Bile acid–sensitive tuft cells regulate biliary neutrophil influx

Inflammation and dysfunction of the extrahepatic biliary tree are common causes of human pathology, including gallstones and cholangiocarcinoma. Despite this, we know little about the local regulation of biliary inflammation. Tuft cells, rare sensory epithelial cells, are particularly prevalent in the mucosa of the gallbladder and extrahepatic bile ducts. Here, we show that biliary tuft cells express a core genetic tuft cell program in addition to a tissue-specific gene signature and, in contrast to small intestinal tuft cells, decreased postnatally, coincident with maturation of bile acid production. Manipulation of enterohepatic bile acid recirculation revealed that tuft cell abundance is negatively regulated by bile acids, including in a model of obstructive cholestasis in which inflammatory infiltration of the biliary tree correlated with loss of tuft cells. Unexpectedly, tuft cell–deficient mice spontaneously displayed an increased gallbladder epithelial inflammatory gene signature accompanied by neutrophil infiltration that was modulated by the microbiome. We propose that biliary tuft cells function as bile acid–sensitive negative regulators of inflammation in biliary tissues and serve to limit inflammation under homeostatic conditions

Genome of the Komodo dragon reveals adaptations in the cardiovascular and chemosensory systems of monitor lizards

Monitor lizards are unique among ectothermic reptiles in that they have high aerobic capacity and distinctive cardiovascular physiology resembling that of endothermic mammals. Here, we sequence the genome of the Komodo dragon Varanus komodoensis, the largest extant monitor lizard, and generate a high-resolution de novo chromosome-assigned genome assembly for V. komodoensis using a hybrid approach of long-range sequencing and single-molecule optical mapping. Comparing the genome of V. komodoensis with those of related species, we find evidence of positive selection in pathways related to energy metabolism, cardiovascular homoeostasis, and haemostasis. We also show species-specific expansions of a chemoreceptor gene family related to pheromone and kairomone sensing in V. komodoensis and other lizard lineages. Together, these evolutionary signatures of adaptation reveal the genetic underpinnings of the unique Komodo dragon sensory and cardiovascular systems, and suggest that selective pressure altered haemostasis genes to help Komodo dragons evade the anticoagulant effects of their own saliva. The Komodo dragon genome is an important resource for understanding the biology of monitor lizards and reptiles worldwide

Bile acid–sensitive tuft cells regulate biliary neutrophil influx

Inflammation and dysfunction of the extrahepatic biliary tree are common causes of human pathology, including gallstones and cholangiocarcinoma. Despite this, we know little about the local regulation of biliary inflammation. Tuft cells, rare sensory epithelial cells, are particularly prevalent in the mucosa of the gallbladder and extrahepatic bile ducts. Here, we show that biliary tuft cells express a core genetic tuft cell program in addition to a tissue-specific gene signature and, in contrast to small intestinal tuft cells, decreased postnatally, coincident with maturation of bile acid production. Manipulation of enterohepatic bile acid recirculation revealed that tuft cell abundance is negatively regulated by bile acids, including in a model of obstructive cholestasis in which inflammatory infiltration of the biliary tree correlated with loss of tuft cells. Unexpectedly, tuft cell-deficient mice spontaneously displayed an increased gallbladder epithelial inflammatory gene signature accompanied by neutrophil infiltration that was modulated by the microbiome. We propose that biliary tuft cells function as bile acid-sensitive negative regulators of inflammation in biliary tissues and serve to limit inflammation under homeostatic conditions

On the cross-population generalizability of gene expression prediction models.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction

On the cross-population generalizability of gene expression prediction models.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction