RSV infection induces a clearly different host response pattern compared with hRV and induced strong innate immune responses both locally and systemically. B cell lymphoma (BCL6) is a hub gene that positively correlates with RSV load and disease severity. Most insights into the cascade of immune events after acute respiratory syncytial virus (RSV) infection have been obtained from animal experiments or in vitro models.In this study, we investigated host gene expression profiles in nasopharyngeal (NP) swabs and whole blood samples during natural RSV and rhinovirus (hRV) infection (acute versus early recovery phase) in 83 hospitalized patients <2 years old with lower respiratory tract infections.Respiratory syncytial virus infection induced strong and persistent innate immune responses including interferon signaling and pathways related to chemokine/cytokine signaling in both compartments. Interferon-α/β, NOTCH1 signaling pathways and potential biomarkers HIST1H4E, IL7R, ISG15 in NP samples, or BCL6, HIST2H2AC, CCNA1 in blood are leading pathways and hub genes that were associated with both RSV load and severity. The observed RSV-induced gene expression patterns did not differ significantly in NP swab and blood specimens. In contrast, hRV infection did not as strongly induce expression of innate immunity pathways, and significant differences were observed between NP swab and blood specimens.We conclude that RSV induced strong and persistent innate immune responses and that RSV severity may be related to development of T follicular helper cells and antiviral inflammatory sequelae derived from high activation of BCL6. Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections (LRTIs) in young children. Although some risk factors associated with severe RSV disease have been identified , the majority of children with RSV that require hospitalization were previously healthy and lacked comorbidities , 2. The In this study, we applied microarray technology to acute and early recovery blood and NP swabs obtained from children <2 years hospitalized for LRTI with laboratory-confirmed RSV or rhinovirus (hRV) infections, which was reported as a second pathogen found in this population .Using a combination of weighted gene coexpression network analysis (WGCNA)  and convThe following Institutional Review Boards approved the study: Children’s Hospitals (CH)1 and 2 and the Hospital for Tropical Diseases  and the Oxford University Tropical Research Ethics Committee . Written informed consent was obtained from parents or legal guardians of children before enrollment into the study.The clinical data and samples for gene expression profiling were collected from a previously reported cohort of 632 children with LRTI under 2 years of age hospitalized at CH1 and CH2 . Severe Total RNAs from whole blood and NP swabs were extracted using the PAXgene blood RNA isolation kit  and the RNeasy Mini kit , respectively. Biotinylated amplified complementary RNA (cRNA) was generated by in vitro transcription of total RNA using Ambion Illumina TotalPrepRNA amplification kit , according to the manufacturer’s instructions. After purification, 800 ng of cRNA from NP samples and 2000-ng labeled cRNA from blood were hybridized to the HumanHT-12 v4 BeadChip array at 55°C for 18 hours and under washing, blocking, and streptavidine-Cy3 staining steps, according to the manufacturer’s instructions. Each array on the HumanHT-12 v4 Expression BeadChip targets more than 31 000 annotated genes with 47 231 probes derived from the National Center for Biotechnology Information Reference Sequence RefSeq Release 38  and other sources. The array was scanned using an iScan confocal scanner .https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97743) under accession number GSE97743.Using R statistical software version 3.2.1 (R Development Core Team) , raw expP < .05. Due to the design of Illumina BeadArray, there were multiple probes for each gene, reflecting the possibility of multiple differentially spliced transcripts for each gene. We collapsed this information as such that each probe represented an average transcript for each gene.To investigate differences in gene expression between 2 time points (early recovery versus acute phase) for each group , the linear modeling framework in the limma package  was usedThe WGCNA uses a stepwise analytical process to interrogate variations in gene coexpression patterns across all samples in the same group (RSV or hRV infection) to identify subsets of coexpressed genes with similar transcriptional patterns together to form a group of transcripts (coexpressed network modules). Because the probability for multiple transcripts to follow a complex pattern of expression across all the samples by chance is low, such sets of genes should constitute coherent and biologically meaningful transcriptional units. The coexpressed network modules that correlate to a clinical trait are assumed to have a coordinated biological function in the clinical condition under study (disease status)  network was constructed by NetworkAnalyst  using thThere were no significant differences in sex and number of household members between the 3 selected patient groups as well, compared with the original cohort . PatientDifferentially expressed genes were identified in the expression data of NP swabs and blood of RSVsi (182 DEGs in blood and 992 in NP swabs), RSVco (106 DEGs in blood and 479 in NP swabs), and hRV (92 DEGs in blood and 325 in NP swabs) .When DEG lists were compared between NP and blood, 36 and 43 overlapping DEGs were observed in RSV single and RSV coinfection, respectively . When weUnsupervised hierarchical clustering heatmaps using DEGs of RSV and hRV in blood and NP swabs confirmed differences in the pattern of gene expression at acute phase and early convalescent phase across 4 patient groups . These hBy using g:Profiler and InnateDB pathway analysis, we revealed the functional significance of the identified DEGs in all RSV infection cases and in hRV. Innate immune responses, ie, interferon (IFN) signaling , cytokine signaling, and the pathway related to Th17 cell population, ie, interleukin (IL)-23-mediated signaling, were the main pathways enriched from up-regulated genes in both NP swabs and blood of RSV cases. In contrast, the down-regulated genes were involved in specific metabolic processes of different cell types from each compartment . For hRVWe explored the coexpressed network modules in the RSV host response by using WGCNA, and we identified 20 and 37 coexpressed network modules in NP swabs and blood during RSV infection, respectively  and b. FWe hypothesized that the common set of genes between the DEGs identified over the course of the RSV infection and the significantly coexpressed gene modules could reveal leading biological pathways and identify key genes related to pathogenesis. Hence, we compared the selected significantly coexpressed gene modules and DEG lists . ExpressP-value = 2.7E-03).The RSV “severe” network (severe NP network) and the RSV “nonsevere” network (nonsevere NP network) in NP arrays were built from 309 genes and 498 genes, respectively . The RSVP = 2.8E-10, degree = 231), IL-7 receptor (IL7R) , and IFN-stimulated gene 15 (ISG15)  (in NP samples) as the most significant hubs in the severe NP network; however, ISG15 , B cell lymphoma 6 (BCL6) , and phospholipid scramblase 1 (PLSCR1)  in the severe blood network were hub genes positively associated with both RSV load and severity.To assess the interaction between the DEGs in the selected modules, a PPI network was explored and visualized using NetworkAnalyst. As shown in P = 1.39E-07, degree = 5) interacts with the RSV nonstructural protein 1 (NS1); (2) DDX58 (DExD/H-box helicase 58)  and (3) IFN induced with helicase C domain 1 (IFIH1)  interact directly with the RSV viral N protein.Comparing these two networks with the recently published RSV-host interaction network , we founTo our knowledge, this is the first study examining the host gene expression response in children with LRTI with RSV or hRV over the course of infection from 2 compartments, ie, NP swabs and blood samples, representing in situ and systemic responses, respectively. Using an unbiased analytical strategy, we found that RSV infection induced similar pathways in both compartments, whereas for hRV infection, no common pathways were shared between the two compartments. For RSV infection, the overall transcriptional responses in blood were reduced in comparison to the NP compartment, with fewer DEGs and enriched pathways as well as lower fold changes. This finding highlights the localized response to RSV infection and the importance of studying the NP compartment, which has also been previously shown to have a similar gene expression pattern as bronchial epithelium in response to cigarette smoking , 41 or iRespiratory syncytial virus infection induced a different host response from hRV in terms of local and systemic responses. Furthermore, RSV appears to be the dominant pathogen during coinfections because similar patterns were observed in RSV coinfection cases. Others also found that in bacterial carriages, the response to RSV was also predominant and independent of the microbiome composition in NP . We coulAlso of note, for RSV infection only, WGCNA showed clear clusters of modules that significantly correlated with severity  and b, wOur study has limitations. Our patients were all hospitalized with severe illness and not representative for the entire spectrum of the RSV infection phenotype, and gene expression patterns may not be similar for milder cases managed as outpatients. Only 83/632 patients from the original cohort were selected for the host expression analysis. The selection was based on availability of paired samples and RNA quality for microarray. The demographic characteristics of this subset of patients were not different from the original cohort. On the other end of the spectrum, because mortality was 0%, we also did not have samples from patients who died. A broader spectrum of clinical severity will facilitate the identification of host markers associated with RSV severity. Using steroids can have an impact on the innate immune response. However, the number of patients under this treatment is small , and so In conclusion, the present study showed that RSV infection induces strong and prolonged innate immune responses with overlap between local and systemic samples, which was not observed in hRV infection. In severe RSV cases, RSV may cause an unbalanced inflammatory response and interact with the BCL6 gene to escape the host immune response.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at Supplementary_table_1Click here for additional data file.Supplementary_table_2Click here for additional data file.Supplementary_table_3Click here for additional data file.Revision_Supp_Figure1Click here for additional data file.Revision_Supp_Figure2Click here for additional data file.Revision_Supp_Figure3Click here for additional data file.Revision_Supp_Figure4aClick here for additional data file.Revision_Supp_Figure4bClick here for additional data file.Revision_Supp_Figure_5Click here for additional data file.SUPPLEMENT_FIGURE_LEGENDClick here for additional data file.


Circulating tumor cells (CTCs) have potential utility as a surrogate biomarker of tumor biology via a liquid biopsy. The aim of this study was to evaluate if the nCounter NanoString assay could be used for accurate gene expression profiling of CTCs using the PAM50 research-use-only CodeSet. Analysis was performed on CTCs isolated by the ANGLE Parsortix system from healthy blood spiked with the breast cancer cell lines Hs578T, SkBr3, MDA-MB-231 or MCF7. Using cell lines as gold standard positive controls and Parsortix processed blood without spiking (unspiked) as negative controls, we found an average of 12 significantly differentially expressed genes among spiked samples versus unspiked controls. We validated our findings with the NanoStringDiff differential expression statistical method. The NanoString recommended targeted pre-amplification introduced false positive results due to pre-amplification bias, and the amplification of non-cancer genes from normal leukocytes confounded gene expression profiling of CTCs. Pre-amplification bias is a concern for other similar assays that may be used as discovery tools or target validation of transcripts of interest in gene expression profiling of CTCs. We recommend the use of an unspiked negative control when evaluating CTC technologies regarding gene expression profiling. Given that the molecular profiling of CTCs as a liquid biopsy may have clinical ramifications for potential treatment selection in future clinical trials, our study emphasizes cautious consideration of pre-analytical variables such as amplification bias in the context of liquid biopsy studies. Similarly, CTC-based clinical studies have provided information about micrometastatic disease in early stages of cancer, patient prognosis, acquisition of resistance to therapy, risk of metastasis and disease progression. Additionally, studies also support the clinical relevance of monitoring CTC counts during treatment to evaluate treatment efficacy, and patient stratification to guide new therapy decisions and treatment management [Over the last years, research focused on circulating tumor cells (CTCs) has captured great attention as a potential “liquid biopsy” with recognized potential utility in cancer diagnosis, prognosis, and personalized treatments in patients with a variety of solid tumors . Since Cnagement , 2, 4–6.in vivo studies. A promising technology, the ANGLE Parsortix cell separation system is an epitope-independent microfluidic platform that captures and recovers viable, intact CTCs from clinical samples. The Parsortix system pumps the blood sample through a disposable cassette formed by a stepped gradient that narrows to a critical gap of 10μm. CTCs are captured at this critical gap, but smaller cells (erythrocytes and most leukocytes) pass through. Harvesting is achieved by applying a gentle reverse flow that allows for the recovery of CTCs for analysis. Various studies have shown the Parsortix system's versatility in capturing and harvesting CTCs suitable for in vitro culture and molecular analysis by techniques such as genomic hybridization, qPCR and RNA-seq [Given the low number of CTCs, cell enrichment strategies are necessary to select and capture rare CTCs from a background of millions of blood cells per milliliter. Regardless of the enrichment method, major obstacles such as CTC purity and CTC rarity pose additional challenges for CTC identification and CTC molecular characterization based on next generation sequencing, targeted multi-marker gene expression analysis by qPCR, and targeted multi-marker hybridization techniques such as the nCounter NanoString assay . The Cel RNA-seq –16.in vitro diagnostic platform utilizing the NanoString technology which has been accepted into certain international treatment guidelines based on clinical validation of the prognostic significance of this gene expression based assay [The rapid decline in sequencing costs, and the advent of multiplexed gene expression assays have made it possible to perform targeted and comprehensive molecular characterization of CTCs by various molecular assays , 17, 18.ed assay . The Naned assay , 22. Theed assay , 23.Although this multiplexed 50-gene test has been validated on FFPE breast tumor tissue, in the present study we aimed to evaluate the feasibility and accuracy of the NanoString nCounter platform for CTC-gene-expression analysis using the PAM50 Research-Use-Only CodeSet following the standard nCounter single cell gene expression protocol.As a proof-of-principle experiment, we determined the CTC enrichment capability of the ANGLE Parsortix system by measuring the CTC recovery rate or the number of harvested CTCs after processing SkBr3 spiked blood samples . CTC recovery rate was calculated by SkBr3-EpCAM+ cell enumeration by flow cytometry. By quantitating the number of endogenous WBCs CD45+ carryover cells, potential leukocyte contamination was also assessed. FACS results indicated that the Parsortix system recovered an average of 5 EpCAM+ cancer cells (20-35%) from blood samples initially spiked with 20 SkBr3 cells Figure . AlthougNext, we sought to test if multiple target enrichment (MTE) is a necessary step when performing NanoString analysis on Parsortix harvested material. Therefore, we created five scenarios in which different ratios of FACS sorted SkBr3 EpCAM+ cells and CD45+ cells were mixed and lysed. Gene expression analysis by NanoString PAM50 was performed on both pre-amplified (MTE) and non-pre-amplified (non-MTE) products from the same cell lysate. Results in Figure To identify a specific CTC PAM50 gene signature among blood samples spiked with breast cancer cell lines, we performed a differential NanoString PAM50 gene expression analysis to discriminate gene expression profile between paired blood samples: spiked and unspiked.nCounter NanoString PAM50 data was normalized using nSolver Data Analysis Software. Intriguingly, we observed differences in gene expression of reference genes among spiked samples and unspiked controls. Among the 7 NanoString PAM50 reference genes included in the MTE primer pool Table , only PSp<0.001).Additionally, we compared the gene expression among bulk PB controls  versus unspiked blood controls (subjected to MTE). We not only observed relatively high gene expression of some genes by unspiked controls compared to bulk PB control Figure , but genFurthermore, transcript counts for individual genes between bulk PB controls and unspiked samples among different donors did not reflect the linear unbiased target pre-amplification expected after the MTE process. We observed inconsistent transcript-count-fold changes between unspiked controls and bulk PB controls among genes and among healthy donors Table . For exaConversely, when performing a global comparison of NanoString PAM50 gene expression among spiked samples with the corresponding cell line control  and specificity (<75%) detecting cell line gene signatures in spiked samples, in conjunction with the relatively high false positive rate (60-80%) shown in Table 2=0.49, R2=0.46, R2=0.62, and R2=0.38 for Hs578T, SkBr3, MDA-231, and MCF-7 controls respectively) between MTE cell line control versus non-MTE ‘gold standard’ cell line RNA.Additionally, we performed a pairwise correlation and linear regression analyses on the PAM50 gene expression between cell line controls and bulk cell line RNA. Since bulk cell line RNA was not subjected to MTE  as shown in Figure q-values are determined as a refined level of significance for every comparison per gene. While p-values measure significance in terms of false positive rate, the q-values measure it in terms of false discovery rate (FDR). p and q-values <0.05 were considered significant.To validate our differential gene expression analysis, we used the open source statistical platform NanoStringDiff . Followip and q-values for the NanoStringDiff comparison analysis between each pair of spiked sample and unspiked control per donor. We found that only 8, 2, 21, and 17 genes (bold font) were statistically significantly expressed in Hs578T, SkBr3, MDA-231, and MCF-7 spiked samples, respectively. By performing the NanoStringDiff statistical method, we could validate some of the differentially expressed genes previously found after nCounter normalization and gene expression analysis. From the differentially expressed genes shown in Figure In Table a priori knowledge of what tumor markers may be present. This very fact allows CTCs to serve as a potential target discovery tool but leaves the approach vulnerable to misinterpretation due to confounding by background peripheral blood leukocytes. The objective of our study was to evaluate the feasibility and accuracy of using the NanoString PAM50 Research-Use-Only code set for gene expression profiling of breast cancer CTCs isolated using the ANGLE Parsortix system. While we have optimized a workflow for transcriptomic profiling of rare CTCs using the Parsortix system, in this report, our data indicate that the NanoString PAM50 panel has limited utility in differentiating CTC mimics from leukocyte background.Circulating tumor cells have enormous potential as a liquid biopsy of the real-time status of tumor biology without In our hands, capture rates for spiked cell line CTC mimics ranged from 61-75% with the Parsortix system, which is similar to other investigators’ reports , 14. OurThe NanoString PAM50 code set has been shown by others to be useful for classifying breast cancers into intrinsic subtypes and in generating risk of recurrence scores (ROR) , 25. TheIn this report, we utilized two types of negative controls – bulk, unsorted peripheral blood from healthy donors, and a second tube of blood from each donor that was processed on the Parsortix to control for the effect of size based microfluidics selection on leukocyte background gene expression (unspiked samples). CTC spike in mimics were also compared to a positive control of known cell lines. Had we not included the unspiked control samples in our study, we would have erroneously concluded that our principle component analysis of NanoString PAM50 data provided conclusive evidence that CTCs could be separated from peripheral blood in terms of gene expression profiling with the PAM50 Figure . Spiked This study emphasizes the importance of either selecting genes that are entirely not expressed in peripheral blood or of performing a careful background subtraction or normalization procedure that considers the peripheral blood gene expression signature that is unique to each patient given that considerable heterogeneity is present both in CTCs and in the peripheral blood specimens from patient to patient. In actual cancer patients, no positive control exists to reliably distinguish signal from noise.We concluded that the NanoString recommended targeted pre-amplification of the PAM50 Research-Use-Only code set introduced false positive results due to pre-amplification bias, and the amplification of non-cancer genes from normal leukocytes confounded gene expression profiling of CTCs. Pre-amplification bias is a concern for other similar assays that may be used as discovery tools or target validation of transcripts of interest in gene expression profiling of CTCs. We recommend the use of an unspiked negative control when evaluating CTC technologies regarding gene expression profiling.The ANGLE Parsortix has broad potential applications in CTC research given that it is capable of rapidly capturing CTCs without a requirement for affinity based marker selection. Our workflow for whole transcriptome RNA Seq profiling with the Parsortix is the basis for ongoing clinical trials at our institution. However, the current study suggests that target validation with the NanoString PAM50 is not a viable option for CTC research.By standard venipuncture procedure, two blood samples of 7.5ml each from 19 different cancer-free female donors were collected in EDTA tubes . Donors were selected based on the inclusion criteria established in the approved IRB protocol. Adhering to institutional HIPPA regulations, female donors between 25 and 50 years of age signed informed consent, and a research numerical identifier was assigned to each participant to keep demographic information blinded from the researchers.One tube of blood was used as a CTC mimic by adding or spiking in the basal-like/triple-negative breast cancer cell lines Hs578T and MDA-MB-231, or the HER2 amplified SkBr3, or the luminal A cell line MCF7. Equal numbers of blood samples (n=6) were spiked with each cell line. Some donors consented for repeated blood draws; therefore some of them were spiked with more than one cell line. These CTC mimic specimens were termed spiked samples. The spiking in process was performed within 20 minutes after blood collection. The other blood specimens were used as non-CTC or negative controls, termed unspiked controls  and MDA-MB-231 (ATTC HTB-26) cells were grown in complete DMEM-Dulbecco's Modified Eagle's Medium supplemented with 10% of FBS . SkBr3 (ATCC HTB-30) was grown in McCoy's 5A Modified Medium with 10% FBS, and MCF7 (ATCC HTB-22) cells were cultured in RPMI 1640 Medium with 10% FBS. Cells were typsinized, manually counted, and a total of 20 cells were spiked into each tube of blood.All cell lines were grown at 37°C with 5% COMycoplasma detection kit , and subjected to short tandem repeat genotyping for cell line authentication at the University of Arizona Genetics Core.All cell lines were tested for Mycoplasma contamination using the MycoAlert Both spiked samples and unspiked controls were subjected to CTC enrichment using the ANGLE Parsortix System . Parsortix cell-harvests were recovered in 200μl of 2% bovine serum albumin BSA  in PBS. Recovered cells were centrifuged at 400xg for 5 minutes at ambient temperature, and the cell pellet was lysed in 5μl of the Prelude Direct Lysis Module . Cell lysates were stored at −80°C.To 7.5 ml of peripheral blood we spiked 20 SkBr3 cells and performed CTC enrichment using the Parsortix system as described above. CTC harvested material was incubated with 1μl of FITC mouse anti-human EpCAM IgG  and 1μl of PE-Cy7 mouse anti-human CD45 IgG  on ice for 30 minutes. Unbound antibodies were removed by washing twice with 2ml of wash buffer (2% BSA in PBS). Cells were resuspended in 200μl of wash buffer and analyzed using a FACS Aria II Cytometer . Cells were sorted and collected in 5μl of cell lysis buffer, and stored at −80°C.NanoString nCounter gene expression assay has been previously described , 23. BriNanoString PAM50 targeted single cell gene expression assays were performed on CTC enriched cell lysates from both spiked samples and unspiked controls. NanoString standard protocol for single cell gene expression analysis consisted of a two-step process: cDNA conversion and multiplexed target enrichment (MTE). Initially, input RNA material (5μl of cell lysate) was converted to cDNA by reverse transcription using 1.5μl of the SuperScript VILO Master Mix . The reaction was incubated at 25°C for 10 minutes, followed by 42°C for 60 minutes and 85°C for 5 minutes in a T-100 Thermal Cycler . Obtained cDNA was PCR amplified using 7.5μl of TaqMan PreAmp Master Mix  and 1μl of a pool of PAM 50 MTE primers , RNA precipitation with isopropanol , and RNA wash with 70% ethanol . Isolated RNA was eluted in RNAase-free water, and both quantity and purity were access by spectrometry using NanoDrop 2000 . A total of 0.1ng of cell line RNA was used for the NanoString PAM50 steps of cDNA synthesis and MTE applying similar conditions used on the spiked samples and the unspiked controls. Additionally, as ‘gold standard’ gene expression positive control we used 100ng of RNA from the cell lines. Neither cDNA conversion nor MTE steps were performed on the ‘gold standard’ control.later solution , and stored at −80°C. PB RNA was isolated using the RiboPure-Blood Kit , and 100ng of it was used as input material for the NanoString PAM50 assay. These specimens were termed bulk peripheral blood controls (bulk PB controls)  RNA extracted from each unspiked blood specimen was included as negative gene expression control. Immediately after blood draw, 200μl of peripheral blood was stabilized by adding 1ml of RNA) Figure .Due to the high sensitivity and high specificity of the NanoString PAM50 platform in FFPE, assays on both samples and controls were performed a single time, without technical replicates, per manufacturer's recommendations even for rare samples.2-transformed data was used for ANOVA testing to compare the mean of the gene expression between all unspiked controls and all peripheral blood RNA samples.nCounter data was analyzed using NanoString nSolver Analysis Software v2.5. Following NanoString's recommended protocol, raw count data normalization included: (i) quality control measurements for imaging quality and sample binding saturation using hybridization positive controls, (ii) elimination of experimental variability using reference genes, and (iii) per sample background correction by setting a gene expression threshold or cut-off value of 2 standard deviations over the mean of the internal hybridization negative controls. Log2 gene expression values per gene. Results were subsequently validated using the NanoStringDiff statistical method for gene expression analysis described by Wang, et al [Differential gene expression analysis between spiked samples and unspiked controls was initially performed by manual comparison of the background corrected Logg, et al . ANOVA t


AGER, encoding RAGE). We aimed to (i) characterise RAGE expression in the lung, (ii) identify AGER transcripts, (iii) ascertain if SNP rs2070600 (Gly82Ser C/T) is associated with lung function and serum sRAGE levels and (iv) identify whether the Gly82Ser variant is functionally important in altering sRAGE levels in an airway epithelial cell model.Genome-Wide Association Studies have identified associations between lung function measures and Chronic Obstructive Pulmonary Disease (COPD) and chromosome region 6p21 containing the gene for the Advanced Glycation End Product Receptor  and protein levels (20 samples). Extensive AGER splicing was identified. The rs2070600T (Ser82) allele is associated with higher FEV1, FEV1/FVC and lower serum sRAGE levels in UK smokers. Using an airway epithelium model overexpressing the Gly82 or Ser82 variants we found that HMGB1 activation of the RAGE-Ser82 receptor results in lower sRAGE production.Immunohistochemical assessment of 6 adult lung samples identified high RAGE expression in the alveoli of healthy adults and individuals with COPD. AGER polymorphism.This study provides new information regarding the expression profile and potential role of RAGE in the human lung and shows a functional role of the Gly82Ser variant. These findings advance our understanding of the potential mechanisms underlying COPD particularly for carriers of this  AGER, pivotal SNP in exon 3 rs2070600 ) as being associated with Forced Expiratory Volume in 1 second (FEV1)/Forced Vital capacity (FVC) [AGER now requires further elucidation in the lung which may provide new insight into the mechanisms underlying several lung diseases where AGER genetic variants have been implicated, including e.g. COPD and lung cancer [AGER encodes RAGE; a member of the immunoglobulin superfamily which interacts with a diverse range of ligands, including damage associated molecular pattern proteins such as High mobility group protein B1 (HMGB1). Activation of RAGE has many downstream effects including the regulation of inflammation, cell migration, proliferation and adhesion [AGER regulation by genetic mechanisms has broad implications. RAGE is expressed abundantly in the membrane and cytoplasm of both pneumocytes and macrophages in lung [AGER in the context of respiratory disease. To do this we have determined the expression profile of RAGE in human lung and during lung development. Additionally, we have investigated the specific functional effects of the key Gly82Ser variant that has been associated with numerous lung phenotypes at both the population and cellular levels providing an insight into the function of this variant in the lung.Lung diseases including chronic obstructive pulmonary disease (COPD) remain a major burden to society and the economy. In recent years there has been substantial progress in our understanding of genetic factors associated with lung function parameters through genome wide association approaches –4. Thereg cancer , 6. AGER70600 Gly2Ser, C/Tadhesion , 9, 10, adhesion –13 and s in lung , suggest in lung  with ove in lung . Increas in lung , 9, 10. www.hdbr.org) at different stages, specifically 19, 21 and 23 days and 9, 10, 11, 12, 13, 14, 15, 16, 17 and 19 weeks post-conception. Immunohistochemistry was performed as previously described [Three control (no history of obstructive lung disease) adult lung samples and three samples from individuals with clinically diagnosed COPD were provided by the Nottingham Health Science Biobank following isolation by lung resection  under ethical approval (08/H0407/1). Twenty fetal lung tissue samples were provided by The Human Developmental Biology Resource  and into the Canalicular (17–26 weeks) stages of development were used in the current AGER analyses. These data are available at NCBI Gene Expression Omnibus , GSE14334. Affymetrix probes; 217046_s_at and 210081_at were used for AGER expression analyses in relation to gestational age (linear regression model (lmFit) as implemented in the Limma package in R/Bioconductor).Prior studies have obtained human fetal lung tissues from the National Institute of Child Health and Human Development tissue databases and the extracted RNA profiled using microarrays to investigate gene expression spanning different gestational ages , 19. RNAAGER mRNA was carried out using total RNA from cultured cells or commercially available total lung tissue (Ambion/Life Technologies) using Taqman™ (Applied Biosystems/Life Technologies) as previously described [AGER  and Human 18S ribosomal RNA 1 endogenous control . qPCR was performed using a Stratagene Mx3005P machine . Data were analysed using the 2-ΔΔCt method.Quantification of total escribed . In eachTotal RNA was extracted from normal human bronchial epithelial cells (NHBECs) from three donors  at either passage 3 or 4 using the GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich). Donor 1 was a Caucasian, 56 years old, Male, Smoker; Donor 2 was a 19 year old, Male, Smoker and Donor 3 was a Caucasian, 50 year old, Male, Smoker. RNA quality was assessed using the Agilent 2100 Bioanalyzer with all samples having a RNA integrity number ≥ 8. A sequencing library was prepared using TruSeq RNA Sample Prep Kit v2 (Illumina). mRNA was poly-A selected using oligo-dT coated magnetic beads and fragmented. cDNA was synthesised using random primers. Finally, a cDNA library was prepared by end-repair, phosphorylation, A-tailing, adapter ligation and PCR amplification. Paired-end sequencing was performed on either the Illumina HiSeq 2000 or the NextSeq 500 with approximately 40 million reads per sample generated. RNA-seq data was analysed as described [Subjects were recruited from five UK centres for smoking history and/or COPD diagnosis, this cohort is the same as that previously described , 23. Thi® Multi Site-directed Mutagenesis kit . A single mutagenic primer ((A)GCTCCCTCTTCCTCGTGTCCTTCCCAAC) was used to generate the alternative allele of SNP rs2070600. All constructs were sequence verified (data not shown). BEAS2B-R1 epithelial cells  [AGER was performed as described. Data from four independent cell experiments were combined for analyses.A plasmid containing the cDNA for the full length, membrane form of RAGE (pcDNA3.1-flRAGE) was a kind gift of Dr. B. Hudson (University of Miami). Site directed mutagenesis of the flRAGE plasmid was carried out using QuikChangere, USA)  were tra1, FVC and FEV1/FVC) and rs2070600 genotype or log serum sRAGE levels as previously described [Linear regression was used to assess association between lung function measures (FEVAGER) was determined in the alveoli and bronchi of control and COPD lungs using immunohistochemistry. Specific staining was observed in both the cytoplasm and membrane , (The abundance of data see . Levels tively), .P value = unadjusted P value, Adj.P.value = adjusted P value controlling for false discovery rate, Beta-coefficient = log (odds ratio) corresponding to the mean change in gene expression per day during the gestational age period studied, 7–22 weeks of gestational age  were assessed. AveExpr = average expression between all samples, t = t-statistic describing differential expression, escribed ).AGER mRNA through fetal development, levels of RAGE protein during lung development were assessed via immunohistochemistry. RAGE expression was also observed to increase during lung development across the 20 fetal lung samples; from absent to low expression in the early embryonic stages to increasing RAGE expression in the developing bronchi/bronchioles of the later embryonic stages (Having observed increasing levels of c stages . RAGE exc stages .AGER quantitative PCR (qPCR) assay was used to determine the expression of AGER mRNA in human lung and in airway cell populations, specifically bronchial epithelium and airway smooth muscle cells. AGER was highly expressed in total lung whereas cultured human airway smooth muscle, NHBEC and BEAS2B-R1 cells exhibited lower levels of AGER mRNA  was used to define AGER splicing and isoform abundance in cultured primary human bronchial epithelial cells from three independent donors .A total GER mRNA . Having g tissue . To ident donors . Using tWe analysed 1042 DNA samples from individuals who currently smoke and have a history of smoking recruited from five UK centres to assess the association of sRAGE levels with lung function by genotype. The minor allele frequency in this population for the Ser82 variant was 10%. Serum samples were analysed from 102 of these subjects who were either homozygous for the Gly82 variant or heterozygous i.e. Gly82/Ser82. Baseline characteristics of the subjects studied are shown in 1 and FEV1/FVC ratio , (P < 0.0001) showing that that the Ser82 variant leads to reduced sRAGE levels in vivo , . Serum ftively), . Serum s in vivo . We did subjects .AGER mRNA (mean 2500 fold change at the mRNA level) suggesting confounding from the very low endogenous expression of RAGE is unlikely to influence our findings. Stimulation with or without HMGB1 resulted in differing levels of sRAGE secretion in a genotype specific manner  suggesting that the effect of the T allele of rs2070600 is not due to altered transcriptional activity.To investigate the functional significance of SNP rs2070600 in the context of the full length membrane bound receptor, we created a recombinant RAGE epithelial cell model, with cells engineered to express the full length RAGE coding region with either the T allele (Ser82) or the C allele (Gly82) of rs2070600. Transient transfection of allele-specific RAGE plasmids into BEAS2B-R1  cells resulted in a significant induction of c manner . Cells t = 0.036 . We did AGER locus on chromosome 6p21 have shown reproducible association with the lung function measures FEV1 and FEV1/FVC [AGER/RAGE expression increases across developmental stages in human lung, both at the level of mRNA and protein, with high expression maintained in adulthood. We characterised AGER gene structure and transcript abundance in bronchial epithelial cells revealing many isoforms confirming the complexity of splice variation for AGER however we can not exclude that additional variation exists as we only investigated samples from three donors. Splice variants for AGER have previously been identified, with at least 10 coding for protein, however, only some are validated [1 and FEV1/FVC ratio and provide new data demonstrating that this allele is associated with lower serum levels of soluble RAGE in UK smokers in keeping with findings of two previous studies [in vivo. This effect appears to be due to altered cell processing of RAGE, as there was no obvious effect of the variants on levels of transcription.A number of polymorphisms within the FEV1/FVC , 26. Thealidated . We conf studies , 27. Lev studies , 28–30. et al, showed high RAGE expression in 37 week old fetal lung [AGER mRNA analyses across fetal stages it is important to note that using two probes to measure gene expression is a strength and based on the data of Hudson et al. [AGER transcripts identified in both adult and fetal lung tissue contain exon 8 and so would be quantified by the 210081_at probe (Lopez-Diez 2013).Our immunohistochemical data confirm and extend previously published findings that showed moderate to high expression of RAGE in the alveolar region, with low expression in the bronchial epithelium , 32 and tal lung . For then et al.  the 2100A limitation of the fetal immunohistochemistry was the low number of samples at each developmental stage . The relationship between increasing RAGE expression in the fetal lung with increasing age may potentially be explained by cell differentiation and the evolving cellular composition within the developing lung. During the embryonic stage, early precursor lung cells are present and with increasing differentiation at the pseudoglandular stage, alveolar type II pneumocytes are present which express RAGE. Furthermore, at the canalicular stage of lung development type I pneumocytes are present alongside a larger differentiated population of type II pneumocytes expressing RAGE.AGER in mammalian lung development. Taken together, these data show that RAGE expression increases through lung development and that high expression is maintained in the adult lung suggesting a role for RAGE in lung homeostasis. This has implications for the mechanisms underlying the development of COPD as these finding potentially suggest that individuals with alterations in AGER homeostasis may have defective lung development and therefore never reach maximal lung function. This is at least in part supported by the association of AGER SNPs and lung function in children [AGER mRNA expression in the BEAS2B-R1 immortalised cell line rather than in lung tissue or primary cells which would be optimal.Importantly, a functional role for RAGE during lung development has recently been demonstrated in mice, with alterations in RAGE levels causing lethality and/or changes to secondary septation, hypoplasia, lung tissue loss and changes in pulmonary microvasculature , 36–38. children . One limd) for the RAGE ligand EN-RAGE or glycolaldehyde-derived AGE respectively compared to RAGE-Gly82 receptors. This reduced Kd for glycolaldehyde-derived AGE for RAGE-Ser82 receptors resulted in an enhanced VEGF mRNA induction demonstrating downstream effects [in vitro whereas RAGE-Gly82 was not [in vitro model only contains this specific variant change, as opposed to other SNPs in linkage disequilibrium (LD) with rs2070600 [Overall, our data suggest that the Gly82Ser polymorphism alters ligand-dependent release of sRAGE in the airway. These data compliment and extend previous reports in non-airway cell systems where it has been shown that Gly82Ser can influence ligand binding and/or receptor activation , 39, 40.s2070600 .It has been robustly demonstrated that sRAGE levels in serum or plasma of COPD patients is lower compared to control subjects and associated with GOLD stage and markers of emphysema . This leAGER genetics, lung function and sRAGE levels is potentially counterintuitive as the rs2070600T allele is associated with better lung function in the general population and lower sRAGE. The molecular mechanisms underlying these observations still remain to be defined, however our novel data provide supporting evidence that rs2070600 is the key SNP driving sRAGE levels in the cell assays. The key SNP(s) driving the association with lung function and COPD diagnosis at the population level remain to be elucidated and similarly whether rs2070600 contributes to lung function and sRAGE via overlapping or distinct mechanisms is an important, unresolved question.The relationship between AGER/RAGE in lung biology and support a functional role for rs2070600 in determining sRAGE levels, which may in turn explain the association seen between this variant and function.In summary, we provide important new information regarding the expression profile of RAGE in the human lung by identifying the isoforms present, and by assessing differential expression in human fetal lung development and in human airways from control individuals and those with COPD. The T allele of rs2070600, which codes for the RAGE-Ser82 variant, was found to be associated with increased lung function in a cohort of UK smokers and the levels of sRAGE were found to be lower in the serum of smoking individuals carrying this variant. These data provide novel insight into the role of S1 Table1 and FEV1/FVC ratio. Minor allele frequency of tested population = 10%. Coded allele = T.rs2070600T was associated with increased FEV(PDF)Click here for additional data file.S2 TableSerum sRAGE levels were lower in individuals with a C:T genotype for rs2070600.(PDF)Click here for additional data file.S3 Table1, FVC or FEV1/FVC when subjects were stratified by genotype. F = statistical significance of the regression equation as a whole.Serum sRAGE levels was not associated with FEV(PDF)Click here for additional data file.


High-resolution  magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal  and multi-modal  approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. Magnetic resonance imaging (MRI) has become one of the most important tools to study human brain function and structure in vivo. Moving from high (3 Tesla [T]) to ultra high 7 and 9.4 T) magnetic fields (UHF), together with improvements in acquisition methods, leads to increases in signal and contrast to noise  –3. The i T magnetSuch studies crucially depend on accurate and precise delineations of the gray matter (GM) ribbon both at the inner  and outer  border. Since the aim of these studies is to investigate how the  MRI (f/MRI) signal varies as a function of small position changes in GM, systematic GM segmentation errors would invalidate the conclusions drawn from these studies. Consider, as an example, an fMRI study conducted with a voxel resolution of 0.8 mm isotropic. Assume an average thickness of human cortex of 2.4 mm and a true signal change at the upper cortical depth level. In this study, under optimal conditions the functional resolution would allow a straight piece of cortical ribbon to be divided in three relative cortical depths, each of them one voxel thick. Falsely labeling an additional fourth voxel as GM would make the difference between reporting an fMRI signal change at most superficial  or mid-superficial  cortical depth level. This example stresses the importance of accurate and precise GM segmentations.Obtaining accurate and precise definitions of the GM ribbon, however, is currently a difficult and time-consuming task for sub-millimeter UHF data. The increases in SNR, CNR and resolution attainable in UHF anatomical data as well as analysis  and recoCBS tools  directlyHere, we show that non-brain voxels misclassified as GM can largely be corrected using a multi-dimensional transfer function that is specified based on a two-dimensional (2D) histogram representation –39 of thThis paper is intended as a demonstration that 2D histogram based methods are useful for improving segmentation of MRI images. In particular, we aim to show that for images acquired at sub-millimetre resolution and at very high field strength (7 T and above) 2D histogram based methods offer an efficient way to obtain a more refined brain mask that excludes usually undesired structures like vessels and dura mater. Several alternatives to the methods presented here exist for data visualization, dimensionality reduction or data fusion, such as principal component analysis , multidiWe structured the paper as follows. In Section 1, we introduce the technique of specifying transfer functions based on 2D histogram representations of voxel intensity and gradient magnitude. We offer theoretical considerations for why this technique is suited to remove vessels and dura mater voxels in high-resolution MRI data (< 1 mm isotropic voxel size). In Section 2, we extend the use of histogram-based transfer functions to multi-modal MRI data sets  by considering MRI data in the compositional data analysis framework . We showIn the context of MRI data visualization, a transfer function can be understood as a mapping of voxel data to optical properties such as color and opacity. Effective transfer functions make structures of interest in the data visible. This can, for example, be achieved by assigning low opacity values to voxels that make up irrelevant structures and by highlighting desired structures with high opacity and salient color values. Multi-dimensional transfer functions assign renderable properties based on a combination of values –38, 51. In theory, multi-dimensional transfer functions could be used to perform exhaustive tissue-type segmentation of human brain MRI data. In this process, each voxel would be classified as either WM, GM, or CSF by specifying appropriate transfer functions. It has been shown, however, that this approach is less successful than other, bespoke brain segmentation algorithms . Here, w2D histogram representations have been shown to greatly facilitate the process of specifying effective, 2D transfer functions , 38. 2D Since different tissue types occupy different regions in the 2D histogram, each tissue type and boundary can, in principle, be isolated using a 2D transfer function based on image intensity and gradient magnitude. For the purposes of this paper, we focus on the distinction between brain  and non-brain  voxels. The intensity-gradient magnitude histogram is particularly suited to distinguish non-brain tissue because voxels containing dura mater and vessels are characterized by high gradient magnitude values. Given the typical voxel sizes of current high resolution studies, gradient magnitude will be high in the entirety of these structures  used in compositional data analysis  = 3 ; field on = 4 subjects  the MP2RAGE data set consisted of: 3D MP2RAGE data  [2; matrix = 320 × 227; slices = 208; 0.7 mm iso. voxels). More details on the MP2RAGE data acquisition and the T2* estimation can be found in [For  voxels) . For thefound in .For every subject, we established ‘ground truth’ GM classifications via manually-guided expert segmentations. All segmentations were created manually by the same expert (OFG), using ITK-SNAP  and a grhttps://github.com/ofgulban/scikit-image/tree/ncut-rag-options).We implemented the creation of transfer function based on 2D histograms in an open source Python package called Segmentator , which ihttps://github.com/ofgulban/segmentator. All the operations of the CoDa analysis described above have been implemented as a separate open source Python package [https://github.com/ofgulban/compoda. This package uses Numpy [The package provides several options to calculate the gradient magnitude image. All the 2D histogram analyses described in this paper were based on gradient magnitude images that were computed as the Euclidean norm of the first spatial derivative estimated using a 3 × 3 × 3 Scharr kernel , 78. Sub package  freely aes Numpy  and Scipes Numpy .For both validation data sets, we followed similar procedures, with modifications where necessary to accommodate for differences in the sequences’ output. Our goal was to obtain initial GM segmentations from existing, fully-automated segmentation algorithms and to quantify the improvement in segmentation accuracy that can be obtained when using the methods described here as post-processing steps. To establish the initial GM segmentations we used FSL FAST  and the For the MPRAGE data set, we first computed ratio images (T1w divided by PDw)  to reducFor the GraMag method  we proceilr transformation. We could therefore proceed with the 2D histogram representation using the first and the second real-space coordinates of the compositions and the hierarchical exploration of normalized graph cut decision trees in this 2D space to separate brain from non-brain tissue voxels. Non-brain tissue voxels were again removed from GM if included in the initial segmentations (of SPM and FSL).For the CoDa method  we folloFor the MP2RAGE data set, the T1 map, T1w (uni) and second inversion image from the MP2RAGE sequence were input to CBS tools . Only thWe observed susceptibility artifacts in some regions of the brain  in the T2*w images. These artifacts make the affected regions noisy and reduce the effectiveness of using T2*w images in the CoDa method. To quantify the effect of these artifacts, we created masks for the artifact-affected regions and ran all our analyses both with and without the artifact regions included. Results shown in the paper were obtained with the affected regions excluded. Results with the affected regions included are shown in the Supplementary Materials.http://www.visceral.eu).The segmentation procedures resulted in three different GM segmentations for each data set and initial segmentation algorithm (SPM or CBS and FSL FAST): (i) an initial segmentation without any further changes, (ii) after correction using the GraMag method and (iii) after correction using the CoDa method. To compare segmentation quality among these three outcomes, we calculated the Dice coefficient (DICE) and the Average Hausdorff Distance (AVHD) using the openly available EvaluateSegmentation Tool  require accurate and precise definitions of the GM ribbon. Creating such definitions is currently a challenging task since sub-millimeter UHF data bring non-brain structures like blood vessels and dura mater into sharper focus. As a result, segmentation algorithms that have been benchmarked at lower resolution data might falsely label part of these structures as GM. Here we presented two methods (GraMag and CoDa) to correct many such mislabeled non-brain voxels efficiently and semi-automatically. The two methods are based on theoretical expectations of how 3D brain data is to be represented in 2D histograms. These expectations imply that brain and non-brain tissue should become separable in 2D histogram representations that are either based on gradient magnitude and intensity or on compositional dimensions. We validated these expectations by implementing the suggested methods in an openly available software package and by quantifying their added benefit using a new high-resolution validation data set. We found that, in general, our suggested methods offered an improvement compared to initial GM segmentations. However, we found some differences in the degree of improvement with respect to (i) the two presented methods, the (ii) type of data and (iii) the algorithm used for initial segmentation.We will discuss these three influences in turn. First, the two methods differ in their prerequisites and their segmentation improvement. The GraMag method only requires uni-modal input such as T1w/PDw or MP2RAGE uni images, while the CoDa method requires multi-modal input of images with different contrast weightings. This makes the GraMag method the method of choice when only a single input image is available. In accordance with our theoretical expectation, the GraMag method identified and removed blood vessels and dura mater tissue. If multi-dimensional input is available, even bigger improvements might be obtained with the CoDa method. Notably, in contrast to the GraMag method, the CoDa method can additionally capture and remove connected tissue of the sagittal sinus. This tissue is usually falsely labeled as GM because of similar intensity values and spatial proximity. It then requires tedious manual removal. How well the CoDa method performs, however, critically depends on the quality of all the input images and the specific combination of contrasts. Performance can be affected by low quality on a single input image, as was the case here with T2* images due to susceptibility artifacts. Furthermore, performance will depend on the specific choice of contrasts and whether these contrasts maximize the compositional difference between brain and non-brain tissue.Second, we found that the improvements were slightly larger and more consistent across subjects for the MPRAGE than for the MP2RAGE data set. This might be explained by the fact that the MPRAGE data conformed more to our theoretical expectations than the MP2RAGE data set. Especially, we found GM values in the MP2RAGE uni image to be less focused on one particular area of the 2D histogram  than theThird, we observed that the performance of the initial segmentation algorithm had an influence on how much we could further improve the GM segmentation. If performance of the initial segmentation algorithm was already relatively high, the improvement obtained with our methods tended to be smaller. Differences in initial segmentation performance might be explained by whether the algorithm has been benchmarked on this particular type of data. We assume FSL FAST and CBS tools to have been benchmarked on MPRAGE and MP2RAGE data respectively, which would explain their relative high performance for these data types.Importantly, our goal here was to aid already existing segmentation pipelines to deal with UHF sub-millimeter resolution data, not to replace those pipelines. Instead, the methods presented here should be considered as an alternative to a large amount of manual slice-by-slice polishing of segmentations and thus as a time-saver. Manually correcting segmentation labels is very time-consuming and can quickly become unreliable. In contrast, our methods greatly reduce the time required for manual polishing because they offer an efficient 2D summary and are more reliable because they are semi-automatic. Although the methods presented here do not entirely eliminate the need for manual corrections, we estimated that for a whole brain cortical ribbon segmentation they do save on average 7.5 hours of manual work .MRI can provide a multitude of informative images that weight tissue properties to generate the image contrast. The compositional data framework is ideally suited for the analysis and visualization of multiple images as it provides a principled way to combine any number of images. In addition, analyzing multiple MRI contrast images in the compositional data framework avoids spatial scale dependence, i.e. dependence on the image resolution and smoothness of the image. As a result, the compositional properties of vessel voxels even at very high resolutions will remain the same or very similar, no matter whether the voxel is at the center or at the border of the vessel. This is similar to analyzing chemical compositions of materials, which are independent of spatial metrics.An envisioned future application of the compositional framework to MRI data is to use it to single out targeted cortical or subcortical structures based on their compositional properties. For an example of identifying subcortical structures see Our theoretical expectations implied that the methods presented here require high-resolution data (< 1 mm). This requirement was unfortunately not met by most available segmentation validation data sets. Simulated phantom (‘BrainWeb’) data  are avaiThese considerations led us to create our own high-resolution segmentation validation data sets for which we established the ‘ground truth’ via manually-guided expert segmentation. While expert segmentations have well-known drawbacks , 89, theThe 2D histogram method presented here is, in principle, capable of generating its own exhaustive tissue-type classifications, i.e. it does not necessarily depend on existing segmentation pipelines to derive GM and WM labels. While we expect the 2D histogram method to give no advantage over existing, fully-automated segmentation algorithms under standard conditions, the histogram method will compare well in cases where standard algorithms fail. Importantly, the 2D histogram method used here does not assume the data to conform to any atlas or template shape. Therefore, it is suitable also for acquisitions with only partial coverage (surface coils) or for specific populations like infant or even dolphin brains see .Using histogram-based methods would be more attractive if the process of specifying transfer functions was fully automatic. We note that there is no principled obstacle to doing this. Indeed, information-theoretic measures have been suggested  that wouWe understand our methods as a secondary, more fine-grained brain extraction. When performing the initial brain extraction or tissue class segmentation, the user can often set parameters of the masking to be either more restrictive (at the risk of excluding brain tissue) or more liberal (at the risk of including a lot of non-brain tissue). We assume that, faced with this trade-off, users will usually lean to the liberal choice of parameters to avoid that relevant brain tissue is excluded. In such cases, we suggest our methods will prove useful. Our methods go beyond simply choosing more restrictive parameters because they focus on information that is relevant to excluding vessels, dura mater and connective tissue .Our comparisons were limited to segmentations obtained from FSL, SPM and CBS tools. While several MRI studies at the mesoscale have used alternative ways of establishing tissue class segmentations , 91, we As is to be expected, we found our methods to be impacted by the CNR of the input data –S4 Figs.By making our validation data sets publicly available, we hope to inspire further algorithmic testing and development. There is currently a lack of validation data for the performance of tissue-type classification of MRI data acquired at ultra-high fields with sub-millimeter resolution. By publishing our data, our code and our work flow, we invite fellow scientists to benefit from our work but also to further contribute to it. The neuro-imaging community can use our data to test the performance of entirely new methods or modifications to existing segmentation algorithms. Contributions could be made in the form of additional high-resolution data, more ground truth segmentations and algorithmic improvement. Anticipating such algorithmic improvements, we envision a future where segmentation of volumetric images will become gradually less laborious despite increasing resolution and volume of the data.S1 FigIntensity (left) and gradient magnitude (right) images are shown for T1w MRI data of a human brain that was either acquired at 0.7 mm isotropic  or at the 0.25 mm isotropic , 65 (bot(TIFF)Click here for additional data file.S2 Figσ = 25) is 16% and high noise (σ = 50) is 32% calculated relative to the mean cortical gray matter intensity. Noise causes structures in the 2D histogram that are initially well-defined to spread outward and, at very high noise levels, to lose shape. Images show a transverse slice for an exemplary subject (sub-02).Shown are intensity images (top row), gradient magnitude images (middle row) and 2D histograms for the GraMag method (bottom row) for a T1w-divided-PDw MRI ratio image without any additional noise (left) and after applying a moderate (middle) and high (right column) amount of additive Gaussian noise with two levels of constant standard deviation of the distribution. The moderate noise ((TIFF)Click here for additional data file.S3 Figσ = 25) is 13% for T1w, 4% for PDw, 5% for T2*w calculated relative to the mean cortical gray matter intensity. High noise (σ = 50) is 27% for T1w, 9% for PDw, 10% for T2*w calculated relative to the mean cortical gray matter intensity. Images show a transverse slice for an exemplary subject (sub-02).Shown are T1w (left), PDw (middle) and T2*w (right column) without any additional noise (top) and after applying a moderate (middle) and high (bottom row) amount of additive Gaussian noise with two levels of constant standard deviation of the distribution. Moderate noise ((TIFF)Click here for additional data file.S4 FigShown are 2D histograms resulting from the CoDa method without any additional noise (left) and after applying a moderate (middle) and high (right column) amount of noise (see (TIFF)Click here for additional data file.S5 FigShown are intensity images (top row), gradient magnitude images (middle row) and 2D histograms for the GraMag method (bottom row) for a T1w-divided-PDw MRI ratio image without any additional noise (left), after applying a high amount of noise (see (TIFF)Click here for additional data file.S6 FigShown are several examples of the variety of existing volumetric datasets for which our methods appear to be useful. Every column represents different images: the brain of a bottle-nose dolphin  (left), (TIFF)Click here for additional data file.S7 Fig(A) Each voxel is considered as a three part composition. The barycentric coordinates of each composition which reside in 3D simplex space are represented in 2D real space after using a isometric log-ratio (ilr) transformation. (B) The ilr coordinates are used to create 2D histograms representing all voxels in the images. The blue lines are the embedded 3D real space primary axes . In this case, the ilr coordinates are not easily interpretable by themselves but they are useful to visualize the barycentric coordinates which are interpretable via the embedded real space axes. Darker regions in the histogram indicate that many voxels are characterized by this particular scale invariant combination of the image contrasts. In this representation, brain tissue  becomes separable from non-brain tissue (black dashed lines and arrows). If desired, subcortical structures like the red nucleus, the globus pallidus and the subthalamic nucleus can additionally be identified (white circle).(TIFF)Click here for additional data file.S8 FigThis diagram provides a detailed overview of all the inputs, processing steps and outputs for MPRAGE GraMag pipeline. Rectangular shapes represent processing steps, rhombic shapes represent input or outputs and cylindrical shapes represent input or output locations.(TIFF)Click here for additional data file.S9 FigThis diagram provides a detailed overview of all the inputs, processing steps and outputs for MPRAGE CoDa pipeline. Rectangular shapes represent processing steps, rhombic shapes represent input or outputs and cylindrical shapes represent input or output locations.(TIFF)Click here for additional data file.S10 FigThis diagram provides a detailed overview of all the inputs, processing steps and outputs for MP2RAGE GraMag pipeline. Rectangular shapes represent processing steps, rhombic shapes represent input or outputs and cylindrical shapes represent input or output locations.(TIFF)Click here for additional data file.S11 FigThis diagram provides a detailed overview of all the inputs, processing steps and outputs for MP2RAGE CoDa pipeline. Rectangular shapes represent processing steps, rhombic shapes represent input or outputs and cylindrical shapes represent input or output locations.(TIFF)Click here for additional data file.S12 FigThe intensity (A) and gradient magnitude (B) values of a T1w-divided-by-PDw MRI image  are represented in a 2D histogram (C). Darker regions in the histogram indicate that many voxels are characterized by this particular combination of image intensity and gradient magnitude. The 2D histogram displays a characteristic pattern with tissue types occupying particular areas of the histogram (D). Voxels containing CSF, dura mater or blood vessels (black dashed lines and arrows) cover different regions of the histogram than voxels containing WM and GM (red dashed lines). As a result, brain tissue becomes separable from non-brain tissue.(TIFF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S1 VideoThe exploration of normalized graph cut decision trees allows for finding a more restrictive brain mask that excludes dura mater and brain vessels in a quick and intuitive manner.(WEBM)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Kras, using Rag1-Cre, gave rise to CNS haemangioblastoma in the cortex and cerebellum in mice that present with highly vascular tumours with stromal cells similar to human haemangioblastomas. The aberrant haemangioblastoma endothelial cells do not express mutant Kras but rather the mutant oncogene is expressed in CNS interstitial cells, including neuronal cells and progeny. This demonstrates a non-cell autonomous origin of this disease that is unexpectedly induced via Rag1-Cre expression in CNS interstitial cells. This is the first time that mutant RAS has been shown to stimulate non-cell autonomous proliferation in malignancy and suggests that mutant RAS can control endothelial cell proliferation in neo-vascularisation when expressed in certain cells.Haemangioblastoma is a rare malignancy of the CNS where vascular proliferation causes lesions due to endothelial propagation. We found that conditionally expressing mutant  Mouse models of human cancer allow for a better understanding of the cell of origin, the disease process and for therapeutic testing. Many different brain tumours are found in humans of which about 1% are haemangioblastomas that are slow growing and are non-invasive caused by vascular endothelium expansion within brain tissueRas (KRas) with glycine 12 mutation to valine (KRasG12V)Rag1) to express Cre recombinaseKRas allele was activated by Rag1-Cre expressed in the brain and because the endothelial cells involved do not themselves express the mutant allele of KRas, signifying a non-cell autonomous proliferation causing this disease.There are currently no mouse models of haemangioblastoma in the CNS. We discovered a mouse model of haemangioblastoma in which endothelial cell proliferation occurs to cause deleterious lesions in the brain when we implemented activation of a conditional form of floxed mutant Kirsten KRasG12V miceRag1-Cre knock in miceoC overnight. 20 uM sections were prepared on a freezing microtome and stained either with cresyl violet or haematoxylin and Immunohistochemistry carried out with anti-CD31 at 1/20 dilution of rabbit anti-CD31 (Abcam-ab28364) with citric acid antigen retrieval method. All experiments with mice were conducted under a Home Office Personal License and with agreement from the University of Oxford Animal Welfare and Ethical Review Committee.Floxed Rag1-Cre mice were bred with ROSA-tdTomato reporter miceE.coli. Three individual clones were selected from Kanamycin plates for each of the AG2 and GFP bands and sequenced using SP6, M13 or T7 primers in the forward and reverse directions. Relevant sequences are shown in LCM was performed using a Zeiss PALM MicroBeam system. Diseased transgenic brains were processed as for pathology and then mounted on special MembraneSlide (Zeiss) slides at a thickness of 7 μm. Before LCM capturing, the slides were de-paraffinised using HistoClear II solution (VWR) twice for 5 minutes each. The slides were allowed to dry completely after dehydration in 100% ethanol twice for 10 minutes each. Different areas of interest were captured in special AdhesiveCap tubes (Zeiss). Extraction of genomic DNA was carried with using a QIAamp DNA FFPE Tissue Kit (Qiagen) according to manufacturer’s instructions. For PCR of the recombined (AG2 band) and non-recombined (GFP band) transgene, a previously described method was usedRas (KRas) with glycine 12 mutation to valine (KRasG12V)Rag1) to express Cre recombinaseKRasG12V gene and the Rag1-Cre . Endothelial layers from the haemangioblastomas were marked in fixed sections  following Cre-mediated deletion (and Gapdh as a control)  were found in 5 out of 39 cases but other genes were considered to be aetiologicalRag-1 gene cause activation of the mutant Kras in this mouse model and that are responsible for the angiogenic effects we observe giving stimulus to the endothelial lesions. Astrocytes have been shown to secrete vascular endothelial growth factor (VEGF) in post-natal brains8KRASRag1 in brain cells remains controversial but our results support the hypothesis that Rag1 is important in brain function. Our work suggests a further study should be undertaken to establish the roles of Rag1 in brain biology and of VEGF-VEGFR interaction in CNS haemangioblastoma. The observation that mutant RAS expressing cells can induce endothelial cell proliferation also has important potential implications for angiogenesis in solid tumour growth where neo-vascularisation enhances tumour growth and supports dissemination of disease to create metastasis. In turn, the metastatic deposits, themselves carrying mutant RAS, could engender neo-vascularisation to sustain their own expansion.Although the exact identity of these cells is unknown, candidates are CNS cells that have Rag1 expression during developmentHow to cite this article: Bao, L. et al. A non-cell autonomous mouse model of CNS haemangioblastoma mediated by mutant KRAS. Sci. Rep.7, 44899; doi: 10.1038/srep44899 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Both IRV vaccination regimens protected against RV antigen shedding in stools, and reduced the cumulative diarrhea scores in the piglets. This study demonstrated that the ID and IM administrations of IRV are immunogenic and protective against RV-induced diarrhea in neonatal piglets. Our findings highlight the potential value of an adjuvant sparing effect of the IRV ID delivery route.Live-attenuated oral rotavirus (RV) vaccines have lower efficacy in low income countries, and additionally are associated with a rare but severe adverse event, intussusception. We have been pursuing the development of an inactivated rotavirus vaccine (IRV) using the human rotavirus strain CDC-9 (G1P) throughnt Wa G1P human RV Rotavirus (RV) infection causes severe dehydrating diarrhea in young children under 5 years of age worldwide. In 2011 the annual estimated number of RV disease-associated death in the <5 year old was 192,700 133,100–284,400) and the majority of the fatalities occur in low income countries of Africa and Asia where healthcare is not readily available or accessible 4,400 and. In addi3 adjuvant and administered by IM injection was highly immunogenic in mice and guinea pigs and conferred protection against homologous rotavirus challenge in gnotobiotic (Gn) piglets .Rotavirus-specific IgG or IgA in sera of piglets was measured by using a modified enzyme immunoassay (EIA) . BrieflyRotavirus IgG avidity assay was performed by modifying the protocol of RV IgG EIA with the denaturant agent diethylamine (DEA) . One dilNeutralizing activity in porcine sera was measured against homotypic and heterotypic human rotavirus strains Wa (G1P), WI61  using Bio-Plex 200  according to manufacturer’s instructions. Data collection and analysis were done using Bio-Plex Manager 5.0.Human RV shedding was detected in rectal swabs using Premier Rotaclone® according to manufacturer’s instructions.Fisher’s exact test was used to compare proportions of animals with diarrhea and RV antigen shedding among groups. The Kruskall-Wallis rank sum test (non-parametric) was used to compare days of onset and duration of diarrhea and RV antigen shedding, cumulative diarrhea scores and cumulative ODs of RV antigen shedding  among groups that were recorded. Negative samples were assigned an arbitrary Ab titer of 1 for the calculation of geometric mean titers (GMTs). Neutralizing and RV Ab titers were log10-transformed prior to statistical analysis. Differences in Ab and neutralization Ab titers among groups were evaluated by comparison of means using a 2-tailed t-test at different time-points post virus inoculation. Statistical significance was assessed at p < 0.05 for all comparisons. Statistical analyses were conducted using IBM® SPSS® Statistics Version 21.We assessed the safety of IRV in piglets by daily observation following each vaccination. No local reactogenicity at injection sites and no fever or other systemic adverse events were found in all placebo or IRV-vaccinated piglets.3 administered with an ID device or IRV formulated with Al(OH)3 administered by IM injection (We examined and demonstrated a dose-dependent rotavirus-specific IgA and IgG response to IRV without Al(OH)njection . Pigletsnjection . PigletsWe also measured sera for neutralizing activity against homotypic and heterotypic human rotavirus strains . ID- andWe further examined sera for 7 circulating cytokines in response to vaccination and oral challenge . No elevWe evaluated the protection of ID- or IM-administered IRV against RV infection in Gn piglets challenged with VirWa RV by comparing RV antigen shedding in rectal swab samples . All 3 pWe also evaluated the protection of ID- or IM-administered IRV against RV diarrhea in Wa RV-challenged Gn piglets by comparing diarrhea scores in vaccinated animals and placebo controls . Two of 3 adjuvant. We demonstrated that both ID- and IM-administered IRV induced comparable IgA, IgG, IgG antibody avidity and neutralizing antibody responses, and conferred complete or at least partial protection against homologous rotavirus infection  and diarrhea in piglets. Of particular interest was the detection of high titers of IgA in sera of both ID and IM IRV-vaccinated animals, an observation that challenges the dogma that parenteral vaccination may not induce IgA response [We previously reported enhanced immunogenicity and apparent dose sparing of IRV coated on a microneedle patch when compared with IM-administered IRV without an adjuvant in mice. In this study we further evaluated the immunogenicity and protective efficacy of ID-administered IRV without adjuvant using a hollow microneedle compared with IM-administered IRV formulated with Al(OH)response –41. Whetresponse .3 in the present study compared with 50 μg antigen formulated with Al(PO)4 in the prior study. These results indicate that Al(OH)3 or Al(PO)4 are effective adjuvants for IRV or even an IRV-containing combination vaccine. The observation that a 1/10 of the previously tested high-dose antigen was as effective in inducing protective immunity is promising for a low cost vaccine.The findings in the present study agree with our previous report that IM-administered IRV was effective in inducing protective immunity against rotavirus infection in piglets . Our res® device used in this study was designed to be user friendly and requires minimum training. In a study that compared trivalent influenza vaccine (TIV) administered ID or IM in adults aged ≥ 65 years showed that ID administration was associated with decreased pain at the site of injection (P < 0.01) compared to the IM groups [The skin is a dynamic organ that harbors elements of the innate and the adaptive immune systems. Skin is particularly rich in dendritic cells (Langerhans cells) in the epidermis which, when activated by infection, can mount a robust immune response. Skin immunization has been demonstrated to be effective against rabies, tuberculosis and other infectious diseases, including successful eradication of smallpox. However, those ID vaccines were delivered using old devices  and the Mantoux technique (regular needle and syringe) which were technically difficult and thus required highly trained personnel and can also be quite painful , 44. TheM groups .Interestingly in our study, IFN-α, which is an innate anti-viral cytokine, was not associated with protection but showed association with HRV replication as indicated by higher fecal shedding OD post challenge in the control group. Our findings line up with the observation in children, where vomiting episodes and RV diarrhea significantly correlated with IFN-α levels . In anim®). The piglets were orally inoculated with a virulent human RV Wa G1P[5 ID50, the identical dose reported in our previous study [The present study is subject to several implications and limitations. We simultaneously compare ID and IM routes for administering IRV. Our findings demonstrated that both ID IRV and IM IRV induced robust IgA response in sera of piglets suggesting that serum IgA antibodies may serve as a proxy to assess the immunogenicity of IRV in clinical trials, similar to the approach used for the development and licensure of oral rotavirus vaccines. Because of volume restriction, we chose two sites to administer the vaccine. For IRV vaccination in humans, we would anticipate a single application site similar to ID-administered concentrated Fluzone vaccine (IntanzaRV Wa G1P at a dosus study . HoweverS1 File(PDF)Click here for additional data file.


This article comments on:Veromann-Jürgenson L-L, Tosens T, Laanisto L, Niinemets Ü. 2017. Extremely thick cell walls and low mesophyll conductance: welcome to the world of ancient living! Journal of Experimental Botany 68, 1639–1653. Mesophyll conductance to CO2et al. (pages 1639–1653) provide evidence of the conservation of ancient structural traits  in evolutionarily old taxa that suggest apparent evolutionary constraints on CO – a key factor in plant photosynthesis – is strongly influenced by leaf anatomy. In this issue, Veromann-Jürgenson 2 fixation. This opens the way for integrated approaches combining evolutionary constraints of diffusive, structural and biochemical factors on plant photosynthesis.2 diffusion through stomata  and the capacity of photosynthetic machinery to convert light to biochemical energy and fix CO2 into sugars  were considered the only two factors constraining plant photosynthesis. However, pioneer studies already suggested that CO2 diffusion from sub-stomatal cavities to carboxylation sites inside chloroplasts  could also limit photosynthesis  differing in nature and size (gm and photosynthesis among species (c). These analyses showed that gm was most strongly correlated with the chloroplast surface area facing intercellular air spaces (Sc/S), thickness of the mesophyll cell walls (Tcw), and chloroplast size; however, depending on foliage structure, the overall importance of gm in constraining photosynthesis and the importance of different anatomical traits in the restriction of CO2 diffusion varied , with little data for ferns, liverworts and hornworts  that are mainly responsible for low gm and photosynthetic performance in several evolutionarily old gymnosperms and herbaceous species with contrasting phylogenetic age. These authors have found, for the first time, striking evidence about the effect of divergence time on structure and physiology, and specifically a negative correlation between estimated evolutionary age of the plant genus and area-based photosynthesis (AN). However, as they recognize, this statement should be treated with caution because species’ evolutionary adaptation to prevailing environmental conditions can actually drive photosynthetic capacity more strongly than their evolutionary age  were observed for the oldest genera.In this context, gm and AN and the corresponding high Tcw for species with widely contrasting ecological strategies. Thereby, these authors hypothesize that (i) the high-CO2 atmosphere when several of these thick-cell-walled species evolved (about 65–200 million years ago) suggests a lower control of diffusional limitations on the rate of photosynthesis, and (ii) the preservation of this ancient trait in spite of the gradual CO2 decrease through evolution has led to stronger control of foliage assimilation rates by gm (The most significant conclusion emerging from the study of es by gm .2 concentration and (B) an evolutionarily modern species which evolved under low CO2 concentration. Photosynthesis in evolutionarily old species at current CO2 concentrations could be constrained by low values of stomatal conductance (gs)   (The schematic representation shows the mesophyll of (A) an evolutionarily old species which evolved under high COεmax), one of the main physiological traits related to the functional role of the cell wall. Higher εmax values are seen as an efficient mechanism for plant performance under dry climates, as low cell-wall elasticity (i.e. high εmax) would allow a rapid recovery after a decrease in soil water content  and the maximum bulk modulus of elasticity ( εmax) for several Quercus species. Mean values of εmax are from The graph shows the relationship between cell wall thickness . This co-regulation between gm and gs is to some extent expected , other factors also determine the rate of plant photosynthesis, such as the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). 2 when analyzing the variability in Rubisco kinetics in different plant species. These authors confirmed that evolution of Rubisco towards increased affinity for CO2  and increased carboxylase catalytic efficiency (kcatc/Kc) in land plants is likely to have been complementary to falling CO2/O2 ratios, as well as to adaptations in leaf architecture, morphology and conductance (Beyond diffusive components (i.e. ductance .


Injury to the adult central nervous systems (CNS) can result in severe long-term disability because damaged CNS connections fail to regenerate after trauma. Identification of regulators that enhance the intrinsic growth capacity of severed axons is a first step to restore function. Here, we conducted a gain-of-function genetic screen in Drosophila to identify strong inducers of axonal growth after injury. We focus on a novel axis the Down Syndrome Cell Adhesion Molecule (Dscam1), the de-ubiquitinating enzyme Fat Facets (Faf)/Usp9x and the Jun N-Terminal Kinase (JNK) pathway transcription factor Kayak (Kay)/Fos. Genetic and biochemical analyses link these genes in a common signaling pathway whereby Faf stabilizes Dscam1 protein levels, by acting on the 3′-UTR of its mRNA, and Dscam1 acts upstream of the growth-promoting JNK signal. The mammalian homolog of Faf, Usp9x/FAM, shares both the regenerative and Dscam1 stabilizing activities, suggesting a conserved mechanism. During CNS development axons grow in a tightly regulated manner to generate an intricate and complex pattern of neuronal connectivity. In most animal species, injury to the adult CNS, either by physical trauma or in the context of neurodegeneration, has devastating long-term consequences in part because of the inability of mature neurons to regenerate severed axons. Functional regeneration requires damaged axons to first start re-growing and then to continue to navigate through a strongly inhibitory environment, before they can reach their synaptic partners and establish functional connections. Both the presence of extrinsic inhibitory factors as well as a lack of intrinsic growth capacity prevent axonal regrowth in the injured CNS  promotes axonal regrowth after injury via the Down syndrome cell adhesion molecule (Dscam1). Our findings suggest that Faf stabilizes Dscam1 by acting on Dscam1 3′-UTR through DLK1/Wnd and that Faf and Dscam1 act upstream of JNK signaling and its nuclear effector Kayak (Fos). The functional role of Faf in promoting axonal regeneration appears to be conserved in mammals, as suggested by the ability of the mouse homolog of Faf, Usp9X/FAM to also stabilize Dscam1 and promote axonal regrowth in the injured fly CNS.Here, we performed a two-step genetic screen of ~300 genes selected by GO term, and identified 13 that promote axonal outgrowth during development in post-mitotic CNS neurons. We then tested those genes in an adult Pdf-Gal4 driver was used to express GFP together with each of the selected genes, and the length of axonal growth was quantified in comparison with controls. Expression of 13 genes (4.2%) promoted significantly increased axonal growth with no obvious adverse effects on neuronal survival or axonal trajectory  showed enhanced regeneration in all three categories  caused axonal phenotypes such as defasciculation, blebbing, or fragmentation, and were excluded from further analysis.To perform a screen for axonal growth and regeneration Figures , we sele Figures . In a se Figures , these 1 Figures . Kayak i Figures , but fai Figures  and ofteDscam1, faf and kay) appeared to consistently promote the most growth across all criteria. We therefore asked whether these genes might be acting together in a novel regeneration pathway linking the cell surface to the nucleus. We began by analyzing the de-ubiquitinating enzyme Faf since it promoted the highest levels of regeneration across all criteria   Figure . However) Figure . In cont) Figure . The mouDrosophila , completely inhibited faf-mediated axonal outgrowth  transgene, and found that loss of Delta function in the sLNvs did not reduce the axonal outgrowth activity of faf (data not shown), suggesting an alternative mechanism in the context of axonal growth. Therefore, we reasoned that Faf might interact with different axon growth-promoting effectors.How might Dscam1  . These data suggest that, at least in this context, Faf does not de-ubiquitinate Dscam1 directly.Faf antagonizes ubiquitination by cleaving the covalent bond between ubiquitin and a substrate protein   to select candidate genes annotated with the terms “Neurite Morphogenesis,” “Transcription Factors,” “Receptors,” “Chromatin Modifiers,” and “Ubiquitin Ligases.” We also included an additional set of genes previously implicated in axonal growth and/or involved in actin dynamics . UAS-Wnd kinase dead (KD) and UAS-Wnd E flies were a gift from C. Collins. The PDF-Gal4 line was obtained from P. Taghert. For Faf overexpression in flies, we used the EP3520 line (Szeged Stock Centrum), which was previously reported to induce Faf gain-of-function  per genotype were stained with an anti-GFP antibody , visualized under a fluorescent microscope equipped with a GFP filter and scored as “growth”  or “no-growth” (when the length of sLNv projections was indistinguishable from controls or shorter). All genes that were scored growth promoting genes were confirmed as such in at least one independent experiment, and their growth inducing ability was analyzed by measuring the axonal sLNv dorsal axonal projections.For the axonal regrowth analysis after injury, flies were reared at 18°C, in order to minimize overexpression effects during development, and shifted to 25°C the day before injury to allow optimal transgene expression.Drosophila Medium (GIBCO). Up to seven brains were placed on the membrane of one culture plate insert and culture medium  was added. sLNv axonal injury was performed using an ultrasonic microchisel controlled by a powered device (Eppendorf). Culture dishes were kept in a plastic box in a humidified incubator at 25°C.Whole-brain explants on culture plate inserts were prepared and injured as described  from the starting point toward the midline (indicated by an asterisk) and as manual trace using Image J. The maximum computed distance was defined as the distance projected by the longest axonal sprout in a straight line and parallel to the commissure. The Average Length was the defined as the average length of the two longest axonal branches traced manually (freehand distance). Imaging was performed on an upright Zeiss Axioscope equipped with a CCD camera, or on a Zeiss 700 or Nikon AR1 confocal microscope. All measurements were performed using ImageJ.de novo growth. Morphometric analysis of axonal regrowth was always performed 4 days after injury, following fixation and GFP staining of the brains in culture. Capacity of regrowth was defined as the ability of the injured sLNv projection to regrow at least one new axonal sprout. Without the support of the head cuticle, brains will flatten and therefore undergo slight morphological changes during the culture process. To be conservative and account for potential inaccuracies in defining the injury point, only regrown axons with a minimum length of 12 μm were defined as de novo growth and taken into account for analysis. To quantify axonal regrowth, newly grown axons were measured in a straight line and manually traced using ImageJ. In this case, the maximum computed distance was defined as the average of the distance of the two longest axonal sprouts in a straight line in any direction. Maximum growth was defined as the sum of the freehand lengths of all de novo grown axons. Images 5 h after injury were acquired on an upright Nikon microscope equipped with a Hamamatsu CCD camera ORCA-R2. Imaging 4 days after injury was performed on a Zeiss 700 or a Nikon A1R confocal microscope after GFP immunostaining. See figure legends for details of individual experiments, including statistical tests used and see Supplementary Table To analyze the role of the candidate genes in axonal regrowth after injury we imaged cultured brains at two different time points after injury: approximately 5 h and 4 days. Comparison between these two timepoints allowed us to define the location at which the injury took place, in order to define Drosophila Schneider's (S2) cells were maintained in Sf-900 II SFM medium (Gibco). To achieve transgene overexpression in Schneider's (S2) cells we electroporated a UAS construct in combination with PMT-Gal4, according to previously developed methods , according to the manufacturer's instructions. Cells were harvested 72–96 h after copper induction, briefly washed with PBS and pellets frozen until cells were lysed in a 1% NP40 buffer in Tris-HCL. Protein concentration was determined by a modified Lowry assay . qPCR was performed using the Taqman Real Time protocol (Applied Biosystems) and probes. Data is shown as mean ± SEM.t-test was used to compare both genotypes. Data is shown as mean ± SEM and significance was set at p ≤ 0.5.Adult brains were dissected and immediately prepared for imaging. Confocal stacks of all sLNv and lLNv cell bodies in each side of the brain were performed. The optimal confocal settings were first adjusted for wild type brains and kept unchanged to allow comparison between genotypes. A maximum projection was created for each brain side and each image was quantified for GFP intensity using the “Image Analysis” module of Zeiss Zen 2.0 software. All quantifications were done by an investigator blind to experimental conditions. Student MK and BH designed the research. MK, MN, and BH analyzed the data. MH contributed expertise and reagents. MK, DS, and BH wrote the manuscript. MK, MN, MZ, NdG, AC, JY, M-LE, and MM performed experiments.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.