The assembly was constructed by integrating long-read sequencing data from PacBio HiFi reads with Hi-C scaffolding techniques. This methodological approach allowed the researchers to anchor the genetic sequences onto larger chromosomal structures. The final assembly size is approximately 1.91 gigabases, with a scaffold N50 of 63.2 megabases, indicating a high level of contiguity. Quality assessments using the BUSCO benchmarking tool recovered 98.2% of complete metazoan single-copy orthologs, suggesting that the assembly is both complete and accurate for comparative genomic studies.

A key feature of the assembly is its organization into 31 pseudochromosomes. Using Hi-C data, 99.92% of the total assembly length was successfully anchored onto these chromosomal structures. This high percentage of anchored data provides a reliable framework for analyzing the physical arrangement of genes and regulatory elements. The resolution of the assembly allows for precise mapping of genomic features, which is essential for understanding the structural organization of the genome in this gastropod species.

Phylogenetic analyses were conducted to clarify the evolutionary position of the white jade snail relative to other giant African snails. By examining single-copy nuclear orthologues and complete mitochondrial genomes, the researchers determined that the white jade snail shares a closer genetic relationship with wild A. fulica than with Achatina immaculata. This finding supports the classification of the white jade snail as a domesticated or farmed variant of A. fulica rather than a distinct species. The phylogenetic results help to contextualize the genetic differences observed between the farmed and wild populations within the broader framework of their shared ancestry.

Genome-wide synteny analyses revealed a largely conserved chromosomal correspondence between the farmed white jade snail and wild A. fulica. The study found near one-to-one collinearity across most chromosomes, indicating that the overall structural architecture of the genome has remained stable despite the process of farming. However, the researchers did detect several localized structural variations. These variations, while limited in scope, suggest that specific regions of the genome may have undergone changes that could be associated with the selection pressures experienced during farming. The conservation of synteny provides a stable baseline against which functional differences can be compared.

Comparative gene family analyses indicated potential functional divergence between the two populations at both the gene and pathway levels. In the white jade snail, candidate functional shifts were primarily associated with processes related to detoxification, molecular transport, metabolism, and pigmentation. These pathways are often linked to adaptation to controlled environments and dietary changes, which are common in farmed organisms. The enrichment of these functions suggests that the white jade snail may have undergone genetic changes that support its survival and growth in captivity, potentially influencing traits such as growth rate and coloration.

In contrast, wild A. fulica showed an enrichment of functions related to environmental responsiveness, signaling, and immune-associated processes. This pattern is consistent with the challenges faced by wild populations, which must constantly adapt to variable environmental conditions and pathogen exposure. The difference in enrichment patterns highlights the distinct selective pressures acting on farmed versus wild snails. While the farmed strain may have reduced the need for robust immune responses due to controlled conditions, the wild population retains a genomic profile geared toward survival in a complex and often hostile natural environment.

Despite the differences in functional enrichment, the study noted that gene families associated with environmental responsiveness and immunity were broadly retained in the white jade snail. This retention suggests that the farmed population has not lost the genetic capacity to respond to environmental stressors or pathogens, even if the expression or regulation of these genes may differ from that of the wild population. The preservation of these gene families indicates a degree of genomic stability and suggests that the white jade snail retains a baseline level of physiological resilience. This finding is important for understanding the long-term sustainability of farming practices and the potential for the farmed strain to adapt to new challenges.

The chromosome-level genome of the white jade snail serves as a valuable resource for future comparative genomics studies. It provides a detailed reference for investigating the candidate genomic basis underlying farming-associated traits. By comparing the farmed genome with that of wild A. fulica and other related species, researchers can identify specific genetic variants that contribute to domestication and adaptation. This resource will facilitate studies on the molecular mechanisms of growth, metabolism, and stress response in gastropods. The availability of this high-quality assembly will likely accelerate research into the biology of Achatina species and their management in agricultural and experimental settings.

Subject of Research: Genomics

Article Title: Chromosome-level genome of the white jade snail reveals conserved genomic architecture and functional divergence relative to wild Achatina fulica

Article References: Liu, H., Niu, Y., Li, S., Fang, Y., & Li, G. (2026). Chromosome-level genome of the white jade snail reveals conserved genomic architecture and functional divergence relative to wild Achatina fulica. BMC Genomics. https://doi.org/10.1186/s12864-026-13355-w

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13355-w

Keywords: Genomics, Achatina fulica, Comparative Genomics, Gastropods, Genome Assembly, Chromosome-level, genome, white, jade, snail, reveals, conserved

The starting point for the framework is a deceptively simple observation: the gut microbiome of social bees is remarkably small and specific compared with that of mammals. Honey bees and bumble bees harbour a core set of bacterial symbionts that have coevolved with their hosts and are transmitted socially, passing between nestmates through shared food, faecal contact and the communal environment of the hive. These microbes contribute to nutrition by aiding the digestion of pollen, to detoxification of harmful compounds, to immune priming against pathogens, and even to behaviour, with measurable consequences for colony performance and resilience. Yet despite the field’s rapid growth, most studies continue to analyse host genetics and environmental exposures separately, and many rely on simple correlations between the abundance of particular microbes and health traits such as parasite resistance or longevity.

That reliance on correlation is where the trouble begins, the authors argue. When researchers observe that bees with a certain microbiome profile are healthier, it is difficult to know what is driving what. The microbiome differences may reflect underlying host genetics, they may be a consequence of environmental exposure, or they may genuinely contribute mechanistically to the health outcome. Disentangling these possibilities is one of the central challenges in bee microbiome research, and the GEM–P framework is designed precisely to make this disentangling more systematic. It treats the genome, the exposome and the gut microbiome as interacting components that jointly shape phenotypes, while phenotypes themselves feed back to modify subsequent exposures and microbiome states, creating a dynamic, looping system rather than a one-directional chain of cause and effect.

Each component of the framework carries specific technical meaning. The genome encompasses host genetic variation, which can filter which microbes are able to colonise the gut, a process the authors describe as genetic filtering. The exposome captures the totality of lifetime exposures, including diet, pesticides, pathogens, temperature and landscape characteristics, all of which can reshape the microbial community or act directly on bee physiology. The microbiome refers to the gut symbionts themselves and their assembly dynamics and functions. The phenome covers the full suite of observable traits, from individual characteristics such as immune gene expression and nutrient processing to colony-level outcomes such as brood production, overwintering success and disease burden. Crucially, the framework operates at both scales simultaneously, recognising that colony phenotypes emerge from the actions and interactions of thousands of individuals.

Perhaps the most intellectually important contribution of the review is its treatment of context dependence. The authors highlight three major sources of it: scale, timing and social transmission. Similar microbiome shifts can be associated with different trait outcomes depending on the life stage and task of the bee, since nurse bees and foragers, or larvae and adults, have distinct physiologies and diets. Exposure history matters too, because a bee that has previously encountered a pesticide may respond differently to a microbial change than a naive individual. And the colony’s transmission structure, the way microbes are shared among nestmates, means that an individual’s microbiome is inseparable from its social environment. A microbial pattern that signals dysbiosis in one context may be an adaptive response in another, which helps explain why apparently contradictory findings recur across the literature.

To illustrate the framework’s utility, the authors use GEM–P to map existing evidence on genetic filtering, exposomal drivers, microbiome assembly and function, and phenotypic outcomes. This mapping exercise reveals where the evidence is strong and where it remains thin. For example, host genetics clearly constrain which symbionts can establish in the bee gut, and dietary exposures demonstrably alter the relative abundance of core taxa, but the mechanistic links connecting specific microbial functions to specific colony outcomes are far less well established. By laying the components out in a single organisational scheme, researchers can identify which pathway connecting genome, exposome, microbiome and phenotype is being invoked in any given hypothesis, and design sampling strategies that test that pathway specifically rather than relying on broad correlational surveys.

Importantly, the authors stress that generating pathway-specific hypotheses does not require comprehensive multi-omics approaches, which remain expensive and technically demanding for many laboratories. Instead, GEM–P supports relatively simple, targeted study designs. Sampling across life stages and castes, recording exposure histories, accounting for colony-level transmission, and measuring phenotypes at matched individual and colony scales can all help distinguish among alternative biological explanations for a microbiome–phenotype association without sequencing everything. The framework thus serves as a guide for experimental economy, pointing researchers toward the comparisons that are most informative for ruling out competing interpretations, such as whether a microbiome shift precedes or follows a change in host state, or whether it is shared across related bees in a way consistent with genetic filtering.

The implications extend beyond basic science into the practical world of beekeeping and pollination services. Social bees underpin agriculture through their pollination of crops and wild plants, and colony losses have economic and ecological consequences that are now felt worldwide. If microbiome-based interventions, such as probiotic supplements or management practices that support beneficial symbionts, are to be developed responsibly, they must rest on an understanding of when microbial change actually causes improvements in health and when it is merely a bystander. GEM–P offers a way to formulate those questions rigorously, linking interventions to specific points in the genome–exposome–microbiome–phenome network and specifying the outcomes and contexts in which effects should be expected. The work also contributes to the objectives of the BeeSustain project, an initiative on integrative modelling for enhanced beekeeping carrying capacity funded through Xjenza Malta’s Research Excellence Programme.

The review, published open access on 3 September 2026, arrives at a moment when the bee microbiome literature has grown rapidly but interpretively fragmented, with different subfields emphasising different components of the system and rarely integrating them. By providing a shared vocabulary and a common structural map, the authors hope the framework will help researchers interpret microbiome–phenotype associations across contexts, generate hypotheses that are explicitly pathway-specific, and design studies whose results genuinely advance mechanistic understanding. For a field in which correlation has often been mistaken for causation, and in which the same microbial signal can mean different things in a larva, a nurse bee or an overwintering colony, that kind of conceptual discipline may prove as valuable as any single experiment. The GEM–P framework does not claim to answer how social bee health is determined, but it tells the research community, with unusual clarity, exactly which questions to ask next.

Subject of Research: An integrative framework linking genome, exposome, gut microbiome and phenome in social bees

Article Title: The Genome Exposome Microbiome–Phenome (GEM–P): a conceptual framework for social bees

Article References: Cutajar, S., Alberoni, D., Di Gioia, D., & Mifsud, D. (2026). The Genome Exposome Microbiome–Phenome (GEM–P): a conceptual framework for social bees. Applied Microbiology and Biotechnology. https://doi.org/10.1007/s00253-026-14016-4

Image Credits: AI Generated

DOI: 10.1007/s00253-026-14016-4

Keywords: social bees, gut microbiome, exposome, genome, phenome, GEM–P framework, colony health, microbial transmission, symbiosis, bee resilience, host genetics, context dependence