In total, we identified tens of thousands of putative lincRNAs expressed at a minimum of one copy per cell, significantly expanding upon prior lincRNA annotation sets. These lincRNAs are specifically regulated and conserved rather than being the product of transcriptional noise. In addition, lincRNAs are strongly enriched for trait-associated SNPs suggesting a new mechanism by which intergenic trait-associated regions may function. These findings will enable the discovery and interrogation of novel intergenic functional elements.Known protein coding gene exons compose less than 3% of the human genome. The remaining 97% is largely uncharted territory, with only a small fraction characterized. The recent observation of transcription in this intergenic territory has stimulated debate about the extent of intergenic transcription and whether these intergenic RNAs are functional. Here we directly observed with a large set of RNA-seq data covering a wide array of human tissue types that the majority of the genome is indeed transcribed, corroborating recent observations by the ENCODE project. Furthermore, using  Much of the human genome is composed of intergenic sequence, the regions between genes. Intergenic sequence was once thought to be transcriptionally silent “junk DNA,” but it has recently become apparent that intergenic regions can be transcribed. However, the scope, nature, and identity of this intergenic transcription remain unknown. Here, by analyzing a large set of RNA-seq data, we found that >85% of the genome is transcribed, allowing us to generate a comprehensive catalog of an important class of intergenic transcripts: long intergenic noncoding RNAs (lincRNAs). We found that the genome encodes far more lincRNAs than previously known. A key question in the field is whether these intergenic transcripts are functional or transcriptional noise. We found that the lincRNAs we identified have many characteristics that are inconsistent with noise, including specific regulation of their expression, the presence of conserved sequence and evidence for regulated processing. Furthermore, these lincRNAs are strongly enriched with intergenic sequences that were previously known to be functional in human traits and diseases. This study provides an essential framework from which the functional elements in intergenic regions can be identified and characterized, facilitating future efforts toward understanding the roles of intergenic transcription in human health and disease. A large fraction of the human genome consists of intergenic sequence. Once referred to as “junk DNA”, it is now clear that functional elements exist in intergenic regions. In fact, genome wide association studies have revealed that approximately half of all disease and trait-associated genomic regions are intergenic Despite this progress, a complete understanding of the extent of intergenic transcription and the identity of these transcripts has remained elusive. The first attempts to analyze the extent and nature of intergenic transcription utilized tiling array technology Long intergenic noncoding RNAs (lincRNAs) are defined as intergenic (relative to current gene annotations) transcripts longer than 200 nucleotides in length that lack protein coding capacity. LincRNAs are known to perform myriad functions through diverse mechanisms ranging from the regulation of epigenetic modifications and gene expression to acting as scaffolds for protein signaling complexes de novo discovery of novel lincRNAs from RNA-seq data has resulted in the compilation of the most comprehensive catalog of human lincRNAs. Owing to the extended breadth of tissues sampled and relaxed constraints on transcript structure, we find significantly more lincRNAs than all previous lincRNA annotation sets combined. Our analyses revealed that these lincRNAs display many features consistent with functionality, contrasting prior claims that intergenic transcription is primarily the product of transcriptional noise In order to bridge the gap between the observation of pervasive intergenic transcription by the ENCODE project and the currently limited set of annotated lincRNAs, we performed an analysis of a unique set of RNA-seq data derived from both novel and published datasets that complements and significantly expands prior efforts http://www.genboree.org/epigenomeatlas/index.rhtml) and 121 previously published RNA-seq datasets representing 23 human tissues under multiple conditions and consisting of over 4.5 billion uniquely mapped reads  an open reading frame (ORF) longer than 100 amino acids. Out of concern that some de novo assembled transcripts may be unannotated extensions of neighboring protein coding genes, as was recently observed for a fraction of GENCODE long noncoding RNAs de novo transcriptome assembly and removed transcripts overlapping these extended gene structures <1, a threshold approximately equivalent to one copy per cell is study . Rather is study . This gris study . These ais study .We evaluated the stringency with which our filtering process removed protein coding transcripts by analyzing ribosomal profiling data from HeLa cells  [22]. Aset al.et al.de novo assembled transcripts discovered in this study. We have also compiled a catalog of lincRNAs expressed at FPKM>1 in at least two datasets  were included . Furthermore, as an additional resource we provide the expression level (FPKM and raw RNA-seq read counts) of all lincRNAs  identified in genome-wide association studies are located in intergenic sequence while only a small portion are in protein coding gene exons There has been a recent debate about whether there is pervasive transcription of the human genome and what the number and abundance of intergenic transcripts is There is an apparent discrepancy between this observed pervasive transcription and the relative paucity of annotated lincRNAs, the most numerous intergenic RNAs. It should be expected that intergenic regions encode far more lincRNAs than are currently annotated. Indeed, here we found that there are many more lincRNAs than previously known, even after aggressive filtering that removed the vast majority of previously annotated long noncoding RNAs and newly discovered intergenic transcripts . These oIn agreement with prior observations of smaller lincRNA annotation sets, our analyses of the expanded lincRNA catalog presented here revealed that most lincRNAs are expressed at lower levels than protein coding genes In order to minimize any potential contamination of the lincRNA catalog with protein coding transcripts, the filtering approach used was very aggressive. In fact, most previously annotated noncoding RNAs failed to pass our filters and were therefore excluded from the lincRNA catalog . The vasOf those previously annotated noncoding RNAs that are intergenic, more than half contain predicted ORFs longer than 100 amino acids. For example, two previously characterized functional human lincRNAs were found to contain ORFs longer than 100 amino acids, Xist and HOTAIR. These results demonstrate that our filtering approach, which eliminates all transcripts with ORFs larger than 100 amino acids, may have removed some lincRNAs with large, nonfunctional ORFs. However, the use of a 100 amino acid ORF cutoff, a commonly used threshold to define potential protein coding genes, is justifiable because ORFs of this size infrequently occur by chance and instead indicate potential for protein coding capacity Rather than discard all transcripts with large ORFs, as we did here, one option to discriminate between transcripts that are coding versus noncoding is to analyze the frequency of synonymous codon substitutions (PhyloCSF) de novo assembled from reads derived from contaminating genomic DNA in RNA-seq data, we took multiple measures to mitigate any contributions of genomic DNA contaminant reads (see Methods).Despite the fact that most previously annotated noncoding RNAs failed to pass our filters, our lincRNA catalog contains significantly more lincRNAs than previously known (>94% of lincRNAs are entirely novel at each expression level). This is the result of two unique features of our study. First, the RNA-seq read depth and diversity of tissues surveyed allowed for the detection of rare and tissue specific transcripts that were previously unknown. Many of these novel transcripts passed all filters and are annotated as novel lincRNAs in our catalog. Second, in contrast to prior lincRNA annotation efforts that were restricted to identification of only spliced or polyadenylated lincRNAs de novo transcriptome assembly using short reads of finite depth, it is not always possible to unequivocally determine the complete structure of a transcript. This is particularly true for lowly expressed transcripts where the number of reads available is limited, and for genomic regions to which reads cannot be uniquely mapped. In the case of shallow read depth, exons of multi-exonic transcripts may lack reads connecting the exons, and de novo assembly could result in separate annotation of each exon as a distinct transcript. In support of this, we found that lower expressed lincRNAs discovered from de novo transcript assembly were less likely to have multi-exonic structures .A key question in the field is whether the transcripts resulting from pervasive transcription of intergenic regions are functional or the result of noisy transcription. The lincRNAs we describe are specifically regulated and contain conserved sequence, attributes inconsistent with transcriptional noise . FurtherThis noncoding RNA catalog represents a major step toward achieving a more complete understanding of this exciting frontier. We have identified a large number of putative lincRNAs with characteristics suggesting functionality. However, many of these lincRNAs are low expressed and definitive proof of functionality for a lincRNA requires functional experiments. High throughput functional genomic approaches, such as RNAi and cDNA overexpression screens, will serve as crucial tools for future efforts to uncover the roles of lincRNAs in diverse biological systems. With the requisite technology now available for these next generation experimental approaches, the time is ripe for this dark matter of the human genome to step further into the spotlight.http://tophat.cbcb.umd.edu/manual.html). Detailed information pertaining to each dataset, including novel datasets, is available in the sources provided in de novo transcript assembly, some datasets with short reads and shallow read depths were included in order to sample as many tissue types as possible. Datasets derived from tissues with mutated genomes, such as cancers, were included to capture tissue specific expression even though some reads from mutated genomic positions would fail to map to the reference hg18 genome. SAMtools v0.1.7 and BEDTools v2.12.0 were used to process aligned read files.127 RNA-seq sequence files , and published lincRNAs from Khalil De novo transcriptome assembly was performed on RNA-seq data with Cufflinks v1.0.1 using the upper quartile normalization (-N) and fragment bias correction (-b) options. This transcript assembly was performed using reads that were prealigned to hg18 using TopHat as described above. In cases where multiple datasets of the same library type from the same tissue were available, these datasets were combined to increase read depth for de novo assembly , alternative and extended 5′ and 3′ UTRs of known human genes from UTRdb, RefSeq NR and XR transcripts annotated as “pseudogenes”, and Ensembl v54 coding sequences.Two steps of filtering were performed to remove both putative protein coding transcripts and their UTRs. First, large ORFs (>100 amino acids) were identified in all transcripts in all reading frames using EMBOSS getorf v6.1.0. In order to account for potentially truncated ORF-containing transcripts in which the start or stop codon may be outside the annotated region, the presence of greater than 300 nt downstream of a start codon without an interrupting stop codon, or 300 nt upstream of a stop codon without an interrupting start codon, sufficed to call a putative ORF. Transcripts with putative large ORFs were removed. These putative large ORF containing intergenic transcripts, some of which may be novel protein coding genes, are provided as a resource in de novo transcriptome assembly with Cufflinks v1.1.0 using RefSeq NM genes as a reference annotation (-g), upper quartile normalization (-N), and fragment bias correction (-b) was performed on all polyA+ RNA-seq libraries in RNA-seq reads may extend beyond annotated 5′ and 3′ ends of annotated protein coding gene structures representing possible extended UTRs as well as, in the case of spliced reads mapping to the gene from distal sites, unannotated exons. In order to avoid cataloging transcripts in these regions as lincRNAs, we created a filter based on extended boundaries of protein coding genes using RNA-seq data. To do this, de novo assemblies prior to the merging of overlapping lincRNA annotations (described below). These redundant annotations are the result of the repeated de novo assembly of the same transcript in multiple different datasets or redundant existing annotations in public databases, each of which have slightly different genomic coordinates yet may represent the same transcript. As such, all reads were distributed fully to each redundant annotation rather than proportioned between them. Read counts were converted to FPKMs using total mapped reads for each dataset calculated by the SAMTools flagstat function and custom scripts. Transcripts not expressed at FPKM>1 in at least one dataset were removed. As a result of this FPKM>1 minimum filter, 99.975% of de novo assembled lincRNAs (pre-merging) have at least 5 reads supporting their expression in at least one of the combined datasets in In order to determine transcript expression levels, mapped RNA-seq reads were distributed to transcripts using a modified version of HTSeq v0.5.3p that allows for reads that are mapped to shared portions of overlapping transcripts to be counted as a full read for each overlapping transcript. This was necessary to properly assign reads to each of multiple redundant annotations of transcripts present in the combined set from all public databases and http://genome.ucsc.edu/cgi-bin/hgLiftOver.To identify a minimal set of distinct lincRNAs, overlapping transcripts were merged if 50% of an exon of a transcript overlapped an exon of another transcript. Furthermore, merged transcripts within 1 kb of each other were placed in the same group but received distinct transcript numbers, and are named based on the FPKM expression level they were derived from, e.g. FPKM1_group_32871_transcript_1. Merging, grouping and naming was performed separately on all FPKM>1 transcripts, FPKM>10 transcripts, and FPKM>30 transcripts. Filtering statistics are presented in de novo assembly, overlapping and nearby lincRNAs were considered to represent different potential models of the same lincRNA gene (rather than isoforms). Therefore, in the rare instances where two or more lincRNA models partially overlap but do not satisfy our merging criteria (above), the reads mapping to these overlapping portions were fully assigned to each lincRNA.After merging these expression filtered, overlapping lincRNAs, FPKMs were recalculated  for the et al.et al.P value < = 0.1 in at least one dataset are cataloged in Datasets S6, S7.For each RNA-seq dataset , an empiet al.et al.An additional set of annotated lincRNA transcripts from Cabili de novo assembly of erroneous transcripts. In principle, only exon-exon junction spanning reads can be unequivocally determined as derived from RNA. Proper de novo assembly of both nonspliced and spliced (aside from the exon-exon junction spanning reads) transcripts may therefore suffer if significant genomic DNA contamination is present. Because our analysis utilized a wide range of novel and previously existing RNA-seq datasets of unknown genomic DNA contamination content, we took multiple steps to mitigate this possibility. First, for all RNA-seq datasets, we analyzed the distribution of reads between protein coding exons compared to other regions with the expectation that read distributions should be similar between RNA-seq datasets generated from libraries of the same type (e.g. polyA+ selected). A dataset with an unusually high percentage of intronic and intergenic reads could contain significant genomic DNA contamination. Our analysis of the datasets used in this study revealed that, as expected, polyA+ specific RNA-seq datasets have a higher fraction of reads mapping to protein coding gene exons than rRNA-depleted or polyA− specific datasets. Furthermore, no obvious outlier datasets were found for any of the library types. The results of this analysis ensured that no datasets with high genomic DNA contamination were used in this study :Publicly available paired-end ditag (PET) cluster annotations derived from 7 cell lines or tissues, generated by the ENCODE project, were downloaded from A549 (wgEncodeGisRnaPetA549CellPapClusters.bedCluster),H1_hESC (wgEncodeGisRnaPetH1hescCellPapClustersRep1.bed),HeLa-S3 (wgEncodeGisRnaPetHelas3CellPapClustersRep1.bed),IMR90 (wgEncodeGisRnaPetImr90CellPapClusters.bedCluster),MCF-7 (wgEncodeGisRnaPetMcf7CellPapClusters.bedCluster),Prostate (wgEncodeGisRnaPetProstateCellPapClustersRep1.bed),SK-N-SH (wgEncodeGisRnaPetSknshCellPapClusters.bedCluster).http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=321010719&c=chr21&g=wgEncodeGisRnaPet. BEDTools was employed to compute overlap between lincRNA and RefSeq NM gene 5′ and 3′ ends and PET cluster 5′ and 3′ end ‘blocks’. In the case of ambiguous stranded lincRNAs, both potential orientations were allowed for determining overlap with the 5′ and 3′ ends of PET clusters.Further description of these PET clusters, including how the annotations were generated, is available at the UCSC Genome Browser site here et al.P values.Ribosome profiling data and matched mRNA-seq data from HeLa cells corresponding to the experiments (mock transfected 12 hr time point) presented in Guo https://main.g2.bx.psu.edu/) and the tool ‘Stitch Gene Blocks’ was used to retrieve multiple alignment files with sequence entries for the following genome builds based on the 44 way Multiz alignment to hg18: hg18 panTro2 rheMac2 tarSyr1 micMur1 otoGar1 tupBel1 mm9 rn4 dipOrd1 cavPor3 speTri1 oryCun1 ochPri2 vicPac1 turTru1 bosTau4 equCab2 felCat3 canFam2 myoLuc1 pteVam1 eriEur1 sorAra1 loxAfr2 proCap1 echTel1 dasNov2 choHof1. Genome build names were converted to common names and PhyloCSF was run using the options –orf = StopStop3 and –frames = 6.The program PhyloCSF (9/16/2010 release) ChIP-seq data from IMR90 cells 2 transformed and the genes (rows) and samples (columns) were normalized by multiplying each log2 transformed FPKM value by a scale factor such that the sum of the squares of the values in each row and column are 1.0. Euclidean distance using centroid linkage was calculated for all samples and the heatmap and dendrogram was generated with Java TreeView. Red corresponds to fully induced expression and blue corresponds to fully repressed expression.RNA-seq datasets from B cells, H1 ESCs, and brain see  were cluBase-wise conservation scores , based on the multiple alignment of placental mammal genomes, were downloaded from the UCSC Genome Browser. The 50 bp window in each lincRNA transcript with the highest average PhyloP score was identified. The process was repeated for RefSeq NM genes and a set of size-matched (to lincRNAs) repetitive elements from RepeatMasker (UCSC Genome Browser). PhyloP scores for the maximally conserved 50 bp windows of each lincRNA are listed in P<10−8 was downloaded from the NCBI dbGaP Association Results Browser . Genomic coordinates of trait-associated SNPs were retrieved from dbSNP 130. To compare enrichment of trait-associated SNPs in lincRNAs versus background loci (nonexpressed intergenic regions), regions of the uniquely mappable genome longer than 200 bp that exclude all evidence of transcription  and the number of common SNPs within RefSeq NM gene exons, lincRNA exons and background loci divided by the number of genomic bases in each of these categories was determined. Fisher's exact test was used to calculate P values and error bars in A table containing all common SNPs  from HapMap release #27 was downloaded from the BioMart HapMap site .(TXT)Click here for additional data file.Dataset S2Primary catalog of lincRNAs identified and analyzed in this study  .(ZIP)Click here for additional data file.Dataset S3et al.Catalog of lincRNAs in Dataset S2 after merging with additional lincRNAs found to be expressed at FPKM>1 exclusively in Cabili (ZIP)Click here for additional data file.Dataset S4Catalog of lincRNAs in Dataset S2 (FPKM>1) that are spliced  .(TXT)Click here for additional data file.Dataset S5Catalog of lincRNAs in Dataset S2 that are expressed at FPKM>1 in at least two RNA-seq datasets  .(TXT)Click here for additional data file.Dataset S6Catalog of lincRNAs in Dataset S2 (FPKM>1) that are statistically significantly (p<0.1) expressed above a random sample of other size-matched intergenic regions in at least one RNA-seq dataset  .(TXT)Click here for additional data file.Dataset S7RNA-seq dataset names, P values and FPKMs corresponding to each significantly expressed lincRNA in Dataset S6.(TXT)Click here for additional data file.Dataset S8RNA-seq FPKM and read counts for all lincRNAs  and NM genes in all individual datasets (TXT). Please note that these are large files: the compressed FPKM file is 32 MB (94 MB uncompressed) and the compressed counts file is 7 MB (29 MB uncompressed).(ZIP)Click here for additional data file.Dataset S9de novo lincRNA that contains an ORF>100 amino acids   (TXT). (F) GENCODEv6 “lincRNAs” and “processed transcripts” that passed the prior filters but overlap an extended protein coding gene structure (149 transcripts)  (TXT). (G) GENCODEv6 “lincRNAs” and “processed transcripts” passing all prior filters except not found expressed at FPKM>1 in any dataset   (TXT). (H) GENCODEv6 “lincRNAs” and “processed transcripts” passing all filters and expressed at FPKM>1 in at least one dataset (945 transcripts)  (TXT).GENCODEv6 “lincRNAs” and “processed transcripts” that were removed at each step of filtering. (A) Unfiltered GENCODEv6 “lincRNAs” and “processed transcripts”   (TXT). (B) GENCODEv6 “lincRNAs” and “processed transcripts” that overlap RefSeq NM (protein coding) genes by at least 1 base pair on either strand   (TXT). (C) GENCODEv6 “lincRNAs” and “processed transcripts” that overlap (see Methods) one or more elements of an expanded set of protein coding genes , pseudogenes, UTRs (UTRdb), or non-lincRNA noncoding RNAs   (TXT). (D) GENCODEv6 “lincRNAs” and “processed transcripts” that passed the protein/pseudogene/non-lincRNA ncRNAs/<200 nt filter, but contain an ORF>100 amino acids in length (964 transcripts)  (TXT). (E) GENCODEv6 “lincRNAs” and “processed transcripts” that do not themselves contain an ORF>100 amino acids, but overlap another annotated or (ZIP)Click here for additional data file.Dataset S10Catalog of intergenic transcripts containing ORFs longer than 100 amino acids  .(TXT)Click here for additional data file.Figure S1Fraction of the human genome with mapped RNA-seq reads at varying minimum read thresholds. The 4.5 billion mapped reads from all 127 RNA-seq datasets were combined and aligned to the uniquely mappable portion of the human genome (see Methods). The fraction of the uniquely mappable genome with at least the minimum read threshold is plotted. The data does not plateau at low minimum read thresholds, indicating that deeper sequencing would result in a further increase in the fraction of genome covered. For split reads (reads spanning an intron), the intervening (intronic) sequence was either inferred to have been transcribed (Including Inferred Bases) or was not (Excluding Inferred Bases). At the 1 read minimum read count threshold, 67.1% and 78.9% of the genome have read coverage when excluding or including inferred bases, respectively.(TIF)Click here for additional data file.Figure S2Fraction of RNA-seq reads mapping to protein coding (RefSeq NM) gene exons versus intronic and intergenic regions for 127 RNA-seq datasets grouped by RNA-seq library type. Read counting was performed using a modified version of HTSeq v0.5.3p (see Methods). Isoforms of protein coding genes were flattened before reads were counted such that reads were distributed only once per gene even if multiple isoforms exist. PolyA+ selected libraries (enriched for mRNAs) contain a higher fraction of reads mapping to protein coding gene exons while ribosomal RNA-depleted RNA-seq libraries and polyA− selected libraries contain a higher fraction of intronic and intergenic reads. In all cases, due to the generally high expression levels of protein coding genes, protein coding gene exons contain a disproportionate number of mapped reads relative to the genomic space they occupy (<3%).(TIF)Click here for additional data file.Figure S3Fraction of lincRNAs  expressed at varying minimum FPKM levels. The fraction of lincRNAs in Dataset S2 that are expressed at or above the corresponding FPKM level in at least one dataset is plotted.(TIF)Click here for additional data file.Figure S42 transformed lincRNA FPKM values for lincRNAs with FPKM>10 in at least one of the analyzed samples. The heatmap displays red for fully induced lincRNAs and blue for fully repressed lincRNAs, where rows and columns were normalized (see Methods).LincRNAs have tissue specific expression patterns. LincRNA expression levels (FPKMs) were used to cluster replicates of RNA-seq data from B cells, H1 embryonic stem cells and brain tissue. Agglomerative hierarchical clustering of both lincRNAs (rows) and samples (columns) by Euclidean distance was performed with log(TIF)Click here for additional data file.Figure S5Polyadenylation of lincRNAs versus protein coding genes. Distribution of ratios of FPKMs in polyA+/polyA− fractions for lincRNAs and NM genes in HeLa and H9 ESCs. Transcripts with reads in both fractions and FPKM>1 in at least one of the two fractions for a specific cell type were included in the analysis of that cell type . Whiskers extend to +/−1.5 times interquartile range or most extreme data point.(TIF)Click here for additional data file.Figure S6Comparison of conservation of the full lincRNA catalog  to GENCODEv6 lincRNAs. The maximally conserved 50 bp windows in each lincRNA, RefSeq NM gene and repetitive element (nonconserved control sequences) were determined. Only the GENCODE lincRNAs that passed all lincRNA filters Click here for additional data file.Figure S7P = 0.0173, ** P<2.2E-16; error bars represent 95% binomial proportion confidence interval).Distribution of common SNPs between lincRNA exons, NM gene exons, and nonexpressed intergenic regions. HapMap II SNPs with minor allele frequency >0.05 located within NM gene exons, lincRNA exons, or background loci (nonexpressed intergenic regions), normalized by total number of base pairs in each region, were counted (*(TIF)Click here for additional data file.Table S1Features of the RNA-seq datasets analyzed. P values correspond to a binomial test of proportion of the FPKM = 1 expression threshold in each dataset (see Methods).(XLSX)Click here for additional data file.Table S2de novo transcriptome assembly.Features of the combined RNA-seq datasets that were used for (XLSX)Click here for additional data file.Table S3LincRNA filtering statistics.(XLSX)Click here for additional data file.Table S4Conservation (PhyloP) score for the maximally conserved 50 bp window of each lincRNA in Dataset S2 (FPKM>1). 532 lincRNAs do not contain 50 contiguous bases with PhyloP scores and therefore are not listed.(XLSX)Click here for additional data file.Table S5de novo assembled lincRNAs (pre-merging) discovered by de novo assembly in each combined dataset (see Fraction of aset see  that are(XLSX)Click here for additional data file.Table S6LincRNA (Dataset S2) and RefSeq NM gene analysis for experimental support of 5′ and 3′ end annotations using combined paired-end ditag (PET) data from 7 tissues/cell lines generated by the ENCODE project (see Methods).(XLSX)Click here for additional data file.


The Mannose-binding lectin (MBL) pathway of complement plays a pivotal role in the pathogenesis of ischemia/reperfusion (I/R) injury after experimental ischemic stroke. As comparable data in human ischemic stroke are limited, we investigated in more detail the association of MBL deficiency with infarction volume and functional outcome in a large cohort of patients receiving intravenous thrombolysis or conservative treatment.In a post hoc analysis of a prospective cohort study, admission MBL concentrations were determined in 353 consecutive patients with an acute ischemic stroke of whom 287 and 66 patients received conservative and thrombolytic treatment, respectively. Stroke severity, infarction volume, and functional outcome were studied in relation to MBL concentrations at presentation to the emergency department. MBL levels on admission were not influenced by the time from symptom onset to presentation (p = 0.53). In the conservative treatment group patients with mild strokes at presentation, small infarction volumes or favorable outcomes after three months demonstrated 1.5 to 2.6-fold lower median MBL levels  compared to patients with more severe strokes. Moreover, MBL deficient patients (<100 ng/ml) were subject to a considerably decreased risk of an unfavorable outcome three months after ischemic stroke  and showed smaller lesion volumes . In contrast, no association of MBL concentration with infarction volume or functional outcome was found in the thrombolysis group. However, the small sample size limits the significance of this observation.MBL deficiency is associated with smaller cerebral infarcts and favorable outcome in patients receiving conservative treatment. Our data suggest an important role of the lectin pathway in the pathophysiology of cerebral I/R injury and might pave the way for new therapeutic interventions. Complement mannose-binding lectin (MBL), a member of the collectin subfamily of C-type lectins, is a key component of innate immunity Numerous studies have acknowledged the deleterious effects of complement activation in the pathogenesis of ischemia/reperfusion (I/R) injury  or intravenous thrombolysis according to the European Guidelines The primary endpoint for this analysis was favorable functional outcome (score 0 or 1) on the modified Rankin scale (mRS) within 90 days after admission. Secondary endpoints included functional status as assessed by the Barthel Index (BI) at day 90 (favorable outcome was defined as BI> = 95%), death from any cause within a 90-day follow-up, severity of stroke as assessed by the National Institutes of Health Stroke Scale (NIHSS) score on admission, and infarction volume assessed by magnetic resonance imaging (MRI) during initial work-up  as described previously For the MBL analysis serum samples drawn immediately on admission to our emergency department (within 0–72 hours after symptom onset) prior to any diagnostic imaging or thrombolytic treatment were available from 353 of 359 patients, of whom 287 and 66 individuals received conservative and intravenous (i.v.) thrombolytic treatment, respectively. For the i.v. treatment group we analyzed additional samples 24 and 72 hours after admission which were available from 56 patients. Quantification of functional MBL was performed by an investigator blinded to any patient data using a commercially available Sandwich-ELISA Kit , and 37 of 353 (10%) individuals showed levels below 100 ng/ml, thereby closely resembling frequency distributions observed in the general population prior to the ischemic event MBL levels remained independently associated with an unfavorable outcome three months after ischemic stroke  in addition to age  had lower MBL levels than individuals with an unfavorable outcome (median MBL level 1116 (IQR 370–2524) vs. 1713 (IQR 420–3018) ng/ml, p = 0.046, n to age .To confirm the influence of MBL levels on functional recovery after ischemic stroke a second outcome measure (Barthel Index) was analyzed. A strong trend towards higher MBL levels in patients with an unfavorable recovery within 90 days after admission (defined as a BI score of > = 95%) could be observed as compared to individuals with a favorable outcome , which was significant in the multivariate analysis .The conservative treatment group consisted of 28/287 (9.8%) patients with MBL levels below 100 ng/ml, a cut-off generally accepted for the definition of functional MBL deficiency. Baseline characteristics were similar in MBL deficient patients as compared to patients with MBL levels ≥100 ng/ml with the exception of a history of hypertension . In this MBL deficient group there were 19 patients (68%) with a favorable recovery  three months after admission, five (18%) with an unfavorable outcome (mRS score of 2–5) and four (14%) individuals who died (mRS score of 6), as compared to 116 (45%), 115 (44%) and 28 (11%) corresponding patients with MBL levels above 100 ng/ml , intermediate in the second  and highest in the third tertile . However, these differences did not reach statistical significance .Patients with a mild stroke at presentation  showed significantly smaller infarctions as compared to MBL sufficient patients (mean size 0.6 (SD 0.8) vs. 18.4 (SD 43.6) ml, p = 0.0025). Notably, none of the MBL deficient patients experienced a medium or large cerebral lesion (volume of >10 ml) as compared to 39 of 140 (28%) patients with MBL levels above 100 ng/ml (p = 0.0073).prior to the ischemic event only MBL levels remained independently associated with lesion size on MRI: The average infarction volume increased by 7.3 ml for every 1000 ng/ml increase in MBL serum levels .After adjustment for covariables being present In contrast to the conservatively treated group MBL levels did not influence any of the above mentioned outcome measures in patients receiving thrombolytic agents after admission. Notably, median MBL levels were similar in patients with a less severe (NIHSS score <7) vs. a severe stroke (NIHSS score >7) at presentation , in patients with small vs. medium/large stroke lesions on MRI , in patients with a complete recovery vs. an unfavorable outcome  and in patients surviving vs. dying during a 90-day follow-up . This holds also true for the cut-off of <100 ng/ml, representing MBL deficient patients.If all study participants (thrombolysis and conservative treatment group) were analyzed together, MBL levels were still independently associated with an unfavorable outcome three months after ischemic stroke  in addition to only age . Furthermore, the association of low MBL levels with smaller infarction size persisted in the pooled analysis (data not shown).Several lines of evidence have emphasized the pivotal role of the complement system in general and the MBL pathway in particular in the pathogenesis of I/R injury, including ischemic stroke These results are in line with data from Cervera et al. who demonstrated a significant association of genetically defined MBL deficiency with favorable outcome albeit in a smaller mixed hemorrhagic and ischemic stroke cohort Notably, the magnitude of association was more evident in the analysis of early infarction volumes than of functional outcome. Hence, the role of MBL seems to be most prominent in the early phase of ischemic stroke, whereas functional recovery might not only be influenced by lesion size and stroke severity (as assessed by the NIHSS) but several other factors such as age, localization of infarct, secondary complications, and early initiation of rehabilitation services mortality after ischemic stroke was not evident in our study. Considering that there were only 32 events in the conservatively treated group (11%), the study was not powered to analyze smaller differences between MBL sufficient and deficient patients. Furthermore, mortality after stroke might be more affected by preexisting comorbidities and infections than MBL status.A significant influence of MBL deficiency on Interestingly, we did not observe a similar protective effect of MBL deficiency in patients receiving i.v. thrombolysis, which in theory should more closely mimic experimental I/R injury than conservative treatment. Several reasons might account for this important difference. First, our thrombolysis group was rather small and only powered to detect substantial differences in outcome, yet results were consistent over all endpoints analyzed. Second, animal as well as human data assessing the effect of MBL deficiency on ischemic stroke outcome after systemic thrombolysis are lacking. Therefore, we a priori chose to separately analyze patients receiving systemic thrombolysis vs. conservative treatment. In theory, massive systemic complement activation occurring after thrombolysis might have masked or offset any favorable effect of MBL deficiency that is usually evident after experimental cerebral I/R injury Recently, recombinant human C1-Inhibitor has been shown to function as a powerful inhibitor of MBL and its detrimental cerebral effects in a mouse model of cerebral I/R injury MBL2 genotypes were not determined could appear as a limitation. However, MBL serum levels show little variation throughout life. In particular, MBL levels have never been shown to strongly fluctuate during acute diseases, and MBL levels in our patients were not influenced by the interval from symptom onset to presentation and blood sampling in our emergency department. Therefore, measurement of MBL serum levels by ELISA allows reliable quantification of the functional activity of the MBL pathway in vivo in this setting MBL2 genotyping cannot account for the significant changes of MBL serum levels induced by thyroid dysfunction Despite the precisely characterized cohort the present prospective observation study has limitations including the post hoc analysis of MBL serum levels. Furthermore, MRI testing was not available for all patients in both groups, which may have biased results. Yet, analysis of functional outcome in patients with MRI data revealed similar effect of MBL deficiency as compared to the whole cohort (data not shown). Regarding the thrombolysis group the small sample size limits the significance of the observed data. At first sight the fact that In conclusion, functional MBL deficiency was associated with smaller infarction volume and favorable functional outcome in patients receiving conservative treatment after acute ischemic stroke. These findings support the concept of a significant contribution of the MBL pathway to cerebral tissue injury in human ischemic stroke. Thus, transient and early blockade of MBL or inhibition of the lectin complement pathway may represent a therapeutically promising strategy for reducing I/R associated cerebral damage.


Loss of androgenic stimulus caused changes in proteins with known roles in meiosis (including Nasp and Hsp70–2), apoptosis (including Diablo), cell signalling (including 14-3-3 isoforms), oxidative stress, DNA repair, and RNA processing. Immunostaining for oxidised DNA adducts confirmed spermatocytes undergo oxidative stress-induced DNA damage during androgen suppression. An increase in PCNA and an associated ubiquitin-conjugating enzyme (Ubc13) suggested a role for PCNA-mediated regulation of DNA repair pathways in spermatocytes. Changes in cytoplasmic SUMO1 localisation in spermatocytes were paralleled by changes in the levels of free SUMO1 and of a subunit of its activating complex, suggesting sumoylation in spermatocytes is modified by androgen action on Sertoli cells. We conclude that Sertoli cells, in response to androgens, modulate protein translation and post-translational events in spermatocytes that impact on their metabolism, survival, and completion of meiosis.The production of mature sperm is reliant on androgen action within the testis, and it is well established that androgens act on receptors within the somatic Sertoli cells to stimulate male germ cell development. Mice lacking Sertoli cell androgen receptors (AR) show late meiotic germ cell arrest, suggesting Sertoli cells transduce the androgenic stimulus co-ordinating this essential step in spermatogenesis. This study aimed to identify germ cell proteins responsive to changes in testicular androgen levels and thereby elucidate mechanisms by which androgens regulate meiosis. Testicular androgen levels were suppressed for 9 weeks using testosterone and estradiol-filled silastic implants, followed by a short period of either further androgen suppression (via an AR antagonist) or the restoration of intratesticular testosterone levels. Comparative proteomics were performed on protein extracts from enriched meiotic cell preparations from adult rats undergoing androgen deprivation and replacement  The production of sperm, known as spermatogenesis, requires androgen action. Spermatogenesis takes place in the seminiferous tubules, where the somatic Sertoli cells co-ordinate the development of germ cells though the various phases of development. The most immature germ cells, the diploid spermatogonia, proliferate prior to their entry into meiosis. Spermatocytes then proceed through meiosis where genetic information is exchanged via homologous chromosome recombination, and the final meiotic divisions produce haploid spermatids. Spermatids then undergo complex remodelling during spermiogenesis to produce the mature streamlined spermatid form. Sertoli cells provide structural and nutritional support to the developing germ cells by establishing a unique microenvironment within the tubules; this includes regulation of paracrine factors and cell-surface protein expression, reviewed in Quantitatively normal sperm production requires the action of both androgens and follicle-stimulating hormone (FSH), reviewed in The completion of meiosis is well known to require androgen action transduced via Sertoli cell AR, reviewed in Pachytene spermatocytes initiate the transcription of genes involved in the completion of meiosis in the mid-spermatogenic stages VII and VIII Taken together, the above studies suggest that Sertoli cells, upon androgen stimulation, secrete factors and/or express cell surface proteins that influence pachytene spermatocyte survival and ability to complete meiosis. Progress has been made towards understanding how androgens act on their receptors in Sertoli cells, reviewed in For an overview of the experimental design and study rationale, see Enriched meiotic cell preparations were prepared from control (untreated rats), androgen-suppressed rats  and androgen-replaced rats (TE+T24). Cell were enriched from whole testis lysates and subjected to elutriation, which separates cells on the basis of size. Because all stages of spermatocytes are represented in each treatment group Comparative proteomics were performed on enriched meiotic cell preparations from each of the four treatment groups . Image analysis of the 7 2D-DIGE gels revealed a total of 738 protein spots present on all gels  which coImmt in Using the above approach, 263 spots were shown to be statistically different between the highest and lowest androgen groups with false discovery values (q) <0.03 and power values >0.75; both parameters indicative of high quality data  and S2. To identify biological processes in spermatocytes that might be changed during testicular androgen manipulation, protein accession numbers  were anaIn order to shed light on the likely role of each protein in the context of spermatocyte function, literature searches were performed to examine known cellular function(s) and whether a role in spermatocytes has been described . Many ofP4hb1/Pdia1, Pdia3 and Pdia6, Hspd1, Hspa2, Hspa9, It was noted that many of the proteins showing significant changes between the high and low testicular androgen groups had roles in metabolism and response to cellular stress . AndrogeFth1, Ftl1, Hspd1  are associated with an increased susceptibility to oxidative stress Prdx6) is a member of a family of antioxidant proteins which protect cells from oxidative stress. Gstm5 is a member of the glutathione S transferases (GSTs) that inactivate various metabolites produced during oxidative stress. Gstm5 is a divergent member of the GST family expressed primarily in testis and may serve to protect spermatogenic cells from oxidative stress Proteins with known roles as protectors of oxidative stress were identified in spermatocytes, and were lower during androgen deprivation (in vivo was examined using immunostaining for 8-hydroxydeoxyguanosine (8OHdG) which labels oxidised DNA adducts , Sfrs7 (known as 9G8), Hnrnph3 (known as hnRNP H) and Hnrnpk (known as hnRNP K) are also present in the spliceosome Several proteins that are components of the spliceosome and are involved in RNA splicing were identified as changed in response to testicular androgen manipulation. nd Sf3a3  are compDdx4 encodes a protein known as DDX4  was lower during AR blockade (TE+Flut) (SUMO activating enzyme subunit 1 (TE+Flut) . SUMO1-3In order to investigate whether SUMO proteins may be modulated in meiotic cells during testicular androgen suppression, immunohistochemical analysis of SUMO1 was performed . The resTaken together, the above results suggest that testicular androgen suppression may cause changes in sumoylation within meiotic cells, both by decreasing the levels of free SUMO and/or decreasing enzymes involved in the sumoylation process. A recent study showed that sumoylation, and the level of free SUMO1, is modulated in meiotic cells in response to oxidative stress induced by hydrogen peroxide or to exposure to tobacco smoke Double strand breaks in DNA are initiated and subsequently repaired during homologous chromosome recombination in meiosis, and thus meiotic cells are well equipped with DNA repair enzymes Prpf19  C7 detergent  as previously described http://www.ludwig.edu.au/Ludwig Institute for Cancer Research  Protein spots of interest were excised using a ProPicII robotic spot picker  based on the X-Y coordinates exported directly from PG240 SameSpots. Protein identification see , S3 by MTestes were fixed in Bouin’s-fixative and embedded in paraffin wax. All immunohistochemical procedures were as previously described One dimensional SDS PAGE was used as described Statistical analysis of proteomic data was performed automatically by the PG240 SameSpots software. Data are given as mean normalised spot volume ± standard deviation. Statistical analysis of immunoblotting results was carried out using SigmaStat v3.5  with all data assessed for normal distribution and equal variance, prior to ANOVA and post-hoc Student Newman-Keuls testing. Results with p<0.05 were considered significant, and are presented as mean ± standard deviation (SD) unless otherwise specified.Table S1Differentially expressed proteins in meiotic cell preparations.(XLS)Click here for additional data file.Table S2Differentially Expressed Proteins Identified by MALDI-TOF MS+MS/MS, PMF/MALDI, LCMS/MS Scaffold and LCMS/MS; detailed mass spectrometry data.(XLS)Click here for additional data file.Table S3Mass spectra for identified proteins.(PDF)Click here for additional data file.Table S4Bioinformatic analysis of androgen-responsive proteins in enriched meiotic cell preparations.(XLS)Click here for additional data file.


Furthermore, among all marker genes, a significant association was found between mRNA expression and MCT-related mortality for FOXM1, GSN, FEN1 and KPNA2 (p<0.05). Finally, marker genes mRNA expression was evaluated in a cohort of 22 independent samples. Data obtained enabled to identify MCT cases with different prognosis. Overall, the molecular characterization of canine MCT transcriptome allowed the identification of a set of 13 transcripts that clearly separated differentiated from undifferentiated MCTs, thus predicting outcome regardless of the histological grade. These results may have clinical relevance and warrant future validation in a prospective study.Prognosis and therapeutic management of dogs with cutaneous mast cell tumors (MCTs) depend on clinical stage and histological grade. However, the prognostic value of this latter is still questionable. In the present study, MCT transcriptome was analyzed to identify a set of candidate genes potentially useful for predicting the biological behavior of MCTs. Fifty-one canine MCT biopsies were analyzed. Isolated and purified total RNAs were individually hybridized to the Agilent Canine V2 4x44k DNA microarray. The comparison of reference differentiated and undifferentiated MCT transcriptome revealed a total of 597 differentially expressed genes (147 down-regulated and 450 up-regulated). The functional analysis of this set of genes provided evidence that they were mainly involved in cell cycle, DNA replication, p53 signaling pathway, nucleotide excision repair and pyrimidine metabolism. Class prediction analysis identified 13 transcripts providing the greatest accuracy of class prediction and divided samples into two categories (differentiated and undifferentiated), harboring a different prognosis. The Principal Component Analysis of all samples, made by using the selected 13 markers, confirmed MCT classification. The first three components accounted for 99.924% of the total variance. This molecular classification significantly correlated with survival time ( Nowadays, molecular profiling technologies provide the potential to comprehend relevant biological networks underlying the cellular and molecular origin of cancer as well as to tailor medical care, both at tumor and patient levels An increasing number of –omic oncologic studies have been recently published in the dog, focusing on mammary tumor Unlike human cutaneous mastocytosis, that is rare and benign The aim of the present study was to characterize the MCT transcriptome by using DNA microarray technology, in order to define a fingerprint of aggressiveness that could predict the biological behavior, possibly overcoming the pitfalls of histological grading.Animal care, surgery and post surgery were carried out in accordance with good veterinary practices; dogs were under the care of licensed veterinarians and participation in the study did not influence decisions of care. According to the Italian law (D. Lgs. n. 116/92), an Institutional Animal Care and Use Committee approval number and date of approval for the study are not requested for both academia and private practice. Only a written informed consent is needed to conduct a clinical trial.In this prospective study, seventy-three histologically confirmed samples of spontaneous canine cutaneous MCTs were collected from veterinary clinics of Northern Italy throughout the years 2007–2013, during routine diagnostic or therapeutic surgical procedure and after written informed consent of the owner. Aliquots from the central part of the tumor mass (up to 100 mg each) were aseptically collected, immediately stored in RNAlater solution  and kept at –20°C until use.For each included case, the following data were retrieved from medical records: breed, age, sex, grade according to Patnaik’s system and Kiupel’s MCTs from 51 dogs yielded high quality total RNA and passed the quality control for gene expression profiling on DNA microarrays. The remaining 22 samples were used in quantitative Real Time PCR (qPCR) confirmatory analyses.For sample class prediction, 18 out of the 51 samples used for gene expression profiling were chosen as “reference samples” on the basis of Patnaik and Kiupel histological classification and mitotic index; these “reference samples” included 13 “differentiated MCTs” , and 5 “undifferentiated MCTs” . The choice of MCT reference samples was based on a 100% concordance among all pathologists.Total RNA was isolated from tissue specimens as previously described Sample amplification, labeling and hybridization were performed following the Agilent One-Color Microarray-Based Gene Expression Analysis protocol. Briefly, for each individual sample, 100 ng of total RNA were linearly amplified and labeled with Cy3-dCTP using Agilent Low Input Quick Amp Labeling kit  according to the manufacturer’s instructions. A mixture of 10 different viral poly-adenylated RNAs  was added to each RNA sample before amplification and labeling, to monitor microarray analysis work-flow. Labeled cRNA was purified with RNeasy Mini Kit , and sample concentration and specific activity (pmol Cy3/µg cRNA) were measured with a NanoDrop ND-1000 UV-Vis spectrophotometer. A total of 1.65 micrograms of labeled cRNA was fragmented by using the Gene Expression Hybridization kit  following the manufacturer’s instructions. A volume of 100 µL of hybridization solution was then dispensed in the gasket slide and assembled to the microarray slide, with each slide containing four arrays. Canine-specific oligo-arrays  were used. Slides were incubated for 17 h at 65°C in an Hybridization Oven , subsequently removed from the hybridization chamber, quickly submerged in GE Wash Buffer 1 to disassembly slides and then washed in GE Wash Buffer 1 for approximately 1 minute, followed by one additional wash in pre-warmed (37°C) GE Wash Buffer 2. Hybridized slides were scanned at 5 µm resolution using a G2565BA DNA microarray scanner . Default settings were modified in order to scan the same slide twice at two different sensitivity levels (XDR Hi 100% and XDR Lo 10%). Total RNA of 4 samples was labeled twice and hybridized separately in different slides to generate technical replicates.Microarray data have been deposited in NCBI's Gene Expression Omnibus, and are accessible through GEO Series accession number GSE50433.http://www.r-project.org), and by using Spike-In control intensities to identify the best normalization procedure. Normalization using limma package always yielded better results than cyclic loess normalization, thus quantile-normalized data were used in all subsequent analysis. After normalization, spike intensities are expected to be uniform across the experiments of a given dataset. All control features and Spike-in were excluded from subsequent analyses. Missing values  were imputed by using the microarray data analysis tool TIGR Multiple Array Viewer (TMEV) The two linked images generated from the scanned slide were analyzed together, data were extracted and background subtracted by using the standard procedures contained in the Agilent Feature Extraction Software version 9.5.1. All 51 samples were then normalized together in a single run to avoid potential biases. Normalization procedures were performed by means of the R statistical software , a two-class unpaired test was implemented in the program SAM  release 4.0 http://www-stat.stanford.edu/~tibs/PAM. This software uses the method of nearest shrunken centroids to find out the minimal set of genes that provides the greatest accuracy of class prediction. The program first performs a discriminant analysis on “known” samples  to choose the smallest panel of genes providing the greatest accuracy of class prediction with the smallest misclassification error. Subsequently, the program uses the same panel of genes to predict a class for a set of “unknown” samples .Gene expression data from 51 MCTs were analyzed to evaluate the ability to classify MCT samples with a reduced set of informative markers, by using a statistical approach for class prediction implemented in the Prediction Analysis of Microarrays (PAM) software, available online at To confirm the correct choice of reference samples and PAM class prediction results, hierarchical clustering (HCL) and principal component analysis (PCA) on gene expression data were carried out by using the TMEV suite.p<0.05.All other statistical tests  were carried out with the GraphPad Prism 5 software . Statistical significance was set at http://david.abcc.ncifcrf.gov) The functional annotation analysis of differentially transcribed genes was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) web-server v.6.7 (http://genome.ucsc.edu and www.ensembl.org).Thirteen target genes selected through class prediction analysis and three internal control genes (ICGs) were chosen for qPCR amplification. For each transcript, gene-specific primers that encompassed one intron and the most appropriate UPL probe were designed by using the Universal Probe Library (UPL) Assay Design Centre web service . Putative intron-exon boundaries were inferred from the UCSC Genome Browser and Ensembl Genome Browser Databases  according to the manufacturer’s protocol and stored at −20°C until use. Primers specificity was evaluated either in silico by means of the BLAST tool than experimentally by Power SYBR Green I  amplification and melting curve analysis. qPCR reactions  consisted of 1X LightCycler 480 Probe Master , 300 or 600 nM forward and reverse primers  according to the assay set-up, 200 nM human UPL probe and 2.5 µL of 1∶100 diluted cDNA. qPCR analysis was performed in duplicate in a LightCycler 480 Instrument  using standard PCR conditions  and LightCycler 480 clear plates . No-template controls and no-reverse transcription controls were included on each plate. Calibration curves, using a 3-fold serial dilution of a cDNA pool, were performed. ICGs assay parameters were comparable to that of the target genes; moreover, no statistically significant differences were observed in their expression profile between differentiated and undifferentiated MCT samples. Data were analyzed with the LightCycler480 software release 1.5.0  by using the second derivative method. Messenger RNA relative quantification was performed by using the ΔΔCt method Twenty-two MCT samples not processed on the array were chosen for qPCR confirmatory analysis. All thirteen transcripts selected with class prediction analysis  were amplified in duplicate as reported above. RQ values were finally analyzed by using Multid-Genex software Survival time was defined as the interval from date of surgery to death and was investigated by means of Kaplan–Meier survival analysis, stratified by MCT molecular classification (differentiated and undifferentiated MCTs). The log rank test was used to compare survival time between groups.p<0.05.For each selected target gene, a receiver-operator characteristic (ROC) curve was created. For areas under curve >0.5, the cut-off value  that better discriminated MCT associated with patient mortality was determined. On the basis of the chosen cut-off value, sensitivity and specificity parameters, with 95% confidence intervals, were calculated. All statistical tests were carried out in GraphPad Prism 5 software . Statistical significance was set at Dogs characteristics are shown in 2 ranging between 0.973 and 0.993 with a p<0.001. Non parametric Spearman correlation analysis of all samples considering RQ values obtained by qPCR target gene amplification and corresponding normalized DNA microarray data showed positive and significant results for all validated genes  were conducted for 4 samples arbitrarily chosen. Linear regression analysis of the entire dataset for each technical replicate revealed a red genes .The transcriptome of reference samples, e.g., differentiated (n = 13) and undifferentiated (n = 5) MCT samples was compared by using the SAM program. The unpaired t-test with FDR of 1% and FC of 2 revealed a total of 597 differentially expressed genes . The complete list of DEGs and corresponding FC is reported in DEGs dataset obtained through the transcriptome comparison of differentiated and undifferentiated reference samples was analyzed by HCL and PCA. Both HCL tree and PCA clearly identified, without overlapping, two main groups, being attributable to differentiated and undifferentiated reference samples –2. The fGene expression data from all 51 MCT samples were analyzed to evaluate the ability to classify “unknown” samples with a reduced set of informative markers. Differentiated and undifferentiated reference MCTs were used as “known” samples. The nearest shrunken centroid analysis implemented in the PAM program was effective in discriminating between differentiated and undifferentiated reference samples with a 100% accuracy (misclassification error of 0), thereby selecting 13 transcripts. This minimal gene set was then used in the cross-validation procedure yielding a probability of correct identification (about 100%) for all 33 remaining samples . The claThe magnitude and pattern of gene expression of selected transcripts are represented in p<0.001) from about 2- up to 7-fold in undifferentiated MCTs. Spearman correlation analysis between FC obtained through DNA microarray and qPCR analysis was statistically significant .The mRNA expression of the 13 target genes was finally evaluated by qPCR. A complete list of the thirteen selected target genes and the three ICGs  is presented in p = 0.0026).The molecular classification into differentiated and undifferentiated MCTs was considered as stratification variable for a survival time analysis . Differep = 0.0092: p = 0.0147: p = 0.0037: p = 0.0024: To understand whether the target gene modulation was associated with an increased incidence of MCT-related mortality, ROC curve analysis was performed for each target. A significant association with MCT-related mortality was evidenced for Forkhead box M1 (FOXM1), GSN, Flap structure specific endonuclease 1 (FEN1) and Karyopherin alpha 2 (KPNA2). Specifically, the area under the ROC curve (AUC) for FOXM1 gene showed a value of 0.79 (95% CI: 0.60–0.98), and this one estimated the test global performance. By using the cut-off of 0.5540 arbitrary units , test sensitivity and the specificity were 87.5%  and 62.79% , respectively. Overall, 1 out of 28 cases with FOXM1<0.5540 AU and 7 out of 23 cases with FOXM1>0.5540 AU died because of MCT. With a RQ value <0.5540 AU the survival probability stabilized at 94% after 15 months, whereas for a RQ value >0.5540 AU the survival probability was 67% after 13 months. The two survival curves significantly differed ; the survival probability of Group 2 stabilized at 38% after 5 months.Data collected from qPCR analysis were analyzed by using the MultiD-Genex software. Clustering and PCA results are reported in The transcriptome of canine mammary tumor, hemangiosarcoma, osteosarcoma, lymphoma, urinary bladder cancer, histiocytic sarcoma and melanoma has been recently characterized by using a DNA microarray approach To the best of authors’ knowledge, with the exception of a recent study comparing the proteome of 5 Kiupel L and 5 H MCTs a priori its biological behavior. The primary objective of this study was to provide a basis for the development of a simple diagnostic platform that could rapidly and accurately distinguish between differentiated and undifferentiated MCTs and support the histological grading, whose prognostic value is still questionable. Such a tool might be of great value to diagnose and treat MCT. Additionally, pet owners would greatly benefit from a more accurate survival time expectance when weighing their dog’s quality of life and their own monetary obligations in treatment decisions. Finally, the analysis of gene signature may allow the elucidation of either a single gene or a gene network that may be manipulated for treatment purposes.In this scenario, we analyzed the transcriptome of 51 canine primary cutaneous MCTs aiming at identifying a gene signature of MCT aggressiveness and predicting To this aim, we performed preliminary HCL and PCA investigations by using the whole dataset  obtained by microarray analysis, but the extreme variability observed among the 51 samples tested did not permit to clearly separate samples into well-defined groups. Indeed, the sum of the first 2 principal components accounted for less than 30% of the total variance (data not shown). Thus, to define a reduced transcriptional profile permitting a better category assignment, we decided to use differentially expressed genes among the categories assigned by histological grading. Accordingly, the transcriptome was compared among Patnaik-G1, G2 and G3 MCTs and between Kiupel L and H MCTs by using SAM tool. In the first case, no differentially expressed genes were observed, due either to the high variability among samples or the imprecise/subjective grading. In the second case, a set of differentially expressed genes was observed, but the resulting PCA evidenced two partially overlapped categories (data not shown). The failure of these approaches prompted us to use an alternative approach, consisting in the choice of known reference samples to be used in class prediction analysis.The choice of MCT cases to be used as known samples was based on histological criteria (2-tier histologic grading system and mitotic index) that permitted to obtain a 100% concordance among all pathologists. Statistical analysis of microarray data supported the choice and confirmed that reference samples belonged to two distinct categories.in vivoPathways analysis of DEGs revealed that cell cycle, DNA replication, nucleotide excision repair, p53 signaling pathway and pyrimidine metabolism were among the pathways deregulated in undifferentiated MCTs compared to well differentiated MCTs. In particular, our data highlights pathways that are important in rate of proliferation, malignant transformation, response to DNA damage and nucleotide metabolism. An altered expression of genes within these functional categories has been previously reported in the literature for other solid tumors, and these genes properly characterized the tumor when compared to its normal counterparts The class prediction analysis identified a set of 13 transcripts involved in malignant transformation that were able to accurately separate MCT samples into differentiated and undifferentiated ones. This panel of genes did not match with the proteins recently identified by using a proteomic approach. In this latter study, 5 Kiupel L and 5 H MCTs were compared, and in H MCTs a modulation of four stress response proteins  as well as of proteins mostly associated with cell motility and metastasis  was observed Cyclin B2 (CCNB2) has a function at the transition from G2 to mitosis. Experimental evidence suggested that CCNB2 promoter activity is down-regulated by tumor suppressor p53 gene The Forkhead box M1 (FOXM1) is an oncogenic transcription factor of the Forkhead family, whose expression is excluded in quiescent or differentiated cells, but highly elevated in proliferating and malignant cells Cell division cycle 20 homolog (CDC20) promotes spindle assembly checkpoint-mediated mitotic arrest through the mitotic checkpoint complex, induces apoptosis through degradation of anti-apoptotic proteins and triggers mitotic exit through CCNB degradation Cell division cycle associated protein 8 (CDCA8) is a component of the chromosomal passenger complex (CPC), a complex that acts as a key regulator of mitosis. The CPC complex has essential functions at the centromere in ensuring correct chromosome alignment and segregation and is required for chromatin-induced microtubule stabilization and spindle assembly. It exhibited frequent and robust up-regulation in cervical cancer and chemoresistant ovarian cancer NUF2  or CDCA1 (cell division cycle associated protein 1) is another component of the essential kinetochore-associated NDC80 complex, which is required for chromosome segregation and spindle checkpoint activity during mitosis. An over-expression of this transcript has been previously observed in colorectal and gastric cancers, as well as in serous adenocarcinomas Nucleolar and spindle-associated protein 1 (NUSAP1) plays a role in spindle microtubule organization. An over-expression of this gene has been reported in cervical cancer, grade III versus grade I meningioma, in recurrent prostate cancer after radical prostatectomy and breast cancer Protein Regulator of Cytokinesis 1 (PRC1) is present at high levels during the S and G2/M phases of mitosis but its levels drop dramatically when the cell exits mitosis and enters the G1 phase. Further than its role in human cervical cancer Centromere protein P (CENPP) is a novel centromere protein that has an important role during interphase of cell cycle and it is required for proper kinetochore function and mitotic progression Ubiquitin-conjugating enzyme E2S (UBE2S) gene encodes a member of the ubiquitin-conjugating enzyme family that works with the anaphase-promoting complex ubiquitin ligase ubiquitylating protein substrates whose degradation regulates progress through mitosis The last gene belonging to mitosis pathway, namely GSN, was the only gene down-regulated in undifferentiated MCTs. It has been reported to be an onco-suppressor participating in the regulation of the apoptotic process and interacting with p53 Other two potential markers, Flap endonuclease 1 (FEN1) and RAD51 are involved in DNA repair pathway. FEN1 is a structure-specific endonuclease best known for its critical roles in Okazaki fragment maturation, DNA repair and apoptosis-induced DNA fragmentation RAD51 protein plays a key role in homologous recombination. It has been shown to be up-regulated in many human cancers, especially higher grade, chemoresistant and radioresistant tumors Finally, Karyopherin α 2 (KPNA2) has recently emerged as a potential biomarker in multiple human cancer forms Molecular pathology, a scientific approach defining cancer subtypes based on the underlying molecular footprints, has led to the discovery of subtypes in several different tumors. Nevertheless, it has been demonstrated that this approach works at its best when cancer subtypes based on genetic profiles are already known: whenever the different subtypes of the same cancer and the number of patients belonging to each of them are known, then a statistical model could be built to associate a specific gene expression profiling, typical of a subtype of cancer, to an individual patient Meanwhile, presented results confirmed the usefulness of a transcriptomic approach in the definition of a signature that could segregate molecular subtypes of the same tumor with different survival probabilities As genome-wide gene expression profiling is cost-prohibitive and impractical, we selected 13 simple qPCR assays to stratify MCTs into differentiated and undifferentiated cases. The reliability of the gene set was tested, by means of a qPCR approach, upon a separate cohort of MCT samples for which a complete follow up was available. Even in this case, we observed a statistical significant association between molecular classification and survival time, confirming the potential usefulness of these results to develop a rapid and relatively cheap benchtop diagnostic test based on the expression of 13 genes that can classify canine MCTs into one of these 2 subgroups, and enabling a direct clinical application of our results. Worth mentioning, in veterinary medicine a benchtop diagnostic test composed of 4 genes has been previously proposed for canine lymphoma As a whole, this study allowed us to fill a gap in the tumor understanding and it contributed to the uncertainty about the relevance and utility of morphological classification systems. In addition, the transcripts here identified not only may predict prognosis , but may also be selected as future targets for therapy (gene therapy or chemotherapy), as previously reported in various human cancers In the future, a study population with a larger sample set of tumors might provide a more robust basis to confirm and refine segregation between differentiated and undifferentiated MCTs.In conclusion, the transcriptome of canine cutaneous MCT was characterized for the first time by using a DNA microarray approach. Data obtained allowed the identification of a signature of aggressiveness in canine MCT; particularly, a set of 13 potential biomarkers identified through a class prediction analysis segregated MCT samples into two distinct molecular categories, namely differentiated and undifferentiated MCTs. The molecular classification into differentiated and undifferentiated MCTs as well as the mRNA expression of FOXM1, GSN, FEN1 and KPNA2 were significantly associated with survival. The prognostic value of the molecular classification was also confirmed in a separate cohort of MCT cases, for which an accurate follow up was available. Thus, this set of genes could be useful to develop a benchtop diagnostic test that could support canine MCT histological grading and consequently having prognostic relevance. Finally, analysis of gene signature allowed elucidation of single genes or genetic pathways (mitosis and DNA repair), which could be considered for future diagnostic and treatment purposes as already performed in human oncology.Table S1Demographic characteristics of complete cohort of 73 dogs with cutaneous mast cell tumor and restricted groups (n = 51 and n = 22) analyzed by gene expression profiling (GEP cohort) and quantitative Real Time PCR confirmatory analysis (dogs not on array), respectively. The table describes the characteristics  of the 73 dogs selected for the study, including the 2 population subsets (gene expression profiling cohort and dogs not on array).(DOCX)Click here for additional data file.Table S2Histological grading and mitotic index of reference samples (differentiated and undifferentiated) used in the class prediction analysis. The table describes the list of reference differentiated and undifferentiated mast cell tumor samples used for class prediction analysis and the corresponding Patnaik and Kiupel histological grading and mitotic index.(DOCX)Click here for additional data file.Table S3Down-regulated genes (n = 147) in undifferentiated reference samples and corresponding –fold changes (FC). The table describes the list of the entire set of down-regulated genes (n = 147) obtained through the comparison of differentiated and undifferentiated reference samples transcriptome. The –fold change for each probe is also reported.(DOCX)Click here for additional data file.Table S4Up-regulated genes (n = 450) in undifferentiated reference samples and corresponding –fold changes (FC). The table describes the list of the entire set of up-regulated genes (n = 450), obtained through the comparison of differentiated and undifferentiated reference samples transcriptome. The –fold change for each probe is also reported.(DOCX)Click here for additional data file.Table S5List of selected genes: Ensembl Genome Browser sequence ID, primer sequences, UPL probe and amplicon size. The table defines target and internal control genes selected for qPCR analysis and all the information about qPCR assay design: transcript Ensembl Genome Browser ID, primer sequences, UPL probe and amplicon size.(DOCX)Click here for additional data file.Table S6qPCR assay parameters: primer concentration, efficiency, error value and dynamic range. The table defines the main features of qPCR assays obtained during their setting up. In particular primer concentration and efficiency, error value and dynamic range are reported.(DOCX)Click here for additional data file.


Prions, the infectious agent of scrapie, chronic wasting disease and other transmissible spongiform encephalopathies, are misfolded proteins that are highly stable and resistant to degradation. Prions are known to associate with clay and other soil components, enhancing their persistence and surprisingly, transmissibility. Currently, few detection and quantification methods exist for prions in soil, hindering an understanding of prion persistence and infectivity in the environment. Variability in apparent infectious titers of prions when bound to soil has complicated attempts to quantify the binding capacity of soil for prion infectivity. Here, we quantify the prion adsorption capacity of whole, sandy loam soil (SLS) typically found in CWD endemic areas in Colorado; and purified montmorillonite clay (Mte), previously shown to bind prions, by BioAssay of Subtracted Infectivity in Complex Solutions (BASICS). We incubated prion positive 10% brain homogenate from terminally sick mice infected with the Rocky Mountain Lab strain of mouse-adapted prions (RML) with 10% SLS or Mte. After 24 hours samples were centrifuged five minutes at 200×g and soil-free supernatant was intracerebrally inoculated into prion susceptible indicator mice. We used the number of days post inoculation to clinical disease to calculate the infectious titer remaining in the supernatant, which we subtracted from the starting titer to determine the infectious prion binding capacity of SLS and Mte. BASICS indicated SLS bound and removed ≥ 95% of infectivity. Mte bound and removed lethal doses (99.98%) of prions from inocula, effectively preventing disease in the mice. Our data reveal significant prion-binding capacity of soil and the utility of BASICS to estimate prion loads and investigate persistence and decomposition in the environment. Additionally, since Mte successfully rescued the mice from prion disease, Mte might be used for remediation and decontamination protocols. Sc) of the normal mammalian prion protein (PrPc) associate with prion infectivity, generally resist protease degradation, and often form insoluble, amyloidogenic aggregates Sc to adsorb strongly to soil components Prions are infectious agents of transmissible spongiform encephalopathies (TSEs) Sc. Mte is prevalent throughout the US mountain west, including CWD endemic areas Experimental evidence suggests that the particularly strong adsorption relationship of prions to soil colloids, or clays (defined as particles <4 µm), may be responsible for the longevity in the environment Sc, specific interactions and adsorption dynamics to soil and humic substrates have yet to be identified.Other soil components such as organic material, quartz, tannins and humic acid have also been implicated in prion adsorption The robust adsorption relationship between the prion protein and soil has proven difficult to measure or reverse, limiting prion detection sensitivity, estimation of prion adsorption capacity of soil Sc bound to soil Sc does not necessarily correlate with prion infectivity and studies estimating infectivity using prion-bound soil fractions have produced conflicting data. Soil-bound prions apparently increase infectivity upon oral inoculation To date, hypothesized soil interactions have largely been demonstrated with recombinant prion proteins c were generated as previously described 50 infectivity analysis TgA20 mice over-expressing mouse PrP2 inhalation to effect followed by decapitation. All efforts were made to minimize suffering.Mice were bred and maintained at Lab Animal Resources, accredited by the Association for Assessment and Accreditation of Lab Animal Care International, in accordance with protocols approved by the Institutional Animal Care and Use Committee at Colorado State University (Protocol ID: 09-1580A). Intracerebral inoculations were performed under Isoflurane anesthesia, and mice euthanized using COWhole SLS used for this study was sourced from a private ranch in Southern Colorado located on the eastern side of the Rocky Mountains and within a game management unit which continues to test negative for CWD in free-ranging cervid populations Mixed-Layer Illite/Smectite – A clay mineral group containing interlayered or interstratified Illite and Smectite. Mixed layer type was identified by the minerals involved (Illite and Smectite), the type of order or stacking along the Z-axis (random or not ordered), and the proportions of the minerals involved (10% Illite and 90% Smectite). Illite and Mica – Common non-expanding minerals which are hydrated silicates containing potassium, silica and aluminum. Kaolinite and Chlorite – Common non-expanding hydrous aluminum silicate clay minerals. Montmorillonite clay was not further analyzed, specifics were obtained from the Material Safety Data Sheet (MSDS) sheet provided by the supplier.Soil classification analysis of whole soil was conducted by the Colorado State University Soil, Water and Plant Testing Laboratory . X-ray diffraction mineralogy analysis of whole soil was conducted by K-T GeoServices, Inc. . Whole soil analysis included XRD weight percentage for bulk (whole rock) and clay fraction (<4 µm), pH, percent organic material, and soil texture classification of basic elements . The folThe Rocky Mountain Lab passage 5 strain of mouse-adapted scrapie (RML5) was previously described We performed an infectivity subtraction assay to estimate binding capacity of SLS and Mte soil . We prep50 determinations for RML in TgA20 mice y = 11.45–0.088x  to calculate infectivity titers as outlined in Reed and Muench Anesthetized mice were intracerebrally inoculated with 30 µl of inoculum (with 1% Pen-Strep added) as previously described −7 dilution prion samples detected positive) and specificity (>98% of NBH samples remain negative) of the detection assay. Each group of samples was processed with at least five NBH negative controls and one positive plate control .Brain tissues of clinical and non-clinical mice, as well as samples of each inoculum were tested by western blotting before and after sPMCA. Prior to assay, brain tissues collected from mice were homogenized following the methods of Meyerett et al. SQ PVDF membranes  in transfer buffer (Invitrogen). Membranes were blocked for 1 hr with 5% nonfat milk in PBS with 0.1% Tween 20, and incubated overnight at 4°C in Superblock  with HRP-conjugated anti-PrP Bar-224 monoclonal antibody (SPI bio) diluted 1∶20,000. Blots were washed 6×10 min in PBS with 0.2% Tween 20 before visualizing proteins using Immobilon chemiluminescent substrate (Millipore) and a Fujifilm LAS 3000 gel documentation system.For visualization by western blot, 18 µl of sample was digested with 2 µl of 50 µg/ml proteinase K  for 30 min at 45°C. The reaction was stopped by adding lithium dodecyl sulfate sample loading buffer  and boiling samples for 5 min at 95°C. Samples were electrophoresed through 12% sodium dodecyl sulfate polyacrylamide gels (Invitrogen) then electro-transferred to Immobilon PSc detection as previously described c. PrPSc was labeled with anti-PrP BAR224 followed by incubation with secondary EnVision HRP-conjugated anti-mouse antibody that was visualized with chromagen 3-Amino-9-ethylcarbazole (DAKO). Hemotoxylin and Glial fibrillary acidic protein (GFAP) stain of activated astrocytes was performed by the Colorado State University Histology and Diagnostic Laboratory as previously described Dissected tissues were prepared and stained for PrPIn this study we collected soil from an area in southern Colorado with similar soil composition to CWD endemic areas but with no reported cases of CWD. Soil component analysis revealed clay content similar to that found in areas of Colorado exhibiting high prevalence . Specifi5±1.2 LD50 units/g soil. Incubation of RML5 with Mte resulted in a near total removal of infectivity with only one mouse becoming ill, equating to at least a 1380-fold reduction in infectivity. Mte completely removed lethal doses of TgA20RML prions (1.7×104 LD50 units), indicating that its RML5 binding capacity is at least 5.63×108 LD50 units/g of Mte.Incubation of 1% TgA20RML and 1% RML5 prions with either SLS or Mte significantly reduced the bioassay infectivity, resulting in delayed clinical disease p<0.05, . SpecifiSc deposits, spongiosis and astrogliosis revealed differences in histopathology between SLS-adsorbed prion-inoculated mice and the single Mte-adsorbed RML5-inoculated mouse that became clinically ill  and Mte-adsorbed TgA20RML (D and J). We detected small deposits of PrPSc and spongiosis and slightly more astrogliosis in the brain of the lone clinically sick mouse inoculated with Mte-adsorbed RML5 (F and L). In contrast, we observed both diffuse and punctate PrPSc aggregates and mild to severe spongiosis and astrogliosis in brains from clinically ill mice inoculated with non-adsorbed prions (B and H) and whole soil-adsorbed TgA20RML (C and I) and RML (E and K). Together with the biochemical analysis, these data confirm prion infection in clinically ill mice, as well as sub-lethal infection in non-clinical mice, which we now call sub-clinically ill mice.To determine whether non-clinically sick mice replicated sub-clinical levels of prions, we attempted to amplify minute quantities of prions from their brains using sPMCA. We detected prions in 2/7 brains from non-clinical mice inoculated with TgA20RML pre-adsorbed with Mte and 2/4 brains from non-clinical mice inoculated with Mte-adsorbed RML , Table 3ally ill . We dete50 calculations and quantitative statements of prion binding capacity of soil and Mte as a function of infectivity. Use of whole brain homogenate, as opposed to recombinant protein or enriched prions, accounts for the complexity of the tissue and competitive matrix binding activity Sc is conceivably a small component of the brain matrix, and will compete with other proteins for binding sites on soil particles. Previous studies revealed the potential for increased adsorption if allowed to incubate for more than 24 hours Environmental persistence and increased transmissibility of soil bound prions remain poorly understood but extremely important aspects of both scrapie and CWD ecology. The use of SLS and Mte allowed us to model the complexity of prion binding in the natural environment, while estimating the relative contribution of a soil component previously shown to avidly bind prions. The use of RML in these studies allowed for LDBASICS circumvents these problems by quantifying prion binding to soil by subtracting residual prion titers present after soil adsorption from initial prion titers before soil adsorption. We are currently titrating several other CWD field isolates and laboratory strains and will use BASICS to quantify binding capacity of relevant soil types to relevant prion strains.4 LD50 units of RML per gram of soil. This amount comprises between 2% and 18% of the total prion binding capacity of soil that we observed for RML5 and TgA20RML, respectively. The estimated 2% binding capacity of Mte for RML5 correlates to the 2% Mte found in SLS, suggesting that the Mte is saturated with prion infectivity. The observation that the 2% of Mte correlates to binding nine-fold more TgA20RML infectivity  in SLS also supports this interpretation, because TgA20RML titers were approximately nine times lower than RML5 titers. This would leave the remaining prions available for binding to other soil components such as other clays, quartz, humic acid or other organic material. Indeed, other soil components have been implicated in protein adsorption, including organic material, tannins, quartz We found a dramatic decrease in infectivity with a simple 24-hour soil incubation. The Mte treatment of TgA20RML bound sufficient amounts of infectious prions to prevent disease onset entirely. Similar binding was seen in RML5 samples, resulting in SLS binding 95.3% of infectious prions and the Mte binding ≥99.98%. These results suggest that the adsorption capacity of the Mte, in these experimental conditions, lies somewhere between the TgA20RML and RML5 titers. If the Mte comprises the majority of smectite clay in the soil (90% of the 2.2% smectite, These data suggest that Mte is not the only factor determining prion binding capacity of soil. However, we propose that prions bind Mte with relatively high avidity and affinity compared to other soil components, whose prion interaction may be more reversible, creating equilibrium between prions bound to soil and free in solution. We hypothesize that Mte concentrations in the soil dictate this equilibrium and likely result in residual infectivity in supernatants in our and other experimental systems Perhaps the most utilitarian finding of this study was the prevention of disease by pre-adsorbing prions with Mte. These data strongly promote Mte for prion remediation applications. Environmental prion mitigation looms as a potential desideratum for agriculture and wildlife management. However, options for degradation and removal of prions have shown limited efficacy In summary, we propose that although constituting a relatively small fraction of total soil, the high binding avidity and affinity of Mte results in high prion occupancy at or near saturation that may drive the likelihood of environmental prion contamination, persistence and transmission in nature, as has been previously suggested


We present results of an analysis of inter and intra batch variability of SS+nebulizers.Guidelines recommend that patients presenting with acute severe asthma with life-threatening features receive the necessary high doses of β2-agonists via the nebulized route.The SideStream Plus  is a breath-enhanced nebulizer designed to maximize respirable output, while minimizing treatment time . Prior tSS+ nebulizers  were assessed in terms of 5 experimental parameters; nebulization time, residual volume, particle size distribution (mass median diameter (MMD), fine particle fraction (FPF)), and emitted dose. Each batch was provided with a driving flow from a different Porta-neb compressor (Philips Respironics). Measurement of particle size distribution was achieved using a laser diffractor  with an extraction flow of 15 l/min. Each nebulizer was weighed and filled with 2.5 ml salbutamol sulphate  solution (1 mg/ml). To assess nebulization duration the nebulizers were run continuously until ‘end of treatment’, defined as when the obscuration level of the sample in the laser diffractor fell below 5% for 5 s. At this point the SS+ were re-weighed to calculate residual volume. The dose delivered to a filter placed between the laser diffractor and extraction air flow was quantified using high performance liquid chromatography. The coefficient of variation (CV) for each experimental parameter was calculated for each batch.Results are shown in TableThe intra batch coefficient of variation indicates that variability in each of the 5 experimental parameters did not differ substantially across the 3 batches tested. The close similarity between mean values for each of the experimental parameters indicates that inter batch variability was low. Taken together, these results suggest that different batches of SS+ nebulizers should perform similarly in terms of nebulization time, particle size distribution, and emitted dose.