Comparison and consolidation of microarray data sets of human tissue expression

<p>Abstract</p> <p>Background</p> <p>Human tissue displays a remarkable diversity in structure and function. To understand how such diversity emerges from the same DNA, systematic measurements of gene expression across different tissues in the human body are essential. Several recent studies addressed this formidable task using microarray technologies. These large tissue expression data sets have provided us an important basis for biomedical research. However, it is well known that microarray data can be compromised by high noise level and various experimental artefacts. Critical comparison of different data sets can help to reveal such errors and to avoid pitfalls in their application.</p> <p>Results</p> <p>We present here the first comparison and integration of four freely available tissue expression data sets generated using three different microarray platforms and containing a total of 377 microarray hybridizations. When assessing the tissue expression of genes, we found that the results considerably depend on the chosen data set. Nevertheless, the comparison also revealed statistically significant similarity of gene expression profiles across different platforms. This enabled us to construct consolidated lists of platform-independent tissue-specific genes using a set of complementary measures. Follow-up analyses showed that results based on consolidated data tend to be more reliable.</p> <p>Conclusions</p> <p>Our study strongly indicates that the consolidation of the four different tissue expression data sets can increase data quality and can lead to biologically more meaningful results. The provided compendium of platform-independent gene lists should facilitate the identification of novel tissue-specific marker genes.</p

ANALYZING SPLIT-PLOT ANDREPEATED-MEASURES DESIGNSUSING MIXED MODELS

We first introduce the general linear mixed model and provide a justification for using REML to fit it. Then, for an irrigation example, we present several mixed models of increasing complexity. The initial model corresponds to a typical split-plot analysis. Next, we present covariance structures that directly describe the variability of repeated measures within whole plots. Finally, we combine the above types into more complicated mixed models, and compare them using likelihood-based criteria

Neuronal Transcriptome from C9orf72 Repeat Expanded Human Tissue is Associated with Loss of C9orf72 Function

A hexanucleotide G4C2 repeat expansion in C9orf72 is the most common genetic cause of familial and sporadic cases of amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The mutation is associated with a reduction of C9orf72 protein and accumulation of toxic RNA and dipeptide repeat aggregates. The accumulation of toxic RNA has been proposed to sequester RNA binding proteins thereby altering RNA processing, consistent with previous transcriptome studies that have shown that the C9orf72 repeat expansion is linked to abundant splicing alterations and transcriptome changes. Here, we used a subcellular fractionation method and FACS to enrich for neuronal nuclei from C9orf72 repeat expanded post-mortem human ALS/FTD brains, and to remove neuronal nuclei with TDP-43 pathology which are observed in nearly all symptomatic C9orf72 repeat expanded cases. We show that the C9orf72 expansion is associated with relatively mild gene expression changes. Dysregulated genes were enriched for vesicle transport pathways, which is consistent with the known functions of C9orf72 protein. Further analysis suggests that the C9orf72 transcriptome is not driven by toxic RNA but is rather shaped by the depletion of pathologic TDP-43 nuclei and the loss of C9orf72 expression. These findings argue against RNA binding protein sequestration in neurons as a major contributor to C9orf72 mediated toxicity

Prion-like proteins sequester and suppress the toxicity of huntingtin exon 1

Expansions of preexisting polyglutamine (polyQ) tracts in at least nine different proteins cause devastating neurodegenerative diseases. There are many unique features to these pathologies, but there must also be unifying mechanisms underlying polyQ toxicity. Using a polyQ-expanded fragment of huntingtin exon-1 (Htt103Q), the causal protein in Huntington disease, we and others have created tractable models for investigating polyQ toxicity in yeast cells. These models recapitulate key pathological features of human diseases and provide access to an unrivalled genetic toolbox. To identify toxicity modifiers, we performed an unbiased overexpression screen of virtually every protein encoded by the yeast genome. Surprisingly, there was no overlap between our modifiers and those from a conceptually identical screen reported recently, a discrepancy we attribute to an artifact of their overexpression plasmid. The suppressors of Htt103Q toxicity recovered in our screen were strongly enriched for glutamine- and asparagine-rich prion-like proteins. Separated from the rest of the protein, the prion-like sequences of these proteins were themselves potent suppressors of polyQ-expanded huntingtin exon-1 toxicity, in both yeast and human cells. Replacing the glutamines in these sequences with asparagines abolished suppression and converted them to enhancers of toxicity. Replacing asparagines with glutamines created stronger suppressors. The suppressors (but not the enhancers) coaggregated with Htt103Q, forming large foci at the insoluble protein deposit in which proteins were highly immobile. Cells possessing foci had fewer (if any) small diffusible oligomers of Htt103Q. Until such foci were lost, cells were protected from death. We discuss the therapeutic implications of these findings.Howard Hughes Medical InstituteNational Institutes of Health (U.S.) (Grant GM25874)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374)G. Harold and Leila Y. Mathers FoundationBeckman Laser Institute FoundationEleanor Schwartz Charitable FoundationWhitehead Institute for Biomedical Researc

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein-protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.DFG [SFB740, 740/2-11, SFB618, 618/3-09, SFB/TRR43 A7]; BMBF(NGFN-Plus) [01GS08169-73, 01GS08150, 01GS08108]; HDSA Coalition for the Cure; EU (EuroSpin) [Health-F2-2009-241498, HEALTH-F2-2009-242167]; Helmholtz Association (MSBN, HelMA) [HA-215]; FCT [IF/00881/2013]info:eu-repo/semantics/publishedVersio

UniHI 4: new tools for query, analysis and visualization of the human protein–protein interactome

Human protein interaction maps have become important tools of biomedical research for the elucidation of molecular mechanisms and the identification of new modulators of disease processes. The Unified Human Interactome database (UniHI, http://www.unihi.org) provides researchers with a comprehensive platform to query and access human protein–protein interaction (PPI) data. Since its first release, UniHI has considerably increased in size. The latest update of UniHI includes over 250 000 interactions between ∼22 300 unique proteins collected from 14 major PPI sources. However, this wealth of data also poses new challenges for researchers due to the complexity of interaction networks retrieved from the database. We therefore developed several new tools to query, analyze and visualize human PPI networks. Most importantly, UniHI allows now the construction of tissue-specific interaction networks and focused querying of canonical pathways. This will enable researchers to target their analysis and to prioritize candidate proteins for follow-up studies

SPICES: Spectro-Polarimetric Imaging and Characterization of Exoplanetary Systems

SPICES (Spectro-Polarimetric Imaging and Characterization of Exoplanetary Systems) is a five-year M-class mission proposed to ESA Cosmic Vision. Its purpose is to image and characterize long-period extrasolar planets and circumstellar disks in the visible (450 - 900 nm) at a spectral resolution of about 40 using both spectroscopy and polarimetry. By 2020/22, present and near-term instruments will have found several tens of planets that SPICES will be able to observe and study in detail. Equipped with a 1.5 m telescope, SPICES can preferentially access exoplanets located at several AUs (0.5-10 AU) from nearby stars ($<$25 pc) with masses ranging from a few Jupiter masses to Super Earths ($\sim$2 Earth radii, $\sim$10 M$_{\oplus}$) as well as circumstellar disks as faint as a few times the zodiacal light in the Solar System

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker