Ying Ding

Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression

Cognitive impairment is highly prevalent among individuals with late-life depression (LLD) and tends to persist even after successful treatment. The biological mechanisms underlying cognitive impairment in LLD are complex and likely involve abnormalities in multiple pathways, or ‘cascades,’ reflected in specific biomarkers. Our aim was to evaluate peripheral (blood-based) evidence for biological pathways associated with cognitive impairment in older adults with LLD. To this end, we used a data-driven comprehensive proteomic analysis (multiplex immunoassay including 242 proteins), along with measures of structural brain abnormalities (gray matter atrophy and white matter hyperintensity volume via magnetic resonance imaging), and brain amyloid-β (Aβ) deposition (PiB-positron emission tomography). We analyzed data from 80 older adults with remitted major depression (36 with mild cognitive impairment (LLD+MCI) and 44 with normal cognitive (LLD+NC)) function. LLD+MCI was associated with differential expression of 24 proteins (P<0.05 and q-value <0.30) related mainly to the regulation of immune–inflammatory activity, intracellular signaling, cell survival and protein and lipid homeostasis. Individuals with LLD+MCI also showed greater white matter hyperintensity burden compared with LLD+NC (P=0.015). We observed no differences in gray matter volume or brain Aβ deposition between groups. Machine learning analysis showed that a group of three proteins (Apo AI, IL-12 and stem cell factor) yielded accuracy of 81.3%, sensitivity of 75% and specificity of 86.4% in discriminating participants with MCI from those with NC function (with an averaged cross-validation accuracy of 76.3%, sensitivity of 69.4% and specificity of 81.8% with nested cross-validation considering the model selection bias). Cognitive impairment in LLD seems to be related to greater cerebrovascular disease along with abnormalities in immune–inflammatory control, cell survival, intracellular signaling, protein and lipid homeostasis, and clotting processes. These results suggest that individuals with LLD and cognitive impairment may be more vulnerable to accelerated brain aging and shed light on possible mediators of their elevated risk for progression to dementia.

Gene Deletions and Amplifications in Human Hepatocellular Carcinomas: Correlation with Hepatocyte Growth Regulation

Tissues from 98 human hepatocellular carcinomas (HCCs) obtained from hepatic resections were subjected to somatic copy number variation (CNV) analysis. Most of these HCCs were discovered in livers resected for orthotopic transplantation, although in a few cases, the tumors themselves were the reason for the hepatectomies. Genomic analysis revealed deletions and amplifications in several genes, and clustering analysis based on CNV revealed five clusters. The LSP1 gene had the most cases with CNV (46 deletions and 5 amplifications). High frequencies of CNV were also seen in PTPRD (21/98), GNB1L (18/98), KIAA1217 (18/98), RP1-1777G6.2 (17/98), ETS1 (11/98), RSU1 (10/98), TBC1D22A (10/98), BAHCC1 (9/98), MAML2 (9/98), RAB1B (9/98), and YIF1A (9/98). The existing literature regarding hepatocytes or other cell types has connected many of these genes to regulation of cytoskeletal architecture, signaling cascades related to growth regulation, and transcription factors directly interacting with nuclear signaling complexes. Correlations with existing literature indicate that genomic lesions associated with HCC at the level of resolution of CNV occur on many genes associated directly or indirectly with signaling pathways operating in liver regeneration and hepatocyte growth regulation.

Sex chromosome complement regulates expression of mood-related genes

BackgroundStudies on major depressive and anxiety disorders suggest dysfunctions in brain corticolimbic circuits, including altered gamma-aminobutyric acid (GABA) and modulatory (serotonin and dopamine) neurotransmission. Interestingly, sexual dimorphisms in GABA, serotonin, and dopamine systems are also reported. Understanding the mechanisms behind these sexual dimorphisms may help unravel the biological bases of the heightened female vulnerability to mood disorders. Here, we investigate the contribution of sex-related factors (sex chromosome complement, developmental gonadal sex, or adult circulating hormones) to frontal cortex expression of selected GABA-, serotonin-, and dopamine-related genes.MethodsAs gonadal sex is determined by sex chromosome complement, the role of sex chromosomes cannot be investigated individually in humans. Therefore, we used the Four Core Genotypes (FCG) mouse model, in which sex chromosome complement and gonadal sex are artificially decoupled, to examine the expression of 13 GABA-related genes, 6 serotonin- and dopamine-related genes, and 8 associated signal transduction genes under chronic stress conditions. Results were analyzed by three-way ANOVA (sex chromosome complementu2009×u2009gonadal sexu2009×u2009circulating testosterone). A global perspective of gene expression changes was provided by heatmap representation and gene co-expression networks to identify patterns of transcriptional activities related to each main factor.ResultsWe show that under chronic stress conditions, sex chromosome complement influenced GABA/serotonin/dopamine-related gene expression in the frontal cortex, with XY mice consistently having lower gene expression compared to XX mice. Gonadal sex and circulating testosterone exhibited less pronounced, more complex, and variable control over gene expression. Across factors, male conditions were associated with a tightly co-expressed set of signal transduction genes.ConclusionsUnder chronic stress conditions, sex-related factors differentially influence expression of genes linked to mood regulation in the frontal cortex. The main factor influencing expression of GABA-, serotonin-, and dopamine-related genes was sex chromosome complement, with an unexpected pro-disease effect in XY mice relative to XX mice. This effect was partially opposed by gonadal sex and circulating testosterone, although all three factors influenced signal transduction pathways in males. Since GABA, serotonin, and dopamine changes are also observed in other psychiatric and neurodegenerative disorders, these findings have broader implications for the understanding of sexual dimorphism in adult psychopathology.

Genome-Wide Methylation Analysis of Prostate Tissues Reveals Global Methylation Patterns of Prostate Cancer

Altered genome methylation is a hallmark of human malignancies. In this study, high-throughput analyses of concordant gene methylation and expression events were performed for 91 human prostate specimens, including prostate tumor (T), matched normal adjacent to tumor (AT), and organ donor (OD). Methylated DNA in genomic DNA was immunoprecipitated with anti-methylcytidine antibodies and detected by Affymetrix human whole genome SNP 6.0 chips. Among the methylated CpG islands, 11,481 islands were found located in the promoter and exon 1 regions of 9295 genes. Genes (7641) were methylated frequently across OD, AT, and T samples, whereas 239 genes were differentially methylated in only T and 785 genes in both AT and T but not OD. Genes with promoter methylation and concordantly suppressed expression were identified. Pathway analysis suggested that many of the methylated genes in T and AT are involved in cell growth and mitogenesis. Classification analysis of the differentially methylated genes in T or OD produced a specificity of 89.4% and a sensitivity of 85.7%. The T and AT groups, however, were only slightly separated by the prediction analysis, indicating a strong field effect. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. These results suggest methylation patterns useful in predicting clinical outcomes of prostate cancer.

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection. Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

General library-based Monte Carlo technique enables equilibrium sampling of semi-atomistic protein models.

We introduce library-based Monte Carlo (LBMC) simulation, which performs Boltzmann sampling of molecular systems based on precalculated statistical libraries of molecular-fragment configurations, energies, and interactions. The library for each fragment can be Boltzmann distributed and thus account for all correlations internal to the fragment. LBMC can be applied to both atomistic and coarse-grained models, as we demonstrate in this proof-of-principle report. We first verify the approach in a toy model and in implicitly solvated all-atom polyalanine systems. We next study five proteins, up to 309 residues in size. On the basis of atomistic equilibrium libraries of peptide-plane configurations, the proteins are modeled with fully atomistic backbones and simplified Go-like interactions among residues. We show that full equilibrium sampling can be obtained in days to weeks on a single processor, suggesting that more accurate models are well within reach. For the future, LBMC provides a convenient platform for constructing adjustable or mixed-resolution models: the configurations of all atoms can be stored at no run-time cost, while an arbitrary subset of interactions is turned on.

Testing the Predictive Value of Peripheral Gene Expression for Nonremission Following Citalopram Treatment for Major Depression

Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

Novel Fusion Transcripts Associate with Progressive Prostate Cancer

The mechanisms underlying the potential for aggressive behavior of prostate cancer (PCa) remain elusive. In this study, whole genome and/or transcriptome sequencing was performed on 19 specimens of PCa, matched adjacent benign prostate tissues, matched blood specimens, and organ donor prostates. A set of novel fusion transcripts was discovered in PCa. Eight of these fusion transcripts were validated through multiple approaches. The occurrence of these fusion transcripts was then analyzed in 289 prostate samples from three institutes, with clinical follow-up ranging from 1 to 15 years. The analyses indicated that most patients [69 (91%) of 76] positive for any of these fusion transcripts (TRMT11-GRIK2, SLC45A2-AMACR, MTOR-TP53BP1, LRRC59-FLJ60017, TMEM135-CCDC67, KDM4-AC011523.2, MAN2A1-FER, and CCNH-C5orf30) experienced PCa recurrence, metastases, and/or PCa-specific death after radical prostatectomy. These outcomes occurred in only 37% (58/157) of patients without carrying those fusion transcripts. Three fusion transcripts occurred exclusively in PCa samples from patients who experienced recurrence or PCaerelated death. The formation of these fusion transcripts may be the result of genome recombination. A combination of these fusion transcripts in PCa with Gleasons grading or with nomogram significantly improves the prediction rate of PCa recurrence. Our analyses suggest that formation of these fusion transcripts may underlie the aggressive behavior of PCa.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each modules first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

High Fidelity Copy Number Analysis of Formalin-Fixed and Paraffin-Embedded Tissues Using Affymetrix Cytoscan HD Chip

Detection of human genome copy number variation (CNV) is one of the most important analyses in diagnosing human malignancies. Genome CNV detection in formalin-fixed and paraffin-embedded (FFPE) tissues remains challenging due to suboptimal DNA quality and failure to use appropriate baseline controls for such tissues. Here, we report a modified method in analyzing CNV in FFPE tissues using microarray with Affymetrix Cytoscan HD chips. Gel purification was applied to select DNA with good quality and data of fresh frozen and FFPE tissues from healthy individuals were included as baseline controls in our data analysis. Our analysis showed a 91% overlap between CNV detection by microarray with FFPE tissues and chromosomal abnormality detection by karyotyping with fresh tissues on 8 cases of lymphoma samples. The CNV overlap between matched frozen and FFPE tissues reached 93.8%. When the analyses were restricted to regions containing genes, 87.1% concordance between FFPE and fresh frozen tissues was found. The analysis was further validated by Fluorescence In Situ Hybridization on these samples using probes specific for BRAF and CITED2. The results suggested that the modified method using Affymetrix Cytoscan HD chip gave rise to a significant improvement over most of the previous methods in terms of accuracy in detecting CNV in FFPE tissues. This FFPE microarray methodology may hold promise for broad application of CNV analysis on clinical samples.