Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset. Droplet-based single cell RNA sequencing (scRNA-seq) , 18, 48 Recently, emerging sample barcoding technologies, such as cell hashing  or MULTImulti-sample multiplets (MSMs), droplets that contain more than one cell from more than one HTO sample; (2) single-sample multiplets (SSMs), droplets that contain more than one cell from a single HTO sample; and (3) singlets, droplets that contain a single cell. We combine singlets and SSMs into a single category called single-sample droplets (SSDs) to differentiate them from MSMs. The relationship between MSM, SSM, singlet, and SSD is summarized in Fig. There are three types of droplets in a sample barcoding scRNA-seq dataset: 1)  multi-sapure-type GEMs whereas GEMs that contain multiple cell types are named phony-type GEMs. An illustration of phony-type GEMs and pure-type GEMs is provided in Fig. GEMs can also be classified based on the number of cell types enclosed in them. GEMs that contain a single cell type are named + B cells, CD3+ T cells, and also a CD3+CD19+ double-positive T-B cell GEM cluster. Similarly, in the CD4-CD8 scatter plot of all CD3+ T cells, besides the CD4+ helper T cells and CD8+ cytotoxic T cells, there also exists a CD4+CD8+ helper-cytotoxic T cell GEM cluster. Both clusters are highlighted in red circles. Existence of such PBMC types at the observed frequencies is unlikely, as CD3+CD19+ cells and CD4+CD8+ T cells are believed to be extremely rare , x∈. Hence, the singlet formula used by Demuxlet under d0=0.01 and Y0=1K can be approximately explained by GMM-Demux as randomly partitioning Y cells among a total of X=100K cell-assay droplets. Despite apparent similarities between their formulas, GMM-Demux and Demuxlet employ different underlying statistical mechanics. Demuxlet uses a discriminative model, which uses regression to subjectively model the multiplet rate as a parametrized curve. GMM-Demux, on the other hand, uses a self-explanatory, generative model that directly simulates the GEM formation process. The generative model allows GMM-Demux to estimate the MSM rates of pure-type and phony-type GEM clusters in a sample barcoding dataset, while the discriminative model of Demuxlet does not. The generative model also enables GMM-Demux to accurately simulate multiplets, including both pure-type and phony-type GEMs; singlets; SSMs; and MSMs, whereas Demuxlet cannot.There are only a few prior studies on modeling multiplet rates. Demuxlet , a genetX, in this case) approaches infinity . According to the GEM formation model, which models partitioning of cells into droplets with a binomial distribution, we have:Let We derive the model parameters by computing:Y is the user-provided total number of cells loaded for library preparation, which can be obtained from the hemocytometer.where Y), the planned number of samples for sample barcoding (M), the estimated number of droplets (X), and the droplet capture rate (rcap) in library preparation as inputs, and it computes the estimated multiplet rates. The online experiment planner assumes cells are evenly distributed among M samples.The online sample barcoding experiment planner estimates the singlet, SSM, and MSM rates of a planned sample barcoding experiment via the GEM formation model. Specifically, it takes the estimated number of cells  rate, defined as the estimated number of SSMs among SSDs. Mathematically, the RSSM rate is defined as:The RSSM rate marks the overall quality of a sample barcoding dataset. It represents the percentage of irremovable multiplets among SSDs, after removing all MSMs in the dataset. If the RSSM rate of the estimated outcome is too high, then the planned experiment should be aborted, as the anticipated outcome will be too noisy for downstream analysis. While dividing the cell assay into more samples drives down the RSSM rate, as it reduces The online experiment planner computes the multiplet rates as follows:M, does not affect the singlet rate. The singlet rate is solely determined by X and Y. However, a greater M reduces the SSM rate and increases the MSM rate. Therefore, we conclude that dividing a cell assay into more samples by sample barcoding transforms more SSMs into MSMs. Transforming SSMs into MSMs improves the quality of the dataset. With fewer SSMs, the RSSM rate of the dataset decreases. In comparison, having more MSMs does not affect the quality of the dataset, as MSMs are removed by GMM-Demux.The above equations show that the number of samples, rcap, the online experiment planner also computes the estimated number of cell-enclosing GEMs in the final output, as well as the estimated number of SSDs after removing MSMs. The number of cell-enclosing GEMs, #non-emptyGEM=#non-emptydrops·rcap (#non-emptydrops is computed in Eq. (#SSD=#non-emptyGEM·(1−P(i∈MSM)).Given d in Eq. ). The nuY and M are user-controlled while X and rcap are largely dictated by the library preparation equipment. However, based on our observations, we found that X mostly varies between 65K and 80K. To account for the wide ranges of variability of the inputs, the online experiment planner uses sliders for selecting X, Y, M, and rcap, which have ranges of 60K– 100K, 1K– 80K, 1–20, and 0–1, respectively. The online experiment planner supports dynamic updates. It computes the estimated multiplet rates in real time as the user updates input parameters. In practice, we recommend that users profile their library preparation equipment once for the total number of droplets (X) in a sequencing run, by performing a small-scale sample barcoding experiment, and use the profiled X (included in the GMM-Demux output) in planning future experiments.Among all four inputs, In novel cell-type identification, a cell-type classifier is used to group GEMs into clusters. Each cluster is assumed to represent a unique cell type. Clusters with average expression profiles that do not match any known cell types are identified as novel cell types .After clustering, phony-type GEMs are grouped into distinct clusters. Phony-type GEM clusters may be incorrectly identified as novel cell types, as their expression profiles do not match known cell types, generating false discoveries. GMM-Demux rectifies true novel cell types by validating if the alleged novel cell-type GEM cluster contains mainly pure-type GEMs. Based on the GEM composition in the cluster, GMM-Demux classifies GEM clusters into three categories: pure-type GEM clusters, phony-type GEM clusters, and mixture clusters. Phony-type GEM clusters contain mostly phony-type GEMs. Pure-type GEM clusters contain mostly pure-type GEMs. Mixture clusters contain large quantities of both pure-type and phony-type GEMs.G represent a GEM cluster. GMM-Demux classifies G by examining the MSM ratio of G. For simplicity, we assume cells are equally randomly divided into the M sample barcoding samples. If G is a phony-type GEM cluster, the MSM ratio of G must be very high. Elaborated in Additional file 1: Section S3, the expected MSM ratio of a phony-type cluster approaches and exceeds G is a pure-type GEM cluster, its MSM ratio should not be greater than the MSM ratio of the entire sample barcoding dataset, which is much smaller than G: in a phony-type GEM cluster, all GEMs are multiplets; hence, the MSM ratio of G, G, respectively; in a pure-type GEM cluster, however, we have G. By comparing the two ratios, we observe that pure-type GEM clusters include singlet counts in the denominator, whereas phony-type GEM clusters do not. As a result, the MSM ratio is much higher in phony-type GEM clusters than in pure-type GEM clusters. Complex situations where cells are not evenly distributed among sample barcoding samples are discussed in Additional file 1: Section S3.Let phony-type hypothesis and the pure-type hypothesis, which assume G being a pure-type or a phony-type GEM cluster, respectively. GMM-Demux tests both hypotheses with the binomial test and computes a p value for each hypothesis. Based on the hypothesis testing results, GMM-Demux classifies G as a pure-type GEM cluster, a phony-type GEM cluster, or a mixture cluster. Details of the hypothesis tests are provided in Additional file 1: Section S3.GMM-Demux uses hypothesis testing to measure the confidence of each classification. GMM-Demux prepares two hypotheses, the G, GMM-Demux recommends different actions. Being classified as a phony-type GEM cluster suggests that the proportion of pure-type GEMs in G, if there exists any, is extremely small and most GEMs in G are phony-type GEMs. GMM-Demux recommends excluding G from further analysis. Being classified as a mixture cluster suggests that G mixes pure-type GEMs and phony-type GEMs together and has non-trivial numbers of GEMs in both categories. This is often a result of poor clustering quality where G becomes a super-cluster over several pure-type and phony-type GEM clusters. GMM-Demux recommends refinement over the clustering method and subdividing G into pure-type GEM and phony-type GEM sub-clusters. Finally, being classified as a pure-type GEM cluster suggests that it is plausible that G defines a real cell type. Further analysis over G is recommended.Based on the classification result of Y), as inputs, and it outputs a double column table as the classification result. The row indices of the output table are GEM barcodes. The two columns are the classification of each GEM and the confidence score of each classification, respectively. With M samples, GMM-Demux classifies GEMs into a maximum of 2M+1 classes. Besides the uncertain class, the negative class, and M SSD classes, there are M-sample class. Additionally, GMM-Demux produces a SSM rate summary file, which includes the SSM rate and the RSSM rate of each sample, and a summary file that includes the multiplet rates of the entire dataset. The summary file also includes the estimated number of cell-assay droplets (X) and the estimated droplet capture rate (rcap) of the library preparation equipment. Example outputs are provided in Additional file 1: Section S4.The GMM-Demux classifier is compatible with CellRanger-3.1.0 from 10X Genomics. It takes the sample barcoding data of post-filtering, non-empty droplets, in the market matrix (ṁtx) format, together with the estimated number of cells (Additional file 1 Supplementary figuresAdditional file 2 Supplementary tablesAdditional file 3 Review history


ABCG2 wt, the SNPs: 34 G > A and 421 C > A, or with empty vector (K562/ve). ABCG2‐expressing cells were enriched using magnetic sorting and the expression was verified using flow cytometry. Intracellular erlotinib concentrations were analyzed by LC–MS/MS after incubation with 1 µmol/L erlotinib for 60 minutes. All recombinant cell lines were confirmed carriers of the vector and expressed ABCG2. Differences in intracellular erlotinib concentrations were observed between K562/ve and K562 ABCG2 wt and between K562/ve and K562 ABCG2 34G > A , indicating that the cell lines carrying ABCG2 wt and ABCG2 34G > A actively transports erlotinib out of the cells. The ABCG2 34G > A cell line had a higher transport capacity compared with ABCG2 wt after adjusting for ABCG2 expression . No differences were observed between K562/ve and K562 ABCG2 421 C > A. Genetic polymorphism in the ABCG2 gene has an influence on the transport of erlotinib which can contribute to the observed variation in erlotinib pharmacokinetics and toxicity.Single nucleotide polymorphisms (SNPs) in the gene for multidrug resistance protein ABCG2, an erlotinib transporter, is a possible contributor to the interindividual variation observed in erlotinib pharmacokinetics and toxicity. Therefore, the aim was to study erlotinib efflux by ABCG2 wild‐type (wt) and ABCG2 polymorphic variants in the K562 cell line. The chronic myeloid leukemia K562 cell line, neither expressing EGFR nor ABCG2, was transduced with vectors containing the  ABCATP‐binding cassetteACNacetonitrileAmAcammonium acetateBCRPbreast cancer resistance proteinEGFRepidermal growth factor receptorEYFPenhanced yellow fluorescent proteinFBSfetal bovine serumHPLChigh‐performance liquid chromatographyLC–MS/MSliquid chromatography‐tandem mass spectrometryMDRmultidrug resistanceMeOHmethanolMFImean fluorescence intensityNSCLCnon–small cell lung cancerPBSphosphate‐buffered salineSNPsingle nucleotide polymorphismTKItyrosine kinase inhibitorwtwild type1The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) erlotinib is used in the treatment of non–small cell lung cancer (NSCLC) patients harboring somatic EGFR mutations, including exon 19 deletion and exon 21 L858R substitution.Erlotinib is a targeted therapy that is administered orally once daily. Large interindividual variation is observed in erlotinib pharmacokinetics. Plasma drug concentrations have been associated with adverse drug reactions,The membrane‐associated protein ABCG2, also known as the breast cancer resistance protein (BCRP), belongs to the ATP‐binding cassette (ABC) transporter family Horsey Aaron et al.Concentrations of erlotinib in tumors are found to vary between <5 nmol/L and 1.1 µmol/L.ABCG2 nonsynonymous single nucleotide polymorphisms (SNPs) 34 G > A (V12M) and 421 C > A (Q141K) are vastly studied. The ABCG2 421C > A polymorphism is known to generate a lower protein expression compared to ABCG2 wild type (wt),ABCG2 34 G > A shows expression levels comparable to ABCG2 wt.In general, the ABCG2 421 C > A carrying cell line were not different compared to the control cell line.Earlier ABCG2 in vitro studies with erlotinib have determined that erlotinib is an ABCG2 substrate and that ABCG2 wt is more efficient in transporting erlotinib compared to control cell lines.ABCG2 wt and ABCG2 polymorphisms influence the intracellular concentrations of erlotinib after drug exposure. Intracellular accumulation of erlotinib was assessed in the chronic myeloid leukemia cell line, K562, stably transduced with ABCG2 wt, the ABCG2 polymorphisms 34 G > A and 421 C > A, and empty vector. By using a non–EGFR‐expressing leukemic cell line, the effect of erlotinib on cell survival is minimized.Therefore, we aim at investigating how 22.1Reference substances for erlotinib and erlotinib‐D6 were purchased from Toronto Research Chemicals. Acetonitrile (ACN) of LC–MS grade was purchased from Merck AB and methanol (MeOH) of HPLC grade from Thermo Fischer Scientific. Ammonium Acetate (AmAc) of analytical grade was obtained from Sigma Aldrich.2.22. The cells were maintained by replacement or addition of fresh media to a concentration of 200 000 cells/ml every 3‐4 days. Cell culture reagents were obtained from Life Technologies. All cell lines were tested negative for mycoplasma infection.The parental K562 cell line (ATCC‐LCG Standards) and the transduced K562 cell lines were cultivated in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and 2% penicillin/streptomycin at 37°C with 5% CO2.3http://www.proteinatlas.org).The K562 cell line was considered to be a suitable modeling system to study how erlotinib is transported by ABCG2 as K562 naturally neither express ABCG2 nor EGFR  retroviral vector (MIY) by utilization of Not1 restriction enzymes. 293T cells were transfected with MIY‐ABCG2 and helper vectors VSVG and POL‐GAG following transduction of K562 using spin infection of the collected viral 293T supernatant. An empty MIY vector was transduced into K562 to serve as a control cell line.K562 was stably transduced with ABCG2 (K562/ve), a cell line with ABCG2 wt (K562 ABCG2 wt), and cell lines with the ABCG2 variants 34 G > A (K562 ABCG2 34) and 421 C > A (K562 ABCG2 421).The final recombinant K562 cell lines were a control cell line with MIY vectors without 2.47 cells were labeled with microbeads and separated on an LS column with a magnetic separator.Enrichment of ABCG2‐expressing cells was performed using magnetic sorting with microbeads conjugated to monoclonal anti‐human ABCG2 (CD338) antibodies (Miltenyi Biotec). Briefly, 1 × 106 cells were labeled with the ABCG2 antibody and washed twice with phosphate‐buffered saline (PBS).Expression of ABCG2 and EYFP was quantified using flow cytometry (Beckman Coulter) after extracellular staining ABCG2 with PerCP‐Cy™5.5 Mouse Anti‐Human CD338 antibody obtained from BD Biosciences. Before analysis, 1 × 102.52 the cells were transferred to a 15 mL tube and centrifuged at 1000g for 5 minutes at room temperature and washed twice with 5 mL cold PBS. The cells were lysed with 200 µL of MeOH containing 6.25 ng/mL erlotinib‐d6 and 200 µL of 10 mmol/L AmAc. The samples were thoroughly vortexted for 10 seconds before centrifugation at 17 000g for 10 minutes at 4°C. The supernatant was transferred to a 96‐well plate and stored at −20°C before LC–MS/MS analysis. Two independent experiments were carried out in triplicates for all cell lines except for K562/ve that contained one replicate in the second experiment.The cell lines were seeded at 400 000 cells/mL in a total volume of 5 mL and incubated with a final concentration of 1 µmol/L erlotinib. After 60 minutes of incubation at 37°C with 5% COThe ABCG2 transport capacity was determined by normalizing the intracellular concentration of erlotinib using the ABCG2 expression. The difference in intracellular erlotinib concentration between the average K562/ve and a replicate from an ABCG2‐expressing cell line was divided by the PerCP‐Cy5.5 response from the ABCG2‐expressing cell line adjusted for isotype expression (Δ ng/mL/Δ MFI).2.6Erlotinib and erlotinib‐d6 were analyzed using a previously described LC–MS/MS method.Calibration and quality control samples for erlotinib were separately prepared at 25, 75, 375, 1200, 375,0 and 5000 ng/mL and, 25, 75, 400, 1000, and 3750 ng/mL in 50% MeOH, respectively. To 50 µL of each sample, 200 µL of MeOH containing 6.25 ng/mL erlotinib‐d6 and 200 µL of 10 mmol/L AmAc was added. The samples were transferred to a 96‐well plate and stored at −20°C before LC–MS/MS analysis.2.7t test using the IBM SPSS Statistics, version 26 (IBM).Statistical differences in intracellular concentrations between cell lines were analyzed with one‐way ANOVA and Tukey HSD post hoc test and differences in transport capacity normalized for ABCG2 expression were analyzed with Student's independent 33.1ABCG2 transduced K562 cell lines confirmed EYFP expression in all transduced cell lines, Figure ABCG2 421 (mean fluorescent intensity (MFI) = 1.18) compared with K562 ABCG2 wt (MFI = 4.68) and K562 ABCG2 34 (MFI = 2.87). Despite low EYFP expression, a signal compared to the K562 (MFI = 0.39) was observed, suggesting that the K562 ABCG2 421 cell line harbor plasmids even though it is low. The EYFP expression in the control cell line K562/ve was extremely high (MFI = 223), Figure In the flow cytometry analysis of the ABCG2 wt (MFI = 49.6) and followed in decreasing order in K562 ABCG2 34 (MFI = 21.0) and K562 ABCG2 421 (MFI = 8.49), Figure ABCG2 wt, K562 ABCG2 34, and K562 ABCG2 421, respectively.The ABCG2 expression, determined based on the PerCP‐Cy5.5 response, was most prevalent in the K562 3.2The performance of the LC–MS/MS method for the quantification of erlotinib was monitored in each run (n = 2). Precision and accuracy for erlotinib in all quality control samples were < 5% and between 92% and 108%, respectively.3.3ABCG2 wt (P ≤ .001) and K562/ve and K562 ABCG2 34 (P ≤ .001), Figure ABCG2 421 were not significantly different from the K562/ve.No statistical differences in intracellular erlotinib concentrations were observed between the parental K562 cell line and the K562/ve cell line. All intracellular‐concentrations in ABCG2‐expressing cell lines were compared with K562/ve. Statistical differences were observed between the K562/ve and the K562 ABCG2 34 showed a higher erlotinib transport capacity compared to K562 ABCG2 wt, despite similar intracellular concentrations due to lower ABCG2 expression in K562 ABCG2 34 (P = .024). The normalized erlotinib transport capacity for K562 ABCG2 421 was widely spread and around zero.To account for variation in ABCG2 expression in the recombinant cell lines, the intracellular erlotinib concentrations were normalized to the ABCG2 expression, Figure 4ABCG2 SNPs 34 G > A and 421 C > A. The intracellular concentrations in K562 ABCG2 wt and K562 ABCG2 34 were significantly lower compared to the K562/ve indicating that the two cell lines actively transport erlotinib out of the cell, Figure ABCG2 34 exhibited a higher transport capacity compared to K562 ABCG2 wt, when the intracellular concentrations were normalized to ABCG2 expression, due to lower ABCG2 expression in K562 ABCG2 34, meanwhile similar amounts of erlotinib were transported out of the cells, Figure ABCG2 421 did not differ from K562/ve, and when normalized to ABCG2 expression, the ABCG2 transport capacity was widely spread and included negative values, Figures In this project, we have studied erlotinib efflux by K562 cells carrying wild‐type ABCG2 and the http://www.proteinatlas.org).The cell line used in this project, the chronic myeloid leukemia K562 cell line, expresses no or very low amounts of ABCG2 and EGFR (The control cell line, K562/ve, containing only an empty vector without the ABCG2 gene, exhibited a very high EYFP expression. This was probably due to the more successful transduction of the empty vector as it was smaller, 6.5kb, compared to the other vectors that additionally contained 4.5kb of ABCG2 cDNA. A more even distribution of EYFP expression among the cell lines would probably have been achieved if the size difference between the empty vector and the ABCG2 containing vectors had been smaller.ABCG2 wt and K562 ABCG2 34 exhibited significantly lower erlotinib intracellular concentrations indicating that erlotinib was actively transported out of the cells. It is in line with previous studies where ABCG2 34 showed similar expression levels and transport capacity as wild type in other studied substances.The two cell lines K562 ABCG2 421 in this study, but it may also be caused by the reduced plasmid content. The results in this study suggest that cells carrying the 421 C > A gene variant are less effective than those with ABCG2 wt and 34G > A to transport erlotinib out of the cell. Our results confirm earlier results that there is no difference between cells carrying the 421C > A and control cell line.It has previously been shown that 421C > A results in a lower ABCG2 expression.ABCG2 wt and K562 ABCG2 34 actively transports erlotinib by efflux while K562 ABCG2 421 exhibits a reduced erlotinib transport capacity. ABCG2 polymorphism can, therefore, be considered as a possible contributor to the interindividual variation observed in erlotinib pharmacokinetics and toxicity.In summary, we have studied the transport of erlotinib in the K562 cell lines transduced with ABCG2 wt and ABCG2 SNPs in this project. We have identified that erlotinib efflux is affected by SNPs in ABCG2 which leads to variation in intracellular concentrations. The results obtained in this study indicate that K562 The authors declare no conflicts of interest.Participated in research design: Green, Vikingsson, Svedberg, and Jacobs. Conducted experiments: Svedberg and Jacobs. Performed data analysis: Svedberg and Jacobs. Wrote or contributed to the writing of the manuscript: Green, Vikingsson, Svedberg, and Jacobs.


Wolbachia are intracellular endosymbionts of several invertebrate taxa, including insects and nematodes. Although Wolbachia DNA has been detected in ticks, its presence is generally associated with parasitism by insects. To determine whether or not Wolbachia can infect and grow in tick cells, cell lines from three tick species, Ixodes scapularis, Ixodes ricinus and Rhipicephalus microplus, were inoculated with Wolbachia strains wStri and wAlbB isolated from mosquito cell lines. Homogenates prepared from fleas collected from cats in Malaysia were inoculated into an I. scapularis cell line. Bacterial growth and identity were monitored by microscopy and PCR amplification and sequencing of fragments of Wolbachia genes. The wStri strain infected Ixodes spp. cells and was maintained through 29 passages. The wAlbB strain successfully infected Ixodes spp. and R. microplus cells and was maintained through 2–5 passages. A novel strain of Wolbachia belonging to the supergroup F, designated wCfeF, was isolated in I. scapularis cells from a pool of Ctenocephalides sp. cat fleas and maintained in vitro through two passages over nine months. This is the first confirmed isolation of a Wolbachia strain from a flea and the first isolation of any Wolbachia strain outside the “pandemic” A and B supergroups. The study demonstrates that tick cells can host multiple Wolbachia strains, and can be added to panels of insect cell lines to improve success rates in isolation of field strains of Wolbachia. Wolbachia is a genus of obligate intracellular endosymbiotic gram-negative bacteria of the family Anaplasmataceae in the order Rickettsiales. Wolbachia infect two phyla in the Ecdysozoa: the Arthropoda and the Nematoda, with a much broader range of host species in the former than in the latter. Although only one species, Wolbachia pipientis, has been formally described [Wolbachia is best known for its ability to induce five distinct reproductive manipulations in arthropod hosts (cytoplasmic incompatibility (CI), induction of parthenogenesis, male killing, feminisation and meiotic drive), all of which favour its spread by reducing resource competition from males (a dead-end host) or imposing a fitness cost on uninfected females [Wolbachia form obligate and putatively beneficial relationships with their hosts, including strains from supergroups C and D in nematodes and E in springtails [escribed , the genescribed ,3,4. Corescribed . Wolbach females ,7,8,9. H females ,11. In oingtails ,13.Wolbachia is found in most of the major groups of haematophagous arthropods, including biting Diptera and Hemiptera, fleas, lice and parasitic mites [Wolbachia strain) die early in development, is common in blood-feeding Diptera [Wolbachia strain in bedbugs is a nutritional mutualist, providing B vitamins for its host that are deficient in the blood meal [Wolbachia has long been of applied interest for disease control, as release of Wolbachia-infected male pest insects can suppress natural populations where the females are uninfected or harbour an incompatible strain [Wolbachia using antibiotics can safely clear adult worm infections, unlike conventional anthelmintics [Wolbachia infections can suppress the dissemination and transmission of pathogens in insects, especially when transinfected into a novel host [Wolbachia-infected Aedes aegypti to reduce the transmission of dengue and other arboviruses [ic mites ,24,25,26 Diptera ,25,27. Iood meal ,20. Wolbe strain . In filalmintics . Finallyvel host . This phoviruses .Wolbachia infections still remains ambiguous. While many studies have reported the presence of Wolbachia in ticks using molecular methods [Wolbachia, or if the bacteria are present within cells of species of the parasitic wasp genus Ixodiphagus [Wolbachia-positive Ixodes ricinus ticks are almost always positive for Ixodiphagus DNA [The order Ixodida is the only large group of haematophagous arthropods in which the status of  methods ,43,44,45diphagus  or otherdiphagus  or mitesagus DNA ,48.Wolbachia. To answer this, we first tested the ability of cell lines with known broad susceptibility to infection with intracellular bacteria, derived from I. ricinus, Ixodes scapularis and Rhipicephalus microplus, to support the replication of two laboratory strains of Wolbachia derived from different insect hosts. We then applied an I. scapularis cell line in an attempt to isolate Wolbachia or other intracellular bacteria from field-collected fleas in Malaysia.However, the question remains whether or not tick cells are capable of supporting infection and growth of I. scapularis cell lines ISE6 [I. ricinus cell line IRE11 [I. scapularis cell line IDE8 [R. microplus cell line BME/CTVM23 [The nes ISE6  and ISE1nes ISE6  and the ne IRE11  were maine IRE11 . The I. ine IDE8  was mainine IDE8  supplemeE/CTVM23  was mainIxodes spp. cell lines, the wStri strain, originally isolated from the small brown planthopper Laodelphax striatellus into the Aedes albopictus cell line NIAS-AeAl-2 [wAlbB strain, originally isolated from the mosquito Ae. albopictus into the Ae. albopictus cell line Aa23 [R. microplus cell line BME/CTVM23, the wAlbB strain, originally provided by Scott O’Neill, Yale University School of Medicine, was transferred from Aa23 cells to Ae. albopictus C6/36 cells [Drosophila medium  and Mitsuhashi and Maramorosch medium  supplemented with 10% FBS and 2 mM L-glutamine. Infected mosquito cell cultures were maintained at 28 °C with weekly medium change and occasional subculture.For experiments with S-AeAl-2  and kindS-AeAl-2 , was proine Aa23  and kind36 cells  maintainwStri in Ixodes spp. cell lines, cell-free Wolbachia were initially used to inoculate tick cell cultures. Wolbachia were released from heavily infected Ae. albopictus cells by forcibly passing infected cell suspensions through a 25 G needle. The resultant suspension was filtered through a 2 µm syringe filter and inoculated into tick cell cultures. To enhance infection rates, filtered Wolbachia were transferred to a 2 mL microfuge tube containing tick cells (1–2 × 106 cells/mL in 1.5 mL) centrifuged at 5000× g for 5 min and allowed to sit at room temperature for 30 min prior to seeding into culture flasks [wAlbB) or 32 °C (wStri). Once established in tick cell lines, Wolbachia were maintained by inoculating uninfected tick cell cultures with a suspension of infected cells at dilutions of 1:5 for wAlbB and 1:10–1:20 for wStri.For experiments with wAlbB and e flasks . CulturewAlbB in R. microplus cells, a 1.2 mL aliquot of resuspended infected C6/36 cells was diluted with a further 0.5 mL Schneider’s modified Drosophila medium, passed ten times through a bent 26-gauge needle and centrifuged at 1500× g for 5 min. A 0.3 mL aliquot of the supernate was added to a 2.2 mL culture of BME/CTVM23 cells in a flat-sided culture tube, and to an uninfected culture of C6/36 cells in L-15 (Leibovitz) medium supplemented as above but with 10% FBS, in a flat-sided tube. Cultures were incubated at 28 °C; once established, Wolbachia were subcultured by passaging resuspended infected BME/CTVM23 onto fresh cells at a dilution of ~1:10.For experiments with Ctenocephalides sp. fleas were collected from domestic cats from a village of indigenous people, also known as the Orang Asli, in Perak, Malaysia . All field sampling was conducted with the approval of the University of Malaya Institutional Animal Care and Use Committee as well as the Department of Orang Asli Development in Malaysia. Live fleas were immobilised at −80 °C for 15 min and identified to genus level using morphological keys [cal keys , followewAlbB and wStri, and at 8 months p.i. for cultures inoculated with flea organs, tick cells were resuspended by pipetting and cytocentrifuge smears were prepared from small aliquots (~50 µL) of cell suspension, stained with Giemsa and examined at ×500–1000 magnification. Wolbachia-infected BME/CTVM23 cells were prepared for transmission electron microscopy as described previously [Live infected tick cell cultures were monitored by weekly inverted microscope examination. At intervals of 1–7 weeks post inoculation (p.i.) for cultures inoculated with eviously .Wolbachia wsp, coxA, fbpA, ftsZ, hcpA and gatB genes was carried out according to published protocols [wStri-infected NIAS-AeAl-2 cells, wAlbB-infected Aa23 or C6/36 cells for Wolbachia-specific PCRs or Rickettsia raoultii-infected BME/CTVM23 cells [wAlbB-infected BME/CTVM23 cells and the absence of contaminating mosquito cells, qPCR amplification of fragments of the Wolbachia 16S rRNA and mosquito 18S rRNA genes was carried out as described previously [Wolbachia counts were normalised against tick cell counts generated by the amplification of fragments of the tick rpl6 gene as described previously [At intervals, 200–500 µL samples of whole culture suspension were collected and DNA was extracted using commercial kits  following the manufacturers’ instructions. Standard PCR amplification of fragments of the pan-bacterial 16S rRNA sequence and rotocols ,33. All 23 cells  for the eviously . Wolbacheviously . All qPCWolbachia MLST database on PubMLST [Wolbachia sp. isolated in this study with other existing Wolbachia sp. was inferred using the Bayesian Markov chain Monte Carlo in MrBayes 3.2 [Wolbachia MLST scheme [coxA, HKY+G for fbpA and GTR+G for the remaining genes.Pair-end sequences obtained from PCR amplicons were assembled to produce a corrected consensus using 4Peaks  and Clustal Omega . The seq PubMLST . The seq PubMLST . The phyayes 3.2 , with coayes 3.2 . The modWolbachia strain wStri bacteria, harvested from heavily-infected AeAl-2 cells  and infection level (from <10 to >50 bacteria per cell) over the subsequent 4 months. Then, during the following 6 weeks, the bacterial growth rate increased rapidly until around 50% of cells were infected with up to >100 bacteria visible per cell. At this point the first subculture onto fresh BME/CTVM23 cells was carried out; four weeks later this passage 1 culture was heavily infected and was passaged onto fresh BME/CTVM23 cells after a further 7 weeks. The passage 2 culture displayed a heavy infection between 8 and 12 weeks later . The Wolbachia wsp gene was also detectable by PCR in passages 1 and 2 at one month after subculture. The infected cells did not exhibit any cytopathic effects detectable by weekly inverted microscope examination. Intracellular bacteria were observed in Giemsa-stained cytocentrifuge smears prepared from the parent culture at eight months p.i., with approximately 25% of cells infected , MT577878 (coxA), MT577879 (fbpA), MT577880 (ftsZ), MT577881 (gatB) and MT577882 (hcpA), and compared with published sequences to determine its relationship with other Wolbachia strains.Seven pools of flea organs were inoculated into separate IDE8 cell cultures; of these, a single pool of five individual infected a,b. To fwCfeF isolate within the clade consisting of arthropod and filarial Wolbachia strains from the F supergroup (posterior probability = 100). These include two Wolbachia strains previously identified in Ctenocephalides felis from Georgia, USA [Wolbachia MLST scheme: fragments of the coxA, fbpA, ftsZ, hcpA and gatB genes [wCfeF with Wolbachia strains from the F clade, including those from insects such as Supella longipalpa (Blattodea), Paratrechina longicornis (Hymenoptera) and Cimex lectularius (Hemiptera)  a. Phylog F clade b–f. In tr clades . AltogetWolbachia can invade and replicate in tick cells, at least in vitro. We demonstrated susceptibility of three cell lines derived from I. scapularis and one each from I. ricinus and R. microplus. Two laboratory-cultured strains and one novel field strain of Wolbachia, originating from diverse insect groups  and belonging to different supergroups (B and F), were able to infect and grow in tick cells. Special procedures, such as the shell vial technique or centrifugation, were not necessary for the initial infection of tick cells with Wolbachia, and infected cultures were maintained for long periods without the need for special incubation conditions. As seen in chronically-infected mosquito cell cultures [Wolbachia strains wAlbB and wCfeF did not display any cytopathic effect over prolonged periods in vitro. In contrast, Ixodes spp. cultures heavily infected with the wStri strain displayed cytopathic effects, manifest as hypertrophied or pyknotic cells; the same strain in mosquito cells (AeAl-2) caused heavily infected cells to lose the ability to attach to a surface [It is clear from our results that cultures ,72, tick surface .Anaplasma [Cardinium [Ehrlichia [Neoehrlichia [Mycobacterium [Rickettsia [Spiroplasma [Wolbachia persica, in a Dermacentor albipictus tick cell line [Francisella [Rickettsia felis [Mycobacterium leprae, the causative agent of leprosy, transmission of which may be associated with biting insects and ticks [Wolbachia of insect origin. The ability of the novel Malaysian flea-derived Wolbachia isolate to infect and grow in a tick cell line confirmed the usefulness of tick cell lines in cultivating bacterial species of which ticks are not the natural hosts.Considered together, all the tick cell lines used in the present study have previously been shown to be permissive for a wide range of intracellular bacteria transmitted and/or harboured by invertebrates. These include representatives of the genera naplasma ,74, Cardardinium ,75, Ehrlhrlichia ,77,78, Nhrlichia ,80, Mycoacterium , Rickettckettsia ,82,83,84roplasma ,85. A prell line , but thincisella . Althougnd ticks ,88. Ticknd ticks . TherefoWolbachia has been detected by molecular techniques: I. scapularis [I. ricinus [R. microplus [Wolbachia DNA include the ixodid ticks Amblyomma americanum [Rhipicephalus sanguineus [Dermacentor silvarum [Haemaphysalis hystricis [Ornithodoros rietcorreai [Wolbachia and nematodes was not demonstrated [I. ricinus [Wolbachia and parasitic insects by screening samples for the presence of DNA from both bacteria and the parasitic wasp Ixodiphagus hookeri.The cell lines used in the present study were also chosen because they belong to species in which apularis ,44, I. r ricinus ,36,38,43icroplus ,41. Otheericanum , Rhipicenguineus , Dermacesilvarum  and Haemystricis  and the tcorreai . None ofnstrated . To date ricinus ,48 have Wolbachia strains, as revealed in Giemsa-stained smears, was quite similar to that of other members of the Rickettsiales propagated in tick cell lines, such as Anaplasma marginale, Ehrlichia ruminantium, Neoehrlichia mikurensis and Rickettsia raoultii [wAlbB strain in BME/CTVM23 cells was more difficult to interpret. Readily-identifiable bacteria, such as those seen in R. raoultii-infected cells of the same line [wMelPop strain of Wolbachia in D. melanogaster brain cells was reported previously [Wolbachia in B. malayi nematodes, immunogold labelling was used to show that vesicles found in association with Wolbachia were of bacterial origin [The intracellular morphology of the raoultii ,73,77,80ame line , were noeviously , and thel origin .Wolbachia strains  can be propagated continuously within insect cell culture systems, they belong exclusively to the more well-characterised A and B supergroups [Wolbachia strain is the only F supergroup member to be isolated into a cell line to date, the only flea-derived strain to be conclusively isolated and propagated over a prolonged period and the only Wolbachia field strain to be isolated into a tick cell line. A previous study did report the isolation of Wolbachia-like organisms from C. felis alongside Rickettsia felis in ISE6 cells; however, these putative Wolbachia were only identified morphologically and molecular confirmation was not presented [Although over a dozen ergroups ,72,90. Tresented .Wolbachia F supergroup is unique as members have been found in both arthropod and nematode hosts [Wolbachia (wCle) and its host, the bedbug Cimex lectularius [Wolbachia infection in a flea was a comparison between laboratory and wild populations of the gerbil flea, Synosternus cleopatrae, that detected a negative impact of infection on reproductive success in the laboratory colony but not in the wild population [C. felis genome from a laboratory colony in California led to the generation of two complete Wolbachia genomes, neither of which was placed in supergroup F [wCfeT, contains a complete biotin synthesis operon and may be involved in providing nutrients to the host cell. The second genome, wCfeJ, encodes a toxin-antidote system similar to that responsible for CI in dipteran hosts. However, experimental confirmation of mutualistic or parasitic roles for these Wolbachia in C. felis has not been published. The availability of a Wolbachia isolate from the F supergroup growing in a cell line will allow for further genetic and phenotypic characterisation at the cellular level, which could be useful in elucidating the Wolbachia-host relationship for this particular strain.The de hosts ,93. A prtularius . To the pulation . Interesrgroup F . One of C. felis genome project and the detection of Wolbachia DNA in Ctenocephalides spp. fleas in several other studies [Wolbachia bacteria had not been