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1.  Bacillus thuringiensis-derived Cry5B Has Potent Anthelmintic Activity against Ascaris suum 
Ascaris suum and Ascaris lumbricoides are two closely related geo-helminth parasites that ubiquitously infect pigs and humans, respectively. Ascaris suum infection in pigs is considered a good model for A. lumbricoides infection in humans because of a similar biology and tissue migration to the intestines. Ascaris lumbricoides infections in children are associated with malnutrition, growth and cognitive stunting, immune defects, and, in extreme cases, life-threatening blockage of the digestive tract and aberrant migration into the bile duct and peritoneum. Similar effects can be seen with A. suum infections in pigs related to poor feed efficiency and performance. New strategies to control Ascaris infections are needed largely due to reduced treatment efficacies of current anthelmintics in the field, the threat of resistance development, and the general lack of new drug development for intestinal soil-transmitted helminths for humans and animals. Here we demonstrate for the first time that A. suum expresses the receptors for Bacillus thuringiensis crystal protein and novel anthelmintic Cry5B, which has been previously shown to intoxicate hookworms and which belongs to a class of proteins considered non-toxic to vertebrates. Cry5B is able to intoxicate A. suum larvae and adults and triggers the activation of the p38 mitogen-activated protein kinase pathway similar to that observed with other nematodes. Most importantly, two moderate doses of 20 mg/kg body weight (143 nM/kg) of Cry5B resulted in a near complete cure of intestinal A. suum infections in pigs. Taken together, these results demonstrate the excellent potential of Cry5B to treat Ascaris infections in pigs and in humans and for Cry5B to work effectively in the human gastrointestinal tract.
Author Summary
Ascaris suum is an intestinal parasitic nematode of pigs that is very closely related to Ascaris lumbricoides, a major intestinal parasitic nematode of humans that infects more than one billion people worldwide. Because of reduced efficacy and the threat of resistance to the current small set of approved drugs to treat Ascaris infections, new treatments are needed. Here we test against A. suum infections the effectiveness of Cry5B, a nematode-killing protein made by the natural soil bacterium Bacillus thuringiensis and representing a promising new class of anthelmintics. We demonstrate for the first time that A. suum possesses the receptors to bind Cry5B and that Cry5B can kill A. suum larvae and adults in culture. Most importantly, we demonstrate that oral administration of Cry5B to pigs infected with A. suum larvae results in a near complete elimination of the infection. Given the similarities between A. suum and A. lumbricoides and the similarity between the pig and human gastrointestinal tracts, our data indicate that Cry5B has excellent potential to treat Ascaris infections in veterinary animals and in humans.
doi:10.1371/journal.pntd.0002263
PMCID: PMC3688533  PMID: 23818995
2.  Phylogeographical Studies of Ascaris spp. Based on Ribosomal and Mitochondrial DNA Sequences 
Background
The taxonomic distinctiveness of Ascaris lumbricoides and A. suum, two of the world's most significant nematodes, still represents a much-debated scientific issue. Previous studies have described two different scenarios in transmission patterns, explained by two hypotheses: (1) separated host-specific transmission cycles in highly endemic regions, (2) a single pool of infection shared by humans and pigs in non-endemic regions. Recently, A. suum has been suggested as an important cause of human ascariasis in endemic areas such as China, where cross-infections and hybridization have also been reported. The main aims of the present study were to investigate the molecular epidemiology of human and pig Ascaris from non-endemic regions and, with reference to existing data, to infer the phylogenetic and phylogeographic relationships among the samples.
Methodology
151 Ascaris worms from pigs and humans were characterized using PCR-RFLP on nuclear ITS rDNA. Representative geographical sub-samples were also analysed by sequencing a portion of the mitochondrial cox1 gene, to infer the extent of variability at population level. Sequence data were compared to GenBank sequences from endemic and non-endemic regions.
Principal Findings
No fixed differences between human and pig Ascaris were evident, with the exception of the Slovak population, which displays significant genetic differentiation. The RFLP analysis confirmed pig as a source of human infection in non-endemic regions and as a corridor for the promulgation of hybrid genotypes. Epidemiology and host-affiliation seem not to be relevant in shaping molecular variance. Phylogenetic and phylogeographical analyses described a complex scenario, involving multiple hosts, sporadic contact between forms and an ancestral taxon referable to A. suum.
Conclusions/Significance
These results suggest the existence of homogenizing gene flow between the two taxa, which appear to be variants of a single polytypic species. This conclusion has implications on the systematics, transmission and control programs relating to ascariasis.
Author Summary
Ascaris lumbricoides, the world's most common human nematode, and A. suum, the pig roundworm, are two of the most important soil-transmitted helminthes of public health and socio-economic concern. However, previously documented similarities at the morphological and genetic level, coupled with evidence for hybridization and gene flow, have clouded the taxonomic distinctiveness of these two nematodes. To date, molecular epidemiological studies have been carried out, mostly in highly endemic regions, where two different transmission cycles have been described. Recently, pigs have been recognized as an important source of human ascariasis in China, opening questions about the zoonotic potential and the efficiency of control programs. Here, samples from non-endemic regions have been analysed using a nuclear marker to identify nematodes to species level plus a mitochondrial marker to investigate the phylogeographic relationships among individuals of the two species from both endemic and non-endemic regions. Results obtained suggested that A. suum and A. lumbricoides may be variants of the same species, with the lack of fixed genetic differences and considerable phylogeographic admixture confirming an extremely close evolutionary relationship among these nematodes. This study highlights the need to further explore the evolutionary affinities of the two taxa to help shed light on the epidemiology of ascariasis.
doi:10.1371/journal.pntd.0002170
PMCID: PMC3623706  PMID: 23593529
3.  Proteomic Analysis of Adult Ascaris suum Fluid Compartments and Secretory Products 
Background
Strategies employed by parasites to establish infections are poorly understood. The host-parasite interface is maintained through a molecular dialog that, among other roles, protects parasites from host immune responses. Parasite excretory/secretory products (ESP) play major roles in this process. Understanding the biology of protein secretion by parasites and their associated functional processes will enhance our understanding of the roles of ESP in host-parasite interactions.
Methodology/Principal Findings
ESP was collected after culturing 10 adult female Ascaris suum. Perienteric fluid (PE) and uterine fluid (UF) were collected directly from adult females by dissection. Using SDS-PAGE coupled with LC-MS/MS, we identified 175, 308 and 274 proteins in ESP, PE and UF, respectively. Although many proteins were shared among the samples, the protein composition of ESP was distinct from PE and UF, whereas PE and UF were highly similar. The distribution of gene ontology (GO) terms for proteins in ESP, PE and UF supports this claim. Comparison of ESP composition in A. suum, Brugia malayi and Heligmosoides polygyrus showed that proteins found in UF were also secreted by males and by larval stages of other species, suggesting that multiple routes of secretion may be used for homologous proteins. ESP composition of nematodes is both phylogeny- and niche-dependent.
Conclusions/Significance
Analysis of the protein composition of A. suum ESP and UF leads to the conclusion that the excretory-secretory apparatus and uterus are separate routes for protein release. Proteins detected in ESP have distinct patterns of biological functions compared to those in UF. PE is likely to serve as the source of the majority of proteins in UF. This analysis expands our knowledge of the biology of protein secretion from nematodes and will inform new studies on the function of secreted proteins in the orchestration of host-parasite interactions.
Author Summary
Ascaris lumbricoides, the most prevalent metazoan parasite of humans, is a public health concern in resource-limited countries. Survival of this parasite in its host is mediated at least in part by parasite materials secreted into the host. Little is known about the composition of these secretions; defining their contents and functions will illuminate host-parasite interactions that lead to parasite establishment. Ascaris suum, a parasite of pigs, was used as a model organism because its genome has been sequenced and it is very closely related to A. lumbricoides. Excretory/secretory products (ESP), uterine fluid (UF) and perienteric fluid (PE) were collected from adult A. suum. Proteins were subjected to LC-MS/MS. ESP proteins (the ‘secretome’) included many also present in UF. Proteins in ESP but not in UF had considerably different characteristics than those in PE or UF, which were similar to each other. We conclude that proteins released from the secretory apparatus have distinct patterns of biological function and that UF proteins are likely derived from PE. Comparing the protein composition of A. suum ESP to ESP from B. malayi and H. polygyrus suggests that the secretome is conserved at the level of both phylogeny and host predilection site.
doi:10.1371/journal.pntd.0002939
PMCID: PMC4046973  PMID: 24901219
4.  Genomic-Bioinformatic Analysis of Transcripts Enriched in the Third-Stage Larva of the Parasitic Nematode Ascaris suum 
Differential transcription in Ascaris suum was investigated using a genomic-bioinformatic approach. A cDNA archive enriched for molecules in the infective third-stage larva (L3) of A. suum was constructed by suppressive-subtractive hybridization (SSH), and a subset of cDNAs from 3075 clones subjected to microarray analysis using cDNA probes derived from RNA from different developmental stages of A. suum. The cDNAs (n = 498) shown by microarray analysis to be enriched in the L3 were sequenced and subjected to bioinformatic analyses using a semi-automated pipeline (ESTExplorer). Using gene ontology (GO), 235 of these molecules were assigned to ‘biological process’ (n = 68), ‘cellular component’ (n = 50), or ‘molecular function’ (n = 117). Of the 91 clusters assembled, 56 molecules (61.5%) had homologues/orthologues in the free-living nematodes Caenorhabditis elegans and C. briggsae and/or other organisms, whereas 35 (38.5%) had no significant similarity to any sequences available in current gene databases. Transcripts encoding protein kinases, protein phosphatases (and their precursors), and enolases were abundantly represented in the L3 of A. suum, as were molecules involved in cellular processes, such as ubiquitination and proteasome function, gene transcription, protein–protein interactions, and function. In silico analyses inferred the C. elegans orthologues/homologues (n = 50) to be involved in apoptosis and insulin signaling (2%), ATP synthesis (2%), carbon metabolism (6%), fatty acid biosynthesis (2%), gap junction (2%), glucose metabolism (6%), or porphyrin metabolism (2%), although 34 (68%) of them could not be mapped to a specific metabolic pathway. Small numbers of these 50 molecules were predicted to be secreted (10%), anchored (2%), and/or transmembrane (12%) proteins. Functionally, 17 (34%) of them were predicted to be associated with (non-wild-type) RNAi phenotypes in C. elegans, the majority being embryonic lethality (Emb) (13 types; 58.8%), larval arrest (Lva) (23.5%) and larval lethality (Lvl) (47%). A genetic interaction network was predicted for these 17 C. elegans orthologues, revealing highly significant interactions for nine molecules associated with embryonic and larval development (66.9%), information storage and processing (5.1%), cellular processing and signaling (15.2%), metabolism (6.1%), and unknown function (6.7%). The potential roles of these molecules in development are discussed in relation to the known roles of their homologues/orthologues in C. elegans and some other nematodes. The results of the present study provide a basis for future functional genomic studies to elucidate molecular aspects governing larval developmental processes in A. suum and/or the transition to parasitism.
Author Summary
In the present study, we constructed a cDNA library enriched for molecules of the infective third-stage larva (L3) of Ascaris suum, the common roundworm of pigs. Using the method of suppressive-subtractive hybridization (SSH), we explored transcription of a subset of molecules by microarray analysis and conducted bioinformatic analyses to characterize these molecules, map them to biochemical pathways, and predict genetic interactions based on comparisons with Caenorhabditis elegans and/or other organisms. The results provide interesting insights into early molecular processes in A. suum. Approximately 60% of the L3-enriched molecules discovered had homologues in C. elegans. Probabilistic analyses suggested that a complex genetic network regulates or controls larval growth and development in A. suum L3s, some of which might be involved in or regulate the switch from the free-living to the parasitic stage. Functional studies of these molecules to elucidate developmental processes in Ascaris could assist in identifying new targets for intervention.
doi:10.1371/journal.pntd.0000246
PMCID: PMC2398786  PMID: 18560474
5.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
doi:10.1038/msb.2011.5
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
6.  Factors Associated with Pleurisy in Pigs: A Case-Control Analysis of Slaughter Pig Data for England and Wales 
PLoS ONE  2012;7(2):e29655.
A case-control investigation was undertaken to determine management and health related factors associated with pleurisy in slaughter pigs in England and Wales.
Methods
The British Pig Executive Pig Health Scheme database of abattoir pathology was used to identify 121 case (>10% prevalence of pleurisy on 3 or more assessment dates in the preceding 24 months) and 121 control units (≤5% prevalence of pleurisy on 3 or more assessment dates in the preceding 24 months). Farm data were collected by postal questionnaire. Data from respondents (70 cases and 51 controls) were analysed using simple logistic regression models with Bonferroni corrections. Limited multivariate analyses were also performed to check the robustness of the overall conclusions.
Results and Conclusions
Management factors associated with increased odds of pleurisy included no all-in all-out pig flow (OR 9.3, 95% confidence interval [CI]: 3.3–29), rearing of pigs with an age difference of >1 month in the same airspace (OR 6.5 [2.8–17]) and repeated mixing (OR 2.2 [1.4–3.8]) or moving (OR 2.2 [1.5–3.4]) of pigs during the rearing phase. Those associated with decreased odds of pleurisy included filling wean-to-finish or grower-to-finish systems with piglets from ≤3 sources (OR 0.18 [0.07–0.41]) compared to farrow-to-finish systems, cleaning and disinfecting of grower (ORs 0.28 [0.13–0.61] and 0.29 [0.13–0.61]) and finisher (ORs 0.24 [0.11–0.51] and 0.2 [0.09–0.44]) accommodation between groups, and extended down time of grower and finisher accommodation (OR 0.84 [0.75–0.93] and 0.86 [0.77–0.94] respectively for each additional day of downtime). This study demonstrated the value of national-level abattoir pathology data collection systems for case control analyses and generated guidance for on-farm interventions to help reduce the prevalence of pleurisy in slaughter pigs.
doi:10.1371/journal.pone.0029655
PMCID: PMC3281815  PMID: 22363407
7.  Streptococcal Toxic Shock Syndrome Caused by Streptococcus suis Serotype 2  
PLoS Medicine  2006;3(5):e151.
Background
Streptococcus suis serotype 2 ( S. suis 2, SS2) is a major zoonotic pathogen that causes only sporadic cases of meningitis and sepsis in humans. Most if not all cases of Streptococcal toxic shock syndrome (STSS) that have been well-documented to date were associated with the non-SS2 group A streptococcus (GAS). However, a recent large-scale outbreak of SS2 in Sichuan Province, China, appeared to be caused by more invasive deep-tissue infection with STSS, characterized by acute high fever, vascular collapse, hypotension, shock, and multiple organ failure.
Methods and Findings
We investigated this outbreak of SS2 infections in both human and pigs, which took place from July to August, 2005, through clinical observation and laboratory experiments. Clinical and pathological characterization of the human patients revealed the hallmarks of typical STSS, which to date had only been associated with GAS infection. Retrospectively, we found that this outbreak was very similar to an earlier outbreak in Jiangsu Province, China, in 1998. We isolated and analyzed 37 bacterial strains from human specimens and eight from pig specimens of the recent outbreak, as well as three human isolates and two pig isolates from the 1998 outbreak we had kept in our laboratory. The bacterial isolates were examined using light microscopy observation, pig infection experiments, multiplex-PCR assay, as well as restriction fragment length polymorphisms (RFLP) and multiple sequence alignment analyses. Multiple lines of evidence confirmed that highly virulent strains of SS2 were the causative agents of both outbreaks.
Conclusions
We report, to our knowledge for the first time, two outbreaks of STSS caused by SS2, a non-GAS streptococcus. The 2005 outbreak was associated with 38 deaths out of 204 documented human cases; the 1998 outbreak with 14 deaths out of 25 reported human cases. Most of the fatal cases were characterized by STSS; some of them by meningitis or severe septicemia. The molecular mechanisms underlying these human STSS outbreaks in human beings remain unclear and an objective for further study.
Clinical description of a 2005 S. suis outbreak in China that affected over 200 individuals and initial characterization of the pathogenic isolates from affected pigs and humans.
doi:10.1371/journal.pmed.0030151
PMCID: PMC1434494  PMID: 16584289
8.  A Tractable Experimental Model for Study of Human and Animal Scabies 
Background
Scabies is a parasitic skin infestation caused by the burrowing mite Sarcoptes scabiei. It is common worldwide and spreads rapidly under crowded conditions, such as those found in socially disadvantaged communities of Indigenous populations and in developing countries. Pruritic scabies lesions facilitate opportunistic bacterial infections, particularly Group A streptococci. Streptococcal infections cause significant sequelae and the increased community streptococcal burden has led to extreme levels of acute rheumatic fever and rheumatic heart disease in Australia's Indigenous communities. In addition, emerging resistance to currently available therapeutics emphasizes the need to identify potential targets for novel chemotherapeutic and/or immunological intervention. Scabies research has been severely limited by the availability of parasites, and scabies remains a truly neglected infectious disease. We report development of a tractable model for scabies in the pig, Sus domestica.
