Embryonic stem cell (ESC) derivation from single blastomeres of 8-cell mouse embryos results in lower derivation rates than that from whole blastocysts, raising a biological question about the developmental potential of sister blastomeres. We aimed to assess the ability of 8-cell blastomeres to produce epiblast cells and ESC lines after isolation, and the properties of the resulting lines. Our results revealed unequal competence among sister blastomeres to produce ESC lines. At least half of the blastomeres possess a lower potential to generate ESCs, although culture conditions and blastomeres plasticity can redirect their non-pluripotent fate towards the epiblast lineage, allowing us to generate up to seven lines from the same embryo. Lines originated from the same embryo segregated into two groups according to their transcriptional signatures. While the expression of genes related to pluripotency and development was higher in one group, no differences were found in their trilineage differentiation ability. These results may help to improve our understanding of the ESC derivation process from single blastomeres and cell fate determination in the preimplantation mouse embryos. Embryonic stem cells (ESC), traditionally derived from the inner cell mass (ICM) of blastocysts , can alsCurrently, the main limitation of using single blastomeres as a source of ESCs is the decreased derivation rate compared to the use of whole embryos, particularly when blastomeres of 8-cell embryos are used. Because ESCs are known to derive from the pluripotent epiblast (EPI) of the preimplantation embryo , this raDuring preimplantation development, blastomeres gradually lose their developmental potential as cleavage progresses, and eventually differentiate into three separate lineages. It is widely accepted that the formation of the ICM and TE in the blastocyst is the first lineage segregation in the mammalian embryo , followeAlthough heterogeneities among blastomeres in cleavage-stage embryos could play a role in cell fate commitment during normal development, it is known that preimplantation embryos are able to adapt to experimental perturbations such as the addition or removal of cells  because The aim of this study was to determine whether, under optimized culture conditions that promote pluripotency maintenance and expansion of the EPI cell population, all blastomeres of the 8-cell stage mouse embryo are able to generate mESCs lines, and whether the lines originating from the same embryo have similar characteristics or whether they differ depending on the blastomere from which they originated. To this aim, we first examined the potential of individual sister blastomeres of 8-cell embryos to develop into mESCs lines and precursor cell aggregates containing pluripotent EPI cells. Next, we compared the transcriptional profile of the mESCs lines derived from blastomeres of the same 8-cell embryos and their ability to differentiate into cells of the three primary germ layers. Our findings will help to improve the generation of ESCs from single blastomeres and to better understand the developmental potential of single blastomeres of 8-cell stage embryos and the determination of cell fate during preimplantation embryo development.Departament d’Agricultura, Ramaderia, Pesca i Alimentació of the Generalitat de Catalunya .Mouse care and procedures were conducted according to the protocols approved by the Ethics Committee on Animal and Human Research of the Universitat Autònoma de Barcelona and by the 2 at −196°C. Cryopreservation allowed us to optimize the use of donor females and to work with the exact number of embryos required for each experimental replicate regardless of superovulation success.Mouse embryos were obtained from 6 to 12 weeks old B6CBAF1/J females (Charles River Laboratories). Females were induced to superovulate by intraperitoneal injection of 5 IU Pregnant Mare’s Serum Gonadotropin  followed by the injection of 5 IU Human Chorionic Gonatropin (Divasa-Farmavic) 48 h later and mated with B6CBAF1/J males. Oviducts were flushed with HEPES-buffered KSOM medium (H-KSOM)  48 h lat® KSOM Medium (Merck Millipore) under mineral oil (Sigma) at 37°C and 5% CO2, and biopsied when they reached the 8-cell stage, as previously reported , as previously reported .3 U/mL leukemia inhibitory factor  and R2i inhibitor cocktail, a combination of MEK inhibitor PD0325901  and TGFβ inhibitor SB431542 . During the first week of culture, 0.1 mg/mL adrenocorticotropic hormone  was also added to the medium.Mouse ESC derivation medium (KSR + R2i) consisted of DMEM high glucose (BioWest) supplemented with 100 μM 2-β mercaptoethanol (Gibco), 1x non-essential amino acids (Gibco), 50 U/mL penicillin and 50 μg/mL streptomycin (Biowest), 20% KSR (Gibco ThermoFisher), 102 for 7–10 days, and the medium was changed every 48 h until outgrowths were observed. After the first passage, the outgrowths were cultured in 4-well plates without ACTH. Putative mESCs lines were passaged once or twice a week using trypsin-EDTA (BioWest) and maintained in culture for at least six passages.Blastomeres were cultured at 37°C and 5% COThe stemness of all putative mESCs lines was assessed at the seventh passage by Alkaline Phosphatase Activity (ALP) staining and immunodetection of pluripotency and differentiation markers.ALP activity was determined using a commercial kit (Sigma AB0300) and a previously reported protocol . Brieflyin vitro differentiation of the putative mESCs lines to confirm their pluripotency and differentiation potential, respectively. Spontaneous differentiation was achieved by culturing s lines in DMEM high glucose supplemented with 10% fetal bovine serum  under feeder-free conditions for 10 days. Cells were fixed in 4% PFA for 20 min at RT, and blocked and permeabilized with a PBS solution containing 0.2% sodium azide (Sigma), 0.5% Triton X-100 (Sigma) and 3% goat serum (BioWest) for 30 min at 37°C. The fixed cells were incubated with primary antibodies overnight at 4°C, washed, and incubated with secondary antibodies for 2 h at RT.Immunofluorescence staining was performed both before and after spontaneous α smooth muscle actin  and mouse monoclonal anti-alpha-fetoprotein  antibodies were used to detect ectoderm, mesoderm, and endoderm differentiation markers, respectively. The secondary antibodies used were anti-mouse IgG Alexa Fluor 488  for OCT4, TUJ1, αSMA and AFP, and anti-rabbit IgG Alexa Fluor 594  for SOX2 and NANOG.Mouse monoclonal anti-OCT4 , rabbit polyclonal anti-NANOG  and rabbit polyclonal anti-SOX2  antibodies were used to detect the pluripotency markers. Mouse monoclonal anti-Tubulin β3 , mouse monoclonal anti-Finally, the cells were mounted with 10 μg/mL Hoechst 33258  diluted in Vectashield (Vector Laboratories) and analyzed using an epifluorescence microscope (Olympus BX61) and Cytovision software .2 for 72 h to allow the formation of cell aggregates. Whole 8-cell embryos previously incubated in acidic Tyrode’s solution to remove the zona pellucida were also cultured under the same conditions and used as controls.Single blastomeres were seeded on a monolayer of inactivated HFF (iHFF) in KSR + R2i medium, as detailed for the derivation of mESCs, and incubated at 37°C and 5% COCell aggregates were fixed and immunostained following the same protocols described above for mESCs. Mouse monoclonal anti-OCT4 and rabbit polyclonal anti-NANOG were used as primary antibodies for the detection of ICM and EPI cells, respectively. The secondary antibodies used were anti-mouse IgG Alexa Fluor 488 for OCT4 and anti-rabbit IgG Alexa Fluor 594 for NANOG.Finally, the cell aggregates were mounted with 10 μg/mL Hoechst 33258 diluted in Vectashield. The number of total cells (Hoechst staining), OCT4+ cells and NANOG + cells were counted using an epifluorescence microscope (Olympus BX61) with the Cytovision and ImageJ software.2. After this short incubation, feeder cells attached to the plates whereas mESCs remained in solution.Feeder cells were removed from mESCs cultures using the protocol described by Total RNA was extracted from mESCs using the Maxwell RSC SimplyRNA Tissue Kit (Promega). Its concentration and quality were assessed using a NanoDrop spectrophotometer (ThermoFisher Scientific).® RNA BR Assay kit (Thermo Fisher Scientific), and RNA integrity was estimated using an Agilent RNA 6000 Pico Bioanalyzer 2,100 Assay (Agilent). All the samples used in the study had RNA integrity numbers (RIN) above 9.Two micrograms of total RNA obtained from each mESC line was sent to the Centre Nacional d’Anàlisi Genòmica (CNAG) in Barcelona (Spain) for RNA sequencing (RNA-seq). The samples were quantified using a QubitRNA-Seq libraries were prepared with the KAPA Stranded mRNA-Seq Illumina Platforms Kit (Roche) following the manufacturer´s recommendations. Briefly, 500 ng of total RNA was used for poly-A fraction enrichment with oligo-dT magnetic beads following mRNA fragmentation. The strand specificity was achieved during the second strand synthesis performed in the presence of dUTP instead of dTTP. The blunt-ended double stranded cDNA was 3′adenylated and Illumina platform compatible adaptors with unique dual indexes and unique molecular identifiers (Integrated DNA Technologies) were ligated. The ligation product was enriched using 15 PCR cycles. The size and quality of the libraries were assessed using a High-Sensitivity DNA Bioanalyzer assay (Agilent).The libraries were sequenced on a NovaSeq 6,000 (Illumina) with a read length of 2 × 51 bp, following the manufacturer’s protocol for dual indexing. Image analysis, base calling and quality scoring of the run were processed using the manufacturer’s software Real Time Analysis (RTA 3.4.4).Mus musculus reference genome (GRCm39) using STAR software version 2.7.8a  enrichment analysis using gProfileR v07.0  plot was generated with the rlog transformed counts using the top 500 most variable genes and the ggplot2 R library . Heatmapin vitro differentiation into cells of the three germ layers, mESCs lines were subjected to embryoid body (EB) formation following the protocol described by 2. Every 2 days, 100 µL of medium was removed and 100 µL of fresh differentiation medium was added to each well.To assess the potential for Oct4, Nanog, and Rex1), endoderm lineage genes , mesoderm lineage genes  and ectoderm lineage genes  by quantitative PCR (qPCR).After 7 days, 20-30 EBs per mESCs line were collected to analyze the expression of pluripotency genes .The qPCR reactions were performed in triplicate on a CFX3846 Real-Time System thermocycler (Bio-Rad) using iTaq universal SYBR Green Supermix (Bio-Rad). The amplification program consisted of an initial denaturation step of 3 min at 95°C, followed by 40 cycles of 10 s at 95°C (denaturing) and 30 s at 60°C . An additional thermal denaturing cycle was performed to obtain the melting curve of the qPCR products.Oct4 , Rex1  and Nanog (qMmuCID0005399) were used to assess pluripotency. Afp (qMmuCID0023288), Foxa2 (qMmuCED0048428) and Ttr (qMmuCID0006861), T-Brachyury (qMmuCID0015169), Nkx2-5 (qMmuCID0020858) and Kdr (qMmuCID0005890), and Nes (qMmuCID0023067), Pax6 (qMmuCID0026137), and Fgf5 (qMmuCID0008576) were used to assess differentiation to endoderm, mesoderm and ectoderm lineages, respectively. A no-template control (NTC) was added for each primer and the results were normalized to Gapdh (qMmuCED0027497) expression. Additionally, the relative expression values in EBs samples were normalized to their respective undifferentiated ESC lines. The cycle quantification value  was determined for each sample using BioRad CFX Maestro™ 195 software, and relative expression was calculated using the ΔΔCq method.Validated commercial PrimePCR SYBR Green Assays (BioRad) for t-test or a Mann-Whitney test. All the statistical analyses were performed using the GraphPad Prism 8 software and p < 0.05 was considered as statistically significant.Aggregate formation rates were analyzed with Fisher’s exact test. The mean numbers of total, OCT4+ and NANOG + cells and the mean ratios of OCT4+/total cells, NANOG+/total cells and NANOG+/OCT4+ were first analyzed using the Shapiro-Wilk test to check the normality of the samples and then compared using either an unpaired Mean values are indicated in the text, tables, and figures as mean ± SEM.in vitro spontaneous differentiation for 10 days) . We obtained a total of 292 mESCs lines (29%), whose stemness was confirmed at the seventh passage by morphological criteria (colonies with rounded morphology and defined edges), pluripotency criteria  activity and OCT4, SOX2 and NANOG markers), and differentiation criteria  .Of the total embryos, 84.1% (106/126) formed at least one mESCs line. Most of the embryos (97/126) gave rise to one to four lines, but two embryos were able to produce seven lines . These rTo assess whether the failure of some blastomeres within each embryo to produce mESCs lines was related to their lack of pluripotent potential, we next examined the ability of single blastomeres to develop into cell aggregates containing pluripotent EPI cells. Cell aggregates are the precursors of outgrowths, and because of their higher formation efficiency and earlier manifestation in the derivation process, their formation could be a better marker of blastomere developmental potential.n = 15) were singly extracted by micromanipulation  were able to develop into cell aggregates containing pluripotent EPI cells after 72 h of culture. This ability was reduced in single blastomeres, as only 62.5% of the initial blastomeres (75/120) developed into aggregates containing OCT4+ and NANOG + cells, even though 72.5% (87/120) of the blastomeres formed aggregates with OCT4+ cells . It mustWhen comparing the mean ratios of OCT4+/Total cells, NANOG+/Total cells and NANOG+/OCT4+ cells between the aggregates derived from single blastomeres  and those derived from control 8-cell embryos , statistically significantly differences were found only in the NANOG+/Total cells ratio .Altogether, these results suggest that single blastomeres isolated from 8-cell embryos have the same potential as whole 8-cell embryos to develop into cell aggregates, but the specification of pluripotent EPI is slightly impaired.Next, we sought to determine how the potential to develop into cell aggregates containing pluripotent cells was distributed among the different blastomeres of the 8-cell embryo. For this purpose, the results obtained from single blastomeres were grouped according to their parental embryos.The number of single blastomeres per embryo that developed into a cell aggregate after 72 h in culture ranged from 3 to 8, with a mean number of 6.5 ± 0.3 . Most emThe sum of aggregates formed by the sister blastomeres of the same parental embryo had an average total number of 190.5 ± 16.8 cells (222.8 ± 15.9 if only embryos forming seven to eight aggregates are considered), a value significantly higher than the average total number of cells in control 8-cell embryo-derived aggregates (95.2 ± 10). These results indicate that when blastomeres from the same embryo are isolated and cultured separately, they can generate a higher number of cells than when they remain together.The mean number of aggregates per embryo that were positive for both OCT4 and NANOG was 5.0 ± 0.4, ranging from 3 to 7 . Thus, tThe number of OCT4+ and NANOG + cells in the cell aggregates was heterogeneous and showed large variation both among sister blastomeres and among parental embryos . The numFinally, given the variation in the number of pluripotent EPI cells among sister blastomeres and that only 29% of the blastomeres were able to produce an mESCs line in the first set of experiments, we tried to estimate the minimum number of NANOG + cells required for the successful establishment of an mESCs line from single blastomeres. To do this, we classified the cell aggregates formed by all the individual blastomeres according to the number of NANOG + cells they contained and selected the 29% upper range, assuming that mESCs lines would most probably be formed from cell aggregates possessing a higher number of NANOG + cells. This 29% upper range contained 5–18 NANOG + cells, indicating that single blastomeres should generate cell aggregates containing at least five NANOG + cells after 3 days in culture for them to be able to subsequently establish an mESCs line. Considering that in the first set of experiments the majority of the embryos  generated one to four mESCs lines, we next calculated the percentage of embryos in the second set of experiments that were able to form one to four aggregates containing the estimated minimal number of NANOG + cells (≥5). Interestingly, the percentage was very similar .To characterize and compare the molecular features of mESCs lines produced from different blastomeres of the same parental embryo in terms of their gene expression patterns, we selected the lines produced by embryos that had given rise to the largest number of mESCs lines in the first set of experiments. In particular, five mESCs lines established from one embryo and seven mESCs lines established from two other embryos  were selected and subjected to RNA sequencing (RNA-seq) . Two intTo reveal possible transcriptional variations among blastomeres-derived mESCs other than those related with the parental embryo of the blastomeres, between-embryo variability was corrected and a new PCA was performed. This new analysis revealed that lines segregated into two clusters (Groups I and II) and, interestingly, half of the lines from each embryo were allocated to each cluster , except Next, a WGCNA was performed on the embryo-adjusted gene counts, in which mESCs line 2.2 was shown to cluster together with lines in group II. Using this analysis, we identified five clusters of highly correlated genes  , which aFinally, to visualize how the two groups of mESCs lines differed in their pluripotency potential, we produced a heatmap of the differential expression of several core, naïve and primed pluripotency genes . Lines iin vitro differentiation for EB formation, and the expression of pluripotency , endoderm lineage , mesoderm lineage  and ectoderm lineage  marker genes was measured by qPCR on the EBs formed. All but one of the mESCs lines  were able to form EBs. Heterogeneity in the expression of these markers was evident among mESCs lines, regardless of the group they belonged to or the parental embryo they originated from  upon differentiation, when compared with mESCs lines from group II, no biased differentiation towards a specific lineage was found among the mESCs lines of the two groups.Finally, we sought to determine whether the transcriptional differences observed between the mESCs lines generated from single blastomeres (groups I and II) would create a bias in their ability to form cells of the three developmental germ layers. To this end, the same blastomere-derived mESCs lines used in the RNA-seq analyses were subjected to spontaneous ted from . Althougn = 126) and blastomeres (n = 1.008) and by keeping track of the parental embryo of each blastomere. We first analyzed the potential of sister blastomeres of 8-cell embryos to produce mESCs lines and cell aggregates containing pluripotent cells (as precursors of mESCs lines). To maximize the development of blastomeres into cell aggregates, outgrowths and mESCs lines, isolated blastomeres were cultured on feeder cells and in the presence of LIF and R2i in the culture medium  was similar to the 85.3% derivation rate obtained by our group from whole blastocysts of the same mouse strain . NonetheThe large variation in the number of mESCs lines obtained per embryo (0-7) suggests that blastomeres from 8-cell stage embryos are not equally competent at establishing mESCs lines and that the number of competent blastomeres is not uniformly distributed among embryos. Nonetheless, among the embryos that produced at least one mESCs line, those in which the mESCs generation potential was restricted to half or less of the blastomeres were much more abundant (91.5%) than those in which more than 50% of the blastomeres produced an mESCs line (8.5%). To the best of our knowledge, no similar studies have been performed with 8-cell embryos, but One of the limiting factors during mESCs establishment from whole blastocysts is the rapid loss of pluripotent EPI cells, the progenitors of mESCs, during the first few days of culture . Therefon = 72) compared with our study (n = 1.008), rather than to the use of ideal experimental conditions for the maintenance of blastomere developmental potential.Several studies have shown that blastomeres isolated from most precompaction embryos differ in their ability to form TE, EPI, and PE cells, indicating their unequal developmental potential . A develIn addition to this biological constraint, the plasticity of embryonic development and the presence of signaling modulators in the derivation medium should also be considered when interpreting the results of our study. Blastomeres of 8-cell embryos show impaired ability to form pluripotent EPI cells after isolation and culture in standard embryo culture medium, and they tend to develop as trophoblastic vesicles instead of forming blastocysts . This coNext, we were interested in determining whether mESCs lines produced from different blastomeres of the same embryo are equivalent in terms of their transcriptional signature. To the best of our knowledge, only one study has addressed this question, using mESCs derived from single blastomeres of 8-cell human embryos . We founversus the naïve pluripotency of mESCs lines. Moreover, only two of the four 8-cell embryos used in this previous study had produced more than two lines, and the other two embryos had resulted in only one line.When we removed between-embryo variability, differences at the transcriptional level between mESCs lines derived from the same embryos became apparent, and interestingly, the lines consistently segregated into two distinct groups. When compared with the lines in group II, the lines in group I displayed upregulation of genes involved in developmental and morphogenesis processes and in signaling pathways associated with stem cell pluripotency, and of core and naïve pluripotency genes. This suggests that half of the mESCs lines generated from each embryo possess a higher pluripotency potential than the other half. Because the derivation of mESCs lines is a long process, it is not possible to completely rule out that differences in the lines’ transcriptomes were acquired during the generation of the line and extended culture. However, the fact that the same pattern was observed in all three parental embryos and that a high number of lines were analyzed for each embryo (5-7), rather suggests that these differences stem from the blastomere that the lines originated from and were maintained during derivation and culture. It is tempting to speculate that lines in group I could have been established from the blastomeres that in the intact embryo possessed the highest developmental potential, whereas lines in group II could derive from those blastomeres with lower developmental potential in which redirection of cell fate towards the EPI lineage after isolation and culture successfully occurred. Because most of the lines used in the transcriptome analysis were obtained from two embryos in which 7 of the 8 blastomeres produced mESCs lines, this reinforces the idea that in the intact 8-cell stage embryo about half of the blastomeres would belong to each of these two categories. It would be interesting to investigate transcriptional differences among sister blastomeres of the 8-cell embryo in future studies to corroborate this idea. Finally, despite the higher pluripotency of mESCs lines belonging to group I and the upregulated expression of genes related to nervous system development in lines belonging to group II, the two groups of lines did not show biased differentiation into any of the three primary germ layers. Terminal differentiation into derivatives of the three germ layers could be performed in future experiments for a more definitive assessment of whether a correlation exists between the transcriptional differences between the two groups of lines and their differentiation potential.In summary, we have demonstrated that it is possible to obtain multiple mESCs lines from the blastomeres of a single 8-cell embryo, but that blastomeres at this developmental stage are unequally competent at producing mESCs lines. We estimated that this competence is related to their ability to produce at least five EPI cells during the first 3 days of culture. Our results also suggest that the current derivation rates probably reflect a combination of three factors: biological constraints, developmental plasticity, and culture conditions. Although in most embryos up to half of the blastomeres would have a higher developmental potential , isolation and culture of the blastomeres could reduce this potential. This could be prevented by culture conditions, such as the use of R2i, which could also allow to “rescue” those blastomeres with reduced developmental potential, by redirecting their cell fate to the EPI lineage. It is possible that the two distinct types of transcriptional signatures identified in the mESCs lines produced from the same embryos reflect the intrinsic developmental potential of the parental blastomeres from which they originated. However, these two different transcriptional signatures did not affect the ability of the lines to differentiate into cells of the primary germ layers. Our findings may help improve the generation of mESCs from single blastomeres and the understanding of cell fate determination in the preimplantation mouse embryo.


O-lkyl naringenin derivatives and their oximes exhibit antimicrobial activity against antibiotic-resistant pathogens. The current study was aimed at determining the modulatory effect of these compounds on the adhesion of selected representatives of the intestinal microbiota: Escherichia coli, a commensal representative of the intestinal microbiota, and Enterococcus faecalis, a bacterium that naturally colonizes the intestines but has disease-promoting potential. To better reflect the variety of real-life scenarios, we performed these studies using two different intestinal cell lines: the physiologically functioning  3T3-L1 cell line and the disease-mimicking, cancerous HT-29 line. The study was performed in vitro under static and microfluidic conditions generated by the Bioflux system. We detected the modulatory effect of the tested O-alkyl naringenin derivatives on bacterial adhesion, which was dependent on the cell line studied and was more significant for E. coli than for E. faecalis. In addition, it was noticed that this activity was affected by the concentration of the tested compound and its structure (length of the carbon chain). In summary, O-alkyl naringenin derivatives and their oximes possess a promising modulatory effect on the adhesion of selected representatives of the intestinal microbiota.Because of the close connection between adhesion and many vital cellular functions, the search for new compounds modulating the adhesion of bacteria belonging to the intestinal microbiota is a great challenge and a clinical need. Based on our previous studies, we discovered that  Cell adhesion is a phenomenon of paramount importance in various physiological processes. It affects a plethora of physiological functions of cells, e.g., growth, differentiation, and migration. Interactions during adhesion can occur either on the cell-to-cell or cell-to-extracellular matrix  pathway, which for eukaryotic cells is essential for the development of tissues, organs, and even whole organisms . Non-spe13–1014 cells , being a concentration able to induce 50% inhibition of cell proliferation. The obtained data are presented in NG) and naringenin oxime (NGOX).In the next step, we decided to determine whether the tested naringenin derivatives negatively affect the viability of the examined intestinal cell lines. The cytotoxic effect of 50 value for these two model compounds was >100 µg/mL.Naringenin and its oxime showed no proliferative effects against both cell lines, 3T3-L1 and HT-29, over the concentration range tested. The ICO-alkyl derivatives of naringenin, e.g., 1a, 3a, 5a, 7a, and 9a. The same compounds, with the exception of 9a, also showed antiproliferative activity against the HT-29 line. It is interesting to note that the cytotoxic effect was observed only for compounds having one carbon chain attached at the C-7 position of the naringenin ring. Furthermore, a correlation between the number of carbon atoms and the level of cytotoxic activity was observed. Indeed, the shorter the attached carbon chain, the greater the effect of the compound on the proliferation of the adenocarcinoma cells examined. In fact, the IC50 value for compound 1a, which contains a hexyl group attached to the C-7 position of naringenin, was 25.39 µg/mL, which was 1.8 times lower than compound 7a (IC50 = 46.72 µg/mL), which possesses a nonyl moiety.Among the tested naringenin derivatives lacking an oxime group, cytotoxic activity against the 3T3-L1 line was demonstrated for monosubstituted 9a having 10 carbon atoms). Naringenin derivatives with two alkyl chains attached (the second at the C-4’ position) with different numbers of carbon atoms  showed no effect on the proliferation of the tested cell lines in the tested concentration range.In contrast, against the 3T3-L1 line, compounds having chains with 6 to 9 carbon atoms showed an effect on proliferation at a level 3–4 times higher than compounds with longer chains , an effect on 3T3-L1 cell proliferation was also noted only for monosubstituted compounds, e.g., 1b, 3b, 5b, and 7b. For them, IC50 values were in the range of 32.12 µg/mL to 49.93 µg/mL. A similar relationship was observed for the human adenocarcinoma cell line because the same compounds  showed cytotoxic effects against this cell line. The IC50 value depended on the length of the attached chain and was higher for the oxime derivatives with a longer alkyl chain attached to the naringenin ring. On the other hand, both 4b and 10b, oximes of O-alkyl naringenin derivatives with attached alkyl chains at the C-7 and C-4’ positions, affected cell differentiation of the HT-29 line with comparable IC50 values of 43.35 and 48.23 µg/mL, respectively. In contrast, the other di-substituted compounds, e.g., 2b, 6b, and 8b, showed no harmful effects against human colon cancer cells in the concentration range tested (IC50 > 100 µg/mL).In the case of naringenin derivatives bearing an additional oxime group  as well as our detailed review of the literature indicating the highest biological activity of flavonoids with a C8–C10 side chain ,31,32,338b at 50 µg/mL had a very strong, pro-adhesive effect on E. coli with respect to both cell lines tested. Here, for the physiological 3T3-L1 cell line, the ratio of the area of bacterial biomass (BB) adhered to the surface of the eukaryotic cells (EC) [the BB/EC ratio] was 0.6 ± 0.12 vs. 0.37 ± 0.13 for compound-exposed and control samples, respectively   without a negative, dysbiotic effect on other representatives of the microbiota .Applying microfluidic conditions, we observed that the use of ectively . For theectively . Interesll line) . RepreseNaringenin is an organic flavonoid compound that is widely distributed in nature. The largest source of naringenin is citrus fruits , although it is also present in smaller amounts in grapes, cherries, fenugreek, and Greek oregano, as well as in coffee, tea, and red wine. The literature review confirms the broad-spectrum, health-promoting effect of this flavonoid on the human body. It has been shown that such activity is closely related to the affinity of naringenin for scavenging reactive oxygen species and increasing the antioxidant defense of the host. This strictly translates into its anti-inflammatory effect and determines its anti-atherosclerotic and neuroprotective features. Additionally, it has been documented that naringenin also has anti-microbial and anti-cancer activity ,28.E. coli adhesion to eukaryotic cells and the decrease in cell membrane fluidity of these bacteria. In another study carried out on four selected bacterial species, it was shown that, together with the entry into the biofilm (sedentary) phase, microorganisms accumulate saturated fatty acids in their cell membranes, leading to a decrease in their membranes’ fluidity  containing one of the tested bacterial strains  and a selected flavonoid, 8b (50 µg/mL), was added to the inlet wells. In the control sample, the above compound was not added (the cell line medium [without antibiotics] together with bacteria). In regard to this, in both tested and control samples, the cell line-to-bacteria ratio was maintained at MOI = 50. After this, the medium was set to flow from inlets to outlets at 0.5 dyne/cm2 for 4 h. In the next step, the inlet wells were emptied, and 100 µL of saline solution containing a mix of two fluorescent dyes, SYTO9  to visualize bacterial biomass and DAPI , to detect eukaryotic cells, was added to each well. The inlet-to-outlet flow was turned on at 0.5 dyne/cm2 for 10 min, followed by a 0.5 h incubation to induce the absorption of the dyes by cells. After completing this stage, photographs of the microcapillaries containing the tested biological materials were taken using an inverted fluorescence microscope  and the Bioflux Montage software . When determining the area occupied by bacterial cells adhered to the surface of eukaryotic cells, the entire bacterial biomass attached was taken into account . These calculations did not include bacterial cells adhering only to the surface of the microcapillaries. The tests were performed in three biological repetitions with three technical replications (constituting images from different fragments of the examined microcapillaries).Determination of the impact of a dynamic medium flow on the adhesive capacity of bacteria was performed using the Bioflux 1000Z  with a coupled microscope environmental chamber , enabling the maintenance of 37 °C and microaerophilic conditions. At the very beginning, all channels of dedicated microfluidic plates  were flushed with 100 µL of αMEM or DMEM high-glucose medium with supplements  applying an intensive flow of 10 dyne/cmt-test was further used. The results of statistical analyses were considered significant for values with p < 0.05.Statistical analysis was performed using GraphPad Prism version 9 . The normality of the distribution was checked by the Shapiro–Wilk test. As all values were normally distributed, Student’s O-alkyl derivatives of naringenin was focused not only on different intestinal cell lines  but also on both static and microfluidic conditions. Therefore, the promising results obtained here provide a basis for in vivo animal studies to clarify the effect of these bioflavonoids on the gut environment. This may provide valuable information on the bioavailability and effect of these compounds on the whole intestinal microbiome.The present study showed that the synthesized naringenin derivatives modulate the adhesion of selected intestinal microbiota to the tested cell lines. It was noticed that this activity was affected by the concentration of the tested naringenin derivatives and their structure (length of the carbon chain). To better reflect the variety of real-life scenarios, the current research on


Once validated, these models can aid in the design of experiments that steer evolution with density-dependent selection.Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, thus affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically would give insights into cellular phenotypes before they unfold temporally. Here, we re-analyzed single cell RNA (scRNA) and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. Using live cell imaging, we quantified differences in the growth rate and contact inhibition between the eight cell lines and used these differences to prioritize the transcriptomic biomarkers of the growth rate and carrying capacity. Using these biomarkers, we found significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We used these predictions to simulate how the clonal composition of a cell line would change depending on density conditions during  Cellular heterogeneity is a defining feature of most cancers, and it is critical to tumor progression and treatment failure ,2. AdvanOne contributor to a cancer cell’s phenotype includes large-scale somatic copy number alterations (SCNAs) of 10 mega base pairs or more. Studies show that SCNAs correlate with progression rates and overall survival, with cancer cells grouped by copy number landscape exhibiting the same resistance to chemotherapy ,5,6,7. THere, we aim to characterize a targeted subset of the phenotypic differences between co-existing karyotypes, which are defined by their ability to out-compete each other at low vs. high cell densities. Cell densities naturally vary in the tumor over space and time, thus creating niches with distinct selection pressures. A cell that is in a densely packed environment will be under more pressure to overcome contact inhibition than a cell that finds itself in sparse conditions. This variation in evolutionary pressures likely contributes to the coexistence of cancer cells with heterogeneous phenotypes. Density-dependent selection occurs when fitness is a function of population density . A relatWe present a framework for examining how density-dependent selection acts on coexisting clones defined by karyotypes and apply it to a set of stomach cancer cell lines. Hereby, we focus on carrying capacity as defined by spatial limitations rather than metabolic constraints.By sequencing the DNA and RNA of >36,000 cells from nine stomach cancer cell lines, our prior work classified cells into groups with unique karyotypes , which ain-vitro experiments.We changed the culture media every 3.05 days on average, with media changes becoming more frequent as cells became more confluent. We compared cell counts derived from a Countess (Life Technologies Countess II FL Automated Cell Counter) to cell counts derived from live cell imaging to confirm segmentation accuracy , thus suWe fit the Gompertz growth model and two instances of the generalized logistic growth model (Richards and Verhulst)  to this ll lines  Table A1ll lines . We thusll lines . Therefoll lines  Table A2r) included the ‘Amoebiasis during G2M’ , the ‘PI3K-Akt signaling pathway’  and carrying capacity (K), respectively.Overall, there was no significant correlation between the growth rate and the carrying capacity across cell lines , most phenotypic differences between karyotypes will be latent ,30, thusin-vivo framework. However, an understanding of the spatial distribution of r- and K-selected sub-populations could facilitate the design of therapy regimens, such as adaptive therapy or double bind therapy [Clones that are far from each other in r–K space may also be spatially segregated and have differential treatment sensitivities. For example, the slower growth rate of K-selected cells may render them more resistant to cytotoxic therapies —an effec therapy , aiming Our in silico results add to the evidence accumulating from many other studies ,29,37 th2) in order to allow for the population’s exponential growth and subsequent plateau at the carrying capacity (range: 2). The end-point for each experiment was determined by fitting a generalized logistic growth curve to the in-vitro data at various points during the time in culture. Across 8 cell lines, the time in culture ranged from 6–21 days, with media being refreshed on average every 48 h until the cells reached confluence, whereupon media was refreshed every 24 h. The experiment was stopped once the fold change in the inferred carrying capacity was less than 4% when removing any subset of the last 20% of time points.The identity of each cell line was determined through independent karyotyping and mycoplasma contamination assessment. Cells were cultured in their recommended media conditions at 37°C. For, HGC-27, EMEM ; KATOIII, RPMI-1640, and EMEM (1:1 mix) were used; for NCI-N87, RPMI-1640 (ATCC modified) was used; the remaining five cell lines  used RPMI-1640. All cell lines were grown in the aforementioned media with 10% fetal bovine serum  and 1% penicillin—streptomycin (Gibco). For cell passaging, Trypsin EDTA (0.25%) with phenol red (Gibco) was added, followed by inactivation using the respective growth media. Cells were seeded at a low initial density . Growth curves were generated to examine the carrying capability of each cell line.Cells were seeded in a T25 flask (Fisherbrand), and sets of 4 phase contrast images were taken at 20× magnification for NCI-N87, and 10× magnification for the aforementioned cell lines on an Evos FL. Segmentation using Cellpose was utilized to quantify the cell count and cellular features . Therefore, we applied pre-processing steps to our pipeline, which consisted of the following: (i) gamma correction to reduce darkness; (ii) histogram equalization to improve image contrast, and (iii) Gaussian blurring for smoothing.TV yes it shouldCell segmentation: Fully automated cell segmentation of phase contrast images was performed using Cellpose software.Cellpose is a deep learning approach based on the U-Net architecture for cell segmentation ,39, wherDetermining relative clonal carrying capacities, growth rates, and loss of contact inhibition parameter values is a necessary first step in modeling density-dependent selection in heterogeneous cell lines. To achieve this, we used gene expression signatures as surrogates of growth parameters as previously described ,41.Quantification of single cell pathway activity from gene expression:In order to identify cells with active gene sets, we utilized AUCell version 3.18 for R version 4.3.1 . AUCell Clonal growth parameters:r) and carrying capacity (K). The test takes the form of a linear model:r or K), and p is the pathway activity. The pathways are then ranked by adjusted We tested these 25 KEGG pathways quantified by AUCell as biomarkers of growth rate . However, even when many of these dependencies are held constant, it is difficult to discern between various models of cancer cell growth ,44,45,46dNidt=Ni∗in-vitro growth using the logistic growth model. To model clonal growth dynamics, we used the ODE45 solver for MatLab version 9.7.0.1216025 to solve Equation (In addition to the two models mentioned above, we also fitted cell line population growth with the Gompertz model . These mll lines  Table A1ll lines  and founll lines . Thus, wEquation  after ben clones identified within a given cell line. For each pair t-test to compare the growth rates predicted for cells assigned to clone x (y (p-value r and K, we further calculate Let


PI3KCA, TP53, BRAF and MUC16. GDF15 suppression by BET inhibition is particularly effective in cancer cells with low activity of the PI3K/Akt axis and high extracellular concentrations of pantothenate. Our findings provide insights that the risk for GDF15 overexpression and concomitant cachexia can be reduced by a personalized selection of anticancer drugs and patients for precision medicine.GDF15 has recently emerged as a key driver of the development of various disease conditions including cancer cachexia. Not only the tumor itself but also adverse effects of chemotherapy have been reported to contribute to increased GDF15. Although regulation of GDF15 transcription by BET domain has recently been reported, the molecular mechanisms of GDF15 gene regulation by drugs are still unknown, leaving uncertainty about the safe and effective therapeutic strategies targeting GDF15. We screened various cardiotoxic drugs and BET inhibitors for their effects on GDF15 regulation in human cardiomyocytes and cancer cell lines and analyzed in-house and public gene signature databases. We found that DNA damaging drugs induce GDF15 in cardiomyocytes more strongly than drugs with other modes of action. In cancer cells, GDF15 induction varied depending on drug- and cell type-specific gene signatures including mutations in  It is a cytokine that mediates the body response to various stress factors such as oxidative stress, tissue injury and drug toxicity. When binding to GDNF-family receptor α-like (GFRAL), localized mainly in the brain stem, it mediates anorexia, alterations in overall metabolism and energy homeostasis and activation of other endocrine stress responses. In health, these mechanisms alert the body to exo- and endogenous toxins by mitigating the exposure and preventing further organ damage through metabolic reprogramming. In disease such as cancer, GDF15 levels can be elevated up to 100-fold, thereby causing pathological conditions4.GDF15 has been found to be a key driver of cachexia syndrome, significant body weight loss that often occurs in chronic diseases including cancer or in case of drug-induced organ injury6. Drug-induced cardiotoxicity is a serious problem often associated with chemotherapy7. Anticancer drugs used in chemotherapy can cause cellular stress and death leading to drastic GDF15 induction in both cancer and host cells10 22 were reported to bind directly to the GDF15 promoter and function as transcription activators. GSK3β23, NFkB24 and p38 MAPK22 were identified to indirectly activate GDF15 expression through transcription activators  proteins, more particularly bromodomain-containing protein 4 (BRD4)31. Currently, treatment of cachexia is predominantly focused on non-specific approaches such as to increase appetite or muscle mass3. However, the identified causal relations of cachexia with chemotherapy32, tumor-secreted factors6, specific molecular pathways and key molecules1 suggest that cachexia is instead a discrete and targetable condition. Specific antibodies targeting GDF15 or its receptor GFRAL have been developed recently to be tested in clinical studies33. These approaches could alleviate the direct effects of GDF15 but do not tackle the underlying transcriptional dysregulation. Although GDF15 overexpression has been identified as an etiological key driver of cachexia, little efforts have been made yet to characterize the molecular basis of GDF15 induction and suppression by drugs. A tool to predict whether a patient will react to a particular anticancer drug with GDF15 overexpression could help reduce risks for chemotherapy-driven development of cachexia. Furthermore, the prevention of GDF15 transcription, e.g., by epigenetic intervention, has the potential to effectively counteract cachexia. This study was therefore designed to understand the molecular basis of drug-specific and cell type-specific regulation of GDF15 gene induction and suppression by screening cardiotoxic drugs in human cardiomyocytes and analyzing in-house and public gene signatures database of cancer cell lines.Cachexia has been considered as an epiphenomenal and unpreventable complication with severe cancer progressions and anticancer treatment. Patients who suffer from cachexia are less responsive to anticancer treatment because of their overall weaknessAs many anticancer drugs are cardiotoxic and GDF15 is a well-known biomarker for heart failure, we employed a human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) model to investigate the effect of drug-induced cardiotoxicity on GDF15 expression in host tissues. To adequately replicate cardiotoxicity using this model, we selected drugs whose cardiotoxic effect in vivo is based on cytotoxicity to cardiomyocytes. Cell viability screening of 105 known cardiotoxic drugs identified 20 drugs which exerted direct cytotoxicity Fig. . Media cTaken together, we found that induction of GDF15 in hiPSC-CMs by cardio- and cytotoxic drugs varies greatly depending on the drug’s mode of action.34. We used linear regression to quantify the cell line’s drug-specific responsiveness regarding GDF15 induction and categorized the cell lines as “responsive” or “resistant”. Comparing the number of responsive cell lines per drug, we found the ratio of responsive cell lines ranged from 4% for sirolimus to 71% for sorafenib , we generated a network comprising key proteins involved in known GDF15 inducing pathways and added reported direct and indirect interactions. We complemented the resulting network with the identified characteristic expression and activity patterns of the four cell line clusters described above. Based on the software-included interactions, we predicted the cluster-specific resulting activities of different GDF15 induction pathways Fig. C.The identified differences in mutations and gene expressions between cell line clusters indicated that cell-line-specific parameters could correlate with the individual extent of GDF15 induction by certain drugs. By differential analysis and statistical testing of gene expression data from individual cell lines with remarkable GDF15 induction behaviour, we identified additional potentially correlating genes. Together with the previously found cluster-typical expression patterns  of GDF15 with BRD4 induction in 50.0% and a negative correlation (Pearson correlation coefficient <  − 0.4) in 18.3% of NCI-60 cell lines. Among the different tissue types 75% of renal and 83% of CNS cancer cell lines showed a positive correlation of GDF15 and BRD4 induction. In a comparison of GDF15 and BRD4 induction between all GDF15 inducing drugs, we found a significant positive correlation for lapatinib, erlotinib and sorafenib  and no significant correlation for other GDF15 inducing drugs.It has recently been reported that basal GDF15 expression depends on BRD4 in pancreatic and melanoma cancer cell lines5. Whilst a personalized selection of chemotherapeutic agents has the potential to mitigate chemotherapy-driven GDF15 overexpression in cancer cells, it leaves basal overexpression of GDF15 unaffected. Through the data analysis described above, we found a correlation between BRD4 and GDF15 expression in 50% of NCI-60, indicating that GDF15 expression might depend on BRD4 in those cell lines. To test this further, we analyzed our own gene expression data of 21 lung and colorectal cancer cell lines treated with the BET inhibitor BI 89499926. It turned out that in 9 of the 21 treated cell lines, GDF15 expression was significantly reduced through BET inhibition . The more GDF15 expression was reduced, the more GDF11 expression tended to get increased upon BET inhibition in the respective cell lines  of each BET inhibitor for BRD2, BRD3 and BRD4, whenever available. GDF15 fold-change thereby meant the ratio of GDF15 expression induced by doxorubicin only and doxorubicin combined with BET inhibitor normalized to cell viability. The strongest negative relation was observed between IC50(BRD4) and GDF15 fold-change  and Wnt-C59  in CDM-3  and metabolic selection with lactate, hiPSC-CMs were plated at 3 × 104 cells/well on 96-well plates or 1 × 106 cells/well on 6-well plates and cultured in CDM3-M medium  supplemented with 500 µg/ml recombinant human albumin , 4 mM sodium L-lactate , 1 mM sodium pyruvate (Sigma Aldrich P5280), 213 µg/ml L-ascorbic acid 2-phosphate , 10 mM D-galactose , 200 ng/ml triiodo-L-thyronine , 20 µg/ml Insulin  and 1 × chemically defined lipid concentrate ) containing 20% FCS  for 24 h before switching to CDM3-M without FCS.hiPSC-CMs were prepared according to the method described in ref.57 and DNA sequencing58 was performed as described previously.Cancer cell lines were authenticated by short tandem repeat analysis performed according to the manufacturer’s instructions and treated with 10 nM, 35 nM or 100 nM BI 894999 BET inhibitor for 4 h or 24 h  library was prepared by serial dilution of 10 mM stocks in DMSO to 9 different concentrations ranging from 0.001 to 10 mM, which were further diluted into assay plates to final concentrations ranging from 0.001 to 10 µM. BI894987 (Boehringer Ingelheim), BI 894999 (Boehringer Ingelheim), CPI203 , GSK1210151A , I-BET762 , JQ1 , OTX015 , PFI-1  and Doxorubicin  were dissolved in DMSO to make 30 mM or 10 mM stocks which were serially diluted in DMSO and assay media to concentrations as indicated. The final concentration of DMSO for hiPSC-CMs assay was 0.1%. To investigate the influence of BET inhibitor treatment on doxorubicin induced GDF15 mRNA expression, hiPSC-CMs were pre-treated with 20 nM or 200 nM doxorubicin for 48 h and kept in medium without compound for 24 h before 24 h treatment with either 100 nM BI 894999 or 500 nM OTX015. We screened the BET inhibitors JQ1, I-BET762, PFI-1, I-BET151, CPI203, OTX015 and BI 894987 for their effect on doxorubicin-induced GDF15 secretion in hiPSC-CMs by simultaneous treatment with 100 nM doxorubicin and 0.1 to 1000 nM of each BET inhibitor. Cancer cell lines were treated with 10 nM, 35 nM or 100 nM of BI 894999 for 4 h or 24 h  and incubated for 2 h at 37 °C. The fluorescence of resorufin generated by the cells was measured on a BioTek Synergy HTX microplate reader equipped with an excitation filter 530/25 nm and an emission filter 590/35 nm. After correction for background medium fluorescence and averaging of biological replicates, we calculated cell viability relative to untreated control for each compound concentration.For GDF15 mRNA quantification, total RNA was isolated from the cells using an RNeasy kit  to synthesize cDNA using a Taqman Reverse Transcription kit . Quantitative real-time PCR analysis was performed using QuantiNova SYBR Green PCR Kit  on a CFX96 Real-Time System (Bio-Rad) under the following amplification conditions: 95 °C for 2 min followed by 40 cycles of 95 °C for 5 s and 60 °C for 10 s. The primer sequences are GDF15-F: 5′-GAGCTGGGAAGATTCGAACA-3′, GDF15-R: 5′-AGAGATACGCAGGTGCAGGT-3′, ACTB-F: 5′-GTCTTCCCCTCCATCGTG-3′ and ACTB-R: 5′-AGGGTGAGGATGCCTCTCTT-3′. PCR data were analyzed using a modified relative expression software tool (REST) and the expression level of GDF15 gene was normalized to that of β-actin. For secreted GDF15 protein quantification, cell culture medium was collected after drug treatment and stored at − 20 °C until ELISA was performed using Human GDF-15 ELISA Kit  according to the manufacturer’s instruction. Resulting GDF15 concentrations were normalized to non-treated control and respective relative cell viability.We obtained basal gene expression values and mutational status of all cancer cell lines from our in-house cancer cell line database. Gene expression data of NCI-60 after compound treatment, basal protein expression, drug sensitivity data, gene dependency data, metabolite expression and compound specifications such as IC50 values and modes of action were collected from different studies and data bases, as described in the Each NCI-60 cell line was assigned to an array containing 15 drug-specific linear regression slopes of GDF15 mean fold-change. We calculated the Pearson correlation coefficient for every cell line pair to build a 60 × 60 correlation matrix. For agglomerative hierarchical clustering, we applied a “Weighted Pair Group Method with Arithmetic Mean” (WPGMA) algorithm to the resulting correlation matrix. The identified clusters represent groups of cell lines that are closely positioned to each other in this 15-dimensional data set and therefore have more similarities regarding GDF15 expression upon treatment with the 15 drugs compared to cell lines of other clusters.59 was used for differential analysis. We searched for significant differences between the four identified NCI-60 cell line clusters in terms of protein expression, gene signature and hallmark gene set scores and mutations. Besides in-house gene expression and DNA sequencing data, we extracted protein expression values from a public data set60. We applied a modified t-test to identify proteins that differed significantly in their expression level between the different cell line clusters. We included proteins with p-values from 0.001 to 0.05 among which are involved in pathways upstream of GDF15 regulation or reported targets of any of the tested drugs. All the proteins with p-values below 0.001 were included regardless of their involvement in GDF15 regulation or drug mechanisms. Differences in mutation prevalence were quantified using Fisher’s exact test and included whenever the p-value was below 0.05 and the respective gene is involved in GDF15 regulation or is a known target of a tested drug. We additionally deduced specific protein and pathway activities from gene and protein expression data using reported gene signatures and hallmark gene set scores. Detailed references are described in the 34. We compared basal GDF15 gene expression and correlation coefficients of GDF15 and BRD4 induction upon treatment between cell lines of different clusters using standard two-sided t-test. Differences in metabolite concentration between cancer cell lines with strong and weak responsiveness to BET inhibition regarding GDF15 and GDF11 , we generated a network consisting of 44 proteins involved in pathways upstream of GDF15. Depending on the studied cell line cluster, this network was supplemented with respective characteristic expression patterns. We connected the entered proteins (Supplementary Table Supplementary Information.


Activating mutations of Ras genes are often observed in cancer. The protein products of the three Ras genes are almost identical. However, for reasons that remain unclear, KRAS is far more frequently mutated than the other Ras isoforms in cancer and RASopathies. We have quantified HRAS, NRAS, KRAS4A and KRAS4B protein abundance across a large panel of cell lines and healthy tissues. We observe consistent patterns of KRAS > NRAS»HRAS protein expression in cells that correlate with the rank order of Ras mutation frequencies in cancer. Our data provide support for the model of a sweet-spot of Ras dosage mediating isoform-specific contributions to cancer and development. We suggest that in most cases, being the most abundant Ras isoform correlates with occupying the sweet-spot and that HRAS and NRAS expression is usually insufficient to promote oncogenesis when mutated. However, our results challenge the notion that rare codons mechanistically underpin the predominance of KRAS mutant cancers. Finally, direct measurement of mutant versus wildtype KRAS protein abundance revealed a frequent imbalance that may suggest additional non-gene duplication mechanisms for optimizing oncogenic Ras dosage. Ras genes are mutated in ~20% of all human cancer cases . There aAt the most fundamental level it must relate to the opportunity and capacity of each Ras isoform to interact with and activate key effector pathways. This is currently best expressed in the Ras “sweet-spot” model that suggests that Ras dosage  will be a major factor in influencing the availability of individual Ras family members to engage cancer pathways . This moRas dosage is known to be important for KRAS-mediated progression of pancreatic and breast cancer and KRAS and NRAS contributions to myeloid malignancies –12. The Importantly, HRAS, KRAS and NRAS protein abundance was never formally measured in these studies to see whether they conformed with the predicted influence of rare codons. Moreover, rather than exhibiting limited expression, KRAS is actually far more frequently amplified in tumors than the other isoforms . KRAS mRIn order to address this, we have quantified Ras protein abundance across a large panel of cell lines and healthy tissues. Whilst our insights do not agree with the predicted influence of rare codons on KRAS protein expression versus other isoforms, we do observe consistent patterns of Ras isoform expression suggesting that relative dosage is an important feature of their biology and disease association. We also observe an imbalance in the abundance of proteins expressed from mutant versus wild type alleles in some cell lines that suggests the existence of additional mechanisms for achieving disease-associated amplification. These datasets and the patterns observed have broad applications in experimental design, network analysis and understanding the contributions of Ras isoforms to normal and disease-associated biology.The patterns of Ras mutations in cancer are suggested to be influenced by rare codon-mediated differences in protein expression. This was established in mice where the KRAS gene is enriched in rare codons versus HRAS . The codTo formally measure Ras dosage, we employed a mass-spectrometry based protein standard absolute quantitation (PSAQ) approach to determine Ras isoform protein copy number per cell Fig.  25, 28,,28, 29. PSAQ was applied to 78 commonly used mouse and human cell lines. All data are derived from three independently processed cell samples where a common Ras standard was spiked into all samples to allow direct comparison between all cell lines. Total Ras abundance derived from H + N + KA + KB peptide measurements ranges over an order of magnitude from ~50,000 to 550,000 proteins per cell Fig. . RelativQuantitation of Ras isoform protein abundance reveal a relative rank order of KRAS4B > NRAS»HRAS > KRAS4A. KRAS4B is the dominant Ras isoform in 52/78 cell lines, whilst KRAS gene products totaled together represent the most abundant Ras in 64/78 of cell lines Fig. . The uppKRAS4A averages only ~22.5% of total KRAS abundance Fig. . This isThe consistent patterns of Ras isoform abundance across a large panel of normal and cancer cell lines derived from two species are compelling. However, it is formally possible that Ras levels may have been influenced by disease of origin, cell derivation and/or culture conditions. To address this, we profiled a panel of tissues freshly derived from three healthy adult CD1 mice. We observed a 4-fold range in total Ras abundance across the tissues Fig. . Our preMany of the cell lines that we profiled harbored KRAS mutations . When normalized to cell size this narrowed to a 2-3-fold difference in the highest versus lowest values of total Ras abundance for a given cell size. Total Ras abundance for an averaged sized cell was ~200,000 copies per cell. This would place Ras in the top 20% of all proteins based on global estimates of mammalian cell proteome copy number , 30, 31.Across cell lines and normal tissues, we observed a consistent pattern of KRAS being the most abundant isoform. This corroborates and significantly extends previous studies that also identified KRAS to be the most abundant Ras isoform in a small panel of cell lines , 25, 32.Intriguingly, the rank order of Ras isoform abundance differed between cell lines and mouse tissues. In all mouse tissues except spleen, HRAS was more abundant than NRAS. In contrast, in all cell lines apart from MCF10a, DLD1 and LU165 cells, NRAS was more abundant than HRAS. The same batches of Ras standards were used for cell and tissue analysis. HT29 cells were included in all PSAQ sample runs and retained the consistent pattern of NRAS > HRAS seen in all previous runs; therefore, we are confident that the mouse tissue data represent a true reflection of Ras abundance in these samples. The switch in abundance may represent general species differences although we note that our cell panel included eight mouse lines that all displayed the same trends as their human counterparts. Alternatively, it may represent adaptive changes between normal versus the disease associated states that our cells were derived from. Further investigation of normal human tissues and relevant mouse models before and after disease initiation are required to test this.The patterns of Ras isoform expression that we observe potentially inform our understanding of why KRAS is more frequently mutated in cancer. Our results challenge the central tenet of the rare codon model that predicted that KRAS expression would be limited relative to the other Ras isoforms , 13, 16 Whilst our data mean that we do not now think that rare codons explain why KRAS is more often mutated in cancer, the rare codon experiments and work by others have conclusively demonstrated that Ras dosage has a significant influence on Ras oncogenic potential –12, 16. The observation in cancer databases of preferential amplification of KRAS versus HRAS or NRAS might seem counterintuitive if it is already the most abundant isoform. If KRAS is already closer to the sweet-spot such that mutation is all that is needed to reach the right level of signaling, then this might suggest that HRAS and NRAS mutations might also be associated with amplifications to allow them to reach the sweet-spot. This would rely on a double genetic perturbation that makes it less likely to be observed. Furthermore, the extent to which these independent mechanisms enhance Ras signaling hasn’t been properly quantified. However, the fact that KRAS is typically targeted suggest that their influence may be similar so that either signaling enhancement mechanism will work for KRAS but will typically be insufficient for the other isoforms in most contexts.Importantly, we are not suggesting that only the most abundant Ras isoform in any given context is going to be the one occupying the sweet-spot when mutated. The position of the sweet-spot will vary depending on cell state, network topology and microenvironmental influences Fig. . In contRas dosage is likely to be more nuanced than measurements of total abundance and it will be interesting to understand if total versus compartment-specific Ras are the critical determinants of the Ras sweet-spot. The type and amount of mutant Ras are also likely to be influential. Mass spectrometry currently represents the only method for directly measuring mutant Ras protein abundance . Our anaOur mutant Ras data provide evidence of fine tuning of cancer-relevant Ras dosage. The dynamic modulation of optimal total and mutant Ras dosage has been elegantly demonstrated in the characterization of allelic imbalances in mutant versus wild type KRAS during tumor outgrowth and in response to therapy in a mouse model . The incRas dosage is also likely to explain Ras isoform-specific contributions to development. KRAS4B is the only essential isoform, deletion results in embryonic lethality as a result of heart defects and cardiovascular problems –41. GeneWhilst our work has provided the first detailed quantitation of patterns of Ras protein abundance, we recognize that there are some important further experiments before we can properly understand the relative contributions of this parameter to total Ras signaling in healthy and diseased states. These include accurately quantifying the impact of different Ras mutations on GTP loading and effector engagement and the extent to which different mutants favor occupation of a signaling competent state. We also need to better understand the context that Ras operates in, at least in part by quantifying relative protein abundance and mutation state of all nodes within the Ras signaling network in relevant human cells and tissues.In summary, we have generated a comprehensive atlas of Ras isoform protein abundance in tissues and commonly used cell lines. Accurately quantifying Ras protein abundance will help parameterize models of signaling networks and inform cell-based Ras studies. Our data reveal new features of Ras biology and challenge and refine models explaining the pattern of Ras mutations in cancer and isoform-specific Ras contributions to development.6 cells were washed twice with ice cold PBS by centrifugation, snap frozen using liquid nitrogen and stored at −80 °C. Pellets were thawed on ice and lysed in NP40 lysis buffer ). Lysate protein concentration was determined using Pierce bicinchoninic acid (BCA) assay. n = 3 independently prepared cell pellets were used for PSAQ analysis.Cell lines were obtained from sources indicated in Supplementary Table °C. Tissue pieces of ~30–50 mg were crushed with a mini pestle chilled on dry ice before lysis in RIPA buffer ). Samples were sonicated on ice and lysate cleared by centrifugation at 17,000 x g. Protein concentration was determined using Pierce BCA assay. Tissues from n = 3 adult mice were used for PSAQ analysis. Sample sizes are based on convention and the high technical reproducibility observed in preliminary experiments, all source data are provided in Supplementary Table Mouse tissues were harvested from adult male B6/129 S mice, snap frozen in liquid nitrogen and stored at −80 13C6-15N2 [ + 8 Da]), L-arginine-U-13C6-15N4 [ + 10 Da])-labelled His-Ras