Biology subjects

Burt, A.

Publications and source records attributed to Burt, A..

At least 19 recordsLinked to original sources

bioRxiv2026

CryoSHIELDs: tunable amphipathic peptide additives for cryo-EM

While cryogenic electron microscopy (cryo-EM) has transformed structural biology, sample preparation remains a major determinant of success. Specifically, the air-water interface (AWI) can damage complexes, impose preferred orientation, and reduce particle density. Here, we introduce cryoSHIELDs, a chemically diverse library of peptide additives designed to passivate the AWI and improve cryo-EM grid preparation. Across 24 protein systems spanning GPCRs, ABC and SLC transporters, soluble and Fab-containing complexes, and enzymes, cryoSHIELDs increased the success rate of structure determination. Individual cryoSHIELDs improved particle density, angular sampling, complex stability, and, for routine targets, map quality and resolution. This toolkit enabled the structural characterization of important targets, including the ABCC6 transporter, revealing unique TMD0-mediated stabilization, and DAMGO-bound -opioid receptor-Gi complex using substantially lower protein concentration. CryoSHIELDs provide a simple, tunable, and broadly applicable platform for more reliable cryo-EM in discovery biology and structure-based drug development.

biophysics↗

bioRxiv2026

Accounting for pseudo-replication of Linkage Disequilibrium for contemporary Ne estimation

The Linkage Disequilibrium (LD) of unlinked loci can be used to estimate contemporary effective population size (Ne) of one to a few generations ago. In genomic datasets loci on different chromosomes are considered unlinked, but there are many more pairs of unlinked loci than there are independent pairs of chromosomes, resulting to confidence intervals (C.I.) being too narrow if the non-independence is not taken into account. Simulations were run to investigate the correlation structure among LD of unlinked loci, which can be expressed by the LD of loci along the same chromosomes, based on a discovery of a novel Random Probe LD estimator. We classify the correlation into two categories: overlapping of loci and disjoint pairs. The former is induced from the same locus being considered twice and is the stronger form of correlation. These correlations feed into {rho}, a parameter to quantify the degree of pseudo-replication in a dataset, and further a correction formula from which C.I. can be properly inferred. We demonstrate the use of our method via an analysis of genomic data from the malaria-transmitting Anopheles gambiae s.s mosquitoes. Apart from the point and C.I. estimates, we find that Var((r^2 ) ) is inflated by about 550 times due to pseudo-replication, highlighting the danger of not handling genetic correlation properly.

bioinformatics↗

bioRxiv2026

A novel unbiased Linkage Disequilibrium estimator through Random Probing

The linkage disequilibrium (LD) or correlation of alleles at different loci is a fundamental statistic in population genetics. This work presents a new estimator for the standardised LD measure r^2 between a pair of loci. The Random Probe (RP) estimator involves generating a set of random dummy loci, before calculating the r^2 between each synthetic locus and the two focal loci using one of the existing estimators (even it is known to be biased). The correlation between the two vectors of r^2 is the LD estimate. Computer simulations show promising results, with the RP estimator at least as unbiased as the current Ragsdale and Gravel estimator in most common scenarios. In the more challenging scenarios with skewed allele frequencies and small sample size, RP is preferred by having notably reduced bias, and that the bias is less sensitive to sample size and underlying LD, while maintaining mean squared error comparable to existing methods. The new estimator will most benefit applications relying on accurate measures of LD. Beyond LD, this study stimulates further discussions on whether RP estimator can be generalised to other genetic summary statistics or measures of relatedness.

bioinformatics↗

bioRxiv2026

Essential function of femaleless in female gametogenesis controls gene drive spread in Anopheles gambiae

Insecticide resistance in mosquito vectors and antimalarial drug resistance in parasites threaten progress towards malaria elimination, prompting the development of alternative control strategies such as CRISPR-Cas9 gene drives. The sex determination gene femaleless (fle, AGAP013051), which is required for female development in Anopheles gambiae, is a promising target for population-suppression approaches aimed at disrupting female-specific genes that affect fertility or viability. However, its functions beyond sex determination remain unknown. Here, we engineered homing gene drives targeting fle and employed germline promoters with distinct temporal expression profiles, early-acting , zero population growth (zpg, AGAP006241) and late-acting sporulation defective 11 (spo11, AGAP010898), to modulate Cas9 activity. The zpg-driven system achieved up to 98% transmission through males but caused complete sterility in hemizygous females due to early biallelic disruption of fle during germline development. Delaying cas9 expression with the spo11 promoter partially restored female fertility, although female transmission remained close to Mendelian levels (59%). These results reveal an essential role for fle in female gametogenesis in addition to its established function in sex determination. Population modelling predicts that releasing zpg-drive males at 16.9% of the wild-type male population could reduce female abundance by 95% within 36 generations. Collectively, our findings reveal a previously unrecognised reproductive function of fle that limits gene-drive spread and provide important insights for the design of vector-control strategies targeting genes with essential germline functions.

synthetic biology↗

bioRxiv2026

Engineered balanced lethal systems for partial suppression or enhancement of wild populations

Genetic interventions to modify wild population densities are typically framed around pest suppression, with parallel approaches for boosting beneficial or endangered populations remaining largely undeveloped. Imposing a sustained but non-eliminative genetic load could in principle address both objectives, but existing designs rely on genes with intermediate fitness effects whose loads are difficult to predict under field conditions. Here we describe engineered balanced lethal systems, in which CRISPR-based gene drive establishes two complementing recessive-lethal alleles at a single locus, producing a sustained 50% load through Mendelian segregation. Modelling shows these systems spread from small releases, and that the resulting population-level consequences depend on density regulation and on the timing of lethality: the same 50% load can suppress pests, boost populations of beneficial or endangered species, dampen boom-bust cycles, or raise effective population size. Additional systems at independent loci scale the effect in stepwise increments, and a split-drive variant localises it geographically. These results demonstrate that gene drives imposing genetic load can be expanded beyond elimination, to support and preserve beneficial and endangered populations.

evolutionary biology↗

bioRxiv2026

Easymode: general pretrained networks for cellular cryo-ET enable flexible approaches to subtomogram averaging

Cellular cryo-electron tomography (cryo-ET) reveals high-resolution details of macromolecules within their native cellular environment. However, in situ cryo-ET datasets are large and highly heterogeneous, which makes comprehensive identification and extraction of the many different elements of cellular architecture for high-resolution analysis a challenging, time-consuming and often tedious task. Here we present easymode, a library of pretrained general segmentation networks for cryo-ET, trained on over 4,000 tilt series spanning a large and diverse variety of sources. Easymode enables in situ structural determination workflows by rendering tomogram content computationally accessible, without requiring any per-dataset training. Beyond directly facilitating high-resolution subtomogram averaging of a selection of widely prevalent complexes, we show how easymode can be used to leverage cellular context in subtomogram averaging workflows, helping identify, align, or filter particle sets, and enabling automated mapping of the cellular landscape surrounding target proteins. We use easymode to determine the in situ structure of rare inosine monophosphate dehydrogenase (IMPDH) filaments at 4.0 A resolution, and to map and visualize the surrounding cellular environment.

molecular biology↗

bioRxiv2026

MissAlignment Teaches Itself Better Cryo-ET Tilt-Series Alignment by Making It Worse

Cryo-electron tomography (cryo-ET) visualizes cellular environments in a near-native state at macromolecular resolution. Accurate alignment of the tilted projection images is essential for data interpretation, yet existing reference-free algorithms often fail due to limited information overlap between images and inaccurate assumptions about the sample. Human experts, however, can easily recognize the misalignment left by these tools. We introduce MissAlignment, a machine learning approach that trains similar intuition to improve tilt-series alignment. A convolutional neural network learns to score alignment accuracy using a contrastive loss that does not require well-aligned ground truth, and gradient back-propagation from this score optimizes individual image alignment parameters. MissAlignment significantly outperforms reference-free techniques, rivals reference-based alignment, and improves all downstream analyses, making cryo-ET applicable to a broader range of biological samples.

biophysics↗

bioRxiv2026

Estimation of adult census size from close-kin dyads in the malaria mosquito Anopheles gambiae

Accurate estimates of adult mosquito abundance are central to the design and evaluation of vector control strategies, yet they are difficult to obtain from natural populations. Conventional mark-recapture methods for estimating adult mosquito census size pose logistical and other challenges. A recently developed close-kin mark-recapture (CKMR) approach is a promising alternative. However, application of CKMR has been largely confined to long-lived vertebrates. Validation on empirical data from short-lived, highly fecund insects is lacking. Here, we apply CKMR to a natural population of Anopheles gambiae, the primary African malaria mosquito, sampled from a small island in Lake Victoria, Uganda. Using a high-diversity amplicon panel of genome-wide markers, we genotyped 714 adult mosquitoes collected over a 20-day period. We classified pairs to close-kin categories by implementing probabilistic latent-kinship estimation rather than using a deterministic threshold-based framework. We observe numerous full-sibling pairs but no parent-offspring pairs, a pattern indicative of extreme variance in reproductive success owing to frequent failure of mosquito egg clutches to produce adults. To adapt the CKMR framework to mosquito life history, we explicitly modeled the mosquito life cycle including the possibility of clutch failure. By incorporating probabilities of both parent-offspring and full-sibling relationships, we estimate an adult female census size of 26,887 (95% credible interval: 6,979 - 146,011), and a clutch failure probability of 97.6% (CrI: 91.3 - 99.6%). We use these estimates to calculate the predicted variance in reproductive success and effective population size. Through individual-based simulations we confirm our estimates and the necessity of modeling reproductive variance to explain observed kinship patterns. Our results demonstrate that CKMR can be applied successfully to mosquitoes, provided that appropriate adjustments are made to account for their natural history.

ecology↗

bioRxiv2026

Assessing alternative methods of using population genomic data to measure changes in population size

Malaria remains a major global health burden, with traditional control methods facing challenges such as insecticide resistance and high operational costs. Genetic biocontrol offers a promising alternative for mosquito population suppression, but its field efficacy would require assessment. This study evaluates the role that population genomic statistics can play in detecting decreases in population size in the context of a cluster randomized control trial (cRCT), investigating the response of nucleotide diversity ({pi}), Tajimas D, segregating sites, and linkage disequilibrium (LD) under both constant and seasonal demographic scenarios. We simulated 90% and 99% population declines with various degrees of between-cluster heterogeneity, and assessed the detection power of each statistic over time and number of clusters per arm. Results show that Tajimas D is highly sensitive and robust across crash severity, seasonality and heterogeneity scenarios. Segregating sites has similar power to Tajimas D when baseline data are available. We further estimated that cRCTs require approximately 3 to 5 villages per treatment arm to achieve adequate statistical power. These findings provide recommendations for genetic monitoring of vector control interventions in wild populations.

evolutionary biology↗

bioRxiv2025

Genomic editors for localised population suppression

Effective control of pest populations remains a major challenge for agriculture, public health, and conservation. While genetic control strategies such as the release of sterile males or individuals carrying dominant lethal alleles have achieved some success, they typically require repeated, large-scale releases due to immediate selection against costly alleles, limiting scalability and applicability to species that are difficult to rear in the laboratory. Recent CRISPR/Cas-based genome editing has enabled the development of more efficient genetic control methods that bias their own inheritance and impose a genetic load to achieve population suppression. These designs can potentially spread throughout an entire species range, making them unsuitable when only local or temporary suppression is needed. Here we explore self-limiting genetic strategies based on releasing males carrying autosomal genomic editors that create deleterious edits in essential genes. Unlike traditional approaches, where costly alleles are immediately purged, these editors can persist through multiple generations when creating female-specific or recessive edits, or when editing rates are less than 100%. This allows editors to survive in males, heterozygous carriers, or individuals unaffected by incomplete editing, trading immediate lethality for accumulated genetic load over time. Using deterministic population modelling, we demonstrate that homozygous releases of editors creating female-specific dominant or recessive edits require 45% and 50% fewer males than releasing individuals carrying dominant lethal alleles (RIDL), respectively, to achieve comparable suppression levels. Efficiency gains are further enhanced by targeting multiple genes simultaneously, with editors making female-specific recessive edits showing approximately 50% reduction in release requirements when targeting three genes compared to one - representing a 73% reduction compared to RIDL. Co-releasing editors with a second construct that temporarily boosts editor frequency can achieve efficiency comparable to previously proposed selectively neutral designs while maintaining temporal self-limitation. These results highlight promising alternative strategies for achieving localised, efficient, and self-limiting pest population control suitable for contexts requiring contained suppression.

evolutionary biology↗

bioRxiv2025

The Global Canopy Atlas: analysis-ready maps of 3D structure for the world's woody ecosystems

Woody canopies regulate exchanges of energy, water and carbon, and their three-dimensional (3D) structure supports much of terrestrial biodiversity. Remote sensing technologies such as airborne laser scanning (ALS) now enable the 3D mapping of entire landscapes. However, we lack the large, harmonized and geographically representative ALS collections needed to build a global picture of woody ecosystem structure. To address this challenge, we developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m2 resolution. The GCA covers 56,554 km2 across all major biomes. 19% of this area has been scanned multiple times, and 87% of all GCA products are openly available, covering 95% of the total area. To showcase its wide range of applications, we applied the GCA in three case studies. First, we validated three global satellite-derived canopy height maps, finding poor performance at native resolution (1-30 m, R2 < 0.38) and moderate performance at 250 m resolution (R2 < 0.65). Second, analyzing global patterns in canopy gap size frequency we discovered an unexpectedly large variation of power law exponents from branch to stand level ( = 1.52 to 2.38), pointing to a fundamental scale-dependence of forest structure. Third, we developed a framework to standardize forest turnover quantification from multi-source, multi-temporal ALS. In a temperate forest in North America it revealed that 21% of canopy gaps closed within 12 years of opening and would thus be missed by infrequent monitoring. As demonstrated by these case studies, the GCA provides a novel data source for ecologists, foresters, remote sensing scientists and the ecosystem modelling community that substantially advances our ability to understand the structure and dynamics of woody ecosystems at global scales.

ecology↗

bioRxiv2025

In-cell structure of a LINC complex reveals the molecular basis for membrane remodelling and head-to-tail coupling in sperm cells

In eukaryotic cells, LINC complexes physically bridge the two nuclear membranes to cytoskeletal filaments, transmitting mechanical forces to the nucleus. Due to their dynamic and membrane-embedded nature, their architecture in native membranes has been hitherto elusive. Here, we combine in-situ electron cryo-tomography, AlphaFold predictions and molecular dynamics simulations to determine the sub-nanometer-resolution architecture of the trimeric [~]130 kDa SUN5-Nesprin3 LINC complex in human spermatozoa. The structure reveals a hexagonal lattice that stitches the nuclear envelope, creating a rigid interface that links the sperm nucleus to the flagellum. Our structure unveils a unique membrane-anchoring mechanism: a SUN5 KASH-lid {beta}-hairpin and a Nesprin3 amphipathic helix insert into the outer nuclear membrane, leading to cooperative remodelling of the nuclear envelope shape by the extended LINC lattice, which results in flattening the caudal sperm nucleus. Our integrative data provides key insights into the architecture and role of a LINC complex in situ, revealing molecular details of a severe male infertility phenotype.

biochemistry↗

bioRxiv2025

Variance partition reveals contrasting random effect contributions on the density and species composition of malaria-transmitting mosquitoes

Spatial-temporal variations exist in the density and species composition of malaria-carrying mosquitoes, which will in turn influence the transmission of the disease. While there has been extensive research on the seasonality and other main drivers of the vector populations, the heterogeneity induced by random effects is just as important but did not quite attract the same attention. To investigate the relative contributions of the between-house, between-village, and between-year variations, as well as other house-level covariates such as inhabitant number and bed net usage, intensive Pyrethroid Spray Catches (PSC) sampling was conducted across a 60-month period between 2012-2019 from four villages in the Sudano-Sahelian region of Burkina Faso. For mosquito density, measured by counts of female Anopheles gambiae s.l., our modelling showed that the between-house variation was the largest component, followed by the between-year then between-village variation, after accounting for seasonality and other covariates. Density increased with the number of inhabitants within a household but was uncorrelated with bed net usage. A subset of female mosquitoes was genotyped for species identification, and the composition of An. coluzzii and An. gambiae, the two dominant vectors in the region, varied hugely across villages without a clear seasonal trend. The between-village variance contributed up to 76% of the total random variation, followed by the between-year variance. The between-house variation was estimated to be statistically insignificant. Neither household size nor bed net usage had any impact on species composition. In short, the relative importance of the random components in mosquito density was in the reverse order from species composition. The estimates and relative strengths help parameterise potential field trials for novel vector control programmes and monitoring.

ecology↗

bioRxiv2025

On the use of generative models for evolutionary inference of malaria vectors from genomic data

Malaria in sub-Saharan Africa is transmitted by mosquitoes, in particular the Anopheles gambiae complex. Efforts to control the spread of malaria have often focused on these vectors, but less is known about the demographic history of populations and species in the Anopheles complex. Here, we first quantify the genetic structure of mosquito populations in sub-Saharan Africa using unsupervised machine learning. We then adapt and apply an innovative generative deep learning algorithm to infer the joint evolutionary history of populations sampled in Guinea and Burkina Faso, West Africa. We further develop a novel model selection approach and discover that an evolutionary model with migration fits this pair of populations better than a model without post-split migration. For the migration model, we find that our method accurately captures population genetic differentiation. These findings demonstrate that machine learning and generative models are a valuable direction for future understanding of the evolution of malaria vectors, including the joint inference of demography and natural selection. Understanding changes in population size, migration patterns, and adaptation in hosts, vectors, and pathogens will assist malaria control interventions, with the ultimate goal of predicting nuanced outcomes from insecticide resistance to population collapse.

genomics↗

bioRxiv2025

A male-drive female-sterile system for the self-limited control of the malaria mosquito Anopheles gambiae

The use of insecticides and antimalarial drugs has been crucial in reducing the mortality and morbidity associated with malaria. However, since 2015, several challenges, including the development of resistance to these insecticides and treatments and changes in mosquito behaviour, have hindered the progress in fighting the disease. As a result, there is a pressing need for new tools to control malaria, including the potential use of genetically modified mosquitoes (GMMs) in the field. Various genetic strategies for vector control are currently being explored, ranging from self-sustaining GMMs with unrestricted geographic and temporal spread to self-limiting alternatives. Here, we propose a self-limiting gene drive strategy called Male Drive Female Sterile (MDFS) targeting Anopheles gambiae, a major malaria vector. The MDFS genetic construct causes dominant sterility in females, while transgenic males remain fertile, allowing them to transmit the female sterility trait at super-Mendelian rates. Laboratory studies have shown that repeated releases of MDFS can lead to the elimination of caged mosquito populations. Based on these findings, modelling suggests that MDFS could be a highly effective and self-limiting strategy for suppressing wild malaria mosquito populations.

molecular biology↗

bioRxiv2025

Assessing the population genetic structure and demographic history of Anopheles gambiae and Anopheles arabiensis at island and mainland populations in Uganda: Implications for testing novel malaria vector control approaches.

Despite substantial investments in malaria control, the disease remains a major burden in sub-Saharan Africa, particularly Uganda. Novel tools such as gene drive systems are being developed to suppress malaria vector populations, but their deployment requires detailed knowledge of mosquito population genetics. We assessed the genetic structure, diversity, and demographic history of Anopheles gambiae and Anopheles arabiensis from six sites in Uganda: three islands in Lake Victoria and three mainland sites. A total of 2918 Anopheles gambiae and 173 Anopheles arabiensis were genotyped using targeted amplicon sequencing of 62 loci across coding and non-coding regions of the genome. Population structure analyses revealed clear separation between the two species but little differentiation within each species across sites. Pairwise FST values among An. gambiae populations were low (0.00054-0.028) but often significant, with mainland populations showing higher connectivity and island populations exhibiting greater isolation. Anopheles arabiensis mainland populations showed no statistically significant differentiation, suggesting panmixia. Principal Component Analysis and Bayesian clustering similarly distinguished species-level structure but no obvious substructure within sites. Mainland An. gambiae populations displayed higher nucleotide diversity than island populations, while An. arabiensis showed the lowest diversity overall. Tajimas D values were negative across sites, consistent with recent population expansions. Effective population size estimates indicated small populations at the islands (146 to 249) compared to large mainland populations (4,054 to 8,190). These findings demonstrate strong genetic differentiation between Anopheles gambiae and Anopheles arabiensis, and subtle but meaningful structure between island and mainland Anopheles gambiae populations. The reduced diversity and small effective population sizes at island sites suggest stronger genetic drift and limited gene flow, in contrast to the highly connected mainland populations. For malaria control, this contrast has direct implications. High connectivity among mainland populations may facilitate the spread of insecticide resistance alleles, while island populations, with their relative isolation and smaller sizes, may serve as suitable sites for contained field trials of gene drive strategies. This study highlights how geographic and ecological factors shape mosquito population structure and provides critical evidence for the design and monitoring of genetic-based vector control interventions.

evolutionary biology↗

bioRxiv2025

Identifying single origin rare variants in population genomic data

Genomic analyses have shown that some mutations in large population genomic datasets may be the result of repeated, independent events at the same locus. However, the possibility of recurrent mutation is often ignored, even when it has the potential to introduce errors, such as when assuming co-ancestry for demographic analysis. Even rare variants such as doubletons, which should be particularly informative about recent demography, may have multiple origins despite arising relatively recently in the population. Here, we develop methods to (1) estimate the frequency of recurrent doubletons in a population genomic dataset from the occurrence of tri-allelic sites with two different singleton mutations, and (2) identify a subset of high confidence single origin doubletons based on the presence of a linked rare variant on the surrounding shared haplotype. Applying these methods to data for the malaria mosquito Anopheles gambiae sampled from across Africa, we estimate that [~]16% of doubletons had independent origins. We then identify a subset of doubletons highly likely ([~]99%) to have a single origin, which consists of [~]68% of all the expected single origin doubletons (and [~]57% of all observed doubletons). The effectiveness of our methods is demonstrated by both further data analyses and coalescent simulations, and these doubletons are then used to test population genetic hypotheses about recombination, selection, and isolation by distance. The methods developed here should be useful for demographic inference when populations or sample sizes are large enough that recurrent mutation cannot be ignored.

evolutionary biology↗

bioRxiv2024

Natural selection of synthetic gene drives for population suppression can favour an intermediate strength of drive

Synthetic gene drives are being investigated as tools to suppress pest populations, and it is important to understand how natural selection will act on variant drivers that may either arise by de novo mutation or are intentionally released. In this study we extend previous spatially implicit stochastic models to examine the evolutionary dynamics of synthetic driving Y chromosomes in patchy environments when population size is responding dynamically to the spread of the driver, and derive conditions for the existence of an evolutionarily stable strategy (ESS) for drive strength. Under broad conditions an intermediate drive strength emerges as the ESS, capable of outcompeting both stronger and weaker variants. Additionally, we show how the intentional release of two drivers straddling the ESS can help stabilise population dynamics. Finally, inbreeding depression has the effect of expanding the range of conditions under which no intermediate ESS exists, with ever stronger drive being selected until the population is eliminated. These results provide insights into the expected evolutionary trajectories of gene drive systems, with important implications for the design and release of gene drives for pest and vector control.

evolutionary biology↗