PIAS1 modulates striatal transcription, DNA damage repair, and SUMOylation with relevance to Huntington's disease.

DNA damage repair genes are modifiers of disease onset in Huntington's disease (HD), but how this process intersects with associated disease pathways remains unclear. Here we evaluated the mechanistic contributions of protein inhibitor of activated STAT-1 (PIAS1) in HD mice and HD patient-derived induced pluripotent stem cells (iPSCs) and find a link between PIAS1 and DNA damage repair pathways. We show that PIAS1 is a component of the transcription-coupled repair complex, that includes the DNA damage end processing enzyme polynucleotide kinase-phosphatase (PNKP), and that PIAS1 is a SUMO E3 ligase for PNKP. Pias1 knockdown (KD) in HD mice had a normalizing effect on HD transcriptional dysregulation associated with synaptic function and disease-associated transcriptional coexpression modules enriched for DNA damage repair mechanisms as did reduction of PIAS1 in HD iPSC-derived neurons. KD also restored mutant HTT-perturbed enzymatic activity of PNKP and modulated genomic integrity of several transcriptionally normalized genes. The findings here now link SUMO modifying machinery to DNA damage repair responses and transcriptional modulation in neurodegenerative disease.

No reliable gene expression biomarkers of current or impending neurocognitive impairment in peripheral blood monocytes of persons living with HIV.

Events leading to and propagating neurocognitive impairment (NCI) in HIV-1-infected (HIV+) persons are largely mediated by peripheral blood monocytes. We previously identified expression levels of individual genes and gene networks in peripheral blood monocytes that correlated with neurocognitive functioning in HIV+ adults. Here, we expand upon those findings by examining if gene expression data at baseline is predictive of change in neurocognitive functioning 2 years later. We also attempt to validate the original findings in a new sample of HIV+ patients and determine if the findings are HIV specific by including HIV-uninfected (HIV-) participants as a comparison group. At two time points, messenger RNA (mRNA) was isolated from the monocytes of 123 HIV+ and 60 HIV- adults enrolled in the Multicenter AIDS Cohort Study and analyzed with the Illumina HT-12 v4 Expression BeadChip. All participants received baseline and follow-up neurocognitive testing 2 years after mRNA analysis. Data were analyzed using standard gene expression analysis and weighted gene co-expression network analysis with correction for multiple testing. Gene sets were analyzed for GO term enrichment. Only weak reproducibility of associations of single genes with neurocognitive functioning was observed, indicating that such measures are unreliable as biomarkers for HIV-related NCI; however, gene networks were generally preserved between time points and largely reproducible, suggesting that these may be more reliable. Several gene networks associated with variables related to HIV infection were found (e.g., MHC I antigen processing, TNF signaling, interferon gamma signaling, and antiviral defense); however, no significant associations were found for neurocognitive function. Furthermore, neither individual gene probes nor gene networks predicted later neurocognitive change. This study did not validate our previous findings and does not support the use of monocyte gene expression profiles as a biomarker for current or future HIV-associated neurocognitive impairment.

Decreased but persistent epigenetic age acceleration is associated with changes in T-cell subsets after initiation of highly active antiretroviral therapy in persons living with HIV.

Introduction: Persons living with HIV (PLWH) experience the early onset of age-related illnesses, even in the setting of successful human immunodeficiency virus (HIV) suppression with highly active antiretroviral therapy (HAART). HIV infection is associated with accelerated epigenetic aging as measured using DNA methylation (DNAm)-based estimates of biological age and of telomere length (TL). Methods: DNAm levels (Infinium MethylationEPIC BeadChip) from peripheral blood mononuclear cells from 200 PLWH and 199 HIV-seronegative (SN) participants matched on chronologic age, hepatitis C virus, and time intervals were used to calculate epigenetic age acceleration, expressed as age-adjusted acceleration residuals from 4 epigenetic clocks [Horvath's pan-tissue age acceleration residual (AAR), extrinsic epigenetic age acceleration (EEAA), phenotypic epigenetic age acceleration (PEAA), and grim epigenetic age acceleration (GEAA)] plus age-adjusted DNAm-based TL (aaDNAmTL). Epigenetic age acceleration was compared for PLWH and SN participants at two visits: up to 1.5 years prior and 2-3 years after HAART (or equivalent visits). Flow cytometry was performed in PLWH and SN participants at both visits to evaluate T-cell subsets. Results: Epigenetic age acceleration in PLWH decreased after the initiation of HAART but remained greater post-HAART than that in age-matched SN participants, with differences in medians of 6.6, 9.1, and 7.7 years for AAR, EEAA, and PEAA, respectively, and 0.39 units of aaDNAmTL shortening (all p < 0.001). Cumulative HIV viral load after HAART initiation was associated with some epigenetic acceleration (EEAA, PEAA, and aaDNAmTL), but even PLWH with undetectable HIV post-HAART showed persistent epigenetic age acceleration compared to SN participants (p < 0.001). AAR, EEAA, and aaDNAmTL showed significant associations with total, naïve, and senescent CD8 T-cell counts; the total CD4 T-cell counts were associated with AAR, EEAA, and PEAA (p = 0.04 to <0.001). In an epigenome-wide analysis using weighted gene co-methylation network analyses, 11 modules demonstrated significant DNAm differences pre- to post-HAART initiation. Of these, nine were previously identified as significantly different from pre- to post-HIV infection but in the opposite direction. Discussion: In this large longitudinal study, we demonstrated that, although the magnitude of the difference decreases with HAART is associated with the cumulative viral load, PLWH are persistently epigenetically older than age-matched SN participants even after the successful initiation of HAART, and these changes are associated with changes in T-cell subsets.

Random generalized linear model: a highly accurate and interpretable ensemble predictor.

BACKGROUND: Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature selection tends to overfit the data and leads to low predictive accuracy. Therefore, it remains an important research goal to combine the advantages of ensemble predictors (high accuracy) with the advantages of forward regression modeling (interpretability). To address this goal several articles have explored GLM based ensemble predictors. Since limited evaluations suggested that these ensemble predictors were less accurate than alternative predictors, they have found little attention in the literature. RESULTS: Comprehensive evaluations involving hundreds of genomic data sets, the UCI machine learning benchmark data, and simulations are used to give GLM based ensemble predictors a new and careful look. A novel bootstrap aggregated (bagged) GLM predictor that incorporates several elements of randomness and instability (random subspace method, optional interaction terms, forward variable selection) often outperforms a host of alternative prediction methods including random forests and penalized regression models (ridge regression, elastic net, lasso). This random generalized linear model (RGLM) predictor provides variable importance measures that can be used to define a "thinned" ensemble predictor (involving few features) that retains excellent predictive accuracy. CONCLUSION: RGLM is a state of the art predictor that shares the advantages of a random forest (excellent predictive accuracy, feature importance measures, out-of-bag estimates of accuracy) with those of a forward selected generalized linear model (interpretability). These methods are implemented in the freely available R software package randomGLM.

A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models.

We report a systems genetic analysis of high density lipoprotein (HDL) levels in an F2 intercross between inbred strains CAST/EiJ and C57BL/6J. We previously showed that there are dramatic differences in HDL metabolism in a cross between these strains, and we now report co-expression network analysis of HDL that integrates global expression data from liver and adipose with relevant metabolic traits. Using data from a total of 293 F2 intercross mice, we constructed weighted gene co-expression networks and identified modules (subnetworks) associated with HDL and clinical traits. These were examined for genes implicated in HDL levels based on large human genome-wide associations studies (GWAS) and examined with respect to conservation between tissue and sexes in a total of 9 data sets. We identify genes that are consistently ranked high by association with HDL across the 9 data sets. We focus in particular on two genes, Wfdc2 and Hdac3, that are located in close proximity to HDL QTL peaks where causal testing indicates that they may affect HDL. Our results provide a rich resource for studies of complex metabolic interactions involving HDL. This article is part of a Special Issue entitled Advances in High Density Lipoprotein Formation and Metabolism: A Tribute to John F. Oram (1945-2010).

Erratum: Accelerated aging with HIV begins at the time of initial HIV infection.

[This corrects the article DOI: 10.1016/j.isci.2022.104488.].

BAC Transgenic Expression of Human TREM2-R47H Remodels Amyloid Plaques but Unable to Reprogram Plaque-associated Microglial Reactivity in 5xFAD Mice.

Background: Genetic study of late-onset Alzheimer's disease (AD) reveals that a rare Arginine-to-Histamine mutation at amino acid residue 47 (R47H) in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) results in increased disease risk. TREM2 plays critical roles in regulating microglial response to amyloid plaques in AD, leading to their clustering and activation surrounding the plaques. We previously showed that increasing human TREM2 gene dosage exerts neuroprotective effects against AD-related deficits in amyloid depositing mouse models of AD. However, the in vivo effects of the R47H mutation on human TREM2-mediated microglial reprogramming and neuroprotection remains poorly understood. Method: Here we created a BAC transgenic mouse model expressing human TREM2 with the R47H mutation in its cognate genomic context (BAC-TREM2-R47H). Importantly, the BAC used in this study was engineered to delete critical exons of other TREM-like genes on the BAC to prevent confounding effects of overexpressing multiple TREM-like genes. We crossed BAC-TREM2- R47H mice with 5xFAD [1], an amyloid depositing mouse model of AD, to evaluate amyloid pathologies and microglial phenotypes, transcriptomics and in situ expression of key TREM2 -dosage dependent genes. We also compared the key findings in 5xFAD/BAC-TREM2-R47H to those observed in 5xFAD/BAC-TREM2 mice. Result: Both BAC-TREM2 and BAC-TREM2-R47H showed proper expression of three splicing isoforms of TREM2 that are normally found in human. In 5xFAD background, elevated TREM2-R47H gene dosages significantly reduced the plaque burden, especially the filamentous type. The results were consistent with enhanced phagocytosis and altered NLRP3 inflammasome activation in BAC- TREM2-R47H microglia in vitro. However, unlike TREM2 overexpression, elevated TREM2- R47H in 5xFAD failed to ameliorate cognitive and transcriptomic deficits. In situ analysis of key TREM2 -dosage dependent genes and microglial morphology uncovered that TREM2-R47H showed a loss-of-function phenotype in reprogramming of plaque-associated microglial reactivity and gene expression in 5xFAD. Conclusion: Our study demonstrated that the AD-risk variant has a previously unknown, mixture of partial and full loss of TREM2 functions in modulating microglial response in AD mouse brains. Together, our new BAC-TREM2-R47H model and prior BAC-TREM2 mice are invaluable resource to facilitate the therapeutic discovery that target human TREM2 and its R47H variant to ameliorate AD and other neurodegenerative disorders.

Comparison of co-expression measures: mutual information, correlation, and model based indices.

BACKGROUND: Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). RESULTS: We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. CONCLUSION: The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects.

BACKGROUND: The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. RESULTS: Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. CONCLUSIONS: Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated.

Is my network module preserved and reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.

Fast R Functions for Robust Correlations and Hierarchical Clustering.

Many high-throughput biological data analyses require the calculation of large correlation matrices and/or clustering of a large number of objects. The standard R function for calculating Pearson correlation can handle calculations without missing values efficiently, but is inefficient when applied to data sets with a relatively small number of missing data. We present an implementation of Pearson correlation calculation that can lead to substantial speedup on data with relatively small number of missing entries. Further, we parallelize all calculations and thus achieve further speedup on systems where parallel processing is available. A robust correlation measure, the biweight midcorrelation, is implemented in a similar manner and provides comparable speed. The functions cor and bicor for fast Pearson and biweight midcorrelation, respectively, are part of the updated, freely available R package WGCNA.The hierarchical clustering algorithm implemented in R function hclust is an order n(3) (n is the number of clustered objects) version of a publicly available clustering algorithm (Murtagh 2012). We present the package flashClust that implements the original algorithm which in practice achieves order approximately n(2), leading to substantial time savings when clustering large data sets.

Accelerated aging with HIV begins at the time of initial HIV infection.

Living with HIV infection is associated with early onset of aging-related chronic conditions, sometimes described as accelerated aging. Epigenetic DNA methylation patterns can evaluate acceleration of biological age relative to chronological age. The impact of initial HIV infection on five epigenetic measures of aging was examined before and approximately 3 years after HIV infection in the same individuals (n=102). Significant epigenetic age acceleration (median 1.9-4.8 years) and estimated telomere length shortening (all p≤ 0.001) were observed from pre-to post-HIV infection, and remained significant in three epigenetic measures after controlling for T cell changes. No acceleration was seen in age- and time interval-matched HIV-uninfected controls. Changes in genome-wide co-methylation clusters were also significantly associated with initial HIV infection (p≤ 2.0 × 10-4). These longitudinal observations clearly demonstrate an early and substantial impact of HIV infection on the epigenetic aging process, and suggest a role for HIV itself in the earlier onset of clinical aging.

Mapping brain gene coexpression in daytime transcriptomes unveils diurnal molecular networks and deciphers perturbation gene signatures.

Brain tissue transcriptomes may be organized into gene coexpression networks, but their underlying biological drivers remain incompletely understood. Here, we undertook a large-scale transcriptomic study using 508 wild-type mouse striatal tissue samples dissected exclusively in the afternoons to define 38 highly reproducible gene coexpression modules. We found that 13 and 11 modules are enriched in cell-type and molecular complex markers, respectively. Importantly, 18 modules are highly enriched in daily rhythmically expressed genes that peak or trough with distinct temporal kinetics, revealing the underlying biology of striatal diurnal gene networks. Moreover, the diurnal coexpression networks are a dominant feature of daytime transcriptomes in the mouse cortex. We next employed the striatal coexpression modules to decipher the striatal transcriptomic signatures from Huntington's disease models and heterozygous null mice for 52 genes, uncovering novel functions for Prkcq and Kdm4b in oligodendrocyte differentiation and bipolar disorder-associated Trank1 in regulating anxiety-like behaviors and nocturnal locomotion.

Uninterrupted CAG repeat drives striatum-selective transcriptionopathy and nuclear pathogenesis in human Huntingtin BAC mice.

In Huntington's disease (HD), the uninterrupted CAG repeat length, but not the polyglutamine length, predicts disease onset. However, the underlying pathobiology remains unclear. Here, we developed bacterial artificial chromosome (BAC) transgenic mice expressing human mutant huntingtin (mHTT) with uninterrupted, and somatically unstable, CAG repeats that exhibit progressive disease-related phenotypes. Unlike prior mHTT transgenic models with stable, CAA-interrupted, polyglutamine-encoding repeats, BAC-CAG mice show robust striatum-selective nuclear inclusions and transcriptional dysregulation resembling those in murine huntingtin knockin models and HD patients. Importantly, the striatal transcriptionopathy in HD models is significantly correlated with their uninterrupted CAG repeat length but not polyglutamine length. Finally, among the pathogenic entities originating from mHTT genomic transgenes and only present or enriched in the uninterrupted CAG repeat model, somatic CAG repeat instability and nuclear mHTT aggregation are best correlated with early-onset striatum-selective molecular pathogenesis and locomotor and sleep deficits, while repeat RNA-associated pathologies and repeat-associated non-AUG (RAN) translation may play less selective or late pathogenic roles, respectively.

Strategies for aggregating gene expression data: the collapseRows R function.

BACKGROUND: Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied. RESULTS: We introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways. CONCLUSIONS: The R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.

Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism- and disease-specific manner.

Findings about chronic complex diseases are difficult to extrapolate from animal models to humans. We reason that organs may have core network modules that are preserved between species and are predictably altered when homeostasis is disrupted. To test this idea, we perturbed hepatic homeostasis in mice by dietary challenge and compared the liver transcriptome with that in human fatty liver disease and liver cancer. Co-expression module preservation analysis pointed to alterations in immune responses and metabolism (core modules) in both human and mouse datasets. The extent of derailment in core modules was predictive of survival in the cancer genome atlas (TCGA) liver cancer dataset. We identified module eigengene quantitative trait loci (module-eQTL) for these predictive co-expression modules, targeting of which may resolve homeostatic perturbations and improve patient outcomes. The framework presented can be used to understand homeostasis at systems levels in pre-clinical models and in humans. A record of this paper's transparent peer review process is included in the supplemental information.

Is human blood a good surrogate for brain tissue in transcriptional studies?

BACKGROUND: Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). RESULTS: We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10⁻9°); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. CONCLUSIONS: Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable.

Detecting network modules in fMRI time series: a weighted network analysis approach.

Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results.

Weighted gene coexpression network analysis: state of the art.

Weighted gene coexpression network analysis (WGCNA) has been applied to many important studies since its introduction in 2005. WGCNA can be used as a data exploratory tool or as a gene screening method; WGCNA can also be used as a tool to generate testable hypothesis for validation in independent data sets. In this article, we review key concepts of WGCNA and some of its applications in gene expression analysis of oncology, brain function, and protein interaction data.

Disease-related Huntingtin seeding activities in cerebrospinal fluids of Huntington's disease patients.

In Huntington's disease (HD), the mutant Huntingtin (mHTT) is postulated to mediate template-based aggregation that can propagate across cells. It has been difficult to quantitatively detect such pathological seeding activities in patient biosamples, e.g. cerebrospinal fluids (CSF), and study their correlation with the disease manifestation. Here we developed a cell line expressing a domain-engineered mHTT-exon 1 reporter, which showed remarkably high sensitivity and specificity in detecting mHTT seeding species in HD patient biosamples. We showed that the seeding-competent mHTT species in HD CSF are significantly elevated upon disease onset and with the progression of neuropathological grades. Mechanistically, we showed that mHTT seeding activities in patient CSF could be ameliorated by the overexpression of chaperone DNAJB6 and by antibodies against the polyproline domain of mHTT. Together, our study developed a selective and scalable cell-based tool to investigate mHTT seeding activities in HD CSF, and demonstrated that the CSF mHTT seeding species are significantly associated with certain disease states. This seeding activity can be ameliorated by targeting specific domain or proteostatic pathway of mHTT, providing novel insights into such pathological activities.