Biology subjects

Sumner, S.

Publications and source records attributed to Sumner, S..

9 recordsLinked to original sources

bioRxiv2026

Chromosome-level genome and caste transcriptomes for Africa's most widespread social wasp (Belonogaster juncea) reveal unexpected genomic diversity

Belonogaster is an African genus of primitively eusocial wasps. They are widespread and conspicuous across sub-Saharan Africa and yet are among the least studied groups of social wasps. Here, we present the first high-quality, chromosome-level genome assembly for a representative of this genus, Belonogaster juncea, complemented by an RNA-seq analysis of brain transcription among individuals representing reproductive and non-reproductive castes. The assembled genome spans 315.8 Mb with a scaffold N50 of 7.91 Mb. The annotation contained 15,159 protein-coding genes and 20,046 isoforms, with Benchmarking Universal Single-Copy Orthologs (BUSCO) completeness of 98.0% for the genome and 97.8% for the annotation. Using this new assembly, we show that the B. juncea genome differs notably from other social wasp genomes: a single satellite repeat makes up a fifth of its chromosome-scale sequence, it has the highest putative chromosome number in a sequenced wasp genome to date, and it shows high levels of syntenic breaks. We find caste neuro-transcription differences between queens and workers, revealing 573 differentially expressed genes (341 worker biased, 232 queen biased), with significant overlap in caste-specific genes when compared to Polistes dominula and Liostenogaster flavolineata. This genomic resource provides a foundation for understanding the molecular mechanisms underlying primitive eusociality and social evolution in wasps, and opens up new research opportunities for a genus of little-studied insects from the Global South.

genomics↗

bioRxiv2026

EXCON: a scalable, reproducible Nextflow pipeline for CAFE-based gene EXpansion and CONtraction analysis

Gene family expansion and contraction analysis is a commonly used approach in evolutionary comparative genomics to identify lineage-specific evolutionary pressures on gene copy/family number. CAFE is one of the most popular tools to measure changes in gene family size, but requires a high level of bioinformatics expertise, manual parameter tuning and preprocessing. Here we present EXCON, an automated, Eco-Flow community-developed Nextflow pipeline that integrates NCBI genome download, annotation quality and format pre-processing, orthologous gene family identification (Orthofinder), ultrametric tree formatting, birth-death modelling (CAFE), and GO enrichment analysis (eggNOG-mapper/TopGO) into a single, portable workflow. EXCON accepts genome assemblies and annotations, supports local, HPC and cloud-based computing environments, and requires minimal user configuration. The pipeline auto-selects appropriate CAFE lambda and error models, and separates high variance orthogroups for separate treatment. Applied to a 24-species primate dataset spanning Catarrhini, Platyrrhini and Strepsirrhini, the pipeline identified 358 rapidly evolving gene families, including a DUX (double homeobox) gene showing repeated, independent copy-number turnover across multiple lineages, consistent with the known instability of the D4Z4 macrosatellite repeat it resides in. EXCON is freely available at https://github.com/Eco-Flow/excon.

evolutionary biology↗

bioRxiv2026

nf-core/genomeqc: a best-practice pipeline for comparing genome and assembly quality

The rapid growth in publicly available genome assemblies has made selecting genomes suitable for downstream analyses increasingly challenging. Differences in assembly and annotation quality can influence gene completeness, duplication rates, contiguity, repeat representation, and other characteristics. Assessing genome quality therefore requires integrating multiple complementary quality metrics that are often generated by independent tools. Here, we present nf-core/genomeqc, a workflow for assessing and comparing genome assemblies. The pipeline accepts RefSeq/GenBank accessions for automatic genome and annotation retrieval, or local genome (FASTA) and annotation (GFF3/GTF) files. It integrates complementary analyses of assembly contiguity, gene completeness, annotation quality, repeat content and other quality metrics using tools such as BUSCO, QUAST, Merqury, and AGAT, before combining the results on a phylogenetic tree for visualisation and comparison across species. GenomeQC is implemented in Nextflow within the nf-core framework, providing an accessible, reproducible, scalable and community-driven workflow for genome quality assessment.

bioinformatics↗

bioRxiv2026

MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits

Genetically regulated molecular phenotypes, such as gene expression and metabolites, are widely thought to mediate the effect of disease-relevant genetic loci identified by genome-wide association studies (GWAS); however, molecular mechanisms connecting genetic variation to disease remain poorly understood. While transcriptome-wide association studies (TWAS) have identified disease-associated genes, strategies that integrate the metabolome remain underexplored. Here, we introduce MetaboXcan, a framework for predicting plasma metabolite levels from genetic data and associating them with complex traits using GWAS summary statistics. We trained lasso regression models on plasma metabolite and genotype data from the Metabolic Syndrome in Men Study (METSIM); these models outperform existing genetic metabolite predictors and generalize well across independent cohorts. MetaboXcan leverages these models to perform four complementary association analyses: (i) gene-to-trait (TWAS), (ii) gene-to-metabolite (M-TWAS), (iii) metabolite-to-trait (MWAS), and (iv) gene-expression-based metabolite-to-trait associations (g-MWAS). The framework further organizes results into metabolic pathways and gene-metabolite interaction networks to facilitate biological interpretation. Applied to chronic kidney disease (CKD), MetaboXcan identified known disease risk genes (PDILT/UMOD, SPATA5L1/GATM), as well as multiple CKD-relevant metabolites (e.g., glycine, homoarginine). Our integrated multiomic analysis enables biological interpretation by revealing glycine availability-centered biochemical axes spanning oxidative stress, cellular energetics, and vascular signaling, while nominating candidate mechanisms for downstream investigation. These results validate known disease-related biology with genetic evidence and generate new hypotheses for further investigation. MetaboXcan is publicly available and broadly applicable to any GWAS phenotype.

genetics↗

bioRxiv2024

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type-specific transcriptome-wide association study framework

Transcriptome-wide association studies (TWAS) help identify disease causing genes, but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here we propose scPrediXcan which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our prediction approach, ctPred, predicts cell-type-specific expression with high accuracy and captures complex gene regulatory grammar that linear models overlook. Applied to type 2 diabetes and systemic lupus erythematosus, scPrediXcan outperformed the canonical TWAS framework by identifying more candidate causal genes, explaining more genome-wide association studies (GWAS) loci, and providing insights into the cellular specificity of TWAS hits. Overall, our results demonstrate that scPrediXcan represents a significant advance, promising to deepen our understanding of the cellular mechanisms underlying complex diseases.

genetics↗

bioRxiv2022

Molecular signatures of alternative fitness strategies in a facultatively social hover wasp

Social insect queens and workers represent ideal models with which to understand the expression and regulation of alternative reproductive phenotypes. Most research in this area has focused on the molecular regulation of reproductive castes in obligately social taxa with complex social systems, while relatively few studies have addressed the molecular basis of caste in species in which the division of reproductive labour is more plastic. As a result, it is not clear whether, and to what extent, the mechanisms of caste in species with reproductive plasticity are the same as those that exist at the highest levels of social complexity. To address this knowledge gap, we analyse brain transcriptomic data for non-reproductives and reproductives of the facultatively social hover wasp Liostenogaster flavolineata, a representative of one of the simplest forms of social living. By experimentally manipulating the reproductive queues exhibited by social groups of this species, we show that reproductive division of labour in this species is associated with surprisingly distinct transcriptomic signatures, similar to those observed in more complex social taxa; that variation in gene expression among non-reproductives reflects their investment into foraging effort more than their social rank; and that distinct co-expressed gene sets are associated with differential investment into alternative reproductive strategies. These results elucidate robust transcriptomic signals that represent the proximate basis of division of labour at the simplest level of insect sociality, and show these signals to be remarkably similar to those in more derived species.

evolutionary biology↗

bioRxiv2020

Genetic toolkit for sociality predicts castes across the spectrum of social complexity in wasps

Major evolutionary transitions describe how biological complexity arises; e.g. in evolution of complex multicellular bodies, and superorganismal insect societies. Such transitions involve the evolution of division of labour, e.g. as queen and worker castes in insect societies. Castes across different evolutionary lineages are thought to be regulated by a conserved genetic toolkit. However, this hypothesis has not been tested thoroughly across the complexity spectrum of the major transition. Here we reveal, using machine learning analyses of brain transcription, evidence of a shared genetic toolkit across the spectrum of social complexity in Vespid wasps. Whilst molecular processes underpinning the simpler societies (which likely represent the origins of social living) are conserved throughout the major transition, additional processes appear to come into play in more complex societies. Such fundamental shifts in regulatory processes with complexity may typify other major evolutionary transitions, such as the evolution of multicellularity.

evolutionary biology↗

bioRxiv2020

The molecular basis of socially-mediated phenotypic plasticity in a eusocial paper wasp

Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Despite this, analyses of the molecular bases of plasticity have been limited by the challenges of linking individual phenotypes with individual-level gene expression profiles, especially in the case of complex social phenotypes. Here, we tackle this challenge by analysing the individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen, a perturbation that induces some individuals to undergo a significant phenotypic shift and become replacement reproductives. Using a machine learning approach, we find a strong response of caste-associated gene expression to queen loss, wherein individuals expression profiles become intermediate between queen and worker states. Importantly, this change occurs even in individuals that appear phenotypically unaffected. Part of this response is explained by individual attributes, most prominently age. These results demonstrate that large changes in gene expression may occur in the absence of detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic but not the phenotypic level. Our findings also highlight the complexity of the relationship between gene expression and phenotype, where transcriptomes are neither a direct reflection of the genotype nor a proxy for the molecular underpinnings of the external phenotype.

bioinformatics↗

bioRxiv2020

Specialisation and plasticity in a primitively social insect

Biological systems not only have the remarkable capacity to build and maintain complex spatio-temporal structures in noisy environments, they can also rapidly break up and rebuild such structures. How such systems can simultaneously achieve both robust specialisation and plasticity is poorly understood. Here we use primitive societies of Polistes wasps as a model system where we experimentally perturb the social structure by removing the queen and follow the re-establishment of the social steady state over time. We combine a unique experimental strategy correlating time-resolved measurements across vastly different scales with a theoretical approach. We show that Polistes integrates antagonistic processes on multiple scales to distinguish between extrinsic and intrinsic perturbations and thereby achieve both robust specialisation and rapid plasticity. The long-term stability of the social structure relies on dynamic DNA methylation which controls transcriptional noise. Such dynamics provide a general principle of how both specialization and plasticity can be achieved in biological systems. One Sentence SummaryA primitive social insect simultaneously achieves specialisation and plasticity by integrating antagonistic dynamics on different scales. HighlightsO_LIWe employ a unique experimental approach correlating dynamics of societies, individuals, and epigenetic gene regulation C_LIO_LIA social insect simultaneously achieves specialisation and plasticity by integrating antagonistic processes on different spatial scales C_LIO_LIRegulation of population-level noise by DNA methylation ensures long-term stability of phenotypic specialisation C_LI

biophysics↗