Comparative analysis of midgut bacterial communities in Chikungunya virus-infected and non-infected Aedes aegypti Thai laboratory strain mosquitoes.

Chikungunya virus (CHIKV) poses a significant global health threat, re-emerging as a mosquito-transmitted pathogen that caused high fever, rash, and severe arthralgia. In Thailand, a notable CHIKV outbreak in 2019-2020 affected approximately 20,000 cases across 60 provinces, underscoring the need for effective mosquito control protocols. Previous studies have highlighted the role of midgut bacteria in the interaction between mosquito vectors and pathogen infections, demonstrating their ability to protect the insect from invading pathogens. However, research on the midgut bacteria of Aedes (Ae.) aegypti, the primary vector for CHIKV in Thailand remains limited. This study aims to characterize the bacterial communities in laboratory strains of Ae. aegypti, both infected and non-infected with CHIKV. Female mosquitoes from a laboratory strain of Ae. aegypti were exposed to a CHIKV-infected blood meal through membrane feeding, while the control group received a non-infected blood meal. At 7 days post-infection (dpi), mosquito midguts were dissected for 16S rRNA gene sequencing to identify midgut bacteria, and CHIKV presence was confirmed by E1-nested RT-PCR using mosquito carcasses. The study aimed to compare the bacterial communities between CHIKV-infected and non-infected groups. The analysis included 12 midgut bacterial samples, divided into three groups: CHIKV-infected (exposed and infected), non-infected (exposed but not infected), and non-exposed (negative control). Alpha diversity indices and Bray-Curtis dissimilarity matrix revealed significant differences in bacterial profiles among the three groups. The infected group exhibited an increased abundance of bacteria genus Gluconobacter, while Asaia was prevalent in both non-infected and negative control groups. Chryseobacterium was prominent in the negative control group. These findings highlight potential alterations in the distribution and abundance of gut microbiomes in response to CHIKV infection status. This study provides valuable insights into the dynamic relationship between midgut bacteria and CHIKV, underscoring the potential for alterations in bacterial composition depending on infection status. Understanding the relationships between mosquitoes and their microbiota holds promise for developing new methods and tools to enhance existing strategies for disease prevention and control. This research advances our understanding of the circulating bacterial composition, opening possibilities for new approaches in combating mosquito-borne diseases.

Nanopore-Based Surveillance of Leishmania Parasites in Culicoides Latrielle (Diptera: Ceratopogonidae) Caught from the Affected Community and Tham Phra Cave in Chiang Rai Province, the Endemic Area of Leishmaniasis in Northern Thailand.

In this research, we elucidated the species composition of Culicoides biting midges, infection prevalence, and genetic diversity of Leishmania parasites circulating in the affected community in Chiang Rai Province, being the most endemic area in Northern Thailand. A total of 146 parous and gravid females, belonging to at least twelve Culicoides species in five subgenera and one species group, were trapped from three collection sites with an overall Leishmania prevalence of 26.7% (39/146). Leishmania was detected, using ITS1-PCR, in C. mahasarakamense (15), C. guttifer (11), C. (Trithecoides) spp. (8), C. jacobsoni (2), C. oxystoma (2), and C. orientalis (1). The evidence of Leishmania infection in these last five species represents new records in Northern Thailand. Given a high infection rate in cavernicolous specimens, this indicates an increased risk of parasite exposure when visiting the cave. Using the nanopore amplicon sequencing, L. martiniquensis was ubiquitously identified in all positives, and more than half of these were also co-infected with L. orientalis. The genetic diversity analysis revealed 13 and 17 unique haplotypes for L. martiniquensis and L. orientalis, respectively. Higher haplotype diversity and relatively low nucleotide diversity were observed in both parasite populations, suggesting recent population divergence. Neutrality tests (Tajima's D and Fu and Li's D) showed to be significantly negative, indicating rapid population growth or a selective sweep. Moreover, dominant haplotypes of both Leishmania species were 100% identical to those in all leishmaniasis patients previously reported from Northern Thailand, strongly supporting the imperative role of Culicoides spp. in disease transmission. Essentially, this research provides the first entomological surveillance data representing the sympatric existence, transmission dynamics, and genetic complexity of two autochthonous Leishmania (Mundinia) parasites in several Culicoides species in the endemic area of Northern Thailand. This would contribute to a more complete understanding of the epidemiology of vector infection and facilitate the development of vector control programs to effectively reduce the transmission of this neglected tropical disease in endemic areas of Northern Thailand.

Natural infection with Leishmania (Mundinia) martiniquensis supports Culicoides peregrinus (Diptera: Ceratopogonidae) as a potential vector of leishmaniasis and characterization of a Crithidia sp. isolated from the midges.

The prevalence of autochthonous leishmaniasis in Thailand is increasing but the natural vectors that are responsible for transmission remain unknown. Experimental in vivo infections in Culicoides spp. with Leishmania (Mundinia) martiniquensis and Leishmania (Mundinia) orientalis, the major causative pathogens in Thailand, have demonstrated that biting midges can act as competent vectors. Therefore, the isolation and detection of Leishmania and other trypanosomatids were performed in biting midges collected at a field site in an endemic area of leishmaniasis in Tha Ruea and a mixed farm of chickens, goats, and cattle in Khuan Phang, Nakhon Si Thammarat province, southern Thailand. Results showed that Culicoides peregrinus was the abundant species (>84%) found in both locations and only cow blood DNA was detected in engorged females. Microscopic examination revealed various forms of Leishmania promastigotes in the foregut of several C. peregrinus in the absence of bloodmeal remnants, indicating established infections. Molecular identification using ITS1 and 3'UTR HSP70 type I markers showed that the Leishmania parasites found in the midges were L. martiniquensis. The infection rate of L. martiniquensis in the collected flies was 2% in Tha Ruea and 6% in Khuan Phang, but no L. orientalis DNA or parasites were found. Additionally, organisms from two different clades of Crithidia, both possibly new species, were identified using SSU rRNA and gGAPDH genes. Choanomastigotes and promastigotes of both Crithidia spp. were observed in the hindgut of the dissected C. peregrinus. Interestingly, midges infected with both L. martiniquensis and Crithidia were found. Moreover, four strains of Crithidia from one of the clades were successfully isolated into culture. These parasites could grow at 37°C in the culture and infect BALB/c mice macrophages but no multiplication was observed, suggesting they are thermotolerant monoxenous trypanosomatids similar to Cr. thermophila. These findings provide the first evidence of natural infection of L. martiniquensis in C. peregrinus supporting it as a potential vector of L. martiniquensis.

Enhancement of VGG16 model with multi-view and spatial dropout for classification of mosquito vectors.

Mosquitoes transmit pathogens that can cause numerous significant infectious diseases in humans and animals such as malaria, dengue fever, chikungunya fever, and encephalitis. Although the VGG16 model is not one of the most advanced CNN networks, it is reported that a fine-tuned VGG16 model achieves accuracy over 90% when applied to the classification of mosquitoes. The present study sets out to improve the accuracy and robustness of the VGG16 network by incorporating spatial dropout layers to regularize the network and by modifying its structure to incorporate multi-view inputs. Herein, four models are implemented: (A) early-combined, (B) middle-combined, (C) late-combined, and (D) ensemble model. Moreover, a structure for combining Models (A), (B), (C), and (D), known as the classifier, is developed. Two image datasets, including a reference dataset of mosquitoes in South Korea and a newly generated dataset of mosquitoes in Thailand, are used to evaluate our models. Regards the reference dataset, the average accuracy of ten runs improved from 83.26% to 99.77%, while the standard deviation decreased from 2.60% to 0.12%. When tested on the new dataset, the classifier's accuracy was also over 99% with a standard deviation of less than 2%. This indicates that the algorithm achieves high accuracy with low variation and is independent of a particular dataset. To evaluate the robustness of the classifier, it was applied to a small dataset consisting of mosquito images captured under various conditions. Its accuracy dropped to 86.14%, but after retraining with the small dataset, it regained its previous level of precision. This demonstrates that the classifier is resilient to variation in the dataset and can be retrained to adapt to the variation. The classifier and the new mosquito dataset could be utilized to develop an application for efficient and rapid entomological surveillance for the prevention and control of mosquito-borne diseases.