CACNA1C, which encodes the alpha-1 subunit of Cav1.2 L-type voltage-gated calcium channels (VGCCs), has been strongly linked to risk for psychiatric disorders including schizophrenia and bipolar disorder. How genetic variation in CACNA1C contributes to risk for these disorders is however not fully known. Both schizophrenia and bipolar disorder are associated with impairments in reversal learning (RL), which may contribute to symptoms seen in these conditions. We used a translational RL paradigm to investigate whether genetic variation in CACNA1C affects RL in both humans and transgenic rats. Associated changes in gene expression were explored using in situ hybridization and quantitative PCR in rats and the BRAINEAC online human database. Risk-associated genetic variation in CACNA1C in healthy human participants was associated with impairments in RL. Consistent with this finding, rats bearing a heterozygous deletion of Cacna1c were impaired in an analogous touchscreen RL task. We investigated the possible molecular mechanism underlying this impairment and found that Cacna1c +/− rats show decreased expression of Bdnf in prefrontal cortex. Examination of BRAINEAC data showed that human risk-associated genetic variation in CACNA1C is also associated with altered expression of brain-derived neurotrophic factor (BDNF) in the prefrontal cortex in humans. These results indicate that genetic variation in CACNA1C may contribute to risk for schizophrenia and bipolar disorder by impacting behavioral flexibility, potentially through altered regulation of BDNF expression in the prefrontal cortex. Tests of RL may be useful for translational studies and in the development of therapies targeting VGCCs.Genetic variation in  CACNA1C, which encodes the alpha-1 subunit of Cav1.2 L-type voltage-gated calcium channels (VGCCs), has been strongly linked to risk for psychiatric disorders including schizophrenia and bipolar disorder.2 The association between common genetic variation in intron 3 of the CACNA1C gene and schizophrenia and bipolar disorder has been confirmed by multiple studies across different populations.3–7 Risk mediated through common variants in CACNA1C has also been found to be shared across disorders, with Smoller et al2 finding similar associations with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Expression studies in cell lines and postmortem tissue have suggested that the intron 3 risk variants may act to decrease expression of CACNA1C, although the exact direction of effect has varied between studies and tissue types.8–12 Rare deleterious mutations in VGCCs have also been found to be associated with risk for schizophrenia.13 Notably, this enrichment was primarily driven by pore-forming alpha-1 subunits and alpha-2-delta auxiliary subunits of VGCCs, including deleterious mutation loss of function variants in CACNA1C.13Genetic variation in CACNA1C with multiple psychiatric disorders has led to the investigation of potential endophenotypes and downstream biological mechanisms that may link genetic risk to disease-relevant behavior. Previous studies have identified a link between risk-associated variation in CACNA1C and a range of tasks requiring cognitive and behavioral flexibility. Specifically, genetic variation in CACNA1C and related VGCCs has been strongly associated with working memory performance as assessed by the N-back test, which is known to depend on prefrontal cortex (PFC) function.14–16 Genetic variation in CACNA1C has similarly been shown to affect logical memory in patients17 and verbal working memory in healthy controls.18CACNA1C genotype has also been found to affect reward learning.20 Genetic imaging studies have been conducted to identify neural circuits affected by genetic variation in CACNA1C and have shown altered activation of fronto-limbic brain circuitry in risk allele carriers.21The robust association of 22 RL involves the inhibition of a previously learnt association between a stimulus and reward and the acquisition of a new opposite contingency or rule. It is a necessary form of behavioral flexibility that allows new experience to influence behavior.23 Deficits in this form of learning can lead to inappropriate perseverative behavior that is not supported by, or beneficial in, the current environment. Impairments in RL have been reliably observed in patients with a range of psychiatric conditions including schizophrenia and bipolar disorder.24–29 Furthermore, deficits in RL have also been observed in first-episode psychosis patients30 and in the context of high polygenic risk for schizophrenia,31 suggesting that RL deficits may reflect a causal mechanism linking common genetic schizophrenia risk to symptomology. Homologous fronto-limbic networks have been found to be involved in RL in both rodents and humans,32 and the development of a touchscreen-based RL paradigm for rodents makes the task highly translatable.34Reversal learning (RL) tasks represent a powerful method for investigating cognitive flexibility with considerable potential for cross-species translational studies.36 Mice lacking the main activity-regulated form of Bdnf show impaired plasticity in the PFC and behavioral deficits in RL tasks.36 Furthermore, manipulations in animal models that decrease prefrontal Bdnf, including maternal separation, have been shown to result in impairments in RL.38 VGCCs, including CACNA1C, have been shown to be central to the activity-dependent regulation of BDNF, suggesting the possibility that genetic variation in CACNA1C may affect cognitive flexibility in part through altering Bdnf expression in the PFC.39–43At a molecular level, the neurotrophin brain-derived neurotrophic factor (BDNF) has been shown to play a critical role in PFC during RL.CACNA1C risk variants in humans and altered Cacna1c dosage in rats. We additionally assessed the impact of genetic variation in CACNA1C on prefrontal BDNF levels in both humans and rats. By conducting these behavioral, genetic, and molecular studies in parallel, we aimed to increase confidence in our findings and develop translational models for the future development of novel therapies for psychiatric disorders targeting VGCCs.On the basis of these findings, we sought to investigate the effect on RL of N = 10) or incomplete/missing behavioral data (N = 6). The data used in this study have previously been used to observe associations between genome-wide polygenic risk for schizophrenia and RL behavior.31One hundred right-handed Caucasian healthy participants (aged 19–47 years) derived from the local community took part in the study. Ethical approval was given by the ethics committee of the School of Psychology, Cardiff University, and informed consent was obtained from each participant prior to the study. Participants had no history of psychiatric illness (themselves or first-degree relative) and did not report taking any psychotropic medication or illegal substances. All participants were right-handed, university graduates (with 17+ years’ education). A total of 84 participants  were used for analysis, after exclusion for quality control of genetic data  using IMPUTE246. For the current analysis, genotypes were extracted from the full single-nucleotide polymorphism (SNP) dataset for rs1006737 and rs2007044 only. These SNPs were selected because rs1006737 is the most consistently reported disease-associated SNP in CACNA1C across disorders,47 and the most recent schizophrenia genome-wide association study found rs2007044 to have the strongest association with schizophrenia (as well as confirming association with rs1006737).48 These 2 SNPs are in high linkage disequilibrium (r2 = .8). Allelic association analyses were conducted to determine whether incidence of each individual allele at each SNP location was associated with performance. Student’s t tests were used to compare performance for each allele.Samples of saliva were collected from participants for genotyping using Oragene DNA (OG-500) kits (DNA Genotek Inc). Genotyping was conducted using a custom HumanCoreExome BeadChip Kit (Illumina), which included 570 038 variants. Data were quality-controlled in PLINK.Cacna1c heterozygous (HET) knockout rats were housed with wild-type (WT) littermates in groups of 1–4. Heterozygous Cacna1c Sprague–Dawley rats were obtained from cryopreserved embryos, created with zinc-finger nuclease (ZFN) technology (Sage Research Labs). A pair of 5-finger ZFNs, recognizing a total of 30 base pairs, was used to target exon 6 (location 460649–460652 bp in genomic sequence), resulting in a frameshift and an early stop codon. Manipulation of founders was confirmed by PCR  and sequencing. Details of basic molecular and behavioral characterization of Cacna1c HET rats are given in the v1.2 alpha-1 subunit antibodies, we used 2 separate antibodies in western blots and an ELISA assay to confirm protein levels are reduced in our heterozygous line  with accompanying Animal Behavior Environment Test (ABET) II software . The overall stages and criteria for moving on to the next phase of the experiment are shown in In this study, 13 WT and 12 HET rats were tested using a touchscreen RL task.Correct responses and number of errors (incorrect responses) for each session were recorded to establish performance across phases and perseveration. For each session of discrimination and RL, % correct response [ × 100] was calculated. The number of sessions and trials required to reach criterion were recorded for each condition. Animals were only included if they had reached criterion for that training phase. The proportion of animals of each genotype to complete each phase was compared to establish success rate. Latencies to respond to stimuli were recorded to indicate speed of decision and response. Latencies were averaged across the first 3 sessions and the last 3 sessions to give an indication of change within phase. Student’s t tests were used to compare the number of sessions taken to complete and error number per session and chi-squared tests were used to compare the frequency of completion for experimental condition by genotype. Repeated measures ANOVAs with time and genotype were conducted to analyze response latencies across sessions.Bdnf and Cacna1c in rodent tissue using established techniques.50 Full details of the in situ hybridization methods and analysis are given in the In situ hybridization was used to quantify the expression of Quantitative PCR (qPCR) was conducted with SensiMix SYBR Green (Bioline) as described in CACNA1C at rs1006737 and rs2007044 on postmortem gene expression using the BRAINEAC online database (www.braineac.org).51 Using the BRAINEAC online general user interface, the expression of BDNF was stratified by rs1006737 and rs2007044 for each individual probe set available. Allele frequencies for the two SNPS were rs1006737: A = 29.1%, G = 70.9%; rs2007044: G = 36.9%, A = 63.1%.We examined the effects of genetic variation in P = .026 and t(165) = −2.230, P = .027, respectively]. There were no significant effects of risk allele at rs1006737 (t(166) = −1.562, P = .120) or rs2007044 on “Total Earnings”  indicating comparative levels of task motivation. Further analysis confirmed that the association between CACNA1C risk allele and performance was specific to trials following reversal and was not affected by correction for relevant covariates including age and gender  = 1.323, P = .202, respectively]. Twelve of the 13 WT animals and 9 out of 12 HETs animals reached criteria for “Visual Discrimination” with no difference in the proportion of completion  or number of sessions taken to reach criteria . Analyses were conducted to compare the proportion of WT and HET animals that completed the “Early” reversal stage, indicating a successful inhibition of response to prior contingency. Fewer HETs completed “Early Reversal,” with 12 WT animals successfully completing early reversal (92%), compared with 7 HET animals (58%)   = 2.176, P = .044].There were no differences between rat genotypes in number of sessions to reach criteria for Habituation or Must Touch  . Mean nuF = 1.134, P = .300]; however, HET animals responded significantly faster than WT during early reversal   = −2.223, P = .039] (P = .408]. In situ hybridization was used to further compare levels of Bdnf mRNA expression in the PFC of a separate cohort of Cacna1c HET (n = 5) and WT (n = 5) rats. Analysis confirmed that HET animals showed significantly reduced expression of total Bdnf mRNA levels in the PFC compared with WT animals   as assessed by probe set 3367273 .13Our results are broadly consistent with previous studies on mice with forebrain-specific total knockout of 57 Previous studies in mice completely lacking forebrain Cacna1c have also demonstrated a reduction in central BDNF levels.58 Furthermore, activity-dependent regulation of BDNF has been shown in rodent models to be critical for both plasticity in the PFC and RL.36 These findings suggest that altered regulation of BDNF in the PFC may contribute to deficits in RL.L-type VGCCs are known to play a critical role in the regulation of BDNF expression.Cacna1c heterozygous rats have decreased expression of Bdnf in the PFC, a result we confirmed with both qPCR and in situ hybridization. These findings demonstrate that decreased dosage of Cacna1c is sufficient to have a significant impact on prefrontal Bdnf expression. In addition, we found that CACNA1C risk-associated genetic variation in humans is associated with decreased BDNF expression in human PFC, as assessed by selected markers of BDNF in postmortem tissue in the BRAINEAC database. Notably, this effect was not seen for all markers across the BDNF gene, suggesting that genetic variation in CACNA1C may affect specific BDNF transcripts in the PFC, an area that will be important for future investigation. These results suggest a mechanism through which genetic variation in CACNA1C may affect cognitive flexibility and RL, because previous studies have shown that mice lacking activity-regulated expression of Bdnf show impaired RL and altered spike-timing-dependent plasticity in the PFC.36 These results also suggest that therapeutic approaches aimed at enhancing prefrontal BDNF levels may be efficacious in alleviating some of the cognitive impairments seen in neuropsychiatric disorders.We found that Schizophrenia Bulletin online.Supplementary material is available at sby146_suppl_Supplementary_MaterialClick here for additional data file.


BRAF mutant subpopulations. A systematic analysis of preclinical data for a failed phase III trial of selumetinib combined with docetaxel in lung cancer suggests potential indications in pancreatic and colorectal cancers with KRAS mutation. This data-informed study exemplifies a method for stratified medicine to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations.Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular  Two drugs, even with the same target, rarely have the same potency across all cancer patients - so how do we objectively select the right patients to treat with each drug? An international effort led by Michael P. Menden and Dennis Wang developed a machine learning approach called SEABED to identify groups of individuals from a population who respond distinctly to specific targeted therapies. They used SEABED to systematically compare 327 pairs of anti-cancer drugs across a panel of >800 cancer cells and 30 cancer types. SEABED identified groups of cells where one drug was more effective than the other. Interestingly, groups responding differently to the pairs of drugs could be treated more effectively when both drugs are given together as a combination, which can be in the presence or absence of drug synergy. This approach enables systematic explorations of personalised medicine to reveal biomarkers and drug combination opportunities. The deep molecular characterisation of these large cell line panels is complemented with high-throughput drug screens, which enables the discovery of drug response biomarkers. For example, analysis of the generic BRAF inhibitors PLX4720 (progenitor of vemurafenib), SB590885, and CI-1040 reproduced drug sensitivity association with BRAF mutation in melanoma, or afatinib sensitivity with ERBB2 amplifications in breast cancer.9 These associations between genetic variants and treatment response have helped identify specific patient subpopulations who are most likely to benefit from treatment. In Phase III clinical trials, however, for new drugs to be successful, they must demonstrate a significant improvement over the existing standard of care. Accurately defining in which subpopulations a new drug demonstrates improved differential efficacy over other drugs targeting the same disease could lead to both better clinical outcomes as well as new targeted therapies.Drug developers face a conundrum in predicting the efficacy of their investigational compound compared to existing drugs used as the standard of care treatment. Systematic screening of drug compounds across a variety of genomic backgrounds in cancer cell lines has improved clinical trial design and personalised treatments.11 there is a need for more objective and unsupervised approaches for identifying subpopulations with differences in drug response , and consequently, systematically gain mechanistic insights from biomarkers. Most approaches capable of comparing multiple drugs measure the overall similarity (or correlation) based on a single response summary metric,12 which permits drug repositioning based on subpopulations with similar behaviour, but neglects ones that behave differently . Segmentation of the overall population occurs top-down and along globally optimal contours that are derived explicitly and maximise the differences between the two resulting subpopulations. The segmentation continues recursively and is modulated by multiple user-defined criteria such as the size or separability of the resulting subpopulations. Higher threshold values for both result in less granular subpopulations but increase certainty that the subpopulations and the quantities estimated from them are both distinct and accurate.While several methods have been proposed to identify drug response biomarkers in cell lines for precision medicine and drug repositioning,BRAF and KRAS mutations,16 and an EGFR inhibitor and MEK inhibitor with expected biomarkers of EGFR, ERBB2, and KRAS mutations.20 Next, we systematically compare how different drugs targeting the MAPK and PI3K–AKT pathway yield different patterns of response within subpopulations. We show how differential drug response may indicate benefit for drug combinations explained through independent action rather than probable synergy by examining subpopulations uniquely sensitive to a single drug,21 which may be precisely targeted by identified biomarkers. This is particularly evident in subpopulations of cell lines with divergent response, where there is sensitivity to either drug but not both. Finally, we demonstrate how the analysis of differential response can guide the design of clinical trials by revealing specific indications where an investigational therapy may be more effective than the standard treatment.We present results from our platform, SEABED , to demonstrate how unsupervised machine learning can discover intrinsic partitions in the drug response measurements of two or more drugs that directly correspond to distinct pharmacological patterns of response with therapeutic biomarkers. Addressing the challenges in comparing the response of two drugs, SEABED initially assesses two gold standards with established clinical biomarkers, namely the differential response of a BRAF inhibitor and MEK inhibitor with anticipated We applied our technique to discover subpopulations of cell lines in which two or more compounds, possibly addressing the same disease state or even targeting the same genetic alteration, have a common pharmacological pattern of response. By further associating enriched genetic alterations in subpopulations with specific patterns of response, we shed light into molecular mechanisms responsible for patient subpopulations that respond differently to two drugs.50) and the area under the dose–response curve . We comprehensively evaluated pan-cancer somatic events to nominate biomarkers, and found that the subpopulation sensitive to both inhibitors was significantly enriched for BRAF mutants , while another subpopulation was exclusively sensitive to the MEK inhibitor and significantly enriched for KRAS mutations .We first considered the specific circumstance in which two drugs engage different targets within the same signalling pathway, namely agents targeting MAPK signalling. SEABED analysed >800 cancer cells for each pair of drugs, and we evaluated two established drug response measures: the drug concentration required to reduce cell viability by half  in APC-deficient mice.22 We reproduced the well-established associations of afatinib with either EGFR and ERBB2 amplifications,23 and surprisingly our unsupervised segmentation returned two subpopulations enriched for EGFR amplifications. The more sensitive subpopulation is solely enriched for EGFR amplifications, while the less sensitive subpopulation additionally includes activating PIK3CA mutations. In concordance with recent literature, PI3K–AKT signalling drives acquired drug resistance to EGFR inhibitors in lung cancer.24In another example we examined a case where one inhibitor might overcome resistance to another inhibitor targeting the same pathway: AZD6244/ARRY-142886 selumetinib (MEK inhibitor) with afatinib  across 839 cell lines Fig. . Strong ons Fig. . A less EGFR, ERBB2, and PI3KCA mutations has an average log(IC50) of 1.01 µM for selumetinib and −0.672 µM for afatinib. In contrast, the BRAF mutation was enriched in a subpopulation where the average log(IC50) for selumetinib was −1.097 µM and 0.625 µM for afatinib. The difference in response between afatinib and selumetinib was significantly greater (t-test P < 0.01) between the subpopulations identified and the total population of PIK3CA or BRAF mutant cell lines  while another had the BRAF mutation enriched . In contrast, another subpopulation was enriched with the RB1 mutation , within which the cell lines were almost uniformly resistant to all MEK inhibitors.In order to demonstrate this approach for drug discovery, we applied SEABED to 745 cell lines across cancer types to evaluate the differential response in those cell lines to five inhibitors  which all target the MEK protein Fig. . The segBRAF and RB1 mutations as well as skin, small-cell lung cancer, lymphoma, and leukaemia tissue types, respectively . The same trend was observed for inhibiting ERK (FR-180204) or RSK (FMK) compared to inhibiting any PI3K–AKT signalling gene , but interestingly there was no mutual sensitivity when comparing to BRAF inhibitors and only two cases for MEK inhibitors.We found 20 drug pairs subpopulations with sensitivity to both PI3K–AKT and MAPK pathway inhibition. This association between subpopulation size and sensitive response was significant when comparing a CRAF inhibitor (TL-2-105) to PI3K-AKT signalling inhibitors (hypergeometric test P < 0.05) exhibiting greater sensitivity to MAPK pathway inhibition. This phenotype is strongly pronounced in pairs with BRAF inhibitors (hypergeometric test P = 0.0166). Twenty drug pairs were found with significantly high proportions of preferential PI3K–AKT pathway inhibition. This is observed when comparing MEK inhibitors (CI-1040 and selumetinib-1) to PI3K–AKT signalling inhibitors .There were 20 drug pairs with a significantly high proportion of subpopulations , and even significant for PI3K inhibitors in comparison with either the EGFR (erlotinib) or MEK inhibitors . Response patterns for all drug pairs can be explored in our portal .In 22 cases, we observed drug pairs with each subpopulation having sensitivity to either an MAPK pathway or an PI3K–AKT pathway inhibitor but not both, i.e. divergent response. This response type was enriched for pairs of any PI3K–AKT pathway inhibitors and BRAF (PLX4720-2) or MEK inhibitors . We observed this phenomenon in 22 drug pair comparisons, including an MEK inhibitor (RDEA119-2) which showed divergent responses to four PI3K inhibitors  of −0.0232 µM for PLX4720-2 and 0.771 µM for PI-103 had 21 cell lines with BRAF mutation . In contrast, the subpopulation with an average log(IC50) of 0.808 µM for PLX4720-2 and 0.562 µM for PI-103 had 22 cell lines with BRAF mutation   Fig. . We obse25 and patient-derived tumour xenograft models (PDXs)26 to investigate whether the drug pairs with divergent response and subpopulations with preferential sensitivity to one drug would be associated with efficacy of their combination treatment , and compared to all drug combinations tested  inhibition of tumour growth compared to all combinations .30 Although there were KRAS mutant cell lines sensitive to selumetinib in preclinical testing,31 we re-examined the pharmacological data with SEABED to assess whether there were distinct subpopulations that justified the patient selection criteria for KRAS mutation.Sometimes, despite strong preclinical evidence, some drugs do not succeed in clinical trials.KRAS mutation. However, those subpopulations enriched for NSCLC KRAS mutants were small in size and mostly exhibited less sensitivity to selumetinib compared to docetaxel  was also no different in selumetinib sensitive subpopulations compared to resistant subpopulations  to selumetinib compared to docetaxel  found across multiple tissue types. NSCLC cell lines accounted for only 8% of this subpopulation, with 50% of those cell lines being KRAS mutants. Colorectal and pancreatic cell lines accounted for 15% and 8% respectively of the subpopulation, and they both had a higher proportion of KRAS mutations  and extract their biomarkers is the basis for personalised therapeutics, which may ultimately increase the likelihood of successful clinical trials.33 Using a network-based segmentation algorithm coupled with biomarker detection (SEABED), we investigated well-established pharmacological targets and clinical biomarkers by comparing the response patterns for BRAF (SB590885) and MEK (CI-1040) inhibition, which expectedly reproduced subpopulations sensitive to both drugs and enriched for BRAF mutants.36 In another example, SEABED compared EGFR/ERBB2 (afatinib) and MEK (selumetinib) inhibition to reveal expected biomarkers such as BRAF, KRAS, and NRAS mutations for selumetinib,16 and afatinib associated with EGFR and ERBB2 amplifications.38 Interestingly, the more afatinib-resistant subpopulation was enriched for PI3KCA-activating mutation, which may cause acquired resistance.24 When we systematically compared inhibitors of the MAPK and PI3K–AKT signalling pathways, we observed subpopulations sensitive to both CRAF, ERK, or RSK targeted drugs and other drugs targeting the PI3K–AKT pathway; however, there were few instances of these subpopulations for inhibitors targeting other genes in the MAPK signalling.39 We found many more subpopulations that were more sensitive to BRAF inhibitors than other PI3K-AKT inhibitors, and as expected, many contained BRAF mutations.34 In contrast, there were not significantly more subpopulations sensitive to MEK inhibition compared to inhibition of PI3K–AKT signalling targets, but BRAF mutant subpopulations may have greater differential response.14 Divergent response was observed when comparing EGFR, BRAF, and MEK inhibitors to drugs targeting the PI3K–AKT pathway. This unique type of response was where subpopulations were found to be sensitive to either drug but not both. Our results comparing the MAPK and PI3K–AKT pathways based on drug response profiles highlights how intertwined those two pathways are in pharmacology space.39The ability to identify distinct subpopulations based on multiple measures of drug response 5 and the Cancer Therapeutics Response Portal (CTRP).8 Furthermore, suggested drug combinations were validated with cell line responses from the AstraZeneca-DREAM challenge dataset25 and patient-derived xenograft (PDX) models from Gao et al.26 Drug synergy was defined as the total efficacy of two drugs that is greater than the individual effects of each drug. Combenefit (v1.31) was used to quantify drug combination synergy by comparing the observed two-dimensional (2-D) combination dose–response surface to a modelled reference based on the Loewe model of additive combinations and the single drug dose–response curves.57The discovery pharmacology dataset was extracted from the GDSC database,58 and consecutively quantified as area under the curve (AUC) or the concentration required to reduce cell viability by half (IC50). GDSC contains 265 compounds tested in 1074 cell lines, while we focus on a subset of 38 drugs targeting either PI3K–AKT signalling, MAPK signalling, or microtubules, which leads to 327 experiments considered for evaluation.For a given cell line in GDSC, the drug response was fitted with a sigmoid curve4 provides the characterisation of >1000 cell lines including whole-exome sequencing, targeted PCR sequencing/split probe FISH analysis and SNP6.0 arrays, which enabled to quantify somatic mutations, gene fusions, and copy number variations (CNVs), respectively. In our analysis, we focus on somatic mutational state of 300 cancer genes and 10 gene fusions. Additionally, we considered 425 recurrent CNVs, split into 117 amplifications and 308 deletions. In total, we consider 735 cancer functional events, which is summarised in the binary event matrix (BEM) from Iorio et al.4 The SPEED algorithm was applied to gene expression profiles in the cell lines to quantify pathway activation as binary scores and then added to the BEM.59The GDSC project50 and AUC values for both drugs was retained. Typically there were roughly 700 cell lines across all cancer types that met these criteria in each experiment. Cancer types and subtypes were stored along with the pharmacological data in a table for each drug for subsequent recall and analysis.For every pair of drugs that was computationally analysed, the subset of GDSC cell lines having valid ICN cell lines, D1 and D2, which results in two sets of M measurement variables, XiWe build network models for a set of G, consisting of N vertices, C) with weighted edges, C. The weight is the similarity, wi,j, between ith and jth composite 2Mx1 dose response profile (DRP), Xi =  for Ci and Xj =  for Cj.We use a network model that is an undirected graph, entclass1pt{minima50 and the AUC values of the dose–response curve  = w. ∆Xi,j is a Xi and Xj and β modulates the similarity between two patients. We selected β=0.5 for our experiments based on experimentation and the observations of previous efforts.The similarity between two cell lines equals one when both have identical covariate values, and approaches zero as their covariates increasingly differ. Additionally, i,j)-pair and captures the variability of individual variables as well as their inter-relationships. While i, and the overbar is the averaging operator. The off-diagonal elements, W, holds the pairwise similarities.The Moore–Penrose pseudo-inverse was used to avoid problems with low-rank during matrix inversion. The framework is generalisable to include more variables and different measures of similarity. The symmeric, positive semi-definite, C, is segmented recursively into distinct subpopulations using the Fiedler eigenvector derived from the eigendecomposition of W.60 Each parent subpopulation of cell lines is successively segmented into two offspring subpopulations until at least one of three constraints is satisfied:The size of the parent subpopulation falls below a user-defined threshold.The size of either offspring subpopulation is below a user-defined threshold.61 a common non-parametric method in which values range between −1 (highly similar) and 1 (highly dissimilar) and values near 0 indicate the two subpopulations are just barely overlapping. Higher values result in fewer, less granular, subpopulations.The offspring subpopulations are not sufficiently dissimilar, where we measure class separability using the silhouette metric,The set of cell lines, K mutually exclusive subpopulations In our experiments, we required the size of the parent subpopulation to be at least 40 in order for segmentation to be performed, and required both offspring subpopulations to have 20 or more members in order to be retained. The silhouette metric threshold was set to 0.25. Generally, criteria and thresholds can be modified and adapted to emphasise relevant factors in a particular problem. The whole segmentation process yields p values were corrected for multiple testing using the Benjamini and Hochberg (BH) procedure  values across all cell lines tested with both drugs, we determined the 20th-percentile of log(IC50) values for each drug to be a user-defined cutoff. The 20th percentile cutoffs P20 for drugs A and B was used to categorise each subpopulation i into four categories based on their average log(IC50) y:We made 324 pairwise comparisons of drugs targeting the MAPK and PI3K–AKT pathways. Based on the distribution of log.After classification of pairwise drug responses, we assessed whether a drug’s subpopulations was significantly over-represented in one category in comparison with all other categories. Testing was carried out using the hypergeometric test (50) values for each drug in subpopulations generated based on their response to the tested drug pairs (Supplementary Table 50) values as circles on a 2-D scatter plot using the Matplotlib Python library. Dashed lines indicative of local 20th percentile of log(IC50) values for each drug were also plotted on the scatter plot unless stated otherwise. The radii of the circles is proportional to the subpopulation size. Due to potential misidentification, cell lines named ‘NA’ found in the output files after segmentation with SEABED were not used for the calculation of the mean log(IC50) values of each subpopulation and, hence, not used for the 2-D visualisation. Sensitivity cutoffs for each drug assumed that 20% of cell lines had an IC50 beyond an arbitrary threshold, which was a consensus among drug development scientists and a lower threshold for clinically approved drugs.62 These human-defined cutoffs were used as a guide for comparing our algorithmically segmented subpopulations.To visualise DRPs across cell lines and drug comparisons, we calculated the average log(ICWe utilised tree diagrams to visualise the data generated. The tree diagrams illustrate how the cancer cell lines are segmented into different subpopulations, based on whether they are sensitive or resistant to the drugs that are being tested. We did not visualise further down the tree in the figures when previously observed significant enrichment of genetic biomarkers is no longer observed in all current subpopulations. The tree diagrams were generated through an open-source Python library called Graphviz. The style of each component of the tree diagram was first initialised through a class. This included the colours, shapes, and fonts of the edges and nodes of the tree diagram. A method to create tree diagrams was developed to accept the number of vertices and leaves, the labels for the leaves, and the tree diagram filename. The tree diagram is finally generated and saved by calling the method.Further information on research design is available in the Supplementary Materialsreporting summarySupplementary Table S4Supplementary Table S5


Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available. Genome-wide association studies (GWAS) have so far uncovered more than 200 loci for multiple sclerosis (MS). Here, the authors integrate data from various sources for a cell type-specific pathway analysis of MS GWAS results that specifically highlights the involvement of the immune system in disease pathogenesis. Fortunately, better powered studies, have increased the number of associations identified enabling biological meaning to be investigated in aggregate . In its simplest form, genes lying closest to the most strongly associated (lead) SNP identified for each association can be grouped into pathways or specific functional memberships via the use of pre-assembled controlled vocabularies 8. This approach can be enhanced by using protein interaction networks to more rigorously assess which of the candidate genes encode proteins that physically interact in any particular pathway9. Using this refined approach, we and others have been able to show that MS-associated genes are indeed more likely to interact in protein space12. Furthermore, this analysis can be extended to include association signals below the genome-wide threshold of significance and thereby nominate new additional potentially meaningful associations14. However, the networks of genes/proteins identified by these approaches are not cell- or tissue-specific, thus limiting the usefulness and interpretability of this information.Translating Genome-wide association studies (GWAS) discoveries into functionally relevant biology has proven to be highly challenging. The extensive linkage disequilibrium (LD) which typically flanks common variants means that most GWAS identified SNPs are likely to be tags for functionally relevant variation rather than exerting any meaningful effects themselves. Furthermore, since the vast majority of associations identified by GWAS map to non-coding regulatory regions it is likely that the underlying functionally relevant variants only exert pertinent effects on gene expression in particular tissues16, raising the possibility of applying network-based approaches in a cell-specific manner. We reasoned this approach would likely be highly informative in diseases like multiple sclerosis (MS) where substantial numbers of associated variants have been identified. MS is an autoimmune disease of the central nervous system (CNS) and leads to a neurodegenerative process. Our recent GWAS meta-analysis and follow-up study have revealed a total of 233 genome-wide significant associations and a further 416 variants potentially associated with MS17.With the completion of efforts like the Encyclopedia of DNA elements (ENCODE) and the Roadmap Epigenomics Project (REP) a wealth of information on regulatory elements is now available from hundreds of cell types and dozens of different tissuesHere we develop a framework to interpret such associations in the context of cell-specific protein networks to identify the most likely process(es) affected by the non-MHC associations as a whole. This approach involves (1) selecting independently associated signals in extended haplotypic blocks; (2) identifying the genomic regulatory processes likely to be altered by the polymorphisms in these blocks in a cell-specific manner; (3) computing a cell-specific gene score for genes in each associated locus; (4) building cell-specific gene/protein networks; (5) Interpreting the biological processes most likely affected for each of the cell types studied. We demonstrate this approach in the latest GWAS meta-analysis in MS involving a total of 47,351 cases and 68,284 controls. Furthermore, we use genotype-level data from a subset of 2370 cases and 412 controls to identify cell-specific intra-individual risk pathways. These individualized scores can be used as a global risk measure in subsequent associations with more detailed phenotypes.17 with cell specific information on regulatory elements available from the ENCODE and Epigenomics Roadmap projects (REP) to identify cell-specific networks likely affected by the susceptibility variants .We integrated genetic association signals from the latest genetic analysis of MSn = 2,444) implicated by the GW significant MS associations for each of the main cell/tissue types . These SNPs would have almost no effect at all in the CNS and lungs (L), as MS-associated variants are unlikely to alter their expression in those tissues. For comparison, a simple proximity approach would have just implicated the FAM213B gene, which, while being of biological interest, may describe an incomplete scenario. Interestingly, TNFRSF14 (a member of the TNF receptor superfamily) encodes a protein involved in signal transduction pathways that activate both inflammatory and inhibitory T-cell immune responses due to its particular ability to interact with multiple ligands in distinct configurations18.All regulatory information retrieved for each MS-associated region was compiled for each cell type in a single master table  its PRE score is low in the M set  how many of the gene products in MS-risk loci predicted to be positively regulated (i.e. PRE > 25th percentile or PRE-25) also interacted in a human protein network containing 15,783 proteins and 455,321 interactions (See methods)  and catalytic activity (33%) categories. Other functions were receptor (13%), signal transduction (6%), and structural molecule (10%). A PANTHER analysis revealed these genes belong to JAK/STAT, IFNgamma, interleukin, and integrin signaling pathways, among others. Altogether, our analyses suggest that susceptibility to MS stems from a core of processes that can be active in any of several immune-related cell types. These findings are in agreement with the “omnigenic” model of inheritance of complex diseases, posing that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes23.Previous studies have shown that the proteins encoded by genes affected by genetic association signals are more likely to interact, in part because they often participate in the same biological pathwaysds) Fig.  shared sds) Fig. . The molCD28 was only present in the T cell network, ELMO1 in B cells and MERTK in the monocyte/macrophage lineage. CD28 is located on the surface of T cells and provides a required co-stimulatory signal to trigger their activation after engagement of the MHC-antigen-T cell receptor trimolecular complex24. ELMO1 encodes a cytoplasmic adaptor protein that interacts with DOCK family guanine nucleotide exchange factors to promote activation of the small GTPase RAC, thus enabling lymphocyte migration25. The MERTK gene encodes a receptor tyrosine kinase that transduces signals from the extracellular matrix into the cytoplasm regulating many physiological processes including cell survival, migration, differentiation, and phagocytosis of apoptotic cells (efferocytosis). Specifically, MERTK plays several important roles in normal macrophage physiology, including regulation of cytokine secretion and clearance of apoptotic cells26. These observations suggest that, in addition to the core susceptibility module, at least part of the risk is cell type-specific.As predicted by the omnigenic model, we noted that several genes were only present in some cell types but not others. For example, r2 > 0.1, r2 > 0.5, and r2 > 0.8) and three PRE thresholds  , and non-replicated (NR) effects (see Methods) SNP sets at three different LD cut-offs  , 0.324 , and 0.246 , representing a significantly higher than expected value for each cell type. Correlations between PRE and RNA expression of mismatched cell types were significantly lower. These results suggest that the computation of PRE can be applied to single patients and individual scores can be generated for each of them.The detailed mapping of regulatory information for each SNP suggests that if PRE are computed for a given cell type in a single individual based on the carriage of relevant risk alleles, these values should capture a non-negligible proportion of the variance in gene expression in that cell type. To test this hypothesis, we interrogated the expression of the entire transcriptome of FACS-sorted CD4r2 > 0.5) for all cell types in this subset of cases and controls are shown as an example  potentially modulate its expression in T cells, this gene is less regulated in B cells and monocytes and strongly silenced in CNS  preferentially in B cells for most subjects  have been reported to express lower levels of CD40 in the surface of their B cells and lower IL-10 levels31. This could carry therapeutic implications considering the prominent role of B cell depletion therapy in this disease33. Another signal revealing B cell involvement in MS risk is the high PRE of all members of the Fc receptor-like (FCRL) gene family in region 53  can be identified for each cell type. For example, subject 201327986 has 110 nodes and 190 edges in this subject’s B cell risk network (blue box), corresponding to the 99While the number of interactions (edges) among proteins encoded by genes in associated loci was variable across cell types, on average, more interactions were observed for cases than for controls in all cell types studied. Supplementary Fig. In summary, this analysis underscores the notion that total MS risk is not only carried by accumulation of risk alleles, but also by how the genes and proteins affected by those polymorphisms interact within each cell type. We anticipate this model could apply to other common diseases.17, which identified the monocyte/macrophage/microglia axis as a key player in directing the autoimmune process to the CNS. However, an important caveat, particularly affecting results for this compartment, must be taken into account: for this analysis, the CNS group was composed of a heterogeneous ensemble of purified primary cells, established cell lines and dissected specimens from specific anatomical regions as available in the ENCODE and REP datasets. Although all derived from CNS tissue, it is highly likely that different regulatory mechanisms are at play in each cell type , thus somehow confounding the overall CNS signature computed here. Thus, the detected effect of MS-associations which map to the CNS could represent the lower boundary of a more widespread phenomenon. A more detailed CNS-specific data set containing genome-wide regulatory element information might be needed to address this question in larger detail.In this work, we provide evidence that integration of associated variants from GWAS with regulatory information and protein interactions, provide plausible models of disease pathogenesis. This approach not only offers a data-driven solution to prioritize which genes within a locus are most likely affected by the risk variant(s) but also provides an interpretable model of risk in a cell/tissue specific manner. While the PRE scores computed here are significantly correlated with actual gene expression from the corresponding cells, the correlation is partial, thus underscoring a potential limitation in our approach, and of available data. However, the statistically significant results obtained for genes and pathways known to be involved in MS further validates this approach. Indeed, the models presented here are consistent with MS genetic risk being driven by the long-term alteration of cellular pathways primarily in monocytes, but also in the B and T cell arms of the human adaptive immune response. The smaller but not negligible contribution of CNS pathways to MS risk is in agreement with our previous analysis37, FUMA38 and PASCAL39 have been proposed and utilized to interpret and integrate summary statistics into a biologically meaningful model. While sharing some of the basic characteristics of previous approaches, our method features a set of unique properties, most notably the introduction of data-driven regulatory effects of associated variants (and those in LD), the ability to create cell-specific networks, and the computing of individual disease burden maps  and individual (genotype) levels.46  corresponding to all non-MHC GW . The 200 GW effects were distributed in 156 unique regions (44 regions contained multiple independent effects). Similarly, the 416 SR effects were distributed in 354 unique regions (62 regions contained multiple independent effects) and the 3695 NR effects were distributed among 1,883 unique regions (1812 regions contained multiple independent affects). Three sets of SNPs were created for each region according to their r2 with their corresponding main effect .Genome-wide regulatory elements from ENCODE and REP were collected from regulomeDBr2 > 0.8, r2 > 0.5, and r2 > 0.1) with each primary effect were also retrieved using chromosomal positions. The main analysis was performed using r2 ≥ 0.5 whereas the other sets were only used for the sensitivity analysis. In total, 538,826 SNPs were included in the analysis.A python tool was written to automatically fetch data from RegulomeDB for these SNPs in all three lists . Similarly, data for SNPs in different levels of LD  and R (repressors/inactivators). Cell or tissue of origin was recorded for each regulatory feature, and cell-specific information was grouped into three main cell types  and one tissue (CNS) that are of interest in MS. In total, 25 brain regions were considered (15 from ENCODE and 10 from REP). In addition, T cell subsets deemed relevant in the pathogenesis of MS  were also analyzed separately. Primary cells and cell lines from a tissue not known to be involved in MS  were included as control. In addition, eQTL data for T cells and monocytes from the IMMVAR projectMultiple regulatory features were considered including protein binding, transcription factor binding sites (TFBS), promoters, enhancers, insulators, histone modifications, and DNAse hypersensitive regions (DHS). We classified these into three broad groups representing promoter/enhancer/transcription (PEX), inert/quiescent (ZQI), and repressor , epigenomic data for CNS cells/tissues in ENCODE or REP is very sparse, thus we decided to merge all data into a single CNS category.An experimentally determined human protein interactome consisting of 15,783 nodes and 455,321 edges was used for this part of the analysisNext, we loaded the gene-level PRE for each cell type as node attributes and conducted a topological analysis by selecting the subnetwork corresponding to the largest connected component of nodes with positive PRE . To eliminate noise from very small or loosely unconnected networks, only those with more than 15 nodes were considered. Sensitivity analysis was performed by defining different thresholds on PRE values  and building networks with only proteins exceeding these thresholds  were sorted into RLT buffer using a MoFlo Astrios cell sorter (Beckman Coulter). Helper T cells were defined as CD3+CD19−CD4+, cytotoxic T-cell were CD3+CD19−CD8+, and monocytes were sorted as CD14+ cells. Total RNA was isolated from sorted cell subsets using RNeasy Mini kit (Qiagen) and assessed RNA quality using Agilent 2100 Bioanalyzer (Agilent Technologies). 3′mRNA-Seq libraries for all cell subsets were prepared from 100 ng total RNA using QuantSeq kit (Lexogen) according to the manufacturer’s instructions and sequenced 50-bp single-end on the HiSeq 4000 (Illumina). Sequence reads were mapped to the human genome reference (GRCh38) with Gencode annotation (r26) using STAR aligner50. Reads were normalized by median of ratios using the DEseq2 package51. The R function featureCounts was used to obtain gene-level read counts52.This work was approved by the Institutional Review Board at the University of California San Francisco (IRB# 10-00104). PBMCs were obtained from 25 individuals by Ficoll method using Vacutainer CPT tube (BD Biosciences). Subjects were consented according to institutional (UCSF) review board (IRB) guidelines. Three different cell subsets (CD4cor.test function in R. The significance of the correlation was confirmed by permutation testing (n = 1000).We selected overlapping genes between RNA-Seq gene counts and PRE scores, and Pearson’s correlation test was performed using the Supplementary informationpeer review fileDescription of Additional Supplementary FilesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5Supplementary data 6Supplementary data 7Supplementary data 8Supplementary data 9Supplementary data 10Supplementary data 11Supplementary data 12Supplementary data 13Supplementary data 14Supplementary data 15Supplementary data 16Supplementary data 17Supplementary data 18Supplementary data 19Supplementary data 20Supplementary data 21Supplementary data 22Supplementary data 23Supplementary data 24Supplementary data 25Supplementary data 26Supplementary data 27Supplementary data 28Supplementary data 29Supplementary data 30Supplementary data 31Supplementary data 32Supplementary data 33Supplementary data 34Supplementary data 35


Infection is the leading cause of non-relapse-related mortality after allogeneic haematopoietic stem cell transplantation (HSCT). Altered functions of immune cells in nasal secretions may influence post HSCT susceptibility to viral respiratory infections. In this prospective study, we determined T and NK cell numbers together with NK activation status in nasopharyngeal aspirates (NPA) in HSCT recipients and healthy controls using multiparametric flow cytometry. We also determined by polymerase chain reaction (PCR) the presence of 16 respiratory viruses. Samples were collected pre-HSCT, at day 0, +10, +20 and +30 after HSCT. Peripheral blood (PB) was also analyzed to determine T and NK cell numbers. A total of 27 pediatric HSCT recipients were enrolled and 16 of them had at least one viral detection (60%). Rhinovirus was the most frequent pathogen (84% of positive NPAs). NPAs of patients contained fewer T and NK cells compared to healthy controls . Viral PCR + patients showed higher NK cell number in their NPAs. The activating receptors repertoire expressed by NK cells was also higher in NPA samples, especially NKp44 and NKp46. Our study supports NK cells relevance for the immune defense against respiratory viruses in HSCT recipients. Recent data suggest that common human coronaviruses (HCoV) are also associated with significant mortality, similar to that seen with RSV, PIV and influenza11. There is less information regarding infections caused by human rhinoviruses (HRV) and human bocavirus (HBoV)13.Allogeneic haematopoietic stem cell transplant (HSCT) is a potentially curative procedure for a variety of pediatric hematologic disorders. Post-transplant infections contribute significantly to the morbidity associated with this procedure. The ablation of HSCT recipient’s immune system by the conditioning regimen increases the susceptibility to infections until they reach immune reconstitution from donor stem cells. The morbidity and mortality from respiratory viruses in immunocompromised patients, including those undergoing HSCT, is well established for respiratory syncytial virus (RSV)16. NK cells play important roles in the host’s immune defense through the production of cytotoxic granules