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Rationale: The role of FSTL-1 (follistatin-like 1) in lung homeostasis is unknown.Objectives: We aimed to define the impact of FSTL-1 attenuation on lung structure and function and to identify FSTL-1-regulated transcriptional pathways in the lung. Further, we aimed to analyze the association of FSTL-1 SNPs with lung disease.Methods: FSTL-1 hypomorphic (FSTL-1 Hypo) mice underwent lung morphometry, pulmonary function testing, and micro-computed tomography. Fstl1 expression was determined in wild-type lung cell populations from three independent research groups. RNA sequencing of wild-type and FSTL-1 Hypo mice identified FSTL-1-regulated gene expression, followed by validation and mechanistic in vitro examination. FSTL1 SNP analysis was performed in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort.Measurements and Main Results: FSTL-1 Hypo mice developed spontaneous emphysema, independent of smoke exposure. Fstl1 is highly expressed in the lung by mesenchymal and endothelial cells but not immune cells. RNA sequencing of whole lung identified 33 FSTL-1-regulated genes, including Nr4a1, an orphan nuclear hormone receptor that negatively regulates NF-κB (nuclear factor-κB) signaling. In vitro, recombinant FSTL-1 treatment of macrophages attenuated NF-κB p65 phosphorylation in an Nr4a1-dependent manner. Within the COPDGene cohort, several SNPs in the FSTL1 region corresponded to chronic obstructive pulmonary disease and lung function.Conclusions: This work identifies a novel role for FSTL-1 protecting against emphysema development independent of smoke exposure. This FSTL-1-deficient emphysema implicates regulation of immune tolerance in lung macrophages through Nr4a1. Further study of the mechanisms involving FSTL-1 in lung homeostasis, immune regulation, and NF-κB signaling may provide additional insight into the pathophysiology of emphysema and inflammatory lung diseases.

Motivation Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. Results We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. Availability and implementation The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv , implemented in Python. [ABSTRACT FROM AUTHOR]

Heart development is a continuous process involving significant remodeling during embryogenesis and neonatal stages. To date, several groups have used single-cell sequencing to characterize the heart transcriptomes but failed to capture the progression of heart development at most stages. This has left gaps in understanding the contribution of each cell type across cardiac development. Here, we report the transcriptional profile of the murine heart from early embryogenesis to late neonatal stages. Through further analysis of this dataset, we identify several transcriptional features. We identify gene expression modules enriched at early embryonic and neonatal stages; multiple cell types in the left and right atriums are transcriptionally distinct at neonatal stages; many congenital heart defect-associated genes have cell type-specific expression; stage-unique ligand-receptor interactions are mostly between epicardial cells and other cell types at neonatal stages; and mutants of epicardium-expressed genes Wt1 and Tbx18 have different heart defects. Assessment of this dataset serves as an invaluable source of information for studies of heart development. A detailed multi-staged single cell atlas of heart development could improve our understanding of cell type diversification during cardiac development. Here, the authors generated a large dataset with cells from embryonic and neonatal hearts to identify the stage and chamber specific features in heart development. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study's conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds , we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds , utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds , on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scd s presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Elimination of the proliferating germline extends lifespan in C. elegans. This phenomenon provides a unique platform to understand how complex metazoans retain metabolic homeostasis when challenged with major physiological perturbations. Here, we demonstrate that two conserved transcription regulators essential for the longevity of germline-less adults, DAF-16/FOXO3A and TCER-1/TCERG1, concurrently enhance the expression of multiple genes involved in lipid synthesis and breakdown, and that both gene classes promote longevity. Lipidomic analyses revealed that key lipogenic processes, including de novo fatty acid synthesis, triglyceride production, desaturation and elongation, are augmented upon germline removal. Our data suggest that lipid anabolic and catabolic pathways are coordinately augmented in response to germline loss, and this metabolic shift helps preserve lipid homeostasis. DAF-16 and TCER-1 also perform essential inhibitory functions in germline-ablated animals. TCER-1 inhibits the somatic gene-expression program that facilitates reproduction and represses anti-longevity genes, whereas DAF-16 impedes ribosome biogenesis. Additionally, we discovered that TCER-1 is critical for optimal fertility in normal adults, suggesting that the protein acts as a switch supporting reproductive fitness or longevity depending on the presence or absence of the germline. Collectively, our data offer insights into how organisms adapt to changes in reproductive status, by utilizing the activating and repressive functions of transcription factors and coordinating fat production and degradation. [ABSTRACT FROM AUTHOR]

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. [ABSTRACT FROM AUTHOR]

Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells—typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation—to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the ‘stem cell matrix’) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein–protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks. [ABSTRACT FROM AUTHOR]

Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures. [ABSTRACT FROM AUTHOR]