ATP23, RAD51C and RPA3 independently associated with poor prognosis in glioblastoma patients. Finally, we observed a substantial upregulation of DDR genes after treatment with TMZ and/or RT, particularly in RT-treated glioblastoma cells, peaking within 24 h after treatment. Our results confirm the potential influence of DDR genes in patient outcome. The observation of DDR genes in response to TMZ and RT gives insight into the global response of DDR pathways after adjuvant treatment in glioblastoma, which may have utility in determining DDR targets for inhibition.Glioblastoma is a highly aggressive, invasive and treatment-resistant tumour. The DNA damage response (DDR) provides tumour cells with enhanced ability to activate cell cycle arrest and repair treatment-induced DNA damage. We studied the expression of DDR, its relationship with standard treatment response and patient survival, and its activation after treatment. The transcriptomic profile of DDR pathways was characterised within a cohort of isocitrate dehydrogenase (IDH) wild-type glioblastoma from The Cancer Genome Atlas (TCGA) and 12 patient-derived glioblastoma cell lines. The relationship between DDR expression and patient survival and cell line response to temozolomide (TMZ) or radiation therapy (RT) was assessed. Finally, the expression of 84 DDR genes was examined in glioblastoma cells treated with TMZ and/or RT. Although distinct DDR cluster groups were apparent in the TCGA cohort and cell lines, no significant differences in OS and treatment response were observed. At the gene level, the high expression of  Glioblastoma is the most common and aggressive type of primary malignant brain tumour in adults. The diagnosis of glioblastoma, updated in the WHO Classification of Tumors of the Central Nervous System 2021, involves a combination of histological (microvascular proliferation or necrosis) and molecular characteristics, including the criteria of having the isocitrate dehydrogenase (IDH) wild-type gene .Evidence suggests that DNA damage response (DDR) pathways respond to treatment-induced damage and aid in tumour survival, leading to treatment resistance in glioblastoma . The basWe investigated the transcriptomic expression of DDR genes and pathways in a TCGA cohort of IDH wild-type glioblastoma patients and its relation to overall patient survival. We identified DDR profiles of 12 patient-derived glioblastoma cell lines and compared TMZ and RT sensitivity of cells stratified on the degree of DDR pathway expression. Lastly, these cell lines were exposed to a clinically relevant dose of RT and/or TMZ to investigate the extent and timing of DDR genes and pathways responding to treatment-induced damage.This study was approved by the Human Research Ethics Committee of the University of Newcastle (H-2020-0389).z-scores for data visualisation. RNAseq by expected maximisation (RSEM) data were collated for 84 DDR genes (p < 0.05), using clinical covariables that were determined to be significant through univariate Cox regression  values were collated from 140 IDH wild-type glioblastoma patients within the TCGA database . FPKM vaDR genes . Patientgression .® -coated tissue culture flasks in StemPro® NSC SFM  containing 100 I.U./mL penicillin and 100 µg/mL streptomycin , and incubated at 37 °C in 5% CO2/95% humified air. Cells were passaged using StemPro® Accutase® solution  for detachment of adherent cells. TMZ was purchased from Sigma-Aldrich , aliquoted in dimethyl sulfoxide (DMSO) (100 mM) and stored between 2 and 8 °C. RT was delivered using a medical linear accelerator (LINAC) at GenesisCare, Gateshead NSW  or RS-2000 Small Animal Irradiator .Twelve patient-derived glioblastoma cell lines were kindly provided by the QIMR Berghofer Medical Research Institute . The cell lines are fully characterised with publicly available molecular and patient data, published by Stringer et al. . Cells whttps://www.qimrberghofer.edu.au/commercial-collaborations/partner-with-us/q-cell/ accessed on 10 May 2021). RNA extraction methods and RNA sequencing analysis from the cell lines are described in Stringer et al. [RNA sequencing data from all 12 glioblastoma cell lines were obtained from the publicly available QMIR database  was added to each well and incubated for 3 h. The medium/MTT solution was aspirated and DMSO (120 µL) added into each well. Each plate was shaken using an IKA® MS 3 basic shaker  at 600 rpm for 2 min. Absorbance was read at 570 nM using the SPECTROstar®Nano microplate reader .To assess cell viability, 96-well plates were coated with Matrigel under ice-cold conditions prior to plating with cells. Adherent glioblastoma cell lines were passaged and seeded at 4000 Cells/well in 96-well plates overnight, before treatment with a clinically relevant dose of TMZ (35 µM)  or RT (2t-test.Glioblastoma cell lines were stratified into two cluster groups (C1 and C2) based on hierarchical clustering of ssGSEA scores in each DDR pathway. Cell lines were also assigned into “high” or “low” expression groups according to each DDR pathway based on a median split of ssGSEA scores. Differences in TMZ or RT cell viability between cluster groups or “high” vs. “low” DDR expression groups was assessed through an unpaired student’s ®Gene Expression Custom Array Card . Samples were run as biological triplicates using the QuantStudio™ 7 Pro Real-Time PCR System . The geometric means of housekeeping genes  were seeded at 300,000 cells/well in 6-well Matrigel-coated plates overnight and treated with a clinically relevant dose of RT (2Gy), followed by TMZ (35 µM) one hour later. Cells were harvested at 2, 24 and 48 h after TMZ treatment and extracted for RNA using the AllPrep DNA/RNA/Protein Mini Kit . RNA was converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit . In accordance with supplier instructions, gene expression of 84 DDR genes  was examng genes  were usep-values < 0.05 were considered significant.The statistical analyses including the Mann–Whitney test, the Kruskal–Wallis test and Dunnett’s multiple comparison test were conducted in GraphPad Prism 7. Unsupervised hierarchical clustering (Ward’s method) of ssGSEA scores was performed in R using the ‘stats’ package and visualised using the ‘ComplexHeatmap’ package. Survival analyses using the log-rank test and Cox regression were performed in R using the ‘survival’ package. MGMT methylated patients when comparing cluster C1 through to C3 (C1 = 34.2% (13/48), C2 = 41.5% (17/41), C3 = 53.3% (16/30)). A similar trend occurred for the proportion of TP53 alterations (C1 = 13% (7/54), C2 = 20.8% (11/53), C3 = 33.3% (11/33)). Although distinct DDR gene profiles were apparent, no survival difference was found across clusters  genes (p = 0.154). To determine whether cell line DDR gene expression clusters had similarities to the TCGA gene expression clusters, hierarchical clustering was performed on combined ssGSEA scores of TCGA samples and cell lines. All cell lines from C1 clustered within a combined cluster resembling TC1, while all cell lines belonging to C2 did not associate with any TCGA DDR cluster  genes A. NHEJ, 4) genes A, was no cluster .MGMT methylation status is a clinical biomarker of TMZ sensitivity [MGMT methylated cell lines had a significantly reduced cell viability than MGMT unmethylated cell lines (p = 0.004) in response to TMZ . Whilst distinct differences in DDR gene expression were observed between clusters C1 and C2, no significant differences in cell viability were observed in response to TMZ (p = 0.32) or RT (p = 0.097)  and/or RT (2Gy) was assessed . Across sitivity . As expee to TMZ . Three c= 0.097) B,C.p = 0.032), while cells with high MMR gene expression were more sensitive to TMZ (p = 0.0039) (p = 0.0038) whilst there was no significant difference in other DDR pathways  D. Interepathways E. CollecMGMT statuses, and responses to TMZ or RT treatment  treated with a clinically relevant dose of TMZ and/or RT at 2, 24 and 48 h post-treatment. These cell lines represented to various degrees different cluster groups, reatment . Within When observing the frequency and distribution of DEGs across all cell lines, several trends appeared. Differences in the frequency of DEGs were apparent between treatments across time points. On average, TMZ induced the lowest frequency of DEGs, whereas RT induced the most robust response causing the highest frequency of DEGs across each time point B,C. The The frequency of DEGs belonging to specific DDR pathways was also distinct for each treatment between time points. Although variability in response was observed between cell lines , trends NEIL3 and CCNO (BER), XRCC2, RAD54L and ATM (HR), DDB2 (NER), and MSH2 (MMR) were among the most differentially expressed and upregulated  and appeared in cells treated with TMZ or TMZ + RT  complex involved in HR and DSB repair and has been implicated in glioblastoma OS outcome [Firstly, we identified three distinct clusters (TC1–3) of TCGA glioblastoma patients based on the overall expression of DDR pathway genes using ssGSEA. Despite the clusters displaying low, moderate, and high DDR gene expression, respectively, no significant OS differences were observed between clusters. A study by Meng et al.  found lostresses . Furtherpatients , its higse to RT . RAD51C ed in HR , while RMGMT methylation status. In regard to these discrepancies between the TCGA data and the baseline data from the 12 patient-derived glioblastoma cell lines, Stringer et al. showed in their original publication of the cell line data that 7 of the 12 cell lines maintained a molecular signature equivalent to that from the original tumour tissue [Stringer et al.  describer tissue . As suchr tissue . HoweverWhen examining individual pathway expression in the cell lines, we showed that genes in DDR pathways are associated with response to TMZ or RT. Cells with a higher expression of NER genes were more resistant to TMZ, while high expression of MMR genes conferred TMZ sensitivity. Previous studies have suggested similar trends with respect to NER components ,25, whilATM), ataxia-telangiectasia and rad3-related (ATR), and DNA-dependent protein kinase (DNA-PK) [6-meG adducts caused by TMZ form DNA breaks only after several replication cycles have occurred [A limitation to this analysis is the bulk transcriptomic lens of tumours prior to any treatment, which does not adequately reflect changes in DDR expression from standard treatment. Thus, studying DDR across a pre- and post-treatment time course may reveal greater insight. In this regard, one of the important aspects of the work presented here is the time course analysis of DDR gene expression response, showing that several genes and pathways are upregulated in response to standard treatment, especially RT. These data may inform the feasibility of targeting DDR components to enhance treatment response in glioblastoma patients. One such approach is being explored through the development of small-molecule inhibitors of DDR proteins including poly (ADP-ribose) polymerase (PARP), Wee1, checkpoint kinase 1 and 2 (Chk1 and Chk2), ataxia-telangiectasia mutated ((DNA-PK) ,30. Acrooccurred ,32, in coccurred . The comNEIL3, XRCC2, CCNO, RAD54L, ATM, DDB2, and MSH2, all of which have been linked to treatment resistance in glioma or solid tumours such as colorectal cancer [RPA3 and RAD51C rarely changed although being associated with OS outcomes in the TCGA cohort, while ATP23, was either upregulated or downregulated depending on the cell line. This suggests that baseline expression and associated patient outcome may not entirely capture the direct role such genes play in DDR when examined across time, and further investigation is needed. This study also shows the upregulation of several NER genes in response to TMZ and RT, which is an underreported DDR pathway in glioblastoma. ATR, a global sensor of DNA damage, has been implicated in NER activation [Across the four cell lines, several DDR genes were frequently observed as differentially expressed. The most notable and frequent included l cancer ,36,37,38tivation  and is ttivation ,41,42. TOverall, this study reveals an influence of DDR genes and subsequent pathways in glioblastoma cellular responses to treatment. Specific clusters of DDR expression failed to show significant differences in patient survival outcome or cell line response to TMZ or RT. However, our analysis revealed that the high expression of three DDR genes associated with poorer overall patient survival, while expression of MMR, NER and HR influenced sensitivity to TMZ or RT in glioblastoma cell lines. Our results suggest that the DDR is primarily upregulated within a 24 h period after treatment of TMZ and/or RT, with distinct trends of DDR activation apparent between treatments. Such data give insight into the changes in DDR gene expression in response to standard treatment and the potential for targeting commonly upregulated DDR components to produce radio- or chemo-sensitising agents.


MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall.Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the  Childhood acute lymphoblastic leukemia is characterised by a range of genetic aberrations. Here, the authors use multi-omics profiling of ALL cell lines to connect molecular phenotypes and drug responses to provide an interactive resource of drug sensitivity. Additionally, ~80% of survivors of childhood ALL will experience a post-treatment life-threatening medical event by age 45, an effect hypothesized to result from the severity and duration of ALL treatment protocols5. Targeted therapy protocols have demonstrated the potential to reduce the likelihood of post-treatment health complications, improve outcomes, and address resistance to chemotherapy. These targeted approaches have been successfully implemented clinically in protocols giving tyrosine kinase inhibitors to Philadelphia chromosome-positive ALL patients (NCT03911128)6. Recently, targeted treatment strategies have expanded to include immunotherapeutic agents such as antibody and chimeric antigen receptor (CAR) -T-cell therapies7. However, the adoption of these agents is limited by challenges in production, identifying suitable antigens, and the clinical emergence of adverse events9. Consequently, there is an immediate need for novel therapeutic modalities for the treatment of high-risk patients and patients who relapse.Acute lymphoblastic leukemia (ALL) accounts for ~30% of all cancers in children, making it the most common childhood cancer. Despite the clinical success of broad chemotherapy protocols and allogeneic hematopoietic stem cell transplantation (HSCT)11, somatic mutations can only partially explain the underlying biology and phenotype, and approximately 25% of childhood ALL patients lack a detectable driving mutation12. Although genetic characterizations have improved risk stratification and hope of new molecular targets for therapy, the primary challenge of identifying novel and effective treatments for ALL is achieving more reliable phenotypic stratification that can improve therapeutic response13.Therefore, bridging the gap in therapeutic options for ALL will likely require the development of both pharmacologic and cellular therapies; and this process will depend on the deeper characterization of both the biomarkers and biology of nonresponsive subtypes. While the genetic landscape of childhood ALL has been extensively studied by the implementation of next-generation genomic, transcriptomic, and epigenetic sequencing tools14, the proteome represents an ideal framework for understanding cellular phenotypes. Recent studies have demonstrated a poor correlation between the proteomic and the genetic phenotypes of cancers17, including childhood ALL18. Studies of tumors and human-derived cell lines22 demonstrated the importance of proteomic characterization, however, our recent characterization of ETV6-RUNX1 and hyperdiploid ALL18 and a study on PDX models23 are the only in-depth studies that have used proteomics methods to understand the biology of childhood ALL.Considering that post-transcriptional and post-translational mechanisms have diverse impacts on the protein levels for each cellular component in ways shaped by cellular fitness requirementsMEF2D-HNRNPUL1 rearranged high-risk subgroup. We also provide a user-friendly web application for exploration of the proteomic, transcriptomic, and drug sensitivity data described in this study, available at https://proteomics.se/forall.In this work, we provide a proteomics-guided analysis of 49 childhood ALL cell lines, generate a comprehensive resource of biomarkers and drug sensitivities, and identify a potential therapeutic vulnerability to target the 24 for all cell lines and available replicates , which correctly indicated B-cell-receptor or T-cell-receptor signaling pathways for each respective lineage  of proteome profiles separated four major lineage-linked groups Fig. . Cell liTCF3-PBX1 fusion26, and ABL1 for BCR-ABL1, NUP214-ABL1, and SPFQ-ABL1 fusions , excluding the two EBV samples. We performed Spearman pairwise correlation for all mRNA-protein pairs (n = 8981), and the median correlation coefficient was 0.55  than at the transcript level (P = 1.4e-75)  Fig. , which w75) Fig. . In lineGINS2 mutation (P19S)35, protein-mRNA differences for GINS-complex members were significantly larger than differences for corresponding protein-mRNA pairs in other cell lines  suggested seven distinct clusters. Hierarchical clustering including all samples and proteins stratified the cell lines into consensus leukemic clusters (CLC)  along with several cell lines with a previously unspecified cytogenetic background. CLC3 contained 16 out of 22 T-ALL cell lines, again with various cytogenetic backgrounds and characterized by G2M checkpoint hallmark, higher spliceosome, and higher E2F target activity when compared to the other six T-ALL cell lines that clustered in CLC7 (Supplementary Data Consensus clustering of the proteome using high variance proteins (LC) Fig. . Althoug38, we performed DE analysis on the transcriptomics data between CLC3 and CLC7 followed by GSEA analysis, which failed to recapitulate the upregulation of the spliceosome  0.61, adjusted P value, q = 1.0)  Fig. . AlteredKMT2A-rearranged subtypes (KMT2A-AFF1 and KMT2A-MLLT1) were classified into RNA cluster 4 and one into RNA cluster 1, whereas the proteomics clustering divided these into three different proteomic clusters , demonstrating that the proteomics analysis delivers an additional angle to reveal phenotypic differences, which may be clinically meaningful. We performed DEqMS analysis between the two KMT2A-rearranged subtypes and identified p53 among the top upregulated proteins in KMT2A-AFF1 cell lines  were classified into the same RNA cluster (cluster 3) but clustered into different proteomic clusters (B-CLC2 and B-CLC5).To gain a better resolution on relative differences between the two different lineages, each lineage was clustered separately. This identified five clusters in the B-lineage (B-CLC) and seven clusters in the T-lineage (T-CLC) cell lines Fig. . Additiones Fig.  suggesti42. To evaluate the lineage states of our cell lines, we used established cellular markers of B-cell and T-cell developmental stages44 to immunotype the BCP-ALL and T-ALL cell lines  is also associated with pathogenic patient risk stratification46. B-CLC5 contained the cell lines most enriched for this protein . Additionally, B-CLC5 was characterized by the abundance of the adhesion molecule CEACAM1, which induces a tolerogenic immune environment through interactions with TIM347, suggesting a common retained affinity for oncogenic immunomodulation48.Within the B-cell state assignments, some cytogenetic subtypes appeared to be confined to one B-cell state, while others occupied multiple cell states and proteomic clusters  against a panel of 528 FDA-approved and investigational oncology drugs at five concentrations. The selective drug sensitivity scores (sDSS) were obtained by normalizing drug sensitivity against drug response of normal bone marrow (BM)49 Fig. .Fig. 4DrKMT2A-AFF1 cell lines (ALL-PO and SEM) with a suspected deregulated p53 pathway which could be due to mutations in TP53 or other dysregulation contributed by the gene fusion50.Broad-spectrum cytotoxic drugs, e.g., conventional chemotherapy drugs demonstrated high activity in a majority of the tested cell lines Fig. . Numerou52, and screening of patient cells ex vivo after diagnosis has been shown to predict response or resistance to these therapeutics53. Most of the cell lines demonstrated substantial sensitivity to dexamethasone, prednisolone, and methylprednisolone, except for 12 cell lines, among them the REH and JURKAT cell lines  protein abundance   are components of standard of care combination chemotherapy protocols for childhood ALL-8) Fig. , consistR ≥ 0.71, P ≤ 2.64e-12) and FLT3 , respectively   while anticorrelating with the target of MDM2 degradation, p53 (R ≤ −0.55)  for each drug, we ranked the drug sensitivity-protein abundance Pearson correlations of each drug and its putative drug target to examine the relationship between target abundance and drug response Fig. . Among tely Fig. . Additio04) Fig. . MDM2 in55) Fig. .58. Tacrolimus does not target this pathway by direct binding, but rather its activity requires the formation of a complex with FKBP1A, a cis-trans prolyl isomerase belonging to the immunophilin protein family59. Despite the crucial role of FKBP1A in this mechanism of action (MoA), we found that the abundance of FKBP1A negatively correlated with sensitivity to tacrolimus . However, another member of the cis-trans prolyl isomerase family, FKBP10, strongly correlated with tacrolimus sensitivity   for twelve ALL cell lines. This compound is widely used clinically as an immunosuppressant due to its ability to inhibit calcineurin-mediated dephosphorylation of NFAT07) Fig. . FKBP10 62. Reverse-phase protein arrays (RPPA)21 using ~230 proteins and data-independent acquisition using MS (~3000 proteins)22 have also been used for protein drug correlations in cancer cell lines, including seven and two ALL cell lines, respectively. These studies and additional meta-analyses63 identified that protein levels had stronger correlations with drug activity than corresponding mRNA levels. To investigate whether these results are confirmed in our childhood ALL cell line panel, we performed pairwise drug sensitivity correlation analysis with our transcriptomics and proteomics data, using drugs with an sDSS of ≥8 in at least two cell lines (n = 281) for the analysis. Cumulatively, relative protein abundances correlated to sDSS significantly higher (P < 1.45e-20) than protein-coding mRNA levels  and RCH-ACV , we monitored HDAC1 engagement using the cellular thermal shift assay (CETSA)67. This demonstrated that tacedinaline treatment thermostabilized HDAC1 to a similar extent (~4 °C) in both sensitive and resistant cells, and that tacedinaline has equivalent target engagement across a concentration range  for two cell lines at minimum, a matrix of Pearson correlation values for proteins correlated to drug sensitivity was used as input for dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP)65. Fig. . We note65. Fig. . For theors Fig. . To studnge Fig. . These oR = 0.87, P = 3.2e-14) and VCP/p97 inhibitor NMS-873   Fig. . These d72. In our dataset, the majority of cell lines were very sensitive to broad BCL2 family member inhibitors   and BCL2L1 , respectively and not exclusively with lineage , a cytokine receptor used as a marker of NOTCH1-dependent developing thymocytes75. However, in BCP-ALL cell lines, IL9R was not correlated with sensitivity , and sensitivity was significantly negatively correlated with PDPK1 protein abundance . Additionally, no PDPK1 interacting proteins from the STRING network database76 correlated with drug activity in BCP-ALL  following OSU-03012 treatment and suggested that HSPs were targets of OSU-0301278. Our dataset identified a significant correlation with HSP90 , but not in T-ALL cell lines . This further suggests that OSU-03012 sensitivity is linked to dependence on chaperonins in BCP-ALL cell lines. Other ongoing clinical, preclinical, and investigational applications of OSU-03012 have also identified the importance of observed changes in chaperone functionality and autophagy induced by drug treatment for several disease indications80.The competitive PDPK1 inhibitor and orphan drug OSU-03012 (AR-12) was another targeted drug with lineage specificity Fig. , where oALL Fig. , and no 81 applied to the sDSS-protein abundance correlations for the BCP-ALL or T-ALL cell lines , but not in T-ALL cell lines . This suggests that the interaction between TXNCD9 and the CCT-complex is more likely to occur in BCP-ALLs, where TXNDC9 abundance correlates with chemosensitivity rather than chemoresistance.To further quantify differences linked to lineage, we used differential correlation analysis (DCA)Together, these differential correlations provide insight into the biology of our cohort which is orthogonal to multi-omics profiling and functionally characterizes important proteins in the context of pharmacologic targeting. Our results demonstrate that integrated proteomics and drug sensitivity analysis is an especially well-suited approach to identify drug-target specificities and to decipher their MoA in ALL. Additionally, our heterogeneous panel of childhood ALL cell lines with varied phenotypes enables a window into the biology of drug response and suggests previously undescribed lineage differences which could have implications in understanding the mechanisms of important therapeutic agents.MEF2D-HNRNPUL1 fusion. MEF2D is a member of the myocyte-specific enhancer factor 2 (MEF2) family of transcription factors, which have key roles in a variety of malignancies83 and in B-cell development84. In the context of childhood ALL, MEF2D has been found fused to at least six different fusion partners86. The MEF2D-rearranged cytogenetic subtype is associated with relatively poor clinical outcomes88, and is also found in adult-onset ALL.Across our analysis of cytogenetic subtype, proteomic clustering, drug sensitivity, and cellular state, we observed striking similarities among a group of cell lines distinguished genetically by a MEF2D-rearrangements are associated with increased HDAC9 levels and MEF2D transcriptional activity. HDAC9 is a class II histone deacetylase that regulates the activity of MEF2 transcription factors89. Two studies demonstrated the sensitivity of xenografted and primary culture samples carrying another MEF2D fusion, MEF2D-BLC9 to broad class I HDAC inhibitors88.MEF2D-rearranged leukemias display a distinct gene expression signature86, which showed substantial overlap and agreement with differentially abundant proteins in the MEF2D-HNRNPUL1 cell lines  derived from an adult patient90. DEqMS analysis identified MEF2C and HDAC9 among the top differentially abundant proteins in comparison to the other BCP-ALL cell lines . Bryostatin-1 is a diacylglycerol (DAG) analog and protein kinase C (PKC) activator92 . By assessing proteomic markers with high Pearson correlations to sDSS, we observed a strong correlation between bryostatin-1 toxicity and markers upregulated in the MEF2D-HNRNPUL1 cell lines , calcium- and DAG-regulated guanine nucleotide exchange factor, which has been identified as a direct mediator of sensitivity to negative selection in pre-B cells93. Negative selection occurs at specific states of B-cell development, and it influences response to BCR stimulus by triggering programmed cell death after sustained, strong BCR signaling. In addition to limiting self-reactive mature B-cells, this mechanism has also been shown to limit the survival of dysfunctional pre-B cells95. In cells that are not vulnerable to negative selection, BCR stimulus can also contribute tonic survival signals, and therefore leukemias often demonstrate alterations in B-cell developmental progression that favor survival96. As cells that have expressed the pre-B-cell receptor at the early pre-B stage, our MEF2D fusion cell lines are at a stage in development that could render them vulnerable to negative selection94. In support of an underlying manipulation of this negative selection pathway, we observed that MEF2D fusion cells had an enriched abundance of the DAG degrading enzyme DGKH . PMA also conferred potent toxicity in MEF2D-HNRNPUL1 cell lines , suggesting that the on-target effects of bryostatin-1 are replicated by PMA. To further confirm that ERK signaling was a mediator of toxicity, we blocked the ERK pathway (phosphorylation) by using the MEK inhibitors tramatenib, UO126, selumetinib, and the ERK inhibitor ERK 11e. In cells treated with 100 nM bryostatin-1 or 25 nM PMA, concurrent dosing with 1 uM of MEK or ERK inhibitors significantly improved viable cell counts for all tested inhibitors . Our data is amenable to methods of analysis that allow subtype-specific multi-omics phenotyping, and which can be applied to obtain specific mechanisms of drug sensitivity in childhood ALL that are relevant for precision medicine.We here report an in-depth multi-omics layered analysis of 49 readily available childhood ALL cell lines, quantifying more than 12,000 proteins and 19,000 protein-coding transcripts as well as sensitivity to 528 oncology and investigational drugs. This represents an in-depth proteomic analysis of childhood ALL cell lines covering numerous cytogenetic subtypes, which complements our previous proteogenomic analysisGINS2 mutation in the REH cell line which we linked to the degradation of the entire GINS complex. Additionally, we perform in-depth characterization of drug–protein correlations, which we used to identify unexpected indications of secondary targets in the sensitivity mechanism for respiratory complex targeting drugs and for the widely used immunosuppressant tacrolimus. This analysis also identified an unexpected drug activity distinction for the HDAC inhibitor tacedinaline, which was specifically less effective in TCF3-PBX1 fusion cell lines relative to other inhibitors of its class, and which was independent of engagement to its putative target HDAC1. Further, we examined the biological impact of cellular lineage by examining lineage-specific drug–protein correlations and drug–drug correlations. The PDPK1 inhibitor OSU-03012 had distinct proteins correlated to toxicity for BCP- and T- lineage cell lines indicating alternative mechanisms of action in each lineage. Further, we performed differential correlation analysis, which indicated TXNDC9 in lineage-dependent roles that had opposing correlations to chemosensitivity, and which could be further explored to understand the response to therapy in childhood ALL and lineage-linked biological differences. These analytical approaches are well suited to identify additional findings and to improve the characterization of biological differences and drug response mechanisms.We illustrate the potential of this dataset by demonstrating correlation analysis to detect post-transcriptional regulation, which can identify specific processes in individual samples, as in the case of the 19 by adding in-depth proteomics profiling of additional 42 childhood ALL cell lines. Overall, our integrative data recapitulated poor mRNA–protein correlations, further highlighting the importance of the addition of proteomic analysis in studying childhood ALL. Although our data is limited to established cell lines which do not cover all known subtypes of childhood ALL, we hope that future studies can cover additional subtypes and close this gap.Genomic profiling has been the major tool utilized for the characterization of childhood ALL. However, protein levels have been demonstrated to have a more direct impact on cellular phenotypes, and our resource and analysis thus provides an improved insight into the biology of leukemia. Our resource complements previous proteomics studies of cancer cell linesMEF2D-HNRNPUL1 cell lines. MEF2D fusions represent ~3.6% of childhood ALL cases, and patients experience 5-year event-free survival of 71%, identifying this patient subset as high-risk of relapse or disease progression in current therapy protocols85. Our observations demonstrate that bryostatin-1 activates the proapoptotic PKC/RASGRP1/ERK signaling pathway in early pre-B cells, the progeny stage at which MEF2D-HNRNPUL1 cell lines are arrested. Bryostatin-1 has successfully completed several phase I and II clinical trials101, and this safety and pharmacology profile makes it an excellent candidate for drug repurposing. Due to limitations of in-vitro cell line models, bryostatin-1 and other drugs represented in the study should be further explored in preclinical models and patient samples that better represent relevant microenvironments and cytokine signaling before possible adaptation into the clinic. Additionally, our identification of negative selection vulnerability in MEF2D-HNRNPUL1 fusion cases represents a biologically therapeutic approach, and as a result of its orthogonal mechanism, it could be especially useful in cases of drug resistance and relapse or pursued in combination therapy. Our results indicate that this drug sensitivity results from on-target DAG analog activity, supported by replicating selective sensitivity using another drug of this class and by rescue experiments. The broad and biologically interconnected landscape of DAG binding proteins should be further characterized to understand the precise mechanism of bryostatin-1 in future studies. Further, we recommend consideration of other orthogonal mechanisms of immune regulation, which were identified to protect against negative selection in pre-B leukemias from other subtypes97 and could be relevant in potential resistance mechanisms.Notably, we identified a DAG analog and proposed PKC activator, bryostatin-1, that demonstrated a phenotype- and subtype-specific efficiency in the https://proteomics.se/forall/) of this childhood ALL cell line panel provide a rich resource for exploration and hypothesis generation.Collectively our proteomics, transcriptomics, pharmacoproteomics analysis, and data portal (l-Ala-l-Gln dipeptide)  supplemented with either 10 or 20% fetal bovine serum , 20 mM HEPES (Gibco/Life Technologies), 1 mM sodium pyruvate (Sigma-Aldrich), 1x MEM non-essential amino acids (Sigma-Aldrich), and 1x Penicillin-Streptomycin (Sigma-Aldrich) was preferably used. For a few cell lines that were not growing well in RPMI, we instead used Iscove’s Modified Dulbecco’s Medium  supplemented with 20% FBS or Nutrient Mixture F-10 Ham supplemented with 10% FBS. Detailed information on the provider of the cell lines and growth media can be found in Supplementary Data 2 to a cell density of ~1–2 million cells/mL. Cells were harvested at 500 × g for 3 min and washed twice with Hank’s Balanced Salt Solution . Aliquots of five million cells were saved for proteomics and transcriptomic analysis. Supernatants were tested negative for mycoplasma by MycoAlert Mycoplasma detection kit (Lonza). All cell lines were authenticated by (short tandem repeat) STR profiling .The 49 childhood ALL cell lines and two EBV-transformed B-cell lines used in this study were obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH , from Children’s Oncology Group Childhood Cancer Repository , from American Type Culture Collection (ATCC), Japanese Collection of Research Bioresources Cell Bank (JCRB), European Collection of Authenticated Cell Cultures  and Banca Biologica e Cell Factory . Roswell Park Memorial Institute (RPMI) 1640 containing 2 mM stable glutamine . Cell debris were removed by centrifugation at 14,000 × g for 15 min. The total protein amount was estimated (Bio-Rad DC). For filter-aided sample preparation (FASP), 250 µg of protein sample was mixed with 1 mM DTT, 8 M urea, and 25 mM HEPES pH 7.6 in a centrifugation-filtering unit with a 10-kDa cut-off . The samples were then centrifuged for 15 min, 14,000 × g, followed by another addition of the 8 M urea buffer and centrifugation. Proteins were alkylated by 25 mM IAA, in 8 M urea, 25 mM HEPES pH 7.6 for 10 min, centrifuged, followed by two more additions and centrifugations with 4 M urea, 25 mM HEPES pH 7.6. Protein samples were digested on the filter, first by incubation the samples with Lys-C ) overnight at 37 °C and an enzyme:protein ratio of 1:50. In the second digestion step trypsin (Thermo Scientific) was added in 50 mM HEPES at an enzyme:protein ratio of 1:100 and incubated for another 8 h at 37 °C. The addition of trypsin is repeated for a final overnight incubation. After digestion, the filter units were centrifuged for 15 min, 14,000 × g, followed by another centrifugation with 50 µL MilliQ water. Peptides were collected and the peptide concentration was determined. The quality of digest was checked for every sample by LC-MS/MS analysis. Peptides were then dried in a speedvac. About 100 µg of peptides were resuspended in 100 mM TEAB pH 8.5 and labeled with isobaric TMT10-tags (Thermo Scientific) according to manufacturer’s instructions, but for 3 h. Labeling efficiency was determined by LC-MS/MS before mixing the samples. In total ten TMT10 sets were prepared, whereas some cell lines were analysed in duplicates to assess the influence of the biological variance. Each TMT set contained a pool for posterior dataset normalization that was composed of lysates of different cell lines and digested together with the individual samples. An overview of the sets and the pool composition is given in table Supplementary Data Samples were prepared using a modified version of the spin filter-aided sample preparation (FASP) protocol103. Sample pools were subjected to peptide IEF-IPG (isoelectric focusing by immobilized pH gradient) in the pI range 3–10 and 3.7–4.9. Dried peptide samples were dissolved in 250 µL rehydration solution containing 8 M urea, and allowed to adsorb to the gel bridge strip by swelling overnight. The 24 cm linear-gradient IPG strips  were incubated overnight in an 8 M rehydration solution containing 1% IPG pharmalyte pH 3–10 or 2.5–5, respectively . After focusing, the peptides were passively eluted into 72 contiguous fractions with MilliQ water/ 35% acetonitrile (CAN)/ 35% ACN + 0.1% formic acid (FA) using an in-house constructed IPG extractor robotics  into a 96-well plate , which were then dried in a SpeedVac. The resulting fractions were dried and kept at −20 °C.The prefractionation method was applied as previously described in ref. −1, the curved gradient went from 2% B up to 40% B in each fraction as shown in Supplementary Data Online LC-MS was performed using a Dionex UltiMate™ 3000 RSLCnano System coupled to a Q-Exactive-HF mass spectrometer (Thermo Fisher Scientific). Each fraction was subjected to MS analysis. Samples were trapped on a C18 guard-desalting column , and separated on a 50 cm long C18 column . The nano capillary solvent A was 95% water, 5% DMSO, 0.1% formic acid; and solvent B was 5% water, 5% DMSO, 95% acetonitrile, 0.1% formic acid. At a constant flow of 0.25 μl minFTMS master scans with 60,000 resolution (and mass range 300–1500 m/z) were followed by data-dependent MS/MS  on the top five ions using higher-energy collision dissociation (HCD) at 30% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 1E6 for MS1 and 1E5 for MS2. Maximum injection times were 100 ms for MS1 and 100 ms for MS2. Dynamic exclusion was set to 30 s duration. Precursors with unassigned charge state or charge state 1 were excluded. An underfill ratio of 1% was used.49 was performed using ALL cell lines cultured as previously described, with media conditions as noted in Supplementary Data ® was used to plate the drugs (labcyte). Following incubation for 72 h at 37 °C and 5% CO2, cell viability (ATP levels) were measured using CellTiter Glo (Promega). Data was collected on an Ensight (Perkin Elmer) system. Data on each plate were normalized to a plate-specific negative control (vehicle) and a positive control . Quality control and selective drug-sensitivity score (sDSS) calculation104 and data analysis was performed using Breeze (breeze.fimm.fi)105. The sDSS are a modified area under the curve-based metric for assessing drug sensitivities. The drug sensitivity to TMP269 was carried out at six different concentrations ranging from 0.0001 to 10 µM at tenfold dilution using DMSO in the same 384-well format as the DSRT described above apart from the data analysis.Drug sensitivity and resistance testing6 cells/mL for each cell line, DMSO was added as a vehicle to control samples and incubated for 3 h at 37 °C and 5% CO2. Cell suspensions were then centrifuged at 300×g for 5 min, the supernatant was discarded and the cells were washed twice with Hank’s Balanced Salt Solution . Pelleted cells were resuspended in HBSS and 75 µL cell suspension were aliquoted to 0.2 mL tubes. Samples were then heated in a temperature range of 37–70 °C in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) for 3 min, followed by 3 min cooling at room temperature and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze-thawing and centrifuged at 21,000×g for 30 min at 4°C. The cleared supernatants were transferred to new tubes, denatured in LDS sample buffer (Thermo Fisher Scientific), and analyzed by western blotting.RCH-ACV and KOPN-8 cells were cultured as previously described, tacedinaline was added to final concentrations of 20 µM to 10 mL suspensions of 1.0 × 106 cells/mL respectively and incubated at 37 °C and 5% CO2 for 3 h. Two replica experiments for each cell line were performed. Cells were then heated at a constant temperature of 53 °C for 3 min in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) followed by 3 min cooling at RT and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze-thawing and centrifuged at 21,000×g for 30 min at 4 °C. The cleared supernatants were transferred to new tubes, denatured in LDS sample buffer (Thermo Fisher Scientific), and analyzed by western blotting.RCH-ACV and KOPN-8 cells were cultured as previously described, tacedinaline was added to final concentrations of 100, 25, 6.25, 1.56, 0.39, 0.098, 0.024 µM and DMSO in 100 µL aliquots of 1.0 × 10Cells were lysed in Cell Signaling Technologies lysis buffer supplemented with protease and phosphatase inhibitors . Protein concentrations were determined using Bio-Rad DC assay (Bio-Rad). Proteins were denatured in LDS sample buffer (Thermo Fisher Scientific), resolved by SDS-PAGE using NuPAGE™ 4 to 12%, Bis-Tris Gel  and NuPAGE MES SDS Running Buffer , and transferred to Nitrocellulose membranes . SeeBlue™ Plus2 Pre-stained Protein Standard was used as protein ladder . Afterward, the membranes were blocked with 5% nonfat dry milk in TBST (Thermo Fisher Scientific) and incubated with primary antibodies for the appropriate target. Following overnight primary incubation at 4 °C, blots were rinsed using TBST and incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies  for rabbit primary ab used at a dilution of 1:5000). All antibody incubations were diluted in 5% nonfat dry milk in TBST. Protein bands were developed with Clarity ECL Substrate Chemiluminescent HRP substrate (Bio-Rad) in an iBright CL1000 Imaging System . Bands were quantified using the ImageJ software version 1.5Oi and iBright Analysis Software version 4.0.1 (Thermo Fisher Scientific). HDAC1 , Phospho-ERK1/2 , ERK1/2 , b-actin  antibodies were used for Western blot to detect corresponding targets.2) in a 96-well sterile tissue culture plate (Corning). All drug treatments and DMSO controls were brought to the same relative DMSO volume of 1:200. Following treatment, non-viable cells were stained using 1:500 Zombie Aqua Live Dead stain (Thermo Fisher), diluted in PBS (Invitrogen), and added directly to the plated cells (1:2 volume). Cell staining was performed for 1.5 h on ice, and during staining and all subsequent steps, cells were protected from light using aluminum foil. Viable cell counts were obtained using a BD Biosciences LSRFortessa flow cytometer, and cells were collected in equal volumes per well using the high throughput sampler (HTS) plate reader. Gating  supplemented with 10% fetal bovine serum , 20 mM HEPES (Gibco/Life Technologies), 1 mM sodium pyruvate (Sigma-Aldrich), 1x MEM non-essential amino acids (Sigma-Aldrich), and 1x Penicillin-Streptomycin (Sigma-Aldrich). All cell lines were diluted to a plating density of 500k cells/mL. Cells were treated with soluble compounds at the stated concentrations for 72 h in standard tissue culture incubation conditions 107 and Percolator (v2.08)108, where search results from eight subsequent fractions were grouped for Percolator target/decoy analysis. All searches were done against the human protein subset of Ensembl 99 in the Galaxy platform109. MSGF + settings included precursor mass tolerance of 10 ppm, fully-tryptic peptides, maximum peptide length of 50 amino acids, and a maximum charge of 6. Fixed modifications were TMT-10plex on lysines and peptide N-termini, and carbamidomethylation on cysteine residues, a variable modification was used for oxidation on methionine residues. Quantification of TMT-10plex reporter ions was done using OpenMS project’s IsobaricAnalyzer (v2.0)110. PSMs found at 1% FDR  were used to infer gene identities.Orbitrap raw MS/MS files were converted to mzML format using msConvert from the ProteoWizard tool suiteProtein quantification by TMT-10plex reporter ions was calculated using TMT PSM ratios to the entire sample set  and normalized to the sample median. The median PSM TMT reporter ratio from peptides unique to a gene symbol was used for quantification. Protein FDR were calculated using the picked-FDR method using gene symbols as protein groups and limited to 1% FDR. The eight technical replicates of the SEM cell lines were combined by taking the protein-wise median levels.https://github.com/NationalGenomicsInfrastructure/ngi_pipeline). The reads were preprocessed for an adapter and quality-based trimming using cutadapt111 and then mapped to the human reference genome GRCh38 (gencode.v31.p12 primary assembly) using STAR aligner112 with enabled chimeric reads detection. All samples showed a good percentage of uniquely mapped reads (average 91%). The mapped reads were summarized/quantified at the gene level using featureCounts113 using gencode.v31 comprehensive gene annotation. The combined counts of all samples were filtered and adjusted for technical variation due to the sequencing batches using the ComBat-seq method implemented in the sva R package114. The adjusted genes counts were normalized using the edgeR package115 using the Trimmed Mean of M-values (TMM) method116. Transcriptomic data for a total of 417 leukemia samples including 18 cases with MEF2D-rearrangements from pediatric patients were obtained from The European Genome-phenome Archive (EGA) (Dataset ID: EGAD00001002704 and EGAD00001002692) after Data Access Agreement (DAC) approval from St. Jude Children’s Research Hospital - Washington University Pediatric Cancer Genome Project Steering Committee. Two additional RNA-seq samples of MEF2D-HNRNPUL1 subtype were obtained from the Shanghai Institute of Hematology (SIH) and were added to the St. Jude cohort and processed accordingly using the same RNA-seq analysis pipeline. The reads from bam files were re-aligned to gencode.v31.p12 human genome then the processing steps and annotation files were used identical to the cell lines RNA-seq data processing steps. STAR mapping statistics summary of 419 clinical patient samples are presented in Supplementary Data Total RNAs were extracted from aliquots harvested at the same time point as proteomics samples using the RNeasy Mini Kit (Qiagen), following manufacturer’s instructions using the option of adding β-mercaptoethanol in the lysis step and using DNAase. RNA concentration and quality were determined using Qubit and RNA Assay kit (Thermo Fisher Scientific) and Bioanalyzer with RNA Nano Chips (Agilent Technologies). RNA libraries were prepared with TruSeq Stranded total RNA RiboZero Kit (Illumina) for ribosomal depletion at the sequencing facilities. Sequencing of the libraries (Paired-end 2 × 150 bp) were performed in three different batches by the NovaSeq6000 S2 platform (Illumina) at the National Genomics Infrastructure (NGI) in Stockholm and SNP&SEQ Technology Platform in Uppsala. Basic quality control of the sequencing data and reads was performed by the facilities using a standard quality control pipeline   using Ensembl database v102 of human genome GRCh38. FusionCatcher was run using default parameters where three aligners  are utilized in combination for detection of potential fusion gene pairs. Genes which showed too many fusion partners (above 99% quantile of a number of partners) were filtered out. A matrix of the (counts per million) CPM of all of the uniquely spanned reads for detected fusions in the cell lines using FusionCatcher are presented in Supplementary Data FusionCatcher v1.33115. FeatureCounts was used to assemble a raw counts matrix, and edgeR used this counts matrix to perform differential expression analysis based on the negative binomial distribution.RNA-based differential expression analyses were performed using edgeR (v.3.32.1)30 were identified while considering variation between the cell lines by calculating a modified “quantile” standard deviation for each protein, ignoring the lowest and highest values for each protein. Then, the distribution of the modified standard deviation was modeled using a mixture of Gaussian distributions and we used an expectation-maximization method (EM) to estimate the different mixture components using the package mixtools, version 1.2.0. The EM process converged in a two distribution solution, which we assumed to represent the highly variable and the unmodulated proteins. Using this model, we estimated the number of highly variable proteins and selected a modified standard deviation threshold, which optimized the number of highly variable minus unmodulated proteins. As the EM process inevitably produces slightly different thresholds every time it is executed, we performed ten iterations and rounded the mean of the iterations to the lower 0.5 in order to have a reproducible solution.Highly variable proteinsWe submitted three different datasets to the clustering procedure: the full panel of cell lines, B cells  only, and T cells (T-ALL) only. For each of these datasets, we identified the set of highly variable proteins as detailed above. We then marked samples that exhibited no Pearson correlation of 0.5 or greater to any other sample using all proteins with valid values only. Sample replicates were excluded. In order to identify the optimal number of clusters, we ran the consensus cluster algorithm using the R package ConsensusClusterPlus, version 1.52.0, applying the following parameters: pFeature =  0.8, pItem = 0.8, reps = 2000, clusterAlg = hc. We filtered the dataset for highly variable proteins and non-outlier samples and clustered with different distance measures  and linkages . The number of clusters was determined by the elbow method and the delta area of the cumulative distribution function  of only protein-coding RNAs. Highly variable protein-coding RNAs were utilized to find an optimal number of clusters using consensus clustering in a similar approach followed for protein data. RNA and protein clusters were compared using the Sankey plot implemented in the ggalluvial R package (version 0.12.3). Uncertainty of RNA clusters were assessed by determining approximate bootstrap probabilities using pvclust R package (version 2.2-0).25. When comparing samples within a specific ALL lineage , we only selected samples belonging to the same lineage. For each specific comparison, we first stratified the samples to be compared into two different groups. The remaining samples were passed in as a combined third group (“other”). Each of the three groups needed to have at least one valid quantification value for a respective protein. In parallel, for each protein, we calculated the sum of quantified PSMs per TMT set. Only sets contributing to the actual DEqMS analysis were taken into account. Then we computed the minimum PSM count across all of these sets while ignoring zero and NA values. If a protein had no quantified PSM, it was excluded from the analysis. This final PSM count per protein was then used to build the empirical Bayes statistics. The significance cut-off was set to an adjusted P value of 0.01 and fold changes to log2(1.5)-fold difference in abundance.Differential abundance of the proteomics data was performed using DEqMS, version 1.6.0n = 8981) between the transcriptomic and proteomic data using 64 matched samples. Correlation between the mRNA and protein abundance values for each of these genes was determined using Spearman’s ρ correlation method. Correlation analysis between sDSS and protein abundance of all quantified proteins  for the 43 cell lines with DSRT were also performed using Pearson Rank Correlation. We used the cor.test function in R which uses t-distribution statistics to calculate the P value of the correlation.Correlation between mRNA and protein levels for each gene was performed using overlapping genes (https://www.gsea-msigdb.org/gsea/index.jsp) of gene lists from DEqMS, edgeR, and drug sensitivity correlation analysis were performed using the GSEA v4.1.0 software against priori-defined gene sets available at available from Molecular Signatures DataBase (MolSigDB). The priori-defined hallmark, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pathway Interaction Database gene sets were used. Weighted enrichment statistics was used for gene set sizes between 15 and 500, running with 1000 permutations.Gene set enrichment analysis  as calculated above. We excluded proteins of the spliceosome, Nop56p-associated pre-rRNA complex, ribosome, and proteasome in order to avoid bias of large complexes. Subcellular localization (neighborhood level) were downloaded from ref. 117. For miRNA analysis, we downloaded miRNA–mRNA interaction data from118. The initial dataset (hsa_MTI.xlsx file) consists of 502,652 interactions that were filtered for “Functional MTI” support corresponding to 8157 unique interactions. These were further condensed to CORUM complex gene targets, allowing 3500 interactions between 572 miRNAs and 835 gene symbol-centric transcripts for downstream analysis. Degradation profiles were extracted from supplementary table 119. Welch’s t-test and ANOVA were used for two- and multi-level comparisons, respectively, after converting correlations to z-scores. For the identification of significant mRNA-protein deviations, we devised a three-step approach:RNA and protein data were preprocessed by averaging replicate cell lines and filtering to include genes with more than 1 TPM and proteins with non-NA values in at least half of the samples.Stability scores for each gene symbol were derived as standardized residuals after regressing protein log2 ratios to RNA TPM values using locally estimated scatter plot smoothing (loess) function to account for lineage-specific transcript expression.Significant stability was called for absolute standardized residuals greater than 3.To investigate protein complex regulation, we used mRNA–protein correlations of all cell lines for CORUM complexes32 (Complete Dataset).Significant cases were annotated for mutations : DepMap 21Q3 Public. figshare. Dataset. 10.6084/m9.figshare.15160110.v2) and CORUM complex membershipn = 336 drugs, n = 9786 proteins). A scaled, centered matrix was generated and used as input for a PCA using the Seurat package in R (https://cran.r-project.org/web/packages/Seurat/index.html). This PCA calculation was used to generate an elbow plot and a jack straw plot, also using functions in the Seurat package. Assessed using the elbow plot and jack straw plot to identify PCs with significant contribution to variance in the dataset, the number of principal components used as input for UMAP initiation was chosen to be 27. Using the uwot package (https://cran.r-project.org/web/packages/uwot/index.html), the Pearson correlation values were input for calculation of UMAP embedding for two dimensions, performed using the following key parameters: n_neighbors = 25, local_connectivity = 1, spread = 3.5, min_dist = 0.05, metric = “euclidean”, pca = 27, init = “normlaplacian”, nn_method = “annoy”. The 2D UMAP was added to the Seurat package as a dimensional reduction and plotted as a ggplot2 object (https://cran.r-project.org/web/packages/ggplot2/index.html) using the DimPlot function in Seurat.Using the Pearson correlation coefficient results calculated as described above, a 2D matrix of values was assembled representing Pearson correlations to gene symbol-centric protein quantification values for each drug. Drugs were limited to only the screen results meeting a minimum sDSS threshold of 8 for at least one cell line (81 was performed using the DGCA package (version 1.0.2), which was applied to Pearson correlations calculated between the sDSS for the drugs and proteins in BCP-ALL (B_Lineage), or in T-ALL (T_Lineage). The differential correlation design detected differentially correlated drug–protein pairs by lineage. No imputation was performed and NA and zero values were excluded. Permutation testing for P values was performed for n = 10 permutations. The classification was performed to characterize differential correlations by their directions and significance in each group, and all results regardless of classification were analyzed and ranked by z-score and significance.Differential correlation analysishttps://cran.r-project.org/web/packages/ggplot2/index.html), and ggpubr (https://cran.r-project.org/web/packages/ggpubr/index.html). P values were obtained from an unpaired t-test between groups performed using the stat_compare_means function in ggpubr. Significance was validated in un-normalized raw counts data. Criteria for exclusion of acquired data was established prior to experimental data acquisition and analysis, and all analyzed wells were obtained from experiments where event counts over time remained constant throughout flow cytometry acquisition and where DMSO-treated controls from the same cell line culture and plate had more than 100 quantified viable cells per HTS collection, to ensure technical robustness of flow cytometry acquisition and cell line preparation.All quantification was performed using the BD FacsDiva software (BD Biosciences). For each experiment, mean DMSO-treated counts were obtained across technical replicates , and this mean value was used to normalize counts to a ratio of viable treated cell count divided by viable mean DMSO cell count. Normalization operations, statistical tests, and data plotting were performed in R using the packages ggplot2  were calculated with the R functions cor or cor.test which uses t-distribution statistics to calculate the P value of the correlation. Linear models built with the R function lm. Two-sided, unpaired t-tests were performed using t.test and two-sided Welch’s t-test using t.test was performed for pairwise comparisons unless otherwise specified. For the multiple group comparisons analysis of variance (ANOVA) test was performed using the function anova. Figure panels were created using base R graphics and ggplot2 (v.3.3.3) using ComplexHeatmap (v.2.2.0) R packages and Graphpad Prism8.No statistical method was used to predetermine sample sizes. No data were excluded from the analyses. The experiments were randomized based on the date of cell lines obtained and cultured as well as cytogenetic type and subtype. Investigator blinding to conditions and outcome assessment was not applicable. No data were excluded from analysis and all data met the quality control standards as described above. All of the statistical analyses were conducted using R (v.3.6.2 or higher), the drug sensitivity statistics of TMP269 which was conducted using Graphpad Prism 8, and the statistical analysis of the flow cytometry was conducted with Microsoft Excel T.TEST function using a two-sided and homoscedastic calculation. Correlations and associated Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Reporting Summary


Bax, Bim, and Bak) and anti-apoptotic  genes and extrinsic  pathways, DNA methyltransferase 1, 3a, and 3b, histone deacetylase inhibitors 1, 2, and 3, cell viability, and apoptosis in hepatocellular carcinoma (HCC) SK-Hep 1, colorectal cancer SW620, and pancreatic cancer PaCa-44 cell lines. The SK-Hep 1, SW620, and PaCa-44 cells were cultured and treated with TSA and zebularine. To determine cell apoptosis, cell viability, and the relative gene expression level, flow cytometry assay, MTT assay, and qRT-PCR were done respectively. The result indicated that zebularine and TSA changed the expression level of the Bax, Bak, Bim Bcl-2, Bcl-xL, Mcl-1, DR4, DR5, FAS, FAS-L, TRAIL, DNA methyltransferase 1, 3a, and 3b, histone deacetylase inhibitors 1, 2, and 3 by which induced cell apoptosis and inhibit cell growth in all three cell lines. Concluding, TSA induced its role through both extrinsic and intrinsic apoptotic pathways in three cell lines, whereas, zebularine played its role via both pathways in the SK-Hep 1cell line, it had no significant effect on Bcl-2, Bcl-xL, and Mcl-1 gene expression in SW620 and PaCa-44 cell lines. Aberrant histone modifications or promoter region hypermethylation of tumor suppressor genes (TSGs) have been recognized as the important epigenetic molecular mechanism in cancer induction. The potential anticancer activities of histone deacetylase inhibitors (HDACIs) and DNA methyltransferase inhibitors (DNMTIs) have been investigated in recent years. The current study was assigned to investigate the effect of trichostatin A (HDACI) in comparison to zebularine (DNMTI) on the intrinsic pro-apoptotic ( Aberrant histone modifications or promoter region hypermethylation of tumor suppressor genes (TSGs) have been recognized as the important epigenetic molecular mechanisms in cancer induction and progression. These mechanisms can inactivate TSGs and cancer-related genes lead to cancer induction and progression. The N-terminal domains of all core histones are subject to diverse modifications, such as methylation, acetylation, phosphorylation, and ubiquitylation at certain residues . AcetylaIn addition to histone modification, DNA methylation, primarily at CpG dinucleotides, has long been identified to play an important role in controlling gene expression. Histone modifications and DNA methylation play a significant role in modulating chromatin structure which can control gene expression. DNA methyltransferases (DNMTs), are responsible for DNA methylation, the activity of these enzymes affects chromatin structure and gene expression . AccordiBCL-2 family divided into three groups: promoters of apoptosis ; inhibitors of apoptosis ; and regulatory BH3-only proteins   and anti-apoptotic  genes and cytoplasmic/extrinsic  pathways, DNA methyltransferase 1, 3a, and 3b, histone deacetylase inhibitors 1, 2, and 3, cell viability, and apoptosis in hepatocellular carcinoma SK-Hep 1, human colorectal cancer SW620, and human pancreatic cancer PaCa-44 cell lines.In-vitro studies have demonstrated that DNMTIs (such as zebularine) and HDACIs  can induce apoptosis through two molecular mechanisms comprising extrinsic and intrinsic apoptotic pathways , 8. The nd PUMA) -11. The nd PUMA) . The curMaterialsHepatocellular carcinoma SK-Hep 1, human colorectal cancer cell lines SW620, and human pancreatic cancer PaCa-44 cell lines were purchased from the National Cell Bank of Iran-Pasteur Institute. The zebularine, TSA, and Dulbecco’s modified Eagle’s medium (DMEM) were obtained from Sigma  and dissolved in dimethyl sulfoxide (DMSO) to make a work stock solution. Further concentrations of the compounds, zebularine, and TSA were obtained by diluting the provided stock solution. Other necessary compounds comprising materials and various kits were purchased as provided for previous works , 14. TheCell culture and cell viability  The SK-Hep 1, SW620, and PaCa-44 cells were cultured in DMEM containing 10% FBS (Sigma) and 1% antibiotics which include 10,000 unit/mL penicillin G sodium(Sigma), 10,000 ug/mL streptomycin sulfate, and 25 ug/mL amphotericin B (Sigma) at 37 °C in 5% CO2 °C for 24 h and then seeded into 96-well plates (3 × 105 cells per well). After 24 h, the medium was replaced with an experimental medium containing various concentrations of zebularine , and TSA . The control groups were treated with DMSO, at a concentration of 0.05 %. After 24 h, the SK-Hep 1, SW620, and PaCa-44 cells, experimental and control, were investigated by MTT assay according to Standard protocols to determine cell viability. In this process, the MTT solution was added to each well for 4 h at 37 ℃ and then the MTT solution was changed by DMSO and shaken for 10 min to dissolve all of the crystals. Finally, the optical density was detected at a wavelength of 570 nM by a microplate reader. Each experiment was repeated three times (triplicates). IC50 was analyzed by GraphPad Prism Version 8.0. software. Non-linear regressions of log (inhibitor) vs response were selected for IC50 estimation. The bottom and top parameters were constrained to 0% and 100%.Cell apoptosis assayTo determine SK-Hep 1, SW620, and PaCa-44 cells apoptosis, the cells were cultured at a density of 3 × 105 cells/well and incubated overnight and then treated with zebularine and TSA, based on IC50 values demonstrated in Real-time Quantitative Reverse Transcription PolymeraseChain Reaction (qRT-PCR)Bax, Bak, Bim, Bcl-2, Bcl-xL, Mcl-1, DR4, DR5, FAS, FAS-L, TRAIL genes, DNA methyltransferase 1, 3a, and 3b, histone deac-etylase inhibitors 1, 2, and 3 qRT-PCR was done. The SK-Hep 1, SW620, and PaCa-44 cells were treated with zebularine and TSA, based on IC50 values demonstrated in To determine the relative expression level of the Result of cell Cell viability by the MTT assay. IC50 values are shown in The cell viability of the SK-Hep 1, SW620, and PaCa-44 cells treated with zebularine and TSA was investigated by MTT assay by which the activity of cellular enzymes reduced the tetrazolium salt MTT and produced a dark-blue formazan crystal. The crystal was dissolvable in DMSO to determine the viable cells. As shown in Result of cell apoptosis assayP < 0.001). Maximal and minimal apoptosis was seen in SK-Hep 1 cell treated with TSA and PaCa-44 cell treated with zebularine after 24 h of treatment respectively, To determine apoptosis, the SK-Hep 1, SW620, and PaCa-44 cells were treated with zebularine and TSA, based on IC50 values, for 24 h and then stained using annexin-V-(FITC) and PI to determine apoptotic cells in the early and late apoptosis stage. As indicated in Determination of genes expressionEffect of zebularine on the gene expressionBax, Bak, Bim, DR4, DR5, FAS, FAS-L, and TRAIL genes and down-regulated the expression of Bcl-2, Mcl-1, Bcl-xL, DNA methyltransferase 1, 3a, and 3b, histone deacetylase 1, 2, and 3 in SK-Hep 1cell line significantly. Additionally, zebularine up-regulated the expression of Bak, Bax, Bim, DR4, DR5, FAS, FAS-L, and TRAIL genes and down-regulated the expression of DNA methyltransferase 1, 3a, and 3b in SW620, and PaCa-44 cell lines significantly. It had no significant effect on Bcl-2, Bcl-xL, and Mcl-1 gene expression in SW620, and PaCa-44 cell lines  and mitochondrial (intrinsic) pathways, both of which converge on the activation of the cascade of caspases . HDACIs DNMTs , HDACs  in SK-Hep 1 cell line significantly. Additionally, zebularine up-regulated the expression of the genes of the extrinsic apoptotic pathway and pro-apoptotic genes (as mentioned in the result part) and down-regulated the expression of DNMTs , HDACs  in SW620, and PaCa-44 cell lines significantly. We did not observe significant changes in the expression level of Bcl-2, Mcl-1, and Bcl-xL in SW620, and PaCa-44 cell lines treated with zebularine. Similarly, in-vitro studies have demonstrated that DNA demethylating agents zebularine and decitabine activate the mitochondrial apoptotic pathway in T cells  and down-regulated the expression of the anti-apoptotic genes,  T cells . Similar T cells . It has  T cells . In recell lines . Several HCT-116 . Experim HCT-116 .Bax, Bak, Bim, DR4, DR5, FAS, FAS-L, and TRAIL genes and down-regulated the expression of Bcl-2, Mcl-1, Bcl-xL, DNA methyltransferase 1, 3a, and 3b, histone deacetylase 1, 2, and 3 in all three cell lines significantly. Inconsistent with our result, other researchers have indicated that histone deacetylase inhibitor sodium butyrate decreases Bcl-XL protein levels by down-regulating its RNA expression in mesothelioma cell lines. Besides, depsipeptide, as an HDACI, decreases the expression of Bcl-2, Bcl-XL, and Mcl-1 in multiple myeloma cells  and pro-apoptotic (BIM)genes in pancreatic cancer cell lines CFPAC1, Miapaca2, HPAF, PSN1, Panc1 PC, Paca44, RT45P1, and T3M4  and pro-apoptotic (BIM)genes in pancreatic cancer cell lines CFPAC1, Miapaca2, HPAF, PSN1, Panc1 PC, Paca44, RT45P1, and T3M4 , Class I HDACs , and Class II HDACs  and up-regulate CIP/KIP Family  resulting in cell apoptosis in colon cancer LS 174T, and LS 180 cell lines (Meanwhile, the extrinsic and intrinsic pathways are not the only molecular mechanisms of zebularine and TSA. The results of current studies indicated that DNA methyltransferase inhibitor zebularine and histone deacetylase inhibitor TSA can induce apoptosis through both mitochondrial/intrinsic and cytoplasmic/extrinsic apoptotic pathways, in hepatocellular carcinoma SK-Hep 1, human colorectal cancer SW620, and human PaCa-44 pancreatic cancer cell lines. Our finding demonstrated that zebularine induced significant apoptosis in all three cell lines. It up-regulated the expression of  T cells . Similar T cells . It has  T cells . In recell lines . Several HCT-116 . Experim HCT-116 . As mentma cells . In huma and p53 . Resent and T3M4 . Similarand T3M4 . In pancand T3M4 . Additioand T3M4 . Meanwhill lines , 43. FinBcl-2, Mcl-1, and Bcl-xL gene expression in SW620, and PaCa-44 cell lines. Therefore, TSA can induce a stronger apoptotic effect through the activation of both pathways. Finally, both agents induced apoptosis significantly, which provided the theoretical basis for the clinical application of these compounds.In conclusion, our results indicated that histone deacetylase inhibitor TSA can induce its apoptotic effects through both mitochondrial/intrinsic and cytoplasmic/extrinsic apoptotic pathways in hepatocellular carcinoma SK-Hep 1, human colorectal cancer SW620, and human PaCa-44 pancreatic cancer cell lines. Further, zebularine can induce apoptosis by an extrinsic apoptotic pathway in three cell lines. It had no significant effect on


To model the problem of radiation resistance in prostate cancer, cell lines mimicking a clinical course of conventionally fractionated or hypofractionated radiotherapy have been generated. Proteomic analysis of radiation resistant and radiosensitive DU145 prostate cancer cells detected 4410 proteins. Over 400 proteins were differentially expressed across both radiation resistant cell lines and pathway analysis revealed enrichment in epithelial to mesenchymal transition, glycolysis and hypoxia. From the radiation resistant protein candidates, the cell surface protein CD44 was identified in the glycolysis and epithelial to mesenchymal transition pathways and may serve as a potential therapeutic target. Localis22.12 incubator at 37°C to allow colonies to form. After 10 days, colonies were stained with crystal violet  and counted. The experiments were performed three separate times.Human PCa adenocarcinoma cell lines DU145 and PC3) were purchased from the American Type Culture Collection . DU145 cells were treated with 10 Gy daily for five fractions over several weeks (DU145‐HF). Generation of DU145‐CF, cell culture, clonogenic survival, cellular proliferation, matrigel transwell invasion, soft agar, and western blot were performed following methods previously described . The gen5 and PC3Radiation therapy remains a definitive treatment modality for prostate cancer with curative intent. While it is an effective treatment for a large majority of patients, cancer relapse can occur and is challenging to salvage. Radiation resistance is seen in both conventional and hypofractionated radiation therapy treatment schedules. Potential therapeutic targets to improve radiation remain elusive. This study is the first published proteomic analysis of prostate cancer cell lines generated from clinically relevant treatment schedules. This study uses an isogenic radiation resistant cell line  to investigate the proteome and elucidate the pathways and proteins implicated in persistence of prostate cancer following radiation therapy. The cell surface protein CD44 has been implicated in radiation resistance in a range of cancer types and we suggest that it may also represent a potential target in prostate cancer.5 cells per well onto a six‐well plate overnight. After 16 h, siRNA control or CD44 siRNA  were transiently transfected into cells using SiTran . One day later, cells were harvested and seeded at 250 and 4000 cells per well onto a six‐well plate in 10% FBS DMEM in triplicates, then mock irradiated (0 Gy) or irradiated with 6 Gy dose of ionizing radiation, respectively. Cells were then placed in a humidified CO2 incubator at 37°C to allow colonies to form. After 10 days, colonies were stained with crystal violet  and counted. To confirm knockdown, the transfected cells were lysed at 48 h post‐transfection and western blotting performed using anti‐CD44 antibody . The experiments were performed three separate times.For CD44 siRNA knockdown experiments, cells  were seeded at 3 × 102.26) were washed three times with Stain Buffer . Cells were then resuspended in 100 μl volume and stained with FITC anti‐CD44  or FITC anti‐IgG1 isotype control  on ice in the dark for 30 min. Flow cytometry was performed using LSR II system  and flow cytometry analysis was done using FlowJo .DU145‐PAR, DU145‐CF, DU145‐HF, PC3‐PAR, and PC3‐CF cells , washed three times with cold phosphate buffered saline (pH 7.4) before cells were pelleted. Cell pellets were resuspended in 150μL of 50% (v/v) 2,2,2,‐Trifluoroethanol and sample preparation was as previously described  with an 2.42 iBAQ values of the total number of proteins (4410) identified across the whole cell lysates from all cell lines, in triplicate. Parameters used for consensus clustering were; max_k = 7, Euclidean P as the similarity metric, pItem = 0.8, seed = 1000, and reps = 1000. For visualisation, abundances were converted to z‐scores.Consensus clustering was performed using ConsensusClusterPlus  (v1.5.0)2.5https://www.inkscape.org). g:Profiler was run on the enriched radiation lists separately, however visualised in the same instance to enable better visualisation of protein overlaps in pathways.Proteins of interest were processed for pathway analysis using the non‐ranked method g:Profiler   . ggPlot2 (3.2.1) , Complex33.1While radiation therapy is an effective treatment modality for many PCa patients, the development of biochemical recurrence due to radiation resistance remains a clinical concern. Recent advances in the clinic have enabled the variation in radiation schedules with the two predominant ones being investigated in this paper using a cell line model; conventional fractionation (CF) radiation therapy and hypofractionated (HF) radiation therapy. To mimic the clinical scenario of resistance to both treatments, DU145 PCa cells were mock irradiated with 0 Gy (DU145‐PAR), irradiated with 2 Gy daily for 59 fractions over several weeks (DU145‐CF) as previously described  or 10 Gy3.2p = 0.0029 for 4 Gy, p = 0.0015 for 6 Gy, p = 0.0001 for 8 Gy; DU145‐HF: p = 0.0011 for 4 Gy, p = 0.0242 for 6 Gy, p = 0.0083 for 8 Gy; Figure p = 0.01 for 0 Gy and p = 0.02 for 6 Gy, Figure p = 0.0156), and proliferation increased following 6 Gy irradiation  from the 4410 proteins detected across the cell lines . These gene lists were compared to the significantly altered proteins (295 proteins for DU145‐CF cells and 194 for DU145‐HF cells) of both radiation resistant cell lines, and presented as the z‐scores of the logOxygen plays a key role in the response of irradiation‐induced reactive oxygen species, a major problem with radiation therapy is hypoxia . It has Most cancer cells display an increase in aerobic glycolysis and use this metabolic pathway for the generation of ATP as the main source of energy . This in3.52 of 0.3521269 was calculated between the fold change in DU145‐CF cells (as compared to DU145‐PAR) and the DU145‐HF cells (as compared to DU145‐PAR), indicating there is a low correlation observed in the fold changes of radiation resistant cells. There were 72 proteins  in both cell lines, as represented in the plot as circles. The significantly altered proteins have been highlighted . It was confirmed that CD44 expression was increased, and the therapeutic utility of an anti‐CD44 blocking antibody in PCa radio‐sensitisation was demonstrated. The study of radioresistant PCa has been limited by technical challenges in obtaining prostate samples following relapse (invasive procedure and very little tissue obtainable). As such, the creation of isogenic radioresistant PCa cell lines is one avenue to address this clinical problem. Although our research is primarily based on an isogenic DU145 PCa cell line model, we were able to validate increased CD44 expression in a second isogenic PC3 PCa cell line model, suggesting that our findings are not cell‐line specific. Further work investigating the therapeutic role of CD44 in an in vivo model, as well its potential as a predictive biomarker  are needThis work was partially funded through an operating grant from the National Cancer Institute Early Detection Research Network (1U01CA214194‐01) to T.K. and P.C.B. A Prostate Cancer Canada Movember Discovery Grant (D2017‐1811) to T.K. and S.K.L. and a Project Grant from the Canadian Institute of Health Research (PJT‐162384) to T.K. and S.K.L.The authors declare no conflict of interest.Supporting informationClick here for additional data file.Supporting informationClick here for additional data file.Supporting informationClick here for additional data file.Supporting informationClick here for additional data file.Supporting informationClick here for additional data file.Supporting informationClick here for additional data file.


