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This is a PDF file of a peer-reviewed paper that has been accepted for publication. Although unedited, the content has been subjected to preliminary formatting. Nature is providing this early version of the typeset paper as a service to our authors and readers. The text and figures will undergo copyediting and a proof review before the paper is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

BackgroundDissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition.ResultsIn this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC).ConclusionsDGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0349-1) contains supplementary material, which is available to authorized users.

BackgroundOligodendrocytes (OLs) and myelin are critical for normal brain function and have been implicated in neurodegeneration. Several lines of evidence including neuroimaging and neuropathological data suggest that Alzheimer’s disease (AD) may be associated with dysmyelination and a breakdown of OL-axon communication.MethodsIn order to understand this phenomenon on a molecular level, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic and proteomic data obtained from human AD postmortem brain samples. We then validated these networks using gene expression datasets generated from mice with ablation of major gene expression nodes identified in our AD-dysregulated networks.ResultsThe robust OL gene coexpression networks that we identified were highly enriched for genes associated with AD risk variants, such as BIN1 and demonstrated strong dysregulation in AD. We further corroborated the structure of the corresponding gene causal networks using datasets generated from the brain of mice with ablation of key network drivers, such as UGT8, CNP and PLP1, which were identified from human AD brain data. Further, we found that mice with genetic ablations of Cnp mimicked aspects of myelin and mitochondrial gene expression dysregulation seen in brain samples from patients with AD, including decreased protein expression of BIN1 and GOT2.ConclusionsThis study provides a molecular blueprint of the dysregulation of gene expression networks of OL in AD and identifies key OL- and myelination-related genes and networks that are highly associated with AD.Electronic supplementary materialThe online version of this article (10.1186/s13024-017-0219-3) contains supplementary material, which is available to authorized users.

Objectives Although diffuse alveolar damage, a subtype of acute lung injury (ALI), is the most common microscopic pattern in coronavirus disease 2019 (COVID-19), other pathologic patterns have been described. The aim of the study was to review autopsies from COVID-19 decedents to evaluate the spectrum of pathology and correlate the results with clinical, laboratory, and radiologic findings. Methods A comprehensive and quantitative review from 40 postmortem examinations was performed. The microscopic patterns were categorized as follows: “major” when present in more than 50% of cases and “novel” if rarely or not previously described and unexpected clinically. Results Three major pulmonary patterns were identified: ALI in 29 (73%) of 40, intravascular fibrin or platelet-rich aggregates (IFPAs) in 36 (90%) of 40, and vascular congestion and hemangiomatosis-like change (VCHL) in 20 (50%) of 40. The absence of ALI (non-ALI) was novel and seen in 11 (27%) of 40. Compared with ALI decedents, those with non-ALI had a shorter hospitalization course (P = .02), chest radiographs with no or minimal consolidation (P = .01), and no pathologically confirmed cause of death (9/11). All non-ALI had VCHL and IFPAs, and clinically most had cardiac arrest. Conclusions Two distinct pulmonary phenotypic patterns—ALI and non-ALI—were noted. Non-ALI represents a rarely described phenotype. The cause of death in non-ALI is most likely COVID-19 related but requires additional corroboration.