Detection, quantification and genetic variability of Mycoplasma hyopneumoniae from apparently healthy and pneumonic swine

Mycoplasma hyopneumoniae is the causative agent of the Porcine Enzootic Pneumonia. However, this mycoplasma can be detected in healthy and symptomatic pigs, that difficults the conclusion for the etiology of this disease. In the present study we aimed to detect, quantify and do molecular analyses of M. hyopneumoniae strains in respiratory clinical samples recovered from healthy pigs and from those with pneumonia or other respiratory symptoms. The analytical sensitivity and specificity of PCR assays directed to Mollicutes detection and porcine mycoplasmas identification in clinical samples were evaluated. The identification of M. hyopneumoniae in the samples was performed using different molecular approaches, Multiplex PCR, Real Time PCR and Multilocus Variable-Number Tandem-Repeat amplification. Molecular characterization of the strains was achieved by determining and comparing the VNTR copy number directly in the samples. The highest number of samples positive to M. hyopneumoniae was identified by the multilocus VNTR amplification assay using labeled primers, followed by capillary electrophoresis. The highest concentration of M. hyopneumoniae was detected in pneumonic lungs (2, 3 * 108 genome copies /mL). The VNTR copy number analysis demonstrated that despite the high genetic variability of the M. hyopneumoniae strains, predominant strains in the swine farms could be identified by means of the VNTR copy number analysis of P97R1 and P146R3. (English)Molecular differences among Mycoplasma hyopneumoniae strains present in pneumonic lungs of swine have been largely studied. However, no comparative studies concerning the strains present in apparently healthy pigs have been carried out. This study aimed to detect, quantify and perform molecular analysis of M. hyopneumoniae strains in pig lungs with and without pneumonic lesions. The detection of M. hyopneumoniae was performed using multiplex PCR (YAMAGUTI, 2008), real-time PCR (STRAIT et al., 2008) and multiple VNTR amplification (VRANCKX et al., 2011). Molecular characterization of the strains was achieved by analysis of the VNTR copy number in P97R1, P146R3, H2R1 and H4. M. hyopneumoniae was detected in samples from healthy and pneumonic pigs and the amount of M. hyopneumoniae positive samples detected varied with the type of assay. The greater number of positive samples was identified by the multiple VNTR amplification combined with capillary electrophoresis. Using real-time PCR, 4.9*104 M. hyopneumoniae genome copies/mL was detected in apparently healthy lungs. A mean quantity of 3.9*106 M. hyopneumoniae genome copies/mL was detected in pneumonic lungs. The analysis of VNTR copy number demonstrated a high genetic variability of the M. hyopneumoniae strains present in apparently healthy and pneumonic lungs. Strains having 3 VNTR copy number in P97R1, were detected only in pneumonic lungs and strains having 40 and 43 VNTR copy number in P146R3 were detected only in apparently healthy lungs. Despite the genetic variability of M. hyopneumoniae, predominant strains in the swine farms could be identified.As diferenças moleculares entre as estirpes de Mycoplasma hyopneumoniae presentes em pulmões de suínos com pneumonia tem sido estudadas. Porém, estudos comparativos relativos as estirpes presentes nos suínos aparentemente saudáveis não foram levados a cabo. O objetivo do estudo foi a detecção, quantificação e analise molecular de M. hyopneumoniae nos pulmões suínos com e sem lesões pneumônicas. Para a detecção de M. hyopneumoniae usaramse o PCR Multiplo (YAMAGUTI, 2008), o PCR a Tempo Real (STRAIT et al., 2008) e a amplificação de múltiplo VNTR (VRANCKX et al., 2011). A caracterização molecular das estirpes foi realizada mediante a análise do número de copias de VNTR em P97R1, P146R3, H2R1 e H4. O M. hyopneumoniae foi detectado em amostras de suínos saudáveis e pneumônicos e a quantidade de M. hyopneumoniae nas amostras positivas variou com o tipo de ensaio. O maior número de amostras positivas foi identificado pela amplificação de múltiplas VNTR combinado com a eletroforese de capilares. Usando o PCR a Tempo Real, 4.9*104 copias de genoma/mL de M. hyopneumoniae foram detectadas em pulmões aparentemente saudáveis. Uma quantidade média de 3.9*106 copias de genoma/mL de M. hyopneumoniae foi detectada em pulmões pneumônicos. A análise do número de copias de VNTR demonstrou uma elevada variabilidade

Caracterização do vírus da raiva isolado de uma colônia de morcegos Eptesicus furinalis, do Brasil

