Anopheles stephensi p38 MAPK signaling regulates innate immunity and bioenergetics during Plasmodium falciparum infection.

BackgroundFruit flies and mammals protect themselves against infection by mounting immune and metabolic responses that must be balanced against the metabolic needs of the pathogens. In this context, p38 mitogen-activated protein kinase (MAPK)-dependent signaling is critical to regulating both innate immunity and metabolism during infection. Accordingly, we asked to what extent the Asian malaria mosquito Anopheles stephensi utilizes p38 MAPK signaling during infection with the human malaria parasite Plasmodium falciparum.MethodsA. stephensi p38 MAPK (AsP38 MAPK) was identified and patterns of signaling in vitro and in vivo (midgut) were analyzed using phospho-specific antibodies and small molecule inhibitors. Functional effects of AsP38 MAPK inhibition were assessed using P. falciparum infection, quantitative real-time PCR, assays for reactive oxygen species and survivorship under oxidative stress, proteomics, and biochemical analyses.ResultsThe genome of A. stephensi encodes a single p38 MAPK that is activated in the midgut in response to parasite infection. Inhibition of AsP38 MAPK signaling significantly reduced P. falciparum sporogonic development. This phenotype was associated with AsP38 MAPK regulation of mitochondrial physiology and stress responses in the midgut epithelium, a tissue critical for parasite development. Specifically, inhibition of AsP38 MAPK resulted in reduction in mosquito protein synthesis machinery, a shift in glucose metabolism, reduced mitochondrial metabolism, enhanced production of mitochondrial reactive oxygen species, induction of an array of anti-parasite effector genes, and decreased resistance to oxidative stress-mediated damage. Hence, P. falciparum-induced activation of AsP38 MAPK in the midgut facilitates parasite infection through a combination of reduced anti-parasite immune defenses and enhanced host protein synthesis and bioenergetics to minimize the impact of infection on the host and to maximize parasite survival, and ultimately, transmission.ConclusionsThese observations suggest that, as in mammals, innate immunity and mitochondrial responses are integrated in mosquitoes and that AsP38 MAPK-dependent signaling facilitates mosquito survival during parasite infection, a fact that may attest to the relatively longer evolutionary relationship of these parasites with their invertebrate compared to their vertebrate hosts. On a practical level, improved understanding of the balances and trade-offs between resistance and metabolism could be leveraged to generate fit, resistant mosquitoes for malaria control

Transcript levels of Toll-Like receptors 5, 8 and 9 correlate with inflammatory activity in Ulcerative Colitis

<p>Abstract</p> <p>Background</p> <p>Dysregulation of innate immune response by Toll-Like Receptors (TLRs) is a key feature in Ulcerative Colitis (UC). Most studies have focused on <it>TLR2, TLR3</it>, and <it>TLR4 </it>participation in UC. However, few studies have explored other TLRs. Therefore, the aim of this study was to evaluate the mRNA profiles of <it>TLR1 to 9 </it>in colonic mucosa of UC patients, according to disease activity.</p> <p>Methods</p> <p>Colonic biopsies were taken from colon during colonoscopy in 51 patients with Ulcerative Colitis and 36 healthy controls. mRNA levels of <it>TLR1 to 9, Tollip</it>, inflammatory cytokines <it>IL6 </it>and <it>TNF </it>were assessed by RT-qPCR with hydrolysis probes. Characterization of <it>TLR9 </it>protein expression was performed by Immunohistochemistry.</p> <p>Results</p> <p>Toll-like receptors <it>TLR8, TLR9</it>, and <it>IL6 </it>mRNA levels were significantly higher in the colonic mucosa from UC patients (both quiescent and active) as compared to healthy individuals (p < 0.04). In the UC patients group the <it>TLR2, TLR4, TLR8 </it>and <it>TLR9 </it>mRNA levels were found to be significantly lower in patients with quiescent disease, as compared to those with active disease (p < 0.05), whereas <it>TLR5 </it>showed a trend (p = 0.06). <it>IL6 </it>and <it>TNF </it>mRNA levels were significantly higher in the presence of active disease and help to discriminate between quiescent and active disease (p < 0.05). Also, <it>IL6 </it>and <it>TNF </it>mRNA positively correlate with TLRs mRNA with the exception for <it>TLR3</it>, with stronger correlations for <it>TLR5, TLR8</it>, and <it>TLR9 </it>(p < 0.0001). <it>TLR9 </it>protein expression was mainly in the lamina propria infiltrate.</p> <p>Conclusions</p> <p>This study demonstrates that <it>TLR2, TLR4, TLR8</it>, and <it>TLR9 </it>expression increases in active UC patients, and that the mRNA levels positively correlate with the severity of intestinal inflammation as well as with inflammatory cytokines.</p

