Prediction of Depression in Individuals at High Familial Risk of Mood Disorders Using Functional Magnetic Resonance Imaging

Objective Bipolar disorder is a highly heritable condition. First-degree relatives of affected individuals have a more than a ten-fold increased risk of developing bipolar disorder (BD), and a three-fold risk of developing major depressive disorder (MDD) than the general population. It is unclear however whether differences in brain activation reported in BD and MDD are present before the onset of illness. Methods We studied 98 young unaffected individuals at high familial risk of BD and 58 healthy controls using functional Magnetic Resonance Imaging (fMRI) scans and a task involving executive and language processing. Twenty of the high-risk subjects subsequently developed MDD after the baseline fMRI scan. Results At baseline the high-risk subjects who later developed MDD demonstrated relatively increased activation in the insula cortex, compared to controls and high risk subjects who remained well. In the healthy controls and high-risk group who remained well, this region demonstrated reduced engagement with increasing task difficulty. The high risk subjects who subsequently developed MDD did not demonstrate this normal disengagement. Activation in this region correlated positively with measures of cyclothymia and neuroticism at baseline, but not with measures of depression. Conclusions These results suggest that increased activation of the insula can differentiate individuals at high-risk of bipolar disorder who later develop MDD from healthy controls and those at familial risk who remain well. These findings offer the potential of future risk stratification in individuals at risk of mood disorder for familial reasons

Intestinal Microbiota Composition of Interleukin-10 Deficient C57BL/6J Mice and Susceptibility to Helicobacter hepaticus-Induced Colitis

The mouse pathobiont Helicobacter hepaticus can induce typhlocolitis in interleukin-10-deficient mice, and H. hepaticus infection of immunodeficient mice is widely used as a model to study the role of pathogens and commensal bacteria in the pathogenesis of inflammatory bowel disease. C57BL/6J Il10[superscript −/−] mice kept under specific pathogen-free conditions in two different facilities (MHH and MIT), displayed strong differences with respect to their susceptibilities to H. hepaticus-induced intestinal pathology. Mice at MIT developed robust typhlocolitis after infection with H. hepaticus, while mice at MHH developed no significant pathology after infection with the same H. hepaticus strain. We hypothesized that the intestinal microbiota might be responsible for these differences and therefore performed high resolution analysis of the intestinal microbiota composition in uninfected mice from the two facilities by deep sequencing of partial 16S rRNA amplicons. The microbiota composition differed markedly between mice from both facilities. Significant differences were also detected between two groups of MHH mice born in different years. Of the 119 operational taxonomic units (OTUs) that occurred in at least half the cecum or colon samples of at least one mouse group, 24 were only found in MIT mice, and another 13 OTUs could only be found in MHH samples. While most of the MHH-specific OTUs could only be identified to class or family level, the MIT-specific set contained OTUs identified to genus or species level, including the opportunistic pathogen, Bilophila wadsworthia. The susceptibility to H. hepaticus-induced colitis differed considerably between Il10[superscript −/−] mice originating from the two institutions. This was associated with significant differences in microbiota composition, highlighting the importance of characterizing the intestinal microbiome when studying murine models of IBD.National Institutes of Health (U.S.) (Grant NIH P01-CA26731)National Institutes of Health (U.S.) (Grant NIH P30ES0026731)National Institutes of Health (U.S.) (Grant NIH R01-OD011141

Critical Assessment of Metagenome Interpretation:A benchmark of metagenomics software

International audienceIn metagenome analysis, computational methods for assembly, taxonomic profilingand binning are key components facilitating downstream biological datainterpretation. However, a lack of consensus about benchmarking datasets andevaluation metrics complicates proper performance assessment. The CriticalAssessment of Metagenome Interpretation (CAMI) challenge has engaged the globaldeveloper community to benchmark their programs on datasets of unprecedentedcomplexity and realism. Benchmark metagenomes were generated from newlysequenced ~700 microorganisms and ~600 novel viruses and plasmids, includinggenomes with varying degrees of relatedness to each other and to publicly availableones and representing common experimental setups. Across all datasets, assemblyand genome binning programs performed well for species represented by individualgenomes, while performance was substantially affected by the presence of relatedstrains. Taxonomic profiling and binning programs were proficient at high taxonomicranks, with a notable performance decrease below the family level. Parametersettings substantially impacted performances, underscoring the importance ofprogram reproducibility. While highlighting current challenges in computationalmetagenomics, the CAMI results provide a roadmap for software selection to answerspecific research questions

Prospective longitudinal study of subcortical brain volumes in individuals at high familial risk of mood disorders with or without subsequent onset of depression

Subcortical volumetric brain abnormalities have been observed in mood disorders. However, it is unknown whether these reflect adverse effects predisposing to mood disorders or emerge at illness onset. Magnetic resonance imaging was conducted at baseline and after two years in 111 initially unaffected young adults at increased risk of mood disorders because of a close family history of bipolar disorder and 93 healthy controls (HC). During the follow-up, 20 high-risk subjects developed major depressive disorder (HR-MDD), with the others remaining well (HR-well). Volumes of the lateral ventricles, caudate, putamen, pallidum, thalamus, hippocampus and amygdala were extracted for each hemisphere. Using linear mixed-effects models, differences and longitudinal changes in subcortical volumes were investigated between groups (HC, HR-MDD, HR-well). There were no significant differences for any subcortical volume between groups controlling for multiple testing. Additionally, no significant differences emerged between groups over time. Our results indicate that volumetric subcortical brain abnormalities of these regions using the current method appear not to form familial trait markers for vulnerability to mood disorders in close relatives of bipolar disorder patients over the two-year time period studied. Moreover, they do not appear to reduce in response to illness onset at least for the time period studied

OTU counts and Good’s coverage (dataset containing 1227 sequences per sample, 8 samples per sample set).

<p>OTU counts and Good’s coverage (dataset containing 1227 sequences per sample, 8 samples per sample set).</p

Principal coordinates analysis (PCoA) of microbiota samples.

<p>Based on Jaccard index distances measuring dissimilarity of OTU-level microbiota composition between individual samples. Axis labels including fractions of variance explained.</p

Rarefaction curves.

<p>A–E, rarefaction curves for individual samples (n = 8 mice), grouped by sample set. F, rarefaction curves for combined data for each sample set (n = 5 sets), including 95% confidence intervals. Lci, lower bound of confidence interval; hci, higher bound of confidence interval. Each sample set consist of either the cecum or the colon samples for one batch of 8 mice. All curves generated after subsampling to 1227 sequences per sample.</p

Phylum-level composition of microbiota.

<p>A. Fraction of sequence counts in each of the individual samples, color coded by sample group. Grey boxes indicate significant differences between sample groups (unequal variances t-Test, p<0.05): a, significantly different between MIT and MHH2009 cecum samples; b, significantly different between MIT and MHH2011 cecum samples; c, significantly different between MHH2009 and MHH2011 cecum samples; d, significantly different between MIT and MHH2009 colon samples. B. Relative abundance of phyla in each of the sample groups, including standard deviation (n = 8).</p

Distribution of OTUs among samples.

<p>Colors encode absolute OTU counts. Information on OTU classification according to RDP classifier. Numbers 1–8 are mouse identifiers. Within one mouse batch, equal numbers refer to cecum and colon samples of the same animal. Heatmap generated after subsampling to 1227 sequences per sample. The numbers on the color key correspond to untransformed OTU abundances.</p

MHH-specific OTUs occurring in at least 4 samples of one MHH sample set.

<p>MHH-specific OTUs occurring in at least 4 samples of one MHH sample set.</p