EPHX2 gene, is in charge of EETs degradation. We aimed to analyze the influence of common, functional polymorphisms in four genes of the donor on the renal biopsy scores independently assigned by pathologists. Additionally, we examined whether this score or the presence of these SNPs were independent risk factors of clinical outcomes in the first year after grafting. A cohort of 119 recipients and their corresponding 85 deceased donors were included in the study. Donors were genotyped for the CYP4F2 V433M, CYP2C8*3, CYP2J2*7, EPHX2 3’UTR A>G, EPHX2 K55R and EPHX2 R287Q polymorphisms. The association of the donors’ SNPs with the biopsy scores and clinical outcomes was retrospectively evaluated by multivariate regression analysis. The CYP2C8*3 polymorphism in the donor was significantly associated with higher scores assigned to pretransplant biopsies . In turn, higher scores were related to an increased risk of acute rejection  and worse glomerular filtration rate (eGFR)  one year after transplant. Patients whose donors carried the CYP4F2 433M variant showed lower eGFR values  and higher risk of acute rejection . The CYP2J2*7 SNP in the donor was associated with elevated risk of delayed graft function . Our results taken together suggest that donor genetic variability may be used as a predictor of tissue damage in the graft as well as to predict clinical outcomes and graft function in the recipient.Cytochrome P450 (CYP) enzymes metabolize arachidonic acid to vasoactive eicosanoids such as epoxyeicosatrienoic acids (EETs) and 20-Hydroxyeicosatetraenoic acid (20-HETE), whilst soluble epoxide hydrolase, encoded by the  Chronic kidney disease is a public health problem characterized by the progressive loss of renal function that affects an important part of the world’s population. The patients in the most advanced state of the disease are diagnosed with end-stage renal disease (ESRD) and they need either dialysis treatment or a kidney transplant. The latter is the first-choice treatment but the unavailability of adequate organs for transplantation to meet the existing high demand has resulted in a severe shortage of deceased donor organs .It has been suggested that in many cases the decision to refuse marginal donor kidneys could be unjustified. There is a need for more rigorous and standardized criteria to accept or reject the organ, in order to minimize the discard of transplantable kidneys , 2. For EPHX2 gene. Therefore, single nucleotide polymorphisms (SNPs) occurring in this pathway hold the potential to affect graft function. Indeed, we have previously shown that the presence of these genetic variants in the donor’s DNA is associated with delayed graft function (DGF) . The remaining SNPs did not display any significant effect , as they obviously had the same polymorphisms and pretransplant scores. The association between the SNPs of the donor and the biopsy score blindly assigned by pathologists to the grafts revealed a statistical trend towards higher scores (more tissue damage) in biopsies from carriers of the t effect .CYP2C8*3 SNP in a logistic regression model with different clinical and demographic covariates of the donors, with the aim to confirm the association between this variant with the biopsies scores. The results showed that CYP2C8*3 variant was significantly associated with kidney damage when controlling for meaningful covariates   displayed scores lower than 5, whilst 31 (26.1%) had scores of 5 or higher. In order to assess whether this biopsy score (low vs. high) was predictive of the evolution of the graft in our series of renal transplant recipients, we studied in different logistic regression models the association with both renal function and with the occurrence of acute rejection and delayed graft function in the first year after grafting. Indeed, we observed that patients whose grafts had been assigned higher scores showed significantly worse eGFR  . The assRegarding the association with acute rejection, patients whose donors had higher scores showed significantly higher risk of this complication  . The scoFinally, we studied the possible influence of donor genetic variability in the clinical course of the transplant, again considering acute rejection, delayed graft function and eGFR one year after grafting. Analyses were adjusted by the same clinical and demographic covariates formerly described.CYP4F2 433M , p = 0.047]. Finally, the CYP2J2*7 variant in the donor was related to higher risk of DGF. Indeed, 17.1% of patients with this complication carried the *7 allele, whilst the variant was present in only 1.4% of patients who did not experience DGF . The regression models are shown in supplementary In addition, patients whose donors carried the same Finally, we reexamined the observed genetic/clinical associations in a subgroup of 68 of the 119 transplanted kidneys that were paired organs, i.e. from the same donor and therefore carrying the same genetic background. There was no significant differences in the eGFR displayed by each of the kidneys in the paired transplants. This was true either for kidneys that carried the variant  or for those pairs which were non-carriers . However, only in one of the kidney pairs we observed the same outcome (rejection), all the remaining pairs had different outcomes. The effect of the A large number of patients with chronic kidney disease depend on a renal transplant to survive. For this reason, a histological evaluation of donor kidney tissue has become an essential aspect in the assessment of renal allograft organ quality, in particular with the increasing use of marginal donors. The identification of genetic biomarkers that determine the predisposition of the donor to have renal damage may therefore be an important tool that adds up to the histological findings to predict how good the graft functionality will be after the transplant. In addition, we hypothesize that the presence of these genetic variants could also correlate with the occurrence of graft-related complications in the recipient.EPHX2 and several CYP genes with the biopsy score of the graft and with clinical outcomes in the recipient in the first year after grafting. We observed that donor genetic variability may be an important factor influencing kidney damage. Specifically, donors who were carriers of CYP2C8*3 had been blindly assigned higher pretansplant biopsy scores (meaning more tissue damage) that non carriers. The EETs synthesized by CYP2C8 have renoprotective properties, acting in numerous situations as vasodilator and anti-inflammatory mediators [In the present work, we studied the possible relationship of six common, functional SNPs of the donor in ediators . Therefoediators ) will beCYP2C8*3 variant, this time in the recipient, was associated with worse graft function, suggesting systemic low EETs levels resulting in an adverse outcome. Moreover, there are some earlier results by Dai et al. reporting how the CYP2C8*3 variant showed only 15% of the in vitro activity of the wild type allele in the metabolism of arachidonic acid to EETs, which also seems to point in this direction. Indeed, the authors proposed that a defective production of these mediators in organs such as the heart, liver and kidney could lead to pathological changes or disease [CYP2C8*3 variant was deficient in the production of EETs (by 75% according to these authors) and, interestingly enough, that this variant in the recipient was associated with kidney damage [In a previous work by our group , we had  disease . Smith ey damage . Our worDonor sex was the other factor that showed a significant association with kidney damage, with males showing slightly better pretransplant scores. A study by Sánchez-Escuredo  found thWe also examined whether the pretransplant renal score established by the pathologists correlated with the clinical evolution of the recipients. Patients whose grafts had higher biopsy scores had lower eGFR values one year after grafting. This is clinically relevant because previous studies have demonstrated that post-transplant renal function in the first year predicts long‐term kidney transplant survival . Our resWe also observed that those recipients whose grafts had higher scores showed an elevated risk of acute rejection compared with recipients that received kidneys with lower scores. A similar observation has previously been reported by Yilmaz et al , who fouCYP4F2 433M variant allele showed worse renal function that non-carriers at that time. In addition, this 433M variant and the CYP2J2*7 SNP were found to increase the risk of acute rejection and delayed graft function, respectively. The results point again to a reduced anti-inflammatory or vasodilator capacity due to an impaired production of EETs and/or to altered production of 20-HETE (synthesized by CYP4F2) in the grafts that carried these variants. There are a few studies, including some conducted by our group in an independent series of renal transplant recipients, [There are ongoing efforts focused on incorporating genetics to the array of existing clinical and histological parameters that try to predict outcomes in renal transplantation , 38. Howipients, , 39, 40 We also aimed to evaluate how influential the recipients’ characteristics were when donor genetics are out of the equation. For this, we re-analyzed the observed clinical associations in a subgroup of paired kidneys (same donor and therefore same genetics). CYP4A11, could have also provided with useful information.The main limitation of this study was its relatively low sample size, which was a consequence of the above-mentioned difficulties to obtain grafts with both histological data and availability of genetic material. This resulted in some wide confidence intervals for the analysis of associations between clinical outcomes and low-frequency SNPs. In addition, being this a retrospective study, we could not measure EETs or 20-HETE plasma/urine concentrations in our patients, which could have help elucidate the mechanisms underlying our observations. In this regard, our group is currently developing a project to measure these concentrations and examine their correlation with donors’ and recipients’ genetic polymorphisms. Finally, the analysis of other relevant genes in this route, e.g. In summary, our findings show, for the first time to our knowledge, that SNPs in donor genes that are involved in the synthesis of vasoactive eicosanoids may contribute to increased graft damage in kidney transplantation. Furthermore, we observed that this increased damage correlated with worse graft function and outcomes in the recipients. Finally, we could also confirm that donor genetic variations may also be independent risk factors for clinical outcomes in patients receiving a kidney transplant. All these results taken together indicate that the addition of genetic testing in the donor prior to transplantation could improve the predictive power of the existing histological and clinical parameters regarding the clinical evolution of the recipient. Nevertheless, further studies with larger series of patients are warranted to corroborate the preliminary findings described herein.S1 Fig*p = 0.038.(TIF)Click here for additional data file.S1 TableBMI, body-mass index; CV, cardiovascular; eGFR, estimated glomerular filtration rate.(DOCX)Click here for additional data file.S2 TableB, regression coefficient; SE, standard error; OR, odds ratio; CI, 95% confidence interval(DOCX)Click here for additional data file.S3 TableB, regression coefficient; SE, standard error; OR, odds ratio; CI, 95% confidence interval; DM, diabetes mellitus.(DOCX)Click here for additional data file.


Diabetes mellitus (DM) and its related sensory peripheral neuropathy (SPN) are the biggest risk factors for foot ulcer formation and lower limb amputation. Reduced vibration perception results in less sensitivity to pressure and is a known risk factor for diabetic foot ulcers. Improving vibration perception in the feet of people with SPN could be protective against foot ulcers. The aim of this study was to determine if a therapeutic vibrating insole, used for 35 consecutive days, could improve vibration perception in a patient with type 2 DM.The patient was a 63 year-old male with a medical history of peripheral vascular disease, controlled hypertension, hyperlipidaemia, artherosclerosis and SPN secondary to controlled type 2 diabetes.The patient used the insoles for 20 min a day for 35 consecutive days. Vibration perception thresholds were measured four times in total: once at baseline, immediately post intervention, 1 month and 10 months later. Vibration perception threshold decreased with an average of 22 V (range 6 V–34 V) dependant on the tested location and time after intervention. The improvement remained after 1 and 10 months.The use of a vibrating insole as a therapeutic device improved this patient’s perception of vibration in his feet. Clinically, vibrating insoles potentially might reduce the risk for ulcer formation and subsequent lower limb amputation in patients with DM and SPN.Using a vibrating insole therapeutically, can potentially improve the perception of vibration and pressure in patients with sensory peripheral neuropathy secondary to type 2 diabetes mellitus. Improved vibration perception might reduce the risk for diabetic ulcer formation and subsequent lower limb amputation. Sensory peripheral neuropathy (SPN) is a common complication of diabetes mellitus (DM)  has been shown to improve sensory perception in patients following stroke, and children with autism, but has not been studied in type 2 diabetic patients with SPN. The brain is known to adapt in response to certain deficits or sensori-motor inputs . If an intervention aimed at improving a patient with SPN’s ability to perceive vibration is successful, it could be used therapeutically to potentially prevent ulcer formation. At an impairment level, diabetic foot ulcers cause pain, which negatively affects patients’ mobility and balance. Decreased mobility may also worsen the already impaired circulation and prolong ulcer healing time in the diabetic population, increasing the risk of wound infection and lower limb amputation. Challenges with mobility also limit the activities of daily living  in which the person can participate. In turn a loss of independence in mobility can lead to psychological problems such as anxiety and depression, decreased participation in society and ultimately social exclusion. Preventing the formation of diabetic foot ulcers may prevent disability across the whole spectrum of impairment, activity and participation and may lead to improved quality of life for persons with DM.Ethical clearance was obtained from the University of the Western Cape’s Biomedical research ethics committee (BM 16_3_23). Written informed consent was obtained from the participant prior to data collection.A 63-year-old retired male with a medical history of peripheral vascular disease, controlled hypertension, hyperlipidaemia, atherosclerosis and controlled type 2 diabetes mellitus participated in this single-case study. He was diagnosed with diabetes mellitus and peripheral vascular disease 15 years ago. He suffered a transient ischaemic attack at the time, and his carotid disease was discovered. It resulted in a left-sided carotid endarterectomy. The participant had artherosclerosis of both the right and left external iliac arteries, with approximately 90% occlusion, confirmed via angiogram (right more occluded than left). Chronic medication included Diaphage 1 g twice a day, Ciplazar 50 mg, Lomanor 5 mg, Simvastatin 4 mg, Disprin and Dopaquel 200 mg twice a day. The participant had been a smoker for the past 50 years and currently smoked 10 to 15 pipes of tobacco per day. He did not consume any alcohol and had been diagnosed with sensory peripheral neuropathy in both feet secondary to type 2 DM 3 years prior to this study.of > 25 V has an increased risk of 19.8% for diabetic ulcers . The Bio-Thesiometer is calibrated and considered as the gold standard for diagnosing SPN. The Bio-Thesiometer is considered as the gold standard to detect SPN because of its strong predictive value for diabetic foot ulceration. A vibratory perception threshold (VPT) of 15 V and lower is considered normal or low risk (2.9%) for foot ulceration, while a VPT A vibrating insole prototype that delivers a mechanical noise signal at a varied frequency was used as the RSS intervention in this case study. This prototype was similar to the initially developed prototype for a study to enhance balance in patients with peripheral neuropathy. The technical specifications of the insole are described in Hijmans et al. . The parA Bio-Thesiometer (Model PVD-LP) was used to establish the participant’s VPT score in volts at baseline. Measures were taken from seven points on the foot. Normal values for each reading are provided in the manual for the Bio-Thesiometer . MeasureThe baseline measurement of the participant’s VPT with the Bio-Thesiometer indicated a mean score of 31 V (unit of measurement for the Bio-Thesiometer) across seven points of both feet in the prone position and a 34.5 V in the supine position. The head of the Bio-Thesiometer was firmly applied to the point of contact on the patient’s foot, and the patient was asked to close his eyes while the first author slowly increased the amplitude of the vibration. The patient then told the first author or research assistant as soon as he detected the vibration, and the first author or research assistant then documented the corresponding reading in volts from the Bio-Thesiometer. Measurements at each point were repeated three times to ensure reliability, and the average was calculated.A clinically important (19.7 V) reduction in the VPT was determined between pre-intervention, mean 32.8 V (± 7.1), and post-intervention, mean 13.1 V (± 2.8), scores across the seven points (by the first author). The findings are considered clinically important because the patient no longer fits the criteria for SPN (VPT > 25 V) (Peters et al. No clinical differences were detected between the prone and supine positions in the initial tests, or between the left and right foot for any of the measurements; so, values reported are for the left foot as assessed by the first author in the supine position . SubsequThe retention of the effect is new information and has not been investigated or documented before. It seems that in this case, the therapeutic use of vibrating insoles has reversed one of the symptoms of peripheral neuropathy.This case study demonstrates a clinical reduction in VPT for a patient with SPN after using a mechanical noise vibrating insole intervention for a period of 20 minutes per day for 35 days. The results from this study may thus have a considerable protecting effect for ulceration.Owing to the nature of single-case studies, the findings of this study cannot be generalised to the diabetic population but can be used to stimulate further research and discussion on the topic. The physiological mechanism of how this intervention reduced VPT and why the effect is being retained in this case study cannot be explained. It could potentially be as a result of increased sensitivity of the mechanoreceptors on the plantar aspect of the feet and a resultant increase in corticomuscular synchronisation (Trenado et al. The findings of this single-case study are relevant and clinically important as they potentially could result in a reduced risk for ulcer formation in patients with DM and potentially reduce the risk of lower limb amputation. A decreased risk for diabetic foot ulcers can contribute to the decrease of the associated disability by maintaining functional mobility for longer duration. Improved mobility will result in participation in daily and socio-economic activities, and inclusion in society, ultimately resulting in improved quality of life.


Here we observed BMSC colony formation in real time via time lapsed optical imaging and analysis, to quantify whether and how heterogeneity emerged over multiple cell divisions spanning the duration of a typical colony formation unit assay. These analyses demonstrate that such colonies are neither homogeneous subpopulations of stem cells nor necessarily derived from single originating cells. While the mechanisms for and causes of this intracolony heterogeneity are not understood fully, we further demonstrate that extensive cell-cell contacts do not correlate with senescence, but that media exchange was concurrent with diversification in even the most uniform single-cell-derived colonies. These direct quantitative observations and visualizations of colony formation provide new insights that are motivated by significant implications for both basic research and stem cell-based therapies.Bone marrow stromal cells (BMSCs) include a subset of stem cells that are considered promising for developmental studies and therapeutic applications. While it is appreciated generally that BMSC populations can exhibit morphological and functional heterogeneity upon  Despite these advances at the lab scale and in ongoing clinical trials, however, we are aware of no therapies to date that have been approved by the U.S. Food & Drug Administration for delivery of BMSCs or putative mesenchymal stem cells .Bone marrow stromal cells (BMSCs), a subset of which are characterized as multipotent mesenchymal stem cells, are considered potential cell therapies for diverse medical conditions–from bone tissue regeneration to autism and Parkinson’s disease –3. Such in vitro-expanded BMSC populations derived thereof [in vitro-expanded BMSC populations that are cultured under ostensibly identical culture protocols [It is discussed broadly that one contributing factor of unsuccessful clinical trials is variability among individual participants. In the case of BMSC autologous therapies, this variability of outcome can be attributed to both variability among patients receiving treatment and to the variability among donors of the initial bone marrow aspirates and the ensuing  thereof –6. Howevrotocols ,7–9. Forrotocols . Neverthrotocols ,12. Furtrotocols , and comrotocols ,15.To avoid the complications inherent in studying heterogeneous cell populations, many studies focusing on basic BMSC biology or therapies have used so-called “clonal” BMSCs. These cells are those within a BMSC population that belonged to or were expanded from a single colony. As such, it is tacitly assumed by most that the colony was derived from a single cell and that cells within the resulting colony were functionally homogeneous stem cells. Such assumptions, which are also implicit in many studies of other cell classes, are sufficiently pervasive that many consider colony formation as a defining criterion for the putative stem cells within a BMSC population. However, a competing consideration is the demonstration that even cells within a BMSC colony are heterogeneous in terms of gene and surface marker expression and morphology  vary substantially from each other in terms of colony size, confluency, and multilineage potential ,19–23. UWe termed colonies originating from a single isolated cell  as single-cell-derived (SCD) colonies and colonies that originated from more than one cell as multi-cell-derived (MCD) colonies. We observed that nearly half (48%) of colonies analyzed at the single-cell level through Day 4 originated from two BMSCs, while a lower fraction (35%) originated from single cells . For eacWe also categorized the progenies comprising each colony in terms of proliferative capacity, in order to understand the proliferation kinetics needed to form a colony in sparsely-seeded conditions. We classified originating cells that yielded more than 16 cells (four population doublings) at Day 4 as “fast proliferators,” and progenies that produced fewer than 8 cells (three population doublings) as “slow proliferators.” The remaining progeny–any progeny containing 8 to 16 cells at the Day 4 time point–were classified as “moderate proliferators.” Of the 52 progenies contributing to colony formation, 28% were classified as fast proliferators, 50% as moderate proliferators, and 22% as slow proliferators. Several combinations of slow, moderate, and fast-dividing progeny contributed to colony formation . Among tWe then sought potential correlations with colony properties that did not require detailed time lapse image analysis in an effort to predict multi-cell origin. While many studies assume that colonies formed in BMSC cultures are functionally uniform , there is evidence to suggest that cells within each BMSC colony are functionally heterogeneous . To testUpon first inspection, PCA indicated no visually distinct clusters of cells . AdditioHaving identified distinguishing metrics of heterogeneity within a colony, we next considered how the extent of intercolony and intracolony heterogeneity–including properties related to colony diameter, cell area, proliferation kinetics, and cell generation–could be represented visually and concurrently to aid in further classification and insights. 2, respectively, each approximately 20% of the standard deviation of all cells for those properties). It should be noted that cells did not differ in their proliferative capacity when they belonged to a colony containing a slow-dividing progeny: cells were not statistically more likely to have senescent daughters, and second-generation cells did not produce a significantly different number of progenies, when in a colony with a slow-dividing progeny.Using cell generation as a metric for heterogeneity, we determined that the standard deviation of generations at Day 4 ranged from 0 to 1.3 generations, with single-cell-derived (SCD) colonies exhibiting statistically smaller standard deviations than multi-cell-derived (MCD) colonies . Analysis of the CFU number at Day 7 would have thus indicated only 26 colonies, leading to 20% error in the CFU measurement . AnotherThis finding underscores the need for rigorous methods to verify single-cell origin of colonies in all studies utilizing colony-derived cells. Even the lowest of plating densities cannot guarantee that cells will not attach near each other, and all SCD colonies in this study were infiltrated by nearby migrating cells within seven days of development. While obtaining colonies of single-cell origin might be thought to be ensured by physically isolating single MSCs, our data suggest the need for more rigorous validation than the observation of an apparent single colony in a well several days after plating. Single-cell isolation is not guaranteed with flow cytometry or serial dilution, as these methods rely on a Poisson distribution with a reported success rate of 26% , and so Despite growing evidence and anecdotal appreciation for emergent or inadvertent heterogeneity within colonies, many experimental designs are fundamentally based on the assumption that all cells derived from the same BMSC colony are homogeneous stem cells. In the present study, we have demonstrated that the morphological and proliferative behavior of MSCs is primarily determined by the generations of the cells (relative to the time point of plating), and not to the colony of origin. In other words, if a functionally uniform subpopulation is desirable, one would obtain a purer population by isolating cells belonging to the same generation rather than cells belonging to the same colony. This finding is evidenced by the trend in generation observed in principal component space  as well in vitro culture.These findings on emergence of biophysical heterogeneity at the intracolony level add to the existing body of evidence of heterogeneity in MSC colonies at the transcription and translation level, as reviewed in Rennerfeldt et al. . For exaThe onset of heterogeneity within colonies–even those that were known to have originated from a single cell and were initially uniform in area and proliferation kinetics –demonstrThough time lapse image analysis is a powerful tool for characterizing cell behavior and can potentially measure hundreds of cell properties, relatively few possible causes of behavioral deviations can be tested without additional coupling to other existing technologies such as RNA sequencing or analysis of surface markers. Nonetheless, some possible causes of heterogeneity onset have been explored in the present study while minimally perturbing the culture system. Heterogeneity was enabled partly by cells within a progeny changing systematically with each new generation. We found that most progenies produced smaller cells with each generation under standard culture conditions . This slColony confluency surprisingly appeared to have no effect on heterogeneity nor the cells’ ability to proliferate, as mitotic divisions frequently occurred even at the center of a confluent colony after 10 days of growth or more Figs  and S9, s = 0.90, p< 10−200) and therefore were possibly exposed not only to a higher concentration of signaling molecules but also decreased nutrients.  Such an empirical trend with generation is particularly interesting considering that the generation assigned to the analyzed cells was relative to the beginning of the experiment. In other words, all “first generation” cells reported here were any number of generations in their previous passages prior to our time lapse imaging experiment. Yet, once the cells were removed from a ~70% confluent culture and plated in very sparse conditions, the cells exhibited biophysical properties more similar to each other than to cells in their own progeny several generations later  and had a larger area before division (p < 0.01) when compared to cells belonging to colonies containing only fast or moderate proliferators; future studies could be designed to explore whether the local chemical environment  differ for cells of different proliferative capacity as another possible source for the seemingly stochastic deviations in the generational trends of a given progeny  was also motivated by the quantitative observation that cell spread area decreased with each new generation. We did not design this study to determine whether heterogeneity was caused by the media exchange protocol or composition, but we recognized that this media exchange step in a conventional culture protocol addresses nutrient depletion and byproduct removal. Indeed, as is demonstrated in Figs eny Figs  and S5. 2) were rarely formed by single cells. Regardless of origin, most colonies demonstrated heterogeneity in terms of proliferative capacity and biophysical properties after only four days of growth .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact and corresponding author, Krystyn Van Vliet  at passage 2 (P2). The cells were obtained from a healthy male donor (age 31–45). The cells were screened by the company for positive adipogenic and osteogenic differentiation staining, as well as for biomarkers via flow cytometry with the following results: <5% positive for CD14, CD34, and CD45; >99% positive for CD166, CD105, CD90, and CD73. These cells are commercially referred to by the company as human bone marrow-derived mesenchymal stem cells.2 in the same expansion medium described above. Cells were fed twice per week and, upon reaching 70% confluency, were trypsinized as P4 cells and replated for experimentation. All reagents were purchased from ThermoFisher Scientific .BMSCs were cultured in expansion medium containing 89% low-glucose Dulbecco’s Modified Eagle Medium, 1% penicillin-streptomycin , and 10% fetal bovine serum (FBS) lot-screened for colony formation, population doublings, and differentiation into the adipogenic and osteogenic lineages. Cells were cultured until 70% confluent, detached using trypsin-EDTA (0.05%) and cryogenically frozen as P3 in a solution of 90% FBS and 10% dimethyl sulfoxide until further use. For experiments, cells were thawed at P3 and plated at 500 cells/cm3.1.1 Motivation. To our knowledge, an assay to estimate actual plating density used in experiments involving BMSCs has not been published prior to this work. In our time lapse imaging experiments, we used glass Petri dishes rather than the traditional tissue culture polystyrene ones to optimize image quality. While we did plasma treat the glass approximately two hours prior to seeding, we hypothesized that fewer cells would attach to the dish than expected, motivating the development of a protocol to estimate actual plating density.3.1.2 Plating into Petri dishes using limiting dilution. Petri dishes that were 60 mm in diameter and that each contained a glass well 35 mm diameter were plasma treated for 10 minutes using a plasma chamber . Dishes were then placed under a UV light in a sterile tissue culture hood for approximately two hours while cells were counted and diluted. Prior to plating, dishes were also rinsed with water to remove any dust acquired from plasma treating and to ensure the glass surface was hydrophilic.2. This entire process was then repeated for each Petri dish using a new, unique dilution, where one experiment consisted of five dishes.BMSCs were expanded to 70% confluency in T-175 flasks and trypsinized as P4 cells. Following trypsin neutralization and centrifugation, the cells were counted using a standard glass hemacytometer. After counting, cells were pipetted again to ensure uniform suspension, and 10% of the cell solution was transferred to a microcentrifuge tube and diluted with expansion medium to 1 mL. Cells in the 1 mL suspension were again pipetted, and 100 μL of that 1 mL was pipetted into a new centrifuge tube and diluted to 1 mL. This process was repeated until a suspension of approximately 1,000 cells/mL was achieved . The final suspension of cells was then plated into the glass well of a Petri dish at a theoretical density of 100 cells/cm3.1.3 Plating density measurements. After approximately six hours (the time at which imaging began in the main experiment), cells were fixed and stained with a 0.5% solution of crystal violet  in 100% methanol . All cells present in each dish were then counted (N = 5 dishes for each of two experiments), and the plating density was calculated by dividing the number of cells by the surface area of the glass well. Counting cells was aided by drawing a grid on the bottom of each dish in a fashion akin to a hemocytometer. In the first experiment, the plating density was 17 +/- 5 cells/cm2, and in the repeat experiment the density was 29 +/- 4 cells/cm2, from which we have concluded that, in our hands, a theoretical plating density of 100 cells/cm2 on glass-bottomed dishes leads to a much lower actual plating density.3.2.1 Initial image acquisition. Time lapse imaging of developing colonies was conducted in two separate, identical experiments. For each experiment, BMSCs were plated into five plasma-treated glass Petri dishes in the same fashion as outlined above (see Plating into Petri Dishes using Limiting Dilution in the Cell Plating Density Assay section prior to this one). After approximately six hours, dishes were inspected to ensure cells had attached. The dishes were then assessed for how far apart individual cells were. The dish with the most isolated cells was used in time lapse imaging, and the remaining four were kept in an incubator at 5% CO2 and cultured in parallel as positive controls of cell growth. Expansion medium was added to all dishes such that the total volume was 5 mL.Imaging was done with the petri dish kept in a stagetop incubator  set at 5% CO2 and the water bath kept filled with filtered, autoclaved water for maximum possible humidity. A temperature probe was kept submerged in the MSC expansion medium at all times as a feedback sensor so that the stagetop incubator could be kept at 37°C, and the instrument was calibrated for temperature control before each use. Imaging was done with an Olympus IX81 microscope  with a phase contrast 10x objective. Beginning at the top of the glass well, the dish was systematically scanned left to right, top to bottom for attached cells. Cells that were especially large and circular in morphology, reminiscent of the often-cited “Type II” morphology [3.2.2 Monitoring of colony development. As the BMSCs migrated and proliferated into colonies, there was a need to increase the number of fields of view (FOV) during the course of the 14-day experiments. However, because our microscope stage was limited to being able to capture 400 images per 15-minutes time point, locations being monitored needed to be omitted with each FOV expansion. This monitoring was made possible through a MATLAB script that organized images by the location they belonged to, stitched the images into a montage, and made a video  to 9 . Locations in which cells were proliferating slowly  were omitted from further monitoring in this transition. This process was continued until the FOV grid needed to be expanded again to a 4x4 FOV grid  and finally a 5x5 FOV grid . A total of 15 monitored locations remained in each experiment after the transition to a 5x5 FOV grid, and these areas were monitored for approximately 14 days. Out of the combined 30 colonies, 28 were suitable for analysis, while the other two migrated too rapidly for single cell tracking. In two of the remaining 28 locations, the colony under development at Day 4 merged with another nearby analyzed colony by Day 7 . The resulting output file was then run through a MATLAB script  that assigned a unique identifier to each cell  as well as the corresponding object number of that cell at every time point. The result was a two-dimensional matrix of object numbers, with rows corresponding to each of 374 time points and columns corresponding to each unique cell over time. Cells that did not exist at any given time point  were given an object number of 0. This matrix was exported as an Excel spreadsheet for further analysis.The resulting spreadsheet was formatted in Excel to highlight all cells with a value of 0 for ease of visualization. The spreadsheet was then inspected for tracking errors: a given column contained non-zero object numbers for all time points (rows) the corresponding cell was in existence. At the time point a column began containing zeros (signifying a supposed division), two columns previously containing zeros would begin yielding non-zero object numbers (signifying the daughter cells of the dividing cell). Any instances where this was not the case was considered an error and subject to further investigation. Further, the object number of a dividing cell at its final time point, as well as the object numbers of the corresponding daughter cells at the next time point, were noted and verified in the tracked images (which displayed the object number of each cell at each time point). Any errors found in this manner  were corrected, and the image set was run through the tracking pipeline again. This process was iterative, with the total active time for correcting each colony amounting to approximately 2–3 hours , and the tracking process requiring approximately 22–36 hours for each iteration (depending on the memory and processing power available for a given computer used).Once no apparent tracking errors were present, the images were run through a more complex tracking pipeline in CellProfiler, which included several more modules in addition to the ones in the previous pipeline. These modules measured the area occupied in each image, information about neighboring cells for each cell (where a neighbor was considered at 15 pixels away), and information about the cells’ size and shape . The pipeline further saved images of the identified objects as .mat files  for further use in data visualization . The resulting output file was then run through a MATLAB script that constructed cell lineage trees , which were inspected for any further possible errors. Errors spotted, if any, were corrected and run through the extensive pipeline again. The occurrence of errors at this stage was rare and took less that one hour to correct . The resulting output file for each colony was analyzed further using MATLAB .Overall, each colony required 4–8 weeks of full-time work  in order to process 374 images per colony at the single-cell level. Together, all processed colonies are a result of the combined efforts of 25 researchers, though we note that improvements and semi-automation of the processing over this period resulted in reduced human time required for validated image processing.3.4.1 Image overlay to represent cell generation and progeny Figs . During tracking, .mat files were saved for each image (time point) in a given colony. Uploading these files into MATLAB displayed a 2-D matrix representative of the image, where each entry in the matrix was a value corresponding to a pixel. The values were 0 for all null-pixels; pixels belonging to an object (cell) identified in the image through the tracking pipeline described above contained the object number in the corresponding location of the matrix. These files were required for relating the analyses of the cells  to the cells qualitatively seen in the original phase contrast images (see www.gimp.org). A MATLAB script then conducted the following steps to create images with overlays corresponding to cell generation and progeny presented in Figs The original phase contrast images were adjusted for brightness and contrast using GIMP  were masked using the same technique described above (see “Image Processing and Cell Segmentation” above). All cells not belonging to the colony at that time point were removed from the image using a black paintbrush in ImageJ. The images were then inverted and saved as binary images. Binary images were imported into Inkscape (www.inkscape.org), and the color of the all black pixels in a given image was changed to represent the number of originating progenies belonging to that colony (as determined by studying the corresponding time lapse video from the first time point onward).3.4.3 Representation intracolony heterogeneity Figs . For a given colony, the output file from single-cell tracking in CellProfiler (see “Tracking and Measurements in CellProfiler” above) was imported into MATLAB, and from the dataset a matrix was constructed containing the x-coordinate, y-coordinate, generation, and cell spread area of each cell at the 374th (approximately Day 4) time point was constructed. The x- and y-coordinates of each cell were then plotted in a scatter plot, with the color of the data point representing the cell generation and the size of the data point set to be relative to the corresponding cell area. The plot was then imported into Inkscape, where rings were constructed to surround the cells, representing the number and proliferative capacity of the progeny comprising the colony. For 4.1.1 Description of data formatting. For a given colony, the output file generated from the tracking pipeline in CellProfiler described above was reorganized in in two general ways: by constructing an array of all raw properties measured in CellProfiler (reorganized by time and unique cell identifier), and by constructing a data table of manually-calculated cell properties based off of the raw measurements. In the former, a unique identification number was assigned to each cell using the same code used to construct cell lineage trees described above. Using this ID and the object numbers of cells at each time point, the data matrix was transformed into an array with properties as columns, time points as rows, and each matrix entry a vector containing the numeric values of a given property and time point, in order of unique cell ID. The second data set constructed was a subset of manually-calculated properties (such as the average and maximum spread area over the course of each cell’s lifetime). These properties are tabulated in 4.1.2 Correlation analysis. The Spearman’s rank correlation coefficient (rs) and associated p value for each pairwise comparison for all pooled colony datasets of the manually-calculated properties described in the paragraph above were calculated using the corr function in MATLAB and are presented in −4, calculated with an initial p value of 0.05, divided by 276 pairwise comparisons). All p values less than 0.05 and greater than the Bonferroni-corrected p value threshold were highlighted in blue. In the main body, statistical significance is only reported for coefficients passing the Bonferroni-corrected p-value threshold.4.1.3 Principal component analysis (PCA). Three types of datasets were constructed and combined to form an input matrix for PCA. The first dataset consisted of all properties measured by CellProfiler for all cells  1 and PC2 were constructed using scatter and gscatter in MATLAB. Each cell’s data point was color-coded by the generation it belonged to, the colony it belonged to, and the cluster ID it belonged to . This analysis was performed for each individual colony as well as for all colonies pooled. Similarly, PCA was also performed with all measured CellProfiler properties at specific time points  for all cells in existence at that time point in all colonies combined .4.1.4 Colony infiltration. Time lapse videos of all developing single-cell-derived colonies were studied at various speeds and directions in time to assess the general time a given colony expanded to the point of approaching nearby colonies or non-migratory cells. During this assessment, it was observed that nearby cells not belonging to the colony were migrating and infiltrating the colony borders, prompting further exploration. In order to determine which, if any, cells in a perceived colony at Day 7 did not actually belong to the originating mother cell at Day 0, an image of a colony was opened in ImageJ for the final time point of Day 7, and the time lapse video of the colony was played backwards and forwards from Day 7 to the start of the experiment until four or more infiltrating cells were identified. Such cells were marked by transparent red dots on the Day 7 phase contrast images in We have developed a novel method for manually tracking the mitotic events of BMSCs in time lapse imaging. For a given colony, images were masked for the first 960 time points (corresponding to ten days of development) using the methods described above (see “Image Processing and Cell Segmentation” above). Cells not belonging to the colony at each time point were then eliminated using a black paintbrush in ImageJ. In some instances, images were further corrected by eliminating cells at the periphery of the colony if they protruded significantly away from the other cells . Confluency was measured at every time point by running these images through a MATLAB script that measured the area occupied by the colony’s convex hull . As a quality control, images displaying the union convex hull were also saved, overlayed on top of the original phase contrast images, and inspected to ensure the measurements were representative of the space occupied by the colony. The area occupied by the cells in the colony was measured by running the masked images through a simple pipeline in CellProfiler that identified primary objects and measured the area occupied by objects in the image. The resulting output file was imported into MATLAB, and the center of the colony at each time point was measured as the centroid of all objects at a given time point.The time and location of mitotic events within the colony were evaluated using a combination of ImageJ and CellProfiler. In ImageJ, two stacks of images were opened side-by-side: a stack of the original phase contrast images and a stack of images the same size containing entirely zero-value pixels. The windows synchronization tool was then utilized, which allows for simultaneous scrolling through time points as well as for making marks added by the researcher in one stack appear in the other. Researchers then flipped through time points, and for every cell division , a white dot