The genomic basis of evolutionary differentiation among honey bees

Abstract

In contrast to the western honey bee, Apis mellifera, other honey bee species have been largely neglected despite their importance and diversity. The genetic basis of the evolutionary diversification of honey bees remains largely unknown. Here, we provide a genome-wide comparison of three honey bee species, each representing one of the three subgenera of honey bees, namely the dwarf (Apis florea), giant (A. dorsata), and cavity-nesting (A. mellifera) honey bees with bumblebees as an outgroup. Our analyses resolve the phylogeny of honey bees with the dwarf honey bees diverging first. We find that evolution of increased eusocial complexity in Apis proceeds via increases in the complexity of gene regulation, which is in agreement with previous studies. However, this process seems to be related to pathways other than transcriptional control. Positive selection patterns across Apis reveal a trade-off between maintaining genome stability and generating genetic diversity, with a rapidly evolving piRNA pathway leading to genomes depleted of transposable elements, and a rapidly evolving DNA repair pathway associated with high recombination rates in all Apis species. Diversification within Apis is accompanied by positive selection in several genes whose putative functions present candidate mechanisms for lineage-specific adaptations, such as migration, immunity, and nesting behavior.

Live-bearing cockroach genome reveals convergent evolutionary mechanisms linked to viviparity in insects and beyond

“…Eight alignments failed to run due to removal of reference species during filtering. Results from RELAX models were corrected for multiple testing as one series using (FDR) (74) and set up our significant threshold at 10% (31).…”

“…The SUMSTAT test is more sensitive than other methods, and minimizes the rate of false positives (77). To be able to use the distribution of loglikelihood ratios of the aBSREL and RELAX tests as scores in the SUMSTAT test, a fourth root transformation was used (31). This transformation conserves the ranks of gene families (78).…”

“…We therefore implemented the “elim” algorithm from the Bioconductor package topGO, to decorrelate the graph structure of the Gene Ontology (20). To account for multiple testing, the final list of p-values resulting from this test was corrected with the FDR (74) and set up our significant threshold at 20% (31). To cluster the long list of significant functional categories before FDR correction, we used REVIGO (30) with the SimRel semantic similarity algorithm and medium size (0.7) result list.…”

“…After correcting for multiple testing, only 2 single-copy orthogroups were under positive selection with an FDR < 10%: an unknown gene with a dynein heavy chain linker protein To identify whether positive selection among viviparous origins quantitatively relates to particular functions, we classified orthogroups based on their GO term annotations from D. melanogaster orthologs and protein domains from a pfam library (see Methods). Using SUMSTAT (31) with the topGO R package (20) to test for gene set enrichment, we identified (P < 0.05), 59, 64, and 104 functional categories that were enriched among positively selected genes in the viviparous cockroach, the tsetse, and the aphid branches, respectively (Fig. 2 A-C).…”

FA-nf: A Functional Annotation Pipeline for Proteins from Non-Model Organisms Implemented in Nextflow

“…As a first example, FA-nf was run against the gene structural annotation of Apis dorsata data [46], consisting of 20,508 translated protein sequences from 12,172 associated genes described in a FASTA and a GFF file, respectively. The pipeline was run using the MySQL engine, opting out external web-based annotation services, against the NCBI NR database (202,010) formatted for DIAMOND in default mode, using the E-value threshold of 1 × 10 −5 , InterproScan 5.48-83, and KOFAMscan with the 202103 dataset.…”