One of the major glyconutrients in EBN is sialic acid, which has a beneficial effect on neurological and intellectual capability in mammals. The aims of this research were to study the effects of sialic acid from EBN on cell viability and to determine its effect on mitochondria membrane potential (MtMP) in Caco-2, SK-N-MC, SH-SY5Y, and PC-12 cell lines. Fourteen samples of raw EBN were collected from four different states in Malaysia. The confluency of the epithelial monolayers measurement of the tight junction for all the cell lines was determined using transepithelial electrical resistance (TEER), and the sialic acid uptake study in cell lines was determined by using ultra-high performance liquid chromatography (UHPLC). The MTT assay was conducted for cell viability study. The MtMP in cell lines was determined using the Mito Probe JC-1 Assay by flow cytometer analysis. We have recorded a statistically significant difference between the uptake of sialic acid from EBN and the standard solution. A higher amount of sialic acid was absorbed by the cells from extract of EBN compared to the standard solution. The amounts of sialic acid uptake in Caco-2, SK-N-MC, SH-SY5Y, and PC-12 cell lines were (0.019 ± 0.001), (0.034 ± 0.006), (0.021 ± 0.002), and (0.025 ± 0.000) µmol/L, respectively. The MTT results indicated that the concentration of sialic acid increased the cell viability and showed no cytotoxicity effects on cell lines when they were exposed to the sialic acid extract and sialic acid standard at all the tested concentrations. The number of active mitochondria was found to be significantly higher in SH-SY5Y cell lines with a 195% increase when treated with sialic acid from EBN. Although many researchers around the globe use SH-SY5Y and SK-N-MC for Alzheimer’s disease (AD) study, based on our finding, SH-SY5Y was found to be the most suitable cell line for AD study by  The mechanism involved here includes conversion of metabolites energy to reduced nicotinamide adenine dinucleotide (NADH) followed by electron transfer from NADH while protons are also pumped across inner membrane to intermembrane space. This process creates transmembrane potential that utilizes ATP synthase for reflow of protons across inner membrane, and finally this energy is the driving factor of adenosine diphosphate (ADP) phosphorylation to ATP . Three fvia oral administration of sialic acid during an initial postnatal period in the rodents  and it is one of the eight glyconutrients which has helped to increase cell tissue repair , promotein vitro cell line. One of the most common cell lines used is the neuroblastoma (SH-SY5Y) cells. These cells were applied as a prototype for Aβ cytotoxicity in AD  until use.Four different types of cell lines were used in this study: the colorectal adenocarcinoma (Caco-2/ATCC cat. no. HTB-37); the neuroepithelioma (SK-N-MC/ATCC cat. no. HTB-10); the neuroblastoma (SH-SY5Y/ATCC cat. no. CRL-2266); and the pheochromocytoma (PC-12/ATCC cat. no. CRL-1721). The entire cell lines were purchased from American Type Culture Collection . Each of the cell lines was seeded and maintained in 25 cml-glutamine . Only PC-12 cell was added with 15% horse serum . All the cells were maintained in the same conditions, at 37°C in an incubator with 5% carbon dioxide, 95% humidity, and air atmosphere. The medium was replaced every 2–3 days. The cells were maintained until they reached 80% confluency.The PC-12 and SH-SY5Y cells were grown in Dulbecco’s Modified Eagle’s Medium  supplemented with 20 and 15% v/v fetal bovine serum (FBS), while SK-N-MC cells were grown in Eagle’s Minimum Essential Medium  supplemented with 20% v/v fatal bovine serum. The entire medium contained 1% v/v nonessential amino acid , 1% antibiotic (penicillin–streptomycin) , and 1% v/v 2  were equilibrated and sterilized according to the manufacturer’s recommendations. Two hundred microliters of culture medium was added to the upper compartment of the cell culture system. The ohmic resistance of a blank (culture insert without cells) was measured in parallel. The blank value was subtracted from the total resistance of the sample, in order to obtain sample resistance. The final unit area resistance (Ω*cm2) was calculated by multiplying the sample resistance by the effective area of the membrane (0.33 cm2 for 24-well millicell insert plates).The cells will reach the maximal levels of differentiation after several days in incubation. To confirm this process, transepithelial resistance values (TEER) can be monitored using EVOM2™ because fully differentiated polarized cells have tight junctions with a TEER of >200 Ω*cm2 . The EVO2 into a 96-well plate. The cells were then incubated overnight in an incubator with 5% carbon dioxide (CO2), 95% air atmosphere, and 95% relative humidity at 37°C. The next day, the medium was changed to medium-free FBS following exposure to (or treatment with) sialic acid at the required concentration , and the cells were incubated for 24 h. MTT reagent at a concentration of 5 mg/ml and a volume of 10 µL was added to each well and incubated for 3–4 h until the purple precipitate was visible. Later, the medium was aspirated from the wells as completely as possible without disturbing the formazan crystals and cells on the plastic surface. Then, 100 µL of dimethyl sulfoxide (DMSO) was added to each well, followed by agitation on a plate shaker for 5 min, and finally, the optical density was read at 570 nm. The number of surviving cells is directly proportional to the level of the formazan product created -2,5-diphenyltetrazolium bromide) assay is commonly used to measure cell viability and proliferation . Adheren created .The absorbance value for the blanks should be 0.00 ± 0.1 optical density (O.D) units. The absorbance range for untreated cells should typically be between 0.75 and 1.25 O.D. units. Selection of a cell number  allows for the measurement of both stimulation and inhibition of cell proliferation. If the absorbance values of the experimental samples are higher than the control, this indicates an increase in cell proliferation. Alternatively, if the absorbance rates of the experimental samples are lower than the control, this indicates a reduction in the rate of cell proliferation or a reduction in overall cell viability.2PO4, 10 mM Mes, 0.5 mM of MgCl2, and 1.0 mM CaCl2 at pH 6.0) and incubated at 37°C for 2 min. Later, 1 ppm (∼3 μmol/L) of sialic acid extract was added to each well and incubated for 15 min at 37°C. Then, the buffer was aspirated, and cells were rinsed with cold buffer for three times before adding 1 ml of 200 mM NaOH to solubilize the cells and they were left overnight at 4°C. After an overnight incubation at 4°C, the cells were subsequently removed; the levels of sialic acid in buffer and cells were determined by using UHPLC. This procedure follows the method of Derakhshandeh and his colleagues with slight modification  was added to the tube and incubated for 5 min. After the incubation, 10 µL of JC-1 stock solution  was added to all tubes and incubated for another 30 min at 37°C. Later, all cells were washed using the warm PBS and centrifuged to obtain the cell pellet. Then, the cells were resuspended by adding 500 µL of PBS to each tube. All tubes were analyzed using a flow cytometer machine (BD FACSAria™) with 488 nm excitation using emission for Alexa Fluor® 488 dye and fluorescence microscopy. The JC-1 dye detection was used with bandpass filters centered around 529 nm (green fluorescence) and bandpass filters centered around 595 nm (orange fluorescence). The logarithmic signal amplification was used with the typical green–orange electronic signal compensation near 4% and orange–green signal compensation around 10% . The kit contains 30 µg powdered dye, DMSO, carbonyl cyanide 3-chlorophenylhhydrazone (CCCP), and 10× phosphate-buffered saline (PBS). The cell lines were fixed with 150 µL of sialic acid extract and standards for 24 h prior to staining the cells for flow cytometer measurement. In brief, all reagents were equilibrated at room temperature before beginning the experiment. Two hundred micromolar of JC-1 powder stock solution was prepared by dissolving the vial with 230 µL of the DMSO. Then, 1 × 10ound 10% . The depp < 0.05 was considered statistically significant.All data were analyzed using the SPSS software version 16 . The viability of the cell lines induced with sialic acid was analyzed using the one-way analysis of variance (ANOVA) with post hoc Tukey’s test to show significant difference between groups. 2 and 292.71 ± 80.61 Ω*cm2, respectively, while SH-SY5Y and PC-12 cell lines reached their 50% confluency at day 15 with 305.91 ± 80.61 Ω*cm2 and 280 ± 127.98 Ω*cm2, respectively. The Pearson Rank correlation analysis was performed to determine the relationship between the days of culture and TEER reading. Based on results in p < 0.01).The determination of cell cultures confluency stage for entire cell lines using EVOM2™ was shown in p < 0.05).The percentage of cell viability in epithelial and neuroblastoma cell lines was significantly higher when they were induced with sialic acid extract compared to sialic acid standard. Above the concentration of 60 μg/ml, all cell lines showed negative effect of sialic acid on cell viability.p < 0.05) and between the extraction and standard extraction (p < 0.05), respectively. However, only Caco-2 cell lines showed a significant difference between the control and standard (p < 0.05). For basal solution, the mean absolute difference (MD) of sialic acid uptake was significant between the control and the extraction in all cell lines. The MD of sialic acid uptake was also significant between the extraction and the standard. In contrast, only SH-SY5Y cell line has recorded the differences in sialic acid uptake between the control and the standard.p < 0.05). The Tukey post hoc test revealed that the number of active mitochondria in SH-SY5Y is significantly higher when induced with sialic acid compared with control (p = 0.000). However, even without treatment with sialic acid, all other cell lines have a higher number of active mitochondria at the start of the experiment.2, and as such the differentiation process is confirmed by careful monitoring of the cell’s polarization process . In addition, it should also be highlighted that translating TEER into a functional estimate of tightness is tough. This is due to the fact that composition of the tight junction complexes and the size of the compound of interest are the underlying factors that influence the endothelial monolayer tightness . In our 00 Ω*cm2 .in vivo studies, in vitro cytotoxicity and cell viability assays were shown to be more advantageous in terms of speed, lower cost, and automation potential. Due to these reasons, studies conducted with human cells are deemed more appropriate than in vivo research , and HCl. None of the sialic acid on the market is obtained from bird’s nests. Since bird’s nests naturally contain higher bioactive compounds including sialic acid, it may be one of the reasons why sialic acid obtained from bird’s nest has a slightly higher absorption rate than the commercially available sialic acid. In addition, the increase in cell viability with higher concentration might be influenced by the mitogenic properties in sialic acid extract from EBN that promoted the cell growth as manifested by previous studies which showed the enhanced cell division in rabbit corneal keratocytes using EBN . AlthougIn general, sialic acid rarely exists free in nature and is usually available as component of oligosaccharide chains of mucins, glycoproteins, and glycolipids. In terms of its position, sialic acid usually occupies terminal, nonreducing positions that are highly exposed and functionally essential. These commonly refer to nonreducing positions of oligosaccharide chains of complex carbohydrates on both outer and inner membrane surfaces, mainly to galactose, N-acetylgalactosamine, and other sialic acid moieties. The highest concentration of sialic acid is present in mammalian central nervous system, where majority of it is found in gangliosides (65%), followed by glycoproteins (32%), while the remaining exists as free sialic acid .To date, there are limited findings on digestion and mechanisms involved among sialic acid compounds. It was reported that rat intestinal walls are highly permeable to free sialic acids. In addition, it was highlighted that sialidases of bacterial origin could possibly cleave sialic acid residues from milk oligosaccharides in colon; however it was not evident if sialic acid is capable of absorption across colonic mucosa . Hence, Based on literature, it was found that the normal range of total sialic acid (TSA) level found within serum or plasma falls in the range of 1.58–2.22 mmol/L. From this, only about 0.5–3.0 μmol/L corresponds to free form of SA . Thus, wEukaryotic cells contain several types of organelles, which may include nucleus, mitochondria, chloroplasts, the endoplasmic reticulum, the Golgi apparatus, and lysosomes. Each of these organelles performs a specific function critical to the cell's survival. Since cell lines were also obtained from the eukaryotic cells, they also have the same organelles. Mitochondria study gained attention in 1970s, when research was focused on studying energy conservation mechanism as well as ATP synthesis. At the same time, chemiosmotic theory of oxidative phosphorylation was also established, which led to conferment of Nobel Prize in Chemistry awarded to Peter Mitchell in year 1978 . Any parIn this study, the JC-1 was used because it is more specific for measuring changes in mitochondria membrane potential (MtMP) . It is ain vitro, SH-SY5Y cell line is more pronounced if the study is focusing on mitochondrial dysfunction. Apart from its suitability for AD, it can also be applicable for other studies focused on mitochondrial dysfunction in other diseases.Previous study showed that the mitochondrial functionality can be restored by natural product such as herbal medicine which helps in preserving the dopaminergic neurons in Parkinson disease , and in in vitro technique. The increase observed in mitochondrial membrane potential in entire cell lines when subjected to sialic acid treatment indicated presence of healthy cells. This is vital to study on MtMP measurement for its effect on mitochondrial dysfunction. The sialic acid uptake also was noticed to occur with varying degrees in cell lines. Based on our findings among all the tested cells, SH-SY5Y was found to be the most suitable cell line especially for studies that will be focused on the expression of active mitochondria.Our finding has indicated that MtMP measurement could be used to study mitochondrial dysfunction by