Campylobacter is undercooked poultry meat, contaminated from intestinal contents during processing. In broilers, Campylobacter can grow to very high densities in the caeca, and is often considered to be a commensal or an opportunistic pathogen in poultry. Reduction of caecal loads of Campylobacter may assist in lowering incidence rates of Campylobacter food poisoning. To achieve this, there needs to be a better understanding of the dynamics of Campylobacter colonization in its natural niche, and the effect of the local microbiome on colonization. Previous studies have shown that the microbiome differed between Campylobacter colonized and non-colonized chicken intestinal samples. To characterize the microbiome of Campylobacter-colonized broilers, caecal samples of 100 randomly selected birds from four farms were analyzed using amplified 16S rRNA gene sequences. Bacterial taxonomic analysis indicated that inter-farm variation was greater than intra-farm variation. The two most common bacterial groups were Bacteroidetes and Firmicutes which were present in all samples and constituted 29.7–63.5 and 30.2–59.8% of the bacteria present, respectively. Campylobacter was cultured from all samples, ranging from 2 to 9 log10 CFU g-1. There was no clear link between Campylobacter counts and Firmicutes, Bacteroidetes, or Tenericutes levels in the 16S rRNA operational taxonomic unit (OTU)-based analysis of the caecal microbiome, but samples with high Campylobacter counts (>9 log CFU g-1) contained increased levels of Enterobacteriaceae. A decrease in Lactobacillus abundance in chicken caeca was also associated with high Campylobacter loads. The reported associations with Lactobacillus and Enterobacteriaceae match changes in the intestinal microbiome of chickens and mice previously reported for Campylobacter infection, and raises the question about temporality and causation; as to whether increases in Campylobacter loads create conditions adverse to Lactobacilli and/or beneficial to Enterobacteriaceae, or that changes in Lactobacilli and Enterobacteriaceae levels created conditions beneficial for Campylobacter colonization. If these changes can be controlled, this may open opportunities for modulation of chicken microbiota to reduce Campylobacter levels for improved food safety.One of the major transmission routes for the foodborne bacterial pathogen  Campylobacter species Campylobacterjejuni and Campylobactercoli are common causative agents of bacterial gastroenteritis in humans  because it is believed that chickens with this level, or higher, of Campylobacter contamination are the most likely to infect consumers  of carcasses of conventionally reared broiler chickens (Table 1) were taken from a poultry processing plant and transported to the Animal and Plant Health Agency (APHA) on the day of slaughter. Caeca were removed whole from the carcasses and a sample of the contents processed immediately for quantitative bacteriology (see below). An entire caecum and contents from each carcass were stored at -20°C until required. Caeca were left to thaw for 1 h at room temperature and their contents transferred to sterile universals and homogenized by stirring with sterile plastic sticks. Caecal contents (25 mg) were transferred to sample tubes for DNA extraction. Community DNA was isolated from the 100 individual caecal samples using Mo Bio Power Soil DNA kits , following the manufacturer’s protocol.Four batches  at 42°C for 48 h.The levels of eviously . BrieflyAliquots of extracted DNA were amplified with universal primers for the V4 and V5 regions of the 16S rRNA gene. The primers U515F (5′-GTGYCAGCMGCCGCGGTA) and U927R (5′-CCCGYCAATTCMTTTRAGT)  were des1. Essentially paired end reads were merged based on overlapping ends using PEAR2, before filtering the data for base-calling quality and amplicon length. The processed sequences were then classified using the pick open reference OTUs process implemented in QIIME v1.9.1  and beta (how different types are distributed among samples) diversity. Individual samples were assigned to specific categories to explore intra-farm differences and the relationship between Campylobacter load and the composition of the microbiome. Relative abundance plots of microbial taxa were generated to visualize the differences between categories. Beta-diversity of the data set was calculated using the weighted UniFrac metric . A test for the difference in the percentage of Proteobacteria isolated from farms was conducted using non-parametric ANOVA  as the data were not normally distributed . Pair-wise, between-farm comparisons were conducted using Dunn’s multiple comparisons test . p-Value of 0.05 was used as cut off for statistical significance.Sequence reads obtained were processed according to the microbiome-helper pipelineE v1.9.1  against c metric , which uERR2206851–ERR2206950, respectively.The sequencing reads (fastq files) have been submitted to the European Nucleotide Archive, with project number PRJEB23657. The individual reads of samples 1–100  sequences per sample. After clustering with a similarity cut off of 97% these sequences were assigned to 96,553 OTUs. Alpha and beta-diversity was assessed using an evenness of 8,500 sequence reads per sample , which excluded a single sample from further analysis. Thus, the microbial diversity in the caeca of 99 birds was compared.After filtering the data for base-calling quality and amplicon length, 4.86 million sequences were retained for further analysis with a mean of 40,475 , Firmicutes  and Tenericutes . The inter-farm variability is confirmed by there being a significant difference in the microbial community composition between the four farms (p < 0.001), as determined by the DirtyGenes test . In the samples coming from one of the farms (Farm 4), a significantly lower mean percentage  of Proteobacteria was observed , compared with the mean percentage of this phylum in the other farms , p-value < 0.0001.According to the sequence data, the caecal microbiome consists mainly of three phyla of bacteria: Bacteroidetes, Firmicutes, and Tenericutes. The proportions of these phyla in the samples varied according to the farm of origin: Bacteroidetes . There was considerable intra-farm variability between individual birds (2 to 9 log10 CFU g-1), and the inter-farm variability was also significant . The samples coming from Farm 3 (samples 26–50) had the highest load of Campylobacter with five samples having more than 9 log10 CFU g-1 and further six having more than 8 log10 CFU g-1, when all other samples had lower loads and only two samples from the other farms had more than 8 log10 CFU g-1. Farm 3 also had the highest average bird weight, comparing all chickens that were slaughtered at roughly the same age (data not available for the chickens of Farm 4). The age of the chickens at the time of slaughter and their mean weights are shown in Table 1.All samples analyzed were culture-positive for Campylobacter counts and the composition of the caecal microbiome was also observed in our study. Analysis of the distribution graphs  indicates an increasing proportion of Firmicutes and Proteobacteria, and a decreasing proportion of Bacteroidetes with increasing levels of Campylobacter. There is no clear trend for the proportion of Tenericutes, although samples with the highest Campylobacter counts (>9 log10 CFU g-1) had the lowest percentage of Tenericutes (0.62%), whereas the percentage of Proteobacteria is increased (14.56%) . The vast majority of these Proteobacteria (91%) belong to the Enterobacteriaceae family, which suggests that Campylobacter spp. colonization of the caeca could be associated with a change of their microbiota and that high counts of Campylobacter spp. could be linked with a high proportion of Enterobacteriaceae. Furthermore, the number of reads for Epsilonproteobacteria were also higher for chickens highly colonized by Campylobacter (>9 log10 CFU g-1).An association between Campylobacter counts from 3 to 7 log CFU g-1. There was a small decrease (2.07%) in samples having 8 log CFU g-1 and a considerable decrease (0.22%) for the samples having 9 log CFU g-1 . A statistical test on the differences in microbial communities between birds with the highest Campylobacter loads and the other birds would not give reliable results because the comparison is post hoc. However, the results do suggest that very high Campylobacter loads could be associated with low Lactobacillaceae loads, a hypothesis that could be tested in future studies.For Lactobacillaceae the percentages are almost constant (2.46–4.89%) for the samples having Figure 5). Intra-farm variation was more obvious in Farm 3, which was the farm with the highest average weight of the birds of roughly the same age (the birds from Farm 4 were 5 days older at slaughter and their weights not recorded) and with the highest load of Campylobacter. The samples from Farm 4 were found to be quite closely related and their diversity was considerably lower than the other farms. In contrast, the samples from Farm 3 were found to have the highest diversity in their microbiota which potentially could be associated with their high load of Campylobacter and increased weight. Figure 6 depicts the principal coordinates analysis (PCoA) of all samples labeled with Campylobacter counts (CFU g-1). Three out of four samples with the highest Campylobacter loads (9 log CFU g-1) cluster tightly together except for one sample that differs slightly. The pattern is similar for the other samples having high Campylobacter counts (8 log CFU g-1), while the samples with lower counts (less than 7 log CFU g-1) tend to be more diverse.Analysis of community composition of the samples indicated that inter-farm variation was greater than intra-farm variation , Proteobacteria (5–64%), and Bacteroidetes (1–30%)  and FirmCampylobacter in all samples in our study precluded a simple comparison between the caecal microbiomes of Campylobacter positive and negative birds. In a recent study  when the Campylobacter counts reached 9 log10 CFU g-1. For Clostridiales, their peak (53.99%) was in samples having 8 log10 CFU g-1 of Campylobacter counts and they presented a small decrease (38.33%) for the samples having 9 log10 CFU g-1 (data not shown).A study by eriaceae . These f10 CFU g-1. This is in line with previous studies. For example, a study in mice demonstrated that a diet-induced alteration of the intestinal microbiota, comprising an increase in the abundance of E. coli and a decrease in Lactobacillus was associated with a greater susceptibility to C. jejuni infection  were all from the same farm, it is possible that there are confounding factors.Recent studies  have shochickens  and Campcrobiota . Accordicrobiota , the caeThe PCoA of the data indicated that there was more between-farm variation in the complete microbial community composition than within-farm variation. The use of weighted uniFrac metrics to determine the distances between samples reveals differences that are due to changes in relative taxon abundance. Given the close proximity of the birds on a farm, the convergence of their caecal microbiomes is not surprising. Differences between farms could be linked to environment, diet or even the age of the birds.Campylobacter in broiler caeca are interrelated with the microbial community structure and particularly associated with an increase in the presence of Enterobacteriaceae. However, further studies need be done to establish causality and to investigate whether the presence of Campylobacter favors the growth of Enterobacteriaceae or vice versa. It is possible that a general dysbacteriosis leads to colonization by both Campylobacter and Enterobacteriaceae. Furthermore, it was found that a decrease in Lactobacillus abundance in chicken caeca could be linked to high Campylobacter loads. Therefore, it can be concluded that Campylobacter colonization might be associated with changes in the intestinal microbiota and the strategies to reduce colonization of Campylobacter in chickens could include shaping the chicken microbiota. The study of the chicken microbiota is considered necessary to provide new and effective approaches to fight Campylobacter colonization and improve public health.The outcomes of the present study suggest that there is strong evidence that high counts of IS was the PI and wrote the manuscript. RE did the sequencing analysis and wrote parts of the manuscript. SC did the microbiological analysis and wrote parts of the manuscript. AV helped with the analysis of the data and wrote parts of the manuscript. DS did the statistical analysis of the data. JP helped with the design of the study and reviewed the manuscript. MC helped with the design of the study, wrote parts of the manuscript, and reviewed it. RLR helped with the design of the study and reviewed the manuscript. AC had the initial idea of this study, helped with the design of the study, and reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The potential growth-promoting effects of antibiotics are not well understood among undernourished children in environments with high pathogen exposure. We aimed to assess whether early antibiotic exposure duration and class were associated with growth to 2 years of age across 8 low-resource sites in the MAL-ED birth cohort study.z scores to 2 years. We assessed the impact of class-specific exposures and duration, and compared these results to effects of antibiotic exposures after 6 months of age.We followed 1954 children twice per week from birth to 2 years to record maternally reported antibiotic exposures and measure anthropometry monthly. We estimated the associations between antibiotic exposure before 6 months of age and weight-for-age and length-for-age (LAZ) Antibiotic use before 6 months of age was associated with increased weight from 6 months to 2 years, whereas associations with length were less consistent across sites and antibiotic classes. Compared to unexposed children, 2 or more courses of metronidazole, macrolides, and cephalosporins were associated with adjusted increases in weight-for-age of 0.24 : 0.04, 0.43), 0.23 , and 0.19  from 6 months to 2 years, respectively.Antibiotic use in low-resource settings was most associated with the ponderal growth of children who had multiple exposures to antibiotics with broad spectrum and anaerobic activity in early infancy. Opportunities for rational and targeted antibiotic therapy in low resource settings may also promote short-term weight gain in children, although longer-term physical growth and metabolic impacts are unknown. What Is KnownAntibiotics are growth-promoting in livestock, and early life exposure to antibiotics has been recently associated with obesity in several high-income countries.The effect of antibiotic use may be different among children in highly contaminated environments in low-income settings in which undernutrition is more common than obesity.What Is NewAntibiotic-associated growth promotion can also occur among children in low-resource settings who are often undernourished.Antibiotic use in the first 6 months of life was associated with increased weight, especially among children with 2 or more exposures to macrolides, metronidazole, and cephalosporins.After decades of antibiotic use to promote growth of livestock in the agricultural industry, researchers have begun to explore the potential role of antibiotic use in promoting growth in humans as well . EvidencIn low-income settings, obesity among young children is less common, and poor growth and undernutrition is often a more pressing public health concern. In these settings, antibiotics are recommended by the WHO as part of the treatment for severe acute malnutrition , despiteTo appropriately account for these factors, we leveraged the high-resolution illness and treatment data from The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED), a birth cohort study performed at 8 sites in South America, sub-Saharan Africa, and south Asia. Antibiotic use was highly prevalent in almost all sites . We aimez scores were calculated using the 2006 WHO child growth standards  and as a categorical variable by number of class-specific courses received, using the models above and additionally adjusting for other antibiotic class exposures to isolate class-specific effects. We also explored effect measure modification by malnutrition (stunted and underweight) at 6 months and pathogen burden from 0 to 5 months (presence of detected ) by inclz scores at the beginning of the age period.To assess the period during which the effects of early antibiotic use (before 6 months of age) were manifested, we estimated these effects on anthropometry at different age periods, from 0 to 5, 6 to 11, 12 to 17, and 17 to 24 months, adjusting for the child's anthropometric To compare early life exposures with later exposures, we used linear regression to estimate the effects of exposures from 6 to 24 months on cross-sectional WAZ and LAZ at 2 years. A child's measurement closest to 24 months, between 23 and 25 months was considered their anthropometry at 2 years. Adjustment variables included the same baseline characteristics as above, including enrollment WAZ, enrollment LAZ (for length models only), illness burden as characterized above over the whole 2 years of follow-up, and antibiotic use in the first 6 months of life. Using these models with cross-sectional outcomes of WAZ and LAZ at 2 years, we demonstrated that the early life effects were insensitive to further statistical adjustment for illnesses and antibiotic use after 6 months of age.Across 8 sites in the MAL-ED cohort, 1954 children were followed until at least 6 months of age and had at least 1 subsequent anthropometric measurement. The majority of these children  remained under surveillance and had anthropometric measurements at 2 years. Baseline characteristics, early antibiotic exposure, and growth outcomes differed across sites Table . Mean enA 7-day increase in duration of antibiotic exposure in the first 6 months of life was associated with an adjusted 0.03  higher WAZ from 6 months to 2 years of age compared to unexposed children. There was no difference between boys and girls, and this association was largely consistent across sites, except in Brazil and South Africa, where antibiotic use was least common and the estimates are least precise Fig. A. The laThe associations of duration of antibiotic exposure with LAZ were more varied across sites Fig. B. There In the assessment of class-specific exposure, macrolide and metronidazole use on at least 1 day during the first 6 months were associated with an adjusted 0.14  and 0.17  increase in WAZ, respectively. Cephalosporins, fluoroquinolones, and penicillins were associated with smaller and nonsignificant increases in WAZ. Associations with LAZ were generally smaller and not statistically significant for any antibiotic class  greater WAZ compared to children receiving 3 courses or less . These children had further gains of 0.08  and 0.04  in WAZ compared to children receiving 3 courses or less from 6 to 11 and 12 to 17 months, respectively. Adjusting for their WAZ at 18 months, there was no further difference in WAZ from 18 to 24 months associated with early antibiotic use . Children who did not, however, receive early antibiotics did not catch-up during this period such that the majority of the overall WAZ difference was maintained to 24 months. More than 3 courses of antibiotics was associated with a similar increase in LAZ by 6 months , but there were no associations in periods after 6 months  or overall as shown above.In contrast to the antibiotic effects in early infancy, duration of antibiotic exposure after 6 months of age were not associated with cross-sectional WAZ or LAZ at 2 years , adjusting for exposure before 6 months and illnesses across the first 2 years of life. Among the antibiotic classes, only fluoroquinolones were associated with increases in size at 2 years of age. Two or more courses of fluoroquinolones after 6 months of age were associated with an adjusted increase of 0.21  in WAZ at 2 years . The associations with other antibiotic classes were near the null and/or did not show a dose-response trend.We demonstrate that the growth-promoting phenomenon of early life antibiotic exposure among healthy children is not unique to high-resource settings and can also occur in populations with low average weights and lengths. Antibiotic exposure was associated with increases in weight in the MAL-ED cohort, and this effect was limited to early exposures in the first 6 months of life, which is consistent with previous studies ,9,13,14.A larger impact of macrolides compared to other antibiotics on BMI was similarly documented in a retrospective US study . Broad-sOur results provide support for the hypothesized mechanism through the gut microbiota. The influence of antibiotics on the diversity and composition of the gut microbiota can persist long after treatment is completed ,31, espeBecause the effect of early life antibiotics did not, however, affect growth rates after 18 months of age, the impact of an altered microbiota may be short lived. This evidence that the microbiota is not necessarily permanently “reprogrammed” to cause increased growth rates long after exposure is supported by a recent large study in the United States that found that early antibiotic use was not associated with higher rates of weight gain after the exposure period . Their rStill, because the unexposed children in the MAL-ED cohort did not complete catch-up growth, such that the weight difference associated with antibiotic exposure was still present at 24 months, these exposures may have sustained impact on a child's attained size. Short-term growth and attained size have been negatively associated with hospitalization and mortality , and posCampylobacter and enteroaggregative E coli in the first 6 months of life  relied on caregiver recall of antibiotic exposures at a minimum of 4 years after exposure and caregiver report of anthropometry . A previThis analysis was limited by the inability to assess long-term growth outcomes 5 to 10 years after exposure. This may explain why there was no observable effect of antibiotic exposure after 6 months of age, which may only become apparent later in childhood ,12,14. TAn association between antibiotics and weight gain in high-income settings is often discussed as a negative side effect of treatment since obesity is a growing public health problem and particularly pernicious among children. The cost-benefit analysis may, however, be different in low-resource settings where children may benefit from improvements in growth and are commonly infected with enteropathogens even in the absence of diarrhea . On the


The challenges of conducting research with hard to reach vulnerable groups are particularly pertinent for people with learning disabilities. Data collection methods for previous cost and cost-effectiveness analyses of health and social care interventions targeting people with learning disabilities have relied on health care/health insurance records or data collection forms completed by the service provider rather than by people with learning disabilities themselves. This paper reports on the development and testing of data collection methods for an economic evaluation within a randomised controlled trial (RCT) for a supported self-management programme for people with mild/moderate learning disabilities and type 2 diabetes.A case finding study was conducted to identify types of health and social care use and data collection methods employed in previous studies with this population. Based on this evidence, resource use questionnaires for completion by GP staff and interviewer-administered participant questionnaires  were tested within a feasibility RCT. Interviewer-administered questionnaires included the EQ-5D-3L . Participants were adults > 18 years with a mild or moderate learning disability and type 2 diabetes, with mental capacity to give consent to research participation.Data collection for questionnaires completed by GP staff requesting data for the last 12 months proved time intensive and difficult. Whilst 82.3% (121/147) of questionnaires were returned, up to 17% of service use items were recorded as unknown. Subsequently, a shorter recall period (4 months) led to a higher return rate but with a higher rate of missing data. Missing data for interviewer-administered participant questionnaires was > 8% but the interviewers reported difficulty with participant recall. Almost 60% (48/80) of participants had difficulty completing the EQ-5D-3L.Further investigation as to how service use can be recorded is recommended. Concerns about the reliability of identifying service use data directly from participants with a learning disability due to challenges in completion, specifically around recall, remain. The degree of difficulty to complete EQ-5D-3L indicates concerns regarding the appropriateness of using this measure in its current form in research with this population.ISRCTN41897033 (registered 21 January 2013).Current Controlled Trials The online version of this article (10.1186/s40814-018-0266-8) contains supplementary material, which is available to authorized users. It is estimated that there are around 1.2 million people in the UK with a mild or moderate learning disability , 4. DispThere are a number of possible explanations for high rates of poorly controlled type 2 diabetes in adults with a learning disability including high prevalence of obesity and unhealthy diets, prescription medications that increase risk of obesity, reduced self-management skills and lack of practical support –11. WhilThis paper reports on the development and testing of data collection methods for an economic evaluation within a randomised controlled trial (RCT) for a supported diabetes self-management programme for people with a mild/moderate learning disability. A full description of the study is reported elsewhere , but in The aim of the phase I case finding study was to identify the different types of health and social care used by individuals with a mild/moderate learning disability and type 2 diabetes. This information would then be used to design a resource use questionnaire for completion by GP staff using the patient’s record for use in an economic evaluation of a supported self-management intervention for this population. In addition, phase I would identify suitable outcome measures for such an economic evaluation.A literature review was undertaken to inform the choice of outcome measure and the development of the health care resource use questionnaires to be completed by GP staff. These health care resource use questionnaires were to be piloted within the phase I case finding study before further refinement and testing for use in the phase II feasibility RCT.The aim of the literature review was fourfold: to identify (i) health and social care resources used by people with type 2 diabetes and a learning disability within self-management interventions (including frequency and drivers of costs) and the methods by which that data were collected; (ii) health and social care resources used by the general population of people with type 2 diabetes within self-management interventions (including frequency and drivers of costs); (iii) health and social care resources used in other settings by people with a learning disability and the methods by which that data was collected; (iv) outcome measures used within any of the cost-effectiveness analyses identified.The search strategies comprised index terms, text words and their synonyms for four concepts: learning disability, self-care interventions, type 2 diabetes and costs . Three separate searches were run in EMBASE, MEDLINE and the Cochrane Library between May 2013 and February 2014. Searches used alternative combinations of three of the four concepts in each. The searches were not limited by language or date of publication. The inclusion criteria were as follows: cost-effectiveness studies for healthy lifestyle interventions; cost-effectiveness studies for diabetes self-management; and studies with costs and/or outcomes for people with a learning disability. Papers were excluded in which studies reported in abstract form only, related to self-monitoring or focussed on pharmaceutical treatment for diabetes. Titles and abstracts were reviewed by two people (CH and JOD) and data extracted using a bespoke data extraction template which included the type of intervention, duration of the study, outcome measures used and type of cost data collected.Based on information retrieved from the literature review  posted the questionnaire to GP practices. A cover letter explaining the study and reimbursement for the time to complete the questionnaire was provided. The CTRU informed the research team if the questionnaire had not been returned after 6 weeks. Reminders to complete questionnaires were by way of a series of emails and unlogged telephone calls by a researcher to the GP practices. For questionnaires still not returned after the 6-week reminder, where feasible, the researcher visited the GP surgery and supported the practice to complete the questionnaire.The completeness of the data collected in the questionnaires was recorded using descriptive statistics, detailing the number and percentage of questionnaires returned and the number and percentage of missing items within the returned questionnaires.The literature review searches identified 1189 unique references.The first search of evidence of cost-effectiveness analysis of self-management interventions in people with diabetes and a learning disability found no papers that met the inclusion criteria. The second search, studies of cost-effectiveness analysis of self-management interventions in people with diabetes, identified eight studies –20 that Cost data were collected using a variety of methods including interviews with health workers, standardised templates for staff, participant questionnaires and participant medical records. Each diabetes self-management intervention included programme costs with staff  being the greatest drivers of cost in relation to intervention delivery. Administration costs such as telephone charges, printing charges and translation costs added to the expense of the interventions. In many of the US studies, patient costs were not included or included in a generalised form using insurance information, with just one study  includinBased on the limited data of health and social care use gleaned from the review in phase I of this study, and in consultation with the experts on the study team, a resource use questionnaire to be completed by GP staff was developed using a modified version of the CSRI for distribution to GP practices.In respect of outcomes, the included diabetes interventions used clinical marker data such as HbA1C levels, body mass index (BMI) and risk of cardiovascular disease; in terms of quality of life measures, the majority of the diabetes interventions used quality-adjusted life years (QALYs) , 17–20, Previous work has suggested that the SF-36 is promising for use with people with a learning disability ; the shoBetween June 2013 and January 2015, a resource use questionnaire to be completed by GP staff for each consented participant was posted to their GP practice for completion using their patient record. The questionnaires asked about the services the participant had used over the previous 12 months. Case finding methods in phase 1 identified 147 eligible and consenting participants, of whom 121 (82.3%) had a resource use questionnaire completed on their behalf by a member of staff at a GP practice or with support from a researcher. Of completed questionnaires, missing data for each item was < 5%; however, items recording service use responses, whilst not missing, were recorded as unknown in up to 17% of items. The data collection process for these questionnaires proved time intensive and difficult. In some instances, researcher telephone reminders to encourage completion exceeded nine or more attempts. Further attempts to contact proved less rewarding with emails being rarely replied to. Often service uses could only be accurately counted by opening every document in the participant’s file to check for referral letters which had been scanned and saved as attachments in a patient record. There were cases where staff at practices refused to complete sections because the process took longer than they had anticipated. Those practices who did not want to complete the questionnaire were offered the option of a research team member visiting the practice to support them in completing the questionnaire; however, this offer was only accepted on three occasions.As shown in Table The questionnaire also included a space to record prescribed diabetes medications. Of the 121 completed questionnaires, diabetes medication information was supplied for 61 participants (50.4%). Additional file In addition to the resource use questionnaire to be completed by GP staff developed for the phase I case finding study, phase II questionnaires for completion by the study participants (through interviewer-administration) were developed based on the findings from the literature review (phase I) and input from the study team. A secondary aim of phase II was to test the data collection questionnaires for use in a subsequent definitive RCT. This included an analysis of the acceptability of interviewer-administered participant questionnaires and participant-reported outcomes from interviews at baseline and 6-month follow-up, and an exploratory analysis of data collected from GP records using a revised version of the phase I questionnaire to be completed by GP staff in order to identify the main drivers of health and social care cost within the intervention and treatment-as-usual (TAU) groups.The resource use questionnaire to be completed by participants took a wider, societal perspective including questions about health care use resource use, employment status, sick leave, their living situation and out-of-pocket expenditures incurred as a result of health care admissions or appointments. Questions were written in plain English and were phrased in a conversational tone. The interviewers recorded post-interview journals reporting on their perceptions of the ease of completion. This gave a more in-depth understanding of the challenges for participants in responding to the questions, and the influence of supporters on participant responses. The questionnaires were analysed using descriptive statistics to assess the level of completeness of data, categorised into resource use, employment and accommodation.In addition to the questionnaire for completion by participants, a revised resource use questionnaire was again sent to GP practices to complete using the patient’s record. As concerns were voiced by staff at the GP practices and study researchers over the time-consuming and problematic collection process in phase I, for phase II, there was a change of approach to the collection of health care resource use data. GP practices were asked for participants’ resource use relating to a shorter duration than in phase I (4 months rather than 12 months). The completion rate improved for phase II, GP practices were contacted regarding completion after 6 weeks had passed on just two occasions.Unit costs were assigned to the data in order to estimate the main drivers of health and social care costs over a 4-month period for the sample. Unit costs were obtained from national sources including the Personal Social Services Research Unit (PSSRU) Costs of Health and Social Care , NHS refBased on findings from the literature review in phase I, the EQ-5D-3L was chosen to be assessed as part of the phase II feasibility study , 33 and Participants were interviewed at baseline and at 6-month follow-up. Interviews were undertaken between September 2014 and September 2015. At baseline, 82 participants were interviewed, 40 males and 42 females and the mean age was 56.4 years; 77 of these participants were interviewed for a second time at follow-up. A full description of the complex recruitment process is reported elsewhere .We asked participants about their living arrangements. In relation to accommodation type at baseline, 57.3% of participants (47/82) lived in domestic housing; 15.9% (13/82) lived in sheltered housing; 18.3% (15/82) lived in a shared supported house. At follow-up, 50.6% of participants (39/77) lived in domestic housing, 16.9% (13/77) lived in sheltered housing and 26% (20/77) lived in a shared supported house. There was only one missing observation across the sample for this question; however, interviewer notes highlight the difficulty in being able to categorise their residence; for the 77 participants interviewed at follow-up, over 40% changed their answer from the one they gave at baseline  indicated they had at baseline, and just one participant indicated sick leave at follow-up. All sick leave recorded was between 1 and 2 days.Participants were asked to recall the health and social care services they had used in the last 4 weeks. The results are shown in Tables “Had to explore if the visit had been in the last four weeks, since participant also mentioned two visits before Christmas.” (#75)- Very little missing data (< 8%) was reported at baseline and follow-up; however, the interviewers did report a number of respondents having difficulty recalling their health and social care use (62% at baseline and 41% at follow-up). The recall time posed particular problems with supporter clarification required:- “Person reported hospital stay that happened a long time ago…..Participant knew she took medication and told researcher what tablet was like. Initially suggested that medication had changed but when researcher enquired further it seemed that it had not.” (#81)When asked about hospitals stays, some participants would report stays which were found to be from a long time ago despite the researcher defining the timeframe we were interested in. Responses to questions about changes in medication were complicated to interpret as people often did not know what they were taking or only knew medication by shape and/or size of tablet:- “Could recall services used but did not know how many times. Participant said had been to hospital in the last six months but on checking with supporter, she had not.” (#344)Paid support workers often had files for a person and could look up hospital visits and confirm or deny self-reported information. Some participants knew they had made some use of the GP but not the number of times; others may have answered positively to hospital use but have been thinking of visits outside the time frame:At the follow-up interview, participants were asked if their medication had changed over the past 6 months. Just seven participants (9.1%) indicated it had; four participants (5.2%) indicated a change in frequency, and three participants (3.9%) indicated the type of medication had changed. This data could not be verified.In order to identify the main drivers of health and social care cost over a 4-month period, we assigned unit costs to each service use The EQ-5D was incomplete for two participants at baseline and one out of 77 participants with a follow-up visit. The average absolute difference between baseline and follow-up was 0.0042 (SD 0.2893). Despite few missing data, interviewers reported that 60% of participants (48/80) at baseline and 53.9% of participants (41/76) at follow-up had some level of difficulty completing the measure. The most important contextual area was the role played by the supporter in the interview process. Although 25% of participants (20/80) did not have a supporter present, for those with a supporter present interviewers recorded supporter intervention on occasion (for 11% of participants at baseline and 14.5% at follow-up) providing explanation of the questions when required, and sometimes when not. Supporters contextualised the question for the respondent, answering questions on the respondent’s behalf and sometimes succeeding in persuading respondents to change answers to match their own perceptions of the respondent’s health state. In the case of the latter, it was clear that often the supporter did not agree with the level chosen by the participant. On occasion, the supporter being present was enough to influence the participant. Researchers recorded the following observation:The linguistic challenges and the role played by the supporter were not mutually exclusive and often overlapped. At baseline, 28.8% of participants (23/80) were assisted by a supporter; at follow-up, this figure decreased to 21.1% (16/76).Based on evidence from the literature and expert advice from the research team, we developed two data collection instruments relating to participants’ health and social care use, assessing two methods of data collection. Resource use questionnaires  completed by GP staff and interviewer-administered participant questionnaires .The analysis of the results from the phase I resource use questionnaire completed by GP staff highlights the high use of primary care services such as GP and nurse care in comparison with hospital-based care. Whilst 82.3% of the questionnaires were returned and with few missing items (< 5% missing), there was however a high level of service use recorded as unknown (which could therefore be considered to be missing data). This highlights the challenge of data collection in primary care. Completion of the resource use questionnaires by GP staff was time consuming and problematic in respect of the intensity of follow-up reminders required to achieve the return rate of over 82% (including researchers helping with completion in the GP surgeries). Potential reasons for the time-consuming nature of the questionnaires and the missing data was the 12-month time period over which resource usage was requested and the GP practice inability or unwillingness to complete the task.In phase II, the time period over which resource usage was collected from GP records was decreased to 4 months, to ease completion. When unit costs were applied, the main drivers of cost were GP care and hospital-based care (A&E and inpatient stays). The types of services used and the main drivers of cost are similar to findings from the literature review , 25, 26.The intensity of reminders and GP visits to assist completion in case finding was not replicated in the feasibility RCT, and in fact, the return rate increased in the second phase of the study. It is not possible to say whether this was because the surgeries became more adept at completion over time or whether it was because the questionnaires were asking about a shorter timespan in the RCT. There were less data reported as ‘unknown’; however, more data was reported as missing. Data on services provided outside the GP surgery team, some of which are part of the Diabetes Annual Care Review, e.g. podiatrist, which were recorded as unknown and missing remained relatively high. If GP records were to be used in future studies with this population, methods to retrieve data on community service use would also need to be factored into the design. The only service not recorded as being used by any of the participants was the diabetes educational course despite being part of recommended first-line treatment of type 2 diabetes in adults . FurtherDespite the difficulty in collecting data directly from GP records, it appears to be a more reliable method than collecting the information from participants themselves. Participants were asked to recall their recent health care use at baseline and follow-up. Interviewers recorded a high occurrence of difficulty with this question (61% at baseline). Rephrasing of questions (e.g. 4 weeks/1 month) and supporter clarification was required. Such a high degree of difficulty with recall indicates concerns about collecting resource use data directly from participants. Interviewers classified accommodation and employment subjectively and changed their interpretation of the response (or it was a different interviewer). A finding would be that these categories need to be better defined for interviewers.The solution as to how to collect resource use data for this population could lie in the use of electronic records, such as Patient Level Information and Costing System (PLICS), as coverage in the UK increases. Providing a central data team with specific data extraction queries could make the data collection process more efficient. These methods bring their own challenges, not least in how to collect out-of-pocket costs for lifestyle change interventions (e.g. cost of changes in diet and exercise) and the costs associated with self-management itself. Accommodation and employment status data may also not be included within GP records.There was almost no missing EQ-5D-3L data reported with only two participants not completing this part of the questionnaire at baseline and one not completing at follow-up. This was most likely because it was interviewer-administered. Despite high completion rates, participants still experienced difficulty answering, with up to 60% being considered to have some level of difficulty with the EQ-5D-3L questions and requiring assistance from the researcher or supporter. The main difficulty experienced was with wording and understanding terms in the questions; rephrasing and explanations of terms helped with completion; however, this is discouraged by the creators of the EQ-5D as it invalidates the measure. Supporters intervened often, sometimes correcting participant’s answers, even if the participant did not appear to have trouble with the question. Participants on occasion disagreed with supporters, placing a different value on their own health.Data collection methods for a future economic evaluation were developed, and their feasibility was tested during this study. In the testing phase (feasibility RCT), there was a higher return rate of the resource use questionnaire from GP practices than had been seen in the development phase (phase I). There were relatively high rates of ‘unknown’ answers for community-provided care than for care provided within the GP surgery or by the GP team; therefore, a requirement for further investigation as to how community team service use can be recorded remains, as does sustaining the high return rate from GP practices.In respect of the participant-completed resource use questionnaires, there were challenges in completion, specifically around the time particular events happened and recall more generally. Difficulty with recall is common in people who have a learning disability , and theIn respect of assessment of quality of life, although rephrasing and provision of explanations facilitated completion of EQ-5D-3L, a substantial proportion of participants had difficulty in understanding the questions. The high degree of difficulty experienced indicates concerns regarding the appropriateness of using this measure in its current form in research with this population. Further research into the language, format and process of completion of the EQ-5D-3L for participants with a learning disability is recommended.Additional file 1:Table S1. Cost effectiveness analyses of self-management interventions in people with diabetes. (DOCX 28 kb)Additional file 2:Table S2. Cost effectiveness analysis of interventions for people with learning disabilities. (DOCX 22 kb)Additional file 3:Other Tables. (DOCX 16 kb)


Anaplasma phagocytophilum that is transmitted by the tick Ixodes ricinus. A sustainable strategy to control tick-infestation is to breed for genetically robust animals. In order to use selection to genetically improve traits we need reliable estimates of genetic parameters. The standard procedures for estimating variance components assume a Gaussian distribution of the data. However, tick-count data is a discrete variable and, thus, standard procedures using linear models may not be appropriate. Thus, the objectives of this study were twofold: 1) to compare four alternative non-linear models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial based on their goodness of fit for quantifying genetic variation, as well as heritability for tick-count and 2) to investigate potential response to selection against tick-count based on truncation selection given the estimated genetic parameters from the best fit model. Our results showed that zero-inflated Poisson was the most parsimonious model for the analysis of tick count data. The resulting estimates of variance components and high heritability (0.32) led us to conclude that genetic determinism is relevant on tick count. A reduction of the breeding values for tick-count by one sire-dam genetic standard deviation on the liability scale will reduce the number of tick counts below an average of 1. An appropriate breeding scheme could control tick-count and, as a consequence, probably reduce TBF in sheep.Tick-borne fever (TBF) is stated as one of the main disease challenges in Norwegian sheep farming during the grazing season. TBF is caused by the bacterium  Sheep farming in Norway is based on grazing unfenced rangeland and mountain pastures in summer. Grazing such pastures do however provide welfare and production challenges with losses to predators, diseases and accidents. There is a yearly national economic loss of approximately 100 million NOK  while it is also a severe animal welfare issue ) varied from low to moderate, depending on the time of measurements, i.e., January 2002: h2 = 0.13, March 2002: h2 = 0.17 and November 2004: h2 = 0.37 [h2 of tick count has been estimated and range widely; from low estimates of h2 (0.11 to 0.13) for the cattle tick Boophilus microplus in crossbred cattle [h2 (0.17 to 0.42) [h2 for tick count is in the range of the previous studies in sheep and cattle, indicating that accuracy in predicting genetic effects for tick count is relatively high and the tick load can be reduced through selective breeding.The mean posterior estimate of 2 = 0.37 . The gend cattle ,58 to moto 0.42) –61. Our h2 for tick count in both sheep and cattle varied due to different statistical models and trait definitions. For the trait definition, we used the sum of tick counts from three positions, which are very likely to be in contact to ticks during grazing season. With this approach, it may not represent the total number of ticks on each lamb as applied in the other studies. Yet, variation of ticks in the hot spots per se should still reflect the variation in susceptibility or a general robustness to tick. Counting ticks of the whole body is more labour-intensive and time consuming than just counting at only three positions. Hence, a future study should quantify the genetic correlation between tick counts from these three positions and the whole body to verify if they can be considered the same trait for which it may be selected using a selection index.The estimate of McMC replicates , will imply a reduction of the number of ticks of approximately 0.37 leading to an average tick count lower than 1. Such expected genetic gain will considerably reduce the need for treatment with insecticide and thereby likely reduce the incidence of tick-borne infections.Given the mean of tick count of this studied population of 1.350, the reduction of the breeding values on the liability in one sire-dam genetic standard deviation Click here for additional data file.


Schizosaccharomyces pombe. Both killers belong to the previously uncharacterized wtf gene family with 25 members in the reference genome. These two killers act in strain-background-independent and genome-location-independent manners to perturb the maturation of spores not inheriting them. Spores carrying one killer are protected from its killing effect but not that of the other killer. The killing and protecting activities can be uncoupled by mutation. The numbers and sequences of wtf genes vary considerably between S. pombe isolates, indicating rapid divergence. We propose that wtf genes contribute to the extensive intraspecific reproductive isolation in S. pombe, and represent ideal models for understanding how segregation-distorting elements act and evolve.Spore killers in fungi are selfish genetic elements that distort Mendelian segregation in their favor. It remains unclear how many species harbor them and how diverse their mechanisms are. Here, we discover two spore killers from a natural isolate of the fission yeast DOI:http://dx.doi.org/10.7554/eLife.26057.001 During evolution, new species emerge when individuals from different populations of similar organisms no longer breed with each other, or when the offspring produced if they do breed are sterile. This process is known as “reproductive isolation” and, for over 100 years, evolutionary biologists have tried to better understand how this process happens.Animals, plants and fungi produce sex cells – known as gametes – when they are preparing to reproduce. These cells are made when cells containing two copies of every gene in the organism divide to produce new cells that each only have one copy of each gene. Therefore, a particular gene copy usually has a 50% chance of being carried by an individual gamete. There are genes that selfishly increase their chances of being transmitted to the next generation by destroying the gametes that do not carry them. These “gamete killer” genes reduce the fertility of the organism and lead to reproductive isolation.Fission yeast is a fungus that is widely used in research. There are different strains of fission yeast that are reproductively isolated from each other, but it is not known whether gamete killers are responsible for this isolation. To address this question, Hu et al. investigated the causes of reproductive isolation in fission yeast.cw9 and cw27. Both genes belong to the wtf gene family. Each gene is believed to encode two different proteins, one that acts as a poison and one that acts as an antidote. The poison is capable of killing all gametes, but the antidote protects the cells that contain the gamete killer gene. Further experiments show that the antidote produced by one of the gamete killer genes cannot protect cells against the poison produced by the other gene.The experiments identified two gamete killers, referred to as wtf family also acts as a gamete killer in fission yeast. Together, these findings shed new light on the causes of reproductive isolation, and will contribute to deeper understanding of speciation and evolution in general.A separate study by Nuckolls et al. found that another member of the DOI:http://dx.doi.org/10.7554/eLife.26057.002 Segregation Distorter in Drosophila and the t haplotype in mouse , at leawtf gene family. We analyzed their killing behaviors in both native and non-native genomic contexts. Furthermore, we performed comparative genomic analysis of wtf genes and revealed interesting patterns of divergence among them.In this study, through investigating the causes of intraspecific hybrid sterility of fission yeast, we uncovered the molecular identities of two active spore killers, which both belong to the S. pombe strain isolated from grapes in Spain . We selected two backcrossed strains that retained the ability to skew inheritance of markers inserted in the left and right peak regions, respectively. These two strains, referred to as backcrossed-1 and backcrossed-2, respectively, were crossed to the laboratory strain and viable progenies were subjected to NGS-assisted bulk segregant analysis, which revealed the locations of the CBS5557 genomic segments present in the two backcrossed strains and confirmed the allele transmission bias (left-side plots in wtf family genes (wtf (for with Tf LTRs) genes were so named because they are often in close proximity to solo long terminal repeats (LTRs) derived from Tf retrotransposons  to wtf9 in the 5’ portion , is a spore killer. The residual allele frequency bias is probably due to additional spore killer gene(s) in the CBS5557 genomic segments present in the backcrossed-1 strain. Within the CBS5557 genomic segment in the backcrossed-2 strain, we found a wtf gene absent in the reference genome, possibly owing to a deletion event mediated by homologous recombination between LTRs   . Two dip process . Interescw9 and cw27 are both flanked by solo LTRs, and upstream of their predicted start codons they share a nearly identical 288-bp-long sequence (hereafter referred to as the conserved_up sequence) (wtf genes (equence) , which itf genes . To detetf genes . The truars1 locus was crossed to a strain with a Tc-version-containing plasmid integrated at the leu1 locus, and found that the Td versions of both cw9 and cw27 can effectively protect spores harboring them from being killed by the respective Tc-version killers  , with four locations containing an extra wtf gene compared to the reference genome , and a suffix for genes at locations where extra genes exist in the CBS5557 genome    . Using Pe genome . We syst7 genome . In addie genome . Like cwbination , suggestwtf genes with syntenic counterparts in the reference genome, cw9 and five others exhibit exceptionally high DNA sequence diversity (<85% identity) from their counterparts in the reference genome  and a yet-to-be-identified killer gene, which are proposed to act together in a fashion similar to the toxin-antitoxin (TA) systems of bacterial plasmids, in which a stable toxin and a labile antitoxin together ensure that bacterial cells cannot lose the plasmid expressing them , whereas wtf genes are predicted to encode multi-transmembrane proteins, suggesting that the detailed molecular mechanisms of these two types of killers are likely to be quite distinct.Despite the similarities they share, ears ago , and hav species , whose l species , suggestwtf killers begs the question: how does a single gene encode both the killing and the protecting activities? Our findings on the mutations that can disrupt one but not the other activity suggest the possibility that more than one type of expression products can be generated from a wtf killer gene. Consistent with this idea, Nuckolls et al. showed that the wtf4 gene from the S. pombe var. kambucha strain (Sk wtf4) acts as a spore killer by expressing two overlapping transcripts, with the shorter transcript encoding a spore-killing poison and the longer transcript encoding an antidote that protects the spores from the killing effect of the poison  and Exon-2-ATG genes , based on the locations of the first ATG codons downstream of the 150 bp sequence , they do not have an in-frame ATG codon in intron one or in the 5’-section of exon 2, suggesting that most of these genes are not able to generate a shorter isoform according to the dual-transcript model, and are thus ‘antidote-only’ genes. Alternatively, it is possible that they may generate two different protein products using other mechanisms, such as alternative splicing , and three wtf genes in the CBS5557 genome , appear to have suffered pseudogenizing mutations , premature stop codons , frameshifting indels , and segmental deletions (wtf2 and wtf17). These ‘dead’ wtf genes presumably are not able to generate either antidote or poison proteins.Five udogenes . Our anawtf genes in the reference genome and the CBS5557 genome are predicted to contain either 5 or 6 exons. Interestingly, the 6-exon genes, including cw9, cw27, and all but one of the other ten 150-bp-sequence-containing genes, clustered together in the DNA sequence-based phylogenetic tree  systems, many toxins are transmembrane proteins and kill cells by damaging cellular membranes . The beswtf genes were named after their association with the solo LTRs of the Tf retrotransposons  appear to have been lost in the laboratory strain due to LTR-mediated recombination, suggesting that the wtf-LTR association promotes wtf gene loss and is detrimental to the long-term persistence of wtf genes. In addition, it may not be coincidental that both cw9 and cw27 require an approximately 90-bp-long sequence between the upstream LTR and the conserved_up sequence for the full protecting activity, raising the possibility that some wtf genes that lack such an intervening sequence (e.g. wtf4 and cw4) may have suffered functional loss due to a Tf insertion too close to the conserved_up sequence or may have become functionally dependent on the upstream LTR.nsposons . It was e family . Howeveransposed . Bowen erulation . Our finwtf genes are probably the most evolutionarily successful gamete killers known to date. During a relatively short evolutionary time span, wtf gene family has emerged and become the largest species-specific gene family in S. pombe (cw9 and cw27 are both around 1.9 kb) and the self-sufficiency of the wtf killers likely have been advantageous to its family size expansion. Furthermore, given the dramatic divergence of wtf genes between the reference genome and the CBS5557 genome, and the mutual-killing behaviors of cw9 and cw27, it is possible that two identical copies of a wtf gene generated by a gene duplication event can undergo relatively rapid sequence diversification to become mutually non-resistant, so that their selective advantage is not diminished by resistance. Ectopic gene conversion is known to play a causal role in the sequence diversification of animal major histocompatibility complex (MHC) genes  genes , plant dC) genes , and Plaar genes . Our phywtf genes locate on chromosome III, the smallest of the three fission yeast chromosomes. To explain this phenomenon, Bowen et al. proposed that the laboratory strain may have inherited only chromosome III but not the other two chromosomes from an ancestor strain in which wtf genes were abundant on all three chromosomes  and another antibiotic resistance marker at a locus on the right arm of chromosome III (rps2802:natMX) (first backcross). Progeny strains with only one of the loci marked were kept. They were then crossed to laboratory-background strains with the other locus marked by kanMX (second backcross). By scoring the marker segregation ratios of the second backcross, we selected those first-backcross progenies that conferred strong segregation distortion at the kanMX-marked locus. Their second-backcross progenies that harbored the kanMX marker were tested by crossing, and those that conferred segregation distortion at the kanMX-marked locus were selected. One of the second-backcross progenies