Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines. The determination of whether cancer cell lines recapitulate the molecular features of corresponding patient tumours remains essential for the selection of appropriate cell line models for preclinical studies. The method developed here, Celligner, integrates cancer cell line and tumour RNA-seq datasets and reveals large differences in their concordance across cell lines and cancer types. These datasets are thus providing new opportunities to identify potential therapeutic targets and connect these vulnerabilities with measurable biomarkers that can be used to develop precision cancer approaches5.Tumor-derived cell line models have been a cornerstone of cancer research for decades. The genomic and molecular features of over a thousand cancer cell line models have now been deeply characterized7. Many existing models have been propagated for decades in vitro, with factors such as clonal selection, cell culture conditions, and ongoing genomic instability all potentially contributing to systematic differences between cell line models and tumors9. Furthermore, many cell line models lack detailed clinical annotations. Therefore, it is critically important to better understand the systematic differences between cell lines and tumors to identify which tumor types have existing cell lines that sufficiently recapitulate their biology and which tumor types do not. Such systematic comparisons may ultimately also help reveal whether patient-derived xenografts, genetically engineered mouse models, or organoid cultures are more, less, or equivalently faithful to human tumors than historical cell lines.The clinical applicability of results derived from cancer cell lines remains an important question, however, due largely to uncertainty as to how well they represent the biological characteristics and drug responses of patient tumors. Historically derived cell line models likely represent an incomplete sampling of the spectrum of human cancers10 and the Cancer Cell Line Encyclopedia (CCLE)1 include the multi-omic features of approximately 10,000 primary tumor biopsy samples and more than 1,000 cancer cell lines. While TCGA focuses primarily on primary tumors (as opposed to metastatic or drug-resistant tumors from which certain cell lines may have been derived) it nevertheless provides a powerful opportunity to begin to perform detailed comparisons of tumors and cell lines systematically across many cancer types. By contrast, previous studies have primarily focused on comparing tumors and cell lines within particular cancer types14.Large datasets such as The Cancer Genome Atlas (TCGA)15, which are complicated by several factors: a lack of paired normal samples for calling mutations in cell lines, systematic differences in the overall rates of copy number variation and mutations19, as well as being limited to known cancer-related lesions.In principle, a global alignment of the datasets would allow for the identification of the best cell line models for a given cancer type, without relying on annotated disease labels. Existing global analyses have mainly compared samples based on their mutation and copy number profiles20, given their demonstrated utility for resolving clinically relevant tumor (sub)types25, as well as predicting genetic2 and chemical vulnerabilities of cancer cells26. However, a key challenge is that gene expression measurements from bulk tumor biopsy samples are confounded by the presence of stromal and immune cell populations not found in cell lines, often comprising a substantial fraction of the cellular makeup of each sample28. The presence of these contaminating cells not only compromises the expression profiles by their own gene expression, but also sends heterotypic signals to the tumors cells that ultimately affect the gene expression within the tumor cells30. Existing approaches for removing the effects of contaminating cells generally require detailed prior knowledge of the expression profiles of each contaminating cell type31, and do not account for other systematic differences between in vitro and tumor expression profiles. Furthermore, more general batch effect correction methods typically require either pre-existing subtype annotations, or assume the cell line and tumor datasets have the same subtype composition32.Comparisons based on information-rich gene expression profiles are a promising alternativeTo address these challenges, we developed Celligner, a method to perform an unsupervised global alignment of large-scale tumor and cell line gene expression datasets. Celligner leverages computational methods recently developed for batch correction of single-cell RNA-Seq data and differential comparisons of high-dimensional data, in order to identify and remove the systematic differences between tumors and cell lines, allowing for direct comparisons of their transcriptional profiles. Notably, Celligner aligns pan-cancer gene expression datasets without the need for any additional information .33 and cancer cell line data from CCLE and the Cancer Dependency Map1. This comparison identifies cell lines that match well to different tumor subtypes, as well as cancer cell lines that are transcriptionally distinct from their annotated primary cancer types.We apply our method to tumor data from TCGA, TARGET, and Treehouse1, 9,806 TCGA tumor samples, 784 pediatric tumor samples from TARGET, and 1,646 pediatric tumor samples from Treehouse33. Although a consistent computational pipeline was used to process all datasets, this analysis revealed a clear separation of cell line and tumor samples type composition of the datasets can greatly confound attempts at globally aligning the distributions of tumor and cell line expression profiles. Finally, even without the confounding effects of normal-cell contamination, differences between in vivo and in vitro conditions, as well as technical artifacts, likely give rise to systematic differences in the cancer cells’ gene expression profiles36.Several features of the problem make the alignment of cell line and tumor expression profiles challenging. First, the degree of tumor purity is highly variable across tumor samples37. Importantly, to avoid biases resulting from the differential disease composition between the two datasets, we first performed an unsupervised clustering of each dataset  to independent estimates of tumor purity based on a consensus measurement of tumor purity28, illustrating that this analysis is able to identify multiple independent signatures of contaminating cells , which on average showed increased expression of immune-related genes, and decreased expression of cell cycle genes, in tumors compared to MNN-matched cell lines  to produce an integrated dataset of cell line and tumor gene expression profiles that have been corrected for multiple sources of systematic dataset-specific differences. Indeed, creating a 2D Uniform Manifold Approximation and Projection (UMAP)To evaluate Celligner, we first tested whether it produced an alignment of known disease types and subtypes present in both the tumor and cell line data. As apparent in Fig. 42.A key advantage of Celligner is that it does not assume that all cell line samples in a dataset are necessarily similar to any tumor samples, and vice versa. As a result, we can utilize the Celligner-aligned expression data to identify which cancer types show good agreement between cell lines and primary tumors, and which do not. Although a high proportion of cell lines clustered with tumors of the same cancer type, not all cell lines aligned well with tumor samples. For example, while many soft tissue, skin cancer, and breast cancer cell lines were similar to corresponding tumor samples, we found that central nervous system (CNS) and thyroid cell lines consistently aligned poorly with tumor samples Fig. . This ob45, and found that Celligner also tended to align tumor and cell line samples of the same subtype  and regression-based tumor-purity correction to calculate adjusted similarity metrics between cell lines and tumors. We also compared Celligner to the results of CancerCellNet46, a method that uses a machine learning model trained on tumor transcriptional profiles to predict the most likely tumor type of each cell line.We also assessed the results of Celligner by comparing it to two previously published methods designed to measure the transcriptional similarity of cell lines and tumors. Firstly, the method of Yu et al.20 , while for CancerCellNet46 we used the tumor type probability estimates for each cell line. This comparison showed that Celligner-based classifications were substantially more accurate compared to those derived using the method of Yu et al.20 , as well as when compared to the estimates from CancerCellNet46 on matched cohorts .Overall, we found that estimates of which cell lines were more or less transcriptionally similar to tumors of the same annotated type were highly concordant between Celligner and both the method of Yu et al.47, which include copy number, methylation, and mutation features identified as recurrent events in tumors (Methods). We found that both Celligner-based and CFE-based similarity estimates produced similar accuracy when classifying the cell lines’ cancer type based on tumor data . However, the rankings of which cell lines were most similar to tumors of the same type differed substantially By providing an unsupervised data integration procedure, Celligner enables joint analyses of the tumor and cell line datasets, providing greater power to detect transcriptionally distinct subpopulations. This is particularly true for the cell line data where there are ~10-fold fewer samples compared to the tumors. Indeed, clustering analysis of the Celligner-aligned dataset revealed a larger number of more distinct clusters among the cell lines compared with the same analysis applied to the cell line dataset on its own type annotations. For example, four cell lines that are not annotated as melanoma samples nevertheless clustered with the melanoma samples  were available for the ALL cell lines, but only for some of the ALL tumor samples. The ALL cell lines formed two distinct clusters, which perfectly matched the labeled B-cell and T-cell subtype. The ALL tumors also largely clustered together with the ALL cell lines, with all of the annotated (B-cell) tumor samples clustering with the B-cell cell lines. The rest of the (un-annotated) tumor samples could easily be classified as either B-cell or T-cell ALL (with some putative AML samples as well) based on their cluster membership Fig. , which aJointly analyzing the Celligner-aligned cell line and tumor data also revealed common structures across cell lines that were distinct from primary tumors. As described above Fig. , cell li43. The majority (11/12) of the thyroid cell lines, which have been observed to be more dedifferentiated than thyroid tumors50, also belonged to this cluster. To further assess how distinct these cell line models were from their lineage-matched counterparts that co-clustered with tumors, we also looked at a set of lineage-specific transcription factors. For example, SOX10 is selectively expressed in melanoma cells, and SOX10 knockout by CRISPR is lethal selectively in melanoma cell lines3. Consistent with the interpretation of this cell-line-specific cluster as representing a more dedifferentiated state, skin cancer lines within the cluster showed much weaker expression of, and dependency on, SOX10  were primarily from cancer types with mesenchymal cell lineages51, and GSEA showed that these samples exhibited elevated expression of genes in the EMT pathway . Notably, however, there were a small number of tumor samples from other lineages present in the cluster -related genes Fig. , reflect52, as well as a recently-generated dataset of clinical compounds screened across 578 cell lines5. These analyses showed that the undifferentiated cell lines have increased sensitivity to tubulin polymerization inhibitors  that aim to address gaps in our current in vitro model representation. Future applications of the method to RNAseq datasets from these and other novel model formats should prove useful.In our global analysis of 1,249 cell lines and 12,236 tumors, we identified pronounced differences across cancer types in how well cancer cell lines reflected the transcriptional patterns of their primary tumor counterparts. While many disease types  were similar between cell lines and tumors, there were few thyroid and CNS cell lines whose gene expression profiles aligned with the corresponding primary tumor samples. Previous studies have identified that CNS cell lines grown in serum-containing media tend to lose their ability to differentiate, and have gene expression profiles that are unlike their primary tumors60. This group of cell lines included those previously annotated as undifferentiated melanoma and basal B breast cancer subtypes . They also showed clear upregulation of mesenchymal genes and downregulation of epithelial markers; all characteristics concordant with an EMT phenotype. Consistently, this group of cell lines was most similar to tumors that arise from mesenchymal tissue, but generally clustered separately from the primary tumor samples. Interestingly, these undifferentiated cell lines also exhibited distinct genetic and chemical vulnerabilities, including increased dependency on integrin genes and sensitivity to tubulin inhibitors, as well as decreased sensitivity to EGFR inhibitors, all of which are consistent with an EMT statepes Fig. , both kn15, have utilized genomic alterations to identify cell lines that are most representative of specific disease subtypes. When we compared the estimates of tumor/cell line similarity produced by Celligner to those based on a set of 1,250 curated copy number, methylation, and mutation features47, we found that they were largely dissimilar, though both approaches yielded similar accuracy at classifying cell lines’ tumor type. We also found that some cell lines previously identified as poor models based on copy number and mutations were identified as non-tumor-like based on our analysis of Celligner-aligned gene expression features as well. For example, Domcke et al. observed that OC316 was hyper-mutated12, Sinha et al. found that SLR20 had an outlier copy number profile67, and Ronen et al. found that COLO320 was dissimilar to colorectal tumors and lacked major colorectal cancer driver genes68. In our analysis, all of these cell lines were also identified as being unlike their respective tumor types. Overall, these analyses suggest that transcriptional and genomic similarity estimates could reflect distinct aspects of the biology, and might provide complementary information, though further work will be needed to understand these relationships in detail. Indeed, a future version of Celligner that also integrates genomic features could enable more detailed comparisons of tumors and cell lines.Our analyses focused on using gene expression data to compare tumor and cell line samples. In contrast, previous efforts, such as CELLector69. The tumor/cell line differences we identified also varied across disease types, emphasizing the importance of using a nonlinear method that allows for disease-type-specific differences. As single-cell data from normal tissues become more readily available, methods that use these data to estimate and remove the effect of different non-cancerous cells71 could be incorporated to further improve comparisons between tumors and cell lines.A key component of Celligner is correcting for systematic differences between tumor and cell line expression profiles, most notably those related to the presence of normal cells in tumor samples. To do this, we utilized an unsupervised approach that did not depend on predefined signatures of the various contaminating cell types, and that also allowed us to account for unknown systematic differences between tumors and cell lines. For instance, we found that cell lines exhibited upregulation of cell cycle expression programs compared with tumors , that allows users to explore a Celligner-aligned integrated resource of cell line and tumor expression profiles, as well as download the data. This tool enables the identification of cell line models that best represent the transcriptional features of a tumor type, or even a particular tumor sample, of interest. We hope that an improved understanding of the similarities between cancer cell lines and tumors will allow for better selection of models and allow for better translation of findings on drug response from preclinical models to clinical samples72. More generally, by identifying and removing many of the confounding differences between cell lines and tumors in an unbiased fashion, Celligner enables integrated analyses of cell line and tumor datasets that can be used to reveal patterns within, and relationships between, these data, helping to improve translation of insights derived from cell line models to the clinic.In order to facilitate the use of Celligner, we have incorporated an interactive web app on the Cancer Dependency Map portal (https://xenabrowser.net)33 and produced by the Treehouse Childhood Cancer Initiative at the UC Santa Cruz Genomics Institute. The data set compiled samples from the UCSC Treehouse Childhood Cancer Initiative, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program, and The Cancer Genome Atlas (TCGA)10. Cell line gene expression data for 1,249 samples were taken from the DepMap Public 19Q4 file: CCLE_expression_full.csv52. All gene expression data were processed using the STAR-RSEM pipeline and are TPM log2 transformed (with a pseudocount of 1 added). Gene expression data were subset to 19,188 protein-coding genes that were present in both the tumor and cell line data.Gene expression data for 12,236 tumor samples were taken from the Treehouse Tumor Compendium V10 Public PolyA dataset, obtained from Xena browser  based clustering method implemented in the Seurat R package73, with a resolution parameter of 5. We then regressed out the first four cPCs (components which had higher variance in the tumor data) from both the tumor and cell line data 39 on the data sets, using the cell line data as the reference dataset. To identify mutual nearest neighbors between the two datasets we used a set of genes that showed high between-cluster variance in each data set. Specifically, we used limma74 to estimate the across-cluster variation in each gene’s expression within each dataset, using the empirical-Bayes moderated F-statistics as a metric of between-cluster variability. We used the union of the top 1000 genes from each data set with the highest F-statistics  for each data set, which was necessary to account for the much larger set of tumor samples used compared with cell lines. Specifically, we used a k value of 5 to identify nearest neighbors in the cell line data and a k value of 50 to identify nearest neighbors in the tumor data. We verified that the output was robust to modest changes in these parameters  correctionTo evaluate the similarity of cell lines to tumor samples we used the Pearson correlation distance between each cell line and tumor in the Celligner-aligned space. Cell lines were classified as a tumor type by identifying the most frequently occurring tumor type within each cell line’s 25 highest correlated tumor neighbors.20 and used the approach described above (‘Measuring tumor/cell line similarity’) to classify each cell line as a tumor type. To compare to the Celligner results we re-classified each cell using the same approach, but this time using the same subset of cell lines and tumors, as well as the disease categories, defined by Yu et al. To evaluate agreement with annotated types we used the annotations from Yu et al. and only considered cell lines where the annotated type was present within the set of tumors (n = 666 cell lines).To calculate tumor type classifications for each cell line we used the pairwise correlation matrix provided by Yu et al.46 To evaluate agreement with annotated types we used the annotations from Peng et al. and only considered cell lines where the annotated type was present within the set of tumors (n = 657 cell lines).To calculate tumor type classifications for each cell line we used the random forest probabilities output by CancerCellNet and used the maximum probability to classify each cell line as one of the 22 cancer types. To compare to the Celligner results we re-classified each cell line by identifying the tumor type most frequently occurring within each of the cell line’s 25 high correlated tumor neighbors , but this time using the same subset of cell lines and tumors, as well as the disease categories, defined by Peng et al.47. To evaluate agreement with annotated types we used the lineage annotations . We only used samples that were present in both the Celligner and CFE data . To compare to the Celligner results we re-classified each cell line using the same approach described above (‘Measuring tumor/cell line similarity’), but this time only using cell lines and tumors included within the Iorio et al. datasetThe undifferentiated cluster was identified using a shared nearest neighbor (SNN) based clustering method implemented in the Seurat R package in the 70-PCA space. Two neighboring clusters were combined to form the undifferentiated cluster, as both clusters were composed of a high proportion of cell lines (compared to tumors) and cell lines within both clusters had similar up-regulated mesenchymal expression profiles.76. We first subsetted the data to genes that had a counts-per-million value greater than one in 10 or more samples. The data were normalized per sample using the ‘TMM’ method from the edgeR package77, and transformed to log2 counts-per-million using the edgeR function ‘cpm’. Linear model analyses, with empirical-Bayes moderated estimates of standard error, were then used to identify genes whose expression was most associated with covariates of interest, such as disease type, or membership in a particular cluster. When analyzing differential gene expression related to the ‘undifferentiated’ cell line cluster  were also taken from ‘repurposing related drug annotations’ from the CLUE data library (clue.io/data).Cell line drug sensitivity data were taken from a dataset of repurposing drugs screened with PRISM79. We used gene-level statistics (log fold change values in Fig. 80. GSEA results shown display the enrichment score normalized to mean enrichment of random samples of the same size and the associated Benjamini-Hochberg adjusted p-values.For gene set enrichment analysis of gene expression profiles we used the fgsea R packagee.g. Figure 40, as implemented in the Seurat v3 package73. The UMAP embedding was computed on the first 70 principal components, using Euclidean distance, with an ‘n.neighbors’ parameter of 10, and a ‘min.dist’ parameter of 0.5.To compute 2D embeddings of gene expression profiles (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Exosomes are cell-generated nano-vesicles found in most biological fluids. Major components of their cargo are lipids, proteins, RNA, DNA, and non-coding RNAs. The miRNAs carried within exosomes reveal real-time information regarding disease status in leukemia and other cancers, and therefore exosomes have been studied as novel biomarkers for cancer. We investigated the impact of exosomes on cell proliferation in pediatric acute lymphocytic leukemia (PALL) and its reversal by silencing of exo-miR-181a. We isolated exosomes from the serum of PALL patients (Exo-PALL) and conditioned medium of leukemic cell lines (Exo-CM). We found that Exo-PALL promotes cell proliferation in leukemic B cell lines by gene regulation. This exosome-induced cell proliferation is a precise event with the up-regulation of proliferative  and pro-survival genes  and suppression of pro-apoptotic genes . Exo-PALL and Exo-CM both show over expression of miR-181a compared to healthy donor control exosomes (Exo-HD). Specific silencing of exosomal miR-181a using a miR-181a inhibitor confirms that miR-181a inhibitor treatment reverses Exo-PALL/Exo-CM-induced leukemic cell proliferation in vitro. Altogether, this study suggests that exosomal miR-181a inhibition can be a novel target for growth suppression in pediatric lymphatic leukemia. Exosomes are spherical nanoparticles 30–150 nm) produced by normal and pathological cells in most body fluids such as serum, urine, breast milk, and ascites. Exosomes originate from the cytoplasm of the cell by inward budding of multivesicular bodies 50 nm pro,3. SeverThe miRNAs are endogenously expressed non-coding regulatory RNAs of 20–25 nucleotides long and play a major role as gene expression regulators. They can act as gene expression silencers at a post-transcriptional level either by inhibiting mRNA translation or by mRNA degradation. Furthermore, miRNAs can destabilize the mRNA transcript through binding to the 5′ or 3′ untranslated regions (UTR) ,12,13. AWe aimed to explore the role of PALL exosomes  of ALL cell lines) and its possible correlation to leukemic cell proliferation. Exo-PALL showed up-regulated miR-181a expression compared to exosomes-derived from healthy donors serum. Moreover, exosome-induced cell proliferation was reversible by silencing of Exo-miR-181a by a specific miR-181a inhibitor. Silencing of miRNA/mRNA at cellular level is widely accepted as an investigational tool, but miRNA-silencing at exosomal level is a rather new concept. Endo et al. demonstrated that cellular gene silencing by lipid nanoparticles, loaded with a gene-specific siRNA, significantly downregulated the expression of indoleamine 2, 3-dioxygenase 1. This study provides a proof-of-concept that microvesicles can be exploited as vehicles and/or delivery systems for siRNA .Confirmation of CD63 and CD81 Expression on Exosomes by Flow Cytometer. CD63 and CD81 are expressed on exosomal surfaces and are well established as their biomarkers . We explExosome isolation: Exosomes were isolated from conditioned media (Exo-CM) of two ALL cell lines (JM1 and SUP-B15) and one control B cell line (CL-01). In addition, exosomes were harvested from human serum samples of either patients with childhood acute lymphocytic leukemia (Exo-PALL) or healthy donors (Exo-HD).Exosome dose titration and exposure time optimization: In order to establish exosome dose titration and time kinetics of the process, Exo-CM (CL-01 and SUP-B15) was added to JM1 cells (paracrine incubation) in one of three different concentrations  with PBS only (no exosomes) as negative control. Cell counting was carried out at 24 and 48 h under light microscopy. Control Exo-CL-01 did not induce cell proliferation at either time points, while Exo-SUP-B15 significantly induced cell proliferation, compared to PBS negative control, both at 24 and 48 h. Similar results were obtained with autologous incubation (autocrine fashion) of Exo-JM1 on JM1 cells (data not shown). Exosome treatment augmented cell proliferation at a starting exosome concentration (100 µg/mL), with optimal proliferation at 250 µg/mL . ConsequExosome-induced leukemia cell proliferation: Consequently, we confirmed that CM-exosomes induce JM1 cell proliferation both in an autocrine and paracrine fashion using Exo-JM1 A and Exon = 6)  C,D. Exo-n = 6) C. To conn = 6) D. Apart n = 6) , supportIn order to evaluate one of the molecular pathways by which Exo-JM1 and Exo-SUP-B15 promotes cell survival and cell proliferation, we analyzed gene expression profiles in the JM1 leukemia cells after exosomes exposure. Both Exo-JM1 and Exo-SUP-B15 altered the mRNA expression of regulatory genes such as Ki-67, BCL2, BAD in JM1 cells A,B. Exo-Next, we tested the effect of exosomes of serum samples from PALL patients and healthy donors on gene induction . Three lExosomes from both CM of cell lines and serum samples (PALL and HD) were analyzed for their miRNAs expression profiles . We chosTransfection efficiency of siRNA inhibitor in exosomes: To support the idea that miR-181a is a major player in leukemia cell proliferation, we explored miR-181a inhibition by a miScript miRNA inhibitor to reverse the induced cell proliferation. We first established and determined uptake-efficiency of a control siRNA by exosomes, by TexRed as per manufacturer recommendations . We tranTargeted silencing of miR-181a by siRNA inhibitor: Once siRNA uptake was confirmed, Exo-JM1 was transfected and loaded with a specific miR-181a inhibitor and the exosomal miR-181a level was determined by q-PCR. Results showed that transfection of exosomes with an inhibitor resulted in a 70–88% silencing of exosomal miR-181a B. To rulWe hypothesized that PALL exosome-induced proliferation could be reversed by silencing of Exo-miR-181a by a specific miR-181a inhibitor . To explTo confirm proof of concept, we treated JM1 cells with exosomes derived from five different PALL patient samples with or without prior miR-181a silencing by an inhibitor in the cell proliferation assay B. We obsIn this study, we identified that exosomes induce cell proliferation in ALL B-lymphocytes as well as in control B-lymphocytes. Furthermore, we showed that cell proliferation is promoted by the up-regulation of exosomal-miR-181a expression and reversible by mi-R181a silencing. The literature illuminates that exosome dose optimization for functional studies varies from cell type to cell type. For example, in gastric cancer, an exosomal dose of 50–400 µg/mL shows a dose-dependent cell proliferation . ExosomeExosomes derived from conditioned media (CM) of ALL cell lines and PALL patient’s serum are capable to induce leukemic cell line proliferation, and this process seems highly specific and not at random. Analysis of proliferative/pro-survival genes and pro-apoptotic genes revealed the upregulation of the former and down-regulation of the latter after exosome exposure. Similarly, others have shown that chronic myeloid leukemia (CML) cell line LAMA84-derived exosomes promote proliferation genes  and downregulate pro-apoptotic genes such as BAD, BAX, and PUMA . HoweverTo explore the physiological mechanism behind the above results, we screened 84 different exo-miRs by use of the Human Cancer Pathway finder PCR array. We found that miR-181a was uniquely and significantly amplified in exosomes derived from P-ALL serum and leukemia cell lines. Furthermore, we showed that inhibition of exosomal miR-181a curbed the exosome induced cell proliferation. Based on our study, we suggest that exo-miR-181a might play a significant part in leukemic cell proliferation. While reports exist on miR-181a and cell proliferation, this is the first report delineating the effect of ALL-derived exosomes and exosomal miR-181a on cell proliferation. Signaling of miR-181a involves target genes of Wnt-signaling which lead to cell proliferation in ALL by inhibiting WIFI signaling . VerduciOur novel data shows that the silencing of miR-181a in leukemia-derived exosomes reverses their effect on leukemic cell proliferation. This suggests that leukemia-derived exosomes are functional and capable to transfer their cargo via exosomal cellular uptake to interfere with the cellular programming of the target cell.The silencing of miR-181a specifically at the exosomal level was challenging. We optimized exosome transfection efficiency and cellular uptake of exosomal contents into the target cells. First, we established that exosome uptake efficiency into the target cells is ~50–65% for Texas-Red conjugated-siRNA, similar to what others have reported for TexRed labeled exosomes in HuH-7 cells , and aleOur data support that miR-181a silencing in exosomes reverses exosome-induced cell proliferation. Although some reports in the literature indicate that cellular inhibition of miR-181a suppresses cell growth and proliferation ,39, noneALL cell lines were purchased from ATCC  and REH cells from DSMZ (Cat# ACC 22). CL-01 was donated by the Chiorazzi lab, Feinstein Institutes for Medical Research, Manhasset, NY. Exosomes were isolated by ultracentrifugation from the CM of cell lines (Exo-CM) as described . The miRn = 11) and healthy donor (HD) (n = 4) serum samples were used in this study . PALL patients  that is added to culture medium, FBS was converted into exosome-free FBS by ultracentrifugation . Briefly6/mL) in 10 mL total volume in a 100-mm tissue culture dish. After 48 h, CM of each cell line was harvested by centrifugation and filtered through a 0.22 micron filter (Millipore) to remove cell debris. Filtered CM was used for exosome isolation by ultracentrifugation.Each leukemic producer cell line was cultured in exo-free FBS cell culture medium during cell expansion in order to harvest exosomes of conditioned medium (CM). Cells were plated . Exosomes were aliquoted and stored at −80 °C for further usage.Exosomes were purified by ultracentrifugation method as described . BrieflyWe used the exosome isolation and analysis kit (cat: ab239682) from Abcam as per manufacturer protocol. Briefly, exosomes (10 µg) were taken into 50 µL PBS. Captured beads (100 µL) were mixed with 50.0 µL of exosomes in a flow cytometer tube. The mixture was incubated at room temperature in the dark overnight. The primary detection antibody (CD81-biotin-conjugated) (5.0 µL) was added and incubated at 2–8 °C for 1.0 h in the dark. Washing steps: 1.0 mL of assay buffer was added to samples, mixed by tapping, and centrifuged at 2000 rpm for 5.0 min. The supernatant was decanted and pellets were reconstituted in 100 µL assay buffer. A secondary antibody (streptavidin-PE conjugated) (5.0 µL) was added to each tube and incubated at 2–8 °C for 30 min in the dark. Then, the washing step was repeated. Exosome pellets were reconstituted in 350 µL assay buffer and read at flow cytometer (Fortessa).6/well) were seeded in 96-well plates and cultured in exo-free FBS medium. We also tested primary exosomes purified from HD and four P-ALL patients at different disease stage: P-ALL D1 (day 1 of diagnosis), and P-ALL D29 (1st remission) (pt #1 and pt #2), P-ALL relapse, P-ALL 2nd remission (pt #3 and pt #4) in three leukemia cell lines. Each well was loaded with exosomes (250 µg/mL) and incubated at 37 °C. After 24 h, cells were mixed by gently pipetting. Cell proliferation was analyzed by live-cell counting using the trypan blue/hemocytometer method under the microscope.JM1, SUP-B15, and NALM-6 leukemia cell lines  method. The quality and quantity of RNA were analyzed by the NanoDrop ND1000 spectrophotometer. Around 2–5 µg of total RNA was used for cDNA synthesis by q-PCR. Oligo-dT primers (Invitrogen) and M-MLV reverse transcriptase  were used for cDNA synthesis as per manufacturer’s protocol.Primer sequences, probe numbers, and gene accession numbers from the universal probe library (UPL) of Roche Applied Science are described . Gene am6 cells/mL) leukemic producer cell lines after 48 h of tissue culture and exosomes were isolated from CM by ultracentrifugation . Collected CM was processed for isolation of exosomal RNA (ExoRNeasy Kit (MAXI)-Qiagen). Similarly, exosomal RNA from human serum samples was extracted utilizing the ExoRNeasy Kit (MIDI) (cat # 77044). RNA was quantitated by NanoDrop.CM was harvested from JM1 and SUP-B15  according to manufactures protocol. Briefly, reaction mixture (150 µL) were prepared in 1.5 mL sterile plastic tube: Exosomes (50–300 µg) in (50 µL 1× PBS) + Exo-Fect solution (10.0 µL) + miR-181a inhibitor (1–8 µM) (20 µL) + 1× PBS (70 µL). Total transfection reaction volume (150.0 µL) were mixed well by flicking/inversion three times and was not vortexed. The reaction mixture was incubated at 37 °C in a shaker for 10 min and then immediately place the tube on ice. Reactions were stopped by adding ExoQuick-TC reagent (30 µL) provided in the kit and mixed by inverting 6 times. Do not vortex. Transfected exosome samples were placed on ice (or at 4 °C) for 30 min. After that, samples were centrifuged for 3 min at 13,000–14,000 rpm and the supernatant was removed. Then, pellets/transfected exosomes were re-suspended in 300 µL 1× PBS.Exo-RNA was converted into cDNA by miScript II RT kit . Expression of miRNA was evaluated by q-PCR using a miScript SYBR@Green PCR kit . Screening of miRNA expression for 84 different miRNAs was performed on Exo-RNA isolated of CM cell lines and human serum exosomes, using a Human Cancer Pathway Finder miScript miRNA PCR Array . Validation of miRNA was carried out by q-PCR using primers  from Qiagen.Confirmation of exosomal uptake of TexRed-siRNA and cellular uptake of exosomes: Exosomes were labeled with TexRed-siRNA (SBI System Bioscience) as per manufacturer’s protocol. TexRed-labeled JM1 exosomes were then co-cultured with JM1 cells for 24 h. Presence of TexRed-siRNA (introduced in target cells by exosomes) into the JM1 cells was assessed by flow cytometer.Dose titration of miR-181a inhibitor: Exo-JM1 were transfected with different concentrations of the miR181a inhibitor  to determine optimal dosing. Then, RNA was isolated from transfected exosomes for cDNA synthesis and miR-181a expression by q-PCR.Exosomal miR-181a silencing: 300 µg of exosomes (either CM- or serum-derived) was transfected under following conditions: (1) transfection reagents only; (2) transfection reagent and control inhibitor; (3) transfection reagent with miR-181a inhibitor (1.0 µM). Each transfection mixture was co-cultured with JM1 cells for 24 h, allowing for exosomal uptake by the target cells. Cell count was carried out by light microscopy.p < 0.05. All results are represented as Mean ± SD. Each experiment was performed in triplicates.To compare the mean values between two groups, the unpaired t-test was used. Statistical significance was defined as Exosomal miR-181a expression is abundantly up-regulated in exosomes derived from P-ALL patients’ serum and CM of ALL cell lines and correlates with leukemia cell proliferation. Subsequent silencing of Exo-miR-181a reverses exosome-induced leukemia cell proliferation. Therefore, exosomal miR-181a inhibition can act as a novel synergistic pharmaceutical target for growth-suppression in pediatric acute lymphocytic leukemia.


The methylation level of the Klotho gene promoter was detected by pyrosequencing. Chromatin immunoprecipitation was used to detect the binding of the Klotho gene promoter to DNMT1 and DNMT3a. The expression of oxidative stress markers (reactive oxygen species (ROS), superoxide dismutase (SOD), malondialdehyde (MDA), catalase (CAT), and 8-hydroxy-2′-deoxyguanosine (8-OHdG)) and inflammatory cytokines (interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α)) in kidney homogenates was also measured using ELISA. Klotho and DNMT3b protein expression was upregulated, while DNMT1, DNMT3a, TGF-β1, and α-SMA protein expression was downregulated after EGCG treatment. EGCG treatment also reduced the methylation level of the Klotho gene promoter as well as the binding of DNMT3a to the Klotho gene promoter. In addition, EGCG treatment significantly decreased the levels of ROS, MDA, 8-OHdG, IL-1β, IL-6, and TNF-α and increased the levels of CAT and SOD. Under HG conditions, EGCG regulated Klotho gene promoter methylation via DNMT3a and decreased the methylation level of the Klotho gene promoter, thereby upregulating the expression of the Klotho protein to exert its protective effect.To explore whether epigallocatechin-3-gallate (EGCG) improves renal damage in diabetic db/db mice and high-glucose- (HG-) induced injury in HK-2 cells by regulating the level of Klotho gene promoter methylation. Western blotting was used to detect the protein expression levels of DNA methyltransferase 1 (DNMT1), DNMT3a, DNMT3b, transforming growth factor- Diabetic kidney disease (DKD) is a chronic microvascular complication of diabetes mellitus (DM); it is also a major cause of deaths in patients with chronic kidney disease (CKD) . DNA metKlotho was accidentally discovered in 1997 by Kuro in the study of spontaneous hypertension and originally identified as an antiaging gene. Klotho, as an antioxidative, antifibrotic, and anti-inflammatory molecule, is highly expressed in distal tubular epithelial cells and is closely associated with kidney diseases. In addition, the Klotho promoter is rich in CpG islands and vulnerable to DNA methylation . MoreoveEpigallocatechin-3-gallate (EGCG), a kind of green tea extract, is the major polyphenolic constituent present in green tea . At presAll animal procedures were approved by the Animal Care Committee of Fudan University. Six-week-old C57BLKS/J db/db mice and normal mice were obtained from the Model Animal Research Center of Nanjing University and housed in a specific pathogen-free animal facility at Pudong Hospital. All the mice had free access to normal rodent diet and distilled water. Prior to treatment, the 6-week-old mice were single-housed for adaptation for two weeks. Then, mice were allocated to the following groups (6 mice per group): normal group (nontreated C57 mice); control group (nontreated db/db mice); and EGCG group (db/db mice + EGCG). The mice of the normal and control groups were intragastrically administered with 0.9% saline, and the mice of EGCG group were intragastrically administered with EGCG 50 mg/kg/d (obtained from preliminary experiments). EGCG (No. E4143) was obtained from Sigma-Aldrich . The treatment lasted for 8 weeks. The graphical illustration of the mouse treatment plan is shown in Figure 2/95% air atmosphere), they were maintained in a serum-free medium for 24 h. Next, HK-2 cells were incubated in medium supplemented with normal (5.5 mM) or high (60.0 mM) levels of glucose, and subsequently, cells were stimulated with EGCG  at doses of 0.1, 1, 20, and 50 μM in high-glucose condition for 24, 48, and 72 h.The human renal proximal tubular epithelial (HK-2) cell line and human embryonic kidney 293 cells (293) were purchased from the Bioresource Collection and Research Center and American Type Culture Collection, respectively. They were cultured in Dulbecco's Modified Eagle's Medium  supplemented with 10% fetal bovine serum . When HK-2 cells reached 80–90% confluency (at 37°C in 5% COCell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay according to the manufacturer's instructions. The absorbance  was determined at 450 nm using an automated microplate reader .Escherichia coli DH5α competent cells. Then, we screened for plasmid inserts directly from E. coli colonies using colony PCR and sent the positive colonies  and reverse transcribed into cDNA using the PrimeScript RT Reagent Kit with gDNA Eraser . Next, we designed PCR primers and cloned Klotho gene fragments using Phusion High-Fidelity DNA Polymerase  . Then, the mRNA overexpression efficiency was determined, and protein levels were detected by real-time PCR and western blotting , HG + Klotho KO, HG + Klotho KO + EGCG, and HG + Klotho OE groups. Cells and clear supernatants in each group were harvested at 72 h post treatment, and used for the detection of fibrosis and inflammatory and oxidative stress markers by enzyme-linked immunosorbent assay (ELISA) and western blotting.HK-2 cells were divided into 6 groups: normal glucose , HG (60 mM), HG + EGCG (1 α (8-iso-PGF2α) ELISA kit  were used to measure mice urine 8-OHdG and 8-iso-PGF2α, respectively. The levels of superoxide dismutase (SOD), malondialdehyde (MDA), catalase (CAT), 8-OHdG, interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in mice kidney tissues and HK-2 cell culture supernatants were measured using commercial kits .The expression of inflammatory and oxidative stress markers was detected using ELISA kits. The 8-OHdG ELISA kit  and the 8-iso-prostaglandin F2β1 (TGF-β1), and α-smooth muscle actin (α-SMA) were evaluated by western blot analysis. Primary antibodies against TGF-β1 and α-SMA were purchased from Santa Cruz Biotechnology, Inc. . Primary antibodies against Klotho, DNMT1, DNMT3a, DNMT3b, β-actin, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and secondary antibodies were obtained from Abcam .The protein levels of Klotho, DNA methyltransferase 1 (DNMT1), DNMT3a, DNMT3b, transforming growth factor-μg of protein was added in each lane and electrophoresed on 10 or 12% sodium dodecyl sulfate-polyacrylamide gels, and then transferred onto PVDF membranes . The membranes were blocked with 5% nonfat milk or 5% bovine serum albumin and blocking buffer  at room temperature (25°C) for 1 h and then incubated with the following primary antibodies overnight at 4°C: Klotho (1 : 1000), DNMT1 (1 : 1000), DNMT3a (1 : 1000), DNMT3b (1 : 1000), TGF-β1 (1 : 100), α-SMA (1 : 100), β-actin (1 : 5000), and GAPDH (1 : 1000). The next day, the membranes were incubated with secondary antibodies  for 1 h at room temperature. Finally, the protein bands were visualized using the SuperSignal West Femto Substrate .Kidney tissue samples and HK-2 cells were lysed with RIPA Buffer  and centrifuged at 2200 g for 10 min at 4°C. Total protein concentration in the supernatants was determined by a BCA assay . Around 30 Genomic DNA from mice kidney tissue and HK-2 cell lines was extracted using the QIAamp DNA Mini Kit . DNA concentration and quality were determined using the NanoDrop 2000c Spectrophotometer . Next, the DNA samples were subjected to bisulfite treatment with the EZ DNA Methylation-Gold™ Kit . All procedures were carried out according to the manufacturer's instructions. The CGI region sequence of mice ) and the HK-2 cell line ) was obtained from the University of California Santa Cruz (UCSC) Genome Browser. The transcription start site was +1. A biotin-labeled reverse primer was used for the second round of PCR. Promoter regions were amplified by PCR using Klotho-specific primers. PCR and pyrosequencing primers for the pyrosequencing experiments were generated manually Tables . Average7) were incubated with 1% formaldehyde for cross-linking at room temperature for 10 min and quenched with 0.125 M for 5 min. Cells were then harvested by scraping and centrifuged at 2,000 g for 1 min. The cell membrane and nuclei were lysed using cell lysis buffer and cell nuclear lysis buffer, respectively. The sonication fragment sizes are typically between 200 bp and 1 kb. The anti-DNMT1 antibody  and the anti-DNMT3a antibody  were coated overnight to pull down chromatin. Rabbit IgG served as a negative control. Around 1/10 volume of IP DNA ultrasonic solution was used as the input DNA. After overnight incubation with proteinase K at 58°C for reverse cross-linking, pure DNA was eluted with the MiniElute PCR Purification Kit . The purified DNA was analyzed by real-time PCR for Klotho promoters. Multiple ChIP-qPCR primers were designed for the CpG island of the Klotho promoter. The most effective primer pair was as follows: forward—5′-TTCAGCGCACGGCGAAGTTC-3′ and reverse—5′-TCCTCTGAGAGCAGCC CTG-3′. Relative data quantification was performed using the 2–ΔΔCt method and expressed as fold enrichment.A ChIP assay was performed following a protocol used in our lab. HK-2 cells  was used for statistical analyses. Results are expressed as means ± standard error of mean (SEM) from triplicate assays. In a normal distribution, differences among groups were determined using one-way analysis of variance (ANOVA). In a nonnormal distribution, differences between groups were analyzed using the Mann-Whitney α and 8-OhdG, were increased in the control group. After treatment of db/db mice with EGCG, the levels of urinary 8-iso-PGF2α and 8-OHdG were decreased remarkably during the 8-week period (P < 0.05). Additionally, EGCG treatment significantly decreased the levels of ROS, MDA, 8-OHdG, IL-1β, IL-6, and TNF-α in kidney tissue samples compared to those in nontreated db/db mice (P < 0.05). Moreover, the level of CAT and SOD was increased in EGCG-treated db/db mice compared with that in nontreated db/db mice (P < 0.05).Table P < 0.01). Likewise, compared with that in the control group, the protein expression of DNMT1 and DNMT3a was also markedly downregulated after oral administration of EGCG for 8 weeks .The levels of Klotho and DNMT3b were upregulated in EGCG-treated db/db mice compared with those in nontreated db/db mice .Site-specific methylation was detected in 94 out of 101 CpG sites in the Klotho promoter by pyrosequencing. Among them, there were significant changes in the 10th, 13th, 14th, 44th, 56th, 62th, 65th, 79th, 88th, 89th, 90th, 91th, and 99th CpG sites. Compared with that in the normal group, the methylation level of the Klotho promoter was significantly increased in nontreated db/db mice (control group), while EGCG treatment for 8 weeks significantly reduced the methylation level of the Klotho promoter in db/db mice compared with that in nontreated db/db mice . Next, HK-2 cells were treated with increasing doses of EGCG in HG conditions, and cell viability was assessed. Compared to HG treatment alone, additional treatment with EGCG  for 72 h significantly increased cell viability (P < 0.01).We first detected the effect of HG (60 mM) on the activity of HK-2 cells, and then investigated whether this effect was time-dependent. Compared to NG treatment (5.5 mM), treatment with HG for 24 h did not induce any significant changes in cell viability. However, we found that HK-2 cell activity was significantly decreased under HG conditions in a time-dependent manner .Furthermore, we examined the changes in Klotho expression in HK-2 cells. The protein expression of Klotho was upregulated in the HG + EGCG group compared to that in the HG group. The concentration of 1 P < 0.01). Moreover, we also found that the protein expression of DNMT1 and DNMT3a was obviously increased in the HG group compared to that in the NG group, but significantly decreased after treatment with EGCG for 72 h .As shown in P < 0.01). We detected site-specific methylation in 166 out of 177 CpG sites in the Klotho promoter. Among them, there were significant changes in the 8th, 9th, 11th, 12th, 27th, 28th, and 29th CpG sites.Next, site-specific methylation of the Klotho promoter in HK-2 cells was analyzed. Compared with that in the NG group, the methylation level of the Klotho promoter was significantly increased in the HG group, but significantly reduced after EGCG treatment for 72 h .To determine the underlying mechanism of EGCG in regulating the methylation level of the Klotho gene promoter, we investigated the enrichment of DNMTs in the Klotho promoter in HK-2 cells by ChIP assay. Compared to the NG group, there was an increase in the enrichment of DNMT3a in the Klotho gene promoter in the HG group. After EGCG treatment, the enrichment of DNMT3a in the Klotho promoter was decreased. However, the enrichment of DNMT1 in the Klotho gene promoter was not significantly different between the HG and NG groups, as well as between the HG and EGCG groups , after EGCG intervention. We also found that Klotho OE attenuated HG-induced oxidative stress and inflammation. Meanwhile, the expression of ROS, TNF-α, IL-6, and IL-1β was increased in Klotho KO HK-2 cells under HG conditions. Moreover, no significant differences in ROS, SOD, MDA, TNF-α, IL-6, and IL-1β expression levels were observed between Klotho KO HK-2 cells with and without EGCG treatment in HG conditions.As shown in Table β1 and α-SMA in HK-2 cells was downregulated by EGCG treatment and Klotho OE. However, the protein level of TGF-β1 and α-SMA showed no obvious change in Klotho KO HK-2 cells with or without EGCG treatment under HG conditions. Thus, these results indicate that Klotho functions as a key target of EGCG and mediates its anti-inflammatory, antioxidative, and antifibrotic effects.As shown in α, and also decreased the expression of renal oxidative stress and inflammatory and fibrosis markers in db/db mice. Moreover, renal DNMT1 and DNMT3a levels were increased, the methylation level of the Klotho gene promoter was increased, and the expression of the Klotho protein was reduced in db/db mice. However, these effects could be reversed with EGCG treatment, suggesting that EGCG downregulated the expression of DNMTs, thereby regulating Klotho gene promoter methylation to increase the expression of the Klotho protein. Similar results were obtained in HK-2 cells. The expression of oxidative stress and inflammatory and fibrosis markers was significantly reduced by Klotho OE under HG conditions, indicating that OE of the Klotho protein can alleviate HG-induced cell damage. In the case of Klotho gene KO, the expression of oxidative stress and inflammatory and fibrosis markers was significantly upregulated by HG stimulation, and EGCG treatment did not reverse this effect, indicating that EGCG did not play a role in mediating the effects of the Klotho gene KO. Hence, Klotho was identified to be a key target of EGCG.In this study, we found that treatment with EGCG (50 mg/kg/d) for 8 weeks decreased the level of 24 h urinary proteins, 8-OHdG and 8-iso-PGF2In China, tea has a long history and is widely consumed by the general population. EGCG, an extract of green tea, has the advantages of being natural and safe, without any side effects . As repoRecent studies have shown that Klotho plays an important role in the development of kidney disease –26. The Epigenetics refers to the ability to regulate gene expression without altering the primary nucleotide sequence, i.e., phenotypic changes, including histone modifications, DNA methylation, and noncoding RNAs can be induced without changing the genotype , 33. In In our study, Klotho and DNMT3b protein expression was upregulated, while DNMT1 and DNMT3a protein expression was downregulated in the EGCG group compared with that in the control group (db/db mice without EGCG treatment). The methylation level of the Klotho gene promoter was increased in the control group but decreased in the EGCG group. Similar to our results, Zhang et al. found that DNMT3b protein expression was decreased and DNMT3a protein expression was increased in chronic nondiabetic kidney disease . Moreoveβ1 and α-SMA in DKD in a direct or indirect manner [β1 and α-SMA are hypertrophic and fibrotic cytokines that play an important role in glomerular hypertrophy and mesangial matrix broadening, which ultimately lead to end-stage renal disease [Oxidative stress, inflammatory response, and fibrosis are the major pathological mechanisms of DKD. ROS are mainly produced by excessive oxidative stress, and renal tubular cells are the main target cells of ROS. ROS can induce the apoptosis of mesangial cells and tubular cells, causing pathological changes, which ultimately lead to glomerular sclerosis , 41. Inft manner . Both TG disease . In thisNext, we investigated the mechanism by which EGCG regulates Klotho expression to protect the kidney in DKD. Klotho is highly expressed in the distal tubules of the kidney and is rich in CpG islands, indicating that the promoter of the Klotho gene is closely related to DNA methylation. Therefore, we examined the methylation level of the Klotho gene promoter and found that its level was increased and the expression of Klotho protein was decreased in db/db mice compared with that in normal mice. Similar results were obtained in HG-induced HK-2 cells. These findings indicated that changes in Klotho promoter methylation were observed in DKD. After 8 weeks of intervention with EGCG (50 mg/kg/d), results showed that the methylation level of the Klotho gene promoter in db/db mice was significantly lower than that in control mice, indicating that EGCG can reverse Klotho promoter methylation. We also found that EGCG could regulate the expression of DNMTs in db/db mice and HK-2 cells. Moreover, ChIP results showed that DNMT3a and Klotho promoters were highly enriched in HG environment, and the enrichment was reduced after EGCG intervention. The effect of DNMT3a on Klotho promoter methylation has been reported in several studies , 46, 47;Hence, these results showed that HG increased the methylation level of the Klotho gene promoter by regulating DNMTs, leading to low expression of Klotho protein and causing changes in downstream molecules, eventually upregulating the expression of oxidative stress and inflammatory and fibrosis markers, and thus accelerating the progression of DKD. Based on our results, EGCG could downregulate the enrichment of DNMT3a and Klotho to decrease the methylation level of the Klotho gene promoter, and it could also upregulate the expression of Klotho to inhibit oxidative stress, fibrosis, and inflammation. In HG-induced HK-2 cells, Klotho OE could inhibit the expression of oxidative stress and inflammatory and fibrosis markers, and ameliorate cell damage. However, in the case of Klotho gene KO, EGCG intervention did not significantly decrease marker expression. These results indicate that EGCG does not exert its protective function in the absence of the Klotho gene.In animal experiments, we have three groups. C57BLKS/J normal mice were set as blank controls, C57BLKS/J db/db mice were set as negative controls, and C57BLKS/J db/db mice treated with EGCG at 50 mg/kg/d were set as the experimental group. The C57BLKS/J normal mice treated with only EGCG were missing. If C57BLKS/J normal mice treated with only EGCG were set, the experiment would be more rigorous.In summary, our results suggested that HG treatment increased the methylation level of the Klotho gene promoter via regulating DNMT3a expression Figures , while E


Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition to ammonia and lactate, many other metabolites accumulate in CHO cell cultures, although their effects remain largely unknown. Elucidating these effects has the potential to further improve the productivity of CHO cell-based bioprocesses. This study used untargeted metabolomics to identify metabolites that accumulate in fed-batch cultures of monoclonal antibody (mAb) producing CHO cells. The metabolomics experiments profiled six cell lines that are derived from two different hosts, produce different mAbs, and exhibit different growth profiles. Comparing the cell lines’ metabolite profiles at different growth stages, we found a strong negative correlation between peak viable cell density (VCD) and a tryptophan metabolite, putatively identified as 5-hydroxyindoleacetaldehyde (5-HIAAld). Amino acid supplementation experiments showed strong growth inhibition of all cell lines by excess tryptophan, which correlated with the accumulation of 5-HIAAld in the culture medium. Prospectively, the approach presented in this study could be used to identify cell line- and host-independent metabolite markers for clone selection and bioprocess development. Chinese hamster ovary (CHO) cells are among the most widely used production hosts for biopharmaceuticals, especially monoclonal antibodies (mAbs), due to their capacity to support proper protein folding and post-translational modifications that are critical for therapeutic efficacy . AdvanceDespite progress, limitations remain with respect to productivity. One major source of productivity limitation in fed-batch bioreactors is the accumulation of metabolic byproducts that reduce cell growth, viability and/or protein production . ControlMetabolomics experiments generally fall into two categories—targeted or untargeted. Targeted experiments focus on a selected subset of the metabolome to obtain quantitative measurements on these metabolites. Untargeted experiments provide a semi-quantitative, but more comprehensive profile of metabolites in a biological system. These experiments are performed without a priori selection of target analytes, and thus provide a useful means of reducing bias for discovery oriented studies .In the context of CHO cell-based bioprocesses, untargeted metabolomics has been used to examine the effects of growth medium composition ,13 and iIn this paper, we describe an untargeted analysis of six CHO cell lines derived from two hosts, with each cell line producing a different mAb. Comparisons of accumulating metabolites across cell lines as well as between different growth phases  point to a tryptophan-derived metabolite as an indicator of significant growth inhibition. Supplementation experiments confirmed that excess tryptophan impairs the growth of multiple CHO cell lines from different hosts.6 cells/mL  of autoscaled LC–MS data from samples collected during the stationary phase shows distinct groupings of cell lines . A scattp-value. However, the significance of this pathway reflects its membership of all 20 naturally occurring amino acids and their cognate tRNAs. Therefore, we focused our subsequent analysis on the other two significant pathways—histidine and tryptophan metabolism.To determine whether the observed growth differences are significantly associated with specific metabolic activities, pathway enrichment analysis was performed on metabolite profiles of stationary phase samples using KEGG pathway maps for the Chinese hamster as reference. Pathways represented by a higher than expected number of metabolites, as determined by a modified Fisher’s exact test, were considered “enriched” in the low-growth cell line 6. The results of this analysis are shown in p-value < 0.05) that meet these criteria  that grew to an intermediate peak VCD  was cultured in shake flasks with varying levels of tryptophan . We observed a significant negative correlation between the specific growth rate on day 3 and level of tryptophan supplementation . Of these, eight features were annotated by BioCAn as tryptophan metabolites per KEGG’s pathway definition. These features included 5-HIAAld, but not IAAld (We next profiled the metabolites in culture medium samples from the shake flask supplementation experiments to determine whether either of the two putatively identified tryptophan metabolites (5-HIAAld and IAAld) accumulated in the shake flask cultures with increasing tryptophan concentration. The samples from these cultures were analyzed using the same LC–MS experiments as the bioreactor study. Analysis of variance identified 112 LC–MS features with responses that significantly associate with tryptophan concentration . For IAA, a high-purity chemical standard is readily available. Using untargeted LC–MS experiments, we correlated the growth of mAb-producing CHO cells cultured in fed-batch reactors with metabolites that accumulated in the culture medium. A novel aspect of the study is that the correlation was identified across multiple cell lines derived from two different hosts, with each cell line producing a different mAb. We found a significant negative correlation between peak VCD and a metabolite annotated as an intermediate in tryptophan metabolism. This finding is supported by results from medium supplementation experiments, which confirmed a growth inhibitory effect of excess tryptophan. Importantly, this effect was observed for multiple cell lines, including a cell line that was not included in the bioreactor study that identified the negative correlation between peak VCD and tryptophan metabolism. We annotated the tryptophan-derived metabolite as 5-HIAAld based on all available evidence. As 5-HIAAld is unavailable for purchase, we were unable to directly determine its effect on cell growth by adding it to the culture medium. However, we show that a metabolite with a spectral signature matching 5-HIAAld accumulates with increasing tryptophan concentration in the culture medium. We also show that this metabolite cannot be IAA, the only other CHO cell metabolite with nearly the same mass (within 0.003 Da of 5-HIAAld), as the RT and MS/MS spectrum clearly do not match A. The MSSeveral recent studies have used metabolomics experiments to investigate growth limitations in CHO cell-based bioprocesses. Mulukutla et al. reported that several amino acids and related metabolites, including tryptophan derivatives indole 3-carboxylate and indole 3-lactate, accumulated in fed-batch cultures of a glutamine synthetase (GS) knockout CHO cell line . Even thIn addition to discovering growth inhibitory byproducts, untargeted metabolomics has also been used to improve medium supplementation strategies. For example, Sellick et al. used GC–MS-based metabolite profiling experiments to find that glucose and several amino acids become depleted at different time points in culture, and that supplementing the feed medium with these nutrients increased peak VCD and maximal titer . More reA common denominator of the above studies is that growth and productivity correlate with intermediates of not only glucose catabolism, but also amino acid metabolism. However, because each study examined a different cell line under varying culture conditions, it is unclear whether any general conclusions can be drawn that are applicable to other hosts and cell lines. The present study takes a step towards addressing this issue by comparing multiple cell lines derived from different hosts that were grown under identically controlled bioreactor conditions. Our supplementation experiments demonstrate that excess tryptophan, but not other amino acids, impairs the growth of cell lines derived from hosts of two different strains (K1 and DG44). This result points to the possibility that cell lines with different genetic backgrounds could share a common mechanism of growth inhibition.A major challenge in metabolomics is metabolite identification, where problems persist regarding low annotation rates, ambiguity, and inconsistency of annotation across studies. There are several factors that contribute to these problems. For a given metabolite, even the largest spectral libraries catalog a handful of experimentally obtained mass features, where the experimental conditions for the library data may or may not match the conditions for the study of interest. Ideally, metabolite annotations are confirmed using high-purity chemical standards run on the same instrument under identical experimental conditions, but these standards are unavailable and too costly to synthesize for many metabolites. Another factor is data preprocessing, which impacts both the quantity of unique mass features that can be extracted from an untargeted experiment as well as the information content of these features. In a recent study, Yeo et al. showed that certain ion products (sometimes termed adducts) of soft (ESI) ionization are highly specific for some subclasses of lipids, and that associating an ion product with the most likely source lipid molecule based on a dominant, preferred ion product of the lipid during preprocessing could improve both the coverage and accuracy of subsequent metabolite annotation .iCHO model [In the present study, we analyzed the annotations for a detected compound suggested by different tools in the context of known enzymatic reactions for the CHO cell . This apHO model  downloadHO model . We alsoHO model  to ensurWhether this tryptophan metabolite directly inhibits CHO cell growth remains to be elucidated. One interpretation of the supplementation experiments is that tryptophan itself is a growth inhibitory metabolite. On the other hand, we did not find a significant negative correlation between tryptophan and peak VCD in the untargeted metabolomics data from the bioreactor experiments . Rather,The accumulation of 5-HIAAld could reflect the depletion of an upstream metabolite that promotes cell growth . In muri13C- and 15N-labeled tryptophan, could be used in conjunction with untargeted metabolomics to characterize the impact of enzyme knockouts (or knockdowns) on the fate of tryptophan or other medium components that potentially give rise to toxic byproducts. Future work should also investigate the impact of lowering the tryptophan concentrations in basal and/or feed media. A resultant improvement in cell growth would provide further supporting evidence that one or more products of tryptophan metabolism leads to growth inhibition. Recently, Mulukutla et al. reported that maintaining several amino acids at low concentrations throughout the culture duration improved both VCD and titer [Obtaining further insights into whether 5-HIAAld modulates CHO cell growth or merely indicates growth inhibition would benefit from molecular approaches that disrupt specific steps in tryptophan metabolism, for example, using CRISPR/Cas9 mediated gene knockout. This strategy has been successfully demonstrated for lipoprotein lipase, achieving near complete knockout of this enzyme in CHO cells . Prospecnd titer .In conclusion, this study demonstrates the benefit of comparing multiple cell lines from different host lineages, as a metabolic indicator identified using this approach is more likely to reflect a general mechanism of growth inhibition. A similar approach could also be used to identify metabolic indicators of product quality, e.g., glycosylation. Prospectively, these cell line- and host-independent metabolic indicators could serve as biomarkers during clone selection and guide rational engineering of CHO cell hosts for improved growth and productivity.Unless otherwise noted, all chemicals and reagents, including LC–MS-grade water and other solvents, were purchased from Sigma Aldrich . Serum-free, chemically defined proprietary basal and feed media (described below) were prepared at Biogen.GLUL) was used as the selection marker. The GLUL selection marker was linked to the heavy chain cassette by an internal ribosome entry site element. The other three cell lines were derived from host 2 [Six CHO cell lines, each producing different human monoclonal antibodies (IgG), were selected for metabolomics experiments. Three of the cell lines were derived from host 1 , a derivm host 2 , a derivm host 2 .2. Cells were counted using a viability analyzer . Cells were inoculated in bioreactors at a seeding density of 1 × 106 cells/mL for all cell lines except cell line 6 (derived from host 2). This cell line was seeded at a lower density (4 × 105 cells/mL), because it did not achieve sufficiently high cell densities in seed flasks to match the inoculation density of the other cell lines. Serum-free, chemically defined proprietary basal and feed media and bioreactor operating conditions were used similar to what has been previously described [All six clones were cultured in 5 L glass bioreactors  using TruBio DV controllers . Cryopreserved cells were thawed and scaled up in shake flasks  by passaging cultures every 3 to 4 days. Shake flasks were kept in a humidified incubator set at 36 °C and 5% COescribed . The basv/v). The mixture was vortexed for 15 sec and centrifuged at 15,000 × g for 15 min at 4 °C to pellet proteins. The supernatant was collected into a fresh sample tube and dried using a SpeedVac concentrator (Eppendorf Vacufuge 5301). The dried sample was reconstituted in one-half sample volume of methanol/water (1:1 v/v). The extraction process was repeated three times for each sample.Previously frozen culture medium samples were thawed on ice and mixed with pure methanol at a 1:3 sample to methanol ratio (m/z) value and LC retention time (RT). Each feature is also associated with an MS/MS spectrum and an area under the curve (AUC) for the corresponding extracted ion chromatogram (XIC).Cell culture supernatant samples were analyzed using information dependent acquisition (IDA) experiments on a time-of-flight (TOF) mass analyzer  as previously described . DetailsThe features were annotated with five different tools: Metlin, HMDB, NIST MS Search 17, CFM-ID and MetFrag ,34,35,365 cells/mL and cultured in a humidified incubator set at 36 °C and 5% CO2. Each batch culture was run in duplicate. The cells were maintained in well-mixed suspension by placing the flasks on an orbital shaker set at 125 rotations per minute. Cells were counted manually by hemocytometer using trypan blue exclusion on days 0, 3, 6, and 9. Each manual count was repeated four times. Metabolites were extracted from culture medium samples and analyzed using LC–MS experiments, as described above.To test whether excess tryptophan inhibits cell growth, we selected a cell line that achieved an intermediate peak VCD in the bioreactor study (cell line 4), and grew this cell line in shake flasks supplemented with different levels of tryptophan  in the basal medium. The cells were inoculated into shake flasks at a density of 2.5 × 105 cells/mL. The plate was incubated with shaking in a humidified incubator set at 36 °C and 5% CO2. After 3 days, cells were counted on an automated cell viability analyzer. For each of the four cell lines, growth in basal medium was compared to growth in amino acid supplemented medium.To determine whether excess amino acids in the culture medium had a similar effect on multiple cell lines, all three cell lines from host 2  and a new cell line from host 1 (cell line 7) were grown in culture media supplemented with one of 8 different amino acids  at 10× their basal level in the basal medium. The cells were first expanded in shake flasks until the growth was exponential, and then transferred to a 24-deep well plate. Each well plate culture was seeded in duplicate at 2.5 × 10p-value was less than 0.05.All statistical calculations were performed in R . Principal component analysis (PCA) was performed on mean-centered and autoscaled AUC data of features detected in samples collected from the bioreactors during the stationary phase . Tests op-values were less than 0.05. Each significant and annotated (putatively identified) feature was matched with associated KEGG pathways in the CHO model. The p-values for pathway enrichment analysis were determined by Fisher’s exact test using the R Stats Package function fisher.test. These p-values were then applied to contingency tables to determine whether a pathway was significantly enriched with metabolites elevated in cell line 6. The count of metabolites mapped to each pathway was reduced by one to provide a more conservative estimate of enrichment, similar to the EASE score [Pathway enrichment analysis was performed to identify pathways associated with metabolites that are significantly elevated in the slowest growing  cell linSE score  modificaSE score . A schemTwo-way analysis of covariance (ANCOVA) was applied to determine whether there were metabolites that accumulated in the bioreactors over time and correlated negatively with VCD. Growth stage was set as a categorical factor with two levels  and peak VCD was set as a continuous factor. One-way analysis of variance (ANOVA) was used to assess the effect of tryptophan on the day 3 growth rate in the supplementation experiments. The specific growth rate (µ) was calculated using the following equation:Two-way ANOVA was used identify metabolites that accumulated in the shake flask cultures in response to increasing tryptophan concentration in the culture medium. The two factors were time in culture and level of tryptophan supplementation.


