Trypanosoma cruzi to resist extended in vivo exposure to highly effective trypanocidal compounds prompted us to explore the potential for dormancy and its contribution to failed drug treatments in this infection. We document the development of non-proliferating intracellular amastigotes in vivo and in vitro in the absence of drug treatment. Non-proliferative amastigotes ultimately converted to trypomastigotes and established infections in new host cells. Most significantly, dormant amastigotes were uniquely resistant to extended drug treatment in vivo and in vitro and could re-establish a flourishing infection after as many as 30 days of drug exposure. These results demonstrate a dormancy state in T. cruzi that accounts for the failure of highly cytotoxic compounds to completely resolve the infection. The ability of T. cruzi to establish dormancy throws into question current methods for identifying curative drugs but also suggests alternative therapeutic approaches.The ability of the Chagas disease agent  Trypanosoma cruzi parasites. It is considered a neglected tropical disease because few effective treatments and preventive methods are routinely used.Chagas disease is one of the most harmful tropical diseases in the Americas. It affects millions of people, predominantly in Latin America. It is usually spread by kissing bugs infected with T. cruzi parasites, but they often fail to cure the infection. Many people with Chagas disease go on to have life-long infections and eventually develop heart failure. The reason for the high rate of treatment failure is not known. It does not appear to be the result of the parasites developing resistance to the drugs. One possibility is that the parasites can hide in a dormant state in the body, dodging the toxic drugs and living to reproduce another day.Several drugs can kill T. cruzi parasite that allows the infection to persist after treatment. In the experiments, a non-reproducing form of the so-called amastigote stage of the T. cruzi parasite inside the host cells was observed in infected mice and human cells. While some of the amastigote parasites continue multiplying, a few stop even without drug treatment – but can resume multiplication at a later time. They may also be able to change into the trypomastigote form of the parasite, which can infect new cells. These non-multiplying amastigotes can survive drug treatment for as long as 30 days, whereas the multiplying amastigotes are killed by such drugs. However, the surviving amastigotes then reestablish active infections after treatment has stopped.Now, Sánchez-Valdéz et al. identify a dormant form of the T. cruzi infections.The experiments explain why treatment so often fails to cure Chagas disease. This suggests new treatment strategies are needed, including using existing drugs for a longer time perhaps with less frequent doses. New therapies that kill the dormant amastigotes may also help. Treatments that overcome the parasite’s ability to hide, could stop the progression of the disease and prevent heart-related deaths in those with persistent  Parasitic diseases have been particularly resistant to the development of effective vaccines and safe and curative therapeutic drugs. The drugs that have been put into use often suffer from one or more of the problems of inefficiency, high toxicity and/or selection of drug resistant mutants.T. cruzi infection in humans is relatively rarely treated. Initially this was because of the misconception that Chagas disease had an autoimmune, rather than pathogen persistence etiology , offersIn addition to classical genetic-based drug resistance, it has become increasingly appreciated that in bacterial infections, ‘persister’ cells develop that are transiently unaffected by chemotherapy, due in most cases to metabolic dormancy (reviewed in ). Such pT. cruzi and link this phenomenon to the resistance of this parasite to otherwise highly effective and cytotoxic drugs. We show that both in vitro and in vivo, T. cruzi exhibits a biphasic kill curve, with the rapid death of most parasites but the persistence of transiently non-replicating intracellular parasites that are resistant to high level and prolonged drug exposure.In this study, we document the occurrence of dormancy in the protozoan T. cruzi drugs BZN and NFX are normally dosed in animals (including humans) for 30–60 days, although shorter treatment times can occasionally be curative  and expand as amastigotes within these host cells during the subsequent ~96 hr . Administration of a single oral dose of BZN reduced intracellular amastigotes at the initial infection site by ~90% within 48 hr  previously determined to be relatively more resistant to BZN in vivo  to determine the replication status of parasites within tissues of infected mice. We first established the in vivo EdU labeling protocol in acutely infected mice with high tissue parasite load, finding that the vast majority of amastigotes incorporated EdU during a 24 hr period after EdU injection  trypomastigotes . The usest cells .T. cruzi amastigotes undergoing minimal replication through 2 rounds of host cell invasion suggests that the cessation of replication can at a minimum happen early after host cell invasion, but did not preclude a more dynamic pattern of transitioning between replication and dormancy during the infection cycle in host cells. In order to better understand the flexibility and heterogeneity of replication within host cells, we monitored the early replication of T. cruzi in host cells by time-lapse video, using violet dye dilution to help mark those parasites undergoing minimal cell division within the same cell where other parasites are actively dividing  is relatively rare. In vitro studies often report very high efficiency of drugs such as BZN to kill >99% of T. cruzi amastigotes – and the assumption has been that this was in fact 100% killing  and even slower dividing, BrdU-non-incorporating intracellular amastigotes in mouse infections but the technical limitations of the labeling procedure prevented a definitive conclusion of dormancy  or in the related African trypanosome Trypanosoma brucei, which although lacking an intracellular stage, makes several well-characterized transitions from replicating to non-replicating forms.Whether stochastic or programmed, the mechanisms for entering and exiting the dormant state by T. cruzi is key to the failure of current drug treatments for T. cruzi infection. While other factors, including the differential tissue tropism of parasite strains and tissue distribution of potential drugs, certainly also impact treatment outcomes, only dormancy has been definitively linked. Given this new understanding of dormancy and its impact on drug treatment, simply identifying more compounds that effectively kill metabolically active parasites is not likely on its own to be the solution to achieving more dependable cure in T. cruzi infection. The development of new assays that can screen for compounds capable of overcoming dormancy should also be part of the paradigm for identifying new, more effective compounds. The use of current drugs in extended, less intensive dosing routines  mice were purchased from Charles River Laboratories and The Jackson Laboratory respectively. The SKH-1 ‘hairless’ mice backcrossed to C57BL/6 were a gift from Dr. Lisa DeLouise (University of Rochester). All mice were maintained in the University of Georgia Animal Facility under specific pathogen-free conditions. This study was carried out in strict accordance with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and Association for Assessment and Accreditation of Laboratory Animal Care accreditation guidelines. The protocol was approved by the University of Georgia Institutional Animal Care and Use Committee.T. cruzi strains were transfected with the pTREX-Luciferase-Neo plasmid, generated by cloning of the firefly luciferase gene from the Luciferase-pcDNA3 plasmid  into the multi-cloning site of the pTREX plasmid  route with D-luciferin  and anesthetized using 2.5% (vol/vol) gaseous isofluorane in oxygen prior to imaging on an IVIS 100 imager  as previously described . Quantif5 cells) were plated onto sterile glass-bottom 35 mm petri dishes and incubated overnight ON at 37°C, 5% CO2, in RPMI-1640 medium plus 10% fresh fetal bovine serum (FBS). Trypomastigotes of the colombiana, CL, Brazil or ARC-0704 strains were used to infect dishes at a ratio of 10 parasites to one host cell. After ON infection, cell cultures were washed with RPMI and incubated with 100 µM EdU for 24, 48 or 72 hr prior to fixation, staining and mounting in ProLong Diamond anti-fade mounting solution  containing 4’,6-diamidino-2-phenylindole dihydrochloride (DAPI) to stain nuclear and kinetoplast DNA. Images were acquired using a laser scanning confocal microscope LSM 710 attached to an EXFO Xcite series 120Q lamp and a digital Zeiss XM10 camera.EdU incorporation into amastigote DNA was detected using the Click-iT EdU Imaging Kit  following manufacturer specifications. Briefly, Vero cells (1 × 105 colombiana T. cruzi strain trypomastigotes co-expressing fluorescent (Tdtomato) and luminescent (luciferase) proteins. Sixty days post-infection, mice were injected i.p. with 10 mM EdU and sacrificed 12, 48 or 72 hr later. Before bioluminescence imaging, mice were transcardially perfused with 1x PBS and then with D-luciferin (0.3 mg/ml) diluted in 1x PBS. Tissue-specific ex vivo luciferase imaging was performed with tissues soaked in D-luciferin (0.3 mg/ml). Luciferase positive thick tissue sections were excised and consecutive imaging and sectioning were performed to reduce non-luminescent area and increase chances to localize amastigote infected tissues. Selected tissue sections were washed in 1x PBS and fixed with cold 4% PFA. After 4°C ON fixation, tissues were washed with 1x PBS and immersed in CUBIC clarifying solution  were infected with 1 × 106 colombiana strain trypomastigotes for approximately 12 hr. Seventy-two hours post-infection, cells were washed twice in PBS, fixed with 4% PFA and then washed again in PBS. After permeabilization with 0.2% Triton X-100, the cells were incubated with TdT reaction cocktail and washed twice with 3% BSA. Finally, cells were incubated with click-iT reaction buffer and washed with 3% BSA. Parasites were pre-treated for 10 min at room temperature with 10 IU/mL DNase I prior to the TUNEL for positive control. A negative control was performed in the absence of the terminal transferase. Cells were mounted using ProLong Diamond anti-fade mounting solution containing DAPI for parasite nuclei visualization. Results were quantified counting about 200 cells in duplicate from three independent experiments.DNA fragmentation indicative of parasite death was assessed by TUNEL  assay according to manufacturer's protocol . Briefly, Vero cells  and CFSE following manufacturer’s instructions. Briefly, 2 × 106 trypomastigotes were incubated for 20 min at 37°C with 10 μM of CellTrace Violet or for 5 min with 5 μM of CFSE, protected from light. Unbound dye was quenched by the addition of five volumes of 10% FBS-RPMI or one volume of FBS respectively. After washing in fresh media parasites were used for infection of cell cultures or mice.Cell suspensions of 2 and imaged at 40X magnification every 15 min for 48 hr in a Lionheart FX imager . Images and time lapse videos were analyzed with the Gene5 software (BioTek). Live cell imaging of cultures at different times post-infection and after drug addition or removal were performed in a Cytation5 cell imager (BioTek) or a Delta Vision II Microscope System .Human foreskin fibroblasts (HFF) or Vero cells were seeded in 96 well glass bottom plates  or 8 well 1 µ-slides  and infected with Tdtomato-expressing colombiana or CL strain parasites labeled with CellTrace Violet in ratios of 2:1 to 10:1 (parasites:host cells). For time-lapse video of amastigote replication, extracellular trypomastigotes were removed by washing and plates were placed in humid chamber with CO2. Free trypomastigotes released to the supernatant were harvested and imaged using an ImageStream Mark II . CellTrace Violet- Tdtomato- positive parasites from those supernatants were sorted using a MoFlo XDP  and placed in fresh Vero cell cultures that were periodically monitored and imaged using a Cytation five imager (BioTek).Vero cell cultures were infected with CellTrace Violet-labeled Tdtomato-expressing parasites of colombiana or CL strain (5:1 ratio parasites:host cells) and incubated for 6 days at 37C in 5% CO50) in complete RPMI media was added. The six replicate wells for each condition were read using BioTek Synergy Hybrid reader and images were taken by BioTek Cytation 5. At the indicated times post-treatment, BZN was removed, cultures washed and fresh medium without BZN added. Spent medium was removed and fresh medium with or without BZN were added weekly. Additional  Vero cells were added biweekly. The relative in vitro resistance to drug treatment by T. cruzi lines was determined as previously described  for 24 hr. After removing non-infecting trypomastigotes, 10 μM BZN  of the GraphPad Prism version 5.0 software were used. Values are expressed as means ± standard error of mean of at least three separate experiments. P values equal to or minor that 0.05 were considered significant. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Trypanosoma cruzi during extended drug exposure" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: John M Kelly and David Sacks.Thank you for submitting your article "Spontaneous dormancy protects The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Trypanosoma cruzi can be found in a dormant/latent state both in vitro and in vivo experimental models. Using CellTRace Violet to track and isolate the few non-dividing parasites from infected cells in vitro, the authors were able to show that the dormant/quiescent parasites can establish a second round of infection in new host cells. They also provided good in vivo evidence that the non-dividing parasites persist following treatment with benzinidazole and are capable of re-initiating an infection. The data presented are novel and will be of much interest to those involved in finding curative treatments for this important neglected tropical infection. Indeed, the existence of non-replicating amastigotes is an important discovery that might explain the widely observed treatment failure as outcome of Chagas disease therapy and the poor efficacy of some compounds despite their promising curing properties.In this study, the authors provide sound evidence that Although no data on the mechanisms of this process are reported, the results are of significant value, and will no doubt stimulate much downstream research to unravel the mechanism(s), determine the biological role, and understand the consequences for drug development.Essential revisions:1) The in vitro infections shown in Figure 3 and 4 could have been exploited to provide more quantitative data regarding the proportion of non-dividing cells that arise within each generation, and to estimate if this frequency changes during successive generations as the numbers of intracellular amastigotes increases. It would also have been informative to more carefully quantify for comparison the subsequent infections initiated by the CellTrace Violet-positive and negative trypomastigote populations for their intracellular growth and the frequency of non-dividing forms that they give rise to. The limited data provided in Figure 5 shows a single Vero cell with a massive infection initiated by the CellTrace Violet-positive population. Only a single non-dividing parasite is indicated, which seems quite low compared to the frequencies observed in Figures 3 and 4.2) The evidence that the non-dividing parasites are the only persisting organisms following treatment and that they are responsible for re-establishing infection is not so clear, particularly from the in vivo data:The experiments shown in Figure 6 and Figure 6—figure supplements 1 and 2 are the key results of the paper but are difficult to assess. The results are meant to indicate that only non-dividing parasites remain after drug treatment, and that these parasites can reinitiate a productive infection. What is actually shown in Figure 6 from the tissue of 3-4 mice treated mice, however, is a single non-dividing parasite. It is not at all clear what this 'representative' result refers to. How many cells, or how much tissue was examined? Were there absolutely no Td-tomato staining cells that were CellTrace Violet-negative? Even a few may have contributed to the re-established infection. Is there some way to quantify this data, by flow cytometry perhaps? It would be especially informative to compare the number of the 'dormant' parasites in the adipose tissue (and/or other tissue) in groups of infected mice before and after treatment.3) Overall the quality of the data presented needs to be improved:Several fluorescence microscopy images are of poor quality, pixelated , saturated , out of focus ; these images cannot be published as presented, considering the importance of the findings.The use of CellTrace Violet to identify the dormant amastigotes is quite clever but some images of these parasites are rather poor . By contrast, Figure 4E is of very good quality. Figure 4F, G: it would be nice to have images of samples from the other representative fractions of the cellTrace/Tc-Tomato plot.4) Figure 7A: the authors assume that rebound fluorescence is due to dormant amastigotes:No direct proof is presented since few resistant parasites  could give rise to the fluorescence peaks. Also, the authors should perform double staining with Tunel/EdU to confirm that dormant parasites are not dying. Biochemical evidence of the dormant amastigotes is lacking: can their ATP consumption be measured? Or the activity of critical metabolic enzyme(s)? Would it be possible to generate parasites transfected with fluorescence reporters for these components in order to directly monitor dormancy in vitro and in vivo? How do the authors distinguish dormancy from slow growth/long generation times? 1) The in vitro infections shown in Figure 3 and 4 could have been exploited to provide more quantitative data regarding the proportion of non-dividing cells that arise within each generation, and to estimate if this frequency changes during successive generations as the numbers of intracellular amastigotes increases. It would also have been informative to more carefully quantify for comparison the subsequent infections initiated by the CellTrace Violet-positive and negative trypomastigote populations for their intracellular growth and the frequency of non-dividing forms that they give rise to. The limited data provided in Figure 5 shows a single Vero cell with a massive infection initiated by the CellTrace Violet-positive population. Only a single non-dividing parasite is indicated, which seems quite low compared to the frequencies observed in Figures 3 and 4.all parasites that are detected are CellTrace Violet-positive – and thus would have had to have arrested early in the replication cycle within host cells. Thus, we conclude that dormancy occurs preferentially and possibly exclusively soon after host cell invasion and conversion to amastigotes. It would be very challenging to generate highly quantitative frequency data on dormant parasites in different experiments , under different culture conditions or with different isolates. Our attempts to do so suggest that there is not an increased frequency of dormancy in particular strains. With specific reference to the comment comparing the frequency of dormant parasites in host cells in Figures 3 and 4 compared to Figure 5, multiple trypomastigotes may enter the same host cell in our standard in vitro assays (where the standard parasite:host cell ratio is 10:1) while this is very highly unlikely in the experiment shown in Figure 5 where a very limited number of parasites were used to infect monolayers of host cells. The images we have selected for inclusion in this manuscript accurately reflect the fact that while dormancy occurs routinely, it is not uniform in every infected cell, hence our conclusion that it is not highly programmed.The reviewers ask interesting questions, but for the most part, they are not approachable with the current technology. For example, with neither the CellTrace Violet nor with EdU is it possible to assess if parasites stop dividing at later points (rounds) during infection in a host cell . We have done such time lapse monitoring of a limited number of host cells and have not yet observed amastigotes that were actively dividing, stop dividing. Further, in the in vivo and in vitro drug treatment/washout experiments, 2) The evidence that the non-dividing parasites are the only persisting organisms following treatment and that they are responsible for re-establishing infection is not so clear, particularly from the in vivo data:The experiments shown in Figure 6 and Figure 6—figure supplements 1 and 2 are the key results of the paper but are difficult to assess. The results are meant to indicate that only non-dividing parasites remain after drug treatment, and that these parasites can reinitiate a productive infection. What is actually shown in Figure 6 from the tissue of 3-4 mice treated mice, however, is a single non-dividing parasite. It is not at all clear what this 'representative' result refers to. How many cells, or how much tissue was examined? Were there absolutely no Td-tomato staining cells that were CellTrace Violet-negative? Even a few may have contributed to the re-established infection. Is there some way to quantify this data, by flow cytometry perhaps? It would be especially informative to compare the number of the 'dormant' parasites in the adipose tissue (and/or other tissue) in groups of infected mice before and after treatment.2 when mounted for microscopic analysis. In tissue harvested on day 6 of infection (prior to treatment), detection of infected cells is relatively easy and 3-4 infected cells can be observed within 15 minutes of scanning. Based on the observation of approximately 30 infected cells containing a total of 140 amastigotes, we estimate the frequency of Violet+ amastigotes among all amastigotes at this point is ~ 2%. Following 12 days of treatment this same amount of tissue must be exhaustively scanned for up to 3 hrs per sample to detect between 4 and 5 infected cells per sample. Every one of these infected cells contained only violet-positive parasite, generally only 1 parasite per cell. Scanning tissues from more than 12 mice using this process never revealed a violet-negative parasite during or at the conclusion of treatment. Figure 6 does indeed show a single dormant parasite – but the purpose of the supplementary figures is to indicate that this was not the only one found – collectively we have found dozens but did not feel that it was necessary to show them all. It is impossible to prove that there is not a non-dormant parasite in mice or animals after 20-30 days of drug treatment – but we have not ever observed them while we do, albeit with great effort, observe non-replicating parasites.We agree that the experiments in Figure 6 and Figure 6—figure supplement 1 and 2 are among the key results in the paper – although not the only ones. For these experiments, the peritoneal adipose tissue sample from each mouse is ~0.3 g of tissue spread over a surface of ~ 1.6 cmSome of this additional description has been added to the figure legends to indicate the vigor with which we searched for dormant and non-dormant parasites.We do not believe that it will be especially informative to quantify dormant parasites before and after treatment in large part because the timing of “awakening” from dormancy is clearly variable. We know that brief BZN treatment of animals or cell cultures results in higher detectable levels of violet+ dormant parasites than longer-term treatment, suggesting that dormant parasites can exit dormancy while under drug treatment and as a result, be killed. One could argue that BZN is slowly killing parasites while they are dormant but then one would have to explain how some dormant parasites can resist BZN killing for 30 days or more.Estimating the number of dormant parasites in the tissue by flow cytometry is quite difficult due to the scarcity of parasites surviving the BZN treatment and the process of the tissue necessary for flow cytometry . We unsuccessfully tried this approach originally with skeletal muscle of animals chronically infected with Td-tomato expressing parasites and moved to the microscopic analysis of adipose tissue with better results.3) Overall the quality of the data presented needs to be improved:Several fluorescence microscopy images are of poor quality, pixelated , saturated , out of focus ; these images cannot be published as presented, considering the importance of the findings.The use of Cell Trace Violet to identify the dormant amastigotes is quite clever but some images of these parasites are rather poor . By contrast, Figure 4E is of very good quality. Figure 4F, G: it would be nice to have images of samples from the other representative fractions of the cellTrace/Tc-Tomato plot.Overall quality of the images has been improved. An ImageStream example of Td-tomato positive- violet-negative parasite for Figure 4F is shown and sharper images selected for ImageStream figures. Images from live cultures using the Cytation 5 or from thick adipose tissue samples represent a challenge and usually those techniques have inherent limitations that prevent obtaining images of the same crispness as for those of monolayers or sections by the confocal microscopy . We increased the resolution of all the pictures to 300dpi. In Figure 2B and C and Figure 7B the saturation and exposure were decreased as much as possible. In Figure 3, saturation and exposure were decreased in the pictures of 24hrs and 72 hrs. Pixelation and focus was corrected/optimized for Figure 4B, Figure 5Figure 6B and 7C. The image in Figure 6C at 6 days was changed to a picture containing a nest with a violet amastigote.4) Figure 7A: the authors assume that rebound fluorescence is due to dormant amastigotes:No direct proof is presented since few resistant parasites  could give rise to the fluorescence peaks. Also, the authors should perform double staining with Tunel/EdU to confirm that dormant parasites are not dying. Biochemical evidence of the dormant amastigotes is lacking: can their ATP consumption be measured? Or the activity of critical metabolic enzyme(s)? Would it be possible to generate parasites transfected with fluorescence reporters for these components in order to directly monitor dormancy in vitro and in vivo? How do the authors distinguish dormancy from slow growth/long generation times?T. cruzi, perhaps because ATP levels in the parasites are not within the reporter range of the cytoplasmic ATP sensor. Measuring enzymatic activity would also be useful, but very challenging in these intracellular parasites, where host enzyme activity and substrate availability must also be dealt with.The reviewers suggest a more detailed biochemical and metabolic characterization of the dormant parasites in order to discriminate them from proliferating amastigotes and to demonstrate true dormancy. We agree that this would be nice, but is well beyond this initial report. We have begun such investigations, attempting as suggested to engineer parasites expressing a reporter for ATP levels. However, although such reporters have been successfully used in mammalian cells , they did not function in We unsuccessfully attempted a TUNEL/EdU double detection – the two protocols were incompatible in our hands. However, as described in Figure 3—figure supplement 1 the detection of TUNEL positive amastigotes dying inside cells is extremely rare and does not correlate with the proportion of EdU negative or violet-positive parasites detected in similar conditions. Further, we show that previously dormant parasites can convert to trypomastigotes, reinvade new host cells, and resume replication – all properties inconsistent with being dead.Our experiments of infection of fresh cultures with sorted violet-positive parasites  as well as the rebound after drug treatment  suggest that parasites resistant to drugs under these conditions are not fixed indefinitely in a non-proliferative dormant state but are totally viable and capable of further proliferation. Therefore, it is not unreasonable to think that those parasites could just display a slower replication. Although we have observed in the time lapse experiments different frequency of division in some amastigotes, the lack of EdU incorporation in some parasites exposed to EdU in vitro for over nearly the entire replication cycle inside the cells, suggests that these parasites have not divided (or did so only just following invasion), in clear contrast to amastigotes actively proliferating in the same cell.Parasites that resist BZN treatment for 20-30 days are not stably resistant to BZN in the classical sense- they can be killed as easily as parasites never exposed to BZN once they become metabolically active Figure 7—figure supplement 7). Thus, a few BZN-resistant parasites are not responsible for our observations, unless BZN-resistance is transient – e.g. the parasites are dormant. The difference between very slow growth and dormancy is ultimately semantics – if very slow growth means not dividing for 30 days, then we would agree with that description. But again, this is a transient property – once dormant parasites exit dormancy, they have a “normal” rate of growth. In short, we believe the sum of the data here argue for a state of dormancy. Determining the metabolic and biochemical markers of dormancy will not be easy but it is in our plans.


This phenotype conversion impairs the intestinal barrier integrity and contributes to the dysregulated immune responses of CD patients. Our next step was to search for pathways to modulate this phenotype switch. The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor. Initial studies of AHR concentrated on its role in the detoxification of xenobiotics. However, recent research has focused on the immune system. Especially, AHR pathway is proven to be essential for the maintenance of intestinal ILC3s in mouse models. We examined whether AHR pathway participated in the human intestinal ILC phenotype change in the inflamed terminal ileum of CD patients. As anticipated, NKp44+ILC3s, NKp44−ILC3s and ILC1s had differential AHR expression. This AHR signaling mediated CD117 expression on the surface of ILC3s. The conversion from ILC3 to ILC1 was accompanied by the downregulation of AHR expression. We further observed that there was a disparity between AHR protein expression and mRNA expression in the inflamed terminal ileum tissues of CD patients compared to unaffected areas. These findings suggest that AHR pathway is also important for human intestinal ILC phenotype regulation and impaired AHR signaling in the inflamed gut of CD patients possibly contributes to the ILC3/ILC1 conversion.Innate lymphoid cells (ILCs) are emerging as important components of our immune system that have critical effector and regulatory functions in both innate and adaptive immune responses. They are enriched at mucosal surfaces, such as lung and intestine. Our previous work has shown that Lineage Recently, a rapid and exciting expansion in our knowledge of the mucosal immunity has occurred. An example is the identification of new cell types, such as ILCs.CD is a chronic inflammatory condition of the gastrointestinal tract and may affect any part from the mouth to the anus. The exact cause of CD remains unknown. It has been suggested that an abnormal immune response against the microorganisms within the gut is responsible for the disease in genetically susceptible individuals –6. In mice, ILC3s play a crucial role in gut immunity, by directly inducing epithelial cell proliferation and glycosylation, promoting anti-microbial peptides production, preventing dissemination of commensal bacteria, and limiting commensal bacteria specific CD4+ T cell response –11. In human, NKp44+ILC3s have been shown to be the major ILC subset in the intestine at the steady state . In the context of CD, our group and other groups have shown that there were significant changes regarding ILCs composition in the inflamed gut. NKp44+ILC3s were found to be significantly reduced while IFN-γ-producing ILC1s accumulated in the inflamed intestinal mucosa of CD patients compared to non-inflamed areas –20. This phenotype alteration switches ILCs’ role from protective to pathogenic in the maintenance of human intestinal immune homeostasis.ILCs are the first line of defense in our immune system to respond to danger signals from the environment by rapidly secreting cytokines and chemokines. They are categorized into three groups: ILC1, ILC2 and ILC3 according to their cytokines production and transcription factors profile –23. Initial studies of AHR focused on its role in mediating toxicity of dioxin-like chemicals. Actually, AHR exerts multiple functions in our body, acting as an essential sensor for environmental factors and human lifestyle factors, such as diet, smoking and psychological stress changes. It is a link between environment and immunity –26.AHR belongs to the family of basic helix-loop-helix/Per-Arnt-Sim (bHLH/PAS) proteins. It is a ligand-dependent transcription factor located in the cytoplasm of cells that upon ligand binding translocates to the nucleus, dimerizes with its nuclear translocator (ARNT), and binds to AHR response elements in the promoter regions of its target genes to initiate their transcription reg cells and natural killer cells –30. Also, AHR pathway is necessary for the maintenance and function of IL-22-producing ILC3s in the gut. AHR knock-out mice have significantly reduced numbers of IL-22-producing ILC3s subset in the gut, lack of lymphoid tissues cryptopatches and isolated lymphoid follicles and less IL-22 production –33. Interestingly, AHR expression has been shown to be down-regulated in the inflamed mucosa of CD patients , 35. However, the association between AHR expression and ILC3s population in the human gastrointestinal tract remains unclear.Recently, accumulating evidence has indicated that AHR signaling is involved in the regulation of differentiation, maintenance and function of various mucosal immune cell subsets in mouse models, including Th17 cells, T+ILC3s subset played a protective role in the pathogenesis of CD and AHR signaling was essential for the maintenance of this subset in the intestinal mucosal lamina propria of CD patients. We found that the three intestinal ILC subsets  had differential AHR expression levels. AHR expression in the NKp44+ILC3s was the highest while its expression in the NKp44−ILC3s was intermediate. ILC1s did not express AHR at all. This means that during the ILC3/ILC1 conversion process, the AHR expression in the NKp44+ILC3s needs to be downregulated. Our data proved that the AHR expression was down-regulated in the inflamed NKp44+ILC3s compared to ILC3s from unaffected tissues. Also, we noticed that there was a disparity between AHR protein expression and mRNA expression in the inflamed terminal ileum of our CD patients. AHR protein expression was downregulated while its mRNA expression was upregulated. It is likely that AHR expression in the inflamed gut of CD patients is regulated by a post-transcriptional mechanism. This is consistent with a recent study which suggests that MicroRNA-124 targets AHR in CD to promote intestinal inflammation .Based on all these information, we hypothesized that NKp44+ILC3s phenotypic instability in the inflamed gut of CD patients. The potential mechanism probably involves the stable expression of surface marker CD117 which is essential for ILCs phenotype . In mouse models, one group demonstrates that intestinal ILC3s of their AHR knock-out mice model has significantly reduced expression CD117 . Their chromatin immunoprecipitation (ChIP) study further suggests that two canonical AHR binding elements are located in the promoter of CD117 gene . In addition, another two groups have shown that CD117 is under the direct transcriptional control of AHR , 38. For human innate lymphoid cells, AHR pathway can regulate their CD117 expression as well , 40. We also observed that CD117 expression was downregulated in the inflamed NKp44+ILC3s. This AHR mediated CD117 expression contributes to the maintenance of NKp44+ILC3s phenotype in the human gut.The impaired AHR signaling pathway causes the NKp44In summary, our findings suggest that impaired AHR signaling involves in the human intestinal ILC3/ILC1 conversion in the inflamed terminal ileum of CD patients and could be a new target of immunotherapy for those patients.


The results show that the TAS trends are significantly affected by 1–2 K century−1, which under 1.5 °C global warming translates into a non-negligible impact of up to 1 K in the regional projections of TAS, similarly affecting projections for maximum and minimum temperatures. In some cases, these differences involve a doubling signal, laying further claim to careful reconsideration of the RCM setups with regard to the inclusion of GHG concentrations as an evolving external forcing which, for the sake of research reproducibility and reliability, should be clearly documented in the literature.Variations in the atmospheric concentrations of greenhouse gases (GHG) may not be included as external forcing when running regional climate models (RCMs); at least, this is a non-regulated, non-documented practice. Here we investigate the so far unexplored impact of considering the rising evolution of the CO The inclusion of greenhouse gas forcing in regional climate model experiments is a non-regulated, non-documented practice. Here, the authors demonstrate the significant of the impact of this lack of regulation on simulated regional warming trends, which can double climate change signals under 1.5 °C global warming. The translation of global temperature increases and targets, such as the encouraged long-term goal of keeping the increase in global mean temperature to well below 2 °C above pre-industrial levels5, into regional impacts and vulnerabilities is of major importance in order to raise awareness of societies and decision-makers6. In order to better describe the consequences of climate change at a proper spatial scale, dynamical downscaling techniques are typically applied to elucidate global warming fingerprints at regional scales and to produce policy-relevant information tailored to the needs of the potentially affected socioeconomic sectors . The dynamical downscaling approach is based on the use of Regional Climate Models (RCMs) running over limited geographical domains with boundary conditions given by Global Circulation Models (GCMs) to be downscaled over the finer RCM spatial grid; although there is another dynamical downscaling approach given by variable resolution GCMs. During the past decades, RCMs have been commonly used by a wide community of researchers and under the umbrella and incentive of many international initiatives, such as CORDEX8, sponsored by the World Climate Research Programme (WCRP), whose aim is to advance and coordinate the science and application of regional climate downscaling through global partnerships.Global warming manifests itself and impacts heterogeneously across the globe11. Although the RCMs explicitly solve mesoscale atmospheric processes, there are still small-scale processes that need to be parameterized12. In particular, radiation schemes are used to manage the radiative forcing, which is affected by the concentration of greenhouse gases (GHG) in the atmosphere. Some radiation schemes, as implemented in RCMs, prescribe GHG concentrations to fixed values by default (probably because they were primarily conceived for weather forecast applications or short-term runs). For instance, in the widely used Weather Research and Forecasting (WRF) model13, the inclusion of variable GHG concentrations as an evolving external forcing is an available option only from its 3.5 version (launched in April 2013) and is not used in the default compilation. The use of constant GHG concentrations in long-term RCM runs leads to the implicit assumption that change in the GHG atmospheric composition has little effect on the regional climate, or that its effect is sufficiently included through the GCMs-provided boundary conditions. No theoretical or empirical scientific evidence supports, however, either of these assumptions. The latter contrasts in particular with the recommended use of large domains as this results, in turn, in weak control by the lateral boundary conditions9.The main added value of high-resolution RCMs is the development of the fine scale, which reaches its optimum by considering large regional domains15) do actually include varying GHG concentrations in their runs , others, as far as we are aware to date, do not (in particular some WRF and HIRHAM runs). This information had to be gathered by personal communication with the modeling groups, since no documentation at this regard is publicly available. In contrast to the well documented experimental design (including the GHG forcing) within GCM coordination frameworks such as the Climate Model Intercomparison Project (CMIP)17, this aspect of the RCMs setup is undocumented in the current scientific literature , which, as little, goes against the code of good practice that should guarantee reproducible research19. Moreover, while many works deal with the sensitivity of RCM runs, for example, to the domain spatial resolution and the choice of parameterization schemes, the inclusion or not of the forcing due to the variations in the atmospheric concentrations of GHG has so far received very little attention (find a single attempt in ref. 20). In spite of its likely paramount importance21 and practical relevance for the accuracy of regional climate change projections, the sensitivity of RCM runs to evolving GHG concentrations has not yet been properly established.Blind to this issue, the benchmark CORDEX framework, for instance, provides scant information on the RCMs implementations and capabilities of using the same climate external forcing data  as the parent GCMs, in spite of its triggering factor in coordinating experimental setups among modeling groups worldwide. In fact, while most of the RCMs contributing to the European branch of CORDEX  trends along the recent past, reporting an overall systematic underestimation that even worsen the signal from the GCMs driving data. As possible causes, they pointed out inadequacies in the characterization and modeling of basic processes and features, such as cloud formation, aerosol variations, and soil properties, which are certainly what climate models miss most27. However, the focus so far has not been on the likely relationship of this issue to how GHG concentrations are treated in RCM runs. The importance of the matter, with its immediate implications for the regional climate modeling community, motivated us to shed light on it.The impact of varying GHG concentrations in RCM runs should be most apparent with regard to temperature trends. In this sense, several studies29, the most challenging one with the highest increases of GHG emissions among those considered in the latest IPCC report1, although in the first half of the 21st century it is very similar to the more moderate RCP4.5. The WRF setup and the GCM data driving WRF (retrieved from the r1i1p1 MPI-ESM-LR CMIP5-experiment31) are described in detail in Methods. For the historical period, WRF was additionally driven by the ERA20C reanalysis data33 (hereafter referred to as ERA). These reanalysis-driven experiments allowed us to elucidate the contribution of varying GHG concentrations also in RCM hindcast runs.Therefore, here we investigate the sensitivity of TAS trends to year-to-year varying GHG concentrations in RCM simulations by analyzing pairs of historical (1951–2005) and scenario (2006–2050) regional climate simulations run with WRF over Europe. Each counterpart was performed allowing or not allowing the GHG concentrations to vary in the RCM . In order to obtain the highest sensitivity to the variations of the GHG atmospheric concentrations, in the scenario period we considered the Representative Concentration Pathway 8.5 (RCP8.5)The fingerprint of evolving GHG concentrations in the regional warming is apparent and robust in all cases, under both past and future conditions, involving even doubled signals under 1.5 °C global warming in some areas (eastern Europe) and seasons (winter). Such a non-negligible impact calls for regulating efforts to include and document systematically the time-varying GHG forcing in the RCM runs as a good practice.Tx) and minimum (Tn) temperatures.Figure Tx and Tn, with the largest signals  appearing over central and northern Europe for the recent past, and also along the south-western Mediterranean strip for the future. By seasons, the signals increase to 8 K century−1 in winter  and summer .All the global and regional datasets and experiments usually depict positive trends, which are more marked over land than sea. The spatial patterns are very similar for TAS, −1 higher , than from the CTE one .Both WRF experiments (VAR and CTE) reproduce the most outstanding features of the spatial patterns retrieved from their respective global driving datasets, while showing some important discrepancies. For instance, at the annual scale, both ERA-driven WRF experiments provide weaker TAS trends in Mediterranean Europe and north-eastern areas compared to the results from ERA  that will guide a special report by the IPCC planned for 201836. Thus we computed changes from the VAR and CTE experiments . The first column depicts the number of sensitive experiments (NSE) to varying GHG concentrations when projecting TAS changes, with positive/negative values indicating larger/smaller TAS increments in the VAR than in the CTE simulations in those sensitive experiments. Over wide areas, three out of four experiments show significant sensitivity to the varying GHG approach, especially when the TAS projections are assessed at the annual scale, with higher TAS increases in the VAR experiments than in the CTE, overwhelmingly. The second column displays the most sensitive experiment (MSE) among all the ensemble members to the varying GHG approach when projecting TAS changes to the future. While it should be acknowledged that one of the two radiation schemes included in the ensemble  and the near future. Other variables and/or RCMs may show different sensitivity, which could be worth to investigate. Anyhow, the worrying issue is that whether the forcing due to the variations in the atmospheric concentrations of GHG is or not included in the regional climate model runs is not sufficiently documented in the scientific literature. This lack of information, together with (1) the fact that the default compilation in one of the most used (and most recent) RCMs, the WRF model, does not include such time-varying forcing, and (2) the information that these authors have gathered regarding the non-regulated practice in this regard within the outstanding EURO-CORDEX initiative, alarmingly incites doubt to emerge.Here we intend to appeal to the regional climate modeling community worldwide by identifying the need to clarify and document the approach when reporting regional climate information. It is clear that the discrepancies between both approaches are large enough for better information on the setup of RCMs to be required, especially when the regional climate simulations are conducted over wide domains and contribute to initiatives demanding coordination efforts . Special attention should be paid to the design of the regional climate modeling experiments that may involve the use of data by a wide community of potential users, including the impacts community.13 was used to run the regional climate simulations over Europe with a spatial resolution of 0.44° in latitude and longitude, using the EURO-CORDEX compliant domain14. For the historical period (1951–2005), both the ERA20C reanalysis33, which assimilates observations of surface pressure and surface marine winds and has an horizontal resolution of approximately 125 km, and the GCM CMIP5-experiment17 r1i1p1 MPI-ESM-LR historical run30 with an approximate horizontal resolution of 200 km over land  were used to initialize and drive (without nudging) the WRF model. Both global datasets incorporate the year-to-year varying annual concentrations of the CO2, CH4, and N2O GHG (as estimated from inventory data28), which show a continuous positive trend1. For the scenario period (2006–2050), the GCM CMIP5-experiment RCP8.5-forced r1i1p1 MPI-ESM-LR run31 was used. RCP8.5 depicts the highest radiative forcing along the XXI century among all RCPs29, with doubled CO2, CH4, and N2O concentrations  by 2050 compared to the last record of the historical period.The 3.6.1 version of the WRF model41, RRTMG shortwave and longwave schemes42, Grell 3D ensemble scheme for cumulus44, Lin scheme for microphysics45 and Yonsei University scheme for the boundary layer46. To construct the multi-physics ensemble we used all the WRF configurations listed in Table 40, these configurations yield results that cover a wide range of the model spread. Simulations other than the CTRL one span only the periods 1971–2000 and 2011–2040, which are enough to assess impacts under 1.5 °C global warming. Also, the GCM-driven CTRL experiments were replicated for these periods over a smaller area domain, with latitude ranging from 44.8° to 53.5° and longitude from 16.2° to 30° . Updates of the boundary conditions, from either ERA or GCM, were performed every 6 h at the borders of the regional domain. The outputs were recorded at one-hour intervals.2, CH4, and N2O GHG, fixed to their values in 2005 , and considering their evolution, their annual estimated values, along the simulated periods as it was included in the driving global datasets (VAR experiments).Each WRF realization was run twice: considering static concentrations of COTx and Tn, respectively) series were also yearly or seasonally averaged and included in the analysis. The linear trends were computed using the Theil–Sen estimator, a non-parametric method that defines the slope as the median among all possible lines through pairs of points. This approach is less sensitive to outliers than the classic least-squares method and is more accurate in case of non-normal data. Trend significance was estimated applying the standard Mann–Kendall trend test. Statistical significance of trend differences  was equally estimated but using the difference time series . This method reduces noise levels by subtracting the variability common to both datasets being faced47. In all cases, we imposed the threshold p < 0.1.The analysis first deals with the temporal trends of TAS over both historical and scenario periods: differences between the VAR and CTE experiments, and between these and their driving global datasets (ERA and GCM). The temporal trends were computed as the linear trends of the yearly-mean series of TAS at either annual or seasonal scales . The daily maximum and minimum TAS  had been 0.46 °C according to the observations6; that is why we looked for an increment of 1.04 °C since 1971–2000. Applying this methodology, Y was found to be 2025. Thus the future period considered here to construct the change patterns of TAS, Tx and Tn was 2011–2040. Statistical significance of the signals appearing in the change patterns and the patterns of differences in the change signals  was accounted for by applying a two-tailed t-test. Again, we imposed the threshold p < 0.1.Second, the analysis deals with climate change regional impacts on TAS, To compare the signals retrieved from the WRF experiments and from their driving global datasets at the grid point level, the former were bilinearly spatially interpolated to the respective global grids.https://www.dkrz.de/daten-en/cera) and the European Center for Medium Range Weather Forecast , and the WRF namelists as used here are provided in the Supplementary Tables https://code.mpimet.mpg.de/projects/cdo/, and the R Project for Statistical Computing  available from https://www.r-project.org/. All codes are as well available from the corresponding author upon request.All datasets used in this study are publicly available. GCM and ERA data can be retrieved from the websites of the Climate and Environmental Retrieval and Archive (Supplementary InformationPeer Review File


Prostate cancer (PCa) is one of the most significant male health concerns worldwide. Single nucleotide polymorphisms (SNPs) are becoming increasingly strong candidate biomarkers for identifying susceptibility to PCa. We identified a number of SNPs reported in genome-wide association analyses (GWAS) as risk factors for aggressive PCa in various European populations, and then defined SNP-SNP interactions, using PLINK software, with nucleic acid samples from a New Zealand cohort. We used this approach to find a gene x environment marker for aggressive PCa, as although statistically gene x environment interactions can be adjusted for, it is highly impossible in practicality, and thus must be incorporated in the search for a reliable biomarker for PCa. We found two intronic SNPs statistically significantly interacting with each other as a risk for aggressive prostate cancer on being compared to healthy controls in a New Zealand population. It has become one of the most significant male health concerns worldwide2. An individual is diagnosed as having high-risk or aggressive PCa based on the classification by the American Urological Association3, when the clinical T stage ≥cT2c, and/or the Gleason score ≥8, and/or the serum prostate serum antigen (PSA) level >20ng/ml4.Prostate cancer (PCa) is highly prevalent, and around 1 in 6 patients are at risk of developing the aggressive form of the disease5, various studies have also shown that genetic interactions with biological and behavioral factors play an important role in the overall risk and prognosis of PCa8. Variations in the genome are a major contributor to the differences in disease susceptibly amongst individuals9. Single nucleotide polymorphisms (SNPs) are the most commonly identified variations in a genome.Although a hereditary aspect is well known for this disease10 is very appealing among researchers working on risk factors for various cancers13, including prostate cancer14. Here we have identified a SNP-SNP interaction as a risk factor for aggressive PCa, by comparing the data generated after carrying out SNP genotyping using the SEQUENOM MassARRAY iPLEX® assay, and the TaqMan® assay (depending on the gene of interest) from the DNA extracted from blood samples. These samples were taken from a New Zealand cohort of men with self-reported European ethnicity that have been clinically diagnosed with aggressive and non-aggressive PCa, and healthy controls with no reported symptoms of the disease. Symptoms include increased urination during night time along with a frequent urge to urinate problems maintaining a steady flow of urine, hematuria and dysuria15. Our results indicate a strong influence of gene x environment interaction in overall gene expression and epistasis.Analysing the role of SNP-SNP interactions and epistasisPatients with a clinically established diagnosis of PCa (aggressive and non-aggressive) from the Auckland Regional Urology Registry , and certain private practices in the Waikato region of New Zealand were sent invitations along with the written consent forms to participate in this study between the years 2006 and 2014. Eventually, a total of 254 patients with various grades of PCa voluntarily participated in our study after providing us with written informed consent. . Additionally, 369 males from the Auckland region of New Zealand with no reported symptoms of PCa were considered as healthy controls for this study , recruited by advertising in and around the University of Auckland. Written informed consent for participation in the study was also obtained from the male healthy controls.16, care was taken to invite men to participate in this study that were between the ages of 40 to 90 years at the time of diagnosis for patients with PCa, and at the time of recruitment for healthy controls. (17). The average age of men with aggressive PCa was calculated to be 66 years, 67 years for men with non-aggressive PCa and 58 years for healthy controls.Each individual participating in this study completed a demographic and lifestyle questionnaire. Because of the influence of age in this diseasentrols. 17. The Patient blood samples were collected at respective outpatient clinics at the Auckland, North Shore and Counties Manukau Hospitals, New Zealand. The blood samples of healthy controls were collected at the Faculty of Medical and Health Sciences, the University of Auckland, New Zealand and the New Zealand Blood Bank, Great South Road Centre, Epsom, Auckland New Zealand.® tubes (Becton Dickinson) containing EDTA by a trained phlebotomist. DNA was extracted using a QIAamp genomic DNA kit  following the manufacturer’s protocol, with the aid of a fully automated QIAcube . The DNA samples were diluted to 5.0ng/μl as per requirements of the SEQUENOM MassARRAY iPLEX® assay protocol.Blood samples from each participant were collected in Vacutainer® assay or by TaqMan® SNP genotyping assay, as discussed in Vaidyanathanet al., 201718.136 SNPs, located in 66 genes and some undefined chromosomal locations were identified by a thorough literature search of GWAS for both aggressive PCa and PCa. Care was taken to select SNPs that were identified as significantly associated with risks for PCa and aggressive PCa in European populations only. Research papers were only considered if published in and after the year 2000, in order to be in concordance with the current trends in PCa research. The final SNPs selected to be genotyped were at the research team’s discretion, either by SEQUENOM MassARRAY iPLEX® assay for the candidate SNPs was carried out in the Auckland UniServices Sequenom Facility at The Liggins Institute, Auckland, and AgResearch Limited, Mosgiel, New Zealand, using a custom-designed multiplex gene panel and iPlex chemistry. The genotype calling was carried out by using the standard post-processing calling parameters from the SEQUENOM Type 4.0 software.SNP genotyping by SEQUENOM MassARRAY iPLEX® SNP genotyping  was carried out on a panel of genes that failed to be genotyped using the SEQUENOM MassARRAY iPLEX® assay. The primers used were either obtained pre-designed from ABI or were custom-made using Assay-by-Design service by ABI, and the protocol provided by the manufacturers was followed20.SNP genotyping using the TaqMan17). SNPs that failed the HWE in patients with PCa were still considered for analysis, as the SNPs may have failed to be in equilibrium in the patient population due to the influence of the risk allele and hence should not be ignored from a case-control study like ours22. The statistical significance was set to p≤0.000123.26 SNPs were removed for being in linkage, and a further 5 SNPs were removed for failing the Hardy-Weinberg Equilibrium (HWE) in the healthy controls, thereby reducing the total SNPs analyzed to 105  clustering, is based on pairing up the SNPs based on similarity of genetic identity. This IBS clustering is used in order to test if the SNPs of two individuals belong to the same population or not18. Following this stratification, we performed a standard case-control association test using the Cochran-Mantel-Haenszel test (1 degree of freedom) to analyse the SNP-disease association that is conditional on the clustering18. The slower ‘--epistasis’ command was used to test for epistasis using logistic regression23. It is the most accurate test to define SNP-SNP interactions using PLINK23.Analysis of the data for SNP-SNP interactions associated with aggressive PCa was carried out using PLINK software version 1.07FGF10) gene24, has been identified to be associated with the SNP rs4809960, an intronic SNP present in chromosomal position 20q13 near the gene cytochrome P450 family 24 subfamily A member 1 (CYP24A1)25, such that the latter SNP raises the odds of having the prior.Raw data from the current studyClick here for additional data file.Copyright: © 2017 Vaidyanathan V et al.2017Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Epistasis results after analysis of the data for SNP-SNP interactionsClick here for additional data file.Copyright: © 2017 Vaidyanathan V et al.2017Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).11. We believe this is the first study on SNP-SNP interactions associated with aggressive PCa carried out with patients from a New Zealand population.Epistatic effects that are crucial to define various biologically-intuitive models of interaction between two SNPs have already been observed in a variety of speciesCYP24A1 has been reported by Holtet al., (2010) to be associated with prostate cancer-specific mortality, and was not evolutionarily conserved25. It was also found to have an effect on the body mass index (BMI), but due to a small sample size the hazard ratios for the BMI strata were not considered reliable enough to be reported25. The protein encoded byCYP24A1 initiates the degradation of the physiologically active form of Vitamin D3 (VD3)26. VD3 is an important hormone that is actively involved in regulating cell proliferation in the prostate, and has also been identified to have increased expression in PCa cell lines27. It is well established that, with ageing, the skin cannot synthesize VD3 as effectively as desirable and the kidney’s ability to convert VD3 to its active form decreases28. This is of relevance because PCa has always been considered as a disease of elderly men29 who have had less exposure to sunlight and thereby Vitamin D330. It is even more intriguing for the other epistatic SNP to be identified inFGF10.The SNP rs4809960 in the geneet al. (2013), during mesenchymal development, eFGF10 protein can trigger PCa development through increased androgen receptor expression in the neoplastic epithelium31. It is also worthy to mention that FGF10 is closest to FGF7 based on its evolutionary history32, and according to Emotoet al. (1997), is suggested to have no activity for fibroblasts32. We do not agree with this, because fibroblasts in certain organs, senesce due to aging33, and can promote tumour invasion34. This logical progression of ageing-led senescence and promotion of tumour invasion holds true for ageing and risk of aggressive PCa16 as well.According to PaulFGF10 may be causing alterations in gene expression, perhaps due to the prevalent external/environmental conditions in the elderly men with PCa. Our theory is based on the recent discovery in a study by Zhanget al. (2007) that even intronic SNPs (such as the ones identified inFGF10 andCYP24A1) can change the outcome and usage of exons36. This unique and novel epistatic finding emphasizes the fact that intronic SNPs (and SNP-SNP interactions) can also have a significant effect on the risk of diseases such as aggressive PCa, and need to be investigated further.We suggest that the intronic SNP rs2121875 in the geneThe data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Vaidyanathan V et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).10.5256/f1000research.11027.d15860517Dataset 1: Raw data from the current study. DOI,10.5256/f1000research.11027.d15860637Dataset 2: Epistasis results after analysis of the data for SNP-SNP interactions. DOI, FGF10) gene24, has been identified to be associated with the intronic SNP rs4809960, an intronic SNP present in chromosomal position 20q13 near the gene cytochrome P450 family 24 subfamily A member 1 (CYP24A1). The protein encoded by CYP24A1 initiates the degradation of the physiologically active form of Vitamin D3 (VD3) 26 which is an important hormone that is actively involved in regulating cell proliferation in the prostate, and has also been identified to have increased expression in PCa cell lines. The epistatic effect of the SNP-SNP interactions suggested by the authors may be relevant in view of many recent studies showing intronic mutations which can exert their effect on protein coding exons. The recent observation of decreasing Vitamin D3 levels worldwide further support the role of environmental factors in these gene environment interactions. Future studies may help in understanding the role of SNPs and environmental interactions.The authors have carried out SNP genotyping in from a New Zealand cohort of men with self-reported European ethnicity that have been clinically diagnosed with aggressive and non-aggressive PCa, and healthy controls. They have identified a number of SNPs from the GWAS from various European populations and described SNP-SNP interaction as a risk factor for aggressive PCa in a New Zealand cohort. The intronic SNP rs2121875, an on chromosomal position 5p12 near the fibroblast growth factor 10 (We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The manuscript identified a number of SNPs in GWAS as risk factors for PCa in various European populations as well as defined SNP-SNP interactions, using PLINK software, with nucleic acid samples from a New Zealand cohort. The approach which authored used to find a gene x environment marker for aggressive PCa gene x environment interactions can be adjusted statistically, however, it is highly impossible in practicality. The manuscript compiled most of recent literature available on the subject and propose justified discussion on the research question.  Since I do not have sufficient expertise in statistical analyses used to elaborate the key findings of the manuscript, I am reluctant to comment on the authenticity and validity of conclusion.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The global increase in Diabetes Mellitus (DM) has led to an increase in DM-Chronic Kidney Disease (DM-CKD). In this cross-sectional observational study we aimed to define phenotypes for patients with DM-CKD that in future may be used to individualise treatment We report 4 DM-CKD phenotypes in 220 patients recruited from Imperial College NHS Trust clinics from 2004–2012. A robust principal component analysis (PCA) was used to statistically determine clusters with phenotypically different patients. 163 patients with complete data sets were analysed: 77 with CKD and 86 with DM-CKD. Four different clusters were identified. Phenotypes 1 and 2 are entirely composed of patients with DM-CKD and phenotypes 3 and 4 are predominantly CKD (non-DM-CKD). Phenotype 1 depicts a cardiovascular phenotype; phenotype 2: microvascular complications with advanced DM-CKD; phenotype 3: advanced CKD with less anaemia, lower weight and HbA1c; phenotype 4: hypercholesteraemic, younger, less severe CKD. We are the first group to describe different phenotypes in DM-CKD using a PCA approach. Identification of phenotypic groups illustrates the differences and similarities that occur under the umbrella term of DM-CKD providing an opportunity to study phenotypes within these groups thereby facilitating development of precision/personalised targeted medicine. The introduction of renin-angiotensin-aldosterone system (RAAS) antagonists in the form of ACEi or ARB, has resulted in the regression of this surrogate marker and slowing of progression of renal dysfunction3. There is increasing appreciation that DN progression to end-stage kidney disease (ESKD) is not always a stepwise progression through albuminuria with different subgroups progressing at different rates and some progress in the absence of proteinuria, hence the need for us to redefine progression of DN4. Progression of the disease and response to the treatment varies in different patients, which may indicate heterogeneity of diabetes chronic kidney disease (DM-CKD). DM-CKD may consist of different sub-population and phenotypes which may require different treatment approaches. In doing so we should be able to identify personalised targeted therapies for people with this potentially devastating disease.Diabetes Mellitus (DM) is increasing worldwide and subsequently as people are treated for complications and enjoy longevity, it is inevitable that more people will develop Diabetic Nephropathy (DN). DN has been described since Egyptian times with the last century providing a classification of DN based on albuminuria5 with a set of reference clinical endpoints. These subgroups have been shown to have physiologically distinct underlying processes that have facilitated the rational use of targeted therapy7. Targeted therapy can be used to specifically target pathways of the disease thereby avoiding the common clinical endpoint. This has led to a revolution in treatment for certain subgroups of this disease8. Clustering methods have been applied to the respiratory epidemiological field and perceived as ‘steps in the right direction’10 with the discovery of these subgroups.Appreciation of heterogeneous disease subgroups has previously been described in Asthma, with distinct subgroupset al.11, recently described non-proteinuric pathways in patients with type 2 DM (T2DM) associated with loss in renal function thereby illustrating phenotypic spectrum of DM that is independent of proteinuria. Given that patients with and without proteinuria with DM may develop ESKD, a new method looking at the spectrum of people with DM-CKD is needed12. The aims of this study were to 1) identify new phenotypes in DM-CKD and 2) compare this with CKD caused by other renal diseases using clinical variables and cytokines to ascertain whether there are more specific markers than albuminuria to determine who will progress to ESKD.Porrini, Determine whether clinical variables may identify heterogeneous subgroups within a cohort of patients with DM-CKD to facilitate further study of underlying mechanisms leading to progression to ESKD which may lead to novel treatment approaches for different sub-groups of DM-CKD.Define and characterise subgroups within the diabetic nephropathy cohort to act as a template for further studies.All methods were performed in accordance with current study guidance and regulations. Following ethics and research and development study approval (NRES Committee London-West London & GTAC 04/Q0406/25) participants with diabetic nephropathy or renal disease without diabetes mellitus were recruited prospectively from renal clinics at Imperial College Healthcare NHS Trust Hospitals London UK, between 2004 to 2012. In this study CKD is used to describe patients with renal disease without DM. Because of the risk/benefit balance, only a limited proportion of patients with DM-CKD had a kidney biopsy (5 patients) with CKD controls having 62 biopsy proven diagnosis. The remainder of the CKD group was diagnosed with imaging or ultrasound showing small kidneys not amenable to renal biopsy. The diagnosis of DN was made by an elevated uACR on at least two occasions or reduction in eGFR and the exclusion of other aetiologies for CKD by history, clinical, and laboratory examinations, including autoantibody screening, urine sediment and renal imaging. Patients with CKD without DM were classified as the control CKD group. The diagnoses of the non-diabetic CKD group are shown in Supplementary Table 13, C-Chemokine ligand-18 (CCL18)14 and macrophage migration inhibitory factor (MIF)15. All of these cytokines have been reported to be raised in DN and may play an important role in pathogenesis of the disease, hence they were chosen to determine whether these change in the different phenotypes and whether they are more specific to DM-CKD than CKD. These cytokines are immunomodulatory and may provide further insight as to whether there are specific changes in different phenotype. Exclusion criteria included: those receiving dialysis therapy or with kidney, pancreas or kidney pancreas transplants, those under 18 years old; and those unable to consent. See supplementary for details of ELISA protocols.Informed consent was obtained prior to any participants entering the study. Participants consenting to enter the prospective cohort study had their clinical and biochemistry data captured using questionnaires at baseline. Biochemistry data included MDRD GFR, serum albumin, urea, creatinine, haemoglobin, HbA1C, total cholesterol and C-reactive protein (CRP). Plasma and urinary samples were collected, processed and stored for cytokine analysis at baseline. Cytokines previously described in DN and studied by our group