visualised or isolated from fleas of this genus prior to the present study, although immunohistochemistry and transmission electron microscopy identified Wolbachia in the sand flea T. penetrans [Ctenocephalides sp. fleas may not only host a wide range of Wolbachia strains from several distinct supergroups, but may also contain strains from a novel, divergent clade [Wolbachia from the B and F supergroups was detected in C. felis [wCfeF isolate. The phylogenies of 16S rRNA and the MLST scheme genes provided compelling evidence for the placement of the wCfeF isolate within the F supergroup, similar to the Wolbachia strains previously reported from North American C. felis [Despite the completion of the  studies ,22,24,26enetrans . Sequencnt clade ,22,26,93C. felis .Wolbachia from Malaysia and the wider Southeast Asian region have primarily concerned the A and B supergroup Wolbachia from mosquitoes, owing to national research priorities placed on mosquito-borne diseases [Wolbachia in other insects, such as the tephritid fruit flies and the butterfly Hypolimnas bolina, as well as filarial nematodes including Brugia pahangi and Onchocerca borneensis [Wolbachia strain from Ctenocephalides sp. fleas in this region.Reports of diseases ,96,97. Arneensis ,100,101.coxA, fbpA and ftsZ) showed that wCfeF appeared to be closely related to Wolbachia strains from the longhorn crazy ant P. longicornis, and the brown-banded cockroach S. longipalpa [Wolbachia between Myrmecophilus spp. ant crickets and their ant hosts, including P. longicornis [Wolbachia strains in C. felis are still not fully understood, although it has been suggested that the predation of fleas by other insect species could be a possible mode of transmission between distantly-related hosts [The phylogenies from the concatenated genes and a subset of individual MLST genes  being found in the region [C. orientis has a strong host preference for dogs and, to a lesser extent, small ruminants [wCfeF in Malaysia and the wider South-East Asian region.It is important to consider the possibility of there being more than one e region . Due to uminants ,105. TheWolbachia from naturally-infected arthropods. The ability to propagate Wolbachia continuously in tick cell lines provides a platform to examine how these bacteria might modulate the replication of tick-borne arboviruses, bacteria and protozoa, potentially leading to novel control strategies for tick-borne diseases. In light of our demonstration, that tick cells are capable of supporting long-term infection with Wolbachia, the exact nature of the relationship between the bacterium, ticks and other parasitic arthropods such as Ixodiphagus in the field should be re-examined.Tick cell lines can now be added to panels of insect, predominantly mosquito, cell lines to enhance the capability to isolate field strains of


The cytotoxic activity of the obtained bicyclic derivatives was evaluated using human hepatocellular carcinoma (HCC), medulloblastoma (MB), and glioblastoma (GBM) cell lines. Chosen compounds were shown to notably reduce cell viability as compared to nonmalignant cells.A series of chiral sulfonamides containing the 2-azabicycloalkane scaffold were prepared from  Chromatographic separations were performed on silica gel 60 (70–230 mesh). Thin-layer chromatography was carried out using silica gel 60 precoated plates.Melting points were determined on the Schmelzpunkt Bestimmer Apotec melting point apparatus  using the standard open capillary. α radiation at 80(2) K. The Cambridge Crystallographic Data Centre (CCDC) reference numbers for 12a and 12d: CCDC 1992131 (12a) and 1992390 (12d) (eq = 1.2–1.5Ueq (parent C atom). Basic crystallographic data are presented in X-ray diffraction data for monocrystals were collected on an Xcalibur Ruby diffractometer  with MoKS,3R,4R)-2-[(S)-1-phenylethyl]-3-aminemethyl-2- azabicyclo[2.2.1]heptane 2 and -2-[(S)-1-phenylethyl]-4-amine-2- azabicyclo[3.2.1]octane 3, were prepared as described previously -3- hydroxymethyl-2-azabicyclo[2.2.1]heptane 4, as previously described octane (bridged azepane) counterparts. The scaffold of the latter can be regarded as less rigid; on the other hand, higher flexibility of the arm containing sulfonamide moiety in 2-azabicylo[2.2.1]heptane-derived compound 10 seems to be of importance as well. Among substituents present in the sulfonamide, biphenyl-containing methoxy group  or fluorine (10c) were found to be most active. For phenyl derivatives, the introduction of two –CF3 moieties resulted in enhanced activity and selectivity , while one trifluoromethyl group in the para position was not sufficient to increase the antiproliferative properties.Although the activities of the investigated sulfonamides varied from one cell line to another, certain regularities could be observed, which are important from the point of view of further developments. Comparison of activities exhibited by isomeric compounds Finally, dimeric species 12i and 13a were not particularly active, and even their toxicity for normal cell lines was higher than for malignant ones.10b, 10c, 10e, 10g, and 11e notably reduce cell viability on the D425 cell line  as compared to HUVEC cells. The D425 cell line is categorized as a group 3 medulloblastoma, with high MYC proto-oncogene amplification [10c (3.11-fold change), followed by compounds 10g and 11e . Thus, compounds 10c, 10g, and 11e showed the highest performance among the agents tested in this study. Although the safety margin seems to be too narrow and remains far below the >30-fold difference recommended for safety reasons [In this study, we evaluate the effect of chiral 2-azabicycloalkane-based sulfonamides on a variety of tumor cell lines, including HCC, MB, and GBM. Our results indicate that compounds fication . In D425 reasons , appropr


TMPRSS4 is a novel Type II transmembrane serine protease found at the surface of the cells and is involved in the development and cancer progression. However, TMPRSS4 functions in breast cancer remain poor understand. The present study investigated the function of TMPRSS4 in the breast cancer cells and the potential mechanistic action underling. The lentiviral vectors causing TMPRSS4 down-regulation and over-expression were established and transfected in MDA-MB-468 and MCF-7 cells, respectively. By using the CCK-8 assay, cell proliferation was analyzed. Moreover, western blot was used to detect the expression of certain proteins related to cell apoptosis (Bax and Bcl2) signaling pathway and telomere maintenance . Cell cycle and cell apoptosis were also analyzed by using the Flow cytometry analysis. TMPRSS4 expression was detected at the mRNA level and protein level by performing qPCR and western blot technique, respectively. TMPRSS4 expression is inhibited in stable transfected MDA-MB-468-shTMPRSS4 cells compared to the control MDA-MB-468-NC and its expression is up-regulated in stable transfected MCF-7-TMPTSS4 compared to its control MCF-7-NC. Moreover, TMPRSS4 silencing in breast cancer reduces cells proliferation by promoting cell cycle arrest in G2/M phase, cell apoptosis, and telomere maintenance impairment while the TMPRSS4 overexpression increases cells proliferation through cell apoptosis reduction and telomere maintenance reinforcement associated with insignificant change in cell cycle progression. TMPRSS4 plays important roles in cancer progression and may be considered as a good therapeutic target for cancer gene therapy especially breast cancer.  Breast cancer is the most common neoplasm diagnosis among women worldwide and is the leading cause of female cancer death . In 2015, 2.4 million incident cases and 523,000 death cases were estimated worldwide and the increase in the number is predicted in the future . The Cancer gene therapy (CGT) which is the modulation of genes implicated in cancer progression, is one of the new cancer treatment strategies developed and used nowadays . Moreover, the Type II Transmembrane serine proteases (TTPs) are a subfamily of serine proteases with a common proteolytic domain, a transmembrane domain, a short cytoplasmic domain and variable length stem region . TTPs are implicated in the regulation of cellular signaling, tumor initiation, and progression via itself deregulation . TMPRSS4 is a member of TTPs and is often overexpressed in many types of cancer tissues such as pancreatic, thyroid, colon, lung, gastric, breast and other cancer tissues, with a poor clinical prognostic . TMPRSS4 is implicated in cell mobility, invasion, proliferation and tumor metastasis . It has been reported that TMPRSS4 down-regulation suppressed cell proliferation rate , induced cell apoptosis , promotes cell cycle arrest , and cell migration and invasion . Moreover, telomeres are DNA proteins complexes found at the ends of eukaryotic chromosomes for the protection of these chromosomes ends from its recognition as chromosome breaks. Loss of telomere protection ( telomere maintenance) activates DNA damage like signaling response which can halt cell proliferation or promote cell death via induction of tumor suppressors P53 and p16 . However, the effect of TMPRSS4 on cell proliferation in relationship with telomere integrity remains unclear, especially in breast cancer. We undertook this study to confirm in breast cancer, the predicted roles of cell cycle and cell apoptosis in TMPRSS4 modulation mediated cell proliferation modulation found before in other types of cancer cell lines and to investigate the effect of TMPRSS4 modulation on cell proliferation in relationship with telomere integrity. From this study, we showed that cell apoptosis and telomeres are implicated in TMPRSS4 expression modulation induced modulation of cells proliferation while cell cycle is only implicated in TMPRSS4 expression silencing reduced cell proliferation in breast cancer. Cell line, cell culture, and reagents2 at 37oC in DMEM  containing 10% fetal bovine serum , 100 U/mL penicillin and 100 ug/mL streptomycin.MDA-MB-468 cells and MCF-7 cells were obtained from the key laboratory of Tumor Biological Behavior of Hubei Province and incubated under 5% COVector, transfection, and generation of stable cell linesHuman breast cancer cells MDA-MB-468 were stably transfected with TMPRSS4 down-regulating vectors PLKO.1-sh-TMPRSS4 versus PLKO.1-sh-NC. The full-length cDNA encoding TMPRSS4 was cloned into the lentiviral vector pHAGE-CMV-MCS-PGK-puro N-HA+C-Flag followed by its transfection in MCF-7 cells and the similar plasmid was used to establish a control clone (Bioeagle/Wuhan/China). Puromycin-resistant clones were selected at 5 ug/mL puromycin after two to three weeks post-transfection. The resultant stably transfected cell lines were respectively called MDA-MB-468-sh-TMPRS4 versus MDA-MB-468-sh-NC and MCF-7-TMPRSS4 versus MCF-7-NC. Quantitative PCR and western blot were used to analyze, in resultant stably transfected cell lines, the expression of TMPRSS4 at mRNA and protein level, respectively.Quantitative PCR (q-PCR) and RNA isolationoC. The resultants cDNA were amplified using the RevertAid™ First-Strand cDNA Synthesis kit . For the quantitative analysis, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. The nucleotide sequence or specific primer for mRNA were designed as follows: TMPRSS4 Sense, 5′-CCGATGTGTTCAACTGGAAG-3′ and anti-sense, 5′-GAGAAAGTGAGTGG GAACTG-3′; GAPDH sense, 5’-GCACCGTCAAGGCTGAGAAC-3′ and anti-sense, 5’-TGGTGAAGACGCCAGTGGA-3’. After denaturation for 5 min at 950C, the reaction was conducted with 40 cycles of 45 sec at 94oC, for 45 sec at 55oC, 60 sec at 72oC and a final extension for 7 min at 72oC. The PCR products were identified using electrophoresis on 1.5% agarose gels containing 0.5% ethidium bromide (ED). The thermal amplification was performed on Mx3000P qPCR System  and the Mx3000P analysis program was used to analyze the results. All experiments were carried out in triplicate.Total RNA was isolated from the resultant stably transfected MDA-MB-468 and MCF-7 cell lines by using TRIzol reagent according to the manufacturer’s protocol, followed by measurement of the RNA concentration. cDNA was generated from extracted total RNA by using the HiScript qRT SuperMix , for 15 min at 50Western blot analysisoC overnight, followed by the incubation of Goat anti-rabbit or anti-mouse horseradish peroxidase-conjugated antibodies  for ,at least, 1 h at room temperature. Membranes were washed three to five times during 10 min per washing prior to each step. The primary antibodies used included: GAPDH , TMPRSS4 , Bax , Bcl2 , UBE2D3 , POT1 , TPP1 . The separated proteins bands were visualized using Enhanced chemiluminescence system ECL . The images were captured and analyzed (quantified) by respectively using ChemiDoc XRS + System  and Image J program. The resultant stably transfected MDA-MB-468 and MCF-7 cells were treated with RIPA lysis buffer  with an addition of 1xPMSF  and phosphatase inhibitor tablets . Same quantities of proteins (30 ug) were separated on 10-12.5% SDS page gels and transfected to polyvinylidene difluoride (PVDF) membrane with transfer buffer. After blocking in TBST supplemented with 5% skim milk powder at room temperature for 2 h, membranes were incubated with primary antibodies at 4Cell proliferation analysisoC for 90 min. The absorbance of each well was determined at 450 nm (A450 nm) with a microplate reader. Each test was repeated in four wells and performed daily for four days.The resultant stably transfected MDA-MB-468 and MCF-7 cells were seeded into the 96 wells plates at 3.103 cells per well in 100 µL DMEM culture medium. At real time post culture for each analysis , CCK-8 (10 μL) was added to each plate and re-incubated at 37Cell cycle assay5 /mL cells in 2 mL DMEM medium. At 3 days post-culture, cells were harvested, fixed in 70% ethanol for 30 min and stained with 500 μL of propidium iodure  for 30 min at 37oC. Samples were analyzed by flow cytometry . All experiments were performed three times.The resultant stably transfected MDA-MB-468 and MCF-7 cells were seeded into the six wells plates at the 10Cell apoptosis assay5 /mL cells in 2 mL DMEM medium. At 3 days post-culture, cells were collected and apoptosis tests were performing using FITC - Conjugated annexin V / Propidium iodide method  according to the manufacturer’s instructions. All tests were carried out in triplicate.The resultant stably transfected MDA-MB-468 and MCF-7 cells were seeded into the six wells plates at the 10Statistical AnalysisGraphPad Prism 5.0 was used for Statistical analysis. One way ANOVA was used for comparison of multiple groups with Turkey’s method and p-Value <0.05. Data are presented as the mean ± SD. Value of P<0.05 was considered to indicate a statistically significant difference.Effect of stable transfection in MDA-MB-468 and MCF-7 on normal TMPRSS4 expressionIn order to analyze the effect of TMPRSS4 down-regulating and up-regulating on normal TMPRSS4 expression in breast cancer, the MDA-MB-468 and MCF-7 breast cancer cell lines were respectively selected to perform the experiments based on the report of Li et al., (2017) where it has been shown that TMPRSS4 is highly expressed in MDA-MB-468 cells and lowly expressed in MFC-7 cell lines. For confirmation of efficient transfection, qPCR and western blot were respectively used and showed that stable transfection in MDA-MB-468 results to the reduction of TMPRSS4 mRNA  and TMPREffect of TMPRSS4 modulation on breast cancer cells proliferation.Cell proliferation was analyzed by utilizing the CCK-8 assay. The results showed that inhibition of TMPRSS4 expression in MDA-MB-468 cell lines reduced the cell proliferation kinetic  and signEffect of TMPRSS4 modulation on breast cancer cell cycleCells cycle was examined using flow cytometry (FACS) assay. The results revealed that silencing TMPRSS4 in MDA-MB-468 cells significantly induced cell cycle arrest in G2/M phase [25.63±0.32% versus 19.60±1.21%] and decrease of cell in S phase [ 30.27±0.66% versus 23.52±0.69%], at 72 h post-culture, in MDA-MB-468-sh-TMPRSS4 groups compare to control MDA-MB-468-sh-NC group  at 72 h Effect of TMPRSS4 modulation on breast cancer cells apoptosisCell apoptosis was investigated by performing flow cytometry assay and analyzing the expression of certain proteins (Bcl2 and Bax) related to cell apoptosis signaling pathway. The results showed that TMPRSS4 expression down-regulation increased the rate of apoptotic index in MDA-MB-468-sh-TMPRSS4 groups compared to control MDA-MB-468-sh-NC groups at 72 h post-culture [(26.29±0.62% versus 19.13±1.18%); . OtherwiEffect of TMPRSS4 modulation on telomere integrity in breast cancer cell linesWestern blot was performed to analyze telomere integrity through certain proteins  related to telomere functions. Our findings showed the impairment of expression related to telomere maintenance proteins. TMPRSS4 down-regulation in MDA-MB-468 cells inhibited, in MDA-MB-468-sh-TMPRSS4 groups versus control MDA-MB-468-sh-NC groups, the expression of POT1 and TPP1 and increase expression of UBE2D3 as the proteins positively and negatively related to telomere integrity, respectively . AdditioThe recent studies showed that TMPRSS4, which is kind of TTPs family and frequently overexpressed in the cancer cells , function as a cancer treatment target biomarker through the use of Cancer gene therapy (CGT) strategies. It has been reported that TMPRSS4 silencing reduced cancer proliferation and its overexpression induction increases cell proliferation in many kinds of cancer cell lines . In the present study, we used MDA-MB-468 triple negative breast cancer which overexpresses TMPRSS4 and MCF-7 triple positive breast cancer cell as a low expressed TMPRSS4 cancer cell  and found that induction of TMPRSS4 expression down-regulation by using down-regulating TMPRSS4 vector inhibited cell proliferation compared to transfected control and the induction of TMPRSS4 up-regulation promotes cell proliferation which confirms the several above mentioned former studies results . Moreover, the cell cycle is the well-established tumor suppressive mechanism which could be activated to reduce tumor progression and response of cancer to treatment . Regarding the effect of TMPRSS4 on cancer cell cycle, our results showed that TMPRSS4 silencing in MDA-MB-468 breast cancer promotes the cell cycle arrest in G2/M phase associated to the reduction of the cell cycle arrest in S phase while induction of the TMPRSS4 overexpression in MCF-7 breast cancer cell has no significant effect on cell cycle process. This difference results could be due to the expression of estrogen receptor in MFC-7 cell line. In a breast cancer cell, Estrogen Receptor status is inversely associated with a p53 expression which is implicated in cell cycle process perturbation. It has been reported that the sub-unit α of Estrogen receptor (estrogen receptor α) binds to and inactivates p53 . P53 induces the expression of p21WAF1/CIT1/Sdi1, an inhibitor of the cyclin-dependent kinases (CDKs)2,3,4 and 6 .This suggested that the expression of ER in the MCF-7 cell may lead to the lack of the p53 and unchanged of cell cycle process by TMPRSS4. Our findings are in correlation with the report of Jung  where it has been reported that TMPRSS4 inhibition reduces cell proliferation by down-regulating cyclin D1 and cyclin E1 expression as the genes related to the cell cycle signaling pathways , and with the report of Lee et al., (2016) which reported that TMPRSS4 overexpression promotes cell proliferation and induces a 1.89 fold increase of the cell cycle gene cyclin D1 promoter (-962/+134) activity. However, our results contradicted the previous studies which found that the TMPRSS4 silencing reduces cell proliferation with no changes in cell cycle phase  and the part of the finding of Lee et al., (2016), where it has been reported that, in prostate cancer cell lines, TMPRSS4 overexpression display smaller G0/G1 phase population and larger S phase population compared to control transfected Lee et al., (2016), suggesting that the effect of TMPRSS4 on cell proliferation in relationship with cell cycle is cancer cell type dependent and remains to be more investigated in the future. Otherwise, cell apoptosis is one of the key steps of cancer development and progression and also considered as an anticancer mechanism . The pro-apoptotic (Bax) and anti-apoptotic (Bcl2) proteins are key proteins in the signaling pathway of cell apoptosis and are also used as a therapeutic target for cancer treatment via apoptotic pathway affection . The present study investigated the link between TMPRSS4 and cell apoptosis in TMPRSS4 modulation mediated cell proliferation modulation and showed that TMPRSS4 silencing reduces cells proliferation by promoting cell apoptosis associated with inhibition of anti-apoptotic protein (Bcl2) and increase of pro-apoptotic (Bax) protein expression whereas TMPRSS4 overexpression induces cells proliferation by reducing cell apoptotic index through increase of anti-apoptotic protein (Bcl2) and decrease of pro-apoptotic protein (Bax) expression. These results confirm the finding of Fan  where it has been reported that TMPRSS4 modulation promotes cell proliferation modulation by modulating cell apoptosis through Bcl2 expression modulation  and the findings of Huang et al., (2014) showing that TMPRSS4 silencing reduced cell proliferation via increase of cell apoptosis. Moreover, the telomere is implicated in cancer development and treatment . The telosome and Ubiquitin Conjugated Enzyme (UBE2D3) are two kinds of factors implicated in the integrity of telomere . By investigating the relationship between TMPRSS4 and Telomere, we found that TMPRSS4 expression down-regulation reduce the telomere integrity via reduction of expression of POT1, TPP1 as the kind of telosome proteins, and up-regulation of UBE2D3 while TMPRSS4 overexpression reinforces its integrity. As it has been reported in our previous studies that UBE2D3 is negatively correlated with cell proliferation and telomere maintenance  and cell proliferation has been inhibited by affecting telomere integrity via silencing of POT1  and TPP1 , the present study suggest that TMPRSS4 modulation modulates cell proliferation by modulating telomere maintenance in breast cancer cells.In conclusion, our findings indicate that TMPRSS4 expression modulation modulates breast cancer cell proliferation by modulating cell apoptosis and telomere maintenance while cell cycle is only implicated in TMPRSS4 silencing mediated inhibition of breast cancer cells proliferation. This confirms the implication of TMPRSS4 in cancer progression and can make TMPRSS4 to be considered as a potential therapeutic target for Cancer Gene Therapy (CGT).


N-acetyltransferase 1 (NAT1), present in all tissues, is classically described as a phase-II xenobiotic metabolizing enzyme but can also catalyze the hydrolysis of acetyl-Coenzyme A (acetyl-CoA) in the absence of an arylamine substrate using folate as a cofactor. NAT1 activity varies inter-individually and has been shown to be overexpressed in estrogen receptor-positive (ER+) breast cancers. NAT1 has also been implicated in breast cancer progression however the exact role of NAT1 remains unknown. The objective of this study was to evaluate the effect of varying levels of NAT1 N-acetylation activity in MDA-MB-231 breast cancer cells on global cellular metabolism and to probe for unknown endogenous NAT1 substrates. Global, untargeted metabolomics was conducted via ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) on MDA-MB-231 breast cancer cell lines constructed with siRNA and CRISPR/Cas9 technologies to vary only in NAT1 N-acetylation activity. Many metabolites were differentially abundant in NAT1-modified cell lines compared to the Scrambled parental cell line. N-acetylasparagine and N-acetylputrescine abundances were strongly positively correlated  with NAT1 N-acetylation activity whereas saccharopine abundance was strongly inversely correlated (r = −0.876). Two of the most striking observations were a reduction in de novo pyrimidine biosynthesis and defective β-oxidation of fatty acids in the absence of NAT1. We have shown that NAT1 expression differentially affects cellular metabolism dependent on the level of expression. Our results support the hypothesis that NAT1 is not just a xenobiotic metabolizing enzyme and may have a role in endogenous cellular metabolism.Human arylamine  A better understanding of the role NAT1 has in breast cancer would aide in the development of novel treatment strategies and therapeutics.Breast cancer is a heterogeneous disease with many underlying genetic transformations that lead to a diseased state. According to the American Cancer Association, 30% of all new cancer cases in women in 2020 will be breast cancer and 1 in 8 women will develop breast cancer in their lifetimeN-acetyltransferase 1 (NAT1) is a phase-II xenobiotic metabolizing enzyme found in almost all tissues. NAT1 can additionally hydrolyze acetyl-Coenzyme A (acetyl-CoA) in the absence of an arylamine substrate using folate as a co-factor3. Many additional novel roles for NAT1 have recently been identified using breast cancer cell models including regulation of matrix metalloproteinase 9 (MMP9)4, providing protection against reactive oxygen species during glucose starvation5, and the loss of NAT1 leading to regulation of mitochondria through inhibition of the pyruvate dehydrogenase complex6. NAT1 expression varies inter-individually and has been shown to be elevated in several cancers including estrogen receptor-positive (ER+) breast cancers9. Additionally, multiple studies have shown that inhibition of NAT1, by both small molecule inhibition and siRNA methods, in breast cancer cells leads to decreased invasive ability and proliferation10 and decreased anchorage-independent colony formation11.Human arylamine 12). Differences in endogenous acetyl-CoA have been observed between MDA-MB-231 breast cancer cells expressing parental, increased, and decreased levels of human NAT113. A previous study investigating the polar metabolome of the same cells has also revealed differences in a number of metabolites, including amino acids and palmitoleic acid13. Given these data, we hypothesize differences in NAT1 activity may be contributing to downstream effects on metabolic pathways that utilize acetyl-CoA such as the TCA cycle and fatty acid synthesis and degradation.To date however, the exact mechanism by which NAT1 expression affects cancer risk and progression remains unclear  cell lines as well as a cell line that has only one copy of NAT1 knocked-out .As metabolic reprogramming is one of the hallmarks of cancer proposed by Hanahan and WeinbergScrambled), increased (Up), decreased , or knockout  human NAT1 N-acetylation activity  and no detectable (knockout) NAT1 activity  were included in this study  however the construction of the CRISPR 2–12 cell line has not been previously described. The CRISPR 2–12 cell line was constructed using the same methodology and guide RNA as the CRISPR 2–19 cell line described in Carlisle et al.19. All cell lines were authenticated by the ATCC Short Tandem Repeat (STR) profiling cell authentication service.Additionally, cell lines constructed using CRISPR/Cas9 technology, expressing approximately 50% of the NAT1 activity of the In vitro NAT1 N-acetylation activity was determined in each constructed cell line via high performance liquid chromatography (HPLC) using slight modifications of procedures previously described21. Briefly, cell lysate from each cell line was incubated with 1 mM acetyl-CoA and 300 μM p-aminobenzoic acid (PABA) at 37 °C for 10 minutes. Reactions were terminated with the addition of 1/10 reaction volume 1 M acetic acid. Reaction products were collected and analyzed using an Agilent Technologies 1260 Infinity high performance liquid chromatography using a LiChrospher 100 RP-18  column to determine the amount of acetylated product.(DMEM), with 10% fetal bovine serum, 1% glutamine, and 1% penicillin/streptomycin added. Cells were allowed to grow for three days in an incubator at 37 °C and 5% CO2.Cells were plated in triplicate per biological replicate at a density of 500,000 cells per 150 × 25 mm cell plate. All cell lines were cultured in high-glucose Dulbecco’s Modified Eagle Medium Cells were then harvested on ice by adding 5 mL 0.25% trypsin and scraping the cells from the plate. Three plates were combined to form one sample  to ensure there was enough cell mass for analysis. After harvesting the cells, cells were washed 3 times with ice-cold 1 x phosphate buffered saline (PBS). All supernatant was removed and the cryovials containing cell pellet samples were then flash frozen by placing in a pool of liquid nitrogen for 1 minute followed by immediate storage at −80 °C. Samples were then shipped on dry ice to Metabolon Inc.  for analysis.22.Following receipt by Metabolon, samples were inventoried and immediately stored at −80 °C until processing. Detailed sample preparation, quality assessment/quality control, ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), data extraction, compound identification, and metabolite quantification methods are described in detail elsewhereBiochemical data was normalized to total protein as determined by Bradford assay to account for differences in metabolite levels due to differences in the amount of material present in each sample. Data for each biochemical was rescaled to set the median equal to 1. Missing values were imputed with the minimum value.23. Data was approximately normally distributed after these transformations. Figure Q-values were then calculated from un-adjusted p-values for preserving the false discovery rate and to account for multiple comparisons24. Dunnett’s post-tests were utilized to compare all groups  to the Scrambled group for those metabolites with one-way ANOVA q ≤ 0.05. Q-values were also determined and reported for Dunnett’s post-tests.All statistical analyses were performed on median-scaled and log-transformed data using R: A Language and Environment for Statistical Computing version 3.3.113. Briefly, the mean abundance for each metabolite was calculated for each group. We then divided the mean of the comparison group  by the mean of the Scrambled group to give us fold-change relative to the Scrambled group. Fold-change and significance of between group differences in global metabolites were visualized using volcano plots. Additionally, abundance data were plotted as box-plots to visualize the distribution of each metabolite by groups. The Pearson correlation coefficient was calculated between NAT1 activity and relative metabolite abundance for all metabolites to generate hypotheses about potential unknown NAT1 substrates or products. Additionally, the correlation coefficient was calculated between carnitine and metabolites whose abundance was concordantly altered in the two NAT1 KO cell lines due to the large proportion of fatty acyl-CoA carnitine conjugates observed. Data was also plotted as a heatmap and hierarchal clustering was conducted using the weighted pair group method with arithmetic mean (WPGMA) method. Principal component analysis was conducted by singular value decomposition of the centered data matrix. The loadings of the first  and second  principal component were plotted. Pathway enrichment analysis was conducted for each group compared to Scrambled. The pathway analysis enrichment score was normalized to mean enrichment of random samples of the same size to determine the relative degree of enrichment.Fold-change was calculated as previously describedp-aminobenzoic acid (PABA) N-acetylation activity was measured in each of the six MDA-MB-231 cell lines. The Scrambled cell line had approximately the same activity as the Parent MDA-MB-231 cell line while the Up cell line had an approximate 700% increase in activity. Additionally, the Down and CRISPR 2-12 cell lines had approximately 65% and 50% of the activity of the Parent and Scrambled cell lines, respectively, while the CRISPR 2-19 and CRISPR 5-50 cell lines had no detectable (limit of detection = 0.05 nmoles acetylated PABA/min/mg) activity  between the six cell lines  were differentially abundant in the Down, Up, CRISPR 2-12, CRISPR 2-19, and CRISPR 5-50 groups, respectively, with a fold change of 2 or greater (in either direction) compared to the Scrambled cell line. Metabolites were further characterized by direction of fold-change compared to Scrambled  of the detected metabolites were found to significantly differ  utilized during the knockout of NAT1, the overlap in significant metabolites with a fold-change greater than or equal to 2 was compared between the two NAT1 KO cell lines and the oup Fig. . EighteeN-acetylasparagine, N-acetylputrescine, saccharopine, cytidine, 1-palmitoyl-2-alpha linolenoyl-GPC (16:0/18:3n3), isovalerylcarnitine (C5), cysteine sulfinic acid, and serotonin, were significantly associated with NAT1 PABA N-acetylation activity  value greater than 0.90 and are N-acetylated compounds, suggesting they may be products of N-acetylation by NAT1. Two of the metabolites significantly correlated with PABA N-acetylation, saccharopine  of all metabolites detected to be altered. Additionally, we expected very few, if any, differences in metabolite abundance between the two NAT1 KO cell lines since each cell line should have the exact same genome. Yet one of the most striking observations of this study is the differences in relative metabolite abundance between the two complete NAT1 KO cell lines. The hierarchical clustering, principal components analysis, and pathway enrichment analysis show there were significant differences between the two cell lines constructed using CRISPR/Cas9 guide RNA 2 and the cell line constructed using CRISPR/Cas9 guide RNA 5. Even though, in terms of NAT1 activity the CRISPR 2-19 and CRISPR 5-50, cell lines are highly similar, their metabolic profiles are extremely different. This suggests there may be additional genetic differences between the two cell lines. The observation of a large number of differentially abundant metabolites between each KO cell line compared to the Scrambled cell line and compared to each other could be because the cell lines have undergone additional unique mutations between them and/or because each CRISPR guide RNA used caused unique off-target effects in addition to targeting NAT1. To focus on metabolome differences associated with varying levels of NAT1 in breast cancer, we have focused our interpretation of this study to metabolites that agreed between the two CRISPR NAT1 KO cell lines  only genetically altered a single gene, NAT1, in each cell line, we expected only a small proportion of metabolites to be significantly different, due to the vast homeostasis mechanisms present32, is formed by carboxylating acetyl-CoA and is known to inhibit CPT1. Decreases in the level of a number of acylcarnitine species clearly indicate that the CPT1 step and subsequent mitochondrial metabolism of fatty acids are disturbed in NAT1 KO cells. We hypothesize the increase in acetyl-CoA observed when NAT1 is knocked out may lead to increased malonyl-CoA which inhibits CPT1 resulting in less acylcarnitines. In addition, 3 out of 18 metabolites that are concordantly increased in both KO cell lines belong to 3-hydroxy-fatty acids  , linolenoylcarnitine (C18:3), linoleoylcarnitine (C18:2), and myristoleoylcarnitine (C14:1)  have been reported to catabolize N-acetylasparagine, albeit at a much lower level than the prototypic ASPA substrate N-acetylaspartate49. Additionally, aminoacylase 1 deficiency (ACY1), a rare inborn error of metabolism disease, is diagnosed by increased N-acetylated amino acids in the urine, including N-acetylasparagine50 suggesting ACY1 can also metabolize N-acetylasparagine. Given the abundance distribution of N-acetylasparagine in our constructed cell lines, it is possible that L-asparagine is a substrate N-acetylated by NAT1 to form N-acetylasparagine but confirmation is needed.The strong positive association between the abundance of N-acetylputrescine and NAT1 N-acetylation activity in the 6 MDA-MB-231 cell lines is consistent with N-acetylputrescine as a product of NAT1 N-acetylation. Putrescine is known to be N-acetylated by spermidine/spermine N1-acetyltransferase 1 (SAT1) and spermidine/spermine N1-acetyltransferase 2 (SAT2) but with much lower affinity than for other SAT substrates such as spermidine51. Redundancy in non-homologous metabolic enzymes has been shown to occur53 and it is possible that NAT1 and the SATs may both N-acetylate putrescine.Similarly, the strong positive association between the abundance of 59. Our data indicates cell lines with higher levels of NAT1 activity have increased amounts of N-acetylated asparagine and putrescine while cell lines with decreased and knocked-out NAT1 activity have decreased amounts of N-acetylated asparagine and putrescine. We hypothesize this could lead to decreased asparagine and putrescine in cell lines with high NAT1 and increased asparagine and putrescine in cell lines with decreased and knocked-out levels of NAT1 relative to cell lines with basal NAT1 activity. However, we did not observe this differential distribution in our dataset. It has been shown that intracellular asparagine exchanges with extracellular amino acids to promote mTORC1 activation, protein and nucleotide synthesis and cell proliferation under normal growth (non-starvation) conditions55. Additionally, it has been reported that the invasiveness of a mouse breast cancer model could be modulated either by altering asparagine biosynthetic capacity or by modifying extracellular asparagine pools with decreases in asparagine leading to decreased metastatic burden54. Polyamines, such as putrescine, are known to facilitate the interactions of transcription factors, such as estrogen receptors with their specific response element and are involved in the proliferation of ER negative breast cancer tumor cells (reviewed in60). Additionally, an increase in intracellular polyamine concentrations has been associated with increased cell proliferation and has been linked to tumorigenesis65. Our data, interpreted in light of the results of previous studies, suggests that knockout NAT1 may increase cell growth capabilities due to higher levels of asparagine and putrescine however studies from our lab do not support this20. This may be because there are additional mechanisms in place regulating the amount of free asparagine and putrescine or other factors regulating growth that override an abundance of these metabolites.Both L-asparagine and putrescine have been implicated in the promotion of cell growth67. This observation may be connected to lysine’s role in carnitine biosynthesis33. Pathway analysis indicated pathways that directly involve acetyl-CoA such as the lysine degradation and tryptophan metabolism pathways were enriched for differences (Fig. The strong negative association between the abundance of saccharopine and NAT1 activity in the 6 MDA-MB-231 cell lines suggests saccharopine is a NAT1 substrate or located upstream of a NAT1 catalyzed reaction. Saccharopine is an intermediate in the main pathway responsible for the catabolism of lysineces Fig. . AdditioN-acetylation by NAT1 but further validation is required. These metabolites have recently been implicated in enhanced cell growth and metastatic potential in breast cancer models suggesting they may be the key to understanding how varying levels of NAT1 affect breast cancer progression. Furthermore, metabolites involved in fatty acid metabolism and pyrimidine biosynthesis showed strong concordant changes in both NAT1 KO cell lines suggesting NAT1 may have an essential role in these processes. Moreover, many of the pathways significantly enriched in pathway analysis are pathways where acetyl-CoA plays a role, adding further evidence for the connection between NAT1 and acetyl-CoA in metabolism.Our results support the hypothesis that NAT1 is not just a xenobiotic metabolizing enzyme and may have a role in endogenous cellular metabolism. Whether this is direct or through NAT1’s effect on acetyl-CoA remains unknown. We have shown that NAT1 expression differentially affects cellular metabolism dependent on the level of expression. Additionally, we have identified potential novel substrates and products of Supplementary information.Supplementary information2.