Search

In addition to specific changes in cis- and trans-regulatory elements, structural changes in the genome are hypothesized to underlie a large number of differences in gene expression between species. Accordingly, we show that species-specific segmental duplications are enriched with genes that are differentially expressed between humans and chimpanzees. [ABSTRACT FROM AUTHOR]

Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. [ABSTRACT FROM PUBLISHER]

Changes in genetic regulation contribute to adaptations in natural populations and influence susceptibility to human diseases. Despite their potential phenotypic importance, the selective pressures acting on regulatory processes in general and gene expression levels in particular are largely unknown. Studies in model organisms suggest that the expression levels of most genes evolve under stabilizing selection, although a few are consistent with adaptive evolution. However, it has been proposed that gene expression levels in primates evolve largely in the absence of selective constraints. In this article, we discuss the microarray-based observations that led to these disparate interpretations. We conclude that in both primates and model organisms, stabilizing selection is likely to be the dominant mode of gene expression evolution. An important implication is that mutations affecting gene expression will often be deleterious and might underlie many human diseases. [Copyright &y& Elsevier]

Although it has been hypothesized for thirty years that many human adaptations are likely to be due to changes in gene regulation, almost nothing is known about the modes of natural selection acting on regulation in primates. Here we identify a set of genes for which expression is evolving under natural selection. We use a new multi-species complementary DNA array to compare steady-state messenger RNA levels in liver tissues within and between humans, chimpanzees, orangutans and rhesus macaques. Using estimates from a linear mixed model, we identify a set of genes for which expression levels have remained constant across the entire phylogeny (∼70 million years), and are therefore likely to be under stabilizing selection. Among the top candidates are five genes with expression levels that have previously been shown to be altered in liver carcinoma. We also find a number of genes with similar expression levels among non-human primates but significantly elevated or reduced expression in the human lineage, features that point to the action of directional selection. Among the gene set with a human-specific increase in expression, there is an excess of transcription factors; the same is not true for genes with increased expression in chimpanzee. [ABSTRACT FROM AUTHOR]

DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a costeffective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array. [ABSTRACT FROM AUTHOR]

Summary DamID sequencing is a technique to map the genome-wide interaction of a protein with DNA. Damsel is the first Bioconductor package to provide an end to end analysis for DamID sequencing data within R. Damsel performs quantification and testing of significant binding sites along with exploratory and visual analysis. Damsel produces results consistent with previous analysis approaches. Availability and implementation The R package Damsel is available for install through the Bioconductor project https://bioconductor.org/packages/release/bioc/html/Damsel.html and the code is available on GitHub https://github.com/Oshlack/Damsel/. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are translational regulatory molecules with recognised roles in heart development and disease. Therefore, it is important to define the human miRNA expression profile in cardiac progenitors and early-differentiated cardiomyocytes and to determine whether critical cardiac transcription factors such as NKX2-5 regulate miRNA expression. We used an NKX2-5eGFP/w reporter line to isolate both cardiac committed mesoderm and cardiomyocytes. We identified 11 miRNAs that were differentially expressed in NKX2-5 -expressing cardiac mesoderm compared to non-cardiac mesoderm. Subsequent profiling revealed that the canonical myogenic miRNAs including MIR1-1, MIR133A1 and MIR208A were enriched in cardiomyocytes. Strikingly, deletion of NKX2-5 did not result in gross changes in the cardiac miRNA profile, either at committed mesoderm or cardiomyocyte stages. Thus, in early human cardiomyocyte commitment and differentiation, the cardiac myogenic miRNA program is predominantly regulated independently of the highly conserved NKX2-5 -dependant gene regulatory network. [ABSTRACT FROM AUTHOR]

Background: Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity. Methods: Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types. Results: Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma. Conclusions: This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening. [ABSTRACT FROM AUTHOR]

Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples. Author summary: Cancer is a disease that continues to evolve. Understanding how it changes can provide key biological insights; for example, it can help to identify recurrent patterns associated with therapy resistance. However, tracking clonal evolution in a cancer from sequencing data is a major analytical challenge. We have developed SuperFreq, an analysis framework purpose built for the detection of intra-tumoural heterogeneity and clonal evolution. SuperFreq encapsulates mutation detection, quality assessment, clonal tracking and phylogeny, for both point mutations and copy number alterations. To demonstrate the major advance that SuperFreq offers, we developed a test dataset with engineered clones, made by blending data from cancer and normal samples from The Cancer Genome Atlas. We demonstrate that SuperFreq exhibits robust performance, both for detecting clones and for assigning mutations to clones. SuperFreq is a powerful and adaptable analysis framework that can be applied to address many research questions. We include an example application, where we identify both early and relapse-specific driver mutations in an acute myeloid leukaemia. [ABSTRACT FROM AUTHOR]

Summary: Bpipe is a simple, dedicated programming language for defining and executing bioinformatics pipelines. It specializes in enabling users to turn existing pipelines based on shell scripts or command line tools into highly flexible, adaptable and maintainable workflows with a minimum of effort. Bpipe ensures that pipelines execute in a controlled and repeatable fashion and keeps audit trails and logs to ensure that experimental results are reproducible. Requiring only Java as a dependency, Bpipe is fully self-contained and cross-platform, making it very easy to adopt and deploy into existing environments.Availability and implementation: Bpipe is freely available from http://bpipe.org under a BSD License.Contact: simon.sadedin@mcri.edu.auSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Motivation Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. Results We have developed propeller , a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity. Availability and implementation The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckl e package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The fetal gene program in DCM cardiomyocytes was involved in upregulation of genes involved in muscle development and downregulation of genes implicated in the innate immune response (Figure [G]). Surprisingly, the phenomenon of fetal gene reactivation in DCM does not appear to be restricted to cardiomyocytes because a similar proportion of genes (<10%) adopts a fetal-like expression pattern in both cardiomyocytes and fibroblasts. Keywords: cardiac development; dilated cardiomyopathy; gene regulation; single cell; transcriptomics EN cardiac development dilated cardiomyopathy gene regulation single cell transcriptomics 1105 1108 4 11/11/22 20221004 NES 221004 A central dogma in cardiac biology is that the postnatal heart adopts a fetal-like transcriptional state in response to stress.[1] Consequently, the so-called "fetal gene program" is frequently used as a surrogate marker of adverse cardiac remodeling and heart failure in preclinical models.[2] Fetal gene reactivation in heart failure is traditionally studied in cardiomyocytes; however, the extent to which the fetal gene program is recapitulated in other cardiac cell types is unknown. [Extracted from the article]

Action myoclonus-renal failure syndrome (AMRF) is an autosomal-recessive disorder with the remarkable combination of focal glomerulosclerosis, frequently with glomerular collapse, and progressive myoclonus epilepsy associated with storage material in the brain. Here, we employed a novel combination of molecular strategies to find the responsible gene and show its effects in an animal model. Utilizing only three unrelated affected individuals and their relatives, we used homozygosity mapping with single-nucleotide polymorphism chips to localize AMRF. We then used microarray-expression analysis to prioritize candidates prior to sequencing. The disorder was mapped to 4q 13-21, and microarray-expression analysis identified SCARB2/Limp2, which encodes a lysosomal-membrane protein, as the likely candidate. Mutations in SCARB2/Limp2 were found in all three families used for mapping and subsequently confirmed in two other unrelated AMRF families. The mutations were associated with lack of SCARB2 protein. Reanalysis of an existing Limp2 knockout mouse showed intracellular inclusions in cerebral and cerebellar cortex, and the kidneys showed subtle glomerular changes. This study highlights that recessive genes can be identified with a very small number of subjects. The ancestral lysosomal-membrane protein SCARB2/LIMP-2 is responsible for AMRF. The heterogeneous pathology in the kidney and brain suggests that SCARB2/Limp2 has pleiotropic effects that may be relevant to understanding the pathogenesis of other forms of glomerulosclerosis or collapse and myoclonic epilepsies. [ABSTRACT FROM AUTHOR]

Despite the increasing diagnostic rate of genomic sequencing, the genetic basis of more than 50% of heritable kidney disease remains unresolved. Kidney organoids differentiated from induced pluripotent stem cells (iPSCs) of individuals affected by inherited renal disease represent a potential, but unvalidated, platform for the functional validation of novel gene variants and investigation of underlying pathogenetic mechanisms. In this study, trio whole-exome sequencing of a prospectively identified nephronophthisis (NPHP) proband and her parents identified compound-heterozygous variants in IFT140 , a gene previously associated with NPHP-related ciliopathies. IFT140 plays a key role in retrograde intraflagellar transport, but the precise downstream cellular mechanisms responsible for disease presentation remain unknown. A one-step reprogramming and gene-editing protocol was used to derive both uncorrected proband iPSCs and isogenic gene-corrected iPSCs, which were differentiated to kidney organoids. Proband organoid tubules demonstrated shortened, club-shaped primary cilia, whereas gene correction rescued this phenotype. Differential expression analysis of epithelial cells isolated from organoids suggested downregulation of genes associated with apicobasal polarity, cell-cell junctions, and dynein motor assembly in proband epithelial cells. Matrigel cyst cultures confirmed a polarization defect in proband versus gene-corrected renal epithelium. As such, this study represents a “proof of concept” for using proband-derived iPSCs to model renal disease and illustrates dysfunctional cellular pathways beyond the primary cilium in the setting of IFT140 mutations, which are established for other NPHP genotypes. [ABSTRACT FROM AUTHOR]

Huntington's disease is caused by polyglutamine (polyQ)-expansion mutations in the CAG tandem repeat of the Huntingtin gene. The central feature of Huntington's disease pathology is the aggregation of mutant Huntingtin (Htt) protein into micrometer-sized inclusion bodies. Soluble mutant Htt states are most proteotoxic and trigger an enhanced risk of death whereas inclusions confer different changes to cellular health, and may even provide adaptive responses to stress. Yet the molecular mechanisms underpinning these changes remain unclear. Using the flow cytometry method of pulse-shape analysis (PulSA) to sort neuroblastoma (Neuro2a) cells enriched with mutant or wild-type Htt into different aggregation states, we clarified which transcriptional signatures were specifically attributable to cells before versus after inclusion assembly. Dampened CREB signalling was the most striking change overall and invoked specifically by soluble mutant Httex1 states. Toxicity could be rescued by stimulation of CREB signalling. Other biological processes mapped to different changes before and after aggregation included NF-kB signalling, autophagy, SUMOylation, transcription regulation by histone deacetylases and BRD4, NAD + biosynthesis, ribosome biogenesis and altered HIF-1 signalling. These findings open the path for therapeutic strategies targeting key molecular changes invoked prior to, and subsequently to, Httex1 aggregation. [ABSTRACT FROM AUTHOR]

Differentiation of naïve CD4+ T cells into effector (Th1, Th2, and Th17) and induced regulatory (iTreg) T cells requires lineage-specifying transcription factors and epigenetic modifications that allow appropriate repression or activation of gene transcription. The epigenetic silencing of cytokine genes is associated with the repressive H3K27 trimethylation mark, mediated by the Ezh2 or Ezh1 methyltransferase components of the polycomb repressive complex 2 (PRC2). Here we show that silencing of the Ifng, Gata3, and Il10 loci in naïve CD4+ T cells is dependent on Ezh2. Naïve CD4+ T cells lacking Ezh2 were epigenetically primed for overproduction of IFN-γ in Th2 and iTreg and IL-10 in Th2 cells. In addition, deficiency of Ezh2 accelerated effector Th cell death via death receptor-mediated extrinsic and intrinsic apoptotic pathways, confirmed in vivo for Ezh2-null IFN-γ-producing CD4+ and CD8+ T cells responding to Listeria monocytogenes infection. These findings demonstrate the key role of PRC2/Ezh2 in differentiation and survival of peripheral T cells and reveal potential immunotherapeutic targets. [ABSTRACT FROM AUTHOR]

Regulatory T cells (Treg) prevent the emergence of autoimmune disease. Prototypic natural Treg (nTreg) can be reliably identified by demethylation at the Forkhead-box P3 (FOXP3) locus. To explore the methylation landscape of nTreg, we analyzed genome-wide methylation in human naive nTreg (rTreg) and conventional naive CD4+ T cells (Naive). We detected 2315 differentially methylated cytosine-guanosine dinucleotides (CpGs) between these 2 cell types, many of which clustered into 127 regions of differential methylation (RDMs). Activation changed the methylation status of 466 CpGs and 18 RDMs in Naive but did not alter DNA methylation in rTreg. Gene-set testing of the 127 RDMs showed that promoter methylation and gene expression were reciprocally related. RDMs were enriched for putative FOXP3-binding motifs. Moreover, CpGs within known FOXP3-binding regions in the genome were hypomethylated. In support of the view that methylation limits access of FOXP3 to its DNA targets, we showed that increased expression of the immune suppressive receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), which delineated Treg from activated effector T cells, was associated with hypomethylation and FOXP3 binding at the TIGIT locus. Differential methylation analysis provides insight into previously undefined human Treg signature genes and their mode of regulation. [ABSTRACT FROM AUTHOR]

Detailed understanding of the genetic regulation of haematopoiesis is essential for blood cell therapies and to faithfully model haematologic disease. Unlike in the mouse, this knowledge is lacking in humans. To address this, we followed endothelial (SOX17) and haematopoietic (RUNX1C) development using in vitro differentiation of human pluripotent stem cells (hPSCs). Mimicking yolk sac haematopoiesis, we identified SOX17-CD34+CD43- endothelial-like cells as the major source of haematopoietic progeny while SOX17+CD34+CD43- cells mostly formed endothelium. Deletion of RUNX1 permitted a single wave of GFI1/1B-dependent yolk sac-like primitive erythropoiesis, but no yolk sac myelopoiesis or aorta-gonad-mesonephros (AGM)-like haematopoiesis. Deletion of SOX17 or all GROUP F SOX genes (SOX7, SOX17, SOX18) did not influence yolk sac-like haematopoiesis, but severely perturbed the development of AGM-like vessels and reduced their haemogenic capacity. This phenotype was largely mediated by reduced NOTCH signaling and recapitulated by inhibiting γ-secretase. Our data extend mouse studies, identifying unique and distinct requirements for RUNX1 and SOX genes during human haematopoiesis. [ABSTRACT FROM AUTHOR]

Background: Respiratory diseases are a common cause of morbidity and hospitalization for children. Despite this, treatment options are limited and are often ineffective. The development of curative or disease-modifying treatments for children relies on a better understanding of underlying immunity in the early airway., Objective: To establish a flow cytometry dataset for immune cells in the pediatric airway, we analyzed 180 samples from 68 children aged between 1 and 15 years. This included 5 tissues of the upper (nasal brushings, palatine tonsils, adenotonsil) and lower (bronchial brushings, bronchoalveolar lavage) airway, as well as whole blood for paired analysis of local and systemic immune response., Methods: Nasal, bronchial, and alveolar samples were analyzed using a 17-plex antibody panel that captures cells of immune and epithelial lineage, while tonsil, adenoid, and blood samples were analyzed using a 31-plex antibody panel that extensively phenotypes mononuclear immune cells. All protocols, panels, and data are openly available to facilitate implementation in pediatric clinical laboratories., Results: We provide age-specific airway cell data for infancy (0-2 years), preschool (3-5 years), childhood (6-10 years) and adolescence (11-15 years) for 37 cell populations. We show tissue-specific maturation of the airway immune system across childhood, further highlighting the importance of developing age-specific references of the pediatric airway. Intraindividual, cross-tissue analysis of paired samples revealed marked correlation in immune cell proportions between paired nasal-bronchial samples, paired tonsil-adenoid samples, and paired adenoid-blood samples, which may have implications for clinical testing., Conclusion: Our study advances knowledge of airway immunity from infancy through to adolescence and provides an openly available control dataset to aid in interpretation of clinical findings in samples obtained from children with respiratory diseases., Competing Interests: Disclosure statement Funded by a Chan Zuckerberg Initiative Single-Cell Biology grant (2021-237883). Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Short tandem repeats (STRs) and variable-number tandem repeats (VNTRs) are repetitive genomic sequences seen widely throughout the genome. These repeat expansions are currently known to cause ∼60 diseases, with expansions in new loci linked to rare diseases continuing to be discovered. Genome sequencing is an important tool for detecting disease-causing variants and several computational tools have been developed to analyze tandem repeats from genomic data, enabling the genotyping and the identification of expanded alleles. However, guidelines for conducting the analysis of these repeats and, more importantly, for assessing the findings are lacking. Understanding the tools and their technical limitations is important for accurately interpreting the results. This article provides detailed, step-by-step instructions for three key use cases in STR analysis from short-read genome sequencing data, which are also applicable to smaller VNTRs. First, it demonstrates an approach for genotyping known pathogenic loci and the identification of clinically significant expansions. Second, we offer guidance on defining tandem repeat loci and conducting genome-wide genotyping studies, which is also applicable to diploid organisms other than humans. Third, instructions are provided on how to find novel expansions at loci not previously known to be associated with disease, aiding in the discovery of new pathogenic loci. Moreover, we introduce the use of newly-developed helper tools that enable a complete and streamlined tandem repeat analysis protocol by addressing the gaps in current methods. All three protocols are compatible with human hg19, hg38, and the latest telomere-to-telomere (hs1) reference genomes. Additionally, this protocol provides an overview and discussion on how to interpret genotyping results. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Genotyping known pathogenic tandem repeat loci Alternate Protocol: Genotyping known pathogenic tandem repeat loci with STRipy Support Protocol 1: Installation of tools and ExpansionHunter catalog modification Basic Protocol 2: Performing genome-wide genotyping of tandem repeats Basic Protocol 3: Discovering de novo tandem repeat expansions Support Protocol 2: Compiling ExpansionHunter Denovo from source code and generating STR profiles., (© 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.)