ZCCHC8, the nuclear exosome targeting component, is mutated in familial pulmonary fibrosis and is required for telomerase RNA maturation.

Short telomere syndromes manifest as familial idiopathic pulmonary fibrosis; they are the most common premature aging disorders. We used genome-wide linkage to identify heterozygous loss of function of ZCCHC8, a zinc-knuckle containing protein, as a cause of autosomal dominant pulmonary fibrosis. ZCCHC8 associated with TR and was required for telomerase function. In ZCCHC8 knockout cells and in mutation carriers, genomically extended telomerase RNA (TR) accumulated at the expense of mature TR, consistent with a role for ZCCHC8 in mediating TR 3' end targeting to the nuclear RNA exosome. We generated Zcchc8-null mice and found that heterozygotes, similar to human mutation carriers, had TR insufficiency but an otherwise preserved transcriptome. In contrast, Zcchc8-/- mice developed progressive and fatal neurodevelopmental pathology with features of a ciliopathy. The Zcchc8-/- brain transcriptome was highly dysregulated, showing accumulation and 3' end misprocessing of other low-abundance RNAs, including those encoding cilia components as well as the intronless replication-dependent histones. Our data identify a novel cause of human short telomere syndromes-familial pulmonary fibrosis and uncover nuclear exosome targeting as an essential 3' end maturation mechanism that vertebrate TR shares with replication-dependent histones.

The Interaction of Borrelia burgdorferi with Human Dendritic Cells: Functional Implications.

Dendritic cells bridge the innate and adaptive immune responses by serving as sensors of infection and as the primary APCs responsible for the initiation of the T cell response against invading pathogens. The naive T cell activation requires the following three key signals to be delivered from dendritic cells: engagement of the TCR by peptide Ags bound to MHC molecules (signal 1), engagement of costimulatory molecules on both cell types (signal 2), and expression of polarizing cytokines (signal 3). Initial interactions between Borrelia burgdorferi, the causative agent of Lyme disease, and dendritic cells remain largely unexplored. To address this gap in knowledge, we cultured live B. burgdorferi with monocyte-derived dendritic cells (mo-DCs) from healthy donors to examine the bacterial immunopeptidome associated with HLA-DR. In parallel, we examined changes in the expression of key costimulatory and regulatory molecules as well as profiled the cytokines released by dendritic cells when exposed to live spirochetes. RNA-sequencing studies on B. burgdorferi-pulsed dendritic cells show a unique gene expression signature associated with B. burgdorferi stimulation that differs from stimulation with lipoteichoic acid, a TLR2 agonist. These studies revealed that exposure of mo-DCs to live B. burgdorferi drives the expression of both pro- and anti-inflammatory cytokines as well as immunoregulatory molecules (e.g., PD-L1, IDO1, Tim3). Collectively, these studies indicate that the interaction of live B. burgdorferi with mo-DCs promotes a unique mature DC phenotype that likely impacts the nature of the adaptive T cell response generated in human Lyme disease.

Excessive O-GlcNAcylation Causes Heart Failure and Sudden Death.

BACKGROUND: Heart failure is a leading cause of death worldwide and is associated with the rising prevalence of obesity, hypertension, and diabetes. O-GlcNAcylation (the attachment of O-linked ß-N-acetylglucosamine [O-GlcNAc] moieties to cytoplasmic, nuclear, and mitochondrial proteins) is a posttranslational modification of intracellular proteins and serves as a metabolic rheostat for cellular stress. Total levels of O-GlcNAcylation are determined by nutrient and metabolic flux, in addition to the net activity of 2 enzymes: O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). Failing myocardium is marked by increased O-GlcNAcylation, but whether excessive O-GlcNAcylation contributes to cardiomyopathy and heart failure is unknown. METHODS: We developed 2 new transgenic mouse models with myocardial overexpression of OGT and OGA to control O-GlcNAcylation independent of pathologic stress. RESULTS: We found that OGT transgenic hearts showed increased O-GlcNAcylation and developed severe dilated cardiomyopathy, ventricular arrhythmias, and premature death. In contrast, OGA transgenic hearts had lower O-GlcNAcylation but identical cardiac function to wild-type littermate controls. OGA transgenic hearts were resistant to pathologic stress induced by pressure overload with attenuated myocardial O-GlcNAcylation levels after stress and decreased pathologic hypertrophy compared with wild-type controls. Interbreeding OGT with OGA transgenic mice rescued cardiomyopathy and premature death, despite persistent elevation of myocardial OGT. Transcriptomic and functional studies revealed disrupted mitochondrial energetics with impairment of complex I activity in hearts from OGT transgenic mice. Complex I activity was rescued by OGA transgenic interbreeding, suggesting an important role for mitochondrial complex I in O-GlcNAc-mediated cardiac pathology. CONCLUSIONS: Our data provide evidence that excessive O-GlcNAcylation causes cardiomyopathy, at least in part, attributable to defective energetics. Enhanced OGA activity is well tolerated and attenuation of O-GlcNAcylation is beneficial against pressure overload-induced pathologic remodeling and heart failure. These findings suggest that attenuation of excessive O-GlcNAcylation may represent a novel therapeutic approach for cardiomyopathy.

Jutils: a visualization toolkit for differential alternative splicing events.

MOTIVATION: Gene alternative splicing plays an important role in development, tissue specialization and disease and differences in splicing patterns can reveal important factors for phenotypic differentiation. While multiple computational methods exist to determine splicing differences, there is a need for user-friendly visualizations that present an intuitive view of the data and work across methods. RESULTS: We developed a toolkit, Jutils, for visualizing differential splicing events at the intron (splice junction) level. Jutils is method-agnostic, converting individual tools' output into a unified representation and using it to create visualizations. Jutils creates three types of visualizations, namely heatmaps of absolute and Z-score normalized splice ratios, sashimi plots and Venn diagrams of results from multiple comparisons. Jutils is lightweight, relying solely on the unified data file for visualizations. AVAILABILITY AND IMPLEMENTATION: Jutils is implemented in Python and is available from https://github.com/Splicebox/Jutils.

Expression of HLA and Autoimmune Pathway Genes in Liver Biopsies of Young Subjects With Autoimmune Hepatitis Type 1.

OBJECTIVES: To test the hypothesis that autoimmune hepatitis (AIH type I) in young subjects is due to genetic differences in proinflammatory genes responding to viral triggers in patients and controls. METHODS: Intrahepatic gene expression was compared between AIH type I (n = 24, age 9-30 years) patients (hereafter referred to as the AIH group) and controls (n = 21, age 4-25 years). RNA sequencing was performed on complementary DNA (cDNA) libraries made from total RNA extracted from formalin-fixed paraffin-embedded (FFPE) liver biopsy samples. Gene expression levels were quantified, and differentially expressed genes were functionally analyzed. Pathway analysis was performed using the databases Kyoto Encyclopedia of Genes and Genomes (KEGG) and PANTHER. The remaining sequences were mapped to the RefSeq complete set of viral genomes. RESULTS: Differential gene analysis identified 181 genes that were significantly differentially expressed (136 upregulated in the AIH group). Autoimmune pathway genes such as CD19 and CD20 which are important in B cell regulation and maturation as well as, CD8 and LY9 , which are T-cell related, were upregulated in our AIH group. Genes implicated in AIH pathogenesis including CXCL10 , which is thought to be associated with AIH severity and progression, complement genes ( C1QA, C1QB , and C1QC ), and human leucocyte antigen ( HLA ) genes ( HLA-DRB1, HLA-DRA, HLA-B , and HLA-C ) were upregulated in samples from the AIH group. Specific viral etiologies were not found. CONCLUSIONS: Unbiased next-generation sequencing and differential gene expression analysis of the AIH group has not only added support for the role of B cells in the pathogenesis and treatment of AIH but also has introduced potential new therapeutic targets: CXCL10 (anti- CXCL10 ) and several complement system-related genes.

Comparison of the skin microbiota in acne and rosacea.

Acne and rosacea, despite their similar clinical presentations, follow distinct clinical courses, suggesting that fundamental differences exist in their pathophysiology. We performed a case-control study profiling the skin microbiota in rosacea and acne patients compared to matched controls. Nineteen rosacea and eight acne patients were matched to controls by age ± 5 years, sex and race. DNA was extracted from facial skin swabs. The V3V4 region of the bacterial 16S rRNA gene was sequenced using Illumina MiSeq and analysed using QIIME/Metastats 2.0 software. The mean relative abundance of Cutibacterium acnes in rosacea with inflammatory papules and pustules (20.454% ±16.943%) was more similar to that of acne (19.055% ±15.469%) than that of rosacea without inflammatory papules and pustules (30.419% ±21.862%). C acnes (P = .048) and Serratia marcescens (P = .038) were significantly enriched in individuals with rosacea compared to acne. Investigating the differences between the skin microbiota in acne and rosacea can provide important clues towards understanding the disease progression in each condition.

Development of Methods to Extract RNA From Archived Pediatric Needle Liver Biopsies to Produce Sequencing Data.

ABSTRACT: Genetic susceptibility has been proposed as etiopathogenic in several pediatric liver diseases including autoimmune hepatitis (AIH). High throughput sequencing (HTPS) has been applied to archived needle liver biopsies obtained from adults but rarely to pediatric biopsies. For conclusive diagnosis of AIH, most subjects have an initial formalin-fixed paraffin embedded (FFPE) needle liver biopsy that is eventually archived and may be stored for decades. OBJECTIVE: Our goal was to develop methods to utilize tissue from archived needle liver biopsies for extraction of RNA sufficient to produce HTPS data. METHODS: We extracted total RNA from 45 FFPE needle liver biopsy samples (24 AIH type 1 patients and 21 controls [ages 15_11 and 19_10]; biopsy storage time 0.5-20 years) and constructed cDNA libraries that were then sequenced on an Illumina HiSeq2000 platform. RESULTS: Forty (89%) of the libraries produced high-quality sequences for further analyses. The average number of sequences obtained per library from HTPS was 55,136,519 (range 14,914,291-184,027,499). There was a significant inverse relationship between the number of human reads obtained and the age of the specimen (P < 2_10_7). It was possible to classify more than 90% of the reads as known genes in samples that had been stored for less than 10 years. CONCLUSIONS: Archived needle liver biopsies can be used for sequence based interrogation of the etiologic origins of complex liver diseases of young subjects, such as AIH.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Motivation: Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results: We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation: SEVA is implemented in the R/Bioconductor package GSReg. Contact: bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

CLASS2: accurate and efficient splice variant annotation from RNA-seq reads.

Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to accurately capture splicing variation has proven difficult. Current programs can find major isoforms of a gene but miss lower abundance variants, or are sensitive but imprecise. CLASS2 is a novel open source tool for accurate genome-guided transcriptome assembly from RNA-seq reads based on the model of splice graph. An extension of our program CLASS, CLASS2 jointly optimizes read patterns and the number of supporting reads to score and prioritize transcripts, implemented in a novel, scalable and efficient dynamic programming algorithm. When compared against reference programs, CLASS2 had the best overall accuracy and could detect up to twice as many splicing events with precision similar to the best reference program. Notably, it was the only tool to produce consistently reliable transcript models for a wide range of applications and sequencing strategies, including ribosomal RNA-depleted samples. Lightweight and multi-threaded, CLASS2 requires <3GB RAM and can analyze a 350 million read set within hours, and can be widely applied to transcriptomics studies ranging from clinical RNA sequencing, to alternative splicing analyses, and to the annotation of new genomes.

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates.

Differences in alternative splicing patterns can reveal important markers of phenotypic differentiation, including biomarkers of disease. Emerging large and complex RNA-seq datasets from disease and population studies include multiple confounders such as sex, age, ethnicity and clinical attributes, which demand highly specialized data analysis tools. However, few methods are equipped to handle the new challenges. We describe an implementation of our programs MntJULiP and Jutils for differential splicing detection and visualization from RNA-seq data that takes into account covariates. MntJULiP detects intron-level differences in alternative splicing from RNA-seq data using a Bayesian mixture model. Jutils visualizes alternative splicing variation with heatmaps, PCA and sashimi plots, and Venn diagrams. Our tools are scalable and can process thousands of samples within hours. We applied our methods to the collection of GTEx brain RNA-seq samples to deconvolute the effects of sex and age at death on the splicing patterns. In particular, clustering of covariate adjusted data identifies a subgroup of individuals undergoing a distinct splicing program during aging. MntJULiP and Jutils are implemented in Python and are available from https://github.com/splicebox/.

Predicting Alu exonization in the human genome with a deep learning model.

Alu exonization, or the recruitment of intronic Alu elements into gene sequences, has contributed to functional diversification; however, its extent and the ways in which it influences gene regulation are not fully understood. We developed an unbiased approach to predict Alu exonization events from genomic sequences implemented in a deep learning model, eXAlu, that overcomes the limitations of tissue or condition specificity and the computational burden of RNA-seq analysis. The model captures previously reported characteristics of exonized Alu sequences and can predict sequence elements important for Alu exonization. Using eXAlu, we estimate the number of Alu elements in the human genome undergoing exonization to be between 55-110K, 11-21 fold more than represented in the GENCODE gene database. Using RT-PCR we were able to validate selected predicted Alu exonization events, supporting the accuracy of our method. Lastly, we highlight a potential application of our method to identify polymorphic Alu insertion exonizations in individuals and in the population from whole genome sequencing data.

CLASS: constrained transcript assembly of RNA-seq reads.

BACKGROUND: RNA-seq has revolutionized our ability to survey the cellular transcriptome in great detail. However, while several approaches have been developed, the problem of assembling the short reads into full-length transcripts remains challenging. RESULTS: We developed a novel algorithm and software tool, CLASS (Constraint-based Local Assembly and Selection of Splice variants), for accurately assembling splice variants using local read coverage patterns of RNA-seq reads, contiguity constraints from read pairs and spliced reads, and optionally information about gene structure extracted from cDNA sequence databases. The algorithmic underpinnings of CLASS are: i) a linear program to infer exons, ii) a compact splice graph representation of a gene and its splice variants, and iii) a transcript selection scheme that takes into account contiguity constraints and, where available, knowledge about gene structure. CONCLUSION: In comparisons against leading transcript assembly programs, CLASS is more accurate on both simulated and real reads and produces results that are easier to interpret when applied to large scale real data, and therefore is a promising analysis tool for next generation sequencing data. AVAILABILITY: CLASS is available from http://sourceforge.net/projects/splicebox.

Multi-platform next-generation sequencing of the domestic turkey (Meleagris gallopavo): genome assembly and analysis.

A synergistic combination of two next-generation sequencing platforms with a detailed comparative BAC physical contig map provided a cost-effective assembly of the genome sequence of the domestic turkey (Meleagris gallopavo). Heterozygosity of the sequenced source genome allowed discovery of more than 600,000 high quality single nucleotide variants. Despite this heterozygosity, the current genome assembly (∼1.1 Gb) includes 917 Mb of sequence assigned to specific turkey chromosomes. Annotation identified nearly 16,000 genes, with 15,093 recognized as protein coding and 611 as non-coding RNA genes. Comparative analysis of the turkey, chicken, and zebra finch genomes, and comparing avian to mammalian species, supports the characteristic stability of avian genomes and identifies genes unique to the avian lineage. Clear differences are seen in number and variety of genes of the avian immune system where expansions and novel genes are less frequent than examples of gene loss. The turkey genome sequence provides resources to further understand the evolution of vertebrate genomes and genetic variation underlying economically important quantitative traits in poultry. This integrated approach may be a model for providing both gene and chromosome level assemblies of other species with agricultural, ecological, and evolutionary interest.

Comparison of genome degradation in Paratyphi A and Typhi, human-restricted serovars of Salmonella enterica that cause typhoid.

Salmonella enterica serovars often have a broad host range, and some cause both gastrointestinal and systemic disease. But the serovars Paratyphi A and Typhi are restricted to humans and cause only systemic disease. It has been estimated that Typhi arose in the last few thousand years. The sequence and microarray analysis of the Paratyphi A genome indicates that it is similar to the Typhi genome but suggests that it has a more recent evolutionary origin. Both genomes have independently accumulated many pseudogenes among their approximately 4,400 protein coding sequences: 173 in Paratyphi A and approximately 210 in Typhi. The recent convergence of these two similar genomes on a similar phenotype is subtly reflected in their genotypes: only 30 genes are degraded in both serovars. Nevertheless, these 30 genes include three known to be important in gastroenteritis, which does not occur in these serovars, and four for Salmonella-translocated effectors, which are normally secreted into host cells to subvert host functions. Loss of function also occurs by mutation in different genes in the same pathway (e.g., in chemotaxis and in the production of fimbriae).

Overexpression screen of chromosome 21 genes reveals modulators of Sonic hedgehog signaling relevant to Down syndrome.

Trisomy 21 and mutations in the Sonic hedgehog (SHH) signaling pathway cause overlapping and pleiotropic phenotypes including cerebellar hypoplasia, craniofacial abnormalities, congenital heart defects and Hirschsprung disease. Trisomic cells derived from individuals with Down syndrome possess deficits in SHH signaling, suggesting that overexpression of human chromosome 21 genes may contribute to SHH-associated phenotypes by disrupting normal SHH signaling during development. However, chromosome 21 does not encode any known components of the canonical SHH pathway. Here, we sought to identify chromosome 21 genes that modulate SHH signaling by overexpressing 163 chromosome 21 cDNAs in a series of SHH-responsive mouse cell lines. We confirmed overexpression of trisomic candidate genes using RNA sequencing in the cerebella of Ts65Dn and TcMAC21 mice, model systems for Down syndrome. Our findings indicate that some human chromosome 21 genes, including DYRK1A, upregulate SHH signaling, whereas others, such as HMGN1, inhibit SHH signaling. Individual overexpression of four genes (B3GALT5, ETS2, HMGN1 and MIS18A) inhibits the SHH-dependent proliferation of primary granule cell precursors. Our study prioritizes dosage-sensitive chromosome 21 genes for future mechanistic studies. Identification of the genes that modulate SHH signaling may suggest new therapeutic avenues for ameliorating Down syndrome phenotypes.

Interleukin-6 Drives Mitochondrial Dysregulation and Accelerates Physical Decline: Insights From an Inducible Humanized IL-6 Knock-In Mouse Model.

Chronic activation of inflammatory pathways (CI) and mitochondrial dysfunction are independently linked to age-related functional decline and early mortality. Interleukin 6 (IL-6) is among the most consistently elevated chronic activation of inflammatory pathways markers, but whether IL-6 plays a causative role in this mitochondrial dysfunction and physical deterioration remains unclear. To characterize the role of IL-6 in age-related mitochondrial dysregulation and physical decline, we have developed an inducible human IL-6 (hIL-6) knock-in mouse (TetO-hIL-6mitoQC) that also contains a mitochondrial-quality control reporter. Six weeks of hIL-6 induction resulted in upregulation of proinflammatory markers, cell proliferation and metabolic pathways, and dysregulated energy utilization. Decreased grip strength, increased falls off the treadmill, and increased frailty index were also observed. Further characterization of skeletal muscles postinduction revealed an increase in mitophagy, downregulation of mitochondrial biogenesis genes, and an overall decrease in total mitochondrial numbers. This study highlights the contribution of IL-6 to mitochondrial dysregulation and supports a causal role of hIL-6 in physical decline and frailty.

Regulation of human interferon signaling by transposon exonization.

Innate immune signaling is essential for clearing pathogens and damaged cells, and must be tightly regulated to avoid excessive inflammation or autoimmunity. Here, we found that the alternative splicing of exons derived from transposable elements is a key mechanism controlling immune signaling in human cells. By analyzing long-read transcriptome datasets, we identified numerous transposon exonization events predicted to generate functional protein variants of immune genes, including the type I interferon receptor IFNAR2. We demonstrated that the transposon-derived isoform of IFNAR2 is more highly expressed than the canonical isoform in almost all tissues, and functions as a decoy receptor that potently inhibits interferon signaling including in cells infected with SARS-CoV-2. Our findings uncover a primate-specific axis controlling interferon signaling and show how a transposon exonization event can be co-opted for immune regulation.

Sim4db and Leaff: utilities for fast batch spliced alignment and sequence indexing.

UNLABELLED: The large number of genomes that will be sequenced will need to be annotated with genes and other functional features. Aligning gene sequences from a related species to the target genome is an economical and highly reliable method to identify genes; unfortunately, existing tools have been lacking in sensitivity and speed. A program we reported, sim4cc, was shown to be highly accurate but is limited to comparing one cDNA with one genomic sequence. We present here an optimization of the tool, implemented in the packages sim4db and leaff. The new tool performs batch alignments of cDNA and genomic sequences in a fraction of the time required by its predecessor, and thus is very well suited for genome-wide analyses. AVAILABILITY: Sim4db and leaff are written in C, C++ and Perl for Linux and other Unix platforms. Source code is distributed free of charge from http://sourceforge.net/projects/kmer/. CONTACT: florea@umiacs.umd.edu

MicroRNA silencing of tumor suppressor DLC-1 promotes efficient hepatitis C virus replication in primary human hepatocytes.

UNLABELLED: MicroRNAs (miRNAs) are approximately 22-nucleotide noncoding RNAs that constitute silencers of target gene expression. Aberrant expression of miRNA has been linked to a variety of cancers, including hepatocellular carcinoma (HCC). Hepatitis C virus (HCV) infection is considered a major cause of chronic liver disease and HCC, although the mechanism of virus infection-associated hepatocarcinogenesis remains unclear. We report a direct role of miRNAs induced in HCV-infected primary human hepatocytes that target the tumor suppressor gene DLC-1 (a Rho GTPase-activating protein), which is frequently deleted in HCC, and other solid human tumors. MicroRNA miR-141 that targets DLC-1 was accentuated in cells infected with HCV genotypes 1a, 1b, and 2a. We present several lines of evidence that efficient HCV replication requires miR-141-mediated suppression of DLC-1. An increase in miR-141 correlated with the inhibition of DLC-1 protein in HCV-infected cells. Depletion of miR-141 with oligonucleotides complementary to the miRNAs inhibited virus replication, whereas artificially increased levels of intracellular miR-141 enhanced HCV replication. HCV-infected hepatocytes showed enhanced cell proliferation that can be countered by overexpression of DLC-1. CONCLUSION: The collective results of this study suggest a novel mechanism of HCV infection-associated miRNA-mediated regulation of a tumor suppressor protein that has the ability to influence cell proliferation and HCV infection-mediated liver cancer.