Saccharomyces cerevisiae as arguably the most well-studied model eukaryote, we still do not have a full catalog or understanding of all its genes. In order to interrogate the transcriptome of S. cerevisiae for low abundance or rapidly turned over transcripts, we deleted elements of the RNA degradation machinery with the goal of preferentially increasing the relative abundance of such transcripts. We then used high-resolution tiling microarrays and ultra high–throughput sequencing (UHTS) to identify, map, and validate unannotated transcripts that are more abundant in the RNA degradation mutants relative to wild-type cells. We identified 365 currently unannotated transcripts, the majority presumably representing low abundance or short-lived RNAs, of which 185 are previously unknown and unique to this study. It is likely that many of these are cryptic unstable transcripts (CUTs), which are rapidly degraded and whose function(s) within the cell are still unclear, while others may be novel functional transcripts. Of the 185 transcripts we identified as novel to our study, greater than 80 percent come from regions of the genome that have lower conservation scores amongst closely related yeast species than 85 percent of the verified ORFs in S. cerevisiae. Such regions of the genome have typically been less well-studied, and by definition transcripts from these regions will distinguish S. cerevisiae from these closely related species.A complete description of the transcriptome of an organism is crucial for a comprehensive understanding of how it functions and how its transcriptional networks are controlled, and may provide insights into the organism's evolution. Despite the status of  Saccharomyces cerevisiae, because of the relative ease of its genetic manipulation and its ease of handling in the laboratory, has long served as a model on which studies in higher organisms have been based. To more fully understand how eukaryotic cells express their genomes, we sought to identify RNA species that are transcribed at very low levels or that are rapidly degraded. We created mutants deficient in the ability to degrade RNA, with the expectation that this would increase the relative abundance of such RNAs, and then used high-resolution microarrays and sequencing technologies to locate and identify from where these RNAs are transcribed. Using this approach, we have identified 365 transcripts that do not appear in the most current list of annotated S. cerevisiae RNA transcripts; of these, 185 are unique to our study. Many of these novel transcripts derive from regions of the genome that are poorly conserved between S. cerevisiae and other closely related yeast species, suggesting that these RNAs may play an important role in the divergent microevolution of S. cerevisiae.The budding yeast  Saccharomyces cerevisiae became the first eukaryote to have its genome fully sequenced Twelve years ago, in a landmark study resulting from the collaborative work of hundreds of scientists around the world, the budding yeast Saccharomyces Genome Database (SGD) S. cerevisiae, it is still not straightforward to identify all of the genes simply based on the DNA sequence.Early analyses of the predicted protein-coding genes showed that about 35% had no known function or homolog ArabidopsisHybridization of RNA to tiling microarrays  has been used to generate genome-wide transcript profiles and to detect previously unannotated transcripts. While this technique has its own caveats, it overcomes the limitations of many previous attempts to find undiscovered transcripts, by providing direct experimental support with high-resolution data. Tiling array studies have revealed more than 5,000 novel transcripts in S. cerevisiae, performed large-scale sequencing of vector-capped cDNA clones Recently, Miura et al. S. cerevisiae growing in rich medium S. pombe growing under several different conditions, including a meiotic time course S. cerevisiae, and 453 novel transcripts in S. pombe; additionally, many transcript boundaries were refined, and novel introns identified. The functions of these novel transcripts remain unknown, with few expected to be protein-coding Recent advances in sequencing technology S. cerevisiae, there are two major pathways that play a role in the decay of mRNAs in the cytoplasm, both of which involve deadenylation , with the hypothesis that the most unstable and/or least abundant transcripts would show the greatest relative change in abundance in such mutants. The mutant-derived RNA was compared to RNA from wild-type cells, using Affymetrix strand-specific tiling microarrays. Novel strand-specific transcripts were identified by segmentation of the relative expression measures from the tiling arrays and subsequently validated using Illumina's Solexa sequencing platform. Using a combined tiling array and RNA-Seq approach, we have identified a total of 365 transcripts that are currently unannotated in SGD. Comparison of our data to various recently published transcriptome studies To identify additional novel transcripts in the yeast Our primary goal was the discovery of novel transcripts based on comparing RNA from mutants deficient in RNA degradation pathways to RNA from a wild-type strain. We wanted to provide, in a high-throughput fashion, distinct and complementary lines of evidence for the existence of each putative transcript. We thus selected two technologies as being appropriate for this aim: tiling arrays and high-throughput sequencing. We used the tiling arrays to discover novel transcribed segments, with their strand of origin information. This approach has been used successfully in previous studies Tiling microarray analysis of mRNA from yeast grown under a diverse set of several different conditions suggested that the greatest fraction of known transcripts are detectable in the presence of high salt (0.8 M NaCl) (our unpublished results); we thus chose high salt as the growth condition used in the experiments described herein. All our deletion strains (the ‘mutant’ strains) and the wild-type strain see .Only perfect match (PM) probes mapping uniquely to the genome were used in the analysis; mismatch probes were discarded. In order to correct for probe-specific effects and to detect only those transcripts that were differentially expressed between a mutant and the wild-type, we used as expression measures the log ratio of mutant PM intensities to wild-type PM intensities. We segmented the log ratios using a piecewise constant change point model as implemented in the ‘segment’ function in the R package ‘tilingArray’ 2 scale, (3) remove segments overlapping known annotation on the same strand, (4) remove segments containing fewer than 5 probes, and (5) remove segments opposite known annotation if they had a log2 fold change less than 2, or there was detectable transcription on the opposite strand  join adjacent segments with similar expression measures, (2) drop segments that are not differentially expressed, using a threshold of <0.5 on the logrrp6Δlsm1Δpat1 mutant library generated 14,745,813 reads, the Δski2Δski8Δrrp6 mutant library 14,973,577 reads, and the Δxrn1Δrrp6Δlsm1Δpat1 mutant library 10,714,094 reads. Following an assessment of the inter-lane variation we combined data across lanes for each strain , which is known to be induced under conditions of osmotic stress. Its average number of reads per unique nucleotide was ∼400 in every case. The least abundant transcript was different in each mutant, but with an average number of reads per base of less than 1. Thus, transcript abundances of the Verified ORFs (as measured by sequencing) span at least 3 orders of magnitude  that were detectable above background in the sequence data, the most abundantly expressed transcript in every mutant, and the wild-type, in terms of number of mapped reads per unique base was that of RPL28) we saw 1399 reads that mapped to the exon-exon junction in the data from the Δrrp6Δlsm1Δpat1 mutant. Of those forty genes whose exon-exon junctions we failed to detect, two were in mitochondrial genes, and 16 were in Dubious or Uncharacterized ORFs. Of the remaining 22, six of the genes are expressed in meiosis, and fourteen have an initial exon of only a few residues. These were less likely to have been detected by our strategy, as we looked for reads that matched the ORF sequence and not the genome, which would have had to start at a few specific residues to be detected. Subsequent analysis, by inclusion of 5′ UTR sequence to capture such exon boundary spanning reads, was able to identify these remaining introns. Thus, only two Verified ORFs, YER014C-A/BUD25 and YPL075W/GCR1, which were not meiosis specific, failed to have reads detected that spanned their exon junctions. BUD25 is opposite two other Verified ORFs in the genome, while Nagalakshmi et al GCR1. Indeed, we were able to identify reads that spanned the 5′ exon-intron junction, and the 3′ intron-exon junction, suggesting that the intron is misannotated.As another measurement of the validity of the sequenced libraries, we determined how many known introns we were able to detect by looking for reads that spanned exon-exon junctions. To detect these intron spanning reads, we identified those reads that mapped to the set of spliced genic ORFs but did not map to the unspliced genome. The wild-type and mutant libraries each generated sequence reads that map to exon-exon junctions, which, when combined, confirm splice junctions in 244 (86%) of the 284 known spliced ORFs reported in the current SGD annotation. In the most extreme case  the transcript detectable above background level, (B) the transcript differentially expressed between the mutant and the wild-type, and (C) the transcript differentially expressed when compared to its surrounding regions  from GenBank. The shortest novel transcript identified was 47 nucleotides long (intergenic), while the longest was 1,869 nucleotides in length , though the longest ORF that it contains only has the potential to encode a peptide 80 amino acids in length. The longest ORF that we discovered within all of our novel transcripts was within an ∼438 bp transcript on the Watson strand of chromosome 7 , with the potential to encode an 87 amino acid polypeptide. However, this potential peptide showed no significant similarity when BLASTed against the GenBank non-redundant protein dataset. The remaining longest ORFs within each novel transcript were