Before my appointment by Imperial College London in June 2005, I was at the European Molecular Biology Laboratory (EMBL), in Heidelberg, Germany. There, I worked in the laboratory of Prof. Fotis C. Kafatos and I was centrally involved in genomic and post genomic research of the malaria mosquito Anopheles gambiae: sequencing of the A. gambiae genome and its comparative analysis with the genome of Drosophila melanogaster, specifically focusing on the comparative genomics of the innate immune system. I was also involved in the establishment of the DNA microarray technology at EMBL, and by using this technology I developed together with my colleague Dr George Dimopoulos the first DNA microarray for A. gambiae. This microarray was used as a pathfinder to explore the mosquito immune system and detect mechanisms of refractoriness to malaria infection. In December 2003, I was elected a Staff Scientist, the first level of the EMBL research faculty, by the EMBL Senior Scientist Committee. In this new role, and in affiliation with the laboratory of Prof Kafatos, I led a research team that focused on the development and exploitation of functional genomic tools towards better understanding of the immune system of A. gambiae and its role in malaria infection.
The first functional genomic tool that we developed was a new EST microarray platform (MMC1) comprising 20,000 elements [1]. At that time, MMC1 was the most advanced A. gambiae microarray platform encompassing approximately 9,000 unique genomic sequences. It was used by us and in collaboration with members of the Kafatos laboratory and external collaborators to study various aspects of the Anopheles biology, such as developmental and spatial gene expression, immune transcriptional networks, mosquito midgut responses to Plasmodium invasion, mosquito responses to viral infections and insecticide resistance. Since many MMC1 ESTs did not correspond to predicted genes in the A. gambiae genome, we provide functional bioinformatics annotation to these ESTs and show that at least 3,000 EST contigs likely correspond to currently non-predicted genes. One of the key discoveries deriving from our transcriptomic studies was LRIM1, a gene with no homologues in other organisms, which is robustly upregulated during bacterial and malaria infections. It turned out that LRIM1 protein is a very important Plasmodium parasite antagonist, implicated in killing and clearance of approximately 80% of Plasmodium berghei ookinetes during invasion of the mosquito midgut. It also targets for melanization the remaining 20% of the parasites; however, two other parasite agonists, CTL4 and CTLMA2, inhibit this reaction [2]. This study attracted great attention and was one of the most influential in the mosquito immunity field. LRIM1 is also central in bacterial phagocytic pathways.
Another area of focus of my group is the recognition of danger signals and signalling through NF-kappaB proteins in A. gambiae. We detected significant differences between mosquitoes and flies of the mechanisms of immune responses to bacterial infections [3]. Both Gram-positive and negative bacteria are dealt with by the mosquito Imd pathway, which is also responsible for killing many parasites during a malaria infection. The pathway controls the expression of antimicrobial peptides and other immune related proteins, including LRIM1. Recent data show that signalling for bacterial and malaria infections are mediated by the pattern recognition receptor PGRPLC.
Recently, we designed a new multifunctional genomic platform, MMC2, from approximately 13,000 amplicons corresponding to equal number of unique A. gambiae genes. MCC2 could be used to (a) construct an almost full mosquito transcriptome microarray, (b) to synthesize double stranded RNA (dsRNA) towards silencing of corresponding transcripts and (c) to produce protein tags to raise antibodies against all mosquito proteins. Microarrays of MMC2 amplicons are now used as a standard operational platform in various transcriptomic studies. Its use in conjunction with MMC1 comprising additional non-predicted genes represents one of the largest collections of unique A. gambiae sequences on chip.