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As the aging population grows progressively around the globe, the need to research and develop strategies to healthy aging is ever more critical and takes on new urgency1. Primary hallmarks of aging include cell autonomous changes linked to epigenetic alterations, genomic instability, telomere attrition and loss of proteostasis (protein homeostasis), which are followed by antagonistic responses such as deregulated nutrient sensing, altered mitochondrial function and cellular senescence. In addition, many functions of the immune system show a progressive decline with age, referred as immunosenescence, leading to a higher risk of infection, cancer, and autoimmune diseases2. Although chronological age is the most powerful risk factor for most chronic diseases, the underlying molecular mechanisms that lead to generalized disease susceptibility are largely unknown. In recent years, rapidly developing high-throughput omics have provided a broader insight, with the identification of a number of longevity-relevant loci based on genome-wide association studies (GWAS) and epigenome analyses. Despite this success, APOE, FOXO3 and 5q33.3 are the only identified loci consistently associated with longevity3. Hence, the complexity of the aging phenomenon, influenced by genetic and epigenetic regulation, post-translational regulation, metabolic regulation, host–microbiome interactions, lifestyle, and many other elements, primarily explains the poor understanding of many of the molecular and cellular processes that underlie the progressive loss of healthy physiology.

Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.

Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.