Some bat species have adapted to the expanding human population by acquiring the ability to roost in urban buildings, increasing the exposure risk for people and domestic animals, and consequently, the likelihood of transmitting rabies. Three dead bats were found in the yard of a house in an urban area of Jundiaí city in the state of São Paulo in southeast Brazil. Two of the three bats tested positive for rabies, using Fluorescent Antibody and Mouse Inoculation techniques. A large colony of Eptesicus furinalis was found in the house's attic, and of the 119 bats captured, four more tested positive for rabies. The objectives of this study were to report the rabies diagnosis, characterize the isolated virus antigenically and genetically, and study the epidemiology of the colony.Algumas espécies de morcegos têm se adaptado ao uso de abrigos em construções urbanas, aumentando a possibilidade de contato desses morcegos com pessoas e animais domésticos e conseqüentemente, o potencial risco de transmissão de raiva. Três morcegos foram encontrados no jardim de uma casa na área urbana da cidade de Jundiaí, Estado de São Paulo, Sudeste do Brasil, dois deles foram positivos para raiva pelas técnicas de imunofluorescência e inoculação em camundongos. Uma grande colônia de E. furinalis foi identificada, vivendo no sótão da casa e 119 morcegos foram encaminhados para diagnóstico de raiva, com mais quatro morcegos positivos. O objetivo desse estudo é apresentar a caracterização genética e antigênica do vírus da raiva isolado desses morcegos e o estudo epidemiológico da colônia

Detection, quantification and genetic variability of Mycoplasma hyopneumoniae from apparently healthy and pneumonic swine

Molecular differences among Mycoplasma hyopneumoniae strains present in pneumonic lungs of swine have been largely studied. However, no comparative studies concerning the strains present in apparently healthy pigs have been carried out. This study aimed to detect, quantify and perform molecular analysis of M. hyopneumoniae strains in pig lungs with and without pneumonic lesions. The detection of M. hyopneumoniae was performed using multiplex PCR (YAMAGUTI, 2008), real-time PCR (STRAIT et al., 2008) and multiple VNTR amplification (VRANCKX et al., 2011). Molecular characterization of the strains was achieved by analysis of the VNTR copy number in P97R1, P146R3, H2R1 and H4. M. hyopneumoniae was detected in samples from healthy and pneumonic pigs and the amount of M. hyopneumoniae positive samples detected varied with the type of assay. The greater number of positive samples was identified by the multiple VNTR amplification combined with capillary electrophoresis. Using real-time PCR, 4.9*104 M. hyopneumoniae genome copies/mL was detected in apparently healthy lungs. A mean quantity of 3.9*106 M. hyopneumoniae genome copies/mL was detected in pneumonic lungs. The analysis of VNTR copy number demonstrated a high genetic variability of the M. hyopneumoniae strains present in apparently healthy and pneumonic lungs. Strains having 3 VNTR copy number in P97R1, were detected only in pneumonic lungs and strains having 40 and 43 VNTR copy number in P146R3 were detected only in apparently healthy lungs. Despite the genetic variability of M. hyopneumoniae, predominant strains in the swine farms could be identified

Prevalence of bat viruses associated with land-use change in the Atlantic Forest, Brazil

Introduction: Bats are critical to maintaining healthy ecosystems and many species are threatened primarily due to global habitat loss. Bats are also important hosts of a range of viruses, several of which have had significant impacts on global public health. The emergence of these viruses has been associated with land-use change and decreased host species richness. Yet, few studies have assessed how bat communities and the viruses they host alter with land-use change, particularly in highly biodiverse sites. Methods: In this study, we investigate the effects of deforestation on bat host species richness and diversity, and viral prevalence and richness across five forested sites and three nearby deforested sites in the interior Atlantic Forest of southern Brazil. Nested-PCR and qPCR were used to amplify and detect viral genetic sequence from six viral families (corona-, adeno-, herpes-, hanta-, paramyxo-, and astro-viridae) in 944 blood, saliva and rectal samples collected from 335 bats. Results: We found that deforested sites had a less diverse bat community than forested sites, but higher viral prevalence and richness after controlling for confounding factors. Viral detection was more likely in juvenile males located in deforested sites. Interestingly, we also found a significant effect of host bat species on viral prevalence indicating that viral taxa were detected more frequently in some species than others. In particular, viruses from the Coronaviridae family were detected more frequently in generalist species compared to specialist species. Discussion: Our findings suggest that deforestation may drive changes in the ecosystem which reduce bat host diversity while increasing the abundance of generalist species which host a wider range of viruses

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost