The susceptibility of Anopheles mosquitoes to Plasmodium infections relies on complex interactions between the insect vector and the malaria parasite. A number of studies have shown that the mosquito innate immune responses play an important role in controlling the malaria infection and that the strength of parasite clearance is under genetic control, but little is known about the influence of environmental factors on the transmission success. We present here evidence that the composition of the vector gut microbiota is one of the major components that determine the outcome of mosquito infections. A. gambiae mosquitoes collected in natural breeding sites from Cameroon were experimentally challenged with a wild P. falciparum isolate, and their gut bacterial content was submitted for pyrosequencing analysis. The meta-taxogenomic approach revealed a broader richness of the midgut bacterial flora than previously described. Unexpectedly, the majority of bacterial species were found in only a small proportion of mosquitoes, and only 20 genera were shared by 80% of individuals. We show that observed differences in gut bacterial flora of adult mosquitoes is a result of breeding in distinct sites, suggesting that the native aquatic source where larvae were grown determines the composition of the midgut microbiota. Importantly, the abundance of Enterobacteriaceae in the mosquito midgut correlates significantly with the Plasmodium infection status. This striking relationship highlights the role of natural gut environment in parasite transmission. Deciphering microbe-pathogen interactions offers new perspectives to control disease transmission.

BackgroundThe deep sea floor is considered one of the most diverse ecosystems on Earth. Recent environmental DNA surveys based on clone libraries of rRNA genes confirm this observation and reveal a high diversity of eukaryotes present in deep-sea sediment samples. However, environmental clone-library surveys yield only a modest number of sequences with which to evaluate the diversity of abyssal eukaryotes.Methodology/Principal FindingsHere, we examined the richness of eukaryotic DNA in deep Arctic and Southern Ocean samples using massively parallel sequencing of the 18S ribosomal RNA (rRNA) V9 hypervariable region. In very small volumes of sediments, ranging from 0.35 to 0.7 g, we recovered up to 7,499 unique sequences per sample. By clustering sequences having up to 3 differences, we observed from 942 to 1756 Operational Taxonomic Units (OTUs) per sample. Taxonomic analyses of these OTUs showed that DNA of all major groups of eukaryotes is represented at the deep-sea floor. The dinoflagellates, cercozoans, ciliates, and euglenozoans predominate, contributing to 17%, 16%, 10%, and 8% of all assigned OTUs, respectively. Interestingly, many sequences represent photosynthetic taxa or are similar to those reported from the environmental surveys of surface waters. Moreover, each sample contained from 31 to 71 different metazoan OTUs despite the small sample volume collected. This indicates that a significant faction of the eukaryotic DNA sequences likely do not belong to living organisms, but represent either free, extracellular DNA or remains and resting stages of planktonic species.Conclusions/SignificanceIn view of our study, the deep-sea floor appears as a global DNA repository, which preserves genetic information about organisms living in the sediment, as well as in the water column above it. This information can be used for future monitoring of past and present environmental changes.

Fungi constitute an important group in soil biological diversity and functioning. However, characterization and knowledge of fungal communities is hampered because few primer sets are available to quantify fungal abundance by real-time quantitative PCR (real-time Q-PCR). The aim in this study was to quantify fungal abundance in soils by incorporating, into a real-time Q-PCR using the SYBRGreen® method, a primer set already used to study the genetic structure of soil fungal communities. To satisfy the real-time Q-PCR requirements to enhance the accuracy and reproducibility of the detection technique, this study focused on the 18S rRNA gene conserved regions. These regions are little affected by length polymorphism and may provide sufficiently small targets, a crucial criterion for enhancing accuracy and reproducibility of the detection technique. An in silico analysis of 33 primer sets targeting the 18S rRNA gene was performed to select the primer set with the best potential for real-time Q-PCR: short amplicon length; good fungal specificity and coverage. The best consensus between specificity, coverage and amplicon length among the 33 sets tested was the primer set FR1 / FF390. This in silico analysis of the specificity of FR1 / FF390 also provided additional information to the previously published analysis on this primer set. The specificity of the primer set FR1 / FF390 for Fungi was validated in vitro by cloning - sequencing the amplicons obtained from a real time Q-PCR assay performed on five independent soil samples. This assay was also used to evaluate the sensitivity and reproducibility of the method. Finally, fungal abundance in samples from 24 soils with contrasting physico-chemical and environmental characteristics was examined and ranked to determine the importance of soil texture, organic carbon content, C∶N ratio and land use in determining fungal abundance in soils.

BackgroundIn crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs.ResultsThis paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators.ConclusionsP-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods.

BackgroundStudies on malaria vector ecology and development/evaluation of vector control strategies often require measures of mosquito life history traits. Assessing the fecundity of malaria vectors can be carried out by counting eggs laid by Anopheles females. However, manually counting the eggs is time consuming, tedious, and error prone.MethodsIn this paper we present a newly developed software for high precision automatic egg counting. The software written in the Java programming language proposes a user-friendly interface and a complete online manual. It allows the inspection of results by the operator and includes proper tools for manual corrections. The user can in fact correct any details on the acquired results by a mouse click. Time saving is significant and errors due to loss of concentration are avoided.ResultsThe software was tested over 16 randomly chosen images from 2 different experiments. The results show that the proposed automatic method produces results that are close to the ground truth.ConclusionsThe proposed approaches demonstrated a very high level of robustness. The adoption of the proposed software package will save many hours of labor to the bench scientist. The software needs no particular configuration and is freely available for download on: http://w3.ualg.pt/∼hshah/eggcounter/.