Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis

Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that development of AH is characterized by defective activity of liver-enriched transcription factors (LETFs). TGFβ1 is a key upstream transcriptome regulator in AH and induces the use of HNF4α P2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4α are not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4α-dependent gene expression. We conclude that targeting TGFβ1 and epigenetic drivers that modulate HNF4α-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Data from: Phylogenetic comparative analysis supports aposematic colouration–body size association in millipede assassins (Hemiptera: Reduviidae: Ectrichodiinae)

The diversity of colour patterns and its importance in interactions with the environment make colouration in animals an intriguing research focus. Aposematic colouration is positively correlated with body size in certain groups of animals, suggesting that warning colours are more effective or that crypsis is harder to achieve in larger animals. Surprisingly, this relationship has not been recovered in studies investigating insects, which may have been confounded by a focus on aposematic taxa that are also gregarious. Millipede assassin bugs (Hemiptera: Reduviidae: Ectrichodiinae) comprise species with cryptic and aposematic colour patterns across a range of body sizes, are typically solitary as adults, and are thus an excellent model for investigating a possible association between colouration and body size. Here, we use a comprehensive phylogeny for Ectrichodiinae, ancestral state reconstruction of colouration, and phylogenetic comparative methods to test for a colouration-body size association. The ancestor of Ectrichodiinae is reconstructed as cryptically coloured, with multiple subsequent transitions between aposematic and cryptic colouration. Aposematic colouration is positively associated with male body length and supports the hypothesis that selection on Ectrichodiinae body size may influence evolutionary transitions between aposematic and cryptic colouration or alternatively that selection for aposematic colouration influences body size evolution

Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis

Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that development of AH is characterized by defective activity of liver-enriched transcription factors (LETFs). TGFβ1 is a key upstream transcriptome regulator in AH and induces the use of HNF4α P2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4α are not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4α-dependent gene expression. We conclude that targeting TGFβ1 and epigenetic drivers that modulate HNF4α-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH