Figures
Abstract
There is a growing concern both inside and outside the scientific community over the lack of reproducibility of experiments. The depth and detail of reported methods are critical to the reproducibility of findings, but also for making it possible to compare and integrate data from different studies. In this study, we evaluated in detail the methods reporting in a comprehensive set of trypanosomiasis experiments that should enable valid reproduction, integration and comparison of research findings. We evaluated a subset of other parasitic (Leishmania, Toxoplasma, Plasmodium, Trichuris and Schistosoma) and non-parasitic (Mycobacterium) experimental infections in order to compare the quality of method reporting more generally. A systematic review using PubMed (2000–2012) of all publications describing gene expression in cells and animals infected with Trypanosoma spp was undertaken based on PRISMA guidelines; 23 papers were identified and included. We defined a checklist of essential parameters that should be reported and have scored the number of those parameters that are reported for each publication. Bibliometric parameters (impact factor, citations and h-index) were used to look for association between Journal and Author status and the quality of method reporting. Trichuriasis experiments achieved the highest scores and included the only paper to score 100% in all criteria. The mean of scores achieved by Trypanosoma articles through the checklist was 65.5% (range 32–90%). Bibliometric parameters were not correlated with the quality of method reporting (Spearman's rank correlation coefficient <−0.5; p>0.05). Our results indicate that the quality of methods reporting in experimental parasitology is a cause for concern and it has not improved over time, despite there being evidence that most of the assessed parameters do influence the results. We propose that our set of parameters be used as guidelines to improve the quality of the reporting of experimental infection models as a pre-requisite for integrating and comparing sets of data.
Citation: Flórez-Vargas O, Bramhall M, Noyes H, Cruickshank S, Stevens R, Brass A (2014) The Quality of Methods Reporting in Parasitology Experiments. PLoS ONE 9(7): e101131. https://doi.org/10.1371/journal.pone.0101131
Editor: Sylvie Bisser, INSERM U1094, University of Limoges School of Medicine, France
Received: January 17, 2014; Accepted: June 3, 2014; Published: July 30, 2014
Copyright: © 2014 Flórez-Vargas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is supported by a scholarship ‘Francisco José de Caldas’ from Colciencias to Oscar Flórez-Vargas and a co-funding scholarship from EPSRC and Epistem Ltd awarded to Michael Bramhall. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: This manuscript has not been published and is not under consideration for publication elsewhere. This research was supported by a scholarship ‘Francisco José de Caldas’ from Colciencias to Oscar Flórez-Vargas and a co-funding scholarship from EPSRC and Epistem Ltd awarded to Michael Bramhall. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Introduction
In this study, we evaluated the reported information on experimental methods in published infectious disease experiments that should enable a valid comparison of research findings. It has been claimed that most published research findings are false [1] and concern about this is spreading beyond the scientific community, making the cover of The Economist recently [2], and potentially undermining public trust in science. Amongst the scientific community there is a growing concern over the related problem of lack of reproducibility [3], [4]. The depth and detail of reported methods directly contributes to the replicability, reproducibility and comparability of experimental work. Replicability is the exact repetition of an experiment to obtain the same results, reproducibility is the repetition of an experiment with small modifications, e.g. the changes that will inevitably occur when conducting the same experiment in different laboratories [5], [6]. If results are replicable but not reproducible they may be of little value since they are likely to be idiosyncratic to the precise conditions used and further inference from the results will be problematic. Comparability is essential to facilitate translational discoveries by making it possible to aggregate data from multiple experiments in a single meta-analysis and answering questions not addressed by the original investigators. The information reported in the Materials & Methods section of an article plays a fundamental role in achieving this aim. In the biomedical field, for instance, the Uniform Guidelines of the International Committee of Medical Journal Editors state that authors should include technical information in sufficient detail to allow the experiment to be repeated by other workers [7]. However, the guidelines are not strictly adhered to and, consequently, the lack of methodological information can make the tasks of replicating, reproducing or comparing results by non-specialists in a field problematic.
Over the past decade sets of minimum items of information have been published that should be reported about a dataset or an experimental process [8]. This allows readers not only to unambiguously interpret and critically evaluate the conclusions reached, but also to potentially replicate, reproduce and compare the experiments. The minimum information checklist or guidelines seek to promote transparency in experimental reporting, enhance accessibility to data and support effective quality assessment, which increases the general value of data, and therefore of the scientific evidence. In this sense, some standard initiatives, such as the Minimum Information About a Microarray Experiment (MIAME) [9] and the Minimum Information About a Proteomics Experiment (MIAPE) [10], have been adopted by several journals, such as Nature Genetics or the Journal of Proteomics, as a requirement for publication.
To address the issue of reproducibility in the context of biomedical experiments, we looked at experimental infection models with a particular focus on the trypanosomiases, which are a widespread group of complex infectious diseases caused by flagellated protozoa of the genus Trypanosoma. These infections affect humans and animals, often with fatal consequences unless treated. In humans, African (sleeping sickness) and American (Chagas disease) trypanosomiases are responsible for considerable morbidity and mortality, affecting millions of people every year [11]–[13]. Moreover, human economic welfare in Africa is also affected by these diseases due to loss of livestock production [14]. The outcome of infection with both American and African trypanosomes depends on both the host and parasite genetic background as well as on environmental variation [15]–[17]. In addition, the trypanosomiases have been labelled as “neglected” because their study hovers in the margins of international health; there is a smaller investment in their research and development and as a result they are less well understood. Hence, an important task is to integrate and compare data from their studies in order to augment the value of this data.
Many studies have been carried out to explore the physiopathology of sleeping sickness and Chagas disease, as well as their genetics. At the time of writing, a PubMed search from 2000–2013 retrieved 1558 and 4248 journal articles containing the MeSH (Medical Subject Headings) terms “Trypanosomiasis, African” and “Chagas disease”, respectively. Despite the large amount of published research, our understanding of the underlying mechanisms involved in these diseases is still limited. It is likely that this can be partly explained by the inherent difficulty in making direct comparisons between the results of independent Trypanosoma infection experiments.
Currently we have data from studies carried out in experimental models of trypanosomiasis. However, a considerable part of this evidence is controversial or contradictory; probably stemming from differences in pre-analytical, analytical and post-analytical variables, as well as experimental design and data analysis. In Chagas diseases, for instance, the role played by the Th17 immune response, T regulatory cells and Nitric Oxide may be critical to the outcome of infection [18]–[20] or these immune factors may have opposing effects or not be required [21]–[23]. Therefore, it is important to know how the data were produced in order to deal not only with the biological complexity of these diseases, but also to permit the replicability, reproducibility and, especially in the case of contradictory results, the comparability of research findings. In order to assess how easy it would be to replicate, reproduce or compare experiments we have undertaken a systematic review of all publications describing gene expression experiments in model organisms infected with these parasites. We have defined a list of essential parameters describing the parasite, the host and the infection that should be reported and for each experiment we have scored the number of those parameters that are reported. In order to determine whether our findings can be generalised to other diseases we have used the same method to assess a subset of papers on Leishmania, Toxoplasma and Plasmodium. A subset of papers that utilised the intestinal helminth parasite Trichuris muris or Schistosoma sp. were used as a comparative control in order to determine the relevance of the checklist in a non-protozoan parasite infection model. In addition, a subset of papers from a non-parasitic infection model (Mycobacterium) were used in order to determine whether this issue is unique to parasitology or has wider implications.
Results
Search strategy
A total of 23 papers on Trypanosoma experiments were identified for inclusion in the review. The search in PubMed provided a total of 5878 references with the MeSH term “Trypanosomiasis”, of which 104 were related with terms “Genes” and “Proteins”, 35 with “Microarray Analysis”, and 27 with “Proteomics”. After adjusting for duplicates 163 remained. The abstracts of these papers were reviewed manually and 139 were discarded because they did not meet the selection criteria (Figure 1 and Table 1). The remaining 23 references [24]–[46] were the corpus of papers identified that reported on gene expression profiling in the host due to an experimental Trypanosoma infection. A subset of 10 articles each of the closely related protozoan parasites Leishmania [47]–[56], Toxoplasma [57]–[66] and Plasmodium [67]–[76] were included for comparison. In addition, 10 articles of Trichuris [77]–[86] and Schistosoma [87]–[96] parasitic worm experiments, and 10 articles of Mycobacterium [97]–[106] experiments as a non-parasitic infection model were included in order to contrast the quality of method reporting in Trypanosoma experiments to other models and to determine the applicability of the checklist to different experimental systems.
Quality of method reporting
To assess the quality of method reporting in Trypanosoma experiments, each paper was checked for reporting of information in three domains: the parasite, the host and the experimental infection. The scores are listed in Tables S1, S2 and S3. A mean of 65.5% (SD = 15.12%) of the information required to reproduce an experiment was reported in this set of papers. No article met all criteria that should be reported in a Trypanosoma experiment according to our checklist (range 32–90%), although two studies [27], [41] scored at 100% out of the available criteria for the parasite and host domains (Tables S1 and S2). The number of articles that met all criteria was higher in the parasite domain (6 out of 23 articles), however the number of criteria met by all the articles was higher in the host domain (7 out of 12 criteria) (Figure 2, Tables S1, S2 and S3). In the experimental infection domain, the inoculum was the only criteria met by all articles, whereas the viability criteria for both cells and parasites were not met in full by any of the studies (Table S3).
The average and range of percentages scored of the quality of methods reporting is shown in brackets.
Bibliometric indices
Different journals have different criteria for publication in order to enhance the quality of research and to prevent publication of poor findings. However, these safeguards are not always successful; limited space for the method section or forms of bias in the peer review process are some of the issues that have generated serious discussion in several scientific journals [107]. Thus, to discover whether there was an association between bibliometric parameters and the quality of method reporting in Trypanosoma experiments, the journal impact factor, the h-index of the corresponding author and the number of citations of the article were compared with the scores for the quality of method reporting. No correlation was observed between method reporting scores and impact factor or h-index (Figures 3A and 3B). However, a significant negative correlation was observed when the scores for method reporting were correlated with the number of citations of the article obtained from Google Scholar (r = −0.42; p = 0.044, n = 23) but not with citations from the Web of Sciences (r = −0.35; p = 0.105, n = 23) (Figure 3C). Interpretation of this observation is confounded by the tendency of older papers to have more citations (Google Scholar: r = −0.40; p = 0.057, n = 23; Web of Sciences: r = −0.42; p = 0.046, n = 23; Figure 3D). There was no correlation between the quality of method reporting and the year of publication, which remained constant during the last 12 years (Figure 4).
Journal impact factor in which the papers were published (A), h-index of the corresponding author (B), and number of citations that the articles have received in other publications (C). Spearman's rank correlation coefficient r is shown alongside the regression lines. The figure shows that there is no correlation between the quality of methods reporting and impact factor [r = −0.04, p = 0.868]. A similar result is shown with h-index, which was searched using the full name of the corresponding author [r = −0.12, p = 0.593; continuous line] and then filtered by the topic Trypanosom* [r = −0.21, p = 0.345; broken line]. There is a weak but significant correlation between the quality of methods reporting and the number of citations recorded by Google Scholar [r = −0.42, p = 0.044; broken line], but not by Web of Science [r = −0.35, p = 0.105; continuous line]. In order to find out if this association is due to a causal effect of the time of publication, a correlation between the number of citations and the time of publication was done (D), and also a weak but significant correlation was shown with the records of Web of Science [r = 0.42, p = 0.046; continuous line], but not with Google Scholar [r = 0.40, p = 0.057; broken line].
The figure shows that there is no correlation [p = 0.711] and that between 2000 and 2012 the quality of methods reporting has remain constant (arithmetic mean = 65.5%).
In order to identify relations between the quality of methods reporting in Trypanosoma experiments and the experience of the journal with publishing papers about trypanosomiasis, we compared the scores achieved for the articles (arithmetic mean was calculated for two or more papers) with the number of articles about trypanosomiasis in the journal in which the articles were published. This comparison showed that the number of articles published in any one journal about trypanosomiasis was not associated with an increase in the quality of methods reporting. The journals with most and fewest articles published about trypanosomiasis between 2000 and 2012 were the American Journal of Tropical Medicine and Hygiene with 172 papers and Genes and Immunity with only three papers (Table S4). Nonetheless, the article that received the lowest score in the reported information (32%) was published in the American Journal of Tropical Medicine and Hygiene [40], whereas the mean score for articles published in Genes and Immunity [36] was almost double this value (60%) (Figure 5).
Number of articles published per journal (black bars) and the percentage of methods reporting (red bars). The figure shows that the quality of method reporting is not related with the number of papers published by any one of the journals.
Comparison with other parasitic diseases
In order to test whether our observations about the quality of method reporting were a general phenomenon or whether they were specific to trypanosomiasis we evaluated 10 articles each on Leishmania, Toxoplasma and Plasmodium; these diseases were chosen because they are also complex and considered public health issues. As in the articles about Trypanosoma experiments, no article about Leishmania, Toxoplasma and Plasmodium experiments met all criteria that should be reported on our checklist, although one publication on Leishmania [49] scored 100% for the parasite and host domains (Table S5 and S6). There was no significant difference in the percentage of reported information between Trypanosoma, Leishmania, Toxoplasma and Plasmodium experiments (Figure 6). The lowest scores were found in the host domain in Leishmania and Toxoplasma experiments (20%, Table S5). Plasmodium experiments had the lowest score in the parasite domain (25%, Table S6) and Leishmania had the lowest score in the experimental infection domain (30%, Table S7). No Toxoplasma or Plasmodium experiment met all of the criteria in any domain (Table S5, S6 and S7).
Articles about “Trypanosomiasis”[MeSH]; “Leishmaniasis”[MeSH]; “Toxoplasmosis”[MeSH]; “Malaria”[MeSH]; “Trichuris”[MeSH]; “Schistosoma”[MeSH] and “Tuberculosis”[MeSH]. The figure shows that the experimental model of colitis induced by Trichuris had the highest scores, followed by tuberculosis, Trypanosoma, Toxoplasma, Leishmania, Plasmodium and Schistosoma experiments. P values less than 0.01 and 0.001 are represented by ** and *** respectively.
In contrast to all protozoan parasite experiments, the quality of method reporting in the helminth model of infection by Trichuris muris showed the highest scores in all three domains (Figure 6). One Trichuris muris experiment [83] successfully scored 100% in all three domains. Trichuris muris experiments reported significantly more information than Trypanosoma (p<0.001), Plasmodium (p<0.001), Schistosoma (p<0.001), Leishmania (p<0.01) and Toxoplasma (p<0.01) experiments (Figure 6). However, the other helminth model, Schistosoma sp., scored poorly with the second lowest mean reported information (61.16%). Mycobacterium (mean reported information 73.96%), the non-parasitic bacterial infection model, scored more highly than Trypanosoma (mean reported information 65.46%) but this was not significant.
Validation of scoring methods
The papers from Trypanosoma experiments were initially scored by the first and second authors. A specialist in trypanosomiasis then independently scored these papers. The evaluation made by the trypanosomiasis specialist scored 61.6% for the number of criteria from the checklist met in the corpus of articles, whereas a strict evaluation scored 65%. These evaluations scored 63.8% and 64.9% respectively after reviewing the results of both examinations. A linear correlation test (Figure 7A) showed a strong and significant linear correlation between the scores (r2 = 0.96; p<0.0001); suggesting that the checklist items measure a common domain and that the personal opinion of the coder does not have an important impact on the scores. In addition, a Bland-Altman test (Figure 7B) was used to verify the agreement between the two evaluations. This analysis showed a good concordance as 16 points were on the line of no difference and 21 fell within the 95% limits of agreement (mean = 0.80 and SD: ±2.91), verifying that the scoring was consistent between the evaluators.
Evaluation based strictly on what was explicitly included in the published paper (Evaluator 1) and on interpretations and assumptions determined by an expert in the field (Evaluator 2).
Discussion
In order to draw conclusions about the quality of method information reported in articles and its impact on the replicability, reproducibility and comparability of experimental work, we have selected trypanosome infection models as a focus of study. Trypanosomiasis as a complex disease is an appropriate example to understand the importance of the subtlety of experimental variables in the outcome of the modelled disease. Our results indicate that the quality of method information reported in articles about experimental infection with Trypanosoma spp is a cause for concern and it has not shown improvement over time, despite there being evidence that most of these variables do influence the results.
Many studies have demonstrated the genetic diversity of Trypanosoma species [108], [109], as well as the diversity of outcome associated with different parasite strains [17]. The classically described differences in humans infected with different subspecies of T. brucei or lineages of T. cruzi are well recognized. T. brucei rhodesiense causes acute disease and T. brucei gambiense causes a more chronic infection [110]. T. b. gambiense is divided into two groups which differ in phenotype including pathology [111]. In addition, the cardiomyopathy and digestive forms of Chagas' disease have been associated with T. cruzi lineage I and T. cruzi lineage II respectively [112]. Strain differences have also been observed in the three major strains of Toxoplasma, which vary greatly in their virulence and infection outcome [113]. In addition, isolates of Trichuris muris not only differ in virulence but can also trigger changes in the immune response elicited in susceptible hosts [114]; whereas eggs from different strains of Schistosoma mansoni cause specific granulomatous responses [115]. Consequently, reporting genus and species of the parasite is not enough; the parasite strain must be reported and if the parasite is a new isolate, it should be characterized.
Virulence of the parasite in all stages of its life cycle plays an important role in the outcome of infection. For example, the failure of laboratory experiments to develop successful malaria vaccines has been attributed to the failure of models to include a skin stage, which is deemed integral to suppressing host immunity and initiating tolerance to the parasite [116]. In T. cruzi, several factors have been implicated in the formation of the infective metacyclic stages. Long-term axenic cultures of T. cruzi exhibit a lower capacity to transform into metacyclic trypomastigotes, in comparison to those maintained by alternate invertebrate/vertebrate passages [117]. In addition, the infectivity of T. cruzi clones is modified when it is grown in different hosts; a clone passaged through mice has been shown to be more virulent to mice and guinea pigs than the same clone passaged through guinea pigs, the virulence of which remained unchanged [118]. Infection route has also been shown to exert significant impact on the overall course and outcome of infection. In Chagas disease, for instance, the outbreaks associated with food/beverage consumption display severe clinical features in comparison with those of patients that have been infected with T. cruzi by vector transmission [119]; a phenomenon that has been associated with the sylvatic biodemes and genotypes of T. cruzi [120], [121]. In addition, in Toxoplasma infections, mice may be susceptible or resistant to infection depending on whether an oral or intraperitoneal challenge is used [122].
Since gender and the corresponding sex steroids affect the immune response [123], [124] it is important to specify the gender of experimental animals used. Sex-differences have been demonstrated previously in several experimental infections. For example, in BALB/k mice, males are more resistant to Toxoplasma gondii than females [125]. Conversely, in BALB/c mice lacking IL-4, and C57BL/6 p55−/− or p75−/− mice, it is the female mice that are better at expelling Trichuris muris than males [84]. However, only 70% of Trypanosoma studies reported the sex of animals used in the experimental infection and only 25% reported the gender of animals used to maintain parasite stocks (Tables S1 and S2). In experimental trypanosome infections a gender-related effect has been shown: using BALB/c mice infected with a natural dose of vector-derived metacyclic trypomastigotes of T. cruzi (100 parasites/mouse) the peak of parasitaemia in males was about four-fold higher than that in females [126]. Similarly, an experimental infection with a strain of T. brucei brucei at 50% of the mouse lethal dose showed that the female were more trypanotolerant than the males and there was no evidence that this was X-linked [127], [128]. Housing conditions and social environment also affect the course of experimental trypanosome infections. For example, the parasitaemia levels vary according to whether the animals are kept individually or in a group due to pheromones of the opposite sex [126], [129]. Furthermore, hormonal profiles during the oestrous cycle are not only modified by the parasite; such as T. congolense [130], but also by the light/dark cycle conditions [131].
In the case of contradictory results, the reporting of the essential parameters that describe a parasitic experimental infection can help to determine the nature of their discrepancies. To exemplify this issue, we have chosen two papers published in the journal Infection and Immunity that were undertaken to assess the role of Nitric Oxide (NO) in immunity to T. cruzi infection and their experiments showed contradictory results. Vespa et al. claim that NO is involved in control of T. cruzi-induced parasitaemia [20], whereas Cummings et al. claim that NO is not required for control of T. cruzi in the acute or chronic stages of the infection [23]. However, although these studies were carried out using female mice on a C57BL/6 background, the experimental infections were performed using different T. cruzi strains, which could explain, at least in part, the differences in their findings: mice infected with 104 trypomastigotes of the Y strain showed peak parasitaemia at day 8 that decreased thereafter [20], whereas mice infected with 103 trypomastigotes of the Brazil strain showed a peak at day 30 and decreased thereafter [23]. Moreover, although both infections were performed with blood-derived trypomastigotes none of them reported species, gender and age of the animals used to culture the parasite; important parameters that modified the infectivity of T. cruzi [117], [118]. In addition, there is experimental evidence that shows significant differences among parasitaemia curves between older and younger BALB/c mice infected with a long-term mouse-passaged clone of the T. cruzi isolate TolAc1; higher parasitaemia levels were observed in older animals (31-day-old) with lower inoculum (3×104 trypomastigotes) than younger animals (8-day-old) with higher inoculum (9×104 trypomastigotes) [118]. However, the age of the animals used to evaluate the role of NO in the control of T. cruzi infection was reported by Vespa et al. but not by Cummings et al. [20], [23]. Thus, these and other conditions that could also influence the parasitaemia and, hence, the researched outcome should be reported in order to understand the complexity of these parasitoses.
Although the information collected through the checklist should be reported for all Trypanosoma experiments, some information could be inferred from the characteristics of the experimental processes, although this depends on the level of expertise of observers (i.e. non-experts and experts). In this way, a factor such as the stage of the parasite used for a T. cruzi infection could be easily inferred by an expert since he/she knows that the infectious stage is the trypomastigote. Moreover, both experts and non-experts could also infer many details of the conditions used in cell cultures by assuming experimenters have opted for the most commonly used parameters. For example temperature and CO2 atmosphere are usually set to 37°C and 5% of CO2. However, neither experts nor non-experts could infer the species and strain of the parasite; age and gender of the host; or the inoculum used in the infection assays, among others. Thus the validation of data becomes a difficult or impossible task when there is not only not enough information about the method used, but also most of the missing information cannot be inferred, even by an expert.
Providing a high-quality description of the experimental method is important not only to replicate and reproduce, but also to compare and integrate that data and, hence, facilitate translational discoveries. The issues found in reporting methods probably stem, at least in part, from the current structure of scientific publishing, which is not adequate to effectively communicate complex experimental methods. This problem has been recognised, with some journals already introducing editorial measures and methods checklists in order to improve the quality of methods reporting [132].
For the field of trypanosomiasis we have created a checklist to guide parasitologists in reporting Trypanosoma experiments (see Annex 1). This checklist included the minimum information that should be provided when describing the parasite, host and infection aspects of those experiments. Our checklist does not cover aspects inherent to each possible experimental assay such as those derived from omics and conventional technologies. In these cases, the BioSharing catalogue [133] should be consulted for checklists: e.g. the Minimum Information About a Microarray Experiment (MIAME) and Proteomics Experiment (MIAPE); and the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE). Moreover, there are other guidelines such as the Minimum Information About a Cellular Assay (MIACA) and the Animals in Research: Reporting In Vivo Experiments (ARRIVE) that provide detailed descriptions of experiments performed on cell and animal models.
In conclusion, it has become clear that biomedical science is plagued by findings that cannot be reproduced and/or compared; and the parasitology community is no stranger to this, as has been shown by this study. Nevertheless, the scientific community that works on trypanosomiases is small and many of them know each other personally so in principle it should be possible to change the way that Trypanosoma experiments are reported. However, it is important that the scientific community as a whole is engaged with that process. Finally, the checklist has been demonstrated to be applicable to several different infection models and could be implemented to improve the quality of methods reporting for all infection experiments in principle.
Materials and Methods
Search strategy
The method of the literature review follows the recommendations outlined in the PRISMA guidelines [134]. A protocol was designed to identify the method information reported in published articles that utilised experimental infection with Trypanosoma species, where the effects on gene expression –transcriptomics and proteomics– of the host were studied. Criteria in three domains were evaluated: characteristics and culture conditions of the parasite, characteristics and maintenance conditions of the host and the infection procedure. The protocol used here for capturing data has not been previously published.
The literature search was conducted using Medline via PubMed. The database was searched in April 2013 for articles that were published between 1st January, 2000 and 31st December, 2012 using the MeSH (Medical Subject Headings) terms as they appear in Table 2. The PubMed Identifier (PMID) numbers were used to identify those articles that were common between “Genes” AND “Trypanosomiasis” and “Proteins” AND “Trypanosomiasis”. The search was not limited by study design or by language of publication. The year 2000 was chosen because it was the year in which the first rough draft of the human genome was completed [135], [136] and these data were used in many fields of medicine including infectious disease. In addition, we chose to focus on gene expression profiling in the host due to an experimental Trypanosoma infection because it provides the broadest evidence about the molecular physiopathology of trypanosomiasis.
In order to compare the quality of method reporting in Trypanosoma experiments with the reporting of other parasitic disease infections we collected a subset of Leishmania, Toxoplasma and Plasmodium experimental infection models, since diseases produced by them are also complex and considered public health issues. In addition, as a comparative control of methods reporting in experimental infections, we sought two models of worm infection: one with a simple life cycle (Trichuris muris) and another with a complex life cycle (Schistosoma sp.); requiring adaptation for survival in fresh water as free-living forms and as parasites in snail intermediate and vertebrate definitive hosts. In addition, we assessed tuberculosis infectious models in order to have a general idea about the quality of method reporting in non-parasitic infection models. Tuberculosis was chosen because it is probably one of the most studied infectious disease.
The same search strategy was carried out where the MeSH term “Trypanosomiasis” was replaced with the following MeSH terms: “Leishmaniasis”, “Toxoplasmosis”, “Malaria”, “Trichuris”, “Schistosoma” and “Tuberculosis”. To avoid selection bias, the articles were randomly ordered and the first 10 articles for each extra parasitosis and the non-parasitic infection model (Mycobacterium) that described gene expression profiling in the host due to an experimental infection were selected.
Study selection was made by one reviewer and checked independently by a second reviewer, any disagreement was resolved by consensus or by discussion with a third reviewer. Only primary research papers were included in the search. The titles and abstracts of articles were reviewed and analysed in detail to filter out those in which the experiments were performed on the parasite or on vector insects and keep those done on the host. This corpus of articles was then used to confirm eligibility and to extract data.
Structure definition and data extraction
A checklist that contains the minimum information required about the parasite, host and infection to describe an experiment carried out with any Trypanosoma species was elaborated by experts in the field of trypanosomiasis research and it is presented in Table 3. Pre-analytical variables in the methods were prioritised in this list because they are critical for interpretation of the results. The terms were classified into three domains according to their roles in a Trypanosoma experiment: the host, the parasite and the infection. A data extraction sheet was developed to annotate the information reported in the methods and results sections. Data extraction and quality assessment were carried out by one author and checked by a second reviewer, and inconsistencies were discussed by both reviewers and consensus reached.
Bibliometric indices
Bibliometric parameters were used to determine if they were associated with the quality of method reporting. The impact factor (IF) of each journal was retrieved from the Institute for Scientific Information (ISI) Web of Knowledge's Journal Citation Reports database science edition 2011. The number of citations was measured by the total recorded for each article by Thomson Scientific's Web of Science and Google Scholar in May 2013. For each corresponding author, the h-index was obtained through Thomson Scientific's Web of Science using a citation window up to one year before the article was published. The h-index was searched in two different ways: first, using the full name of the corresponding author and second, filtering the result by topic, using the term “Trypanosom*”. The number of articles published for each journal about trypanosomiasis was sought in PubMed using the short name of the journals and the MeSH term “Trypanosomiasis”. The search was filtered by time; from 1st January, 2000 to 31st December, 2012.
Validity of scoring methods
An expert in trypanosomiasis tested the quality of reported information on Trypanosoma experiments. The expert scored the corpus of articles using the checklist that contains the minimum information required to describe a Trypanosoma experiment (Table 3). This evaluation was based strictly on what was explicitly included in the published paper and its results are presented throughout this article. The validity of this assessment was tested based on its agreement with another evaluation based on interpretations and assumptions determined by another expert in the field in order to avoid bias of the retrieval results by interpretation.
Statistical analysis
For each article, the percentage of reported information in each article domain was obtained by direct counting. Linear and Spearman's rank correlations and Bland-Altman comparison were calculated using STATA software [137] and the equivalence of between scores obtained by the evaluators was determined by a correlation test. Comparisons between experimental infection models were performed using a one-way ANOVA in GraphPad PRISM 4 software [138].
Supporting Information
Table S1.
Quality measures of the studies that failed to fulfil any one of data of minimal information about the parasite in Trypanosoma experiments.
https://doi.org/10.1371/journal.pone.0101131.s001
(PDF)
Table S2.
Quality measures of the studies that failed to fulfil any one of data of minimal information about the host in Trypanosoma experiments.
https://doi.org/10.1371/journal.pone.0101131.s002
(PDF)
Table S3.
Quality measures of the studies that failed to fulfil any one of data of minimal information about the experimental infection in Trypanosoma experiments.
https://doi.org/10.1371/journal.pone.0101131.s003
(PDF)
Table S4.
Bibliometric indices in reporting Trypanosoma experiments.
https://doi.org/10.1371/journal.pone.0101131.s004
(PDF)
Table S5.
Quality measures of the studies that failed to supply any one of the criteria for minimal information about the parasite in Leishmania, Toxoplasma, Plasmodium, Trichuris, Schistosoma and Mycobacterium experiments.
https://doi.org/10.1371/journal.pone.0101131.s005
(PDF)
Table S6.
Quality measures of the studies that failed to supply any one of the criteria for minimal information about the host in Leishmania, Toxoplasma, Plasmodium, Trichuris, Schistosoma and Mycobacterium experiments.
https://doi.org/10.1371/journal.pone.0101131.s006
(PDF)
Table S7.
Quality measures of the studies that failed to supply any one of the criteria for minimal information about the experimental infection in Leishmania, Toxoplasma, Plasmodium, Trichuris, Schistosoma and Mycobacterium experiments.
https://doi.org/10.1371/journal.pone.0101131.s007
(PDF)
Author Contributions
Conceived and designed the experiments: OF-V RS AB. Performed the experiments: OF-V MB HN SC. Analyzed the data: OF-V MB HN SC. Wrote the paper: OF-V MB HN SC RS AB.
References
- 1. Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2: e124.
- 2.
Anon (19 Oct 2013) Unreliable research: Trouble at the lab. The Economist. Available: http://www.economist.com/news/briefing/21588057-scientists-think-science-self-correcting-alarming-degree-it-not-trouble. Accessed 20 October 2013.
- 3.
Anon (14 Ags 2012) The Reproducibility Initiative. Available: https://www.scienceexchange.com/reproducibility. Accessed 17 January 2014.
- 4. Sandve GK, Nekrutenko A, Taylor J, Hovig E (2013) Ten Simple Rules for Reproducible Computational Research. PLoS Comp Biol 9: e1003285.
- 5. Casadevall A, Fang FC (2010) Reproducible science. Infect Immun 78: 4972–4975.
- 6.
Drummond C (2009) Replicability is not Reproducibility: Nor is it a good science. Paper presented at: Evaluation Methods for Machine Learning Workshop at the 26th International Conference on Machine Learning; June 2009; Montreal, Quebec, Canada. Available at: http://cogprints.org/7691/7/ICMLws09.pdf.
- 7.
International Committee of Medical Journal Editors Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publications. http://www.icmje.org/#prepare.
- 8. Taylor CF, Field D, Sansone SA, Aerts J, Apweiler R, et al. (2008) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol 26: 889–896.
- 9. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, et al. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29: 365–371.
- 10. Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK Jr, et al. (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25: 887–893.
- 11. Simarro PP, Diarra A, Ruiz Postigo JA, Franco JR, Jannin JG (2011) The human African trypanosomiasis control and surveillance programme of the World Health Organization 2000-2009: the way forward. PLoS Negl Trop Dis 5: e1007.
- 12. Schmunis GA, Yadon ZE (2010) Chagas disease: a Latin American health problem becoming a world health problem. Acta Trop 115: 14–21.
- 13. Hotez PJ, Dumonteil E, Betancourt Cravioto M, Bottazzi ME, Tapia-Conyer R, et al. (2013) An Unfolding Tragedy of Chagas Disease in North America. PLoS Negl Trop Dis 7: e2300.
- 14. Kristjanson PM, Swallow BM, Rowlands GJ, Kruska RL, de Leeuw PN (1999) Measuring the costs of African animal trypanosomosis, the potential benefits of control and returns to research. Agr Syst 59: 79–98.
- 15. Goodhead I, Archibald A, Amwayi P, Brass A, Gibson J, et al. (2010) A comprehensive genetic analysis of candidate genes regulating response to Trypanosoma congolense infection in mice. PLoS Negl Trop Dis 4: e880.
- 16. Vasconcelos RH, Montenegro SM, Azevedo EA, Gomes YM, Morais CN (2012) Genetic susceptibility to chronic Chagas disease: an overview of single nucleotide polymorphisms of cytokine genes. Cytokine 59: 203–208.
- 17. Goodhead I, Capewell P, Bailey JW, Beament T, Chance M, et al. (2013) Whole-genome sequencing of Trypanosoma brucei reveals introgression between subspecies that is associated with virulence. mBio 4: e00197–00113-e00197-00113.
- 18. da Matta Guedes PM, Gutierrez FR, Maia FL, Milanezi CM, Silva GK, et al. (2010) IL-17 produced during Trypanosoma cruzi infection plays a central role in regulating parasite-induced myocarditis. PLoS Negl Trop Dis 4: e604.
- 19. de Araujo FF, da Silveira AB, Correa-Oliveira R, Chaves AT, Adad SJ, et al. (2011) Characterization of the presence of Foxp3(+) T cells from patients with different clinical forms of Chagas' disease. Hum Pathol 42: 299–301.
- 20. Vespa GN, Cunha FQ, Silva JS (1994) Nitric oxide is involved in control of Trypanosoma cruzi-induced parasitemia and directly kills the parasite in vitro. Infect Immun 62: 5177–5182.
- 21. Miyazaki Y, Hamano S, Wang S, Shimanoe Y, Iwakura Y, et al. (2010) IL-17 is necessary for host protection against acute-phase Trypanosoma cruzi infection. J Immunol 185: 1150–1157.
- 22. Kotner J, Tarleton R (2007) Endogenous CD4(+) CD25(+) regulatory T cells have a limited role in the control of Trypanosoma cruzi infection in mice. Infect Immun 75: 861–869.
- 23. Cummings KL, Tarleton RL (2004) Inducible nitric oxide synthase is not essential for control of Trypanosoma cruzi infection in mice. Infect Immun 72: 4081–4089.
- 24. Mukherjee S, Belbin TJ, Spray DC, Iacobas DA, Weiss LM, et al. (2003) Microarray analysis of changes in gene expression in a murine model of chronic chagasic cardiomyopathy. Parasitol Res 91: 187–196.
- 25. Mukherjee S, Nagajyothi F, Mukhopadhyay A, Machado FS, Belbin TJ, et al. (2008) Alterations in myocardial gene expression associated with experimental Trypanosoma cruzi infection. Genomics 91: 423–432.
- 26. Garg N, Gerstner A, Bhatia V, DeFord J, Papaconstantinou J (2004) Gene expression analysis in mitochondria from chagasic mice: alterations in specific metabolic pathways. Biochem J 381: 743–752.
- 27. Hashimoto M, Nakajima-Shimada J, Ishidoh K, Aoki T (2005) Gene expression profiles in response to Fas stimulation in Trypanosoma cruzi-infected host cells. Int J Parasitol 35: 1587–1594.
- 28. Goldenberg RC, Iacobas DA, Iacobas S, Rocha LL, da Silva de Azevedo Fortes F, et al. (2009) Transcriptomic alterations in Trypanosoma cruzi-infected cardiac myocytes. Microbes Infect 11: 1140–1149.
- 29. Chessler AD, Unnikrishnan M, Bei AK, Daily JP, Burleigh BA (2009) Trypanosoma cruzi triggers an early type I IFN response in vivo at the site of intradermal infection. J Immunol 182: 2288–2296.
- 30. Costales JA, Daily JP, Burleigh BA (2009) Cytokine-dependent and-independent gene expression changes and cell cycle block revealed in Trypanosoma cruzi-infected host cells by comparative mRNA profiling. BMC Genomics 10: 252.
- 31. Soares MB, de Lima RS, Rocha LL, Vasconcelos JF, Rogatto SR, et al. (2010) Gene expression changes associated with myocarditis and fibrosis in hearts of mice with chronic chagasic cardiomyopathy. J Infect Dis 202: 416–426.
- 32. Soares MB, Lima RS, Souza BS, Vasconcelos JF, Rocha LL, et al. (2011) Reversion of gene expression alterations in hearts of mice with chronic chagasic cardiomyopathy after transplantation of bone marrow cells. Cell Cycle 10: 1448–1455.
- 33. Manque PA, Probst CM, Pereira MC, Rampazzo RC, Ozaki LS, et al. (2011) Trypanosoma cruzi infection induces a global host cell response in cardiomyocytes. Infect Immun 79: 1855–1862.
- 34. Genovesio A, Giardini MA, Kwon YJ, de Macedo Dossin F, Choi SY, et al. (2011) Visual genome-wide RNAi screening to identify human host factors required for Trypanosoma cruzi infection. PLoS One 6: e19733.
- 35. Tanowitz HB, Mukhopadhyay A, Ashton AW, Lisanti MP, Machado FS, et al. (2011) Microarray analysis of the mammalian thromboxane receptor-Trypanosoma cruzi interaction. Cell Cycle 10: 1132–1143.
- 36. Kierstein S, Noyes H, Naessens J, Nakamura Y, Pritchard C, et al. (2006) Gene expression profiling in a mouse model for African trypanosomiasis. Genes Immun 7: 667–679.
- 37. Li SQ, Reid SA, Fung MC, Inoue N, Lun ZR (2009) Analysis of gene expression profiles in the liver and spleen of mice infected with Trypanosoma evansi by using a cDNA microarray. Parasitol Res 104: 385–397.
- 38. Noyes HA, Alimohammadian MH, Agaba M, Brass A, Fuchs H, et al. (2009) Mechanisms controlling anaemia in Trypanosoma congolense infected mice. PLoS One 4: e5170.
- 39. O'Gorman GM, Park SD, Hill EW, Meade KG, Coussens PM, et al. (2009) Transcriptional profiling of cattle infected with Trypanosoma congolense highlights gene expression signatures underlying trypanotolerance and trypanosusceptibility. BMC Genomics 10: 207.
- 40. Amin DN, Ngoyi DM, Nhkwachi GM, Palomba M, Rottenberg M, et al. (2010) Identification of stage biomarkers for human African trypanosomiasis. Am J Trop Med Hyg 82: 983–990.
- 41. Li SQ, Luckins A, Lun ZR (2011) Trypanosoma brucei brucei: A comparison of gene expression in the liver and spleen of infected mice utilizing cDNA microarray technology. Exp Parasitol 128: 256–264.
- 42. Mekata H, Konnai S, Mingala CN, Abes NS, Gutierrez CA, et al. (2012) Kinetics of regulatory dendritic cells in inflammatory responses during Trypanosoma evansi infection. Parasite Immunol 34: 318–329.
- 43. Meade KG, O'Gorman GM, Hill EW, Narciandi F, Agaba M, et al. (2009) Divergent antimicrobial peptide (AMP) and acute phase protein (APP) responses to Trypanosoma congolense infection in trypanotolerant and trypanosusceptible cattle. Mol Immunol 47: 196–204.
- 44. Hill EW, O'Gorman GM, Agaba M, Gibson JP, Hanotte O, et al. (2005) Understanding bovine trypanosomiasis and trypanotolerance: the promise of functional genomics. Vet Immunol Immunopathol 105: 247–258.
- 45. Lopez R, Demick KP, Mansfield JM, Paulnock DM (2008) Type I IFNs play a role in early resistance, but subsequent susceptibility, to the African trypanosomes. J Immunol 181: 4908–4917.
- 46. Graefe SE, Streichert T, Budde BS, Nurnberg P, Steeg C, et al. (2006) Genes from Chagas susceptibility loci that are differentially expressed in T. cruzi-resistant mice are candidates accounting for impaired immunity. PLoS One 1: e57.
- 47. Park AY, Hondowicz BD, Scott P (2000) IL-12 is required to maintain a Th1 response during Leishmania major infection. J Immunol 165: 896–902.
- 48. Kinjyo I, Inoue H, Hamano S, Fukuyama S, Yoshimura T, et al. (2006) Loss of SOCS3 in T helper cells resulted in reduced immune responses and hyperproduction of interleukin 10 and transforming growth factor-beta 1. J Exp Med 203: 1021–1031.
- 49. Guerfali FZ, Laouini D, Guizani-Tabbane L, Ottones F, Ben-Aissa K, et al. (2008) Simultaneous gene expression profiling in human macrophages infected with Leishmania major parasites using SAGE. BMC Genomics 9: 238.
- 50. Ehrchen JM, Roebrock K, Foell D, Nippe N, von Stebut E, et al. (2010) Keratinocytes determine Th1 immunity during early experimental leishmaniasis. PLoS Pathog 6: e1000871.
- 51. Biswas A, Bhattacharya A, Kar S, Das PK (2011) Expression of IL-10-triggered STAT3-dependent IL-4Ralpha is required for induction of arginase 1 in visceral leishmaniasis. Eur J Immunol 41: 992–1003.
- 52. Bertholet S, Debrabant A, Afrin F, Caler E, Mendez S, et al. (2005) Antigen requirements for efficient priming of CD8+ T cells by Leishmania major-infected dendritic cells. Infect Immun 73: 6620–6628.
- 53. Brunner C, Sindrilaru A, Girkontaite I, Fischer KD, Sunderkotter C, et al. (2007) BOB.1/OBF.1 controls the balance of TH1 and TH2 immune responses. EMBO J 26: 3191–3202.
- 54. Filippi C, Hugues S, Cazareth J, Julia V, Glaichenhaus N, et al. (2003) CD4+ T cell polarization in mice is modulated by strain-specific major histocompatibility complex-independent differences within dendritic cells. J Exp Med 198: 201–209.
- 55. Jayakumar A, Widenmaier R, Ma X, McDowell MA (2008) Transcriptional inhibition of interleukin-12 promoter activity in Leishmania spp.-infected macrophages. J Parasitol 94: 84–93.
- 56. Vivarini Ade C, Pereira Rde M, Teixeira KL, Calegari-Silva TC, Bellio M, et al. (2011) Human cutaneous leishmaniasis: interferon-dependent expression of double-stranded RNA-dependent protein kinase (PKR) via TLR2. FASEB J 25: 4162–4173.
- 57. Gail M, Gross U, Bohne W (2001) Transcriptional profile of Toxoplasma gondii-infected human fibroblasts as revealed by gene-array hybridization. Mol Genet Genomics 265: 905–912.
- 58. Knight BC, Kissane S, Falciani F, Salmon M, Stanford MR, et al. (2006) Expression analysis of immune response genes of Muller cells infected with Toxoplasma gondii. J Neuroimmunol 179: 126–131.
- 59. Ju CH, Chockalingam A, Leifer CA (2009) Early response of mucosal epithelial cells during Toxoplasma gondii infection. J Immunol 183: 7420–7427.
- 60. Okomo-Adhiambo M, Beattie C, Rink A (2006) cDNA microarray analysis of host-pathogen interactions in a porcine in vitro model for Toxoplasma gondii infection. Infect Immun 74: 4254–4265.
- 61. Zhou DH, Yuan ZG, Zhao FR, Li HL, Zhou Y, et al. (2011) Modulation of mouse macrophage proteome induced by Toxoplasma gondii tachyzoites in vivo. Parasitol Res 109: 1637–1646.
- 62. Watford WT, Hissong BD, Durant LR, Yamane H, Muul LM, et al. (2008) Tpl2 kinase regulates T cell interferon-gamma production and host resistance to Toxoplasma gondii. J Exp Med 205: 2803–2812.
- 63. Tato CM, Villarino A, Caamano JH, Boothby M, Hunter CA (2003) Inhibition of NF-kappa B activity in T and NK cells results in defective effector cell expansion and production of IFN-gamma required for resistance to Toxoplasma gondii. J Immunol 170: 3139–3146.
- 64. Fux B, Rodrigues CV, Portela RW, Silva NM, Su C, et al. (2003) Role of cytokines and major histocompatibility complex restriction in mouse resistance to infection with a natural recombinant strain (type I-III) of Toxoplasma gondii. Infect Immun 71: 6392–6401.
- 65. Fang R, Nie H, Wang Z, Tu P, Zhou D, et al. (2009) Protective immune response in BALB/c mice induced by a suicidal DNA vaccine of the MIC3 gene of Toxoplasma gondii. Vet Parasitol 164: 134–140.
- 66. Desolme B, Mevelec MN, Buzoni-Gatel D, Bout D (2000) Induction of protective immunity against toxoplasmosis in mice by DNA immunization with a plasmid encoding Toxoplasma gondii GRA4 gene. Vaccine 18: 2512–2521.
- 67. Ylostalo J, Randall AC, Myers TA, Metzger M, Krogstad DJ, et al. (2005) Transcriptome profiles of host gene expression in a monkey model of human malaria. J Infect Dis 191: 400–409.
- 68. Carapau D, Kruhofer M, Chatalbash A, Orengo JM, Mota MM, et al. (2007) Transcriptome profile of dendritic cells during malaria: cAMP regulation of IL-6. Cell Microbiol 9: 1738–1752.
