Symbiotic Associations in the Phenotypically-Diverse Brown Alga Saccharina japonica

The brown alga Saccharina japonica (Areschoug) Lane, Mayes, Druehl et Saunders is a highly polymorphic representative of the family Laminariaceae, inhabiting the northwest Pacific region. We have obtained 16S rRNA sequence data in symbiont microorganisms of the typical form (TYP) of S. japonica and its common morphological varieties, known as “longipes” (LON) and “shallow-water” (SHA), which show contrasting bathymetric distribution and sharp morphological, life history traits, and ecological differences. Phylogenetic analysis of the 16S rRNA sequences shows that the microbial communities are significantly different in the three forms studied and consist of mosaic sets of common and form-specific bacterial lineages. The divergence in bacterial composition is substantial between the TYP and LON forms in spite of their high genetic similarity. The symbiont distribution in the S. japonica forms and in three other laminarialean species is not related to the depth or locality of the algae settlements. Combined with our previous results on symbiont associations in sea urchins and taking into account the highly specific character of bacteria-algae associations, we propose that the TYP and LON forms may represent incipient species passing through initial steps of reproductive isolation. We suggest that phenotype differences between genetically similar forms may be caused by host-symbiont interactions that may be a general feature of evolution in algae and other eukaryote organisms. Bacterial symbionts could serve as sensitive markers to distinguish genetically similar algae forms and also as possible growth-promoting inductors to increase algae productivity

Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h2∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol