Insulin resistance (IR) plays a key role in gestational diabetes mellitus (GDM) pathogenesis. The antiaging protein klotho has been proven to be closely related to IR. The purpose of this study was to investigate the effect of klotho on IR in GDM trophoblast cells.  The GDM cell model of HTR-8/SVneo cells was induced by high glucose (HG). Plasmid transfection was used to mediate the overexpression or silencing of klotho. The effects of klotho on cell viability, IR, and the IGF-1/PI3K pathways were observed by RT-qPCR, western blot, Cell Counting Kit-8 detection, glucose uptake assay, and immunofluorescence detection. α, INSR-β, IRS1, IRS2, and GLUT4), and glucose uptake in HTR-8/SVneo cells of the HG group. In addition, the overexpression of klotho inhibited the levels of IGF-1, IGF-1R/p-IGF-1R, and the phosphorylation and activation of the signal transduction molecules PI3K/Akt/mTOR. On the contrary, klotho deletions could reverse these changes of HTR-8/SVneo cells induced by HG. Conclusion. In a word, the results of this study showed that the regulation of klotho played an important role in the IR of trophoblast cells induced by HG, which was mediated at least in part by the IGF-1/PI3K/Akt/mTOR pathway. Klotho expression was up-regulated in HG-induced cells. Overexpression of klotho could reduce the cell viability, insulin signaling molecules (INSR- Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, which refers to the abnormal glucose tolerance that occurs during pregnancy or is first discovered . GDM oftThe intracellular signal of placental insulin is mediated by stimulating its tyrosine kinase receptor and activating insulin receptor substrate -1 (IRS-1) . Activatβ-klotho increased in the GDM placenta, which could be one of the reasons for IR -2-deoxy-glucose (2-NBDG) . Briefly, the cells were cultured in a medium with a 100 The expression and distribution of p-IGF-1R were detected by immunofluorescence staining. Cells were pre-seeded on the slide and fixed with 4% paraformaldehyde for 15 minutes. After scouring with PBS to remove residual paraformaldehyde, the cells were infiltrated with 0.1% TritonX-100 for 30 minutes to permeate the cell membrane. Then, goat serum was used to block the nonspecific antigen for 15 minutes and spent the night with the anti-p-IGF-1R antibody  at 4°C. After washing with PBS, the cells were incubated with secondary bodies anti-Rabbit IgG (H + L) for 90 minutes. The nucleus was incubated with DAPI. Finally, the cells were sealed with buffered glycerin and stored away from light for observation under a fluorescence microscope .t-tests were used for the statistical analysis between the two groups, and one-way analysis of variance (ANOVA) determined three or more groups. p < 0.05 was considered significant.The statistical analysis was performed by using the GraphPad Prism 8 software. All data were represented as the mean ± SD. 'Student's To study the important influence of klotho on GDM trophoblast cells, we simulated the HTR-8/SVneo cell model by HG induction. RT-qPCR was performed to test klotho levels in HTR-8/SVneo cells . The resα, INSR-β, IRS1, and IRS2. The results showed that GLUT4, INSR-α, INSR-β, IRS1, and IRS2 levels in trophoblast cells under HG significantly decreased than in the control group  were significantly decreased in HG-induced HTR-8/SVneo cells, which is consistent with previous studies [Many studies showed that IR played an important role in GDM , 27. At  studies . Moreove studies , which wStudies have shown that klotho-mediated insulin metabolism and aging are related to IR , 31, 32.β signaling pathway [It was reported that klotho inhibits the IGF signaling pathway, induces SOD expression to reduce oxidative stress, and inhibits the Akt-mTOR signaling pathway to inhibit abnormal growth of the kidney . Klotho  pathway . In addi pathway . Inhibit pathway . The upr pathway .Our study found that the expression of IGF-1R, p-IGF-1R, and IGF-1 was significantly increased in the HG-treated HTR-8/SVneo cells after silencing klotho. Over-expression of klotho significantly reduced the levels of these proteins. It is consistent with previous studies . We furtIn a word, the results of this study showed that silencing of klotho reversed the decrease of cell activity induced by HG and alleviated insulin resistance. In addition, these results were consistent with klotho's view that it could inhibit the activation of IGF-1 and PI3K-Akt-mTOR signals to promote IR. This study proved that klotho might regulate trophoblast cells' IR under HG conditions, which indicated that klotho might be a promising target for treating GDM.


PSEN1, which cause familial Alzheimer’s disease alter the processing of amyloid precursor protein, leading to the generation of various amyloid-β peptide species. These species differ in their potential for aggregation. Mutation-specific amyloid-β peptide profiles may thereby influence pathogenicity and clinical heterogeneity. There is particular interest in comparing mutations with typical and atypical clinical presentations, such as E280G. We generated PSEN1 E280G mutation induced pluripotent stem cells from two patients and differentiated them into cortical neurons, along with previously reported PSEN1 M146I, PSEN1 R278I and two control lines. We assessed both the amyloid-β peptide profiles and presenilin 1 protein maturity. We also compared amyloid-β peptide profiles in human post-mortem brain tissue from cases with matched mutations. Amyloid-β ratios significantly differed compared with controls and between different patients, implicating mutation-specific alterations in amyloid-β ratios. Amyloid-β42:40 was increased in the M146I and both E280G lines compared with controls. Amyloid-β42:40 was not increased in the R278I line compared with controls. The amyloid-β43:40 ratio was increased in R278I and both E280G lines compared with controls, but not in M146I cells. Distinct amyloid-β peptide patterns were also observed in human brain tissue from individuals with these mutations, showing some similar patterns to cell line observations. Reduced presenilin 1 maturation was observed in neurons with the PSEN1 R278I and E280G mutations, but not the M146I mutation. These results suggest that mutation location can differentially alter the presenilin 1 protein and affect its autoendoproteolysis and processivity, contributing to the pathological phenotype. Investigating differences in underlying molecular mechanisms of familial Alzheimer’s disease may inform our understanding of clinical heterogeneity.Mutations in the presenilin 1 gene,  et al., generate patient-derived induced pluripotent stem cells containing the Presenilin 1 E280G mutation. The E280G lines displayed a high relative production of Abeta43, a finding matched in post-mortem tissue with the same mutation. This mutation also disrupts presenilin 1 protein maturity. Together these findings indicate mutation-specific pathogenic mechanisms.Willumsen   However, FAD is caused by rare autosomal dominant mutations in the presenilin 1 (PSEN1), PSEN2 or amyloid precursor protein (APP) genes and APP duplications.4–9Alzheimer’s disease is defined by the cerebral accumulation of pathological misfolded proteins into amyloid beta (Aβ) plaques and neurofibrillary tangles containing tau.11 In the amyloidogenic pathway, APP is cleaved by β-secretase (BACE1) releasing the sAPPβ fragment.12 The remaining membrane-bound C99 fragment undergoes an initial epsilon cleavage, releasing an APP intracellular C domain and a membrane-bound Aβ peptide. Sequential carboxypeptidase-like cleavages of Aβ by the γ-secretase complex releases Aβ peptides of varying length.13 Epsilon Aβ cleavage occurs at residue 48 or 49, giving rise to two cleavage pathways of decreasing peptide length (Aβ49 > 46 > 43 > 40 and Aβ48 > 45 > 42 > 38).14 In PSEN1-FAD, there is a loss of processivity leading to qualitative shifts towards longer Aβ peptides.15The presenilin 1 protein (PSEN1) is the catalytic component of the γ-secretase complex, responsible for the cleavage of APP.16 Studies have revealed that specific FAD mutations produce very distinct Aβ ratios, as shown in both iPSC differentiated into neurons17 and in other cell-based and activity assay models,18–20 recapitulating aspects of disease in vitro. Importantly, these Aβ ratios are likely to reflect early perturbations in Aβ processing, revealing early disease mechanisms.Recently, there have been multiple papers that have generated induced pluripotent stem cells (iPSCs) from FAD and Down’s syndrome patient fibroblasts, reviewed in.PSEN1, most pathogenic PSEN1 mutations cause an increase in the ratio of Aβ42 to total Aβ, and notably an increased Aβ42:40 ratio.21 However, the mechanism by which ratios are altered differs by mutation. Specific PSEN1 mutations affecting γ-secretase activity can influence both absolute amounts of Aβ and the Aβ cleavage pathway selection, leading to altered ratios of the peptides within those pathways.22Compared with wild-type (WT) PSEN1 mutations appear to consistently hinder sequential carboxypeptidase cleavage, leading to the release of longer Aβ peptides, alterations of which can be measured via the Aβ42:38 and Aβ43:40 ratios. This results in a relative increase in, and release of, longer Aβ peptides which are more prone to aggregation,22 in turn leading to more oligomeric Aβ and greater extracellular deposition.23 Mutations that severely compromise γ-secretase carboxypeptidase activity affect residues that map near the proposed γ-secretase active site.24Pathogenic 25 Mutations near the endoproteolytic site are proposed to inhibit endoproteolysis,26 which can lead to altered processivity.27 Interestingly, even within the γ-secretase complex bearing full-length PSEN1, the catalytic pore is still able to be formed,28 which suggests that proteolytic activity can still occur, although it may be reduced.Upon γ-secretase complex assembly full-length PSEN1 undergoes autoendoproteolysis to produce cleaved PSEN1, consisting of the PSEN1 N-terminal fragment (NTF) and C-terminal fragment.PSEN1 R278I mutation has previously been shown to have increased full-length PSEN1 and Aβ43 ratio in patient iPSC-derived neuronal lines by our group.17 Increased Aβ43 and full-length PSEN1, with reduced Aβ40, was also observed in the R278I mutation mouse embryonic fibroblast CF-1 (MEF) cell line30 and mouse models.30 Importantly, alterations in PSEN1 maturity were not caused by differences in PSEN1 expression, and APP expression was not altered,17 implying specific mutations affect disease via influencing PSEN1 structure/function. Along with R278I, other mutations associated with high Aβ43 are located around the intracellular loop. Another important mutation located in this area is the E280G mutation. Clinically, the E280G mutation is associated with atypical clinical presentations, including spastic paraparesis and marked white matter hyperintensities on MRI.32 Pathologically, a brain biopsy from a PSEN1 E280G carrier showed numerous large non-cored Aβ deposits as cotton wool plaques (CWPs) and severe cerebral amyloid angiopathy (CAA).31 An individual with the PSEN1 E280Q mutation has also had a similar CWP pathology observed post-mortem.33 HEK lines expressing the E280G mutation show significantly increased Aβ42 concentration, non-significantly increased Aβ40 concentration and an overall significantly increased Aβ42:40 ratio in conditioned media compared with controls.34 However, as discussed by the authors, these results suffer from the caveat that only mutated PSEN1 is expressed in these lines compared with heterozygous mutation carriers where WT PSEN1 is also expressed. In another study, cloned γ-secretase with a range of PSEN1 mutations was purified, WT or mutant γ-secretase was mixed with APP-C99 in a detergent-based assay and incubated at 37°C for 16 h. Aβ peptides produced in the detergent-based assay were measured using an AlphaLISA assay. The assays revealed greater amounts of Aβ42 compared with Aβ40 and a higher Aβ42:40 ratio in association with the PSEN1 E280G mutation, while western blot images also suggested the presence of full-length PSEN1, possibly due to reduced endoproteolytic processing.20 Aβ43 has not, however, been assessed in the PSEN1 E280G mutation.In some mutations, an increase in the ratio of Aβ43 compared with other peptide lengths or increased absolute levels has been linked to an increase in the full-length form of PSEN1. The PSEN1 R278I mutation. Finally, we compared these findings to post-mortem tissue from E280G carriers.In this study, we generated E280G iPSCs from two clinically affected FAD patient donors. We went on to investigate Aβ profiles using ratios measured via an enzyme-linked immune-sorbent assay (ELISA) and an electrochemiluminescence assay, and PSEN1 protein maturation in iPSC-derived neurons, with the hypothesis that the E280G mutation decreases PSEN1 autoendoproteolysis, resulting in the enhanced generation of Aβ43, similar to the proximal 35 Fibroblasts underwent episomal reprogramming according to Okita et al.36 Fibroblasts underwent nucleofection with episomal DNA coding for OCT4 and shRNA against p53, KLF4, SOX2 and c-MYC using plasmids #27077, #27078, and #27080 which were obtained from Addgene. Nucleofection was performed using the Lonza P2 Nucleofection kit (Amaxa PBP2-00675) as per manufacturer’s instructions.Cell lines used in this study are summarized in After nucleofection, cells were grown on geltrex  , maintained in fibroblast media which was changed every three days.TM) 10 cm dishes . On Day 8 after nucleofection, media was switched to human embryonic stem cell media , 20 ng/ml Fibroblast Growth Factor-basic (Peprotech)) and changed daily. Nascent iPSC colonies were picked and transferred into feeder-free conditions . iPSCs were passaged using 0.5 mM ethylenediaminetetraacetic acid.Six days after electroporation, cells were lifted with 1 × trypsin  and resuspended in fibroblast media on cultured on geltrex-coated MEF feeder layer coated  and 1 μM dorsomorphin (Tocris)]. N2B27 media consists of a 1:1 mixture of Dulbecco’s modified eagle medium F12 (DMEM-F12) and Neurobasal supplemented with 0.5X N2, 0.5X B27, 0.5X non-essential amino acids, 1 mM L-glutamine, 25 U/ml pen & 25 µg/ml strep, 50 μM β-mercaptoethanol and 2.5 μg/ml insulin. At Day 12, the precursors were passaged by using a cell scraper  onto laminin-coated wells and at Day 18 passaged using dispase and plated in laminin-coated wells (Sigma) in N2B27 media. The final passage was performed at Day 35 using accutase and maintained in N2B27 media on poly-L-ornithine (Sigma) and laminin (Sigma) coated wells in N2B27 media until the required time point. Neurons were analysed at 100 days post-neural induction. One hundred days were selected as it represents the end of corticogenesis and detectable levels of secreted Aβ.38iPSCs were differentiated into cortical neurons according to the Shi (av), 4°C for 15 mins. After isolating and removing the aqueous phase, the DNA-containing interphase was removed from the organic phase by adding 300 µl of 100% ethanol was added and samples were shaken, incubated for 3 mins and centrifuged at 2000 g(av), 4°C for 5 mins. The DNA was washed twice by the addition of 0.1 M trisodium citrate in 10% ethanol, pH 8.5 followed by incubation for 30 mins with regular inversion followed by centrifugation at 2000g(av), 4°C for 5 mins. The pellet was resuspended in 1.5 ml 75% ethanol and incubated for 20 mins with regular inversion followed by centrifugation at 2000 g(av), 4°C for 5 mins. The pellet was air dried for 10 mins before resuspension in 300 µL 8 mM NAOH, followed by centrifugation at 12 000g(av), 4°C for 10 mins. Genomic DNA from the two patient PSEN1 E280G lines, named E280G.A and E280G.B, were analysed for chromosomal abnormalities using the hPSC Genetic Analysis Kit (Stem Cell Technologies) and the StemCell genetic analysis app. Amplification in a minimal critical region of chromosome 17 of the E280G.A line was observed, with a calculated copy number of 2.44. There were no significant amplifications in the E280G.B line. Additional chromosomal sites  showed no significant abnormalities.DNA was harvested using the TRIzol™  protocol according to manufacturer’s instructions. Briefly, cells were homogenized in 1 ml TRIzol™ followed by the addition of 200 µl Chloroform , shaken vigorously and then incubated for 3 mins followed by centrifugation at 12 000gRNA was harvested from iPSCs 2 days after passage via Monarch total RNA miniprep kit (NEB). 100 ng was used for Nanostring analysis on the nCounter Stem Cell Characterization Panel. The expression of 770 genes was analysed on the nSolver software to compare grouped control (CTL1 and CTL2) and grouped E280G (E280G.A and E280G.B) data. Linear regression analysis was performed using GraphPad Prism.°C. Coverslips were blocked in 5% BSA/0.3% PBS-triton-X-100 followed by primary antibody incubation  and fixed for 15–20 min in 4% formaldehyde followed by excess 1X PBS wash and stored in PBS at 4cubation  in blockantibody  in block17 Conditioned media was centrifuged at 2000 g(av) for 5 min at RT, with 1 ml aliquoted in Sarstedt 2 mL PP tubes (72.694.406) and stored at −80°c.Cortical neurons were incubated in N2B27 media for 48 h prior to collection, as described previously.DCTM Protein Assay, 5 µl of sample and standards (1:1 dilutions of Bovine Serum Albumin in RIPA from 4 mg/ml to 0.125 mg/ml + a RIPA blank) was added to reagent A + S, followed by reagent B, in triplicate. After plate shaking and 15 min development time, absorbance was measured at 750 nm on a SparkTM 10 m plate reader (Tecan). Concentration was calculated from the standard curve using the equation x = (y-m)/b. Lysates of 30 µg loaded with NuPageTM LDS sample buffer and NuPAGETM reducing agent were denatured at 70°c for 10 min followed by brief centrifugation. Electrophoresis was conducted on a NuPageTM 10% Bis-Tris gel in MES SDS running buffer at 150 V for 1 h 30 mins. Transfer to nitrocellulose membrane was conducted at 30 V for 1 h. The membrane was blocked in 5% BSA/0.1% PBS-tween20 (PBS-T) and incubated in anti-PSEN1 overnight at 4°C. The membrane was washed three times with 0.1% PBS-T and incubated in a secondary antibody (Cells that had been used for conditioned media collection were lysed in radioimmunoprecipitation assay buffer (RIPA) containing phosphatase and protease inhibitors (Roche). Using the Bio-Rad antibody  for 1 h,antibody  for 1 h,n = 2).Solid phase sandwich, ELISA for Aβ1-43  was conducted using D100 neuron-conditioned cell media according to manufacturer’s instructions. Briefly, 100 µl of calibrator, control and samples were added in duplicate to individual wells coated with an Aβ1-43 peptide-specific capture antibody. A labelled detection antibody (82E1) was applied followed by chromogen incubation and the eventual addition of the stop solution. The plate was then read at 450 nm, with a reference of 650 nm . Aβ38/40/42 in D100 neuron-conditioned cell media were measured by electrochemiluminescence using the V-PLEX Aβ Peptide Panel 1 (6E10) Kit (1 Plate) , according to manufacturer’s instructions. Briefly, samples were diluted 1:1 in diluent 35 and added in duplicate to individual wells which were coated in mouse monoclonal peptide-specific capture antibodies for human Aβx-38/x-40/x-42. Samples were incubated with anti-Aβ antibody (6E10 clone) as the detection antibody conjugated with an electrically excitable SULFO-TAG. Measurements were made using the MESO QuickPlex SQ 120, MSD. Concentrations were calculated from the ECL signal using a four-parameter logistic curve-fitting method with the MSD Workbench software package. Absolute levels of secreted Aβ43, Aβ42, Aβ40, and Aβ38 were measured and converted to ratios for comparison. Data from the two control lines (CTL1 and CTL2) were pooled and comparisons were made between all cell lines, except for the E280G.B line in Aβ38 analyses due to the low number of inductions  and random hexamers following the manufacturer’s instructions. qPCR for each induction (run in triplicate) was performed using Power SYBR™ Green (Thermo Fisher) on the Mx3000P qPCR system (Aligent). The following primers were used: T-box Brain Transcription Factor 1 , Special AT-rich sequence-binding protein 2 , Class III beta-tubulin  and PSEN1 (F: CAATACTGTACGTAGCCAGA R: AATGGGGTATAGATTAGCTG). Data were normalized to housekeeping gene RPL18a (F: CCCACAACATGTACCGGGAA R: TCTTGGAGTCGTGGAACTGC).RNA was harvested using the TRIzol™  protocol and stored at −8039 For a summary of cases, see 202 in methanol and non-specific binding with 10% dried milk solution. Sections were incubated with the primary antibodies  with the Indica Labs standard macro (Area Quantification v1) optimized for each Aβ antibody.All three FAD cases assessed in this study were obtained through the brain donation programme of the Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology. The protocols used for brain donation and ethical approval for this study were approved by a London Research Ethics Committee and tissue is stored for research under a license from the Human Tissue Authority. The standard diagnostic criteria for the neuropathological diagnosis of Alzheimer’s disease were used in all cases.tibodies  overnighP < 0.05 considered significant. When ANOVA analysis reached statistical significance, Tukey’s post hoc test was used, adjusting the significant level appropriately for the analysis conducted. All cell data statistics were conducted on GraphPad Prism.Cell data were checked for normality using QQ plots. For statistical analysis, the CTL1 and CTL2 lines have been pooled. When needed for western blot and qPCR analysis, CTL2 was used for normalization. Cell data were assessed using one-way ANOVA with a probability value of less than PSEN1 E280G mutation carriers were reprogrammed to iPSCs via episomal reprogramming. Confirmation of successful reprogramming in the newly generated PSEN1 E280G lines was confirmed by immunocytochemistry (ICC) which showed that all iPSC lines expressed the markers of pluripotency NANOG and SSEA4  with E280G lines (E280G.A and E280G.B) displayed a high correlation (R2 = 0.9932), representing a similar stem cell expression profile in the newly derived lines compared with established iPSCs (Fibroblasts from two nd SSEA4 . Chromos of 2.44 . The exaed iPSCs . Sanger ed iPSCs .PSEN1 M146I and R278I),17 the newly generated lines were subjected to cortical differentiation to generate the cell type affected in FAD. ICC was conducted on all control and FAD cell lines to investigate forebrain patterning at Day 25 via Forkhead Box G1 (FOXG1) and Paired box protein Pax-6 (PAX6) staining  with a significant difference between the control and E280G.B lines . There was no significant difference in SATB2 expression . There was a significant difference in the Aβ42:40 ratio, indicative of both γ-secretase epsilon cleavage efficiency and selection of the Aβ48 or 49 pathway, across the cell lines (P < 0.0001). Compared with controls, there was a significantly increased ratio in the M146I, E280G.A and E280G.B lines . M146I and E280G.A also had significantly increased Aβ42:40 ratios compared with R278I . The M146I line also had a significantly increased Aβ42:40 ratio compared with E280G.B (P = 0.01), To explore Aβ processing down the Aβ48/49 pathways in vitro , we asseP < 0.0001). Compared with controls, there was a significantly higher Aβ43:40 ratio in the R278I, E280G.A and E280G.B lines . The Aβ43:40 ratio in the R278I, E280G.A and E280G.B lines was also significantly higher compared with M146I , The Aβ43:40 ratio, indicative of γ-secretase carboxypeptidase cleavage efficiency in the Aβ49 pathway, was significantly different between cell lines (n number (n = 2). The ratio was significantly different across the cell lines . Compared with controls, M146I, R278I and E280G.A had a significantly increased Aβ42:38 ratio . The M146I line had a significantly increased ratio compared with R278I, and significantly reduced ratio compared with E280G.A (Both P < 0.0001). The R278I line had a significantly reduced ratio compared with E280G.A (P < 0.0001), The ratio of Aβ42:38, indicative of γ-secretase carboxypeptidase cleavage efficiency in the Aβ48 pathway, was compared across all lines, excluding E280G.B due to low P < 0.0001), which indicates γ-secretase epsilon cleavage efficiency in the Aβ48:49 pathways. Controls had a significantly increased Aβ38:40 ratio compared with R278I and E280G.A lines . The M146I line had a significantly increased Aβ38:40 ratio compared with E280G.A (P = 0.0002). R278I also had a significantly increased Aβ38:40 ratio compared with E280G.A (P = 0.02), There was a significant difference across the lines (excluding E280G.B) in the ratio of Aβ38:40 (P = 0.002). Compared with controls, there was a significantly decreased Aβ42:43 ratio in the R278I, E280G.A and E280G.B lines . Additionally, compared with M146I, there was a significantly decreased Aβ42:43 ratio in the R278I, E280G.A and E280G.B lines , Pathway 43, also indicative of γ-secretase epsilon cleavage to the Aβ48 or 49 pathway, significantly differed across cell lines .These data support PSEN1 mutations have been shown to alter γ-secretase stability.41 By using western blots to probe for the PSEN1-NTF, the presence of cleaved and full-length PSEN1 was assessed in the neurons, PSEN1 M146I, PSEN1 R278I and two PSEN1 E280G). Full-length PSEN1 levels statistically differed across lines (P = 0.04), with significantly increased levels in the R278I line compared with the control group (P = 0.03), with a notable trend for increased levels in the E280G lines, see Firstly, the amount of full-length PSEN1 (46 kDa), relative to actin, was normalized to CTL2. Protein expression was then compared between the control lines (pooled data), and the four FAD patient-derived lines (P = 0.0007). Compared with the control group, PSEN1-NTF was significantly higher in the E280G.A and E280G.B lines  and significantly higher in the E280G.A and E280G.B lines compared with the R278I lines , see Secondly, the amount of PSEN1-NTF (25 kDa), relative to actin and normalized to CTL2, was then compared between the control lines (pooled data), and the four FAD patient-derived lines. PSEN1-NTF levels indicate effective autocleavage of PSEN1 into the N and C-terminal fragments. PSEN1-NTF significantly differed across lines (P < 0.0002) with higher full-length PSEN1 levels in the R278I line compared with the control group (P < 0.0001), M146I line (P = 0.0008), E280G.A line (P = 0.002) and E280G.B line (P = 0.002), see Finally, the levels of full-length PSEN1 relative to PSEN1-NTF, normalized to the CTL2 line was compared between the control lines (pooled data), and the four FAD patient-derived lines. This ratio was significantly different across lines , and the four FAD patient-derived lines . TogethePSEN1 mutations that were studied in our cell lines  were available for immunohistochemical investigation. Note, the tissue and iPSC lines are not from the same individuals. The percentage area of Aβ-specific antibody coverage was assessed in the temporal and occipital cortices.Post-mortem cortical brain tissue from three individuals with FAD carrying the same Immunohistochemical analysis displayed intense Aβ42 deposition in all cases in the temporal A–C and oAβ40 pathology was present in all cases, notably as CAA, and cortical deposition . Aβ40 waAβ43 IHC shows mutation-specific differences in Aβ43 deposition . Aβ43 dePSEN1 E280G mutation, which is associated with atypical clinical (spastic paraparesis) and pathological features (amyloid plaques of the cotton wool type). We have shown that in patient-derived iPSC-cortical neurons, the E280G mutation leads to increased ratios of Aβ42 and of Aβ43 compared with other length peptides, which is linked with both reduced PSEN1 autoendoproteolysis and relative abundance of both full-length and PSEN1-NTF.The pathogenic effect of mutations in PSEN1 is mediated through alterations in APP processing with different mutations producing distinct effects on that processing. This may potentially underlie some of the clinical heterogeneity seen in FAD mutation carriers. Here, for the first time, we have defined the consequences of the 43 Mutations which effect the endopeptidase activity could affect both absolute Aβ levels, and which pathway of Aβ production is selected, leading to altered ratios of the peptides within those pathways.22 Whilst PSEN1 mutations vary in whether or not they alter the endopeptidase activity of γ-secretase, they have been found to consistently decrease the efficiency of its subsequent carboxypeptidase activity by destabilizing enzyme–substrate complexes between γ-secretase and the Aβ peptides it is processing.44 The destabilization and compromised carboxypeptidase activity results in the release of longer Aβ peptides which are more prone to aggregation22 This in turn leads to more oligomeric Aβ and greater extracellular deposition.23 We observed a greater Aβ42:38 ratio in the M146I and E280G.A lines, compared with controls, indicative of consistently impaired carboxypeptidase activity in the Aβ48 pathway for these lines. Mutations near the autoendoproteolysis site are proposed to inhibit the cleavage that converts the full-length form into the mature, cleaved product.26 This can then lead to altered processivity by the enzyme.27 Interestingly, even within the full-length PSEN1, the catalytic pore is still able to be formed,28 which suggests that γ-secretase activity can still occur, although it is likely reduced, possibly explaining altered Aβ peptide production in mutations near this location. Such mutations in this region include PSEN1 R278I and E280G. Compared with controls and with M146I, the R278I and E280G lines displayed an increased Aβ43:40 ratio, indicating reduced carboxypeptidase activity in the Aβ49 pathway in a mutation dependant manner. Mutations not within the catalytic domain can likely affect the protein structure/stability and this may also impact interactions with substrates which will contribute to disease.44 For instance, the M146I mutation is not within the catalytic domains, yet displayed altered Aβ production profiles compared with controls, and other mutations.One way in which mutations can affect PSEN1 is via the initial endopeptidase activity of γ-secretase.PSEN1 R278I cell line compared with the control group. This has previously been shown for the R278I line, with a significant increase in full-length PSEN1 observed, and has been linked to Aβ43 production.30 It has also been observed for the E280G mutation,20 with our results suggestive of a trend for increased full-length PSEN1 in the E280G cell lines, although this did not reach significance.Western blot analysis revealed a significantly increased full-length PSEN1 in the The levels of PSEN1-NTF protein were also observed to be higher, and significantly so in the E280G neurons. As PSEN1 expression was not higher in these lines, it suggests that in the E280G lines there is an accumulation of PSEN1-NTF. This increase in PSEN1-NTF may explain the high levels of Aβ42 observed in these lines, in addition to the high Aβ43.Finally, the level of full-length relative to PSEN1-NTF was significantly higher in the R278I line, which reflects the increase in products of the Aβ49 pathway only in this line. This measure also highlights that the R278I mutation specifically affects the processing of full-length to cleaved PSEN1, compared with other lines.PSEN1 R278I and E280G mutation lines. It is likely that the mutation location hinders the processivity of full-length to cleaved PSEN1. The R278I and E280G mutations have a similar impact on Aβ ratios, however, the differential impacts on protein maturity effect may impact the amounts of mutant PSEN1 that are incorporated into the γ-secretase complex. Thus, absolute levels of Aβ could differ. To investigate this, isogenic lines with the homozygous mutations can be generated, as has previously been done for the PSEN1 Intron 4 deletion mutation.45This would allow the impact of these mutations to be assessed on protein maturity, Aβ ratios, and absolute Aβ levels, in the absence of a WT PSEN1 allele. It should be noted there was some variability between the two E280G lines. We cannot rule out that this could be due to the karyotypic abnormality in the E280G.A line in the 17q region. Although we were not able to identify the exact region that is duplicated, it is worth noting that the MAPT gene is located on chromosome 17q, encoding for tau, which forms pathological deposits in multiple dementias including FAD. Reassuringly, analysis of gene expression profiles in this line was normal  ε2/ε3 genotype. Generally, the APOE ε4 allele is linked to sporadic CAA, with a meta-analysis indicating APOE ε4 as a risk variant in grouped dementia patients and controls while APOE ε2 may result in less CAA,58 although a positive APOE ε4 carrier status and CAA severity were not associated in a study of PSEN1 mutation cases.59 Post-mortem analysis in 93 sAD patients revealed a significant association of CAA pathology with the APOE ε4 allele, with increasing APOE ε4 allele number associated with increasing severity of CAA.60 However, the role of the APOE genotype in CAA is not clear cut, as carriers of the APOE ε2 allele have been found to have more severe CAA than those with the APOE ε3/ε3 genotype, despite the APOE ε2 allele conferring lower risk for Alzheimer’s disease.61 Additionally, Alzheimer’s disease patients who were APOE2 carriers had worse small vessel disease 62 and a meta-analysis found APOE ε2 to be associated with certain markers of cerebrovascular disease.63 These factors may contribute to the association between APOE ε2 and CAA. Thus, the APOE ε2 carriership in the M16I mutation case may influence the Aβ40 pathology displayed in this case. However, the R278I case also has an APOE ε2/ε3 genotype, which could also impact the histological observations. In future work, it would be of interest to investigate the Aβ peptides deposited in a larger number of individuals carrying the same FAD mutation but different APOE genotypes. Such larger studies will be needed in order to fully investigate correlations between clinical and pathological features and mutation-associated differences in Aβ processing. While studying pathology and iPSCs from the same individuals would be a gold standard, the altered Aβ ratios observed in iPSC studies are conserved in FAD and also in sAD human tissue, validating the use of iPSCs as models and implying γ-secretase alterations do occur in vivo and can occur in sAD.64 Furthermore, the approach used here has enabled some confirmation of cell line results to human pathological tissue, highlighting the validity of the models to recapitulate FAD mechanisms and their appropriateness to investigate clinical and pathological associations.The mutation-associated differences in PSEN1 and subsequent Aβ production identified in this study are of particular interest in view of the distinct clinical and pathological features associated with the mutations we studied. E280G and R278I mutations both lie within exon 8, near the endoproteolytic cleavage domain. Mutations that are post-codon 200, and particularly within exon 8, have been linked to clinically atypical cognitive presentations and associated with spastic paraparesis.fcac321_Supplementary_DataClick here for additional data file.


