SAMMSON lncRNA in uveal melanoma (UM), the most common primary intraocular malignancy in adults. Currently, there are no effective treatments for UM patients with metastatic disease, resulting in a median survival time of 6–12 months. We aimed to investigate the therapeutic potential of SAMMSON inhibition in UM. Antisense oligonucleotide (ASO)-mediated SAMMSON inhibition impaired the growth and viability of a genetically diverse panel of uveal melanoma cell lines. These effects were accompanied by an induction of apoptosis and were recapitulated in two uveal melanoma patient derived xenograft (PDX) models through subcutaneous ASO delivery. SAMMSON pulldown revealed several candidate interaction partners, including various proteins involved in mitochondrial translation. Consequently, inhibition of SAMMSON impaired global, mitochondrial and cytosolic protein translation levels and mitochondrial function in uveal melanoma cells. The present study demonstrates that SAMMSON expression is essential for uveal melanoma cell survival. ASO-mediated silencing of SAMMSON may provide an effective treatment strategy to treat primary and metastatic uveal melanoma patients.Long non-coding RNAs (lncRNAs) can exhibit cell-type and cancer-type specific expression profiles, making them highly attractive as therapeutic targets. Pan-cancer RNA sequencing data revealed broad expression of the  GNAQ or GNA11 (90% of UM tumors). These mutations result in the activation of the pathways downstream of Gαq and Gα11 such as the Ras Homolog Family Member/Ras-related C3 botulinum toxin/Yes Associated Protein (Rho/Rac/YAP) pathway, Phosphatidylinositol -bisphosphate 3-kinase (PI3K)/AKT pathway and Phospholipase C (PLC) which subsequently activates the Mitogen-Activated Protein Kinase (MAPK) pathway (RAF/MEK/ERK) [GNAQ and GNA11 mutations occur in most UM, they are not associated with metastasis [BAP1) have been frequently detected in UM tumors and are associated with the development of metastasis [BAP1, located on 3p21.1, is frequently mutated on the remaining allele present in monosomy 3 tumors, which might explain the poor prognosis of monosomy 3 tumors [Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, with an incidence of 5−7.4 cases per million annually –3. Curretastasis . In addi3 tumors . Loss of3 tumors SAMMSON) was discovered on chromosome 3p13 as a lineage survival oncogene in skin melanoma [SAMMSON with proteins involved in ribosomal RNA (rRNA) maturation and protein synthesis such as p32 (C1QBP), CARF and XRN2 and sequestration of CARF in the cytoplasm result in an elevated cytosolic and mitochondrial rRNA processing and protein synthesis [SAMMSON is consistently expressed in UM and conjunctival melanoma (CM) cells, the latter of which are phenotypically and genetically more related to skin melanoma. SAMMSON silencing, by means of locked nucleid acid (LNA) antisense oligonucleotides (ASOs), revealed an essential role for SAMMSON in UM survival in vitro and in vivo and provides perspectives for RNA-targeted therapy.Long non-coding RNAs are an emerging class of regulatory RNA molecules that interact with proteins, DNA, and other RNA molecules. Compared to protein-coding mRNAs, lncRNAs show a more tissue restricted expression profile, making them attractive targets for therapy. LncRNAs are regulating a variety of cellular functions such as transcription, splicing, mRNA stability and translation, and do so via different mechanisms. Recently, Survival Associated Mitochondrial Melanoma Specific Oncogenic Non-coding RNA  followed by uveal melanoma (SAMMSON expression in >80% of tumor samples) . While SAMMSON expression in uveal melanoma tumors is independent from patient survival, tumor stage, primary tumor localization site  and metastatic stage of the patient, SAMMSON expression is elevated in metastatic tumors compared to matched primary tumors  and metastatic tumors (OMM2.3 and OMM1)  cell lines CRMM1 and CRMM2 that are genetically and phenotypically more related to skin melanoma [SAMMSON is invariably co-amplified with Melanocyte Inducing Transcription Factor (MITF) on chromosome 3, uveal melanoma cells are characterized by frequent (50−60%) loss of an entire copy of chromosome 3 (ref. [(ROBO1) , are significantly downregulated in monosomy 3 UM tumors , 81% of those genes are significantly downregulated (n = 472/581)), SAMMSON expression is independent of chromosome 3 copy number . This suggests a compensation mechanism by which UM tumors cells maintain high SAMMSON levels in the presence of chromosome 3 loss.Pan cancer RNA sequencing data from more than 10 000 tumor samples representing 32 cancer types  showed the highest and most consistent M1) Fig. , as wellM1) Fig. . SAMMSONquent 50−0% loss o 3 (ref. ) . Both ASOs (ASO 3 i.e. GapmeR3 and ASO 11 i.e. GapmeR11) have previously been validated by Leucci et al. [SAMMSON negative cell lines. Transfection of 100 nM of both SAMMSON inhibiting ASOs significantly decreased SAMMSON expression in four UM  and two CM cell lines (CRMM1 and CRMM2) . SAMMSON knock-down resulted in a strong and significant decrease in cell viability (based on ATP measurements) in multiple UM and two CM cell lines . In line with earlier observations in skin melanoma cells [To evaluate the importance of i et al.  for its ma cells , these eSAMMSON knockdown depends on ASO dosing, we performed a dose response experiment with both ASOs in the UM cell line OMM1 and CM cell line CRMM1. We observed a dose dependent decrease in SAMMSON expression for both ASOs . We also observed a dose dependent reduction in cell growth and proliferation in UM cell lines OMM1, 92.1 and CM cell line CRMM1, measured via real-time cell imaging . These effects were accompanied by a dose dependent induction in apoptosis, measured by annexin V, in both UM cell lines .To evaluate whether SAMMSON expression in 92.1 and OMM1 . Consequently, SAMMSON knockdown was associated with a significant decrease in cell viability in 92.1 and OMM1 . Taken together, these results reveal an important role for SAMMSON in maintaining UM and CM cell survival in vitro. Due to the genetic similarity between CM and SKCM, where SAMMSON has been extensively studied [SAMMSON in UM.To verify that the observed effects were not related to the lipid-based delivery of the ASOs, we evaluated an alternative non-lipid-based delivery method using the TransIT-X2 transfection reagent. Transfection of 100 nM of ASO 3 significantly decreased  studied , 14, thiSAMMSON contributes to UM tumor cell survival, we studied SAMMSON interaction partners in UM cell lines. First, we evaluated SAMMSON binding to p32 and XRN2, 2 interaction partners previously identified in skin melanoma [SAMMSON RNA, respectively, indicating that SAMMSON binding to these factors is conserved in uveal melanoma cells . We detected 83 and 84 proteins that were significantly enriched upon SAMMSON pull down in OMM1 and OMM2.3, respectively, of which 57 were found in both cell lines , in maintaining mitochondrial function was assessed by measuring the oxygen consumption rate (OCR) as a proxy for mitochondrial respiration. Differences in OCR after injections of oligomycin, fluoro-carbonyl cyanide phenylhydrazone (FCCP) and rotenone/antimycin A were measured [p < 0.001 for all tested MRP siPOOLs in 92.1 and OMM1, unpaired t-test) resulted in a significant decrease in mitochondrial spare respiratory capacity (SRC) of 49% in 92.1 and 32% in OMM1  and p = 0.0063 (OMM1), unpaired t-test). Together with validated p32 and XRN2 interactions, these results suggest a role for SAMMSON in regulating translation and mitochondrial function in uveal melanoma. To further investigate the effect of SAMMSON knockdown on translation, we quantified global, mitochondrial and cytosolic translation levels using a puromycin incorporation assay  and p = 0.0077 (92.1), two-way ANOVA). Fractionation revealed a reduction of both mitochondrial and cytosolic translation rates by 38% and 44%, respectively in OMM1  and p < 0.0001 (cyto), one-way ANOVA).To investigate the mechanism by which melanoma , 14, usills Fig. . We thennes Fig.  that areRNA Fig. . The impmeasured . Knockdo (SUnSET ). SAMMSOSAMMSON for proper mitochondrial function by measuring the OCR. Upon SAMMSON knockdown, we observed a significant decrease in mitochondrial spare respiratory capacity (SRC) of 82%, 42% and 72% in 92.1, OMM1 and OMM2.3, respectively . This was further verified by a 5,5′,6,6′-tetraethylbenzimidazolyl-carbocyanine iodide (JC-1) fluorescence staining, where the electric membrane potential (ΔΨ) is quantified based on the relative red over green fluorescence [SAMMSON knockdown decreases ΔΨ with 39%, 14% and 31% in 92.1, OMM1 and OMM2.3, respectively, indicating oxidative phosphorylation (OXPHOS) impairment  and p < 0.0001 (OMM1 and OMM2.3), unpaired t-test). These data suggest that the mechanism by which SAMMSON affects translation is conserved between UM and SKCM, resulting in an impairment of mitochondrial function. In addition, knockdown of MRPL13, together with Mitochondrial Ribosomal Protein L4 and L37 (MRPL4 and MRPL37), three SAMMSON interaction partners identified in both OMM1 and OMM2.3, resulted in a significant decrease in spare respiratory capacity of 49% in 92.1 and 32% in OMM1, demonstrating their importance in mitochondrial function.We then explored the importance of rescence . SAMMSONSAMMSON expressing UM cell lines 92.1 and OMM1 and non-SAMMSON expressing cell lines CT5.3hTERT and HEK293T were treated with tigecycline, a bacteriostatic antibiotic of the tetracyclines family [p < 0.0001 (confluence) and p < 0.0001 for all concentrations, except 3.125 µM in 92.1 and p≤0.01 for the two highest concentrations in OMM1 (apoptosis), one-way ANOVA), verifying that UM cells strongly depend on mitochondrial translation. While the growth of non-SAMMSON expressing cells HEK293T and CT5.3hTERT is also affected by tigecycline treatment  and p ≤ 0.05 for all concentrations, except 3.125 µM (CT5.3hTERT), two-way ANOVA), tigecycline was markedly less effective in CT5.3hTERT cells .To assess the importance of mitochondrial translation in UM cell survival, s family –23. In bSAMMSON on the UM transcriptome, we performed shallow RNA sequencing of UM cell lines 92.1 and OMM1 treated with either NTC ASO or ASO 3. Differential gene expression analysis revealed 378 up- and 255 downregulated genes . Gene set enrichment analysis (GSEA) revealed a significant upregulation of gene sets associated with apoptosis and p53 response upon SAMMSON silencing  and FDR q = 0 (p53) and Supplemental Table SAMMSON inhibition also resulted in a transcriptional activation of genes involved in translation .To assess the impact of SAMMSON knockdown were evaluated in three independent experiments using two UM patient derived xenografts (PDX) models (MP46 (GNAQQ209L) and MEL077 (derived from a patient progressing on the immune checkpoint inhibitor pembrolizumab)) showing high SAMMSON expression levels . Tumor growth, monitored for 22 days, was significantly delayed  and tumor weight significantly reduced in MEL077 PDX mice treated with ASO 3 compared to NTC ASO . A similar trend could be observed in an independent repeat in PDX model MEL077 , further investigation of the mitotic index showed a significant reduction in the number of mitotic cells in the ASO 3 treated tumor samples . The discrepancy between Ki67 and MI results might be explained by the selected regions and cell cycle phase coverage of both methods, which has also been described in other tumors [SAMMSON knockdown induces apoptosis in vivo  analysis with anti-Ki67 of the MEL077 model revealed highly aggressive tumors, with over 80% of proliferating cells in all tumor samples Fig. . While tr tumors –26. In aivo Fig. . Moreovep < 0.0001, Fisher exact test) between pathways induced in the PDX model and pathways induced in the UM cell lines upon SAMMSON knockdown. Gene sets related to apoptosis and p53 response were significantly upregulated , p = 0.0357 (LINE-1), p = 0.0202 (SVA), unpaired t-test).Although no macro-metastatic lesions were observed in both PDX models, we investigated the presence of tumoral DNA in blood and various murine tissues resected from both PDX models . To thisSAMMSON was discovered as a melanoma specific lncRNA [SAMMSON is expressed in more than 80% of uveal melanoma tumors at levels that are substantially higher compared to non-melanoma tumors. Furthermore, SAMMSON is significantly higher expressed in metastatic UM tumors compared to matching primary tumors. In addition, SAMMSON expression could be observed in conjunctival melanoma (CM) cells that are genetically and phenotypically more related to skin melanoma. While 50% of uveal melanoma tumors are characterized by loss of an entire copy of chromosome 3, SAMMSON expression levels are not reduced in monosomy 3 tumors whereas the majority of genes on chromosome 3 do show a clear gene-dosage effect. These observations suggest the existence of a compensation mechanism that requires further investigation. Potentially, monosomy 3 tumors upregulate one or multiple transcription factors that drive SAMMSON expression from the remaining allele.Long non-coding RNAs are crucial players in many cellular and biological processes including regulation of gene expression, cell growth, differentiation and development. Aberrant expression of lncRNAs is observed in virtually all tumor types and some lncRNAs appear to be cell type- and tissue-specific , 29. Thec lncRNA , but sevc lncRNA –34. In tSAMMSON knockdown in various in vitro and in vivo UM models induces a potent anti-tumor response including growth reduction, induction of apoptosis, and reduced levels of tumor-derived DNA in lung tissue. Whether this DNA is cell-free or derived from tumor cells invading these tissues remains to be investigated. The fact that we did not observe tumor DNA in blood may be indicative of the latter. Additional studies in uveal melanoma xenograft models that metastasize [SAMMSON knockdown on metastatic disease. Furthermore, the in vivo kinetics of SAMMSON expression upon ASO treatment should be explored in more detail. While we observed a trend of SAMMSON knockdown in vivo 3 weeks after start of treatment, the difference was not significant. Differences in baseline SAMMSON expression levels and (time-dependent) variability in knockdown efficiency between mice may explain these observations. SAMMSON expression quantification at earlier treatment time points could provide more insights in knockdown dynamics. Our in vitro data, based on multiple cell lines with a different genetic background, and in vivo data, based on two genetically different UM PDX models, suggest that the genetic background of the tumor does not influence the response to SAMMSON inhibition. Furthermore, cell lines originating from primary and metastatic UM tumors showed similar responses to SAMMSON knockdown, including viability reduction and induction of apoptosis. Since SAMMSON expression levels are elevated in metastatic tumors, these results highlight the broad relevance of SAMMSON inhibition as a potential therapeutic strategy, which may be primarily of interest for metastatic UM patients, where the therapeutic need is high. On-target toxicity of the ASO treatment could not be investigated due to the absence of a SAMMSON homologue in the mouse genome, which is a limitation of the current study. On the other hand, the fact that SAMMSON is expressed in most uveal melanoma tumors (irrespective of genetic background or tumor stage) and absent in differentiated human tissues [SAMMSON could selectively kill tumor cells without on-target toxicity in other tissues.The lack of treatments for UM patients with metastatic dissemination that occurs in 50% of the patients and is accompanied by extremely poor survival  demonstrastasize –40 shoul tissues  suggestsSAMMSON interaction with proteins involved in mitochondrial and cytosolic translation, such as p32, XRN2 and CARF [SAMMSON in regulating mitochondrial and cytosolic translation. Additional mechanistic studies are required to further elucidate the role of these novel candidate interactors. Of note, XRN2 and p32 were not identified by mass-spectrometry analysis, suggesting that we may have missed other interaction partners. In support of these interactions and the established role of SAMMSON in SKCM, SAMMSON inhibition effectively impairs mitochondrial and cytosolic translation rates. Since all proteins required for mitochondrial translation, including the mitoribosomal proteins and the mitochondrial translation factors, are translated by cytosolic ribosomes [SAMMSON inhibition indeed impairs mitochondrial function, as shown in three UM cell lines. Moreover, knockdown of the identified SAMMSON interaction partners MRPL4, MRPL13 and MRPL37 also impaired mitochondrial respiration, demonstrating their involvement in mitochondrial function. This results in mitochondrial precursor overaccumulation stress (mPOS), characterized by the toxic accumulation and aggregation of unimported mitochondrial proteins in the cytosol [SAMMSON inhibition affects global translation, transcriptome analysis in both UM cell lines and PDX models revealed a transcriptional upregulation of various components involved in the translational machinery. These observations have also been observed in yeast when using translation inhibitors such as cycloheximide (CHX) [Earlier studies in SKCM demonstrated and CARF . In UM, ibosomes , furtheribosomes . SAMMSON cytosol . Cells nde (CHX)  and uprede (CHX) . Ribosomde (CHX) . TranscrSAMMSON impairs cytosolic and mitochondrial translation, which consequently affects mitochondrial function and ultimately results in decreased cell viability and induction of apoptosis, both in vitro and in vivo. Together, our findings suggest that SAMMSON is an attractive therapeutic target for UM and that SAMMSON can be targeted in vivo using ASO technology.In conclusion, our work shows that inhibition of SAMMSON negative cell lines HEK293T and CT5.3hTERT were obtained from ATCC  and the Department of Radiation oncology and experimental cancer research, Ghent University, Belgium, respectively. The cell lines 92.1, OMM1, OMM2.3 and MEL270 were grown in Dulbecco’s modified Eagle’s medium /F12––Roswell Park Memorial Institute  1640 (1:1) medium. SK-MEL28 was grown in DMEM/F-12 GlutaMAX medium (Gibco), CT5.3hTERT in DMEM medium, CRMM1 and CRMM2 in Ham’s F-12K (Kaighn’s) medium (Gibco), MM28, MP65, MP46 and MP38 in Iscove’s Modified Dulbecco’s Medium (IMDM) and HEK293T and MEL077 in RPMI 1640 medium. Media of MM28, MP65, MP46 and MP38 were supplemented with 20% fetal bovine serum (FBS) and all other media with 10% FBS, 2 mM L-glutamine (Gibco) and 100 IU/ml penicillin/streptomycin (Gibco) and all cell lines were incubated in a humidified atmosphere containing 5% CO2 at 37 °C. For executing experiments, cells were grown in L-glutamine and penicillin/streptomycin free media. Short tandem repeat (STR) genotyping was used to validate cell line authenticity and absence of mycoplasma was verified on a monthly basis for all cell lines in culture.For all human cell lines, an ethical approval was obtained from the Ghent University commission for medical ethics. The human uveal melanoma cell lines 92.1 , OMM1 1, OMM2.3,Supplementary legendsSupplementary methodsSAMMSON expression is independent from patient survival, tumor stage, tumor localization site and metastatic state of the UM patient.Supplemental Fig 1. Supplemental Fig 2. Expressed genes in monosomy 3 UM tumors compared to disomy 3 UM tumors.SAMMSON knockdown affects cell growth in CM cell line CRMM1.Supplemental Fig 3. Supplemental Fig 4. Non-lipid-based delivery of ASOs in UM cells.SAMMSON interaction partners.Supplemental Fig 5. Identification of Supplemental Fig 6. Uncropped images of WB-SUnSET analysis.SAMMSON knockdown in UM cell line OMM2.3.Supplemental Fig 7. Impairment of mitochondrial function upon SAMMSON inhibition in UM cells.Supplemental Fig 8. Phenotypic results using tigecycline are comparable to SAMMSON inhibition slows down tumor growth in vivo.Supplemental Fig 9. Supplemental Table 1.Supplemental Table 2.


The mutations observed in iPSC lines affect a higher proportion of mtDNA molecules, favouring non-synonymous protein-coding and tRNA variants, including known disease-causing mutations. Analysing 11,538 single cells shows stable heteroplasmy in sub-clones derived from the original donor during differentiation, with mtDNA variants influencing the expression of key genes involved in mitochondrial metabolism and epidermal cell differentiation. Thus, the dynamic mtDNA landscape contributes to the heterogeneity of human iPSCs and should be considered when using reprogrammed cells experimentally or as a therapy.Individual induced pluripotent stem cells (iPSCs) show considerable phenotypic heterogeneity, but the reasons for this are not fully understood. Comprehensively analysing the mitochondrial genome (mtDNA) in 146 iPSC and fibroblast lines from 151 donors, we show that most age-related fibroblast mtDNA mutations are lost during reprogramming. However, iPSC-specific mutations are seen in 76.6% (108/141) of iPSC lines at a mutation rate of 8.62 × 10 Here the authors describe high depth mitochondrial DNA (mtDNA) sequence analysis of 146 human induced pluripotent stem cell (hiPSC) lines as well as single cell RNA-seq (scRNAseq) of hiPSCs undergoing differentiation from 125 donors; reporting mtDNA diversity and some variants favoured after reprogramming. Even iPSCs derived from the same donor cell line show phenotypic differences that are poorly understood. However, independently differentiating the same cell line several times reproduces similar cell phenotypes4, implicating cell-intrinsic factors driving the heterogeneity, rather than the external environment.There is a growing interest in the use of induced pluripotent stem cells (iPSCs) to model human disease mechanisms and in cell-based therapies, but iPSCs derived from the same tissue show considerable phenotypic heterogeneity that can affect their capacity to differentiate into organ-specific lineages. Bulk and single-cell analysis has shown common genetic variation contributes to differences in gene expression profiles between donors5. Most human cells contain >1000 copies of mtDNA, and deep sequencing has shown that most humans harbour a mixed population of mitochondrial genomes (heteroplasmy)6. mtDNA mutations can arise de novo during life or be inherited down the maternal line, and specific mtDNA variants compromise oxidative metabolism and the synthesis of adenosine triphosphate (ATP). High percentage levels of specific mtDNA mutations cause severe multi-system metabolic diseases that affect ~1 in 8000 humans, but they also contribute to the pathology of common age-related disorders, including Parkinson’s disease5. mtDNA mutations influence canonical cell signalling pathways, reactive oxygen species production, amino acid metabolism and cell growth through indirect effects on nuclear gene transcription10.Although there has been considerable effort describing nuclear genetic and epigenetic differences that arise during reprogramming, the 16.5 Kb mitochondrial genome (mtDNA) remains largely uncharacterised. mtDNA codes for 13 essential peptide components of the oxidative phosphorylation systems, and 2 rRNAs required for intra-mitochondrial protein synthesis11, including mitochondrial respiration in derived cardiomyocytes12. However, with <25 donors studied to date, the landscape of mtDNA variation in human iPSCs poorly understood13.mtDNA mutations also accumulate in somatic tissues throughout life, raising the possibility that mtDNA variation contributes to the molecular heterogeneity of human iPSCs. In keeping with this, several small studies have described mtDNA variants present in iPSC lines that were not detected in parental fibroblast lines, with some having detrimental effects on metabolismIn this work, we analysed high-depth mtDNA sequences in 146 iPSC lines as part of the Human-Induced Pluripotent Stem Cells Initiative (HipSci). We show extreme mtDNA diversity, with selection favouring some variants during reprogramming. Single-cell analysis shows that mtDNA variants define sub-clones that modulate gene expression within iPSCs and subsequent differentiated cell lineages.http://www.hipsci.org/). After sample quality control (QC), 141 iPSC lines and 146 fibroblast lines were included in the analysis (see 'Methods'), with one iPSC line from 25 donors and 2 iPSC lines from 58 donors included  . We filtered out calls likely to be due to systematic errors, including nuclear-encoded mitochondrial DNA sequences (NUMTs) and length variants flanking polynucleotide tracts (see 'Methods'). We then determined the frequency distribution of major population-specific mtDNA haplogroups (macro-haplogroups) , confirming that the vast majority of donors were representative of the United Kingdom population. In total, 143 of the 146 donors (98%) belonged to one of the macro-haplogroups H, I, J, K, T, U, V, or W. As a further QC step, we only included heteroplasmic variants with bulk heteroplasmy allele fractions (HFs) between 2 and 98% (see 'Methods').Initially, we analysed high-depth mtDNA sequences derived from whole-genome sequence (WGS) data on 146 iPSC and 151 fibroblast lines obtained from 151 different donors aged 27–77 years recalled through the NIHR BioResource as part of the Human Induced Pluripotent Stem Cells Initiative   data generated from 102 iPSCs (see 'Methods'). As expected, the depth of RNA-seq was less uniform than WGS   , but there was a neither detectable correlation with age seen for the other genomic regions nor across the whole mtDNA genome   of 146 fibroblast lines carried at least one heteroplasmy on bulk analysis (HF > = 2%). The mean number of heteroplasmic mtDNA variants per fibroblast line was 8 (s.d. = 4), with a greater mean number in fibroblasts from older individuals (l) Figs. , where tel) Fig. . Overallel) Fig. .Fig. 2Ch16. The previously observed m.2623G and m.13369C variants13 were also seen in 12.3%, despite being exceptionally rare in blood from the same population6. These mutations occurred on different mtDNA haplogroup backgrounds, indicating recurrent mutation events.Eight heteroplasmic variants were present in >5% of the fibroblast lines Figs. . m.414G Each mutation initially affects a single mtDNA molecule, but to be detected by our WGS pipeline, at least 2% of molecules must be affected in the bulk DNA sample . Thus, the mutations we observed are a consequence of the original mutation event followed by subsequent copying  and propagation of the variant into many daughter cells. Although it is conceivable that some high-level heteroplasmic variants could reflect recurrent mutations in the same individual, this seems unlikely because the majority (64.5%) of heteroplasmic variants were seen in only one fibroblast line. Clonal expansion must therefore be playing a key role in generating the higher heteroplasmy values seen in fibroblasts from older subjects, and explain the correlation between the HF and age for m.414G Fig. .Next, we carried out a similar analysis of high-depth mtDNA sequences in 141 iPSCs derived from 83 of the original fibroblast lines Fig. . In contP = 6.38 × 10−20, paired Wilcoxon test)  . There was no detectable difference in either the mean HF, the mean number of heteroplasmic variants, or the mean number of iPSC-specific mutations in iPSC lines analysed at different passages   Fig. . In totast) Fig. . By contst) Fig. , making −3/base pair, D-loop vs. expected P = 2.03 × 10−16, 95% CI 1.84–2.63, Fisher’s exact test). The D-loop had a higher mutation frequency than all of the other mtDNA regions  . By contrast, in the iPSCs, the mutation frequency in the D-loop was lower than the rest of the mtDNA     Fig. . Heteropst) Fig. , and ovest) Fig. . In keepst) Fig. . Taken tst) Fig.  with thest) Fig. .Fig. 3Th−5 per base pair per genome per reprogramming , which is a conservative estimate based on mutations with a HF > =2% in the iPSCs and a HF > 0 in their matched fibroblast cell lines. Finally, to determine whether our initial detection threshold had a major impact on the measured iPSC-specific mutation rate, we decreased the detection threshold in iPSCs by 0.5% increments. This showed the anticipated increased mutation rate, but only by approximately fourfold, indicating that our approach is reliable to within one order of magnitude  of the unique heteroplasmic variants present in each donor fibroblast were detected in both iPSC lines (shared both); 11.0% (49/436) were present in one of the two iPSC lines (shared one/lost one); and 80.6% (351/436) were not detected in either iPSC line (lost from both). By contrast, 78.0% (184/236) of the unique heteroplasmic variants in each donor iPSC line were only present in one of the two iPSCs (iPSC-specific one) and 6.8% (16/236) in both iPSCs (iPSC-specific both) Fig. . Thus, t18. A similar genetic bottleneck effect could explain why variants not detectable in fibroblasts become detectable in iPSCs. However, given the inherent inefficiency of cellular reprogramming, an alternative explanation is that there is a ‘cellular bottleneck’, where only a small proportion of the fibroblasts contribute to the iPSC cell population. In keeping with this, the average amount of mtDNA per cell was greater in iPSCs than in the matched fibroblast lines   Fig. , althougP = 4.75 × 10−14, shared vs iPSC-specific in iPSCs P < 2.2 × 10−16, Wilcoxon rank-sum test) . Note that this correction reflects the fold change in HF, but the size of this change is influenced by the initial HF.As expected, the HF for shared heteroplasmic variants found in both the fibroblasts and iPSCs lines was greater than HF seen for lost and iPSC-specific heteroplasmic variants (shared vs lost in fibroblasts st) Fig. , in keepst) Fig.    , and the HSs for non-synonymous and tRNA variants were more likely to increase than other regions   Fig. . Of 161 P = 1.15 × 10−3, coefficient estimate = 0.78, s.d. = 0.24, tRNA P = 4.93 × 10−3, coefficient estimate = 0.89, s.d. = 0.32, logistic regression model)  of a variant as a function of the age of the individual when the skin biopsy was taken, the HF in the original fibroblast line, the mitochondrial genome location, and the macro-haplogroup of the donor (see 'Methods'). Variants in the D-loop region were less likely to increase than variants in non-synonymous and tRNA regions (non-synonymous el) Fig. . Thus, m6. Thus, tissue or context-specific factors influence the segregation of mtDNA heteroplasmy in humans, implying a more complex mechanism than simply the effect of the variants on oxidative phosphorylation and ATP synthesis. This is in keeping with work in mice, where pluripotent cells modulate heteroplasmy in a different way to mouse embryonic fibroblasts (MEFs), and reprogramming MEFs to iPSCs recapitulates the pluripotent cell behaviour19. For the D-loop, one potential explanation involves the selection against variants that compromise mtDNA replication, given the increased mtDNA content seen in iPSC lines. To gain insight into the selection of the D-loop region, we further defined the heteroplasmic variants in different D-loop regions  were more likely to be lost than expected when compared to the average across D-loop region , particularly in L-strand promoter (MT-LSP)  which generates the RNA–primer required for DNA synthesis and includes the age-related mutation m.414G. By contrast, iPSC-specific mutations were enriched in MT-OHR , particularly in MT-CSB1 . Although it is difficult to explain all of these patterns at present, their distribution is likely to reflect the function of D-loop structures.The preferential loss of D-loop variants and propagation of non-synonymous and tRNA variants are the complete opposite of what is seen during germline transmissionons Fig. . VariantOf the 12 known pathogenic mtDNA mutations present in 9 donor fibroblast lines Figs.  and 3d, −6 per base pair per genome (95% CI 7.88 × 10−7–5.31 × 10−6 per base per genome) . Accounting for differences in the definition of a pathogenic mutation, the iPSC-specific mutation rate during iPSC reprogramming was ~5.9-fold greater than estimated for the germ line6.Five iPSC-specific pathogenic mutations were not seen in the fibroblast lines from four donors, giving a iPSC-specific pathogenic mutation rate of 2.14 × 1021, next we determined the mutational signature of the heteroplasmic mtDNA variants. As expected, in fibroblasts, C > T and T > C substitutions were the most common. We did not observe the L-strand T > C mutation bias seen in whole blood and 18 cancer types20 , with more C > A, C > G, T > A variants  and less T > C variants  than donor fibroblasts , and C > G substitutions were strongly enriched on the H-strand  , with the shared and lost variants having the similar signature profile   Fig. . The L-sst) Fig. 20. iPSC-es) Fig. . Thus, c 24 Fig. , althougn = 9661), mesoderm , definitive endoderm  and undefined cells (n = 6278)3 (see 'Methods'). We studied 11,538 single cells  from 60 donors. The bulk WGS data of 60 progenitor fibroblast cells were also available and included in our analysis above. In total, 6288 of 11,538 single cells from 36 iPSC lines also had the bulk WGS data from the same iPSC lines. In all, 5607 cells from 30 iPSC lines had matched bulk RNA-sequencing data  from 125 donors at three canonical stages of endoderm differentiation: iPSCs    (see 'Methods'). Overall, 1647 pseudo-bulk variants (HF > 0 at the pseudo-bulk level) were observed across the whole dataset Fig. . There wNext, we studied mtDNA heteroplasmy levels during differentiation at the single-cell level. We only included the cell lines where at least 20 cells were available at each stage of development to minimise sampling bias. We estimated the proportion of cells that carried the same variant within each cell line, the mean HF per variant within each cell line, and pseudo-bulk HF at all three stages Fig. . In all,P < 2.2 × 10−16R2 = 0.90, iPSC vs defendo P < 2.2 × 10−16R2 = 0.92, iPSC vs mesendo P < 2.2 × 10−16R2 = 0.88, Pearson’s correlation test)  Fig. , with a 25 based solely on the mtDNA heteroplasmic variant data  ata Fig. , revealiata Fig.  (see 'Mest) Fig. . The numst) Fig. , with sist) Fig. . This inst) Fig. , which w3. However, analysing the mean HF of non-synonymous mtDNA variants uncovered additional differences in gene expression attributable to the mtDNA variants. In the 12 tested cell lines, mtDNA variants accounted for >5% of the variance in expression for 897 genes in one of three developmental stages. Up to 129 genes in an individual line showed differential expression attributable to non-synonymous mtDNA variants . As seen previously, the cell developmental stage was the main source of variation, followed by the experimental batch and cell line of origin  was greater, and variants in different mtDNA regions were subject to purifying selection or were enriched through a selective advantage. We also saw iPSC-specific mtDNA mutations emerging in individual iPSC lines which displayed a distinct mutational signature, and known pathogenic mtDNA mutations reached high percentage heteroplasmy levels. These findings are in keeping with previous observations in human embryonic stem cells (hESCs) which also harbour a wide range of different mtDNA mutations at the single-cell level13 in this context. For this reason, we have not referred to these variants as ‘de novo’, because we cannot be certain that these variants are not present in a very small number of molecules in the original fibroblast line. A more sensitive detection technique might reveal ultra-rare mutations in fibroblasts that are present in iPSCs at much higher levels, lowering the iPSC-specific mutation rate. However, it is also likely that a more sensitive technique would detect additional mutations in iPSCs not present in the fibroblasts, counterbalancing this effect. At this point, we can only speculate what the implications of a more sensitive detection method might be. However, this does not change our main conclusion that the heteroplasmy levels change during cell reprogramming. A re-analysis of published data13 from human 30 iPSC clones derived from three blood samples yields a very similar mutation rate (8.85 × 10−5/base pair) to our result calculated from the reprogramming of human fibroblasts (8.62 × 10−5/base pair). This adds weight to our conclusion that reprogramming shapes the mtDNA landscape, and appears to be independent of the original cell type. However, definitive evidence will require a much more extensive comparison of iPSCs derived from different tissues from the same individual.It is important to note that, as with any sequencing experiment, there are technical limitations to our approach. For example, we are unable to completely exclude rare variants in the fibroblast lines that fell well below the detection threshold of 2% used in the bulk WGS, either because they were at low heteroplasmy levels in many cells, or because a very small number of cells carried homoplasmic variants. Technical limitations in the sequencing approach, such as the introduction of base errors, mean that we are unable to absolutely exclude the possibility of any variant present in iPSCs being present in fibroblasts, and our reported iPSC-specific mutation rate must be interpreted27, packaging of mtDNA into ‘segregating units’ which reduced the effective population size of mtDNA molecules28, the focal replication of mtDNA in different regions of the cell29, and related mechanisms involving the fission and fusion of mitochondria30. A bottleneck effect could also occur at the cellular level due to the preferential replication or loss of cells containing specific mtDNA variants during reprogramming. This is supported by recent evidence in mice, where mtDNA variants have been shown to influence reprogramming efficiency from fibroblasts to iPSCs19.What mechanisms could explain the changing heteroplasmy levels we have observed during reprogramming? Rapid shifts in heteroplasmy could be due to a genetic bottleneck effect. This has been extensively studied in the female germline where the bottleneck could be due to a reduction in mtDNA content within the cell31. At the cellular level, differences in cellular proliferation or cell death could influence heteroplasmy levels in the whole population of cells32. Selection could also occur at the mitochondrial level, through increased biogenesis or selective destruction by autophagy or mitophagy33. Some mtDNA variants could also influence mtDNA replication itself, particularly those in the non-coding D-loop. Given emerging evidence that cell culture conditions such as the oxygen level of the addition or antioxidants can influence heteroplasmy levels34, including during iPSCs generation19, it is possible that the precise conditions used to generate the iPSCs influenced the heteroplasmy dynamics that we observed. However, the HipSci resource provides an opportunity to study all of these mechanisms now that the mtDNA variation has been characterised in detail.The selection could occur at several levels3, the mtDNA defined sub-clones defined within each lineage showed differences in gene expression, including key genes involved in cellular metabolism, including ATP synthesis, telomere maintenance and organisation and epidermal cell differentiation. Most of the variants (93.7%) influencing gene expression were from iPSC/mesendo/defendo-specific variants during reprogramming. Thus, the process of cell reprogramming shapes the mtDNA landscape clone-by-clone, adding to the heterogeneity of derived iPSCs. It is highly likely that these findings will have an impact on iPSC disease models, and the use of iPSC cell lines in cellular therapies. These cell lines are available to the scientific community through the human induced pluripotent stem cell initiative (https://www.hipsci.org/)1.Analysing single-cell transcriptomes at three states of differentiation allowed the identification of distinct cell lineages based on their mtDNA sequence. Unlike the reprogramming, heteroplasmy levels remained stable during the three stages of differentiation. Although only making a minor contribution to the overall variance in gene expression between donorshttp://www.hipsci.org/)1. The donors’ age was between 27 and 77 years . Samples were collected initially under ethical approval for iPS cell derivation , with later samples collected under a revised consent .In all, 146 iPSC and 151 fibroblast lines from 151 healthy donors  were obtained from the Human Induced Pluripotent Stem Cells Initiative ). On reaching confluence after ~30 days, fibroblasts were passaged into 25-cm2 flasks, and further passaged when 80–90% confluent into a 75-cm2 flask. Cells were then expanded to confluency in 225-cm2 flasks at a split ratio of 1:3, and cryopreserved at 1–2 million cells per vial in FBS and 10% DMSO or seeded immediately for reprogramming. Fibroblasts were transduced using Sendai vectors expressing human (h)OCT3/4, hSOX2, hKLF4 and hMYC51  and cultured on an irradiated mouse embryonic fibroblast (MEF-CF1) feeder layer in a 10-cm2 tissue-culture dish in iPSC medium consisting of advanced DMEM (Life Technologies) supplemented with 10% Knockout Serum Replacement , 2 mM l-glutamine (Life Technologies), 0.007% 2-mercaptoethanol (Sigma-Aldrich), 4 ng ml−1 recombinant zebrafish fibroblast growth factor-2, and 1% pen/strep (Life Technologies). Cells with an iPS cell morphology appeared ~25–30 days after transduction. Six undifferentiated colonies per donor selected between days 30–40 were transferred onto 12-well MEF-CF1 feeder plates and cultured in iPS cell medium with daily medium change, and passaged every 5–7 days. Three of the six lines were selected based on morphological qualities  and expanded for banking and characterisation. Each iPS cell line was passaged ~16 times before the initial molecular data for quality control, including genotyping, gene expression data, and an assessment of the pluripotency and differentiation potential of each line.The iPSCs were generated in bulk culture without physical subcloning. Details of the generation of the iPSC lines including fibroblast isolation, iPSC derivation, iPSC culture, iPSC line selection, molecular assays and cell culture for maintenance and differentiation in scRNA sequencing are available at A summary of the iPSC lines used in this study is available in Supplementary Data http://www.hipsci.org/). The average depth of WGS was 44-fold (s.d. = 9-fold)  on the resulting smaller BAM files to generate the realigned mtDNA BAM files36. During this process, any read pairs for which either of the two reads in the pair mapped to multiple locations were discarded, including potential nuclear mitochondrial sequences in the nuclear genome and amplification artefacts. The realigned bam files were used to call the variants and generate the vcf file for each sequence. We also used the second variant caller VarScan237 to call mtDNA variants from the realigned bam file . The mpileup files used in VarScan2 were generated by Samtools with options -d 0 -q 30 -Q 30. We then filtered the variants as follows: (1) we retained variants for which the allele fractions (AFs) were above 2% from both MToolbox and VarScan2, and the allele fractions were extracted from VarScan2. The average sequencing depth of mtDNA was 1824-fold (s.d. 2249-fold), which ensured us to detect the low-level variants; (2) we retained only single-nucleotide polymorphisms (SNPs); (3) we removed variants with depth < 200×; (4) we removed variants where there were less than two reads on each strand for the minor allele; (5) we removed variants falling within low-complexity regions .Whole-genome sequencing (WGS) data of 146 iPSC and 151 fibroblast lines were obtained from HipSci  was based on our previous work40.Mitochondrial DNA haplogroup assignment was performed using variants with allele fraction above 95% by HaploGrep2Potential DNA cross-contamination was checked using mtDNA variant calls. Two samples carrying more than ten heteroplasmic variants with similar HFs were excluded from this study. We also removed four sequences where the average depth of mtDNA was below 400×. A further four iPSCs were removed because their matched fibroblasts were unavailable or failed quality controls. After all of the sample QC steps, 146 fibroblasts and 141 iPSCs were included in the analysis. From 25 donors one iPSC line was included and from 58 donors 2 iPSC lines were included Fig. .P = 0.907, paired Wilcoxon test). Likewise, the average number of heteroplasmic variants, the heteroplasmy fraction (HF), and the average number of low-level heteroplasmic variants (HF < 5% or HF < 10%) were no detectable difference between the two clusters   shared, if the variant was present in both fibroblasts and their derived iPSCs and was heteroplasmic in at least one of the variants; (2) lost, if the heteroplasmic variant was present in the fibroblast but not detected in their derived iPSC and (3) iPSC-specific, if the heteroplasmic variant was present in the iPSC and not detected in their fibroblast progenitor. If two iPSCs were derived from the same fibroblast, we defined them as two separate fibroblast and iPSC pairs.Next, we defined sub-categories of heteroplasmic variants detected in the fibroblast cell lines with two derived iPSCs and their derived iPSCs: (1) shared both, if the variant was present in the fibroblast and both derived iPSCs and was heteroplasmic in at least one of the variants; (2) shared one/lost one, if the variant was present in the fibroblast and only one of two iPSCs and was heteroplasmic in at least one of the variants; (3) iPSC-specific both, if the same heteroplasmic variant was present in both iPSCs and not detected in their matched fibroblast; (4) iPSC-specific one, if the heteroplasmic variant was present in only one of two iPSCs and not detected in their matched fibroblast; (5) lost both, if the heteroplasmic variant was present in the fibroblast but not detected in any of their derived iPSCs.41.To understand the relationship between donor age and the mean number of heteroplasmic variant and average heteroplasmy fraction in fibroblast cell lines and iPSC lines, we applied linear regression to each dataset using RS = 1 if the normalised heteroplasmic shift was above 0, and S = 0 if normalised heteroplasmic shift was below 0, HFvfibro was the logit transformed heteroplasmy fraction from each variant in the fibroblast cell lines, Rmt was the mtDNA regions , Hap was the macro-haplogroup from each donor, and age was the donor age.To understand whether multiple factors affect the heteroplasmic shifts between fibroblast cell lines and iPSC lines, logistic regression was applied using R.42 and the previous study38. We then reviewed each variant by searching online literatures. Based on previously published criteria43, a total of 87 single-nucleotide mutations were included in the final list of pathogenic mutations44.The initial list of pathogenic mutations was from MitoMap (Disease Mutations with mutation status as ‘Cfrm’)24. The substitution rate for each trinucleotide context was estimated by the number of substitutions divided by the frequency of the trinucleotide context present in the mtDNA reference genome (rCRS), in the light (L) and heavy (H) strands. The signature profiles from the total heteroplasmic variants observed in fibroblasts and iPSCs, lost heteroplasmic variants in fibroblasts, iPSC-specific heteroplasmic variants in iPSCs and shared heteroplasmic variants between fibroblasts and iPSCs were considered. To compare the signature profiles between the different mtDNA regions, we estimated the frequency of six point mutations in the L and H strands. To explore the mechanism of mutagenesis we correlated the signature profiles of mtDNA substitutions from fibroblasts, iPSCs, shared and iPSC-specific heteroplasmic variants with 30 cancer-specific mutational signatures in the nuclear DNA identified24.Mutational spectra were derived from the revised Cambridge Reference Sequence (rCRS) and alternative alleles at each variant site. The resulting spectra were composed of the six distinguishable point mutations . Each signature was displayed using a 96 substitution classification defined by the substitution class and the sequence context immediately 3’ and 5’ of the mutated baseWe estimated the relative mtDNA copy number from WGS by comparing the mean read depth of the autosomes to the mean depth of mtDNA. Mean autosomal depth (DPautosome) was calculated from 2.685 Gb autosome regions (without chromosome gaps). And mean mitochondrial depth (DPmt) was obtained from the full length of mtDNA genome . Relative mtDNA copy number (CNmt) was calculated in a diploid cell as below:45.Bulk RNA-seq FASTQ files for 332 iPSC lines were obtained from the ENA project: ERP007111 and EGA projects: EGAS00001000593. Raw sequencing data were used to detect mitochondrial variants using the same pipeline as described above. Variants with low-quality RNA-seq were discarded based on the criteria applied in WGS data. In addition, we removed the highly heteroplasmic variants that were specific to mtRNA , including three variants  previously reported as artefacts . 102 of 332 iPSC lines that had matched WGS data available were included in this studyhttps://zenodo.org/record/3625024#.Xil-0y2cZ0s. The details of cell culture and single-cell sequencing were previously described3. The pipeline developed for the WGS data was used to detect mtDNA variants from raw FASTQ files, with several additional filtering steps: (1) retaining the cells with mtDNA genome sequencing depth above 200×, (2) excluding the iPSC/mesendo/defendo-specific variants only detected in a single cell from the same cell line which was more likely from sequencing error, (3) defining heteroplasmic variants with HF between 2% and 95%, due to lower sequencing depth in single-cell RNA-sequencing data compared to WGS, (4) excluding the cells with >20 heteroplasmic variants, (5) retaining the mtDNA regions with mean sequencing depth above 200× . This resulted in 11, 538 cells from 60 cell lines which also had WGS data available from their matched fibroblast cell lines.Single-cell RNA-seq FASTQ files for 36,044 cells were obtained from the ENA project: ERP016000 and EGA projects: EGAS00001002278 and EGAD00001005741. Processed single-cell count data were obtained from Zenodo: https://zenodo.org/record/3625024#.Xil-0y2cZ0s. The stage labels post iPSC (mesendo and defendo) were defined using the methods previously described3. Briefly, cells were assigned to the mesendo stage if they were collected at day 1 or day 2 and had pseudotime values between 0.15 and 0.5. Cells were assigned to the defendo stage if they were collected at day 2 or day 3 and had pseudotime values higher than 0.7. Cells with intermediate pseudotime (between 0.5 and 0.7) were assigned as undefined. Overall, we assigned 9969 (86.4%) cells to any of the stages .The pseudotime of the single cells from single-cell RNA-seq was obtained from Zenodo: To compare the variant profiles between three cell stages, we included the cell lines where at least 20 cells were available at each stage of development to minimise sampling bias, which included 6881 cells from 24 cell lines. The average allele frequency of each variant was estimated across all the cells from the same cell line at each cell stage, which we termed pseudo-bulk variants.25 of the cells from the different cell lines and within each cell line using the mitochondrial DNA variants described above. UMAP was analysed using the UMAP package46 with default parameters in R and visualised using the M3C package47 in R.For the analysis of the cell lineages, we only included 692 variants detected in more than 1% cells within the same cell line from 24 cell lines which had >20 cells assigned to each cell stage. In all, 27 of 692 variants were also present in the matched fibroblast cell lines, and 665 were iPSC/mesendo/defendo-specific variants in the single cells. We performed a Uniform Manifold Approximation and Projection (UMAP)48. Variance component analysis was performed on top 4000 most variable genes using a linear mixed model in the variancePartition R package49. We included the cell stage, the experiment and the cell line identity as random effects. To estimate the effects of mtDNA variants on gene expression, we identified top 4000 most variable genes in the cells from the same cell stage, the same experiment and the same cell line identity. Variance component analysis was performed by partitioning gene expression variation into mean heteroplasmy fractions of mtDNA non-synonymous variants and residual noise.We only included the cells and variants meeting the following filtering criteria: 1) cells were assigned to one of three cell stages , (2) cell lines with at least 100 cells and >20 cells assigned to each cell stage and (3) retaining variants where >15 cells carried one of two alleles in each cell stage  enrichment analysis was performed using Enrichr53.To further understand the effect of each mtDNA variant on gene expression, we tested the differences in gene expression between cells carrying different genotypes of mtDNA variants from the same cell line. We fitted a generalised linear model for the gene expression data within each cell line from the same experiment with the cell stages as covariates using MAST R package41 and Python (http://www.python.org). Figures were generated using Matplotlib (https://matplotlib.org) in Python and R. Circos plots were made using Circos54.All statistical analyses in this study were performed using RFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-5


They induced the accumulation of a comparable amount of a subG1 population, but slightly lower extent of caspase activation, as compared with actinomycin D, used as an apoptosis inducer. The quantitative structure–activity relationship analysis suggests the significant correlation of tumor-specificity with a 3D shape of molecules, and possible involvement of inflammation and hormone receptor response pathways. Conclusions: Compounds 7 and 15 can be potential candidates of a lead compound for developing novel anticancer drugs.Background: Very few papers covering the anticancer activity of azulenes have been reported, as compared with those of antibacterial and anti-inflammatory activity. This led us to investigate the antitumor potential of fifteen 4,6,8-trimethyl azulene amide derivatives against oral malignant cells. Methods: 4,6,8-Trimethyl azulene amide derivatives were newly synthesized. Anticancer activity was evaluated by tumor-specificity against four human oral squamous cell carcinoma (OSCC) cell lines over three normal oral cells. Neurotoxicity was evaluated by cytotoxicity against three neuronal cell lines over normal oral cells. Apoptosis induction was evaluated by Western blot and cell cycle analyses. Results: Among fifteen derivatives, compounds  Azulene is a 10 π-electron non-benzenoid aromatic hydrocarbon with a fused structure of five- and seven-membered rings, showing a deep blue coloration. The resonance structure of azulene contains ionic cyclopentadienide and tropylium substructures, resulting in electrophilic substitution reactions at the 1- and 3-positions and nucleophilic addition reactions at the 2-, 4-, 6-, and 8-positions, along with the 2-position at the five-membered ring in some cases   × 100] .The cells were washed, lysed, and their protein extracts subjected to Western blot (WB) analysis, as described previously . All pro6 cells) were fixed with paraformaldehyde  in PBS(−) and treated with ribonuclease (RNase) A (Sigma-Aldrich). After staining with propidium iodide  in the presence of 0.01% Nonidet-40  to prevent cell aggregation, the cells were filtered through Falcon® cell strainers  and then subjected to cell sorting , as described previously [Cells (approximately 10eviously . Cell cyeviously .50) was used for the comparison of the cytotoxicity between the compounds, since the CC50 values had a distribution pattern close to a logarithmic normal distribution. The mean pCC50 values for normal cells and tumor cell lines were defined as N and T, respectively [50 against OSCC), N represents −log . The chemical structures were cleaned and standardized ). Then, the 3D-structure of each compound was generated by CORINA Classic  and determined optimal 3D-structure with force field calculations (amber-10: EHT) in Molecular Operating Environment (MOE) version 2020.09 . Chemical 2D and 3D descriptors were calculated with MOE version 2020.9 and Mordred version 1.2.0 (Python library) [pCC50   based onThe activities against 59 signaling pathways , agonistt-test was performed using JMP Pro 6 . The significance level was set at p < 0.05.Each experimental value was expressed as the mean ± standard deviation (SD) of triplicate or quadruplicate measurements. One-way ANOVA and Dunnett’s post-test were performed using IBM SPSS 27.0 statistics . The correlation between chemical descriptors and cytotoxicity or tumor specificity was investigated using simple regression analyses by scikit-learn and SciPy with Python 3.8.5. Student’s 1. The 3D-structure may be the determinant of tumor-selectivity. There was possible involvement of inflammation and hormone receptor pathway rather than caspase pathway in the selective cytotoxicity against OSCC cells. At present, TS values of compounds 7, 9, and 15 are not so high compared with anticancer drugs (9 was cytotoxic, further chemical modification of this compound that enhances the TS value should be done. On the other hand, compound 7, compound 15, and 5-FU showed cytostatic growth inhibition, and their action may be potentiated through combination with other types of anticancer drugs [The present study demonstrated that three compounds from fifteen 4,6,8-trimethyl azulene amide derivatives selectively inhibited the growth of human oral squamous cell carcinoma cell lines. Their actions are either cytotoxic or cytostatic, accumulating subGer drugs . Since cer drugs .


