Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk.

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation.

MOTIVATION: Genome-wide association studies are identifying single nucleotide variants (SNVs) linked to various diseases, however the functional effect caused by these variants is often unknown. One potential functional effect, the loss or gain of protein phosphorylation sites, can be induced through variations in key amino acids that disrupt or introduce valid kinase binding patterns. Current methods for predicting the effect of SNVs on phosphorylation operate on the sequence content of reference and variant proteins. However, consideration of the amino acid sequence alone is insufficient for predicting phosphorylation change, as context factors determine kinase-substrate selection. RESULTS: We present here a method for quantifying the effect of SNVs on protein phosphorylation through an integrated system of motif analysis and context-based assessment of kinase targets. By predicting the effect that known variants across the proteome have on phosphorylation, we are able to use this background of proteome-wide variant effects to quantify the significance of novel variants for modifying phosphorylation. We validate our method on a manually curated set of phosphorylation change-causing variants from the primary literature, showing that the method predicts known examples of phosphorylation change at high levels of specificity. We apply our approach to data-sets of variants in phosphorylation site regions, showing that variants causing predicted phosphorylation loss are over-represented among disease-associated variants. AVAILABILITY AND IMPLEMENTATION: The method is freely available as a web-service at the website http://bioinf.scmb.uq.edu.au/phosphopick/snp. CONTACT: m.boden@uq.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence.

Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets - assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate - requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made. We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins. PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.

PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events.

MOTIVATION: The determinants of kinase-substrate phosphorylation can be found both in the substrate sequence and the surrounding cellular context. Cell cycle progression, interactions with mediating proteins and even prior phosphorylation events are necessary for kinases to maintain substrate specificity. While much work has focussed on the use of sequence-based methods to predict phosphorylation sites, there has been very little work invested into the application of systems biology to understand phosphorylation. Lack of specificity in many kinase substrate binding motifs means that sequence methods for predicting kinase binding sites are susceptible to high false-positive rates. RESULTS: We present here a model that takes into account protein-protein interaction information, and protein abundance data across the cell cycle to predict kinase substrates for 59 human kinases that are representative of important biological pathways. The model shows high accuracy for substrate prediction (with an average AUC of 0.86) across the 59 kinases tested. When using the model to complement sequence-based kinase-specific phosphorylation site prediction, we found that the additional information increased prediction performance for most comparisons made, particularly on kinases from the CMGC family. We then used our model to identify functional overlaps between predicted CDK2 substrates and targets from the E2F family of transcription factors. Our results demonstrate that a model harnessing context data can account for the short-falls in sequence information and provide a robust description of the cellular events that regulate protein phosphorylation. AVAILABILITY AND IMPLEMENTATION: The method is freely available online as a web server at the website http://bioinf.scmb.uq.edu.au/phosphopick. CONTACT: m.boden@uq.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting the dynamics of protein abundance.

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

Integration mapping of cardiac fibroblast single-cell transcriptomes elucidates cellular principles of fibrosis in diverse pathologies.

Single-cell technology has allowed researchers to probe tissue complexity and dynamics at unprecedented depth in health and disease. However, the generation of high-dimensionality single-cell atlases and virtual three-dimensional tissues requires integrated reference maps that harmonize disparate experimental designs, analytical pipelines, and taxonomies. Here, we present a comprehensive single-cell transcriptome integration map of cardiac fibrosis, which underpins pathophysiology in most cardiovascular diseases. Our findings reveal similarity between cardiac fibroblast (CF) identities and dynamics in ischemic versus pressure overload models of cardiomyopathy. We also describe timelines for commitment of activated CFs to proliferation and myofibrogenesis, profibrotic and antifibrotic polarization of myofibroblasts and matrifibrocytes, and CF conservation across mouse and human healthy and diseased hearts. These insights have the potential to inform knowledge-based therapies.

The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening.

A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.

Quantitative trait and transcriptome analysis of genetic complexity underpinning cardiac interatrial septation in mice using an advanced intercross line.