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM) Large Marine Ecosystem (LME) (“Atlantis-GOM”). This methodology consists of: (1) compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S.) GOM since 2000; (2) compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3) fitting binomial generalized additive models (GAMs) to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs) to the large monitoring database; and (4) employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by the data from the large monitoring database; for those, only GAM predictions were employed to obtain distribution maps for Atlantis-GOM. Pearson residuals were computed to validate geostatistical binomial GLMMs. Ultimately, 53 annual maps and 64 seasonal maps (for 32 different functional groups/life stages) were produced for Atlantis-GOM. Our methodology could serve other world\u27s regions characterized by a large surface area, particularly LMEs bordered by several countries

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM) Large Marine Ecosystem (LME) (“Atlantis-GOM”). This methodology consists of: (1) compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S.) GOM since 2000; (2) compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3) fitting binomial generalized additive models (GAMs) to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs) to the large monitoring database; and (4) employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by the data from the large monitoring database; for those, only GAM predictions were employed to obtain distribution maps for Atlantis-GOM. Pearson residuals were computed to validate geostatistical binomial GLMMs. Ultimately, 53 annual maps and 64 seasonal maps (for 32 different functional groups/life stages) were produced for Atlantis-GOM. Our methodology could serve other world\u27s regions characterized by a large surface area, particularly LMEs bordered by several countries

Representing Species Distributions in Spatially-explicit Ecosystem Models from Presence-only Data

Spatially-explicit ecosystem models are increasingly considered for informing fisheries management. The inputs of these models that determine species distributions in the modeled system are critical. There is a need for methods to estimate species distributions in spatially-explicit ecosystem models from presence-only data. To address this need, we used a method relying on binomial generalized additive models integrating environmental covariates. This method allows for the production of distribution maps for ecosystem models such as Atlantis models, and of preference functions for Ecospace models; preference functions define the preferences of species groups for certain environmental parameter values and are employed by Ecospace to allocate species group biomasses spatially. The key step of the method we used is the objective generation of pseudo-absences for each month, by sampling with replacement the centroids of the cells of fine-scale spatial grids defined for each month. To demonstrate the method, we applied it to the diving bird and surface-feeding bird groups represented in the Atlantis model for the Gulf of Mexico (GOM), and to the seabird group represented in the Ecospace model for the West Florida Shelf. We also employed the distribution maps we constructed to provide a basis for a hypothetical marine protected area (MPA) planning scenario aiming to secure food for seabirds. Specifically, we produced a hotspot map for seabirds for the U.S. GOM from the distribution map of the species group, and then combined the hotspot map for seabirds with a hotspot map for their main prey, forage fish (Clupeidae and Exocoetidae), to determine where the hotspots of seabirds and forage fish overlap. This analysis suggested that, to secure forage fish for seabirds in the U.S. GOM, hypothetical MPAs should be implemented primarily within the coastal regions of the Louisiana-Texas shelf, within the coastal region of West Florida located between Sarasota and Naples and/or within Apalachee Bay, Florida

Improving the Spatial Allocation of Marine Mammal and Sea Turtle Biomasses in Spatially Explicit Ecosystem Models

Ecosystem-based fisheries management (EBFM) is gaining traction worldwide, including in the Gulf of Mexico (GOM). Ecosystem models, such as applications of the Atlantis and Ecospace modeling approaches, are key tools for assisting EBFM. Patterns of spatial overlap between exploited fish species, other species of concern such as marine mammals and sea turtles, and human activities can have a large influence on the predictions made by ecosystem models, but these patterns are usually not well defined. We developed methods for producing distribution maps for the cetacean, sirenian, and sea turtle groups represented in the Atlantis model of the GOM, and employed a method, initially designed for fish and invertebrates, for generating preference functions for the dolphin species represented in the Ecospace model of the West Florida Shelf. Preference functions specify the preferences of species for certain environmental conditions and are used by Ecospace to allocate species biomasses in space. We also took advantage of our mapping outputs to estimate the percentage of spatial overlap between the hotspots of cetaceans and sea turtles in the US GOM and their areas of bycatch in the US pelagic longline fishery. The present study provides new insights into the spatial distribution patterns of marine mammals and sea turtles in the GOM large marine ecosystem, including the first quantitatively supported maps of Florida manatee (sirenian) distribution along the entire US GOM coast. Efforts such as ours should be continued for improving the reliability of ecosystem models and, thereby, advancing EBFM worldwide