N-isopropylacrylamide)n-block-poly((3-acrylamidopropyl) trimethylammonium chloride)m (PNIPAAMn-b-PAMPTMA(+)m) can efficiently deliver DNA and siRNA to HeLa [n:m) [n-b-PAMPTMA(+)m copolymers by investigating their cytotoxicity in human cancer cell lines and in a normal breast cell line.Thermoresponsive polymers have been widely investigated in modern drug delivery and gene delivery systems, due to their potential to load and release the payload by simply modifying local temperature . Previou to HeLa  and HT10La [n:m) ,3. In ann:m ratio 48:6, 48:10 and 65:20) were dissolved in varying concentrations in cell culture medium, as described before [Cell lines: HeLa , HCC1806 (breast cancer) and MCF10A .The copolymers (n-b-PAMPTMAm(+) copolymers was shown to be dependent on the polymer concentration, n:m ratio and cell line. MCF10A cells were very tolerant to the presence of the copolymer 48:6, even at the highest concentration (Discussion and conclusions: The low toxicity shown particularly for the non-tumorigenic cell line but higher toxicity towards the cancer cell lines makes the tested copolymers highly appealing as drug carriers in anti-cancer therapy.In this preliminary study the cytotoxicity of the PNIPAAMntration , followe


Patients with esophageal malignancy have a 5-year survival rate of only 14% in Canada. This high mortality rate is due to three factors: late diagnosis, difficulty to surgically remove the tumor due to its localization and treatment resistance. Treatment resistance has been ascribed to the presence of cancer stem cells (CSCs) inside the tumor. However, no treatment specifically directed against CSCs is available to patients. Therefore, targeting CSCs is a promising strategy to improve survival of patients with esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer worldwide.Herein, we developed an unbiased approach to identify new players in chemotherapy and radiotherapy resistance in ESCC.high cells and CD24high/CD44high cells in flow cytometry. We then used a proteomic approach to identify new players in treatment resistance.We established radioresistant (RR), chemoresistant (CR) and radiochemoresistant (RCR) human ESCC cell lines using weekly radiation and/or continuous treatment with increasing doses of chemotherapeutic agent 5-FU. We validated that the process of resistance acquisition correlates with enrichment in CSCs as revealed by higher ALDH1 expression, and increased proportion of ALDH1high/CD24high cell proportion is decreased in control and RR cell lines suggesting that proline is a key regulator of CSC population in ESCC.Interestingly, pathway analysis demonstrated enrichment in energy metabolism as well as amino acid metabolism. Seahorse assays showed a more quiescent metabolism in all three types of resistant cells compared to the control cell line. More precisely, resistant cell lines have a lower respiration rate than control cell line, while glycolysis remains unchanged. Surprisingly, our results show a metabolic rewiring very different from the well-known Warburg effect. To further characterise these metabolic changes, we performed an unbiased metabolomic pilot study and confirmed a decrease in amino acid levels such as proline, in resistant cell lines. Preliminary data show that when cultured in DMEM with proline addition, CD44To conclude, our results suggest an important role of metabolism in ESCC treatment resistance. This study is a first step towards the identification of new targets to fight treatment resistance in ESCC patients.CAG, CIHRCanada research chair TIER 2


Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in  Biomedical Research 10H (formerly Life Sciences 10H). Since that time, the URCFG has conducted several large-scale genetic research projects that have yielded publishable data and research resources  was established at UCLA in 2003 as an entity representing the collaborative research effort of undergraduates, typically first- and second-year students, participating in a discovery-based laboratory course called Drosophila melanogaster. Over the last two decades, the fly has become an increasingly popular model for investigating the molecular mechanisms regulating blood cell specification, development, and function  in the fruit fly, Drosophila  occurs in two separate specification events that differ in space and time. The first wave of hematopoiesis occurs in the embryonic head mesoderm and creates blood cells that quickly mature and migrate throughout the developing embryo, eventually becoming the circulating blood cells of the larva. A subset of these cells, many of which appear to retain progenitor characteristics, become sessile, attaching to the lateral body wall around the chordotonal organs and to various internal organs  give rise to blood progenitors that belong to either myeloid or lymphoid lineage, which further differentiate into a variety of mature forms . Likewisl organs . The secl organs , 2015.Drosophila has three defined terminally differentiated blood cell types called plasmatocytes, crystal cells, and lamellocytes  region, from which early blood cells are derived, both require FGF, BMP, and Wnt signaling .P]S}{Hand-GAL4 Hml Chr.  was used as previously described .For the primary screen (expression throughout the hematopoietic system), the Drosophila RNAi Center , the National Institute of Genetics , and the Bloomington Drosophila Stock Center . Acquired RNAi lines were randomly assigned to students participating in the primary screen and the secondary screen, and each RNAi line was assigned to a minimum of two students. Each RNAi line was continually screened until two complete data sets (see below) were acquired. For target gene validation, the BDSC was searched for alternate RNAi lines targeting 24 candidate genes identified by Δ-GAL4Hml in our secondary screen (those causing strong increases in Δ-DsRedHml fluorescence); 14 alternative RNAi lines were available, obtained, and screened .Transgenic RNAi lines for screening were obtained from the Vienna GAL4 females were crossed to males from individual UAS-hpRNA lines or to males from 1118w (BDSC 5905) as a control. Crosses to HHLT-GAL4 and Δ-GAL4Hml were reared at 29°C to maximize RNAi-based phenotypes. Crosses to Antp-GAL4 and PG14-GAL4dome were placed directly at 29°C or reared for one day at 18°C before shifting to 29°C. Crosses to Antp-GAL4 and MESO-GAL4dome with elav-GAL80 were reared at room temperature for one day before shifting to 29°C.Virgin GAL4 driver line, larvae were sometimes prescreened for the presence of GFP and DsRed expression. Four immobile larvae were aligned dorsal side up along the anterior/posterior axis on the bottom (flat surface) of a glass spot well plate that was chilled on ice. Larvae were then imaged for GFP or DsRed fluorescence using a Zeiss Stemi SV11 fluorescence stereo dissection microscope  equipped with an AxioCam MRm camera, controlled by Zeiss AxioVision imaging software. Imaging 12 larvae (three sets of four larvae) for each cross was considered as a complete dataset.Wandering third-instar larvae (non-Tb) were collected, washed with water, and placed into glass spot well plates (Fisher) on ice to minimize movement. Depending upon balancer chromosomes present in the parental HHLT-GAL4 UAS-GFP) screen, students noted changes to fluorescence associated with the lymph gland region, including the posterior pericardial cells, and the circulating blood cell population. Changes noted were varying levels of increased or decreased fluorescence for lymph glands , whether pericardial cells were absent, increased or decreased circulating cell density (including clumps and melanotic tumors). For the secondary screen with Δ-DsRedHml as a marker, students noted changes to fluorescence associated with the lymph gland region and the circulating blood cell population. Changes noted were varying levels of increased or decreased fluorescence for lymph glands  and increased or decreased circulating cell density (including clumps and melanotic tumors). RNAi phenotypes were scored by two or more students in both the primary and the secondary screens, with “hits” being selected by causing reproducible phenotype scores at each stage. Because circulating cell phenotypes varied in several ways, scoring was more subjective. Thus, RNAi line reproducibly causing circulating cell phenotypes were consolidated into a single group that cause any relative change (Supplementary Table S3).Reporter gene expression (fluorescence) in progeny larvae activating RNAi within the hematopoietic system was compared with that of progeny larvae in which RNAi was absent (from control crosses). For the primary , 46.1% male (n = 142); of 294 respondents, 10.1% were URM (n = 31), where URM includes American Indian/Alaskan Native, Black/African American, or Hispanic/Latinx; student make-up by year: first-year, 33.1% (n = 102), second-year, 41.6% (n = 128), third-year, 20.8% (n = 64), and fourth-year, 4.5% (n = 14). SURE II survey participants (January 2015 through May 2018) identified as follows: of 17,810 respondents, 64.6% were female , 35.4% were male (n = 6298); of 17,638 respondents, 17.8% were URM (n = 3142); of 17,328 respondents, student make-up by year: first-year, 35.2% (n = 6103), second-year, 26.2% (n = 4547), third-year, 20.2% (n = 3496), and fourth-year, 18.4% (n = 3182). UCLA student demographic data were obtained under UCLA IRB#16-001388.Learning gains were assessed using the Survey of Undergraduate Research Experiences (SURE) II , which oGAL4 driver lines are available upon request from the Biomedical Research Minor and Banerjee laboratory (UCLA). RNAi lines are available from the Bloomington Drosophila Stock Center , NIG-FLY, National Institute of Genetics (Japan), and the Vienna Drosophila Resource Center (Austria). Supplementary Figure S1 shows phenotypic validation data for a subset of RNAi lines. Supplementary Table S1 contains a list of additional RNAi lines added to the collection identified by the primary phase of the genetic screen. Supplementary Table S2 lists the duplicate screening completion rate for each GAL4 driver/RNAi line combination. Supplementary Table S3 lists candidate genes regulating circulating blood cells. Supplementary Table S4 list Gene Ontology (GO) terms enriched among genes identified in the primary phase screen with HHLT-GAL4. Supplementary Table S5 lists all enriched Reactome groups among genes identified in the primary phase screen with HHLT-GAL4. Supplementary Figure S1 is in TIF format. All Supplementary Tables are in Microsoft Excel (.xlsx) format and have been uploaded to figshare: https://doi.org/10.25387/g3.13166891.GAL4/UAS gene expression system derived from yeast  in whole animals using fluorescence microscopy. An overview of the experimental design is shown in To identify hematopoietic genes, 339 URCFG students used RNA interference (RNAi) to disrupt the function of approximately 3500 genes within the developing blood system. In our experimental approach, pseudo-double-stranded hairpin RNAs (hpRNAs) are produced within cells from a transgene containing an inverted-repeat DNA sequence corresponding to a specific target gene  nor what the specific impact was on blood lineage development. To address these limitations and further delineate the functions of the identified candidate genes, the secondary screen was conducted in which RNAi was directed to either differentiating cells using the Δ-GAL4Hemolectin  that target either the primary screen candidate genes redundantly (20 genes) or genes predicted to function in related processes or pathways. Over the course of five academic quarters (Winter 2015–Spring 2016), students crossed RNAi lines from the 202-line collection with the three GAL4 drivers described above and analyzed DsRed fluorescence (Δ-DsRedHml expression) in whole, wandering third-instar larvae. Each RNAi line was assigned to two or more students, with the goal of collecting at least two complete data sets for each GAL4 driver/RNAi line cross combination. The collection of imaging data for 12 progeny larvae from a given cross was considered a complete data set, and individual RNAi lines remained within the assignment pool until two complete image data sets were obtained. The duplicate completion rate for the entire RNAi line collection was 41% (83 lines) for all three GAL4 drivers, 78% (158 lines) for at least two of three GAL4 drivers, and 95% (191 lines (95%) for at least one GAL4 driver. If single-complete data sets are included, the completion rate increases to 75% (151 lines) across all three drivers, to 99% (199 lines) for at least two of three GAL4 drivers, and to 100% (202 lines) for at least one GAL4 driver (Supplementary Table S2). With respect to RNAi in the PSC . Of the 180 viable lines, 160 (79%) were completed in duplicate. For RNAi screening in progenitor cells , students successfully screened 186 RNAi lines , of which 137 (68%) were completed in duplicate. Similar screening in the maturing blood cell population (Δ-DsRed HmlΔ-GAL4Hml) was successful for 182 RNAi lines , 135 (67%) of which were completed in duplicate (Supplementary Table S2).We compiled a collection of 202 RNAi lines comprised of the 148 lines identified in the primary screen, as well as 54 lines (Δ-DsRedHml reporter expression between RNAi and control (non-RNAi) backgrounds as viewed in whole, third instar larvae. While this is a highly specific and, therefore, powerful molecular genetic tool, the usefulness of Δ-DsRedHml in this RNAi screen is offset by variability in lymph gland phenotypes, possibly due to incomplete phenotypic penetrance and/or expressivity, within the 12 RNAi-larvae sample group. Additionally, the relative inexperience of the undergraduate researchers, with Drosophila in general and the hematopoietic system in particular, sometimes made their identification of subtle phenotypic changes difficult. Therefore, to increase the likelihood that RNAi lines (candidate genes) are identified correctly, the developmental phenotype caused by each RNAi line was independently scored by two or more students. Scoring consisted of first determining whether a phenotype was present and, if so, then describing and categorizing the nature of the phenotype. RNAi lines identified more than once and causing similar hematopoietic phenotypes (the majority of lines identified) were subdivided into those causing an increase in lymph gland Δ-DsRedHml expression and those causing a decrease in lymph gland Δ-DsRedHml expression. Though not our focus, changes in Δ-DsRedHml expression among circulating cells were also noted .As described previously, phenotypic analysis in the secondary screen involved discerning the variance of Antp-GAL4 identified 20 RNAi lines (representing 19 genes) that cause an increase in Δ-DsRedHml lymph gland fluorescence and 15 RNAi lines (representing 15 genes) that cause a decrease in Δ-DsRedHml lymph gland fluorescence (see Supplementary Table S3). Of these 13 RNAi lines, three overlap with RNAi lines increasing lymph gland DsRed fluorescence and four overlap with RNAi lines decreasing lymph gland DsRed fluorescence. The use of dome-GAL4 to disrupt gene function in lymph gland progenitor cells identified 34 RNAi lines (representing 33 genes) increasing lymph gland Δ-DsRedHml expression and 18 RNAi lines (representing 17 genes) decreasing lymph gland Δ-DsRedHml expression (see Supplementary Table S3), with six lines in common with those increasing lymph gland DsRed fluorescence and six lines in common with those decreasing lymph gland DsRed fluorescence. Lastly, analysis of RNAi lines in maturing/mature cells using Δ-GAL4Hml identified 50 RNAi lines (representing 48 genes) causing an increase in lymph gland Δ-DsRedHml expression and 8 RNAi lines (representing 8 genes) causing a decrease in lymph gland Δ-DsRedHml expression (see Supplementary Table S3), with 27 lines overlapping with those causing an increase in lymph gland Δ-DsRedHml fluorescence and four lines overlapping with those causing a decrease in lymph gland Δ-DsRedHml fluorescence.Directing RNAi to the PSC using ence see . This ansion see . Anothersion see . A totalΔ-GAL4Hml) using screen RNAi lines, cause an increase in lymph gland Δ-DsRedHml fluorescence. Subsequent analysis showed that 11 of 14 validation RNAi lines caused either the same or a highly similar phenotype as the original RNAi line . Several such RNAi line cross-validations also appeared within the screen itself. For example, in the primary screen using HHLT-GAL4, seven genes  were identified by two different RNAi lines, and two genes (Nup93-1 and Chc) by three different RNAi lines  identified several components of the COP9 Signalosome (CSN), an important negative regulator of cullin-RING ubiquitin ligases (CSN1b (identified twice), CSN2 , CSN5, and CSN6 (Nup88 (mbo), Nup93-1 (found twice), Nup214, and Nup358, genes encoding subunits (nucleoporins) of the Nuclear Pore Complex (NPC), which regulates nucleocytoplasmic shuttling, gene expression, and a variety of other cellular processes , Amphiphysin, three of four AP-2 adapter genes , Rab5, and Syntaxin7 suggests an important regulatory role for endosome formation and trafficking in hematopoiesis. The secondary screening, which examined the primary screen RNAi lines as well as additional RNAi lines targeting the same or related genes, identified CSN5 and CSN6 again  within this group . Not surprisingly, a large number of Gene Ontology (GO) terms, many of which are defined very broadly, were also found to be enriched within the Biological Process (456 GO terms), Molecular Function (40 GO terms), and Cellular Component (109 GO terms) categories (Supplementary Table S4). Comparison of our genes with the KEGG pathway database (q = 1.35e–07). KEGG analysis also identified the Spliceosome  and mRNA surveillance  groups, which, collectively, indicates an important role for RNA processing regulation during hematopoietic development. Gene set analysis by the Reactome pathway database (Supplementary Table S5), 11 of which coincide with RNA regulation (q = 1.1e–04), Phagosome , and SNARE interaction in vesicular transport , while 14 related pathways were identified by Reactome (Supplementary Table S5).To better understand the nature of the genes identified in the primary and the secondary screens, we analyzed each gene set using the online STRING protein interaction database  were found to be enriched among the secondary screening gene subsets  were also found to be enriched specifically among the secondary screen gene subsets. Notch signaling pathway gene enrichment shows up twice, identified by RNAi knockdown in PSC cells by Antp-GAL4, and in maturing blood cells by Δ-GAL4Hml  and Protein processing in endoplasmic reticulum (dome-GAL4), both enrichments were associated with a decrease in Δ-DsRedHml fluorescence. The Δ-DsRedHml phenotypes associated with the seven functional enrichment groups overlapping with the primary screen (HHLT-GAL4) can be found in The numbers of candidate hematopoietic genes identified by the secondary screening, using cell-type-specific RNAi along with the  subsets . Three amlΔ-GAL4 . FoxO siGAL4/UAS gene expression system, specifically the HHLT-GAL4 driver, the activity of which is highly restricted to the lymph gland and circulating blood cell populations (HHLT-GAL4 UAS-GFP). While indirect, this approach was advantageous because general lymph gland morphologies and circulating cell densities could be easily evaluated and compared across genotypes in situ, while also increasing the analytical throughput  that were either redundant or targeted genes that were functionally related to candidate hits from our primary screen. Second, we generated new GAL4 driver lines that (1) target RNAi to lymph gland sub-populations and (2) report on the development of mature blood cells. Our collection of 202 RNAi lines was then screened using these driver lines , and DsRed fluorescence was evaluated in whole animals, similar to GFP in the primary screen , and of how its functional disruption impacted lymph gland development. To achieve this, we first added additional RNAi lines  identified 34 genes regulating blood cell maturation and/or proliferation in the lymph gland, 19 causing an increase and 15 causing a decrease in Δ-DsRedHml expression  also identified a number of candidate genes regulating blood cell maturation. Specifically, progenitor-cell RNAi identified 50 genes, 33 that cause an increase and 17 that cause a decrease in Δ-DsRedHml fluorescence in the lymph gland. Previous work has shown lymph gland progenitor cells to be regulated by several paracrine and metabolic signaling mechanisms (Δ-GAL4Hml) identified the largest cell-type-specific subset with 56 candidate genes, 48 of which increase and 8 of which decrease Δ-DsRedHml fluorescence in the lymph gland. Previous work has demonstrated that interaction between mature cells and progenitor cells, via the “equilibrium signaling pathway,” is important for balancing progenitor cell maintenance and differentiation  by any single GAL4 driver in the secondary, cell type-specific screen. One possible reason is that HHLT-GAL4 phenotypes for most RNAi lines are complex, arising only because of functional disruption in multiple cell types simultaneously. Another possibility is a threshold effect owing to differences in GAL4 driver strength, i.e., the individual cell-type GAL4 drivers may not induce RNAi as robustly as HHLT-GAL4. For RNAi lines that do cause phenotypes both with HHLT-GAL4 and with a cell type-specific GAL4 driver, it is not clear that these are equivalent phenotypes. The absence of a hematopoietic marker in the HHLT-GAL4 screen and the differences in GAL4 expression levels and patterns contribute to this uncertainty. Thus, while we are confident that the candidate hematopoietic genes identified by HHLT-GAL4 in the primary phase of the screen are valid, it seems that determining the functional specificities of candidate genes may be more straightforward for those causing phenotypes when disrupted in a single hematopoietic cell type.It is interesting to note that many of the RNAi lines identified in the primary screen using P-value = 1.0e–16). Likewise, large numbers of Gene Ontology terms were also enriched for this network (Supplementary Table S4), though many of the terms are broad and overlapping. However, network analysis using the KEGG Pathway database identified a smaller number of enriched functional groups or pathways. Of the eight groups identified by KEGG analysis  and another three pointed to vesicular trafficking  as having major hematopoietic roles.Our analysis of the primary screen candidate genes using the online STRING database helped to reveal important genes subsets. The protein–protein interaction (PPI) network for our 137 gene dataset is composed of 599 edges (known or predicted interactions), a number significantly greater than the 350 edges expected for a randomly selected network of the same size , FoxO signaling pathway (dme04068), and Protein processing in endoplasmic reticulum (dme04141). It is interesting that Notch signaling pathway was identified twice by RNAi, once in the PSC (Antp-GAL4) and once in maturing cells (Δ-GAL4Hml), as both cell types have known roles for Notch signaling during hematopoiesis  is based upon identifying the genes chico, encoding the Insulin Receptor Substrate homolog, and babo, encoding the TGF-β/Activin receptor  can cause a variety of cellular defects including loss of apicobasal polarity, increased proliferation, and aberrant activation of signaling pathways such as Notch and EGFR , and a multifunctional chaperone called Hsc70-4. It is well established that functional disruption of early endosomal trafficking  and Prp19 are interesting because both encode components of the NineTeen Complex , as well as supernumary limbs  and Cullin 1   offered by the UCLA Minor in Biomedical Research. This RNAi-based screen for new hematopoietic genes represents the third iteration of a CURE-based pedagogical approach to teaching UCLA URCFG undergraduates about science and scientific research. The two previous research projects completed by the URCFG were mosaic analysis of lethal P-element insertional mutants in the fly eye  as the basis for the genetic screen was particularly beneficial. Using RNAi in the context of the To explore how students might benefit from participating in the RNAi screen, we used the SURE II survey , which an = 626). For URM students in particular, the increase in STEM retention is even greater . Our findings add to a growing body of evidence that authentic research experiences in the classroom context create highly effective learning environments for undergraduate students and can improve engagement and persistence in STEM  majors. It has been previously reported that the STEM retention rate nationally (through degree completion) is approximately 40%, dropping to as low as 25% among underrepresented minority (URM) students . As rece


Bcr-Abl and p210Bcr-Abl. While p210Bcr-Abl is the hallmark of chronic myeloid leukemia (CML), p190Bcr-Abl occurs in the majority of Philadelphia-positive acute lymphoblastic leukemia (Ph + ALL) patients. In CML, p190Bcr-Abl occurs in a minority of patients associating with distinct hematological features and inferior outcomes, yet the pathogenic role of p190Bcr-Abl and potential targeting therapies are largely uncharacterized. We employed next generation sequencing, phospho-proteomic profiling, and drug sensitivity testing to characterize p190Bcr-Abl in CML and hematopoietic progenitor cell line models (Ba/f3 and HPC-LSK). p190Bcr-Abl CML patients demonstrated poor response to imatinib and frequent mutations in epigenetic modifiers genes. In contrast with p210Bcr-Abl, p190Bcr-Abl exhibited specific transcriptional upregulation of interferon, interleukin-1 receptor, and P53 signaling pathways, associated with hyperphosphorylation of relevant signaling molecules including JAK1/STAT1 and PAK1 in addition to Src hyperphosphorylation. Comparable to p190Bcr-Abl CML patients, p190Bcr-Abl cell lines demonstrated similar transcriptional and phospho-signaling signatures. With the drug sensitivity screening we identified targeted drugs with specific activity in p190Bcr-Abl cell lines including IAP-, PAK1-, and Src inhibitors and glucocorticoids. Our results provide novel insights into the mechanisms underlying the distinct features of p190Bcr-Abl CML and promising therapeutic targets for this high-risk patient group.The oncogenic protein Bcr-Abl has two major isoforms, p190 BCR and ABL1 genes [BCR partner, different hybrid genes encoding distinct Bcr-Abl isoforms are formed [Bcr-Abl  and p210Bcr-Abl (referred to as p190 and p210 hereafter), based on apparent molecular weights [Bcr-Abl is a hybrid oncokinase protein, which is expressed upon t9;22) reciprocal translocation resulting in fusion between 2 recipro weights . Except  weights , 5. RAG  weights . The p19 weights –9.In CML, p190 occurs as the sole Bcr-Abl isoform at diagnosis in a minority of patients (1–2%) , 11 and Various models have been developed to investigate leukemogenic potential of different Bcr-Abl isoforms –18. In mIn order to explore the unique pathways involved in p190-CML leukemogenesis, we integrated exome sequencing, RNA sequencing and phosphorylation profiling to characterize a small cohort of p190-CML patients. We validated our findings by directly comparing the gene expression and phosphorylation profiles between p190 and p210 in two different hematopoietic progenitor cell lines (Ba/f3 and HPC-LSK). Furthermore, we used a comprehensive drug testing to investigate isoform-specific sensitivities and identified promising targeted therapies. Our study provides the first consolidated view of the molecular pathogenesis of p190-CML and identifies promising drug candidates which can be translated into tailored treatment strategies for these high-risk patients.n = 4), p210-CML patients (n = 3), and Ph+ALL patients (n = 10) are summarized in Supplementary Table Clinical and hematological data of p190-CML patients (−4 M 1-Thioglycerol (Sigma), 12.5 ng/ml recombinant human IL6 (Peprotech), 2 mM L-glutamin, and antibiotics as indicated for Ba/f3 cells  in addition to inhouse-prepared stem cell factor .Mouse Ba/f3 cell line retrovirally transfected with BCR-ABL1-GFP encoding either p190/p210 was a kind gift from Prof. Nikolas von Bubnoff . HPC-LSK progenitor cell lines  were genGenomic DNA was extracted from diagnostic PMNCs samples of four p190-CML patients. Processing of WES samples was performed as previously described  using HiTo screen the drug sensitivity profile of Ba/f3 cell lines, a comprehensive oncology drug library consisting of 156 approved drugs and 372 preclinical and investigational compounds was used. For the less proliferating HPC-LSK cells, a custom library of selected 65 compounds were designed and applied  featuring 228 phospho-tyrosine sites was used. Sample preparation and processing were performed according to manufacturer’ instructions. Array scanning and image analysis were performed by Full Moon Bioscience and data were normalized to the median antibody signaling value. Western blotting was used to validate findings in cell lines and CML patient samples (three p190 and three p210). For Ph+ALL samples, phosphorylation of selected proteins was investigated by flowcytometry using iQue Screener Plus flow cytometer (Intellicyt). Further details are described in Supplementary materials.3 cells were re-suspended in 0.4 ml IMDM culture medium supplemented by 2% FBS and the samples were mixed with 4 ml of methylcellulose  without adding cytokines. Drugs  and recombinant mouse IFN-alpha  were added in the indicated concentrations. 1.1 ml were plated on 35–mm-dishes in triplicate according to instructions of the manufacturer. Colonies were counted after 14 days using an inverted microscope. For phenotyping of the clones, cells were harvested from colonies at day 14 and stained with a panel of antimouse antibodies including CD19-PE , CD11b-PerCP-Cy™5.5 (#561114), CD45R/B220-BV421 (#562922) from BD Bioscience and CD3-APC (#100236), CD45-APC-Cy7 (# 557659) from Biolegend. Cells were acquired by FACSVerse (BD Biosciences).For HPC-LSK cell lines, 2.5 × 10t test, Fisher Exact test, Pearson’ correlation, and Spearman correlation tests were computed using GraphPad Prism 8 software or R 3.5.0.Mann–Whitney U-test, Two-tailed Student ASXL1, DNMT3A, and KDM4D epigenetic modifier genes   were identified as differentially expressed (DE) and were all upregulated in p190-CML compared to p210-CML samples. Among DE genes were CCL20, CXCL8 (IL8), IL1R2 , IFIT3, IFIT2 (type-I interferon-induced genes), GPR84 and S100P genes  showed notable hyperphosphorylation in p190-CML compared to p210-CML samples. Other STAT molecules including STAT3 and STAT5 showed mild hyperphosphorylation in P190-CML samples Fig. .Next, we aimed to get deeper insights of the p190-specific transcriptional and signaling pathways in hematopoietic progenitor cell lines to overcome the limitation of scarcity of p190-CML samples. We used two different models: Ba/f3 cell line, a mouse pro-B that has been extensively used in Bcr-Abl signaling comparative studies , 24 and Transcriptomic comparisons of the cell lines revealed a clear segregation between the parental and p190/p210-transduced cells in both models Fig. . DespiteQ < 0.05, Fig. Gbp), Stat1 and Stat2 signaling molecules, lymphocyte-related kinases and markers , and cytokine and chemokine molecules. In contrast, the overexpression of Cxcr4, Il15, Rab32, and Kalrn genes were demonstrated in p210 cells . Comparable to p190-CML, p190-Ph+ALL demonstrated increased Src, and to lesser extent STAT5, phosphorylation in contrast with STAT3 that was more phosphorylated in p210 samples  was downregulated in p190-CML (Supplementary Fig. GPR84 gene which is tightly linked to aberrant β-catenin activity [Jup was significantly upregulated in p190 HPC-LSK progenitor cell line model (Supplementary Fig. Transcriptomic regulation in p190 and p210 cells highlighted the differences in phenotypic potentials between the different Bcr-Abl isoforms. Earlier studies have shown that p190 and p210 induce distinct leukemia phenotypes in mouse models , 53, In Cxcr4  and Rab34 [Rab32  in p210 4 [Rab32 , that mi4 [Rab32 . In concactivity . In contIn summary, our data provides novel insights into the p190-specific genomic and signaling regulation in CML. Our data suggests a critical role of IFN/STAT1 signaling in p190-CML that represents a promising target of treatment. In addition, p190-CML patients typically showing inferior response to imatinib could benefit from frontline dasatinib treatment that additionally targets activated Src signaling. Drug sensitivity profiling also highlighted IAP-, PAK- and MDM2-inhibitors as potentially effective therapeutic options that can be combined with TKIs warranting their testing in personalized treatment strategies of p190-CML.SUPPLEMENTAL MATERIALSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5


Tissue of origin, therefore, must be considered while assessing the effects of any particular heavy metal or metalloid. Among the metals and metalloid, arsenic appears to have relatively more pleiotropic influences on cellular metabolic pathways including those known to have association with diabetes. Although only two stem cell derived datasets are included in the current study, effects of heavy metals on these cells appear to be different from other mature cells of similar tissue origin. This study provides useful information about different heavy metal affected pathways, which may be useful in further exploration using wet-lab based techniques.Various anthropogenic and natural events over the years have gradually increased human exposure to various heavy metals. Several of these heavy metals including cadmium, mercury, nickel, chromium, and the metalloid arsenic among others, have created major public health concerns for their high level of toxicities. Identification of the general as well as the differentially affected cellular metabolic pathways will help understanding the molecular mechanism of different heavy metal-induced toxicities. In this study, we analyzed 25 paired (control vs. treated) transcriptomic datasets derived following treatment of various human cells with different heavy metals and metalloid  to identify the affected metabolic pathways. The effects of these metals on metabolic pathways depend not only on the metals  ArsenArsenic is the 20th most abundant element on earth’s crust  and wellCadmium (Cd) is another non-essential heavy metal that poses significant health risks to human. Chronic exposure to cadmium leads to systemic toxicity and cancers in the lung, breast, prostate, nasopharynx, pancreas, and kidney ,24. The Chromium in small amount is an essential element for normal metabolic functions. However, at high concentrations this heavy metal, especially in the hexavalent form (Cr (VI)), it is toxic and carcinogenic . Cr (VI)Other heavy metals like mercury, nickel, vanadium, etc also have adverse effects on human health. Human exposure to mercury occurs through inhalation of elemental  mercury vapor via industrial practices, dental amalgam or ingestion of organic mercury compounds  primarily through consumption of contaminated fish . Mercuryin silico tools to analyze 25 global gene expression datasets  of control vs. metal/ metalloid  treated cells of human origin to explore the metabolic pathways that are affected following exposure to arsenic and other heavy metals.In this study, we used 22.125 paired global gene expression datasets  from 13 studies in which different human cells  were exposed to arsenic, cadmium, chromium, mercury, nickel, iron, and vanadium were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database  Table 1Table 1. 2.2The filtered list of genes from each paired dataset was used as an input into the NetworkAnalyst platform  and path33.1Irrespective of their tissue of origin, a particular heavy metal treated cell lines tend to cluster together A. The afArsenic exposed cells form a relatively distant cluster from the other heavy metal treated cells A. This mHg, Ni, Fe, and V affected pathways have many considerable overlaps and include purine metabolism, HIF-1A signaling pathway, fluid shear stress and atherosclerosis, protein digestion and absorption, glycerolipid metabolism, phosphatidylinositol signaling and ferroptosis pathways B). The dOne distinguishable pathway between Ni and the other  heavy metal treated cells is the pentose and glucuronate interconversion pathway, which appears to be under-represented in Ni treated cells. This particular pathway is under-represented in the Cr-treated cells as well. Except this one pathway, Cr appears to have the least impact on cellular metabolism. Effects of Fe, Hg, and V on purine metabolism and HIF-1A signaling pathways are more pronounced than those of Ni. In fact, Vanadate- a vanadium oxoanion, is known to induce expression of HIF-1α in a hypoxia-like manner, which in turn leads to progression and metastasis of cancer by increasing expression of vascular endothelial growth factor (VEGF) . Nickel As shown in The distinct cluster of the arsenic treated cells (except #8 and #9 in 3.2Datasets used in this study were derived from different cells of human origin. Two of these are human cancer cell lines- HepG2 (from liver cancer cells)  and MCF7Although different cell lines treated with same heavy metal/metalloid tend to cluster close to one another A, within3.3Impacts of arsenic on the cellular pathways in HepG2 cell line compared to the other cell lines are shown in Arsenic has drastically different effects on HepG2 cell line compared to other cell lines . All theThe PLS-DA plot along with the overrepresented pathways in cadmium treated cell lines are shown in Compared to arsenic, cadmium has less pronounced effect on metabolic pathways in HepG2 than the other cell lines . Among tCadmium is known to affect tight junctions, influenza A and Rap1 signaling pathways in the other cell lines. Influenza A virus up-regulates the expression of IL-27 in human lung epithelial cells and peripheral blood mononuclear cells through Cyclooxygenase-2 and Protein Kinase A (PKA) signaling . Rap1 pa3.4in vivo environment well despite its tumorigenic origin [c origin . The patc origin ,107. Addc origin . Hence, 4per se, but also the nature and origin of the cells. Therefore, the tissue origin must be considered while assessing the effects of a particular heavy metal/metalloid. Among the heavy metals and metalloid, arsenic appears to have more pleiotropic effects on cellular pathways, including a few known to have association with diabetes. One important limitation of this study is the inability to predict whether the affected pathways were induced or inhibited by the heavy metals. Despite this limitation, this study provides useful information about the pathways that are affected by particular heavy metals and may be directive in further exploration using wet-lab based techniques.This study explored the effects of several commonly encountered heavy metals and metalloid on the metabolic pathways of different cell lines of human origin. The affected metabolic pathways are modulated by not only the heavy metal This study neither involved any human nor animal, and hence no ethical approval was required.Ministry of Education, Bangladesh.Grant for Advanced Research in Education (GARE) from the Design of the work- AAS; Acquisition, analysis, and interpretation of data- KF, SSS, TA, ZH, AFS; Manuscript preparation and reviewing- SSS, KF, TA, AASThe authors declare no conflict of interest.