- 69. Miu J, Hunt NH, Ball HJ (2008) Predominance of interferon-related responses in the brain during murine malaria, as identified by microarray analysis. Infect Immun 76: 1812–1824.
- 70. Albuquerque SS, Carret C, Grosso AR, Tarun AS, Peng X, et al. (2009) Host cell transcriptional profiling during malaria liver stage infection reveals a coordinated and sequential set of biological events. BMC Genomics 10: 270.
- 71. Delic D, Dkhil M, Al-Quraishy S, Wunderlich F (2011) Hepatic miRNA expression reprogrammed by Plasmodium chabaudi malaria. Parasitol Res 108: 1111–1121.
- 72. Rosanas-Urgell A, Martin-Jaular L, Ricarte-Filho J, Ferrer M, Kalko S, et al. (2012) Expression of non-TLR pattern recognition receptors in the spleen of BALB/c mice infected with Plasmodium yoelii and Plasmodium chabaudi chabaudi AS. Mem Inst Oswaldo Cruz 107: 410–415.
- 73. Randall LM, Amante FH, McSweeney KA, Zhou Y, Stanley AC, et al. (2008) Common strategies to prevent and modulate experimental cerebral malaria in mouse strains with different susceptibilities. Infect Immun 76: 3312–3320.
- 74. Oakley MS, McCutchan TF, Anantharaman V, Ward JM, Faucette L, et al. (2008) Host biomarkers and biological pathways that are associated with the expression of experimental cerebral malaria in mice. Infect Immun 76: 4518–4529.
- 75. Lovegrove FE, Pena-Castillo L, Mohammad N, Liles WC, Hughes TR, et al. (2006) Simultaneous host and parasite expression profiling identifies tissue-specific transcriptional programs associated with susceptibility or resistance to experimental cerebral malaria. BMC Genomics 7: 295.
- 76. Delahaye NF, Coltel N, Puthier D, Barbier M, Benech P, et al. (2007) Gene expression analysis reveals early changes in several molecular pathways in cerebral malaria-susceptible mice versus cerebral malaria-resistant mice. BMC Genomics 8: 452.
- 77. Betts J (2000) deSchoolmeester ML, Else KJ (2000) Trichuris muris: CD4+ T cell-mediated protection in reconstituted SCID mice. Parasitology 121 Pt 6: 631–637.
- 78. Bickle Q, Helmby H (2007) Lack of galectin-3 involvement in murine intestinal nematode and schistosome infection. Parasite Immunol 29: 93–100.
- 79. Cliffe LJ, Humphreys NE, Lane TE, Potten CS, Booth C, et al. (2005) Accelerated intestinal epithelial cell turnover: a new mechanism of parasite expulsion. Science 308: 1463–1465.
- 80. Humphreys NE, Worthington JJ, Little MC, Rice EJ, Grencis RK (2004) The role of CD8+ cells in the establishment and maintenance of a Trichuris muris infection. Parasite Immunol 26: 187–196.
- 81. Villarino AV, Artis D, Bezbradica JS, Miller O, Saris CJ, et al. (2008) IL-27R deficiency delays the onset of colitis and protects from helminth-induced pathology in a model of chronic IBD. Int Immunol 20: 739–752.
- 82. Massacand JC, Stettler RC, Meier R, Humphreys NE, Grencis RK, et al. (2009) Helminth products bypass the need for TSLP in Th2 immune responses by directly modulating dendritic cell function. Proc Natl Acad Sci U S A 106: 13968–13973.
- 83. Dixon H, Blanchard C (2006) Deschoolmeester ML, Yuill NC, Christie JW, et al (2006) The role of Th2 cytokines, chemokines and parasite products in eosinophil recruitment to the gastrointestinal mucosa during helminth infection. Eur J Immunol 36: 1753–1763.
- 84. Hepworth MR, Hardman MJ, Grencis RK (2010) The role of sex hormones in the development of Th2 immunity in a gender-biased model of Trichuris muris infection. Eur J Immunol 40: 406–416.
- 85. Hasnain SZ, Thornton DJ, Grencis RK (2011) Changes in the mucosal barrier during acute and chronic Trichuris muris infection. Parasite Immunol 33: 45–55.
- 86. Svensson M, Russell K, Mack M, Else KJ (2010) CD4+ T-cell localization to the large intestinal mucosa during Trichuris muris infection is mediated by G alpha i-coupled receptors but is CCR6- and CXCR3-independent. Immunology 129: 257–267.
- 87. Burke ML, McGarvey L, McSorley HJ, Bielefeldt-Ohmann H, McManus DP, et al. (2011) Migrating Schistosoma japonicum schistosomula induce an innate immune response and wound healing in the murine lung. Mol Immunol 49: 191–200.
- 88. Zhang M, Gao Y, Du X, Zhang D, Ji M, et al. (2011) Toll-like receptor (TLR) 2 and TLR4 deficiencies exert differential in vivo effects against Schistosoma japonicum. Parasite Immunol 33: 199–209.
- 89. Singh KP, Gerard HC, Hudson AP, Boros DL (2006) Differential expression of collagen, MMP, TIMP and fibrogenic-cytokine genes in the granulomatous colon of Schistosoma mansoni-infected mice. Ann Trop Med Parasitol 100: 611–620.
- 90. Perry CR, Burke ML, Stenzel DJ, McManus DP, Ramm GA, et al. (2011) Differential expression of chemokine and matrix re-modelling genes is associated with contrasting schistosome-induced hepatopathology in murine models. PLoS Negl Trop Dis 5: e1178.
- 91. de Oliveira Fraga LA, Torrero MN, Tocheva AS, Mitre E, Davies SJ (2010) Induction of type 2 responses by schistosome worms during prepatent infection. J Infect Dis 201: 464–472.
- 92. de la Torre-Escudero E, Valero L, Perez-Sanchez R, Manzano-Roman R, Oleaga A (2012) Proteomic identification of endothelial cell surface proteins isolated from the hepatic portal vein of mice infected with Schistosoma bovis. J Proteomics 77: 129–143.
- 93. Bystrom J, Dyer KD, Ting-De Ravin SS, Naumann N, Stephany DA, et al. (2006) Interleukin-5 does not influence differential transcription of transmembrane and soluble isoforms of IL-5R alpha in vivo. Eur J Haematol 77: 181–190.
- 94. Burke ML, McManus DP, Ramm GA, Duke M, Li Y, et al. (2010) Co-ordinated gene expression in the liver and spleen during Schistosoma japonicum infection regulates cell migration. PLoS Negl Trop Dis 4: e686.
- 95. Angyalosi G, Neveu R, Wolowczuk I, Delanoye A, Herno J, et al. (2001) HLA class II polymorphism influences onset and severity of pathology in Schistosoma mansoni-infected transgenic mice. Infect Immun 69: 5874–5882.
- 96. Ray D, Nelson TA, Fu CL, Patel S, Gong DN, et al. (2012) Transcriptional profiling of the bladder in urogenital schistosomiasis reveals pathways of inflammatory fibrosis and urothelial compromise. PLoS Negl Trop Dis 6: e1912.
- 97. Xu Y, Xie J, Li Y, Yue J, Chen J, et al. (2003) Using a cDNA microarray to study cellular gene expression altered by Mycobacterium tuberculosis. Chin Med J (Engl) 116: 1070–1073.
- 98. Volpe E, Cappelli G, Grassi M, Martino A, Serafino A, et al. (2006) Gene expression profiling of human macrophages at late time of infection with Mycobacterium tuberculosis. Immunology 118: 449–460.
- 99. Silver RF, Walrath J, Lee H, Jacobson BA, Horton H, et al. (2009) Human alveolar macrophage gene responses to Mycobacterium tuberculosis strains H37Ra and H37Rv. Am J Respir Cell Mol Biol 40: 491–504.
- 100. Sharbati J, Lewin A, Kutz-Lohroff B, Kamal E, Einspanier R, et al. (2011) Integrated microRNA-mRNA-analysis of human monocyte derived macrophages upon Mycobacterium avium subsp. hominissuis infection. PLoS One 6: e20258.
- 101. Ragno S, Romano M, Howell S, Pappin DJ, Jenner PJ, et al. (2001) Changes in gene expression in macrophages infected with Mycobacterium tuberculosis: a combined transcriptomic and proteomic approach. Immunology 104: 99–108.
- 102. Orlova MO, Majorov KB, Lyadova IV, Eruslanov EB, M'Lan C E, et al. (2006) Constitutive differences in gene expression profiles parallel genetic patterns of susceptibility to tuberculosis in mice. Infect Immun 74: 3668–3672.
- 103. Magee DA, Taraktsoglou M, Killick KE, Nalpas NC, Browne JA, et al. (2012) Global gene expression and systems biology analysis of bovine monocyte-derived macrophages in response to in vitro challenge with Mycobacterium bovis. PLoS One 7: e32034.
- 104. Maddocks S, Scandurra GM, Nourse C, Bye C, Williams RB, et al. (2009) Gene expression in HIV-1/Mycobacterium tuberculosis co-infected macrophages is dominated by M. tuberculosis. Tuberculosis (Edinb) 89: 285–293.
- 105. Keller C, Lauber J, Blumenthal A, Buer J, Ehlers S (2004) Resistance and susceptibility to tuberculosis analysed at the transcriptome level: lessons from mouse macrophages. Tuberculosis (Edinb) 84: 144–158.
- 106. Beisiegel M, Mollenkopf HJ, Hahnke K, Koch M, Dietrich I, et al. (2009) Combination of host susceptibility and Mycobacterium tuberculosis virulence define gene expression profile in the host. Eur J Immunol 39: 3369–3384.
- 107. Sugimoto CR, Zhang G, Cronin B (2013) Bias in peer review. J Am Soc Inform Sci Tech 64: 2–17.
- 108. Zingales B, Miles MA, Campbell DA, Tibayrenc M, Macedo AM, et al. (2012) The revised Trypanosoma cruzi subspecific nomenclature: rationale, epidemiological relevance and research applications. Infect Genet Evol 12: 240–253.
- 109. Majiwa PA, Hamers R, Van Meirvenne N, Matthyssens G (1986) Evidence for genetic diversity in Trypanosoma (Nannomonas) congolense. Parasitology 93 (Pt 2): 291–304.
- 110. Barrett MP, Burchmore RJ, Stich A, Lazzari JO, Frasch AC, et al. (2003) The trypanosomiases. Lancet 362: 1469–1480.
- 111. Capewell P, Clucas C, DeJesus E, Kieft R, Hajduk S, et al. (2013) The TgsGP gene is essential for resistance to human serum in Trypanosoma brucei gambiense. PLoS Pathog 9: e1003686.
- 112. Miles MA, Cedillos RA, Povoa MM, de Souza AA, Prata A, et al. (1981) Do radically dissimilar Trypanosoma cruzi strains (zymodemes) cause Venezuelan and Brazilian forms of Chagas' disease? Lancet 1: 1338–1340.
- 113. Saeij JP, Boyle JP, Boothroyd JC (2005) Differences among the three major strains of Toxoplasma gondii and their specific interactions with the infected host. Trends Parasitol 21: 476–481.
- 114. Johnston CE, Bradley JE, Behnke JM, Matthews KR, Else KJ (2005) Isolates of Trichuris muris elicit different adaptive immune responses in their murine host. Parasite Immunol 27: 69–78.
- 115. Zuim NR, Allegretti SM, Linhares AX, Magalhaes LA, Zanotti-Magalhaes EM (2012) A Study of the Granulomatous Responses Induced by Different Strains of Schistosoma mansoni. Interdiscip Perspect Infect Dis 2012: 953524.
- 116. Guilbride DL, Guilbride PD, Gawlinski P (2012) Malaria's deadly secret: a skin stage. Trends Parasitol 28: 142–150.
- 117. De Lima AR, Navarro MC, Arteaga RY, Contreras VT (2008) Cultivation of Trypanosoma cruzi epimastigotes in low glucose axenic media shifts its competence to differentiate at metacyclic trypomastigotes. Exp Parasitol 119: 336–342.
- 118. Perez Brandan C, Padilla AM, Diosque P, Basombrio MA (2006) Trypanosoma cruzi: infectivity modulation of a clone after passages through different hosts. Exp Parasitol 114: 89–93.
- 119. Shikanai-Yasuda MA, Carvalho NB (2012) Oral transmission of Chagas disease. Clin Infect Dis 54: 845–852.
- 120. Camandaroba EL, Pinheiro Lima CM, Andrade SG (2002) Oral transmission of Chagas disease: importance of Trypanosoma cruzi biodeme in the intragastric experimental infection. Rev Inst Med Trop Sao Paulo 44: 97–103.
- 121. Ramirez JD, Montilla M, Cucunuba ZM, Florez AC, Zambrano P, et al. (2013) Molecular epidemiology of human oral Chagas disease outbreaks in Colombia. PLoS Negl Trop Dis 7: e2041.
- 122. Johnson AM (1984) Strain-dependent, route of challenge-dependent, murine susceptibility to toxoplasmosis. Z Parasitenkd 70: 303–309.
- 123. Schuurs AH, Verheul HA (1990) Effects of gender and sex steroids on the immune response. J Steroid Biochem 35: 157–172.
- 124. Klein SL (2012) Immune cells have sex and so should journal articles. Endocrinology 153: 2544–2550.
- 125. Roberts CW, Cruickshank SM, Alexander J (1995) Sex-determined resistance to Toxoplasma gondii is associated with temporal differences in cytokine production. Infect Immun 63: 2549–2555.
- 126. Schuster JP, Schaub GA (2001) Experimental Chagas disease: the influence of sex and psychoneuroimmunological factors. Parasitol Res 87: 994–1000.
- 127. Turay AA, Nwobu GO, Okogun GR, Igwe CU, Adeyeye K, et al. (2005) A comparative study on the susceptibility of male and female albino mice to Trypanosoma brucei brucei. J Vector Borne Dis 42: 15–20.
- 128. Greenblatt HC, Rosenstreich DL (1984) Trypanosoma rhodesiense infection in mice: sex dependence of resistance. Infect Immun 43: 337–340.
- 129. Schuster JP, Schaub GA (2001) Trypanosoma cruzi: the development of estrus cycle and parasitemia in female mice maintained with or without male pheromones. Parasitol Res 87: 985–993.
- 130. Mutayoba BM, Gombe S, Kaaya GP, Waindi EN (1988) Trypanosome-induced ovarian dysfunction. Evidence of higher residual fertility in trypanotolerant small East African goats. Acta Trop 45: 225–237.
- 131. Giammanco S, Ernandes M, La Guardia M (1997) Effects of environmental lighting and tryptophan devoid diet on the rat vaginal cycle. Arch Physiol Biochem 105: 445–449.
- 132.
Nature (2013) Reporting Checklist For Life Sciences Articles. In: checklist.pdf, editor: Nature Publishing Group.
- 133.
Maguire E, Gonzalez-Beltran A, Rocca-Serra P, Sansone S BioSharing. http://biosharing.org/.
- 134. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, et al. (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 6: e1000100.
- 135. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, et al. (2001) Initial sequencing and analysis of the human genome. Nature 409: 860–921.
- 136. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, et al. (2001) The sequence of the human genome. Science 291: 1304–1351.
- 137.
StataCorp. Stata Statistical Software: Release 10.College Station, TX, USA: StataCorp LP.
- 138.
GraphPad. GraphPad Prism version 4.0 for Windows. La Jolla California USA, www.graphpad.com.