containing molecules such as perforins and granzymes that lyse infected cells. Other important features of NK cells are the production of cytokines including interferons, their enhancement of local immune responses by directly acting on target cells, and their role in the recruitment and activation of other immune cells, including T cells and macrophages17.Natural Killer (NK) cells in the nasal secretions are a first line of defense against respiratory viruses both in healthy individuals and in those undergoing HSCT. They have a protective role against many viral infections commonly encountered after HSCT, including herpes simplex virus, varicella-zoster virus, cytomegalovirus and influenza virus as well as some bacterial infections17. Membrane bound Killer-cell Activation Receptors (KARs) work together with inhibitory Killer Immunoglobulin-like Receptors (KIRs), in order to regulate the NK cells functions on infected or transformed cells. The interaction between the KIR family on NK cells and HLA Class I molecules results in inhibitory signals. KARs, like the natural cytotoxicity receptors (NCRs), comprising NKp30, NKp46, and NKp44 are key receptors in the recognition and elimination of virally infected and tumor cells19.NK cells recognize virally infected cells through the integration of signaling from activating and inhibitory germ line encoded receptors on the NK cell surface26, very little is known about the immune reconstitution in HSCT recipients’ mucosa. In the present study, our goals were: 1) to determine NK and T cell numbers in the nasal passages of HSCT recipients; 2) to determine the activation phenotype of NPAs’ NK cells using multiparametric flow cytometry; 3) to determine the presence and functional profile of these cells within the context of a viral infection; and 4) to assess the effects of viral infections on nasal immune reconstitution.Because immune cells in the nasal mucosa can control and regulate viral infections via killing infected respiratory epithelial cells, low numbers of NK and or T cells in the nasopharyngeal aspirates (NPAs) of HSCT pediatric recipients could be associated with an increased susceptibility to viral respiratory infections. Although blood immune reconstitution after an HSCT has been widely studied28.Prospective study including patients under 18 years of age who received allogenic HSCT at La Paz University Hospital , from January 2017 to June 2018. Exclusion criteria included chronic lung disease prior to HSCT. HSCT was performed according to current centre protocols, using bone marrow or peripheral blood stem cells as haematopoietic stem cell source. Acute and chronic graft versus host disease (GVHD) were defined and graded as previously reportedAge- and sex-matched controls were recruited among healthy patients undergoing elective surgery. Cases and controls were also matched according to procedure date  ± 1 week.All research was performed in accordance to local regulations. Informed consent in accordance with the Declaration of Helsinki was obtained from the parents of all children. The study was approved by the Clinical Research Ethics Committee at La Paz University Hospital.PB: total, T and NK cell counts.NPAs: total T and NK cell counts, natural cytotoxicity receptors, viral detection.Study samples were peripheral blood (PB) and NPAs that were collected prior to the conditioning regimen and after HSCT at days 15 and 30 for PB mononuclear cells and before the conditioning regimen and at days 0, 10, 20 and 30 after HSCT for NPAs. The following tests were performed in each sample type:For NPA collection, 1 mL of 0.9% saline solution was instilled into each nostril and the nasopharynx was suctioned using a sterile catheter with a mucus trap. NPAs were not performed if the patient had active bleeding or his platelet count was below 20.000/mm3. NPA samples containing blood were considered not valid and discarded. Each specimen was sent to the Respiratory Virus and Influenza Unit at the National Microbiology Center  for viral analysis and to the Translational Research in Paediatric Oncology, Hematopoietic Transplantation & Cell Therapy Unit at La Paz Hospital Research Institute  for flow cytometry assays. NPAs were processed within 24 hours after collection. In the control group, NPAs were collected on the day of surgery.31. Influenza A, B and C viruses were detected by using previously described primer sets only to amplify influenza viruses in a multiplex PCR assay29. A second multiplex PCR was used to detect parainfluenza viruses 1 to 4, human coronaviruses 229E and OC43, enteroviruses and HRV30. Presence of RSV A and B types, HMPV, HBoV and adenoviruses was established using a third multiplex RT-nested PCR−BRQ method31.Upon reception of the samples, three aliquots were prepared and stored at −80 °C. Both the reception and the NPA sample processing areas are separated from those defined as working areas. Three independent RT-PCR assays were performed to detect sixteen respiratory viruses as previously published by our groupFor flow cytometry assays, NPA samples were filtered with a 70 µm cell strainer , centrifuged at 1400 rpm (5 min) and suspended in 100 µl of PBS. Total sample volume was acquired using a Navios flow cytometer (Beckman Coulter).The total cell count per sample was determined. T cell  and Natural killer (NK) cell  populations were identified using the following labelled antibodies: CD45-FITC/APCCy7 , CD56-APC  and CD3-PECy7 . Fluorescent cell viability dye 7-AAD (BD Pharmingen) was used to exclude dead cells. FlowJo v10.0.7 software (TreeStar) was used for data analysis.The expression of natural cytotoxicity receptors (NCRs), NKG2A and NKG2D receptors on NK cells subpopulation in NPA was evaluated with the following fluorochrome-conjugated antibodies: NKG2D CD314-PE ; NKp46 CD335-FITC ; NKp44 CD-336-PE ; NKp30 CD337-PE ; NKG2A-PE .P < 0.05 was considered statistically significant. All analyses were performed using the SPSS statistical software, version 21.0 (IBM Corp.).Values are expressed as percentages for discrete variables and as median and interquartile range (IQR) for continuous variables. Comparisons between groups  were made using Mann-Whitney U test, and Fisher’s exact test) as appropriate. A value of During the study period, 27 HSCT recipients were recruited. Their main characteristics are summarized in Table All HSCT were allogenic. Twenty-five patients underwent HSCT for the first time (93%) and two received their second HSCT (7%).Eighteen healthy children were included in the control group, 10 male (56%) and 8 female (44%), with a median age of 8.7 years (IQR 9). There were no significant differences regarding age and sex between HSCT recipients and healthy controls.A total of 83 samples were collected from the 27 HSCT recipients, and 77 were valid for viral studies . Twenty-five samples (32%) were positive, and 16 of 27 HSCT recipients (60%) had at least one viral detection. Among HSCT recipients with viral infection, the median number of positive samples per patient was 1 (IQR 1). HRV was isolated in 21 samples (84% of positive NPA) from 12 patients, followed by adenovirus and parainfluenza type 1 . There were no viral coinfections among HSCT recipients. Detailed information regarding positive samples is given in Table Infections caused by HRV were symptomatic in 2 of 12 patients (17%): one had low-grade fever and the other persistent rhinorrhea. Both patients with adenovirus infections had fever, mucositis and elevated levels of C-reactive protein (above 100 mg/L). Infections by parainfluenza type 1 virus were also symptomatic (one patient with fever and another with laryngitis and pneumonia). None of the patients required admission to the intensive care unit (ICU) nor died as a result of a viral infection. There were no differences regarding age between HSCT recipients with and without viral infections , but patients below two years of age tested positive more frequently .A total of 17 samples from healthy controls were analyzed, and viruses were identified in 4 24%): two single infections (HRV and adenovirus) and two coinfections  .The NPAs of patients prior to HSCT conditioning contained fewer T and NK cells as compared with healthy controls p = 0.0132 and p = 0.120, respectively Fig. . AdditioWe further analyzed T and NK cell kinetics after the HSCT. We observed an increase in T cells in the NPA samples at infusion day. This increase is reverted at day 10 post HSCT, when NPA cell counts reach levels similar to those observed prior to the HSCT. This effect was observed both in PCR+ and in PCR− patients )  had finished their infusions prior to NPA collection. Pre-transplant conditioning, such as high-dose chemotherapy is toxic to many cells and render the endothelium vulnerable19. The most overexpressed activating receptors we found in NK cells of NPAs were NKp44 (NCR2) and NKp46 (NCR1). This is consistent with Shemer-Avni et al.’s findings41. In nasal lavage samples of patients infected with respiratory viruses, they found NKp46 as the most abundantly expressed NCR. However, in their RTqPCR analysis they detected no NKp44 expression and very little of NKp30. Altogether, our findings point to NKp44 as a key player in nasal mucosa elimination of virally infected cells by NK cells.Natural killer cell activity is regulated through the balance of signals from inhibitory and activating receptors. The natural cytotoxicity receptors (NCRs) NKp46 (NCR1), NKp44 (NCR2), and NKp30 (NCR3), as well as NKG2D, are the main activating receptors involved in mediating NK cell function in health and disease26. Collectively, these factors predispose the host to a variety of infections. In fact, despite the routine use of prophylactic antimicrobials in the peri-transplant period43, infections occur is about 80–85% of HSCT recipients and are one of the leading causes of non-relapse-related mortality after allogeneic HSCT45.Conditioning regimens used for allogeneic HSCT, high dose chemotherapy and/or radiotherapy, induce serious damage in mucosal, humoral and cellular immune function. Moreover, those regimens can harm the host’s thymopoiesis resulting in further delayed immune recovery48. Asymptomatic respiratory infections in children undergoing HSCT have been barely studied. A prospective study of 33 patients detected respiratory viruses in 24% of them before HSCT, mainly HRV and adenovirus49. In our series, infections caused by HRV were the most common, and were not associated with increased morbidity. These findings are in concordance with those reported by Srinivasan et al. in children with cancer or post-HSCT and upper or lower respiratory infection13 and asymptomatic children before HSCT49. Other authors have reported implementing routine extensive pre-HSCT lung screening, including bronchoalveolar lavage for the detection of viruses, bacteria and fungi. In that study, almost one third of asymptomatic patients had viral respiratory infections, mainly caused by rhinovirus, and none of these worsened during HSCT50. On the other hand, infections caused by adenovirus and parainfluenza virus were less frequent but more often symptomatic in our patients. Adenovirus has been described as a cause of lower respiratory tract infection with high mortality after HSCT in studies involving symptomatic children51. We did not find a correlation between viral infection and HSCT outcomes, which could be caused by small sample size and low incidence of symptomatic infection.More than half of the HSCT recipients included in our study presented a viral respiratory infection at some point. To date, most studies of these infections in pediatric HSCT recipients have focused on symptomatic individuals, reporting an incidence of 5–11%51.Important strengths of our study include its prospective design and the determination of number, activation phenotype and functional profile of NK cells together with respiratory virus detection, assessing HSCT recipients over time and comparing them with healthy controls. Potential limitations of our study relate to sample size and losses to follow-up. NPA cell counts have great variability so larger cohorts would be desirable to draw stronger conclusions. Many patients did not tolerate well NPAs and withdrew during the course of the study. The small number of patients further precluded correlation of viral detection and transplantation outcomes, and limits our ability to examine the implications of asymptomatic viral respiratory infections in HSCT recipients. NPAs were collected up to 30 days after HSCT, and some infections occurring later may have been missed. However, most respiratory viral infections in HSCT recipients occur soon after transplantation, and adverse clinical outcomes are more likely to occur in the same periodIn conclusion, we provide data suggesting NK cells as major effectors in respiratory viral control in the nasal mucosa. We also characterized this NK cell population finding a highly activated phenotype compared with that of their PB counterparts. Further study of this unique mucosal immune cell population will be beneficial in assessing the effects of viral infections on upper respiratory immune responses in HSCT recipients as well as in healthy individuals.Supplementary material


Adult children who are uninformed about their parents’ preferences for end-of-life care may not be prepared to advocate on their behalf when the circumstances arise. The purpose of the current analysis was to examine how well adult children understand their parents’ end of life preferences. We analyzed responses from adult children (n = 70) of 40 older adults (65+) who participated in an intervention to improve family communication about end-of-life care. We compared children’s and parents’ responses on the same set of 6 questions about healthcare decision-making  and 4 questions about living preferences . We also examined demographic differences between children who had higher agreement  versus lower agreement . On average, children provided the same response as their parents on approximately 5 out of 10 questions. Overall, adult children answered more questions correctly about living preferences compared to preferences about healthcare decisions (t(69) = 6.59, p < 0.001). In terms of demographic characteristics, there were no significant differences between children with higher and lower agreement with their parents’ preferences on variables including gender, frequency of contact with parents, and living proximity to parents. Our results underscore the need for increased communication between adult children and their parents about topics likely to influence quality of care at end of life.


GC). Kinship was found to play a major role in correcting population confounding effects and results advocate a ‘trait-specific’ fit of different GWAS models. A survey of genome-wide linkage disequilibrium (LD), one of the critical factors affecting GWAS, is also presented and our findings are compared to other recent studies in potato. The genetic material used here, and the outputs of this study represent a novel resource for genetic analysis in potato.Genome-wide association studies (GWAS) have become a powerful tool for analyzing complex traits in crop plants. The current study evaluates the efficacy of various GWAS models and methods for elucidating population structure in potato. The presence of significant population structure can lead to detection of spurious marker-trait associations, as well as mask true ones. While appropriate statistical models are needed to detect true marker-trait associations, in most published potato GWAS, a ‘one model fits all traits’ approach has been adopted. We have examined various GWAS models on a large association panel comprising diverse tetraploid potato cultivars and breeding lines, genotyped with single nucleotide polymorphism (SNP) markers. Phenotypic data were generated for 20 quantitative traits assessed in different environments. Best Linear Unbiased Estimates (BLUEs) for these traits were obtained for use in assessing GWAS models. Goodness of fit of GWAS models, derived using different combinations of kinship and population structure for all traits, was evaluated using Quantile-Quantile (Q-Q) plots and genomic control inflation factors (λ Solanum tuberosum Group tuberosum is a highly heterozygous, autotetraploid crop. Due to the many genetic complexities posed by autopolyploidy, most potato genetic studies have been performed at the diploid level using bi-parental populations. Significant recent progress has been made in the development of improved algorithms and software for linkage and QTL analysis in autopolyploid crop species  and simple sequence repeats (SSRs)  in potato.th edition . These trait BLUEs were used in GWAS as well as all other analyses involving phenotypic values.Field trials were conducted in 2012 and 2013 at two different sites . At each site replicated trials (two replicates) with two nitrogen levels (100 and 200 kg/ha) were conducted according to an alpha design, thereby leading to a total of eight ‘environments’. Twenty quantitative traits were measured, but not all were phenotyped in every environment (Table S1). The traits analyzed were plant height, yield, days to emergence (assessed as days to reach 50% emergence), stolon attachment , tuber uniformity , foliage , total number of tubers, tuber dry matter (percentage dry matter based on air-dried and water-immersed weights), eye depth , tuber shape (length to breadth ratio), tuber skin brightness , mean number of stems per plant , tubers per stem, tuber flesh color , after cooking blackening , enzymatic browning , tuber fry quality (percentage glucose in tubers stored at 6° with CIPC (Chlorpropham) for ∼3 months after harvest), dormancy break , ethylene treated tuber sprouting, and maturity. To generate phenotypic values for each trait, genotype was modeled as a fixed effect while all other effects were treated as random, and the Best Linear Unbiased Estimates (BLUEs) for all traits were calculated using REML implemented in Genstat 15https://www.sasa.gov.uk/) for further validation against the varietal SSR genotypic database held at SASA. Correspondence of phenotypic data between replicates, sites and years was also assessed for any gross inconsistencies. For all traits, residuals plots from REML analysis were examined for presence of any outliers and adherence to a normal distribution.Field trials were rogued by potato field staff for any discrepancies in the observed phenotypes and misidentification/mislabeling of clones. Genotypes which showed inconsistent phenotypes were sent for DNA fingerprinting at Science and Advice for Scottish Agriculture  and multiplied in 2011, the year preceding the first field trial. In addition, we included seven diverse diploid potato genotypes in the genotyping panel to act as ‘outgroups’ and to aid validation of population structure analysis. The diploid material included the sequenced doubled monoploid 44 clone , hereaftGenomic DNA was extracted from young leaf tissue from individual field grown plants using the Qiagen DNeasy Plant Maxi Kit (Qiagen), quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit , and normalized to a concentration of 30 ng/μL. The panel was genotyped at GenProbe  for the Infinium 8k Potato SNP Array  values which should follow a uniform distribution under the null hypothesis. Models were ranked using genomic control inflation factor (λGC) metric calculated using R package ‘GenABEL’  plots of the observed GenABEL’ . Bonferr2) was used to calculate correlations between marker-pairs using SNP dosage scores (0 to 4). LD was calculated based on marker pairs located within (a) chromosomal short arm, (b) chromosomal long arm, (c) euchromatin , (d) heterochromatin, and (e) whole chromosomal region for all 12 chromosomes. Extent of LD decay was estimated by implementing Quantile regression  and where r2 equals 0.1 on the 90th percentile, respectively. For whole chromosomal regions, extent of LD decay was also estimated using non-linear regression of r2 against the physical map distance according to Pearson correlation coefficient (rThe measure of polymorphic information content (PIC) for each SNP was calculated according to https://doi.org/10.25387/g3.6716033.Supplemental Material is available at figshare and brief captions for the uploaded material are provided here, as follows: Table S1 (traits phenotyped); Table S2 (germplasm included in the study); Table S3 (List of SNPs mapping to multiple positions); Table S4 ; Table S5 ; Table S6 (Size of pericentromeric heterochromatin); Table S7 (Pairwise measures of population differentiation); Table S8 (Significant marker-trait associations); Table S9 (Annotation of SolCAP SNPs used in the study); Table S10 (Genotype and phenotype data); Figure S1 ; Figure S2 (PCA performed on the genomic relationship matrix of the association panel clones); Figure S3 (Traits Pairs plot); Figure S4 and S5 ; Figure S6 (nMDS Screeplot); Figure S7 (population structure present in the association panel); Figure S8 .The germplasm panel displays a broad range of genotypic and phenotypic diversity, reflecting the diverse geographic origins, release dates, and market classes of the selected potato varieties and breeding clones. Distribution of minor allele frequencies (MAF) across all tetraploid genotypes is shown in The association panel was phenotyped for 20 quantitative traits in up to eight environments . A ‘pairs’ plot showing a scatterplot of the relationship between traits, the distribution of individual traits and the correlation between traits is presented in Figure S3. The residuals for all traits were normally distributed (data not shown). 2 statistic using pairwise combinations of SNPs present across all 12 chromosomes. Previously LD in potato has been assessed using a varying level of structural units ranging from a few fragments per chromosome  was within twofold limits of that observed for the euchromatin (2.73 Mb). Chromosome-scale LD blocks and decay patterns for all 12 chromosomes are shown in 1/10,90 at the whole chromosomal-region level, ranging from 2.54 Mb (Chromosome 9) to 4.68 Mb (Chromosome 3), whereas chromosome 8 displayed extremely conserved LD1/10,90 of 20.04 Mb. 1/10,90 = 8 Mb) in their autotetraploid Andigena population (N = 652). The r2 equalling 0.1 within a distance of 5 cM and 10 cM, respectively. In our study, overall LD1/10,90 for euchromatin and whole chromosomal regions were 2.73 Mb and 3.27 Mb, respectively. Relating physical size of the assembled potato genome  as implemented by 1/10 was 1.09 Mb which despite being derived from average r2 fitted using non-linear regression is still within reasonable LD decay limits expected for potato but much slower than that reported by Genotyping was carried out for 351 tetraploid and 7 diploid potato genotypes. Initial data quality checks revealed ten pairs of tetraploid genotypes showing more than 99% similarity. These genotypes also contained those clones which during the roguing of field trials had shown morphological features incompatible with those expected from previous knowledge. The identical pairs were DNA fingerprinted with SSRs and only the individuals, one per pair, for which phenotypic assessments and fingerprinting patterns matched with the potato database were retained, thereby, reducing the net number of genotypes in the tetraploid panel from 351 to 341.The individual relatedness between genotypes in the revised panel was estimated using a genomic relationship matrix. Kinship analysis assigned more closely related lines such as those belonging to individual breeding programs and/or possessing resistance to specific pest or disease  into ten smaller clusters (hereafter referred to as ‘Kinship groups’). Although the categorization of clones into distinct groups is subjective, the clustering of the panel into ten kinship groups is supported by the hierarchical clustering derived dendrogram . KinshipFst, Table S7) which further supports the absence of any strong population structure in the panel used here. These findings are in accordance with the observations made in previous studies  with a criterion of representing the full data set in the smallest possible number of dimensions. At the chosen nMDS solution, there was no clear visual separation of clones into distinct subpopulations indicating a lack of strong population structure within the panel . However, nMDS analysis was still able to detect subtle relationships among the clones, possibly resulting from the weak underlying stratification among genotypes, as depicted in the nMDS-based dendrogram  showing e.g., ‘GL’ series) formed distinct clusters and others were interspersed among the cultivar groups. The four diploid wild genotypes clustered into a separate group, which surprisingly included one tetraploid clone, 3053-18, a ‘late blight R-gene differential’ for the resistance gene R5. This result is likely due to close genetic relationship of 3053-18 to the late blight resistant wild species in this cluster, close relatives of which were used to introgress late blight resistance (S. tuberosum group Phureja cv. MayanGold and S. stenotomum) were clustered with one of the tetraploid clusters predominantly containing older  cultivars and advanced breeding lines.The correspondence of kinship groups to the nMDS-derived subpopulations was very high with six kinship groups nesting into their respective subpopulations as single large blocks. Members of the remaining kinship groups did show their assignment to separate subpopulations but still the allocation was in cohesive blocks of at least three genotypes. This allocation of members of individual kinship groups to more than one subpopulation could be attributed to their shared lineage. The details of individual members belonging to these kinship groups and subpopulations are present in Table S2a. The germplasm was also analyzed using phylogenetic analysis  by inclusistance . The remPopulation structure was also assessed using PCA  and STRUCTURE . Population structure obtained using PCA and nMDS was compared to visualize patterns of variation in terms of ‘country of origin’ and ‘year of release’. The assessment using ‘year of release’ failed to reveal any specific patterns for both PCA as well as nMDS (data not shown). This observation contrasts with that of Visualization of nMDS-based population structure in terms of ‘country of origin’ of each genotype yielded a more harmonious representation of the germplasm as compared to the PCA. The subpopulations were not clearly defined but a country-specific pattern was apparent . One subpopulation  comprised modern UK cultivars while another subpopulation  contained older UK varieties. Two interspersed subpopulations at the center (cyan and brown) primarily contained ‘mainland’ European germplasm; and the remaining one  included mainly international non-European material. The analysis suggests the majority of the UK potato cultivars represent more diverse genetic pools than rest of the European and non-European cultivated potato germplasm with limited admixture. Similarly in other studies, population stratification within a region has been reported. For example, two subpopulations of European winter wheat from multiple national programs  were prevs. observed –log10(p) values and genomic control inflation factors (λGC) achieved for each ‘trait x model’ combination. The success (ranking) of GWAS models in controlling the overall genome-wide type I error rate , summarized over 20 traits, is shown in All GWAS analyses described here as well as in the subsequent sections were performed on 290 tetraploid clones. This reduction in the association panel was due to clone identity issues, as described in the preceding section, as well as loss of some genotypes due to lack of enough seed tubers for successive field trials. Seed tubers for these genotypes were not supplemented from other sources to avoid inducing any bias arising due to variation in seed tuber generation as the seed material for the reported study was generated from a designated seed production site in the preceding year. Regarding markers, a total of 5,157 SNPs were retained for GWAS after filtering for missing data (>= 20%) and minor allele frequency (MAF=<0.05) thresholds. Performance of GWAS models was evaluated using Q-Q plots of the expected viz. STRUCTURE, principal component analysis and non-metric multidimensional scaling. GC estimates for Q and QK GWAS models involving Q matrices from above three methods and their overall rankings are provided in p-value inflation than nMDS and PCA while the latter two performed better for 6 traits each. Evaluation of these models as QK models  controlled the p-value inflation observed in Q models with different methods for population structure estimation and brought λGC close to 1. This further implies that with the inclusion of correction for kinship effects, the method used for population structure estimation is not very critical for performing GWAS in potato. Figure S13 and Figure S14 illustrate Q-Q plots obtained from Q and QK models tested using three different Q-matrices, respectively. Overall the results indicate that no single method was able to capture population confounding effects better than the others for all 20 traits. Nevertheless, when overall model performance was taken into consideration, nMDS seemed to provide slight advantage over PCA and STRUCTURE (Various methods have been used for controlling the confounding effects of population structure for GWAS. Here we examined GWAS models with population structure estimates (Q matrices) from three different methods TRUCTURE  and was GC values and rankings for these four models achieved for all 20 traits are presented in p-values close to the expected (λGC∼1), were comparable where K model was most effective for 7 traits and QK for 13 traits. Furthermore, comparison of the individual components of the QK model revealed that correction for Kinship was more efficient in overcoming the germplasm confounding effects than adjusting for population structure. As compared to K Model, Q Model never ranked first or second for any of the 20 traits and performed close to K and QK models for only one trait  to the K model. p-value inflation further confirms lack of significant population structure in potato.Four principal GWAS models  were investigated for all 20 traits. The λA careful assessment of kinship and population structure in GWAS studies is required to avoid spurious associations arising from systematic differences in allele frequencies due to the difference in sample ancestries. However, the results described above indicate that this may only be critical when the trait in question is also confounded with the kinship and/or population structure present in the germplasm. Overcompensation of mixed models for population structure and relatedness can lead to false negatives (type II error) as also reported by viz. Naïve, K, Q and QK for performing GWAS in potato and validate our findings using previously published reports. i.e., (a) from models where p-value inflation was close to the expected normal value (λGC∼1) and (b) which passed the set Bonferroni correction p-value (α = 0.05) threshold are included for reporting GWAS results. Effectiveness of K and QK models over Q and Naïve models in controlling type I error is clearly evident in the Manhattan plots  gene  lower and thus more significant than those reported by the QK model  with large effect QTL will have greater success with 8k SNP array than with the polygenic traits  with complex architecture as observed in the current and previous studies (The current study analyses LD in various subsets of the genome and reports a moderate level of LD decay in potato. The primary aim of GWAS is to filter real marker-trait associations that arise from physical linkage of loci from spurious effects that result as a consequence of potential confounders such as kinship and population structure. The germplasm examined here shows multiple levels of relatedness with a prominent kinship and weak population structure, despite which the nMDS analysis was able to reveal the underlying patterns of subpopulations in the panel. These subpopulations showed an apparent structuring according to country of origin and further provide novel insights into the breeding strategies adopted in Europe. Evaluation of various GWAS models for 20 traits further demonstrated the importance of applying trait-specific models and exploiting different population structure methods for performing association mapping in potato. The main finding reveals kinship, rather than population structure, as the major factor controlling the level of spurious associations in cultivated potato.