Many microbial populations rapidly adapt to changing environments with multiple variants competing for survival. To quantify such complex evolutionary dynamics in vivo, time resolved and genome wide data including rare variants are essential. We performed whole-genome deep sequencing of HIV-1 populations in 9 untreated patients, with 6-12 longitudinal samples per patient spanning 5-8 years of infection. The data can be accessed and explored via an interactive web application. We show that patterns of minor diversity are reproducible between patients and mirror global HIV-1 diversity, suggesting a universal landscape of fitness costs that control diversity. Reversions towards the ancestral HIV-1 sequence are observed throughout infection and account for almost one third of all sequence changes. Reversion rates depend strongly on conservation. Frequent recombination limits linkage disequilibrium to about 100bp in most of the genome, but strong hitch-hiking due to short range linkage limits diversity.DOI:http://dx.doi.org/10.7554/eLife.11282.001 th century. The virus is now found around the globe and has evolved into several different subtypes. Also, HIV continues to evolve inside each infected individual so that different variations of the virus are often present in the same person at any given time.HIV was transmitted from apes to humans multiple times before the virus began to spread among humans some time in the early 20When a person initially becomes infected, immune cells called killer T-cells seek out and destroy infected cells. However, HIV quickly acquires genetic mutations that allow it to escape the immune system and multiply in the body. There is a cost to this evasion strategy: the mutant forms of the virus often don’t multiply as rapidly as the variant that first infected the individual. This may explain why the virus often mutates back to its previous form in newly infected people. But many questions remain about how these pressures influence the evolution of the virus in individuals and in populations over time.Now, Zanini et al. have sequenced the genomes of HIV samples collected from nine people with HIV in Sweden over the course of five to eight years. These individuals were diagnosed between 1990 and 2003 – when HIV medications were not as widely used as they are now – and did not receive any treatment during the course of the study. Therefore, the study provides a rare opportunity to look at how HIV evolves in the absence of drugs that target the virus.Zanini et al. sequenced the entire genetic code of each form of the virus identified in the samples. The data show that HIV follows predictable patterns of evolution within individuals as well as across human populations. Mutations happen frequently all over the HIV genome. However, the mutant viruses often revert to a common or “optimal” form of the virus throughout the course of infection. This suggests that there is a tradeoff between the benefits of acquiring new mutations and maintaining a set of traits that have enabled the virus to spread so successfully in humans.To make it easier for other researchers to explore the data, Zanini et al. created a web application that allows others to access and create visual representations of viral evolution.Together these findings suggest that it will be possible to achieve a fuller understanding of RNA virus evolution that integrates the molecular biology of the virus and the immune response of the host with the evolutionary changes.DOI:http://dx.doi.org/10.7554/eLife.11282.002 The human immunodeficiency virus 1 (HIV-1) is a paradigmatic example of a rapidly adapting population characterized by high diversity, strong selection, and recombination. HIV-1 has originated from multiple zoonotic transmissions from apes in the early part of the 20th century , one of The evolution of HIV-1 ultimately takes place within infected individuals and can be observed directly in longitudinal samples of virus populations from the same individual. The detailed knowledge of HIV-1 biology paired with historical samples makes HIV-1 an ideal system to study general features of evolution at high mutation rates and strong selection that are otherwise only accessible in evolution experiments .During the first few months of an HIV-1 infection, the viral population typically acquires several mutations that mediate escape from cytotoxic T-cells (CTL). The properties and dynamics of these mutations and their effect on epitope presentation and virus control have been extensively studied . Such esLongitudinal sequence data has also been used to track evolution driven by the humoral immune response against HIV-1 which occurs throughout infection and results in high rates of evolution in the variable loops of HIV-1 surface proteins . In a piThe earlier studies discussed above have either focused on early infection or particular regions of the HIV-1 genome. However, most virus evolution happens during chronic infection simultaneously at many locations in the genome. To develop a comprehensive and quantitative understanding of the evolution and diversification of HIV-1 populations, we generated a whole-genome, deep-sequencing data set covering nine patients over 5–8 years with 6–12 time points per patient. Importantly, the data set covers the entire genome such that no substitution is missed and includes early samples defining the initial population. To our knowledge, this is the only whole-genome deep sequencing data set with long follow-up of multiple patients. We provide interactive and intuitive web-based access to the data set and hope it will become a resource for many future studies, like the data set by Below, we first describe the methodology we developed to sequence the entire HIV-1 genome at great depth. We then analyze the intrapatient evolution of HIV-1 and show that the minor variants in the virus population explore sequence space in predictable fashion at the single site level. At the same time, we observe a strong tendency for reversion towards the global HIV-1 consensus that is not limited to early infection but occurs at an approximately constant rate throughout chronic infection. Reversion is more frequent at sites that are more conserved at the global level, suggesting a direct relationship between intrapatient fitness cost and global conservation. Together with reproducible patterns of intrapatient variation, this link explains why HIV-1 fitness landscapes can be inferred from cross-sectional data . We furtThe study included nine HIV-1-infected patients who were diagnosed in Sweden between 1990 and 2003. Data from two additional patients (p4 and p7) were excluded from analysis because of suspected superinfection and failure to amplify early samples with low virus levels, respectively. The patients were selected to have a relatively well-defined time of infection and to have been treatment-naive for a minimum of 5 years. Patients diagnosed in recent years rarely fulfill these inclusion criteria because therapy is almost universally recommended, but this was not the case when the study patients were diagnosed. Basic characteristics of the patients are presented in μl patient plasma and used for one-step RT-PCR amplification with six overlapping primer sets that span almost the complete HIV-1 genome, similar to the strategy developed by The basic steps of our amplification and sequencing pipeline are illustrated in Importantly, sequencing depth is determined not only by coverage but also by template availability and sequencing errors . We perfWe quantified the number of HIV-1 genomes that contributed to each sequencing library by PCR-limiting dilution (median: 120 quartiles: 50–500). Hence template availability, rather than coverage, determined the sequencing depth, see We estimated the error rate of the PCR and sequencing pipeline by amplifying and sequencing a plasmid clone. We minimized PCR recombination by reducing the number of PCR cycles and optimizing the reaction protocol . In some cases, however, the template number was low or template recovery poor such that only presence or absence of a variant could be called. Furthermore, SNPs remained linked through cDNA synthesis, PCR and sequencing.hiv.tuebingen.mpg.de, will encourage others to further analyze these data.Deep sequencing data sets like the one presented here require substantial filtering, mapping, and processing before they can be used to address specific biological questions. Hence depositing the raw data (while important) is of limited use for follow-up analyses. To facilitate reuse of our data, we developed a web application to explore the data set interactively and visualize the patterns of HIV-1 evolution in the study patients. In addition, the website provides easy access to processed data. These include phylogenetic trees, viral loads and CD4+ T cell counts, consensus sequences, major and minor haplotypes in different regions of the genome, frequencies of single nucleotide polymorphisms, and clean sequencing reads. The application allows browsing the data either by patient or by genomic region and provides composite interactive plots to explore how the virus population has changed over time. We hope that this web application, available at In most patients, the virus populations were initially homogeneous and diversified over the years, as expected for an infection with a single or small number of similar founder viruses . In two To measure the rate at which the virus population accumulates mutations, we calculated the average distance of each sample from the approximate founder sequence in 300 bp windows. Regressing this distance against time yields the rate of divergence in different regions of the genome, see Having found that coarse patterns of divergence are comparable among patients, we asked whether intrapatient diversity at individual sites in the viral genome follows general and predictable patterns. To this end, we compared diversity at each position to the diversity observed in HIV-1 group M .Taken together, the observations in In addition to the reproducible patterns of minor diversity, virus evolution is characterized by adaptations that are specific to the host. Immune selection results in escape mutations that rapidly spread through the population . Such muTo quantify the degree at which the evolutionary dynamics of HIV-1 is dominated by selective sweeps, we calculated divergence and diversity separately at nonsynonymous and synonymous sites in different parts of the HIV-1 genome. Indeed, we observe a strong anti-correlation between synonymous diversity and nonsynonymous divergence, which is further quantified in -10). This corroborates the interpretation that, due to substantial recombination, sweeping nonsynonymous mutations only occasionally ‘drag’ adjacent synonymous mutations to fixation. Synonymous mutations rarely rise in frequency because of their own effect on fitness, since they usually have small or deleterious phenotypic effects and do not contribute directly in immune evasion , corresponding to 5.5 excess nonsynonymous substitutions, whereby the total number of nonsynonymous substitutions per patient is on average 43 . The set of predicted epitopes will contain false positives and lack true epitopes, hence the actual number of CTL driven substitutions could be higher as for example suggested by Next, we sought to quantify what fraction of nonsynonymous divergence is driven by escape from cytotoxic T-lymphocytes (CTLs). Four-digit HLA types were determined for all patients and a set of putatively targeted HIV-1 epitopes were determined using the epitope binding prediction tool MHCi (tools.immuneepitope.org/mhci). We then asked whether we observed more nonsynonymous substitutions in epitopes predicted to be targeted than expected by chance . We found a significant enrichment by a factor 1.9 in the putatively targeted region . Similar results are found when comparing with the subtype consensus of each patient virus (24 ± 2.5%). Reversions are between 4 and 5 times more frequent than expected in the absence of a reversion bias . These findings agree with results by By focusing on sites where the founder virus differed from the group M consensus, we are predominantly looking at weakly conserved sites. To control for conservation, we carried out the same analysis after stratifying sites by overall level of conservation. To quantify the decoupling of SNPs by recombination, we calculated linkage disequilibrium (LD) between SNPs as a function of distance for each of the six fragments, see −5/bp/day as estimated in  were retrieved from the biobanks of the Karolinska University Hospital and the Public Health Agency of Sweden. These samples had been stored at -70°C following routine HIV RNA quantification. Information about the patients and the samples are summarized in The inclusion criteria for the study patients were: a) a relatively well-defined time of infection based on a negative HIV antibody test less than 2 years before a first positive test or a laboratory documented primary HIV infection; b) no antiretroviral therapy (ART) for a minimum of approximately 5 years following diagnosis; and c) availability of biobank plasma samples covering this time period. The patients were selected from the Venhälsan HIV clinic in Stockholm, Sweden and were diagnosed between 1990 and 2003. Seven to twelve plasma samples per patient (200–1000 μl corresponding to 1.35 × 1010 copies/μl), SF162 , pZM246F_10 .In the control experiments, we used HIV DNA from the following plasmids; NL4-3 , 38540  and 38304 .We also used HIV RNA from the following virus isolates LAI III . Infection was considered to have occurred 17 days prior to date of first hospital visit based on an average incubation time from infection to development of PHI of 14 days  and an eFiebig staging  was usedFor patients in Fiebig stage VI, we calculated time since EDI using a published time-continuous model of development of antibodies reactive in the BED assay . HoweverComprehensive information about the data used for EDI determinations is provided in www.hiv.lanl.gov. Because of the redundancy of the long terminal repeats (LTRs), this means that all genomic regions except positions 482-571 in the R region of the LTRs were sequenced. Primer design was performed using the subtype reference alignment and the PrimerDesign software available at www.hiv.lanl.gov  was divided into two 200 μl aliquots. Total RNA was extracted using RNeasy Lipid Tissue Mini Kit (Qiagen Cat. No. 74804). Each aliquot was eluted twice with 50 μl RNase free water to maximize HIV RNA recovery. The four eluates were pooled, giving a total volume of 200 μl of RNA per sample. The RNA was divided into twelve 14 μl aliquots for duplicate one-step RT-PCR with the outer primers for fragments F1 to F6 and Superscript III One-Step RT-PCR with Platinum Taq High Fidelity Enzyme Mix .For each sample, 400 μl of the product from the first PCR was amplified with Platinum Taq High Fidelity. The second PCR consisted of a denaturation step at 94°C for 2 min, followed by 30 PCR cycles with denaturation at 94°C for 15 s, annealing at 50°C for 20 s and extension at 72°C for 90 s and a final extension at 72°C for 6 min. Other PCR conditions were also tried during assay development.Remaining RNA was used for template quantification (see below). The one-step RT-PCR was started with cDNA synthesis at 50°C for 30 min and denaturation step at 94°C for 2 min followed by 30 PCR cycles of denaturation at 94°C for 15 s, annealing at 50°C for 30 s and extension at 68°C for 90 s and a final extension step at 68°C for 5 min. A second nested PCR was used for template quantification and in some of the control experiments. For the second PCR, 2.5 After PCR, the duplicate amplicons from each of the six overlapping PCRs were pooled and purified with Illustra GFX PCR DNA and Gel Band Purification Kit  or AGENCOURT AMPure XP PCR purification kit . Purified amplicons from each sample were quantified with Qubit assays  and thereafter diluted and pooled in equimolar concentrations.μl to reduce overtagmentation; (ii) the number of post-tagmentation PCR cycles was raised up to 14 for samples with very low input DNA; (iii) post-PCR purification was done using Qiagen Qiaquick columns to maximize large-inserts throughput as compared to magnetic-bead based protocols; (iv) sze selection was performed using the SageScience BluePippin system with 1.5% agarose gel cassettes and internal marker R2, selecting sizes of 550–900 bp ; (v) size-selected eluates were pooled, buffer-exchanged into EB (10 mM Tris-HCl), and reconcentrated to at least 2 nM.The Illumina Nextera XT library preparation protocol and kit were used to produce DNA libraries. The original protocol was optimized for longer reads and amplicon input in the following fashion: (i) input DNA concentration to tagmentation was increased to 0.3 ng/The Illumina MiSeq instrument with 2 × 2 bp or 2 × 2 bp sequencing kits (MS-102-2003/MS-102-3003) was used to sequence the DNA libraries. We performed 26 paired-end sequencing runs. Overall, we obtained around 200 Gbases of output, i.e., 300 Mbases for each PCR amplicon. The median number of reads per amplicon was 80,000 . All read files have been uploaded to ENA with study accession number PRJEB9618.Bases were called from the raw images using Casava 1.8. The reads were analyzed from that point on using a custom pipeline written in Python 2.7 and C++. We favored this pipeline over existing programs because HIV-1 is a diverse species and both coverage and genetic diversity typically fluctuate by many orders of magnitude across the genome. The pipeline works as follows: (i) reads were mapped onto the HIV-1 reference HxB2, using the probabilistic mapper Stampy ; (ii) maWe added 1–5% of PhiX control DNA to all our sequencing runs. Comparison of reads that mapped to PhiX with the PhiX reference sequence revealed that the Phred quality score were a rather reliable indicator of error rates, with saturation around 0.1%. SNP calls were calculated from the reads using a custom pile-up restricted to reads with a PHRED quality >=30 corresponding to a minimal theoretical error rate of 0.1% per base. However, in a dedicated set of control experiments, we established that the main source of error is PCR, see 4 template molecules. The distribution of SNP frequencies found in the sequencing reads generated from the plasmid were compared with sequencing results from a typical patient sample, see We estimated the error rate of the PCR and sequencing pipeline by amplifying and sequencing plasmid clones . In most of these experiments, we used 10To roughly quantify the actual number of HIV RNA templates that were subjected to sequencing, we analyzed a dilution series of the RNA  with the nested primers for fragment F4 using the PCR conditions described above. Tests were done in duplicate on the same plate as amplifications for sequencing and the amplicons were visualized by agarose gel electrophoresis. Templates numbers were estimated based on the assumption of Poisson sampling. Thus, for each sample, this dilution series provides an estimate of the number of templates that make it into the sequencing library. The results from the limiting dilution experiments correlated well (rank correlation p = 0.7) with routine plasma HIV-1 RNA level measurements that had been performed at time of sampling, see In addition to quantification of template input for each sample, we separately estimated the recovery of sequence variation in each of the six sequenced amplicons. Adjacent fragments overlap each other by a few hundred bases. Variation in the overlapping regions is amplified and sequenced on both the leading and trailing fragment completely independently and the concordance of the observed variant frequencies, see Ln and Tn correspond to effective template numbers specific to leading (L) and trailing (T) fragment. For each SNP in the overlap, we calculate the difference δ and the mean x of the frequencies reported on the L/T fragment. An estimate of If deviations of SNP frequencies are due to random sampling from the pool of RNA molecules and stochastic PCR efficiency, the variance between the frequencies observed on two neighboring fragments should be k is the number of SNPs in the overlap. To increase robustness of this estimate, we include p pseudo counts each of which contributes k = 5 SNPs in the overlap at an average frequency above 0.03. We used p = 3. Changing the parameters does not change the numbers qualitatively.where p = 0.5 for most of the fragments), but indicated that variant recovery in fragment F5 was sometimes poor, consistent with the difficulties we encountered when amplifying fragment F5. Furthermore, the agreement of SNP frequencies in the overlaps indicate that the primers were well-designed and did not induce significant primer-related PCR amplification biases. These fragment-specific estimates are only available for samples and fragments with sufficient diversity in the overlap regions and are often lacking for early samples and fragment F6. The latter is due to the very conserved overlap with fragment F5.The fragment specific estimates of template numbers are consistent with the estimates based on limiting dilution . In some cases, however, the template number was low or recovery poor such that no more than presence absence of a variant could be called. All available fragment specific estimates are shown in The paired-end reads obtained correspond to inserts of up to 700bp in length and therefore provide information on linkage of mutations up to that distance. However, cDNA synthesis and PCR have both the potential to generate in vitro recombination, and true biological linkage is preserved only if the frequency of in vitro recombination is low. To estimate the in vitro recombination in our experimental setup, virions from two subtype B HIV isolates, LAI III and 38304 (the latter obtained from a Swedish HIV-1 patient infected in Brazil), were mixed in equal concentrations. Aliquots of this mix (approximately 1250 RNA molecules per PCR fragment) were amplified with the six overlapping one-step RT-PCRs as described above in both single and nested PCR mode and then sequenced.PCR recombination is known to occur predominantly at high amplicon concentrations due to heteroduplex formation of incompletely extended molecules . Consistgithub.com/neherlab/HIVEVO_figures. In these scripts, all parameters, settings, and calculations are explicitly documented.Python scripts that generate each figure shown in the manuscript are available at Average divergence defined as the fraction of non-founder alleles in a sliding window of 300bp was regressed against the estimated times since infection using a linear model without intercept. Different data points were weighted by the total divergence, reflecting the expected scaling of the variance if divergence is due to changes at many independent sites. The evolutionary rate was than mapped to the corresponding coordinate in the HxB2 reference.To construct trees in different regions of the HIV-1 genome, we extracted reads covering the desired region, restricted them to haplotypes above the desired frequency threshold and constructed a maximum likelihood phylogeny using FastTree . Trees wn1 at position x1 was jointly observed with nucleotide n2 at position x2. These matrices, reduced to variable positions, are available on the website. From these matrices, we calculate the joint frequency p12  of the majority nucleotide at positions . The normalized linkage disequilibrium measure is then given byTo calculate linkage disequilibrium, we first constructed four-dimensional matrices for each fragment and each sample that report the number of times reads mapped such that a nucleotide p1,p2 are the frequencies of the majority nucleotide at the respective positions, andwhere Values of Synonymous and non-synonymous divergence was analyzed on a per site basis to avoid confounding by different numbers of transitions and transversions. Positions were classified as synonymous based on the following criterion: The consensus amino acid at this position did not change throughout infection and at least the transition at this site is synonymous. In this case, the observed mutations at this site are almost always synonymous.tools.immuneepitope.org/mhci). This tool uses several prediction methods ranked based on previously observed performance: Consensus  to MHCi and obtained a list of peptides putatively presented by the HLA alleles (A/B/C) of the respective host ranked by prediction score.We considered the parts of the HIV-1 genome that putatively is targeted in any of the nine patients, and asked whether the density of nonsynonymous substitutions is higher in the part that is predicted to be presented in a focal patient compared to the part that is not presented. We excluded the variable loops of gp120 and the external part of gp41 from this analysis to avoid confounding by antibody selection. Repeating the same analysis for synonymous mutations did not yield any enrichment as expected.Full genome HIV-1/SIVcpz sequences were downloaded from LANL HIV database. Sequences belonging to subtypes B, C, and 01_AE were individually aligned to HxB2  to consWhen comparing HIV variation within patients to cross-sectional diversity, only positions of the founder sequence were used that mapped to HxB2. Similarly, only positions in the cross-sectional alignment were used that had less than 5% gaps relative to HxB2. This effectively masked regions of the genome that frequently experience insertions of deletions.To investigate divergence and reversion, intrapatient evolution was studied separately at sites where the approximate founder sequence agreed/disagreed with the respective subtype or group M consensus. Divergence was assessed on a per site basis.The website was realized using the Flask web framework, Bootstrap from Twitter as a frontend engine and jQuery and D3.js as a JavaScript library for interactive plots . The web In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Diethard Tautz as the Senior Editor.Thank you for submitting your work entitled "Population genomics of intrapatient HIV-1 evolution" for consideration by The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.Summary:eLife.In this paper, you use deep sequencing to track the genotypic evolution of HIV in individuals over multiple years. You observe patterns in the virus's evolution over time, and note a surprising amount of parallelism in the fate of specific mutations in the few patients you study. Although others have conducted similar studies, your study is more comprehensive than past efforts. Another major strength is that you develop a web-based visualization platform that enables both expert and non-expert users to inspect the results in detail. However, the current manuscript does not properly describe past efforts adequately, and this is the primary criticism that will need to be addressed before we can justify publication in For example, in the Introduction, you may want to discuss the work described in the following references as part of the background and then make clear what is novel about your studies: Bernardin J Virol 2005; Bar KJ Plos Path 2012; Brockman J Virol 2010; Schneidewind A JVirol 2010. Also, Salazer-Gonzalez 2009, did not examine T cell escape in acute HIV infection; escape was examined Goonetilleke et al. 2009  followed by Liu et al. JCI 2013. Key papers from the lab of P Borrow should also be recognized. Iyer S Plos One 2015 (lab of J Mullins) is a recent cross-sectional study in HIV-1 using deep sequencing techniques that is also relevant. Finally, the point made in the Discussion about how your results provide direct justification for past efforts to extract universal fitness landscapes of HIV from cross-sectional data might be considered for inclusion in the introduction as well ; Fergusson, Immunity (2013); Mann PLOS CB (2014)).In addition, it is important that you address the following comments in order to improve the paper:Specific comments:1) It is clear from 2) A major finding of the paper is the evidence for parallel evolution in the sense of similar positions accumulating similar divergence among patients, and of strong tendency of reversion to the same consensus identities in all strains. However, it is often unclear if this trend is occurring primarily on the protein level, primarily on the nucleotide level, or both. For instance, in 3) How does the consistency of evolution between patients correlate with distance between those patients' founder viruses? A central claim in this study is that: "At a single nucleotide level, the spectrum of mutational possibilities is explored reproducibly." The evidence supporting this claim includes the correlation between the site-specific entropy of patient samples and natural sequences . However4) More discussion is needed on the clinical characteristics of patients . Of note, participants had to be ARV naïve for at least 5 years, and thus some had a low viral load. How might factors leading to low viral load affect evolution? You note that some patients received ART during the study, and were included in an ongoing sub-study. However, it is not clear if the time points after ART initiation were included in the present study. If they were included, are there any interesting differences in evolutionary patterns between patients that received ART and patients that did not?5) Five of nine subjects in the cohort were identified in either Fiebig V or VI. This staging precludes confident determination of the transmitted/founder viruses in these subjects and therefore the statement that “initial consensus sequence approximates the sequence of the founder virus(es)” is not supported. You may want to revise the terminology to reflect that, in these subjects, you deduced a consensus sequence of an early virus sequence.Regarding the Fiebig staging and virus loads: These data are very confusingly described and presented. Was Fiebig staging performed during a visit prior to the available plasma samples tested? If so, when? Going through the supplementary file, the VL of the first sample available for sequencing is clearly outside the acute window for several of the patients described as detected in Fiebig II-IV. As above, how can you justify describing the first sequence as the founder? As shown by multiple groups, significant immune selection and recombination occurs within the first months of HIV infection.6) You provide a convincing demonstration of reversal to global consensus and provide a discussion of the how this observation can be reconciled with the observed intra-patient and global HIV diversity. It might help the reader to have the discussion of the balance of divergence and reversal (presently in the fifth paragraph of the Discussion) sooner, e.g. in the subsection "Extensive reversion toward consensus". The findings in this regard relate to a major ongoing debate in molecular evolution about the extent to which diverged homologs retain strong propensities for specific amino acids. Pollock et al.  and Shah et al.  have argued that pervasive epistasis means that the propensities for specific amino acids means will diverge rapidly among homologs, which will quickly come to favor different amino acids at homologous sites. On the other hand, Ashenberg et al. , Risso et al. , and Doud et al.  have argued that the propensities for specific amino acids are similar among homologs. How do your results bear on this issue?7) In the tree in 8) It is confusing that there is one Methods section called "diversity and divergence" and another called "divergence and diversity".9) The y-axis of 10) You state: "11) A*32 is overrepresented in this group; a much higher frequency than found in the broader Swedish population. Can the authors comment? Are these subjects in any way linked? In this paper, you use deep sequencing to track the genotypic evolution of HIV in individuals over multiple years. You observe patterns in the virus's evolution over time, and note a surprising amount of parallelism in the fate of specific mutations in the few patients you study. Although others have conducted similar studies, your study is more comprehensive than past efforts. Another major strength is that you develop a web-based visualization platform that enables both expert and non-expert users to inspect the results in detail. However, the current manuscript does not properly describe past efforts adequately, and this is the primary criticism that will need to be addressed before we can justify publication in eLife.Thank you very much for the constructive and helpful criticism, which has helped us to connect our findings better to existing literature and articulate more clearly the novel insights that can be obtained from longitudinal whole genome deep sequencing. While evolution during early infection or in specific regions of the HIV-1 genome have been extensively studied, we present for the first time a deep whole genome data set that allows quantification of the evolutionary dynamics throughout infection.For example, in the Introduction, you may want to discuss the work described in the following references as part of the background and then make clear what is novel about your studies: Bernardin J Virol 2005; Bar KJ Plos Path 2012; Brockman J Virol 2010; Schneidewind A JVirol 2010.We have expanded the part of the Introduction discussing prior studies of HIV evolution. Specifically, we have included the suggested references when discussion early CTL escapes, Nab escapes, fitness costs, and stability of compensated transmitted variants. All these references focus on patterns of evolution during the first few month after infection, while we study genome wide patterns of evolution from early to chronic infection.Also, Salazer-Gonzalez 2009, did not examine T cell escape in acute HIV infection; escape was examined Goonetilleke et al. 2009  followed by Liu et al. JCI 2013.Our apologies, we mixed up these back-to-back papers. We now cite Goonetilleke et al. 2009 and Liu et al. 2013 when discussing early escape. We included the reference to Salazar-Gonzales et al. 2009 when discussing founder sequences.Key papers from the lab of P Borrow should also be recognized. Iyer S Plos One 2015 (lab of J Mullins) is a recent cross-sectional study in HIV-1 using deep sequencing techniques that is also relevant. Finally, the point made in the Discussion about how your results provide direct justification for past efforts to extract universal fitness landscapes of HIV from cross-sectional data might be considered for inclusion in the introduction as well ; Fergusson, Immunity (2013); Mann PLOS CB (2014)).Thank you for these suggestions. We included a reference to work by the lab of P Borrow  when discussing previous work on CTL escapes in early infection. We further included a reference to Iyer et al. when discussing the necessity of quantifying templates and the limitations of next generation sequencing of diverse viral populations. We have added a brief discussion about the link between reversion, reproducible intrapatient diversity and inference of fitness landscapes from cross-sectional data. We also included the additional references as suggested.In addition, it is important that you address the following comments in order to improve the paper:Specific comments: 1) It is clear from The number of input templates varies greatly from sample to sample. We now discuss the distribution of template input at greater length. The median template input per fragment is 120 with an interquartile range of 50-500. In most cases we have therefore more templates than indicated in The deviations in 2) A major finding of the paper is the evidence for parallel evolution in the sense of similar positions accumulating similar divergence among patients, and of strong tendency of reversion to the same consensus identities in all strains. However, it is often unclear if this trend is occurring primarily on the protein level, primarily on the nucleotide level, or both. For instance, in We thank the reviewers for suggesting this analysis, which we also find to be an interesting point. We now provide 3) How does the consistency of evolution between patients correlate with distance between those patients' founder viruses? A central claim in this study is that: "At a single nucleotide level, the spectrum of mutational possibilities is explored reproducibly." The evidence supporting this claim includes the correlation between the site-specific entropy of patient samples and natural sequences . HoweverWe added 4) More discussion is needed on the clinical characteristics of patients . Of note, participants had to be ARV naïve for at least 5 years, and thus some had a low viral load. How might factors leading to low viral load affect evolution? You note that some patients received ART during the study, and were included in an ongoing sub-study. However, it is not clear if the time points after ART initiation were included in the present study. If they were included, are there any interesting differences in evolutionary patterns between patients that received ART and patients that did not?We have added more information about the samples, viral load, and CD4 count in the Results section. We have also included All samples in this study were obtained before any start of ART. To avoid misunderstanding we have removed the mentioning of the substudy, which is not relevant to this manuscript (beginning of Results).5) Five of nine subjects in the cohort were identified in either Fiebig V or VI. This staging precludes confident determination of the transmitted/founder viruses in these subjects and therefore the statement that “initial consensus sequence approximates the sequence of the founder virus(es)” is not supported. You may want to revise the terminology to reflect that, in these subjects, you deduced a consensus sequence of an early virus sequence.Regarding the Fiebig staging and virus loads: These data are very confusingly described and presented. Was Fiebig staging performed during a visit prior to the available plasma samples tested? If so, when? Going through the supplementary file, the VL of the first sample available for sequencing is clearly outside the acute window for several of the patients described as detected in Fiebig II-IV. As above, how can you justify describing the first sequence as the founder? As shown by multiple groups, significant immune selection and recombination occurs within the first months of HIV infection.It is true that our earliest sample is between one and seven month after infection and some mutations have likely spread through the population by that time. Hence we can only approximate, rather than exactly identify the founder sequence. However, the difference between the true founder and our approximation will be small compared to the evolution in the many years of follow-up after the first sample. All observation that we make in the manuscript should become even stronger if we knew the exact founder sequence. We added a discussion of the approximate nature of the inferred founder sequence to the manuscript in the subsection “Consistent evolution across the entire genome”.We agree that the presentation of the Fiebig staging, virus loads and estimation of date of infection (EDI) was unclear. This includes the dates for sampling for the different tests  which sometimes differed. For this reason we provide a new supplement table with comprehensive data, dates and comments, see 6) You provide a convincing demonstration of reversal to global consensus and provide a discussion of the how this observation can be reconciled with the observed intra-patient and global HIV diversity. It might help the reader to have the discussion of the balance of divergence and reversal (presently in the fifth paragraph of the Discussion) sooner, e.g. in the subsection "Extensive reversion toward consensus". The findings in this regard relate to a major ongoing debate in molecular evolution about the extent to which diverged homologs retain strong propensities for specific amino acids. Pollock et al.  and Shah et al.  have argued that pervasive epistasis means that the propensities for specific amino acids means will diverge rapidly among homologs, which will quickly come to favor different amino acids at homologous sites. On the other hand, Ashenberg et al. , Risso et al. , and Doud et al.  have argued that the propensities for specific amino acids are similar among homologs. How do your results bear on this issue?This is an interesting discussion and our results certainly suggest largely conserved amino acid preferences at the level of divergence observed between HIV-1 subtypes. This does not exclude that the preference at some sites have changed, but the majority is conserved. We have added a discussion of this issue.7) In the tree in We apologize for the lack of explanation. This sequence is the HxB2 reference sequence. This is now explicitly stated in the caption.8) It is confusing that there is one Methods section called "diversity and divergence" and another called "divergence and diversity".Thank you very much for pointing out this oversight. We have now given these sections headings that more accurately reflect their content.9) The y-axis of We have added the units to the caption.10) You state: "Thank you very much for spotting this. We meant to refer to 11) A*32 is overrepresented in this group; a much higher frequency than found in the broader Swedish population. Can the authors comment? Are these subjects in any way linked?We agree that the overrepresentation of A*32 is surprising. Dr Bratt has carefully checked if there could exist some relatedness or similar ethnic background of patients 8-11, but no such factors could be identified. Patients 4 and 7, who were sampled but excluded from the current analyses because of poor PCR performance or suspected superinfection, did not have the A*32 allele. Some reports  have indicated that A*32 is associated with long-term survival. Our patients had relatively slow disease progression and we cannot exclude that our inclusion criteria resulted in overrepresentation of certain HLA types among our patients. We have refrained from discussing this in the manuscript.


The present study examined to examine whether improvement of insomnia is mediated by a reduction in sleep-related dysfunctional beliefs through cognitive behavioral therapy for insomnia. In total, 64 patients with chronic insomnia received cognitive behavioral therapy for insomnia consisting of 6 biweekly individual treatment sessions of 50 minutes in length. Participants were asked to complete the Athens Insomnia Scale and the Dysfunctional Beliefs and Attitudes about Sleep scale both at the baseline and at the end of treatment. The results showed that although cognitive behavioral therapy for insomnia greatly reduced individuals’ scores on both scales, the decrease in dysfunctional beliefs and attitudes about sleep with treatment did not seem to mediate improvement in insomnia. The findings suggest that sleep-related dysfunctional beliefs endorsed by patients with chronic insomnia may be attenuated by cognitive behavioral therapy for insomnia, but changes in such beliefs are not likely to play a crucial role in reducing the severity of insomnia. Insomnia is defined as having difficulty initiating or maintaining sleep, waking up too early, or sleep that is chronically nonrestorative or poor in quality, causing daytime impairments such as decreased attention and worries concerning sleep Cognitive behavioral therapy for insomnia (CBT-I) is commonly used for treatment of the disorder. CBT-I has been shown to be effective for improving insomnia symptoms in 70–80% of patients with chronic insomnia Schwartz and Carney Previous studies have examined the relationship between sleep-related dysfunctional beliefs and treatment outcomes with CBT-I Considering these issues, the present study set out to clarify whether improvement of insomnia, as evaluated with the Athens Insomnia Scale (AIS), is mediated by a reduction in sleep-related dysfunctional beliefs through CBT-I.Individuals were eligible for this study if they were 20 years old or above and met criteria for psychophisiological insomnia  according to the second edition of the International Classification of Sleep Disorders In all, 64 patients participated in the study; however, 11 patients answered at least one of the above-indicated scales incompletely. Therefore, the total number of participants with sufficient data for analysis was 53 .This study was approved by the ethical review board of the Neuropsychiatric Research Institute, Tokyo, Japan. Written informed consent was obtained from all participants. After diagnoses were made by board-certified sleep disorder specialist physicians, eligible participants were offered CBT-I th Revision (ICD-10) no problem at all) to 3 (very serious problem). Participants were asked to rate corresponding items  as positive  when they had experienced sleep difficulty at least three nights per week during the previous month. This scale was also used to measure concomitant change in insomnia symptoms over the course of treatment with CBT-I. A score of 6 points or greater on this scale indicates clinical levels of insomnia The AIS was used to assess the severity of insomnia according to criteria for insomnia outlined in the International Statistical Classification of Diseases and Related Health Problems, 10strongly disagree) to 10 (strongly agree). The total score is calculated from the average score of all the items on the scale and could range from 0 to 10, with higher scores indicating higher levels of dysfunctional beliefs about sleep.The DBAS has been utilized in previous mediation analyses, making it an effective tool for assessing the sleep-related dysfunctional beliefs of participants in our study. The DBAS-16 is a self-rating inventory consisting of 16 items  with 10-point scales, ranging from 0 , progressive muscle relaxation (Session 3), sleep scheduling , and coping with worry (Session 6) based on the manual of a previous study t-tests and correlation analysis. The effect size (Cohen’s d) was used to investigate the extent of differences in scale scores between survey points (baseline vs. endpoint) in order to evaluate treatment effect. In general, Cohen's d values of 0.2 or lower, around 0.5, and 0.8 or more indicate small, moderate, and large effect sizes 2R) is considered small, whereas one around 0.13 is deemed moderate. One of 0.26 or greater is generally considered large Descriptive and inferential statistics were computed using SPSS version 19.0 . The relationships between the scales at baseline and at the end of treatment were evaluated using paired t-tests showed significant decreases in participants’ AIS  and DBAS  scores at the end of treatment as compared to when they began. The effect sizes (d) were 1.21 and 0.72, respectively , (2) AIS scores at baseline and at the end of treatment , and (3) DBAS scores at baseline and at the end of treatment   the AIS and DBAS at baseline , but did not have a direct effect on change in DBAS . In the multiple regression equation with dysfunctional beliefs as the second independent variable (Model 3), the β-value of the AIS-T1 increased to 0.55 (p = 0.00), while change in DBAS did not show a direct effect on AIS-T2 .The first regression analysis revealed that AIS-T1 had a standardized direct effect on AIS-T2, with a The present study examined whether improvement in insomnia is mediated by a reduction in sleep-related dysfunctional beliefs as manifested by a decrease in DBAS score after CBT-I. The results suggest that DBAS scores decreased after CBT-I, but the change in these beliefs did not seem to play a crucial role in improving insomnia.n = 335, mean score = 2.96, SD = 0.13) and individuals with insomnia  in a previous study involving a large sample t386 = −14.34, p<0.01), but were not significantly different from those of the insomnia patients . Therefore, the DBAS scores of our study sample were thought to not differ significantly from those of insomnia patients in general.We compared the DBAS scores of insomnia patients in this study with those of good sleepers  and that reductions in maladaptive beliefs lead to improved sleep Schwartz and Carney Our study had several limitations. First, the present study only examined the association between change in DBAS scores and improvement in AIS scores. As mentioned earlier, several candidate mediators of the persistence of insomnia exist other than dysfunctional beliefs, and significant interactions have been found between symptoms of insomnia and behavioral, cognitive, and hyperarousal variables Finally, all participants were drawn from a single sleep disorder clinic. Therefore, the participants of our study might not be representative of the general chronic insomniac population, although the demographic data and effect size of insomnia severity were quite similar to those of previous studies In summary, the sleep-related dysfunctional beliefs endorsed by patients with chronic insomnia were decreased through CBT-I, but changes in these beliefs did not play a crucial role in reducing the severity of insomnia. Therefore, the impact of cognitive-behavioral approaches such as the CBT-I on behavioral and hyperarousal aspects should be studied in greater detail in order to clarify underlying mechanisms by which insomnia is significantly improved. It would also be important to examine potential long-term mediating effects of dysfunctional beliefs and attitudes about sleep on AIS scores and whether CBT-I outcomes in individual therapy setting differ from those of group therapy settings in future research.


Chronic pain remains a serious public health problem worldwide. A spinal cord stimulation (SCS) therapy called HF10 SCS uses 10-kHz high-frequency stimulation to provide pain relief without paresthesia. In this article, we describe the therapy, device, and the methods of implant and then review the safety and effectiveness data for this therapy.HF10 SCS uses a charge-balanced stimulation waveform that has been shown to be safe in both animal and human studies. Data from a multicenter, prospective clinical trial shows that the therapy provides substantial back and leg pain relief. Numerous additional reports suggest improved pain relief in other body areas and for complex pain patterns, even for patients who have previously failed other neuromodulation therapies.The clinical experience reported in this article supports the efficacy and pain relief provided by HF10 SCS therapy. Clinical studies have also concluded that HF10 SCS does not generate paresthesia nor was it necessary to provide adequate coverage for pain relief. As clinical evidence accumulates and technological innovation improves patient outcomes, neuromodulatory techniques will be sought earlier in the treatment continuum to reduce the suffering for the many with otherwise intractable chronic pain. Chronic pain remains a serious public health problem. A recent survey estimates that almost one in five (19%) adults in Europe suffer from chronic pain with almost two-thirds of chronic pain patients reporting inadequate pain control at times Spinal cord stimulation (SCS) is an accepted cost-effective treatment option for chronic pain 4–7. SCS is typically recommended after conventional medical management and/or back surgery has failed. SCS has the advantages of being reversible and less invasive than surgeries and may cause fewer issues over time than long-term pharmacological treatments, therefore causing some to call for its use earlier in the treatment continuum Nevertheless, traditional SCS therapy still has limitations. The objective of traditional SCS is to induce comfortable paresthesia, tingling sensations, that overlap the existing distribution of pain by modulating neuronal activity Some patients find SCS-induced paresthesia to be uncomfortable. Furthermore, paresthesia variability can also increase with postural changes and body movement to the point of discomfort or in other cases diminishing to subtherapeutic levels An SCS system capable of delivering stimulation frequencies up to 10 kHz has been developed . It delivers HF10™ SCS therapy, a therapy that uses proprietary waveform with stimulation frequencies up to 10 kHz. In contrast to other currently available systems that use frequencies in the range of 50 Hz, this innovation does not require or produce paresthesia to achieve clinical efficacy Since its European regulatory approval in 2010, the Senza system has been implanted in over 2,000 patients. The device is approved in Europe and Australia for the management of chronic intractable pain of the trunk and/or limbs and is currently in preapproval clinical trials in the United States. In this report, we summarize the device, the implanting and programming procedures, as well as review the available clinical safety and effectiveness data. Lastly, implications on future pain management practice are discussed. The focus of this report is to provide a descriptive summary of an approved device and an up-to-date review of the current published clinical experience, rather than to address mechanisms of action, or comparative efficacy with other treatment modalities.HF10 SCS is SCS provided at a much higher frequency than traditional SCS systems. The HF10 SCS waveform consists of a biphasic charge-balanced pulse train with pulse widths usually set to 30 μsec and a pulse rate of 10 kHz. Table Under fluoroscopic guidance, stimulation leads are placed according to anatomical landmarks. Leads are placed in the midline epidural space with a staggered offset to cover the target areas in a contiguous fashion. For back and leg pain, the first electrode of one lead is usually placed at the top of the T8 vertebra and the last electrode of the second lead placed at the bottom of the T11 vertebra with some overlap of the leads at the T9/T10 disc. For neck and arm pain, the electrodes are placed along the C2–C7 vertebrae. These positions have been determined by empirical observations to be effective. The overlap provides some protection against lead migration, which can be especially common during the trial phase. This large span is provided by SCS octopolar leads that have 5-mm spacing between the contact edges. The anatomy-based surgical placement simplifies the implant procedure compared with traditional SCS, where the physician must take time to position the leads to assure that stimulation from the electrodes covers the patient's painful areas with paresthesia. This process often referred to as “paresthesia mapping” involves changing sedation and verbally interacting with the patient to discuss perceived sensations. The physician often must physically move the leads and perform intraoperative programming with the aid of a technician to optimize paresthesia distribution and comfort. With HF10 SCS, because the physician does not need to check for paresthesia coverage, the patient is allowed to remain under sedation continuously, improving surgical workflow and increasing patient comfort. After the leads are placed, the impedances are checked to ensure the integrity of the stimulation system, and the leads are verified to be in the epidural space before completing the procedure.With HF10 SCS therapy, the patient undergoes a stimulation trial similar to traditional SCS. The leads are externalized and connected to an external trial stimulator for a period ranging from a few days to a few weeks depending on the local standard of care. Patients are provided three programs initially to evaluate which program provides the best pain relief. These programs involve multiple contacts and typically utilize amplitude between 1 and 5 mA, with amplitude gradually increased if no pain relief is experienced. A program is generally tried for a period of 24 hours and continued if successful analgesia is obtained or changed to a different program if insufficient analgesia is obtained. If none of the three programs is found to provide relief, the patient will return to the clinic for another set of programs. A permanent implantable pulse generator (IPG) is typically implanted if the trial of HF10 provided greater than 50% pain relief.Patient follow-up is similar to traditional SCS, with patients coming in every 3 months to 1 year depending on the physician's usual practice. As in traditional SCS, patients are given instructions that if their pain relief changes or additional issues arise, they should return to the clinic for evaluation and potential reprogramming or lead revision. Clinical evaluation may include assessment of new pain, and a technical assessment may include impedance testing and x-rays to determine if the leads have migrated or if some of the contacts are not functioning, similar to traditional SCS.Overall, as summarized in Table The following sections review the clinical evidence collected to date on the Senza system and HF10 SCS therapy.The Senza device has been extensively reviewed and deemed safe by regulatory agencies and has since been approved for use in Europe and Australia. This section will summarize the system's design as it relates to safety and clinical safety results.The Senza system has been designed to deliver a safe waveform—a current-controlled pulse delivered at 10,000 Hz. The waveform maintains charge balance, an important aspect of neurostimulation safety, through the use of “active charge recovery.” With the Senza system, the current is “pushed out” then “pulled in” by the electronic circuits, where both phases are actively controlled in a precise manner. Active charge recovery can allow the charge balance to happen much more quickly and controllably than “passive recharge” where the current is pushed out and is then passively recovered, as is the case with traditional low-frequency SCS therapy. Active recharge allows the stimulation frequency to reach the high rates used in HF10 SCS while still maintaining charge balance, important for safely delivering electrical pulses to the tissues.To test for safety, Butt et al. More recent safety data comes from a prospective, multicenter, open-label clinical trial at two European centers: Department of Anesthesia and Pain Management, AZ Nikolaas, St Niklaas, Belgium, and The Pain Management and Neuromodulation Centre, Guy's and St. Thomas’ Hospital, London, UK In addition to collection of adverse events, the centers performed neurologic exams at baseline and at study follow-ups. This data were reviewed by an independent panel of neurologists. No evidence of neurologic deficit or dysfunction related to the stimulation was observed.Overall, this long-term data demonstrates that the Senza system's safety profile is similar to that of traditional SCS and that when adverse events do occur, they are generally mild and reversible.The effectiveness of the Senza system can be evaluated by assessing the outcomes from two prospective multicenter studies, as well as a number of smaller patient cohorts. The most compelling results originate from patients with back and leg pain, but there are also limited data on patients with other conditions, including complex regional pain syndrome (CRPS).P < 0.001]) and back pain scores (8.12–1.88 [P < 0.001]) with HF10 SCS. The vast majority  of patients preferred HF10 SCS therapy over conventional therapy.The first clinical study of HF10 SCS was a feasibility study in 24 patients at five U.S. centers with chronic predominant back pain who were candidates for traditional SCS As just described, efficacy results are also available from a prospective, multicenter, open-label clinical trial for patients with chronic intractable back pain with 2-year follow-up conducted in Europe In this study, patients were also evaluated in subpopulations based on their clinical history P < 0.001) and a 62% reduction at 12 months (P < 0.001). Most patients (65%) had at least 50% leg pain relief at 12 months compared with baseline. Back pain was also reduced, with a 62% reduction at 12 months compared with baseline, indicating that HF10 SCS can simultaneously improve leg pain and back pain.Another post hoc subgroup analysis presented by Smet and colleagues P ≤ 0.001) from baseline to post-trial (N = 172) as well as 3.6 overall NRS reduction  from baseline to 12 months post-implant (N = 58). The NRS for back pain was collected in a subset of patients with back pain only (N = 30) and showed a reduction from 7.1 ± 1.7 at baseline to 3.3 ± 1.8 at 3 months and 3.2 ± 2.2 at 6 months (P < 0.001), with the results being similar to those published for the European study In addition to the formal clinical studies, data from four Combined, these results provide evidence that HF10 SCS therapy is able to provide effective relief for many patients with chronic back, leg, and upper body pain due to various underlying diagnoses.In addition to the larger studies, a number of single centers have confirmed that HF10 provides relief of chronic pain under real-world conditions and in a wider variety of pain conditions through retrospective data analyses.Al-Kaisy and colleagues Verrills et al. report on a cohort of chronic back pain patients who failed traditional SCS Finally, Salmon This report describes recent experience with the HF10 SCS to treat chronic pain. HF10 SCS has been shown to address pain areas that traditional SCS therapy is known to be less effective in treating, including the low back This therapy overcomes many of the known limitations of traditional SCS therapy. In particular, HF10 SCS does not require the patient to feel paresthesia, which some patients may find uncomfortable One of the most compelling aspects of this therapy is that the pain relief results obtained are at least as favorable as traditional SCS therapy  reduce the risk of overestimating the magnitude of the treatment effect It is worthwhile noting the results from a 33-patient, randomized, two-period, cross-over study on 5-kHz SCS Overall, we believe that HF10 SCS may have a significant impact on a physician's ability to treat selected patients in pain and may offer an improved patient experience with the HF10 SCS therapy.We have described an innovative approach to SCS, HF10 SCS, which uses 10-kHz high-frequency stimulation to provide pain relief without paresthesia and with more predictable procedure times. Data from multiple sources, including a multicenter, prospective, clinical trial, show that the therapy appears to provide substantial back pain relief and may provide improved pain relief in other body areas and for complex pain, even for patients who have previously failed SCS and/or PNFS. This therapy may change the way pain patients are treated with neuromodulation, potentially moving SCS to a place earlier in the treatment continuum.


The burden of disease due to norovirus infection has been well described in the general United States population, but studies of norovirus occurrence among persons with chronic medical conditions have been limited mostly to the immunocompromised. We assessed the impact of norovirus gastroenteritis on health care utilization in US subjects with a range of chronic medical conditions.We performed a retrospective cohort study using MarketScan data from July 2002 to December 2013, comparing the rates of emergency department visits, outpatient visits and hospitalizations among patients with chronic conditions  with those in a healthy population. We estimated the rates of these outcomes due to norovirus gastroenteritis using an indirect modelling approach whereby cases of gastroenteritis of unknown cause and not attributed to a range of other causes were assumed to be due to norovirus.Hospitalization rates for norovirus gastroenteritis were higher in all of the risk groups analyzed compared with data in otherwise healthy subjects, ranging from 3.2 per 10,000 person-years in persons with chronic respiratory conditions, to 23.1 per 10,000 person-years in persons with chronic renal conditions, compared to 2.1 per 10,000 among persons without chronic conditions. Over 51% of all norovirus hospitalizations occurred in the 37% of the population with some form of chronic medical condition. Outpatient visits for norovirus gastroenteritis were also increased in persons with chronic gastrointestinal or immunocompromising conditions.Norovirus gastroenteritis leads to significantly higher rates of healthcare utilization in patients with a chronic medical condition compared to patients without any such condition. Norovirus is the most common cause of acute gastroenteritis (AGE) . TransmiAs molecular diagnostic techniques capable of identifying norovirus are not in routine use there has been limited direct laboratory-confirmed case evaluation of the NGE burden, indirect modelling techniques that do not require a viral time series based on laboratory confirmed cases have been used recently to assess the NGE burden –11.Modelling studies estimate that in the United States (US) the all-age annual incidence of NGE-associated hospital discharges was 2.4/10,000 population 1996–2007) . Norovir–2007 . NBased on a similar modelling approach to that used previously to estimate the overall burden of norovirus disease , we usedThis was a retrospective cohort study designed to compare episode rates of AGE and NGE in terms of ED visits, outpatient visits and hospitalizations in the patient population with chronic conditions , against rates in the general population. A secondary study objective was to compare ED visits, outpatient visits and hospitalization episode rates per age category .The Truven Health MarketScan® Commercial Claims and Encounters Database used in this study contains healthcare utilization data for over 140 million individuals in the US enrolled from a variety of public and private healthcare plans since 2001. The study population included all individuals with at least 6 months of previous continuous enrollment record in the database between July 2002 and December 2013.th revision (ICD-9) code indicating cause unspecified and cause-specified AGE  of ED visits, outpatient visits and hospitalizations between July 2002 until June 2013 with an International Classification of Disease 9fied AGE . For casfied AGE . PatientClostridium difficile, and parasite-associated events) in each month  = the relative contribution of each of the four specified pathogens,i,j = counts of cause-specified events in month i of the year j, by age and by risk group,N-eventsƴ = the relative contribution of time,i,j = month j in year i.TimeAssuming that any remaining seasonality not explained by the regression model is due to NGE, the number of NGE events in each month was calculated as the difference between predicted cause-unspecified counts and the observed cause-unspecified counts . Also assuming that there is a minimum monthly residual for each seasonal year with no NGE events, the estimated number of NGE events was calculated as the difference between the residual of the model and the minimum residual for that seasonal year (from July through June) . EstimatThe AGE and NGE episode rate was defined as the ratio of the number of events during the study period, divided by the total person-time  of eligible population (or specific patient population) per age group. Person-time was estimated by adding up the number of person-days contributed, per month or per season from July to June of the following year, by each person to each specific age group and each specific risk group. Rates were expressed per 10,000 person-years. We thus estimated the rates of healthcare visits due to AGE and NGE for the overall population, for each of the chronic condition groups, for those who had more than one of these conditions, and for those without any chronic condition. To account for important differences in the age distribution of some of these conditions, we age-standardized all rates to the overall population in the MarketScan database, using the same age categories described above. The 95% confidence intervals (CIs) for the NGE rates were estimated using Monte Carlo simulations accounting for the model uncertainties.The study population comprised 9,028,466 individuals registered in the MarketScan database, of which 56.4% were female , particuUtilization rates were rather constant over the years of the study, with some increases in the season 2002–2003 and the seasons 2005–2006 and 2006–2007 , correspThe presence of a chronic condition was identified in 36.5% of the MarketScan population . With thMean annual rates of AGE and NGE-attributable outpatient and ED visits in patients with a chronic medical condition were similar to rates in the total population, with the exception of increases in both AGE and NGE-attributable outpatient visits in persons with gastrointestinal conditions, and of NGE-attributable outpatient visits in persons with immunocompromising conditions . In contConsidering each age category, NGE-attributable hospitalization rates in patients with a chronic medical condition were higher in all age groups and for all chronic condition groups, relative to the healthy population without a chronic condition . The higThe total proportion of NGE hospitalizations across all age groups that occurred in the population with a chronic condition was 51.6%. In the different age groups the proportions were 14.3%, 43.4%, 51.7%, 71.7%, 67.5% and 85.5% among 0–4, 5–17, 18–64, 65–74, 75–84 and 85+ year olds, respectively. The most important contributing groups were patients with gastrointestinal conditions, and the group of patients with two or more chronic conditions. In these groups the proportion of NGE hospitalizations  was 19.9% and 7.3%, respectively. The proportion of NGE-attributable ED and outpatient visits occurring in the population with any chronic medical conditions across all age groups was 18.0% and 20.4%, respectively.Our study is the first to assess the burden of NGE in individuals with chronic medical conditions. NGE-attributable hospitalizations were substantially higher in patients with a chronic condition compared to the total population. Higher rates of hospitalization, in the absence of increased rates of higher ED or outpatient department visit rates, may reflect more severe disease or a higher propensity to admit patients with underlying chronic conditions.In contrast to other chronic conditions, patients with chronic gastrointestinal or immunocompromising conditions had higher rates of all health utilization outcomes for NGE. This finding is consistent with previous reports suggesting that NGE may be particularly severe or protracted in immunocompromised patients ,13–15. TSeveral recent modelling studies have shown the substantial burden caused by NGE in terms of hospitalizations, deaths and ambulatory visits in the US and countries in Europe ,10,11. HFor groups over 5 years of age, our age-specific estimates of NGE-attributable outpatient and ED visits for the overall population are comparable to those reported by Gastañaduy et al , who useThe causal agent in cases of AGE is infrequently diagnosed, which means that direct estimates of the disease burden using laboratory-confirmed cases are likely to be substantial underestimates. While indirect modelling techniques overcome this limitation in the data, the present model relies on the assumption that NGE makes up the vast majority of AGE when an etiologic agent was not identified, leading to possible over-estimation of the disease burden. Other potential limitations include the inability of the model to control for seasonal factors that may have influenced health care utilization behaviors, such as holiday periods, the assumption that ICD-coding practices remained constant or that the propensity of patients to seek care or be hospitalized did not change over the study period, and changes in trends for certain etiological agents such as decreases in rotavirus hospitalizations following the introduction of rotavirus vaccination in 2009. In addition, our use of a database based on insurance claims automatically excludes the uninsured, thereby limiting the extrapolation to the entire US population. In the present study, when comparing the rates among different risk groups, these limitations may be less important as they can be expected to occur similarly across the different groups.We identified pre-existing chronic medical conditions exclusively based on ICD-9 codes. We cannot be sure that every subject classified as having a chronic condition was actively suffering from the condition. Reassuringly, our estimated rates of chronic conditions are very comparable to previous reports for the US population, although such comparisons are hampered by differences in classification of chronic conditions and prevalence estimation methods. In a recent analysis of the prevalence of multiple chronic conditions among US adults, Ward et al found 24.3% of noninstitutionalized US adults to have only 1 chronic condition and 25.5.1% to have more than 1 . In our We observed the highest relative risk estimates for NGE-associated hospitalizations among 5–17 and 18–64 year olds with chronic medical conditions, as compared with the same age groups in the healthy population. This is not totally unexpected as hospitalization rates in healthy 5–17 or 18–64 year olds can be expected to be relatively low, whereas hospitalization rates among healthy small children and elderly will be relatively high already. Similar observations have been made for populations at high versus low risk of influenza complications, in which the increase in hospitalization outcomes was proportionally higher in at-risk 18–64 year olds than in the at-risk elderly . The praDevelopment of norovirus vaccines is underway with data from a Phase 1 clinical study showing a candidate bivalent VLP norovirus vaccine to be immunogenic and reducing the severity of norovirus disease in a human challenge model ,25. StudThe presence of a chronic pre-existing medical condition leads to higher rates of health care utilization due to NGE. A disproportionate segment (52%) of all norovirus hospitalizations occurred in the 37% of the population that had at least one chronic medical condition, who along with children under 5 and elderly adults, may merit special attention in NGE prevention strategies. The use of modeling techniques to derive norovirus specific AGE rates and the reliance upon coded diagnoses suggest caution in interpreting our results pending further confirmation.S1 Fig(TIFF)Click here for additional data file.S1 Table*includes subjects with only one chronic condition. ImmunoC = immunocompromising condition, Hep & Panc = hepatic and pancreatic condition, ≥ 2 = having at least 2 chronic conditions.(DOCX)Click here for additional data file.


Central nervous system (CNS) diseases are difficult to treat because of the blood-brain barrier (BBB), which prevents most drugs from entering into the brain. Intranasal (IN) administration is a promising approach for drug delivery to the brain, bypassing the BBB; however, its application has been restricted to particularly potent substances and it does not offer localized delivery to specific brain sites. Focused ultrasound (FUS) in combination with microbubbles can deliver drugs to the brain at targeted locations. The present study proposed to combine these two different platform techniques (FUS+IN) for enhancing the delivery efficiency of intranasally administered drugs at a targeted location. After IN administration of 40 kDa fluorescently-labeled dextran as the model drug, FUS targeted at one region within the caudate putamen of mouse brains was applied in the presence of systemically administered microbubbles. To compare with the conventional FUS technique, in which intravenous (IV) drug injection is employed, FUS was also applied after IV injection of the same amount of dextran in another group of mice. Dextran delivery outcomes were evaluated using fluorescence imaging of brain slices. The results showed that FUS+IN enhanced drug delivery within the targeted region compared with that achieved by IN only. Despite the fact that the IN route has limited drug absorption across the nasal mucosa, the delivery efficiency of FUS+IN was not significantly different from that of FUS+IV. As a new drug delivery platform, the FUS+IN technique is potentially useful for treating CNS diseases. Central nervous system (CNS) diseases are difficult to treat as most systemically administered therapeutic agents cannot penetrate the blood-brain barrier (BBB). The BBB prevents the access of ∼100% of large-molecule drugs and ∼98% of small-molecule drugs  have been found to provide unique pathways for the noninvasive delivery of therapeutic agents to the CNS FUS in combination with microbubbles can increase the BBB permeability for targeted brain drug delivery with no or minimal tissue damage The physical mechanisms for FUS-enhanced drug delivery have been under extensive investigation Therefore, we hypothesized that FUS can enhance IN drug delivery efficiency at the targeted brain sites. There were two objectives of this study: (1) to explore the feasibility of this new brain drug delivery strategy–FUS in combination with IN (FUS+IN); (2) to compare the drug delivery outcomes of this new strategy with the conventional approach–FUS coupled with IV injection (FUS+IV). Wild-type mice were used as the animal model and a 40 kDa fluorescently-labeled dextran was used as the model drug This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Columbia University Institutional Animal Care and Use Committee. All surgery was performed under isoflurane anesthesia, and all efforts were made to minimize animal suffering. The animal body temperature was maintained using a heating pad.A total of 26 male C57BL/6 mice  were used in this study. Among these 26 mice, 20 were divided into the following four experimental groups with n = 5 for each group. (1) Control group: no dextran delivery and no FUS. (2) IN sham group: IN administration of the dextran without FUS. (3) IN treatment group: IN administration of the dextran with FUS applied on the left side of the caudate putamen while the contralateral right side was not sonicated. (4) IV treatment group: IV injection of the dextran with FUS applied on the left side of the caudate putamen while the contralateral right side was not sonicated. The remaining 6 mice were treated following the same protocol as groups 3 and 4 with n = 3 for each group for the purpose of assessing the safety of the FUS treatment.8 number of microbubbles per mL.Microbubbles comprised of a 90 mol% 1,2-distearoyl-sn-glycero-3- phosphocholine (DSPC) and 10 mol% 1,2-distearoyl-sn-glycero-3- phosphoethanolamine-N-[methoxy(polyethylene glycol)2000] (DSPE-PEG2000)  lipid-shell and a perfluorobutane  gas-core were manufactured in-house. Size-selected microbubbles with a median diameter of 4–5 µm were isolated from a poly-dispersed microbubble distribution using a differential centrifugation method In the control group (group 1), no dextran was administered.In the IN sham group (group 2) and IN treatment group (group 3), ∼2 mg of 40 kDa Texas Red-labeled dextran  was administered intranasally following procedures used before For the IV treatment group (group 4), the same amount of dextran  was administered by IV bolus injection through the tail vein.For the IN treatment group (group 3) and IV treatment group (group 4), the mice were sonicated at a targeted brain location using an experimental setup illustrated in A single-element FUS transducer  was driven by a function generator  through a nominal 50 dB gain power amplifier . The lateral and axial dimensions of the FUS focal region measured in water were 1.2 mm and 13.0 mm. A custom-built truncated cone was attached to the transducer and filled with degassed water to provide acoustic coupling. The cone was immersed in a degassed-water container. The bottom of the water container had a window sealed with an almost acoustically and optically transparent membrane. The container was placed on the mouse head and coupled with degassed ultrasound gel. Acoustic emissions arising from microbubble cavitation were acquired by a pulse-echo transducer . It was positioned through a central hole of the FUS transducer and confocally aligned with the FUS transducer. The signals received by the pulse-echo transducer were amplified by 20 dB  and then digitized  at a sampling frequency of 50 MHz.Before FUS sonication, each mouse was positioned prone with its head immobilized by a stereotaxic frame . The fur on the mouse head was removed with an electric clipper and a depilatory cream. A modified 27G×½ butterfly catheter  was inserted into the tail vein for IV injection. The FUS transducer was moved 2 mm lateral of the sagittal suture and 6 mm anterior of the lambdoid suture using a previously described grid positioning method For all the mice used in the current study, a 1-h period was allowed after finishing IN and IV dextran administration to enable the dextran to diffuse into the brain parenchyma . At the The acoustic emission analysis method was the same as that described previously For each ultrasound pulse, its frequency spectrum between 3 and 9 MHz was used for stable and inertial cavitation quantification to eliminate any contributions from the FUS beam (frequency  = 1.5 MHz). The broadband emission from inertial cavitation was obtained using a comb filter, which had a cut-off band around each harmonic and ultraharmonic frequencies with bandwidths of 350 kHz and 100 kHz, respectively. The root mean square of the filtered spectra was calculated to represent the inertial cavitation level for each pulse. The stable cavitation level was obtained by first calculating the root mean square of the amplitudes of all harmonics and then subtracting the corresponding inertial cavitation level. The inertial cavitation activity was thus not included in the stable cavitation quantification. The stable and inertial cavitation doses were calculated by integrating the stable and inertial cavitation levels over the total ultrasound exposure time, respectively. The final doses were then calculated by subtracting baseline doses obtained based on signals acquired prior to microbubble injection.The dextran delivery outcomes were determined by quantifying the fluorescence intensities within the targeted caudate putamen. Nine horizontal sections with four dorsal sections, four ventral sections, and a reference midline section were selected from each brain for the analysis. All the fluorescence images were first normalized by their corresponding exposure time. Then, a circular region-of-interest  was manually aligned with the sonicated and control caudate putamen on each section, and the spatial average fluorescence intensity within the ROI was calculated using ImageJ . The diameter of the ROI was selected to be the same as the FUS transducer lateral focal region dimension (1.2 mm). The reported fluorescence intensity for each side of the brain was the sum of the calculated fluorescence intensities within the ROI of all nine sections.An unpaired two-tailed Student's t-test using GraphPad Prism  was used to compare between groups. A P value of 0.05 was considered to represent a significant difference in all the analyses. All data were expressed as mean ± standard deviation.When IV injection was used for dextran delivery instead of IN, localized dextran accumulation was observed at the targeted caudate putamen. It should be noted that although the fluorescence intensities of FUS+IN and FUS+IV groups were at a similar level, distinct dextran distribution characteristics were observed, as representatively shown in Preliminary safety assessment of FUS sonication was performed by histological analysis. Minor microhemorrhages were observed in both FUS+IN and FUS+IV groups. Our findings validated the hypothesis that FUS can enhance the delivery of IN-administered drugs at a targeted brain location. It is the first time that this novel combination of these two different platform techniques is proposed. Numerous studies have demonstrated that the IN route can provide a non-invasive approach for brain drug delivery, but none of them have shown that an enhanced delivery at a targeted location was possible. The FUS+IN technique offers a potentially useful drug delivery platform for treating CNS diseases.By combining FUS with IN drug delivery, we achieved a significant increase in drug accumulation in the targeted brain site, thus improving the bioavailability and biodistribution of the IN administered drug. We confirmed that IN-administered dextran could be delivered to the brain, bypassing the BBB, but it had low delivery efficiency and was non-targeted . IN admiOur findings should not be interpreted as if to establish that the IN administration when combined with FUS is superior for targeted brain drug delivery than FUS+IV. On the contrary, FUS+IV is better at targeted delivery than FUS+IN. IN administration is associated with drug delivery to the whole brain, whereas the dextran injected through IV can only be delivered at the FUS targeted region where the BBB was opened. Therefore, for drugs exhibiting high stability in circulation and low systemic side-effects, such as dextrans (the half-life of the 40 kDa dextran in blood is about 7.5 hours We clearly showed brain uptake of the IN administrated dextran in both the IN only group and FUS+IN group . In addiFurther investigation is warranted on the mechanisms for FUS-enhanced IN drug delivery. It has been demonstrated that FUS and microbubbles can generate shear stress on the tissue and increase hydraulic conductivity of the interstitial space, leading to increased drug diffusion Whole brain histological examination showed minor microhemorrhages within the FUS targeted region. The damage induced by the FUS technique for FUS+IN and FUS+IV groups were similar . Figure IN administration is a promising approach for brain drug delivery. The present study demonstrated for the first time that FUS can enhance IN drug delivery efficiency at the targeted brain location. Despite the fact that the IN route has limited drug absorption across the nasal mucosa, the FUS+IN technique achieved similar drug delivery efficiency within the targeted region compared with the conventional FUS+IV approach. Future studies will explore its potential as a drug delivery platform in treating CNS diseases.


Neurospora crassa is an important model organism for filamentous fungi as well as for circadian biology and photobiology. Although the community-accumulated tool set for the molecular analysis of Neurospora is extensive, two components are missing: (1) dependable reference genes whose level of expression are relatively constant across light/dark cycles and as a function of time of day and (2) a catalog of primers specifically designed for real-time PCR (RT-PCR). To address the first of these we have identified genes that are optimal for use as reference genes in RT-PCR across a wide range of expression levels; the mRNA/transcripts from these genes have potential for use as reference noncycling transcripts outside of Neurospora. In addition, we have generated a genome-wide set of RT-PCR primers, thereby streamlining the analysis of gene expression. In validation studies these primers successfully identified target mRNAs arising from 70% (34 of 49) of all tested genes and from all (28) of the moderately to highly expressed tested genes. To achieve this, programs to aid in primer design have been developed . However, existing Internet-based primer design web sites are not high-throughput, leaving the user to provide individually not only the gene but also the genomic sequence of interest. Designing individual primers for target genes is a time-consuming process, and very often multiple primers for a single gene are required as part of the validation process; designing multiple RT-PCR primers per gene for multiple genes can quickly become tedious, and a genome-scale data set is optimal for large-scale efforts  is a rapid means of assessing an organism’s response to changing environmental conditions. In comparison to other techniques that also track the steady-state levels of mRNA such as Northern blot analysis, microarrays, and RNA-Seq, RT-PCR is cheaper, faster, requires less mRNA, and produces large amounts of data with limited effort. Two key pieces contributing to the success of this technique are (1) the efficient design of optimal primers that are used in the reaction and (2) reference genes to serve as internal controls for template inputs. RT-PCR primer design must take into account sequence position, exon position, product size, melting temperature (Thttp://www.nih.gov/science/models/). Its 43-Mb genome contains approximately 11,000 predicted genes (http://fungidb.org/fungidb/). Haploid progeny, regulatable promoters   and pass them to Primer3 , which automatically generated a list of candidate primers with designated parameters (Table S1). Three to five primer pairs were selected and primers were ordered for 49 genes and supplied in 96-well plates , diluted, and mixed by a Biomek NX robot . The 49 genes were selected because they were potentially circadianly regulated genes based on previous work  using the auto Ct command. Each Ct value represents a single technical replicate from the mix of the cDNA sample.The functionality of each of the 49 selected primer pairs was tested. Each unique primer pair was assigned a C10 FPKM values for the remaining genes, comparing within time point variation against between time point variation for each gene and rejected genes for which P < 0.05. Because we chose genes to reject, we did not adjust for multiple comparisons because that would introduce more type I errors while reducing type II errors; our goal is to screen out genes with strong variation and the cutoff is itself arbitrary, making such an adjustment unnecessary. This left us with a set of expressing, noncircadian, ANOVA selected (ENCAS) genes. In the last step of our analysis, linear regressions were performed on the same log10 FPKM values used for all previous calculations for each ENCAS gene using the scikit learn package (a set of data mining and machine learning tools) in python. These linear regressions were used to create prediction intervals (95%) for the expected mean at each time point for each gene. Genes were then scored based on the sum of the absolute value of the differences of the upper and lower bounds of these prediction intervals with respect to the overall mean expression of the gene at each time point. Under this scoring metric, which we have termed the prediction interval ranking score (PIRS), genes that are more stable across circadian time will have lower scores. For both the light-induction and quinic acid–induction (QA) time courses, neither the JTK cycle nor the ANOVA-based screening methods were applicable because the time courses were not circadian in nature and did not contain replicates. It is important to note that the light-induction data were all normalized to the initial time point for each gene; because of this, we could not examine absolute expression levels and identify the best genes from each expression quintile for the light-induction data set.To determine which genes showed the least variation across time and would be optimal for use as reference genes over circadian time, we first rejected extremely low-expressing and nondetected genes with a log-transformed fragments per kilobase per million (FPKM) value cutoff of −2 from our RNA-Seq data. Next, we used our Jonckheere-Terpstra-Kendall (JTK) cycle .In addition, as an independent analysis, normalized, log-transformed FPKM values  we assigned a prediction interval ranking score (PIRS) to each gene. The resulting PIRS was minimized for genes that exhibit both flat expression as a function of time and low variance in observed expression at each time point . The average FPKM values of the noncircadian genes ranged from log10 0.087 to log10 3.16, whereas the non-QA-induced genes ranged from log10 −1.30 to log10 1.49, giving a wide variety of expression levels from which to choose from when selecting the appropriate reference gene. Not surprisingly, of the genes with predicted function, classifications included ribosomal, cytoskeletal, or protease genes, all proteins that could logically be expected to be housekeeping genes.For both the circadian and QA induction datasets, we selected the top two genes in each quintile of expression . Becauseigure S3 ]. In allFigure S3).As a way to identify genes that could be used as reference genes, we subjected all of the genes that were in our ENCAS set to pareto optimization to select nondominated genes across the three data sets. Pareto optimization is a technique for multi-objective optimization that aims to find the set of all choices that might be considered optimal under any of the possible combinations of weightings of individual scores . The algMaterials and Methods). In addition, each gene should have five primer pair sets that are not identical to one another. In the case of mono-exonic genes, the primer pairs were all selected from sequence in the last 500 base pairs of the gene  (File S1). The outline of the database can be seen in Using our program we were able to successfully design primers for 10,798 (99.9%) of the 10,802 ORFs predicted by Assembly 12 of the Neurospora genome  were successfully able to return Ct values under 30. The remaining 15 (31%) genes had Ct values over 30 across the three primer pairs, indicating that they were very poorly expressed. For five of the 15 genes that had Ct values over 30, we tested the remaining two primer sets to see if we could obtain a reliable signal. None of the genes tested were able to generate meaningful Ct values, suggesting that genes that were not detected by our designed primers are either in extremely low abundance under the conditions tested or poor candidates for RT-PCR analysis rather than being an inherent program primer design failure. Furthermore, when comparing the average Ct values of all the tested primer pairs for genes with Ct values below 30 to the average Ct values of test primer pairs for genes with Ct values above 30, the SD of the average Ct value was strikingly lower in genes with Ct values below 30 than those genes with Ct values over 30 . The average SD for genes where all primer pairs reported a Ct values below 30 was 0.52 , whereas the average SD of those genes with a Ct value above 30  was 2.45.Of the 49 genes analyzed, 34 (69%) returned a Cer pairs , meaning over 30   . To develop manually a single RT-PCR primer sequence that meets all of the standards set forth in our criteria takes approximately 5–10 min, suggesting that these predesigned primers may afford considerable time-savings.Finding a functional and truly constitutive “housekeeping” gene to serve as a reference gene in RT-PCR has often been difficult because changing cell or experimental conditions have been shown to have a large influence on gene expression levels , i.e., rFigure S1) and therefore have the potential to be useful to other species that serve as model organisms.Circadian rhythms present a unique challenge for those investigating mRNA levels via RT-PCR. Because as much as 40% of the genome could be rhythmic at the mRNA level (