Despite potent anticancer activity, the clinical utilization of cisplatin is limited due to nephrotoxicity. As Organic Cation Transporter 2 (OCT2) has been shown to be one of the key transporters involved in the uptake of cisplatin into renal proximal tubules, OCT2 inhibitors such as cimetidine have been explored to suppress cisplatin-induced nephrotoxicity. Nonetheless, the impact of OCT2 inhibition or cimetidine on the anti-cancer effects of cisplatin has not been extensively examined. The main objective of the present study was to quantitatively characterize the anticancer effects of cisplatin and cimetidine and determine their nature of interactions in two cancer cell lines, OCT2-negative hepatocellular carcinoma (HCC) cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468. First, we determined the static concentration-response curves of cisplatin and cimetidine as single agents. Next, with the help of three-dimensional (3D) response surface analyses and a competitive interaction model, we determined their nature of interactions at static concentrations to be modestly synergistic or additive in Huh7 and antagonistic in MDA-MB-468. These results were consistent with the cell-level pharmacodynamic (PD) modeling analysis which leveraged the time-course effects of drugs as single agents and drug combinations. Our developed PD model can be further used to design future preclinical studies to further investigate the cisplatin and cimetidine combinations in different in vitro and in vivo cancer models. Cisplatin is one of the most potent chemotherapeutic agents and is widely used for the treatment of a variety of cancer types. However, its clinical use is limited due to severe side effects, including nephrotoxicity, ototoxicity, and peripheral neurotoxicity ,2,3. Of Cimetidine is an OCT2 substrate and competitively inhibits the cellular uptake of cisplatin via OCT2 transporters. Therefore, the combination of cimetidine with cisplatin therapy has been investigated to potentially reduce cisplatin-induced nephrotoxicity ,8,10. CiWhile targeting OCT2 seems like an attractive strategy to circumvent cisplatin-induced nephrotoxicity, it is imperative to ensure that such a strategy does not compromise the anticancer efficacy of cisplatin. For instance, OCT2 inhibition or co-treatment with cimetidine can block, at least partially if not fully, the transport of cisplatin into cancer cells , thereby suppressing its activity. While the nephroprotective effects of cimetidine have been extensively characterized in preclinical studies, very few studies have investigated the impact of cimetidine on the anticancer effects of cisplatin ,11. Besi50s). Based on this, CIS and CIM concentrations were selected for static and time-course drug combination studies. Next, the static combinatorial effects of CIS and CIM and the nature of their interactions in the two cancer cell lines were determined with the help of a three-dimensional (3D) response surface analysis using a competitive interaction model. Finally, the time-course effects of the CIS, CIM and their combinations were determined with the help of a mathematical in vitro pharmacodynamic (PD) model, and relevant parameters including interaction parameters were estimated. Thus, with the help of experimental and PD modeling approaches, the impact of OCT2 inhibition or co-treatment with cimetidine on the anticancer effects of cisplatin was evaluated in human cancer cell lines.The main objective of the current study is to quantitatively characterize the anticancer effects of CIS and CIM as single agents and combinations in two cancer cell lines, OCT2-negative liver cancer cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468, using a combination of experimental data and in vitro pharmacodynamic (PD) modeling approaches. First, the effects of CIS and CIM as single agents in both the cancer cell lines were evaluated with the help of concentration-response curves and by determining their respective in vitro half-maximal inhibitory concentrations (ICThe human hepatocellular carcinoma (HCC) cell line, Huh7, and breast cancer cell line, MDA-MB-468 cells were purchase from American Type Culture Collection (ATCC) . Both cell lines were cultured, maintained, passaged, and used per manufacturer’s instructions and as extensively described previously ,17. Cisplatin and cimetidine were purchased from Millipore Sigma-Aldrich Co. . Cimetidine was dissolved in molecular biology grade water while sodium chloride (NaCl) solution in water was used for cisplatin as per manufacturer’s instructions. While cimetidine stock solutions were stored at −80 °C, but cisplatin stocks were always prepared fresh and stored, if required in dark at 2–8 °C. Fresh serial dilutions from the drug stocks were prepared each time prior to experiments.Cell culture reagents including Dulbecco’s Modified Eagle’s Medium (DMEM), MEM non-essential amino acids, sodium bicarbonate, Penicillin/Streptomycin, Phosphate Buffered Saline (PBS), molecular biology grade water and 0.25% trypsin/2.21 mM EDTA were purchased from Corning . Fetal Bovine Serum (FBS) and Cell Counting kit-8 (CCK-8) were purchased from Millipore Sigma-Aldrich . 3 cells per well (100 µL) of a 96-well plate. After overnight incubation, the cell lines were exposed to varying concentrations of CIS (0.001 to 25 µM), CIM (0.05 to 10 mM), or their combinations for 72 to 96 h for various sets of experiments. The CCK8 cell viability assay was subsequently carried out at different time points as indicated per the manufacturer’s instructions. Briefly, cells are incubated in CCK-8 solution (10 µL/well of a 96-well plate) for an hour with absorbance measured at 450 nm using a microplate spectrophotometer Biotek . Experiments were performed in at least triplicates and compared against controls. Based on growth patterns, Huh7 and MDA-MB-468 cells were seeded at a density of 5 × 10maxI) and concentrations required to achieve 50% of maximal effects (50IC) were estimated for CIS and CIM as single agents in the two human cancer cell lines, Huh7 and MDA-MB-468, at 72 h:R is the response to drug treatments (% cell viability), 0R is the baseline response (% cell viability in absence of drug treatments or control), maxI is the maximal inhibitory effect of drug treatments, 50IC is the drug concentration required to achieve 50% of maxI, C is the drug concentration and γ is the Hill coefficient (Table 1). The concentration-response curves were used to select concentrations for single agents and combinations during time-course and subsequent studies. An inhibitory Hill function was used to characterize the concentration-response curves. The maximal inhibitory effects , max,AI and max,BI are the maximal effects of the two drugs , AC and BC are concentrations of the drugs, ICA50 and ICB50 are the half-maximal inhibitory concentrations of the drugs, Aγ and Bγ are the respective Hill coefficients and ψ is the interaction term. All individual drug-related parameters were fixed from the CIS and CIM concentration-response curve fittings and the interaction term, ψ, was estimated. The apparent interaction between the drugs was antagonistic when ψ > 1, synergistic when ψ < 1 and additive when ψ = 1. Finally, three-dimensional (3D) response surface plots of cell viability versus CIS and CIM concentrations were constructed to visually evaluate the interaction between the drugs for both cell lines. An additive interaction was assumed between both drugs  and 3D response surface plots were generated for both cell lines using Equation (2) with MATLAB version 2017a The MathWorks, Inc. . Finally, observed cell viability data were overlaid on the additive response surface plots to assess location of the data points relative to the additive surface, thus enabling visual assessment of additive, antagonistic or synergistic effects between CIS and CIM in Huh7 and MDA-MB-468. A competitive interaction model ,19 was uThe in vitro cellular level pharmacodynamic model (PD) was developed to evaluate the effects for the single agents, CIS, CIM and their combinations on the cell viability of Huh7 and MDA-MB-468. The corresponding parameters, their definitions and estimated values are listed in Table 2. It was assumed that there was no change in drug concentrations and no degradation of drugs over time in cell culture media. In addition, it was assumed that the two drugs do not interact with each other in the cell culture media for their combination group. R is the % cellular viability (cellular response) at time t, gk is the first-order growth rate constant for cancer cell lines, while R0 is the % cellular viability at time zero.The cellular growth of Huh7 and MDA-MB-468 in the control group (no drug treatment) was best described by an exponential growth function as follows: The PD effect of CIS or CIM on the cell viability of Huh7 and MDA-MB-468 was described by a stimulatory effect (Hill function) on the cell death of cancer cells ,21. Threx represents either CIS or CIM, max,xS is the maximal killing rate constant while SCx50, is the concentration of CIS or CIM required to induce half-maximal cell-killing effect in Huh7 and MDA-MB-468. xK is the cytotoxicity Hill function, 1xK to 3xK are transit compartments with xƮ being the mean transit time between compartments and Cx represents the drug concentration. The differential equations for the effect of CIS or CIM as single agents in Huh7 and MDA-MB-468 are as follows:Both drugs are assumed to exert cytotoxic effects (stimulation of death function) in Huh7 and MDA-MB-468 cancer cell lines. Hence, the PD model for the drug combinations include an interaction parameter, ψ, which was applied to Equation (5a), where ψ = 1, <1, or >1 indicate additive, synergistic or antagonistic interactions, respectively. The following differential equations were used to characterize the combinatorial effects of CIS and CIM in Huh7 and MDA-MB-468:The time-course data of the single agents were characterized first with the single-agent PD models, parameters related to the single agents were estimated, and then fixed while estimating the interaction parameter and characterizing the drug combination data in the two cell lines. All model fittings and simulations were performed using Monolix suites version 2016R1 or higher . 50s for CIS were 1.96 µM and 0.39 µM while that for CIM were 3.19 mM and 3.28 mM in Huh7 and MDA-MB-468, respectively. Thus, MDA-MB-468 was more sensitive towards CIS as compared to Huh7 (~ 5-fold), while both cell lines showed similar sensitivities for CIM as evidenced by their estimated IC50s in the respective cell lines. On the other hand, both CIS and CIM showed similar maximal killing (close to 1) for both the cancer cell lines. A hill coefficient was used to accurately characterize the concentration-response curves for CIM and was estimated at 3.12 and 1.83 for Huh7 and MDA-MB-468, respectively. The inhibitory Hill model captured the concentration-response data well, as demonstrated with observations versus individual prediction plots shown in The two cancer cell lines, Huh7 (OCT2-negative) and MDA-MB-468 (OCT2-positive), were treated with a wide range of either CIS (0.001 to 25 µM) or CIM (0.1 to 10 mM) concentrations for 72 h. The inhibitory Hill model was fitted to the static concentration-response curves of CIS and CIM as single agents in both the cell lines as shown in After evaluating the concentration-response curves of CIS and CIM as single agents, Huh7 and MDA-MB-468 were exposed to a range of CIS and CIM concentrations, both as single agents as well as their combinations for 72 h as indicated in The time course effects of CIS and CIM as single agents as well as their combinations on the cell viability of Huh7 and MDA-MB-468 was characterized with the help of an in vitro cellular level PD response model as shown in The model fittings for the time-course effects of CIS or CIM as single agents in Huh7 and MDA-MB-468 are shown in −1 and 0.018 (±1.4%) h−1, for Huh7 and MDA-MB-468, respectively. The time course effects of CIS or CIM on the cell viability of cancer cell lines over 96 h was adequately captured with the stimulatory effects on cell death characterized with a capacity-limited, Hill function (Equations (4a)–(4e)). The estimated, maximal killing rate constant  for CIS was 0.038 (± 9.63%) h−1 and 0.108 (± 5.75%) h−1 while the CIS concentration inducing 50% of maximal cell killing rate  was estimated to be 4.27 (± 6.32%) µM and 2.48 (± 8.13%) µM for Huh7 and MDA-MB-468, respectively. On the other hand, the estimated maximal killing rate constant  for CIM was 0.106 (± 2.25%) h−1 and 0.096 (± 17.1%) h−1 while the CIM concentration inducing 50% of maximal cell killing rate  was estimated to be ~37 (± 22.7%) mM and ~21 (± 21.3%) mM for Huh7 and MDA-MB-468, respectively. Thus, time-course data analysis also confirmed CIS to be far more potent as compared to CIM  which is consistent with the concentration-response analysis. The delayed effects of CIS and CIM were captured well with the help of three transit compartments (K1 to K3) on the stimulation of death function, with CISƮ and CIMƮ representing the mean transit time. The cell viability of cancer cell lines (in absence of drug treatments) was first characterized with an exponential growth function (Equation (3)) using a first-order growth rate constant, kg which was estimated to be 0.011 (±3.32%) hNext, the time-course combinatorial effects of CIS and CIM on the cell viability or stimulation of death of Huh7 and MDA-MB-468 cell lines was characterized by fixing the parameter estimates of single agents and incorporating an interaction parameter ψ in the model structure as shown in Despite its high antitumor efficacy, the clinical utilization of CIS is limited due to organ toxicities including nephrotoxicity, which can often be dose-limiting ,2,3. Pre50 = 0.39 µM) demonstrated higher sensitivity towards CIS as compared to Huh7 (IC50 = 1.96 µM) by ~5-fold which could be explained, at least partially, due to higher OCT2 expression levels in the former as compared to the latter. The breast cancer cell line, MDA-MB-468 was previously reported to express high levels of OCT2 protein ,23, henc50s for CIM as a single agent were estimated at 3.28 mM and 3.19 mM in MDA-MB-468 and Huh 7, respectively  which are consistent with previous reports [50 estimates, a lower range of CIS concentrations (0.05 µM to 25 µM) and a higher range of CIM concentrations (from 0.05 to 4 mM) were used for subsequent static combination studies. With the help of a competitive interaction model and 3D response-surface analyses of the static single agent and combination profiles, the nature of interaction was determined as slightly synergistic or additive for CIS and CIM in Huh7 but antagonistic in MDA-MB-468 (In addition, we observed that CIM alone also mediated cytotoxicity in the cancer cell lines, although at much higher concentrations . This is reports ,13,14,15 reports . Based oA-MB-468 . FinallyFinally, few studies have previously reported that CIM did not impact the antitumor effects of CIS in vitro or in vivo in OCT2 expressing cancer models; however, those studies evaluated the impact of CIM at only one or two concentrations or dose levels ,11. HencTaken together, the above results indicate that combining CIM can potentially be beneficial in improving anticancer activity of CIS, especially in OCT2-negative cancer cells. Future studies should also aim to investigate the underlying mechanisms contributing to the synergistic interactions of CIS and CIM in OCT2-negative cancer cell lines. While antagonistic interactions were determined in the OCT2-positive cancer cells, it should be noted that the combination can still be beneficial to patients with OCT2-positive tumors in suppressing CIS-induced nephrotoxicity, especially at higher doses or for a longer duration and thereby benefit from the anticancer activity of CIS. Our developed cell-level PD model can be further used to better design future preclinical studies to further investigate the impact of OCT2 inhibition on the effects of cisplatin in different in vitro and in vivo cancer models.


