e these results are extremely important in order to generate correct estimates of SARS-CoV-2 infections in the general population. Furthermore, the assessment of antibody responses against the trimeric S protein will be critical to evaluate the durability of the antibody response and for the characterization of a vaccine-induced antibody response.Middle East respiratory syndrome coronavirus (MERS-CoV) causes a highly lethal pneumonia that emerged in 2012. There is limited information on MERS-CoV pathogenesis, as data from patients are scarce and the generation of animal models reproducing MERS clinical manifestations has been challenging. Human dipeptidyl peptidase 4 knock-in (hDPP4-KI) mice and a mouse-adapted MERS-CoV strain (MERSMA-6-1-2) were recently described. hDPP4-KI mice infected with MERSMA-6-1-2 show pathological signs of respiratory disease, high viral titers in the lung, and death. In this work, a mouse-adapted MERS-CoV infectious cDNA was engineered by introducing nonsynonymous mutations contained in the MERSMA-6-1-2 genome into a MERS-CoV infectious cDNA, leading to a recombinant mouse-adapted virus (rMERS-MA) that was virulent in hDDP4-KI mice. MERS-CoV adaptation to cell culture or mouse lungs led to mutations and deletions in genus-specific gene 5 that prevented full-length protein expression. In contrast, analysis of 476 MERS-CoV firating severe MERS-CoV pathogenesis.IMPORTANCE Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic virus causing human infections with high mortality rate (∼35%). Animal models together with reverse-genetics systems are essential to understand MERS-CoV pathogenesis. We developed a reverse-genetics system for a mouse-adapted MERS-CoV that reproduces the virus behavior observed in humans. This system is highly useful to investigate the role of specific viral genes in pathogenesis. In addition, we described a virus lacking gene 5 expression that is more virulent than the parental one. The data provide novel functions in IFN modulation for gene 5 in the context of viral infection and will help to develop novel antiviral strategies. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with limited treatment options. Cabozantinib, an orally bioavailable multikinase inhibitor is now approved by Food and Drug Administration (FDA) for HCC patients. https://www.selleckchem.com/products/kynurenic-acid.html We evaluated the therapeutic efficacy of cabozantinib, either alone or in combination, in vitro and in vivo. Human HCC cell lines and HCC mouse models were used to assess the therapeutic efficacy and targeted molecular pathways of cabozantinib, either alone or in combination with the pan-mTOR inhibitor MLN0128 or the checkpoint inhibitor anti-PD-L1 antibody. Cabozantinib treatment led to stable disease in c-Met/β-catenin and Akt/c-Met mouse HCC while possessing limited efficacy on Akt/Ras and c-Myc liver tumours. Importantly, cabozantinib effectively inhibited c-MET and ERK activity, leading to decreased PKM2 and increased p21 expression in HCC cells and in c-Met/β-catenin and Akt/c-Met HCC. However, cabozantinib was ineffective in inhibiting the Akt/mTOR cascade. Intriguingly, a strong inhibition of angiogenesis by cabozantinib occurred regardless of the oncogenic drivers. However, cabozantinib had limited impact on other tumour microenvironment parameters, including tumour infiltrating T cells, and did not induce programmed death-ligand 1 (PD-L1) expression. Combining cabozantinib with MLN0128 led to tumour regression in c-Met/β-catenin mice. In contrast, combined treatment with cabozantinib and the checkpoint inhibitor anti-PD-L1 antibody did not provide any additional therapeutic benefit in the four mouse HCC models tested. c-MET/ERK/p21/PKM2 cascade and VEGFR2-induced angiogenesis are the primary targets of cabozantinib in HCC treatment. Combination therapies with cabozantinib and mTOR inhibitors may be effective against human HCC. c-MET/ERK/p21/PKM2 cascade and VEGFR2-induced angiogenesis are the primary targets of cabozantinib in HCC treatment. Combination therapies with cabozantinib and mTOR inhibitors may be effective against human HCC.Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5,596 metagenome-assembled genomes (MAGs) from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome data sets. The MAGs comprised 1,522 species-level genome bins (SGBs), most of which were novel at the species, genus, or family level, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1,986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host or environment specific. Our MAG and gene data sets greatly expand the microbial genome repertoire and pr of microbial genomic diversity in the vertebrate gut.Human immunodeficiency virus type 1 (HIV-1) depends on a class of host proteins called host dependency factors (HDFs) to facilitate its infection. So far experimental efforts have detected a certain number of HDFs, but the gene inventory of HIV-1 HDFs remains incomplete. Here, we implemented an existing network-based gene discovery strategy to predict HIV-1 HDFs. First, an encoding scheme based on a publicly available human tissue-specific gene functional network (GIANT; http//giant.princeton.edu/) was designed to convert each human gene into a 25,825-dimensional feature vector. Then, a random forest-based predictive model was trained on a data set containing 868 known HDFs and 1,736 non-HDFs. Through 5-fold cross-validation, an independent test, and comparison with one existing method, the proposed prediction method consistently revealed accurate and competitive performance. The highlight of our method should be ascribed to the introduction of the GIANT encoding scheme, which contains rich information regarding gene interactions.