proportional hazard models were computed with surviver, and Kaplan–Meier plots were generated with survminer packages, respectively. Hazard ratio (HR) and significance p-values for each gene are reported in the text and in Supplementary Table For survival analysis, certain rate-limiting enzymes or transporters were tested for possible correlation with patients’ survival. The criteria for inclusion in analysis were for the genes to show significant difference in expression between tumor and normal as well as correlation being in the same direction as the LFC difference. The significance of correlation with survival was first explored on OncoLnc database (Supplementary Information 1.Supplementary Information 2.


Drosophila cellularization, Rho inhibition by RhoGTPase-activating proteins (GAPs) remains to be studied. We have found that the RhoGAP, GRAF, inhibits actomyosin contractility during cellularization. GRAF is enriched at the cleavage furrow tip during actomyosin assembly and initiation of ring constriction. Graf depletion shows increased Rho-GTP, increased Myosin II and ring hyper constriction dependent upon the loss of the RhoGTPase domain. GRAF and RhoGEF2 are present in a balance for appropriate activation of actomyosin ring constriction. RhoGEF2 depletion and abrogation of Myosin II activation in Rho kinase mutants suppress the Graf hyper constriction defect. Therefore, GRAF recruitment restricts Rho-GTP levels in a spatiotemporal manner for inhibiting actomyosin contractility during cellularization.Actomyosin contractility is regulated by Rho-GTP in cell migration, cytokinesis and morphogenesis in embryo development. Whereas Rho activation by Rho-GTP exchange factor (GEF), RhoGEF2, is well known in actomyosin contractility during cytokinesis at the base of invaginating membranes in  Metazoan embryogenesis involves a variety of cell shape changes during cytokinesis, cell migration and tissue morphogenesis . The corDrosophila embryogenesis is an excellent model system to study the molecular mechanisms regulating actomyosin-driven contraction in a spatiotemporal manner. The Drosophila embryo begins development as a syncytium  in regulating contractile ring formation in cellularization in Drosophila embryogenesis.In contrast to RhoGEF2 function, RhoGAP domain-containing proteins are likely to play a key role in inhibiting the activation of Myosin II. The function of RhoGAPs during tissue morphogenesis has been generally less studied as compared to RhoGEFs. Depletion of RhoGAPs, RGA3 and RGA4 in the yosin II . CGAP, arulation . CGAP murulation . The hexrulation . HoweverDrosophila cellularization. We found that GRAF loss showed a distinct hyper constriction phenotype dependent upon its RhoGTPase activity during cellularization. GRAF was present at the furrow tip in early stages of cellularization during Myosin II assembly, enriched in mid cellularization and depleted from the ring in late stages. Graf depletion led to increased Myosin II recruitment at the contractile ring. Loss of RhoGEF2 and Myosin II activation suppressed the hyper constriction phenotype of GRAF. Our studies show a crucial role for GRAF in inhibiting actomyosin contractility in cellularization.GRAF is a multidomain protein containing a BAR, PH, RhoGAP and SH3 domain . GRAF waDrosophila is a multidomain protein containing a BAR, PH, RhoGAP and SH3 domain  (Graf shRNA2 (Graf2i) . Control and mutant embryos were stained with fluorescently coupled phalloidin, which marks cortical F-actin   and Graf(Graf2i)  were cro F-actin . During  F-actin . The pol F-actin . Grafi ay stages . In Grafrization . Quantifrization .Graf locus were generated by the Crispr-Cas9 strategy. The transgenic fly stock containing a guide RNA (gRNA) against exon 1 (at 2751 residue from transcription start site) of Graf was crossed to maternally expressing Cas9 (nanos-Cas9) . Like the Grafi expressing embryos, GrafCR57 embryos obtained from homozygous GrafCR57/GrafCR57 parents did not show polygonal F-actin architecture in early cellularization. They showed premature ring formation in early cellularization and enhanced ring constriction in mid and late cellularization  . A singld Graf2i . 155 inds intact . The flyanalysis . GrafCR5 embryos . GrafCR5rization . Loss ofrization . Taken trization .Drosophila embryos during cellularization, we generated an antibody against full-length GRAF protein . Control embryos were co-stained with GRAF and the polarity protein Dlg to mark lateral membranes. GRAF antibody stained the furrow tip basal to Dlg throughout cellularization , sqh; Sqh-mCherry (Sqh-mCherry)   and 2. TmCherry) . GRAF-GFmCherry)  and 2.Drosophila blastoderm embryo, has been previously shown to have a similar distribution with enrichment at the furrow tip during the polygonal phase along with a role in inhibition of constriction  of Anillin (AnillinRBD-GFP) expressed under the control of ubiquitin promoter . GrafEP09461 contains a gypsy transposon tagged line with a UASp element inserted in the 5′UTR region of GRAF in the same orientation of native gene  caused ring hyper constriction as compared to controls in cellularization  and visualized furrow length and ring dynamics with Sqh-mCherry in cellularization    in contr embryos . Rho-GTPcontrols .mbsi)   maternal embryos .Graf mutant phenotype of hyper constriction with Rok depletion shows that Myosin II activation is a necessary step to execute the ring constriction in cellularization  the function of GRAF in spatiotemporal regulation of rate of constriction during cellularization, (2) coregulation of actomyosin assembly by actin remodeling or endocytic proteins along with GRAF and (3) redundancy of RhoGAPs in morphogenesis in cellularization and gastrulation.In this study, we show that the function of the RhoGTPase domain containing protein GRAF is imperative in causing a delay in the onset of contraction during cellularization to ensure appropriate closure of cells at the base of nuclei after membrane extension. The spatial pattern of GRAF recruitment varies with the dynamics of the actomyosin network. GRAF localizes precisely to the furrow tip in early cellularization, increases in mid cellularization and finally becomes cytoplasmic at late stages . Graf nule units . Graf loGraf mutant embryos lead to increased Myosin II recruitment and, along with the F-actin remodeling activities of Anillin and Peanut, results in enhanced ring constriction in cellularization. Taken together, our study shows that spatiotemporal recruitment of GRAF to the contractile ring is required for fine-tuning of Rho activity for regulated constriction during cellularization.GRAF is present uniformly at the furrow tip when Myosin II is beginning to assemble in early stages of cellularization and a complete overlap between GRAF and Myosin II occurs during mid cellularization. Rho-GTP and Myosin II are present at the furrow tip in late cellularization, whereas GRAF is lost from the furrow tip in late stages. Constriction of the cleavage furrow in cellularization occurs in successive steps by the action of Myosin II and F-actin polymerization . Myosin Graf mutants will reveal their interaction during actomyosin network stabilization in cellularization.Whereas Anillin and Peanut lead to constriction of the contractile ring in cellularization, Cheerio and Bottleneck are actin crosslinkers that inhibit contractility similar to GRAF. Bottleneck and Cheerio organize a basal polygonal non-contractile actin network in mid cellularization . GRAF loDrosophila  using primers 1 and 3 (Key resources table). The cDNA in LD28528 contained three mutations causing premature stop codon when compared to the gene sequence available at FLYBASE:FBgn0030685. These mutations were rectified by site-directed mutagenesis as follows: C212T, A420G and deletion of T468 residue using primers 13–18 (Key resources table). The modified residues were confirmed using sequencing. This intact Graf cDNA sequence was used to generate a fluorescently tagged GRAF-GFP construct with GFP at the C-terminus. PCR amplification was performed on the GRAF and GFP gene using primers 1, 3 and 2, 4, respectively (Key resources table). Overlap PCR was performed to give rise to GRAF with a serine linker followed by GFP tagged at C-terminal of the Graf cDNA-amplified products using primers 1 and 4 (Key resources table). The PCR product at the end has a sequence homologous to BamH1 digested pUASp vector ends. The purified product from the overlap PCR was digested with BamH1. The pUASp vector was used in a 10:1 ratio for transformation in DH10B-derived Escherichia coli strain PPY .The rain PPY . This stGraf cDNA-amplified products using primers 1 and 4 (Key resources table). This purified product had an end homologous region to BamH1, and both the PCR product and the pUASp vector were individually digested with BamH1. The purified product and vector were used in a 10:1 ratio to transform in E. coli strain PPY. Finally, primers 1 and 4 (Key resources table) were used for confirming the product with PCR, and the DNA sequence was confirmed by sequencing.The deletion of the RhoGAP domain of GRAF that spans the residues 1177–1764 in the ORF was achieved using overlap PCR. PCR amplification was performed on pUASp GRAF-GFP vectors using primers 1,5 and 6,4. The overlap PCR was performed to give rise to GRAFΔRhoGAP with a serine linker followed by GFP tagged at C-terminal region of E. coli strain PPY in 10:1 ratio. PCR and sequencing were performed to confirm the presence of the GRAF insert using primers 7 and 10 (Key resources table). The N-terminal region of the GRAF gene had 6X His tag, which was used for protein purification.The pUASP GRAF-GFP vector was used to amplify GRAF full-length sequence using primers 7 and 10 (Key resources table), and pET-15b expression vector amplification was carried out by primers 8 and 9 (Key resources table). The GRAF full-length amplified product and vector were transformed into 2SO4), optical density (OD) values were measured at 450 nm using 800 TS absorbance reader . Antibodies from the sixth bleed were purified on Protein A agarose beads (Sigma).Polyclonal antibodies were developed against full-length GRAF protein in New Zealand White rabbit at Bioklone, India, by the following procedure. Immunizations were given subcutaneously. After primary immunization of the rabbit with 200 g of GRAF in Freund’s adjuvant (Sigma), boosters were administered with 150 g of GRAF at 21-day intervals. Production bleeds were collected from the immunized rabbit on the 14th day after each booster. The antibody titers in rabbit sera were measured in indirect ELISA. 100 μl of purified GRAF protein was coated in the wells of microtiter plates or strips (Nunc maxisorp) at a concentration of 3 g/ml, overnight at 4°C. After blocking with 5% skimmed milk in PBS for 1 hr at 37°C, the wells were washed twice with PBS. Varying dilutions of rabbit sera were added to the wells and incubated overnight at 4°C. After washes with PBS-Tween 20 (PBST) followed by PBS, the wells were incubated with goat anti-rabbit IgG-HRP , 1:2000, for 45 min at room temperature. Following washes with PBST and PBS, wells were incubated with TMB (Invitrosense ultra blue) at room temperature for 20 min in dark. After the addition of the stop solution  for 1 min and instantly adding ice-cold 1X washing buffer. After cooling on ice, embryos were devitelinized in 1:1 mix of MeOH and heptane followed by three washes in 1X PBST (1X PBS with 0.3% Triton X-100) for 5 min each. After washing, embryos were blocked in 2% BSA in 1X PBST for 1 hr and then incubated in the primary antibody (Key resources table) overnight at 4°C. This was followed by three 1X PBST washes and incubated in fluorescently coupled secondary antibodies (Molecular Probes) at 1:1000 dilution for 1 hr at room temperature. Embryos were washed three times in 1X PBST for 5 min each. DNA was labeled with Hoechst 33258  for 5 min in the second 1X PBST wash. Finally, embryos were mounted in Slow fade Gold antifade (Molecular Probes).To visualize F-actin, embryos were fixed using 1:1 mix of 8% PFA in 1X PBS:heptane for 20 min and followed by hand devitellinization. Fluorescently coupled phalloidin (Molecular Probes) was used to label F-actin in embryos in cellularization for 1 hr at room temperature followed by washes and DNA labeling.For live imaging, 2–2.5 hr embryos were collected on sucrose agar plates and dechorionated with 100% bleach for 1 min, and mounted on two-well coverslip bottom Labtek chambers. Mounted embryos were filled with 1X PBS  and imagZeiss laser scanning confocal microscope LSM710, LSM780 and Leica laser scanning microscope SP8 containing laser lines at 488, 561 and 633 nm were used to image immunostained fixed or live embryos. The 40X objective having NA 1.4 of these microscopes was used for imaging. The laser power, scan speed and gain were adjusted with the range indicator mode such that 8-bit image acquisition was in 0–255 range. For both fixed or live imaging, an averaging of 2 was used during image acquisition. Optical sectioning of 1.08 μm and 0.68 μm was used to acquire images at Zeiss and Leica confocal microscopes, respectively.GrafCR57 embryos expressing Sqh-mCherry were used for visualizing contractile rings for laser ablation experiments using the Zeiss LSM780 microscope. Laser ablation was achieved by using 800 nm multiphoton femtosecond pulsed Mai Tai laser. The region of interest used for ablations was set to a line of 510 pixel length (42.51 μm) and captured with a speed of 1.58 μs per pixel and 20 iterations. The Sqh-mCherry was imaged using a 561 nm laser excitation with a time interval of 1.27 s. A sagittal section was taken before ablations to estimate furrow length for early (less than 6 μm) and mid stages (6–16 μm) of cellularization. Three sections were taken before the ablations in approximately 3 s, the time taken for ablations is approximately 3.22 s and imaging was carried out for approximately 70 s after ablations.Control and http://fiji.sc/wiki/index.php/Fiji)  . The rinhp/Fiji) , GrafCR5GrafCR57 , GRAF-OEGrafCR57  and Zipp57 .Thank you for submitting your article "Spatiotemporal recruitment of RhoGTPase protein GRAF inhibits actomyosin ring constriction in The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:Drosophila cellularization is modulated by the RhoGAP GRAF. The authors demonstrate that GRAF localizes at the tip of the invaginating furrows and that its depletion or over-expression causes changes in the dynamics of ring contraction. The authors then argue that GRAF affects myosin II localization and activity by balancing the levels of Rho-GTP at the contractile rings. This study carefully dissects the role of GRAF during cellularization and nicely shows its importance to achieve the correct levels of actomyosin contraction during this process. Due to the central role of actomyosin mechanics in several morphogenetic processes, as well as in disease, this work is potentially relevant for a wide audience. Furthermore, the authors have developed an arsenal of tools to explore the role of GRAF that should be useful to the fly community. However, I believe that some issues should be addressed before publication particularly regarding the quantification and interpretation of the data.In this manuscript Sharma and Rikhy show that actomyosin contraction at invaginating furrows during Essential revisions:1. The authors provide a number of data points consistent with hypercontractility but never demonstrate that the ring is actually hypercontractile. Ideally laser ablations to analyze recoil, or experiments using tension sensors could be performed. However, RLC phosphorylation could suffice. Analysis of phosphorylated light chains with/without GRAF help support the model that the myosin being recruited is active/contractile.2. Validation of KD lines. For some, I could be convinced that previous literature is sufficient (eg Rok KD), but for GRAF, which is central in this work, I would like to see in author's hands that they are seeing protein depletion, what the maternal contribution level is, etc. Similarly, the authors do an excellent job of presenting their CRISPR KO validation up to the point of showing loss of protein. I have seen examples where N-terminal targeted CRISPR KO lines were bypassed by unknown alternative starts. It would be nice to demonstrate that no shorter proteins are being produced in the KO, as far as the author's antibodies can determine.3. The authors lean heavily on the area measurements as a readout for rescues (GFP-GRAF +/- GAP) and genetic interactions . More quantitative investigation of these rescues would be appropriate. The authors have the tools to do this. For example, the zipper (or RLC-P as suggested above) recruitment would be a nice downstream readout to confirm the area differences are myosin 2 dependent.4. GRAF-OE (with or without Rho-GEF2) appears to have more lateral actin. Were the images in Figure 6 collected and presented identically? Acquisition and post-acquisition modifications were identical? The question regarding acquisition/post-acquisition modifications to the images applies throughout the paper. It is not clear to me if the reader can directly compare intensities between early/mid/late samples or if they were all adjusted independently for presentation. Please make this clear. In a number of the images, the noise appears to increase in Late, is this because there is more diffuse GRAF or because the laser/signal was adjusted to enhance the image for presentation?CR57. However, I am not convinced of this based on the data. Is the initial area of the rings significantly different between control embryos and GRAFdeltaRhoGAP-GFP? In that case, are the dynamics of ring contraction over time different, or simply rings are contracting at similar rates and are smaller in GRAFdeltaRhoGAP-GFP because their original size was smaller? Initially, the dynamics of contraction in GRAFCR57embryos seems significantly faster than in GRAFdeltaRhoGAP-GFP, which could imply that the RhoGAP domain of GRAF is important at later stages of cellularization. Differences in dynamics would be more obvious if the curves were normalized to the initial size of the rings. The authors should look at the rate of contraction to be able to claim changes in contractility speed.5. In figure 3C the authors claim that in the GRAFdeltaRhoGAP-GFP embryos the phenotype is similar to that in GRAF6. For your graphs measuring the change in ring area of over time  an ANOVA analysis has been performed on each time point or the overall rate? The figure legend indicates these are statistically significant, but there is no indication in the figure. It is just hard to follow exactly what is statistically significant in these graphs.7. The authors are looking at the mean fluorescence of different proteins like myosin and GRAF. Although this is a very informative metric, I think they should also look at the total levels of these molecules to see if they are increasing or not. For example, the authors say that the levels of GRAF and myosin increase from early to mid-cellularization. However, usually mean fluorescence is normalized to (divided by) the area of the structure under study. As the rings are decreasing in area during cellularization, the increase in mean fluorescence they are observing could simply be an increase in the density of myosin or GRAF as the rings become smaller. Therefore to talk about an increase in the recruitment of these molecules, the authors should look at total fluorescence levels (the product of mean fluorescence and area of the ring). This is particularly important when comparing the levels of molecules in phenotypes where the ring sizes differ at a given time point, like figure 5D.8. Figure 8: The lower diagram in C shows Graf and ROK KD leads to "Ring constriction similar to controls". This is not supported by the data. You've broken the chain below the GAP, and therefore the phenotype should mimic ROK-KD alone, which is what 8B shows. The blue line is more similar to the green than to the black. ROK isn't in the competitive cycle like RhoGEF2 is. It is downstream. Regardless of what happens upstream, ROK-KD should largely prevent myosin activation and constriction, which the authors data show. And "Dephosphorylated myosin 2" would not lead to "Ring Constriction similar to controls" as the model suggests, it would lead to the results presented in the data. Essential revisions:1. The authors provide a number of data points consistent with hypercontractility but never demonstrate that the ring is actually hypercontractile. Ideally laser ablations to analyze recoil, or experiments using tension sensors could be performed. However, RLC phosphorylation could suffice. Analysis of phosphorylated light chains with/without GRAF help support the model that the myosin being recruited is active/contractile.We have attempted the following different experiments to address change of active Rho-GTP and contractility in the controls and GRAF mutant embryos:CR57 as compared to controls. This new experiment has been added as a part of revised Figure 3 and Video 3.1. As suggested by the reviewers, we performed laser ablations at the ring for controls and mutants to test changes in contractility in early and mid cellularization. We found that the initial recoil velocity, rate of displacement and displacement at the final time point of the ring at the furrow base is increased at early and mid stages of cellularization in GrafGrafCR57 and controls. AnillinRBD-GFP is more spread in mid cellularization between adjacent rings in GrafCR57 as compared to controls. This allows a documentation of change in Rho-GTP levels in embryos in cellularization in GrafCR57. This is included as a part of revised Figure 4.2.Since GRAF has a RhoGAP domain, we further tested the levels of Rho-GTP in mutant embryos. For this, we add new data of Rho-GTP distribution as visualized by AnillinRBD-GFP in GrafCR57 as compared to controls. This is included as a part of revised Figure 6.3. We have measured the changes in Myosin II recruitment to the contractile ring by estimating the intensity of Zipper immunofluorescence in fixed embryos and Sqh-mCherry in live videos in controls and mutants. We find that Sqh-mCherry and Zipper levels are increased in 4. As suggested by the reviewers, we also attempted an analysis of phosphorylated light chain with a phosphospecific antibody against Sqh obtained from Robert Ward. We add images here for reference of the reviewers . We trieDrosophila embryos but plan to do this in future experiments.5. We have not been able to test the tension sensor  in cellularization in 2. Validation of KD lines. For some, I could be convinced that previous literature is sufficient (eg Rok KD), but for GRAF, which is central in this work, I would like to see in author's hands that they are seeing protein depletion, what the maternal contribution level is, etc. Similarly, the authors do an excellent job of presenting their CRISPR KO validation up to the point of showing loss of protein. I have seen examples where N-terminal targeted CRISPR KO lines were bypassed by unknown alternative starts. It would be nice to demonstrate that no shorter proteins are being produced in the KO, as far as the author's antibodies can determine.GrafCR57 mutant by a premature stop codon which gave rise to a truncated product of 27aa. The reason to target exon1 instead of targeting the deletion of the entire gene was because of the presence of another gene CG8260 in the opposite direction from the intronic region of Graf. Even though CG8260 does not show the presence of transcripts in the development as per the ModEncode data base, we did not want to affect its synthesis by deleting the entire genomic region. This information has been added to the revised text on creation of Crispr mutants of Graf.1. We used a guide RNA to target exon1 of the Graf gene. This gave rise to the GrafCR57 mutant embryos show loss of GRAF antibody staining and the mutant phenotype of hypercontractility is rescued by the addition of full length GRAF-GFP in embryos. This rescue is not seen when a deletion construct missing the RhoGAP domain is combined with GrafCR57 mutant embryos. This rescue shows a specific requirement of the RhoGAP activity of GRAF in inhibiting contractility in cellularization.2. The Graf is partially viable and escapers can be used to maintain these homozygous flies as a stock. It does not show any defects in development of other organ structures such as the wing, eye, thoracic bristles, etc. We performed RNAi expression against GRAF with maternal, wing and eye specific Gal4s. These experiments showed a specific defect in embryonic stages with maternal Gal4s and not with wing specific or eye specific Gal4s or even tubulin Gal4. These data together suggest that maternal deposition and/or synthesis of GRAF is present in early development and is essential for these developmental processes.3. The Crispr mutant of GrafCR57 embryos as compared to Graf RNAi expressing embryos. This also correlates with the stronger ring constriction phenotype seen in Graf CR57 as compared to Graf RNAi. Both the RNAi and the Crispr mutant show the loss of function, hyper constriction phenotypes. It is very unlikely that other products produced in the GRAF Crispr mutant affect the phenotype.4. We also show that there is a depletion of GRAF antibody staining in Graf RNAi expressing embryos. We have added this data to revised Figure 2—figure supplement 1. GRAF levels are decreased to a greater extent in GrafCR57 embryos. We get multiple bands in the wild type extracts from adults and embryos with the antibody against GRAF and the control blots with anti-tubulin antibody show a specific single band. The western blot analysis from mutants shows loss of protein bands in mutants in a couple of blots but this is not reproducible. As per the data on ModEncode and Fly Proteome, the GRAF mRNA and protein are present in very small quantities. It is likely that for this reason and possibly due to less stability of the protein, we are unable to detect this on western blots with specificity and reproducibility. Our current and future studies involve attempts to standardise conditions to increase the stability of the protein for biochemistry experiments.5. We attempted western blot analysis to test whether smaller fragments are seen in the 6. For embryos expressing RNAi against RhoGEF2 and ROK and with overexpression of RhoGEF2 we include new measurements of Sqh-mCherry at late cellularization in revised Figures 6, 7, 8 and 9.GrafCR57 and decreased in intensity in the inter ring region in late cellularization. We therefore quantified the ring to inter ring region ratio for all the genotypes from live videos with Sqh-mCherry in cellularization. This analysis shows that Sqh-mCherry fluorescence is reduced at the ring in RhoGEF2 and ROK RNAi expression embryos and increased in GrafCR57, GrafCR57;GRAFΔRhoGAP-GFP and RhoGEF2-OE embryos.Sqh-mCherry shows a patchy distribution in early stages of cellularization during Myosin II assembly the furrow tip. It shows localization at the ring in mid and late stages of cellularization. The intensity in mid stages is higher than late stages of cellularization in controls. Sqh-mCherry is more spread in the inter ring region in Graf mutants in mid cellularization. This has also been seen in actin remodelling protein Cheerio mutant embryos which show hyper constriction . Sqh-mCherry increased in intensity at the contractile ring in We have also measured the Sqh-mCherry and Zipper intensity in syncytial embryos in prophase and metaphase in RhoGEF2 RNAi, RhoGEF2-OE and ROK RNAi in previous studies (Dey and Rikhy 2020). Sqh-mCherry is present in the cortical regions close to the membrane in control embryos in prophase and is cytoplasmic in metaphase. We find that Sqh-mCherry and Zipper are cytoplasmic in prophase in ROK RNAi and RhoGEF2 RNAi expressing embryos and are retained in the cortical regions in metaphase of RhoGEF2-OE embryos as compared to controls. The reagents for knockdown and overexpression of Rho and Myosin II have therefore been confirmed in several ways of shape analysis and Myosin II intensity estimation in cellularization in the revised manuscript and in syncytial stages of development previously. We have added the appropriate description from literature for each of these mutants in the Results section.3. The authors lean heavily on the area measurements as a readout for rescues (GFP-GRAF +/- GAP) and genetic interactions . More quantitative investigation of these rescues would be appropriate. The authors have the tools to do this. For example, the zipper (or RLC-P as suggested above) recruitment would be a nice downstream readout to confirm the area differences are myosin 2 dependent.CR57 mutants. Hence we could not use this method to document changes of p-Sqh levels in single or double mutant embryos.As mentioned in response to point 1, we did not get a significant staining for p-Sqh at the ring in controls and Graf GrafCR57 mutant embryos and found increased levels in early, mid and late cellularization. Sqh-mCherry is recruited to the ring in late stages and is not present in inter-ring regions in control embryos. We found that Sqh-mCherry levels remained high at the ring in GrafCR57 mutant embryos in late cellularization. We therefore estimated the levels of Sqh-mCherry in Graf CR57 in late cellularization at the ring relative to the inter ring from videos. We found an increase in Sqh-mCherry relative to inter ring regions in GrafCR57, GrafCR57;GRAFΔRhoGAP-GFP when compared to controls and GrafCR57; GRAF-GFP .We performed Zipper antibody stainings in GrafCR57;RhoGEF2 RNAi . A decrease in Sqh-mCherry was seen in ROK RNAi mutant embryos and this decrease remained in GrafCR57, ROK RNAi embryos thus confirming that hyper contractility in GrafCR57 embryos is dependent upon Myosin II activation via Rho-kinase .We also estimated the levels of Sqh-mCherry as a ratio to the inter ring intensity in all the mutants used in the manuscript. This data is included as a part of revised Figures 7, 8 and 9. We found that there is an increase in Sqh-mCherry at the ring in RhoGEF2-OE and this was no longer seen in combinations of GRAF-OE; RhoGEF2-OE . We found that there is a decrease in Sqh-mCherry in RhoGEF2 RNAi expressing embryos and this was not seen in the combinations of 4. GRAF-OE (with or without Rho-GEF2) appears to have more lateral actin. Were the images in Figure 6 collected and presented identically? Acquisition and post-acquisition modifications were identical? The question regarding acquisition/post-acquisition modifications to the images applies throughout the paper. It is not clear to me if the reader can directly compare intensities between early/mid/late samples or if they were all adjusted independently for presentation. Please make this clear. In a number of the images, the noise appears to increase in Late, is this because there is more diffuse GRAF or because the laser/signal was adjusted to enhance the image for presentation?The immunostainings for GRAF-OE are imaged at similar laser power settings to controls on the confocal microscope. There is an increase in GRAF all along the Z axis in GRAF-OE embryos . As noted by the reviewer, this also coincides with an increase in Phalloidin and therefore increase in F-actin in GRAF-OE embryos. It may be possible that an increase in GRAF gives rise to an increase in F-actin along the furrow.Graf mutant embryos and will be able to highlight the impact of GRAF on regulating actin remodelling in a future study.We are currently investigating the change in recruitment of actin remodelling proteins in CR57. However, I am not convinced of this based on the data. Is the initial area of the rings significantly different between control embryos and GRAFdeltaRhoGAP-GFP? In that case, are the dynamics of ring contraction over time different, or simply rings are contracting at similar rates and are smaller in GRAFdeltaRhoGAP-GFP because their original size was smaller? Initially, the dynamics of contraction in GRAF CR57 embryos seems significantly faster than in GRAFdeltaRhoGAP-GFP, which could imply that the RhoGAP domain of GRAF is important at later stages of cellularization. Differences in dynamics would be more obvious if the curves were normalized to the initial size of the rings. The authors should look at the rate of contraction to be able to claim changes in contractility speed.5. In figure 3C the authors claim that in the GRAFdeltaRhoGAP-GFP embryos the phenotype is similar to that in GRAF GrafCR57; GRAFΔRhoGAP-GFP and GrafCR57; GRAF-GFP has now been redone using the Sqh-mCherry localization as a readout for making the ROI for estimating area. The earlier distribution was estimated using the GFP fluorescence of GRAF-GFP or GRAFΔRhoGAP-GFP, this tends to label the edges better than the vertices and often appears to be rounded. This gave an underestimate of the area especially in early cellularization. The revised Figure 4 shows that there is a decrease in area in GrafCR57, GRAFΔRhoGAP-GFP similar to GrafCR57 when compared to both controls and in GrafCR57, GRAF-GFP embryos throughout cellularization.The quantification of area for 6. For your graphs measuring the change in ring area of over time  an ANOVA analysis has been performed on each time point or the overall rate? The figure legend indicates these are statistically significant, but there is no indication in the figure. It is just hard to follow exactly what is statistically significant in these graphs.We have added a description of the curves that are statistically different in the figures and legends in the revised manuscript. One-way ANOVA with the modification used compares the overall curve for area change between different genotypes.GrafCR57, GRAF-OE and controls in Figure 6—figure supplement 1B. The description for this has been added to the legends.We have used the two-way ANOVA to compare each time point for Sqh-mCherry intensity between 7. The authors are looking at the mean fluorescence of different proteins like myosin and GRAF. Although this is a very informative metric, I think they should also look at the total levels of these molecules to see if they are increasing or not. For example, the authors say that the levels of GRAF and myosin increase from early to mid-cellularization. However, usually mean fluorescence is normalized to (divided by) the area of the structure under study. As the rings are decreasing in area during cellularization, the increase in mean fluorescence they are observing could simply be an increase in the density of myosin or GRAF as the rings become smaller. Therefore to talk about an increase in the recruitment of these molecules, the authors should look at total fluorescence levels (the product of mean fluorescence and area of the ring). This is particularly important when comparing the levels of molecules in phenotypes where the ring sizes differ at a given time point, like figure 5D.All the fluorescence intensities for the GRAF antibody, Zipper antibody, GRAF-GFP, AnillinRBD-GFP and Sqh-mCherry are represented as a mean per pixel at any given time point or cellularization stage in the previous and the revised manuscript.We have shown line scans of the relative fluorescence intensity across a line with fixed thickness and length at the furrow tip for Sqh-mCherry, GRAF-GFP and AnillinRBD-GFP. We find that Sqh-mCherry and GRAF-GFP colocalize in early stages. GRAF-GFP increases at the furrow tip in mid stages and becomes cytoplasmic in late cellularization . Sqh-mCherry on the other hand increases in mid cellularization and is still present in the ring in late cellularization.The mean fluorescence intensities per pixel for Sqh-mCherry are obtained from sum projections of 5 sections in revised Figure 6—figure supplement 1B. The mean fluorescence intensities per pixel are represented as a ratio to the maximum fluorescence per embryo. This shows that there is an increase in Sqh-mCherry and GRAF in every pixel in mid cellularization followed by a decrease in late cellularization. We have included this description in the revised Materials and methods.The mean fluorescence intensities per pixel obtained from the sum intensity images relative to inter ring intensity per pixel in late stages are now added in Figures 6, 7, 8 and 9 to estimate the relative change in Myosin II recruitment in different mutant and rescue combinations. We have included this description in the revised material and methods.8. Figure 8: The lower diagram in C shows Graf and ROK KD leads to "Ring constriction similar to controls". This is not supported by the data. You've broken the chain below the GAP, and therefore the phenotype should mimic ROK-KD alone, which is what 8B shows. The blue line is more similar to the green than to the black. ROK isn't in the competitive cycle like RhoGEF2 is. It is downstream. Regardless of what happens upstream, ROK-KD should largely prevent myosin activation and constriction, which the authors data show. And "Dephosphorylated myosin 2" would not lead to "Ring Constriction similar to controls" as the model suggests, it would lead to the results presented in the data.GrafCR57 ROK RNAi combinations. ROK depletion suppresses the hyper constriction phenotype of GrafCR57.We thank the reviewers for pointing this out. Yes this was indeed a typographical error. We have made a new summary diagram in Figure 9 and corrected this to show that there is loss of constriction in ROK RNAi and this is maintained in the