Epigenomic features associated with body temperature stabilize tissues during cold exposure in cold-resistant pigs.

Cold stress in low-temperature environments can trigger changes in gene expression, but epigenomics regulation of temperature stability in vital tissues, including the fat and diencephalon, is still unclear. Here, we explore the cold-induced changes in epigenomic features in the diencephalon and fat tissues of two cold-resistant Chinese pig breeds, Min and Enshi black (ES) pigs, utilizing H3K27ac CUT&Tag, RNA-seq, and selective signature analysis. Our results show significant alterations in H3K27ac modifications in the diencephalon of Min pigs and the fat of ES pigs after cold exposure. Dramatic changes in H3K27ac modifications in Min pigs are primarily associated with genes involved in energy metabolism and hormone regulation, whereas those in ES pigs are primarily associated with immunity-related genes. Moreover, transcription factors PRDM1 and HSF1, which show evidence of selection, are enriched in genomic regions presenting cold-responsive alterations in H3K27ac modification in the Min pig diencephalon and ES pig fat, respectively. Our results indicate the diversity of epigenomic response mechanisms to cold exposure between Min and ES pigs, providing unique epigenetic resources for studies of low-temperature adaptation in large mammals.

Epigenomics analysis of miRNA cis-regulatory elements in pig muscle and fat tissues.

Although large-scale and accurate identification of cis-regulatory elements on pig protein-coding and long non-coding genes has been reported, similar study on pig miRNAs is still lacking. Here, we systematically characterized the cis-regulatory elements of pig miRNAs in muscle and fat by adopting miRNAomes, ChIP-seq, ATAC-seq, RNA-seq and Hi-C data. In total, the cis-regulatory elements of 257 (85.95%) expressed miRNAs including 226 known and 31 novel miRNAs were identified. Especially, the miRNAs associated with super-enhancers, active promoters, and "A" compartment were significantly higher than those associated by typical enhancers, prompters without H3K27ac, and "B" compartment, respectively. The tissue specific transcription factors were the primary determination of core miRNA expression pattern in muscle and fat. Moreover, the miRNA promoters are more evolutionarily conserved than miRNA enhancers, like other type genes. Our study adds additional important information to existing pig epigenetic data and provides essential resource for future in-depth investigation of pig epigenetics.

HSP27 Protein Dampens Encephalomyocarditis Virus Replication by Stabilizing Melanoma Differentiation-Associated Gene 5.

Heat shock proteins (HSPs) are a protein family that respond to physiological stress, such as heat, starvation, and infection. As cellular protein chaperones, they play an important role in protein folding, assembly, and degradation. Though it is well known that HSP27 is involved in a range of viral infections, its role during an encephalomyocarditis virus (EMCV) infection is not known. Here, we report that EMCV degrades HSP27 and that EMCV proteins 2Cpro and 3Apro are primarily responsible for its degradation. Consequently, loss of cellular HSP27 augmented EMCV proliferation, an effect that could be reversed upon HSP27 overexpression. Importantly, we found that HSP27 positively regulated EMCV-triggered type I interferon (IFN) production. Moreover, overexpression of 2Cpro and 3Apro significantly blocked type I IFN production. We also found for the first time that HSP27, as a molecular chaperone, can specifically interact with MDA5 and stabilize the expression of MDA5. Collectively, this study shows that HSP27 dampens EMCV infectivity by positively regulating EMCV-triggered retinoic acid-inducible gene (RIG)-I-like receptor (RLR)/melanoma differentiation-associated gene 5 (MDA5) signal pathway, while EMCV proteins 2Cpro and 3Apro interact with HSP27 and degrade HSP27 protein expression to allow EMCV proliferation. Our findings provide further mechanistic evidence for EMCV partaking in immune escape mechanisms, and that 2Cpro and 3Apro could serve as potential antiviral targets.

Host protein, HSP90ß, antagonizes IFN-ß signaling pathway and facilitates the proliferation of encephalomyocarditis virus in vitro.

Encephalomyocarditis virus (EMCV) is a small, non-enveloped, single stranded RNA virus which infects a wide variety of mammalian species, and has zoonotic importance. Many host proteins are known to regulate EMCV proliferation by interacting with its structural or nonstructural proteins, but the regulatory role and mechanism of heat shock protein 90ß (HSP90ß), in EMCV infection has not been reported yet. Here, we report that overexpression of HSP90ß significantly promotes the growth and proliferation of EMCV in vitro. On the contrary, down-regulation of HSP90ß by RNAi or geldanamycin inhibits EMCV replication. HSP90ß suppresses IFN-ß responses in the RLRs pathway by targeting the expression of the key adaptor molecules MAVS, TBK1, and IRF3, but not MDA5. This study demonstrates the firsthand information that HSP90ß plays a positive role in viral proliferation by inhibiting EMCV induced IFN-ß production. Collectively, the results reveal new insights into HSP90ß-assisted progression of EMCV infection.

LADV: Deep Learning Assisted Authoring of Dashboard Visualizations From Images and Sketches.

Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.

Transmembrane Protein 39A Promotes the Replication of Encephalomyocarditis Virus via Autophagy Pathway.

Encephalomyocarditis virus (EMCV) causes encephalitis, myocarditis, neuropathy, reproductive disorders, and diabetes in animals. EMCV is known to induce cell autophagy; however, the molecular mechanisms underlying this remain unclear. Here, we show that the type III-transmembrane protein, transmembrane protein 39A (TMEM39A), plays a critical role in EMCV replication. We showed that EMCV GS01 strain infection upregulated TMEM39A expression. Importantly, EMCV induced autophagy in a range of host cells. The autophagy chemical inhibitor, 3-MA, inhibited EMCV replication and reduced TMEM39A expression. This is the first study demonstrating TMEM39A promoting the replication of EMCV via autophagy. Overall, we show that TMEM39A plays a positive regulatory role in EMCV proliferation and that TMEM39A expression is dependent on the autophagy pathway.

Author Correction: Typeface Reveals Spatial Economical Patterns.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Typeface Reveals Spatial Economical Patterns.

Understanding the socioeconomic and demographic characteristics of an urban region is vital for policy-making, urban management, and urban planning. Auditing socioeconomic and demographic patterns traditionally entails producing a large portion of data by human-participant surveys, which are usually costly and time consuming. Even with newly developed computational methods, amenity characteristics such as typeface, color, and graphic element choices are still missing at the city scale. However, they have a huge impact on personalized preferences. Currently, researchers tend to use large-scale street view imagery to uncover physical and socioeconomic patterns. In this research, we first propose a framework that uses deep convolutional neural network to recognize the typeface from street view imagery in London. Second, we analyze the relationship between 11 typefaces and the average household income in 77 wards of London. The result show that the typefaces used in the neighborhood are highly correlated with economic and demographic factors. Typeface could be an alternative metric to evaluate economic and demographic status in large-scale urban regions. More generally, typeface can also act as a key visual characteristic of a city.