Unlike single-gene mutations leading to Mendelian conditions, common human diseases are likely to be emergent phenomena arising from multilayer, multiscale, and highly interconnected interactions. Atrial and ventricular septal defects are the most common forms of cardiac congenital anomalies in humans. Atrial septal defects (ASD) show an open communication between the left and right atria postnatally, potentially resulting in serious hemodynamic consequences if untreated. A milder form of atrial septal defect, patent foramen ovale (PFO), exists in about one-quarter of the human population, strongly associated with ischaemic stroke and migraine. The anatomic liabilities and genetic and molecular basis of atrial septal defects remain unclear. Here, we advance our previous analysis of atrial septal variation through quantitative trait locus (QTL) mapping of an advanced intercross line (AIL) established between the inbred QSi5 and 129T2/SvEms mouse strains, that show extremes of septal phenotypes. Analysis resolved 37 unique septal QTL with high overlap between QTL for distinct septal traits and PFO as a binary trait. Whole genome sequencing of parental strains and filtering identified predicted functional variants, including in known human congenital heart disease genes. Transcriptome analysis of developing septa revealed downregulation of networks involving ribosome, nucleosome, mitochondrial, and extracellular matrix biosynthesis in the 129T2/SvEms strain, potentially reflecting an essential role for growth and cellular maturation in septal development. Analysis of variant architecture across different gene features, including enhancers and promoters, provided evidence for the involvement of non-coding as well as protein-coding variants. Our study provides the first high-resolution picture of genetic complexity and network liability underlying common congenital heart disease, with relevance to human ASD and PFO.

Fibulin-3 is necessary to prevent cardiac rupture following myocardial infarction.

Despite the high prevalence of heart failure in the western world, there are few effective treatments. Fibulin-3 is a protein involved in extracellular matrix (ECM) structural integrity, however its role in the heart is unknown. We have demonstrated, using single cell RNA-seq, that fibulin-3 was highly expressed in quiescent murine cardiac fibroblasts, with expression highest prior to injury and late post-infarct (from ~ day-28 to week-8). In humans, fibulin-3 was upregulated in left ventricular tissue and plasma of heart failure patients. Fibulin-3 knockout (Efemp1-/-) and wildtype mice were subjected to experimental myocardial infarction. Fibulin-3 deletion resulted in significantly higher rate of cardiac rupture days 3-6 post-infarct, indicating a weak and poorly formed scar, with severe ventricular remodelling in surviving mice at day-28 post-infarct. Fibulin-3 knockout mice demonstrated less collagen deposition at day-3 post-infarct, with abnormal collagen fibre-alignment. RNA-seq on day-3 infarct tissue revealed upregulation of ECM degradation and inflammatory genes, but downregulation of ECM assembly/structure/organisation genes in fibulin-3 knockout mice. GSEA pathway analysis showed enrichment of inflammatory pathways and a depletion of ECM organisation pathways. Fibulin-3 originates from cardiac fibroblasts, is upregulated in human heart failure, and is necessary for correct ECM organisation/structural integrity of fibrotic tissue to prevent cardiac rupture post-infarct.

Macrophage ACE2 is necessary for SARS-CoV-2 replication and subsequent cytokine responses that restrict continued virion release.

Macrophages are key cellular contributors to the pathogenesis of COVID-19, the disease caused by the virus SARS-CoV-2. The SARS-CoV-2 entry receptor ACE2 is present only on a subset of macrophages at sites of SARS-CoV-2 infection in humans. Here, we investigated whether SARS-CoV-2 can enter macrophages, replicate, and release new viral progeny; whether macrophages need to sense a replicating virus to drive cytokine release; and, if so, whether ACE2 is involved in these mechanisms. We found that SARS-CoV-2 could enter, but did not replicate within, ACE2-deficient human primary macrophages and did not induce proinflammatory cytokine expression. By contrast, ACE2 overexpression in human THP-1-derived macrophages permitted SARS-CoV-2 entry, processing and replication, and virion release. ACE2-overexpressing THP-1 macrophages sensed active viral replication and triggered proinflammatory, antiviral programs mediated by the kinase TBK-1 that limited prolonged viral replication and release. These findings help elucidate the role of ACE2 and its absence in macrophage responses to SARS-CoV-2 infection.

Extracellular Matrix Protein-1 as a Mediator of Inflammation-Induced Fibrosis After Myocardial Infarction.

Irreversible fibrosis is a hallmark of myocardial infarction (MI) and heart failure. Extracellular matrix protein-1 (ECM-1) is up-regulated in these hearts, localized to fibrotic, inflammatory, and perivascular areas. ECM-1 originates predominantly from fibroblasts, macrophages, and pericytes/vascular cells in uninjured human and mouse hearts, and from M1 and M2 macrophages and myofibroblasts after MI. ECM-1 stimulates fibroblast-to-myofibroblast transition, up-regulates key fibrotic and inflammatory pathways, and inhibits cardiac fibroblast migration. ECM-1 binds HuCFb cell surface receptor LRP1, and LRP1 inhibition blocks ECM-1 from stimulating fibroblast-to-myofibroblast transition, confirming a novel ECM-1-LRP1 fibrotic signaling axis. ECM-1 may represent a novel mechanism facilitating inflammation-fibrosis crosstalk.

Hif-1a suppresses ROS-induced proliferation of cardiac fibroblasts following myocardial infarction.

We report that cardiac fibroblasts (CFs) and mesenchymal progenitors are more hypoxic than other cardiac interstitial populations, express more hypoxia-inducible factor 1α (HIF-1α), and exhibit increased glycolytic metabolism. CF-specific deletion of Hif-1a resulted in decreased HIF-1 target gene expression and increased mesenchymal progenitors in uninjured hearts and increased CF activation without proliferation following sham injury, as demonstrated using single-cell RNA sequencing (scRNA-seq). After myocardial infarction (MI), however, there was ∼50% increased CF proliferation and excessive scarring and contractile dysfunction, a scenario replicated in 3D engineered cardiac microtissues. CF proliferation was associated with higher reactive oxygen species (ROS) as occurred also in wild-type mice treated with the mitochondrial ROS generator MitoParaquat (MitoPQ). The mitochondrial-targeted antioxidant MitoTEMPO rescued Hif-1a mutant phenotypes. Thus, HIF-1α in CFs provides a critical braking mechanism against excessive post-ischemic CF activation and proliferation through regulation of mitochondrial ROS. CFs are potential cellular targets for designer antioxidant therapies in cardiovascular disease.

Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction.

Endothelial cells play a critical role in the adaptation of tissues to injury. Tissue ischemia induced by infarction leads to profound changes in endothelial cell functions and can induce transition to a mesenchymal state. Here we explore the kinetics and individual cellular responses of endothelial cells after myocardial infarction by using single cell RNA sequencing. This study demonstrates a time dependent switch in endothelial cell proliferation and inflammation associated with transient changes in metabolic gene signatures. Trajectory analysis reveals that the majority of endothelial cells 3 to 7 days after myocardial infarction acquire a transient state, characterized by mesenchymal gene expression, which returns to baseline 14 days after injury. Lineage tracing, using the Cdh5-CreERT2;mT/mG mice followed by single cell RNA sequencing, confirms the transient mesenchymal transition and reveals additional hypoxic and inflammatory signatures of endothelial cells during early and late states after injury. These data suggest that endothelial cells undergo a transient mes-enchymal activation concomitant with a metabolic adaptation within the first days after myocardial infarction but do not acquire a long-term mesenchymal fate. This mesenchymal activation may facilitate endothelial cell migration and clonal expansion to regenerate the vascular network.

Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data.

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 'UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra .

Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.

Besides cardiomyocytes (CM), the heart contains numerous interstitial cell types which play key roles in heart repair, regeneration and disease, including fibroblast, vascular and immune cells. However, a comprehensive understanding of this interactive cell community is lacking. We performed single-cell RNA-sequencing of the total non-CM fraction and enriched (Pdgfra-GFP+) fibroblast lineage cells from murine hearts at days 3 and 7 post-sham or myocardial infarction (MI) surgery. Clustering of >30,000 single cells identified >30 populations representing nine cell lineages, including a previously undescribed fibroblast lineage trajectory present in both sham and MI hearts leading to a uniquely activated cell state defined in part by a strong anti-WNT transcriptome signature. We also uncovered novel myofibroblast subtypes expressing either pro-fibrotic or anti-fibrotic signatures. Our data highlight non-linear dynamics in myeloid and fibroblast lineages after cardiac injury, and provide an entry point for deeper analysis of cardiac homeostasis, inflammation, fibrosis, repair and regeneration.

Transcriptional heterogeneity of fibroblasts is a hallmark of the aging heart.

Aging is a major risk factor for cardiovascular disease. Although the impact of aging has been extensively studied, little is known regarding the aging processes in cells of the heart. Here we analyzed the transcriptomes of hearts of 12-week-old and 18-month-old mice by single-nucleus RNA-sequencing. Among all cell types, aged fibroblasts showed most significant differential gene expression, increased RNA dynamics, and network entropy. Aged fibroblasts exhibited significantly changed expression patterns of inflammatory, extracellular matrix organization angiogenesis, and osteogenic genes. Functional analyses indicated deterioration of paracrine signatures between fibroblasts and endothelial cells in old hearts. Aged heart-derived fibroblasts had impaired endothelial cell angiogenesis and autophagy and augmented proinflammatory response. In particular, expression of Serpine1 and Serpine2 were significantly increased and secreted by old fibroblasts to exert antiangiogenic effects on endothelial cells, an effect that could be significantly prevented by using neutralizing antibodies. Moreover, we found an enlarged subpopulation of aged fibroblasts expressing osteoblast genes in the epicardial layer associated with increased calcification. Taken together this study provides system-wide insights and identifies molecular changes of aging cardiac fibroblasts, which may contribute to declined heart function.

Identification of active signaling pathways by integrating gene expression and protein interaction data.

BACKGROUND: Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. RESULTS: Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development. CONCLUSIONS: The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/ .