that conferred segregation distortion at the mug123 locus is the backcrossed-1 strain used in the bulk segregant analysis shown in rps2802 locus underwent additional rounds of backcross to yield the backcrossed-2 strain used in the bulk segregant analysis shown in To construct strains with only a portion of chromosome III originating from CBS5557 and the rest of the genome coming from the laboratory strain, we first introduced cw9∆ × laboratory strain (cross-derived diploid strain was named DY26100), SRR5131579; viable progenies from backcrossed-2 strain × laboratory strain (cross-derived diploid strain was named DY26095), SRR5131577; viable progenies from backcrossed-2 cw27∆ strain × laboratory strain (cross-derived diploid strain was named DY26092), SRR5131576. Sequencing reads were mapped to the reference genome using BWA-MEM . SAM files were converted to BAM files and duplicates were removed using SAMtools . The average read depths were 14.9× for DY9974 × DY8531, 10.4× for DY26097, 18.4× for DY26100, 16.2× for DY26095, and 23.1× for DY26092. The software bam-readcount version 0.7.4 was run with the option -q 60 -b 35  to obtain the read counts of four different bases at each of the 38783 SNP positions identified previously . Sequencing library construction and sequencing on the PacBio RS II platform using P6C4 chemistry were performed by BGI . After filtering out low-quality reads and removing adaptor sequences, we obtained 91,333 subreads no shorter than 1000 bp (mean length 7370 bp). These reads were used for de novo assembly by the SMRT Analysis software. The assembly was polished using Pilon version 1.21 (RRID:SCR_014731) together with our previously published CBS5557 Illumina sequencing data . At least 40 tetrads (160 spores) were analyzed for each cross. Based on empirical evidence, such a sample size is large enough for reliably assessing spore viability. Numerical data of the tetrad analysis in Excel format are provided as ectively .http://www.bioinformatics.org/sms2/ident_sim.html)  .SCR_011811) with the command line option --localpair --maxiterate 16 --reorder  and was visualized using Phylo.io  with the parameter settings --genemodel=exactlyone --sample=100 --keep_viterbi=true --alternatives-from-sampling=true --minexonintronprob=0.08 --minmeanexonintronprob=0.3 --maxtracks=20 , the selected prediction is the one with the second best score. The final predictions for all 57 genes in plain text format are provided as Gene structures of the 57 racks=20 . When muwtf genes of the CBS5557 genome have been deposited at GenBank under the accession numbers KY926712-KY926743.Illumina and PacBio sequencing data reported in this paper are available at the Sequence Read Archive (SRA) under the accession number SRP095878 (BioProject PRJNA358837) . The DNA 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 favorably evaluated by Diethard Tautz (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The following individuals involved in review of your submission have agreed to reveal their identity: Nick Rhind (Reviewer #2); Michael Lichten (Reviewer #3).Thank you for submitting your article "A large gene family in fission yeast encodes spore killers that subvert Mendel's law" 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:S. pombe strain CBS5557 two members of the wtf repeated gene family that serve as meiotic drive elements when crossed to the reference strain. Using deletions of the two loci, they show that the protective function can be separated from the killing function, and show that the C-terminus of the putative open-reading frame is important for killing but not for protection. In addition, they show that the two genes actually kill their sister spores, using both viability and EM-based assays. They then analyze the ensemble of wtf/cw loci in CBS5557 and show that these elements appear to frequently "move" by ectopic gene conversion. Combined with Nuckolls et al., this paper makes an intriguing and interesting story.Hu et al. have identified, in the genome of Essential revisions:k1wtf and k2wtf to cw9 and cw27, is needlessly confusing. Why not simply call these loci cw9 and cw27 from the start, and just say that the reasons for doing so will be presented below? Along similar lines, the wtf nomenclature depicted in cw[3-4]a and cw[3-4]b should be named cw3 and cw4, respectively, likewise the homologs of wtf11, 12, 14, 15, 17 and 18 are unambiguous.1) The switch in nomenclature, from 2) k1wft∆ does not restore reference allele recovery to 50% in the left-peak strain x laboratory strain cross  It's not clear why in cross . Is it pcw8a and cw24 are considered to be a homeologous pair, but wtf24 and cw6b are not. Furthermore, the phylogeny should be shown as an unrooted tree – the authors reconstructed the phylogeny as unrooted and they should depict it that way too.4) k1∆ k2∆ double mutant spores (5) For readers interested in further numerical analysis, it would be useful if the actual numerical data underlying all of the bar graphs were included in a supplementary file. For example, we were wondering if the loss of spore viability in t spores  is greatcw loci are "dead", "antidote-only", or "antidote-toxin". Similarly, since the C-termini of cw9 and cw27 appear to be important for toxin activity, is this region more conserved among the wtf loci than other parts of the protein? In "antidote-only" or in "antidote-toxin" loci?6) It would be useful to use the translation start site-criterion outlined in Nuckolls et al. for whether the 7) It is not clear why the variation of allele frequency depicted in 8) The authors should extend the discussion of their findings and take into account the findings of Nuckolls et al. In particular:wtf genes do? Could they be acting as rescuers but not as killers? Could they have assumed other unrelated functions?What do the other wtf genes in chr III. Why is that the case? (Could it have to do with the fact that it is the only chromosome where disomy is tolerated?)On the second page of the Discussion, the authors speculate as to how one locus could produce both a toxin and an antidote. It should be possible to eliminate, or at least deprioritize, one or more of the proposed mechanisms by inspecting the sequence of the loci and perhaps publicly available RNA-seq data. It would be of great interest to have some speculation on how a putative trans-membrane protein could function in a toxin-antidote system. Essential revisions:1) The switch in nomenclature, from k1wtf and k2wtf to cw9 and cw27, is needlessly confusing. Why not simply call these loci cw9 and cw27 from the start, and just say that the reasons for doing so will be presented below? Along similar lines, the wtf nomenclature depicted in wtf genes in the CBS5557 genome.We have followed the suggestions of the reviewers and changed the nomenclature of the 2) 3) It's not clear why k1wft∆ does not restore reference allele recovery to 50% in the left-peak strain x laboratory strain cross . Is it pcw genes remaining in the backcrossed-1 strain. Future work will be needed to determine which one(s) are active killer genes.We agree with the reviewers that the residual allele frequency bias is probably due to additional active spore killer gene(s) in the CBS5557 genomic regions still present in the backcrossed-1 strain. We have added a sentence about this possible explanation in the Results section. There are 12 4) cw genes, we have generated a new phylogenetic tree, which has a topology similar to, but not exactly the same as, the tree in the original manuscript. This new tree is shown in During the revision, we have improved the de novo assembly of CBS5557 genome by adding a polishing step using the software Pilon. With polished sequences of the 5) For readers interested in further numerical analysis, it would be useful if the actual numerical data underlying all of the bar graphs were included in a supplementary file. For example, we were wondering if the loss of spore viability in k1∆ k2∆ double mutant spores  is greatNumerical data are now provided in 6) It would be useful to use the translation start site-criterion outlined in Nuckolls et al. for whether the cw loci are "dead", "antidote-only", or "antidote-toxin". Similarly, since the C-termini of cw9 and cw27 appear to be important for toxin activity, is this region more conserved among the wtf loci than other parts of the protein? In "antidote-only" or in "antidote-toxin" loci?wtf genes in the reference genome and the CBS5557 genome, and uncovered a within-intron-1 150-bp sequence shared by all currently known active wtf killers . We added a new figure  It is not clear why the variation of allele frequency depicted in For cost reasons, we usually limit the Illumina sequencing of the bulk segregant analysis (BSA) samples to an average read depth of between 10× and 20×. Therefore, the read depth at any SNP loci is much lower than the number of segregants, and as a consequence, the allele frequency data shown in 8) The authors should extend the discussion of their findings and take into account the findings of Nuckolls et al. In particular:What do the other wtf genes do? Could they be acting as rescuers but not as killers? Could they have assumed other unrelated functions?wtf genes in the reference genome and the CBS5557 genome into "poison-and-antidote" genes, "antidote-only" genes, and "dead" genes.We have added several paragraphs in the Discussion section to describe the classification the 57 wtf genes concentrate on chromosome III. We have added a paragraph in the Discussion section to discuss the possible reasons behind this intriguing phenomenon.We agree with the reviewers that tolerance of chromosome III disomy may be a reason why On the second page of the Discussion, the authors speculate as to how one locus could produce both a toxin and an antidote. It should be possible to eliminate, or at least deprioritize, one or more of the proposed mechanisms by inspecting the sequence of the loci and perhaps publicly available RNA-seq data. It would be of great interest to have some speculation on how a putative trans-membrane protein could function in a toxin-antidote system.wtf genes in the reference genome and the CBS5557 genome into "poison-and-antidote" genes, which conform to the dual-transcript model of Nuckolls et al., "antidote-only" genes, and "dead" genes. We speculate that it remains possible that the "antidote-only" genes may be able to generate poison isoforms through mechanisms other than alternative transcription start sites. We have also added a paragraph in the Discussion section to speculate on how transmembrane proteins can act as spore killers.In the newly added paragraphs of the Discussion section, by more carefully inspecting the sequences, we have classified the 57


Supplemental Digital Content is Available in the Text.Low back pain trajectories identified previously appear generalizable. Effective management tailored to individual trajectories needs to be identified. Low back pain (LBP) is a major health challenge globally. Research has identified common trajectories of pain over time. We aimed to investigate whether trajectories described in 1 primary care cohort can be confirmed in another, and to determine the prognostic value of factors collected 5 years prior to the identification of the trajectory. The study was conducted on 281 patients who had consulted primary care for LBP, at that point completed a baseline questionnaire, and then returned a questionnaire at 5-year follow-up plus at least 3 (of 6) subsequent monthly questionnaires. Baseline factors were measured using validated tools. Pain intensity scores from the 5-year follow-up and monthly questionnaires were used to assign participants into 4 previously derived pain trajectories , using latent class analysis. Posterior probabilities of belonging to each cluster were estimated for each participant. The posterior probabilities for the assigned clusters were very high (>0.90) for each cluster except for the smallest “fluctuating” cluster (0.74). Lower social class and higher pain intensity were significantly associated with a more severe trajectory 5 years later, as were patients' perceptions of the greater consequences and longer duration of pain, and greater passive behavioural coping. Low back pain trajectories identified previously appear generalizable. These allow better understanding of the long-term course of LBP, and effective management tailored to individual trajectories needs to be identified. It is the leading cause of years lived with disability worldwide.15 In the long-term follow-up of that cohort, we have shown evidence that these trajectories persist over many years.11 Other studies have since also described trajectories of back pain.25 Despite some differences between studies, common trajectories have been identified across settings and countries. However, no research investigated if the patterns already described in 1 cohort can be confirmed in new cohorts.25 We had the opportunity to replicate methods we have previously used in 1 cohort (BaRNS study),15 within the follow-up of a separate cohort of primary care patients with back pain (BeBack study),19 thereby facilitating examination of the generalisability of findings between samples, and allowing investigation of the potential for wider use and application of the findings.In our previous work among primary care patients with back pain, we identified, for the first time, 4 trajectories of change in back pain over time: persistent mild, recovering, severe, and fluctuating.40 Studies have described associations with identified trajectories,37 but none, to date, have been able to determine predictors of trajectory membership at a time point prior to the identification of the trajectory. This is important to establish a clear time sequence between the predictive factor and the outcome .Predictors of back pain outcome have been identified in a range of studies, but these studies have commonly used the presence or level of back pain at a single point as the outcome.The aims of this study were to, therefore, investigate whether back pain trajectories found in 1 cohort of patients with LBP consulting in primary care are observed in a separate sample, and whether predictors of those trajectories can be identified.19 Ethical approval for all phases of the study was obtained from the North Staffordshire and North West Cheshire Research Ethics Committees.This was a prospective cohort study of patients seeking health care for LBP in 8 general practices within the North Staffordshire and Cheshire area, England (BeBack Study). Consecutive adults aged 18 to 60 years, who visited their general practitioner about back pain between September 2004 and April 2006, were sent information about the study and invited to take part. Further details about recruitment are reported elsewhere.19 The eligible subjects for this 5-year follow-up study were derived from 1289 patients who responded to the initial baseline questionnaire and gave permission for further contact; 810 (63%) responded again after 6 months, and 696 of these (86%) were traced and contacted 5 years later. This eligible sample was sent a questionnaire at the 5-year follow-up stage, followed by 6 shorter monthly questionnaires. In total, 488 responded at the 5-year follow-up stage (70%) and 281 (40%) completed the 5-year follow-up questionnaire and at least 3 subsequent monthly questionnaires. Participants in this analysis were those 281 patients.A total of 1591 participants participated in the cohort at the initial baseline.16 Physical disability associated with back pain was measured using the Roland–Morris Disability Questionnaire .36 Pain duration was measured as time since the last pain-free month,13 and the presence of leg pain and distal leg pain was reported for the previous 2 weeks. These are classified as pain-related factors.In all questionnaires, back pain intensity was derived from the mean of 3 self-reported 11-point numeric rating scales (0-10) for the least and usual pain in last 2 weeks, and current pain.20 These were measured in the initial baseline questionnaire, using the Illness Perception Questionnaire-Revised.31 The Illness Perception Questionnaire-Revised contains 5 subscales relating to the illness (in this case pain): consequences , emotional representation , personal control , treatment control , and timeline . The Coping Strategies Questionnaire 24 was used to assess the level of catastrophising in relation to pain ,21 the Hospital Anxiety and Depression Scale was used to measure affect ,42 the Tampa Scale for Kinesiophobia was used to measure fear of movement ,27 and the Pain Self-efficacy Questionnaire was used to assess the ability of the person to cope and manage despite their current pain levels .33 Finally, passive behavioural coping items were included measuring aspects such as withdrawal from activities, avoidance, and resting .41Psychological factors were selected based on previous prognostic findings within the 2004 to 2006 data set.Baseline questionnaires also included the sociodemographic and occupational factors of age, sex, educational level (education up to the age of 16 years vs education beyond age 16), social class , and current working status .19 and is supported by evidence that individuals scoring less than the midpoint on a pain intensity scale were unlikely to suffer a significant level of disability.38 Questionnaires were scored according to the systems suggested by the developers, where appropriate.From the 5-year questionnaire and the subsequent 6 monthly questionnaires, pain intensity scores were trichotomized into no pain (a score <1.0), mild-moderate pain (a score ≥1.0 and <5.0), and high pain (a score ≥5.0), analysed as an ordinal variable. This cutoff has been established in our previous studies5Baseline characteristics were grouped by domain: sociodemographic and occupational , pain-related , and psychological , similar to previous analyses.15 The assumption behind latent class analysis is that there exist a certain number of distinct pathways of LBP, and participants can be grouped into a small number of clusters representing these pathways based on their profiles of pain over time, with each participant belonging to 1 cluster. The 4 trajectories  identified at 7-year follow-up from 112 participants in the BaRNS study11 were used as the basis for this analysis, and each of the BeBack study participants were allocated to the predefined cluster best matching their pain profile. To do this, the 281 BeBack participants were merged into a single data set with the 112 participants from the BaRNS study, who were preclassified into their LLCA clusters. A 4-class restricted LLCA model was applied based on the 4 pre-established clusters. The posterior probabilities of belonging to each of the 4 clusters for the BeBack participants were then freely estimated within this model. Participants were allocated to the cluster for which they had the highest probability. The goodness of fit of the model was assessed by determining the mean posterior probabilities for the BeBack study participants allocated to each cluster, and subjective assessment of how well individual trajectories within a cluster followed the cluster-specific trajectory. Participants should be allocated to their assigned cluster with a high probability of belonging to that cluster; lower probabilities might suggest that the model has difficulty discriminating between clusters and that participants may not match the trajectory described by their assigned cluster. Mean posterior probabilities above 0.70 are generally considered to show clear allocation of participants to clusters.32 LatentGOLD 4.0 was used for this analysis.The categorised pain intensity scores from the 5-year questionnaire and the following 6 monthly questionnaires were used to cluster participants into different courses of pain, using longitudinal latent class analysis (LLCA), as in the BaRNS study.15 However, there is no definitive method of identifying the best fitting model, and so, both statistical goodness of fit indices  and interpretation of the resultant clusters are generally used. This means selection of the optimal model is somewhat subjective with potential for bias through our knowledge of the trajectories identified in our previous study. Therefore, we carried this out purely as a sensitivity analysis by first using statistical goodness of fit indices to assess whether a 4-cluster model appeared optimal for this cohort. We then used the monthly cluster-specific probabilities of having each level of pain to assess whether these 4 clusters yielded similar trajectories as in the previous study. See supplementary file 1 for full details of the methods (available online at http://links.lww.com/PAIN/A499).An alternative approach to assess the generalizability of the previously derived trajectories is to assess whether we would identify the same number of clusters and trajectory patterns for this cohort using the same modelling method used in the previous study.24 Possible collinearity between potential prognostic factors was tested. Unadjusted relative risk ratios (RRRs) were calculated  to show the univariable association between each potential prognostic factor and the 5-year cluster group using univariable multinomial logistic regression models. Multivariable multinomial logistic regression modelling was then used within each domain  to assess the independent associations of the significant factors  from the univariable analysis with pain course at 5 years. Then, all significant variables in the within-domain analyses were included in a final model, with all variables entered simultaneously. The “no or occasional mild” group was set as the reference group. Given the small prevalence of the fluctuating trajectory in the BaRNS study11 and the relative small cohort size of this study, the fluctuating group was combined with the “persistent severe” group.To determine factors predictive of pain course at 5-year follow-up, we used a stepped process based on an approach we have used previously.We further determined whether the baseline prognostic factors had similar relationships with a single assessment  as identified for the patient clusters based on multiple assessments . Additional multinomial logistic regression models using the same stepped approach but using the trichotomized pain score at 5 years  as the dependent variable were performed. Analysis was performed using STATA 14 .http://links.lww.com/PAIN/A499).Characteristics of the included sample from their initial baseline BeBack study questionnaires are presented in Table The 281 participants in the current analysis were allocated to the 4 predefined clusters using LLCA. Seventy nine (28%) were included in the “no or occasional mild” pain cluster, 131 (47%) in the “persistent mild” cluster, 60 (21%) in the “persistent severe” cluster, and 11 (4%) in the “fluctuating” cluster. The mean posterior probabilities for the assigned clusters were over 0.90 for each cluster except for the fluctuating cluster where it was 0.74. The probability of belonging to each nonassigned class was under 0.10 except for those allocated to the fluctuating cluster that had a mean probability of 0.22 of being allocated to the persistent mild cluster (Table 11 are shown and indicate very similar monthly cluster-specific mean scores in the 2 cohorts.The mean monthly pain intensity scores (trajectories) for each of the clusters have been plotted in Figure Comparison of the initial baseline characteristics of participants in the clusters at 5-year follow-up indicates that people in milder clusters were more highly educated and less likely to not work or have reduced their work than those in more severe clusters. Participants allocated to the milder clusters also reported shorter pain duration, less leg pain, and had lower scores on all of the measures of psychological factors Table .All the selected baseline factors, except for age and sex, were found to be associated with 5-year cluster group in the univariable analyses Table . After aIn the final model, the baseline factors significantly associated with more severe 5-year pain course were as follows: lower social class , higher pain intensity , greater perception on serious consequence from pain , lower emotional representation , greater perception that the pain will last a long time , less beliefs in the personal controllability of pain , and a higher passive behavioural coping score  (Table http://links.lww.com/PAIN/A499).Statistically significant predictors of a worse 5-year outcome when based on a single assessment  were higher baseline pain intensity, longer pain duration, greater perception that the pain will last a long time, and a higher passive behavioural coping score .The sensitivity analysis deriving latent classes for this cohort using the same approach as in the original study showed that a 4-cluster model fitted this cohort's data well. The derived clusters were similar in their patterns of pain as the original clusters. The mean posterior probabilities for the assigned clusters were over 0.95 for each cluster except for the “fluctuating” cluster, where it was 0.88. The probability of belonging to each nonassigned cluster was low (<0.12). Comparison of the assignment of participants to the clusters to their cluster assignments based on the previously identified clusters used in the main analysis showed that 259/281 (92%) participants were assigned to the same clusters  are key predictors of pain trajectory 5 years later.This study shows that LBP trajectories identified within 1 primary care consultation cohort are generalizable to another. Predictors of those trajectories, apparent 5 years before the identification of the trajectories, have also been identified. It is the first time that the external validity of identified trajectories has been assessed using comparable methods within a new sample of patients with LBP, and the analysis shows that the previous findings of 4 trajectories1 and this type of analysis has been recommended.25 Our analyses using the single pain score at 5 years as the outcome generated fewer associations with the baseline prognostic factors. Trajectories of pain in this group of patients with back pain were relatively stable over time. However, this may not be the case in other groups of patients with pain, for example, patients with new episodes of back pain, pain in other body sites, or different age groups. For example, common trajectories of pain in knee osteoarthritis included both improvement and deterioration,34 as did pain across several sites in adolescents.17 These trajectories can only be captured by repeated measurements. Although repeated monthly pain assessments involve increased measurement burden for patients, it better reflects patterns of pain over time and reduces recall bias.1 New data collection methods such as web-based questionnaires, mobile devices, and the visual trajectories questionnaire for pain12 may be helpful to reduce the measurement burden. There were missing monthly pain scores within the sample used in our analysis; however, analysis of just those with no missing data did not affect the prevalence of each cluster and slightly increased the mean posterior probabilities for the assigned cluster. The long-term follow-up and use of validated questionnaires are also strengths. However, the sample size for the analysis of predictors was limited due to loss to follow-up at 5 years and the small size of some of the trajectories. Comparison with study participants not included in the full analyses or the whole cohort subjects19 showed few differences other than included participants were slightly older. Ideally, we would have kept the fluctuating cluster as a separate group when exploring cluster predictors; however, given the small number of participants in this cluster, this was not possible. Our study shows that the trajectories identified in another sample of back pain consulters appear generalizable but further work should assess the generalisability of the identified predictors for these trajectories, in particular whether a fluctuating pattern of pain has different predictors to a persistent severe pattern.A strength of this study is the prospective design, meaning that the measurement of prognostic factors associated with 5-year trajectory clusters clearly preceded the data collection period used to derive the trajectories. The use of pain trajectories as the outcome in the analysis of prognostic factors is also a strength because studies have shown that trajectories are more accurate measures of pain status than single or scattered follow-up points,15 and assessed how well these participants fitted their allocated trajectory. An alternative approach to assess the generalizability of the previously derived trajectories would have been to derive the trajectories for this cohort using the same modelling method used in the previous study. However, deciding on the optimal number of clusters may have then been influenced by knowledge of these prior trajectories, given there is no definitive method using statistical goodness of fit measures of determining the optimal number of clusters.25 Hence, we performed this as a sensitivity analysis which again indicated good generalisability of the clusters. The approach we have taken utilises a strength of latent class analysis of using information on people with established and validated clusters to identify the most likely cluster membership of a new group of people. This approach has shown that a distinct group of patients with LBP could be clearly allocated to the same trajectories identified previously. Our study suggests that these trajectories can now be applied more widely in research for classifying back pain consulters.We allocated participants to the 4 trajectories of LBP derived in a previous study29 reported that disability and self-efficacy were associated with trajectories, and Axén et al2 reported that pain intensity and duration were associated with trajectories, although in neither of these studies did the measurement of predictors clearly precede the derivation of trajectories. Other prognostic factors such as social class status and patients' perceptions about back pain have not been identified in previous trajectory studies. The latter finding supports the idea that people develop personal beliefs about their LBP and these influence subsequent reactions and behavior, which then may affect their long-term outcomes. Identification of these factors has potential clinical impact because these perceptions are modifiable factors and could be revised, for example, through education or cognitive restructuring.Our findings on the predictors of cluster membership have similarities with other studies of associations with back pain trajectories. For example, Macedo et al15 and their long-term persistence, have key implications. Knowledge of these long-term trajectories should enable better understanding of the long-term course of LBP. If the trajectory that an individual is likely to belong to can be identified, the challenge is then to identify effective management tailored to individual trajectories. This may mean more intensive treatment for those on a more severe trajectory, but for those likely to be in the milder trajectories, this may mean avoiding unnecessary investigations or overtreating. However, the finding that pain intensity at baseline predicts pain trajectory 5 years later, along with previous findings that trajectory membership11 and presence of LBP28 have long-term stability, indicates the challenge of shifting patients from more severe trajectories, and helping people better manage and cope with their symptoms may be the best current alternative. Improved understanding of how people get into these stable pain trajectories in the first place is required. Given the evidence of relatively trajectory stability in adult back pain populations, 1 potential direction would be a focus on children or young adult populations as a way of developing preventative interventions.14The findings from this analysis that LBP trajectories have good external validity, combined with findings from previous studies showing the clearly different characteristics of patients in these trajectories,Our results provide clear evidence of the generalizability of LBP trajectories in patients consulting in primary care and provide direction for future research and clinical practice.The authors have no conflicts of interest to declare.This work was supported by the following grants: Arthritis Research UK [13413], the Wellcome Trust [083572], and the Medical Research Council Prognosis Research Strategy (PROGRESS) Partnership [G0902393/99558]. Time from NEF was supported by an NIHR Research Professorship (NIHR-RP-011-015). NEF is an NIHR Senior Investigator. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.