were analysed; monocyte chemoattractant protein-1 (MCP-1)All data analysed during this study are included in this published article and the Supplementary Information files.Participant selection was unbiased and reflects the widespread presentations of diabetic nephropathy to the renal clinic. However selection bias exists as these patients were already diagnosed with CKD or DM-CKD and hence referred to the specialist renal clinics.MCP-1, CCL18 and MIF levels were measured in plasma and urinary samples by ELISA. Details of antibodies used in Table We report cross-sectional analysis of baseline data in CKD and DM-CKD. We collected 43 variables to begin with and these were reduced to 30 in order to optimize the utility and exclude variables with predominantly missing data in the total dataset for principal component analysis  and derived four clusters or Phenotypes. The spatial distribution of patients across the PCs, are described in PCA plots  , reducing the original 30 variables into 3 high level variables , simultaneously creates equivalents of major traits or dimensions of the underlying dataset, in our case CKD and DM-CKD. The PC1, PC2 and PC3 establish a new coordinate system to describe the CKD and DM-CKD patients; the PCs can be considered as disease ‘forces’ that act on individual CKD and DM-CKD patients. In Agusti Closer examination of these diagrams reveals three traits in CKD and DM-CKD. The first trait/PC is aligned to traditional complications seen with long term diabetes such as insulin use, retinopathy, duration of diabetes, and peripheral vascular disease, with ethnicity also being one of the major variables in this domain. The second trait/PC has major variables determining the severity of CKD including: baseline GFR, Urea, Urine Albumin/Creatinine ratios. The third trait/PC with major variables of diastolic and systolic blood pressure may suggest a disease domain of a cardiovascular renal CKD type.As described in the methods section, the principal components were subjected to K-Means cluster analysis. The cluster analysis resulted in four clusters Fig.  with the2 compared with phenotype 2, whose GFR was 29.3mls/min/1.73 m2. Phenotypes 3 and 4 were predominantly CKD patients that showed a similar grouping of renal function as that seen in phenotypes 1 and 2. GFR for phenotype 3 and 4 was 27.3mls/min/1.73 m2 and 67.8mls/min/1.73 m2, respectively. The different phenotypes of patients with DM-CKD are evident as there were also DM-CKD patients in phenotypes 3 and 4. Urinary and serum MCP-1, CCL18 and MIF data did not reach significant differences between the phenotypes yet each group had a different cytokine profile that requires larger numbers to confirm these preliminary findings (see supplementary for cytokine profile graphs for the different DM-CKD phenotypes).Phenotype 1 consists of 34 patients all of whom have DM-CKD. This phenotype has a significantly higher systolic and diastolic blood pressure than the other phenotypes. The total cholesterol is high in this group compared with those in phenotype 2 despite a high use of statins. HbA1c levels were comparable to phenotype 2 and therefore cannot be the differentiating factor for the different phenotypes: 8.31% (67.3 mmol/mol) phenotype 1 and 8.06% (64.6 mmol/mol) phenotype 2. Albuminuria or proteinuria did not significantly affect the phenotyping of DM-CKD Table . The meaPhenotype 2 was entirely composed of DM-CKD patients, 40 in total with significantly worse GFR than phenotype 1. This group had a longer duration of diabetes compared with phenotype 1, mean 21.76 years compared to 17.59 years, respectively; however this was not significant and was not a determinant of the clustering. Those in phenotype 2 were more anaemic compared with those in phenotype 3, who had similar renal function Table .Phenotype 1 and 2 had more ischaemic heart disease (IHD) and stroke (CVA) than phenotypes 3 and 4 that were predominantly CKD. Angiotensin converting enzyme inhibitors and statins were used more in phenotypes 1 and 2 compared with 3 and 4.2, similar to phenotype 2 but without microvascular complications. The DM-CKD patients have a shorter duration of diabetes in phenotype 3 and anaemia was less prominent compared to phenotype 1 and 2. Patients in phenotypes 3 and 4 with CKD alone are also on immunosuppression unlike phenotypes 1 and 2. Urinary MIF and serum and urinary CCL18 were higher in this phenotype however this was not significantly different and a larger study is required to determine whether this profile is specific to this phenotype  in a well characterised population. To our knowledge this is the first time this approach has been used to establish the treatable traits and phenotypes in adults with DM-CKD. We are reporting preliminary results and suggest further exploration of both the treatable traits and the phenotypes to derive effective and high precision treatment modalities in DM-CKD.This study illustrates a novel approach to characterise DM-CKD patients in two ways. First, we introduce novel thinking on the variability of the disease using high level disease traits  and these alone may form a basis for treatable traits in DM-CKDThis was a predominantly male cohort with phenotypes 3 and 4 having more females than phenotypes 1 and 2. Phenotype 1 appears to have predominantly South Asian patients with phenotype 2 including a wide ethnic variety. Phenotypes 3 and 4 had predominantly more white people however this cohort had larger numbers of white people and thus with a larger cohort an ethnic and gender effect may be determined.Microvascular complications were more commonly seen in phenotype 2, where a longer duration of diabetes was reported. A prospective study is needed to determine whether individuals stay in these phenotypes or move from one phenotype to another with the progression of their disease. This will be established in the following study that will allow identification and recruitment of patients into these phenotypes with genetic and molecular mapping further characterising patients in these phenotypes. The follow-up will elucidate whether these phenotypes are fixed or whether people move between phenotypes allowing further mapping of the disease. The phenotypes, however, show that despite the similar GFRs in the DM-CKD and CKD population there are differences that arise in people with diabetes that are specific and individual to the person.The variability in the phenotype of patients with DM-CKD is illustrated by patients with DM-CKD found in all four phenotypes, albeit a predominance in phenotypes 1 and 2. The spectrum of phenotypes within this disease has been captured using the principal component analysis and as a concept this may explain the overlap we see between the traditional T1DM and T2DM classification, whilst also providing a possible explanation for people responding well to intensive glycaemic and blood pressure control whilst others with DM-CKD continue to progress to ESKD despite this. Access to increasing genome, molecular, cellular and tissue data will further allow specific pathways to be identified in different subgroups of patients with DM-CKD, similar to those seen in asthma. Further large cohorts are required to determine whether these phenotypes, based on predominantly clinical variables, are conceptually robust with a further overlay of genomic, molecular and proteomic data. This may lead to further elucidation of predominant signalling pathways that may subsequently be targeted more effectively in the right population.This study’s phenotypes were derived from 3 PCs/traits from the PCA that included the long term complications of diabetes (Trait 1), the severity of CKD (Trait 2) and the cardiovascular burden in CKD (Trait 3). Further clarification of these traits may reveal treatable traits (18) in CKD and DM-CKD that may lead the way in changing how we treat these patients.The limitations to this study include: the small number of T1DM patients (6 patients) recruited to the cohort and lack of long term follow up to determine whether these phenotypes are predictive of outcome or whether the phenotype changes with duration of the disease. The small proportion of T1DM in comparison to T2DM is a reflection of the common pattern in general nephrology clinics. Therefore, the main driver for identification of DM-CKD phenotypes were data from T2DM patients (80/86). T1DM patients have been included to suggest that a similar model and statistical approach can be used for a larger T1DM data set in future studies. At present no conclusion can be made to compare T1DM and T2DM patients because of the small size of T1DM patients A further study recruiting more T1DM will determine how similar or different the DM-CKD entity is between T1DM and T2DM and whether a common pathway really exists between the two or whether there is a T1DM DM-CKD and T2DM DM-CKD that do not share phenotypes. More patients are also needed in each phenotype to determine whether the high urinary MIF levels seen in phenotype 4 remains in view of the small sample size of DM-CKD in this group. A further limitation to this study is the lack of histopathology renal biopsies in patients within the different DM-CKD phenotypic groups which is limited by current clinical practice and concern on the risk/benefit ratio in patients with DM. DM-CKD has traditionally been a kidney disease that has been clinically diagnosed in view of the risk of renal biopsy however there is an increasing appreciation amongst the renal community of the important role of renal biopsy in further understanding the disease process and facilitating a more personalised medical treatment approach.In addition, many CKD patients are on immunosuppression that may have influenced the clusters and the cytokine biomarkers assessed in this study. The follow-up of this study is in progress and we hope to report our findings soon. A longer study is planned to increase number of patients with these characteristics to confirm these phenotypes.We believe that we are the first group to describe different phenotypes in DN using a PCA approach. Our results may form a platform to use a combination of clinical variables and cytokines to group patients with predominant phenotypes. This approach allows a structure to combine more clinical data and biological results to determine different endotypes within the phenotypes identified. A similar approach has been used in Asthma and has led to the discovery of specific phenotypes that have subsequently been amenable to more effective targeted personalised therapy. We hope this approach will help map further understanding of DN in a structured way whereby biomarkers may reflect disease progression within these groups.Supplementary Material