Methodology/Principal Findings
Over five years and involving ten independent cohorts, we have developed a protocol for continuous passage of Sarcoptes scabiei var. suis. To increase intensity and duration of infestation without generating animal welfare issues we have optimised an immunosuppression regimen utilising daily oral treatment with 0.2mg/kg dexamethasone. Only mild, controlled side effects are observed, and mange infection can be maintained indefinitely providing large mite numbers (>6000 mites/g skin) for molecular-based research on scabies. In pilot experiments we explore whether any adaptation of the mite population is reflected in genetic changes. Phylogenetic analysis was performed comparing sets of genetic data obtained from pig mites collected from naturally infected pigs with data from pig mites collected from the most recent cohort.
Conclusions/Significance
A reliable pig/scabies animal model will facilitate in vivo studies on host immune responses to scabies including the relations to the associated bacterial pathogenesis and more detailed studies of molecular evolution and host adaption. It is a most needed tool for the further investigation of this important and widespread parasitic disease.
Author Summary
Scabies, a neglected parasitic disease caused by the microscopic mite Sarcoptes scabiei, is a major driving force behind bacterial skin infections in tropical settings. Aboriginal and Torres Strait Islander peoples are nearly twenty times more likely to die from acute rheumatic fever and rheumatic heart disease than individuals from the wider Australian community. These conditions are caused by bacterial pathogens such as Group A streptococci, which have been linked to underlying scabies infestations. Community based initiatives to reduce scabies and associated disease have expanded, but have been threatened in recent years by emerging drug resistance. Critical biological questions surrounding scabies remain unanswered due to a lack of biomedical research. This has been due in part to a lack of either a suitable animal model or an in vitro culture system for scabies mites. The pig/mite model reported here will be a much needed resource for parasite material and will facilitate in vivo studies on host immune responses to scabies, including relations to associated bacterial pathogenesis, and more detailed studies of molecular evolution and host adaptation. It represents the missing tool to extrapolate emerging molecular data into an in vivo setting and may well allow the development of clinical interventions.
doi:10.1371/journal.pntd.0000756
PMCID: PMC2907415  PMID: 20668508
9.  Diffusion based Abnormality Markers of Pathology: Towards Learned Diagnostic Prediction of ASD 
NeuroImage  2011;57(3):918-927.
This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using Support Vector Machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from Diffusion Tensor Imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with autism spectrum disorder (ASD) (mean age 10.5 ± 2.5 yrs) as compared to 30 typically developing (TD) controls ( mean age 10.3 ± 2.5 yrs). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p < 0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.
doi:10.1016/j.neuroimage.2011.05.023
PMCID: PMC3152443  PMID: 21609768
Diffusion tensor imaging; support vector machines; pattern classification; abnormality score
10.  Influenza A virus infection of healthy piglets in an abattoir in Brazil: animal-human interface and risk for interspecies transmission 
Memórias do Instituto Oswaldo Cruz  2013;108(5):548-553.
Asymptomatic influenza virus infections in pigs are frequent and the lack of measures for controlling viral spread facilitates the circulation of different virus strains between pigs. The goal of this study was to demonstrate the circulation of influenza A virus strains among asymptomatic piglets in an abattoir in Brazil and discuss the potential public health impacts. Tracheal samples (n = 330) were collected from asymptomatic animals by a veterinarian that also performed visual lung tissue examinations. No slaughtered animals presented with any noticeable macroscopic signs of influenza infection following examination of lung tissues. Samples were then analysed by reverse transcription-polymerase chain reaction that resulted in the identification of 30 (9%) influenza A positive samples. The presence of asymptomatic pig infections suggested that these animals could facilitate virus dissemination and act as a source of infection for the herd, thereby enabling the emergence of influenza outbreaks associated with significant economic losses. Furthermore, the continuous exposure of the farm and abattoir workers to the virus increases the risk for interspecies transmission. Monitoring measures of swine influenza virus infections and vaccination and monitoring of employees for influenza infection should also be considered. In addition regulatory agencies should consider the public health ramifications regarding the potential zoonotic viral transmission between humans and pigs.
doi:10.1590/0074-0276108052013003
PMCID: PMC3970599  PMID: 23903968
influenza virus; interspecies transmission; zoonosis; pork industry
11.  caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data 
BMC Bioinformatics  2008;9:383.
Background
The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables.
Results
In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering (wherein phenotype labels for samples are known), albeit with minor algorithm modifications customized to each of these tasks.
Conclusion
VISDA achieved robust and superior clustering accuracy, compared with several benchmark clustering schemes. The model order selection scheme in VISDA was shown to be effective for high dimensional genomic data clustering. On muscular dystrophy data and muscle regeneration data, VISDA identified biologically relevant co-expressed gene clusters. VISDA also captured the pathological relationships among different phenotypes revealed at the molecular level, through phenotype clustering on muscular dystrophy data and multi-category cancer data.
doi:10.1186/1471-2105-9-383
PMCID: PMC2566986  PMID: 18801195
12.  Scabies Mites Alter the Skin Microbiome and Promote Growth of Opportunistic Pathogens in a Porcine Model 
Background
The resident skin microbiota plays an important role in restricting pathogenic bacteria, thereby protecting the host. Scabies mites (Sarcoptes scabiei) are thought to promote bacterial infections by breaching the skin barrier and excreting molecules that inhibit host innate immune responses. Epidemiological studies in humans confirm increased incidence of impetigo, generally caused by Staphylococcus aureus and Streptococcus pyogenes, secondary to the epidermal infestation with the parasitic mite. It is therefore possible that mite infestation could alter the healthy skin microbiota making way for the opportunistic pathogens. A longitudinal study to test this hypothesis in humans is near impossible due to ethical reasons. In a porcine model we generated scabies infestations closely resembling the disease manifestation in humans and investigated the scabies associated changes in the skin microbiota over the course of a mite infestation.
