Equine recurrent laryngeal neuropathy (RLN) is a bilateral mononeuropathywith an unknown pathogenesis that significantly affects performance inThoroughbreds. A genetic contribution to the pathogenesis of RLN issuggested by the higher prevalence of the condition in offspring ofRLN-affected than unaffected stallions. To better understand RLNpathogenesis and its genetic basis, we performed a genome-wide association(GWAS) of 282 RLN-affected and 268 control Thoroughbreds.LCORL/NCAPG locus on ECA3 previously shown to affectbody size in horses. Using height at the withers of 505 of these horses, weconfirmed the strong association of this locus with body size, anddemonstrated a significant phenotypic and genetic correlation between heightand RLN grade in this cohort. Secondary genetic associations for RLN onECA18 and X did not correlate with withers height in our cohort, but didcontain candidate genes likely influencing muscle physiology and growth:myostatin (MSTN) and integral membrane protein 2A(ITM2A).We found a significant association of RLN with theThis linkage between body size and RLN suggests that selective breeding toreduce RLN prevalence would likely reduce adult size in this population.However, our results do not preclude the possibility of modifier loci thatattenuate RLN risk without reducing size or performance, or that the RLNrisk allele is distinct but tightly linked to the body size locus on ECA3.This study is both the largest body size GWAS and the largest RLN GWASwithin Thoroughbred horses to date, and suggests that improved understandingof the relationship between genetics, equine growth rate, and RLN prevalencemay significantly advance our understanding and management of thisdisease. Musculus cricoarytenoideusdorsalis (CAD), that prevents maximal abduction of the arytenoid cartilageand contributes to the development of airway obstruction. The degree of CADparesis/paralysis is graded endoscopically from 1 to 4 [Equine recurrent laryngeal neuropathy is a common upper respiratory tract disorder of horses and a significant cause oftheir reduced athletic performance . RLN repm 1 to 4  and a sim 1 to 4 .Recurrent laryngeal neuropathy is an important axonopathy for horses in competitiveevents, because the resultant paresis of the intrinsic laryngeal muscles leads toobstruction of air flow during intense exercise, an abnormal inspiratory noise knownas “roaring,” and, most importantly, reduced performance. Despite itsimportance, however, the etiology of the axonopathy is unknown. RLN has particulareconomic importance in Thoroughbred racehorses because these animals enter racetraining as 2-year-olds. Even though all horses going through sales areendoscopically examined for evidence of RLN, the condition may not yet be clinicallyevident in some animals  so a diath and 18thcentury that were crossbred to local mares [The prevalence of RLN varies, ranging from between 2-11% in Thoroughbreds ,9 and 35al mares . As a coal mares , making P = 1.62 × 10-6) and 31(P = 1.69 × 10-5) associatedwith RLN. Neither of these chromosome regions contained regions associated withneuropathies in other mammals or candidate genes that could be immediately linked toperipheral neuropathy. The present study reports on use of a newer Illumina EquineSNP70 BeadChip to conduct a GWAS on a large cohort of Thoroughbred horses that werephenotypically characterized for RLN and height. Specifically, we sought to identifyrisk loci associated with RLN and in doing so, gain additional insight into theetiopathogenesis of this disease. Because of the clinically reported connectionbetween height and RLN, we also examined their genetic association.The presence of genetic factors contributing to the pathogenesis of RLN is suggestedby the observation that offspring of RLN-affected stallions are more likely to beaffected with the disorder than are offspring from unaffected stallions ,16. ElucThe protocol for phenotypic characterization and obtaining blood samples and theinformed consent forms were approved by the Animal Use and Care Committee ofMichigan State University (MSU) and Cornell University. Information on the risksand benefits of the investigation was provided to the horse trainers/owners andinformed consent was signed before at the time the sample was taken. Phenotypicdata and venous blood samples (collected in 10 ml EDTA tubes) were obtainedfrom 573 Thoroughbred horses evaluated by large-animal specialists from twoveterinary teaching hospitals (Michigan State University and Cornell University)and by participating board-certified veterinary surgeons at private practicesthroughout the US (n = 10). A total of 505 horses at least2 years old had recorded heights. In each unsedated horse, avideoendoscopic examination of the larynx was performed and a laryngeal grade of1 to 4 [Samples submitted by practitioners were shipped on dry ice to the PulmonaryLaboratory, MSU and then stored at -20°C. All samples were then processedby personnel in the Cornell Veterinary Biobank . DNA wasThe 573 Thoroughbred samples were genotyped using Illumina Equine SNP70 arrayscontaining 65,157 markers. Genotype calls were made in GenomeStudio using aGenCall threshold of 0.25. Twenty samples with high missingness were re-run andan additional 29 samples passing QC were run multiple times as technicalreplicates. A total of 1,470 SNPs were excluded for high missingness (>10%)and 7,751 monomorphic or rare SNPs  were excluded. Duplicate concordance at theremaining sites was >99.99%.P < 1 × 10-4based on Hardy-Weinberg equilibrium) and 24 were excluded for low heterozygosity(P < 1 × 10-32);filtering was more relaxed for low heterozygosity because population structurewould be expected to depress heterozygosity somewhat from Hardy-Weinbergexpectation. The final unphased dataset contained 566 horses genotyped at 56,679markers with a mean call rate of 99.77%. Principal component analysis wascomputed using Eigenstrat [For the final dataset, only one array per sample was retained, and an additionalseven samples were excluded because their imputed genotypic sex did not matchthe phenotypic records, indicating a possible misidentification of thesesamples. Two SNPs (BIEC2_403636 and BIEC2_424368) were excluded because theyshowed evidence of probe hybridization to multiple genomic locations, and twoother SNPs (BIEC2_346860 and BIEC2_840760) were excluded due to low duplicateconcordance rates. Additionally, 197 markers were excluded for highheterozygosity  and afamily-wise error rate of α = 0.05.Genome-wide association was performed using GEMMA  after imTo assess association to haplotypes in the ECA3 candidate region a set of SNPscentered on BIEC2_808543 and encompassing approximately 2 Mb(ECA3:104737609–106517004) was selected. We further filtered this set toinclude only markers with a genotyping rate > 99% and aMAF > 0.01, leaving 45 remaining markers. Linkage disequilibriumbetween markers was examined using Haploview v4.2 . HaplotyLCORL) and non-SMC condensing I complex,subunit G (NCAPG) genes and correcting for sex and gelding, the strongest association with RLNwas centered on a SNP, BIEC2_808543, adjacent to the ligand-dependent nuclearreceptor corepressor-like (P = 1.3 × 10-22) centeredon the same SNP, BIEC2_808543, in LCORL/NCAPG locus that was thestrongest association for RLN. All 12 SNPs passing the Bonferroni significancethreshold were in this region . We estimated thatthe entire SNP70 panel explains 0.24 ± 0.11 of the variation inwithers height after accounting for sex and gelding [We performed GWAS on height using sex  as acategorical covariate for the 505 horses of at least two years of age withrecorded heights Figure B. The st gelding .LCORL/NCAPG minor (G) allele count at BIEC2_808543 hadan effect of increasing height 3.7 cm (S.E. = 0.4) and explained 16.6% ofthe variation in height in the dataset. This effect was roughly additive andconsistent in sires, mares, and geldings (Table P = 0.08). GWASfor height including BIEC2_808543 genotype as a covariate did not reveal anyadditional associated regions  analysis  to estimP = 8.18 × 10-23). Thisgenomic region includes both the LCORL and NCPG genes , acompact muscle mass close to the trunk, and high rates of maximal oxygenconsumption. Although selection for height apparently has been accompanied by anincreased prevalence of RLN, the genetic basis for this is currently unknown.The current study, which used the largest cohort of RLN-affected and unaffectedhorses ever assembled from a single breed revealed a very strong genetic correlationbetween RLN and height. This association was previously well known across breeds, buthas never been demonstrated over the small range of heights (152 to 178 cm)spanned in the present study. This was evident not only in the clinical observations, but also in the GWAS results. The haplotypeassociations for height and RLN were coincident and the genetic correlation betweenthe traits was >90%.The phenotype of RLN was defined based on the presence (cases) or absence (controls)of endoscopically visible failure of abduction of the left arytenoid and associatedvocal fold. This clinical and functional definition was used because veterinariansin the field use it and because it has important implications for athleticperformance. There were two reasons for selection of grade 3 as the threshold scorefor a case: first, horses with this grade are unable to maintain vocal foldabduction and therefore require surgical correction to restore their performance;and second, failure of abduction is easily recognized endoscopically by experiencedveterinarians . However, histopathological examination ofthe intrinsic muscles of the larynx can reveal muscle fiber-type regrouping(7evidence of axon death and reinnervation from a neighboring axon) in some horsesthat do not have clinical evidence of laryngeal paresis  and therLCORL/NCAPG region and body size in the horse [LCORL and NCAPG in growth is supported by well-validatedassociations to human height [Recently, several independent studies have documented strong associations between thehe horse ,31, but n height ,33, and n height .HMGA2, ZFAT, and LASP1) as well as aThoroughbred-specific QTL on ECA28, but no signal for these loci was detected inthis dataset [HMGA2 is poor on the IlluminaSNP70 product, the same markers for ZFAT are included in this analysis andsegregate at ~ 20% in our cohort, but do not appear to impact withers height. Theassociation of ZFAT with height is replicated in a study of 1,077Franches-Montagnes horses [ZFAT may bedependent on genetic background. The previous estimate that 59% of the variation inheight in Thoroughbreds was explained by LCORL/NCAPG and the QTLon ECA28 is significantly higher than our estimate of 24% based on the entire SNPdataset. This may reflect an overestimation in their analysis due to small samplesize (N = 48) or an underestimation in ours due to the small range ofThoroughbred heights, environmental variability, and/or measurement error.Within the Thoroughbred breed, the ECA3 locus appears to have the single largesteffect on withers height Figure B. Previo dataset . Althougs horses , suggestLCORL and NCAPG are genes encoding transcriptionfactors and could have diverse targets with varying physiological functions. Thus,the same causative mutation may be implicated for both body size and RLN phenotypes.Lastly, given extensive LD across the ECA3 locus, RLN and body size may beinfluenced by two independent genetic changes associated by linkage. Further workwill be needed to distinguish which of these three hypotheses is the most likelyexplanation for the association between this ECA3 locus and RLN.Our study confirms anecdotal evidence and previous reports in draft breedsidentifying an association between RLN and large size in horses ,12. HoweLCORL/NCAPG locus, the chromosome 18 and X candidate loci arenear to genes that are likely to affect muscle physiology and growth in horses(MSTN and ITM2A). Interestingly, the primary associationsignal in [MYO9B), a candidate gene thatmay be very relevant to the pathology of a distal axonopathy like RLN. Yet, ourstudy suggests that QTLs on chromosome 21 and 31 identified by Dupuis etal.[Besides the ignal in  is also is etal. are not is etal.; Dupuis The results of this study add further confirmation to the association between heightand the clinical occurrence of RLN and indicate the close relationship between thegenetic loci determining these two characteristics. However, despite beingstatistically significant, LCORL/NCAPG accounted for only 6% of the variation inRLN. However, in these Thoroughbreds, the proportion of variation of RLN explainedby the entire SNP70 set (“chip heritability”) was 45%, which, althoughmuch higher than in most complex diseases , suggestSome of the information in this paper has been used in a patent application.ARB and SAB participated heavily in experimental design, data analysis, andmanuscript preparation. ALB was instrumental in establishing the cohort of samples.EC prepared DNA samples, established the DNA bank, and participated in dataanalysis. KCO, RJT and ZZ participated in experimental design and data analysis. MCprepared samples and was involved in data analysis. DMA contributed samples to thecohort, was involved in data analysis and manuscript preparation. JES wrote thefirst plan to establish the cohort and conducted the genetic analysis (she is now inprivate industry). NER (the corresponding author) assisted JES with the initialexperimental design, oversaw and participated in collecting the majority of thesamples for the cohort, participated in discussions of data, and prepared the finalmanuscript submission. All authors reviewed the final manuscript.Principal component analysis (PCA) of genotype data shows nostratification according to RLN status or evidence for significantpopulation substructure within the cohort. Red = RLNaffected, black = control.Click here for fileSummary of horses used for height and RLN association mapping.Click here for fileLCORL/NCAPG. The RLN association (A) is shown with sex asthe covariate in GEMMA  and height (B). There is little genomic inflation ofP-values in the data in uncorrected PLINK (gray) or population structurecorrected GEMMA (black) association scans for height (B) in our dataset.The top dozen SNPs are all linked to the ECA3 locus nearA Figure B. GenomiClick here for fileA: GWAS of RLN using sex, gelding, and height as covariates yieldsadditional suggestive associations on ECA18 and ECA23 (489 horses: 274cases and 215 controls). B: GWAS of RLN without covariates yields anadditional signal on chromosome X.Click here for fileLCORL/NCAPG) allele count as covariates shows noother significant genetic associations with these traits.Genome-wide association of RLN (A) and height (B) using sex, gelding, andBIEC2_808543 (Click here for filePartitioning the variance explained by each chromosome for height(x-axis) and RLN grade (y-axis) in our sample (550 horses) afteraccounting for sex and gelding as covariates. The variance explained forRLN case/control was not estimable by the REML method with sex andgelding covariates; therefore, RLN grade (1-4) was used instead. REMLestimated total variance explained genome-wide for height and RLN gradeis 59% and 61%, respectively, somewhat higher than the estimatesobtained by the LMM method or by bivariate REML (see Methods).Click here for fileP-values resulting from a haplotype association test for withersheight.Click here for fileP-values resulting from a haplotype association test for RLN status(case/control).Click here for file


However, combination therapy increased cancer cell apoptosis, decreased tumour cell proliferation and enhanced survival, owing to a synergistic effect of the drugs on suppression of the PI3K pathway . Therefore, this model represents a valuable preclinical system for advancing current therapies and for testing novel drugs and drug combinations against GBM. 45Page Gliobastoma multiforme (GBM) is an aggressive malignant form of brain tumour for which effective therapies are limited. Genetically engineered mouse models (GEMMs) harbouring mutations in components of the main signalling pathways that are altered in human GBM – including the receptor tyrosine kinase (RTK)–RAS– phosphoinositide-3-kinase (PI3K) pathway – are available. However, these GEMMs display a long latency to tumorigenesis and advanced tumour heterogeneity, which represents a challenge in preclinical drug testing. In this study, Zoë Weaver Ohler and colleagues developed an orthotopic and tractable mouse model of GBM by transplanting brain tumour cells derived from GBM-GEMMs into the brain  of syngeneic mice . This model develops GBM with features of the human disease, including high vascularisation and aggressive invasion of surrounding tissues, and thus was used to test the effects of two drugs that are currently in clinical trials for GBM and other solid tumours, namely BKM120 and PD0325901 . When tested


Inteins are nature's escape artists; they facilitate their excision from flanking polypeptides (exteins) concomitant with extein ligation to produce a mature host protein. Splicing requires sequential nucleophilic displacement reactions catalyzed by strategies similar to proteases and asparagine lyases. Inteins require precise reaction coordination rather than rapid turnover or tight substrate binding because they are single turnover enzymes with covalently linked substrates. This has allowed inteins to explore alternative mechanisms with different steps or to use different methods for activation and coordination of the steps. Pressing issues include understanding the underlying details of catalysis and how the splicing steps are controlled. Saccharomyces cerevisiae Sce VMA1 vacuolar ATPase gene was so large. Two years later two groups showed that a section of the VMA1 gene was absent in the mature ATPase , the intein penultimate HisG:6, and the less conserved positions 4 and 13 in block F .–20.17–20– block F . Positiotrans by the same mechanisms used in cis-splicing inteins 36–Inteins come in many flavors. Most inteins are large chimeras containing both a splicing domain and the same type of endonucleases that mediate intron mobility  20–22).–22.20–22–23– inteins –32. Bothinteins –. Intein inteins –, 35. IntEstablishing a universal mechanism for all inteins at the atomic level is unlikely because the methods used to promote each rearrangement and the roles played by the assisting residues vary. Instead, we will discuss both the canonical and the alternative protein splicing mechanisms, and general strategies for catalysis and coordination of the steps. Detailed mechanism reviews are available , 9, 40, Conversion of amide bonds to enzyme-linked thioesters or esters, as in step 1 of protein splicing, is a common method of catalysis used by proteases and autoprocessing enzymes including glycosyltransferases and pyruvoyl enzymes , 9, 40. Hedgehog-intein) fold of the intein splicing domain  uses Lys58 to activate the N-terminal splice site by possibly stabilizing the initial N–S acyl shift tetrahedral intermediate (58 lies outside the conserved intein motifs (B:10) that potentially activates the N-terminal nucleophile , stabilizing the tetrahedral intermediate, and/or influencing how the tetrahedral intermediate is resolved. Likely contributors again include ThrB:7, HisB:10, and AspF:4. A close look at the Mycobacterium tuberculosis Mtu RecA intein provides several lines of evidence to support these strategies. NMR and quantum mechanical/molecular mechanics studies suggest that HisB:10 may deprotonate the thiol of the N-terminal Cys1 to drive formation of the tetrahedral intermediate and then donate the proton to the Cys1 α-amino group to resolve the tetrahedral intermediate as a thioester  the tetrahedral intermediate is stabilized by charged HisF:13 and HisG:6 residues, and 3) the electrophilicity of the backbone amide may be increased by HisG:6  Hisy HisG:6 , 67–69. isG:6 67–, 70, 71,isG:6 67–. SeparatisG:6 67–.F:13 and HisG:6 are not required for Asn cyclization in all inteins  that can undergo cyclization to cleave the C-terminal splice site  intein can splice with a native C-terminal Gln more efficiently than with Asn ester intermediate ester intermediate  that activate class 1 N-terminal splice sites are also required in class 2 inteins  conformational changes triggered by a preceding step result in formation of a robust active site for the next step and 2) differences in kinetic rates for each step ensure correct reaction order. Conformational changes may be as simple as fixing different rotamer positions, or they may involve larger movements. Evidence for conformational control can be inferred from the absolute coupling of N-terminal and C-terminal reactions observed in some inteins where C-terminal cleavage only occurs if preceding steps have been completed , 84, 85.G:6, AsnG:7, and Cys+1 (G:8) backbone torsional angles could enable a close approach of the Cys+1 nucleophile to the N-terminal scissile bond (The most common argument for larger scale movement in intein active sites comes from intein structures. Only the Sce VMA intein  and the ile bond . It remaF:4 hydrogen-bonds to AsnG:7, preventing Asn cyclization until branched intermediate formation causes reorientation of the AspF:4 side chain (−1 preventing proper orientation of ArgB:11 until formation of the linear and/or branched thioester intermediate results in movement of the Tyr−1 side chain, allowing the ArgB:11 side chain to reorient and assist Asn cyclization (Movement of side chains during splicing can coordinate the reaction by the gain or loss of hydrogen bonds and changes in van der Waals packing interactions to align catalytic residues. For example, structures of the Pho RadA intein suggest that Aspde chain . Anotherlization . In the lization .G:7 with the more common AsnG:7 accelerated C-terminal cleavage by 20-fold and the overall splicing reaction by 3-fold. The naturally split Npu and Ssp DnaE inteins have been extensively investigated as model systems for intein kinetics because it is easy to initiate reactions by mixing fragments (87–Kinetic data can provide further insight into how inteins control the steps of splicing. Asn cyclization is the slowest step for most inteins studied to date, including the Pab Pol II intein , the splragments , 87–90. ments 87–. Whereasments 87–, 49. Altments 87–, 89. Recments 87–.The class 2 Mja KlbA intein was studied using a semisynthetic intein precursor that could be induced to splice with a redox switch . BrancheIn all of these inteins, the rates of Asn cyclization are comparable or slower than preceding steps, ensuring that Asn cyclization will not precede extein ligation, which occurs during branched intermediate formation. Kinetic control complements coordination strategies involving conformational changes. This was shown experimentally in the Mxe GyrA intein, where the rate of C-terminal cleavage increased 10-fold when a branched intermediate was present .cis-splicing and trans-splicing inteins are sensitive to inhibition by divalent cations (96–Inteins have evolved to tightly regulate the steps of splicing. This is essential as inteins interrupt highly conserved domains of proteins important to their host organisms, including DNA polymerases and helicases . However cations , 96–98, tions 96–, 99, 100Remaining mechanistic challenges include deciphering how reactions are coordinated and illuminating the diverse ways that inteins promote catalysis. Going forward, detailed studies of catalytic mechanisms, intein kinetics, and structures must occur in the context of native host exteins, which will distinguish between physiologically significant observations and those that may be artifacts of heterologous model systems. Furthermore, detailed studies of multiple inteins will determine whether catalytic strategies are universal or specific to a subset of inteins.The plethora of reactions performed by HINT domain proteins highlights the robust and flexible nature of catalysis when rapid turnover and substrate binding are not required. This allows for survival of mutated inteins as long as compensatory residues are present to permit a low level of splicing and provides time for the intein to evolve into a more efficient enzyme by testing new catalytic strategies. Thus the flexibility of inteins, BILs, and Hedgehog proteins provides a blueprint for modifying enzyme activity by varying nucleophiles and strategies to activate these nucleophiles.


Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy.n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity . Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods.The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity.A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens. Prostate carcinoma accounts for a quarter of cancer diagnoses in men in the United Kingdom and is the fourth most common cancer worldwide with an estimated 1.1 million men diagnosed in 2012 . It is eLate toxicity following irradiation for prostate cancer includes damage to the bladder, bowel and erectile function  associated with prostate cancer susceptibility (N=533), RADIOGEN (N=597), GenePARE (N=290) and CCI (N=150). Informed consent was obtained from all patients. RAPPER was approved by the Cambridge South Research Ethics Committee (05/Q0108/365). RADIOGEN was approved by the Galician Ethical Committee. GenePARE was approved by the Mount Sinai Medical Center Institutional Review Board. The CCI study was approved by the Health Research Ethics Board of Alberta (Cancer).This prospective study involved four prostate cancer radiotherapy cohorts: RAPPER (The UK RAPPER study (UKCRN1471) recruited patients who received neoadjuvant androgen suppression and external beam radiotherapy (EBRT) from two clinical trials RT01 (ISRCTN47772397) and CHHiP (ISRCTN97182923). A full description of the cohort is available elsewhere  was used in patients undergoing brachytherapy alone and 103Pd (100 Gy) in patients also receiving EBRT. The EBRT regimen was delivered using 3D conformal technique using 24–50 Gy. External beam radiotherapy alone was delivered using IMRT using 66.6–81 Gy, and further full details can be found elsewhere  or conventional (72–82 Gy delivered in 2 Gy per fraction) schedule. Approximately 50% of patients received androgen suppression. Further treatment details can be found elsewhere . Samples that had >3% of all variants missing were removed. Allele frequencies are known to vary by ancestral background, and hence principle component analysis (PCA) was used to identify and exclude individuals with non-European ancestry in order to avoid false positive associations arising from population substructure because of the small number of participants with other ethnicities. Comparable sets of variants were produced through imputation using SHAPEIT , rectal volume , transurethral resection of the prostate (TURP) before radiotherapy (urinary end points only) and baseline toxicity  and polygenic risk score would be 99% for a toxicity end point with 15% prevalence (grade ⩾1) and 96% for a toxicity end point with 6% prevalence (grade ⩾1). This difference in mean risk would be equivalent to a relative risk of toxicity of 1.4 for the subset of patients with a higher mean polygenic risk of prostate cancer.This study was well powered to detect significant associations between prostate cancer polygenic risk scores and common radiotherapy toxicity end points. Assuming a moderate difference of 0.34 in mean polygenic risk score between prostate cancer patients who experience toxicity and those who do not, with a significance level of R2>0.3; The distributions of patient characteristics, toxicity end points and STAT scores are summarised in P>0.35 and P>0.33 for non-weighted and weighted scores respectively; P-value <5 × 10−4 in any of the individual studies or on meta-analysis , the development of fibrosis  and scavenging of reactive oxygen species . Although significant associations were reported, replication was often unsuccessful (Studies investigating genetic variation in relation to risk of radiotherapy toxicity focused initially on ccessful .The study reported here had a number of limitations. First, the findings are limited to prostate cancer risk conferred by common variants only – many thousands of participants will need to be studied to assess a role for rare variants. Second, our analysis was limited to men who were genetically of European ancestry and therefore the conclusions may not be generalisable to men of other ethnicities. Third, many genes that predispose to prostate cancer have not yet been identified. Fourth, there are likely to be unrecorded toxicities in patients because underreporting is a known issue of data collection in radiotherapy studies (Only 33% of common germline variants that predict the familial risk of developing prostate cancer have so far been discovered (In summary, this work showed that there is no association between genetic susceptibility to developing prostate cancer and the development of late radiation toxicity. The implication of this finding is that standard RT for prostate cancer can be given to patients with an increased genetic burden for prostate cancer without the risk of increased late radiotherapy toxicity.


Xanthomonadaceae and Rhizobiaceae growth, supporting their role in nitrate metabolism. (Per)chlorate showed distinct effects on microbial community structure compared with nitrate and resulted in a general suppression of the community relative to the untreated control combined with a significant decrease in sulfate reducing species abundance indicating specific toxicity. Furthermore, chlorate stimulated Pseudomonadaceae and Pseudoalteromonadaceae, members of which are known chlorate respirers, suggesting that chlorate may also control sulfidogenesis by biocompetitive exclusion of sulfate-reduction. Perchlorate addition stimulated Desulfobulbaceae and Desulfomonadaceae, which contain sulfide oxidizing and elemental sulfur-reducing species respectively, suggesting that effluent sulfide concentrations may be controlled through sulfur redox cycling in addition to toxicity and biocompetitive exclusion. Sulfur isotope analyses further support sulfur cycling in the columns, even when sulfide is not detected. This study indicates that (per)chlorate show great promise as inhibitors of sulfidogenesis in natural communities and provides insight into which organisms and respiratory processes are involved.Microbial sulfate reduction is a primary cause of oil reservoir souring. Here we show that amendment with chlorate or perchlorate [collectively (per)chlorate] potentially resolves this issue. Triplicate packed columns inoculated with marine sediment were flushed with coastal water amended with yeast extract and one of nitrate, chlorate, or perchlorate. Results showed that although sulfide production was dramatically reduced by all treatments, effluent sulfide was observed in the nitrate (10 mM) treatment after an initial inhibition period. In contrast, no effluent sulfide was observed with (per)chlorate (10 mM). Microbial community analyses indicated temporal community shifts and phylogenetic clustering by treatment. Nitrate addition stimulated  As such, global reliance on fossil energy and oil recovery will likely continue to dominate in the near future. An important aspect of oil recovery is control of reservoir bio-souring, which is the result of in situ hydrogen sulfide (H2S) biogeneration, typically after initiation of secondary recovery processes involving injection of sulfate-rich seawater , the generation of H2S by sulfate reducing microorganisms (SRM) poses significant health  is significantly higher than that coupled to sulfate reduction (Δ Go' = −179 kJmol−1 toluene) chlorate into injection waters and the stimulation of the activity of dissimilatory (per)chlorate reducing bacteria (DPRB) in oil reservoirs. The advantage of this approach is that in addition to thermodynamic preference  relative to sulfate reduction (Eo' = −217 mV), previous studies  was monitored by analyzing influent and effluent geochemistry and sulfur isotopes, while community 16S ribosomal RNA gene analysis was used to gain insight into the shifts in microbial community composition.2) San Francisco Bay water (19–33 mM sulfate concentration) containing 1 g.L−1 yeast extract as a non-selective labile carbon source. Treatments consisted of 10 mM sodium nitrate, 10 mM sodium chlorate, 10 mM sodium perchlorate, or a no treatment control. The treatment concentration was briefly reduced to 5 mM for all three treatments at day 35 for a period of 3 days and then returned to 10 mM to study the impact of lower treatment concentration on the column geochemistry. The control columns were unchanged during this time period. All four treatments were run with triplicate columns and identical flow rates. During the initial 28 days of the study, the columns were continuously flooded at 0.1 mL.min−1 for 2 days (estimated retention time 2.78 h) with subsequent 2 days of no feed. After 20 days the flow rate was decreased to 0.025 mL.min−1 (2 days of flow at 0.025 mL/min followed by 2 days of no flow). Continuous flow at 0.025 mL.min−1 was established from day 28 with an estimated retention time of 11.11 h and a variance of less than 1% (±0.00025 mL.min−1). The columns were run for a total of 51 days. As total flow was very similar between all columns regardless of treatment, a cross-comparison of column treatments could be reasonably achieved.Triplicate flow-through columns were constructed from sealed 50 mL glass syringes packed with a mixture of about 50% San Francisco Bay sediment  and about 50% glass beads . The constructed columns were flooded with autoclaved anoxic (boiled and degassed with N−1. Perchlorate was quantified on a Dionex IC 2100 equipped with an AS16-HC anion-exchange column (Dionex IC2100) with a 25–65 mM potassium hydroxide gradient at a flow rate of 1 mL.min−1. Sulfide concentrations were quantified using a Cline assay  × 1000, where R = 34S/32S, and the value is reported in per mil (‰) units relative to the Canyon Diablo Troilite standard (Rstd = 0.0441216).Sulfur isotope analysis of dissolved sulfate was conducted on samples selected from one replicate column of each treatment. Sulfate was first precipitated as barium sulfate by adding excess barium chloride. The precipitate was rinsed three times in deionized water before being dried for analysis and sulfur isotope ratios were measured using a Eurovector 3028 elemental analyzer in helium continuous flow mode with a GV Isoprime isotope ratio mass spectrometer. Instrumental precision as assessed on external standards was ±0.2‰. Sulfur isotope ratios are reported in standard delta notation, δTo characterize changes in the microbial community due to the various treatments sediment samples were collected from the top (outlet) of the columns, DNA was isolated from the initial columns before flow began (designated inoculum) and from each of the triplicate columns for each treatment at four other time points  using a Mo Bio PowerSoil DNA isolation kit  following the manufacturers protocol. DNA was quality assessed by agarose gel electrophoresis. PCR amplification was conducted as previously described  and Excel . OTU data was square root transformed and normalized. Hierarchical clustering based on group average  and nonmetric multidimensional scaling (nMDS), both based on Bray-Curtis similarity matrices were used to assess clustering amongst the samples. Both had low stress values  indicating that the plots were indeed a good representation of the data. Similarity Profile (SimProf) was used as a statistical measure to determine significance to the groupings identified in the Hierarchical clustering at a 5% significance level.Similarity percentage (SIMPER) based on the Bray-Curtis similarity matrix was used to determine the OTUs contributing to the top 10% of differences between the groups. These OTUs were sorted by family and phylum. Relative abundance values for all OTUs within a family or phylum were summed. The total abundance differences between groups for each family were then calculated for each treatment.Relative richness (measure of the number of different OTUs in a community) was calculated using OTU presence/absence calls from the PhyloChip data. Relative diversity and evenness were calculated using Shannon's diversity and equitability measures, respectively. Evenness is a measure of equality of the community (the relative abundance of the different OTUs present in the community). These calculations were based on the intensity values from the PhyloChip. Because PhyloChip abundance is based on hybridization scores (intensity) relative, not absolute, diversity, and evenness were calculated.3− + 6Fe2+ + 9H2O → 6FeOOH + Cl− + 12H+  + 3So + 12H2O resulting in redox cycling of the iron as a catalyst and abiotic sulfide oxidation coupled to chlorate reduction. The rapid decrease in effluent chlorate concentrations is probably a combination of both abiotic consumption catalyzed by the Fe(II) content of the column matrices and microbial community adaptation resulting in microbial chlorate respiration. In contrast to both nitrate and chlorate, perchlorate concentrations remained above 6 mM for the first 17 days of the study and then decreased to below detection by day 21 chlorate on souring in comparison to nitrate in a dynamic sediment system, we monitored the influent and effluent ion concentrations of triplicate up-flow columns for each treatment. Influent nitrate, chlorate, and perchlorate concentrations were kept constant (~10 mM) throughout the study except for a brief period (day 35–38) when the treatment concentration was decreased by 50% to 5 mM Figure . Of note+ Figure . The fer1 Figure . The dif1 Figure   after 11 days, which is equivalent to approximately 40% removal  by the final day of the study (day 51). During the last 10 days of column operation the average effluent sulfide concentration was 16.44 ± 4.83 mM, which stoichiometrically balanced the sulfate removal (18.2 ± 4.55 mM) during the same timeframe.Throughout operation, some variation in influent sulfate concentrations (19–33 mM) occurred due to tidal mixing with freshwater sources (Sacramento River) at the location of water collection in San Francisco Bay Figure . In the t-test, p-value = 0.41). In support of this, sulfide concentrations in the effluent remained below detection (<0.1 mM) during this timeframe , despite variations in the influent sulfate concentration , with high values corresponding to samples taken after periods of no-flow alongside a greater extent of sulfate reduction within the columns  reflect the period of stable continuous flow and a general community δ34S fractionation value.Sulfur isotope ratios of dissolved sulfate were used to gain further insight into sulfur cycling within the columns. Microbial sulfate reduction results in a well-established shift in the δt Figure . It is nn Figure  providin34S values . This discrepancy between the (per)chlorate and nitrate treatment is consistent with active redox cycling between sulfide and sulfate in the nitrate columns in which the isotopic sulfate-S signature is masked by the complete microbial oxidation of sulfide to sulfate by sulfur-oxidizing nitrate-reducing bacteria as previously observed  indicated an overall grouping by treatment Figure . In two-Relative diversity and evenness calculations indicated that the perchlorate treatment had the highest relative evenness Figure . The perDesulfovibrionaceae and Desulfobacteraceae  and Gammaproteobacteria containing the majority of the known chlorate reducing species (Pseudomonas) , Verrucomicrobia, Euryarcheota, and Tenericutes Figure . Chlorat Figures , 5B. IntXanthamonadaceae and Pseudomonadaceae within the Gammaproteobacteria. These families contain ubiquitous members known as canonical nitrate reducing organisms (Xanthamonas and Pseudomonas species).In contrast to both perchlorate and chlorate, while nitrate treatment did have some inhibitory effect on members of the microbial community relative to the inoculum, there was also a strong enrichment of specific phyla Figures . This reDesulfovibrionaceae, Desulfobacteraceae, and Desulfomicrobiaceae), while nitrate only showed inhibition of the Desulfobacteraceae relative to the control reactors  relative to the no treatment control . This result further suggests that community adaptation to perchlorate was slow relative to its inhibitory effect as a whole and diverse growth was not associated with its presence. The result also suggests that members of one or more of the Desulfuromonadaceae, Desulfobulbaceae, and Xanthamonadaceae families have the previously unrecognized ability to respire perchlorate. Helicobacteraceae includes the known perchlorate reducer Wolinella succinogenes. Relative to the chlorate treatment, perchlorate had the greatest inhibitory impact on members of the Deltaproteobacteria and Gammaproteobacteria chlorate on microbial sub-populations and the preferential thermodynamics of (per)chlorate respiration.34S sulfate values and elevated sulfide in the nitrate treatment is consistent with active sulfur redox cycling in the nitrate columns in which the δ34S isotopic signature of sulfate reduction is  masked by the microbial oxidation of sulfide to sulfate by NDSO microorganisms as previously observed  and the light biogenic sulfate from sulfide oxidation . Ultimately, in a nitrate treated column, effluent sulfate δ34S will depend on the relative importance of the biocompetitive exclusion mechanism and the sulfide re-oxidation pathway, as seen previously by Hubert et al.  that are present in a broad diversity of bacterial phylotypes in mixed microbial ecosystems  reducing members of this class include Desulfurellaceae, Pelobacteraceae, and Desulfuromonadaceae  to be primarily SRMs, in our reactors they may act as elemental sulfur reducers or sulfide oxidizers because members of this family are primarily enriched in treated columns where no net sulfide production is observed. Obligately S0 reducing families (Desulfuromonadaceae) are also enriched in (per)chlorate treatments vs. both nitrate treatment and the no treatment controls. Based on known metabolic capabilities of members of the Desulfobulbaceae  and the Desulfuromonadaceae, the enrichment of these organisms may involve the redox cycling of sulfur between H2S and elemental sulfur which supports earlier studies demonstrating that all DPRB innately oxidize H2S to elemental sulfur chlorate columns.Despite the fact that no sulfide was measured in the effluent from the (per)chlorate columns both the isotopic measurements and community analysis support the fact that (per)chlorate indeed stimulates sulfur cycling in the column system. (Per)chlorate treatment enriches for organisms known to be capable of sulfide oxidation, STaken together our findings indicate that (per)chlorate and nitrate are mechanistically distinct inhibitors of sulfide production in complex natural ecosystems. The apparently greater inhibitory potency of (per)chlorate relative to nitrate and potentially favorable shifts in geochemistry and microbial communities suggest that (per)chlorate may be a promising alternative to nitrate for controlling bio-souring in industrial ecosystems. Currently, the potential for perchlorate respiration in an oil reservoir is unknown. Future studies are needed to investigate the application of perchlorate with sulfidogenic samples from industrial ecosystems including oil reservoirs using crude oil or produced water organic components as the electron donor(s).The corresponding author has an IP submission based on souring control. The Associate Editor M. Youngblut declares that despite being affiliated to the same institution as the author(s) J. D. Coates, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mammalian photoreceptors contain specialised connecting cilia that connect the inner (IS) to the outer segments (OS). Dysfunction of the connecting cilia due to mutations in ciliary proteins are a common cause of the inherited retinal dystrophy retinitis pigmentosa (RP). Mutations affecting the Retinitis Pigmentosa GTPase Regulator (RPGR) protein is one such cause, affecting 10–20% of all people with RP and the majority of those with X-linked RP. RPGR is located in photoreceptor connecting cilia. It interacts with a wide variety of ciliary proteins, but its exact function is unknown. Recently, there have been important advances both in our understanding of RPGR function and towards the development of a therapy. This review summarises the existing literature on human RPGR function and dysfunction, and suggests that RPGR plays a role in the function of the ciliary gate, which controls access of both membrane and soluble proteins to the photoreceptor outer segment. We discuss key models used to investigate and treat RPGR disease and suggest that gene augmentation therapy offers a realistic therapeutic approach, although important questions still remain to be answered, while cell replacement therapy based on retinal progenitor cells represents a more distant prospect. •We discuss the proposed function(s) of the RPGR protein in photoreceptor maintenance.•We highlight the animal models used in the field to research Rpgr.•We discuss recent advances in gene therapy as a possible treatment for RPGR mutations. There are syndromal and non-syndromal forms of RP and digenic forms have also been described (RPGR gene account for 70–90% of the X-linked form of RP (XLRP) and 10–20% of all RP. Mutations in the RP2 gene account for most of the remaining ∼20% of XLRP. XLRP is associated with a severe phenotype with marked rod and later cone cell death, extinguished rod electroretinogram and visual loss, commonly starting within the first few decades of life. At present, there is no treatment for disease caused by RPGR mutations (“RPGR disease”). However, an improved understanding of the protein's function has emerged in parallel with the emergence of novel technologies to model or potentially treat RPGR disease. Recent gene augmentation therapy successes using animal models have brought renewed hope for those affected by this debilitating illness , an inherited retinal dystrophy affecting 1 in 3000 people that commonly causes severe visual loss and blindness in middle life . Mutatioescribed . Mutatio illness .2RPGR gene is located on the short arm of the X chromosome (Xp21.1)   and expr(Xp21.1) . Express(Xp21.1) , which f studied a , connecting the inner and outer segments, but shows a slightly different developmental expression pattern and affinity for the axonemal (detergent insoluble) fraction compared with the other major RPGR isoform (RPGRORF15), suggesting overlapping but also distinct functions  mice showed that RPGR is not essential for mammalian photoreceptor development  and juvenile RP    as well (type 6) . A recenling LCA .Bbs2, Ahi1, Rp1, Rpgrip1, Cep290 mice), including several RPGR disease models. The latter include a naturally occurring Rpgr mutant mouse . In contrast, mutants that are defective in vesicular transport of opsins  show accumulation of vesicles near the base of the IS while mutants with absent OS (e.g. Rho−/− mice) show vesicle accumulation at the distal tip of the CC, neither of which were found in Rpgrip1, Spata7 or Rpgr KO mice  and RP/LCA mouse models , but since proposed ciliary gate proteins such as RPGR, RPGRIP1 and CEP290 are all required for opsin localisation to the OS it is currently more plausible that this defect is secondary to defective ciliary gate functions.RP2 gene product greatly accelerates the hydrolysis of GTP-bound ARL3. RP2 null mutations are therefore thought to impair the trafficking of prenylated proteins to the photoreceptor OS   Fig. 1bbFig. 1b,eptor OS . A rare hthalmia . A PDEδ hthalmia . Non-preutations . DespiteORF15 and both proteins form part of a microtubule-associated protein complex at the centrosome , suggesting that RPGR is also involved in sorting/loading of specific IFT and other protein cargoes at this site (Another RPGR-interacting protein is CEP290 (NPHP6), which localises to centrosomes throughout the cell cycle and to the photoreceptor CC in mouse photoreceptors . It was ntrosome . Experimproteins . RPGR ishis site .CEP290 mutations are associated with retinal degeneration in six partially overlapping ciliopathy syndromes, consistent with a central role in ciliary and/or outer segment maintenance (Joubert Syndrome (JBS), nephronophthisis (NPHP), Leber congenital amaurosis (LCA), Senior-Loken syndrome (SLS), Meckel-Gruber syndrome (MKS) and Bardet-Biedl syndrome (BBS) (CEP290 has numerous interacting partners  and CEP2me (BBS) ). In sumORF15 co-immunoprecipitates with a variety of basal body  and both anterograde and retrograde microtubular transport proteins , suggesting a role in CC trafficking although whether as a cargo or a regulator is unclear.RPGR also interacts with Structural Maintenance of Chromosome (SMC) proteins SMC1 and SMC3 , whose fNPHP5/IQCB1, encoding an IQ-domain protein which localises to the photoreceptor CC, cause the ciliopathy SLS. An interaction between NPHP5 and RPGRORF15 has been demonstrated  for a Ran-like GTPase . RAB8 is in RPGR  and the  in RPGR , raisingRPGR exon ORF15 repeat domain is a hotspot for disease but its function is unclear. It is predicted to be unstructured and, so far, interacting partners have not been identified. In contrast, the basic C-terminal domain , a scaffold protein expressed in cochlear hair cells and photoreceptors . WHRN ha cargoes  Fig. 3)ORF15 to Rpgrip1 KO mouse reported by The Usher protein network shows clear links to the actin cytoskeleton  and RPGRORF15 functions are likely to be involved in some aspect(s) of the ciliary gate, and trafficking or sorting of cargoes, some of which originate from the periciliary membrane complex. Whether this function is mediated by RPGRIP1, PDEδ, nephrocystins, RAB8, WHRN, the actin or microtubule based cytoskeleton, or a combination of these, remains to be resolved.In summary, RPGR is localized to the photoreceptor CC and to the corresponding structures (transition zone) in primary cilia, as a result of its interaction with one or more RPGRIP1 isoforms. RPGR51 or PDEδ. More recently, high-resolution 3-D structural data for the RPGR RCC1-like domain in complex with PDEδ  mutations tend to cause more severe disease than ORF15 mutations  and someith PDEδ  and RPGRith PDEδ  was deteith PDEδ .RPGR mutations are affected by 'modifier genes.' Recently a SNP screen of RPGR patients displaying varying disease severity showed that SNPs in IQCB1 and RPGRIP1L were associated with disease severity , cone dystrophy, cone-rod dystrophy or atrophic macular degeneration (3%) and ciliopathy (2%) . Furtherseverity . Until XRPGR carriers are generally asymptomatic but can display clinical abnormalities with tapetal reflexes, peripheral retinal thinning and severe rod and cone dysfunction  and field loss patterns . Interesfunction . This hi66.1RPGR disease models have been studied. RPGR knockdown in ciliated cell lines is one approach that has helped to unravel disease mechanisms  genetic background showed a faster rate of retinal degeneration and visual loss than the Rpgr KO mouse  by gene augmentation therapy also argues against significant GOF mutations in XLRP patients.Various sms e.g. . Animal 2 months . Cone oppression . A recenKO mouse . A naturneration . Differe effects . RpgrORF protein . Howeverkgrounds , suggestRPGR disease models exist in dogs. Canine X-linked progressive retinal atrophy (XLPRA) occurs in the Siberian Samoyed husky  and in a mixed breed dog . The XLPRA1 mutation allows normal photoreceptor development and function until 6 months of age followed by a slow degeneration of rods, which die by apoptosis. The XLPRA2 phenotype is severe, with abnormal retinal development leading to disorganised OS and rapid degeneration  mediated the transfer of full-length human RPGRORF15 (driven by the human IRBP promoter) into photoreceptors, preventing degeneration in both canine RPGR disease models and resulting in increased numbers of photoreceptors, preserved structure and absence of rhodopsin and L/M cone opsin mislocalization  on an Rpgr null background led to a more severe phenotype than in the Rpgr null mouse -derived retinal pigment epithelium (RPE) for cell replacement in RPE dystrophies is already in clinical trials and appears to be safe . The opt8RPGR mutations are responsible for 10–20% of all RP patients and cause severe disease for which there is no treatment. This review has sought to summarise current understanding of RPGR biology, including proposed roles in the ciliary gate that regulates protein trafficking to and from the photoreceptor OS. The results of treating animal models of RPGR disease show considerable promise and suggest that gene replacement therapy and, in the future, cell replacement therapy, could lead to improved visual function in this disorder.


Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. It is well known that many cancer drugs are effective only in a subset of cancer patients, and selection of drug responsive biomarkers is crucial to find the right patients for the right drug. Also, early identification of biomarkers that can select sensitive and resistant patient subpopulations is particularly important in order to be able to test biomarker hypotheses in early clinical drug development and utilize those biomarkers during later clinical development to stratify patients into appropriate treatment arms. This will facilitate the development of companion diagnostic tests along with novel treatments. Therefore, it is ideal to discover drug response biomarkers prior to clinical trials using pre-clinical data sets, for example, by in vitro cell line screening of drug sensitivity.Numerous studies of in vitro drug sensitivity screens –3 coupleAlthough there are a few successful examples of predictive gene expression signatures to predict disease prognosis, for example the 70-gene MammaPrint signature  and the The poor model performance could be improved if more appropriate modeling approaches for the complex clinical endpoints of interest were used. For instance, poor prediction of overall survival for Multiple Myeloma patients in the MAQCII study could be partly due to applying an arbitrary survival cutoff (24 month) for patients . Both geIn order to generate translational drug-sensitive models, e.g. building predictive models using cancer cell line data that can be used to predict patient response in clinical trials, more detailed considerations are needed along the model building/testing process. For example, as observed from the MAQC II study, a simple application of t-test for feature selection is not sufficient . Insteadhttp://www.Eurofinspanlabs.com). IC50 values were used as dependent variables and gene expression data from untreated cells was used as independent variables to build the predictive models. A Partial Least Squares Regression (PLSR) modeling approach was utilized because it can effectively handle a high number of independent variables with minimal demands on sample size [In the current work, a combination of novel statistical and functional feature selection approaches were used to build in vitro gene transcription based predictive models, for Erlotinib and Sorafenib response using a 240 cancer cell line panel . A 240 tumor cell line panel (Oncopanel) was used to identify cell lines that were sensitive or resistant to Erlotinib treatment . The Oncopanel contains cancer cell lines covering a variety of cancer indications . For eacA PLSR model workflow was developed and an Erlotinib sensitivity model was trained using OncoPanel data . The keyFirst, an RMA normalization of the baseline gene expression of the OncoPanel cell lines was conducted. Next, an intensity cutoff of 40% of the whole genome to remove genes with low intensities was applied. Third, a variance cutoff of 1 to keep genes with the highest variability of expression among the panel cell lines was applied. This data reduction step narrowed down the number of probesets by an order of magnitude from 54,675 to 3,787.2(IC50)) for all cell lines. For each probeset, permutation testing was run by randomly assigning a response sign to the cell lines panel. After Fisher's transformation (f = 0.5 * ln ((1+r) / (1-r), where r is the correlation coefficient on each permutation), the permutation resulted in f-values that follow a normal distribution and a raw p-value of each gene was calculated based on the fitted mean and standard deviation from the permutation data. An adjusted p-value was calculated using Benjamini-Hochberg control of false discovery rate [Feature selection was conducted on the 3787 probesets based on correlation between gene expression level and drug response  and balance validation (30%) subsets: we first equally divided the cell lines into three groups by IC50 values, then conducted random splits on each group to get training and validation subsets. When merging the training and validation subsets from these three groups, both training and validation subsets having some sensitive and some resistant cell lines  were generated. The training subset was then further divided into random training (60% of training) and random validation (40% of the training) subsets. Overall, the training set was divided into three parts: random training (42%), random validation (28%) and balance validation (30%) subsets . HundredPearson correlation between experimental and predicted IC50s. The AUC measure uses a data-driven cutoff to divide cell lines into sensitive- and resistant-groups using experimental IC50s, and use a series of cutoffs on predicted IC50s to define predicted sensitive- and resistant groups, to generate the Receiver Operating Characteristic (ROC) curve and calculate AUC values. In this sense, although a regression-based modeling framework was built, some classification measures to help select top models were still used; 2. Among all splits, a correlated AUC and correlation measures on random validation  were selected based on all five criteria out of 150 initial balance split model sets. Then, one top model with best correlation between predicted and experimental IC50 in balance validation set was selected as a top model in each of the top five model sets, resulting in 5 top models from 150*1000 = 150000 total splits. Pairwise overlapping scores among these models were then calculated (the overlapping score was defined as Jaccard similarity index), on the real random training cell line sets as well as generated an overlapping score distribution on randomly select two sets with the same size of cell lines as in the random training (a permutation test). In order to remove top models that were significantly overlapping to each other, a 90% quantile cutoff on pairwise overlapping scores was used as a significant overlapping cutoff.There are multiple potential ways to identify key biomarker genes from a single training/testing dataset, for example, several sparse PLSR approaches were developed by Chun and Keles , 30, Lê Starting from the five highest weighted genes and adding one gene at a time, the representative PLSR model was retrained/retested by increasing signature gene size one at a time (a forward selection approach). The core PLSR model was identified as an early plateau point on the model performance curves on both AUC and correlation measures . The resA subset of the core PLSR model as the pathway-based PLSR model was selected using the functional analysis platform MetaCore (Thomson Reuters) . The 191In the 51-gene signature , 22 geneTo test for drug-specificity of the Erlotinib PLSR model, a predictive model to Sorafenib was built using data from Oncopanel cell lines treated with Sorafenib to generate IC50 values as well as the same baseline gene expression data and modeling framework as for Erlotinib. Therefore, the data reduction is the same for both Erlotinib and Sorafenib and the models are directly comparable.Unlike Erlotinib’s IC50 distribution , the SorA network of Sorafenib signature genes was generated using protein-protein interactions and canonical pathway information . ActivatThe BATTLE clinical trial data was used as an independent testing dataset, to evaluate the performance of the OncoPanel cell line data derived drug sensitivity models. In this Phase II clinical trial , subsetsThe baseline tumor gene expression data in patients was generated using the Affymetrix HG Gene 1.0 ST array, which is a different platform than the U133plus2 array used in the OncoPanel experiments. To address the issue of platform incompatibility, the Affymetrix “U133PlusVsHuGene_BestMatch” file was used to identify matching probesets between U133plus2 and HG Gene 1.0 ST arrays, then a quantile normalization of the BATTLE data against OncoPanel gene expression was performed. All 51 genes from the Erlotinib functional pathway-based model were present in the BATTLE dataset, as well as all 113 Sorafenib signature genes.The cutoffs of Erlotinib or Sorafenib predicted scores were data-driven by re-predicting IC50s on the Oncopanel cell lines, using the corresponding drug sensitivity models . The disThe patients’ PFS cutoffs for Erlotinib and Sorafenib were selected at 2.4 and 4.2 months based on their PFS distributions, respectively . We are As shown in Similarly, the Sorafenib model built from OncoPanel Sorafenib screen data (IC50s) can predict drug response for the 39 Sorafenib treated patients . The SorThe Erlotinib model was further tested to predict the response of Sorafenib treated patients. For these heavily pre-treated patients in the BATTLE clinical trial, both the PFS and model-predicted scores suggested that the majority of the patients are not Erlotinib sensitive. The overall accuracy of the Erlotinb model in predicting Sorafenib treatment outcome was 64% . InteresThe Sorafenib model failed to predict the BATTLE Erlotinib treated patients’ response, with a positive predictive value of 14% and an overall accuracy of 48% . It is wPutting together, the Erlotinib or Sorafenib models trained from Oncopanel data can be used to predict patients’ response upon corresponding treatments. However, the models performed poorly in cross-evaluation (i.e. Sorafenib signature for predicting Erlotinib response and vice versa). This suggests that both the Erlotinib and the Sorafenib models are drug specific.Another way to assess cell line data derived drug sensitivity models was to test the models for their ability to predict PFS. Patients in the BATTLE clinical trial were assigned to a marker positive (drug sensitive) or a marker negative (drug resistant) sub-group based on the corresponding PLSR drug predicted sensitivity scores, then marker +/- patient groups were compared based on the clinical output (PFS).When the Erlotinib model is used to stratify BATTLE patients in the Erlotinib arms, the median PFS for the model-predicted Erlotinib-sensitive patient group was 3.84 month while the PFS for model-predicted Erlotinib-resistant patients was 1.84 month, corresponding to a p-value of 0.09 and Hazard ratio of 0.43  over the marker-resistant group , with a Importantly, the signatures were drug-specific and did not predict across arms. The Erlotinib predictive model failed to separate marker-sensitive vs. marker-resistant groups for Sorafenib treatment arm and vice versa, suggesting that the predictive models are drug specific .After the Erlotinib and Sorafenib sensitivity models were tested using the BATTLE clinical trial data, they were further used to predict patients’ tumor samples from gene expression data in the public domain. The percentage of predicted Erlotinib or Sorafenib sensitive samples were generated based on cancer indications, and a higher percentage of predicted drug sensitivity for a cancer type was considered as a potential indication that the drug may work well on treating that type of cancer.An internal data repository was built based on the tranSMART translational medicine platform  using daExtensive manual curation and text mining were performed to standardize metadata on patient and clinic features. For the purpose of applying the drug sensitivity models on patients’ cancer samples, our quality control contains the following aspects: (1). Remove cell line data; (2). Remove normal tissue samples; (3). In some solid tumor studies, the samples were actually collected from blood or saliva—we also removed data from these studies. After these quality control steps, the resulting 11331 samples was categorized into various cancer indications.Each sample’s baseline gene expression was used to predict its potential Erlotinib or Sorafenib response, and percentage of predicted drug sensitive samples were calculated upon each cancer indication. Overall, differential pattern of drug sensitive indications is generally consistent with clinical trials outcomes for Erlotinib and Sorafenib, respectively . Lung cahttp://www.clinicaltrials.gov/). In addition, Sorafenib model predictions suggested there are a high percentage of drug sensitive samples in bone cancer and haematopoietic neoplasms , which creates balance validation subsets as well as random validation subsets. The feature selection was done on the whole dataset, so that the same feature genes could be used to train and compare models among different splits. This also enabled finding the best model that represented consensus information among the whole dataset. By identifying minimally overlapping high score models and calculating consensus weighting among them, the common features in the training dataset were captured. Besides the Erlotinib and Sorafenib case studies described in the current work, the method was further validated through our participation in the NCI-DREAM challenge. The NCI-DREAM challenge was a community effort comprised of 44 teams to predict drug-sensitivity to 28 drugs .A translational medicine case study consisting of generating predictive models of Erlotinib and Sorafenib sensitivity from a cell line panel dataset, followed by model validation in a clinical setting is presented. The models predict clear clinical benefits: for example, the Erlotinib drug sensitivity model separated marker-sensitive and marker-resistant patient groups with the former demonstrating twice as long survival than the latter  as the clinical end-point. Moreover, the training data (Oncopanel) covers a mixture of cancer indications, while non-small cell lung cancer (NSCLC) was the only cancer indication in the BATTLE trial. Also, the signatures were generated on the Affymetrix U133plus2 platform and tested on the data generated on Affymetrix Human Gene 1.0 ST platform. Despite all these differences, the cell line derived Erlotinib and Sorafenib sensitivity models predicted BATTLE trial PFS outcomes with high accuracy of 84% and 79%, respectively .Given the fact the Erlotinib is an EGFR inhibitor while Sorafenib has multiple tyrosine kinase receptors as targets, one would expect that model specificity between Erlotinib and Sorafenib would be difficult to achieve. Strikingly, the Erlotinib and Sorafenib drug sensitivity models were drug specific, i.e. the Erlotinib model failed to predict Sorafenib patients’ PFS and vice versa Figs  and 4.The drug sensitivity models were used to predict percentage of drug sensitive samples per cancer indication for a large number of cancer expression datasets in various indications. The predicted Erlotinib or Sorafenib sensitive cancer indications were generally consistent with known FDA approvals on Erlotinib or Sorafenib . Therefohttps://www.eurofinspanlabs.com). For testing the Erlotinib predictive model, the BATTLE trial patients’ baseline gene expression [Among the 240 Eurofin cancer cell lines, baseline gene expression data was downloaded from 170 cell lines from ArrayExpress (E-MTAB-37). The remaining 70 cell lines gene expression data was purchased from Eurofins (pression  (GSE3307pression .http://www.affymetrix.com) and was used to identify good matching probesets in Affymetrix U133plus2 array (for training dataset) and in Human Gene 1.0 ST array (for testing dataset). Based on the observations from both MAQC II project [Since gene expression data was generated from different microarray platforms, it is necessary to normalize them before model training/testing. A “U133PlusVsHuGene_BestMatch” file was downloaded from Affymetrix website . There are 213 cell line IC50 data on both Erlotinib and Sorafenib datasets.Erlotinib and Sorafenib sensitivity screen data were purchased from Eurofins (Two-layers of cell line data QC were conducted that reduced the number of cell lines from 213 to 183 before model training and testing. The first layer involved curve-fitting on dose-response data for each compound. The following criteria were used to QC each compound’s dose-response curve: (1). Cell lines were removed if there were three or more missing data points on the dose-response curve; (2). Cell lines were removed if there were four or more data points with CV > 30%; (3). Cell lines where the dose-response curve displays unusual behavior (e.g. has a stronger inhibitory effect at lower concentrations then at higher concentrations) were removed; (4). Cell lines that only demonstrate inhibitory effects at the highest tested concentration were removed. The second layer of QC evaluates whether cell line screen results actually reflect a drug-treatment effect. Clustering analysis was conducted among a group of internal and standard of care compounds. Compounds that have similar mechanism of action were clustered together, suggesting that cell line screen data actually reflects real drug-treatment effect and therefore could be possible to identify drug-sensitive predictive models from cell line data (data not shown).We designed and implemented a PLSR based modeling framework on drug-sensitivity predictive model building. More details on the PLSR method and its biological applications have been previously published –26. We sIn the current work, we use the “pls” package from R (version 3.0.2) for PLS regression. The loading numbers from first and second PLS components were used to calculate gene weights as sqrt(PLS1^2 + PLS2^2). The gene weights were used to compare top splitting cases and forward feature selection steps.To reveal functional connectivity between the genes from Sorafenib and Erlotinib predictive models, we reconstructed the causal molecular network by connecting these genes via canonical molecular pathways. The network was built in several steps.First, in addition to the predictive models’ genes, we identified “topologically significant” genes for the signature genes. The algorithm of selection of topologically-significant direct regulators, or “overconnected” genes, is described in  and remoNext, we selected molecular pathways significantly enriched with both genes of predictive signature and topologically significant genes using enrichment analysis in the Pathway Maps ontology in MetaCore (Thomson Reuters).Finally, the identified enriched molecular pathways were manually combined in the causal molecular network. The resulting network included both sensitivity (negative correlation of expression with IC50) and resistance-specific (positive correlation of expression with IC50) genes. The networks were further populated with direct interactions between proteins encoded by signature genes. Also, we included genes with genetic alterations correlated with the drug response phenotype in OncoPanel cancer cell line panel in the reconstructed causal networks.S1 Fig(TIF)Click here for additional data file.S2 FigPairwise comparison was done among top performing models, as well as with the consensus model. The numbers in the lower left part of the figure are PLSR model derived loading values for individual genes, and the numbers in the top right part of the figure are Pearson correlations between models.(TIF)Click here for additional data file.S3 FigThe network was reconstructed from canonical signaling pathways regulated by signature genes and signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes are red thermometers; topologically significant genes are highlighted with yellow thermometers. White starts in object images mark groups of proteins.(TIF)Click here for additional data file.S4 FigThe network was reconstructed from canonical signaling pathways regulated by signature genes and signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes are highlighted with red thermometers; topologically significant genes are highlighted with yellow thermometers. White starts in object images mark groups of proteins.(TIF)Click here for additional data file.S5 Fig(A) Erlotinib IC50 distribution on Oncopanel cancer cell line panel; (B). Sorafenib IC50 distribution on Oncopanel cancer cell line panel. The top figure was for the whole Ricerca cell line panel and the bottom figure was after removing data for middle one-third IC50s.(TIF)Click here for additional data file.S6 FigRed points were top performing models on 1000 random splits on this balanced split, based on both AUC and correlation measures.(TIF)Click here for additional data file.S7 FigRed vertical line was cutoffs selected to separate drug sensitive vs resistant cases for each drug.(TIF)Click here for additional data file.S8 FigRed vertical line was cutoffs selected to separate patients into responder and non-responder sub-groups for each drug.(TIF)Click here for additional data file.S1 TableA raw p-value of each gene was calculated during feature selection step based on the fitted mean and standard deviation from the permutation data (see (DOCX)Click here for additional data file.S2 TableA raw p-value of each gene was calculated during feature selection step based on the fitted mean and standard deviation from the permutation data (see (DOCX)Click here for additional data file.S3 TablePearson’s correlation coefficient was calculated for a vector of log2(IC50) and a vector of genetic events. Positive value of correlation for a given gene was interpreted as correlation with resistance. Negative value can was interpreted as an association with sensitivity. Correlation P-values were calculated by a permutation test with 1000 shuffles. Fisher’s exact test was used for association analysis between resistance/sensitivity classes and genetic events. “R” stands for resistance, “S” stands for sensitivity.(DOCX)Click here for additional data file.S1 TextDescription and discussion of reconstructed causal models omitted from main manuscript and shown in (DOCX)Click here for additional data file.S2 TextAdditional results and methods omitted from the main manuscript. We provide results of association analysis between genetic events and drug response supporting major observations in the manuscript.(DOCX)Click here for additional data file.


Vibrio parahaemolyticus is a bacterial pathogen responsible for mild to severe gastroenteritis, wound infections, and septicemia resulting from the ingestion or handling of raw or undercooked contaminated seafood. Here, we report the draft whole-genome sequences and annotations of 14 Canadian V. parahaemolyticus clinical isolates that were serologically identified as K group II using polyvalent antisera but were not specifically K serogrouped using monovalent antisera. Vibrio parahaemolyticus is a halophilic marine bacterium that is widely distributed in temperate estuaries and is one of several etiological agents of human vibriosis. Since 2000, there has been an increasing prevalence of V. parahaemolyticus infections in Canada , since several serovariants can simultaneously be associated with illness (V. parahaemolyticus CC36 (serotype O4:K12) has been published , British Columbia Centre for Disease Control (BCCDC), and the Bureau of Microbial Hazards (BMH) . Twenty-six of these isolates were identified as ST36 and O4, indicating inclusion in CC36, but only weakly agglutinated with the polyvalent antiserum K group II and failed to agglutinate with any of the seven associated monovalent antisera   Prokaryotic Genome Annotation Pipeline (PGAP)  .These nucleotide sequences have been deposited at DDBJ/EMBL/GenBank as BioProject PRJNA272927 under the accession numbers provided in