Drosophila transgenic strain with neuronal-specific RNAi of an ubiquitous gene was profiled using RNA-seq. After observing the existence of an unusual variance in our dataset, we showed that the expression profile of a small panel of marker genes, including GAL4 under control of a tissue specific driver, can identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We further analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we showed that trimming 5′ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of systematic technical errors.RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order to achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of “biological quality” are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system (CNS) of a  Whole transcriptome profiling can provide fundamental insights into the changes in cellular functions that occur as a consequence of genetic mutations and therefore help unravel the molecular pathways underlying human disease. Over the last few years, the enormous complexity of higher eukaryotic genomes, which encode a multitude of non-coding and tissue-specific transcripts and isoforms has been abundantly reported . SMN1 encodes the Smn protein, a key component of the SMN complex, which is essential for the assembly of the splicing machinery. Smn is required for the assembly of spliceosomal snRNP complexes and low levels of Smn were shown to result in a strong reduction in the rate of biogenesis of these complexes in patient derived cell lines  is a devastating and early onset neurodegenerative disorder caused by loss-of-function (LOF) mutations of Drosophila transgenic RNAi models have been developed using a binary system that uses the offspring obtained from a cross of two transgenic fly lines. One of the lines contains a ubiquitous or tissue-specific promoter upstream of the yeast Gal4 transcription factor coding sequence. The second line has an integrated copy of an intron-spliced hairpin transcript that produces a double stranded RNA for any gene of interest, fused to the yeast upstream activator sequence (UAS) that is bound by Gal4. The resultant offspring will therefore only down-regulate the human disease gene in a tissue-specific manner, depending on the GAL4 driver used  in Drosophila, transgenic RNAi models of the neurodegenerative disorder SMA, we conducted a pilot study comparing RNA-seq libraries generated from pooled samples obtained from control and hypomorphic Smn RNAi knockdown fly strains.Given its small size, obtaining tissue specific samples from Drosophila neuronal transcriptome from CNS samples of a transgenic fly strain with cell-type specific down-regulation of an ubiquitous gene. While conducting our investigation, we observed the existence of an unusual variance of gene expression within each fly strain, most likely caused by the presence of uneven contamination from surrounding tissues. In order to circumvent such effects, we demonstrate that in addition to standard procedures for read quality filtering, other quality control methods must be implemented to discard libraries that do not accurately reflect the transcriptome of the target tissue. Furthermore, we discuss the limitations of unraveling cell type specific changes in gene expression in the context of complex tissue samples and evaluate the effect of trimming 5′ ends of reads, which display nucleotide frequency biases on sampling errors.Here we present and discuss the possibility and limitations of using RNA-seq for characterization of changes in the w; P{w+mC=GAL4−elav.L}3 (Bloomington), w; P{UAS−PWIZ}15, w; P{UAS−SmnRNAi−C24} SmnX7, P{UAS−SmnRNAi−C24}/TM6B, Dfd-YFP. To obtain animals with intended genotype, w; P{w+mC=GAL4−elav.L}3 females were crossed to males of w; P{UAS−PWIZ}15 (in text: elavGAL4 or WT), w; P{UAS−SmnRNAi−C24} (in text: elavGAL4-C24 or C24), or w; Df(3L)SmnX7, P{UAS−SmnRNAi−C24}/TM6B, Dfd-YFP (in text: SmnX7/elavGAL4-C24 or X7/C24). Considering the technical limitations associated to the isolation of neuronal cells, i.e., the lack of well-described cell surface markers, the impact of exogenous GFP overexpression in the maintenance of a stable differentiated phenotype  and the difficulty of maintaining the physical integrity and phenotype of these cells upon tissue disruption, an approach based on manual dissection of CNS tissue was considered to be the most appropriate for profiling RNAi-dependent changes in neuronal gene expression. Approximately 200 late third instar larvae were dissected in order to generate one CNS biological replicate of the corresponding genotype in ice cold PBS. Dissected CNS samples were quickly frozen in TriPure Isolation Reagent . After, each ~200 CNS samples were pooled and crude total RNA for each sample was extracted using TriPure Isolation Reagent. The crude RNA extract was treated with rDNase set  to digest contaminated DNA and was, subsequently, cleaned-up with NucleoSpinRNA Clean-up XS kit  to remove impurities. The purified total RNA was quantified using spectrophotometry  and microfluidic analyzer, Agilent RNA 6000 Nano kit . In total we generated, six biological replicates for WT, seven biological replicates for C24 and 4 biological replicates for X7/C24.Fly strains we used in this study: On average mRNA-libraries were generated from 10 μg of total RNA and prepared using the TruSeq RNA Sample preparation protocol . Information regarding all the used kits for library construction, cluster formation and sequencing is listed in Supplementary Table D. melanogaster genome assembly build BDGP5 . Primer and probe sequences are presented in Supplementary Table Total RNA was extracted from CNS samples using the TriPure RNA extraction reagent. RNA samples were treated with rDNase set followed by purification with NucleoSpin RNA Clean-up XS kit. 1 μg of total RNA was reverse transcribed using the PrimeScript® RT reagent Kit  by following the protocol for TaqMan Probe Assays. qPCR was performed using Premix Ex Taq™ (TaKaRa Bio Inc.) with 100 ng of cDNA in a ABI7500 Real time PCR system (Applied Biosystems). TBP primers and Zen™ double quenched probe sets were designed using the PrimerQuest primer design tool provided by Integrated DNA Technologies, Inc.  and synthesized by the same company. Primers for Drosophila transgenic strain for neuronal specific downregulation of Smn expression by double-stranded RNAi (Smnwt/elavGAL4-C24) was engineered by integrating a GAL4 inducible anti-Smn shRNA construct into an Smnwt/elavGAL4 background  and Smnwt/elavGAL4-C24 (C24) backgrounds and total RNA was isolated and pooled to generate a pilot RNA-seq dataset for WT and C24 strains . Following poly-A RNA enrichment, paired-end RNA-seq (mRNA-seq) libraries with ~100 million reads were generated from the pooled samples  for each of these genes were determined for each mRNA-seq library , which could have been inadvertently introduced during the delicate step of dissecting hundreds of larvae. In order to test this hypothesis, we defined a panel of five marker genes to which we could confidently assign an expected CNS expression pattern in our fly strains. These included the y Figure . The resAfter this pilot sequencing experiment, six additional paired-end mRNA-seq libraries, corresponding to three biological replicates of WT and C24 pooled CNS samples were generated using higher amounts of input cDNA. This reduced the PCR incurred bias during library preparation and consequently resulted in a higher coverage of protein coding genes, increasing the robustness of the dataset to study the CNS transcriptome Table . We nextSmn-dependent NMJ phenotype , supporting the initial conclusion that Smn is not differentially expressed between WT and C24 CNS mRNA-seq libraries. These results suggest that the expression of a transgenic shRNA transcript does not interfere with target gene quantification using non-strand specific, polyA plus paired-end mRNA-seq.The transgenic shRNA knockdown of s Figure . In caseSmn expression levels in WT and C24 CNS, RT-qPCR quantification in total RNA samples was performed  background  between both datasets. This implies that the elav-C24 strain displays Smn-dependent changes in neuronal gene expression. We reasoned that DE of Smn-dependent ubiquitous genes might be difficult to detect in the elav-C24 background due to signal dilution in a complex sample. On the other hand, it is predicted that the sensitivity to detect changes in the expression of neuronal specific genes should not be impaired or even display a relative higher efficiency in this mutant background that in the X7/C24 flies. In order to test this hypothesis, we retrieved from Flybase  elav; (b) the GAL4 transgene; (c) the glia specific gene repo SmnX7/TM6B, Dfd, YFP (X7(±)) mutant line, which contains a deletion of the Smn gene. These flies have a somatic down-regulation of Smn, compounded by the neuronal specific-expression of the anti-Smn shRNA and therefore exhibit a much more severe phenotype. In agreement with this, Smn down-regulation was very prominent in these flies and a much higher number of genes was found to be differentially expressed when compared to the WT background. Moreover, this group of differentially expressed genes showed a highly significant overlap with the much smaller subset identified in the C24 vs. WT comparison, suggesting that the DE analysis of C24 mRNA-seq libraries was nevertheless able to identify Smn-specific changes in neuronal gene expression. The occurrence of loss of sensitivity as a consequence of the presence of non-neuronal cells in the isolated tissue samples was expected to have a more significant effect on ubiquitously expressed genes, while neuronal genes should be more efficiently detected in a C24 background. By performing a classification of the differentially expressed genes identified in each comparison regarding their described tissue specificity, we suggest that the major limitation in the transcriptome profiling of the C24 flies is a signal dampening effect arising from the presence of non-neuronal cells. Therefore, the use of this bulk approach in similar contexts of cell-type specific up or down-regulation of a target gene should be considered with caution when approaches to isolate the target cell population are not an option. However, our results suggest that the increased sensitivity in the identification of cell-type specific genes afforded by these models may still justify the combined use of tissue specific down-regulation and compound tissue libraries in specific cases.As part of the general workflow of bioinformatics analysis after data filtering, the correlation and spatial relation between samples in our dataset was investigated. We observed poor correlations (<0.90) between libraries of the same strain and consequently observed a spurious hierarchical clustering regarding the biological origin of libraries in our pilot sequencing round. Considering the strong correlations observed between some libraries of different biological origin (over 0.98 in some cases), we put forth the possibility that the mRNA-seq libraries were being biased by contaminations from non-CNS tissues originating during the complex step of larvae dissection and tissue isolation, rather than reflecting stochastic biases occurring during library preparation, as reported by others (Mamanova et al., While assessing the quality of our sequencing dataset, we observed a bias in the nucleotide frequencies of the first 10 nucleotides of the 5′ ends of all reads. This bias has been attributed to the use of random hexamer primers in the preparation of mRNA-seq libraries (Hansen et al., In conclusion, we have observed that applying a small set of key genes allowed us to eliminate mRNA-seq libraries with a high level of contamination from non-CNS tissues, purging the dataset from libraries that would otherwise completely impair the identification of differentially expressed genes between the CNS transcriptome. Moreover, we address the limitations associated with the use of complex tissue samples to profile gene expression changes caused by a cell-type specific down-regulation of a gene of interest. Our results suggest that although the magnitude of detected changes—in particular for ubiquitously expressed genes—may be significantly masked by the detection of transcripts from other, non-targeted cell types, the ability to identify changes in genes that are specifically expressed in the targeted cell type is still maintained. Therefore, the combined use of models displaying tissue specific and somatic down-regulation of a gene of interest may provide complementary information. Finally, we demonstrate that trimming the 5′ end portion of mRNA-seq reads when significant biases in nucleotide frequencies are detected during quality control analysis, can increase the coverage of protein coding genes, causing a change in the number of DE genes, mostly likely to a decrease of incurred sampling bias.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.


There is an emerging consensus that protected areas are key in reducing adverse land-cover change, but their efficacy remains difficult to quantify. Many previous assessments of protected area effectiveness have compared changes between sets of protected and unprotected sites that differ systematically in other potentially confounding respects , have considered only forest loss or changes at single sites, or have analysed changes derived from land-cover data of low spatial resolution. We assessed the effectiveness of protection in reducing land-cover change in Important Bird Areas (IBAs) across Africa using a dedicated visual interpretation of higher resolution satellite imagery. We compared rates of change in natural land-cover over a c. 20-year period from around 1990 at a large number of points across 45 protected IBAs to those from 48 unprotected IBAs. A matching algorithm was used to select sample points to control for potentially confounding differences between protected and unprotected IBAs. The rate of loss of natural land-cover at sample points within protected IBAs was just 42% of that at matched points in unprotected IBAs. Conversion was especially marked in forests, but protection reduced rates of forest loss by a similar relative amount. Rates of conversion increased from the centre to the edges of both protected and unprotected IBAs, but rates of loss in 20-km buffer zones surrounding protected IBAs and unprotected IBAs were similar, with no evidence of displacement of conversion from within protected areas to their immediate surrounds (leakage). Protected areas (PAs) form a central pillar in efforts to combat biodiversity loss and absorb a high proportion of global conservation investment If investment in site protection is to be enhanced, the effectiveness of PAs in reducing deleterious land-cover change needs to be quantified Although several approaches have been proposed for assessing the effectiveness of PAs We quantified the extent to which protection reduces the rate of loss of natural land-cover in sites of recognised biodiversity value using a dedicated land-cover change assessment tool which uses remotely-sensed data at a 30-m resolution - a suitable scale for measuring land-cover change 2 were excluded as the small sample sizes obtained from the sampling protocol might have resulted in a non-representative assessment of land-cover. Of the remaining 692 IBAs, 509 were classed as protected and 183 as unprotected.Protected and unprotected IBAs were identified by intersecting the digitised boundaries of the 793 IBAs in sub-Saharan Africa and Madagascar  with the boundaries of all 1580 nationally designated African PAs (from the World Database of Protected Areas [IUCN categories I to VI]) that had digitised boundaries Land-cover change assessment” below), we applied a more stringent matching process at the level of individual sample points to identify a subset of these points that were very closely matched between protected and unprotected IBAs. This was done using variables expected to influence the likelihood of land-cover change , mean human population density . We then sub-sampled these points to produce a set of matched points from within the protected and unprotected IBAs. We used 0.5 SD calipers Matching was done using the MatchIt package 2) and 3 km apart in their buffers  or purchased Aster images. All images had a spatial resolution of 30 m. Points for which no data were available  in one or more of the sampling periods were excluded. A small number of points in the buffers of unprotected IBAs that overlapped nearby PAs were excluded.We used a grid-point sampling protocol  buffers . Visual 2 = 0.35, P = 0.6) or final  land-cover assessments. Analysts were unaware at the time of analysis of the protection status of the IBA or whether individual points were in IBAs or in buffers.Land-cover in each of the three time periods was allocated to one of 11 broad categories (based on the Land-cover Classification System (LCCS); The accuracy of interpretations was evaluated by comparison with very high resolution images from Google Earth at 20 IBAs. Nine of these IBAs were dominated by closed or open forest, three by shrub, three by arable agriculture, two by herbaceous, one by a mosaic of natural and agricultural vegetation, one by bare and one by open water. Forty sample points were selected at random from each of these 20 IBAs (including buffer zones) and the land-cover shown in Google Earth images (dating from 2006–2009) was compared with land-cover identified from the Landsat/Aster images for the 2005–2009 time period. Grouping of land-cover categories into non-natural cover  and natural cover  gave an overall interpretation accuracy of 93.9% (±0.008). Accuracy was 87.8% (±1.48) when split into five broad categories of forest (closed forest and open forest), shrub/herbaceous/flooded  agricultural , open water, and bare).We modelled the loss rate of natural land-cover at each of the matched points that held natural land-cover in the initial year of assessment using a generalised linear mixed model. Retention (0) or conversion (1) of natural land-cover was modelled as a binary dependent variable, with the number of years between observations (i.e. exposure time) fitted as a binomial denominator. This accounted for differences in the time interval between assessments and expressed the dependent variable as an estimate of annual rate of conversion that was comparable between sites. Conversion was defined as either the loss of open or closed forest to any other land-cover type or replacement of any of the other natural land-cover types  with non-natural  If the natural land-cover was retained throughout, the number of years between the first and third assessments was taken as the exposure time. If land-cover changed between the initial and second assessments or between the second and final, we assumed that conversion had occurred midway through the intervening period and calculated the exposure time accordingly. None of the points converted between the initial and second assessments had returned to their original state by the final assessment.Models were fitted with PROC GLIMMIX in SAS, specifying a binomial error distribution. The Laplace approximation was specified to allow comparison of model fit using AIC values between mixed-effects models containing different sets of fixed factors The sets of protected and unprotected IBA points identified by the matching process were well balanced, with similar distributions of the matching covariates in the two groups . Whilst Across all of the matched points, rates of forest loss were greater than rates of loss of shrub, herbaceous and flooded land-cover. Forest cover remained intact on only 81.79% of originally forested points, compared to 91.76% for shrub, herbaceous and flooded cover  . The extThe parameter estimates from the null model indicated that the annual rate of loss of natural land-cover across all points was 0.34%±0.004 per year  The models indicated that the annual rate of forest loss alone was over twice that of all natural land-cover (0.75%±0.009). Across all land-cover types the model with greatest support contained protection class (the 4-level factor), distance to PA boundary and their interaction term , as wellRates of loss of natural land-cover were 58% lower inside protected IBAs than inside unprotected IBAs . Rates oAnnual rates of loss of natural land-cover in protected IBAs were less than half of those in IBAs with no legal protection. Estimates of annual rates of loss of all natural land-cover types (0.34%) and of forest alone (0.75%) were similar to estimates of 0.3% and 0.7% respectively, previously reported across sub-Saharan Africa between 1975 and 2000 Our finding that protection is effective at reducing land-cover change on sites of conservation importance supports previous studies that have suggested that site protection has a positive effect on natural land-cover across PAs in general . Therefore, only extensive or less subtle changes are likely to be identified. Despite this, our approach could fill gaps in field-based monitoring In summary, protection is effective at reducing (though not stopping) loss of natural land-cover within sites of high conservation importance. This conclusion is based on changes in all natural land-cover types (unlike many previous studies which consider only forest), at sites of defined conservation importance. Our results suggest that the proposed expansion of the global terrestrial protected area network Figure S1Distribution of all African IBAs (stars), showing the 93 IBAs covered in the assessment of land-cover change (circles). Blue: protected IBAs, red: unprotected IBAs.(DOCX)Click here for additional data file.Figure S2Example of distribution of sample points within an IBA (shaded area) and in a 20-km buffer around it. Points within the IBA are 1.5 km apart, those in the buffer are 3 km apart, reflecting the higher density of sampling points used inside IBAs.(DOCX)Click here for additional data file.Figure S3Conversion rates of forest alone (based on parameter estimates from model with 95% CL) for classes of points estimated from the model with the strongest support . Point classes sharing the same letters did not differ significantly from each other.(DOCX)Click here for additional data file.Table S1IBAs selected for land-cover change assessment based on site-level matching, with characteristics used in this site-level matching.(DOCX)Click here for additional data file.Table S2Summary of and comparison between matching covariates across 28,490 points split equally between those with and without protection, following matching in MatchIt using calipers  = 0.5 SD. Land-cover variables were identified from visual assessment. In addition to the basic statistics, the differences in the empirical cumulative distance function are also given, for which values closer to 0 indicate better matching.(DOCX)Click here for additional data file.Table S3Descriptions of land-cover classes used in visual interpretation, based on Land Cover Classification System (LCCS).(DOCX)Click here for additional data file.


Previous studies have documented that advanced life support (ALS) provided by anaesthesiologists in emergency medical service (EMS) leads to life years gained . EspeciaAdvanced interventions performed by the anaesthesiologist on call at three helicopter and car EMS units and one search-and-rescue unit were reported on a standard form for 12 months during 2011. Data on advanced airway management and surgical- and anaesthesiological procedures are reported here. Based on the number of prehospital working hours reported by each physician we calculated the number of procedures performed per 24-hour for each individual. With the mean value for each procedure per 24 hours per physician we have calculated the expected number of procedures performed in one year based on full time HEMS physician engagement equivalent to 84 24-hour shifts. The Data Protection Official of all involved institutions approved the data collection as a quality improvement project.We received forms from 82.7% of the 1460 duty-days covered by the study. Thirty-six individual physicians reported on their activities during the study and the mean number of duty hours per physician was 804.9 hours . The expected number of procedures performed per year per physician is listed in Table Anaesthesiologists in Norwegian EMS can expect relatively little exposure pre-hospital to crucial life-saving interventions. Concomitant in-hospital clinical practice and other skills retraining should therefore be maintained to ensure adequate proficiency in these interventions.


Somatic hypermutation (SHM) in the variable region of immunoglobulin genes (IGV) naturally occurs in a narrow window of B cell development to provide high-affinity antibodies. However, SHM can also aberrantly target proto-oncogenes and cause genome instability. The role of aberrant SHM (aSHM) has been widely studied in various non-Hodgkin's lymphoma particularly in diffuse large B-cell lymphoma (DLBCL). Although, it has been speculated that aSHM targets a wide range of genome loci so far only twelve genes have been identified as targets of aSHM through the targeted sequencing of selected genes. A genome-wide study aiming at identifying a comprehensive set of aSHM targets recurrently occurring in DLBCL has not been previously undertaken. Here, we present a comprehensive assessment of the somatic hypermutated genes in DLBCL identified through an analysis of genomic and transcriptome data derived from 40 DLBCL patients. Our analysis verifies that there are indeed many genes that are recurrently affected by aSHM. In particular, we have identified 32 novel targets that show same or higher level of aSHM activity than genes previously reported. Amongst these novel targets, 22 genes showed a significant correlation between mRNA abundance and aSHM. In normal GC B cells, SHM can also target the non-IGV loci such as the 5' sequences of the BCL6 and FAS/CD95 (TNFRSF6) genes , 2. Thisic cells . Due to ic cells . The mosic cells , 11–13, ic cells .Aberrant SHM (aSHM) does not target proto-oncogenes in all subtypes of lymphomas originating from GC or post CG B-cells. In fact, aSHM activity in PIM1, PAX5, RHOH/TTF and MYC proto-oncogenes, have been acknowledged as a molecular feature exclusive to DLBCL. While aSHM of oncogenic loci affects more than 50% of DLBCL, it is rarely or never observed in other B-cell malignancies . SomaticWe have performed a genome wide study on single nucleotide variations (SNVs) from whole genome data derived from 40 previously described DLBCL patients to identify recurrent SHM targets in DLBCL. Matched RNA seq and matched normal whole genome data was available for all of these samples and used in this study, however, only the SNVs derived from the whole genome data were included in the study as the variations derived from RNA-seq data are biased in the regions with high expression level. Our cohort consisted of 13 Activated B-cell (ABC) and 23 Germinal B-cell (GCB) subtypes .Since SHM activities occur within a 2kb region downstream of TSS, we analyzed the mutations in this target region for annotated genes in the UCSC knownGene track . We refeSince SHM activities are only associated with densely mutated regions, we would expect a decline in SHM feature values as the mutation rate decreases in the SHM-targets. In order to validate this hypothesis we divided the list of genes, sorted by mutation rate, into three groups. Expectedly, the IG loci were most highly ranked in the list. In particular, more than 20% of the top 60 SHM-targets belong to IG loci. Removing the IG loci and using them as a positive control group we divided the rest of the hypermutated SHM-targets into the following groups. Group I consists of the candidate SHM-targets (discussed earlier) that includes 44 SHM-targets with a mutation density above that of the known SHM targets. Group II consists of SHM-targets that are only moderately mutated. In particular, it includes SHM-targets that contain 3 to 5 SNVs observed in the input samples (92 SHM-targets) and group III consists of the remaining of the SHM-targets that contains at least two distinct SNVs (470 SHM-targets). Table Another indication of predominant SHM activity in the SHM-targets of group I and the IG group is the geographical pattern of mutations in these regions. Since the probability of mutations drops exponentially as the distance from TSS increases, the mutation density curve in the SHM-target region is expected to form a bell shape curve with its peak located in a region 150 to 1000 bases upstream the TSS. Such a trend can be observed more strongly in group I compare to groups II and III .Another aspect of SHM is its reliance on active transcription. It has been shown that the elimination of transcription across an IG locus results in a loss of SHM  and alsoWe also investigated the correlation between aSHM and translocations. To do so, we first identified genome wide translocation events independent from the results of the aSHM study. The translocation events were identified using ABySS , which a−20) out of which 13 genes (i.e. 30%) were known proto-oncogenes (P < 10−4).We also screened recurrently targeted aSHM genes (i.e. the 44 genes in group I) against genes that are known to be cancer related. We selected a total of 3632 cancer related genes through a union of several credible cancer gene repositories including the CancerGenes database  that com−10), on the other hand it shows that most of the mutations reported in this study are novel in lymphoma.The list of aSHM-targeted genes that have a role in cancer can be found in It would be of interest to determine whether differences exist in the mutations patters of individual genes in the Activated B-cell (ABC) and Germinal B-cell (GCB) subtypes of Group 1. We investigated this question on our cohort that consists 13 ABC and 23 GCB . Our analysis shows that although some genes are favorably mutated in the samples of one subgroup , however, the statistical power in our data is not sufficient to show that aSHM favorably targets one subgroup compare to another See . For insThe genes in group 1 were also tested for enrichment of certain functional classes using the DAVID functional annotation clustering tool. This revealed significant enrichment for genes involved in lymphocyte activation  and transcription factor activity  including known lymphoma-related genes such as the oncogenes BCL2 and BCL6 but also novel genes including CXCR4, RHOH, CD74 and MS4A1 . The SHM targets were also enriched for genes involved in regulation of phosphorylation  including SOCS1, DUSP2, SGK1 and PIM1.We described genome wide recurrent targets of somatic hypermutations in diffuse large B-cell lymphoma. The mutation characteristics and distributions in the targeted regions resemble those of the SHM mutations in IGH loci and other known targets of aSHM in B-cell malignancies. Our analysis further confirms a significant concordance between genome rearrangements and SHM activities in the affected genes, an observation that has been previously reported. We observed aSHM at sites known ot be involved in translocations but in the absence of translocations, which is consistent with a model wherein aSHM may precede the genetic events that result in these rearrangements. It is possible that some of the observed SHM events arise in the B-cells prior to malignant transformation however unlikely, since it is known that other B-cell derived malignancies do not display aSHM events  other thSample preparation and sequencing were conducted as previously described . The datHere we explain how the statistical measures are calculated for the variations in SHM-target regions. Note that some of these measures are only included in the Supplementary tables and not in the main tables Translocation events were identified using ABySS  and thenRead alignments relative to the breakpoint and read mapping quality were taken into account. The gene expression values were calculated as reads per kilo base gene model per million mapped reads (RPKM) values from RNA-seq data derived from the tumors .


The interactions between biochemical processes and mechanical signaling play important roles during various cellular processes such as wound healing, embryogenesis, metastasis, and cell migration. While traditional traction force measurements have provided quantitative information about cell matrix interactions in two dimensions, recent studies have shown significant differences in the behavior and morphology of cells when placed in three-dimensional environments. Hence new quantitative experimental techniques are needed to accurately determine cell traction forces in three dimensions. Recently, two approaches both based on laser scanning confocal microscopy have emerged to address this need. This study highlights the details, implementation and advantages of such a three-dimensional imaging methodology with the capability to compute cellular traction forces dynamically during cell migration and locomotion. An application of this newly developed three-dimensional traction force microscopy (3D TFM) technique to single cell migration studies of 3T3 fibroblasts is presented to show that this methodology offers a new quantitative vantage point to investigate the three-dimensional nature of cell-ECM interactions. The exchange of physical forces in cell-cell and cell-matrix interactions plays a significant role in regulating a variety of physiological and pathological processes including wound healing, angiogenesis, metastasis and embryogenesis Traction force microscopy utilizes optical phase and wide-field microscopy to track substrate surface displacements due to cellular traction forces through the spatial correlation of fluorescent particles embedded in the substrate. Polyacrylamide gels are among the most commonly used substrate materials in studying cell force responses due to their mechanical tunability, optical translucency, and elastic material behavior To record cell surface deformations, cells are initially seeded on the substrate material and allowed to spread or migrate. After some time, a first image is captured optically, where typically both the cell and tracker particles are recorded simultaneously. Then, cells are detached from the surface through trypsinization or similar treatment. A second image is captured (without moving the microscope's objective) to serve as the undeformed or reference configuration. Cell-induced substrate displacements are then determined from the two images by using either a single particle tracking or a digital image correlation algorithm. The resulting gel displacements are converted into traction forces using the inverse Boussinesq formulation, where the Boussinesq theory describes the displacement equilibrium solutions inside a semi-infinite elastic half-space with applied forces at its free boundary While previously published traction force microscopy studies have contributed a great deal to our understanding of cell behavior and local cell-ECM interactions, they remain inherently restricted to two dimensions. Current cell motility and mechanotransduction models are based on experimental findings from two-dimensional studies This study presents a three-dimensional traction force microscopy (3D TFM) technique capable of measuring cellular deformations in three dimensions with submicron accuracy. While this technique has been used previously to measure deformation fields within soft biomaterials and around migrating fibroblast cells A similar methodology has been demonstrated by Hur et al. by combining LSCM with finite element modeling (FEM) to compute the resulting cell traction fields Here we describe straightforward and accurate calculations of cellular displacements and tractions via the 3D TFM technique, without the need to invoke complex mathematical frameworks, for both planar and fully three-dimensional systems. The 3D TFM methodology is utilized to investigate the dynamic evolution of traction forces during migration of 3T3 fibroblasts on polyacrylamide substrates.Full field displacement measurements were carried out using the LSCM-DVC technique applied to migrating 3T3 fibroblast cells on polyacrylamide gels with a Young's modulus of E Each cell was visualized simultaneously with the displacement of the fluorescent particles inside the polyacrylamide gels using two separate photodetectors. This procedure allowed correlation of the position of the cell (determined by GFP-actin) with the substrate displacement field. However, due to the finite life-time and degradation of the GFP, parts of the cell were occasionally not visible at locations where substantial deformations were observed.a priori assumptions regarding the stress state  and geometry . This removes several conditions needed to determine traction forces from two-dimensional data sets, such as an infinite substrate requirement or the inverse calculations required within the framework of the Boussinesq theory TAs illustrated in As shown earlier, cell-induced deformations can extend through 50% of the substrate thickness or up to 25% of cell's body length (long axis). Unless experiments are designed with such considerations in mind, it is likely that cells will respond to an effective modulus that is a function of film thickness, rather than to the modulus of the gel layer alone This study also shows that cells can actively modulate in-plane  or FEM-based analysis. The study highlights the highly three-dimensional deformation fields around migrating 3T3 fibroblasts and shows the local dynamic force modulation of these cells at different times. It also presents experimental evidence for a three-dimensional peeling or rolling motion during cell locomotion characterized by changes in the orientation and magnitude of the cell-induced three-dimensional traction forces. While this study focused on single cell matrix interactions, future studies might envision quantifying cell-substrate interactions for multiple cells, cell sheets or cell clusters. Since the technique can accurately resolve traction forces as a function of time in three dimensions, it should create new avenues for quantifying cell-matrix interactions in three-dimensional tissues  and biomaterials and provide a tool for studying the intrinsic coupling of mechanical and biochemical interactions outside and inside cells in all three dimensions.This section provides a general overview of the newly developed three-dimensional traction force microscopy technique. This method has the capability of determining the three-dimensional cell-induced full-field displacements and traction forces inside transparent biomaterials, such as polyacrylamide and collagen gels. n)),within the material as well as on its surface, by simple matrix multiplication of the normal with the material stress tensor. As the schematic illustrates, this method does not rely on any mathematical frameworks or stress-state assumptions, such as the Boussinesq theory or FEM analysis. Because all material displacements can be calculated from three-dimensional LSCM images, the tractions can be calculated in a forward way based solely on mechanics principles as described in detail in the following sections. This obviates the need for any inverse formulation and stabilization schemes that are generally necessary for 2D TFM calculations.First, submicron-sized fluorescent particles are embedded within the transparent biomaterial of interest and three-dimensional time-lapsed volumetric images are recorded using laser scanning confocal microscopy (LSCM). Next, cell-induced material deformations  are calculated using a recently developed digital volume correlation (DVC) algorithm that tracks the center displacement of individual spatial subsets of the recorded LSCM images in all three dimensions Glass coverslips  were chemically modified to allow for covalent attachment of polyacrylamide sheets using a previously established protocol Polyacrylamide films were generated and fused to functionalized coverslips using a previously adapted protocol In order to promote cell attachment to polyacrylamide films, fibronectin was conjugated to the gel surface using the heterobifunctional crosslinker, sulfo-SANPAH . Adopting a previously outlined procedure Prior to depositing cells, FN-modified gel samples were equilibrated in growth medium at 37The mechanical properties of the polyacrylamide substrates were determined by performing both unconfined and confined compression testing on cylindrical polyacrylamide specimens using a custom-built compression setup For unconfined tests, gel samples were cast in a circular washer secured to the bottom of a 60 mm diameter plastic Petri dish. Following polymerization . Samples were hydrated following the protocol described above. The samples were compressed following the same loading-unloading protocol used for the unconfined test. Using the determined Young's modulus value of the unconfined test case and observing that any further compression beyond an initial compression strain of Three-dimensional image stacks were acquired using a Nikon C-1 confocal system mounted on a TE-2000-U inverted optical microscope. A 40× CFI planar fluor air objective with a numerical aperture of 0.6 was used in all experiments. Three laser lines were used to image the cells and the fluorescent microparticles: An Argon (488 nm) laser for the GFP-actin, a green Helium Neon (543 nm) laser for the microparticles inside the polyacrylamide gels, and a red Helium Neon (633 nm) laser to excite Mitotracker Deep Red for mitochondrial labeling. Confocal stacks were acquired every 35 minutes for several hours at a resolution of 512×512×Z voxels  from several experiments on polyacrylamide gels with cells present and cells absent (control). For both systems the summation of forces and moments of each control volume was on the same order of magnitude, Figure S1Close-up Cell Traction Image. Expanded contour plot of the magnitude of the three-dimensional traction vector as shown in (TIFF)Click here for additional data file.Figure S2Close-up Cell Traction Image. Expanded cross-section depth contour plot of the magnitude of the three-dimensional traction vector as shown in (TIFF)Click here for additional data file.Figure S3Close-up Cross-sectional Traction Image underneath Cell Nucleus. Expanded view of (TIFF)Click here for additional data file.


Notch mRNA is a potential target of PTB, (2) PTB and Notch functions in the dorso-lateral regions of the Drosophila embryo are linked to actin regulation but not their functions in the ventral region, and (3) the actin-related Notch activity in the dorso-lateral regions might require a Notch activity at or near the cell surface that is different from the nuclear Notch activity involved in cell fate specification in the ventral region. These data raise the possibility that the Drosophila embryo is divided into zones of different PTB and Notch activities based on whether or not they are linked to actin regulation. They also provide clues to the almost forgotten role of Notch in cell adhesion and reveal a role for the Notch pathway in cell fusions.Polypyrimidine Tract Binding (PTB) protein is a regulator of mRNA processing and translation. Genetic screens and studies of wing and bristle development during the post-embryonic stages of Drosophila suggest that it is a negative regulator of the Notch pathway. How PTB regulates the Notch pathway is unknown. Our studies of Drosophila embryogenesis indicate that (1) the  Genetic screens and developmental studies performed on post-embryonic stages of this organism indicate that PTB regulates oogenesis in adult females hephaestus is a negative regulator of Notch pathway activities hephaestus somatic mutant clones the level of active Notch molecule  is known to increase Notch gene is negatively regulated at the level of mRNA 3′ processing during Drosophila embryogenesis Notch mRNA and protein production during development.Polypyrimidine Tract Binding (PTB) protein  plays critical roles in mRNA metabolism. It contains four RNA Recognition Motifs (RRMs) and binds to pyrimidine-rich sequences with motifs such as UCUUC and CUCUCU. Biochemical studies, most of them performed using mammalian intra/NICD) from the cell surface. Nintra/NICD translocates to the nucleus, associates with transcription factors including Suppressor of Hairless and Mastermind, and turns on transcription of target genes. A Mastermind-dependent process degrades Nintra/NICD, possibly to keep transcription commensurate with the amount of Nintra/NICD production. This mechanism, called canonical Notch signaling, is used as a binary switch during cell fate specification. Cells that block Nintra/NICD production commit to one fate  and cells that produce Nintra/NICD acquire the alternative cell fate. Depending on the developmental event or context, Nintra/NICD The Notch protein is a cell surface receptor that is required for the development of all animal tissues. At any particular developmental stage, it is involved in the differentiation of many different tissues in different regions. The mechanism underlying Notch function is generally thought to be the same everywhere: In response to binding of a ligand such as Delta, the Notch receptor is proteolytically cleaved to release the Notch intracellular domain  or are confined to distinct regions of the developing animal.There is suggestive evidence in assorted systems that some Notch functions are not based on canonical Notch signaling. Many of these functions, particularly those related to actin, cytoskeletion, or extracellular matrix appear to be based on a mechanism that does not involve Suppressor of Hairless or NThe ventral region of the Drosophila embryo was the region where the role of Notch in cell fate specification was first discovered about 70 years ago hephaestus negatively regulated Notch functions in embryos (as it does during wing development), its loss of function was expected to have a similar effect on Notch features in the ventral and the dorso-lateral regions.The dorso-lateral regions of the developing Drosophila embryo support many processes that require the cell fate specification functions of Notch, for example the specification of neuronal precursor cells that differentiate the peripheral nervous system or the pericardial cells that differentiate the dorsal vessel (heart). These regions are also involved in actin-based processes such as gastrulation and dorsal closure processes. There are no published reports of the role of Notch in these actin-based processes. It is possibly because of epistasis: loss of Notch function suppresses the production of epidermal cells that are required for many aspects of gastrulation and dorsal closure hephaestus embryos the level of Notch mRNA and protein activity is increased indicating that Notch mRNA might be targeted by the Hephaestus protein for negative regulation. Remarkably, in hephaestus embryos the Notch protein accumulates at or near the cell surface in the dorso-lateral regions but not in the ventral region. Notch accumulation in the dorso-lateral regions is associated with actin accumulation, cell fusions, and disruption in dorsal closure and cardiogenesis processes. The same phenomenon is observed in mutant Notch embryos that have lost negative regulation at the level of mRNA 3′ processing suggesting that the hephaestus phenotypes are possibly consequences of increased Notch activities. Over-expression of Nintra, thereby canonical Notch signaling, recapitulates the molecular and morphological phenotypes in the ventral regions of mutant hephaestus or Notch embryos but not the phenotypes in the dorso-lateral regions. These data suggest that the Drosophila embryo is zoned based on whether or not PTB and Notch activities are directly linked to actin-based processes. Thus, cells that appear to be the same  and part of the same epithelial layer might differ in developmental potential depending on their place of origin in the embryo .Results reported here show that in mutant hephaestus alleles used in our studies are 03429heph and 2heph. They are loss of function alleles due to P element insertions. 03429heph appears to be the stronger allele as its homozygotes do not survive to adulthood but a few 2heph homozygotes do . The yellow white (yw) fly stock served as the wild type (WT) control. The Notch null mutants used are 55e11N or 264-47N; the Notch gain-of-function allele used was Nnd1-dse 03429heph; N- embryos were obtained using standard genetic crosses and stocks with green (GFP) and blue (lacZ) balancers. Stocks were maintained at 18°C and experimental embryos were collected at room temperature (23°C to 27 °C) or 29–30°C (yw control and Nnd1-dse) over a 0–24-hour period. They were processed immediately for immuno-labeling studies or aged for an additional day or two before processing . Embryos were immuno-labeled with antibodies against Notch gotes do , FlyBaseintra/NICD, we used a UAS-Nintra/NICD transgene daughterless Gal4  or heat shock protein 70 Gal4  drivers provided through the males. hsGal4 flies and embryos were reared at 18°C and transferred to 30°C at different stages. All offspring embryos would express Nintra/NICD, as homozygous parents were used.RNA expression was analyzed by northern blotting Although similar results were obtained with imuno-fluorescence, detailed studies were done with immuno-cytochemical procedures  because they were efficient and cost-effective, enabling us to examine simultaneously thousands of embryos and determine stage-specific expressivity and penetrance of mutant phenotypes. Although Phalloidin and actin antibody gave similar results, we relied on the latter for double labeling studies because it was less tedious. Wild type and mutant embryos were processed identically and mounted in Phosphate Buffered Saline with 0.5% triton×100 (PBT) using strips of glass cover-slip as props so that embryos could be rolled to desired position for imaging.In vitro cell fusion assays were performed with clone-8 cells that endogenously express Notch (but not Delta), Schneider (S2) cells that express neither Notch nor Delta actin5C promoter (S2-actDelta). For generating the S2-actDelta cell line, actin5C promoter and the Delta cDNA were cloned the into pUAST vector .For determining the effect of Notch and Delta interaction on F-actin in cultured cells, cell aggregations were performed using S2 cells expressing heat shock inducible Notch or Delta and procedures described in DeltaVision imaging was done at the Neuroscience Core Imaging facility, University of Vermont College of Medicine. All other imaging was done using an upright Nikon SMZ1500 stereoscope or a Nikon 2500 inverted fluorescence microscope fitted with a Spot RT Slider CCD camera. Wild type and mutant embryos were imaged together or with identical settings. Extra Long Working Distance (ELWD) 40×objective was used to image live cells through the bottom of the microplate . Images were processed using Photoshop (Adobe) and Canvas (Deneba) programs. Any adjustment made to brightness/contrast was applied to the whole image and the same settings were used for all compared samples.hephaestus function affected Notch function during embryogenesis, we examined the CNS development in mutant hephaestus embryos as this process is under the control of canonical Notch signaling and is the most widely accepted assay for this signaling. The neuronal precursor cells and many of their progeny express the neuronal marker protein Hunchback. When there is loss of canonical Notch signaling, as in Notch null embryos, an excess of Hunchback signal is observed. When there is an excess of this signaling, as in transgenic embryos over-expressing Nintra/NICD from an exogenous promoter, a suppression of Hunchback signal is observed . Examination of 03429heph and 2heph embryos showed that Hunchback expression is suppressed . Results showed that 03429heph embryos express a high level of Notch mRNA as well as high levels of E(spl)C mRNA, the target of canonical Notch signaling during neurogenesis . In these embryos the amnioserosa (AS) persists in the dorsal region and most of other embryogenesis events are arrested. Although the amnioserosa in heph mutant embryos retains the morphology (and can be easily identified using it), its physiology appeared to be altered as expression of markers such as Kruppel 03429heph embryos appeared to cease development at about stage 14 to 16 (12–16 hours of embryogenesis), only about 20% of 2heph embryos appeared to cease development at these stages. These determinations are gross approximations as not all parts of the dead embryos ceased development at the same stage . Embryos that had ceased development died of necrosis between 48–72 hours after egg laying.Our studies also revealed that the dorsal closure process is severely disrupted in  embryos Fig. 3. heph mutant embryos is specific to the dorsal closure process, we examined development of the dorsal vessel. Its development is initiated shortly after gastrulation and well before the initiation of the dorsal closure process. Pairs of cardioblasts develop on either side of the embryo, from the lateral myoblast cells along the anterior-posterior axis. Notch activity in one cell of each pair suppresses the cardioblast fate and specifies the alternate pericardial cell fate. While cardioblasts are responsible for producing the dorsal vessel proper, pericardial cells are responsible for producing support structures and for secreting the extracellular matrix protein Pericardin that is required for attaching the cardioblasts to the dorso-lateral epidermal cells. The two longitudinal rows of pericardial cell-cardioblast pairs that form on either side of the embryo migrate to the dorsal midline by hitchhiking on the dorso-lateral epidermal cells involved in dorsal closure. After reaching the dorsal midline, they fuse and differentiate the dorsal vessel. Notch activity is also required for this differentiation Notch null embryos), Pericardin is not expressed, as pericardial cells are not formed .To determine whether the defect in the dorso-lateral lateral region of 03429heph embryos both the pericardial cell specification process  as well as migration of these cells to the dorsal mid-line  were affected  embryos have not even begun to express Pericardin (these embryos begin expression at stage 13). Pericardial cells were in high numbers and studies of numerous independently processed samples indicated that the pericardial cell specification process proceeded in a haphazard manner. For example, Pericardin was found in regions that do not normally express it, such as the head and the tail regions (see 03429heph embryo in 4D). Embryos in 4E show the ventral region of the embryos in 4D to point out that the ventral epidermis is developed, which is consistent with canonical Notch signaling activity in the region. Abnormal phenotypes in 03429heph embryo persisted to the time when dorsal vessel is fully formed in the wild type embryo (embryos in 4F). We observed similar defects in 2heph embryos but they were milder or in a smaller fraction of embryos. These data indicate that the loss of hephaestus function affects not just the dorsal closure process but also additional processes that are dependent on the dorso-lateral regions of the embryo.In affected Fig. 4. hephaestus function affected Notch protein levels, we studied 03429heph embryos using an antibody made against the carboxyl terminus of the Notch protein 03429heph embryo but not in the ventral region. Notch accumulation was very pronounced in stage 15 embryos but could be detected even in stage 9 embryos when the effect of the zygotic loss of hephaestus function begins to manifest . However, it is clearly discernible in stage 9 embryos in which Notch protein expression is confined to the outer layer of cells. We observed the same pattern of Notch protein expression  even in 2heph embryos but it was not as obvious and was observed in a smaller fraction of embryos.To determine whether loss of manifest Fig. 5. hephaestus embryos. We found that actin accumulated to a high level and in a disorganized manner in the dorso-lateral regions of 03429heph embryos . These results rule out cell death (apoptosis) as the cause of actin accumulation in mutant hephaestus embryos.As the dorsal closure process is primarily dependent on actin-based processes  embryos Fig. 6. ryos see . We obse03429heph embryos, we examined whether there is overlap between Notch and actin accumulation. We performed high-resolution analysis using the DeltaVision restoration microscopy, which like confocal microscopy provides expression information in a single optical plane. We chose to focus on the dorso-lateral cells in late stage 15 03429heph embryos, as they show the highest level of Notch and actin accumulation. We used Alexa Fluor 488 conjugated and highly cross-adsorbed secondary antibody against the Notch antibody made in hamster, Alexa Fluor 647 conjugated and highly cross-adsorbed secondary antibody against actin antibody made in mouse, and DAPI for nuclear labeling. These fluorophores have no overlap in emission spectra. In all embryos we examined (n = 15), there was significant overlap in Notch and actin accumulation. Low magnification images of dorso-lateral regions of embryos are shown in Fig. S5). Thus, the loss of hephaestus function resulted in high levels of Notch, actin, and cell fusions.As Notch and actin protein accumulation patterns were comparable in the dorso-lateral regions of intra/NICD over-expression mimics the classic gain-of-canonical-Notch-signaling phenotype: suppression of neurogenesis in the ventral region of 03429heph embryos. Therefore, we examined if exogenous Nintra/NICD expression also mimicked the high levels of Pericardin and actin in the dorso-lateral region of the embryo. Results of experiments examining pericardin level in UAS-Nintra/NICD-daGal4 embryos are shown in Figure S6. We did not observe high levels of pericardin at any stage. In fact, pericardin level was suppressed. Comparable stages of wild type embryos showed normal Pericardin levels (embryos D–E). We obtained similar results with both daGal4 and hsGal4 drivers.Accumulation of Notch could imply excess canonical Notch signaling. As shown in intra/NICD-hsGal4 embryos showed no effect at earlier stages (stages 11–12) but showed suppression of actin level at later stages (stages 13–14) . The latter result is the opposite of what we observed in mutant 03429heph embryos. We also observed the loss of aminoserosa (AS) in Nintra/NICD expressing embryos, which is the opposite of what we observed in 03429heph embryos. We then examined embryos of the hyperactive Notch allele Bl(1)N. This allele contains a mutation in the extracellular region that is a negative regulator of Nintra/NICD production. As a consequence, Nintra/NICD and canonical Notch signaling is produced at a high level in B/Yl(1)N embryos and these embryos die near the end of embryogenesis (stage 17) or in early larval stages (Bl(1)N embryos showed that actin level is not increased . We also did not observe increased levels of pericardin in Bl(1)N embryos. In fact, we found that the dorsal vessel was missing or partially formed. These observations are in accord with our Gal4/UAS-Nintra/NICD transgene data. Thus, it appears that while Nintra/NICD and canonical Notch signaling are sufficient to explain the mutant phenotypes in the ventral region of 03429heph embryos, they are insufficient to explain the mutant phenotypes in the dorso-lateral regions.Results of our experiments examining actin level in UAS-Nl stages , FlyBaseintra/NICD over-expression failed to reproduce the dorsal closure or actin phenotypes. To distinguish between the two possibilities, we examined embryos of the temperature-sensitive nd1-dseN allele that produces constitutive and high levels of endogenous Notch activities at the restrictive temperature. The lesion in nd1-dseN is a mutation in the Down Stream Element (dse) that is critical for the negative regulation of Notch mRNA 3′ processing and protein production. At the restrictive temperature, more than two-thirds of the embryos die at various stages 03429heph embryos, we expected to observe similar phenotypes in nd1-dseN embryos. We found that actin protein accumulated in the dorso-lateral regions of more than 50% of un-hatched nd1-dseN embryos that had developed past embryonic stage 14 , we expected a fraction of Notch; hephaestus zygotic double mutant embryos to develop beyond stage 13. These embryos would enable us to determine if the actin accumulation in hephaestus mutant embryos is suppressed in the absence of Notch. Therefore, we generated double zygotic mutant embryos of 03429heph and one of the Notch null alleles (55e11N and 264-47N). About 70% of these embryos manifested the Notch null phenotype of excess neuronal cell types at the expense of epidermal cells. In other words, Notch is indeed epistatic to hephaestus, which is also consistent with data from studies in wing development indicating that hephaestus functions after Notch activation Notch null phenotypes. A sample of embryos probed with the actin antibody is shown in 55e11/YN embryos than in comparable wild type embryos, possibly because they are composed of mostly neuronal cells. That high level of actin was drastically reduced when the embryos were also homozygous for the 03429heph. Reduction in the ventral region was likely to be due to partial suppression of the neurogenic phenotype . In the dorso-lateral regions of the same embryos, actin accumulation was not observed. We conclude from these observations that (1) actin accumulation in 03429heph is dependent on Notch activity and (2) this Notch activity occurs later than the canonical Notch signaling activity in the ventral region. While these results are not conclusive (due to the complexity of hephaestus and Notch interactions), they are consistent with our hypothesis that hephasestus negatively regulates Notch that in turn positively regulates actin levels in the dorso-lateral regions of the Drosophila embryo.Since Notch activity is required for producing epidermal cells, including those in the dorso-lateral regions, loss of Notch function can be expected to be epistatic to the loss of 03429heph and nd1-dseN embryos, we wondered if Notch accumulation at the cell surface results in actin accumulation. To find out we relied on the phenomenon of Notch receptor clustering, as it is the most natural way to enrich for Notch at the cell surface. When cultured Drosophila cells expressing Notch are treated with cells expressing a Notch ligand , the first and most rapid response is Notch receptor clustering at the sites of contact with ligand expressing cells. In the absence of the ligand expressing cells, Notch expression is distributed all around the cell membrane and in the cytoplasm. Delta does not cluster in response to Notch binding 03429heph and nd1-dseN embryos is also F-actin, we probed these embryos with fluorescently labeled Phalloidin. These experiments confirmed that most of actin that accumulated in 03429heph and nd1-dseN embryos is indeed F-actin , whether it is directly involved in cell fusions as our data suggest is unknown. Therefore, we examined this issue in cultured cells that are simpler than embryos and have served as an excellent in vitro model system for the analysis of Notch and/or Delta activities for more than 20 years  served as control cells. We treated cl-8 cells with either S2-actDelta cells or S2 cells for varying periods from 2 hours to 20 hours. After about 4 hours, we started to notice large cells. Samples from a high-cell-density experiment and a low-cell-density experiment are shown in Fig. S8). We have also not observed cell fusion in cells expressing Nintra/NICD. These results suggest that persistent interaction between Notch and ligand (Delta) expressing cells (made possible only by binding between Notch and ligand {Delta} at the interface between cells) has the potential to lead to cell fusions and supports our observation of cell fusions in 03429heph and nd1-dseN embryos. The low level of fusions observed in vitro could be due to lack of proper conditions or additional factors (fusogens) that promote cell fusions in embryos.To find out whether the large cells were products of cell fusion, we rendered clone 8 cells red and S2-actDelta cells or S2 cells green with cytoplasmic dyes that are confined to the treated cells for several cell generations. We mixed red clone 8 cells with either green S2-actDelta cells or green S2 cells, and processed them as described above but in the dark (to prevent fluorescence quenching). Double colored large cells indicating cell fusions became apparent in cl-8/S2-actDelta cell samples at about six hours and progressively increased over time. Eighty to ninety percent of cell aggregates (of 25–100 cells each) showed one or few fused cells (it is difficult to rule out fusion in the remaining aggregates as fused cells were easily dislodged). Two examples of cell fusion after 12 hours of incubation are shown in hephaestus (dmPTB) function affects the ventral and the dorso-lateral regions of Drosophila embryos very differently. In the ventral region, development of the CNS is suppressed and there was a discernible depletion in the level of the Notch protein. Suppression of the CNS development is consistent with the known role of hephaestus as a negative regulator of canonical Notch signaling during wing and bristle development in the larval and pupal stages intra/NICD is turned over by a proteolysis process linked to the activity of the transcription factor Mastermind intra/NICD production and degradation is higher than Notch synthesis. If that were the case, it would suggest that the mechanism responsible for down regulating Notch activity targets not Nintra/NICD production or degradation but Notch synthesis. Combining the data from heph alleles and the nd1-dseN allele . The simplest explanation is that Notch function is not required in these regions and de-repression of Notch protein synthesis results in the accumulation of Notch protein in these regions . This explanation, however, does not account for Pericardin accumulation, actin accumulation, or the block in dorsal closure. Pericardin level during cardiogenesis is well established to depend on Notch activity Notch null embryos). Studies of others show that Notch activity is associated with higher actin level heph and nd1-dseN embryos.The surprising finding in our study is the different response of the dorso-lateral regions of the embryo to the loss of intra/NICD expression does not lead to actin or Pericardin accumulation in the dorso-lateral region, the simplest explanation is that Notch function in the dorso-lateral region is not completely based on Nintra/NICD activity in the nucleus. There is evidence for the existence of Notch activity independent of Nintra/NICD. For example, a Notch function independent of Presenilin  development from the dorso-lateral regions 03429heph and nd1-dseN embryos, which raises the question of why we do not see depletion of Notch and actin proteins in the dorso-lateral regions as a consequence of constitutive Nintra/NICD and canonical Notch signaling. There are two possible explanations. One, a minority of cells are involved in the PNS development. Two, the canonical Notch signaling activity might precede the non-canonical Notch signaling activity and the latter determines the ultimate phenotype. Regardless of which explanation is correct, it is remarkable that cells in the ventral and the dorso-lateral regions respond so differently to the loss of hephaestus function or to the loss of negative regulation of Notch activity due to the nd1-dseN mutation. At an earlier stage (stage 8 or 9), these cells were all the same, as they all adopt the default neuronal fate in Notch or Delta null embryos. At later stages, the ventral epidermal cells appear to diverge by blocking non-canonical Notch signaling altogether. It appears that this block is not an intrinsic lack of competence because actin and Notch enriched cells are occasionally observed in the ventral region  but we do not know anything about the process that is defective in the dorso-lateral regions. Our data contains two clues to the latter process. One clue is in 03429heph embryos probed with the actin antibody. A close examination of this image reveals that the strong actin signals in the 03429heph embryo are more or less amplified and expanded versions of the above-background actin signals in the wild type embryo. The second clue is in 03429heph and nd1-dseN embryos probed with phalloidin. It appears that these embryos form enlarged versions of the cable-like actin structures that traverse almost the entire length of the dorso-lateral regions in the body of the wild type embryo  is delayed to stage 17 (end of embryogenesis) when the mother was heterozygous for the Green TM3 balancer with the 1Ser mutant allele. If the mother was heterozygous for a null allele of Notch, 03429heph embryos hatched into larvae (data not shown). 1TM3actGFPSer homozygotes ceased development at about stage 6, were severely deformed, or died in the larval stages. Animals were arranged in a multi-well plate and imaged under brightlight and UV light with filter to detect GFP fluorescence.Manifestation of (TIF)Click here for additional data file.Figure S2intra/NICD-overexpressing embryos. A. Too many neural cells were produced in embryos lacking Notch function (55e11/YN). B. Too few neural cells were produced in embryos expressing high levels of Nintra/NICD and canonical Notch signaling. All embryos were from the same experiment and were processed identically.Expression of Hunchback in Notch null embryos and in N(TIF)Click here for additional data file.Figure S355e11N/Y embryos that lack Notch function. All embryos were from the same experiment and were processed identically.Cardiogenesis  requires Notch function. Pericardial cells were not formed in (TIF)Click here for additional data file.Figure S4wingless null embryos that are known to experience increased apoptosis do not accumulate actin in the dorso-lateral regions. cx4wg is a null allele of wingless. Both embryos were from the same experiment and were processed identically.(TIF)Click here for additional data file.Figure S503429heph embryos. This is the full DeltaVision image that was the source for The sizes of nuclei are similar inside and outside the regions of high actin accumulation in the dorso-lateral regions of (TIF)Click here for additional data file.Figure S6intra/NICD over-expression does not result in increased Pericardin level. Embryos from stage 9 to the end of embryogenesis were studied but only Stage 12 and 14 embryos are shown. Pericardin expression became apparent in wild type embryos only at stage 14. A stage 16 wild type embryo with fully formed dorsal vessel is also shown for comparison. All embryos were from the same experiment and were processed identically.N(TIF)Click here for additional data file.Figure S7A. Excess canonical signaling due to Nintra/NICD over-expression did not result in increased actin level. Nintra/NICD over-expression did not affect actin level at early stages (stage 11) although other phenotypic consequences of its expression were apparent, such as the loss of aminioserosa (AS) or the block in germ-band retraction (resulting in the U-shaped phenotype commonly observed in embryos deficient for function of genes involved in germ-band retraction). However, Nintra/NICD over-expression at later stages (stages 13–14) suppressed actin levels. B. Excess canonical Notch signaling due to expression of a hyper-active classical allele Bl(1)N also did not result in increased actin level. Embryonic stages 13–14 are shown. All embryos were from the same experiment and were processed identically.Excess canonical Notch signaling does not result in increased actin level. (TIF)Click here for additional data file.Figure S8cl-8+S2 cell mixtures do not show evidence of cell fusion. CellTracker Red labeled cl-8 cells were treated with CellTracker green labeled S2 cells. Note that cells are either red or green.(TIF)Click here for additional data file.


Biomarkers derived from gene expression profiling data may have a high false-positive rate and must be rigorously validated using independent clinical data sets, which are not always available. Although animal model systems could provide alternative data sets to formulate hypotheses and limit the number of signatures to be tested in clinical samples, the predictive power of such an approach is not yet proven. The present study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC). Tissue samples were obtained from tumor (TU), adjacent non-tumor (AN) and distant normal (DN) liver in Tet-operator regulated (TRE) human c-MET transgenic mice (n = 21) as well as from a Chinese cohort of 272 HBV- and 9 HCV-associated HCC patients. Whole genome microarray expression profiling was conducted in Affymetrix gene expression chips, and prognostic significances of gene expression signatures were evaluated across the two species. Our data revealed parallels between mouse and human liver tumors, including down-regulation of metabolic pathways and up-regulation of cell cycle processes. The mouse tumors were most similar to a subset of patient samples characterized by activation of the Wnt pathway, but distinctive in the p53 pathway signals. Of potential clinical utility, we identified a set of genes that were down regulated in both mouse tumors and human HCC having significant predictive power on overall and disease-free survival, which were highly enriched for metabolic functions. In conclusions, this study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials have been initiated. Hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide, with over 300,000 new cases per year in China and with a rising incidence in western countries Several intricate transgenic mouse models of human cancer have been suggested to accurately mimic the pathophysiology and molecular features of human malignancies Biomarkers derived from microarray expression profiling data can be subject to high false-positive rate due to multiple hypothesis testing inherent to working with large numbers of genes and gene combinations. A predictive biomarker signature or gene set determined from a given set of samples (the training set) must be validated with data from independent samples  In this study, we performed molecular profiling of normal liver and tumor tissues from the c-MET driven mouse model, to understand the molecular changes in these mice. We determined how well the model approximates human disease and confirmed the expression of specific cancer targets. We used the data derived from the c-MET model to generate signatures distinguishing tumor (TU) from adjacent non-tumor (AN) and wild-type (WT) normal tissues, and tested the prognostic power of these signatures in a data set from human HCC.The Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (HKU/HA HKW IRB) approved this study, and each patient gave his/her written informed consent on the use of the clinical specimens for research. All studies involving animals were fully approved by the Merck Boston Institutional Animal Care and Use Committee (protocol numbers: #07-08-044 and #08-08-041) and were conducted according to the institutional animal ethics guidelines.The mice used in this study have been described  [18], [2All patients that were enrolled in this study underwent a curative hepatectomy for HCC at Queen Mary Hospital, Pokfulam, Hong Kong between 1993 and 2007 Total RNA was extracted and purified from the clinical liver specimens (n = 272 HBV-HCC tumor (TU), 257 HBV-HCC tumor-adjacent normal (AN), 9 HCV-HCC tumor (TU).www.affymetrix.com/products/software/specific/gcos.affx) and were further normalized and processed to derive the sequence-based intensities using the RMA algorithm as implemented in Affymetrix Power Tools (http://www.affymetrix.com/support/developer/powertools). Data used for this analysis passed two levels of quality controls (QCs) . Log10(ratio) of each gene in each sample were computed by subtracting mean of log10(Intensity) of that gene across all adjacent non-tumor samples, to make them comparable to the c-MET mouse model data where the references are the pool of wild-type mouse liver tissues. Raw gene expression profiling data were deposited to GEO with the following accession numbers: GSE25142 (derived from the c-Met mouse model) and GSE25097 (derived from human HCC).9 HCV-HCC tumor-adjacent normal (AN)) using the SV96 Total RNA Isolation System (Promega) according to a custom automated protocol. The extracted RNA was quantified using RiboGreen RNA Quantitation Reagent (Invitrogen) and its quality was assessed using Agilent RNA 6000 Pico Kit  in an Agilent 2100 Bioanalyzer (Agilent). Only those samples passing the minimum thresholds for quantity and quality (RIN>6) were amplified and labeled using the Ovation WB protocol , according to the manufacturer's instructions. In brief, 50 ng of total RNA was amplified using the Ribo-SPIA technology (NuGEN Technologies),fragmented and labeled with biotin using the FL-Ovation cDNA Biotin Module V2 (NuGEN Technologies). The resulting amplified cRNAs were hybridized to Affymetrix gene expression chips  We used one-way ANOVA to define tumor signature in mice c-MET experiment, say, for a comparison between WT Vs. tumor, we identified 6277 mouse probesets with ANOVA P-value < 0.001. The false discovery rate (FDR) here is estimated to be 0.62% from 1000 permutations. Among those signature probesets, 3114 showed tumor down- and 3163 tumor up-regulations compared with WT. The mouse tumor signature was then mapped to human probesets in Affymetrix human chip. We also identified mouse tumor signature from comparisons of adjacent non-tumor vs. tumor and distant non-tumor vs. tumor, by ANOVA analysis.We compiled many databases include gene sets with known biological functions or properties from a variety of public , licensed , and proprietary sources . These annotated gene sets were used in the enrichment test. The enrichment P-values (the chance probability of observing overlapping genes between the input geneset and the geneset in the database) is computed using the hypergeometric distribution To estimate the prognostic power of each of mouse signatures, the signature was mapped to human probesets and then treated as a metagene To determine tumor-specific gene expression in the c-MET mouse model of HCC, we compared tissue from tumor-bearing mice to several control tissues including adjacent and distant normal liver tissue from tumor bearing mice, wild-type liver tissue and liver tissue from two single transgene parental lines . We usedWe performed unsupervised clustering and found that the expression profiles of adjacent and distant normal tissue samples were interspersed, segregated by the animal from which they were taken and distinct from the tumor profiles . This reAlthough both HCV- and HBV-infection have been shown to cause HCC, HBV infections were predominant in this cohort (272 HBV-HCC tumors and 9 HCV-HCC tumors). To identify potential molecular differences between HCV- and HBV-infected HCC, we analyzed all available HCV samples in this cohort (nine) and compared them to an equal number of randomly selected HBV samples (nine). The HBV samples were randomly distributed in an unsupervised cluster of all tumor samples (data not shown) indicating that they were not skewed toward a particular molecular profile. We did not observe any consistent molecular difference among these samples (data not shown).To determine whether parallels between human and mouse HCC exist at the gene level, we performed direct comparison of the mouse and human HCC profiles. The mouse samples were normalized against wild-type liver tissue  and the human samples were normalized against an average of all adjacent non-tumor samples. Unsupervised clustering of the human and mouse samples showed that the molecular profiles of the two tumor sets were more closely related to each other than to their cognate adjacent non-tumor tissues , red boxAmong the genes differentially expressed in the majority of mouse and human tumors, we identified three gene subsets with distinct characteristics  in human HCC p53 pathway activity in the mouse HCC model is of interest as We identified gene signatures in the mouse tumors by comparing the tumor gene expression pattern to the three non-tumor tissues indicated in For each pair-wise tissue comparison, we identified sets of genes that were down- or up-regulated in the tumor and generated heat maps using these genes and the mouse c-MET tumor and FVB-WT wild-type samples shown in Interestingly, we found that for all comparisons, the genes down-regulated in the tumors had highly predictive power for patient survival  and diseTo determine whether the predictive power of the mouse-derived signatures is specific to the c-MET model, we analyzed the predictive power of a signature derived from independent mouse models Next, we analyzed the expression pattern of the mouse gene signatures in the human samples and found that the genes identified in the mouse model showed significant expression changes in the human tumors  to define the signatures in the same or opposite direction between mouse and human. Similar to The present study shows that the mouse c-MET tumor model has similarities with human HCC at the molecular level including down-regulation of metabolic processes and up-regulation of cell cycle genes. Tumor-specific gene signatures derived in the mouse model can distinguish tumor from non-tumor tissue in human HCC. The genes down-regulated in tumor compared with adjacent non-tumor tissue in both the mouse and human samples had significant predictive power on overall survival and disease-free survival in HCC patients. These genes were highly enriched for metabolic function indicating that loss of normal liver function is related to poor outcome in HCC patients. The predictive power of the mouse-derived signatures likely stems from their tumor properties rather than c-MET-driven properties, and underscores the utility of mouse tumor models for identification of gene signatures relevant to human disease.The tumors from the c-MET model had uniform gene expression profiles as expected for tumors induced by a single oncogene in an inbred mouse strain. This is in contrast with human HCC samples, which showed significant differences in oncology pathway activity and mRNA expression Human cancers are thought to initiate from a single mutated cell in the context of a normal organ, whereas in the c-MET mouse model, all liver cells overexpress the oncogene, potentially creating a different microenvironment for the pre-neoplastic cell. The mechanism by which only certain cells within the c-MET-overexpressing liver develop into cancer is unclear at present, although the presence of a secondary mutation is necessary. We compared the gene signatures from c-MET overexpressing non-tumor tissues (adjacent or distant) to signatures from liver tissue of wild-type mice and did not find any biological annotation or predictive power (data not shown). The similar predictive power and biological annotations of the signatures regardless of the control tissue used in the comparison  suggests that, on a global level, gene expression changes in the tumor are minimally influenced by c-MET overexpression and, for the most part, reflect downstream consequences of tumor formation. In support of this hypothesis, the c-MET-regulated genes identified in primary hepatocytes from the c-MET knock-out mouse With respect to human disease, we found that the down-regulated signature in tumor vs. adjacent non-tumor tissue that is shared by mouse and human samples has very significant predictive power on overall survival and disease free survival. Further refinement of this signature may identify genes that can be tested as predictive biomarkers in the clinic. The down-regulated genes in tumor samples are highly enriched for metabolic function indicating that loss of normal liver function is related to poor outcome in HCC patients who received surgical treatment. Our recent study also identified down regulation of microRNA-122 significantly impaired liver mitochondrial metabolic functions in HCC TP53. Activation of the p53 pathway in the mouse tumors may be part of a stress response caused by overexpression of the c-MET oncogene. Inactivation of the p53 pathway in the c-MET mouse model or overexpression of c-MET in a TP53 mutant mouse may generate a mouse model that is more highly representative of human HCC.We observed the greatest difference between human HCC and the mouse c-MET model with respect to the activity of the p53 pathway. In human HCC, the p53 pathway is frequently inactivated due to inherited or sporadic mutations in Our results highlight the potential value of investing in molecular profiling of animal models of human disease. The approach described here is widely applicable to a variety of diseases for which both relevant animal models and clinical samples are readily available. Furthermore, from biomarker development point of view, any predictive or prognostic biomarkers need to go through two stages: biomarker identification (or hypothesis generation), and biomarker validation (hypothesis testing). This usually requires at least two independent datasets: training and validation set. Without appropriate validation set, due to the high dimensional nature (more than thousands of genes or signatures) of the microarray platform, signatures derived from the training set are subjective to over fitting or false positives (a property of multi-testing). Ideally this is done by two independent clinical cohorts. However, in many cases they are not readily available. This is especially true for new treatments entering the early clinical phases (Phase I & II). The HCC example described above shows it's possible to use the mouse model as the first step for signatures (and hypotheses) generation, to effectively limit the number of hypotheses to a few , to be quickly tested in the first clinical set available. By using the mouse model as training set and limiting the number of hypotheses, we can help to reduce false positives in clinical setting and speed up the biomarker development.Figure S1Mouse tumor signatures predict human patient survival. Heat maps show the expression of genes that were differentially expressed between tumor and distant normal tissue in the mouse.(TIF)Click here for additional data file.Figure S2Gene expression signatures in both mouse c-MET liver tumor and human HCC.(A) TP53 pathway signature shown in the same gene order both in mouse (upper panel) and human (lower panel) HCC. Tissue types in mouse: 0 = WT, 1 = DN, 2 = AN, 3 = TU; in human: 0 = AN; 1 = TU. (B) Wnt signaling pathway signatures shown in the same gene order both in mouse (upper panel) and human (lower panel) HCC. Tissue types in mouse: 0 = WT, 1 = DN, 2 = AN, 3 = TU; in human: 0 = AN; 1 = TU.(TIF)Click here for additional data file.Figure S3Genes derived from several mouse models of HCC have predictive power in human HCC. Log rank test p-value tested in human HCC samples for genes derived by Lee JS compared with permutation.(TIF)Click here for additional data file.Table S1Mouse tissues used to identify tumor specific signatures.(DOCX)Click here for additional data file.Table S2Top 5 GO annotation categories for differentially expressed genes in c-Met tumors.(DOCX)Click here for additional data file.Table S3Top 5 GO annotation categories for differentially expressed genes in human HCC tumors.(DOCX)Click here for additional data file.Table S4Liver-enriched genes up-regulated in tumors(DOCX)Click here for additional data file.Table S5Mouse signatures identify gene sets with predictive power for survival in human samples.(DOCX)Click here for additional data file.Table S6Mouse signatures split according to their expression in human samples have prognostic power for disease-free survival.(DOCX)Click here for additional data file.Table S7Genes down-regulated in both c-Met tumors and human HCC that have significant predictive power for survival.(DOCX)Click here for additional data file.Table S8Gene ontology of different mouse liver tumors versus human HCC gene sets.(XLSX)Click here for additional data file.


We suspected that the kit itself might have been contaminated with small traces of endogenous MLV genome or XMRV and attempted to evaluate the quality of the kit in two independent laboratories. We purchased four one-step RT-PCR kits from Invitrogen, TaKaRa, Promega and QIAGEN in Japan. To amplify the partial gag gene of XMRV or other MLV-related viruses, primer sets  which have been widely used in XMRV studies were employed. The nucleotide sequences of the amplicons were determined and compared with deposited sequences of a polytropic endogenous MLV (PmERV), XMRV and endogenous MLV-related viruses derived from CFS patients. We found that the enzyme mixtures of the one-step RT-PCR kit from Invitrogen were contaminated with RNA derived from PmERV. The nucleotide sequence of a partial gag region of the contaminant amplified by RT-PCR was nearly identical (99.4% identity) to a PmERV on chromosome 7 and highly similar (96.9 to 97.6%) to recently identified MLV-like viruses derived from CFS patients. We also determined the nucleotide sequence of a partial env region of the contaminant and found that it was almost identical (99.6%) to the PmERV. In the investigation of XMRV infection in patients of CFS and prostate cancer, researchers should prudently evaluate the test kits for the presence of endogenous MLV as well as XMRV genomes prior to PCR and RT-PCR tests.During pilot studies to investigate the presence of viral RNA of xenotropic murine leukemia virus (MLV)-related virus (XMRV) infection in sera from chronic fatigue syndrome (CFS) patients in Japan, a positive band was frequently detected at the expected product size in negative control samples when detecting a partial  Xenotropic murine leukemia virus (MLV)-related virus (XMRV), which resembles endogenous MLV, was discovered in prostate cancer patients in 2006 ,2. In 20gag region of XMRV. We suspected that the test kit itself might have been contaminated with small traces of endogenous MLV genome or XMRV and attempted to evaluate the quality of the kit in two independent laboratories, in JRC and Institute for Virus Research (IVR), Kyoto University .In studies investigating XMRV infection, a PCR approach to detect proviral DNA and/or a RT-PCR approach to detect viral RNA have been commonly employed ,8,13-15.®III One-Step RT-PCR System with the Platinum® Taq High Fidelity Kit (Cat. no. 12574-030)  (abbreviated as Kit I); AccessQuick™RT-PCR Sysytem (Cat. no. A1701)  (abbreviated as Kit P); One Step RT-PCR Kit (Cat. no. PRO24A)  (Abbreviated as Kit T); One Step RT-PCR Kit (Cat. no. 210210)  (Abbreviated as Kit Q).We used the following RT-PCR kits which were purchased in Japan: SuperScriptgag gene of XMRV or other MLV-related viruses, primers 419F (5'-ATCAGTTAACCTACCCGAGTCGGAC-3') and 1154R (5'-GCCGCCTCTTCTTCATTGTTCTC-3') . To enhance one-step RT-PCR reactions, 2.5 μl of 1 μg/μl carrier RNA from QIAamp UltraSens™ Virus Kit (Cat. no. 53704) (QIAGEN) was added to the reaction mixtures of the one-step RT-PCR reactions as indicated in Figure To amplify the partial By adding carrier RNA in the samples to enhance the RT-PCR reaction, we consistently detected a positive band using Kit I in negative controls using two primer sets  which are widely used to amplify XMRV Figure . These rTaq DNA polymerase) and the buffer contained in the kit were tested by adding the individual components to three different one-step RT-PCR kits   Figure . As a regag leader region and are nearly identical to polytropic endogenous MLVs encoded in multiple chromosomal locations of the C57BL/6J mouse genome. The nucleotide sequences of the representative clone [GenBank: AB597300] were aligned with sequences deposited in GenBank as follows: MLV-like virus from CFS patients types 1, 2 and 3  [NC_007815]   and sequenced for both strands. Three clones (two clones at JRC and one clone at IVR) were sequenced and found to be nearly identical (one nucleotide difference between one another). These sequences have a 9 nucleotide deletion observed in some endogenous polytropic MLVs in place of the XMRV-specific 24 nucleotide deletion in the 5' M630559] , XMRV stenv regions with two primer sets  based on the sequence of the PmERV chr 7, and then sequenced the amplicons directly. We determined 674 bp of the N-terminal env region [GenBank: AB597301] and found that the contaminant was nearly identical (99.6% identity) to the PmERV chr 7   in Kit I can be purchased separately from the manufacturer, we attempted to detect the MLV genome in the Platinum Taq polymerase using the same protocol as the one performed in Figure It should be noted that Kit I contains an anti-DNA polymerase monoclonal antibody to accomplish hot start-PCR and to reduce non-specific amplification. Mice have enormous copy numbers of endogenous retroviruses in their genomes; and hybridomas, for manufacturing monoclonal antibodies, have been found to produce high amounts of retroviral particles . Therefoy others ,18. BecaTaq DNA polymerase for the contamination of MLV-related genomes and found that the test kit and the Taq DNA polymerase from Invitrogen were contaminated with MLV-related genomes.Surveys have been conducted by several research groups on XMRV infection in CFS patients, but the results have been inconsistent. Although all research groups carefully performed their experiments to test XMRV infection by PCR and/or RT-PCR, it is still difficult to conclude that the positive results linking XMRV with CFS are not laboratory artifacts. Xenotropic (or polytropic) MLVs are widespread, and there may be many opportunities for samples to get contaminated with such ubiquitous viruses in laboratories when conducting biological or medical research . In thisTaq polymerases from several suppliers and was used to test the patient samples [The findings in the present study indicate that contaminating nucleic acids in the test kits can potentially produce false-positive PCR results in studies of XMRV and other MLV-related viruses. In particular, our results raise the possibility that the PCR products described by Lo et al.  were der samples . They al samples . As poin samples  respondi samples , assuranThe requirement for quality control to avoid contamination of endogenous retroviral genomes in test kits may differ depending on the intended purpose. However, in XMRV studies, many researchers conduct ultra-sensitive PCR or RT-PCR to detect extremely small amounts of XMRV. Therefore, in the investigation of XMRV infection, researchers should prudently evaluate test kits for the presence of genomes of endogenous MLV as well as XMRV.The authors declare that they have no competing interests.RAF, ES, and TM designed the experiments. RAF and ES performed the experiments. TM wrote the manuscript. All authors read and approved the final manuscript.