Methodology/Principal Findings
In a 21 week trial, skin scrapings were collected from pigs infected with S. scabies var. suis and scabies-free control animals. A total of 96 skin scrapings were collected before, during infection and after acaricide treatment, and analyzed by bacterial 16S rDNA tag-encoded FLX-titanium amplicon pyrosequencing. We found significant changes in the epidermal microbiota, in particular a dramatic increase in Staphylococcus correlating with the onset of mite infestation in animals challenged with scabies mites. This increase persisted beyond treatment from mite infection and healing of skin. Furthermore, the staphylococci population shifted from the commensal S. hominis on the healthy skin prior to scabies mite challenge to S. chromogenes, which is increasingly recognized as being pathogenic, coinciding with scabies infection in pigs. In contrast, all animals in the scabies-free cohort remained relatively free of Staphylococcus throughout the trial.
Conclusions/Significance
This is the first experimental in vivo evidence supporting previous assumptions that establishment of pathogens follow scabies infection. Our findings provide an explanation for a biologically important aspect of the disease pathogenesis. The methods developed from this pig trial will serve as a guide to analyze human clinical samples. Studies building on this will offer implications for development of novel intervention strategies against the mites and the secondary infections.
Author Summary
Scabies is a neglected, contagious skin disease caused by a parasitic mite Sarcoptes scabiei. It is highly prevalent world-wide, and now recognized as a possible underlying factor for secondary bacterial infections with potential serious downstream complications. There is currently few experimental data demonstrating directly that mite infestation promotes bacterial infections. Due to remarkable similarities in terms of immunology, physiology and skin anatomy between pigs and humans, we developed a sustainable porcine model enabling in vivo studies of scabies mite infestations. Here, we investigated the impact of the scabies mite infection on the normal pig skin microbiota in the inner ear pinnae in young piglets. Samples obtained prior to, during infection and after acaricide treatment were analyzed by sequencing of bacterial 16S rDNA. We report that scabies infestation has an impact on the host's skin microbiota. Staphylococcus abundance increased with the onset of infection and remained beyond treatment and healing. A shift from commensal to pathogenic Staphylococci was observed. This study supports the link between scabies and Staphylococcus infections, as seen in humans. It is the first in vivo demonstration of a mite induced shift in the skin microbiota, providing a basis for a similar study in humans.
doi:10.1371/journal.pntd.0002897
PMCID: PMC4038468  PMID: 24875186
13.  BPEX Pig Health Scheme: a useful monitoring system for respiratory disease control in pig farms? 
Background
Respiratory diseases account for significant economic losses to the UK pig industry. Lesions indicative of respiratory disease in pig lungs at slaughter e.g. pneumonia and pleuritis are frequently recorded to assess herd health or provide data for epidemiological studies. The BPEX Pig Health Scheme (BPHS) is a monitoring system, which informs producers of gross lesions in their pigs' carcasses at slaughter, enabling farm-level decisions to be made. The aim of the study was to assess whether information provided by the BPHS regarding respiratory lesions was associated with respiratory pathogens in the farm, farm management practices and each other.
Results
BPHS reports were obtained from a subset of 70 pig farms involved in a cross-sectional study conducted in 2008-09 investigating the epidemiology of post-weaning multi-systemic wasting syndrome. The reports were combined with data regarding the presence/absence of several pathogens in the herd and potential farm-level risk factors for respiratory disease. Principal component analysis (PCA) performed on BPHS reports generated three principal components, explaining 71% of the total variance. Enzootic pneumonia score, severe pleurisy and acute pleuropneumonia had the highest loadings for the principal component which explained the largest percentage of the total variance (35%) (BPHS component 1), it was thought that this component identifies farms with acute disease. Using the factor loadings a score for each farm for BPHS component 1 was obtained. As farms' score for BPHS component 1 increased, average carcass weight at slaughter decreased. In addition, farms positive for H1N2 and porcine reproductive and respiratory disease virus (PRRSV) were more likely to have higher levels of severe and mild pleurisy reported by the BPHS, respectively.
Conclusions
The study found statistical associations between levels of pleurisy recorded by BPHS at slaughter and the presence H1N2 and PRRSV in the herd. There is also some evidence that farms which submit pigs with these lesions may have reduced productivity. However, more research is needed to fully validate the scheme.
doi:10.1186/1746-6148-7-82
PMCID: PMC3285094  PMID: 22208847
14.  Exploring relationships between whole carcass condemnation abattoir data, non-disease factors and disease outbreaks in swine herds in Ontario (2001–2007) 
BMC Research Notes  2014;7:185.
Background
Improving upon traditional animal disease surveillance systems may allow more rapid detection of disease outbreaks in animal populations. In Ontario, between the years 2001 – 2007, widespread outbreaks of several diseases caused major impacts to the swine industry. This study was undertaken to investigate whether whole carcass condemnation data of market pigs from provincial abattoirs from 2001 – 2007 could have provided useful information for disease surveillance of Ontario swine. The objective was to examine the suitability of these data for detection of disease outbreaks using multi-level models and spatial scan statistics. We investigated the ability of these data to provide spatially-relevant surveillance information by determining the approximate distance pigs are shipped from farm to provincial abattoirs in the province, and explored potentially biasing non-disease factors within these data.
Results
Provincially-inspected abattoirs in Ontario were found to be located in close proximity to the hog farms of origin. The fall season and increasing abattoir capacity were associated with a decrease in condemnation rates. Condemnation rates varied across agricultural regions by year, and some regions showed yearly trends consistent with the timing of emergence of new disease strains that affected the Ontario swine population. Scan statistics identified stable clusters of condemnations in space that may have represented stable underlying factors influencing condemnations. The temporal scans detected the most likely cluster of high condemnations during the timeframe in which widespread disease events were documented. One space-time cluster took place during the beginning of the historical disease outbreaks and may have provided an early warning signal within a syndromic surveillance system.