Chlamydia trachomatis infection, has been reported among (mainly HIV-positive) men who have sex with men (MSM) since 2003. In the Netherlands, LGV testing recommendations changed from selective to universal testing in 2015. Changes in tested populations could have led to incomparable LGV positivity rates over time.Lymphogranuloma venereum (LGV), an invasive form of We investigated LGV trends among MSM attending Centres for Sexual Health using surveillance data between 2011 and 2017.Chlamydia infection and MSM tested specifically for LGV. With multivariable logistic regression analysis, the association between years and LGV was adjusted for testing indicators and determinants.LGV positivity was calculated among MSM tested for rectal We included 224,194 consultations. LGV increased from 86 in 2011 to 270 in 2017. Among LGV-positives, proportions of HIV-negative and asymptomatic MSM increased from 17.4% to 45.6% and from 31.4% to 49.3%, respectively, between 2011 and 2017. Among MSM tested for rectal chlamydia, LGV positivity increased from 0.12% to 0.33% among HIV-negatives and remained stable around 2.5% among HIV-positives. Among LGV-tested MSM, LGV positivity increased from 2.1% to 5.7% among HIV-negatives and from 15.1% to 22.1% among HIV-positives. Multivariable models showed increased odds ratios and significant positive associations between years and LGV.Although increased testing and changes in LGV incidence are difficult to disentangle, we found increasing LGV trends, especially when corrected for confounding. LGV was increasingly attributed to HIV-negative and asymptomatic MSM, among whom testing was previously limited. This stresses the importance of universal testing and continuous surveillance. Chlamydia trachomatis (CT). Rectal LGV has been reported at a low level among men who have sex with men (MSM) in Europe since 2003, while urogenital infections are rarely seen [Lymphogranuloma venereum (LGV) is a sexually transmitted infection (STI) caused by invasive L1, L2 and L3 serovars of ely seen ,5. In adely seen ,7. Diffeely seen ,9.To date, the exact LGV transmission dynamics are not well understood. A previous Dutch study found a rectal-urethral LGV ratio of 15:1 and therefore concluded that urethral LGV could not explain all rectal LGV transmissions . IndicatPrevious studies and the European Centre for Disease Prevention and Control underlined the importance of active LGV testing and the implementation of surveillance systems ,7,12,13.Between 2006 and 2012, the number of LGV diagnoses fluctuated, but the incidence remained consistently higher than before the outbreak in 2003 most probably resulting from increased testing after 2003. However, it was estimated that selective LGV testing could have resulted in an underdiagnosis of 20% between 2006 and 2012, as LGV was also diagnosed among asymptomatic MSM and among HIV-negative MSM .Changes in the indications for LGV testing over time complicate the interpretation of LGV positivity trends (confounding by indication). A decreased positivity is to be expected when MSM at lower risk for LGV are increasingly tested, the incidence remains stable and LGV is truly uncommon in the population among whom testing was limited before. Because of selective testing procedures, positivity is usually calculated among only those positive for rectal CT who were further tested for LGV, thus excluding those negative for rectal CT . In a unThis study aimed to investigate rectal CT testing, LGV testing and LGV positivity using different denominators among MSM attending CSH between 2011 and 2017. In addition, we aimed to explore LGV trends with changes in the characteristics of the population tested for LGV taken into account.This surveillance study used routinely collected data from all consultations performed at CSH in the Netherlands between 2011 and 2017 [The CSH offer low-threshold and free of charge STI/HIV care exclusively for predefined high-risk populations. MSM are eligible for STI/HIV testing independent of risk behaviour. A selection of information registered in the electronic patient records of the CSH, including demographic variables, sexual risk behaviour, STI/HIV history, STI testing and diagnosis, was sent to the CSH surveillance database for national surveillance purposes. For the current study, all MSM (men who had sex only with men and men who had sex with both men and women) consultations between 2011 and 2017 were included. Trends including earlier years have been published previously .Testing for LGV applied a two-step procedure. First, samples were tested by a generic nucleic acid amplification test (NAAT) for CT. Second, positive samples were further tested by a second PCR to discriminate between LGV and non-LGV genotypes. Only laboratory-confirmed LGV cases are reported to the surveillance system. Indeterminate LGV test results were considered as negative in the analyses.A client identification number was not available over the full study period and therefore the unit of analysis is ‘STI consultation’. Therefore, repeat testers could have been counted several times over the period analysed. We calculated the proportion of consulting MSM who were tested for rectal CT infection, the proportion of rectal CT-positive MSM among those tested and the proportion of MSM who were tested for LGV serovars among those with a rectal CT infection. LGV positivity was calculated by dividing the number of LGV-positives by the number of MSM who were (i) tested for rectal CT-infection, including those positive but not tested for LGV as ‘non-LGV’ and (ii) tested for LGV, thus excluding rectal CT-negative MSM. Proportions and positivity were stratified by year of consultation and by HIV status. All trends were explored using the using the Cochran–Armitage test for trends.We performed uni- and multivariate logistic regression to adjust for possible confounders that could influence the association between year  and LGV. Indicators for LGV testing and variables that were previously identified as LGV risk factors using the same Dutch CSH surveillance data between 2006 and 2012, were added to the multivariate model: age group , STI symptoms , HIV status , sexual contact , number of sexual partners in the past 6 months  and condom use in last contact  [Ethical committee approval was not sought since we used de-identified routinely collected surveillance data.Supplementary Table S1). Unprotected sex with the most recent partner decreased from 73.3%  in 2011 to 62.6%  in 2017.A total of 224,194 consultations among MSM were registered between 2011 and 2017. The median age was 36 years, 69.5% were ethnic Dutch, 14.1% were known HIV-positives, 20.5% had a history of STI, 83.8% had sex with men only, 52.9% had 0–5 sex partners in the past six months, 60.9% had unprotected sex with the most recent partner, 22.9% experienced STI related symptoms and 19.4% were notified for STI/HIV exposure . The proThe proportion of MSM who were tested for rectal CT infection increased from 77.6% to 96.3% between 2011 and 2017 , whereasSupplementary Table S2).The total number of diagnosed LGV infections increased from 86 in 2011 to 270 in 2017 . The proChlamydia infection. Among HIV-positives the trend was stable around 2.5% (p = 0.534) and among HIV-negatives, the trend increased slightly but significantly from 0.12%  to 0.33%  p < 0.001). LGV positivity calculated among LGV-tested MSM was higher and increased from 15.1% (71/470) to 22.1% (147/664) among HIV-positive MSM (p = 0.340) and from 2.1% (15/717) to 5.7%  among HIV-negative MSM.Overall, LGV positivity was higher among HIV-positives than among HIV-negatives over the full study period, but the trends were comparable for both groups, with a rapid increase and decrease in 2012 and 2013, respectively . LGV posThe Dutch CSH surveillance data showed that rectal CT testing, LGV testing and the number of LGV diagnoses increased among MSM between 2011 and 2017, with an increasing proportion of LGV attributed to HIV-negative or asymptomatic MSM. Given the increasing rectal CT and LGV testing trends, we expected positivity to decrease. In line with this, we found a decreasing trend for rectal CT. However, LGV positivity remained stable when calculated among all MSM tested for rectal CT infection and increased when calculated among LGV-tested MSM. These crude LGV positivity trends were affected by changes in the population tested over time, as we found stronger and significant positive associations between years and LGV after correction for LGV testing indicators and risk factors.A strength of the current study is that CSH surveillance data have national coverage including all consultations at all CSH, resulting in a large number of observations. Unique is the collection of data on LGV since the first outbreak in 2003. Limitations of the current study include the lack of information on LGV-related symptoms, LGV risk factors such as unprotected receptive anal sex and LGV testing and diagnoses at healthcare settings other than CSH. In addition, we were not able to correct for repeat testing in the logistic regression analyses as a unique client identifier was not available for the full study period (available only July 2014–2017). However, the results of a generalised estimating equation model correcting for within-subject correlations, including the years 2015 to 2017 only, did not differ from the results of a logistic regression model (data not shown). Lastly, the interpretation of LGV positivity is challenging because changes in rectal CT positivity are also captured if LGV positivity is calculated among all MSM tested for rectal CT infection. Using LGV-tested MSM only as denominator, selection bias limits the interpretation of LGV positivity. Therefore, all trends and changes in the characteristics of the population tested should be considered.Internationally, testing procedures vary from country to country and long-term surveillance data on LGV is lacking, which hampers comparison of trends between countries. The number of reported LGV cases continues to increase in European countries, which may be explained by increased testing . In the The increasing proportions of HIV-negative MSM and asymptomatic cases among LGV-infected MSM could be explained by increased testing and previous underdiagnosis. Besides changes in the testing policy, the increasing proportion of HIV-negative MSM among LGV-positives may also be related to HIV biomedical interventions in two different ways: On the one hand, greater awareness of ‘undetectable is untransmittable’ for HIV and the availability of pre-exposure prophylaxis (PrEP) might have resulted in increased risky behaviour including sexual mixing between HIV-negative and HIV-positive MSM . On the Chlamydia infection) may effectively treat these infections. However, strong evidence is not available yet.In the Netherlands, LGV control is based on offering universal rectal CT testing and universal LGV testing to all MSM attending CSH, followed by treatment and (voluntary) partner notification for those with LGV. Although recommended by the Dutch guideline for general practitioners (GPs), rectal testing is rarely performed by GPs . TherefoIt is challenging to disentangle the effects of increased testing, previous underdiagnosis and changes in LGV incidence over time. Current LGV control measures are mostly performed by CSH, which may not be enough to interrupt LGV transmission. To guide additional prevention efforts, insight into LGV occurrence among MSM attending clinical settings other than CSH and insight into circulating LGV strains are needed. Furthermore, it is important to take changes in the characteristics of the tested population into account in the interpretation of trends as we found stronger and significant increasing LGV trends when corrected for confounding. Notably, we found increasing proportions of LGV attributed to HIV-negative and asymptomatic MSM, among whom testing was previously limited. These findings stress the importance of universal rectal CT and LGV testing for MSM irrespective of behaviour, HIV status or symptoms and the importance of continuous surveillance to improve LGV control and to improve insight into the epidemic.