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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.

Some of the fastest evolving regions of the human genome are conserved noncoding elements with many human-specific DNA substitutions. These human accelerated regions (HARs) are enriched nearby regulatory genes, and several HARs function as developmental enhancers. To investigate if this evolutionary signature is unique to humans, we quantified evidence of accelerated substitutions in conserved genomic elements across multiple lineages and applied this approach simultaneously to the genomes of five apes: human, chimpanzee, gorilla, orangutan, and gibbon. We find roughly similar numbers and genomic distributions of lineage-specific accelerated regions (linARs) in all five apes. In particular, apes share an enrichment of linARs in regulatory DNA nearby genes involved in development, especially transcription factors and other regulators. Many developmental loci harbor clusters of nonoverlapping linARs from multiple apes, suggesting that accelerated evolution in each species affected distinct regulatory elements that control a shared set of developmental pathways. Our statistical tests distinguish between GC-biased and unbiased accelerated substitution rates, allowing us to quantify the roles of different evolutionary forces in creating linARs. We find evidence of GC-biased gene conversion in each ape, but unbiased acceleration consistent with positive selection or loss of constraint is more common in all five lineages. It therefore appears that similar evolutionary processes created independent accelerated regions in the genomes of different apes, and that these lineage-specific changes to conserved noncoding sequences may have differentially altered expression of a core set of developmental genes across ape evolution.

Next-generation sequencing technology enables the identification of thousands of gene regulatory sequences in many cell types and organisms. We consider the problem of testing if two such sequences differ in their number of binding site motifs for a given transcription factor (TF) protein. Binding site motifs impart regulatory function by providing TFs the opportunity to bind to genomic elements and thereby affect the expression of nearby genes. Evolutionary changes to such functional DNA are hypothesized to be major contributors to phenotypic diversity within and between species; but despite the importance of TF motifs for gene expression, no method exists to test for motif loss or gain. Assuming that motif counts are Binomially distributed, and allowing for dependencies between motif instances in evolutionarily related sequences, we derive the probability mass function of the difference in motif counts between two nucleotide sequences. We provide a method to numerically estimate this distribution from genomic data and show through simulations that our estimator is accurate. Finally, we introduce the R package motifDiverge that implements our methodology and illustrate its application to gene regulatory enhancers identified by a mouse developmental time course experiment. While this study was motivated by analysis of regulatory motifs, our results can be applied to any problem involving two correlated Bernoulli trials.

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available.

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease.

Gene expression divergence and chromosomal rearrangements have been put forward as major contributors to phenotypic differences between closely related species. It has also been established that duplicated genes show enhanced rates of positive selection in their amino acid sequences. If functional divergence is largely due to changes in gene expression, it follows that regulatory sequences in duplicated loci should also evolve rapidly. To investigate this hypothesis, we performed likelihood ratio tests (LRTs) on all noncoding loci within 5 kb of every transcript in the human genome and identified sequences with increased substitution rates in the human lineage since divergence from Old World Monkeys. The fraction of rapidly evolving loci is significantly higher nearby genes that duplicated in the common ancestor of humans and chimps compared with nonduplicated genes. We also conducted a genome-wide scan for nucleotide substitutions predicted to affect transcription factor binding. Rates of binding site divergence are elevated in noncoding sequences of duplicated loci with accelerated substitution rates. Many of the genes associated with these fast-evolving genomic elements belong to functional categories identified in previous studies of positive selection on amino acid sequences. In addition, we find enrichment for accelerated evolution nearby genes involved in establishment and maintenance of pregnancy, processes that differ significantly between humans and monkeys. Our findings support the hypothesis that adaptive evolution of the regulation of duplicated genes has played a significant role in human evolution.

Motivation Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology's utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation, computational methods to annotate and remove doublets form scRNA-seq datasets are needed. Results We introduce vaeda (Variational Auto-Encoder for Doublet Annotation), a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to 16 benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. Altogether, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. Availability and implementation Vaeda is available at https://github.com/kostkalab/vaeda , and the version used for the results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783). Supplementary information Supplementary data are available at Bioinformatics online. [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 [...]