Anopheles gambiae genome reannotation through synthesis of ab initio and comparative gene prediction algorithms

BACKGROUND: Complete genome annotation is a necessary tool as Anopheles gambiae researchers probe the biology of this potent malaria vector. RESULTS: We reannotate the A. gambiae genome by synthesizing comparative and ab initio sets of predicted coding sequences (CDSs) into a single set using an exon-gene-union algorithm followed by an open-reading-frame-selection algorithm. The reannotation predicts 20,970 CDSs supported by at least two lines of evidence, and it lowers the proportion of CDSs lacking start and/or stop codons to only approximately 4%. The reannotated CDS set includes a set of 4,681 novel CDSs not represented in the Ensembl annotation but with EST support, and another set of 4,031 Ensembl-supported genes that undergo major structural and, therefore, probably functional changes in the reannotated set. The quality and accuracy of the reannotation was assessed by comparison with end sequences from 20,249 full-length cDNA clones, and evaluation of mass spectrometry peptide hit rates from an A. gambiae shotgun proteomic dataset confirms that the reannotated CDSs offer a high quality protein database for proteomics. We provide a functional proteomics annotation, ReAnoXcel, obtained by analysis of the new CDSs through the AnoXcel pipeline, which allows functional comparisons of the CDS sets within the same bioinformatic platform. CDS data are available for download. CONCLUSION: Comprehensive A. gambiae genome reannotation is achieved through a combination of comparative and ab initio gene prediction algorithms

Fine Pathogen Discrimination within the APL1 Gene Family Protects Anopheles gambiae against Human and Rodent Malaria Species

Genetically controlled resistance of Anopheles gambiae mosquitoes to Plasmodium falciparum is a common trait in the natural population, and a cluster of natural resistance loci were mapped to the Plasmodium-Resistance Island (PRI) of the A. gambiae genome. The APL1 family of leucine-rich repeat (LRR) proteins was highlighted by candidate gene studies in the PRI, and is comprised of paralogs APL1A, APL1B and APL1C that share ≥50% amino acid identity. Here, we present a functional analysis of the joint response of APL1 family members during mosquito infection with human and rodent Plasmodium species. Only paralog APL1A protected A. gambiae against infection with the human malaria parasite P. falciparum from both the field population and in vitro culture. In contrast, only paralog APL1C protected against the rodent malaria parasites P. berghei and P. yoelii. We show that anti-P. falciparum protection is mediated by the Imd/Rel2 pathway, while protection against P. berghei infection was shown to require Toll/Rel1 signaling. Further, only the short Rel2-S isoform and not the long Rel2-F isoform of Rel2 confers protection against P. falciparum. Protection correlates with the transcriptional regulation of APL1A by Rel2-S but not Rel2-F, suggesting that the Rel2-S anti-parasite phenotype results at least in part from its transcriptional control over APL1A. These results indicate that distinct members of the APL1 gene family display a mutually exclusive protective effect against different classes of Plasmodium parasites. It appears that a gene-for-pathogen-class system orients the appropriate host defenses against distinct categories of similar pathogens. It is known that insect innate immune pathways can distinguish between grossly different microbes such as Gram-positive bacteria, Gram-negative bacteria, or fungi, but the function of the APL1 paralogs reveals that mosquito innate immunity possesses a more fine-grained capacity to distinguish between classes of closely related eukaryotic pathogens than has been previously recognized

A major genetic locus controlling natural Plasmodium falciparum infection is shared by East and West African Anopheles gambiae

Background: Genetic linkage mapping identified a region of chromosome 2L in the Anopheles gambiae genome that exerts major control over natural infection by Plasmodium falciparum. This 2L Plasmodium-resistance interval was mapped in mosquitoes from a natural population in Mali, West Africa, and controls the numbers of P. falciparum oocysts that develop on the vector midgut. An important question is whether genetic variation with respect to Plasmodium-resistance exists across Africa, and if so whether the same or multiple geographically distinct resistance mechanisms are responsible for the trait. Methods: To identify P falciparum resistance loci in pedigrees generated and infected in Kenya, East Africa, 28 microsatellite loci were typed across the mosquito genome. Genetic linkage mapping was used to detect significant linkage between genotype and numbers of midgut oocysts surviving to 7–8 days post-infection. Results: A major malaria-control locus was identified on chromosome 2L in East African mosquitoes, in the same apparent position originally identified from the West African population. Presence of this resistance locus explains 75% of parasite free mosquitoes. The Kenyan resistance locus is named EA_Pfin1 (East Africa_ Plasmodium falciparum Infection Intensity). Conclusion: Detection of a malaria-control locus at the same chromosomal location in both East and West African mosquitoes indicates that, to the level of genetic resolution of the analysis, the same mechanism of Plasmodium-resistance, or a mechanism controlled by the same genomic region, is found across Africa, and thus probably operates in A. gambiae throughout its entire range

Exceptional Diversity, Maintenance of Polymorphism, and Recent Directional Selection on the APL1 Malaria Resistance Genes of Anopheles gambiae

The three-gene APL1 locus encodes essential components of the mosquito immune defense against malaria parasites. APL1 was originally identified because it lies within a mapped QTL conferring the vector mosquito Anopheles gambiae natural resistance to the human malaria parasite, Plasmodium falciparum, and APL1 genes have subsequently been shown to be involved in defense against several species of Plasmodium. Here, we examine molecular population genetic variation at the APL1 gene cluster in spatially and temporally diverse West African collections of A. gambiae. The locus is extremely polymorphic, showing evidence of adaptive evolutionary maintenance of genetic variation. We hypothesize that this variability aids in defense against genetically diverse pathogens, including Plasmodium. Variation at APL1 is highly structured across geographic and temporal subpopulations. In particular, diversity is exceptionally high during the rainy season, when malaria transmission rates are at their peak. Much less allelic diversity is observed during the dry season when mosquito population sizes and malaria transmission rates are low. APL1 diversity is weakly stratified by the polymorphic 2La chromosomal inversion but is very strongly subdivided between the M and S “molecular forms.” We find evidence that a recent selective sweep has occurred at the APL1 locus in M form mosquitoes only. The independently reported observation of a similar M-form restricted sweep at the Tep1 locus, whose product physically interacts with APL1C, suggests that epistatic selection may act on these two loci causing them to sweep coordinately

Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii

Mosquito control remains a central pillar of efforts to reduce malaria burden in sub-Saharan Africa. However, insecticide resistance is entrenched in malaria vector populations, and countries with a high malaria burden face a daunting challenge to sustain malaria control with a limited set of surveillance and intervention tools. Here we report on the second phase of a project to build an open resource of high-quality data on genome variation among natural populations of the major African malaria vector species Anopheles gambiae and Anopheles coluzzii. We analyzed whole genomes of 1142 individual mosquitoes sampled from the wild in 13 African countries, as well as a further 234 individuals comprising parents and progeny of 11 laboratory crosses. The data resource includes high-confidence single-nucleotide polymorphism (SNP) calls at 57 million variable sites, genome-wide copy number variation (CNV) calls, and haplotypes phased at biallelic SNPs. We use these data to analyze genetic population structure and characterize genetic diversity within and between populations. We illustrate the utility of these data by investigating species differences in isolation by distance, genetic variation within proposed gene drive target sequences, and patterns of resistance to pyrethroid insecticides. This data resource provides a foundation for developing new operational systems for molecular surveillance and for accelerating research and development of new vector control tools. It also provides a unique resource for the study of population genomics and evolutionary biology in eukaryotic species with high levels of genetic diversity under strong anthropogenic evolutionary pressures

Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus

Vector population control using insecticides is a key element of current strategies to prevent malaria transmission in Africa. The introduction of effective insecticides, such as the organophosphate pirimiphos-methyl, is essential to overcome the recurrent emergence of resistance driven by the highly diverse Anopheles genomes. Here, we use a population genomic approach to investigate the basis of pirimiphos-methyl resistance in the major malaria vectors Anopheles gambiae and A. coluzzii. A combination of copy number variation and a single non-synonymous substitution in the acetylcholinesterase gene, Ace1, provides the key resistance diagnostic in an A. coluzzii population from Coˆte d’Ivoire that we used for sequence-based association mapping, with replication in other West African populations. The Ace1 substitution and duplications occur on a unique resistance haplotype that evolved in A. gambiae and introgressed into A. coluzzii, and is now common in West Africa primarily due to selection imposed by other organophosphate or carbamate insecticides. Our findings highlight the predictive value of this complex resistance haplotype for phenotypic resistance and clarify its evolutionary history, providing tools to for molecular surveillance of the current and future effectiveness of pirimiphos-methyl based interventions

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University