Unraveling the evolutionary origin of the complex Nuclear Receptor Element (cNRE), a cis-regulatory module required for preferential expression in the atrial chamber.

Cardiac function requires appropriate proteins in each chamber. Atria requires slow myosin to act as reservoirs, while ventricles demand fast myosin for swift pumping. Myosins are thus under chamber-biased cis-regulation, with myosin gene expression imbalances leading to congenital heart dysfunction. To identify regulatory inputs leading to cardiac chamber-biased expression, we computationally and molecularly dissected the quail Slow Myosin Heavy Chain III (SMyHC III) promoter that drives preferential expression to the atria. We show that SMyHC III gene states are orchestrated by a complex Nuclear Receptor Element (cNRE) of 32 base pairs. Using transgenesis in zebrafish and mice, we demonstrate that preferential atrial expression is achieved by a combinatorial regulatory input composed of atrial activation motifs and ventricular repression motifs. Using comparative genomics, we show that the cNRE might have emerged from an endogenous viral element through infection of an ancestral host germline, revealing an evolutionary pathway to cardiac chamber-specific expression.

Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data.

The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.

The African killifish: A short-lived vertebrate model to study the biology of sarcopenia and longevity.

Sarcopenia, the age-related decline in muscle function, places a considerable burden on health-care systems. While the stereotypic hallmarks of sarcopenia are well characterized, their contribution to muscle wasting remains elusive, which is partly due to the limited availability of animal models. Here, we have performed cellular and molecular characterization of skeletal muscle from the African killifish-an extremely short-lived vertebrate-revealing that while many characteristics deteriorate with increasing age, supporting the use of killifish as a model for sarcopenia research, some features surprisingly reverse to an "early-life" state in the extremely old stages. This suggests that in extremely old animals, there may be mechanisms that prevent further deterioration of skeletal muscle, contributing to an extension of life span. In line with this, we report a reduction in mortality rates in extremely old killifish. To identify mechanisms for this phenomenon, we used a systems metabolomics approach, which revealed that during aging there is a striking depletion of triglycerides, mimicking a state of calorie restriction. This results in the activation of mitohormesis, increasing Sirt1 levels, which improves lipid metabolism and maintains nutrient homeostasis in extremely old animals. Pharmacological induction of Sirt1 in aged animals was sufficient to induce a late life-like metabolic profile, supporting its role in life span extension in vertebrate populations that are naturally long-lived. Collectively, our results demonstrate that killifish are not only a novel model to study the biological processes that govern sarcopenia, but they also provide a unique vertebrate system to dissect the regulation of longevity.

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

Parallel use of human stem cell lung and heart models provide insights for SARS-CoV-2 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe coronavirus disease 2019 (COVID-19). To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR-Cas9-mediated knockout of ACE2, we demonstrated that angiotensin-converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but that further processing in lung cells required TMPRSS2, while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems.

Kidney Organoids Generated Using an Allelic Series of NPHS2 Point Variants Reveal Distinct Intracellular Podocin Mistrafficking.

BACKGROUND: NPHS2 variants are the most common cause of steroid-resistant nephrotic syndrome in children >1 month old. Missense NPHS2 variants were reported to cause mistrafficking of the encoded protein, PODOCIN, but this conclusion was on the basis of overexpression in some nonpodocyte cell lines. METHODS: We generated a series of human induced pluripotent stem cell (iPSC) lines bearing pathogenic missense variants of NPHS2 , encoding the protein changes p.G92C, p.P118L, p.R138Q, p.R168H, and p.R291W, and control lines. iPSC lines were also generated from a patient with steroid-resistant nephrotic syndrome (p.R168H homozygote) and a healthy heterozygous parent. All lines were differentiated into kidney organoids. Immunofluorescence assessed PODOCIN expression and subcellular localization. Podocytes were transcriptionally profiled and PODOCIN-NEPHRIN interaction interrogated. RESULTS: All variant lines revealed reduced levels of PODOCIN protein in the absence of reduced transcription. Although wild-type PODOCIN localized to the membrane, distinct variant proteins displayed unique patterns of subcellular protein trafficking, some unreported. P118L and R138Q were preferentially retained in the endoplasmic reticulum (ER); R168H and R291W accumulated in the Golgi. Podocyte profiling demonstrated minimal disease-associated transcriptional change. All variants displayed podocyte-specific apoptosis, which was not linked to ER stress. NEPHRIN-PODOCIN colocalization elucidated the variant-specific effect on NEPHRIN association and hence NEPHRIN trafficking. CONCLUSIONS: Specific variants of endogenous NPHS2 result in distinct subcellular PODOCIN localization within organoid podocytes. Understanding the effect of each variant on protein levels and localization and the effect on NEPHRIN provides additional insight into the pathobiology of NPHS2 variants. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2023_01_05_JASN2022060707.mp3.

Systematic molecular profiling of acute leukemia cancer stem cells allows identification of druggable targets.

Acute myeloid leukemia (AML) is one of the most prevalent and acute blood cancers with a poor prognosis and low overall survival rate, especially in the elderly. Although several new AML markers and drug targets have been recently identified, the rate of long-term cancer eradication has not improved significantly due to the presence and drug resistance of AML cancer stem cells (CSCs). Here we develop a novel computational pipeline to analyze the transcriptomic profiles of AML cancer (stem) cells and identify novel candidate AML CSC markers and drug targets. In our novel pipeline we apply a top-down meta-analysis strategy to integrate The Cancer Genome Atlas data with CSC datasets to infer cell stemness features. As a result, a set of genes termed the "AML key CSC genes" along with all the available drugs/compounds that could target them were identified. Overall, our novel computational pipeline could retrieve known cancer drugs (Carfilzomib) and predicted novel drugs such as Zonisamide, Amitriptyline, and their targets amongst the top ranked drugs and drug targets for targeting AML. Additionally, the pipeline applied in this study could be used for the identification of CSC-specific markers, drivers and their respective targeting drugs in other cancer types.

Parallel use of pluripotent human stem cell lung and heart models provide new insights for treatment of SARS-CoV-2.

SARS-CoV-2 primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe COVID-19. To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR- Cas9 mediated knock-out of ACE2, we demonstrated that angiotensin converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but further processing in lung cells required TMPRSS2 while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems. One-sentence summary: Rational treatment strategies for SARS-CoV-2 derived from human PSC models.

Human pluripotent stem cell-derived macrophages host Mycobacterium abscessus infection.

Human macrophages are a natural host of many mycobacterium species, including Mycobacterium abscessus (M. abscessus), an emerging pathogen affecting immunocompromised and cystic fibrosis patients with few available treatments. The search for an effective treatment is hindered by the lack of a tractable in vitro intracellular infection model. Here, we established a reliable model for M. abscessus infection using human pluripotent stem cell-derived macrophages (hPSC-macrophages). hPSC differentiation permitted reproducible generation of functional macrophages that were highly susceptible to M. abscessus infection. Electron microscopy demonstrated that M. abscessus was present in the hPSC-macrophage vacuoles. RNA sequencing analysis revealed a time-dependent host cell response, with differing gene and protein expression patterns post-infection. Engineered tdTOMATO-expressing hPSC-macrophages with GFP-expressing mycobacteria enabled rapid image-based high-throughput analysis of intracellular infection and quantitative assessment of antibiotic efficacy. Our study describes the first to our knowledge hPSC-based model for M. abscessus infection, representing a novel and accessible system for studying pathogen-host interaction and drug discovery.

Activation of Nkx2.5 transcriptional program is required for adult myocardial repair.

The cardiac developmental network has been associated with myocardial regenerative potential. However, the embryonic signals triggered following injury have yet to be fully elucidated. Nkx2.5 is a key causative transcription factor associated with human congenital heart disease and one of the earliest markers of cardiac progenitors, thus it serves as a promising candidate. Here, we show that cardiac-specific RNA-sequencing studies reveal a disrupted embryonic transcriptional profile in the adult Nkx2.5 loss-of-function myocardium. nkx2.5-/- fish exhibit an impaired ability to recover following ventricular apex amputation with diminished dedifferentiation and proliferation. Complex network analyses illuminate that Nkx2.5 is required to provoke proteolytic pathways necessary for sarcomere disassembly and to mount a proliferative response for cardiomyocyte renewal. Moreover, Nkx2.5 targets embedded in these distinct gene regulatory modules coordinate appropriate, multi-faceted injury responses. Altogether, our findings support a previously unrecognized, Nkx2.5-dependent regenerative circuit that invokes myocardial cell cycle re-entry, proteolysis, and mitochondrial metabolism to ensure effective regeneration in the teleost heart.

Transcriptomic profile dataset of embryonic stem cells (Wild-type and IPO13-Knock Out) with and without oxidative stress.

The transcriptional response to cellular stress relies upon trafficking of regulators of transcription between the nuclear and cytoplasmic compartments, which occurs through action of members of the importin (IPO) superfamily. As a result of stresses such as oxidative or osmotic stress, one consequence is that importins become mislocalised, leading to inhibition of conventional nuclear transport. Here, we examine IPO13, which has a number of nonconventional characteristics, in the context of cell stress. We used Next Generation RNA Sequencing using the Illumina platform to compare the transcriptomes of Wild-type (WT) and IPO13-Knockout (KO) mouse embryonic stem cells in the absence and presence of oxidative stress. Differences in the mRNA expression profiles were observed between the cell lines in the absence and in the presence of stress. This data will be a key resource to enable characterization of the contribution of nuclear transporter IPO13 to cellular transcription in the absence and presence of oxidative stress, as well as more broadly, in the study of stem cell biology and effect of stress on embryonic stem cell transcription.

CD90 Marks a Mesenchymal Program in Human Thymic Epithelial Cells In Vitro and In Vivo.

Thymic epithelium is critical for the structural integrity of the thymus and for T cell development. Within the fully formed thymus, large numbers of hematopoietic cells shape the thymic epithelium into a scaffold-like structure which bears little similarity to classical epithelial layers, such as those observed in the skin, intestine or pancreas. Here, we show that human thymic epithelial cells (TECs) possess an epithelial identity that also incorporates the expression of mesenchymal cell associated genes, whose expression levels vary between medullary and cortical TECs (m/cTECs). Using pluripotent stem cell (PSC) differentiation systems, we identified a unique population of cells that co-expressed the master TEC transcription factor FOXN1, as well as the epithelial associated marker EPCAM and the mesenchymal associated gene CD90. Using the same serum free culture conditions, we also observed co-expression of EPCAM and CD90 on cultured TECs derived from neonatal human thymus in vitro. Single cell RNA-sequencing revealed these cultured TECs possessed an immature mTEC phenotype and expressed epithelial and mesenchymal associated genes, such as EPCAM, CLDN4, CD90 and COL1A1. Importantly, flow cytometry and single cell RNA-sequencing analysis further confirmed the presence of an EPCAM+CD90+ population in the CD45- fraction of neonatal human thymic stromal cells in vivo. Using the human thymus cell atlas, we found that cTECs displayed more pronounced mesenchymal characteristics than mTECs during embryonic development. Collectively, these results suggest human TECs possess a hybrid gene expression program comprising both epithelial and mesenchymal elements, and provide a basis for the further exploration of thymus development from primary tissues and from the in vitro differentiation of PSCs.

Adult mouse fibroblasts retain organ-specific transcriptomic identity.

Organ fibroblasts are essential components of homeostatic and diseased tissues. They participate in sculpting the extracellular matrix, sensing the microenvironment, and communicating with other resident cells. Recent studies have revealed transcriptomic heterogeneity among fibroblasts within and between organs. To dissect the basis of interorgan heterogeneity, we compare the gene expression of murine fibroblasts from different tissues (tail, skin, lung, liver, heart, kidney, and gonads) and show that they display distinct positional and organ-specific transcriptome signatures that reflect their embryonic origins. We demonstrate that expression of genes typically attributed to the surrounding parenchyma by fibroblasts is established in embryonic development and largely maintained in culture, bioengineered tissues and ectopic transplants. Targeted knockdown of key organ-specific transcription factors affects fibroblast functions, in particular genes involved in the modulation of fibrosis and inflammation. In conclusion, our data reveal that adult fibroblasts maintain an embryonic gene expression signature inherited from their organ of origin, thereby increasing our understanding of adult fibroblast heterogeneity. The knowledge of this tissue-specific gene signature may assist in targeting fibrotic diseases in a more precise, organ-specific manner.

MonaGO: a novel gene ontology enrichment analysis visualisation system.

BACKGROUND: Gene ontology (GO) enrichment analysis is frequently undertaken during exploration of various -omics data sets. Despite the wide array of tools available to biologists to perform this analysis, meaningful visualisation of the overrepresented GO in a manner which is easy to interpret is still lacking. RESULTS: Monash Gene Ontology (MonaGO) is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing GO enrichment analysis and visualising the results. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is a unique platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. CONCLUSION: MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

Spatially resolved transcriptomics in immersive environments.

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

Detection and identification of cis-regulatory elements using change-point and classification algorithms.

BACKGROUND: Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation. RESULTS: We extend an alignment-based method, changept, and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite. CONCLUSIONS: The analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.

The RNA-binding protein SRSF3 has an essential role in megakaryocyte maturation and platelet production.

RNA processing is increasingly recognized as a critical control point in the regulation of different hematopoietic lineages including megakaryocytes responsible for the production of platelets. Platelets are anucleate cytoplasts that contain a rich repertoire of RNAs encoding proteins with essential platelet functions derived from the parent megakaryocyte. It is largely unknown how RNA binding proteins contribute to the development and functions of megakaryocytes and platelets. We show that serine-arginine-rich splicing factor 3 (SRSF3) is essential for megakaryocyte maturation and generation of functional platelets. Megakaryocyte-specific deletion of Srsf3 in mice led to macrothrombocytopenia characterized by megakaryocyte maturation arrest, dramatically reduced platelet counts, and abnormally large functionally compromised platelets. SRSF3 deficient megakaryocytes failed to reprogram their transcriptome during maturation and to load platelets with RNAs required for normal platelet function. SRSF3 depletion led to nuclear accumulation of megakaryocyte mRNAs, demonstrating that SRSF3 deploys similar RNA regulatory mechanisms in megakaryocytes as in other cell types. Our study further suggests that SRSF3 plays a role in sorting cytoplasmic megakaryocyte RNAs into platelets and demonstrates how SRSF3-mediated RNA processing forms a central part of megakaryocyte gene regulation. Understanding SRSF3 functions in megakaryocytes and platelets provides key insights into normal thrombopoiesis and platelet pathologies as SRSF3 RNA targets in megakaryocytes are associated with platelet diseases.

3D-cardiomics: A spatial transcriptional atlas of the mammalian heart.

Understanding the spatial gene expression and regulation in the heart is key to uncovering its developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information in organs such as the heart, but few utilize intuitive true-to-life three-dimensional representations to analyze and visualise results. Here we combined transcriptomics with 3D-modelling to interrogate spatial gene expression in the mammalian heart. For this, we microdissected and sequenced transcriptome-wide 18 anatomical sections of the adult mouse heart. Our study has unveiled known and novel genes that display complex spatial expression in the heart sub-compartments. We have also created 3D-cardiomics, an interface for spatial transcriptome analysis and visualization that allows the easy exploration of these data in a 3D model of the heart. 3D-cardiomics is accessible from http://3d-cardiomics.erc.monash.edu/.

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease.

BACKGROUND: Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development. RESULTS: We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes. CONCLUSIONS: Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

Towards spatio-temporally resolved developmental cardiac gene regulatory networks in zebrafish.

Heart formation in the zebrafish involves a rapid, complex series of morphogenetic events in three-dimensional space that spans cardiac lineage specification through to chamber formation and maturation. This process is tightly orchestrated by a cardiac gene regulatory network (GRN), which ensures the precise spatio-temporal deployment of genes critical for heart formation. Alterations of the timing or spatial localisation of gene expression can have a significant impact in cardiac ontogeny and may lead to heart malformations. Hence, a better understanding of the cellular and molecular basis of congenital heart disease relies on understanding the behaviour of cardiac GRNs with precise spatiotemporal resolution. Here, we review the recent technical advances that have expanded our capacity to interrogate the cardiac GRN in zebrafish. In particular, we focus on studies utilising high-throughput technologies to systematically dissect gene expression patterns, both temporally and spatially during heart development.