proteins were prepared exactly as described [His-Ras-encoding pTrcHisA plasmids were generated by subcloning Ras coding sequences from constructs kindly provided by Dominic Esposito (NCI Ras Initiative), or available from previous work  These Hiescribed . Ras pro°C, alkylated in 55 mM iodoacetamide for 30 mins at room temperature, quenched with 10 mM DTT for 5 min at room temperature, then digested with 5 ng / μl Trypsin Gold (Promega) in 9% Acetonitrile and 40 mM Ambic, overnight at 37 °C. Trypsin was quenched using Formic acid and extracted peptides dried using a SpeedVac. Peptides were subjected to C18 desalting using an Agilent 1260 Infinity LC system equipped with an MRP-C18 Hi-recovery column  before SpeedVac drying.20 μg of cell lysate or 40 μg of mouse lysate containing spike-ins of the relevant heavy Ras standard were fractionated using SDS PAGE. The region of the gel containing endogenous and His-Ras standards was excised and dissected into ~1 mm gel cubes. These were reduced using 10 mM DTT in 100 mM Ammonium bicarbonate (Ambic) for 1 h at 56 via a Dionex U3000 nano-LC system (Thermo) mounted with a NanoAcquity 5 µm, 180 µm x 20 mm C18 trap and 1.7 µm, 75 µm X 100 mm analytical column (Waters) maintained at 40 °C. A gradient of 2−50% acetonitrile/0.1% formic acid (v/v) over 45 min was applied to the columns at a flow rate of 300 nL/min. The NanoSpray III source of the mass spectrometer was fitted with a 10 μm inner diameter PicoTip emitter (New Objective). The mass spectrometer was operated in positive ion mode using Analyst TF1.6 software (Sciex) and the MIDAS approach (MRM-initiated detection and sequencing) was used to quantify and confirm the identity of the analytes of interest. The optimized transitions are shown in Supplementary Table Desalted samples reconstituted in 0.1% formic acid were delivered into a QTRAP 6500 (Sciex) Area under curve was extracted for at least 3 combined transitions in Analyst software (Sciex), and further analysis performed in Excel. Briefly, ratios of Light:Heavy for each peptide were used to determine the quantity of peptide present per lane in moles, before conversion into copies per cell using the number of cells counted per μg lysate produced. Three repeats of each experiment were performed, and for each condition a mean was calculated from at least 2 values for inclusion in the final data. For KRas4B up to 6 values were obtained as this was analysed in both “wildtype” and “mutant” heavy spike ins. The same analysis was performed for mouse tissue lysates, although copies per ng total protein were calculated. For mouse tissues, KRas4A and 4B proved to be less consistent than NRas and HRas, so a combined value for KRas copies per ng total protein was calculated by subtracting copies of NRas and HRas from the pan-Ras copy number.Supplementary legendsSupplementary Figure 1Supplementary Figure 2Supplementary Table 1


Alloreactive donor-derived T-cells play a pivotal role in alloimmune responses after allogeneic hematopoietic stem cell transplantation ; both in the relapse-preventing Graft-versus-Leukemia (GvL) effect and the potentially lethal complication Graft-versus-Host-Disease (GvHD). The balance between GvL and GvHD can be shifted by removing T-cells via T-cell depletion (TCD) to reduce the risk of GvHD, and by introducing additional donor T-cells (donor lymphocyte infusions [DLI]) to boost the GvL effect. However, the association between T-cell kinetics and the occurrence of allo-immunological events has not been clearly demonstrated yet. Therefore, we investigated the complex associations between the T-cell kinetics and alloimmune responses in a cohort of 166 acute leukemia patients receiving alemtuzumab-based TCD alloSCT. Of these patients, 62 with an anticipated high risk of relapse were scheduled to receive a prophylactic DLI at 3 months after transplant. In this setting, we applied joint modelling which allowed us to better capture the complex interplay between DLI, T-cell kinetics, GvHD and relapse than traditional statistical methods. We demonstrate that DLI can induce detectable T-cell expansion, leading to an increase in total, CD4+ and CD8+ T-cell counts starting at 3 months after alloSCT. CD4+ T-cells showed the strongest association with the development of alloimmune responses: higher CD4 counts increased the risk of GvHD  and decreased the risk of relapse . Similar models showed that natural killer cells recovered rapidly after alloSCT and were associated with a lower risk of relapse . The results of this study advocate the use of joint models to further study immune cell kinetics in different settings. These TTo reduce the risk of severe GvHD, donor T-cell depletion (TCD) can be applied, although this will decrease the GvL effect . In ordede novo T-cell generation from infused hematopoietic stem cells starting at least 6 months after alloSCT, homeostatic proliferation of T-cells present in the patient or graft, T-cell expansion during infections and expansion of alloreactive T-cells responsible for GvL and GvHD. Especially cytomegalovirus (CMV) reactivations are common during the first 3 months after alloSCT and strongly affect the kinetics of both T-cells and NK cells after alloSCT (could have been measured if kept under follow-up). Likewise, DLI and the use of posttransplant prophylactic immunosuppression are known to affect the risks of relapse and GvHD, but may also affect the T-cell kinetics . All patients received an alemtuzumab-based TCD alloSCT after nonmyeloablative conditioning without any posttransplant prophylactic immunosuppression. Patients with an anticipated high risk of relapse were scheduled to receive an early low-dose DLI prophylactically at 3 months after alloSCT, while prophylactic DLI administration for the other patients started at 6 months. In this unique setting we investigated the impact of the early low-dose DLI on the T-cell and NK cell kinetics during the first 6 months after transplant and the association between these kinetics and the development of clinical events.22.1This retrospective study included all adult patients with acute myeloid leukemia, acute lymphoblastic leukemia or MDS in complete morphologic remission after intensive induction therapy who received their first alloSCT from a 9 or 10 out of 10 HLA matched donor using nonmyeloablative conditioning and alemtuzumab-based TCD  between 2.22 orally or 30 mg/m2 intravenously) and busulfan (2 days 4x0.8 mg/kg intravenously), or the FLAMSA regimen: fludarabine (5 days 30 mg/m2 intravenously), cytarabine (4 days 2000 mg/m2 intravenously), amsacrine (4 days 100 mg/m2 intravenously) and busulfan (4 days 4x0.8 mg/kg intravenously). In both regimens, TCD was performed by adding 20 mg alemtuzumab  to the graft before infusion and by administering 15 mg alemtuzumab intravenously on days -4 and -3. Patients with an unrelated donor (UD) received rabbit-derived anti-thymocyte globulin  additionally on day -2 (until April 2010 2mg/kg and thereafter 1mg/kg). None of the patients received posttransplant GvHD prophylaxis.As conditioning regimen patients received either fludarabine (6 days 50 mg/m6 or 1.5x106 T-cells/kg for patients with a related donor (RD) or an UD, respectively. Early low-dose DLIs given at 3 months after alloSCT contained 0.3x106 or 0.15x106 T-cells/kg for patients with a RD or an UD, respectively. Since May 2010, all patients without any relapse and without GvHD requiring systemic immunosuppressive treatment at 6 months after alloSCT prophylactically  were planned to receive the standard DLI. Patients who were considered to have a high risk of relapse based on the disease characteristics or MRD status at time of alloSCT or who received the FLAMSA regimen were also scheduled to receive the early low-dose DLI prophylactically at 3 months after alloSCT. All patients, including those transplanted before May 2010, could receive pre-emptive DLIs in case of MC or MRD positivity, starting from 3 months after alloSCT. Additionally, as part of several clinical trials, patients could receive modified T-cell products prophylactically or virus-specific T-cell infusions to treat severe viral infections.The dose of unmodified pre-emptive and prophylactic DLIs was based on donor type and timing after alloSCT. Standard DLIs given at 6 months after alloSCT contained 3x102.36 cells/L.CMV serostatus was assessed in all patients and donors before alloSCT. After transplant CMV was monitored routinely by PCR on peripheral blood samples in all patients. Absolute numbers of circulating total (CD3+), CD4+CD8- and CD4-CD8+ T-cells, B cells and NK cells were measured routinely at predefined timepoints on anticoagulated fresh venous blood by flow cytometry with bead calibration . Samples were measured either on a FACSCalibur using anti-CD3-APC, anti-CD4-FITC, anti-CD8-PE, and anti-CD45-PerCP or with anti-CD3-FITC, anti-CD16-PE, anti-CD19-APC, anti-CD45-PerCP, and anti-CD56-PE, or on a FACSCanto using anti-CD3-APC, anti-CD4-PB, anti-CD8-FITC, anti-CD16-PE, anti-CD19-PE Cy7, anti-CD45-PerCP, and anti-CD56-PE . The lower detection limit was 0.5x102.49/l) in the absence of relapse or ongoing myelotoxic medication.Relapse was defined as the recurrence of at least 5% blasts on cytomorphologic bone marrow examination or at least 1% blasts in peripheral blood . We defined clinically significant GvHD as the start of therapeutic systemic immunosuppression for GvHD . We defiFor this study we analyzed the T-cell and NK cell kinetics and events during the first 6 months after alloSCT, during which the early immunological recovery and most CMV reactivations take place. Furthermore, during this period the impact of the early low-dose DLI can be assessed, as the standard DLI is given to all eligible patients around 6 months after alloSCT. As part of the analyses assessing the net impact of the early low-dose DLI on the T-cell and NK cell kinetics and clinical events, patients receiving a standard DLI or modified T-cell product as part of a clinical trial were censored at 7 days after this infusion. We considered this to be non-informative censoring, since these interventions were prophylactic and not driven by the clinical course of the patient. For the T-cell kinetics we considered the circulating cell counts of the total (CD3+) T-cell population and the two major T-cell subpopulations: the CD4+CD8- and the CD4-CD8+ T-cells.2.5Probabilities of overall survival (OS) and relapse-free survival (RFS) after alloSCT with associated 95% confidence intervals (95%-CI) were calculated by the Kaplan-Meier method. The cumulative incidences of clinically significant GvHD and relapse from time of alloSCT were estimated by means of the Aalen-Johansen method, treating other failure (as described in the previous section) as a third competing risk.To study the complex interplay between the immune cell kinetics, DLI and clinically relevant endpoints (GvHD and relapse), two joint models were developed; model I starting at time of alloSCT and model II at time of the early low-dose DLI.Shared-parameter joint models consist of two components: a longitudinal submodel, and a time-to-event submodel . The forBelow follows a concise description of the joint