Conclusions
Spatial disease surveillance methods may be applicable to whole carcass condemnation data collected at provincially-inspected abattoirs in Ontario for disease detection on a local scale. These data could provide useful information within a syndromic disease surveillance system for protecting swine herd health within the province. However, non-disease factors including region, season and abattoir size need to be considered when applying quantitative methods to abattoir data for disease surveillance.
doi:10.1186/1756-0500-7-185
PMCID: PMC3975458  PMID: 24674622
15.  Genome-Wide Tissue-Specific Gene Expression, Co-expression and Regulation of Co-expressed Genes in Adult Nematode Ascaris suum 
Background
Caenorhabditis elegans has traditionally been used as a model for studying nematode biology, but its small size limits the ability for researchers to perform some experiments such as high-throughput tissue-specific gene expression studies. However, the dissection of individual tissues is possible in the parasitic nematode Ascaris suum due to its relatively large size. Here, we take advantage of the recent genome sequencing of Ascaris suum and the ability to physically dissect its separate tissues to produce a wide-scale tissue-specific nematode RNA-seq datasets, including data on three non-reproductive tissues (head, pharynx, and intestine) in both male and female worms, as well as four reproductive tissues (testis, seminal vesicle, ovary, and uterus). We obtained fundamental information about the biology of diverse cell types and potential interactions among tissues within this multicellular organism.
Methodology/Principal Findings
Overexpression and functional enrichment analyses identified many putative biological functions enriched in each tissue studied, including functions which have not been previously studied in detail in nematodes. Putative tissue-specific transcriptional factors and corresponding binding motifs that regulate expression in each tissue were identified, including the intestine-enriched ELT-2 motif/transcription factor previously described in nematode intestines. Constitutively expressed and novel genes were also characterized, with the largest number of novel genes found to be overexpressed in the testis. Finally, a putative acetylcholine-mediated transcriptional network connecting biological activity in the head to the male reproductive system is described using co-expression networks, along with a similar ecdysone-mediated system in the female.
Conclusions/Significance
The expression profiles, co-expression networks and co-expression regulation of the 10 tissues studied and the tissue-specific analysis presented here are a valuable resource for studying tissue-specific biological functions in nematodes.
Author Summary
Tissue-specific gene expression provides fundamental information about the biology of diverse cell types within an organism and interactions among tissues within multicellular organisms. However, such studies are experimentally challenging in smaller organisms such as many nematodes species, including the species (Caenorhabditis elegans) that is widely used in biomedical research. Ascaris suum (the large roundworm of swine), however, is of particular interest as a model nematode because it is large enough to allow for the dissection of individual tissues, and equally important because it is closely related to A. lumbricoides, which infects ∼1 billion people worldwide. Here, we build significantly on the previous tissue-specific gene expression research in A. suum by producing the first nematode RNA-seq dataset that spans multiple specific tissues, including three non-reproductive and two reproductive tissues in both male and female A. suum worms. This analysis provides significant details on the biological functions occurring within each of these tissues, which has not been previously explored. It also provides insight into specific gene regulation pathways active in each of the tissues, which have broad applicability across other nematodes, including both non-parasitic and parasitic species.
doi:10.1371/journal.pntd.0002678
PMCID: PMC3916258  PMID: 24516681
16.  Individual risk factors for Mycoplasma hyopneumoniae infections in suckling pigs at the age of weaning 
Background
In recent years, the occurrence and the relevance of Mycoplasma hyopneumoniae infections in suckling pigs has been examined in several studies. Whereas most of these studies were focused on sole prevalence estimation within different age groups, follow-up of infected piglets or assessment of pathological findings, none of the studies included a detailed analysis of individual and environmental risk factors. Therefore, the aim of the present study was to investigate the frequency of M. hyopneumoniae infections in suckling pigs of endemically infected herds and to identify individual risk factors potentially influencing the infection status of suckling pigs at the age of weaning.
Results
The animal level prevalence of M. hyopneumoniae infections in suckling pigs examined in three conventional pig breeding herds was 3.6% (41/1127) at the time of weaning. A prevalence of 1.2% was found in the same pigs at the end of their nursery period. In a multivariable Poisson regression model it was found that incidence rate ratios (IRR) for suckling pigs are significantly lower than 1 when teeth grinding was conducted (IRR: 0.10). Moreover, high temperatures in the piglet nest during the first two weeks of life (occasionally >40°C) were associated with a decrease of the probability of an infection (IRR: 0.23-0.40). Contrary, the application of PCV2 vaccines to piglets was associated with an increased infection risk (IRR: 9.72).
Conclusions
Since single infected piglets are supposed to act as initiators for the transmission of this pathogen in nursery and fattening pigs, the elimination of the risk factors described in this study should help to reduce the incidence rate of M. hyopneumoniae infections and thereby might contribute to a reduced probability of high prevalences in older pigs.
doi:10.1186/1751-0147-55-44
PMCID: PMC3698135  PMID: 23731650
Mycoplasma hyopneumoniae; Enzootic pneumonia; Suckling pig; Epidemiology; Risk factor analysis
17.  Microbiomes of Unreactive and Pathologically Altered Ileocecal Lymph Nodes of Slaughter Pigs 
Microbe-laden dendritic cells are shifted to ileocecal lymph nodes (ICLNs), where microbes are concentrated and an adequate immune response is triggered. Hence, ICLNs are at a crucial position in immune anatomy and control processes of the local immune system. Pathological alterations in ICLNs, such as reactive hyperplasia, lymphadenitis purulenta, or granulomatosa, can harbor a multitude of pathogens and commensals, posing a potential zoonotic risk in animal production. The aim of this study was to characterize the microbial diversity of unreactive ICLNs of slaughter pigs and to investigate community shifts in reactive ICLNs altered by enlargement, purulence, or granulomatous formations. Pyrosequencing of 16S rRNA gene amplicons from 32 ICLNs yielded 175,313 sequences, clustering into 650 operational taxonomic units (OTUs). OTUs were assigned to 239 genera and 11 phyla. Besides a highly diverse bacterial community in ICLNs, we observed significant shifts in pathologically altered ICLNs. The relative abundances of Cloacibacterium- and Novosphingobium-associated OTUs and the genus Faecalibacterium were significantly higher in unreactive ICLNs than in pathologically altered ICLNs. Enlarged ICLNs harbored significantly more Lactobacillus- and Clostridium-associated sequences. Relative abundances of Mycoplasma, Bacteroides, Veillonella, and Variovorax OTUs were significantly increased in granulomatous ICLNs, whereas abundances of Pseudomonas, Escherichia, and Acinetobacter OTUs were significantly increased in purulent ICLNs (P < 0.05). Correlation-based networks revealed interactions among OTUs in all ICLN groups, and discriminant analyses depicted discrimination in response to pathological alterations. This study is the first community-based survey in ICLNs of livestock animals and will provide a basis to broaden the knowledge of microbe-host interactions in pigs.
doi:10.1128/AEM.03089-13
PMCID: PMC3911030  PMID: 24141125
18.  Cost of post-weaning multi-systemic wasting syndrome and porcine circovirus type-2 subclinical infection in England – An economic disease model 
Preventive Veterinary Medicine  2013;110(2):88-102.
Post-weaning multi-systemic wasting syndrome (PMWS) is a multi-factorial disease with major economic implications for the pig industry worldwide. The present study aimed to assess the economic impact of PMWS and porcine circovirus type 2 (PCV2) subclinical infections (PCV2SI) for farrow-to-finish farms and to estimate the resulting cost to the English pig industry.
A disease model was built to simulate the varying proportions of pigs in a batch that get infected with PCV2 and develop either PMWS, subclinical disease (reduce growth without evident clinical signs) or remain healthy (normal growth and no clinical signs), depending on the farm level PMWS severity. This PMWS severity measure accounted for the level of post-weaning mortality, PMWS morbidity and proportion of PCV2 infected pigs observed on farms. The model generated six outcomes: infected pigs with PMWS that die (PMWS-D); infected pigs with PMWS that recover (PMWS-R); subclinical pigs that die (Sub-D); subclinical pigs that reach slaughter age (Sub-S); healthy pigs sold (H-S); and pigs, infected or non-infected by PCV2, that die due to non-PCV2 related causes (nonPCV2-D). Enterprise and partial budget analyses were used to assess the deficit/profits and the extra costs/extra benefits of a change in disease status, respectively. Results from the economic analysis at pig level were combined with the disease model's estimates of the proportion of different pigs produced at different severity scores to assess the cost of PMWS and subclinical disease at farm level, and these were then extrapolated to estimate costs at national level.
The net profit for a H-S pig was £19.2. The mean loss for a PMWS-D pig was £84.1 (90% CI: 79.6–89.1), £24.5 (90% CI: 15.1–35.4) for a PMWS-R pig, £82.3 (90% CI: 78.1–87.5) for a Sub-D pig, and £8.1 (90% CI: 2.18–15.1) for a Sub-S pig. At farm level, the greatest proportion of negative economic impact was attributed to PCV2 subclinical pigs. The economic impact for the English pig industry for the year 2008, prior to the introduction of PCV2 vaccines, was estimated at £52.6 million per year (90% CI: 34.7–72.0), and approximately £88 million per year during the epidemic period.
This was the first study to use empirical data to model the cost of PMWS/PCV2SI at different farm severity levels. Results from this model will be used to assess the efficiency of different control measures and to provide a decision support tool to farmers and policy makers.
doi:10.1016/j.prevetmed.2013.02.010
PMCID: PMC3652492  PMID: 23490147
Post-weaning multi-systemic wasting syndrome; Porcine Circovirus type 2 subclinical infection; Enterprise budget analysis; Partial budget analysis; Stochastic economic model
19.  Ascaris suum in Saskatchewan Pigs: An Abattoir Survey of Prevalence and Intensity of Infection 
The Canadian Veterinary Journal  1980;21(11):307-309.
In a survey of 2500 market weight pigs in a Saskatchewan abattoir, 37% were infected with adult Ascaris suum and in 46% there were milkspot hepatic lesions. A total of 60% of the pigs examined had some evidence of A. suum infection. Most infected pigs contained less than 50 adult ascarids.
In a second abattoir survey of a further 2500 market weight pigs, 44% of the animals had milk-spot hepatic lesions. In approximately 12% of the affected pigs these lesions were very severe and a very similar proportion of pigs' livers were condemned or sent for use as animal feed.
The results of these surveys are discussed with particular reference to the significance of A. suum infection in growing pigs in Saskatchewan.
PMCID: PMC1789823  PMID: 7459794
20.  Genome-Wide Association Studies of Virulent and Avirulent Haemophilus parasuis Serotype 4 Strains 
Genome Announcements  2014;2(5):e00884-14.
Haemophilus parasuis is a normal commensal of the upper respiratory tract of healthy pigs. However, in conjunction with stress and/or viral infections, or in immunocompromised animals, H. parasuis can transform into a pathogen causing Glasser’s disease, which is typically characterized by fibrinous polyserositis, polyarthritis, meningitis, and sometimes acute pneumonia and septicemia. H. parasuis serotype 5 is highly virulent and more frequently isolated from respiratory and systemic infection in pigs. Recently Newport Laboratories isolated highly virulent H. parasuis serotype 4 strains from the tissues of diseased pigs. This study was undertaken to identify the genes responsible for H. parasuis serotype 4 virulence. To achieve this objective we performed genome-wide association studies (GWAS) across two virulent and three avirulent H. parasuis serotype 4 strains.
doi:10.1128/genomeA.00884-14
PMCID: PMC4155596  PMID: 25189591
21.  Evaluation of the presence and zoonotic transmission of Chlamydia suis in a pig slaughterhouse 
BMC Infectious Diseases  2014;14(1):560.
Background
A significant number of studies on pig farms and wild boars worldwide, demonstrate the endemic presence of Chlamydia suis in pigs. However, the zoonotic potential of this pathogen, phylogenetically closely related to Chlamydia trachomatis, is still uninvestigated. Therefore, this study aims to examine the zoonotic transmission in a Belgian pig abattoir.
Methods
Presence of Chlamydia suis in pigs, contact surfaces, air and employees was assessed using a Chlamydia suis specific real-time PCR and culture. Furthermore, Chlamydia suis isolates were tested for the presence of the tet(C) gene.
Results
Chlamydia suis bacteria could be demonstrated in samples from pigs, the air and contact surfaces. Moreover, eye swabs of two employees were positive for Chlamydia suis by both PCR and culture. The tet(C) gene was absent in both human Chlamydia suis isolates and no clinical signs were reported.
Conclusions
These findings suggest the need for further epidemiological and clinical research to elucidate the significance of human ocular Chlamydia suis infections.
doi:10.1186/s12879-014-0560-x
PMCID: PMC4216655  PMID: 25358497
Chlamydia suis; Swine; Zoonosis; Tetracycline; Public health
22.  Localized Th1-, Th2-, T Regulatory Cell-, and Inflammation-Associated Hepatic and Pulmonary Immune Responses in Ascaris suum-Infected Swine Are Increased by Retinoic Acid▿ †  
Infection and Immunity  2009;77(6):2576-2587.
Pigs infected with Ascaris suum or controls were given 100 μg (low-dose) or 1,000 μg (high-dose) all-trans retinoic acid (ATRA)/kg body weight in corn oil or corn oil alone per os on days after inoculation (DAI) −1, +1, and +3 with infective eggs. Treatment with ATRA increased interleukin 4 (IL4) and IL12p70 in plasma of infected pigs at 7 DAI and augmented bronchoalveolar lavage (BAL) eosinophilia observed at 7 and 14 DAI. To explore potential molecular mechanisms underlying these observations, a quantitative real-time reverse transcription (RT)-PCR array was used to examine mRNA expression in tissue. Ascaris-infected pigs had increased levels of liver mRNA for T-helper-2 (Th2)-associated cytokines, mast cell markers, and T regulatory (Treg) cells, while infected pigs given ATRA had higher IL4, IL13, CCL11, CCL26, CCL17, CCL22, and TPSB1 expression. Gene expression for Th1-associated markers (IFNG, IL12B, and TBX21), the CXCR3 ligand (CXCL9), IL1B, and the putative Treg marker TNFRSF18 was also increased. Expression of IL4, IL13, IL1B, IL6, CCL11, and CCL26 was increased in the lungs of infected pigs treated with ATRA. To determine a putative cellular source of eosinophil chemoattractants, alveolar macrophages were treated with IL4 and/or ATRA in vitro. IL4 induced CCL11, CCL17, CCL22, and CCL26 mRNA, and ATRA increased the basal and IL4-stimulated expression of CCL17 and CCL22. Thus, ATRA augments a diverse Th1-, Th2-, Treg-, and inflammation-associated response in swine infected with A. suum, and the increased BAL eosinophilia may be related to enhanced induction of eosinophil chemokine activity by alveolar macrophages.
doi:10.1128/IAI.00827-07
PMCID: PMC2687331  PMID: 19332534
23.  On the statistical assessment of classifiers using DNA microarray data 
BMC Bioinformatics  2006;7:387.
Background
In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data.
Results
We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed.
Conclusions
The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.
doi:10.1186/1471-2105-7-387
PMCID: PMC1564153  PMID: 16919171
24.  Slaughterhouse Pigs Are a Major Reservoir of Streptococcus suis Serotype 2 Capable of Causing Human Infection in Southern Vietnam 
PLoS ONE  2011;6(3):e17943.
Streptococcus suis is a pathogen of major economic significance to the swine industry and is increasingly recognized as an emerging zoonotic agent in Asia. In Vietnam, S. suis is the leading cause of bacterial meningitis in adult humans. Zoonotic transmission is most frequently associated with serotype 2 strains and occupational exposure to pigs or consumption of infected pork. To gain insight into the role of pigs for human consumption as a reservoir for zoonotic infection in southern Vietnam, we determined the prevalence and diversity of S. suis carriage in healthy slaughterhouse pigs. Nasopharyngeal tonsils were sampled from pigs at slaughterhouses serving six provinces in southern Vietnam and Ho Chi Minh City area from September 2006 to November 2007. Samples were screened by bacterial culture. Isolates of S. suis were serotyped and characterized by multi locus sequence typing (MLST) and pulse field gel electrophoresis (PFGE). Antibiotic susceptibility profiles and associated genetic resistance determinants, and the presence of putative virulence factors were determined. 41% (222/542) of pigs carried S. suis of one or multiple serotypes. 8% (45/542) carried S. suis serotype 2 which was the most common serotype found (45/317 strains, 14%). 80% of serotype 2 strains belonged to the MLST clonal complex 1,which was previously associated with meningitis cases in Vietnam and outbreaks of severe disease in China in 1998 and 2005. These strains clustered with representative strains isolated from patients with meningitis in PFGE analysis, and showed similar antimicrobial resistance and virulence factor profiles. Slaughterhouse pigs are a major reservoir of S. suis serotype 2 capable of causing human infection in southern Vietnam. Strict hygiene at processing facilities, and health education programs addressing food safety and proper handling of pork should be encouraged.
doi:10.1371/journal.pone.0017943
PMCID: PMC3065462  PMID: 21464930
25.  Building gene expression profile classifiers with a simple and efficient rejection option in R 
BMC Bioinformatics  2011;12(Suppl 13):S3.
Background
The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers.
Results
This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention.
Conclusions
This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be available.
doi:10.1186/1471-2105-12-S13-S3
PMCID: PMC3278843  PMID: 22373214

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