Functional genomics of a symbiotic community : shared traits in the olive fruit fly gut microbiota

The olive fruit fly Bactrocera oleae is a major pest of olives worldwide and houses a specialized gut microbiota dominated by the obligate symbiont “Candidatus Erwinia dacicola”. Ca. E. dacicola is thought to supplement dietary nitrogen to the host, with only indirect evidence for this hypothesis so far. Here, we sought to investigate the contribution of the symbiosis to insect fitness and explore the ecology of the insect gut. For this purpose, we examined the composition of bacterial communities associated with Cretan olive fruit fly populations, and inspected several genomes and one transcriptome assembly. We identified, and reconstructed the genome of, a novel component of the gut microbiota, Tatumella sp. TA1, which is stably associated with Mediterranean olive fruit fly populations. We also reconstructed a number of pathways related to nitrogen assimilation and interactions with the host. The results show that, despite variation in taxa composition of the gut microbial community, core functions related to the symbiosis are maintained. Functional redundancy between different microbial taxa was observed for genes involved in urea hydrolysis. The latter is encoded in the obligate symbiont genome by a conserved urease operon, likely acquired by horizontal gene transfer, based on phylogenetic evidence. A potential underlying mechanism is the action of mobile elements, especially abundant in the Ca. E. dacicola genome. This finding, along with the identification, in the studied genomes, of extracellular surface structure components that may mediate interactions within the gut community, suggest that ongoing and past genetic exchanges between microbes may have shaped the symbiosis

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Monitoring urban sealing from space: The application of remote sensing to identify and measure changes in the area of soil prevented from carrying out functions by sealing

Overview Urban development presents the greatest driver of soil loss due to sealing-over by buildings, pavement and transport infrastructure. To this end, soil sealing is recognised as one of the major threats to soil. The ability to monitor the rates, types and geo-spatial distribution of soil sealing is crucial to understanding the severity of pressure on soils and their impact on European and global socio-economic and environmental systems. The overall objective of this work was to test the feasibility of using space-derived information to support the Defra Soils Team (ST) in monitoring the extent and pattern of soil sealing. The rate and nature of sealing should be routinely measured in order for it to be managed to best effect. Monitoring soil sealing is intended to be a part of a national soil monitoring scheme and to inform policy creation. This report identifies appropriate Earth Observation (EO) technology and processing procedures to deliver a range of baseline and monitoring information, and assesses the practical scope for the routine use of EO information to support the delivery of the required tasks of the Defra ST1 . The project was funded under the British National Space Centre’s GIFTSS2 programme with support from Defra.Technical report of GIFTSS project BNSC/ITT/54, Defra code SP054

Enhanced activity of NLRP3 inflammasome in peripheral blood cells of patients with active rheumatoid arthritis

Introduction: Interleukin-1β (IL-1β) is a major inflammatory cytokine, produced predominantly by innate immune cells through NLRP3-inflammasome activation. Both intrinsic and extrinsic danger signals may activate NLRP3. Genetic variations in NLRP3-inflammasome components have been reported to influence rheumatoid arthritis (RA) susceptibility and severity. We sought to assess the activity of NLRP3-inflammasome in patients with active RA compared to healthy individuals. Method: Intracellular protein expression of NLRP3, ASC, pro- and active caspase-1, pro- and active IL-1β was assessed by immunoblotting both at baseline and upon inflammasome activation. NLRP3 function (IL-1β secretion) was assessed upon priming of TLR2 (Pam(3)CysSK(4), TLR3 (poly(I:C)) or TLR4 (LPS) and ATP sequential treatment. We used caspase inhibitors (casp-1, 3/7 and 8) to assess their contribution to IL-1β maturation. All experiments were performed in whole blood cells. Results: Active RA patients (n = 11) expressed higher basal intracellular levels of NLRP3 (p < 0.008), ASC (p < 0.003), active caspase-1 (p < 0.02) and pro-IL-1β (p < 0.001). Upon priming with TLR4 (LPS) and ATP, RA-derived cell extracts (n = 7) displayed increased expression of NLRP3 (p < 0.01) and active caspase-1 (p < 0.001). Secreted IL-1β in culture supernatants from whole blood cells activated with TLR4 (LPS) or TLR3 agonist (poly(I:C)) plus ATP was higher in RA patients (n = 20) versus controls (n = 18) (p < 0.02 for both). Caspase-1 inhibition significantly reduced IL-1β secretion induced by all stimuli, whereas caspase-8 inhibition affected only TLR4 and TLR3 cell priming. Conclusion: Patients with active RA have increased expression of NLRP3 and NLRP3-mediated IL-1β secretion in whole blood cells upon stimulation via TLR3 and TLR4 but not TLR2. In these patients, IL-1β secretion seems to be predominately driven by caspase-1 and caspase-8. Targeting NLRP3 or downstream caspases may be of benefit in suppressing IL-1β production in RA. © 2015 Choulaki et al