models developed for the present application. Detailed explanation of the statistical models and the underlying rationale can be found in the 2.5.1To investigate the effect of early low-dose DLI on the kinetics of the T-cell and NK cell counts after TCD alloSCT, we performed an intention-to-treat (ITT) analysis with a baseline group distinguishing between those scheduled for early low-dose DLI because of a high anticipated risk of relapse (henceforth ‘high risk’ group) and those who were not (‘non-high risk’ group). We chose this approach instead of a per-protocol analysis since we could not properly define a control group of patients who did not receive early DLI but could have been candidates as we did not know for each patient who was not scheduled for early DLI whether he/she would have been able to receive it.To investigate whether the current slope (i.e. rate of increase or decrease of counts at a given moment) of the T-cell counts was associated with the development of GvHD, we also extended the models by adding the current slope of the log counts in addition to the current value to the time-to-event submodel .2.5.2To further investigate the T-cell kinetics after the early low-dose DLI, we constructed a joint model including only the patients who actually received the early low-dose DLI without any prior event of interest  . As shown in To investigate whether administration of the early low-dose DLI increased the numbers of circulating T-cells during the first 6 months after alloSCT, we performed an ITT analysis using model I (see Methods) to compare the 62 high risk patients who were scheduled for early low-dose DLI with the 104 non-high risk patients who were not. All patients had at least 2 T-cell measurements with a median of 6 measurements per patient (interquartile range: 5-8). Although patients showed very different T-cell kinetics over time Figure 23.2.2To investigate whether the T-cell counts increased after the early low-dose DLI as the ITT-analysis suggested, we used model II including only the 42 patients who actually received this DLI without any prior event and modelled the kinetics during the first 3 months after DLI. One of the 42 patients did not have any T-cell measurement during this period and was excluded. Baseline characteristics of the 41 included patients are described in 3.33.3.1To study the association between the T-cell kinetics and the development of GvHD or relapse after TCD alloSCT and DLI, we added disease risk and donor type as time-fixed covariates alongside the time-dependent T-cell counts in the cause-specific submodels  of model I. As shown in We hypothesized that not only the current value but also the slope of the T-cell counts would be associated with the development of an alloimmune response. To investigate this, we extended the time-to-event submodel of model I by additionally including the current slope of the T-cell counts as a covariate for all endpoints. However, we observed no association between the slope of any of the T-cell subsets and the development of GvHD . We therefore retained the simpler version of model I with only the current value.3.3.2To investigate whether higher T-cell counts were associated with a lower risk of relapse, we examined the risk factors for relapse in the time-to-event submodel of model I. Despite the ATG, patients with an UD had a significantly lower risk of relapse than patients with a RD (HRs ranging between 0.2 (95%-CI 0.1-0.5) and 0.3 (95%-CI 0.1-0.8), Of the 36 patients who experienced other failures, 6 died, 8 developed graft failure, 18 required systemic immunosuppression for a non-GvHD indication (of whom 9 received rituximab for EBV) and 4 received a virus-specific T-cell infusion for a severe viral infection. Only in the CD8 model a trend was observed for a higher risk of other failure in patients with an UD receiving an ATG-containing conditioning regimen . Higher CD4+ T-cell counts significantly lowered the hazard of the composite endpoint other failure .3.3.3To investigate whether the T-cell kinetics were also associated with the development of alloimmune responses in the postDLI setting, we used the time-to-event submodel of model II starting from early low-dose DLI with GvHD and non-GvHD events as competing events. We observed no significant association between the current values and the very heterogenous composite endpoint of relapse and other failure Figure 63.46 NK cells/l for almost all patients within 2 months, before the time of administration of the early low-dose DLI. As shown in both CD4 and NK counts as time-dependent covariates. In this model, CD4 counts were significantly associated with the development of GvHD  while the HR for the NK cell counts was 1.07 , supporting that the CD4+ T-cells were the important drivers for the development of GvHD.To investigate the NK cell kinetics and their association with GvHD and relapse, we returned to model I starting at alloSCT. As shown in 4In this study we investigated the interplay between immune cell kinetics and alloimmune responses after both TCD alloSCT and subsequent DLI using joint modelling. In the ITT analysis we observed significantly more GvHD in the high risk patients intended to receive an early low-dose DLI and an increase in T-cell counts starting at 3 months after alloSCT in high risk patients with an UD receiving an ATG-containing conditioning regimen. The ITT allocation was solely based on the disease characteristics of the patients. Since all patients were in complete remission at time of alloSCT, the TCD strategy was similar between the disease risk groups, and all GvHD in the high risk group only occurred after DLI, the only plausible explanation for both the higher risk of GvHD and the associated T-cell expansion is the administration of the early low-dose DLI. We also observed significant associations between the CD4 counts and alloimmune responses after TCD alloSCT and DLI: an increase in CD4+ T-cells was associated with a higher risk of GvHD and at the same time a lower risk of relapse suggesting establishment of a GvL effect. Interestingly, we only observed DLI-induced T-cell expansion in patients transplanted using an UD. This likely reflects an alloimmune response as GvHD was mainly seen in patients with an UD after receiving a DLI, and the T-cell counts after DLI were associated with the development of GvHD. The alloreactive T-cell expansion may have been more easily detectable in patients with an UD compared to RD because of the deeper lymphopenia at time of DLI due to the long-lasting immunosuppressive effect of ATG that patients with an UD received . In addiOur results suggest a DLI-induced T-cell expansion measurable in total numbers of the major T-cell subsets where others did not observe a significant effect of DLI on the T-cell kinetics –21. ThisThere are several limitations to our study. The total CD3, CD4 and CD8 counts are crude measures for potentially alloreactive T-cells, as only donor-derived T-cells can induce GvHD and GvL and the counts are not informative about the subpopulations, activation status or kinetics of specific T-cell clones. Thus, if we had measured the chimerism status and clonality, we might have expected to find stronger associations between the T-cell kinetics and the clinical events. Moreover, our ITT approach attenuated the observed effects of DLI on the T-cell kinetics and clinical endpoints as not all high risk patients received the early low-dose DLI and most patients who did receive this DLI did not receive it at exactly the same time after transplant. Therefore, we constructed model II starting from early low-dose DLI to see whether similar associations were observed. Joint modelling requires substantial numbers of both clinical events and longitudinal measurements to estimate associations with sufficient accuracy. Despite our comparatively larger sample size, the modest numbers of clinical events limited both the accurate estimation of association parameters (between T-cell counts and the endpoints), as well as the inclusion of additional risk factors for each endpoint. This was especially noticeable in our models focusing on the subset of the patients actually receiving an early low dose DLI. Due to the limited number of events, we used suboptimal composite endpoints such as ‘other failure’ and ‘relapse and other failure’, which hampered estimation of the association between the T-cell kinetics and these endpoints.jointly as part of a multivariate joint model, which will account for the correlation between each subset, but may be complicated to fit and will require larger sample sizes. In our study, we were not able to present such a multivariate joint model because of both sample size and software limitations. Nevertheless, results from the exploratory time-dependent cause-specific Cox model for GvHD with both the CD4 and NK counts hint at the importance of modelling immune subsets jointly.Further studies are necessary to assess the clinical implications of the findings from the present work. Aside from validation of our findings, larger studies must be performed to investigate the predictive utility of the T-cell and NK cell counts. While these counts are crude measures, they are often measured standardly and therefore attractive biomarkers for predicting alloimmune responses in patients receiving alloSCT and/or DLI. Further investigation of the immune cell kinetics in other alloSCT settings is needed to see whether similar associations between the T-cell and NK cell kinetics and alloimmune responses can be observed when using joint modelling. For instance, the recent machine learning analysis by McCurdy et al. also suggested important roles of CD4+ T-cells in the development of acute GvHD and of NK cells in the development of relapse after alloSCT with posttransplant cyclophosphamide . For DLIGenerally speaking, further characterization of the circulating T-cell subsets, differentiation and metabolic fitness could provide valuable additional insight in future studies on T-cell kinetics , 41.In summary, joint modelling allowed us to capture the associations between DLI, T-cell and NK cell counts, GvHD and relapse in a very complex clinical setting, even with modest numbers of patients and events. NK cells recover early after alloSCT and may have a protective effect against relapse. We demonstrate that DLI can induce detectable T-cell expansion and observe that the CD4+ T-cells show the strongest association with the development of alloimmune responses. Higher CD4 counts increase the risk of GvHD and decrease the risk of relapse.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Medical Ethical Committee of Leiden University Medical Center. The patients/participants provided their written informed consent to participate in this study.EK, EB, LW, CH, HP and JF designed the study. EK and EB collected and prepared the data, and performed the data analyses under supervision of LW, CH, HP and JF. All authors helped interpreting the data. EK and EB wrote the manuscript with contributions from LW, CH, HP and JF. PvdB, PvB, EM, JT, TS, DL, HV, JF and CH provided the patients care. All authors critically reviewed and approved the manuscript.