Convergent functional genomic studies of ω-3 fatty acids in stress reactivity, bipolar disorder and alcoholism

Abstract

Omega-3 fatty acids have been proposed as an adjuvant treatment option in psychiatric disorders. Given their other health benefits and their relative lack of toxicity, teratogenicity and side effects, they may be particularly useful in children and in females of child-bearing age, especially during pregnancy and postpartum. A comprehensive mechanistic understanding of their effects is needed. Here we report translational studies demonstrating the phenotypic normalization and gene expression effects of dietary omega-3 fatty acids, specifically docosahexaenoic acid (DHA), in a stress-reactive knockout mouse model of bipolar disorder and co-morbid alcoholism, using a bioinformatic convergent functional genomics approach integrating animal model and human data to prioritize disease-relevant genes. Additionally, to validate at a behavioral level the novel observed effects on decreasing alcohol consumption, we also tested the effects of DHA in an independent animal model, alcohol-preferring (P) rats, a well-established animal model of alcoholism. Our studies uncover sex differences, brain region-specific effects and blood biomarkers that may underpin the effects of DHA. Of note, DHA modulates some of the same genes targeted by current psychotropic medications, as well as increases myelin-related gene expression. Myelin-related gene expression decrease is a common, if nonspecific, denominator of neuropsychiatric disorders. In conclusion, our work supports the potential utility of omega-3 fatty acids, specifically DHA, for a spectrum of psychiatric disorders such as stress disorders, bipolar disorder, alcoholism and beyond.

Figure 1

Effects of docosahexaenoic acid (DHA) on stressed mice behavior: DBP (+/+) wild-type (WT) and DBP (−/−) knockout (KO) mice on a diet either high or low in DHA were subjected to a chronic stress paradigm consisting of isolation (single housing) for 28 days, with an acute stressor (behavioral challenge tests, including forced swim test) at day 21.On day 28, video-tracking software was used to measure locomotion (total distance traveled, in centimeters) during a 30-min period in open field. Two-factor analysis of variance (ANOVA) was done for genotype and diet. Additionally, one-tail t-tests with ^*P<0.05 are depicted.

Figure 2

Convergent functional genomics (CFG). Bayesian integration of multiple animal model and human lines of evidence to prioritize disease-relevant genes.

Figure 3

Top candidate genes changed in DBP knockout (KO) stressed (ST) mice on high- vs low-docosahexaenoic acid (DHA) diet. (a) Female mice and (b) male mice.

Figure 4

Effects of docosahexaenoic acid (DHA) on male DBP knockout (KO) stressed (ST) mice alcohol (EtOH) consumption: mice on a diet supplemented with either low or high DHA were subjected to alcohol free-choice drinking paradigm. (a, b) Fluid consumption (water or 10% ethanol) monitored for a period of 2 weeks (14 days). (c, d) Fluid consumption (water or 10% ethanol) monitored for a period of 4 weeks (28 days) with an acute stressor (behavioral challenge tests represented by the dotted vertical line) at day 21, as described in the Materials and methods Section. ^*P<0.05.

Figure 5

Effects of docosahexaenoic acid (DHA) on alcohol (EtOH) consumption in male alcohol-preferring (P) rats. Experimentally naive, male P rats, 4–6 months of age at the start of the experiment, were used as subjects. These rats were placed on one of the three diets: (1) low-DHA diet, (2) control diet or (3) high-DHA diet. Rats were given continuous free-choice access in the home cage to 15% v/v ethanol and water. Ethanol intake was measured daily throughout the experiment. (a, b) Fluid consumption from both bottles was monitored for a period of 2 weeks (14 days). ^*t-test P<0.05 for rats on low-DHA compared with rats on high-DHA diet.

Figure 6

High-docosahexaenoic acid (DHA) diet has a stabilizing effect on mood in stressed mice. A model integrating the behavioral and genomic data.