Genome sequencing will be increasingly used in the clinical setting to tailor antimicrobial prescribing and inform infection control outbreaks. A recent technological innovation that could reduce the delay between pathogen sampling and data generation is single molecule sequencing. An example of this technology, which is undergoing evaluation through an early access programme, is the Oxford Nanopore MinION.We undertook a feasibility study on six clinically significant pathogens, comparing the MinION to the Illumina MiSeq and PacBio RSII platforms. Genomic DNA was prepared and sequenced using the MinION as instructed by the manufacturer, and Illumina MiSeq and PacBio sequencing was performed using established methods.mecA gene in an MRSA isolate.An evaluation of the accuracy of the MinION based on sequencing of an MRSA isolate showed that error rates were higher in the MinION reads, but provided an even coverage across the entire genome length. The MinION detected all of the expected carbapenemases and ESBL genes in five Gram-negative isolates and the The MinION can detect the presence of acquired resistance genes, but improvements in accuracy are needed so that antimicrobial resistance associated with mutations in chromosomal genes can be identified. Achieving this using WGS technology has the potential advantage that this reveals the entire gene repertoire, and if linked to automated interpretation tools would represent an improvement on other molecular methods such as PCR that target specific gene panels. A recent technological innovation that has the potential to generate rapid sequence data is single-molecule sequencing, and a device based on this approach (Oxford Nanopore MinION) is under evaluation by the scientific community through an early access programme. This device is about the size of a mobile phone, connects to a computer via a USB port, and streams data via the Internet to the base-calling software. The first publication of its performance using the version R6 flow cell reported high error rates, with only 10% of reads from a 36 h sequencing run of lambda phage mapping to the reference, and <1% of the data generated matching the reference.3 A second publication reported whole-genome shotgun sequencing of Escherichia coli using the R7 and R7.3 flow cells, in which the authors concluded that further work was required to determine appropriate algorithms for common analyses.4 A subsequent publication reported the use of the MinION to resolve the structure and chromosomal insertion site of a composite antibiotic resistance island in Salmonella Typhi Haplotype 58.5 Recently, the ability of the MinION to rapidly distinguish outbreak from non-outbreak strains of Salmonella Enteritidis was evaluated as part of a wider study into a Salmonella outbreak.6 Here, we report the results of a comparative study of clinically significant antibiotic-resistant pathogens, in which sequence data generated by the MinION were compared with those generated by the Illumina MiSeq and PacBio RSII platforms.The emergence of mobile genetic elements encoding ESBL and carbapenemases in Gram-negative bacilli represents a major threat to human health.http://www.ebi.ac.uk/ena/) .Bacterial genomic DNA was extracted using the QiaAMP DNA Mini kit . MinION sample preparation was performed according to the manufacturer's instructions and sequenced on R7 flow cells. The sequencing script was run in MinKNOW V0.45.3.9, and Metrichor V0.17 was used for basecalling using the R7 1.2 and 1.3 2D workflows. Illumina sequencing was carried out as described previously. PacBio SMRT  sequencing was used to generate long reads of MRSA SASCBU26. Library preparation (SMRTbell) was carried out following the manufacturer's protocol . The SMRTbell library was bound with version P4 polymerase, and the subsequent complexes loaded on to V3 SMRTcells using MagBeads. The complexes were immediately sequenced using version C2 chemistry. All sequence data have been submitted to the European Nucleotide Archive (ENA) , which produced a relatively stringent alignment. Under these mapping criteria only 25% of the MinION reads aligned to the reference, although this provided an even mapping depth across the genome . Combined data from two MinION R7 flow cells were compared with sequence data generated previously using the Illumina MiSeq and PacBio RSII platforms.me Table . Error rme Table . There wAcinetobacter baumannii AB223 positive for an OXA-23 carbapenemase; Enterobacter cloacae EC1a positive for an IMP-1 carbapenemase; Klebsiella pneumoniae KP652 and E. coli Eco216 both positive for CTX-M-15 ESBL; and E. cloacae EC302, which was previously reported not to contain any ESBL or carbapenemase genes.2 The sixth isolate was MRSA SASCBU26, which is positive for the methicillin resistance gene (mecA) present on a chromosomally integrated SCCmec type IVc element.8 Miseq data were assembled as described in the Supplementary Methods section. It was not possible to assemble MinION reads due to the high error rate but, as the long reads generated by the MinION often span the length of a gene, the full gene sequence was available from read data without assembly. A consensus sequence was called for each gene as described in the Supplementary Methods section. Sequence data generated by the MiSeq and MinION were independently interrogated for the presence of genes encoding resistance using a publicly available database of bacterial antimicrobial resistance genes . A clustered version of this database was used, in which gene sequences in the database with 90% sequence identity or greater have been clustered together.7 In total, 75 gene cluster matches were identified for the six isolates, of which 64 (85%) matches were found in both the MinION and MiSeq results, and 11 were identified in data generated by one of the two technologies. Table S1 shows further detail of the matches found for each isolate in the MiSeq and MinION data. All of the expected carbapenemases and ESBL genes in the Gram-negative isolates and the mecA gene in the MRSA isolate were identified from MiSeq and MinION data. Comparison of the percentage identity (%ID) of the matches common to MiSeq and the MinION data for each isolate demonstrated a correlation between the two .Next, we evaluated the performance of the MinION against the MiSeq platform for the detection of acquired genes associated with clinically significant antimicrobial resistance using a panel of six isolates. Five were Gram-negative bacterial species that had been sequenced previously, and were known to possess phenotypic antibiotic resistance and a range of plasmid-mediated ESBLs and carbapenemases, as follows: aac3-IIa in the data from the E. coli isolate Eco216. By checking the MiSeq data generated for the same sample in a previous study,2 we confirmed the presence of these genes. Both genes are commonly plasmid encoded, suggesting that the plasmid was lost during preparation of DNA for our new MiSeq sequencing run. Conversely, a plasmid-encoded β-lactamase gene (blaMIR) that had gone undetected in our previous study2 was identified in E. cloacae EC302 from both the MiSeq and MinION data. PacBio data were also available for SASCBU26, which allowed a comparison with MinION. This demonstrated an identical set of matches to those found in the MinION reads (Table S1). There was a correlation between the number of MinION reads matching a gene and the %ID of the match to the database .Eight matches were only identified in the MiSeq data, five of which were low-identity matches (<85%). The MinION data yielded three matches not found in the MiSeq data, of which one was found in a single read with a very low identity (52%) to an AmpH β-lactamase cluster. Both the MiSeq and MinION data for this isolate identified matches to a second AmpH β-lactamase cluster, suggesting that this represented assignment of the read to the wrong cluster. The remaining two matches found in the MinION data, but not the corresponding MiSeq data, were high-identity matches (>87%) to TEM-1D and 10 Affordability is also an essential criterion for NHS laboratories and other publically funded institutions. The MiSeq Dx sequencing instrument, approved for clinical use by the FDA, retails at around $125 000.11 The MinION has low capital costs, with current sample preparation requiring minimal equipment. Oxford Nanopore have proposed a model for the future in which users would be charged based on ‘pay-as-you-go’, with 3 h of sequencing on the MinION MkI projected to cost around $270.12 One potential advantage of the MinION is its portability and ease of use, which could facilitate its uptake by diagnostic laboratories that anticipate low demand for microbial sequencing, but choose to develop in-house sequencing capabilities. Based on portability, the MinION could also find utility in more remote laboratories, particularly where pathogen sequencing could be used to investigate emerging pathogens and outbreaks of unknown cause.The on-going development of a range of sequencing instruments provides an opportunity to match the needs of a specific application to performance qualities. Sequencing for clinical purposes must be both rapid and accurate if the results are to lead to information that changes the course of individual patient care or a public health intervention. The MinION can detect the presence of clinically important acquired resistance genes, but does not provide sufficient accuracy to detect resistance associated with point mutations in chromosomal genes. The inability to detect point mutations in relation to resistance is not only relevant for chromosomal genes as detection of sub-variants within a gene (cluster) can be very important, for example when distinguishing between TEM-1 and TEM52.13 However, the major barrier to clinical implementation is the availability of automated interpretation tools—which affects all platforms equally. These early evaluations suggest that direct sequencing using a mobile device could find utility in diagnostic microbiology, given further improvements to speed, accuracy and yield.A potential benefit of single-molecule sequencing is the speed at which sequence data could be generated, and the fact that data can be analysed in real time. In the event that high-quality data could be generated within the first few hours of a sequencing run, this could be combined with other methods such as rapid DNA extraction from bacterial colonies grown from the primary sample to minimize time from culture to sequence data.Department of Health, the Wellcome Trust and the Health Innovation Challenge Fund . S. R. H. and J. P. were supported by Wellcome Trust grant 098051. The MinION and reagents supplied by Oxford Nanopore were received as part of Oxford Nanopore's MinION access programme.This study was funded by a grant from the J. P. and S. J. P. have received funding for travel and accommodation from Illumina Inc. All other authors: none to declare.JAC Online .Supplementary Methods, Table S1 and Figure S1 are available as Supplementary data at


Results showed that sequence variation within CTL epitopes at the first time point (within six months of the estimated date of seroconversion) was consistent with most mutations being transmitted in the infecting viral strain rather than with escape arising within the first few weeks of infection. Escape arose throughout the first three years of infection, but slowly and steadily. Approximately one third of patients did not drive any new escape in an HLA-restricted epitope in just under two years. Patients driving several escape mutations during these two years were rare and the median and modal numbers of new escape events in each patient were one and zero respectively. Survival analysis of time to escape found that possession of a protective HLA type significantly reduced time to first escape in a patient (p = 0.01), and epitopes escaped faster in the face of a measurable CD8+ ELISpot response (p = 0.001). However, even in an HLA matched host who mounted a measurable, specific, CD8+ response the average time before the targeted epitope evolved an escape mutation was longer than two years.The existence of viral variants that escape from the selection pressures imposed by cytotoxic T-lymphocytes (CTLs) in HIV-1 infection is well documented, but it is unclear when they arise, with reported measures of the time to escape in individuals ranging from days to years. A study of participants enrolled in the SPARTAC  clinical trial allowed direct observation of the evolution of CTL escape variants in 125 adults with primary HIV-1 infection observed for up to three years. Patient HLA-type, longitudinal CD8+ T-cell responses measured by IFN-γ ELISpot and longitudinal HIV-1  The cytotoxic T-lymphocyte (CTL) arm of the immune response is thought to play a significant role in the control of HIV-1 infection. Mutations within the HIV-1 genome allow the virus to escape recognition by CTLs and so evade the immune response. These escape mutations have been well documented but observed waiting times to escape within an individual have ranged from days to years. Many studies describing CTL escape have taken a detailed look at a few patients. Our analysis is based on a cohort of 125 clinical trial participants with immunologic and viral sequence data taken at regular longitudinal time points within the first few years of infection. Results suggested that the majority of CTL-related mutations present early in infection had been transmitted in the infecting viral strain as opposed to arising in the new host due to selection pressure imposed by CTLs. Whilst the prevalence of CTL escape mutations in the dataset was high, the incidence of new escape was relatively low; around one third of patients did not drive an escape within the first two years. Patients possessing a ‘protective’ HLA genotype had a significantly shorter waiting time to first escape than those without. In ad of AIDS . However of AIDS , 9.+ T-cells or prevent antigen presentation by HLA class I molecules and so evade the immune response [env mutations that rise to fixation in the first few years represent viral adaptation to CD8+ T-cell responses [Many studies have demonstrated the capability of the HIV-1-specific CTL response to select for viral variants that escape recognition by CD8response –14. Indeesponses  whilst aesponses .+ T-cell response as well as pre-existence of compensatory mutations in sequences surrounding epitopes [CTL escape has been observed to occur throughout the different stages of HIV-1 infection, from soon after seroconversion , 17–20 tepitopes , 21, 27.epitopes –30. In tepitopes .gag, pol, nef and env genes. We used longitudinal viral sequence data covering the first three years of infection to count escape events in each of 46 epitopes in all available patient samples, and only considered periods when patients were off therapy. Approximately one third of patients drove escape in an epitope for which they were HLA-matched within their first year of infection and escape continued to be seen into the second year. However an estimated 33% of patients had still not driven any escape in their HLA-restricted epitopes across gag, pol or nef by the end of their second year off therapy. In looking at whether escape was clustered in patients, we found little evidence to suggest that this was the case. We also observed that time to escape for individual epitopes was faster in HLA-matched patients and in patients who were able to mount a measurable CD8+ T-cell response, and that patients with protective HLA alleles were more likely to have their first escape sooner.In this study we analysed the timing and extent of CTL escape in 125 patients across the HIV-1 gag, pol, env and nef), and IFN-γ CD8+ T-cell ELISpot data were collected longitudinally. Participants were followed for up to three years whilst off ART ; some participants initiated long-term ART before the end of the three years, upon reaching the trial clinical endpoint. The amount of data available at each time point is summarised in The cohort consisted of 125 treatment-naive, HIV-1 subtype B infected adults who were recruited soon after the estimated date of seroconversion  as part of the SPARTAC clinical trial and followed for up to three years see . Particiany amino acid variation in previously identified  escape sites within optimal defined epitopes in gag, pol, env or nef  the data comprised a total of 3831 epitope sequences from 122 patients in which we were able to look for variation within known escape sites. The range of times from seroconversion amongst these patients matched that of the whole cohort. Of these, 1235 contained escape mutations. However, this escape at baseline was, for almost all epitopes, equally prevalent in HLA-matched and HLA-mismatched hosts . This paed hosts . Furtherenv gene, the vast majority of epitopes in env  were already escaped at baseline, and hence whilst these were included in our analyses we were not able to comprehensively study incident escape in env. In the 2297 candidate epitopes, just 108 ‘escape’ events were observed in patients studied while off ART and only 37 of those were in epitopes in an HLA-matched patient. To see if there was evidence for a separate group of particularly fast escaping patients we compared the distribution of the number of escape events per host (in their HLA-restricted epitopes) with a random distribution, calculated using a Poisson process, and found no significant difference between the distributions . Due to the high variability of the ibutions . In factibutions  we used ibutions . Even afThis analysis is meaningful if patients with missing data would have had similar rates of escape to those observed for a full two years whilst untreated. We believe this to be a valid assumption and assess it in further detail later.Time to escape can be described either for each patient in a population  or for each epitope within each patient . By considering the first such escape event (in patients or in epitopes) the well-developed statistical tools of survival analysis become applicable. Since only 9% of patients have more than one incident escape event in an epitope for which they are HLA-matched  very litIn assessing the suitability of these analyses we also needed to address the question of whether the time at which patients are censored from the survival analysis (which occurs when they have no further sequence data) is independent of the rate at which they drive escape mutations. For some patients the fact that we were unable to obtain sequence data at later time points was a direct consequence of the fact that they had reached the trial primary endpoint (a CD4 cell count < 350 cells /μ l or commencing long-term ART) within the time frame of our observation period. Hence if, as some might hypothesise, escape events can drive clinical failure, it is possible that censored patients would have high rates of escape that we’d fail to observe.We looked in more detail at those patients who reached the trial endpoint  within our three-year observation period. An incident escape was observed in 6/20 patients for whom we had no sequence data after clinical failure compared to 7/24 patients who did have sequence data after clinical failure. In the 7 patients in the latter group who drove at least one escape event, there were just two cases where an escape mutation was first seen at the time point after clinical failure (see nef and gag (the genes for which the most data were available) were used to avoid underestimating the occurrence of escape due to missing data. This left 65 patients for this analysis. Note that this number includes both treated and untreated patients (range of previous ART duration 2–12 months). We also considered these patient groups separately  whilst an estimated 33% of patients did not drive any new escape in their HLA-restricted epitopes in 92 weeks off ART %). Note also that the slope of the curve is close to constant; there is no indication of a propensity for many incident escape events to arise early during the observation period and fewer later.Although there was no evidence for a separate group of patients with a high frequency of escape , there w+ T-cell ELISpot response to that epitope in that host . Even here the incidence of escape was low , and by extending the list of epitopes beyond the set of optimal defined epitopes see . Repeate+ T-cells or the antibody response. But understanding the impact of these alternative selective pressures is not necessary for assessing the concern that our methods are blind to CTL escape occurring in previously unidentified epitopes. We have previously shown that HLA-association studies favour the detection of rapid escape mutations. Hence any escape in unknown or poorly characterised epitopes that we have not looked in is likely to be slower than the time frame of this study and consequently not affect our conclusions [We acknowledge that even this extended epitope list is unlikely to be complete, and that the Los Alamos ‘A-list’ will evolve to include new peptides and HLA-restrictions , 41. Indclusions .Our definition of escape was also based on consensus viral sequence data; hence we were unable to detect the presence of any low frequency variants unlike with deep sequencing methods , 17. HowThe final aspect of our definition of escape to address is the use of HXB2 as our comparison sequence. Although there is no ‘gold standard’ comparator for this analysis, there were only four epitopes in which HXB2 differed from the ‘A list’ optimal at a documented escape site. We therefore re-ran the analysis re-classifying these four epitopes and found no significant difference in the distribution of baseline escape prevalence between HLA-matched and-mismatched hosts. We did identify 4 new incident escapes . Although the EVK escape variant is only poorly defined, its inclusion would mean that an estimated 25% of patients had not driven an escape by the end of 2 years off-therapy (as opposed to 33%).In conclusion, this study of a cohort from a randomised controlled trial, the proper design for assessing rates of escape across a population, reveals that escape is on average both less frequent and less concentrated in the first few months of infection than would be expected from recent intense studies of much smaller numbers of acutely infected individuals.The SPARTAC trial was approved by the following authorities: Medicines and Healthcare products Regulatory Agency (UK), Ministry of Health (Brazil), Irish Medicines Board (Ireland), Medicines Control Council (South Africa), and The Uganda National Council for Science and Technology (Uganda). It was also approved by the following ethics committees in the participating countries: Central London Research Ethics Committee (UK), Hospital Universitrio Clementino Fraga Filho Ethics in Research Committee (Brazil), Clinical Research and Ethics Committee of Hospital Clinic in the province of Barcelona, Spain, The Adelaide and Meath Hospital Research Ethics Committee (Ireland), University of Witwatersrand Human Research Ethics Committee, University of Kwazulu-Natal Research Ethics Committee and University of Cape Town Research Ethics Committee (South Africa), Uganda Virus Research Institute Science and ethics committee (Uganda), The Prince Charles Hospital Human Research Ethics Committee and St Vincent’s Hospital Human Research Ethics Committee , and the National Institute for Infectious Diseases Lazzaro Spallanzani, Institute Hospital and the Medical Research Ethics Committee, and the ethical committee Of the Central Foundation of San Raffaele, MonteTabor . All participants signed a written informed consent.3 (> 3 months after randomisation and confirmed within 4 weeks) or initiated long-term ART. This provided an immunological surrogate of clinical progression, but also allowed inclusion of those participants who commenced ART at CD4 cell counts greater than cells/mm3.The design of the SPARTAC trial is reported elsewhere . In brieThe participants studied here consisted of a sub-group 125 HIV-1 subtype B positive individuals of whom 42, 41 and 42 were randomised to ART-12, ART-48 and SOC, respectively. Peripheral blood mononuclear cells (PBMCs) were collected at regular intervals during the treatment period and follow-up. The sub-group of 125 participants was based on those individuals infected with subtype B HIV-1 for whom both sequence and ELISpot data were available, predominantly determined by specimen availability.pol, gag, nef and env genes were amplified separately using nested PCR reactions and primers described previously [Viral RNA was extracted from patient plasma  and the HIV-1 eviously , 37, 43.+ T-cell response was performed by interferon gamma ELISpot analysis in all subtype B patients, using methods described elsewhere [6 cells. ELISpot responses were measured at 0, 24 and 60 weeks post-recruitment.Quantification of CD8lsewhere . ResponsPatients’ HLA type was determined to the oligo-allelic level using Dynal RELITM Reverse Sequence-Specific Oligonucleotide kits for the HLA-A, -B and -C loci . To obtain four-digit typing, Dynal Biotech Sequence-Specific priming kits were used, in conjunction with the Sequence-Specific Oligonucleotide type.any amino acid variation in previously identified  escape sites within an ‘optimal’ defined epitope. Known escape sites were defined as within-epitope sites for which escape (E), calculated escape (CE), inferred escape (IE) or literature escape (LE) had been documented in the ‘CTL/CD8+ Epitope Variants and Escape Mutations’ HIV database table, as downloaded on 05/11/12 [A CTL escape mutation was defined as 05/11/12 . Any ami05/11/12  download+ T-cells, a comparison was made between the magnitudes of CD8+ T-cell ELISpot responses for the pre-defined optimal peptides with those for autologous epitope variants observed in the population at baseline. Variants for 7 Gag and 2 Pol epitopes were tested. In all cases except Gag EVD  the major variant(s) elicited a diminished response. In 6/9 cases responses to all ‘escape’ variants were reduced by at least 90%  of the Fisher’s exact test p-values for whether escape was independent of HLA-matching for that epitope. A permutation test was used to calculate the distribution of this mean under the null hypothesis; 2,000 random data sets were generated by permuting which patients the escape events occurred in for each epitope (i.e. there was no permutation between epitopes). The mean of the p-values for the observed data was then calculated and compared to the null distribution  across all epitopes in all patients and tp be the total observation time for all epitopes of a particular patient, p. Then, under the assumption that the average rate of incidence of escape in all patients is r = E/T, the probability of patient p driving i escape events is:tp = ×108.To account for missing data for , it was Note that this model does not impose the condition that only one escape is possible in each patient-epitope pair. We believe this to be a reasonable simplification given that the total number of escape events is small.2 goodness-of-fit test with 4 degrees of freedom  was used to test the null hypothesis that the observed data followed the expected distribution.A XSurvival analysis. For the survival analysis shown in Figs. S1 Text(PDF)Click here for additional data file.S1 FigHistograms show the distribution of the mean of the Fisher’s exact test p-values for independence of HLA-matching and escape in each epitope, under the null hypothesis that ‘escape’ is equally likely to be seen in HLA-matched and-mismatched hosts. (A) at baseline, (B) at 52 weeks. The red lines show the observed mean of the p-values. 2000 random simulations were used to obtain the null distributions. There is significant evidence to reject the null hypothesis at 52 weeks but not baseline, suggesting that little escape has already been driven before baseline.(EPS)Click here for additional data file.S2 FigEach patient-epitope pair was categorised according to if and when the patient reached the trial endpoint and how their time of clinical failure related to the last available sequence data point, as shown here diagrammatically. The three categories are (A) censoring may have resulted from clinical failure and there were no observations after failure, (B) censoring may have resulted from clinical failure but there was at least 1 further observation after failure, and (C) censoring is not related to clinical failure since long course ART was started after the last observed data point. Numbers on the right hand side represent the number of HLA-matched patient-epitope pairs and in brackets the number of distinct patients in each category. Note that three patients have epitopes in both A and B. One patient was removed as they were suspected to have been superinfected during the observation period.(EPS)Click here for additional data file.S3 FigAll HLA-matched host-epitope pairs are considered. Epitopes restricted by a protective HLA-allele as defined in (EPS)Click here for additional data file.S4 Figgag or nef, the two genes for which the most data was available, were not included so as not to skew the results due to lack of data. Time to first escape in an HLA-restricted epitope is plotted. These survival curves are very similar for the patients who have received no ART and who have received < 6 months of ART. Patients who have received > 6 mo ART were observed for less time off ART and hence the number of patients in this group with enough data to be included in the analysis is smaller (15 patients compared to 22 with no ART and 24 with < 6 mo ART). Therefore, while there appears to be a slower rate of escape in patients who have received > 6 months ART this difference is not significant (p = 0.24) and the effect of removing this group from the data is small Click here for additional data file.S5 Figgag or nef, the two genes for which the most data was available, were not included so as not to skew the results due to lack of data. (A) Time to first escape in an HLA-restricted epitope (solid line) is plotted along with 95% confidence intervals (dotted line). (B) Patients are split according to whether they have one of the more ‘protective’ HLA alleles or not. The set of beneficial alleles was taken to be B*58, B*27, B*57, A*26, B*51, A*11, B*14, B*18, B*08 . Having(EPS)Click here for additional data file.S6 Fig+ T-cell ELISpot response to them (light red) or not (dark red). As in -4, HR = 2.5) and then faster again if the host has a measurable CD8+ T-cell ELISpot response to that epitope . p-values and hazard ratios were calculated using a mixed effects model as in As with time to first escape in a patient, time to escape in epitopes was longer for epitopes in patients who have previously received > 6 mo ART, but there was no difference between epitopes in those who had received no ART and in those who had received < 6 mo ART. (A) Time to a first escape in epitopes within HLA-mismatched (dark blue) and-matched (light blue) hosts is shown. (B) Epitopes in HLA-matched hosts are split according to whether they have a measurable CD8(EPS)Click here for additional data file.S7 FigEpitopes are labelled where possible with their first three amino acids. Bars represent 95% binomial confidence limits (Agresti-Coull method). The dotted black line is y = x. Epitopes labelled in bold have a significantly higher proportion of escape in matched hosts than in mismatched hosts . Note that this plot is noisier that (EPS)Click here for additional data file.S8 Figgag or nef, the two genes for which the most data was available, were not included so as not to skew the results due to lack of data. (A) Time to first escape in an HLA-restricted epitope (solid line) is plotted along with 95% confidence intervals (dotted line). (B) Patients are split according to whether they have one of the more ‘protective’ HLA alleles or not. The set of beneficial alleles was taken to be all the HLA-A and-B alleles down to B*08 that are present in our data, taken from the ranking in [Patients who were either missing data for nking in . Having (EPS)Click here for additional data file.S9 Fig+ T-cell ELISpot response to them (light red) or not (dark red). As in -4, HR = 2.2) and then faster again if the host has a measurable CD8+ T-cell ELISpot response to that epitope . p-values and hazard ratios were calculated using a mixed effects model as in (A) Time to a first escape for epitopes in HLA-mismatched (dark blue) and-matched (light blue) hosts is shown. (B) Epitopes within HLA-matched hosts are split according to whether they have a measurable CD8(EPS)Click here for additional data file.S10 FigEpitopes are labelled where possible with their first three amino acids. Bars represent 95% binomial confidence limits (Agresti-Coull method). The dotted black line is y = x. Epitopes labelled in bold have a significantly higher proportion of escape in matched hosts than in mismatched hosts . (A) At baseline the majority of epitopes have escape at equal prevalence in HLA-matched and-mismatched hosts and the distribution of points is not significantly different from as would be expected if escape was equally likely to occur in HLA-matched and-mismatched hosts  (B) 52 weeks later within host evolution has resulted in a higher prevalence of escape in HLA-matched hosts for many epitopes, with the eight epitopes and the distribution as a whole lying significantly ‘below the line’ Click here for additional data file.S11 Figgag or nef, the two genes for which the most data was available, were not included so as not to skew the results due to lack of data. (A) Time to first escape in an HLA-restricted epitope (solid line) is plotted along with 95% confidence intervals (dotted line). (B) Patients are split according to whether they have one of the more ‘protective’ HLA alleles or not. The set of beneficial alleles was taken to be all the HLA-A and-B alleles down to B*08 that are present in our data, taken from the ranking in [Patients who were either missing data for nking in . Having (EPS)Click here for additional data file.S12 Fig-10, HR = 3.2). The effect of a response could not be analysed as CD8+ T-cell ELISpot responses were not measured for all of the extended list of epitopes. The p-value and hazard ratio was calculated using a mixed effects model as in Time to a first escape for epitopes in HLA-mismatched (dark blue) and-matched (light blue) hosts is shown. As in (EPS)Click here for additional data file.S1 TableNote that some sequences do not cover the whole gene and so the number given is an upper bound for the number of patients who have sequence data for any one epitope. For each patient ELISpot data was either available for all 181 optimal peptides across all four genes or not at all. Data was available in 115 patients at week 0, 109 patients at week 24 and 115 patients at week 60. The final column gives the number of patients with at least 2 time points of sequence data and ELISpot data at at least one of 0, 24 or 60 weeks.(PDF)Click here for additional data file.S2 Table+ T-cell responses to autologous variants were assessed and for which there was at least one HLA-matched patient with a positive response to the optimal epitope are shown. The top sequence for each epitope gives the pre-defined optimal ELISpot peptide, which is also the most prevalent variant in each case. The ‘Escape’ column records whether or not each variant is classified as escape according to our definition. The prevalence of each variant in the cohort at baseline and the proportion of patients with a particular variant who are HLA-matched for the epitope are also recorded. The final column gives the average ELISpot response to the variant (number of SFCs) in those that are HLA-matched and respond to the optimal, as a percentage of their response to the optimal.All epitopes for which CD8(PDF)Click here for additional data file.S3 TableAll p-values < 0.2 are given for a Fisher’s exact test with the alternative hypothesis that the proportion of escape in matched hosts is greater than that in mismatched hosts.(PDF)Click here for additional data file.S4 TableAll p-values < 0.2 are given for a Fisher’s exact test with the alternative hypothesis that the proportion of escape in matched hosts is greater than that in mismatched hosts.(PDF)Click here for additional data file.S5 TableSince a few patients did not have available sequence data at baseline, this table is similar but not identical to (PDF)Click here for additional data file.S6 TablePeptides that are also in the ‘A-list’ of optimal epitopes are shown in bold.(PDF)Click here for additional data file.


Aging is characterized by a profound remodeling of the epigenetic architecture in terms of DNA methylation patterns. To date the most effective tool to study genome wide DNA methylation changes is Infinium HumanMethylation450 BeadChip (Infinium 450k). Despite the wealth of tools for Infinium 450k analysis, the identification of the most biologically relevant DNA methylation changes is still challenging. Here we propose an analytical pipeline to select differentially methylated regions (DMRs), tailored on microarray architecture, which is highly effective in highlighting biologically relevant results. The pipeline groups microarray probes on the basis of their localization respect to CpG islands and genic sequences and, depending on probes density, identifies DMRs through a single-probe or a region-centric approach that considers the concomitant variation of multiple adjacent CpG probes. We successfully applied this analytical pipeline on 3 independent Infinium 450k datasets that investigated age-associated changes in blood DNA methylation. We provide a consensus list of genes that systematically vary in DNA methylation levels from 0 to 100 years and that have a potentially relevant role in the aging process. In the last two years the Infinium HumanMethylation-450 BeadChip (Infinium 450k)  has beenUsing the Infinium 450k, researchers have identified several CpG sites that either get hypermethylated or hypomethylated during aging in different tissues , and a sAlthough defining a list of CpG sites whose methylation status is age-dependent is an essential step in aging research, the real challenge is to identify biologically relevant DNA methylation changes and their relative contribution to the aging process.The difficult task of extracting relevant information from microarray data can be made easier if the number of microarray features is reduced on the basis of a biologically meaningful criterion. In this way the top ranking groups of features are more likely to be functionally linked to the phenotype under study than the single features. For expression microarrays this task has been successfully addressed by grouping genes that share common biological functions , but thist EXON, GENE BODY, 3′ UTR, ILSAND, NSHORE, SSHORE, NSHELF, SSHELF) and calculates their mean or median methylation values, which are then compared between the samples under analysis [At present, different region-centric approaches have been proposed. Illumina Methylation Analyzer (IMA) defines for each gene 11 region categories .The main point is that the Infinium 450k probes are not evenly distributed across the genome, but they are enriched in specific regions while others are underrepresented. To deal with this issue, a methodology in which differentially methylated regions (DMRs) are defined as regions in which at least two contiguous probes within 1-kb distance have a significant differential statistic was recently proposed  and usedHere we implemented an alternative pipeline for the analysis of Infinium 450k data that is based on a careful description of CpG probes distribution within the array. The proposed methodology: 1) classifies CpG probes based on their genomic localization 2) defines groups of adjacent CpG probes based on their density in the region 3) depending on the previous classifications, applies a single-probe or a region centric analysis which considers the concomitant variation of a group of adjacent CpG probes.As a proof of principle, we used our approach to conduct a meta-analysis on 3 independent datasets in which the Infinium 450k was used to investigate age-associated variations in blood DNA methylation profiles. From this meta-analysis, we extracted a short list of genes that potentially have a biologically relevant role in the aging process.i) Class A, including probes in CpG islands and CpG islands-surrounding sequences (shores and shelves) that map in genic regions; ii) Class B, including probes in CpG islands and CpG islands-surrounding regions (shores and shelves) which do not map in genic regions; iii) Class C, including probes in genic regions which are not CpG rich; iv) Class D, including probes in non-genic regions which are not CpG rich. These four classes have different epigenetic functions, as their methylation status can affect gene function and chromatin structure in different ways [The method we propose focuses on grouping CpG probes into clusters, hereafter referred as “blocks of probes” (BOPs). CpG probes were grouped taking into consideration not only their contiguity in DNA sequence, but also their genomic localization, which represents a critical aspect for data interpretation . Using Ient ways . Class AThen, we defined BOPs as follows Fig. : for proClass A, B and C included 77202, 34448 and 20273 BOPs respectively  to test for general changes in methylation of a genomic region. However, as described above, Class A and Class B BOPs contain a largely variable number of CpGs, spanning from 1 to 75 for Class A BOPs and from 1 to 41 for Class B BOPs. We considered BOPs containing 1 or 2 CpG probes not informative enough for a region-centric approach but more suitable for a single-probe approach. On the contrary, the remaining BOPs  are analysed by applying a sliding-window MANOVA Fig. . Indeed,p-values and, if present, associated genes, is provided as output. Then, authors can choose to select significant BOPs containing at least 2 adjacent CpG sites for which the DNA methylation