was placed on the dividing cell with the paintbrush tool at the time point just before division. Dots were placed on the original phase contrast images, and so appeared instead on the corresponding blank image in the same location. This process was conducted for 960 time points per colony, and blank images (including those that were marked with a cell division) were saved in order of their representative time point. These images were then run through a simple pipeline in CellProfiler that identified the pixel location of each marked division for all time points. The resulting output file was then imported into MATLAB, and the distance of each mitotic event from the colony center  was tabulated and plotted.Classification processes of individual cells and colonies are described below and were used in statistical analyses described either above in “Single Cell Time Lapse Image Analysis” or below in “Whole-Colony Analysis.”4.3.1 Senescence. For each colony, an image at 374th time point , which displayed the object number of each cell, was used as a reference point in determining which cells divided at future time points (referred to as the “reference image” from this point forward). Time lapsed videos as well as individual images at time points beyond the reference image were then studied extensively; each cell present in the reference image was tabulated by its object number and manually tracked in the videos and/or images. Cells that divided within the next three days after the reference image were marked as proliferative, and cells that did not divide were marked as senescent.4.3.2 Degree of isolation. Time lapse videos of each colony were studied and scored for how isolated they were from neighboring cells as they developed. A scoring system categorized colonies with a score of 1 through 5 for the following observations: 1) the colony developed with other cells present in the 2x2 fields of view (1.7 x 1.3 mm) from the start of the experiment (neighboring cells were close enough to appear in the combined field of view but migrated out of view and therefore could not be tracked); 2) neighboring cells crawled into the combined fields of view (FOVs) within the first two days of the experiment; 3) neighboring cells were revealed when the number of FOVs was expanded from a 2x2 to a 3x3 grid (2.6 x 2.1 mm) at Day 2; 4) neighboring cells migrated into the combined FOVs after they were first expanded, approximately 2–4 days after the start of imaging; 5) neighboring cells were not revealed until the FOVs were again expanded from a 3x3 to 4x4 grid (3.5 x 2.6 mm) at Day 6.4.3.3 Asynchronicity of cell lifetime preceding cell division. The mean and standard deviation of lifetimes for all cells belonging to all colonies existing at any time point during the single-cell analysis  were calculated in MATLAB. The difference in lifetimes between each cell and its twin was then calculated, and cells differing in lifetime by more than the overall standard deviation (0.27 days) were tabulated as “asynchronous” for further analysis.4.3.4 Fast vs. slow proliferators. Lineage trees of each progeny contributing to colony formation were studied to tabulate how many cells were present at the final time point analyzed at the single-cell level. Progenies were categorized as “slow dividers” if they contained less than eight cells at the final time point (corresponding to three population doublings), “fast dividers” if they contained more than 16 cells at the final time point (corresponding to four population doublings), and “moderate dividers” if they contained 8–16 cells at the final time point. Each colony was then tabulated in terms of the number of fast, moderate, and slow dividers they comprised of . The p-value threshold for significance for these three properties under this filter was therefore 0.05/3 = 0.167.4.4.1 Measurements. Image processing was performed on all colonies at the 672nd time point (corresponding to seven full days of colony development) as described above (see “Representation of Whole-Colony Properties” in Method Details). For each colony, the area occupied by the colony’s convex hull was calculated in MATLAB using the bwconvexhull function, and the approximate diameter reported was calculated from this measured area. In the few cases where the colony observed at Day 7 was much larger than the fields of view, the fraction of the colony imaged was approximated by visual inspection and factored into the calculations accordingly. Similarly, the confluency of the colony was calculated as the total area occupied by the cells divided by the area of the convex hull . The number of neighboring colonies at Day 7 was tabulated by careful observation of the original phase contrast images at the 672nd time point by multiple observers. All other colony-wide properties, such as the number of senescent cells at Day 4, were tabulated from the results of the classification analyses described in the previous section.4.4.2 Statistical analysis. The Spearman’s rank correlation coefficient (rs) and associated p-value for all combinations of pairwise comparisons for the 14 whole-colony properties described above . Top center: phase contrast image of the colony at Day 4, with cells color-coded by their generation. Top right: glyph representing cell area, generation, relative location in colony, and proliferative capacity of the progeny/progenies that formed the colony  are indicated (see key). Bottom: phase contrast images of the colony at Days 4, 7, and 10. Images underwent a process of background flattening and brightness/contrast adjustment (see lony see . Filled ment see . Transpa(PDF)Click here for additional data file.S2 Fig(A) The confluency of colonies at Day 7, organized by number of originating progenies studied at the single-cell level up to Day 4. In general, single-cell-derived (SCD) colonies tended to be lower in confluency relative to colonies originating from two or more cells, though there was no statistical correlation between colony confluency and number of cells it originated from. (B) The approximate diameter of all colonies studied at Day 7 is reported; again, no statistical correlation was found between colony diameter and the number of originating progenies. (C) A categorical analysis of the degree of isolation the colonies developed in is presented. Four colonies developed from originating cells that attached relatively close to neighboring cells. In these cases, the initial 1.7 x 1.3 mm montaged field of view (FOV) contained one or more cells close enough to the studied progeny/progenies to be observed at the first time point, yet far enough away that they and their progeny migrated into and out of view and therefore could not be analyzed at the single-cell level. In the next category, neighboring colonies migrated into the FOV of the developing colony within the first two days of development. Many of the colonies were classified into the middle category: no neighboring cells were observed until the FOV was expanded to 2.6 x 2.1 mm at the end of Day 2 (see (D) Boxplots reporting pairwise comparisons of single-cell-derived (SCD) versus multi-cell-derived (MCD) colonies of several properties using a Student’s t-test (see ay 2 see . In the (TIF)Click here for additional data file.S3 Fig(A) Coefficient values of principal component 1 (PC1) for the properties analyzed in the PCA presented in (B) Similarly, the coefficients for PC2 are presented for the PCA in (C) PCA was conducted on all cells existing at the indicated time points, where the input measurements were properties measured directly by CellProfiler at that time point in the experiment Click here for additional data file.S4 Fig(A) Average generation of cells for each single-cell-derived (SCD) colony. Error bars represent one standard deviation. (B) Average generation of cells for each multi-cell-derived (MCD) colony at Day 4. The number of originating progenies is indicated by color. MCD colonies had statistically higher standard deviations in generation at Day 4 compared to SCD colonies in a t-test (p < 0.001). (C) The average cell generation for three SCD colonies analyzed up to Day 6 or 7. The range of generations for each colony increased after Day 4 in all three colonies.(TIF)Click here for additional data file.S5 Fig(A) Further examples of area-vs-time curves for individual cells are given for several progenies to demonstrate the relationship between cell area, generation (color scheme provided), and senescence (labeled black). The Spearman correlation coefficient (rs) and associated p- value for the comparison of average cell area versus generation is also given for each progeny. The top two graphs represent two progenies with a strong decreasing trend in average cell area and generation. The middle two graphs show examples of cells within single progenies that deviate from this trend due to senescence, and the bottom two graphs demonstrate that not all deviating cells were senescent. (B) Area vs time for all cells studied, with time labeled relative to the start of the experiment (left) and relative to the birth time point of each cell (right). While a few senescent cells grew to a very large spread area, area alone could not distinguish senescent from proliferating cells.(TIF)Click here for additional data file.S6 FigThe location of each cell division was measured over the course of ten days of colony development and is reported as the location relative to the colony center (left). Colony confluency was also measured at each time point (indicated in the color bar), and a phase contrast image of the colony at Day 10 qualitatively demonstrates the confluent conditions in which mitotic events were detected . Scale bar = 0.5 mm.(TIF)Click here for additional data file.S7 Fig: Lineage trees for each SCD colony. Lineage lines are color-coded by cell generation, and the width of the lines represents the area of the cell at each 15-minute time point. : Glyphs representing cell area, generation, location, and proliferative capacity of their progeny at fixed time points : Boxplots of cell area, categorized by cell generation. Circles outside of whiskers represent statistical outliers. Cell area was calculated as the average area of each cell over all 15-minute time points for the first 0.83 days of their lifetimes. One outlier data point in panel M  was removed from this figure for scaling purposes. : Area-vs-time curves for each cell over the course of analysis. Data line color represents cell generation, and the vertical dashed line indicates the time point at which cells were fed. : Principal component analysis (PCA) of all cells belonging to the colony over the course of analysis. Input variables were averaged values of 54 properties measured by CellProfiler (see Three single-cell-derived (SCD) colonies  were analyzed at the single-cell level up to Day 6 or Day 7. iler see , and dat(PDF)Click here for additional data file.S8 FigThis time lapse video was constructed using MATLAB at 10 frames per second, where each frame corresponds to one 15 min time point.(AVI)Click here for additional data file.S9 FigThis time lapse video was constructed using MATLAB at 10 frames per second, where each frame corresponds to one 15 min time point.(AVI)Click here for additional data file.S10 FigThis time lapse video was constructed using MATLAB at 10 frames per second, where each frame corresponds to one 15 min time point. Images were altered post-experiment to color code the cells by cell generation.(MP4)Click here for additional data file.S1 TableCorrelations passing thresholds for each are highlighted.(XLSX)Click here for additional data file.S2 TableCorrelations passing thresholds for p value and critical values of the Spearman correlation coefficient are highlighted in green. p values which pass the Bonferroni-corrected p value threshold for significance, but whose corresponding correlation coefficient does not pass the threshold for significance, are highlighted in blue and may possibly represent weak correlations by less-stringent criteria.(XLSX)Click here for additional data file.


In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.First, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.Our results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.The online version of this article (10.1186/s12859-018-2222-2) contains supplementary material, which is available to authorized users. According to Eder et al.  the averMolecular Docking, which constitutes the second step of the RDD, is an attractive technique to identify and optimize drug candidates because of its ability to quickly screen large libraries of potential leads for identifying native-like poses and filtering out compounds that are likely nonbinders , 7. It hThe protein flexibility is a vital issue in docking programs since they perform satisfactorily taking care only the flexibility of ligands. , 11. TheTo account for the dynamic behavior of proteins, we make use of an ensemble of conformations obtained from a Molecular Dynamics (MD) simulation , 15. MD A number of different methods were proposed in the literature to reduce the elapsed time taken for performing docking-based virtual screening . Most ofA different approach to deal with ensemble docking is addressed by wFReDoW , our preIn this study, we show that Reduced Fully-Flexible Receptor (RFFR) models can be generated by identifying promising MD conformations to the ligands during the docking experiments without previous assessments about the best free energy of binding or any other evaluation associated with ligand binding quality. To reach this goal, we developed a selective method for optimizing ensemble docking-based experiments for FFR models. This method aims to discard groups of unpromising snapshots for specific ligands at runtime and scale ensemble docking-based experiments on an INhA FFR model out onto cloud virtual machines (VMs). It was deployed on e-FReDock, the cloud-based scientific workflow to perform exhaustive molecular docking simulations of FFR models and multiple ligands . As a reThis paper describes the implementation of the proposed method in the e-FReDock workflow  and evaltrans-enoyl-ACP (CoA) reductase (E.C.1.3.1.9) enzyme or InhA-NADH complex from Mycobacterium tuberculosis [Mycobacterium tuberculosis cell wall. Inhibition of InhA by the drug isoniazid, for instance, kills the bacteria [The FFR model employed in this study was generated from an MD simulation of the 2-rculosis . InhA isbacteria . The Inhbacteria , 38.The MD simulation was performed by the SANDER module from the Amber9 suite of programs  using thTo reduce the size of the FFR Model and, consequently, the number of ensemble docking experiments, without affecting the accuracy of the produced RFFR models, we decided to use a clustering of MD conformations as input data for the method proposed. The clustering of MD conformations applied in this study was generated by De Paris et al. . They prThe e-FReDock workflow was developed in e-Science Central (e-SC) , a workfThe previous specification of e-FReDock deployed on e-SC is presented in De Paris et al. . It was The e-FReDock workflow uses the e-SC API Java client to control the invocations of both sub-workflows. This API has a set of e-SC components to execute workflow instances on cloud resources and manage data files by accessing the e-SC file system. We decided to use this API to deal with the quality assessment of the groups of snapshots at docking runtime since the e-SC enactment system is a directed acyclic graph based workflow, i.e., it can not repeat workflow tasks. Thus, besides creating new blocks of activities to meet the needs of the proposed method, we also performed some changes in the e-SC API to monitor the selective ensemble docking-based experiments.The cloud platforms selected for performing the ensemble docking-based experiments in this study were: Microsoft Azure public cloud  and ClouThe second cloud platform used to execute our experiments was CIC. This private cloud is located at Newcastle University (UK) and built by the School of Computing Science to support cloud research, staff and students’ mass-scale virtualization requirements and third-party partners. CIC private cloud infrastructure is a virtualization platform, consisting of 27 nodes with 20 cores each, resulting in a total of 540 cores and 7424 GB total RAM. The storage area network uses a 10 Gb Ethernet LAN and 4 nodes with 12 cores, 64 GB RAM and 37 TB storage per node. Furthermore, 3 nodes with 12 cores, 64 GB RAM and 1.4 TB storage each are used for management purposes. Horizon Dashboard  is the wThe selective approach aims to identify and discard snapshots with unfavorable receptor-ligand bound conformations in groups of MD conformations with similar properties in their substrate-binding cavities. Favorable binding modes are discovered and ranked during the docking experiments, based on predicted FEB values extracted from snapshots already docked. The approach developed to perform selective ensemble docking experiments is divided into preprocessing and processing stages. The schematic process from these both stages is given in the flowchart shown in Fig. An experiment is created when a clustering of MD Conformations and a ligand are submitted as input for docking executions. Before starting the experiment, the user should define the percentage and the number of minimum and maximum snapshots per batch. Based on these parameters, the preprocessing phase splits clusters of snapshots into batches. Even though the proposed method allows to choose a type of analysis, we performed evaluations for both, batch and cluster, and concluded that performing analyses in small samples of snapshots  identifies more precisely promising snapshots than in cluster analyses. For this reason, all results presented in this study were performed by using analyses per batch.Each batch contains its status and priority, used for determining the order in which the snapshots will be processed. Priority indicates how promising a group of snapshots is on a scale from 0 to 5 (5 being the most promising), whereas status denotes one of the following four possibilities: (A) Active, (C) Calibrate phase, (D) Discarded and (F) Finished. In this approach, when a docking experiment is submitted to be executed, all batches receive status “A” and priority 5. Snapshots are processed until the percentage threshold to start the analysis, which is a parameter defined by the user, is reached by all batches of an experiment. The highest priority is set to accelerate the end of the calibrate phase. When all batches reach the percentage threshold to start the analysis , their statuses are simultaneously changed to “A”, and a set of metrics are computed to define the experiment baseline. Figure lqi), sampling FEB 13th percentile (pi), and sampling FEB minimum value (mini). The estimated FEB average is defined by Hübler et al. [The set of metrics computed after the calibrate phase are sampling FEB average . A batch with the status equal to “D” is stopped as it contains snapshots with poor quality of docking results for a specific ligand. A batch may be discarded for two reasons: (i) if it is unable to reach the experiment baseline metrics see Fig.  or; (ii)lqi is less or equal to LQlq; (c) pi is less or equal to LQp; and (d) mini is less or equal to LQmin. If a batch does not meet such conditions, its priority is decreased, tending to zero when th percentile, and sampling minimum values since we expect to outperform the quality of the RFFR models produced not only by considering the FEB values average but also by identifying the snapshots that account for at least 25% more negative FEB values of a batch.In the analyses of docking results, the desirable batches (i.e. batches with priority 5 are those where: (a) The primary objective of introducing the proposed method into the e-FReDock scientific workflow was to assist in performing practical virtual screening on FFR models by speeding up ensemble docking experiments. Towards this end, we made improvements and refinements in the original e-FReDock workflow version by the approach described in the previous section. Figure The Analyze Docking Result block, which was added in the selective ensemble docking sub-workflow, computes the priority and determines the status of each group of snapshots by using the set of metrics described in the previous section. Priorities, status and other data necessary for handling the proposed method are stored in the MongoDB database, which in turn, is also accessed by the e-SC API for discarding groups of unpromising snapshots. The e-SC API is one of the essential components of the e-FReDock conceptual architecture and it is based on the workflow scheme from Fig. To better understand which choices to make regarding costs and performance of a commercial cloud system, we performed and evaluated a set of experiments on e-FReDock, using Azure Dv2-series instances located in the North Europe data center. docking The Dv2-series Ubuntu 14.04 instances are based on the 2.4 GHz Intel Xeon E5-2673 v3 processor with Intel Turbo Boost Technology 2.0 that can go up to 3.2 GHz. Table In these experiments, the Lamarckian Genetic Algorithm (LGA) from AutoDock4.2 and its parameters were used to execute the molecular docking simulations between snapshots from the InhA FFR model  and the As can be seen in Fig. The Fig. It is worth emphasizing that LGA is a non-deterministic algorithm and its overall time execution may vary according to the global search space of genetic algorithms. This search randomly generates a population of ligand poses until either the maximum number of evaluations or the maximum number of generations limits is reached . As the To execute the selective ensemble docking-based experiments on e-FreDock, we select a set of 74 ligands from two databases: 12 from PDB  and 62 fDocking parameters were set up to perform 20 LGA independent runs with a maximum of 500,000 energy evaluation. The grid box was centered in the middle coordinates of the binding cavity with a dimension of 48Å X 48Å X 44Å for ZINC’s compounds, and customized sizes were configured to the PDB’s ligands. All ligands were treated as flexible during the docking experiments. To provide the reference pose of each PDB ligand, we first fit all snapshots of the FFR model to the first MD conformation. After that, we placed the reference pose of each PDB ligand based on the first MD conformation and reproduced it for all MD conformations. A PDBQT file for each snapshot from the FFR model was created before starting the experiments and placed into the e-SC Share Library. We set the atom types used by AutoDock4.2, added the Kollman charges and merged all receptor snapshots from the FFR model with the nonpolar hydrogens. For each experiment, groups were divided into batches of 20%, limiting the number of snapshots between 50 and 150. The percentages of processed snapshots defined to start the analyses and to discard a batch were 10 and 40%, respectively. These values were obtained based on preliminary test analises.The e-FReDock experiments were performed on the two cloud environments: CIC  and MicrH0): the method does not result in gains; (ii) Alternative Hypothesis (H1): the method results in gains. To reject the null hypothesis, the accuracy of all RFFR models produced should be higher than the selective ensemble docking at random, considering the same percentage of processed snapshots.The method proposed in this study aims to eliminate groups of unpromising snapshots at docking runtime using the approach to perform selective ensemble docking experiments presented in the Implementation Section. This method generates an RFFR model for each ligand based on a set of metrics computed to assign the priority and status for each batch. To validate the e-FReDock results, we statistically compared the set of snapshots that constitutes the RFFR model with a set of snapshots selected by chance from the ensemble docking experiment. Thus, the following hypotheses are addressed: (i) Null Hypothesis , the worst results were obtained only on 3 structures . These findings suggest that MD conformations from the FFR model used in this study are unable to reproduce structures with tight-binding InhA inhibitors and with sub-nanomolar affinities, i.e. structures that have very similar mode of action to triclosan .“the method does not result in gains". A random selection of 9837 snapshots - equivalent to 50.45% of processed snapshots for PDB ligands - and 8453 - equivalent to 43.35% of processed snapshots for ZINC compounds - would statistically take around 43.00 to 50.00% of the best 10, 20, 30, 100 and 200 receptor-ligand interactions. Tables The analyses on e-FReDock results provide support to reject the null-hypothesis defined as To further validate the gains of the proposed method, the alternative hypothesis, we also assessed the RMSD values of the RFFR models produced for ligands extracted from PDB. The goal of this analysis is to investigate if, in addition to cover the best interactions, e-FReDock is also able to select the best RMSD values. For that, a comparative analysis of the variation of RMSD values between the FFR model and the RFFR models is presented in Fig. 3 to 2,852.9Å3 for the entire 20 ns MD trajectory [Regarding docking accuracy, Fig. RFFR model winner: If the best predicted FEB value reached by the RFFR model outperforms the best predicted FEB value from the 1ENY structure in more than 1 kcal/mol.Tie for RFFR model: If the best predicted FEB value reached by the RFFR model outperforms the best predicted FEB value from the 1ENY structure with a difference equal to or less than 1 kcal/mol.Tie for 1ENY structure: If the best predicted FEB value reached by the 1ENY structure outperforms the best predicted FEB value from the RFFR model with a difference equal to or less than 1 kcal/mol.1ENY structure winner: If the best predicted FEB value reached by the 1ENY structure outperforms the best predicted FEB value from the RFFR model in more than 1 kcal/mol.In this set of experiments, we intend to evaluate the quality of the RFFR models produced based on the assumption that our selective method was able to outperform docking results when compared with the rigid structure that originated the FFR model . TowardThe rate threshold of 1 kcal/mol used to identify the winners is based on the approach used by Huey et al. . They vaThe best FEB values obtained by ligands for the 1ENY crystallographic structure and the RFFR model were assessed and ranked  presented FEB values very close to the 1ENY structure, while the RFFR model outperforms its rigid model for only 2 ligands (16.67%). These results may be related to the fact that InhA structures from PDB are unable to be reproduced by any MD conformation of the FFR model . ConversThis set of experiments was performed to investigate the performance of the proposed selective method when it is applied to a different docking method. We decided to use AutoDock Vina  since itH1) defined to validate the e-FReDock results, which is accepted when our selective method results in gains by comparing statistically to a random selection. Even though some ligands present the percentage of processed snapshots higher than 50%, the minimum percentage of top best docking results was 54.00%. According to this finding, we can infer that the selective docking-based virtual screening method proposed in this study may be applicable to different docking methods.As we expected, our method is able to reduce the number of processed snapshots on average by 56.33% in AutoDock Vina, while keeping the quality of the resulting RFFR models. The positive accuracy is evidenced by the percentage of the best docking results selected for each ligand, which ranges from 54.00 to 100.00% with an average superior to 83.00% for all top best results analyzed. These results provide further support for the alternative hypothesis , with the remaining on CIC private cloud. In total 244.66 h were taken from D2 instance, which was the e-SC Server used to run e-FReDock performance tests and e-FReDock experiments, and 1,900.80 h for all 10 D2 v2 instances (e-SC engines). Tables  ligands,This study introduced a method developed to identify groups of snapshots with proper conformational states to accommodate a particular ligand at docking run-time and incorporated it into the e-FReDock cloud-based workflow. A strategic solution was created in the e-SC API to allocate more virtual processors to batches with high priority, and fewer processors to batches with low priority. Experimental results revealed the high accuracy reached by the proposed method for a set of 74 ligands using AutoDock4.2, thereby reducing the model size on average by 53% while keeping the quality of the model by at least 86%. Further experiments were performed for a set of 12 ligands using AutoDock Vina, which also exhibited good accuracy by reducing the percentage of processed snapshots, on average, by 44% and preserving the quality of the RFFR models by at least 83%. The results concluded that, in addition to identifying the best binding affinity of a specific ligand in the FFR model under study, our method was also able to: (1) perform well with different docking methods; (2) select the best docking poses even using a FEB-based selective method; and (3) outperform docking results obtained from the rigid structure of the FFR model.A natural progression of this study is to work on the overhead imposed when fast docking experiments are executed using more than 8 Azure VMs. Another direction for future research would be to execute our method in larger InhA FFR models and perform docking experiments with more compounds, particularly those already ranked as drug candidates to the FFR model  based onAdditional file 1Table S1 - Best FEB values obtained from e-FReDock and cross-docking experiments. (XLSX 11 kb)


The KERALINK trial tests the hypothesis that corneal cross-linking (CXL) treatment reduces the progression of keratoconus in comparison to standard care in patients under 17 years old. KERALINK is a randomised controlled, observer-masked, multicentre trial in progressive keratoconus comparing epithelium-off CXL with standard care, including spectacles or contact lenses as necessary for best-corrected acuity.2 on corneal topography. The primary outcome measure is between-group difference in K2 at 18 months adjusted for K2 at baseline examination. Secondary outcomes are the effect of CXL on (1) keratoconus progression, (2) time to keratoconus progression, (3) visual acuity, (4) refraction, (5) apical corneal thickness and (6) adverse events. Patient-reported effects will be explored by questionnaires.A total of 30 participants will be randomised per group. Eligible participants aged 10–16 years with progressive keratoconus in one or both eyes will be recruited. Following randomisation, participants will be followed up 3-monthly for 18 months. The effect on progression will be determined by KResearch Ethics Committee Approval was obtained on 30 June 2016 (ref: 14/LO/1937). Current protocol: V.5.0 (08/11/2017). Study findings will be published in peer-reviewed journals.European Union clinial trials register (EudraCT) 2016-001460-11 This is the first randomised trial of corneal cross-linking (CXL) in keratoconus in children, in which group disease onset is at an early age, is perceived to be at high risk of progression to corneal transplantation and in which only observational studies have been published.A total of 60 patients aged 10–16 years with progressive keratoconus will be randomised to CXL or standard care including spectacles and contact lenses as required for best-corrected vision.2 at 18 months adjusted for K2 at baseline examination and measured by masked optometrists.The trial is designed to examine safety and efficacy of CXL in reducing progression, the primary outcome measure being between-group difference in KSecondary outcome measures at 18 months include keratoconus progression, visual acuity, refraction, adverse events and quality of life measurements.Follow-up to 18 months after randomisation is relatively short and any benefit found following CXL would require longer term analysis of efficacy.Keratoconus is characterised by thinning and distortion of the cornea that results in visual loss from complex refractive error and corneal opacification. The prevalence in Europe has been reported as 1:11632) and the point of maximum steepness (Kmax).The most important parameters used in the assessment of keratoconus are the curvature of the cornea (presented as dioptre power (D)), apical corneal thickness in µm, refraction and best-corrected visual acuity. Earliest disease can be detected by corneal topography, which demonstrates thinning and irregularity of corneal curvature. Quantification of steepness of the corneal curvature in horizontal, vertical and multiple oblique meridians identifies the meridian of maximum corneal steepness  between CXL and control eyes was reported: an improvement in CXL-treated eyes with flattening of Kmax by −1.03±0.19 D compared with an increase in Kmax for control eyes of +1.75 ± 0.38 D at 36 months. Adverse effects were not uncommon but mostly transient, including corneal oedema, superficial opacification and recurrent corneal erosions. Despite increasing information in relation to the efficacy of CXL a Cochrane Review conducted in 2015 concluded that evidence for the use of CXL in the management of keratoconus is limited due to the lack of properly conducted RCTs.While the standard care described above involves treatment of the refractive consequences of keratoconus or replacement of the diseased cornea by a transplant, the concept of stabilising keratoconus and arresting its progression at a stage when there is still good unaided or spectacle-corrected vision is relatively recent. Corneal cross-linking (CXL) increases the stiffness of the cornea, which can arrest the progression of early keratoconus.et al reported an uncontrolled study of 152 keratoconus patients ranging in age from 10 to 18 years, of whom follow-up post-CXL was available on only 61% of patients.max by −0.4 D was found. Vinciguerra et al reported 40 CXL-treated eyes in patients with progressive keratoconus aged 9–18 (mean 14.2) years in a non-randomised prospective study.et al reported progression in 22% within 5 years of CXL.In younger subjects, a number of observational studies of CXL in keratoconus patients <19 years have been published, each with limitations but each reporting effectiveness. Caporossi Evidence of effectiveness of CXL is of particular interest in young patients and has specifically been requested by the National Institute for Health and Care Excellence in the UK. KERALINK is a multicentre RCT in this patient group evaluating epithelium-off CXL, the technique of CXL which has been demonstrated to be effective in adults. If the trial demonstrates efficacy of CXL compared with standard care, and in particular if CXL is arrests keratoconus progression, this would have important implications for clinical management. Although we intend to follow-up the trial patients for several years after the proposed trial concludes in order to ascertain the duration of keratoconus stability, it is clear that arrested progression in a paediatric patient is likely (1) to obviate the need for contact lens correction and for later corneal transplant surgery and (2) to have correspondingly greater health and cost benefit than if the CXL were undertaken in adults. Trial findings will inform ophthalmologists, optometrists and inform future research and treatment policy.www.controlled-trials.com and the European Union clinical trials registry. It was approved by the UK Health Research Authority, the Medicines and Healthcare Regulatory Agency and ethical approval was granted by the Brent Ethics Committee (reference 16/LO/0913). The trial is supervised by a trial management group (TMG), with independent oversight by a trial steering committee (TSC) and a data monitoring committee. Eligible patients are randomised in a 1:1 ratio to receive either CXL or standard care including spectacles or contact lenses as necessary (standard care of early keratoconus in the UK includes correction of refractive error and not CXL). Following randomisation, participants are followed for 18 months at 3-monthly intervals. Inclusion and exclusion criteria are shown in KERALINK is a randomised controlled, observer-masked controlled trial in five centres in the UK. The study adheres to the tenets of the Declaration of Helsinki and is registered at max) or in the steepest corneal meridian (K2) of at least 1.5 D was used as threshold for eligibility in one or both eyes. Based on this, eligibility was defined by an increase from baseline in Kmax or K2 of >1.5 D between two topography examinations separated by 3 or more months. For each patient, the eye with the more advanced keratoconus at baseline will be categorised as the study eye for the primary analysis, unless that eye had undergone prior surgery such as corneal transplantation.To differentiate true keratoconus progression from measurement artefact or minimal progression, an increase on topography  in the steepest keratometry , used for assessment of the primary outcome. To improve repeatability, three measurements of each eye will be taken at baseline and follow-up examinations and the mean used in comparisons. Contact lenses will be removed at least 7 days prior to topography.Corneal topography for measurement of corneal power in the steepest meridian , logMAR measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart at a starting distance of 4 m in both eyes.Subjective refraction, both eyesApical corneal thickness measurement, both eyes, by ultrasound and Scheimpflug imaging at topographyQuality of life will be assessed by visual function  and generic paediatric health outcome  questionnaires. CVAQC is a 25-item vision-specific questionnaire designed for children.https://www.sealedenvelope.com). The system is customised to trial requirements, using minimisation with stratification by treatment centre and whether progression is confirmed in one eye or both eyes at randomisation. Following a dedicated consent/screening and randomisation visit for eligible patients and their parents, patients will be randomised to one of two trial arms . At completion of the procedure one drop of povidone iodine and a therapeutic contact lens will be applied to the treated eye. Management post-CXL is (1) proxymetacaine drops every 2 hours and naproxen 250 mg two times per day, both as required for analgesia, (2) moxifloxacin 0.5% drops every 6 hours for 1 week as infection prophylaxis, (3) dexamethasone 0.1% drops every 6 hours for 1 week, every 12 hours for 1 week, then fluorometholone 0.1% drops every 12 hours for 1 week. Patients randomised to CXL will attend for an extra examination at 1 week post-CXL for removal of the contact lens and confirmation of corneal re-epithelialisation.Corneal cross-linking in one or both eyes (according to whether progression is confirmed in one eye or both), under general or local anaesthesia as applicable, followed by standard management. The surgical procedure will be as follows: insertion of lid speculum, removal of corneal epithelium with a spatula, administration of riboflavin drops  every 2 min for 10 min, application of pulsed UV light using standardised parameters of 10 mW/cmThe trial control arm is standard management alone, including refraction testing with provision of glasses and/or contact lens fitting for one or both eyes as required for best-corrected visual acuity.2) of >1.5 D on corneal topography between two examinations or the requirement for change from spectacle to rigid contact lenses correction of vision, as the latter precludes reliable topography measurements.To differentiate true keratoconus progression from measurement artefact, we will define progression as an increase in power in the steepest corneal meridian  defined as >1.5 D increase from baseline in KTime to keratoconus progression.Uncorrected and best-corrected visual acuity (logMAR) measured with an ETDRS chart at a starting distance of 4 m.Refraction .Apical corneal thickness.Quality of life as assessed by paediatric health outcome and visual function questionnaires.2 will need to have this confirmed at a subsequent visit . Participants who have unconfirmed progression at the 18-month follow-up visit will need this confirmed at a further visit at 21 months.All patients will be assessed at baseline, 3, 6, 9, 12, 15 and 18 months. Any patient found to have >1.5 D increase in KPatients will be assessed for adverse events at the 1 week post-CXL follow-up and at all visits following randomisation.Any reversible or short-term corneal abnormality, for example, prolonged eye pain, delayed corneal epithelialisation, transient corneal oedema.Any visually significant corneal abnormality, for example, opacity resulting from sterile inflammatory infiltrates, corneal infection or stromal melting.Any untoward medical occurrence in a study patient which does not necessarily have a causal relationship with the treatment under study, for example, abnormal laboratory findings, or disease symptoms and signs.The Independent Data Monitoring Committee (IDMC) will monitor adverse events and serious adverse events during the trial to inform their recommendations to the TSC. Participants in the standard care arm with significant progression confirmed at two successive examinations will be considered for other keratoconus management options including cross-over to CXL2 at 18 months, adjusted for K2 at baseline, in the study eye recorded by an optometrist masked to the treatment group. A difference between the groups in the change in K2 of >1.5 D from randomisation to 18 months is considered to be a clinically important difference  to allow for up to 24% loss to follow-up. These estimates are based on 12-month and 24-month data reported by Wittig-Silva et al from which we estimated a pooled SD of the changes of 1.476 D. We expect that on average there will be 10% loss to follow-up in both groups. In the study by Wittig-Silva et al, 19% of patients withdrew, crossed over to CXL or had a transplant by 18 months. However, 18% of patients in the control group either received CXL or a transplant. If we specifically adjust the sample size to take account of 10% loss to follow-up and up to 20% of the control arm cross-over to CXL or transplant, then our planned total sample size of 60 patients would still provide at least 80% power to detect the clinically important difference. The trial protocol states that participants cannot cross over to CXL before 9 months.The primary outcome is KPatients and parents were first involved in this research at a patient event hosted by Moorfields Eye Hospital. Topics on which opinions were collected included randomisation, cross-over and the duration of follow-up of trial patients. The research questions, design and trial outcome measures in the protocol were finalised following the above meeting and additional input from the UK Keratoconus Self-Help and Support Association. This Association supported the trial by publicising the trial and by providing representatives on the TMG and the trial IDMC. The investigators will communicate a summary of the trial results to participants and their parents. The UK Keratoconus Self Help and Support Association will disseminate in their website and other communications the results to keratoconus patients. The burden of the intervention was discussed at our initial meeting with patients and parents and at the consent-taking stage in the trial.The primary analysis will be conducted following the intention-to-treat (ITT) principle where all randomised patients will be analysed in their allocated group whether or not they receive their allocated treatment. Patient characteristics at the time of randomisation will be summarised using mean and SD for continuous variables which are approximately normally distributed, median and IQR for variables which are not normally distributed, or by frequencies and percentages for categorical variables. All statistical tests will use a two-sided p value of 0.05 unless otherwise specified. All CIs presented will be 95% and two-sided. A detailed statistical analysis plan will be developed for approval by the TSC and review by the IDMC and finalised before the first statistical analysis of unmasked outcome measures. No formal interim analysis is planned, but reports concerning patient safety and key efficacy outcomes will be prepared for regular review by the IDMC who may request an interim analysis if a report raises concern. The IDMC is independent from the sponsor and funders. The membership, frequency of meetings, activity  and authority will be covered in the UCL CCTU IDMC terms of reference.2 values recorded after randomisation. K2 at randomisation, treatment group, follow-up time, the interaction between treatment and time, and the stratifying variables centre and whether each patient has one or both eyes eligible will be included as fixed effects. A random patient effect will be included to take account of clustering by patient. The regression coefficient for treatment group in this model estimates the difference between the mean changes in K2 of each group.For each patient, the eye with the more advanced keratoconus at the time of randomisation will be defined as the study eye for the primary analysis, unless that eye has previously been treated by CXL or corneal transplantation. The analysis of the primary outcome will be performed using a linear mixed model fitted to all KAn ITT analysis will be performed for all secondary outcomes. Secondary continuous outcomes such as uncorrected and best-corrected visual acuity measured at randomisation and on more than one occasion during follow-up will be analysed using similar linear mixed models. Uncorrected and best-corrected visual acuity will be measured in logMAR using an ETDRS chart at a distance of 4 m. In patients for whom both eyes show progression at the time of randomisation, information from both eyes will be included in a secondary analysis including eye as a fixed effect and patient as a random effect.Fisher’s exact test will be used to compare the proportion of study eyes with keratoconus progression in each treatment group. Cox regression analysis will be used to estimate time to keratoconus progression in the study eye for each treatment group. The model will adjust for the stratifying variables, centre and whether each patient has one or both eyes eligible. Patients who do not progress during the course of the trial will be censored at their last follow-up visit.2. The impact of missing data will be mitigated against by incorporating information from all observed time points using a mixed model approach.We will also explore how visual disability and health in children and young patients with keratoconus relate to changes in KPlanned subgroup analyses will be conducted to investigate whether the effect of CXL differs between patients who had progression at randomisation in one or both eyes. This will be explored by adding an interaction between the number of eyes with progression at randomisation and CXL treatment to the primary efficacy outcome analysis mixed model.The trial was approved by the UK Health Research Authority and the Medicines and Healthcare Regulatory Agency. Trial investigators will ensure that the study (including any approved amendments) is conducted in accordance with the principles of the Declaration of Helsinki.The results of the trial will be reported in accordance with Consolidated Standards of Reporting Trials guidance and will be disseminated regardless of the direction of effect. Publications generated from the trial will be attributed to the TMG, which consists of all those who have wholeheartedly collaborated in the trial. The main report will be drafted by the TMG, and the final version will be reviewed by the TSC before submission for publication. Trial findings will be disseminated to the patients, UK Keratoconus Self-Help and Support Group and also doctors, optometrists, advisory bodies and healthcare commissioners. This will take the form of papers in peer-reviewed open-access medical journals and presentations at conferences.