Background: We have previously reported that the endocannabinoid receptor inverse agonist AM630 is a potent inhibitor of isocitrade dehydrogenase-1 wild-type glioblastoma (GBM) core tumour cell proliferation. To uncover the mechanism behind the anti-tumour effects we have performed a transcriptional analysis of AM630 activity both in the tumour core cells (U87) and the invasive margin cells (GIN-8), the latter representing a better proxy of post-surgical residual disease. Results: The core and invasive margin cells exhibited markedly different gene expression profiles and only the core cells had high expression of a potential AM630 target, the CB1 receptor. Both cell types had moderate expression of the HTR2B serotonin receptor, a reported AM630 target. We found that the AM630 driven transcriptional response was substantially higher in the central cells than in the invasive margin cells, with the former driving the up regulation of immune response and the down regulation of cell cycle and metastatic pathways and correlating with transcriptional responses driven by established anti-neoplastics as well as serotonin receptor antagonists. Conclusion: Our results highlight the different gene sets involved in the core and invasive margin cell lines derived from GBM and an associated marked difference in responsiveness to AM630. Our findings identify AM630 as an anti-neoplastic drug in the context of the core cells, showing a high correlation with the activity of known antiproliferative drugs. However, we reveal a key set of similarities between the two cell lines that may inform therapeutic intervention. Cell fate decisions are key in the homeostatic maintenance of the cellular milieu as well as in the survival of tissues and organisms. Uncontrolled cell proliferation occurs in tumoral cancer cells as a direct consequence of dysregulated cell cycle phases due to a lack of effective cell cycle DNA checkpoints as the cell grows and divides. In the central nervous system (CNS), different systems control cell fate decisions to maintain an effective and functional brain circuitry . PatholoThe high-grade malignant brain tumour, isocitrate dehydrogenase-1 wild-type glioblastoma (GBM), is the most frequent and aggressive primary de novo tumour of the CNS, with a median survival of 14.6 months from diagnosis in patients multimodally treated with surgery, radiotherapy, and chemotherapy . DespiteIn the developed brain the neuromodulatory activity of the endocannabinoid (eCB) system is the control of neural excitability through retrograde signalling to inhibit presynaptic transmitter release . HoweverIn previous studies, we have found that the pharmacological blockade of the CB1 or CB2 receptor signalling pathways impairs ) the in vitro proliferation of human GBM cells obtained from the core region (U87) suggesting that CB2 cannabinoid receptors are somehow involved in the proliferation of these GBM cells . The in With the aim of uncovering the biological mechanisms behind the anti-tumour activity associated with eCB receptor inhibition, we performed a detailed investigation into the gene expression changes driven by receptor inhibition in the context of the core and invasive margin cell populations of GBM. Specifically, we performed a microarray analysis of the gene expression perturbation driven by the CB2 receptor inverse agonist AM630 in GBM primary cell cultures from the central U87 cells and the more clinically relevant invasive margin GIN-8 cells.The selective CB2 inverse-antagonist (AM630) was purchased from Tocris . All other reagents and materials for cell cultures were obtained from standard suppliers.2. Cells grown in 6-well plates were treated with AM630 (5 microM) or vehicle for 24 h at 37 °C. Cells were then harvested for microarray analysis.All cell lines (U87 and GIN-8) used in this project were of human origin, obtained from Dr Ruman Rahmam (University of Nottingham). U87 cells confirmed on short tandem repeat (STR) genotyping, isolated from the core of a GBM tumour and sourced commercially, were used as a biological positive control for GBM cells. The glioma invasive margin (GIN-8) cell line, isolated from the tumour infiltrative edge, were derived in-house from surgeries at the Queen’s Medical Centre, Nottingham (comparable to their respective primary tissue on STR). Monolayer cells cultures were prepared as described previously ,21. Briehttps://www.selectscience.net/products/ovation-rna-seq-system-v2, accessed on 20 December 2021). The resulting expression data were pre-processed using RMA normalisation with the Bioconductor affy package [Following cell treatment, the expression changes relative to control were assayed on a microarray chip with quadruplicate samples. Cells were lysed in Absolutely RNA Miniprep Kit lysis buffer and β-mercaptoethanol . RNA was then extracted, and quality assessed (RNA integrity number ≥ 8) using a Bioanalyser (Agilent Technology). RNA expression levels were measured on Affymetrix Human Genome U133 plus 2.0 (GPL570) chip following library preparation and labelling by Nugen Ovation V2 and Nugen Encore as per the supplier’s recommended procedure . The panel consists of 7932 samples from 17 cancer types. Samples corresponding to a given cancer type were grouped and the cancer type gene expression levels were assigned to the median of the group, resulting in 17 profiles of median expression. The median fragments per kilobase million (FPKM) for the 17 cancer types are given in https://gtexportal.org/, accessed on 20 December 2021) to generate relative rank profiles similar to the microarray procedure discussed above. Because of the non-parametric nature of the profiles, we compared our profiles with the TCGA profiles using the Spearman rank correlation analysis.The GRP corresponding to the untreated cell samples were queried against RNAseq data for a panel of cancer cell lines hosted by The Cancer Genome Atlas (TCGA)   data comTranscription factor co-expression profiles (TFCEP) were generated by collecting pairs of genes with the highest co-expression patterns across the 600,000 expression samples comprising SPIED . Co-exprPathway enrichment analysis was based on a variant of the Kolmogorov–Smirnoff (KS) statistic. This is similar to the approach used in Gene Set Enrichment Analysis (GSEA) . The expWe observe that the distribution of https://cancergenome.nih.gov/, accessed on 20 December 2021) further strengthens our understanding of the basal transcriptomic profile of the core cell population  but not the CB2 receptor (CNR2) see . This leOur next objective was to probe for differences in the the GBM-derived cell lines by looking at the expression changes driven by the CB2 receptor inverse agonist AM630. We found that AM630 drove a substantial transcriptional response in the core cell population. The transcriptional response was symmetrical and substantial, with 1514 and 2168 two-fold up and downregulated genes see . This waThe muted transcriptional response to AM630 in the invasive margin population had only a moderate correlation with the core cell response. Comparing probes with significant expression differences (at the three standard deviation from the null level) between the treatment and control in the two cell populations, we saw a slight correlation in the AM630 response see . This obPathway enrichment of the genes perturbed in the core population showed a clear up regulation of immune response genes and down regulation of cell cycle and metastatic pathways see . In contThe main affected pathways in the boundary of the tumour after AM630 exposure  were epiwww.spied.org.uk, accessed on 20 December 2021) [Another insight into the activity of AM630 can be gained by a direct correlation analysis with the transcriptional activity of other compounds. To this end, we performed a search of the Connectivity Map (CMAP)  databaseer 2021) . As expeer 2021) ,32. Specer 2021)  with thier 2021) . Metergoer 2021) . Anotherer 2021)  with vorer 2021)  and scrier 2021) . The toper 2021) .In contrast, a CMAP analysis of the invasive AM630 margin profile does not return many significantly correlating drugs. Noting the moderate correlation in the core and invasive margin responses we reasoned that a combined profile populated with genes with a significant combined expression change may capture the shared activity in the two cellular contexts. To this end, we generated a combined profile constituting genes with combined Z scores of above five standard deviations from the null using Stouffer’s method  see Tab. Here, tLarge-scale transcriptional data enables the discovery of functional associations between genes , where gDespite multiple clinical trials and research efforts, GBM continues being the most aggressive of all forms of cancer with very limited clinical options to stop progression and dissemination of the tumour. GBM constitutes a complex of interacting cell types with a core population responding to treatments and an invasive margin population that has proved refractory to intervention . UnfortuDifferent signalling pathways associated with the brain endocannabinoid machinery are being investigated as potential therapeutic targets , with reRelative expression analysis of the untreated cells highlights the differences between the core and invasive margin populations. The core cells show a clear correlation with published GBM expression data, whereas the invasive margin population shows no correlation with any of the TCGA cell lines. This is in agreement with recent evidence that reveals an extensive degree of intra-tumour heterogeneity in GBM resulting from a clonal evolution process producing a completely different genetic pattern in the core compared with the invasive margin . This diAs regards the potential activity of AM630, we found a low primary target, the CB2 receptor, expression in either population. This led us to consider potential off-target effects for AM630 activity. Of the off-targets, the CB1 receptor is highly expressed in the core, and the serotonin receptor (HTR2B) is expressed at moderate levels in both populations. This observation is in agreement with the relatively high potency of AM630 in the core population. We hypothesise that AM630 elicits a substantial response in the core cells through antagonism of the CB1 receptor and any activity shared by the drug across the core and invasive margin cells may be driven by serotonin receptor antagonism.Analysing the pathways involved, we found that in the core of the tumour, AM630 is associated with the down regulation of the cell cycle and cancer-associated pathways. In contrast, AM630 up-regulates an immune response together with the TP53 and interferon pathways. The TP53 gene encodes a tumour suppressor protein P53, that plays a critical role in tumour suppression by orchestrating a wide variety of cellular responses inducing tumoral cell death . We can Investigating the drug-driven transcriptional profiles from the CMAP database we found that anti-neoplastic agents have high levels of correlation with the core cell response profile, with some drugs reported to have anti GBM activities. Different studies showed a high degree of expression for different serotonin receptor subtypes in glioblastoma. Of particular interest was the high degree of correlation with serotonin receptor antagonists which may speak to the serotonin receptor HTR2B being the sole off-target of AM630 with significant expression in both the core and invasive margin cell populations. This is of interest in the context of GBM as the serotonin levels have been shown to be elevated in some cancer types . HoweverThe correlation with serotonin receptor antagonists is of interest as the 5-HT serotonin receptors are highly expressed in different types of cancers, including GBM and modulate mitogenic signalling and impact tumour cell viability . In thisGiven the heterogeneity of glioblastoma, further studies are required to elucidate the molecular mechanisms of the observed AM630 anti-tumoral actions and if they can potentially be used in the future as an addition to current therapy.


Aim: Bendamustine is primarily used for treatment of indolent lymphomas but has shown efficacy in some patients with diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM). Molecular-based patient stratification for identification of resistant patients, who will benefit from alternative treatments, is important. The aim of this study was to develop a resistance gene signature (REGS) from bendamustine dose-response assays in cultures of DLBCL and MM cell lines, enabling prediction of bendamustine response in DLBCL and MM patients.Methods: Bendamustine response was determined in 14 DLBCL and 11 MM cell lines. Using baseline gene expression profiles and degree of growth inhibition after bendamustine exposure, a bendamustine REGS was developed and examined for the risk stratification potential in DLBCL (n = 971) and MM  patients divided into prognostic subtypes.Results: Bendamustine resistance significantly correlated with resistance to cyclophosphamide in DLBCL and melphalan in MM cell lines. The bendamustine REGS showed significantly lower bendamustine resistance probabilities in DLBCL patients with GCB subtype tumors and in tumors of the differentiation dependent centrocyte and plasmablast subtypes. In MM patients, pre-BII classified tumors displayed high bendamustine resistance probabilities and the plasma cell subtype had lower bendamustine resistance probability than memory cells. Furthermore, tumors belonging to the 4p14, MAF, and D2 TC subclasses consistently displayed high bendamustine resistance probabilities.Conclusion: Significant differences in predicted response to bendamustine were found in molecular subtypes of DLBCL and MM, encouraging validation in prospective bendamustine-treated cohorts with available gene expression profiles and follow-up data. It contains three structural components: (1) a nitrogen mustard (2-chloroethylamine group) which confers the alkylating function; (2) a benzimidazole ring  giving it the antimetabolite function; and (3) a butyric acid group that confers water solubility. The mechanisms of action of bendamustine include DNA cross-linking, activation of DNA damage response leading to apoptosis, inhibition of mitotic checkpoints, and induction of mitotic catastrophe - a form of non-apoptotic cell death. Bendamustine is known to be well-tolerated and has also been shown to be effective as a single agent in heavily pretreated, high-grade NHL patients.Bendamustine is a bi-functional drug used for treatment of chronic lymphocytic leukemia (CLL) and rituximab-refractory indolent non-Hodgkin’s lymphoma (NHL). DLBCL is an aggressive form of NHL and the standard treatment is immuno-chemotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Unfortunately, up to one third of patients eventually develop refractory disease or relapse (R/R) in which case they are treated with high dose salvage therapy and autologous stem cell transplantations (ASCT). Not all diagnostic patients are eligible for full-dose R-CHOP due to toxicities which might lead to dose reduction, substitution, or discontinuation of one or more of the drugs. Moreover, a substantial fraction of R/R patients are ineligible for ASCT due to comorbidities and age, and for these no standard salvage therapy exist,8,9. Thus, effective alternative therapies with manageable toxicities are needed. In that context, bendamustine in combination with rituximab therapy has been suggested due to long-term remission for some patients together with low toxicity,10,11. Several clinical studies have tested the effect of combined bendamustine and rituximab treatment in DLBCL patients and reported an acceptable toxicity profile with modest activity, meaning only a subset of the patients respond [Supplementary Table 1],12-18. Thus, strategies for patient stratification based on bendamustine response are needed.The most common type of NHL is diffuse large B-cell lymphoma (DLBCL) and develops from post-germinal, antibody-secreting plasma cells that have undergone somatic hypermutation before they infiltrate the bone marrow. Over the past decades, the survival of MM patients has improved drastically, mainly attributed by advances in novel therapies,22. Melphalan combined with prednisone was the first successful therapy in achieving relatively high response rates in MM and has remained the gold standard for several decades. Of note, a phase III study has later revealed a generally superior outcome when using bendamustine in combination with prednisone in comparison to melphalan combined with prednisone, and it was thus approved as first-line therapy for MM patients not eligible for ASCT. In R/R MM patients, the overall response to bendamustine monotherapy is 30%-55% and higher when combined with other chemotherapy agents [Supplementary Table 1],25-27. As with DLBCL, bendamustine combination treatment has been shown to be safe, effective, and well-tolerated in both diagnostic and R/R MM patients,28,29.Multiple myeloma (MM) accounts for up to 10% of all hematological malignancies,31. MM can be subclassified based on the translocation and cyclin D (TC) classification which distinguishes eight subtypes based on immunoglobin translocations, activation of cyclin D genes, and chromosomal ploidy. A more refined cell-of-origin (COO) based classification system, the “B-cell associated gene signature” (BAGS), also exist for DLBCL and MM where primary cancer cells at the time of diagnosis are associated to normal B-cell subset phenotypes from tonsils or bone marrow, respectively. DLBCL patients are classified into one of the following subtypes: naïve, centrocyte, centroblast, memory B-cell, or plasmablast, and MM patients into: Pre-BI, Pre-BII, immature, naïve, memory, or plasma cell. BAGS provide for both DLBCL and MM independent and significant prognostic information as compared to ABC/GCB and TC, and for DLBCL when compared to the clinical international prognostic index. In addition, the BAGS subtypes have distinct and specific responses to chemotherapeutic agents used in a routine clinical setting for treatment of DLBCL and MM,34. Machine learning models have previously been developed to predict response to components of the R-CHOP regimen in DLBCL and melphalan in MM. These models were successfully generated based on dose-response studies and resistance gene signatures (REGS) in human B-cell cancer cell lines (HBCCL)-37.Various prognostic molecular classifications have been developed based on gene expression profiles (GEPs) of DLBCL and MM cell lines and patient tumor samples. According to the 2016 revision of the World Health Organization classification of lymphoid malignancies, the standard molecular subclassification of DLBCL is activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCL which has proven to have prognostic impact favoring the GCB-DLBCL subtypesIn this study, we hypothesize that bendamustine dose-response assays and GEP of cell lines can be used to find gene expression patterns in which the expression of a group of genes predicts the cellular response to bendamustine. The predicted response can be applied to samples from DLBCL and MM patients to identify the subset of patients that will be sensitive or resistant to treatment. To test this, we developed a REGS from DLBCL and MM cell lines based on GEP and bendamustine dose-response assays, and subsequently investigated their stratification potential of DLBCL and MM patients based on molecular profiles.,38. The cells were cultured at 37 °C in a humidified atmosphere of 95% air and 5% CO2 with the appropriate medium, serum, and supplements HBCCLs consisting of 14 DLBCL cell lines  and 11 MM cell lines  were used. Culturing and DNA barcoding for verification of cell line identities were performed as previously described.Prior to the dose-response experiments, the optimal seeding concentration for each cell line was determined as the highest concentration at which the cells grew at an exponential rate throughout the experiment without drugs added. The effect of bendamustine on viable proliferating cells was measured for the 25 HBCCL. Cells were seeded in 120 µL culture media per well in a 96 well plate 24 h before 16 bendamustine concentrations of two-fold increments were added. The highest bendamustine concentration used was 500 µg/mL in isotonic saltwater. Viability was measured immediately after addition of bendamustine (0 h plate) and after 48 h of drug exposure (48 h plate) by addition of CellTiter reagent  and subsequent absorbance measurement at 492 nm using FLUOstar Optima . Isotonic saltwater was used as controls alongside bendamustine. Border effects were circumvented by only including non-border wells for analysis for each experiment, and experiments were performed in triplicates. Time independent summary statistics were obtained as previously described.CEL files for all cell lines are deposited with GEO accessions GSE53798, GSE99634, and GSE22759. In DLBCL, three individual, diagnostic patient cohorts with available GEP datasets from tumors were used to test the bendamustine REGS, and GEP data were pre-processed as previously described. These encompass the IDRC dataset , the LLMPP dataset , and the MDFCI dataset . Similarly, the following three separate and previously described MM patient cohorts were used to test the bendamustine REGS: HOVON-65 dataset , MRC Myeloma IX dataset , and UAMS dataset .GEPs for each untreated cell line using the Affymetrix Human Genome U133 Plus 2.0 arrays were used. These were generated by purification of RNA from cells, cDNA conversion, biotin-labelling, and lastly hybridization to the GeneChip. The . An Rmarkdown document detailing the analysis is available as Supplementary File 1.For all statistical analyses R version 4.0.2 was used0). In this aspect, previously generated summary statistics for the other drugs, used in the treatment of hematological cancers, were obtained in addition to the bendamustine dose-response assays,40. A generalized pairs plot was created using the R package GGally.Correlations between bendamustine, cyclophosphamide, doxorubicin, vincristine, and melphalan response were determined by assessing the pairwise Pearson’s correlation of the area under the positive parts of the dose-response curves  pre-processed using the Bioconductor package frma. Cell lines were split into tertiles according to their AUC0 values separately for each disease to avoid comparison of diseases and assigned values of sensitive, intermediate, or resistant. Sensitive and resistant cell lines were treated as binary outcomes and GEPs as predictors in a logistic regression model with an elastic net penalty using the R package glmnet. Optimal parameters were obtained using leave-one-out cross-validation, with misclassification error as the loss function.The bendamustine REGS is based on regularized multivariate regression analysis with cross-validated tuning parameters and can assign probability of resistance to a specific drug utilizing GEP and dose-response data from cell lines,34. Assignment to a BAGS group was determined by the highest predicted probability across the classes, setting the 15% of patients with lowest classification probability in each cohort as unclassified. Kaplan-Meier curves of DLBCL patients were trichotomized based on predicted bendamustine response including p-values from a log-rank test generated using the R package survminer.Cell lines and patient samples from cohorts were assigned to DLBCL or MM BAGS classes using the BAGS classifiers. However, faster growing cells generally appear more sensitive to chemotherapy drugs when using a fixed timepoint, thus to account for this bias under the assumption of exponential growth, the growth rate from treated cells was normalized using untreated cells at both 0h and 48h time points as reference. This allows for a time independent determination of growth inhibition where growth rates are accounted for and hereby the bendamustine response among HBCCL panel can be ranked and compared. The four summary statistics from the drug response experiments are: 50% growth inhibition compared to untreated cells (GI50), total growth inhibition (TGI), the concentration at which the cell population is halved after 48 h (LC48), and AUC0 (the area under the curve above 0) Using the relative growth inhibition for a drug, it is possible to generate cell line specific dose-response curves0 ranging from 219.79  to 351.16  in DLBCL and 250.41  to 378.98  in MM cell lines. The most sensitive cells were SU-DHL-5 and MOLP-2 in DLBCL and MM, respectively, and the most resistant were SU-DHL-10 and RPMI-8226, respectively.Increasing concentrations of bendamustine induced growth inhibition at varying degrees for both DLBCL and MM cell lines 50 and AUC0 values with GI50 ranging from 1.80  to 1.84  and AUC0 ranging from 333.5  to 350.8  Interestingly, the six MM cell lines, U266, KMS-12-PE, NCI-H929, KMS-11, OPM-2, and AMO-1 displayed very similar GI0 values for bendamustine from this study and the cornerstone drugs of standard DLBCL and MM treatment: cyclophosphamide, doxorubicin, vincristine, and melphalan obtained from previous dose-response assays [Supplementary Table 2],40. A significant and positive correlation was regarded as an indication of shared resistance mechanisms between two compared drugs.To investigate co-occurrence of resistance and sensitivity among the HBCCL towards various drugs used in the treatment of hematological cancers, we assessed the pairwise Pearson’s correlation between AUCR = 0.747, P < 0.05) R = 0.44 for both). Melphalan dose-response data was not included for DLBCL cell lines since there were too few treated cell lines for comparison, and it is not widely used in the treatment of DLBCL. In MM, correlation coefficients between the drugs were overall lower R = 0.49) and cyclophosphamide (R = 0.43) - two other alkylating agents. Interestingly, these two drugs displayed the overall strongest and most significant correlation in MM  R = 0.085). In summary, a stronger correlation in response between agents with the similar mechanisms of action was observed, indicating that there are inherent response patterns to similar drugs in the HBCCL.In DLBCL cells, the highest and only significant correlation coefficient was found between bendamustine and cyclophosphamide treatment  [Supplementary Table 3]. Both DLBCL and MM tumors display molecular heterogeneity whereby cells from these diseases can be subclassified into different subtypes showing different prognosis after treatment,54. Thus, we evaluated whether there was a difference in the predicted bendamustine response using the bendamustine REGS on GEPs restricted to DLBCL and MM patients and on different subtypes hereof [Supplementary Table 4].A combination of GEPs from untreated cells in the HBCCL panel and outcome from the bendamustine dose-response assays was used to develop the bendamustine REGS. The minimum misclassification error in the cross-validation was 0.33. The final prediction model trained on the complete dataset, using the optimal parameters obtained from the cross-validation, generated a bendamustine REGS with 13 genes , where ABC patients displayed the highest bendamustine resistance probability in contrast to GCB patients P < 0.01) despite the large variations in resistance probability. The DLBCL patients with reminiscent transcriptional expression patterns matching centrocyte and plasmablasts displayed lower probabilities of bendamustine resistance in comparison to the naïve, centroblast, memory, and unclassified subtypes [Supplementary Figure 1C and D]. Of notice, tumors of the memory subtype in ABC-DLBCL patients displayed high probabilities of bendamustine resistance, whereas they were predicted to be the least bendamustine resistant subtype along with plasmablasts in the GCB-DLBCL patients.In DLBCL, the bendamustine REGS was tested in four independent, diagnostic DLBCL cohorts. As there was no significant difference in bendamustine resistance probabilities between the four cohorts [Supplementary Figure 1B]. Thus, subsequent comparison to the BAGS classification was performed in each individual dataset Supplementary Figure 1E]. Significant differences between the TC subclasses were found in all three datasets, where the highest bendamustine resistance probability was consistently found for the TC classes: 4p14, MAF, and D2 For the MM cohorts there was a significant difference in the distribution of bendamustine resistance probabilities between the three datasets used in vitro, the predictive value of the bendamustine REGS was evaluated in patients treated with cyclophosphamide containing drug regimens. The LLMPP clinical DLBCL dataset, which is split into CHOP and R-CHOP treated patients, was used, and patients were trichotomized into sensitive, intermediate, and resistant groups based on predicted bendamustine response. Bendamustine resistance probabilities using the bendamustine REGS were significantly associated with overall survival in the CHOP treated cohort (P < 0.01), but not in the R-CHOP treated cohort [Supplementary Figure 2A and B]. However, when the bendamustine REGS and a previously generated cyclophosphamide REGS was compared in patients within each of the clinical cohorts, no significant correlation was found between the two REGSs [Supplementary Figure 3].We did not find any bendamustine treated DLBCL cohorts usable for retrospective validation of the prediction model. However, as there was a significant correlation between bendamustine and cyclophosphamide response in the HBCCL n = 971, MM: n = 1,126) with individually assigned bendamustine resistance probabilities, we found that the ABC subtype was the most resistant in DLBCL, and the centrocyte and plasmablast subtypes were predicted to be the least resistant in the DLBCL patient cohorts. Bendamustine resistance probabilities in memory subtype tumors depended on ABC/GCB subclassification. In MM, the Pre-BII subtype had a high bendamustine resistance probability along with TC classes 4p14, MAF, and D2. Thus, molecular and COO based subclassification of patient cohorts can stratify patients based on bendamustine resistance probabilities.In summary, in clinical samples  regarding the drugs before reaching the tumor, and lack of consideration for intra- and inter-patient clonal heterogeneity of tumors. Despite these limitations, malignant cancer cell lines, like the HBCCLs, serve as strong pre-clinical models for disease specific pharmacogenomic studies  as they harbor many of the same mutations and genomic alterations found in malignant B-cells from DLBCL and MM,61. In addition, the HBCCLs were originally developed from patients and display many of the cytogenic characteristics of DLBCL and MM which contribute to oncogenesis and possibly response to treatments. The translational advantage of the HBCCL is the number of DLBCL and MM tumor specific cell lines in contrast to other cell panels such as NCI-60, which only contains two DLBCL and MM like cell lines.The use of malignant HBCCLs . Furthermore, a combined REGS developed from cyclophosphamide, doxorubicin, and vincristine treated cell lines also showed prognostic value in DLBCL.The prognostic potential in patient stratification using the cell line based bendamustine REGS should be validated in large, independent, and prospective randomized clinical cohorts treated with or without bendamustine in combination with standard treatment regimens to assess its clinical potential. At the current time and to our knowledge, no bendamustine treated DLBCL and MM validation cohorts with available GEP data and follow-up exist. We have previously validated a melphalan REGS, where high probabilities of resistance were associated with inferior clinical outcome in the HOVON-65 datasetin vitro, significant differences were found in the overall survival between patients trichotomized based on predicted bendamustine resistance in the CHOP treated cohort. This could suggest that the bendamustine REGS is a pseudo marker for response to treatment with alkylating agents sharing similar mechanisms of action with bendamustine in DLBCL. It is, however, worth noting that the DLBCL patients from the clinical datasets were subjected to a combination treatment containing either four or five drugs with different mechanisms of actions, some of which both interact with each other and the surrounding tumor microenvironment affecting the clinical outcome,62. Additionally, the bendamustine resistance probabilities did not correlate with the cyclophosphamide resistance probabilities in patients. Thus, a bendamustine treated cohort is needed for validation of the bendamustine REGS. Strong and validated predictors of treatment outcome based on clear molecular signatures have a great potential in the clinical setting for the identification of resistant patients who will benefit more from alternative treatments. However, many biological and statistical considerations are needed prior to their development and clinical application.In addition to the correlation between cyclophosphamide and bendamustine response In conclusion, the use of disease-specific cell lines is a strong tool for prognostic classification of patient cohorts as they can be used to create REGSs which can be further applied to clinical samples and used in risk stratification, either alone or in combination with other molecular subclassification models. Here, we evaluated the REGS for bendamustine in COO based subtypes in DLBCL and MM. For DLBCL, we found that the GCB subtype and the differentiation dependent BAGS subtypes of centrocyte, plasmablasts are predicted to have lower bendamustine resistance along with the plasma cell subtype in MM. However, these findings need to be validated in large, independent, prospective patient cohorts with bendamustine-treated patients and with available GEP and follow-up.


TRB) sequencing of sorted CD4+CD25+CD127low regulatory T cells (Treg cells) and CD4+ conventional T cells (Tcon cells) in order to track longitudinal changes in the TCR repertoire. GVHD following DLI was associated with less diverse but clonally expanded CD4+CD25+CD127low Treg and CD4+ Tcon TCR repertoires, while patients without GVHD exhibited healthy-like repertoire properties. Moreover, the diversification of the repertoires upon GVHD treatment was linked to steroid-sensitive GVHD, whereas decreased diversity was observed in steroid-refractory GVHD. Finally, the unbiased sample analysis revealed that the healthy-like attributes of the CD4+CD25+CD127low Treg TCR repertoire were associated with reduced GVHD incidence. In conclusion, CD4+CD25+CD127low Treg and CD4+ Tcon TRB repertoire dynamics may provide a helpful real-time tool to improve the diagnosis and monitoring of treatment in GVHD following DLI.Donor lymphocyte infusion (DLI) can (re-)induce durable remission in relapsing patients after allogeneic hematopoietic stem-cell transplantation . However, DLI harbors the risk of increased non-relapse mortality due to the co-occurrence of graft-versus-host disease (GVHD). GVHD onset may be caused or accompanied by changes in the clonal T-cell receptor (TCR) repertoire. To investigate this, we analyzed T cells in a cohort of 21 patients receiving DLI after alloHSCT. We performed deep T-cell receptor β ( Donor lymphocyte infusions (DLIs) are used as a curative treatment option in relapsing patients (therapeutic DLIs). They are also applied for the conversion of mixed chimerism or persisting minimal residual disease (pre-emptive DLIs) as well as to prevent relapse in high-risk disease (prophylactic DLIs) . The donrom GVHD . Howeverrom GVHD , the onsrom GVHD . Thus, a+ B cells, as well as naïve CD8+ cells, CD27+ NK cells and mononuclear cells, is associated with higher rates of GVHD [reg) cells was not protective for GVHD in that study. Treg cells have been described as the main T-cell subset maintaining self-tolerance in the immune system. Consequently, they contribute to the prevention of autoimmune and inflammatory diseases such as GVHD [reg cells was shown to prevent GVHD while retaining GVL activity after alloHSCT [reg-depleted DLI was shown to improve GVL [A recent report analyzing the cell subset composition of DLI products suggested that a higher proportion of CD27 as GVHD . The adoalloHSCT ,11, and rove GVL .reg TCR repertoire in the peripheral blood early after alloHSCT was associated with protection from GVHD within the first 100 days post alloHSCT [+ TCR repertoire analysis after alloHSCT, reduced diversity and clonal expansion were observed in acute GVHD (aGVHD) [+ TCR repertoire in the blood [The use of the T-cell receptor (TCR) repertoire analysis has improved our knowledge about overall T-cell diversity kinetics, including the outgrowth of individual specific T-cell clones. Easy-to-handle tools to decipher TCR-based repertoire dynamics in the context of DLI are just evolving and mostly involve spectratyping ,14,15. HalloHSCT . In an u (aGVHD) ,22,23,24he blood .+CD25+CD127low Treg cells and sorted CD4+ Tcon cells while observing the emergence and clinical course of GVHD during a study period of 36 months post DLI. In this study, we show that patients without GVHD post DLI exhibited broad CD4+CD25+CD127low Treg and CD4+ Tcon TRB repertoires similar to healthy individuals. Moreover, we observed a diversification of the CD4+CD25+CD127low Treg and CD4+ Tcon repertoires upon steroid treatment, which was indicative of GVHD treatment response. Finally, we examined whether the features of healthy-like repertoires early after DLI could predict the occurrence of GVHD. Overall, these findings could guide the diagnosis and monitoring of treatment in GVHD following DLI.In the setting of DLI, where immune-cell and TCR reconstitution has fully taken place, the role of immune-cell repertoire dynamics with regard to the risk of developing GVHD remains to be elucidated. Here, in a cohort of patients receiving DLI after alloHSCT, we longitudinally assessed the TCR repertoire via the deep sequencing of the TCR beta chains of sorted CD4p = 0.057; p = 0.024) and a trend towards higher NRM (p = 0.067) for GVHD compared with noGVHD during the 36 months of follow-up , we performed correlational analyses between absolute cell counts and 1/D. Higher values of 1/D indicated a more diverse repertoire. There were no correlations between 1/D and absolute cell counts neither for patient nor for healthy control (HC) samples in both CD4+CD25+CD127low Treg and CD4+ Tcon populations . Furthermore, a lower CD4+CD25+CD127low Treg diversity was observed in GVHD samples compared with noGVHD (p = 0.006), and patients without GVHD exhibited a CD4+CD25+CD127low Treg repertoire similar to that of healthy individuals . The noGVHD patient samples were matched for timing with regard to the sampling time points post DLI. For the control group, we used the average diversity of each healthy individual’s repertoires. The intergroup comparison of these subgroups revealed aGVHD to be the main driver of the observed differences in the diversity of both CD4 and HCs B. Again,+CD25+CD127low Treg 1/D  and CD4+ Tcon 1/D  but not for CD4+ Tcon 1/D  after GVHD was diagnosed) compared with the last time point prior to the onset of aGVHD (median of 66 d (5–70 d) before GVHD was diagnosed). Upon the occurrence of aGVHD, we observed a decrease in CD4Tcon 1/D D.+CD25+CD127low Treg and CD4+ Tcon repertoires compared with patients without GVHD or HCs. Conversely, patients not developing GVHD after DLI showed healthy-like repertoires.Taken together, GVHD after DLI was associated with less diverse CD4+CD25+CD127low Treg and CD4+ Tcon repertoire diversity in GVHD, which suggests the expansion of GVHD specific clonotypes, we visualized the repertoire proportion of individual clones comparing four representative samples for aGVHD, c/oGVHD, noGVHD and HCs , whereas there was a drop in diversity of −58.58% in steroid-refractory GVHD  linked to steroid sensitivity. Clonality analyses complemented the diversity findings by showing an increased clonality in the top 25% of the repertoires as well as expanded top 20 clones in steroid-refractory samples, while the opposite was the case in the steroid-sensitive samples for both CD4+CD25+CD127low Treg and CD4+ Tcon repertoires  of all patient samples taken at the first time point after DLI and categorized them into expanded (<25th percentile of HC 1/D) and not expanded, i.e., healthy-like samples (1/D ≥ 25th percentile of HC 1/D). The comparison of the clinical characteristics of patients with expanded vs. healthy-like diversity revealed no significant differences between the groups induce long-term remission via donor T-cell clones directed against recurring or persisting malignant cells, GVL often co-occurs with GVHD as result of unselective allo-reactivity ,7. To ru+CD25+CD127low Treg and CD4+ Tcon TRB repertoires in patients treated with DLI. Although the absolute count of immune cells did not influence the repertoire diversity  and functional assays to assess the role of cell functionality in the protection from GVHD.To date, the monitoring of GVHD following DLI depends on clinical assessment and histological confirmation. There is emerging evidence that GVHD after alloHSCT is associated with an expanded TCR repertoire, and the role as a potential biomarker has been discussed ,22,23,24iversity  and a biratyping ,22, as eeg cells , were noeg cells , we sortlonality . Immune-ed aGVHD . This isalloHSCT . HoweveralloHSCT ,21. In aficities ,30; howe+CD25+CD127low Treg and CD4+ Tcon repertoires, approaching healthy-like properties in steroid-sensitive patients, while steroid refractoriness was associated with opposite effects upon steroid treatment .The treatment of higher-grade GVHD relies on systemic steroids as first-line therapy. Therapy might be escalated with additional immunosuppression when criteria for steroid-refractory GVHD are met . Our detreatment . Evidenc T cells ; however T cells . However+CD25+CD127low Treg repertoire  prior to the application of DLI. However, this remains a challenging goal considering the highly individual and thus very complex immune-cell repertoire interactions in allo-reactivity.As +CD25+CD127low Treg and CD4+ Tcon TRB repertoires. Upon steroid therapy, we observed the diversification of the repertoires in steroid-sensitive GVHD patients. Moreover, a healthy-like CD4+CD25+CD127low Treg repertoire early after DLI was linked to a reduced incidence of GVHD during the 36 months of follow-up. While these findings warrant further testing, they hold promise to improve the diagnosis and monitoring of treatment in GVHD following DLI, thereby improving the safety of DLI.In conclusion, GVHD following DLI was associated with less diverse and clonally expanded CD4n = 5), second alloHSCT before DLI (n = 1), ongoing cGVHD at DLI (n = 1) and withdrawal from treatment (n = 1) were excluded from the trial. Details of study recruitment and sampling are given in A total of 29 patients treated with unmanipulated DLI for clinical or molecular relapse or increased host chimerism after alloHSCT were recruited between 2015 and 2017 in the department of Hematology, Hemostasis, Oncology and Stem-Cell Transplantation at Hannover Medical School. The institutional review board approved the study #2604-2015), and written informed consent was obtained from all study participants. Patients with an HLA-mismatched donor  and (overlap) chronic GVHD (c/oGVHD) were categorized according to the EBMT-NIH-CIBMTR task force panel recommendation , using tn = 3), individuals taking immunosuppressive medication (n = 1) and those with cold symptoms requiring symptomatic medication on more than three concurrent study visits (n = 1) were excluded from the trial. Moreover, samples of healthy individuals with self-reported wounds, cold symptoms or dental procedures prior to the blood draw were excluded  provided their written informed consent. Smokers (excluded . In tota+CD25+CD127low Treg and CD4+ Tcon was performed on a BD FACSAria Fusion (BD Biosciences) cell sorter  were extracted from patient whole-blood samples using the Ficoll gradient as described elsewhere  and storl sorter . Flow cy2O) were run together with the patient samples during all PCR steps.RNA was extracted from the sorted cells; this was followed by reverse transcription using SMARTer RACE 5′-3′ PCR Kit (Clontech) using a customized protocol . Next, thttps://github.com/MHHIMMUNOLOGY/MHHTCR_ANALYSIS, accessed on 21 August 2022). For TCR repertoire analysis we used VDJtools [http://cran.R-project.org, accessed on 21 August 2022) treemap package.Sequencing data analysis included the preparation of FastQ files, followed by extraction using MIXCR (version 2.1.11) with the alignment of nucleotide sequences based on the IMGT database (22.05.2018), including the correction of PCR errors. MIXCR default options were used for alignment and assembling. To avoid biased sample analysis due to diverging sequencing depths, we randomly normalized all samples to 20,000 reads using a custom bash script resulting in a standardized downsampling function. This was part of our streamline data analysis pipeline, ensuring a standardized and reproducible analysis of sequencing samples, including decompression, conversion into the FASTQ file format and random normalization  or the two-tailed Wilcoxon matched-pairs signed rank test  was used. Hazard ratios for the comparison of cumulative incidence curves were calculated with the Mantel–Haenszel test. The competing risk analysis of the cumulative incidence of relapse (CIR) and non-relapse mortality (NRM), analyzing the means of cumulative incidence curves, was performed with Gray’s test [Statistical analyses were performed with Prism 8 (GraphPad). Normality distribution was tested with the Shapiro–Wilk test. The group comparisons of normally distributed numerical variables was conducted with unpaired two-tailed Student’s y’s test .