all shorter, with no significant similarities to any known proteins. It is not clear whether this means they do not encode proteins, or whether they encode novel, short proteins, which are currently uncharacterized due to their low conservation. We also analyzed each of our novel transcripts for any matches to known RNA structures present in the RFAM database rrp6Δlsm1Δpat1 data, we detected 65.3% of the 176 Davis and Ares transcripts  has resulted in an ever more detailed knowledge of the yeast transcriptome.In our approach, we utilized a high-throughput discovery and validation pipeline. Clearly, much work needs to be done to characterize and understand the transcripts discovered here as well as those discovered in previous studies, however a first step in characterizing the transcripts is localization and then validation. In our computational analysis we employed a strategy of being lenient in identification of putative novel transcripts . This was followed by a strict validation step  with the current protocols for RNA-Seq using the Solexa 1G Genome Analyzer. It is likely that modified protocols will soon address this shortcoming, and indeed such protocols for the ABI SOLiD sequencing system have been recently published We can now ask the important and obvious question: has the yeast transcriptome been completely described, and what does completion mean? It is possible that if we sequence deeply enough, we may observe that every nucleotide within the genome is transcribed at some level see , though S. cerevisiae to determine if these transcripts are ubiquitous within the species, and to determine whether the syntenic (but non-conserved) regions within closely related species within the Saccharomyces sensu stricto are also transcribed. With such data, we can hope to discover and hopefully appreciate not only how each of these species are related to one another, but also how their transcriptional potential and networks have diverged.Since many of the recently discovered transcripts (including those in this study) have been found in regions of the genome where there is little or no sequence conservation  underscores how much work remains to be done in understanding and cataloging the transcriptomes of even the most well-studied model organisms.Since the landmark publication of the Saccharomyces cerevisiae strain which was created by crossing strains FY23 and FY86 All deletions were created in a diploid PCR was performed  was added in an equal volume of YPD prewarmed to 30°C . Cells were harvested after 30 minutes by filtration, frozen in liquid nitrogen, and kept at −80°C until RNA extraction and purification.Our unpublished studies suggested that among two dozen or so different conditions that we have assayed, exposure to high salt (0.8 M NaCl) results in the expression of the greatest fraction of known and novel transcripts, and thus was chosen as the experimental condition to use to find previously unannotated and low abundance transcripts. Cells were grown at 30°C in YPD to approximately 1×10RNA was extracted from the cells using a slightly modified version of the traditional hot phenol protocol S. cerevisiae Tiling 1.0R Array (Reverse) (part number: 900645) for Watson strand expression or GeneChip S. cerevisiae Tiling 1.0F Array (Forward) (part number: 520286) for Crick strand expression.All microarray analyses were carried out using Affymetrix GeneChip The arrays each contain more than 2.5 million perfect match probes, which are offset from one another by 4 bases across the genome (21 bp overlap). Thus, each residue in the genome is interrogated on average by 6 oligonucleotide probes.Total RNA samples were prepared following the protocol exactly as described in David et al. 260/280. 2 µg of the PolyA purified RNA were then used in the generation of cDNA as per Affymetrix First Strand and Second Strand Synthesis protocols utilizing a T7-Oligo(dT) as the primer for the First Strand, followed by in vitro transcription to generate biotin labeled cRNA, as outlined by Affymetrix protocols. The cRNA was fragmented as described by Affymetrix, and then sent for hybridization and scanning by the PAN facility at Stanford (http://cmgm.stanford.edu/pan/) according to standard Affymetrix protocols.PolyA RNA samples were prepared as follows. 500 µg of total RNA were PolyA purified using Qiagen Oligotex suspension to produce approximately 10 µg of PolyA RNA as determined by ODOur goal was to identify short-lived transcripts based on measured intensities of probes tiling the genome. It is well known that probe affinities significantly bias the relationship between measured intensity and actual transcript abundance. In David et al. this was addressed by effectively forming log ratios between wild-type and genomic DNA hybridization. In order to highlight the changes between mutants and wild-type transcription and to reduce the effect of probe affinities we formed log ratios between mutant and wild-type intensities. This approach has the same effect on probe affinities as the approach used by David et al., see Saccharomyces Genome Database on May 19th 2008, using MUMmer The probes on the tiling array were mapped to the yeast genome, as downloaded from the The resulting data were segmented using the ‘segment’ function in the R package ‘tilingArray’ a priori uninteresting clusters. The pipeline for constructing clusters from segments and producing a set of putative clusters to be validated using the sequencing data worked as follows:Following the segmentation we were left with a set of segments for each of the three mutants and two RNA sample types . Our analyses indicated that transcripts are often split into a number of segments due to various artifacts of the array data . At this stage, we wished to both join appropriate segments into adjacent co-expressed segments (clusters) as well as filter out 2(mutant/wild-type) for either the segment or the entire microarray.Label each segment as upregulated (med(seg) − med(microarray) ≥.5), downregulated (med(seg) − med(microarray) ≤−.5), or baseline ( −.5<med(seg) − med(microarray) <.5). Here med(.) is the median of logDrop any baseline segment containing 5 probes or less. This step attempts to avoid the creation of separate segments due to non-responsive probes.Join adjacent segments if they have the same regulation label , unless the following criteria hold: the absolute difference in medians between the two segments exceeds 1 and the lengths of the two segments are greater than 30 probes and span more than 150 bp. Uneven spacing in the tiling array probes occurs due to repeat regions often leaving an area of the chromosome tiled at a lower density. In order to keep the cutoffs consistent through these areas we employ the strategy of enforcing a minimum length at the base and probe level. These joined segments were then referred to as clusters.Drop all baseline clusters as well as any cluster with fewer than 5 probes or a length less than 40 bp.Remove any cluster that overlaps any known transcribed annotation on the same strand. We extend each annotated element by 100 bp on both the 5′ and 3′ end to account for UTRs.2 fold change of at least 2 as well as a log2 fold change of less than 1 on the strand opposite the cluster.For any cluster that overlaps annotation on the opposite strand we further required a logThis process resulted in a set of putative clusters that were subsequently considered for validation by Solexa sequencing.In order to generate libraries for the Solexa platform, various reagents and kits were required. At the time that these experiments were performed, Illumina did not have an RNA-Seq specific kit, and thus parts of various kits were utilized. Note that not all of the reagents from the kits provided by Illumina were used, as these kits were adapted for use in the protocol below and not necessarily used as described in the instructions that came with the kit. They are as follows:For protocols desiring PolyA purified RNAs:NlaIII Sample Prep Kit (part number 1002390)Illumina Digital Gene Expression-Tag Profiling for Illumina Genomic DNA Sample Prep Kit (part number 1000181)Invitrogen SuperScript III Reverse Transcriptase (part number 18080-044)Invitrogen Random (N6) Primers (part number 48190-011)Qiagen MinElute Reaction Cleanup Kit (part number 28204)Qiagen QIAquick PCR Purification Kit (part number 28104)Zymo Research Zymoclean Gel DNA Recovery Kit (part number D4001)Amersham Biosciences MicroSpin G-50 Columns (part number 27-5330-01)Millipore Microcon Ultracel YM-30 Centrifugal Filter Devices (part number 42410)Also required was a magnetic stand that can accommodate 1.5 ml microcentrifuge tubes. The protocol as described below was done using DNase/RNase certified free siliconized 1.5 ml microcentrifuge tubes.Strains used for our UHTS experiments are GSY147 and GSY1289 see . GSY147 Two consecutive purifications using oligo dT conjugated magnetic beads were performed as follows. 100 µg of Total RNA were diluted in a final volume of 100 µl water and heated at 65°C for two minutes and then placed on ice. 200 µl of beads were equilibrated by two consecutive 100 µl washes in binding buffer (mixed gently by hand), using a magnetic stand to separate the beads from the buffer. The beads were then resuspended in 100 µl of binding buffer. The RNA was added to the beads, and the tube was mixed gently by hand for 5 minutes at room temperature and then placed on the magnetic stand to separate the beads from the supernatant. The supernatant was discarded, and the beads underwent two consecutive washes with 200 µl washing buffer. The beads were resuspended in 10 mM Tris-HCl pH 7.5, and the tube was heated at 80°C for two minutes and then immediately placed on the magnetic stand where the supernatant was transferred to a new tube. The beads were saved and prepared for the second round of PolyA purification by washing them once with 200 µl washing buffer. The entire process was then repeated once for a second round of purification, beginning with the dilution of the RNA and the denaturing of the RNA secondary structure.PolyA purified treated RNA samples were then fragmented to ensure an unbiased binding of the random hexamers during cDNA synthesis. 5× Fragmentation Buffer  was made, of which 5 µl was added to the RNA sample, and the total reaction was brought up to 25 µl. The sample was heated at 94°C for 2.5 minutes and immediately placed on ice. The sample was then run through a G-50 spin column that has been equilibrated with 3×400 µl of nuclease free water to remove ions from the fragmentation. The sample was concentrated to 10.5 µl with a Micron filter.First Strand Synthesis:st strand buffer, 2 µl 100 mM DTT, 1 µl 10 mM dNTP, and 0.5 µl RNaseOUT (40 U/µl). The tube was mixed and left at room temperature for 2 minutes. 1 µl SuperScript III (200 U/µl) was added, and the sample was placed in a thermocycler with the following program: 25°C for 10 minutes, 42°C for 50 minutes, 70°C for 15 minutes, 4°C hold.10.5 µl of fragmented RNAs were transferred to a PCR tube and 1 µl of random hexamer (3 µg/µl) was added. The tube was heated to 65°C for 5 minutes and then placed on ice. The following reagents from the Illumina kit were then added: 4 µl 5×1Second Strand Synthesis:nd strand buffer, 3 µl 10 mM dNTPs, 1 µl RNase H (2 U/µl), and 5 µl DNA Pol I (10 U/µl). The sample was vortexed and placed in an Eppendorf Thermomixer R (set at 16°C and programmed to spin at 1400 rpm for 15 seconds and stand for 2 minutes) overnight (minimum 2.5 hours).The first strand synthesis reaction was transferred to a 1.5 ml siliconized microcentrifuge tube and placed on ice. 61 µl nuclease free water was added to the sample, along with the following reagents from the Illumina kit: 10 µl 2The newly synthesized cDNA was purified with a QIAquick PCR spin column as per Qiagen protocols and eluted in 30 µl EB solution.The following reagents from the Illumina kit were added to the 30 µl sample as follows: 45 µl nuclease free water, 10 µl T4 DNA ligase buffer with 10 mM ATP, 4 µl 10 mM dNTPs, 5 µl T4 DNA polymerase (3 U/µl), 1 µl Klenow DNA polymerase (5 U/µl), 5 µl T4 PNK (10 U/µl). The sample was vortexed and incubated at 20°C for 30 minutes. Afterwards, the sample was purified with a QIAquick PCR spin column as per Qiagen protocols and eluted in 32 µl EB solution.The following reagents from the Illumina kit were added to the 32 µl sample as follows: 5 µl Klenow buffer, 10 µl 1 mM dATP, and 1 µl Klenow 3′ to 5′ exonuclease (5 U/µl). The sample was vortexed and incubated at 37°C for 30 minutes. Afterwards, the sample was purified with a MinElute spin column as per Qiagen protocols and eluted in 10 µl EB solution.The following reagents from the Illumina kit were added to the 10 µl sample as follows: 25 µl DNA ligase buffer, 2 µl adaptor oligo mix, and 5 µl DNA ligase (1 U/µl). The sample was vortexed and incubated at 25°C for 15 minutes. Afterwards, the sample was purified with a MinElute spin column as per Qiagen protocols and eluted in 10 µl EB solution.The 10 µl sample was loaded onto a 1% TAE agarose gel at least one lane away from a 100 bp ladder. The sample was run sufficiently far enough and a gel slice corresponding to approximately 200 bp+/−50 bp was excised out of the gel with a scalpel (note that no cDNA may be visible on the gel). The cDNA was purified using a Zymo Research Zymoclean Gel DNA Recovery Kit and eluted in 10 µl nuclease free water.The 10 µl sample was transferred to a PCR tube. The following reagents from the Illumina kit were added to the 10 µl sample as follows: 27 µl nuclease free water, 10 µl 5× cloned Phu buffer, 1 µl oligo 1.1, 1 µl oligo 2.1, 0.5 µl 25 mM dNTPs, 0.5 µl Phu polymerase. The sample was then run on a thermocycler using the following program: 98°C hold for 30 seconds, 98°C for 10 seconds, 65°C for 30 seconds, 72°C for 30 seconds, 72°C hold for 5 minutes, 4°C hold, for 50 cycles. The sample was purified with a QIAquick PCR spin column as per Qiagen protocols and eluted in 30 µl EB solution. The sample was then run through a G-50 spin column that had been equilibrated with 3×400 µl of nuclease free water to remove any remaining unincorporated nucleotides that would interfere with the concentration determination of the library. The DNA was concentrated through the use of a Speed Vac until the final volume of the library was 10 µl. The cDNA was quantified using a Nanodrop. A concentration range between 10–100 ng/ml final concentration of an RNAseq library is required for good quality sequencing. The sample was then sent for sequencing in the Genetics Department Solexa machine at Stanford.Saccharomyces Genome Database (SGD) th, 2008, using ELAND, which is part of the Solexa analysis pipeline Sequence reads that passed Solexa's quality filters were aligned to both the yeast genome and the spliced yeast ORF set , downloaded from the We examined the goodness of fit for a simple Poisson model described below, using the chi-square goodness of fit statistic see . QQ-plotIn order to validate each putative transcript identified by tiling array data analysis, we investigated the following three criteria:the transcript is expressed above a suitably defined background level;the transcript is differentially expressed in the mutant as compared to the wild-type;the transcript is differentially expressed as compared to the surrounding region;An important consideration in all subsequent analyses was that certain areas of the genome are unmappable due to repeated sequences. We defined a base as non-unique if the 25mer starting at that position occurs elsewhere in the genome. We excluded all such bases from consideration in subsequent analyses.First, we determined whether the transcript was above background level. Background regions were defined in the following fashion:All regions that are intergenic on both strands were obtained.Any region which overlaps the segments reported by Nagalakshmi et al., David et al., Miura et al., or Davis and Ares was removed  indexed by j = 1,…,J, and lane indexed by i = 1,…,I. Let jK denote the length of ROI j and let a(i) denote the type of sample assayed in lane i, i.e., a(i)∈{wt, mt}, where the short-hand notation wt and mt refers to the wild-type and mutant yeast strains, respectively. As a first-pass modeling attempt, suppose the counts iXj, have a Poisson distribution with mean λai),j,j( is the parameter of interest representing the expression level of ROI j in samples of type a(i)∈{wt, mt} and βi is a lane effect. The maximum likelihood estimator (MLE) of the parameter λa,j,, subject to the identifiability constraints Σjλaj, = 1 for each a, isaj, is the proportion of the total read counts for type a samples that fall in ROI j.In order to construct statistics for differential expression, we considered the following model. Let j, a natural measure of differential expression between mutant and wild-type strains is the log-ratio log. Using the delta method, it can be argued that the estimator mtj,/λwtj,) and estimated varianceFor a given ROI mtj, = λwtj,.Thus, one can identify differentially expressed ROI between the mutant and wild-type strains based on the following test statistics:j and j′ for a given strain a∈{wt, mt}. In this case, a natural measure of differential expression is the log-ratio log/), which adjusts for differences in ROI length. Another application of the delta method suggests the following test statistic for determining whether ROI j and j′ are differentially expressed within strain a,aj,/jK = λaj′, /jK′.Another question of interest is the comparison of expression levels between two ROI We applied the detected above background statistic described above with a cutoff of .8. Results are available in All raw data have been deposited in the GEO database with accession number GSE11802.Figure S1Microarray data for A. Pre-Normalization and B. Post-Normalization stretches of Chromosome 4. The plots indicate that by forming the log ratio between the mutant and wild-type samples, we highlight differences between the two samples. At approximately base 248,000, we can see an unannotated upregulation in the mutant versus the wild-type. This region stands out much more prominently in the Post-Normalization plots, which was the intention of using the wild-type data.(0.50 MB PDF)Click here for additional data file.Figure S2Here we plot a goodness of fit statistic computed under the model described in the text. We compute an expected number of counts for each gene and compare this to the observed number of counts. This gives us a chi-squared statistic for each gene. If the gene counts are distributed as Y_j,i ∼ Poisson(\lambda_j\beta_i), then the test statistic will have a null distribution of Chisquare with lanes-1 degree of freedom. The plots demonstrate a very strong correspondence between our model and the observations.(1.48 MB PDF)Click here for additional data file.Figure S3Coverage plots as described in (0.05 MB PDF)Click here for additional data file.Figure S4Coverage plots as described in (0.05 MB PDF)Click here for additional data file.Figure S5Unannotated non-intergenic transcripts found in this study. Each page shows one transcript, with the following information tracks from top to bottom: SGD annotation on the Watson and Crick strands), our tiling microarray data from the Crick and Watson strands , our UHTS data for the mutant and wild-type strains, tiling microarray data from David et al. for the Crick and Watson strands, UHTS data from Nagalakshmi et al., nucleosome position, data from Miura et al., and degree of conservation. The name and chromosome of origin of each transcript are indicated below each panel. For the UHTS data, each point plotted corresponds to the 5′ end of sequence reads, and the y position of the plotted point above the axis indicates  how many reads mapped to that position. Horizontal lines in a track indicate novel segments found in the corresponding study (black for forward strand and blue for reverse strand).(6.83 MB PDF)Click here for additional data file.Figure S6As in (13.33 MB PDF)Click here for additional data file.Figure S7Density plot of conservation scores for different categories of segment.(0.04 MB PDF)Click here for additional data file.Table S1Table of validated transcripts. Here there are 566 rows, each corresponding to an individual cluster. The column metaName groups the clusters together into transcripts, so that there are 365 unique different metaNames.(0.13 MB TXT)Click here for additional data file.Table S2This table shows which previously reported unannotated transcripts were above background level in this study .(3.90 MB TXT)Click here for additional data file.Table S3Table of RPKMs for our different datasets for SGD annotated features.(0.66 MB TXT)Click here for additional data file.Text S1Supplementary File Descriptions.(0.09 MB DOC)Click here for additional data file.


