Influence of Host Blood Meal Source on Gut Microbiota of Wild Caught Aedes aegypti, a Dominant Arboviral Disease Vector.

Blood feeding is an important behavior of Aedes aegypti, a dominant arboviral disease vector, as it can establish and transmit viruses to humans. Bacteria associated with the mosquito gut can modulate the biological characteristics and behavior of disease vectors. In this study, we characterized the gut microbiota composition of human-blood-fed (HF), non-human-blood-fed (NHF) and non-fed (NF) field-collected Ae. aegypti mosquitoes, using a 16S metagenomic approach, to assess any association of bacterial taxa with the blood-feeding behavior of Ae. aegypti. A significant difference in the microbiota composition between the HF and NF mosquito group was observed. A significant association was observed in the relative abundance of families Rhodobacteraceae, Neisseriaceae and Dermacoccaceae in the HF group in contrast to NF and NHF Ae. aegypti mosquitoes, respectively. At the class level, two classes (Rhodobacterales and Neisseriales) were found to be in higher abundance in the HF mosquitoes compared to a single class of bacteria (Caulobacterales) in the NF mosquitoes. These results show that human-blood feeding may change the gut microbiota in wild Ae. aegypti populations. More research is needed to determine how changes in the midgut bacterial communities in response to human-blood-feeding affect the vectorial capacity of Ae. aegypti.

An assessment of remotely sensed environmental variables on Dengue epidemiology in Central India.

In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.