A global map of the Zika virus phosphoproteome reveals host-driven regulation of viral budding

Flaviviruses are enveloped, positive-strand RNA viruses that cause millions of infections in the human population annually. Although Zika virus (ZIKV) had been detected in humans as early as the 1950s, its reemergence in South America in 2015 resulted in a global health crisis. While flaviviruses encode 10 proteins that can be post-translationally modified by host enzymes, little is known regarding post-translational modifications (PTMs) of the flavivirus proteome. We used mass spectrometry to comprehensively identify host-driven PTMs on the ZIKV proteome. This approach allowed us to identify 43 PTMs across 8 ZIKV proteins, including several that are highly conserved within the Flavivirus genus. Notably, we found two phosphosites on the ZIKV envelope protein that are functionally important for viral propagation and appear to regulate viral budding. Additionally, we discovered 115 host kinases that interacted with ZIKV proteins and determined that Bosutinib, an FDA-approved tyrosine kinase inhibitor that targets ZIKV interacting host kinases, impairs ZIKV growth. Thus, we have defined a high-resolution map of host-driven PTMs on ZIKV proteins as well as cellular interacting kinases, uncovered a novel mechanism of host driven-regulation of ZIKV budding, and identified an FDA-approved inhibitor of ZIKV growth.

Structure Determination of Inactive-State GPCRs with a Universal Nanobody

Cryogenic electron microscopy (cryo-EM) has widened the field of structure-based drug discovery by allowing for routine determination of membrane protein structures previously intractable. However, despite representing one of the largest classes of therapeutic targets, most inactive-state G protein-coupled receptors (GPCRs) have remained inaccessible for cryo-EM because their small size and membrane-embedded nature impedes projection alignment for high-resolution map reconstructions. Here we demonstrate that a camelid single-chain antibody (nanobody) recognizing a grafted intracellular loop can be used to obtain cryo-EM structures of different inactive-state GPCRs at resolutions comparable or better than those obtained by X-ray crystallography. Using this approach, we obtained the structure of human neurotensin 1 receptor (NTSR1) bound to antagonist SR48692, of μ-opioid receptor (MOR) bound to the clinical antagonist alvimopan, as well as the structure of the previously uncharacterized somatostatin receptor 2 (SSTR2) in the apo state; each of these structures yields novel insights into ligand binding and specificity. We expect this rapid, straightforward approach to facilitate the broad structural exploration of GPCR inactive states without the need for extensive engineering and crystallization.

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.

Gene-regulatory network study of rheumatoid arthritis in single-cell chromatin landscapes of peripheral blood mononuclear cells

Rheumatoid arthritis is a chronic autoinflammatory disease with an elusive etiology. Assays for transposase-accessible chromatin with single-cell sequencing (scATAC-seq) contribute to the progress in epigenetic studies. However, the impact of epigenetic technology on autoimmune diseases has not been objectively analyzed. Therefore, scATAC-seq was performed to generate a high-resolution map of accessible loci in peripheral blood mononuclear cells (PBMCs) of RA patients at the single-cell level. The purpose of our project was to discover the transcription factors (TFs) that were involved in the pathogenesis of RA at single-cell resolution. In our research, we obtained 22 accessible chromatin patterns. Then, 10 key TFs were involved in the RA pathogenesis by regulating the activity of MAP kinase. Consequently, two genes (PTPRC, SPAG9) regulated by 10 key TFs were found that may be associated with RA disease pathogenesis and these TFs were obviously enriched in RA patients (p<0.05, FC>1.2). With further qPCR validation on PTPRC and SPAG9 in monocytes, we found differential expression of these two genes, which were regulated by eight TFs (ZNF384, HNF1B, DMRTA2, MEF2A, NFE2L1, CREB3L4 (var. 2), FOSL2::JUNB (var. 2), MEF2B). What is more, the eight TFs showed highly accessible binding sites in RA patients. These findings demonstrate the value of using scATAC-seq to reveal transcriptional regulatory variation in RA-derived PBMCs, providing insights on therapy from an epigenetic perspective.

High-resolution map of sugarcane cultivation in Brazil using a phenology-based method

Sugarcane is the most important source of sugar, and its cultivation area has undergone rapid expansion, replacing other crops, pastures, and forests. Brazil is the world's largest sugarcane producer and contributed to approximately 38.6 % of the world's total production in 2019. Sugarcane in Brazil can be harvested from April to December in south-central area and from September to April in northeast area. The flexible phenology and harvest conditions of sugarcane in Brazil make it difficult to identify the harvest area at state to country scales. In this study, we developed a phenology-based method to identify the harvest area of sugarcane in Brazil by incorporating the multiple phenology conditions into a time-weighted dynamic time warping method (TWDTW). Then, we produced annual 30-m spatial resolution sugarcane harvest maps (2016–2019) for 14 states in Brazil (over 98 % of the harvest area) based on the proposed method by using Landsat-7/8 and Sentinel-2 optical data. The proposed method performed well in identifying sugarcane harvest area with limited training sample data. Validations for the 2018 harvest year displayed high accuracy, with user's, producer's, and overall accuracies of 97.00 %, 88.08 %, and 92.99 % in Brazil, respectively. In addition, the identified harvest area of sugarcane exhibited good correlations with the agricultural statistical data provided by the Brazilian Institute of Geography and Statistics (IBGE) at the municipality, microregion, and mesoregion levels. The 30-m Brazil sugarcane harvest maps can be obtained at https://doi.org/10.6084/m9.figshare.14213909 (Zheng et al., 2021).

A NOVEL OF COMPARISON BETWEEN 3D HIGH DEFINITION MAPS CREATED BY PHOTOGRAMMETRY AND LASER SCANNING APPLIED FOR AN AUTONOMOUS VEHICLE

In this paper, based on the selected mathematical algorithm, the performing of two methods for building high-resolution 3D maps that are Photographmetry and Laser scanning was analyzed to find out the advantages and disadvantages of each one. The results showed that the high-resolution map constructed by using lidar was more accurate and detailed, whereas the map constructed by using images with coordinates was more intuitive. A mapping method using lidar-camera fusion was proposed in which the detailed roads are created by Lidar and the rest area built by optical imaging method.

Phage display demonstrates durable differences in serological profile by route of inoculation in primary infections of non-human primates with Dengue Virus 1

Natural dengue virus (DENV) infections occur by mosquito bite but how the inoculation route affects the humoral immune response is unknown. We serologically profiled 20 non-human primates (NHP) from a prior study of DENV1 infection where animals were inoculated by mosquito (N = 10) or subcutaneous injection (N = 10). Using a comprehensive, densely tiled and highly redundant pan-flavivirus programmable phage library containing 91,562 overlapping 62 amino acid peptides, we produced a high-resolution map of linear peptide sequences enriched during DENV seroconversion. Profiles in mosquito-inoculated and subcutaneously-inoculated animals were similar up to 90 days after primary infection, but diverged at 1 year with differences in sero-reactivity in the Envelope (E; residues 215–406; p < 0.08), and Nonstructural-3 (NS3; residues 549–615; p < 0.05) proteins in mosquito-inoculated versus subcutaneously-inoculated animals. Within the E protein, residues 339–384 in domain III accounted for > 99% of the observed sero-reactivity difference. Antibody breadth did not vary by mode of inoculation. The differential reactivity to E domain III seen by phage display validated orthogonally by ELISA, but did not correlate with late neutralization titers. Serological profiling of humoral immune responses to DENV infection in NHP by programmable phage display demonstrated durable differences in sero-reactivity by route of inoculation.

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

The cell cycle is a fundamental process of life, however, a quantitative understanding of gene regulation dynamics in the context of the cell cycle is still far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to its dynamics without externally perturbing the cell. Here, we build a high-resolution map of the cell cycle transcriptome based on scRNA-seq and deep-learning. By generating scRNA-seq libraries with high depth, in mouse embryonic stem cells and human fibroblasts, we are able to observe cycling patterns in the unspliced-spliced RNA space for single genes. Since existing methods in scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach to fit these cycling patterns sorting single cells across the cell cycle. We characterize the cell cycle in asynchronous pluripotent and differentiated cells identifying major waves of transcription during the G1 phase and systematically study the G1-G0 transition where the cells exit the cycle. Our work presents to the scientific community a broader understanding of RNA velocity and cell cycle maps, that we applied to pluripotency and differentiation. Our approach will facilitate the study of the cell cycle in multiple cellular models and different biological contexts, such as cancer and development.