difference between the considered groups  or two selected ranges of values of the continuous variable is higher than a set threshold.We implemented the proposed pipeline in R software environment. Starting from a table containing the beta-values of interest , the script annotates the probes and divides them in Class A, Class B, Class B and Class D. Class A and Class B probes are grouped in BOPs and BOPs containing 3 or more probes are analysed by the region-approach (MANOVA on sliding windows of 3 CpG probes within the same BOP). The remaining CpG probes are analysed by ANOVA. Both categorical and continuous covariates can be used. A list of significant CpG probes is outputted from the single-probe analysis. For the region-centric approach, a list of BOPs ranked by nominal or FDR-corrected Significant BOPs can be ranked according to this criterion allowing authors to identify “bubbles” of different methylation between the conditions under study, which are likely to be biologically meaningful. The pipeline provides as outputs several useful plots, including schematic diagrams in which the chromosomal position of the probes within the selected BOPs is plotted against the methylation level of each probe  includes data from 656 subjects ranging from 19 to 101 years (average 64 ± 15 years) . The secIn each dataset, probes containing missing values in at least one sample and probes on X and Y chromosome were removed. Age-associated DMR were identified in D1 and D3 using the age as a continuous variable and in D2 the group (newborns or nonagenarians) as a categorical variable. Ethnicity (Caucasian-European or Hispanic-Mexican) was used as covariate in the D1 analysis. As the relative proportions of the different types of blood cells can vary significantly with age, we inferred blood cell counts from methylation data and use them as covariates in the analysis.q-values. Based on these criteria 21083, 517 and 2736 BOPs were identified as age-associated DMRs in D1, D2 and D3 respectively.Here we present results only for the region-centric analysis performed on Class A BOPs containing at least 3 CpG probes. MANOVA results were corrected for multiple comparisons through Benjamini-Hochberg False Discovery Rate correction; 0.05 was used as significant threshold for The considerably lower number of BOPs identified in D2 is ascribable to the effect of blood cell types correction, as cord blood and whole blood significantly differ for this parameter.To validate our approach, we compared the results of the region-centric approach with the results of a single-probe approach by analysis of variance (ANOVA). As shown in Figure To confirm that our region-centric approach reduces spurious results and is more likely to identify biologically relevant regions, we compared the results from the three datasets.q-value < 0.01) and that showed a difference in average beta-values between newborns and nonagenarians greater than 0.20. We considered only the CpG probes belonging to Class A probes, that is 45 and 800 probes in D1 and D2 respectively, and we matched them with the corresponding BOPs. In this way, we achieved a list of 34 and 472 BOPs identified by Hannum et al. and by Heyn et al.. Only one BOP was shared by the two lists  and CpG probes  and we calculated the number of common DMRs between the three datasets. As shown in Figure Finally, we provided a short list of genomic regions whose methylation levels vary according to age in D1, D2 and D3  or of bisulfite-treated genomic DNA (BS-seq) provide a more comprehensive picture of DNA methylome  but, desA recent comprehensive review has collected the bioinformatic tools developed to specifically analyze Infinium 450k data . Apart fHere we reasoned that the statistical analysis of the Infinium 450k microarray should take into account the specific architecture of the microarray. Indeed, we clearly showed that different genomic regions present different level of coverage in terms of probes distribution. We grouped probes in blocks (BOPs) based on microarray annotations and verified that only in CpG islands and in the surrounding regions (shores and shelves) the mean distance between probes in a BOP was within 500 bp, a range in which the methylation values of CpG sites are usually correlated . On the We used multivariate ANOVA (MANOVA) to test for general changes in methylation in the region-centric analysis. This approach has been previously adopted for the analysis of methylation data , 30 as iOverall the strength of our approach is that not only microarray features are grouped in biologically meaningful groups, but also that the ranking criterion is based on a multivariate approach. Additionally, significant BOPs can be ranked also on the basis of the “bubble” of differential methylation, defined as at least two adjacent probes whose mean methylation in the groups under investigation differs of at least a minimum value. As a confirm of the validity of the approach we demonstrated that, when considering the top ranking BOPs, our method increased the number of common genes identified in all three studies compared to a single-probe analysis, indicating that it is likely to provide a more informative overview of biologically relevant results.The analysis pipeline we propose is implemented in R software environment and is therefore freely available. Researchers just need to define the analysis parameters, such as the covariates to be used in the ANOVA/MANOVA, the FDR correction method, the significance threshold and the minimum difference between mean methylation values between the groups under investigation.et al. and by Heyn et al. or was included in Horvath's epigenetic clock. Inferring how the methylation of these regions can contribute to the aging process is out of the scope of this paper, although the life-long variations in DNA methylation that we described are suggestive of a profound link between development and aging [We used our analysis pipeline to perform a meta-analysis on 3 Infinium 450k datasets that investigated age-associated changes in DNA methylation. The 3 datasets are considerably different in terms of both samples number and age range. Nevertheless, we were able to identify a core of genomic regions whose methylation profiles systematically vary with aging, from newborns to nonagenarians. To our knowledge, this is the first report describing a relatively large number of genomic regions with such characteristics. Only a subset of these genes was identified by Hannum nd aging . Many gend aging . Moreovend aging –38. COL1nd aging  in whichnd aging .Collectively the above observations indicate that this short list of genes, selected by means of an analytical pipeline that is tailored on the architecture of the microarray and that is more likely to provide biologically relevant findings, can be used as the basis for deeper investigations to shed light on the molecular basis of the aging process.http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE40279 and GSE30870 respectively. D3 includes 32 mother–offspring couples and is part of a larger datasets submitted to GEO with accession number GSE52588.D1 and D2 are publicly available at NCBI Gene Expression Omnibus (GEO)  Definition of Classes and BOPs; 2) DMRs identification 3) FDR correction, selection of significant DMRs/probes, sorting of probes and plots. Step 2 and step 3 are separately provided for Class A BOPs containing 3 or more probes, Class A BOPs containing 1 or 2 probes, Class B BOPs containing 3 or more probes, Class B BOPs containing 1 or 2 probes, Class C probes and Class D probes. Detailed explanations of input and output files are provided. A general description of the 3 analytical steps is provided below:The analytical pipeline is implemented as an R script freely available at p-value greater than 0.05 in more than 75% samples were removed, together with those localized on sexual chromosomes. Probes that contained SNPs were annotated as previously described [Step 1: Probes containing missing values and probes with a detection escribed . For procar. For sliding windows MANOVA, the function is applied on sliding windows of 3 consecutive CpGs within the same BOP. For each BOP, the lowest p-value among those calculated for the different sliding windows is kept. Both MANOVA and ANOVA analysis support the use of both categorical and continuous covariates. Parallel processing can be used if the computational environment supports it.Step 2: DMRs identification is based on MANOVA and ANOVA functions from the R package p-values for multiple testing using the correction methods implemented in the R package multtest. BOPs/probes can be selected on the basis of a significance threshold on either nominal or FDR-corrected p-values. In the case of BOP analysis, BOPs can be ranked on the basis of a user-defined minimum mean methylation difference between adjacent CpG probes. MDS plots, heatmap plots and line plots are generated as outputs.Step 3: The analytic pipeline allows to correct MANOVA/ANOVA


The expression and subsequent purification of mammalian recombinant proteins is of critical importance to many areas of biological science. To maintain the appropriate tertiary structure and post-translational modifications of such proteins, transient mammalian expression systems are often adopted. The successful utilisation of these systems is, however, not always forthcoming and some recombinant proteins prove refractory to expression in mammalian hosts. In this study we focussed on the role of different N-terminal signal peptides and residues immediately downstream, in influencing the level of secreted recombinant protein obtained from suspension HEK293 cells. Using secreted alkaline phosphatase (SEAP) as a model protein, we identified that the +1/+2 downstream residues flanking a heterologous signal peptide significantly affect secreted levels. By incorporating these findings we conducted a comparison of different signal peptide sequences and identified the most productive as secrecon, a computationally-designed sequence. Importantly, in the context of the secrecon signal peptide and SEAP, we also demonstrated a clear preference for specific amino acid residues at the +1 position , and a detrimental effect of others . When proteins that naturally contain these “undesirable” residues at the +1 position were expressed with their native signal peptide, the heterologous secrecon signal peptide, or secrecon with an additional alanine at the +1 or +1 and +2 position, the level of expression differed significantly and in an unpredictable manner. For each protein, however, at least one of the panel of signal peptide/adjacent amino acid combinations enabled successful recombinant expression. In this study, we highlight the important interplay between a signal peptide and its adjacent amino acids in enabling protein expression, and we describe a strategy that could enable recombinant proteins that have so far proved refractory to expression in HEK293 cells, to be produced in sufficient quantities to answer important biological questions. E. coli provide a convenient and cost-effective means for producing recombinant proteins, however, the proteins obtained via these systems are not glycosylated and there is a risk that they will not be folded properly [The ability to recombinantly express and purify a protein of interest of sufficient quantity and quality is critical for many areas of biological science, not least in the production of biopharmaceuticals and recombinant antigens for therapeutic antibody generation. In the latter case, the target antigen needs to be available in a form that most closely resembles the native protein. Prokaryotic expression hosts such as properly . Due to properly , alterinproperly  and tranproperly , incorpoproperly , codon oproperly , using pproperly . In someproperly  is requiDuring the translation of secreted proteins, the signal peptide is recognised as it emerges from the ribosome; it is bound by the signal recognition particle (SRP) and translation is halted. This entire complex is transported to the external face of the ER where it binds to the SRP receptor, and the signal sequence is transferred to a translocon. Whilst bound to the translocon, translation is reinitiated and the protein passes through the ER membrane and into the lumen. As it does this, the signal peptide is recognised by a signal peptidase and is cleaved to generate the mature protein that can be trafficked through the Golgi network before being secreted from the cell via the classical secretory pathway .Signal peptide sequences were first described in 1975 as short removable N-terminal sequences that mark a protein for removal from the cell . SubsequThe potential posed by utilising heterologous signal peptides from highly secreted proteins to improve protein yields is an attractive one and the identification of a universally applicable strategy involving the use of a single signal peptide for all recombinant proteins would constitute an ideal scenario. In an attempt to investigate this potential further, we chose to study the expression of a widely used model secreted protein, secreted alkaline phosphatase (SEAP), but in the context of non-native signal peptides. In this study we identify an optimal signal peptide for the secretion of SEAP, but importantly we also identify a critical role for amino acids in the mature protein (positions +1 and +2). We identify amino acids that negatively influence the expression of SEAP when inserted at the +1 position and by investigating the expression of proteins with unfavourable native +1 amino acids we demonstrate that refractory expression can be restored by replacing the signal peptide or by also placing small neutral amino acids at the +1/+2 positions. It is anticipated that these findings could be applied widely to improve the production of proteins of significant importance to many areas of biological research as well as the biopharmaceutical industry.http://www.signalpeptide.de/). A total of 24 sequences were obtained and the frequencies of residues at each position were calculated  in length from human proteins were obtained from the Signal Peptide Database  and sub-cloned into this vector. The resultant pDest12.2oriP_CD33_SEAP_FLAG10His vector formed the template for the construction of all further SEAP-containing expression vectors, whereby signal sequences and adjacent residues were replaced by equivalent versions (i.e. using the same codon usage where amino acids were unchanged) using a restriction site upstream of the start codon, and an internal restriction site in the SEAP-encoding sequence. Expression vectors for interferon alpha 2a (IFNα2), interleukin 25 (IL-25), sclerostin, mimecan and prostaglandin-H2 D-isomerase were generated entirely by gene synthesis, incorporating the different signal peptide/adjacent residue combinations.The expression vector pDest12.2oriP_CD33_FLAG10His, based on the pDest12.2 vector (Thermo Fisher), was generated via gene synthesis and sub-cloning to encode a human CD33 signal peptide, followed by a 6/ml using ExpiFectamine™ 293 according to the manufacturer’s instructions. Following transfection, the cells were grown at 37°C by shaking at 350 rpm in 5%CO2, 70% humidity for six days. The conditioned media, containing secreted protein, were harvested on day 6 by centrifugation at 3500rpm for 20 minutes. The clarified media from each of the three replicates for each expression vector were pooled and stored at -20°C. Each transfection was performed in duplicate, on separate days, and the conditioned media was analysed separately by Western blot analysis and a SEAP activity assay.All proteins were expressed in a suspension-adapted HEK293 cell line . Cells, growing in deep 96-well blocks, were transfected in triplicate at a density of 2.5x10An equal volume of each pooled conditioned media sample was incubated in a final concentration of 1 mM DTT and 1xLDS NuPAGE LDS sample buffer (Thermo Fisher) at 70°C for 15 minutes before loading onto a 4–12% Bis-Tris NuPAGE gel (Thermo Fisher), alongside a protein molecular weight marker . The gels were run at 150V, 400 mAmp for 100 minutes in MOPS buffer and the proteins were subsequently transferred onto PVDF membranes using iBlot transfer stacks (Thermo Fisher). The membranes were blocked at 4°C overnight in Odyssey-PBS blocking buffer, followed by an hour with mouse anti-His antibody  and a further hour with IRDye 800 CW donkey anti-mouse antibody at ambient temperature. Between each antibody incubation, the membranes were washed five times with Odyssey-PBS blocking buffer containing 0.1% Tween 20 and finally the membranes were rinsed with PBS and left to dry in the dark. The membranes were scanned using the Odyssey scanner (LiCor) at 800nm to enable visualisation of protein bands.Purification of His-tagged proteins from the conditioned media of HEK293 cells was performed using a high-throughput 24-well filter plate method. A 500ul sample of conditioned media was applied to a 25ul packed bed of HisTrap Excel resin  that had been equilibrated with 2xD-PBS/10mM imidazole. The sample was incubated for 90 minutes with the resin prior to a 25mM imidazole wash step, and elution with 400mM imidazole. Buffer exchange was then performed using a 7,000 Da cut off Zeba desalting spin column (Thermo Scientific) into 2 x DPBS buffer.The amount of SEAP present in the conditioned media of HEK cells transiently transfected with the different SEAP constructs was determined using the Great EscAPe™ SEAP Chemiluminescence Kit 2.0 , accordingly to the manufacturer’s instructions. A standard curve, using purified SEAP was generated and a linear regression analysis performed. All samples were diluted 1000-fold and analysed in replicate (6). The amount of SEAP was calculated using the linear regression equation from the standard curve, and the percentage change to the control sample (Native-SEAP) in each case, was determined. Statistical analyses of each experiment were performed using a one-way ANOVA (Dunnett’s correction for multiple comparisons), and graphs combining the data from the two independent experiments were generated.To enable N-terminal sequencing of proteins, the conditioned media was subjected to Nickel chelate chromatography as described above. The purified proteins were mixed with an equal volume of a reducing sample buffer, subjected to SDS-PAGE electrophoresis, transferred on 0.22 μm Immobillon PSQ PVDF membrane (Millipore) and stained with Coomassie Blue. The main band from each sample was cut out from the membrane and used for sequencing via chemical Edman degradation of the N-terminus. Analysis was performed using an automatic sequencer PPSQ-31A .IFNα2 was transiently expressed in HEK293F cells, as described above, in 50 ml Erlenmeyer flasks and the conditioned media was harvested after 4 days of expression. The supernatant was diluted two-fold in 2 x DPBS, loaded onto 5 ml His Trap column , washed with 40 mM Imidazole and eluted with 400 mM Imidazole. The sample was concentrated using a 5K cut-off Vivaflow 200 concentrator (VWR) and loaded onto a Superose 12 size exclusion chromatography column  that had been equilibrated with 2 x DPBS buffer. Fractions containing the purified protein were collected and pooled.TM Type I IFN Assay Ready cells (EuroDiagnostica) that express firefly luciferase under the control of an IFNα/β response element. Cells were incubated with a three-fold serial dilution of purified IFNα2 (four replicates per sample) for 18 hours at 37°C with 5% CO2 and the luminescence was measured following addition of a luciferase substrate . The concentration of IFNα2 required for a half-maximal (EC50) response was determined using non-linear regression analysis  in GraphPad Prism .Activity of purified human IFNα2 was measured using iLiteTo investigate the effect of changing the signal peptide of SEAP to that of a non-native signal peptide, we first chose to investigate the commonly used human CD33 signal peptide that has been shown to improve the secretion of proteins such as IL-15 . The DNATo investigate whether this phenomenon would be observed for other signal peptides and to identify potentially superior sequences, a panel of alternative signal peptides were chosen for further analysis . These iTo determine whether two alanine residues are required to enable secretion of SEAP with a heterologous signal peptide, or whether a single alanine would be sufficient, an additional expression plasmid containing a single alanine between secrecon, the high-yielding signal peptide, and SEAP was generated. The amount of secreted SEAP was determined in comparison to equivalent versions with two or no alanine residues. The quantitative data obtained  suggestsTo verify that the processing of the secrecon signal peptide had occurred between the expected amino acids, N-terminal sequencing was performed on the purified proteins. The following N-terminal amino acids were observed, confirming that the heterologous secrecon signal peptide had been processed correctly: Native = IIPVE; secrecon-A = AIIPV; secrecon-AA = AAIIP (data not shown).The experiments conducted so far have demonstrated that a signal peptide and the adjacent +1 residue need to be compatible to allow efficient recognition and processing of the signal peptide enabling SEAP to be secreted. The secrecon signal peptide in combination with a single alanine proved to be more efficient than the native SEAP signal peptide. In an attempt to identify whether the preference for certain amino acids in the +1 position was restricted to alanine, we systematically replaced alanine with all 19 alternative amino acids via gene synthesis and assessed their ability to enable secretion of SEAP using Western blot quantification and a SEAP activity assay . The resThe observation that tyrosine, cysteine and proline were not favoured at the +1 position correlates with a lower prevalence of these amino acids in the +1 position of endogenous proteins ,18, whicThe successful production of recombinant proteins often requires the optimisation of several factors including expression conditions, gene sequence manipulation and addition of appropriate purification tags. Here we focussed on the potential of identifying a non-native signal peptide that would have universal applicability in improving the secretion of recombinant proteins, in particular those that prove refractory to expression with their native signal peptide.In conducting these experiments we identified that, in the context of a model protein (SEAP), simply adjoining a heterologous signal peptide to a mature protein sequence does not guarantee secreted expression. On the contrary, the expression of SEAP, when placed directly adjacent to any of the non-native signal peptides tested, was completely abrogated. Restoration of SEAP expression was achieved when additional amino acids  were placed upstream of the isoleucine that represents the start of the mature SEAP sequence. This data demonstrates that the combination of the native SEAP signal peptide and its mature protein is preferred over all of the heterologous alternatives tested, and that the identity of the amino acids downstream of a signal peptide are critical for successful protein secretion.EcoRI restriction site. The data from Barash et al. [At first glance it would appear that the lack of expression for the secrecon-SEAP protein is contradictory to that obtained by Barash et al., 2002 , where sh et al. , therefoOnce the requirement for additional amino acids between the signal and mature sequences of SEAP had been identified, a comparison of different heterologous signal peptides, incorporating the additional alanine residues, was conducted with the ultimate aim of identifying one that could improve secreted protein levels beyond that obtained with the native sequence. Indeed, we were successful in achieving improved SEAP levels using the synthetic sequence, secrecon, further supporting the data from Barash et al. . The CD3As the secrecon signal peptide contains an alanine at the -1 position, the addition of further alanine residues between the secrecon peptide and SEAP could affect the recognition of the signal peptide by creating different and/or multiple potential cleavage sites, and thus generate a heterogeneous preparation consisting of proteins with different N-terminal residues. In conducting N-terminal sequence analysis, however, we were able to rule out this possibility as the protein species sequenced contained the anticipated N-termini, indicating correct signal peptide processing. It should be noted, however, that although the addition of two small neutral amino acids to the N-terminus of a protein is unlikely to have a detrimental effect on the activity or folding of a protein, and did not affect the activity of SEAP, there may be occasions where this would indeed be the case. Therefore, minimising the number of additional amino acids incorporated is preferable to obtain a recombinant protein that more closely resembles the native mature protein. Indeed, we were able to obtain high levels of SEAP from the secrecon-SEAP variant with a single alanine (secrecon-A-SEAP), in addition to the variant with two additional alanines (secrecon-AA-SEAP).The striking difference observed in secreted levels of SEAP when an alanine is placed at the +1 position, adjacent to secrecon as opposed to the isoleucine of the mature SEAP protein, led us to question the impact of other amino acids in the +1 position. The data obtained from the systematic analysis where we replaced alanine for every other amino acid suggests that most residues are accepted at that position, but there is a clear negative impact of some amino acids on the secreted levels of SEAP. In particular tyrosine, proline, cysteine and glutamine are not tolerated in this position. Interestingly, in a study in which the N-terminus of 270 eukaryotic proteins was empirically determined by N-terminal sequencing following expression in CHO cells, the prevalence of proline and tyrosine residues in the +1 position was strikingly low . In addiWhen a representative set of proteins containing the four “detrimental” amino acids or alanine at the +1 position was expressed in various signal peptide/spacer combinations, the results obtained were difficult to interpret in a systematic way. Three of the four proteins with undesirable +1 amino acids indeed did not express well in HEK293 cells, but one, IL-25 (tyrosine at +1), did. In addition, the protein with a naturally occurring alanine residue at +1 (Prostaglandin-H2 D-isomerase) did not express when its native signal peptide was used, but did when the secrecon signal peptide was used either alone or in combination with spacer residues. For the three proteins that were not successfully detected when the native signal peptide was used, one or more of the three alternative secrecon-based strategies did yield secreted protein. For mimecan that encodes a proline at the +1 position, no secreted protein was detected with the native signal peptide, as was also the case when proline was inserted at the +1 position for secrecon-SEAP. Furthermore, no secretion was observed when the signal peptide was replaced with the secrecon sequence. When one or two alanine residues were placed between secrecon and the mature peptide sequence, secreted protein expression was achieved—a result that correlates well with the analysis performed by Zhang et al  showing In conducting this study we have confirmed findings previously reported that the secreted level of proteins can be significantly influenced by replacing the signal peptide sequence with that of a heterologous protein, or with a synthetic signal peptide. Moreover, we have demonstrated that amino acids present in the mature peptide play a crucial role in the determining whether a protein is secreted from a cell. Whether this effect is purely a post-translational one involving recognition by the SRP receptor or cleavage by the signal peptidase, or involves transcription and translation rates was not the subject of this study, but warrants further attention. From the results presented here it is difficult to make any overarching conclusions and recommendations based on the use of a universal signal peptide and/or additional residue combination as the impact of such modifications appears to be context specific. A systematic study encompassing a larger panel of proteins and signal peptide sequences is required to be able to identify any rules and requirements for particular primary sequence features, as well as size, charge and secondary structure considerations for successful secreted protein production. The availability of crystal structures for the key components of the signal peptide recognition and cleavage pathways would also enable predictive modelling studies to be performed, that could drive a more rational design of optimal signal peptide and mature protein combinations and avoid the need for additional residues.In conclusion, we have reinforced the importance of the mature protein sequence in combination with the signal peptide for achieving recombinant protein expression. Our data, with a small panel of secreted proteins, suggests that it will be challenging to predict whether or not a protein will express recombinantly in mammalian cell culture solely by taking into account the +1 amino acid, and that replacing the signal peptide with a “universal” signal peptide will not overcome expression issues in every case. Nevertheless, by employing the secrecon signal peptide and generating a limited number of alternative expression plasmids, the successful expression of four different recombinant proteins from mammalian cells that could not be obtained with their native sequence was achieved. It is anticipated that this approach could be used to successfully generate proteins that are of significant biological and clinical value.S1 Fighttp://weblogo.berkeley.edu/logo.cgi).The frequency of residues was analysed from an alignment of 24 human 16 amino acid long signal peptide sequences to generate a “consensus” sequence signal peptide. The height of each residue represents the frequency of that amino acid in the 24 signal peptides analysed. The figure was generated using the WebLogo tool ((PDF)Click here for additional data file.S2 FigA and B represent duplicate Western blots for M = two-colour Odyssey molecular weight marker). Blots correspond to Panels (PDF)Click here for additional data file.S3 FigA and B represent duplicate Western blots for M = two-colour Odyssey molecular weight marker). Blots correspond to Panels (PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigA and B represent duplicate Western blots for M = two-colour Odyssey molecular weight marker). Blots correspond to Panels (PDF)Click here for additional data file.S6 FigA and B represent duplicate Western blots for C and D represent duplicate Western blots for M = two-colour Odyssey molecular weight marker). Blots correspond to Panels (PDF)Click here for additional data file.S7 FigBlots correspond to (PDF)Click here for additional data file.S8 FigTM Type I IFN Assay Ready cells (EuroDiagnostica) were incubated with increasing concentrations of purified IFNα2 for 18 hours and the luminescence was measured following addition of a luciferase substrate . Error bars represent the 5–95 percentiles of the mean from four replicate assay wells. The concentration of IFNα2 required for a half-maximal (EC50) response was determined using non-linear regression analysis  in GraphPad Prism .iLite(PDF)Click here for additional data file.


Patients who require positive pressure ventilation through a tracheostomy are unable to phonate due to the inflated tracheostomy cuff. Whilst a speaking valve (SV) can be used on a tracheostomy tube, its use in ventilated ICU patients has been inhibited by concerns regarding potential deleterious effects to recovering lungs. The objective of this study was to assess end expiratory lung impedance (EELI) and standard bedside respiratory parameters before, during and after SV use in tracheostomised patients weaning from mechanical ventilation.A prospective observational study was conducted in a cardio-thoracic adult ICU. 20 consecutive tracheostomised patients weaning from mechanical ventilation and using a SV were recruited. Electrical Impedance Tomography (EIT) was used to monitor patients’ EELI. Changes in lung impedance and standard bedside respiratory data were analysed pre, during and post SV use.p < 0.001). EtCO2 showed a significant drop during SV use (p = 0.01) whilst SpO2 remained unchanged. Respiratory rate (RR (breaths per minute)) decreased whilst the SV was in situ (p <0.001), and heart rate (HR (beats per minute)) was unchanged. All results were similar regardless of the patients’ respiratory requirements at time of recruitment.Use of in-line SVs resulted in significant increase of EELI. This effect grew and was maintained for at least 15 minutes after removal of the SV . ACTRN:  Invasively ventilated patients are unable to phonate due to either the endotracheal tube positioning through the vocal folds, or when ventilating through the tracheostomy, the air bypassing the vocal folds. Speaking valves (SVs) can be used in-line with mechanical ventilation, but use of these requires deflation of the tracheostomy cuff . Cuff deThe key concern raised by physicians is that by deflating the cuff, and thus, losing positive end-expiratory pressure (PEEP) this could lead to loss of lung volume through alveolar collapse. It has been demonstrated that loss of PEEP in other events such as suctioning , 3 and vOne small case series has described the apparently safe use of SVs during weaning from mechanical ventilation . AnotherCurrently there are no data on the effect of SVs on end-expiratory lung volume (EELV), a critical point when the lungs are at most risk of collapsing. An SV is a one-way valve that allows for inspiration via the tracheostomy tube whilst expiration is redirected to the upper airway via the vocal folds, enabling phonation  and restElectrical impedance tomography (EIT) is a radiation-free real-time bedside imaging tool capable of measuring the air movement in and out of the thorax –14. It h2) and end-tidal carbon dioxide (EtCO2). The potential effect on respiratory mechanics of talking versus quietly breathing with the SV in situ, and the effects of the SV and its dependence on the patients’ ventilatory requirements at the time, were also investigated.The aim of this study was to assess the effect of SVs on EELI. Based on the findings of prior case studies it was hypothesised that there is an increase in global EELI with the SV in situ when patients are performing trials off the ventilator . This may potentially be similar when patients are constantly supported by mechanical ventilation, given restored physiological PEEP. Secondary aims included determining the effects of SV on the patient’s respiratory rate (RR), heart rate (HR), oxygen saturation  a prospective observational study (ACTRN12615000589583) using a repeated measures design was conducted. The study took place in a primarily cardiothoracic ICU at a metropolitan tertiary teaching hospital. Consecutive patients who were tracheostomised and being weaned from mechanical ventilation, from November 2013 to December 2014, were considered for inclusion in the study if they were tolerating a SV for a minimum of 30 minutes, as jointly assessed by a speech pathologist and a physician. Patients were excluded if they had significant language or cognitive deficits, or were not suitable to wear an EIT belt . In total 20 patients were recruited into two groups: 1) 10 patients on pressure support ventilation (PSV) and 2) 10 patients having trial periods off mechanical ventilation (and transferred onto high-flow or low-flow oxygen via the tracheostomy). All patients provided written informed consent, or for those unable to sign for written consent, the consent was provided by a legally authorised person  or by the patient’s nurse witnessing verbal consent. The study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.Following informed consent, patients were enrolled in the study. EIT  measurements were taken continuously for 60 minutes with the frame rate set to 10 Hz to give the EELI per breath. Transitions to and from SV were followed by 15-minute periods, to allow for stabilisation .2) data were collected from the ventilator . HR and SpO2 were measured with pulse oximeter . Airway pressure (Paw) was measured directly via a neonatal feeding catheter (6 F) introduced through the Luer port of an adaptor  advanced to lie just distal to the tracheostomy cannula in the trachea, and measured with a pressure transducer . Oximeter and pressure data were collected at 200 Hz . EtCO2 was sampled from the feeding tube and measured . EtCO2 was measured continuously throughout the 60 minutes apart from 2 minutes before, during and after SV use when continuous Paw measurements were taken through the same catheter. There was no flow through the catheter during pressure measurements, to ensure highest possible fidelity. All data were collected on a breath-to-breath basis using custom-written software.Set ventilator-delivered PEEP and fraction of inspired oxygen . This included switching the system to non-invasive (NIV) mode for PSV  and reducing the set ventilator-delivered PEEP by 5 cmH2O [Fifteen minutes of data were recorded continuously during four discrete periods: (1) baseline – prior to placement of the SV in-line with mechanical ventilation; (2) quiet breathing with SV in-line; (3) talking with SV in-line; and (4) post removal of the in-line SV. After the baseline period the tracheostomy cuff was deflated with simultaneous tracheal suctioning to clear secretions pooling above the cuff and minimise aspiration. The SV  was then inserted in-line with the ventilation circuit following the adapter that accommodated the EtCO 5 cmH2O . This ch 5 cmH2O , no set t test, for RR, EtCO2, HR and SpO2. The level of significance was set at p <0.05 throughout, with 95 % confidence intervals quoted where appropriate. All statistical analyses were conducted using STATATM (version 12.0).Data were analysed offline post data collection using commercially available Draeger software . EELI was averaged across the readings and displayed as mean EELI for each of the four data collection periods. A mixed effects regression model was used to investigate the changes in EELI compared to baseline. Planned comparisons between baseline and each subsequent data collection period were conducted using the paired During the study period 55 tracheostomised patients used an SV, and all were assessed for inclusion in the study. Of these patients 20 met the inclusion criteria and were enrolled in the study. Figure n = 13, 65 %) or respiratory disease (n = 5). Nineteen of the patients (95 %) had received a tracheostomy due to prolonged need for mechanical ventilation. Patient number 3 had the tracheostomy initially inserted for surgery in the upper airway, but required prolonged respiratory support following cardiac surgery. See Table The mean age of the patients in the study was 60.4 ± 14.9 years . The mean age for all tracheostomised patients in the ICU throughout the recruitment period was 57.1 ± 17.4 years . On average, patients used an SV for 2.5 days prior to recruitment to the study. There were 10 patients assessed whilst being ventilated with PSV, and 10 assessed during periods off the ventilator  for the duration of data collection. All but one of the patients who were assessed off ventilator were still requiring >12 h/day of mechanical ventilation. See Table p = 0.034). Further increase from baseline by 83.6 % (905 units) (p <0.001) and 120 %  (p <0.001) were seen in data collection period 3 and 4, respectively  occurred from baseline to period 2  and returned to baseline for period 4. RR decreased significantly from baseline while SV was used in-line with the ventilation circuit , and returned to baseline once the SV was removed. HR and SpO2 did not change significantly throughout data collection.EtCOaw were captured . These data all indicated similar drops in Paw coinciding with the reduction of ventilator-delivered PEEP for the duration of the SV use. Ventilator data showed that there was minimal expired tidal volume when the SV was in-line  and its deflated cuff, ensuring more residual air in the lungs at the end of expiration. Further analysis is required to confirm that lung hyperinflation did not occur as it could be argued that an increase in EELI may correlate to tidal hyperinflation. We used SpO2 and EtCOThe subsequent increase in EELI when patients talked is explicable through the additional, but variable, upper airway resistance caused by the glottis  with voc2 during the SV use. One reason may be a drop in EtCO2 due to using one’s voice, as observed in a study of healthy subjects [2 was measured was being diluted with fresh inspiratory flow in all patients on high-flow oxygen, and some on PSV while the cuff was deflated. Transcutaneous carbon dioxide (TcCO2) and arterial pressure of carbon dioxide (PaCO2) may need to be measured in similar studies in the future.There are several potential explanations for the drop in EtCOsubjects . Anothersubjects , 30 to caw were captured, due to rapid and repeated obstruction of the fine-bore catheter with secretions due to presence of no flow through the catheter during the numerous 2-minute measurements. A similar reduction in Paw coinciding with the turning down of the set ventilator-delivered PEEP for the duration of the SV use was noted. However, due to lack of data, it is difficult to draw any conclusions. Further studies are needed to further look at Paw and ventilator-delivered PEEP with and without an SV in circuit.Only limited data on PIt was surprising to observe that the ventilator demonstrated substantial exhaled tidal volume whilst the SV was in situ. This may indicate the presence of a leak in the SV or some form of back-pressure. This means that the ventilator may actually still be delivering PEEP when a one-way valve is in place, and will be the subject of further studies.Communication is a key issue for ventilated patients, who find the inability to speak distressing –33. DiffIncreased use of SV brings with it multiple questions, such as, for how long should the SVs be used at any one time? Does this lead to fatigue? Should the SVs be used with patients during mobilisation? Future studies are needed to look at the efficacy of SVs in the weaning and rehabilitation process of mechanically ventilated tracheostomised ICU patients.This study was conducted on a specific cohort of ICU patients, mostly cardiothoracic, and extrapolation of these data to patients with different pathological conditions may not be wise. This is even more relevant in patients with spinal and brain injuries in whom central control of breathing might be affected.No patients in this study were ventilated using volume-controlled modes, hence there is a need to determine whether restored physiological PEEP through the SV helps compensate for the leak in the ventilatory circuit similarly in volume-controlled ventilation.aw data may be a poor representation of the actual Paw across the time points in the study, and was therefore not reported in detail. Different methods to obtain these important data are recommended for future similar studies. Minor difficulties also occurred with EtCO2 measurements  through the same catheter. However, due to the presence of airflow in the catheter during EtCO2 measurement, this reduced the likelihood of the catheter becoming blocked with secretions, and resulted in almost full data collection across 60 minutes obtained from all patients.Airway pressures were only measured for the second half of the study with limited data obtained as described above. Hence the reported PRoutine suctioning was performed as per patient needs throughout data collection. It is known that tracheal suctioning causes a degree of de-recruitment . The quaThe duration of the study was only a total of one hour with the SV in situ for 30 minutes. Clinically the same patients would be using the SV for several hours at a time. Due to the inability to compare the change in EELI between sessions and the patients needing to remain in the same position, the EIT belt stayed in situ for the duration of the study with the patients sitting up. Therefore it was not feasible to monitor the patients for longer.2.When SVs were used in this cohort of cardio-respiratory patients, we observed no evidence of lung de-recruitment whilst weaning from mechanical ventilation. Deflation of the tracheostomy cuff with restoration of the airflow via the upper airway with a one-way valve facilitated an increase in EELI both during and after a period of SV use in our cohort of patients, which may indicate recruitment of the lungs. Use of the SV resulted in reduced RR and a reduced end-tidal COSpeaking valve use facilitated an increase in end-expiratory lung impedance in tracheostomised cardiothoracic ICU patients weaning off mechanical ventilationIncreased end-expiratory lung impedance was maintained and further increased for at least 15 minutes post removal of the speaking valve from the ventilation circuit2 when used in tracheostomised cardiothoracic ICU patients weaning off mechanical ventilationSpeaking valve use resulted in a reduced respiratory rate and reduced end-tidal CO