Multi-modal analysis reveals tumor and immune features distinguishing EBV-positive and EBV-negative post-transplant lymphoproliferative disorders.

The oncogenic Epstein-Barr virus (EBV) can drive tumorigenesis with disrupted host immunity, causing malignancies including post-transplant lymphoproliferative disorders (PTLDs). PTLD can also arise in the absence of EBV, but the biological differences underlying EBV(+) and EBV(-) B cell PTLD and the associated host-EBV-tumor interactions remain poorly understood. Here, we reveal the core differences between EBV(+) and EBV(-) PTLD, characterized by increased expression of genes related to immune processes or DNA interactions, respectively, and the augmented ability of EBV(+) PTLD B cells to modulate the tumor microenvironment through elaboration of monocyte-attracting cytokines/chemokines.

Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases.

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility.

Signatures of immune dysfunction in HIV and HCV infection share features with chronic inflammation in aging and persist after viral reduction or elimination.

Chronic inflammation is thought to be a major cause of morbidity and mortality in aging, but whether similar mechanisms underlie dysfunction in infection-associated chronic inflammation is unclear. Here, we profiled the immune proteome, and cellular composition and signaling states in a cohort of aging individuals versus a set of HIV patients on long-term antiretroviral therapy therapy or hepatitis C virus (HCV) patients before and after sofosbuvir treatment. We found shared alterations in aging-associated and infection-associated chronic inflammation including T cell memory inflation, up-regulation of intracellular signaling pathways of inflammation, and diminished sensitivity to cytokines in lymphocytes and myeloid cells.

Cellular senescence impairs the reversibility of pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) in congenital cardiac shunts can be reversed by hemodynamic unloading (HU) through shunt closure. However, this reversibility potential is lost beyond a certain point in time. The reason why PAH becomes irreversible is unknown.

Increased monocyte count as a cellular biomarker for poor outcomes in fibrotic diseases: a retrospective, multicentre cohort study.

Background: There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical presentation. We aimed to investigate whether a specific immune cell type from patients with idiopathic pulmonary fibrosis could identify those at higher risk of poor outcomes. We then sought to validate our findings using cytometry and electronic health records.

A 20-Gene Set Predictive of Progression to Severe Dengue.

There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue.

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases.

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method.

Author Correction: A multi-cohort study of the immune factors associated with M. tuberculosis infection outcomes.

The spelling of author Qianting Yang was corrected; the affiliation of author Stephanus T. Malherbe was corrected; and graphs in Fig. 4b and c were corrected owing to reanalysis of the data into the correct timed intervals.

A multi-cohort study of the immune factors associated with M. tuberculosis infection outcomes.

Most infections with Mycobacterium tuberculosis (Mtb) manifest as a clinically asymptomatic, contained state, known as latent tuberculosis infection, that affects approximately one-quarter of the global population. Although fewer than one in ten individuals eventually progress to active disease, tuberculosis is a leading cause of death from infectious disease worldwide. Despite intense efforts, immune factors that influence the infection outcomes remain poorly defined.

Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry.

Modifications of histone proteins are fundamental to the regulation of epigenetic phenotypes. Dysregulations of histone modifications have been linked to the pathogenesis of diverse human diseases. However, identifying differential histone modifications in patients with immune-mediated diseases has been challenging, in part due to the lack of a powerful analytic platform to study histone modifications in the complex human immune system.

KLRD1-expressing natural killer cells predict influenza susceptibility.

Background: Influenza infects tens of millions of people every year in the USA. Other than notable risk groups, such as children and the elderly, it is difficult to predict what subpopulations are at higher risk of infection. Viral challenge studies, where healthy human volunteers are inoculated with live influenza virus, provide a unique opportunity to study infection susceptibility.

Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging.

Post-translational modifications of histone proteins and exchanges of histone variants of chromatin are central to the regulation of nearly all DNA-templated biological processes. However, the degree and variability of chromatin modifications in specific human immune cells remain largely unknown. Here, we employ a highly multiplexed mass cytometry analysis to profile the global levels of a broad array of chromatin modifications in primary human immune cells at the single-cell level.

Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD.

Objective: Although anti-tumour necrosis factor alpha (anti-TNFα) therapies represent a major breakthrough in IBD therapy, their cost-benefit ratio is hampered by an overall 30% non-response rate, adverse side effects and high costs. Thus, finding predictive biomarkers of non-response prior to commencing anti-TNFα therapy is of high value.

Design: We analysed publicly available whole-genome expression profiles of colon biopsies obtained from multiple cohorts of patients with IBD using a combined computational deconvolution-meta-analysis paradigm which allows to estimate immune cell contribution to the measured expression and capture differential regulatory programmes otherwise masked due to variation in cellular composition.

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis - the ontology and the annotations - evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade.

META-ANALYSIS OF CONTINUOUS PHENOTYPES IDENTIFIES A GENE SIGNATURE THAT CORRELATES WITH COPD DISEASE STATUS.

The utility of multi-cohort two-class meta-analysis to identify robust differentially expressed gene signatures has been well established. However, many biomedical applications, such as gene signatures of disease progression, require one-class analysis. Here we describe an R package, MetaCorrelator, that can identify a reproducible transcriptional signature that is correlated with a continuous disease phenotype across multiple datasets.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices.

Methods to increase reproducibility in differential gene expression via meta-analysis.

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies.

Origin and consequences of the relationship between protein mean and variance.

Cell-to-cell variance in protein levels (noise) is a ubiquitous phenomenon that can increase fitness by generating phenotypic differences within clonal populations of cells. An important challenge is to identify the specific molecular events that control noise. This task is complicated by the strong dependence of a protein's cell-to-cell variance on its mean expression level through a power-law like relationship (σ2∝μ1.

Validating single-cell genomics for the study of renal development.

Single-cell genomics will enable studies of the earliest events in kidney development, although it is unclear if existing technologies are mature enough to generate accurate and reproducible data on kidney progenitors. Here we designed a pilot study to validate a high-throughput assay to measure the expression levels of key regulators of kidney development in single cells isolated from embryonic mice. Our experiment produced 4608 expression measurements of 22 genes, made in small cell pools, and 28 single cells purified from the RET-positive ureteric bud.

Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data.

Next-generation sequencing (NGS) is becoming a common approach for clinical testing of oncology specimens for mutations in cancer genes. Unlike inherited variants, cancer mutations may occur at low frequencies because of contamination from normal cells or tumor heterogeneity and can therefore be challenging to detect using common NGS analysis tools, which are often designed for constitutional genomic studies. We generated high-coverage (>1000×) NGS data from synthetic DNA mixtures with variant allele fractions (VAFs) of 25% to 2.

Population-based rare variant detection via pooled exome or custom hybridization capture with or without individual indexing.

Background: Rare genetic variation in the human population is a major source of pathophysiological variability and has been implicated in a host of complex phenotypes and diseases. Finding disease-related genes harboring disparate functional rare variants requires sequencing of many individuals across many genomic regions and comparing against unaffected cohorts. However, despite persistent declines in sequencing costs, population-based rare variant detection across large genomic target regions remains cost prohibitive for most investigators.

Detection of rare genomic variants from pooled sequencing using SPLINTER.

As DNA sequencing technology has markedly advanced in recent years(2), it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought(3). In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease(4,5). Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants(6-8).

Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families.

Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. Mutation screening in these genes has been performed in either very small series or in single families with late onset AD (LOAD). Similarly, studies in single families have reported mutations in MAPT and GRN associated with clinical AD but no systematic screen of a large dataset has been performed to determine how frequently this occurs.

Rare missense variants in CHRNB4 are associated with reduced risk of nicotine dependence.

Genome-wide association studies have identified common variation in the CHRNA5-CHRNA3-CHRNB4 and CHRNA6-CHRNB3 gene clusters that contribute to nicotine dependence. However, the role of rare variation in risk for nicotine dependence in these nicotinic receptor genes has not been studied. We undertook pooled sequencing of the coding regions and flanking sequence of the CHRNA5, CHRNA3, CHRNB4, CHRNA6 and CHRNB3 genes in African American and European American nicotine-dependent smokers and smokers without symptoms of dependence.

High-throughput discovery of rare insertions and deletions in large cohorts.

Pooled-DNA sequencing strategies enable fast, accurate, and cost-effect detection of rare variants, but current approaches are not able to accurately identify short insertions and deletions (indels), despite their pivotal role in genetic disease. Furthermore, the sensitivity and specificity of these methods depend on arbitrary, user-selected significance thresholds, whose optimal values change from experiment to experiment. Here, we present a combined experimental and computational strategy that combines a synthetically engineered DNA library inserted in each run and a new computational approach named SPLINTER that detects and quantifies short indels and substitutions in large pools.