Utilization of Never-Medicated Bipolar Disorder Patients towards Development and Validation of a Peripheral Biomarker Profile

There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated BPD patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD. We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p<0.001). The 10-gene predictor was further explored via testing on an independent cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p<0.001). We have developed a peripheral gene expression biomarker profile, that can classify healthy controls from patients with BPD receiving antipsychotic or mood stabilizing medication, which has both high sensitivity and specificity. Moreover, assay of three first-episode patients who had never received such medications, to first enrich the expression dataset for disease-related genes independent of medication effects, and then to test the 10-gene predictor, validates the peripheral biomarker approach for BPD

Experimental Strategy and Gene Expression Data for the 10-Gene Predictor Model.

<div><p><b>A</b>: This panel depicts the study design and order of experimental analysis.</p> <p><b>B</b>: Box plot of log expression data for each of the 10 genes in the model developed on the training sample cohort. The horizontal line within each box represents the group median (BPD or C) for each gene (genes 1-10). The box indicates the interquartile range (IQR), the whiskers represent 25th and 75th percentiles of the data, and outliers are depicted as circles. 84% of the classes were correctly predicted using this 10-gene model in a DLDA (p<0.001), with 89% sensitivity and 75% specificity.</p> <p><b>C</b>: Log expression data of the enrichment sample cohort for each of the 10 genes. 88% of the sample were correctly predicted, with a sensitivity of 67% and specificity of 92% (p=0.015).</p> <p><b>D</b>: Log expression data of the replicate samples for each of the 10 genes. Six of the control subjects with B samples in the replication sample were originally randomly sampled to the training sample, and eight originally randomly sampled to the enrichment sample. 88% of the replicate samples were correctly predicted, with a sensitivity of 90% and specificity of 86% (p<0.001). Concordance between replicates (for each of the 34 individual subjects with a replicate sample, class prediction of the A replicate = class prediction of the B replicate), was greater than 85%.</p> <p><b>Key</b>: BPD= bipolar disorder patients; C=control subjects; NM= Never-Medicated, DLDA= Diagonal Linear Discriminant Analysis.</p> <p><b>Key to Gene List (Probeset ID, Gene Symbol, description)</b>:</p> <p>Gene 1: 212282_at, TMEM97, transmembrane protein 97.</p> <p>Gene 2: 236769_at, LOCI158402, Hypothetical protein LOCI158402.</p> <p>Gene 3: 231798_at, NOG, noggin.</p> <p>Gene 4: 1568983_a_at, unknown transcript, unknown.</p> <p>Gene 5: 1560527_at, NF-E4, transcription factor NF-E4.</p> <p>Gene 6: 208304_at, CCR3, chemokine (CC motif) receptor 3.</p> <p>Gene 7: 230000_at, RNF213, ring finger protein 213.</p> <p>Gene 8: 225252_at, SRXN1, sulfiredoxin 1 homolog.</p> <p>Gene 9: 210425_x_at, GOLGA8B, golgi autoantigen, golgin subfamily a, 8B.</p> <p>Gene 10: 227884_at, TAF15, TAF15 RNA polymerase II, TATA box binding protein (TBP)- associated factor, 68kDa.</p></div

PhyloFisher : A phylogenomic package for resolving eukaryotic relationships

Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (&gt;100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher (https://github.com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic "single-copy orthogroup" datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset

The Oxymonad Genome Displays Canonical Eukaryotic Complexity in the Absence of a Mitochondrion

The discovery that the protist Monocercomonoides exilis completely lacks mitochondria demonstrates that these organelles are not absolutely essential to eukaryotic cells. However, the degree to which the metabolism and cellular systems of this organism have adapted to the loss of mitochondria is unknown. Here, we report an extensive analysis of the M. exilis genome to address this question. Unexpectedly, we find that M. exilis genome structure and content is similar in complexity to other eukaryotes and less "reduced" than genomes of some other protists from the Metamonada group to which it belongs. Furthermore, the predicted cytoskeletal systems, the organization of endomembrane systems, and biosynthetic pathways also display canonical eukaryotic complexity. The only apparent preadaptation that permitted the loss of mitochondria was the acquisition of the SUF system for Fe-S cluster assembly and the loss of glycine cleavage system. Changes in other systems, including in amino acid metabolism and oxidative stress response, were coincident with the loss of mitochondria but are likely adaptations to the microaerophilic and endobiotic niche rather than the mitochondrial loss per se. Apart from the lack of mitochondria and peroxisomes, we show that M. exilis is a fully elaborated eukaryotic cell that is a promising model system in which eukaryotic cell biology can be investigated in the absence of mitochondria