Genome-wide RNA Tomography in the zebrafish embryo.

Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. Although genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using in situ hybridization, for example, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. Importantly, our technique enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs.

Genome sequencing reveals loci under artificial selection that underlie disease phenotypes in the laboratory rat.

Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models.

Identification of an RNA Polymerase III Regulator Linked to Disease-Associated Protein Aggregation.

Protein aggregation is associated with age-related neurodegenerative disorders, such as Alzheimer's and polyglutamine diseases. As a causal relationship between protein aggregation and neurodegeneration remains elusive, understanding the cellular mechanisms regulating protein aggregation will help develop future treatments. To identify such mechanisms, we conducted a forward genetic screen in a C. elegans model of polyglutamine aggregation and identified the protein MOAG-2/LIR-3 as a driver of protein aggregation. In the absence of polyglutamine, MOAG-2/LIR-3 regulates the RNA polymerase III-associated transcription of small non-coding RNAs. This regulation is lost in the presence of polyglutamine, which mislocalizes MOAG-2/LIR-3 from the nucleus to the cytosol. We then show biochemically that MOAG-2/LIR-3 can also catalyze the aggregation of polyglutamine-expanded huntingtin. These results suggest that polyglutamine can induce an aggregation-promoting activity of MOAG-2/LIR-3 in the cytosol. The concept that certain aggregation-prone proteins can convert other endogenous proteins into drivers of aggregation and toxicity adds to the understanding of how cellular homeostasis can be deteriorated in protein misfolding diseases.

Alternative Splicing Is a Major Factor Shaping Transcriptome Diversity in Mild and Severe Chronic Obstructive Pulmonary Disease.

The role of alternative splicing in chronic obstructive pulmonary disease (COPD) is still largely unknown. We aimed to investigate the differences in alternatively splicing events between patients with mild-to-moderate and severe COPD compared with non-COPD control subjects and to identify splicing factors associated with aberrant alternative splicing in COPD. For this purpose, we performed genome-wide RNA-sequencing analysis of bronchial brushings from 23 patients with mild-to-moderate COPD, 121 with severe COPD, and 23 non-COPD control subjects. We found a significant difference in the frequency of alternative splicing events in patients with mild-to-moderate and severe COPD compared with non-COPD control subjects. There were from two to eight times (depending on event type) more differential alternative splicing events in the severe than in the mild-to-moderate stage. The severe COPD samples showed less intron retention and more exon skipping. It is interesting that the transcript levels of the top 10 differentially expressed splicing factors were significantly correlated with the percentage of many alternatively spliced transcripts in severe COPD. The aberrant alternative splicing in severe COPD was predicted to increase the overall protein-coding capacity of gene products. In conclusion, we observed large and significant differences in alternative splicing between bronchial samples of patients with COPD and control subjects, with more events observed in severe than in mild-to-moderate COPD. The changes in the expression of several splicing factors correlated with prevalence of alternative splicing in severe COPD. Alternative splicing can indirectly impact gene expression by changing the relative abundance of protein-coding isoforms potentially influencing pathophysiological changes. The results provide a better understanding of COPD-related alternative splicing changes.

The developmental stage of the medulloblastoma cell-of-origin restricts Sonic hedgehog pathway usage and drug sensitivity.

Sonic hedgehog (SHH) medulloblastoma originates from the cerebellar granule neuron progenitor (CGNP) lineage, which depends on Hedgehog signaling for its perinatal expansion. Whereas SHH tumors exhibit overall deregulation of this pathway, they also show patient age-specific aberrations. To investigate whether the developmental stage of the CGNP can account for these age-specific lesions, we analyzed developing murine CGNP transcriptomes and observed highly dynamic gene expression as a function of age. Cross-species comparison with human SHH medulloblastoma showed partial maintenance of these expression patterns, and highlighted low primary cilium expression as hallmark of infant medulloblastoma and early embryonic CGNPs. This coincided with reduced responsiveness to upstream SHH pathway component Smoothened, whereas sensitivity to downstream components SUFU and GLI family proteins was retained. Together, these findings can explain the preference for SUFU mutations in infant medulloblastoma and suggest that drugs targeting the downstream SHH pathway will be most appropriate for infant patients.

Nasal epithelial gene expression identifies relevant asthma endotypes in the ATLANTIS study.

INTRODUCTION: Asthma is an inflammatory airways disease encompassing multiple phenotypes and endotypes. Several studies suggested gene expression in nasal epithelium to serve as a proxy for bronchial epithelium, being a non-invasive approach to investigate lung diseases. We hypothesised that molecular differences in upper airway epithelium reflect asthma-associated differences in the lower airways and are associated with clinical expression of asthma. METHODS: We analysed nasal epithelial gene expression data from 369 patients with asthma and 58 non-asthmatic controls from the Assessment of Small Airways Involvement in Asthma study. Unsupervised hierarchical clustering was performed on asthma-associated genes. Asthma-associated gene signatures were replicated in independent cohorts with nasal and bronchial brushes data by comparing Gene Set Variation Analysis scores between asthma patients and non-asthmatic controls. RESULTS: We identified 67 higher expressed and 59 lower expressed genes in nasal epithelium from asthma patients compared with controls (false discovery rate<0.05), including CLCA1, CST1 and POSTN, genes well known to reflect asthma in bronchial airway epithelium. Hierarchical clustering revealed several molecular asthma endotypes with distinct clinical characteristics, including an endotype with higher blood and sputum eosinophils, high fractional exhaled nitric oxide, and more severe small airway dysfunction, as reflected by lower forced expiratory flow at 50%. In an independent cohort, we demonstrated that genes higher expressed in the nasal epithelium reflect asthma-associated changes in the lower airways. CONCLUSION: Our results show that the nasal epithelial gene expression profile reflects asthma-related processes in the lower airways. We suggest that nasal epithelium may be a useful non-invasive tool to identify asthma endotypes and may advance personalised management of the disease.

Transcription factor achaete scute-like 2 controls intestinal stem cell fate.

The small intestinal epithelium is the most rapidly self-renewing tissue of mammals. Proliferative cells are confined to crypts, while differentiated cell types predominantly occupy the villi. We recently demonstrated the existence of a long-lived pool of cycling stem cells defined by Lgr5 expression and intermingled with post-mitotic Paneth cells at crypt bottoms. We have now determined a gene signature for these Lgr5 stem cells. One of the genes within this stem cell signature is the Wnt target Achaete scute-like 2 (Ascl2). Transgenic expression of the Ascl2 transcription factor throughout the intestinal epithelium induces crypt hyperplasia and ectopic crypts on villi. Induced deletion of the Ascl2 gene in adult small intestine leads to disappearance of the Lgr5 stem cells within days. The combined results from these gain- and loss-of-function experiments imply that Ascl2 controls intestinal stem cell fate.

FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets.

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.

A proteomics approach to identify COPD-related changes in lung fibroblasts.

Lung fibroblasts are implicated in abnormal tissue repair in chronic obstructive pulmonary disease (COPD). The exact mechanisms are unknown and comprehensive analysis comparing COPD- and control fibroblasts is lacking. The aim of this study is to gain insight into the role of lung fibroblasts in COPD pathology using unbiased proteomic and transcriptomic analysis. Protein and RNA were isolated from cultured parenchymal lung fibroblasts of 17 patients with stage IV COPD and 16 non-COPD controls. Proteins were analyzed using LC-MS/MS and RNA through RNA sequencing. Differential protein and gene expression in COPD was assessed via linear regression, followed by pathway enrichment, correlation analysis, and immunohistological staining in lung tissue. Proteomic and transcriptomic data were compared to investigate the overlap and correlation between both levels of data. We identified 40 differentially expressed (DE) proteins and zero DE genes between COPD and control fibroblasts. The most significant DE proteins were HNRNPA2B1 and FHL1. Thirteen of the 40 proteins were previously associated with COPD, including FHL1 and GSTP1. Six of the 40 proteins were related to telomere maintenance pathways, and were positively correlated with the senescence marker LMNB1. No significant correlation between gene and protein expression was observed for the 40 proteins. We hereby describe 40 DE proteins in COPD fibroblasts including previously described COPD proteins (FHL1, GSTP1) and new COPD research targets like HNRNPA2B1. Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis and indicates that different types of information are generated with both methods.

Liver transcriptomic and methylomic analyses identify transcriptional mitogen-activated protein kinase regulation in facultative hibernation of Syrian hamster.

Hibernation consists of alternating torpor-arousal phases, during which animals cope with repetitive hypothermia and ischaemia-reperfusion. Due to limited transcriptomic and methylomic information for facultative hibernators, we here conducted RNA and whole-genome bisulfide sequencing in liver of hibernating Syrian hamster (Mesocricetus auratus). Gene ontology analysis was performed on 844 differentially expressed genes and confirmed the shift in metabolic fuel utilization, inhibition of RNA transcription and cell cycle regulation as found in seasonal hibernators. Additionally, we showed a so far unreported suppression of mitogen-activated protein kinase (MAPK) and protein phosphatase 1 pathways during torpor. Notably, hibernating hamsters showed upregulation of MAPK inhibitors (dual-specificity phosphatases and sproutys) and reduced levels of MAPK-induced transcription factors (TFs). Promoter methylation was found to modulate the expression of genes targeted by these TFs. In conclusion, we document gene regulation between hibernation phases, which may aid the identification of pathways and targets to prevent organ damage in transplantation or ischaemia-reperfusion.

GeneNetTools: tests for Gaussian graphical models with shrinkage.

MOTIVATION: Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues. RESULTS: We derive the statistical properties of the partial correlation obtained with the Ledoit-Wolf shrinkage. Our result provides a toolbox for (differential) network analyses as (i) confidence intervals, (ii) a test for zero partial correlation (null-effects) and (iii) a test to compare partial correlations. Our novel (parametric) methods account for the number of variables, the sample size and the shrinkage values. Additionally, they are computationally fast, simple to implement and require only basic statistical knowledge. Our simulations show that the novel tests perform better than DiffNetFDR-a recently published alternative-in terms of the trade-off between true and false positives. The methods are demonstrated on synthetic data and two gene-expression datasets from Escherichia coli and Mus musculus. AVAILABILITY AND IMPLEMENTATION: The R package with the methods and the R script with the analysis are available in https://github.com/V-Bernal/GeneNetTools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Smoking increases expression of the SARS-CoV-2 spike protein-binding long ACE2 isoform in bronchial epithelium.

After more than two years the COVID-19 pandemic, that is caused by infection with the respiratory SARS-CoV-2 virus, is still ongoing. The risk to develop severe COVID-19 upon SARS-CoV-2 infection is increased in individuals with a high age, high body mass index, and who are smoking. The SARS-CoV-2 virus infects cells of the upper respiratory tract by entering these cells upon binding to the Angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is expressed in various cell types in the lung but the expression is especially high in goblet and ciliated cells. Recently, it was shown that next to its full-length isoform, ACE2 also has a short isoform. The short isoform is unable to bind SARS-CoV-2 and does not facilitate viral entry. In the current study we investigated whether active cigarette smoking increases the expression of the long or the short ACE2 isoform. We showed that in active smokers the expression of the long, active isoform, but not the short isoform of ACE2 is higher compared to never smokers. Additionally, it was shown that the expression of especially the long, active isoform of ACE2 was associated with secretory, club and goblet epithelial cells. This study increases our understanding of why current smokers are more susceptible to SARS-CoV-2 infection, in addition to the already established increased risk to develop severe COVID-19.

Variant calling: Considerations, practices, and developments.

The success of many clinical, association, or population genetics studies critically relies on properly performed variant calling step. The variety of modern genomics protocols, techniques, and platforms makes our choices of methods and algorithms difficult and there is no "one size fits all" solution for study design and data analysis. In this review, we discuss considerations that need to be taken into account while designing the study and preparing for the experiments. We outline the variety of variant types that can be detected using sequencing approaches and highlight some specific requirements and basic principles of their detection. Finally, we cover interesting developments that enable variant calling for a broad range of applications in the genomics field. We conclude by discussing technological and algorithmic advances that have the potential to change the ways of calling DNA variants in the nearest future.

A survey of current methods to detect and genotype inversions.

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

Telomerase subunit Est2 marks internal sites that are prone to accumulate DNA damage.

BACKGROUND: The main function of telomerase is at the telomeres but under adverse conditions telomerase can bind to internal regions causing deleterious effects as observed in cancer cells. RESULTS: By mapping the global occupancy of the catalytic subunit of telomerase (Est2) in the budding yeast Saccharomyces cerevisiae, we reveal that it binds to multiple guanine-rich genomic loci, which we termed "non-telomeric binding sites" (NTBS). We characterize Est2 binding to NTBS. Contrary to telomeres, Est2 binds to NTBS in G1 and G2 phase independently of Est1 and Est3. The absence of Est1 and Est3 renders telomerase inactive at NTBS. However, upon global DNA damage, Est1 and Est3 join Est2 at NTBS and telomere addition can be observed indicating that Est2 occupancy marks NTBS regions as particular risks for genome stability. CONCLUSIONS: Our results provide a novel model of telomerase regulation in the cell cycle using internal regions as "parking spots" of Est2 but marking them as hotspots for telomere addition.

Chromosomal Instability Characterizes Pediatric Medulloblastoma but Is Not Tolerated in the Developing Cerebellum.

Medulloblastoma is a pediatric brain malignancy that consists of four transcriptional subgroups. Structural and numerical aneuploidy are common in all subgroups, although they are particularly profound in Group 3 and Group 4 medulloblastoma and in a subtype of SHH medulloblastoma termed SHHα. This suggests that chromosomal instability (CIN), the process leading to aneuploidy, is an important player in medulloblastoma pathophysiology. However, it is not known if there is ongoing CIN in medulloblastoma or if CIN affects the developing cerebellum and promotes tumor formation. To investigate this, we performed karyotyping of single medulloblastoma cells and demonstrated the presence of distinct tumor cell clones harboring unique copy number alterations, which is suggestive of ongoing CIN. We also found enrichment for processes related to DNA replication, repair, and mitosis in both SHH medulloblastoma and in the highly proliferative compartment of the presumed tumor cell lineage-of-origin, the latter also being sensitive to genotoxic stress. However, when challenging these tumor cells-of-origin with genetic lesions inducing CIN using transgenic mouse modeling, we found no evidence for large chromosomal aberrations in the cerebellum or for medulloblastoma formation. We therefore conclude that without a background of specific genetic mutations, CIN is not tolerated in the developing cerebellum in vivo and, thus, by itself is not sufficient to initiate medulloblastoma.

The 'un-shrunk' partial correlation in Gaussian graphical models.

BACKGROUND: In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes ('high dimensional problem'). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation. RESULTS: We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as 'un-shrinking' the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus. CONCLUSIONS: GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the 'high-dimensional problem'. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

InvertypeR: Bayesian inversion genotyping with Strand-seq data.

BACKGROUND: Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the detection of inversions smaller than 10 Kb. Moreover, the methods rely on manual inspection to separate false and true positives. RESULTS: Here we describe "InvertypeR", a method based on a Bayesian binomial model that genotypes inversions using fixed genomic coordinates. We validated InvertypeR by re-genotyping inversions reported for three trios by the Human Genome Structural Variation Consortium. Although 6.3% of the family inversion genotypes in the original study showed Mendelian discordance, this was reduced to 0.5% using InvertypeR. By applying InvertypeR to published inversion coordinates and predicted inversion hotspots (n = 3701), as well as coordinates from conventional inversion discovery, we furthermore genotyped 66 inversions not previously reported for the three trios. CONCLUSIONS: InvertypeR discovers, genotypes, and phases inversions without relying on manual inspection. For greater accessibility, results are presented as phased chromosome ideograms with inversions linked to Strand-seq data in the genome browser. InvertypeR increases the power of Strand-seq for studies on the role of inversions in phenotypic variation, genome instability, and human disease.

breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data.

MOTIVATION: Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. RESULTS: Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package https://bioconductor.org/packages/breakpointR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Construction of Whole Genomes from Scaffolds Using Single Cell Strand-Seq Data.

Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments such as contigs and scaffolds in relation to each other is a critical and often challenging step in the construction of robust genome references. We previously identified misoriented regions in the mouse and human reference assemblies using Strand-seq, a single cell sequencing technique that preserves DNA directionality Here we demonstrate the ability of Strand-seq to build and correct full-length chromosomes by identifying which scaffolds belong to the same chromosome and determining their correct order and orientation, without the need for overlapping sequences. We demonstrate that Strand-seq exquisitely maps assembly fragments into large related groups and chromosome-sized clusters without using new assembly data. Using template strand inheritance as a bi-allelic marker, we employ genetic mapping principles to cluster scaffolds that are derived from the same chromosome and order them within the chromosome based solely on directionality of DNA strand inheritance. We prove the utility of our approach by generating improved genome assemblies for several model organisms including the ferret, pig, Xenopus, zebrafish, Tasmanian devil and the Guinea pig.