Hepatocellular carcinoma (HCC) is the fourth most frequent cause of cancer-related mortality worldwide. While ablation, resection and orthotopic liver transplantation are indicated at an early stage of the disease, Sorafenib (Sfb) is the current most administrated first-line treatment for advanced HCC, even though its therapeutic benefit is limited due to the appearance of resistance. Deep knowledge on the molecular consequences of Sfb-treatment is essentially required for optimizing novel therapeutic strategies to improve the outcomes for patients with advanced HCC. In this study, we analyzed differential gene expression changes in two well characterized liver cancer cell lines upon a Sfb-treatment, demonstrating that both lines responded similarly to the treatment. Our results provide valuable information on the molecular action of Sfb on diverse cellular fundamental processes such as DNA repair, translation and proteostasis and identify rationalization issues that could provide a different therapeutic perspective to Sfb.Hepatocellular carcinoma (HCC) is one of the most frequent and essentially incurable cancers in its advanced stages. The tyrosine kinase inhibitor Sorafenib (Sfb) remains the globally accepted treatment for advanced HCC. However, the extent of its therapeutic benefit is limited. Sfb exerts antitumor activity through its cytotoxic, anti-proliferative and pro-apoptotic roles in HCC cells. To better understand the molecular mechanisms underlying these effects, we used RNA sequencing to generate comprehensive transcriptome profiles of HepG2 and SNU423, hepatoblastoma- (HB) and HCC-derived cell lines, respectively, following a Sfb treatment at a pharmacological dose. This resulted in similar alterations of gene expression in both cell lines. Genes functionally related to membrane trafficking, stress-responsible and unfolded protein responses, circadian clock and activation of apoptosis were predominantly upregulated, while genes involved in cell growth and cycle, DNA replication and repair, ribosome biogenesis, translation initiation and proteostasis were downregulated. Our results suggest that Sfb causes primary effects on cellular stress that lead to upregulation of selective responses to compensate for its negative effect and restore homeostasis. No significant differences were found specifically affecting each cell line, indicating the robustness of the Sfb mechanism of action despite the heterogeneity of liver cancer. We discuss our results on terms of providing rationalization for possible strategies to improve Sfb clinical outcomes. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults . HCC hasSfb is a multiple tyrosine kinase receptor inhibitor such as the vascular endothelial growth factor receptors 2 and 3 (VEGFR2 and VEGFR3), the platelet-derived growth factor receptor beta (PDGFR-ß), FLT3 and c-KIT, as well as the Raf kinases, which are integral components of the Ras/Raf/mitogen-activated protein (MAP)/extracellular signal-regulated kinase (ERK) kinase (MEK)/ERK signaling cascade ,11; thusPrevious studies have examined the expression level of numerous transcripts in selected HB and HCC cell lines, which differ in their genetic characteristics, treated with Sfb using DNA microarray technology . The aim2 until reaching a density of 100,000 cells/cm2. Sfb was added at the concentration of 10 µM at 24 h after plating, and lysates were obtained after 12 h treatment. Sorafenib  was dissolved in dimethyl sulfoxide (DMSO) as a stock solution (100 mM).HepG2 and SNU423 cell lines were obtained from the American Type Culture Collection . As described earlier, cell lines were selected according to their origin, cellular differentiation stage, and p53 genetic status: HepG2 (wild-type p53. HB-8065™)  and SNU42). Two hours before cell harvesting, 20 μL of 10 μM BrdU was added to the cultures. DNA was denaturalized with 200 μL FixDenat solution included in the commercial kit for 30 min at room temperature. After removal, cells were incubated with 100 μL of monoclonal anti-BrdU antibody HRP conjugated for 90 min at room temperature. Afterwards, cells were washed with PBS buffer  and incubated with 100 μL revealing solution for 15 min at room temperature. Absorbance at 370 nm (A370) was measured using an Infinite 200 PRO microplate reader . Cell cycle progression was assessed by flow cytometric analysis. For this, cells were seeded 24 h before treatment to a final confluence of 70% in a 6-well plate. Afterwards, cells were treated with 10 µM Sfb for 12 h and then harvested and fixed in 70% ethanol in PBS buffer overnight at 4 °C. After this, cells were resuspended in PBS buffer and incubated with 0.5 mg/mL RNase A during 1 h at 37 °C. Propidium Iodine (PI) was added to a final concentration of 0.05 mg/mL for 20 min at room temperature. Finally, cells were filtered to avoid aggregation and cell cycle progression was assessed using a FACSCantoTM Flow Cytometer and analyzed using the FACSDiva software .The measurement of bromodeoxyuridine (BrdU) incorporation was completed as exactly described in  using a Total RNA was extracted from each sample using a RNeasy mini kit according to the manufacturer’s instructions . RNA from each sample was then stored at −80 °C before further analyses.25 beads  according to the manufacturer’s instructions. Samples were normalized to an equivalent concentration of 67.3 ng/µL and prepared for RNA Ilumina Sequencing. For HepG2 cell lines, three biological replicates were obtained, while for SNU423 cells only two biological replicates were collected.Libraries were prepared using RNA of cells treated either with the vehicle or with Sfb. Total RNA was prepared as described above. Then, concentration and quality of the RNA were assessed with Qubit  and a 2100 Bioanalyzed Nano Chip , respectively. RNA Integrity Number (RIN) values were > 9 in all RNA samples. Polyadenylated RNA was isolated from the total RNA using NEBNext Oligo d(T)p-value < 0.5 and [log2(fold changes)] ≥±0.5 were selected. Data presented in this study has been submitted to the Gene Expression Omnibus database under the accession number GSE186280.RNA sequencing was performed with the NextSeq500 Mid-Output and 2 × 75 pb length parameters (paired-end). RNA-Seq data were first filtered using the FASTQ Toolkit v1.0.0 program and then analyzed using the BaseSpace Onsite v3.22.91.158 from Illumina. Only genes that were upregulated or downregulated with a https://reactome.org; last accessed 3 February 2022) )Both HepG2 and SNU423 are epithelial liver cancer cell lines despite the well or moderate differentiation they respectively display; as such, both cell lines have high levels of E-cadherin but not increased levels of vimentin ,70. ReceHCC is one of the deadliest human cancers. Sfb, an oral multikinase inhibitor, has been considered the standard care for patients with advanced unresectable HCC . Howeverhttps://livercenter.pitt.edu/omics-data-liver-diseases; last accessed 3 February 2022). We identified DEGs in two different liver cancer cell lines. HepG2 in contrast to SNU423 cells harbord a wild-type version of p53, although this fact has not been reflected in significant differences in increasing cell proliferation or inducing apoptosis of the former versus the latter cell line [To deepen understanding on the cellular response to Sfb, we studied its effects at a clinical reliable dosage on the global gene expression in two different model HB and HCC cell lines, HepG2 and SNU423, respectively, using RNA-Seq. This study complements those previously obtained using DNA microarray technology or available as a global GEO dataset in the literature . In ell line ,78). AccFew insignificant alterations in the gene expression patterns were identified as specific for HepG2 versus SNU423 cells. In our conditions, Sfb has an overall unified response, despite the fact that SNU423 are mildly more resistant to the proapoptotic and antiproliferative properties of Sfb than HepG2 cells .In summary, the present study provides differential gene expression changes in an HB and an HCC cell line treated with Sfb. We not only validated previously reported findings, but also identified novel issues in terms of the basis for further research for novel therapeutic perspectives. Data presented herein will allow to enlighten previously unconnected molecular mechanisms induced by Sfb in liver cancer cells.


Human Schlafen 5 (SLFN5) is reported to inhibit or promote the proliferation of several specific types of cancer cells by our lab and other researchers. We are curious about its implications in lung adenocarcinoma (LUAC), a malignant tumor with a high incidence rate and high mortality.  Lentiviral stable transfections of SLFN5-specific shRNA for knockdown and SLFN5 full-length coding sequence for overexpression were performed in LUAC cell for proliferation analysis in vitro and in vivo in nude mice. Clinical LUAC samples were collected for immunohistochemical analysis of SLFN5 protein levels. in vitro and in vivo and promoted apoptosis. As to the signaling pathway, we found phosphatase and tensin homolog on chromosome 10 (PTEN) was positively regulated by SLFN5, while its downstream signaling pathway AKT/mammalian target of rapamycin (mTOR) was inhibited. Moreover, compared with adjacent normal tissues, SLFN5 protein levels were markedly decreased in lung adenocarcinoma tissues. In conclusion, these suggest that human SLFN5 plays inhibitory roles in LUAC progression through the PTEN/PI3K/AKT/mTOR pathway, providing a potential target for developing drugs for lung cancer therapy in the future. We found that knockdown of endogenous SLFN5 upregulates cancer cell proliferation while inhibiting apoptosis. Besides, SLFN5 inhibition on proliferation was also observed in a nude mouse xenograft model. In contrast, overexpression of exogenous SLFN5 inhibited cell proliferation  Increasing evidence has shown that the Schlafen family (SLFN for human/Slfn for mouse) functions in both the immune system and malignant tumors –10. The β-catenin pathway  × 0.52  [SLFN5 knockdown or shRNA control cells (2 × 10= width) . When thμm. Slides were incubated with anti-SLFN5 antibody, or anti-PTEN, at 1 : 100 dilution for 2 hr followed by secondary antibody incubation and reaction with DAB peroxidase substrate. Slides were also stained with H&E routinely. The staining results were read and given the results back to back by two experienced pathologists.The collection and treatment of tissue samples from lung adenocarcinoma patients met the requirements of the ethics committee of Shanghai University of Medical and Health Sciences. The samples were fixed with 10% paraformaldehyde, dehydrated with xylene gradient, embedded in paraffin, and sliced to a thickness of 4 SLFN5 is reported to exert differential effects on the proliferation of distinct cancer cell types. It inhibits melanoma cell proliferation but has no effect on renal cell carcinoma cell proliferation, while promoting glioblastoma growth. The lung adenocarcinoma cell line A549, the most commonly used cell line in the lung cancer studies, exhibits moderate expression levels of SLFN5 and was therefore adopted for both knockdown and overexpression assays in the present study. We constructed SLFN5 stable knockdown cell lines using SLFN5-specific shRNA (ShSLFN5) and scrambled shRNA as a control (ShCtrl). Meanwhile, SLFN5 overexpressing cell lines were created with a lentiviral SLFN5 recombinant vector (SLFN5) and empty vector as a control (Vector). Knockdown and overexpression efficiencies were confirmed as shown in Whether the SLFN family is related to the apoptosis pathway has not been previously reported. To evaluate the relationship of SLFN5 with apoptosis, flow cytometry analyses of apoptosis were performed. In ShSLFN5-A549-transfected cells, both early and late apoptotic percentages were lower than in ShCtrl-A549 cells, and total apoptotic percentages decreased . In contin vitro cell culture; however, under the much more complex in vivo environment, rich in multiple regulatory cytokines and growth factors, various matrices, infiltrated cells, and vascular tissues, the functions of SLFN5 are largely unknown. To determine the effects of SLFN5 on tumor growth in vivo, A549 lung adenocarcinoma cells with SLFN5 knockdown or overexpression were subcutaneously injected into male nude mouse axilla regions. Over approximately 30 days, tumors growing from A549 cells transfected with ShSLFN5 grew significantly larger than those from ShCtrl-transfected cells, both with respect to tumor volume and weight , and Bax (its phosphorylation causes degradation through ubiquitination).PTEN is a suppressor gene in lung cancer that is regulated by several transcription factors and methylation modifications , 45–47. In conclusion, SLFN5 exerts inhibitory roles in lung cancer cell tumorigenesis and progression, providing a potential target for future drug development in lung cancer therapy. To further clarify SLFN5's regulation and function, additional studies investigating some aspects such as regulatory factors, signaling details, and structural mutants of SLFN5 need to be undertaken in the future.


Heterocephalus glaber, provides an interesting model for studying biomarkers of longevity due to its long lifespan of more than 30 years, almost ten times longer than that of mice and rats. α-Klotho (klotho) is an aging-suppressor gene, and overexpression of klotho is associated with extended lifespan in mice. Klotho is predominantly expressed in the kidney. The expression profile of klotho in the NMR has not previously been reported. The present investigation studied the expression of klotho in the kidney of NMR with that of Rattus Norvegicus (RN) and demonstrated that klotho was expressed in the kidney of NMR at the same level as found in RN. Besides, a significant expression of Kl mRNA was found in the liver of NMR, in contrast to RN, where no hepatic expression was detected. The Klotho expression was further confirmed at the protein level. Thus, the results of the present comparative study indicate a differential tissue expression of klotho between different species. Besides its important function in the kidney, Klotho might also be of significance in the liver of NMR. It is suggested that the hepatic extrarenal expression of klotho may function as a further longevity-related factor in supplement to the Klotho in the kidney.The Naked Mole Rat (NMR),  In the past few years, the NMR has been used as a model for longevity studies due to its lifespan, exceptional features and extraordinary biology, that has been adapted as a consequence of the challenges posed by a subterranean niche4. The NMR is a mouse-sized (approximately 35 g in weight) rodent, which does not show any increase in the age-specific hazard of mortality, in defiance of Gompertzian laws5. This species shows no decrease in physiological capacity, cardio-vascular function, gastrointestinal function, glucose tolerance, and reproductive capacity until the third decades of life7. NMR is further characterized by resistance to sarcopenia and potential low susceptibility to cancer11. The longevity of NMR, together with their low growth rate, might be a consequence of a very high energy consumption in these rats as they are exposed to high levels of oxidative stress early in life12.The Naked Mole Rat (NMR) is an eusocial, subterranean rodent that lives more than 30 years, which is almost ten times longer than mice and rats in captivity (app. 3–4 years) or of any other small rodent14. It is distinctly expressed in the kidney and found in small amounts in the parathyroid glands and the plexus choroideus in the brain15. The Klotho gene (Kl) is located on chromosome 13 in humans and 12 in rats and contains five exons and four introns. In humans and mice, along with the full-length transcript of membrane-bound α-klotho (mKl), an alternatively spliced Klotho has also been identified as a nonsense-mediated mRNA decay target17, known as sKl.α-Klotho  is an evolutionarily, highly conserved kidney hormone, that is related to longevity18. This is partially effectuated by its circulating forms that are excreted into serum, urine, and the cerebrospinal fluid, and which might have a direct effect on tissues or cells that do not express klotho 23.Klotho has a critical role in longevity and healthy agingklotho in mice results in a shortened lifespan, and klotho overexpression in transgenic mice extends the lifespan by 30%24. The klotho deficient mice develop accelerated aging features besides shortened lifespan, organ and tissue atrophy, osteoporosis, sarcopenia, frailty, and a severe vascular phenotype of calcification, arteriosclerosis, and impaired endothelial function25. Klotho is connected to functions that protect the vasculature against osteochondrocytic conversion, including inhibition of high phosphate (P) induced vascular calcification through inhibition of the sodium dependent phosphate transporters, Pit-1 and Pit-2, or by Wnt antagonism28. In humans circulating levels of Klotho decrease with age31. Reduction in Klotho levels has also been proposed in patients with several aging-related diseases such as cancer, Alzheimer’s disease, hypertension, and uremia36. Measuring of plasma Klotho protein levels is however, difficult due to lack of good antibodies and reliable commercial assays37.Knockout of 38. FGF23 decreases the type II sodium-dependent P co-transporters (NaPi2a and NaPi2c) in proximal tubules, and is thereby inhibiting P reabsorption39. In NMR plasma P levels has been found to be considerably lower as compared to that of Rattus Norvegicus (RN) and other small rodents. Furthermore, an inverse correlation has been demonstrated between plasma P and longevity in mammals indicating a relationship between longevity and regulators of mineral homeostasis40.Klotho is an important renal hormone involved in the calcium (Ca) and P homeostasis as an obligatory co-receptor for the action of fibroblast growth factor 23 (FGF23) in the kidney via the fibroblast growth factor receptor (FGFR)2 vitamin D by inhibition of the 25-OH vitamin D 1α-hydroxylase (Cyp27b1) and by stimulation of 1,25 (OH)2 vitamin D 24α-hydroxylase (Cyp24a1) via the KL/FGFR-ERK pathway41. Recently, the crystal structure of the KL-FGFR1c-FGF23 complex has been resolved, providing a structural basis for understanding the unique feature of FGF23 and its requirement for Klotho to exert their hormonal actions42. Thus, Klotho enhances the renal activity of FGF23, and P depletion has been shown to rescue most of the aging phenotypes of klotho knockout mice44. The physiological and biochemical factors responsible for these effects are not well characterized, and it is not known whether the long-living NMR represents a specific expression of the anti-aging and longevity hormone, Klotho45.FGF23 and thus Klotho reduce the synthesis of 1,25 (OH)In the present investigation, we therefore examined the level of expression of Klotho in the kidney of NMR and further investigated whether a potential kidney expression of Klotho was increased, as compared to that of the common rat, RN. Our comparative study further examined whether Klotho was expressed in other tissues  of the long-living NMR versus RN. The hypothesis was that the longevity of NMR was associated with increased expression levels of Klotho.. Kidney, liver, skin, and lung samples from five redundant NMRs were kindly obtained from a Danish Zoo-housed population . The genders and ages of these animals were unknown. Another five NMR sets of organs were kindly provided as specimens from a research colony at the Gorbunova and Seluanov laboratory . This was a mix of both genders and different ages. In comparison, corresponding tissues from adult male Wistar rats  with a weight of 200 g were used.The Naked Mole Rat belongs to a very selective group of protected mammals, which can’t be bought commercially for experimental researchThe experimental protocols were approved by the Danish Animal Experiments Inspectorate, Ministry of Environment and Food of Denmark, the Danish Veterinary and Food Administration (reference no: 2017-15-0201-01214) and performed in accordance with the National Institute for Health’s Guidelines for the Care and Use of Laboratory Animals. The study was carried out in compliance with the ARRIVE guidelines.klotho. The genomic sequence of klotho was obtained from the Ensembl genome browser for Human and Rat and from NCBI genome browser for NMR . The first-strand cDNA was synthesized from 1.5 μg of RNA with Superscript III cDNA kit . Jumpstart  and LightCycler 480II  were used for qPCR. The cDNA levels were normalized to the mean of reference genes Table , which wmKl cDNA from a pool of five kidney samples and five liver samples was performed by Taq DNA polymerase  following the manufacturer’s protocol. Amplification of a long fragment of mKl cDNA was performed by one pair of primer (Kl-FOR_1/Kl-R2) (Table mKl transcript was cut out. The QIAquick Gel Extraction Kit (# 28704) was used according to the manufacturer’s protocol for cleaning the band from the agarose gel.PCR amplification of 2) Table  using thhttp://blast.ncbi.nlm.nih.gov/) was used to align sequences to check possible identical sequences between proximal and distal breakpoints. Primers used for Sanger sequencing are listed in Table The cDNA from 5 NMRs was pooled together for kidney and liver, respectively, and amplified by PCR, as described above. The quality of the PCR product was confirmed on 2% agarose gel. The amplicons were then sequenced in both directions using BigDye Terminator version 1.1 Cycle sequencing kit (Life Technologies) and sequenced on ABI 3130XL genetic analyzer by BigDye 3.1. Sequence traces were aligned to the NMR (XM_021251513.1) gene reference sequence. Nucleotide BLAST Program  with Rattus Norvegicus (BAA34740.1: NBCI) was performed.47. Endogenous peptides that are quantifiable surrogates of Klotho were selected from the first domain of Klotho protein, KL1  at Biogenety, Aalborg, Denmark. PRM is an ion monitoring technique based on high-resolution and high-precision mass spectrometry, which can detect peptides with an attomole-level in complex samplesp ≤ 0.05.Normal distributed data are presented as mean ± standard deviation (SD). Statistical significance was tested using unpaired two-tailed t-test calculated in GraphPad Prism 8.0 to compare means between groups. Significance level was set at 48. The specificity of the samples used in the present study was examined by electrophoresis of total RNA following by staining with ethidium bromide. The samples belonging to the NMR expressed a cleaved 28S characteristic for this species, in contrast to uncleaved 28S of rRNA in RN, confirming that the tissues used belonged to NMR  fragmentNMR Fig. .Figure 1klotho in NMR tissues was examined by designing specific primers for amplification of mKl and reference genes, as the NMR rats do not share the same sequence as RN  and the research colony population (Gorbunova and Seluanov laboratory). The expression of klotho in the kidney of NMR was similar to that of RN  region of the coding sequence by PCR. No expression of klotho was detected in the lung and skin of NMR, while a clear expression of klotho was observed in the liver of NMR  in the NMR kidney and liver was amplified  with annealing to two different positions on the gene, and confirmed on agarose gel run together with controls  in four NMR livers and kidneys (positive control). Since the protein sequence of NMR Klotho was not annotated, unique peptides were designed to detect predicted NMR Klotho protein. These peptides were aligned to human and rat Klotho protein and found identical. The peptides for Klotho detected by PRM are shown in Fig. 20. Cleavages that produce the 130 kDa and 70 kDa soluble forms of Klotho were termed α and β-cut, respectively. Two potential recognition sites for sheddase on Klotho, at β and α-cut sites, have been identified. PPLPENQPLE recognition site sequence (between KL1 and KL2 domains) at the β-cut site is a conserved sequence in mice, rats, and humans. At the α-cut site (close to the transmembrane region), PGPETLERF in humans and LGSGTLGRF in mice and rats have been detected. We found that NMR contains the conserved metalloprotease recognition sequences PPLPENQPLE at its potential β-cut site and LGPETLGRF at its potential α-cut site. The latter is less well conserved. It shares total similarity with human besides the first amino acid in the sequence, which however is similar to the rat and mouse amino acid sequences  with three exons and two introns and a 3000 bp coding region. However, in Ensemble the same genome assembly for NMR predicted gene for klotho was shown to contain 6 exon and 5 introns (2763 bp) (KL-201 ENSHGLT00000007403.1). Klotho is flanked by PDS5B and STARD13 as seen in other species, including humans, rats, and mice16. The chromosome location of Kl in NMR is unknown. The sKL contains a 50 bp insertion between exon 3 and 4 in human Kl17. sKl occurs in human and mice due to alternative splicing and has not been detected in rats16.In 2012 the genome for NMR  was sequenced, and predicted gene sequences for klotho expressed in the kidneys of NMR and RN were equal the existence of a potential extrarenal expression of mKl was further examined in other organs of NMR, such as the lungs, skin, and liver. It was found that mKl was not expressed in the skin or the pulmonary tissue of NMR, while a highly significant expression of mKl was demonstrated in the liver of NMR.As in the present investigation the levels of mKl in the liver and its alignment to kidney mKl, confirmed the expression of mKl in the NMR liver. Furthermore, the presence of the sKl in the liver of NMR was excluded. Amplification and sequencing of the predicted region of alternative splicing, which potentially could result in nonsense-mediated decay of mRNA as demonstrated in both humans and mice17 was performed, and it was shown that NMR hepatic klotho did not include the 50 bp introns, which have been shown in the alternatively spliced region.A large piece of the amplified and sequenced mKl codes for a transmembrane Klotho, a type-1 single-pass protein, with a long extracellular region consisting of two separate glucosyl hydrolase domains, repeats Kl1 and Kl2, which are connected to a short 20 amino acid single transmembrane domain. Cleavage of Klotho generates two soluble circulating forms20. Metalloproteases, mainly ADAM 10 and ADAM 17, have previously been shown to be responsible for Klotho shedding20. In silico analysis of the sequence of the NMR klotho gene as well as amino acids show the existence of similar secretase recognition cleavage sites, as seen in humans, mice and rats. Sanger sequencing of NMR liver and kidney klotho confirmed the existence of recognition sites for both α and β-cut cleavage  from cytoplasmic and mitochondrial sources53. These metabolic pathways likely contribute to NMR’s healthy and successful aging process. ROS-sensitive apoptosis signal-regulating kinase 1 (ASK1)-signalosome is a mediator of cell senescence and the aging phenotype of the klotho knockout mice model54. Klotho deficiency in vivo is associated with decreased hepatic nuclear and cytoplasmic levels of nuclear factor erythroid 2-related factor 2 (Nrf2), and an inverse correlation has been demonstrated between Klotho and Nrf254. Overexpression of klotho results in nuclear translocation of Nrf2 and activation of the antioxidant response element (ARE) in the promoter of antioxidant genes55. We hypothesize that NMR presumably uses its extrarenal hepatic Klotho to reduce the generation of ROS, which arise from mitochondria and cytosol in the liver. This is likely mediated by enhanced Nrf2 and its antioxidant enzyme network, which have been found to be to be elevated in the liver of NMR57.Another interesting aspect which might link the expression of Klotho in the liver of NMR to longevity is that NMR is highly adapted to very harsh living conditions. NMRs live in dark underground tunnel systems (up to two meters under the surface) with low oxygen and high carbon dioxide59 is interesting, and it might be assumed that the extrarenal production of Klotho in the liver of NMR and the low plasma P60 might be involved in elevated autophagy and the prolonged life span in NMR61. NMR has a low plasma P60, which potentially can induce an increased autophagy flux e.g. by disturbing the binding between beclin1 (BCL1) and its negative regulator (BCL2)58. Experimental and clinical observations provide strong evidence for a toxic effect of high P, which accelerates the aging process and implies a role for P homeostasis as a toxic factor in the mammalian aging process similar to that of knockout of klotho62. Thus, long-term intake of high P is associated with decreased renal expression of Klotho, low circulating Klotho, and both increased PTH and FGF2363. Therefore, a defect in Klotho causes not only P retention, but also a premature-aging syndrome in mice, which can be rescued by resolving the hyperphosphatemia64. As previously mentioned, a comparative longevity analysis has revealed existence of a significant inverse correlation between plasma P and lifespan in different mammals. Interestingly plasma P levels of the NMR is half that of mice and rats65.Low P upregulates the expression of Klotho and enhances autophagy independently. The link between P toxicity, Klotho and, autophagyklotho in the kidneys is at the same level, as that found in the common rats, RN. Besides, for the first time, it is shown that the Naked Mole Rats exhibit a highly significant extrarenal expression of Klotho in the liver, a finding which is in contrast to no liver expression of Klotho in Rattus Norvegicus. Thus, differential tissue expression of Klotho in different species is demonstrated. The results of the present comparative study indicate that the longevity-related gene, klotho, which in the Naked Mole Rat also is expressed in the liver, might have a specific function as a further longevity and health-span-expanding factor.The results of the present investigation clearly demonstrate that in the Naked Mole Rat with an extreme longevity, the expression of Supplementary Figures.


Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination. Melanoma can be a devastating disease, one in which incidence is on the rise, and treatment options are limited for the most aggressive forms of the disease. Several gene mutations are widely expressed in melanoma incidences. In particular, the BRAFV600E mutation occurs in ~60% of patients. This mutation results in constitutive activation of BRAF signaling in the ERK/MAP Kinase pathway with a result of increased cell proliferation and survival . Having large datasets available can provide a powerful tool for identifying pathways and gene expressions that are critical in determining prognosis and overall sensitivity to treatments. In this study, we have selected the melanoma cell lines from the high-dimensional CCLE dataset and used various machine learning methods to identify genes of interest that distinguish these 62 cell lines into three discrete clusters. To date, the use of biomarker data from these databases has provided limited success and will require confirmation in biologic systems to determine true correlative benefits. The overall hope is that these analyses will lead to the development of molecular signatures for targeted therapies and the ability to individualize the treatments for the most durable response.One approach to address this question is to profile gene expression using CDNA microarray screening or transcriptome analyses from patient tumor libraries. These have the advantage of providing known information on tumor response to different therapies, tumor staging, prognosis, and survival data. The downside to this approach is limited access to tissues, tissue heterogeneity, and overall tissue availability -competitive with the optimal clustering obtained by augmenting k-means with an improved initialization technique. Computational experiments showed that the augmentation improves both the speed and the accuracy of k-means, often quite dramatically. Likely due to the extreme scarcity and sparseness of our data, more complex clustering techniques did not prove useful.We used gene expression and copy number features for 62 melanoma cell lines from the CCLE to systematically assess features of interest for distinguishing genomic clusters of melanoma cell lines. CCLE skin cancer data included expression for >19,000 genes, copy number for >20,000 genes and 24 drug sensitivity features. To identify the distinct clusters of the cell lines, we applied the k-means++  which reflects the sum of Euclidean distances of cluster members to cluster centers. An elbow-point with this metric suggests a natural k in the data such that the clustering explains a large percentage of variability in the data with a small k. Each data point corresponding to each candidate k reflects the results of ten randomized trials. The solution with the minimum error is selected.When plotting a goodness-of-fit metric against the number of clusters specified for the k-means++ algorithm, we can search for a specific number of clusters, k when considering the copy number variation features alone indicated by a near-straight line in the graph. Combining the copy number variations with the gene expressions, shown in As shown in Information from the American Type Culture Collection (ATCC) suggested the origin of the two cell lines exclusively clustered together in To identify the patterns of gene expressions best separating the three clusters, we applied a pipeline of various feature selection and classification methods with cluster index used as the response variable. We tailored our methods to work with our high-dimensional dataset.Feature selection methods consist of three main groups: filter, wrapper, and embedded methods. Filter methods rank each feature according to some univariate metric with the response variable. Wrapper algorithms search for the best subset of features concerning the classification performance of an underlying model. The wrapper algorithm typically treats the classification algorithm as a black box, such that any classifier is suitable for the wrapper. One can use standard optimization techniques  as the wrapper algorithm. Embedded methods search among different feature subsets, but unlike wrappers, the process is embedded into a classification model and takes place naturally as a part of learning the classifier. Decision Trees are a famous example of a classification algorithm with embedded feature selection properties. For more information on feature selection methods and their applications to genomic and proteomic data, refer to Dubitz  and the k-nearest neighbors (KNN) algorithm. Specifically, the objective function of the search is the average test set classification performance of KNN when k is varied from three to nine. We randomize the training and test splits for each trial in the last step. Each random trial of the search algorithm returns a 20 feature subset of the 500 feature pool. We record the features which occur in the results the most often. This procedure consolidates the results from three highly varied algorithms.In part because of the high dimensionality and sparseness of the CCLE melanoma data, one is often led to finding entirely different results from applying feature selection on randomized trials or between different algorithms. The results depend entirely on algorithm details and not the structures within the data itself. Also, in part because of the power of various classification algorithms: using a large enough subset of features, even if they are simply randomly selected, can yield good classification accuracy. The classifier can learn patterns in the data that are circumstantial artifacts as opposed to biologically relevant information. We propose that the most general features which distinguish the data will score well across different kinds of feature selection algorithms and will also yield high classification performances across many different types of classifiers. To reach this end, we first employ a statistical univariate technique, called the Fisher Score  on Input Data and select the top 500 gene expressions based on the SVM-RFE+CBR ranking.S2. Perform the SVM-RFE+CBR method  and (S2). After eliminating duplicates, the resulting reduced data contains 49 cell lines with 928 gene expressions.S3. Reduced Data and record the genes that occur most often in the resulting optimal 20 feature subsets.S4. Perform 500 randomized trials of SFFS wrapped around KNN on Output Data: 49 cell lines with the top 15 genes obtained from the randomized sequential trials of k-Nearest Neighbors in (S4).S5. ith feature is calculated asThe Fisher Score has been used in several studies on genetic datasets due to its computational simplicity  were sufficient for perfect or near-perfect classification performance across various classifiers. These genes are displayed in order of frequency (importance) in Of these 15 genes, some have links to melanoma prognosis. While other genes have links to other cancers, their relationship to melanoma was never previously shown. Here are brief descriptions of some of the genes which have recently been linked to various cancers and have the potential to serve as prognostic biomarkers:TBC1D16 a GTPase-activating protein for RAB family proteins is suggested to regulate EGFR in melanoma as a result of a hypomethylation event. This may confer poor survival but at the same time may increase BRAF and MEK response  which is currently the target of several large trials showing substantial benefit with anti-PD-L1 for late-stage melanoma  is encoded by PIK3CD. PIK3CD has been reported to be overexpressed in colorectal cancer (CRC) and associated with poor survival and may be a prognostic biomarker for CRC but no relation to melanoma has been observed to date  for Cluster 1. TRIM9, ARHGEF6, C16orf52, and SMTN are also lower expressing genes for Cluster 1.There are 22 cell lines that partitioned into Cluster 2. Of these, 21 are catalogued for mutations. All 21 are positive for the BRAF V600E mutation. We observe that none of these cell lines have mutations in any of the RAS genes, which stands out from the cells in Cluster 1.Examples, of preferentially essential genes in some of the cell lines in Cluster 2, as determined by CRISPR and/or RNAi screens, included BRAF, MAPK1, SOX10, MDM2, and DUSP4.Based on our analysis, TRIM9, YPEL2, ARHGEF6, SPATA13, AHR, C16orf52, and SMTN are high expressing genes present in the cell lines of Cluster 2. Of this group, RIM9, ARHGEF6, C16orf52, and SMTN are low expressing genes in cell lines of Cluster 1.depmap.org/portal). Of the six represented cells lines, five had mutations in the BRAF gene: four with the typical BRAF V600E mutation and one with BRAF I326V, which has been found in other cancers .There are seven cells lines that partitioned into Cluster 3. One is a fibroblast cell line and is not represented in the Cancer Dependency Map portal from the Broad Institute  cross-validation experiments: Randomly partition the data into k = 10 approximately equal parts. Designate one of these subsets as a test set. Build a model on the remaining k−1 = 9 subsets that form the training dataset. Then, evaluate classification performance on the test set. Finally, average performance while using each fold as the test set once. We evaluated the performance of the Decision Tree model through 10 times Analysis of the drug response data provided by the CCLE suggests that these 15 genes also play a role in predicting drug responses. The CCLE reports drug response in a subset of the cancer cell lines as ActArea—the area above the fitted dose-response curve—and includes responses across 24 different anti-cancer drugs. Of these 24 anti-cancer drugs, we focused on two MEK inhibitors. These are believed to be crucial in the treatment of melanoma. Upon analyzing the mean response of these two drugs across all three of our clusters separately, we found a decreasing response from cluster 1 to cluster 3 across both drugs as shown in Through a machine learning pipeline of clustering and feature selection, our study identifies 15 genes whose levels of expression categorize melanoma into three genomic clusters. A Decision Tree model needs only three genes—GSTO1, TBC1D16, and TRIM9—to reliably classify nearly all of the cell lines into their respective clusters. These results shed some light on the globally relevant genetic intravariability among melanoma cell lines. These results may indicate extra considerations in choosing a cancer therapy in addition to the treatment of the BRAF mutation that exists in 42/48  of the CCLE cell lines across all clusters  Research Experience for Undergraduates (REU) Grant no. 1359341. Publication of this article was funded in part by the Open Access Subvention Fund and the John H. Evans Library.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


AREG, EREG, HBEGF, TGFA, and EPGN) is associated with osimertinib sensitivity. Novel putative biomarkers of response including those involved in immune signalling and cell cycle were found to be associated with sensitivity and resistance to MEK inhibitors respectively. We have also developed an RShiny webpage facilitating interactive visualization to fuel further hypothesis generation for drug repurposing in HNSCC. Our study provides a rich reference database of HNSCC drug sensitivity profiles, affording an opportunity to explore potential biomarkers of response in prioritized drug candidates. Our approach could also reveal insights for drug repurposing in other cancers.Effective treatment options for head and neck squamous cell carcinoma (HNSCC) are currently lacking. We exploited the drug response and genomic data of the 28 HNSCC cell lines, screened with 4,518 compounds, from the PRISM repurposing dataset to uncover repurposing drug candidates for HNSCC. A total of 886 active compounds, comprising of 418 targeted cancer, 404 non-oncology, and 64 chemotherapy compounds were identified for HNSCC. Top classes of mechanism of action amongst targeted cancer compounds included PI3K/AKT/MTOR, EGFR, and HDAC inhibitors. We have shortlisted 36 compounds with enriched killing activities for repurposing in HNSCC. The integrative analysis confirmed that the average expression of EGFR ligands ( Since 2006, the EGFR-targeting cetuximab remained the only approved molecular-targeted drug for HNSCC. In more recent years, two anti-PD1 immunotherapy drugs (pembrolizumab and nivolumab) have been approved for the treatment of recurrent and metastatic HNSCC. However, despite these advancements, only a minority of patients respond to these drugs, underscoring the unmet need to expand the treatment options for HNSCC.Head and neck squamous cell carcinoma (HNSCC) is a deadly disease affecting more than 700,000 people worldwide2, the Cancer Therapeutics Response Portal (CTRP)3 and the Genomics of Drug Sensitivity in Cancer (GDSC)5. More recently, a highly scalable method of pooled screening of a mixture of barcoded cell lines, called PRISM, an acronym for “profiling relative inhibition simultaneously in mixtures”6 presents a rich resource for drug repurposing opportunities that includes non-oncology drugs7. Amongst the cancer cell lines included in this resource, 28 human papilloma virus (HPV)-negative HNSCC cell lines were tested with 4,518 compounds, making this the largest dataset of drug response data for HNSCC. Herein, we endeavour to conduct a systematic in silico analysis of this PRISM repurposing dataset, focusing specifically on HPV-negative HNSCC, which is known to have poorer prognosis compared to HPV-positive HNSCC8. We aim to uncover the top mechanisms of action (MOA) in the compounds that show active killing in HNSCC and to identify promising compounds with biomarkers for HNSCC using computational approaches. Through this study, we also provide a readily accessible, and exploitable drug response reference dataset for HNSCC for the research community. Notably, our analysis helped to eliminate non-active compounds, revealing those that are most efficacious for HNSCC from the PRISM dataset. Further, we provide insights on the MOA landscape of active compounds, enabling the focus on downstream validation of these compounds for the treatment of HNSCC. We also demonstrated the utility of exploiting this pharmacogenomic dataset by providing examples of potential drug candidates, such as osimertinib and trametinib, along with markers of drug response, and suggestions of drug combinations to overcome drug resistance. Despite utilizing a publicly available dataset, cancer-specific analysis represent a valuable resource that will offer meaningful insights in uncovering unique drug candidates and pharmacogenomic biomarkers, which can otherwise be masked and remain unidentified in the large pan-cancer dataset.High-throughput drug screening on cancer cell lines has been the starting point behind important drug discoveries and affords the opportunity to explore drug repurposing systematically. Some of these earlier large-scale efforts include the Cancer Cell Line Encyclopedia (CCLE)https://depmap.org/repurposing/-“primary_replicate_collapsed_logfold_change.csv”, 19Q4). The identities of the cell lines were mapped using the “primary-screen-cell-line-info.csv” file, while the information of the compounds tested were mapped using the “2- Compounds tested” tab from “Corsello_supplemental_tables.xlsx”. A total of 4,686 sets of log fold change values (logFC) [relative to dimethyl sulfoxide (DMSO)] was available for 4,518 compounds. Duplicated data for 161 compounds and triplicated data for three compounds  were averaged accordingly, generating a final matrix of 4,518 unique compounds on 28 HNSCC cell lines , onto the Morpheus tool . Other combinations of the epsilon and perplexity were also tested , where compounds including EGFR inhibitors, MEK inhibitors (MEKi) were shown to cluster togetherhttps://depmap.org/portal/interactive/), to compare the means of drug sensitivity data between two groups and generate the estimates of effect size and corresponding p-value. The “drug sensitivity (PRISM repurposing Primary screen 19Q4)” was selected as input dataset, with the 28 HNSCC cell lines being placed in the “in” group cell lines, while all other cell lines tested in PRISM were used as “out” group cell lines. A total of 179 unique compounds  were found to have preferential activity in HNSCC   and four most osimertinib-sensitive cell lines  were used as input file for gene set enrichment analysis (GSEA) using GenePattern11. Gene set database “c2.cp.kegg.v7.3.symbols.gmt [curated]” was selected. Full results of the GSEA comparing osimertinib-sensitive and -resistant HNSCC can be found in Table To identify gene expression signatures and pathways that are associated with drug response, gene expression data for the HNSCC cell lines were extracted from the DepMap portal, Public 19Q4 release-“CCLE_expression.csv”https://doi.org/10.6084/m9.figshare.16443759.v1]. Hierarchical clustering was performed using a metric of “one minus spearman correlation” and “complete” linkage method. A distinct cluster of MEKi-resistant cell lines was observed, which included six lines: FADU, SNU1041, BICR18, HSC2, SNU46 and YD8. For the compounds, a few sub-branches consisting of eight active MEKi were found to share largely similar drug sensitivity profiles. This cluster consists of the three MEKi that were differentially enriched for killing activity in HNSCC , as well as another five compounds . The clinical indication and drug development status of these eight MEKi were manually curated ” was selected, with the rest following default parameters. Similar GSEA was also performed using the two following datasets as described.GCT file with gene expression matrix of the six MEKi-sensitive and six MEKi-resistant HNSCC was used as input for GSEA using GenePatternhttps://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html).Among 26 HNSCC lines tested with MEKi (trametinib) in GDSC, we identified the six most sensitive HNSCC  and six most resistant HNSCC . The gene expression dataset  for these 12 cell lines were downloaded from the GDSC data portal  with the accession number GSE108062. GSEA was performed as described above, with Illumina HumanHT-12 V4.0 expression bead chip selected as the chip platform.For the drug screen by Lepikhova et al.Hallmark gene sets enriched in MEKi-resistant groups of respective datasets  were compared for similarity. Hallmarks commonly identified from all three datasets were highlighted in the Venn diagram. Full results of the GSEA are available in Table P-value < 0.05 is considered as statistically significant.The differences in the mean of logFC between the two groups were evaluated for their statistical difference using the two-tailed, unpaired Mann–Whitney test in the GraphPad Prism software 8.0.2. Pearson correlation between gene expression and drug sensitivity was also computed using GraphPad Prism, 2, CTRP13, GDSC14 and PRISM7. Additionally, other independent research groups have also published their HNSCC screen outcomes, including Ghasemi et al.15 and Lephikova et al.12. These drug screens vary diversely in terms of screening techniques, platforms, cell lines and compounds. Amongst these, the “PRISM Repurposing dataset” conducted by the Broad Institute7, represents the largest resource for drug repurposing, and is the only dataset that includes non-oncology compounds published to date , where it resulted in a median of 46% reduction in tumour size among 17 HNSCC patients23. However, the biomarkers that were being investigated (p-ERK1/2 and CD44) did not correlate well with response23.We found three MEKi  among the compounds that were enriched for killing activity in HNSCC when compared to non-HNSCC Fig. D. TrametIL1A, SAA1, LCN2, CSF2, IL1B and CXCL1), where their gene expression significantly correlated with MEKi sensitivity across the 28 HNSCC cell lines  of combining cobimetinib with atezolimumab (anti-PDL1 mAb) in HNSCC and other cancers. Some HNSCC however, were intrinsically resistant towards MEK inhibition. In this regard, we found cell cycle and proliferation-related pathways to be consistently associated with resistance towards MEKi. This supports the combination of CDK4/6 inhibitors with MEKi for better synergy, which has been investigated in other cancers, such as melanoma, colorectal and NSCLC56. However, these were investigated in the context of the presence of mutation(s) in the Ras/Raf/MAPK pathway which is lacking in HNSCC. Instead, in HNSCC, we showed that the immune-cytokines enriched HNSCC could promote responsiveness to MEKi. The potential utility of those shortlisted biomarkers of response, as well as the efficacy of combining MEKi with CDK4/6 warrant further investigation in HNSCC.Another class of targeted cancer compounds with promising efficacy in HNSCC is MEKi. Clear selectivity of MEKi are seen where subsets of HNSCC cell lines demonstrated distinct responses to MEKi suggesting that predictive markers of response are crucial for patient selection. Unlike in melanoma, where BRAF mutation status could help to identify responders to vemurafenib, other mechanisms of response must be at play in HNSCC as BRAF mutations are uncommon in HNSCCIn summary, our HNSCC-specific analysis of the PRISM repurposing dataset has helped to reveal the targetable MOAs within HNSCC and shortlisted 36 drug candidates to prioritize for repurposing in HNSCC. Our processed HNSCC drug response dataset that is made available in the form of interactive heatmaps or RShiny app webpage is an important resource that could accelerate drug development in the context of HNSCC. We have also identified several HNSCC-specific biomarkers for EGFR inhibitor and MEKi that warrant further validation. Furthermore, using transcriptomic-based pathway-level enrichment analysis, we demonstrated how rational drug combination may be proposed, such as in the case of MEKi and CDK4/6 inhibitor. These exemplify the potential of our study in enabling new discoveries in identifying promising new drugs and biomarkers for further investigation, to expand the treatment options for HNSCC. In addition, our in silico analysis pipeline can also be applied to other cancers covered in this PRISM dataset, for their corresponding drug repurposing exploration.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


However, Graft-versus-Host-Disease (GvHD), often co-occurring with GvT, remains an obstacle for therapeutic efficacy. Hence, approaches, which selectively alleviate GvHD without compromising GvT activity, are needed. As already explored for autoimmune and inflammatory disorders, immuno-metabolic interventions pose a promising option to address this unmet challenge. Being embedded in a complex regulatory framework, immunological and metabolic pathways are closely intertwined, which is demonstrated by metabolic reprograming of T-cells upon activation or differentiation. In this review, current knowledge on the immuno-metabolic signature of GvHD-driving T-cells is summarized and approaches to metabolically interfere are outlined. Furthermore, we address the metabolic impact of standard medications for GvHD treatment and prophylaxis, which, in conjunction with the immuno-metabolic profile of alloreactive T-cells, could allow more targeted interventions in the future.Allogeneic-hematopoietic stem cell transplantation  represents the only curative treatment option for numerous hematological malignancies. Elimination of malignant cells depends on the T-cells’ Graft- Primarily (and like GvT) driven by alloreactive donor T-cells, with immune responses directed against foreign (host) antigens, GvHD can result in severe damage of host tissue, accounting for the majority of allo-HSCT-related morbidity and mortality  is a well-established and potential curative option for numerous high-risk hematological malignancies. Its therapeutic success, which depends on a mainly T-cell-driven Graft-ortality , 2.In GvHD treatment, T-cell responses are mitigated by immunosuppressive agents. However, increased susceptibility to infections, high mortality rates in steroid-refractory GvHD, and tumor relapses, emphasize the need for a deeper understanding of the T-cell pathobiology . As of tT-cell metabolism, function, and differentiation are tightly interconnected in health and disease . InterfeHence, this mini-review will provide an overview of known metabolic T-cell alterations in GvHD  and upon activation and differentiation, T-cells undergo metabolic reprogramming. This crosstalk between substantial metabolic- and immune-signaling pathways is regulated by metabolic checkpoints  with immune-modulatory functions , 27. A dGlycolysis is essential for T-cell activation, supporting T-cell growth and proliferation , 34. Imp+ effector T-cell (Teff) expansion but also for GvHD induction , Th2, and Th17 T-cells are pathogenic in GvHD and preferentially utilize glycolysis  susceptibility of alloreactive CD4+ T-cells towards glycolytic interference could be therapeutically exploited -1-(4-pyridinyl)-2-propen-1-one (3-PO), an inhibitor of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3 (PFKFB3), which represents a glycolytic rate-limiting factor, efficiently controlled GvHD (Targeting glycolysis has shown promising results in murine models: treatment with 2-deoxy-D-glucose (2-DG), a glucose-analog inhibiting initiation of glycolysis, diminished proliferation and activation of allogenic T-cells. However, that short-term led GvHD .Nonetheless, interpretation of those findings should be approached cautiously. A recent study revealed metabolic reprogramming of donor T-cells by AML blasts. AML-derived lactic acid (LA) was found to be responsible for diminished metabolic activity (including glycolysis) in T-cells of relapsing allo-HSCT patients . Thus, oin vivo monitoring of glycolytic activity by FDG-PET was shown to allow GvHD detection in murine models, with current efforts for clinical translation being ongoing  cycle with metabolic products and generating ATP in the mitochondrial electron transport chain (ETC), OXPHOS is an efficient provider of energy , 29.Increased OXPHOS and oxygen consumption  was detected in murine alloreactive T-cells. Enhanced superoxide production, decreased cellular antioxidant levels, and a hyperpolarized mitochondrial membrane potential suggest an abundant reactive oxygen species (ROS) production that is promoted by increased OXPHOS and an overall enhanced mitochondrial activity , 14, 44.Targeting TCA- and ETC-activity with mitochondrial F1-F0-ATPase-inhibitors (BZ-423/LYC-31138), improved survival and limited GvHD in murine models , 10. As Noteworthy, the levels of TCA intermediates  were found reduced in the allo- and syngeneic setup, which suggests pyruvate being predominantly converted to LA rather than TCA intermediates . HoweverThe key metabolic sensor AMP-activated protein kinase (AMPK) has been implicated as a driver of oxidative metabolism. Being reciprocally interconnected with the mechanistic target of rapamycin (mTOR), AMPK restricts anabolism (e.g. fat and protein synthesis) while improving catabolic pathways including OXPHOS and autophagy , 46. AMPAlthough the role of OXPHOS in alloreactive T-cells is still not conclusively clarified, ROS resulting from mitochondrial activity and required for proper T-cell activation as second messengers, may contribute to a continuous allo-activation upon BMT. In concurrence with this, targeting ROS pathways in preclinical models has reduced GvHD severity without impairing the GvT-effects .In addition to glucose metabolism, anabolism, and catabolism of fatty acids (FAs) regulate T-cell fate, proliferation, and differentiation of effector, memory, and Treg subsets .de novo FAS via acetyl-CoA-carboxylase-1 (ACC1) inhibition in donor T-cells prevented acute GvHD and decreased glycolytic activity (Alloreactive T-cells exhibited increased FA-synthesis (FAS), with increased long-chain FA-transport and upregulated FAS-associated enzymes, alongside with enhanced FAO early after transplantation in a murine model , 14. Genactivity . This coactivity .in vitro blockade of alloreactive T-cell expansion by sorafen A, a specific ACC1/2 inhibitor, might constitute a promising therapeutic strategy , the enzyme responsible for shuttling FAs into the mitochondria, selectively affected alloreactive T-cells in vitro and in vivo  or by transplantation of 5-LO-deficient leukocytes, improved survival and alleviated GvHD  mediates intrinsic lipolysis by catalyzing the hydrolysis of cholesteryl esters and triglycerides in lysosomes and is required for physiological T-cell development and function . Pharmacted GvHD .In addition to the dysregulated FA-metabolism, data from clinical trials suggests that dysbiosis of gut microbiota leads to abundance of FAs that are linked to GvHD outcomes. A multicenter-study with adults found high levels of circulating short-chain FAs  to be associated with protection from chronic GvHD . FurtherGlutamine is an anabolic energy source for DNA/RNA synthesis and an alternative carbon source fueling the TCA-cycle in activated T-cells [leading to production of the citrate precursor α-ketoglutarate (α-KG)] .Alloreactive T-cells upregulate their glutamine-dependent TCA anaplerosis, which is reflected by increased glutamine, decreased glutamate, and increased levels of aspartate and ornithine (products of glutamate conversion to α-KG) , 10. IncConversely, emerging evidence suggests glutamine supplementation to be beneficial in view of GvHD. In a murine acute GvHD model, systemic glutamine administration boosted Treg frequency, limited pro-inflammatory immune responses, protected from GvHD, and improved survival . In patiAs a part of the anabolic metabolism, the PPP is critical for nucleotide synthesis and is implicated in maintenance of the cellular redox balance (via NADPH regeneration) , 37.Murine allogeneic T-cells exhibited an overall increased PPP-activity and enhanced levels of PPP-regulating enzymes . Although inhibition of g6pd by dehydroepiandrosterone (DHEA), did not affect donor T-cell expansion, it decreased the frequency of IFN-γ-secreting T-cells . ChronicIn addition to specific metabolic targeting, immunosuppressive drugs, commonly utilized for GvHD prophylaxis and treatment, can affect T-cell metabolism:Immunosuppression by GCs, the first line treatment against GvHD, has profound effects on T-cell development, differentiation, and function . GCs arein vitro and in vivo, which was accompanied by impaired memory T-cell formation and decreased tumor clearance in vivo . Th. Th64). in vitro experiments leading to improved survival and reduced GvHD mortality in vivo. Importantly, T-cell responses against cytomegalovirus, an opportunistic virus that remains a major cause for morbidity in GvHD, were not affected , an important regulator of aerobic glycolysis downstream of mTOR, with echinomycin, effectively reduced acute GvHD while preserving GvT by reducing glucose-dependent Th1 and Th17 cells and promoting Treg induction .2+, dephosphorylates its prime target nuclear factor of activated T-cells (NFAT), enabling NFAT translocation into the nucleus and subsequent NFAT target gene induction [crucial for T-cell activation and cytokine production ..2+, dephAlthough CNIs, such as ciclosporin A (CsA) or tacrolimus (FK506), are extensively and successfully used in GvHD prophylaxis, dose-dependent negative effects on GvT-efficacy have been reported . InteresHowever, despite that CNIs affect metabolic checkpoints, data on their metabolic impact in GvHD still remain limited.et al. demonstrated store-operated Ca2+-entry (SOCE) to regulate metabolic reprogramming via NFAT and the PI3K-AKT-kinase-mTOR pathway. Further, cell cycle entry of T-cells was found to be SOCE-dependent with SOCE-deficient T-cells being stuck in G0 phase, potentially explaining the regulation of T-cell proliferation by calcineurin blockade. These data propose a novel molecular mechanism by which SOCE, calcineurin, and NFAT control T-cell metabolism and function , which may explain some of the CNI-mediated adverse systemic effects , 72. Morde novo purine synthesis inhibitors methotrexate (MTX) and mycophenolate-mofetil (MMF) on T-cell metabolism  is rather limited. However, several in vitro studies implicate an effect of MMF on T-cell metabolism (i.e. via suppression of glycolysis), by interfering with AKT/mTOR signaling, thereby contributing to T-cell anergy and reduced T-cell proliferation , project number 324392634 - TRR 221.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.