Left ventricle non-compaction cardiomyopathy (LVNC) has gained great interest in recent years, being one of the most controversial cardiomyopathies. There are several open debates, not only about its genetic heterogeneity, or about the possibility to be an acquired cardiomyopathy, but also about its possible overdiagnosis based on imaging techniques. In order to better understand this entity, we identified 38 LVNC patients diagnosed by cardiac MRI (CMRI) or anatomopathological study that could underwent NGS-sequencing and clinical study. Anatomopathological exam was performed in eight available LVNC hearts. The genetic yield was 34.2%. Patients with negative genetic testing had better left ventricular ejection fraction (LVEF) or it showed a tendency to improve in follow-up, and a possible trigger factor for LVNC was identified in 1/3 of them. Nonetheless, cerebrovascular accidents occurred in similar proportions in both groups. We conclude that in LVNC there seem to be different ways to achieve the same final phenotype. Genetic testing has a good genetic yield and provides valuable information. LVNC without an underlying genetic cause may have a better prognosis in terms of LVEF evolution. However, anticoagulation to prevent cerebrovascular accident (CVA) should be carefully evaluated in all patients. Larger series with pathologic examination are needed to help better understand this entity. Left ventricular (LV) non-compaction (LVNC) is characterized by prominent myocardial trabeculations in a thick, non-compacted layer adjacent to a thin compacted layer. LVNC is the most recently categorized cardiomyopathy, and probably the most controversial one, without available clinical guidelines. The American Heart Association classified LVNC as a distinct primary cardiomyopathy with a genetic aetiology . HoweverLVNC had historically been categorized as congenital condition, secondary to a failure of the compaction process during embryonic cardiac development ,5,6,7,8.Agreement between the three most commonly cited transthoracic echocardiogram (TTE) diagnostic criteria, described by Chin et al. , Jenni eMoreover, the yield of genetic testing in LVNC varies from 9% to 41%, depending on patient selection and the number of genes screened ,29,40,41On the other hand, although a gold-standard diagnostic technique for LVNC is missing , anatomoThe aim of the current investigation was to provide a comprehensive clinical view of LVNC based on genetic and anatomopathological information.Adult patients (>21 years old) with LVNC diagnosis were recruited consecutively from a tertiary hospital from Spain, referral for cardiogenetics. Due to the controversy about the diagnostic criteria of LVNC, only patients diagnosed with LVNC, either by Petersen CMRI criteria, or Burke APE criteria, were included. Reports from 824 CMRI (from 2007 to 2015) and 89 transplanted hearts (from 2009 to 2015) were reviewed. At this step, LVNC was considered irrespective of its co-occurrence with other primary cardiomyopathies.According to these criteria, 43 consecutive patients with LVNC diagnosis, either from CMRI criteria (from 2007 to 2015), or form APE criteria (from 2009 to 2015) were identified. Next-generation sequencing was performed in all patients who met the inclusion criteria (excluding significant CHD) and were still alive at the time this study was performed. Three patients could not be included in the clinical study due to decease without genetic testing available. Two patients with LVNC associated with significant CHD (that could induce significant hemodynamic changes) were excluded. Therefore, clinical and genetic study was available for the remaining 38 alive patients with LVNC.Apart from that, histopathological exam was performed in all available LVNC hearts, including those patients who had died without the possibility of a genetic test.A retrospective medical record review of the recruited individuals evaluated was performed. Probands and available family members studied were evaluated by history taking, physical examination, 12-lead electrocardiography, 24-h Holter monitoring, TTE and CMRI or APE. Left ventricular ejection fraction (LVEF) evolution will be categorized into normal, slightly depressed (LVEF <55%), moderately (LVEF 45–35%) depressed, or severely depressed (LVEF <35%).Possible trigger factors for LVNC  were specifically investigated. Available relatives were screened with the same protocol.MYBPC3, MYH7, TNNI3, TNNT2, TPM1, TNNC, MYL1, MYL2, ACTC1, FLNC, MIB1, TAZ, LDB3, DTNA, HCN4, RYR2, LMNA, NKX2-5, MYH6, PRDM16, ACTN2, DMD, DNAJC19, FHL1, PLN, and TTN genes by Ion Torrent semiconductor chip technology in a Ion GeneStudio S5 Sequencer , according to previously described protocols [Genetic screening was carried out with DNA samples from the 38 LVNC recruited patients. All of them were NGS sequenced for a gene panel including rotocols ,46. OverData acquisition and analysis was performed in compliance with protocols evaluated by the Ethical Local Committee of the Hospital Universitario Central de Asturias (No. 2020.224). Written informed consent was obtained from all 38 participants, prior to genetic study.Interpretation of all gene variants with an allele frequency <0.01 was based the American College of Medical Genetics and Genomics (ACMG-AMP) 2015 Standards and Guidelines ,47,48. AAll 89 available hearts between 2009 and 2015 from our tertiary referral hospital with a heart transplant program were evaluated. Moreover, a patient diagnosed of LVNC by CMR was also transplanted. APE found eight hearts that fulfilled APE criteria for LVNC: six patients with isolated LVNC, one with congenital heart disease associated and one with concomitant three vessels ischemic disease.The examination was performed by an experienced pathologist expert in the field, based on the LVNC anatomopathological criteria from Burke et al. ,44. First test or Mann–Whitney U test. p < 0.05 was considered to be significant.Statistical analyses were performed with SPSS v.19 . Descriptive data for continuous variables are presented as mean + SD and as frequencies or percentages for categorical variables. The Chi-square test or Fisher exact test were used to compare frequencies, whereas differences in continuous variables were evaluated with either the Student A total of 38 isolated LVNC patients diagnosed by CMRI, APE, or both , were evA P/LP variant was found in 13 patients , meaning a genetic yield of 34.2%. Genetic testing was negative (no relevant genetic variants were identified) in patients 18 to 38 (group 2). Another five patients were carriers of VUS and, therefore, considered separately . Family screening was performed in all available relatives, supporting variants segregation criteria in P/LP variants .Principal clinical characteristics of patients with isolated LVNC are summarized in With genetic screening, up to 22 relatives with P/LP variant carriers and 27 non-carriers relatives were identified. Intrafamilial phenotypic variability was frequently found. As expected, in LMNA families, DCM phenotype was present and HCM in those with sarcomeric pathogenic variants . MoreoveMain clinical differences between patients with P/LP variants (group 1) and those with negative genetic result, carriers of benign or likely benign variants (group 2) are shown in An internal cardiac defibrilator (ICD) was implanted in 12 patients. Almost half of patients from group 1 had an ICD (46.1%), 50% with at least one appropriate therapy during follow-up. Conversely, only three patients from group 2 (14.3%) received an ICD. What is more, in all three LVEF improved during follow-up and even normalized in two of them.p = 0.4). Atrial fibrillation or flutter had been detected in six patients form group 1 and 4 from group 2. However, in both groups, a patient suffered a cerebrovascular accident without previous known arrhythmias.Apart from that, despite these differences in LVEF, patients suffered cerebrovascular accidents in similar proportions in both groups . The genetic yield of this small but severely affected cohort of isolated LVNC is 80%.Over the past few decades, technological advances in genetic sequencing have allowed to perform genetic testing worldwide. The number of genetic variants to analyse has increased massively  and so hA study performed about 10 years ago, found a genetic “mutation” in 17.5% of their TTE-based cohort . The genIn 2017, an interesting study in children classified variants according to ACMG  with a gIn our study, genetic testing identified a genetic cause in up to 34.2% of patients, a percentage within the expected ranges according to previously reported series. However, better understanding of VUS may improve this yield. Thanks to genetic screening not only 22 relatives at risk were identified, but also 27 relatives could be discharged. LVNC have been accepted to be a possible inherited condition. Therefore, we believe that genetic screening should be strongly recommended, like in any other kind inherited cardiomyopathy. Moreover, in this entity, genetic testing may be useful not only for family screening, but also to help in differential diagnosis with hypertrabeculation mimicking LVNC. In fact, if genetic testing had only been performed in those with moderate to severe LV dysfunction in follow-up, genetic yield would have improved to 75% (9/12).The strength of our cohort relies not only in the genetic variants’ classification based on ACMG-AMP guidelines , but alsWe believe that our data reinforce the hypothesis that there are different ways to reach the same final phenotype, whether as congenital heart defect or as an inherited cardiomyopathy or as an acquired one. Although in this study we included LVNC patients based on the same criteria, they had different etiologies. It seems obvious that patients from group 1 have an inherited kind of cardiomyopathy. However, the presence of trigger factors in 1/3 of patients from group 2 support the hypothesis that LVNC phenotype could also be an acquired cardiomyopathy ,9,10,11.In addition, our data also supports the theory that LVNC could actually been underdiagnosed previously, even in autopsies ,21,44,52Furthermore, APE has played a critical role in helping to describe and classify LVNC as a novel cardiomyopathy and to settle its diagnostic criteria ,19. NeveFamily screening was not available for all relatives. All genetic variants identified in this cohort were reviewed by two biologists and two cardiologists trained in cardiogenetics, according to current published guidelines and available data . DespiteThere seem to be different ways to achieve the same final phenotype: LVNC. As genetic testing in LVNC has a good genetic yield and provides valuable information, it should be recommended for all LVNC patients. LVNC without an underlying genetic cause may have a better prognosis in terms of LVEF evolution in time. However, anticoagulation to prevent CVA should be carefully evaluated in all patients. Larger series with pathologic examination are needed to help better understand this entity.


Is motor response inhibition supported by a specialised neuronal inhibitory control mechanism, or by a more general system of action updating? This pre-registered study employed a context-cueing paradigm requiring both inhibitory and non-inhibitory action updating in combination with functional magnetic resonance imaging to test the specificity of responses under different updating conditions, including the cancellation of actions. Cortical regions of activity were found to be common to multiple forms of action updating. However, functional specificity during response inhibition was observed in the anterior right inferior frontal gyrus. In addition, fronto-subcortical activity was explored using a novel contrast method. These exploratory results indicate that the specificity for response inhibition observed in right prefrontal cortex continued downstream and was observed in right hemisphere subcortical activity, while left hemisphere activity was associated with right-hand response execution. Overall, our findings reveal both common and distinct correlates of response inhibition in prefrontal cortex, with exploratory analyses supporting putative models of subcortical pathways and extending them through the demonstration of lateralisation. •Anterior specificity in cortical activity to response inhibition was identified.•Methodological development of a compound contrast methodology optimising data usage.•Exploratory demonstration of lateralised subcortical action control pathways. Studies have repeatedly indicated that, neuroanatomically, the right inferior frontal gyrus (IFG) and the pre-supplementary motor area (pre-SMA) are crucial in motor inhibition e.g. . HoweverOne commonly used task to measure response inhibition is the stop-signal task SST; . During The context-cuing paradigm and fMRI allows us to explore another key set of regions associated with control; the basal ganglia (BG) and thalamic nuclei. Converging neuroanatomical evidence suggests that the IFG and pre-SMA exert influence over subcortical activity in the control of actions, including response inhibition e.g. . Furtherhttps://osf.io/zqefx/, section The primary pre-registered goal of the current work was to establish the neuroanatomical distribution of inhibitory and non-inhibitory action updating, in terms of their specificity and overlap (see https://osf.io/zqefx/ and https://osf.io/27gmh/).The main hypotheses and methods for this study were pre-registered prior to data collection , were included in the study. All had normal or corrected-to-normal vision, were neurologically healthy and screened for contraindications to MRI. Informed consent was received from each participant and all methods were approved by the ethics committee at the School of Psychology, Cardiff University. Participants were reimbursed at a rate of £10 per hour for their time.Thirty right-handed participants , aged 18–29 years .The study protocol was pre-registered prior to data collection, see here: 2.3https://osf.io/zqefx/ and a clarification point 1 here: https://osf.io/27gmh/). Signal presentation was pseudo-randomised so that a maximum of three signals were presented in succession. Fixation crosses appeared during the inter-trial interval (ITI) for either 500 ms, 1000 ms or 2000 ms to reduce automation of participants’ responses. Arrow direction and ITIs were randomised but occurred with equal probability in each block. Further technical detail is reported in SI 1.2.The task design is outlined in 2.4A 3T GE GDx scanner, equipped with an 8-channel head coil was used. Whole-brain functional images were acquired using an echo planar imaging (EPI) sequence with AC-PC alignment . Interleaved slices were acquired in an ascending direction. 156 vol were acquired over the course of each run , such that a single fMRI run covered the duration of each of the behavioural runs. Each run was preceded by the acquisition of 4 dummy scans. A T1-weighted anatomical scan  and 2 field maps  were acquired after EPIs for each participant. In addition, to localise hypointense STN  a suscep2.5https://osf.io/zqefx/). Before testing, participants completed one block of each task, presented in a randomised order, to remind them of the task instructions. This data was not saved or analysed. The testing session included 8 fMRI runs of the behavioural task. Each task block consisted of 18 trials  and the context pseudo-randomly switched every block, so that no context was repeated across successive blocks. For each fMRI run, 3 blocks of each task were presented, and each block was separated by task context cues. As such, 432 trials per task  were presented in each testing session. The delays between stimulus and signal onsets were adjusted after completion of every 2nd fMRI run to ensure successful inhibition on stop signal trials remained at ~50%. If performance on any of the tasks fell beyond pre-registered benchmarks, standardised feedback was provided (see sections 4.2.3 and 5.3 here: https://osf.io/zqefx/). Throughout testing, physiological measures of cardiac and respiration rate, O2 troughs and end-tidal CO2 were taken. Full details are reported in SI 1.3.Prior to the fMRI session all participants completed an initial training session . Bayesian equivalents of all frequentist tests were computed using either the ‘default’ prior settings in JASP (JASP Team (2019), version 0.9.2.0) or using the JZS ‘default’ prior with a scale factor of r = √https://osf.io/g4chs/). For repeated measures ANOVA in JASP, Bayes Factors (BFs) were taken for the model compared to the null (i.e. BF10). For interaction effects, BF10 of the model with the interaction was compared against the BF10 of the model with only the main effects , version 23). The Holm-Bonferroni method  was used = √12  in custo2.72.7.1In-house scripts were used to remove physiological regressors from the EPI data prior to pre-processing . Subsequ2.7.2p < 0.05, using Gaussian Random Field theory  = 0.626, p < 0.001). Only regions identified by both as the STN were included in the final masks. All other ROIs were specified by the Atlas of the Basal Ganglia  were created for all cortical and subcortical structures of interest. IFG ROIs were defined as the combination of the al atlas . For anang atlas . THAL wang atlas . STN wera . Contrasts were divided into those pertaining to response execution and response inhibition, and then further subdivided into contrasts representative of proactive (preparatory) inhibition and reactive inhibition . Here response execution related contrasts were expected to result in activation patterns consistent with the direct pathway, and response inhibition contrasts were expected to result in activity conforming to the indirect and hyperdirect pathways, where the indirect pathway might link to proactive contrasts and the hyperdirect ROIs might be expressed more clearly by reactive contrasts, following their putative role  the pattern of activations in cortical and subcortical ROIs, and 2) whether these patterns were consistent with the putative pathways. Across the neuroimaging literature a variety of contrasts are often used to refer to similar states of interest  and stop signal > null (implicit baseline) have both been used to demonstrate activity associated with response inhibition; ive role . These cp<0.05 or BF>3, separately), were regressed upon one another. The linear model of these regressions include intercept coefficients, derived as t-statistics. These statistics were representative of one region being active, relative to mean zero, when the covariant was taken into account. p-values and BFs were then derived.Initial analysis applied a repeated measures ANOVA to the compound data with factors of site  and condition (response execution vs. response inhibition). Single sample t-tests were then applied to each ROI under each condition. Interrelationships between activated regions was assessed with a series of partial (moderator/mediator) analyses . Here, at-test (as described above), for each region proposed to be involved in each pathway, separately. For all subcortical regions not proposed to be involved, the inverse of the BF was taken as representative of evidence of zero activity. Then, taking advantage of BFs being transitive, the product of these was taken as evidence of the consistency between activity of the proposed pathway and observed activity under behaviourally relevant conditions. For example, the product BF for the hyperdirect pathway was the result of BFs from right GPi, right STN, right SN and right THAL (i.e. regions proposed to contribute to the pathway) and inverse BFs from the right GPe, right STR and all left subcortical structures (i.e. regions not proposed to contribute to the pathway). By applying this to the corresponding compound contrasts we were able to assess the weight of evidence for each pathway under their behaviourally relevant conditions (e.g. direct pathway with response execution contrasts). Finally, to confirm that any correspondence was not the result of over-fitting, the analyses were applied to the behaviourally inappropriate compound data (e.g. %BOLD from response execution contrasts applied to the indirect (inhibitory) pathway). Alternative modelling approaches are reported here: https://osf.io/zkq7h/.The second set of analyses made use of the compound contrast data sets applied to lateralised subcortical regions. To assess the consistency between the pattern of activity observed and that expected according to the pathways descriptions, we applied models representative of each pathway to the data. A BF was derived for each pathway under their behaviourally relevant condition (e.g. %BOLD from contrasts reflective of response inhibition from ROIs comprising the indirect pathway). This involved taking the BFs based on the single sample 3post hoc exploratory analyses that describe observed patterns in the data, subject to future confirmation.We first describe the outcome of pre-registered behavioural analyses and our primary imaging analyses aimed at delineating the pattern of cortical activity under inhibitory and non-inhibitory action updating conditions. These are followed by analyses aimed at exploring the expression of the putative subcortical pathways. Additional pre-registered brain-behaviour analyses are reported in SI 2.3. The results therefore reflect a combination of pre-registered confirmatory analyses and, separately, 3.1https://osf.io/zqefx/). Accuracy rates across all no-signal (go) trials were greater than 85% , as were signal trials in the DT (93.21% ± 3.42%) and IT (95.68% ± 3.26%). Successful stopping on signal trials in the SST was in accord with pre-registered target performance of ~50% (45.45% ± 6.03%). Reaction times (RTs) across correct trials were also within pre-registered target range for no-signal trials across all tasks  and signal trials in the DT (1st response = 421.55 ms ± 43.33 ms) and IT (396.70 ms ± 43.73 ms).Analysis of the behavioural data confirmed participants performed the behavioural tasks in line with, and in excess of, the levels to which they were trained  = 1280.59, p < 0.001, BF = 9.46 × 1012) and trial type  = 2394.53, p < 0.001, BF = 2.36 × 109) and an interaction effect  = 1788.71, p < 0.001 (degrees of freedom Greenhouse-Geisser corrected), BF = 6.32 × 10105) were found. Of specific interest, no-signal trial performance significantly differed across task contexts  and within contexts, signal and no-signal trial accuracy rates differed for the SST  and DT , but not for the IT . These findings potentially indicate differences in task difficulties across context. For RTs, significant main effects of context  = 44.32, p < 0.001, BF = 5.90 × 1020) and trial type  = 6.43, p = 0.017, BF = 0.23) and an interaction effect  = 140.20, p < 0.001, BF = 131451.61) were again observed (degrees of freedom are Greenhouse-Geisser corrected). Here, across task contexts, RTs to no-signal trials significantly differed , consistent with the common observation of proactive or preparatory slowing in the SST  and IT  and shorter in the SST . Longer RTs are likely due to distraction effects  and trial types  . For acc the SST  and pote effects  and shor effects . CollectAdditional task-specific measures were computed for use with brain-behaviour analyses and are reported in SI 2.1.3 as they are not used in analyses reported here.3.2Central to this investigation was to test the specificity of regions under conditions of inhibitory and non-inhibitory action updating, with a focus on the right IFG and pre-SMA. Analysis revealed right frontal dominance associated with response inhibition and left lateralisation associated with double-responding .pars triangularis (68.33% of this region), relative to the most posterior right IFG, the pars opercularis (14.74%). In comparison, no right IFG voxels were uniquely associated with non-inhibitory action updating. In the pre-SMA, disjunction analyses revealed exclusive activity under conditions of response inhibition (16.14% of this region), which appeared in the anterior portion. Conversely, more posterior activity was observed under conditions of non-inhibitory action updating  NOT  and  NOT ). Inhibiting a response was associated with exclusive activity in right frontal regions , with 40 15.59%; b.Fig. 4C = 19.81, p < 0.001, BF = 149.32; task: F = 7.42, p = 0.001, BF = 348.14), but no clear interaction effect  = 2.10, p = 0.132, BF = 0.26; p0.05<0.001, BF = 226.40). Further, right IFG was recruited to a significantly greater extent under SST conditions relative to DT  and IT  conditions, with no difference between DT and IT requirements . In the pre-SMA, %BOLD was stronger under SST conditions relative to IT  conditions, and while graded recruitment is indicated across tasks .To further quantify this specificity a series of exploratory tests were applied to %BOLD acquired from signal > no-signal contrasts from each task . Repeated measures ANOVA between ROI (right IFG and pre-SMA) and task  revealed significant main effects  and task . Significant main effects  = 59.58, p < 0.001, BF = 57776.94; task: F = 9.79, p < 0.001, BF = 8122.72) and a significant interaction  = 10.91, p < 0.001, BF = 2.94) were revealed , but not updating in the absence of inhibition .Given the differences in regional recruitment across the right IFG sub-divisions, a separate exploratory repeated measures ANOVA was conducted between ROI , as opposed to the anterior right IFG, the pars triangularis  is in accord with the results reported above and suggests that the pre-SMA supports both action updating and the execution of simple responses.Despite differences in regional recruitment, common activity between task conditions was also evident as revealed by pre-registered conjunction analysis:  ∩   relative to non-inhibitory action updating (DT) and no updating (IT) conditions. Further, the anterior spread of activity associated with response inhibition suggests the olvement .3.3The three putative action-control pathways  involve a specific pattern of activity according to excitation/inhibition relationships between the structures involved . The fol3.3.13.3.1.1 = 15.80, p < 0.001, BF = 3.86 × 1044) indicated differential recruitment of ROIs when responses were executed or inhibited (degrees of freedom are Greenhouse-Geisser corrected).A two-way ANOVA and t-tests were used to assess the spatial distribution of activity across cortical (preSMA and IFG) and subcortical (BG and THAL) ROIs under behaviourally relevant conditions. This involved factors of condition (execution vs. inhibition) and ROI . A significant interaction effect  under conditions of response execution or response inhibition , but exerts a moderating influence if the original difference is reduced but its significant status remains (i.e. p < 0.05 or BF > 3). It should be noted, however, that while a decision criterion (e.g. p < 0.05) is required for interpretation, this criterion is to some extent arbitrary.The preceding analyses demonstrate patterns of context specific activity but are silent to the interrelationships between structures. The simplest way to test for these correlational relationships was to apply a series of moderator and mediator analyses , appendeWhen responses were executed, four regions demonstrated significant differences from baseline  = 3.22, p = 0.003, BF = 12.10). These results are likely due to the active nature of reactive stopping which is likely more hemodynamically demanding than implementing slower proactive control. Additionally the lack of differentiation can also be attributed to there only being two structures (GPe and SN) which are involved in the indirect and not hyperdirect pathways, whereas all other structures are expected to respond in the same way for both pathways  sub-units of activity supporting inhibitory control.pars opercularis , with the more rostral, pars triangularis, receptive to ambiguous responding  and independently of covariance relationships, and the temporal dynamics of interrelations between structures were not taken into account in these analyses. Even so, our data indicates interdependencies, such as upstream relationships (from THAL to STR), not described by the classic models, and suggest areas for future investigation. Similarly, mutual interdependent effects were also found under conditions of response execution between left GPe and left THAL. Such mutual interaction between regions hints at the presence of continual feedback loops .This study was supported by Leah Maizey: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition. C. John Evans: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - review & editing, Supervision. Nils Muhlert: Conceptualization, Methodology, Software, Validation, Resources, Writing - review & editing, Visualization. Frederick Verbruggen: Conceptualization, Methodology, Software, Resources, Writing - review & editing, Supervision, Funding acquisition. Christopher D. Chambers: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Christopher P.G. Allen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition.The authors have no competing interests.


Positron emission tomography (PET) is a molecular imaging technique that can be used to investigate the in vivo pharmacology of drugs. Initial preclinical evaluation of PET tracers is often conducted in rodents due to the accessibility of disease models as well as economic considerations. Compared to larger species, rodents display a higher expression and/or activity of efflux transporters such as the P-glycoprotein (P-gp). Low brain uptake could, therefore, be species-specific and uptake in rodents not be predictive for that in humans. We hypothesized that a better prediction from rodent data could be achieved when a tracer is evaluated under P-gp inhibition. Consequently, we compared the performance of eight neuroreceptor tracers in rats with and without P-gp inhibition including a specific binding blockade. This data set was then used to predict the binding of these eight tracers in pigs.2A receptors , serotonin 5-HT7 receptors  and dopamine D2/3 receptors  were used in the study. The brain uptake and target-specific binding of these PET radiotracers were evaluated in rats with and without inhibition of P-gp. Rat data were subsequently compared to the results obtained in pigs.PET tracers targeting serotonin 5-HTWithout P-gp inhibition, the amount of target-specific binding in the rat brain was sufficient to justify further translation for three out of eight evaluated tracers. With P-gp inhibition, results for five out of eight tracers justified further translation. The performance in pigs could correctly be predicted for six out of eight tracers when rat data obtained under P-gp inhibition were used, compared to four out of eight tracers without P-gp inhibition.P-gp strongly affects the uptake of PET tracers in rodents, but false prediction outcomes can be reduced by evaluating a tracer under P-gp inhibition. Positron emission tomography (PET) is a powerful molecular imaging technique that has earned an established position in the clinic as a diagnostic tool. It provides a combination of high sensitivity, tissue penetration and quantitativity that is unmatched by any other molecular imaging technique . Apart fDuring preclinical development of novel PET tracers, it is important to use animals that represent human physiology as closely as possible. Rodents are usually tested first, for a number of reasons including lower costs compared to larger animals and the availability of disease models . Compare11C]GR205171 and [18F]Altanserin, was three–sixfold lower in rats than in humans, which is unsatisfactorily low [18F]Altanserin in the minipig brain was 3.8-fold higher than in the rat brain, apparently due to a lower P-gp activity in pigs. Detecting brain uptake of this magnitude during initial evaluation of a tracer would warrant further translation to humans.We have previously demonstrated that the brain uptake of two PET tracers used for brain receptor imaging, Altanserin, [11C]Cimbi-36, [11C]Pimavanserin and [18F]MH.MZ, serotonin 5-HT7 receptor tracers [11C]Cimbi-701, [11C]Cimbi-717 and [11C]BA-10 and dopamine D2/3 receptor tracer [18F]Fallypride were studied.Eight PET tracers for three different receptor systems were used in this study. Serotonin 5-HTResults of the tracer evaluation via both the traditional workflow (baseline and target block conditions) and the new workflow  were used to predict the performance of the same tracer in pigs. The prediction algorithm is illustrated in Additional file 18F]Altanserin, [11C]Cimbi-36, [11C]Pimavanserin, [18F]MH.MZ, [18F]Fallypride, [11C]Cimbi-701, [11C]Cimbi-717 and [11C]BA-10 were synthesized as previously reported [[reported –19. Chem18F]Altanserin) female rats of 200–300 g (8–10 weeks)  were housed in groups of 2–3 animals per cage in a climate-controlled rodent facility with a 12-h light/dark cycle. The animals had free access to water and were fed ad libitum. All procedures were conducted in accordance with the European Commission’s Directive 2010/63/EU, FELASA and ARRIVE guidelines for animal research and, with approval from The Danish Council for Animal Ethics  as well as the Department of Experimental Medicine, University of Copenhagen.Long-Evans WT  or 90 min (18F), followed by a transmission scan at speed 10 (acquisition time approximately 6 min). The animals were scanned at baseline (no pre-treatment before tracer injection) and after receiving P-gp inhibition with or without target block. Details of inhibition and blocking regimens are described in Table 7 receptor antagonist SB-269970 , the sigma and dopamine D2/3 receptor antagonist haloperidol  or the 5-HT2A receptor antagonist ketanserin . Elacridar was given 30 min prior to tracer injection through the intravenous catheter, and receptor blocking drugs were given 15 min before tracer injection. Chosen dosages and pre-treatment intervals were based on literature data . Th. Th11]. ure data .Table 1T18F]Altanserin were performed in a MicroPET Focus 120 scanner . Hyponorm/midazolam  was used as anaesthesia. The rats received 11 ± 2 MBq of the tracer as a bolus injection. Three of the rats were administered with 22.5 mg/kg bolus of cyclosporin A  followed by a constant infusion of 7.5 mg/kg/h, starting 20–25 min before radiotracer administration, as previously described [Studies with [escribed .18F]Altanserin), 60-min list-mode PET data were transformed into 33 dynamic frames , while 90-min list-mode PET data were transformed into 35 dynamic frames . Attenuation maps were reconstructed from transmission scans using maximum a posteriori algorithm for transmission data (MAP-TR) with human head (HH) segmentation/thresholding scans [18F]Altanserin scans were performed on the Focus 120 camera. Ninety-minute list-mode PET data were transformed into 25 dynamic frames  and reconstructed using the filtered back-projection method. Reconstructed PET image frames consisted of 95 planes of 128 × 128 voxels of 0.87 × 0.87 × 0.80 mm.For HRRT scans , AUC values of 18F-labelled tracers were calculated for both the full duration and the first 60 min of the scan.For data analysis, the software PMOD 3.7  was used. Summed PET images were generated based on all counts recorded in the time intervals specified in Table  tracers , thalamu tracers  and strir tracer . CerebelApparent target-specific binding of the tracers in the target regions was expressed as specific binding ratios (SBR), which were calculated from mean full scan length AUC values applying Eq. Evaluation of a tracer was considered successful if (whether with or without P-gp inhibition) the tracer showed an SBR value of at least 0.15 (15% higher uptake in the target region relative to reference region), and this SBR value decreased by at least 30% under target block condition.2A receptor tracers investigated, only [18F]MH.MZ showed substantial uptake in the rat brain at baseline: AUC for the target receptor-rich mPFC was 60 ± 5 min × g/mL . Under P-gp inhibition with elacridar, the AUC for the mPFC rose to 147 min × g/mL (+ 143%), and the SBR value for the mPFC reached 1.04. Combining target blockade with P-gp inhibition decreased the SBR to 0.04 (− 96%). Thus, the 5-HT2A receptor-specific binding and the blocking of it were clearly detectable both with and without P-gp inhibition for [18F]MH.MZ  in the range of 23–36 min × g/mL , while SBR values for [18F]Altanserin and [11C]Pimavanserin actually grew to 0.89 (+ 172%) and 0.04 (+ 110%), respectively. Thus, for these three tracers, blockable target-specific binding could only be detected for [11C]Cimbi-36.In contrast, 8FAltanse18F]Altanserin, [11C]Pimavanserin and [11C]Cimbi-36 increased to 53 min × g/mL (+ 93%), 180 min × g/mL (+ 689%) and 174 min × g/mL (+ 391%), while SBR values reached 0.56, 0.03 and 0.65, respectively. Ketanserin blockade combined with P-gp inhibition resulted in SBR values of 0.32 (− 42%) for [18F]Altanserin and 0.14 (− 79%) for [11C]Cimbi-36. For [11C]Pimavanserin, AUC in the cerebellum after combined ketanserin and elacridar pre-treatment was greater than AUC in the mPFC, resulting in a negative SBR value of –0.10. However, a change in SBR from 0.03 to –0.10, although high in relative terms (-395%), can hardly be interpreted as a sign of target-specific binding because both values are very low.After P-gp inhibition, mPFC AUC values for [18F]Altanserin, 5-HT2A receptor-specific binding and the blocking effect became observable after P-gp inhibition, for [11C]Cimbi-36 the use of P-gp inhibition led to an amplification of both apparent specific binding at baseline and the blocking effect, while [11C]Pimavanserin did not show any sign of specific 5-HT2A receptor binding either with or without P-gp inhibition . Thalamic AUC values reached 62 min × g/mL (+ 47%) for [11C]Cimbi-717, 142 min × g/mL (+ 277%) for [11C]Cimbi-701 and 65 min × g/mL (+ 121%) for [11C]BA-10 after P-gp inhibition with elacridar. Respective SBR values were 0.14, 0.18 and 0.16. Simultaneous inhibition of P-gp and blocking of the 5-HT7 receptor decreased [11C]BA-10 SBR to 0.09 (–44%), while SBRs for [11C]Cimbi-717 and [11C]Cimbi-701 actually increased to 0.21 (+ 46%) and 0.22 (+ 23%), respectively. In summary, specific binding could only be detected for [11C]BA-10 after P-gp inhibition.Among tracers targeting 5-HT1C]BA-10 , 17, 27.2/3 receptor ligand [18F]Fallypride was included into the study as a non-serotonergic tracer. Baseline striatal AUC equalled 95 min × g/mL. Striatal SBR was 3.11 at baseline and fell to 0.01 (− 100%) after haloperidol pre-treatment  block decreased striatal SBR to 0.22 (− 95%). Therefore, target-specific binding was clearly visible both without and with P-gp inhibition.The dopamine Dr ligand 8FFallypr18F]MH.MZ, [18F]Altanserin, [11C]Pimavanserin, [11C]Cimbi-36, [11C]Cimbi-717 and [11C]Cimbi-701 in pigs was reported previously [11C]BA-10 and [18F]Fallypride in pig brain are presented in Additional file Evaluation of Cimbi-717 (47%) and [18F]Altanserin (93%), while for all other tracers, AUC values increased by 100–700%. Brain uptake of [18F]Altanserin was previously reported to increase 2.6-fold (by 160%) in response to P-gp inhibition MH.MZ, [11C]Cimbi-36 and [18F]Fallypride showed preferential accumulation  in target-rich regions and more than 30% reduction in SBR values after a selective target block  at baseline without P-gp inhibition, but ketanserin pre-treatment led to a drastic decrease of tracer uptake in both frontal cortex and cerebellum, with uptake in the cerebellum falling even more drastically  showed a pronounced reduction of apparent specific binding in target-rich regions (> 30% SBR decrease) in response to target block under these conditions. These included [18F]Altanserin and [11C]BA-10, for which no such reduction could be demonstrated using the traditional workflow. For [11C]Pimavanserin, [11C]Cimbi-717 and [11C]Cimbi-701, no receptor-specific binding could be detected under P-gp inhibition: target region SBR values were either too low at baseline . Table ND) of the tracer in the target region (by 30% or more). For all tracers except [11C]Cimbi-701 and [11C]Cimbi-717, which had shown specific binding in pigs but not in rats, baseline BPND values in pigs highly correlated with baseline SBR values in rats, both with and without P-gp inhibition  and without additional experiments in non-rodent species, the remaining tracers would likely have been discarded. The use of P-gp inhibition helped to additionally identify two tracers ([18F]Altanserin and [11C]BA-10) that showed specific binding. [11C]Pimavanserin did not show any specific binding either with or without P-gp inhibition in rats, and behaved in the same way in pigs. Agreement between pig and rat data on [11C]Pimavanserin is a positive finding within the framework of this study, even though it may be perceived as disappointing that a ligand with demonstrated sub-nanomolar affinity and high selectivity towards 5-HT2A receptors [11C]Cimbi-701 and [11C]Cimbi-717, on the other hand, demonstrate the limitations of brain radioligand evaluation in rodents: even with the use of P-gp inhibition, these tracers would have been discarded based on rodent data only. Apart from efflux transporter expression and activity, inter-species differences in receptor abundances or metabolic pathways are factors that may have a crucial impact on the translational validation of PET tracers Cimbi-701 and [11C]Cimbi-717 evaluation in these two species.In our study, blocking experiments in rats without P-gp inhibition detected specific binding for three tracers , even though all tracers were strong substrates of P-gp. A traditional evaluation workflow without P-gp inhibition could only predict the outcome for four out of eight tracers (50% success rate). Our results demonstrate how addition of P-gp inhibition can aid in the interpretation of initial tracer evaluation in rats, improve the translatability and minimize unjustified discontinuations of promising tracers. We believe that the proposed workflow allows for a more effective initial in vivo screening using rats.Additional file 1. Detailed overview of the experimental design, description of experiments in pigs, chemical structures of all studied compounds, investigated brain regions on the MRI template, time–activity curves for rat and pig  scans, SBR and AUC values for all tracers, numbers of rats scanned per experimental condition are detailed in the Additional file.


Molecular dynamics and docking simulations predicted the potential binding sites and the binding mode of the three pesticides on the structure of the two key targets, providing a rational for their mechanism as EDCs. The results demonstrate that the three pesticides are potential EDCs as glyphosate acts as an aromatase inhibitor, whereas imidacloprid and thiacloprid can interfere with estrogen induced signaling.The intensive use of pesticides has led to their increasing presence in water, soil, and agricultural products. Mounting evidence indicates that some pesticides may be endocrine disrupting chemicals (EDCs), being therefore harmful for the human health and the environment. In this study, three pesticides, glyphosate, thiacloprid, and imidacloprid, were tested for their ability to interfere with estrogen biosynthesis and/or signaling, to evaluate their potential action as EDCs. Among the tested compounds, only glyphosate inhibited aromatase activity (up to 30%) via a non-competitive inhibition or a mixed inhibition mechanism depending on the concentration applied. Then, the ability of the three pesticides to induce an estrogenic activity was tested in MELN cells. When compared to 17β-estradiol, thiacloprid and imidacloprid induced an estrogenic activity at the highest concentrations tested with a relative potency of 5.4 × 10 Under the modern lifestyle, humans are exposed to various chemicals such as pesticide residuals in fruits and vegetables, antibiotics in meat and milk, preservatives in cosmetics and personal care products ,2. TheseThe modern industrial and agricultural system relies heavily on pesticides. The compelling need of high food crop increasingly demands the use of chemicals. This results in the extensive applications of millions of tons of pesticides every year ,10. AmonThe intensive use of glyphosate-based herbicides and neonicotinoid insecticides has caused the contamination of soil, water, air, and agricultural products ,26,27,28Aiming at establishing the interference of pollutants with human health, we monitored the effect of glyphosate and two neonicotinoids on two critical targets of the endocrine system: Aromatase, the enzyme responsible for estrogen biosynthesis, and estrogen receptor (ER) alpha, the main protein promoting estrogen signaling.Aromatase catalyzes the transformation of androgens to estrogens . In vitrHere, a recombinant form of human aromatase is used to test the possible effect of the three pesticides on estrogen biosynthesis, gaining new information at molecular level ,40,41. MAt a cellular level, the estrogen-regulated signaling is mainly due to estrogen receptors (ERs). The main ERs are the ERα and ERβ nuclear receptors and the G-protein-coupled estrogen receptor 1, which is a membrane receptor . EstrogePrevious studies have investigated the ability of glyphosate to induce an estrogenic activity mediated by ERs. The results of the study of Thongprakaisang et al., 2013  demonstrIn this work, in order to increase the knowledge on the estrogenic activity of glyphosate, imidacloprid, and thiacloprid, the gene reporter assay on estrogen-sensitive human breast cancer MCF-7 cells transfected with the ERE-βGlob-Luc-SVNeo plasmid (MELN cells) is applied to test the three pesticides for their possible ER-mediated estrogenic activity. Possible additive and/or antagonist effects are also investigated. Moreover, docking simulations provide atomic level insights on the potential binding mode of these molecules to the primary ligand (estrogen) binding site as well as to a peripheral allosteric site which may be responsible for the experimental observed additive effect of the pollutant with the endogenous ligand.All reagents are analytically pure by purchase from Sigma-Aldrich . Stock solutions of chemical compounds were prepared in absolute ethanol or dimethyl sulfoxide (DMSO). Before each experiment, the test sample was diluted into a fresh buffer solution, and the final organic solvent concentration was less than 0.1%. The recombinant human aromatase (Aro) and the human recombinant cytochrome P450 reductase (hCPR) were expressed and purified as previously described ,49.An estrone direct competitive ELISA kit  was used to evaluate the effect of pesticides on aromatase activity. Different reaction mixtures were set up by mixing 5 nM Aro, 5 nM hCPR, 0.5 mM NADPH, 50 nM androstenedione, and three concentrations of pesticides  in a 100 mM potassium buffer (KPi) containing 20% glycerol, 1 mM β-mercaptoethanol at pH 7.0. Reactions were carried out for 10 min at 30 °C, heat-inactivated for 10 min at 90 °C, and centrifuged for 10 min at 11,000 rpm. After centrifugation, the supernatant was diluted 1:8 in the Calibrator A provided by the ELISA kit and the product estrone quantified performing ELISA according to the manufacturer’s instructions. Reactions in the presence of anastrozole or without hCPR were used as negative controls. The concentration of estrone was extrapolated from a calibration curve with known concentrations of estrone.For the experiment where the catalytic parameters were derived, four substrate concentrations were applied in the reaction mixture (ranging from 25 to 250 nM) in the absence and presence of 1000 and 5000 nM of glyphosate.In order to explain the molecular terms for the action of glyphosate on the aromatase enzyme we docked it into the two possible allosteric sites previously identified . DockingIn order to account for its flexibility of the receptor and since is flexibility resulted to be of paramount importance for the identification of novel allosteric inhibitors , we perfThe Parm99SB AMBER force field (FF) ,60 and lThe Shahrokh et al. parameters were used for the heme moiety and Cys437 . SimulatThe system was then explicitly solvated using the TIP3P water model, leading to a total of 131,454 atoms. Topology, built with AmberTools 18, was later converted in a GROMACS format using the acpype algorithm . MD simu−1 nm−2, reaching a constant value (92 × 92 × 151 Å3) of the simulation box size. Constraints were then slowly released, and the system was thermalized to the target temperature of 300 K in about 10 ns. Then, the aromatase in complex with glyphosate was relaxed by performing a 100 ns MD simulation rescaling the motion of the center of mass of aromatase and the ligand, followed by an unbiased 100 ns MD simulation.An initial equilibration of the membrane was performed for 100 ns with the protein atoms harmonically restrained with a force constant of 1000 kJ molβGlob-Luc-SVNeo)  = 4.79) and 10−6 M (Log[nM] = 3), respectively . Since both the neonicotinoid pesticides induced a dose-dependent increase of the relative luciferase activity, in particular from 10−4 M (Log[nM] = 5) to the highest tested dose  and 1.5 × 10−3 M (IC 95% 2.5 × 10−4–3.6 × 10−2 M), respectively, while the EEF was 5.4 × 10−10 (IC 95% 3.3 × 10−9–2.5 × 10−11) and 3.7 × 10−9 (IC 95% 2.2 × 10−8–1.5 × 10−10), respectively.The estrogenic activity of imidacloprid and thiacloprid was also quantitatively evaluated by the estimate of the concentrations of E2 and pesticides at which 50% of biological effect is achieved (EC50) and the E2 equivalency factor (EEF). The EC50 of E2 and pesticides was calculated by dose-response curves whereas the EEF was calculated through the formula: EEF = E2 EC50/pesticide EC50. The EC50 of imidacloprid and thiacloprid was 1.0 × 10The exposure of cells to imidacloprid and thiacloprid in combination with tamoxifen confirmed that the estrogenic activity of the two pesticides was induced by the activation of ER. Indeed, the wells treated with the neonicotinoid pesticides and tamoxifen showed a relative luciferase activity that was lower compared to the wells treated with these pesticides alone. Furthermore, the relative luciferase activity of wells treated with the neonicotinoid pesticides and tamoxifen was similar to the relative luciferase activity measured in the negative control A,C.Finally, the exposure of cells to imidacloprid and thiacloprid in combination with E2 induced an increase of the relative luciferase activity with respect to the E2 alone B,D. The In order to provide a rationale for the estrogenic activity exerted by the two neonicotinoids, thiacloprid and imidacloprid, we have performed docking calculations on ERα. First, the two molecules were docked into the ERαs estrogen binding site, using the crystal structure of 17-β-estradiol bound to the ERα dimer (PDB id: 1qku) . Both neNext, in order to disclose if and how the two neonicotinoids exert an additive effect to estrogen binding, by occupying an allosteric cavity, we looked for the presence of druggable allosteric pockets in the protein. Interestingly, a high-ranking binding pocket was found in the proximity of the estrogen binding site of one monomer of ERα . The docIn this study, the effect of three pesticides on two key targets of the endocrine system was evaluated using a combination of experimental and in silico methods that allowed an investigation at the molecular level. The first target considered was aromatase, the key enzyme for estrogen production that resulted in being partially inhibited by glyphosate. The increase obtained in the aromatase activity in the presence of imidacloprid and thiacloprid, even if not statistically significant, can be interesting to be further investigated. Indeed, these two compounds could directly act on aromatase as allosteric activators or they could exert their action indirectly on CPR that has an essential role in catalysis and acts as an effector on human aromatase conformation ,81.Glyphosate is the most widely used active compound among herbicides and was already reported to affect aromatase expression and activity in cells ,35. The However, the direct interaction between glyphosate and the enzyme together with the inhibition mechanism was not reported yet. In this study, the ELISA assay revealed that the inhibition of aromatase by glyphosate is partial and weak and strongly depends on the substrate concentration. Moreover, at the lower concentration of glyphosate tested (1 μM), the inhibition was found to be non-competitive, while at the higher concentration used (5 μM), the inhibition turned into a mixed inhibition mode. These data suggest that glyphosate binds to an allosteric site both when the enzyme is free and in complex with the substrate. Classical MD simulations supply a structural model for glyphosate binding to aromatase, explaining at the atomic-level that this pollutant may exert its inhibitory activity at a low concentration by binding to the allosteric Site 1 previously identified by Magistrato et al.  and demoThe second important target studied was the estrogen receptor that is responsible for estrogen binding and signal transduction in cells. MCF-7 cells, stably transfected with an estrogen-regulated luciferase gene (MELN cells), were used to assess the estrogenic activity of the pesticides. Glyphosate induced no estrogenic activity on MELN cells, moreover glyphosate did not change the effect induced by tamoxifen or E2. These results are in accordance with the results of the studies of Kojima et al., 2004  and Gasn−4 M. A consistent result was found in the study of Kojima et al., 2004 [−5 M; a contradicting result was found by Westlund and Yargeau, 2017 [In the present study, both imidacloprid and thiacloprid induced a dose-response estrogenic activity mediated by the ER activation, in particular starting from 10l., 2004  in whichau, 2017 , who didau, 2017 . Indeed,au, 2017 , as repoau, 2017 .The estrogenic activity of imidacloprid and thiacloprid was observed at concentrations higher than the pesticide levels measured in human biological samples ,84. AlthInterestingly, the results of the present study showed that, when the ER-antagonist tamoxifen was added to these pesticides, the estrogenic activity was still higher than the negative control. This result can be explained by the competition between each pesticide and tamoxifen for ER binding. Moreover, in the presence of high concentrations of imidacloprid and thiacloprid, an additive effect with E2 was also observed. The docking calculation suggested that these two neonicotinoids may bind to both the orthosteric and allosteric pockets of ERα, suggesting a putative mechanism to rationalize their observed estrogenic activity.In conclusion, this study provides further evidence about the action of some pesticides as endocrine disrupting chemicals (EDCs) targeting important proteins of the endocrine system. In particular, it shows that the inhibitory effects of the three compounds tested on aromatase are partial and their estrogenic effects occur at relatively high concentrations. However, possible additive estrogenic effects with the physiological hormone 17-βestradiol are present. Furthermore, it has to be taken into account that pesticides are usually introduced in the environment with their co-formulants that can be also biologically active as EDCs. Previous studies already showed that aromatase is inhibited by co-formulants of glyphosate-based herbicides ,86. MoreOur study also provides an integrated approach based on different assays and computational methods that allowed gaining new information about the possible interaction of pesticides and key targets at a molecular level. Such information can be exploited to predict the possible impact of other compounds on estrogen production and signaling in order to develop safer compounds for human health and environment.


This provides a unique route towards colloids capable of modulating ice growth.Chemical tools to modulate ice formation/growth have great (bio)-technological value, with ice binding/antifreeze proteins being exciting targets for biomimetic materials. Here we introduce polymer nanomaterials that are potent inhibitors of ice recrystallisation using polymerisation-induced self-assembly (PISA), employing a poly graft macromolecular chain transfer agent (macro-CTA). Crucially, engineering the core-forming block with diacetone acrylamide enabled PISA to be conducted in saline, whereas poly(2-hydroxypropyl methacrylate) cores led to coagulation. The most active particles inhibited ice growth as low as 0.5 mg mL IRI-active materials have been found to improve post-thaw recovery of cryopreserved cells,21 proteins22 and bacteria.23 It is still not possible to rationally design new IRIs, and in particular generating IRI-active polymer colloids/nanomaterials is challenging. Immobilisation of PVA onto gold particles by ‘grafting to’ retains, but does not enhance, activity24 as do coacervates.25 Similarly, assembly of Type III AFPs into dendrimers26 or on gold particles27 allows binding of multiple ice faces, but affords no overall increase in activity. This is surprising as AFP/IBPs typically show more activity as molecular weight or aggregation increases29 and theory predicts that the rate of ice nucleation requires large protein aggregates to function.30 Graphene oxide nanosheets also show strong particle size effects on ice formation.31 How large particles can bind and inhibit ice is not clear and if the design rules are related to that of protein/polymers is unknown. There is a clear opportunity to develop nanomaterials containing ice binding macromolecules with controlled presentation and architecture.Ice formation and growth presents major challenges in the maintenance of infrastructure,32 The versatility of this method has allowed the synthesis of nano-objects for drug delivery,34 cell storage,36 permeable nano-reactors,37,38 and has been extensively reviewed.42 A challenge with many PISA formulations is the need to conduct PISA in biological media, which invariably contain salts. Armes and co-workers engineered stable nanoparticles by changing the steric stabilizer block to poly(sulfobetaines), to enhance solubility in the presence of salt.43 Cationic diblock spherical copolymer nanoparticles have also shown surprisingly strong aggregation resistance in electrolyte solutions.44 However, if PISA is to be used to design nanoparticles to interface and modulate the growth of ice, the corona-forming block is limited to polymers which can bind ice (e.g. PVA).Polymerisation-induced self-assembly (PISA) has emerged as a powerful tool to generate multivalent polymeric nanomaterials of controlled morphology and size at high solid contents.Taking into account the above, we here address the challenges of (i) conducting PISA in saline without restricting the corona-forming block, and (ii) introducing ice recrystallisation inhibition activity into polymer particles. This is achieved using diacetone acrylamide, rather than the common 2-hydroxypropyl methacrylate coreforming monomer, with a poly based macrograft-RAFT agent. The resulting particles are potent IRI agents and present a unique approach to employ self-assembled colloidal dispersions for IRI.45 Commercially available PVA (10 kDa/DP = 181) was selected which was grafted with 4-cyano-4-[(ethylsulfanylthiocarbonyl)sulfanyl]pentanoic acid CTA (CEPA), by DCC coupling to afford a water soluble PVA-based graft macro-CTA. The macro-CTA was thoroughly dialysed to remove unfunctionalised RAFT agent. Due to overlapping signals, quantification of the grafting density by 1H NMR spectroscopy was not possible. Thermogravimetric analysis (TGA) showed that a ratio of four CEPA molecules per a hundred hydroxyl groups were present, meaning that approximately seven CTA groups were grafted per PVA chain  was selected as the corona-forming block, as it is the most active polymeric IRI agent known.VA chain . SEC anao PVA181 .46 In water, monomer conversions above 95%  and stable PISA dispersions were obtained when targeting PHPMA degrees of polymerisation of 50–300. Dynamic light scattering revealed nanoparticles ranging in diameter from 200–400 nm as a function of PHPMA chain length. Dry-state TEM (181-g7-PHPMA50 (51 but further investigation is beyond the scope of this work. Attempts at dispersing these particles into NaCl (0.1 to 0.01 M) led to macroscopic precipitation. Alternatively, PISA was attempted directly in saline solutions, but no stable particles could be obtained. Therefore, this formulation, despite containing the desired PVA corona, could not be used for IRI activity measurements.45 The intolerance of many monomers used in PISA to saline solutions, specifically their inability to self-assemble and tendency to precipitate in such polar media, is a common challenge with PISA formulations and therefore saline-free buffers are often used.38The most widely used core-forming monomer for PISA, 2-hydroxypropyl methacrylate (HPMA), was used for thermally-initiated dispersion polymerisations at 60 °C (10% w/w) in water, and later in 0.01 M saline. NaCl is essential for IRI activity testing and lack of saline dispersibility would prevent assessment of activity.tate TEM  revealed-PHPMA50 . We hypoIt is not possible to change the PVA corona for another polymer as the PVA is the crucial ice-binding unit. To overcome the saline instability challenge, we therefore reasoned that tuning the core-forming block may enable saline-tolerant PISA. Diacetone acrylamide (DAAm) was investigated as an alternative. In contrast to HPMA, the DAAm reactions (using the same polymerisation conditions as above) led to stable nanoparticle dispersions in both water and in 0.01 M NaCl, with high conversions achieved (>97%). It should be noted that we conducted the screening of PISA in saline, to ensure we selected stable particles, and to avoid problems due to osmotic pressures upon changing [NaCl], but we anticipate that aqueous synthesis followed by addition of saline could also be used. DLS and TEM analysis  confirme13,46 It is important to note that this assay requires saline to create channels between ice crystals, enabling recrystallisation to readily occur and hence avoid false positive results. For this reason the importance of our saline-stabilised PISA system cannot be understated. Polynucleated ice wafers were obtained from 10 μL droplets at –80 °C, then annealed at –8 °C for 30 minutes before being photographed and the average crystal size determined. This method separates nucleation from growth, and lower values of mean grain size (MGS) indicates higher activity. The IRI activity of particles from –1, and in some cases as low as 0.5 mg mL–1, placing them in the highly to very highly active range of antifreeze-protein mimetics.45 To enable comparison of multivalent effects (as the particles have different sizes) –1 PVA181-g7-PDAAm50 showed IRI activity (MGS 40%) but as the core block length increased to PVA181-g7-PDAAm300 there was an increase with larger particles inhibiting growth at 0.1 mg mL–1 PVA. This activity, in terms of [PVA] was greater than the macro-CTA alone, implying a multivalent enhancement. Previous reports of PVA and antifreeze proteins grafted to particles or dendrimers27 gave no relative enhancement. We hypothesise that the dense packing of polymer chains in a PISA particle, compared to those obtained by ‘grafting to’ is crucial to enhance the IRI activity. It is important to note that the macro-CTA had lower IRI activity than pure PVA homopolymer. We used commercial PVA (~80% hydrolysis), which along with the grafted RAFT agent, breaks up the consecutive hydroxyl group sequence, which has been shown to be crucial for PVA IRI.13,52,53To evaluate the ice recrystallisation inhibition activity, the ‘splat’ assay was used.54 To further demonstrate the IRI activity, an alternative assay was conducted in 45 wt% sucrose (‘sucrose sandwich’).55–1 of PVA181-g7-PDAAm300 display complete inhibition of ice recrystallisation, demonstrating activity in a range of different formulations. Finally, ice morphology analysis was conducted. In these assays the temperature is varied to encourage crystal growth and to visualise any morphology changes. PVA is known to bind prismatic faces and hence produces faceting  available: This includes full synthetic methods and additional characterisation.Supporting Information


SPARK is both a deductive verification tool for the Ada language and the subset of Ada on which it operates. In this paper, we present a recent extension of the SPARK language and toolset to support pointers. This extension is based on an ownership policy inspired by Rust to enforce non-aliasing through a move semantics of assignment. In particular, we consider pointer-based recursive data structures, and discuss how they are supported in SPARK. We explain how iteration over these structures can be handled using a restricted form of aliasing called local borrowing. To avoid introducing a memory model and to stay in the first-order logic background of SPARK, the relation between the iterator and the underlying structure is encoded as a predicate which is maintained throughout the program control flow. Special first-order contracts, called pledges, can be used to describe this relation. Finally, we give examples of programs that can be verified using this framework. The programming language SPARK  has beenHowever, it was unclear if programs traversing recursive data structures such as lists and trees could be supported in this setting. In particular, iteration using a loop requires an alias between the traversed structure and the iterator. In this paper, we detail an approach, inspired by recent work by Astrauskas et al. , that en Ada 2012 introduced contract based programming to Ada. In particular, it is possible to attach pre- and postconditions to subprogramsAda  is a genSPARK is the name of a tool that provides formal verification for Ada. It uses the user-provided contracts and attempts to prove that the runtime checks cannot fail and that postconditions are established by the corresponding subprograms. As formal verification for the whole Ada language would be intractable, SPARK is also the name of the subset of the Ada language that is supported by the SPARK toolThe SPARK tool uses the Why3 platform to generate verification conditions for SMT solvers via a weakest-precondition calculus .access types. It is possible to declare an access type using the access keyword. Objects of an access type are null if no initial values are supplied. It is possible to allocate an object on the heap using the keyword new. An initial value can be supplied for the allocated object. A dereference of a pointer is written as a record component access, but using the keyword all. When a pointer is dereferenced, a runtime check is introduced to make sure that it is not null. Ada does not mandate garbage collection. Memory allocated on the heap can be reclaimed manually by the user using a generic function named Unchecked_Deallocation, which also sets its argument pointer to null. There are several kinds of access types. The basic access types, like Int_Acc defined above, are called pool specific access types. They can only designate objects allocated on the heap. General access types, introduced by the keyword all, can also be used to designate objects allocated on the stack or global data.Pointers in Ada are called Pointers were excluded from the SPARK subset until recently. Indeed, allowing pointers in a straightforward way would break the absence of aliasing in SPARK. In addition, pointers are associated with a list of classes of bugs such as memory leaks, use-after-free and dereferencing a null-pointer.To support pointers in SPARK, we designed a subset of Ada’s access types which does not introduce aliasing and avoids some pointer-specific issues, while retaining as much expressivity as possible. The first restriction we selected is the exclusion of general access types. This means that SPARK can only create pointers designating memory allocated on the heap, and not on the stack. As a result, pointers can only be made invalid by explicit deallocation, and deallocation of a valid pointer is always legal. To eliminate aliasing between (heap) pointers, ownership rules inspired by Rust have been added on top of Ada’s legality rules. These rules enforce a single writer/multiple readers policy. They ensure that, when a value designated by a pointer is modified, all other objects can be considered to be preserved.moved. The source of the assignment loses the ownership of the designated data while the target of the assignment gains it. The ownership system makes sure that the designated data is not accessed again through the source of the assignment.  As the ownership policy ensures that no aliasing can occur between access objects, it is possible to reason about the program almost as if the pointer was replaced by the data it points to. When an object containing a pointer is assigned to another variable, it is safe to consider that the designated data is copied by the assignment. Indeed, any effects that could occur because variables are sharing a substructure cannot be observed because of the ownership rules.The basis of the ownership policy of SPARK is the move semantics of assignments. When a pointer is assigned to a variable, both the source and the target of the assignment designate the same memory region: assigning an object containing a pointer creates an alias. To alleviate this problem, when an object containing a pointer is assigned, the memory region designated by the pointer is said to be maybe, or option types: access objects are either null, or they contain a value. In addition, access objects also contain an address, which can be used to handle comparison . When a pointer is dereferenced, a verification condition is generated to make sure that the pointer is not null, so that its value can be accessed.  Note that the ownership policy is key for this translation to be correct, as it prevents the program from observing side-effects caused by the modification of a shared reference, which would not be accounted for in the verification model.Pointers are handled in the verification model of the SPARK proof tool as incomplete declaration, introduces a place-holder for the type, which can only be used in restricted circumstances. In particular, this place-holder can be used to declare an access type designating pointers to values of this type. Using this mechanism, it is possible to declare a recursive data structure, since the access type can be used in the type definition as it comes afterward.  There are no specific restrictions concerning recursive types in SPARK. However, the ownership policy of SPARK implies that it will not be possible to create a structure which has either cycles (e.g. doubly linked lists) or shared substructures (e.g. DAGs) in it. The ownership policy may also impact how recursive structures can be manipulated. In general, working with such structures involves a traversal, which can be done either recursively, or iteratively using a loop. Algorithms working in a recursive way are generally compliant with the ownership policy of SPARK. Indeed, the recursive calls will allow reading or modifying the structure in depth without having to deconstruct it Algorithms involving loops are trickier. The declaration of the iterator used for the loop creates an alias of the traversed data structure. As per SPARK’s ownership policy, this is considered to be a move, so it makes it illegal to access the initial structure. Further assignments to the iterator during the traversal contribute to losing definitively one by one the ownership of every node in the structure, making it impossible to restore the ownership at the end.  To traverse recursive data structures, a move is not what we want. Here we need a way to lend the ownership of a memory region for a period of time and automatically restore it at the end. A similar mechanism, called borrowing, is available in the Rust language. We have adapted it to SPARK.In Ada, recursivity can only be introduced through pointers. The idea is to first declare a type, but without giving its definition. This declaration, called an anonymous access type is used for the borrower A borrower can be used to modify the underlying structure. This makes it effectively an alias of the borrowed object. To allow the tool to statically determine the cases of aliasing, SPARK restricts the initial value of a local borrower to be the name of a part of an existing object. This forbids for example borrowing one of two structures depending on a condition.As Ada is an imperative language, losing the possibility to traverse a linked data structure using a loop was deemed too restrictive. To alleviate this problem, a notion of ownership borrowing was introduced in SPARK. It allows the users to declare a variable, called a borrower, which is initialized with a reference to a part of an existing data structure. To state that this initialization should not be considered a move, an  Borrowing can be used to allow simple iterative traversals of a recursive data structure like the loop of Set_All_To_Zero. More complex traversals, involving stacks for example, cannot be written iteratively in SPARK.  Using reborrows, local borrowers allow one to indirectly modify a data structure at an arbitrarily-deep position, which may not be statically-known. While in the scope of the borrower, these indirect modifications can be ignored by the analysis, as the ownership policy makes them impossible to observe. However, after the end of the borrow, ownership is transferred back to the borrowed object, and SPARK needs to take into account whatever modifications may have occurred through the borrower.  To be able to reconstruct the borrowed object from the value of the borrower, we must track the relation between them. As this relation cannot be statically determined because of reborrows, SPARK handles it as an additional object in the program. This allows us to take advantage of the normal mechanism for handling value dependent control-flow in SPARK . The idea is the following. When a borrower is declared in Ada, we create two objects: the borrower itself, which is considered as a stand-alone structure, independent of the borrowed object, and a predicate. The predicate, which we call the borrow relation, encodes the most precise relation between the borrower and the borrowed object which does not depend on the actual value designated by the borrower. The value of the borrow relation is computed by the tool from the definition of the borrower, and is updated at each reborrow. Modifications of the underlying data structure don’t impact this relation. At the end of the borrow, the borrowed object is reconstructed using both the borrow relation and the current value of the borrower. It is possible to update a borrower to change the part of the object it designates (as opposed to modifying the designated object). This is called a reborrow. In SPARK, the value assigned to the borrower in a reborrow should be rooted at the borrower. This means that reborrows only go deeper into the structure. SPARK performs deductive verification, which relies on user-specified invariants to handle loops. When traversing a linked data structure, the loop body contains a reborrow, which means that the borrow relation is modified in the loop. As a general rule, if a variable is modified in a loop, it should be described in the loop invariant, lest nothing is known about its value afterward. Thus, we need a way to describe the borrow relation in the loop invariant.pledges [As part of their work on the Prusti proof tool for Rust, Astrauskas et al. found the need for a similar annotation that they call pledges . In Rustghost function  which has two parameters. The first one is used to identify the borrower on which the pledge should apply, while the second holds the assertion. Note that a call to a pledge function isn’t really a call for the SPARK analyzer. It is simply a marker that the expression in argument is a pledge.  When a pledge function is called in an assertion, SPARK recognizes it and identifies its parameter as a pledge. It therefore attempts to show that the property is implied by the borrow relation .  Using pledges, we can formally verify the Set_All_To_Zero procedure. Its postcondition states that all elements of the list have been set to 0 using the Nth function. To be able to express the loop invariant in a similar way, we have introduced a ghost variable C to count the number of iterations. Its value is maintained by the first loop invariant. The second and third invariants are pledges, describing how the value of X can be reconstructed from the value of the iterator Y. The second invariant gives the length of the list, while the third describes the value of its elements using the Nth function. Elements which have already been processed are frozen by the borrow. Their value is known to be 0. Other elements can be linked to the corresponding position in the iterator Y.  Note that, in general, it is not necessary to write a pledge to verify a program using a local borrower. Indeed, the analysis tool is able to precisely track the borrow relation through successive reborrows. Pledges need only be provided when the borrow relation itself cannot be tracked by the tool, for example because of a loop, like in our example.Since the Ada syntax has no support for pledges, we have resorted in SPARK to introducing special functions (dedicated to each access type) called pledge functions, which mark expressions which should be considered as pledge expressions by the tool. A pledge function is a We could not try the tool on any pre-existing benchmark since SPARK codebases do not have pointers, and Ada codebases usually violate some SPARK rules. In particular, Ada codebases have no reason to abide by the ownership policy of SPARK. So instead, we mostly had to write new tests to assess the correctness and performance of our implementation. The public testsuite of SPARK contains more than 150 tests mentioning access types, be they supported cases or not.To assess expressivity and provability on programs dealing with recursive data structures, we have written 6 examples, none of them very big, but ranging over various levels of complexityFigure Though these examples are small, we think they demonstrate that it is possible to define recursive data structures in SPARK, and to verify iterative programs using them. When writing the algorithms, we found that the limitations mostly come from the ownership policy of SPARK. Some data structures are not supported, requiring either to switch to full Ada for their implementations, or to change the algorithm to work around the missing links. In general, we found that the annotation effort required to describe the borrow relations, though non-negligible, was acceptable. In particular, it uses the standard SPARK expressions, with no mentions of memory separation or permission.typed memory model where different arrays are used for the basic types of C [Program verification tools for mainstream languages such as C or Java generally support aliasing, because the concept of pointer or reference is more central. They deal with it by modeling the heap. The WP plugin of Frama-C uses by default a es of C . The Veres of C  toolset es of C . In SPARThe ownership rules introduced in SPARK are largely inspired by the Rust language . The difThe Prusti verification tool for Rust  allows uIn a recent work , MatsushWe have presented a recent extension of the SPARK language and toolset to support pointers. It is based on an ownership policy enforcing non-aliasing. To support pointer-based recursive data structures, a restricted form of aliasing is introduced in SPARK through local borrowers, which can be used to iterate through a linked data structure in an imperative way. We have described how local borrowers can be supported by the verification tool, without introducing a memory model, by using a mutable predicate named the borrow relation. This borrow relation can be described when necessary using special annotations named pledges, which solely consist of SPARK standard expressions, and do not expose the underlying verification technique. Our work is available in the 20.1 release of SPARK Pro and will be part of the next community release.As for future work, we would like to extend the subset of Ada pointers supported in SPARK. In particular, we would like to introduce function pointers to model callbacks, pointers to constants with a more permissive ownership policy, and local borrowing of objects allocated on the stack.


Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects. As the cost of sequencing a human genome decreased dramatically, large-scale whole-genome sequencing projects were launched alongside the rising demand for precision medicine. Previous studies have stated that interethnic differences in drug response are substantial, and precision medicine relies on genotype-based prescribing decisions aimed at mitigating the risks and maximizing the efficacy of pharmacotherapy. Accordingly, national projects, such as those in the UK, United States, EU, Mexico, India, China, Sweden, Korea, and Taiwan biobank –8, have https://gnomad.broadinstitute.org/about) as part of population genetic studies. In 2016, the Google Brain team announced DeepVariant [To the best of our knowledge, the GATK –11 is onDeepVariant's superior accuracy comes at the cost of computational intensity, as it requires approximately two times longer wall-clock times than GATK in variant identification. DeepVariant is composed of three steps: (1) make_examples obtains BAM files and converts them to images, (2) call_variants performs the variant calling using the ML-trained model, and (3) post process_variants transforms the variant calling output into a standard VCF file. Following the authors' instructions, call_variants is the only step that can make use of GPUs to reduce the variant calling time by more than 50% . HoweverOur specific aim in constructing DeepVariant-on-Spark is to enable multi-GPU support and optimize resource allocation in the DeepVariant pipeline. DeepVaraint-on-Spark leverages the Apache Spark and Hadoop technologies to launch DeepVariant processes in parallel and ensure that we can fully utilize all GPU resources. To make DeepVariant-on-Spark more accessible to everyone, we have deployed DeepVariant-on-Spark onto the Google Cloud Platform (GCP). Users can follow our step-by-step instruction, and all necessary packages can be installed automatically by Ansible  within 2https://cloud.google.com/dataproc/) to manage services for running Apache Spark [https://cloud.google.com/storage/docs/gsutil_install). Cloud Dataproc provides a mechanism for automating cluster resource management, which enables flexible addition and subtraction of cluster nodes. After successfully launching the DataProc cluster, Ansible is used to automate the deployment of all necessary packages to the entire cluster, which includes DeepVariant [https://storage.cloud.google.com/sparkdv/performance-test/DeepVariant-On-Spark_Tutorial.html?hl=zh-TW).DeepVariant-on-Spark is designed as a cloud-based application, utilizing Google Cloud Dataproc , which was sequenced by an Illumina NovaSeq 6000 instrument with a 150 bp paired-end sequencing protocol. Reads were aligned to the human reference genome—Gencode GRCh38.p13 by bwa-mem. We deposited the aligned sequences in Google Storage as a BAM file (gs://sparkdv/performance-test/NA12878-novaseq.bam) and used Qualimap2 to evaluate the alignment quality of the obtained BAM file .Currently, DeepVariant only supports single GPU acceleration in variant calling, and we cannot obtain any benefit on machines with multiple GPUs. Therefore, DeepVariant-on-Spark was designed to leverage Apache Spark to launch multiple DeepVariant processes in parallel to address the scalability problem of variant calling. The input BAM file was first uploaded to the Hadoop Distributed File System (HDFS), followed by segmentation into several 1 Mbp data blocks. Then, we transformed the resulting data blocks into Apache Parquet file format (∗.parquet) to enhance the transfer performance through data compression. To overcome the load imbalance introduced by the uneven length of chromosomes, we aggregated those 1 Mbp data blocks into 155 approximately equal-sized BAM files according to contiguous unmasked regions of the human genome. These BAM files were transformed into resilient distributed datasets (RDD) data structure, which can be further partitioned and distributed across a Spark cluster. Finally, the Spark PipeRDD was used to parallelize the DeepVariant processes on Spark to ensure that all the GPU resources can be fully utilized across compute nodes .ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/NA12878_HG001/NISTv3.3.2/GRCh38/) to determine the F-score, recall, and precision values for SNPs and Indels. The RTG vcfeval tool  was used to generate intermediate VCF for variant comparison [https://github.com/Illumina/hap.py/blob/master/doc/happy.md), a quantify tool, was utilized to count and stratify variants.We performed variant calling using DeepVariant and DeepVariant-on-Spark to obtain variant call format (VCF) files from the NA12878 reference sample. DeepVariant version 0.7.0 was used for constructing the original DeepVariant pipeline and our accelerated DeepVariant-on-Spark pipeline on the cloud computing environment. The results from both pipelines with different CPU/GPU combinations were compared to the GIAB NIST v3.2.2 HG001 truth data , 24  for DeepVariant-on-Spark as compared to DeepVariant. As shown in First, we need to confirm that DeepVariant-on-Spark can achieve comparable accuracy to DeepVariant. Before we can perform a side-by-side comparison between DeepVariant-on-Spark and DeepVariant, we need to have a standard BAM file for the evaluation of the variant calling accuracy. The 30x WGS of the NA12878 reference sample with 611,997,146 reads was aligned to the GRCh38.p13 reference genome to obtain the BAM file, with 99.82% aligned reads, 41.25% GC, and 29.08x mean coverage. Our analysis showed that the To explore the bottleneck of DeepVariant, we evaluated the DeepVariant pipeline on the GCP using a virtual machine equipped with 16 CPUs (2.0 GHz) and 60 GB of memory, with flexibility for adding or removing CPU/GPU on existing virtual machine instances. The reference runtime for DeepVariant was established using 16 CPUs (2.0 GHz), which took 17.5 hours to finish variant calling from 30x WGS. The results show that “Make_Examples” and “Call_Variants” are computational bottlenecks of the DeepVariant pipeline. Increasing the number of CPUs may have improved both steps, but the impact on the “Call_Variants” step is not apparent . HoweverHeterogeneous computing, which uses a different type of processor (CPU or GPU) to gain efficiency and performance, therein incorporating specialized processing capabilities to handle specific tasks, has been gaining popularity in the past few years. Accordingly, we attempted to incorporate GPUs into existing virtual machine instances on the GCP. The results show that GPU acceleration is trivial at the “Make_Examples” step, whereas it introduces significant improvements at the “Call_Variants” step. The speedup rate increases after incorporating the GPUs Figures ; howeverThe primary purpose of this study is to provide a reference guide for users who plan to initiate a small-scale genome analysis to select their ideal solutions for their preliminary study. When computing time is not a major concern, the original DeepVariant pipeline with 16 CPUs can be a relatively cost-effective solution, which can analyze as many as 21 WGS datasets within the 300 USD free credits provided by the GCP. However, the default virtual machine architecture provided by the GCP has poor scalability, with a constraint on CPU number, making it difficult to optimize the trade-offs between cost and performance in running DeepVariant. At the same cost of 25 USD, DeepVariant takes 5.5 hours to finish a 30x WGS variant calling task with its inherited constraint of using 96 CPUs, while DeepVariant-on-Spark has the flexibility to adjust the cost-performance ratio to finish the same task in 2.5 hours. If pressed for time, DeepVariant-on-Spark would be a cost-efficient option that can finish the variant calling of 10 WGS data in just one day. In this study, we provide a flexible and scalable framework for DeepVariant acceleration. DeepVariant is the most precise variant caller that outperforms existing tools in SNV and Indel identification, therein having great potential for implementation in routine genetic diagnosis. Our proposed framework, DeepVariant-on-Spark, can not only reduce the wall-clock time while maintaining the same accuracy but also break the limitation on the number of utilizable CPUs and GPUs of DeepVariant.The user needs to set up a new account on the GCP to receive the free credits, which can provide a convenient place to run DeepVariant with CPU and GPU support. Following the author's instructions, we can easily set up the CPU version of DeepVariant without any hurdles. However, we have to recompile a compatible NVIDIA GPU driver for a specific DeepVariant version on the GCP to enable the support of GPU hardware, which can be a challenge for inexperienced users. On the other hand, DeepVaraint-on-Spark provides step-by-step instructions on how to prepare a Google DataProc cluster. The installation script will recognize the hardware specifications and automatically deploy related drivers and packages to enable CPU or GPU acceleration of DeepVariant-on-Spark within 20 minutes. To the best of our knowledge, substantial efforts, such as Nextflow, DNAnexus, DNAstack, and Parabricks, have been devoted to accelerating the DeepVariant pipeline. Nextflow offers parallel processing of multiple samples for DeepVariant at a time , produciTo conclude, we present DeepVariant-on-Spark, a flexible and scalable tool for variant calling based on the Spark framework. DeepVariant-on-Spark implements the parallelization of the DeepVariant algorithm on a multinode cluster and enables the support of multiple GPUs, therein accelerating the processing of the DeepVariant pipeline while maintaining accuracy. Following our instructions, users can easily deploy the DeepVariant-on-Spark on the GCP within 20 minutes and start to analyze WGS datasets on the GCP, which is also useful for researchers who plan to initiate a small-scale genome analysis for preliminary study and makes DeepVariant, the TensorFlow-based variant caller, more attractive to general users, simplifying the usage to catalyze DeepVairant to a more broadly used tool.