MFN1 , approximately 156 kb upstream from alternate-splicing SOX2OT  and PSARL , with the loci showing modest to strong evidence of association for the Map 2 discovery samples . Using an unselected independent sample of 1,430 individuals, results replicated for the MFN1 (p = 0.006), SOX2OT (p = 0.0002), and PSARL (p = 0.0005) gene regions. MFN1 and PSARL both interact with OPA1 to regulate mitochondrial fusion and the inhibition of mitochondrial-led apoptosis, respectively. That two mitochondrial regulatory processes in the retina are implicated in the aetiology of myopia is surprising and is likely to provide novel insight into the molecular genetic basis of common myopia.Refractive error is a highly heritable quantitative trait responsible for considerable morbidity. Following an initial genome-wide linkage study using microsatellite markers, we confirmed evidence for linkage to chromosome 3q26 and then conducted fine-scale association mapping using high-resolution linkage disequilibrium unit (LDU) maps. We used a preliminary discovery marker set across the 30-Mb region with an average SNP density of 1 SNP/15 kb (Map 1). Map 1 was divided into 51 LDU windows and additional SNPs were genotyped for six regions (Map 2) that showed preliminary evidence of multi-marker association using composite likelihood. A total of 575 cases and controls selected from the tails of the trait distribution were genotyped for the discovery sample. Malecot model estimates indicate three loci with putative common functional variants centred on  OPA1 mutations causing the unrelated Mendelian disease Autosomal Dominant Optic Atrophy (ADOA) by triggering mitochondrial-led retinal ganglia cell apoptosis.Successful gene mapping strategies for common disease continue to require careful consideration of basic study design with the advent of genome-wide association studies. Here, we take advantage of prior information that the heritability of the quantitative trait myopia in the general population is high and shows evidence of replicated linkage to chromosome 3q26. Based on this, we conducted a fine map linkage disequilibrium association study for the region, using a high-resolution genetic map derived from population-based HapMap Phase II data. For analysis, we used efficient multi-locus tests of association using single nucleotide polymorphism markers genotyped for our sample data and placed on the genetic map measured in linkage disequilibrium units. We followed up preliminary evidence of association for the discovery samples with further genotyping in the same samples to improve the model location estimates for the common functional variants we identified. Three locations were replicated using an independent sample. Two of the identified genes are likely to play an unexpected role in myopia with both pivotal in the healthy housekeeping metabolism of retinal mitochondria. Both proteins interact with OPA1, with nonsynonymous  Myopia is the most common eye disorder, affecting an estimated 36% of adults over 20 years in the United States Epidemiological studies have identified close visual work  to be a significant risk factor for myopia development in children and that outdoor activity appears to be protective The aims of this study were twofold. The first was to replicate the linkage signals at these four loci using an independent sample of DZ twins to the original study, using measures of refractive error (optician prescription) obtained via a postal questionnaire. The second aim was to conduct a follow-up association study of the genomic region with strongest evidence of replicated linkage, using linkage disequilibrium mapping to identify possible susceptibility genes and to replicate results using an independent sample.Refraction data, either from autorefractor or postal prescription, were available for 4273 UK twin subjects (1716 complete pairs) with SE data. Overall, the SE mean (between sib-pair standard deviation) was −0.29D (2.36), range −20D to +8.75D with an inter-quartile range of −1.06D to +1.125D and 26% of the subjects were myopic using a threshold of SE< = −1D. A total 1846 autorefractor measures (915 complete pairs) and 997 postal prescriptions (485 pairs) were available for the discovery phase of this study and an independent sample of 1430 twins for replication . The meaFor this study, we attempted to replicate linkage to four loci previously reported by our group Marginal evidence for replicated linkage using the original Généthon map and microsatellite data for independent samples was also observed for MYP7 (11p13) and MYP9 (4q26), but not for MYP10 (8p23). These loci are currently subject to further investigation.Autorefractor rather than postal prescription data were used for discovery stage association mapping, since autorefractor data are observed to be more precise with a smaller standard deviation . Subjects measured using an autorefractor were measured in the same standardized manner with no transcription errors that tend to be associated with postal prescription data.For the initial fine mapping study of the 3q26 region (Map 1), a total of 243 cases and 257 controls selected from myopic and hyperopic concordant sib-pairs, respectively, were defined from the lower and upper quartiles of the SE quantitative trait using 915 twin pairs with complete autorefractor data see . SeventyThe Map 2 data contained 154 new samples and approximately 70% of the samples from Map 1, yielding a total of 443 case-controls. Hence a total of 575 cases and controls were genotyped for either the Map 1 (n = 421) or Map 2 (n = 443) discovery samples , with 28For the Map 1 samples, we attempted to genotype a total of 2304 SNPs. After removing non-polymorphic SNPs (384 SNPs), SNPs with a call rate ≤90% (84), evidence of Hardy-Weinberg disequilibrium (38) and MAF< = 1% (0), a total of 1800 out of 1920 polymorphic SNPs remained for Map 1 analysis.For the second stage of the association study (Map 2), in order to further refine the location of detected association, we genotyped an additional set of 382 SNPs for those LDU regions from Map 1 that showed evidence of association with myopic case-control status. Hence Map 2 had a high local LD resolution. After removing non-polymorphic Map 2 SNPs (33), SNPs with a call rate ≤90% (19), evidence of Hardy-Weinberg disequilibrium (20) and MAF< = 1% (7), a total of 307 SNPs remained for Map 2 analysis.MYNN (p = 0.0028), MFN1 (p = 0.02), upstream of SOX2OT (p = 0.0082), a “gene desert” region downstream from SOX2OT , MCF2L2/PSARL (p = 0.014) and LPP (p = 0.003), with each analytical LDU window spanning 939 kb, 672 kb, 965 kb, 753 kb, 944 kb and 465 kb, respectively. Additional Map 2 genotyping within Map 1 provided the same or increased evidence of association for the MYNN (p = 9.2×10−5), MFN1 (p = 1.54×10−8), upstream of SOX2OT (p = 1.1×10−11), downstream of SOX2OT (p = 1.6×10−5) and MCF2L2/PSARL regions (p = 0.01), but not LPP (p = 0.03). Hence four of the regions provided statistically significant evidence of association at the discovery phase with a significance threshold of α = 10−4 .Map 1 provided preliminary multi-marker evidence of association to six gene regions , namely −2 was used for the replication tests  from the TwinsUK register, to obtain 1430 individuals complete for autorefractor or postal SE and genotypes at 3q26 based upon the Illumina genome-wide Hap300 chip made available from other ongoing studies. Using quantitative tests of association, the same Malecot models and analytical LDU windows were fitted to the replication data. A significance threshold of α = 10ests see .All single-SNP allelic tests of association results , but the confidence interval for this estimated location also includes the genes ZNF639 (ZASC1), MFN1 and GNB4  for single-SNP tests of association, with SNPs rs6794192 and rs7618348 genotyped and providing low p-values for all three samples  and combined sample single-SNP p-values of 10−3 and 10−4, respectively , rs10460887 , rs9822116 , rs17293193  and rs7618348  using a quantitative test of association, the same analytic LDU window and a different panel of markers for the 3q26 region derived from the Hap300 chip.The SOX2OT gene region . The physical location for the putative associated common variant in the region using the more informative Map 2 data was estimated to be at 182,595 kb with a 95% confidence interval of 182,533–182,688 kb , rs733422  and rs4855026 . 21 = 14, p = 1.8×10−4, These results were also observed for the replication sample using a quantitative test of association with Hap300 SNPs covering the same region  was stronger than that for MCF2L2 (χ21 = 6.2).Strong evidence of association to this LDU window was also observed for the replication sample using a quantitative test of association for Illumina Hap300 SNPs genotyped for the same LDU window . However, when a higher-resolution map was genotyped for the locus, association was detected at genome-wide significance for MFN1 and SOX2OT.The approach succeeded, with evidence of replicated association to the MCF2L2/PSARL gene region using Map 2 data remained the same (p = 0.01), but the same LDU window was subsequently replicated more strongly using a different panel of Hap300 SNPs (p = 0.0005). The diverging location estimates in the MCF2L2/PSARL region using two different SNP marker sets suggests the possibility of more than one common functional variant and co-incidental association for this LDU window  genotyped at different centres.Some of the regions we have investigated on 3q26 are complicated with high recombination rates or a high density of genes. We have used a model that assumes common susceptibility loci with little or no allelic heterogeneity. As such we recognise there are likely to be more variants and genes in this region that will be identified and replicated by further mapping studies.MFN1 gene region  with a 95% confidence interval ranging from 180,505–180,655 kb. Although the strongest evidence of association peaks at 180,565.8 kb in the middle of the MFN1 gene at exon 7, the confidence interval includes two neighbouring genes, ZNF639 and GNB4.The Malecot model delimits the MFN1) is a mitochondrial outer membrane protein, widely expressed in human tissues but varying in mRNA expression levels between tissues OPA1, a dynamin-related protein of the inner membrane which is mutated in autosomal dominant optic atrophy OPA1 requires Mfn1 to regulate mitochondrial fusion OPA1 is expressed in embryonic retina at many levels, not just the ganglion cells leading to the optic nerve, and continues to be expressed in adult retina with unknown function Mitofusin-1  non-coding RNA gene SOX2OT, which may play a regulatory role in SOX2 expression SOX2OT is a highly complex locus, which appears to produce several proteins with no sequence overlap, with 14 documented alternative splicing mRNAs, 5 non-over-lapping alternate last exons and 7 validated alternative polyadenylation sites. Upstream of SOX2OT there are also 5 possible alternative promoters SOX2OT in regulating SOX2 is unknown.PSARL) is a mitochondrial inner membrane protease, which interacts with OPA1 to inhibit the mitochrondrial remodelling process that signals apoptosis The protein presenilin-associated rhomboid-like protein  protein, which is transported from the nucleus to the outer surface of the inner mitochondrial membrane and interacts with Mfn1 and PARL to cause mitochondrial fusion and suppress mitochondrial-led apoptosis, respectively. OPA1 is widely expressed throughout the body, but most abundantly in the retina, followed by the brain. In the eye, OPA1 is present in the cells of the retinal ganglion cell layer, inner and outer plexiform layers and inner nuclear layer The MFN1, SOX2OT and PSARL using a multi-marker approach that models LD structure. We performed a two-stage design to ensure adequate SNP coverage using a high-resolution LDU map. Prior evidence of replicated linkage to this region means these associations are likely to be real with MFN1, GNB4, PSARL genes and regulatory non-coding RNAs in the vicinity of SOX2, all plausible candidates. Although the mechanisms are not clear, this study strongly suggests that two fundamental mitochondrial molecular pathways are implicated in the aetiology of myopia.In summary, we have detected and replicated three novel loci at MFN1 and PSARL, can be taken forward to clarify the molecular genetic aetiology of common myopia.We are confident that additional mapping studies for these data are likely to replicate further candidate genes at 3q26-28 and genome-wide, which along with Twins in this study volunteered through media campaigns to be on the TwinsUK Adult Twin Registry at St Thomas' Hospital, London A postal enquiry was also initiated in 2002–2003, asking about ocular history and requesting subjects' ocular refraction prescription from their optometrist. We used postal data for those subjects without autorefraction data. Included in the questionnaires were questions about spectacle wear to cross-check refractive error data supplied. Subjects were excluded if they gave a history of cataract surgery, laser refractive surgery, retinal detachment or other ocular problems that might have influenced refractive correction.Spherical equivalent (SE) was recorded in the standard manner as the sum of the spherical power and half the cylindrical power in diopters (D). The mean SE for left and right eye was calculated for each individual, and where data was available for only one eye, this was used as the SE for the subject.Although there has been little evidence of population stratification in population-based studies of self-reported Britons, we assessed for possible stratification with little or no evidence of stratification observed for these data We attempted to replicate the evidence from our original genome-wide analysis using AR data The most informative individuals were selected for genotyping from the lower and upper quartiles of the continuous SE diopter distribution. We selected individuals from a dataset of 915 twin pairs with complete autorefractor data, of whom 431 were monozygotic and 484 DZ pairs, which included the 221 DZ autorefracted pairs from the original linkage study. From the 915 twin pairs, a total of 575 unrelated cases and controls were selected for the discovery sample – 255 monozygotic and 320 DZ singletons. To enrich for genetically informative cases and controls, individuals were selected if they were myopic and had a myopic twin (a “super” case) or alternatively, were hyperopic and had a hyperopic twin (a “super” control). The most myopic individuals (cases), with a diopter score of less than <−1, were selected from each twin pair, where the pair mean was equal to or less than  = <−0.75 diopters. Similarly, the more hyperopic individuals (controls), with a diopter score of at least >+1, were selected from twin pairs with a pair mean greater than >+1 diopters.This resulted in an ascertained sample, designed to differentially increase allele frequencies between cases and controls for disease susceptibility alleles that predispose individuals to develop myopia. We chose hyperopic rather than normal sighted controls as a strategy to increase power, on the assumption that the aetiology for myopia and hyperopia lie on a continuum between health and disease and that both share genetic risk mechanisms. The discovery data were analysed using case-control status, on the supposition that most of the information would be captured by affection status, but we also tested the case-control data for quantitative association using the original diopter measurements for the selected data.Case and control samples were simultaneously genotyped using the same platform and arbitrarily allocated to the same plates. Case-control status was independent of plate and well assignment (data not shown).The LD maps Map 1 (the entire 3q26 region) was first partitioned into 51 non-overlapping windows based on the LDU map with a minimum length of 10 LDU per window and by default, not breaking LDU blocks. For the six out of 51 LDU windows showing strongest evidence of association for the Map 1 data , an addiFurther to the Map 1 and Map 2 studies, we also examined an independent replication sample of 1430 individuals for quantitative association, from which discovery samples and their relatives were excluded. The replication sample was composed of 460 unrelated individual female monozygotic and 338 dizygotic twin singletons and 316 DZ twin pairs. Tests of association were calculated using robust standard errors (clustered by family identifier) to account for relatedness with samples complete for refraction error  and 3q26 genotype data. SNP genotypes for the replication samples were derived from a genome-wide Illumina HumanHap 300 dataset made available at the Twin Research Unit from other studies To assess the validity of postal SE with AR measures, we compared 138 individuals with both types of measure. The correlation between AR and postal data was 0.93, with a mean difference  of −0.241 (standard deviation = 0.93) and no observed relationship between the differences and the means for the two measures (p = 0.75).21> = 12), SNP-wise missing rates greater than 10% or MAF≤0.01. Samples with a total of more than 30% case-wise missing were also removed before analysis.Discovery Map 1 and Map 2 sample handling, DNA genotyping and genotype calls were performed by Ellipsis Biotherapeutics Corporation (Toronto) using an Illumina Beadstation. SNPs were screened for quality control before analysis and rejected if the marker showed strong evidence of Hardy Weinberg disequilibrium  with fully compatible SNP arrays, the Hap300 Duo, Hap300, and Hap550. Quality control measures taken for these data are detailed in f is the frequency of myopic individuals in the sample and R is the minor allele frequency Allelic tests of association were initially performed for each marker. The association measure, z, from the 2×2 table between the myopia phenotype  and the two alleles of each SNP marker were obtained for Map 1 and Map 2 as z = |D|/f(1−R), where D is the covariance between myopia-status and the marker alleles, thi SNP, the observed association iz has an expectation E(iz) estimated by the model as: E(iz) = (1−L)MeεΔ(Si−S)−+L. The parameter M (intercept) reflects a monophyletic or polyphyletic origin of susceptibility alleles . The parameter L (asymptote) is the spurious association at long distance.The significance of each window (or LDU region) was tested using a composite likelihood approach that simultaneously combines information from all markers within each window S, which is the estimated location of the putative disease gene in the map. The parameter ε measures the rate of exponential decline in association with distance and hence iS is the LDU location of the ith marker. The Kronecker Δ is used for map direction and assures a correct sign, where Δ = 1 if Si≥S or Δ = −1 if Si<S. Given the observed associations for iz, the Malecot parameters are estimated iteratively by combining information over all loci within a window. The composite likelihood is calculated as Λ = i∑ K [iz−E(iz)]2, where z and E(z) are the observed and expected association values, respectively, at the ith marker SNP. Their squared difference is weighted by an information index Ki, which is estimated as: Kz = χ21/z2, where χ21 is the Pearson's χ21 from the 2×2 table .The object of LD mapping is to estimate et al.Null where M = 0. The alternative model Full allows the estimation of both M and S. Hence the contrast between these two models tests for association to a region and for a disease determinant at location S. The difference in marker-density between Map 1, Map 2 and the replication samples genotyped for Hap300 SNPs, was taken into account by the use of an F statistic with 1df and 2df degrees of freedom. The degrees of freedom 1df was the number of SNPs minus 2df parameters estimated in the Full model. The F-value was estimated as F = [(NullΛ−FullΛ)/2df]/FΛ/1df. Subsequently, to facilitate model fit comparison between tests with different degrees of freedom, p-values from the F-statistic were converted to a χ21  for each SNP marker (i) and the expected E(ib), which was estimated using the Malecot model for every thi distance in LDU.Same procedures were used for the replication samples . However, as the Hap300 chip had been genotyped for a large number of unselected twins, for this analysis we used the quantitative phenotype instead of the case-control status. Therefore the composite likelihood was calculated using the observed regression coefficient  to indicate genome-wide significance. For evidence of linkage replication presented in this study, we lowered the threshold to LOD 2 (α≈2×10−3), since replicating a true initial linkage result for complex traits is recognized to be difficult due to upward bias in discovery sample estimates For the original discovery sample −4, which is conservative, since a Bonferoni correction would provide a threshold of α≈10−3 (0.05/51) based upon approximately 51 independent tests .Based upon Map 1 results, we took forward six LDU windows for further genotyping (Map 2) that corresponded to the six most statistically significant results. For Map 2 we used a threshold of α = 10Replication association  We attempted to replicate the six analytic LDU windows, with each test window independent of one another. Hence we considered replication using a threshold of α = 10−2 (0.05/6) based upon a Bonferoni correction.The URLs for data software presented herein are as follows:http://www.hapmap.org/ (for HapMap data)HapMap, Figure S1Pairwise LD plot (D') for the MFN1 gene region  using HapMap Phase II SNPs . The top ideogram represents the whole of chromosome 3, with the yellow bar high-lighting the gene region of interest. Below that shows the local physical distance in kb, local coalescent recombination rates (cM/Mb) and gene locations. Note that two annotated hotspots (cM/Mb) either side of the MFN1 gene coincide with the linkage disequilibrium unit (LDU) steps depicted in (0.13 MB DOC)Click here for additional data file.Figure S2Pairwise LD plot (D') for the SOX2 gene region  using HapMap Phase II SNPs, showing local physical distance in kb (Build 35), local coalescent recombination rates (cM/Mb) and gene locations. Both the LDU map and the HapMap annotated recombination rates indicate elevated levels of recombination in the SOX2OT gene region, especially downstream. The estimated location for the common functional variant is at approximately 182,600 kb in a ∼155 kb region with extended LD .(0.09 MB DOC)Click here for additional data file.Figure S3Pairwise LD plot (D') for the MCF2L2/PSARL gene region using HapMap Phase II SNPs showing local physical distance in kb (Build 35), local coalescent recombination rates (cM/Mb) and gene locations. The estimated physical location for the common functional variant at MCF2L2 based upon linkage disequilibrium unit (LDU) study Map 2 is at approximately 184,390 kb (on the left side of the diagram), while the estimated location at PSARL based on the LDU map for Hap300 marker SNPs is at 185,100 kb (on the right).(0.09 MB DOC)Click here for additional data file.Table S13q26 HapMap II LDU map.(2.06 MB XLS)Click here for additional data file.Table S2Discovery sample single SNP p-value results (Map 1).(0.26 MB XLS)Click here for additional data file.Table S3Discovery sample single SNP p-value results (Map 2).(0.05 MB XLS)Click here for additional data file.Table S4Replication sample single SNP p-value results (Hap300).(0.54 MB XLS)Click here for additional data file.


Physical activity modulates expression of metabolic genes and may therefore be a prerequisite for metabolic responses to environmental stimuli. However, the extent to which exercise interacts with environmental conditions to modulate metabolism is unresolved. Hence, we tested the hypothesis that even low levels of physical activity are beneficial by improving metabolic responsiveness to temperatures below the thermal neutral zone, thereby increasing the capacity for substrate oxidation and energy expenditure.Rattus fuscipes) to avoid potential effects of breeding on physiological phenotypes. Exercise acclimation  at 22°C increased mRNA concentrations of PGC1α, PPARδ, and NRF-1 in skeletal muscle and brown adipose tissue compared to sedentary animals. Lowering ambient temperature to 12°C caused further increases in relative expression of NRF-1 in skeletal muscle, and of PPARδ of brown adipose tissue. Surprisingly, relative expression of UCP1 increased only when both exercise and cold stimuli were present. Importantly, in sedentary animals cold acclimation (12°C) alone did not change any of the above variables. Similarly, cold alone did not increase maximum capacity for substrate oxidation in mitochondria (cytochrome c oxidase and citrate synthase activities) of either muscle or brown adipose tissue. Animals that exercised regularly had higher exercise induced metabolic rates in colder environments than sedentary rats, and temperature induced metabolic scope was greater in exercised rats.We used wild rats (Physical activity is a necessary prerequisite for the expression of transcriptional regulators that influence a broad range of physiological functions from energy metabolism to cardiovascular function and nutrient uptake. A sedentary lifestyle leads to decreased daily energy expenditure because of a lack of direct use of energy and a muted metabolic response to ambient temperature, which can be reversed even by low levels of physical activity. Animal health and reproductive success is largely determined by a balance between energy intake and expenditure. Too little chemical energy  available to cells will constrain animal function Aerobic, or oxidative, metabolism is the principal avenue for ATP production in all animals, and also for heat production in endotherms. In the cell, mitochondria are the site for oxidative metabolic pathways. The capacity of mitochondria to oxidise substrates derived from food is important for whole animal energy expenditure In the absence of either cold or activity, metabolic responses will be muted, which may explain the limited metabolic response of experimental stimulation of metabolic regulators Rattus fuscipes is 29–31°C. Metabolic responses triggered by ambient temperature therefore constitute a principal avenue of daily energy expenditure In the context of endothermic metabolism, “cold” may be defined as an ambient temperature below the thermal neutral zone when metabolic rates increase above basal rates We tested the null-hypothesis that chronic cold and regular low level exercise act independently to elicit similar metabolic responses. Hence, an animal that is sedentary and exposed to cold will have similar metabolic responses as an animal that is exercised in the warmth. The alternatively hypothesis is that there is a differential, and possibly an interactive effect of cold exposure and exercise on metabolic responses. To test these hypotheses, we exposed rats chronically to different thermal and exercise conditions in a two-factorial experimental design, and examined a functional cascade of responses from the expression of regulatory transcription factors to running performance. Our results show that metabolic responses to cold are dependent on low levels of physical activity, so that the principal avenues of daily energy expenditure are removed by a sedentary lifestyle.1,23>55.76, p<0.0001; 1,23 = 4.98, p<0.05), which means exercised and warm acclimated animals had a lower expression than exercised, cold acclimated animals , but there was no significant interaction between exercise and thermal acclimation . Only exercise had a significant effect on PPARδ relative expression , and neither thermal acclimation nor the interaction were significant .In muscle, relative expression of all target genes was significantly greater in exercised animals , and warm acclimated animals had lower expression when exercised but higher expression when sedentary . Similarly, COX activity in BAT of exercised rats was significantly higher than in sedentary rats , but there was an interaction between exercise and thermal acclimation indicating that cold acclimated, exercised rats had the greatest COX activity  activity, an indicator of the maximal capacity of mitochondria for substrate oxidation, was significantly higher in skeletal muscle  and warm acclimated rats , and thermal acclimation did not  activity is a measure of mitochondrial abundance. In muscle, its activity was significantly higher in exercised  but cold exposure had no effect  nor thermal acclimation  affected LDH activity (We determined lactate dehydrogenase (LDH) activity to assess whether there is a change in glycolytic, anaerobic ATP production. LDH activity was significantly higher in muscle of exercised rats compared to sedentary animals  regardless of thermal acclimation . Importantly, exercise induced metabolic rate increased significantly in exercised rats at 12°C but not in sedentary rats  and exercised  rats. Interestingly, at low temperature scope increased in cold acclimated and exercised rats but not in animals from other treatments  increased with decreasing test temperature in exercised rats, and it was lower in exercised rats compared to sedentary rats at the higher ambient temperature of 22°C (exercise x test temperature interaction: Fcrit) varied with exercise, thermal acclimation and test temperature , but none of the main effects was significant .Critical sustained running performance  animals. Here we show that physical activity and cold exposure interact in their effects on metabolism. Cold alone has no effect on transcriptional regulators and causes only a limited increase in mitochondrial enzyme activities. Coordination and quantitative adjustment of metabolic pathways depends on induction or repression of transcription of a small number of transcription factors and their coactivators Cold can stimulate mitochondrial metabolism and abundance via the sympathetic nervous system, and adrenergic receptors on the cell surface can stimulate PGC-1α transcription via cAMP and CREB signalling R. fuscipes in 29–31°C , which the animals almost never experience in the wild PGC-1α also regulates non-shivering thermogenesis in brown adipose tissue Both of our acclimation treatments were below the thermal neutral zone of the species, which mean that rats even in our warm (22°C) treatment may have had increased metabolic capacities compared to minimum levels at temperatures within the thermal neutral zone. Nonetheless, the muted response of sedentary rats to a further decrease in acclimation temperatures from 22°C to 12°C is extraordinary, both with respect to the implications discussed above and with respect to the consensus in the literature that cold stimulates metabolic responses, particularly in BAT . Our acclimation and test temperatures were purposely moderate, and cold-induced metabolic responses in the laboratory are commonly observed at much lower ambient temperatures . More extreme cold exposure will stimulate uncoupling proteins and BAT oxidative capacity in the absence of physical activity . Responses of BAT to very low temperatures alone are adaptive for animals that have to cope with extreme cold, and it suggests that there may be a threshold of activation below which exercise becomes relatively unimportant for BAT activation. This latter suggestion is speculative, but provides the basis for an interesting and feasible hypothesis to test.There was no training effect on locomotor performance as a result of the exercise regime we imposed on the animals. Hence, metabolic responses to exercise do not require intensive exercise or training regimes that lead to increased locomotor performance. Thermal acclimation had a greater effect on locomotor performance than exercise, which is in contrast to the metabolic responses we report and indicates that running at this level is not constrained by metabolic capacity.Regular physical activity not only increases energy expenditure directly, but also modulates gene expression patterns of metabolic regulators. Gene expression patterns induced by a sedentary lifestyle result in metabolic patterns that are the precursors of metabolic diseases prevalent among humans When combined with cold exposure, exercise promotes uncoupling of oxidative phosphorylation by increasing the expression of UCP1 in BAT. Increased expression of UCP1 increases the proportion of energy derived from fatty acid oxidation that is released as heat, rather than being deposited as white adipose tissue; this provides an important avenue by which exercise facilitates increased energy expenditure and counteracts obesity All experimental procedures were approved by the University of Sydney Animal Ethics Committee (permit L04/12-2006/3/4512) and the NSW National Parks and Wildlife Services (Scientific License S12186).Rattus fuscipes, as a model to avoid potential problems from inbreeding and thereby altered exercise responses in laboratory strains. Bush rats were trapped near Sydney, Australia  and kept individually in plastic cages (350×220×220 mm). We used only adult animals, and excluded any pregnant females; all experimental groups had a even sex ratio. Rats were fed ad libitum on a mixture of commercial rodent mix and diced apples. Median body mass of experimental animals was 115.0 g and body mass did not change significantly during the acclimation treatments in any experimental group. For more information on food intake and body mass of the animals please see We used a wild rodent, the bush rat After a 14 day habituation period, rats were randomly allocated to either a cold (12°C) or a warm (22°C) acclimation treatment with a constant photoperiod of 12L:12D in all treatments; acclimation treatments lasted for 30 days. The rationale for our treatments was to expose animals to conditions naturally and routinely experienced rather than testing physiological limits under extreme conditions. Hence, acclimation temperatures were based on mean winter and summer temperatures naturally experienced by the population from which the rats were sampled −1 i. p.) on a day following an exercise day, but the animals were not exercised on the day of euthanasia.Rats in the exercise treatments were run in a wheel on five days per week throughout the acclimation period. In each exercise session, bush rats were placed individually into an enclosed, motorised running wheel for 30 minutes at 60% of the running speed that resulted in exercise induced metabolic rate as determined in preliminary studies (according to methods described in Locomotor performance below). Bush rats were euthanased by an overdose of sodium pentabarbitone  were used for analyses of PPARδ, PGC-1α and NRF-1 mRNA concentrations according to the manufacturer's instructions.Skeletal muscle and BAT samples were collected at the time of euthanasia and immediately stored in RNAlater  at −20°C. RNA extraction and reverse transcription were conducted according to published protocols Rattus norvegicus uncoupling-protein 1  and nuclear 28S RNA . For these genes we used real-time PCR reactions with SYBR green that contained 1× SensiMixPlus SYBR , 4.5 mM MgCL2, 600 nM primer and 50 ng cDNA. The cycle consisted of 95°C for 7 min, 40 cycles of 95°C for 15 s and 66°C for 1 min; 95°C for 15 s; 60°C for 1 min, and 95°C for 15 s. We performed melt curve analysis to ensure that only a single PCR product was amplified, and all assays were run in duplicate. Relative gene expression of the target gene was calculated according to Primers were designed from sequences obtained from GenBank for Skeletal muscle  and BAT were collected at the time of euthanasia and were transferred into liquid nitrogen immediately after collection and stored at −80°C for later analysis of lactate dehydrogenase (LDH), cytochrome c oxidase (COX) and citrate synthase (CS) activities. Enzyme activities were determined according to published protocols 2) and pure N2 (0% O2) used for calibration of the gas analyser −1. Gas sampling rates were set at 100 mL.min−1, and flow rates were measured with a flowhead  connected to a spirometer  and the PowerLab system (ADInstruments). When oxygen consumption had plateaued, generally after 2 hours, resting metabolic rate was determined as the mean of the five lowest consecutive oxygen consumption measurements. Mass specific oxygen consumption (mL. O2. hr−1. g−1) was calculated using the standard equation VO2 = V1 (F1O2-FeO2)/(1-FeO2) where V1 is the inlet air flow rate (ml.h−1), F1O2 is the inlet fractional O2 content (0.2095) and FeO2 is the excurrent fractional O2 content Standard flow through respirometry was used to measure whole animal rates of oxygen consumption at 12°C and 22°C Ucrit) was measured at 12°C and 22°C in ramped speed trials in a motorised running wheel fixed within a Perspex metabolic chamber (220×120×240 mm), which also allowed measurements of oxygen consumption (see oxygen consumption above). Wheel speed was controlled by a motor regulated with a DC power supply  and rotations were counted with a cyclometer calibrated to the circumference of the running wheel. The settings on the power supply were calibrated for wheel rotations (km.h−1) so that running speed could be controlled by adjusting the DC power input. The running wheel was placed in a temperature-controlled room and the temperature within the wheel was monitored with a calibrated thermocouple connected to a computerised recording system .Critical sustained running speed (Ucrit = Uf+[(tf/ti)Ui], where Uf is the greatest running speed maintained for a whole time interval, tf is time spent at the final speed, ti is the time interval between speed increments and Ui is the speed increment ti (150 s), Ui (0.25 km.h−1) and initial running speed (0.75 km.h−1). Animals were placed individually in the wheel and allowed to habituate at the test temperature for 2 h. All but one rat in the warm-exercised treatment ran in the wheel. Animals were run until fatigued, which was defined as the time when animals could no longer maintain position in the wheel. Each rat was run at 12°C and at 22°C in random order, with a minimum of 24 h between runs.Critical sustained running speed was determined as −1. Exercise induced metabolic rate was calculated as the highest instantaneous VO2 averaged over 1 min intervals Oxygen consumption was measured during the running trials to give exercise induced metabolic rate at 12°C and 22°C. Oxygen consumption was measured using the same set-up as described above for resting metabolic rate. However, for measurements of exercise induced metabolic rate the flow rate was increased to 1400 mL.minWe analysed relative expression of target genes To explore relationships between PGC-1α and its target genes, we performed regression analyses between PGC-1α relative expression and that of PPARδ, NRF1, and UCP1 across all treatments. All data are expressed as means + s.e.m. and the level of significance was α = 0.05.We used the truncated-product method


Napa gene that results in the substitution of a methionine for isoleucine at position 105 (M105I) of αSNAP has been detected in these animals. αSNAP is a ubiquitous protein that plays a key role in membrane fusion and exocytosis. In this study, we found that male hyh mice with a mild phenotype produced morphologically normal and motile sperm, but had a strongly reduced fertility. When stimulated with progesterone or A23187 , sperm from these animals had a defective acrosome reaction. It has been reported that the M105I mutation affects the expression but not the function of the protein. Consistent with an hypomorphic phenotype, the testes and epididymides of hyh mice had low amounts of the mutated protein. In contrast, sperm had αSNAP levels indistinguishable from those found in wild type cells, suggesting that the mutated protein is not fully functional for acrosomal exocytosis. Corroborating this possibility, addition of recombinant wild type αSNAP rescued exocytosis in streptolysin O-permeabilized sperm, while the mutant protein was ineffective. Moreover, addition of recombinant αSNAP. M105I inhibited acrosomal exocytosis in permeabilized human and wild type mouse sperm. We conclude that the M105I mutation affects the expression and also the function of αSNAP, and that a fully functional αSNAP is necessary for acrosomal exocytosis, a key event in fertilization.Hydrocephalus with hop gait (hyh) is a recessive inheritable disease that arose spontaneously in a mouse strain. A missense mutation in the  Intracellular transport of macromolecules is an essential process in cell physiology. Most steps of this process require apposition and fusion of membrane-bound compartments. Soluble N-ethylmaleimide sensitive factor attachment protein α (αSNAP) is a ubiquitous protein present in all eukaryotic cells playing a key role in membrane fusion Regulated secretion requires the fusion of exocytic granules with the plasma membrane and depends on αSNAP Napa -the gene encoding for αSNAP- is mutated in hyh mice Many diseases have been associated to mutations in proteins involved in intracellular transport The aim of this study was to asses whether the M105I mutation in αSNAP could affect the sperm acrosome reaction, and hence, the fertility of hyh male mice. Our results show that animals displaying a slowly progressive phenotype produce morphologically normal and motile sperm, but have strongly reduced fertility. We demonstrate that these cells have a defective acrosomal exocytosis due to a functional deficiency of αSNAP. Moreover, we show that αSNAP carrying the M105I mutation is not fully functional for the acrosome reaction.We have previously described that hyh mice present a heterogenous neuropathological and clinical phenotype SP mice present a preserved general motor activity Several causes can render a spermatozoon unable to fertilize an oocyte. We analyzed the morphology, viability, and motility of SP sperm. The results summarized in It has been previously reported that the amount of αSNAP is strongly reduced in the brain of hyh mice, indicating that the mutation causes an alteration in the steady state balance of this protein Supporting the results obtained by Western blot, a strong difference in αSNAP immunostaining was observed in the seminiferous epithelium from wild type and SP animals . This diThe above results indicate that the acrosomal exocytosis defect in hyh mice was not due to a diminished amount of αSNAP. A possible explanation for this observation is that the low concentrations of αSNAP in the testis and epididymis alter the normal maturation of the exocytic machinery of the sperm. Hence, even with a normal quantity of the protein, the sperm may be unable to exocytose. To test this hypothesis, we assessed whether we could restore exocytosis by adding wild type αSNAP to sperm from SP mice. To this end, we set up a plasma membrane-permeabilized mouse sperm model similar to the one we routinely use for human sperm Although αSNAP is a required factor for intracellular transport, in some experimental models -including the acrosomal exocytosis- an excess of the wild type protein inhibits membrane fusion The acrosome reaction is a special type of regulated secretion Napa gen coding for αSNAP is the first example of a fertility problem directly related to a factor necessary for the general mechanism of regulated secretion that is specifically affecting the acrosome reaction. Mutant mice with a mild clinical and neuropathological phenotype are able to copulate and the amount of live and motile sperm that they produce is similar to that of wild type mice; however, they have a much reduced fertility. Even when fertilization was assessed in vitro, sperm from hyh mice behave poorly as compared with sperm from wild type mice. Notwithstanding, the difference between genotypes was less dramatic in the in vitro assays than in the mating studies. Thus, other defects not assessed by in vitro fertilization might contribute to this phenotype. In this context, although SP mice present a preserved general motor activity and are able to copulate, they do have gait and equilibrium impairments Acrosome reaction is a required event to achieve fertilization in mammals Acrosome reaction experiments indicate that the basic problem in sperm from hyh mice is a reduced capacity to undergo exocytosis upon stimulation with progesterone and even with a calcium ionophore. The fact that exogenous αSNAP can rescue acrosomal exocytosis in these cells is a strong evidence that the principal defect is a malfunction in the endogenous protein. Worth noticing is that the M105I mutation is in a region of αSNAP that does not interact with the SNARE complex The deletion of αSNAP is embryonically lethal in mice Handling, care and processing of animals were carried out according to the regulations approved by the Bioethics Committee of the Universidad Austral de Chile and the Council of the American Physiological Society. The protocol for handling human sperm samples has been approved by the Bioethics Committee of the Medical School of the Universidad Nacional de Cuyo, Argentina.Pisum sativum (FITC-PSA) was from Sigma Chemical Co. . Hoechst 33258, Sytox Green, A23187, and Alexa Fluor 488-conjugated goat anti-mouse immunoglobulin G were from Molecular Probes . Mouse monoclonal anti-α-SNAP (clone 4E4) and anti-α/β-SNAP  antibodies were purchased from Exalpha Biologicals  and Synaptic Systems , respectively. Mouse anti-NSF monoclonal antibody (clone 7) was purchased from BD Biosciences Pharmingen. Monoclonal mouse anti-actin antibody (JLA20) was obtained from the Developmental Studies Hybridoma Bank  and monoclonal mouse anti-β-tubulin (clone 2-28-33) was from Sigma Chemical Co. All other chemicals were analytical-grade and were purchased from Sigma Chemical Co. or ICN Biochemicals .Recombinant streptolysin O (SLO) was obtained from Dr Bhakdi . Tetramethylrhodamine isothiocyanate-labeled peanut agglutinin (TRITC-PNA) and VECTASTAIN Universal Elite ABC kit (PK-6200) were from Vector Laboratories Ltd. . Fluorescein isothiocyanate-coupled 6-tagged αSNAP wild type and αSNAP.M105I were purified from the cytosolic fraction of Escherichia coli XL-1 blue  on Ni-NTA-agarose based on previously published methods Plasmid pQE9 encoding wild type αSNAP was a kind gift from Dr S.W. Whiteheart . Plasmid pET28 encoding αSNAP.M105I was a generous gift from Dr. Phillys Hanson . Recombinant Hishyh mutation originally arose in the C57BL/10J inbred strain and was subsequently placed on a B6C3Fe-a/a  hybrid background ad libitum with rodent food and kept under a constant photoperiod of light/dark 12∶12 h and room temperature of 22°C.Mice were obtained from The Jackson Laboratory , where the Mice (8–12 week old) were genotyped and mated using the monogamous mating system, except in the case of mating homozygous mutant male with homozygous mutant female were polygamous mating was utilized . Vaginal plug was used as an indication that mating had occurred. If no litters were produced after 10 weeks the mating was considered non-productive.6 sperm/ml, and incubated for 1 h at 37°C in an atmosphere of 5% CO2/95% air. Human semen samples were obtained from normal healthy donors. Highly motile sperm were recovered following a swim-up separation in HTF supplemented with 0.5% BSA for 1 h at 37°C in an atmosphere of 5% CO2/95% air. Concentration was adjusted to 5–10×106/ml, and incubation proceeded for at least 2 h.Metaphase II eggs were obtained from 6 to 8 week old wild type female mice superovulated by injection of 5 IU of pregnant mare serum gonadotrophin  followed 48 h later by 5 IU of human chorionic gonadotrophin . Eggs were collected in Whitten's medium 5 sperm/ml. Eggs and sperm were mixed in a 500 µl fertilization drop of HTF medium containing 0.5% BSA, and then cultured at 37°C in an atmosphere of 5% CO2/95% air. After 3 hours, eggs were washed six times to remove any loosely attached sperm, and then cultured for 5 h. Finally, embryos were fixed in 3.7% paraformaldehyde in PBS for 15 min, permeabilized with 0.1% Triton X-100 and stained with 10 µM Sytox Green (Molecular Probes) for DNA staining. Fertilization was evaluated by the presence of the second polar body and the formation of both the male and female pronuclei by epifluorescence microscopy.All IVF procedures were performed approximately 14 hours post hCG injection using HTF medium. Metaphase II eggs were obtained from 5–6 wild type female mice. Only eggs with visible polar body were collected and separated in two groups. Sperm from two wild type male mice and from at least two hyh male mice were obtained as explained above. Following capacitation, sperm from wild type and hyh mice were diluted to 2 or 4×106 cells/ml in HTF. To determine morphology of sperm and acrosome, a 5 µl aliquot was stained with Coomassie G-250 Wild type and mutant sperm were collected as explained and adjusted to a concentration of 5–10×10Testes were rapidly removed from hyh and wild type mice and fixed in Bouin fixative solution for 48 h at room temperature. Paraffin sections were processed for immunohistochemistry as previously described Sperm suspensions from hyh and wild type mice were fixed in 2% paraformaldehyde. Indirect immunofluorescence was performed as described Proteins from hyh and wild type mice brain, testis and epidydimis were extracted by homogenizing in a modified RIPA buffer  with a protease inhibitor cocktail  plus 1 mM phenylmethanesulphonylfluoride (PMSF). The homogenates were centrifuged at 6000×g for 10 minutes at 4°C to sediment unbroken cells and nuclei. Supernatants were stored at −80°C until used. Sperm proteins were isolated according to For Western blotting, 2% (v/v) 2-mercaptoethanol was added to the samples. After boiling for 2 min, 15 µg  or 30 µg (sperm) of total protein were loaded on 10% polyacrylamide gels according to Motile sperm were obtained as explained above. In some experiments, spermatozoa without further treatment were used to study acrosome reaction. Permeabilization of human and mouse sperm was accomplished as previously described