Biology subjects

Forster, A. J.

Publications and source records attributed to Forster, A. J..

4 recordsLinked to original sources

bioRxiv2026

COSMOS: integrated vector-pathogen metagenomics for mosquito-borne disease

By 2050, almost half of the global population will be at risk of mosquito-borne disease. Effective response requires monitoring both pathogen transmission and vector abundance, yet these are typically assessed using separate assays with limited taxonomic breadth. Here we present COSMOS (COmprehensive Sequencing for MOSquito-borne disease), a targeted metagenomics workflow designed to reconstruct pathogen genomes and mosquito community composition directly from mixed field-collected samples. Over three annual rounds of a major Australian surveillance program, we evaluated >120,000 mosquitoes in 219 traps, with 98.7% agreement with surveillance data across seven monitored arboviruses, and additional detections of Sindbis virus and the state's first detection of encephalitic Little Sussex virus. Of 137 viral detections, 101 yielded near-complete genomes (>90% coverage) at median 95x depth. Using an accompanying computational workflow, COSMOS quantified mosquito composition, matching entomological estimates of reported vector species (CCC 0.78-0.89). Joint analysis of virus and vector composition identified structured mosquito communities associated with spatial and temporal environmental variation, allowing the ecological context of arbovirus detections to be characterised. Detections during the Murray Valley encephalitis virus outbreak in 2023 were concentrated within a narrowly defined mosquito-community space corresponding to rural communities dominated by the inland S-AUS sub-lineage of Culex annulirostris. Integrated vector-pathogen metagenomics provides ecological and evolutionary context for arbovirus activity within a single scalable workflow for surveillance and outbreak response.

microbiology↗

bioRxiv2026

Differential sensitivity of k-mer based identification tools using closely related database sequences affects relative abundance estimation

Deep metagenomic sequencing is increasingly used for lineage profiling of complex biological mixtures, including applications such as wastewater virus surveillance and microbiome profiling. For profiling, sequence read sets are compared against a database of reference sequences to identify component genetic lineages, and estimate their relative abundance. However, there is currently a lack of clarity regarding the impact of two ubiquitous features of biological mixtures on compositional profiling: (1) population diversity, where reads contain genetic diversity not represented in the reference set, and (2) the presence of high sequence similarity between references, which causes ambiguity in read assignment. Here we show, using both simulated and real data, that performance of two commonly used tools for lineage profiling, KMA and Kallisto, is differently affected by sample-reference distance and the presence of highly similar competing reference sequences. Increasing sample-reference distance reduced read assignment for both tools, but more strongly for Kallisto, which led to directional bias in abundance estimates when distance differed between mixture components. In contrast, when sample-reference distance was low, Kallisto more specifically discriminated between highly similar competing references, whereas KMA was less able to partition reads accurately between them. This specificity-sensitivity trade-off was reflected in real datasets: KMA produced more accurate abundance estimates for mosquito COX1 mixtures, whereas Kallisto performed better for mixtures of closely related SARS-CoV-2 lineages, although neither tool fully resolved the most similar SARS-CoV-2 variants. Tool choice is therefore dependent on the degree of expected sample-reference distance and inter-reference distance within datasets.

bioinformatics↗

bioRxiv2025

Geographical variation drives adaptive equilibrium of the P. falciparum sickle-associated mutations

The recent discovery of genetic mutations in Plasmodium falciparum--the most lethal malaria parasite--that enable it to overcome the protective effects of sickle cell trait, raises fundamental questions about the underlying biological and evolutionary interactions. Here we develop a geostatistical model to compare sickle haemoglobin genotype frequencies to the Plasmodium falciparum sickle-associated alleles across global populations, and find a robust association at multiple geographical scales, implying that sickle drives positive selection for these parasite mutations. A model of parasite evolution and an analysis of local haplotype patterns suggest that key features of these mutations - that they are polymorphic in all African populations and are mutually correlated despite lying in different genome regions - are caused by geographical variation in selection pressure, and that the alleles may have been maintained by balancing selection over timescales comparable to the age of the sickle mutation itself. The predicted impact of this host-parasite interaction on disease outcomes varies widely across populations, and functional data are needed to discover the biological mechanisms involved.

genetics↗

bioRxiv2021

Ultra-conserved sequences in the genomes of highly diverse Anopheles mosquitoes, with implications for malaria vector control.

DNA sequences that are exactly conserved over long evolutionary time scales have been observed in a variety of taxa. Such sequences are likely under strong functional constraint and they have been useful in the field of comparative genomics for identifying genome regions with regulatory function. A potential new application for these ultra-conserved elements has emerged in the development of gene drives to control mosquito populations. Many gene drives work by recognising and inserting at a specific target sequence in the genome, often imposing a reproductive load as a consequence. They can therefore select for target sequence variants that provide resistance to the drive. Focusing on highly conserved, highly constrained sequences lowers the probability that variant, gene drive-resistant alleles can be tolerated. Here we search for conserved sequences of 18bp and over in an alignment of 21 Anopheles genomes, spanning an evolutionary timescale of 100 million years, and characterise the resulting sequences according to their location and function. Over 8000 ultra-conserved elements were found across the alignment, with a maximum length of 164 bp. Length-corrected gene ontology analysis revealed that genes containing Anopheles ultra-conserved elements were over-represented in categories with structural or nucleotide binding functions. Known insect transcription factor binding sites were found in 48% of intergenic Anopheles ultra-conserved elements. When we looked at the genome sequences of 1142 wild-caught mosquitoes we found that 15% of the Anopheles ultra-conserved elements contained no polymorphisms. Our list of Anopheles ultra-conserved elements should provide a valuable starting point for the selection and testing of new targets for gene-drive modification in the mosquitoes that transmit malaria.

genomics↗