Tissue-specific immunopathology in fatal COVID-19

Funding: Inflammation in COVID-19: Exploration of Critical Aspects of Pathogenesis (ICECAP) receives funding and support from the Chief Scientist Office (RapidResearch in COVID-19 programme [RARC-19] funding call, “Inflammation in Covid-19: Exploration of Critical Aspects of Pathogenesis; COV/EDI/20/10” to D.A.D., C.D.L., C.D.R., J.K.B., and D.J.H.), LifeArc (through the University of Edinburgh STOPCOVID funding award to K.D., D.A.D., and C.D.L.), UK Research and Innovation (UKRI) (Coronavirus Disease [COVID-19] Rapid Response Initiative; MR/V028790/1 to C.D.L., D.A.D., and J.A.H.), and Medical Research Scotland (CVG-1722-2020 to D.A.D., C.D.L., C.D.R., J.K.B., and D.J.H.). C.D.L. is funded by a Wellcome Trust Clinical Career Development Fellowship(206566/Z/17/Z). J.K.B. and C.D.R. are supported by the Medical Research Council (grant MC_PC_19059) as part of the International Severe AcuteRespiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC-4C). D.J.H., I.H.U., and M.E. are supported by the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics. S.P. is supported by Kidney Research UK, and G.T. is supported by the Melville Trust for the Cure and Care of Cancer. Identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and sequencing work was supported by theU.S. Food and Drug Administration grant HHSF223201510104C (“Ebola Virus Disease: correlates of protection, determinants of outcome and clinicalmanagement”; amended to incorporate urgent COVID-19 studies) and contract 75F40120C00085 (“Characterization of severe coronavirus infection inhumans and model systems for medical countermeasure development and evaluation”; awarded to J.A.H.). J.A.H. is also funded by the Centre of Excellence in Infectious Diseases Research and the Alder Hey Charity. R.P.-R. is directly supported by the Medical Research Council Discovery Medicine North Doctoral Training Partnership. The group of J.A.H. is supported by the National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England and in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.Rationale: In life-threatening Covid-19, corticosteroids reduce mortality, suggesting that immune responses have a causal role in death. Whether this deleterious inflammation is primarily a direct reaction to the presence of SARS-CoV-2 or an independent immunopathologic process is unknown. Objectives: To determine SARS-CoV-2 organotropism and organ-specific inflammatory responses, and the relationships between viral presence, inflammation, and organ injury. Methods: Tissue was acquired from eleven detailed post-mortem examinations. SARS-CoV-2 organotropism was mapped by multiplex PCR and sequencing, with cellular resolution achieved by in situ viral spike protein detection. Histological evidence of inflammation was quantified from 37 anatomical sites, and the pulmonary immune response characterized by multiplex immunofluorescence. Measurements and main results: Multiple aberrant immune responses in fatal Covid-19 were found, principally involving the lung and reticuloendothelial system, and these were not clearly topologically associated with the virus. Inflammation and organ dysfunction did not map to the tissue and cellular distribution of SARS-CoV-2 RNA and protein, both between and within tissues. An arteritis was identified in the lung, which was further characterised as a monocyte/myeloid-rich vasculitis, and occurred along with an influx of macrophage/monocyte-lineage cells into the pulmonary parenchyma. In addition, stereotyped abnormal reticulo-endothelial responses, including excessive reactive plasmacytosis and iron-laden macrophages, were present and dissociated from viral presence in lymphoid tissues. Conclusions: Tissue-specific immunopathology occurs in Covid-19, implicating a significant component of immune-mediated, virus-independent immunopathology as a primary mechanism in severe disease. Our data highlight novel immunopathological mechanisms, and validate ongoing and future efforts to therapeutically target aberrant macrophage and plasma cell responses as well as promoting pathogen tolerance in Covid-19.Publisher PDFPeer reviewe

SHIV-162P3 Infection of Rhesus Macaques Given Maraviroc Gel Vaginally Does Not Involve Resistant Viruses

Maraviroc (MVC) gels are effective at protecting rhesus macaques from vaginal SHIV transmission, but breakthrough infections can occur. To determine the effects of a vaginal MVC gel on infecting SHIV populations in a macaque model, we analyzed plasma samples from three rhesus macaques that received a MVC vaginal gel (day 0) but became infected after high-dose SHIV-162P3 vaginal challenge. Two infected macaques that received a placebo gel served as controls. The infecting SHIV-162P3 stock had an overall mean genetic distance of 0.294±0.027%; limited entropy changes were noted across the envelope (gp160). No envelope mutations were observed consistently in viruses isolated from infected macaques at days 14–21, the time of first detectable viremia, nor selected at later time points, days 42–70. No statistically significant differences in MVC susceptibilities were observed between the SHIV inoculum (50% inhibitory concentration [IC50] 1.87 nM) and virus isolated from the three MVC-treated macaques (MVC IC50 1.18 nM, 1.69 nM, and 1.53 nM, respectively). Highlighter plot analyses suggested that infection was established in each MVC-treated animal by one founder virus genotype. The expected Poisson distribution of pairwise Hamming Distance frequency counts was observed and a phylogenetic analysis did not identify infections with distinct lineages from the challenge stock. These data suggest that breakthrough infections most likely result from incomplete viral inhibition and not the selection of MVC-resistant variants

Exploring Demographic, Physical, and Historical Explanations for the Genetic Structure of Two Lineages of Greater Antillean Bats

Observed patterns of genetic structure result from the interactions of demographic, physical, and historical influences on gene flow. The particular strength of various factors in governing gene flow, however, may differ between species in biologically relevant ways. We investigated the role of demographic factors (population size and sex-biased dispersal) and physical features (geographic distance, island size and climatological winds) on patterns of genetic structure and gene flow for two lineages of Greater Antillean bats. We used microsatellite genetic data to estimate demographic characteristics, infer population genetic structure, and estimate gene flow among island populations of Erophylla sezekorni/E. bombifrons and Macrotus waterhousii (Chiroptera: Phyllostomidae). Using a landscape genetics approach, we asked if geographic distance, island size, or climatological winds mediate historical gene flow in this system. Samples from 13 islands spanning Erophylla's range clustered into five genetically distinct populations. Samples of M. waterhousii from eight islands represented eight genetically distinct populations. While we found evidence that a majority of historical gene flow between genetic populations was asymmetric for both lineages, we were not able to entirely rule out incomplete lineage sorting in generating this pattern. We found no evidence of contemporary gene flow except between two genetic populations of Erophylla. Both lineages exhibited significant isolation by geographic distance. Patterns of genetic structure and gene flow, however, were not explained by differences in relative effective population sizes, island area, sex-biased dispersal (tested only for Erophylla), or surface-level climatological winds. Gene flow among islands appears to be highly restricted, particularly for M. waterhousii, and we suggest that this species deserves increased taxonomic attention and conservation concern

Three Novel Downstream Promoter Elements Regulate MHC Class I Promoter Activity in Mammalian Cells

BACKGROUND: MHC CLASS I TRANSCRIPTION IS REGULATED BY TWO DISTINCT TYPES OF REGULATORY PATHWAYS: 1) tissue-specific pathways that establish constitutive levels of expression within a given tissue and 2) dynamically modulated pathways that increase or decrease expression within that tissue in response to hormonal or cytokine mediated stimuli. These sets of pathways target distinct upstream regulatory elements, have distinct basal transcription factor requirements, and utilize discrete sets of transcription start sites within an extended core promoter. METHODOLOGY/PRINCIPAL FINDINGS: We studied regulatory elements within the MHC class I promoter by cellular transfection and in vitro transcription assays in HeLa, HeLa/CIITA, and tsBN462 of various promoter constructs. We have identified three novel MHC class I regulatory elements (GLE, DPE-L1 and DPE-L2), located downstream of the major transcription start sites, that contribute to the regulation of both constitutive and activated MHC class I expression. These elements located at the 3' end of the core promoter preferentially regulate the multiple transcription start sites clustered at the 5' end of the core promoter. CONCLUSIONS/SIGNIFICANCE: Three novel downstream elements (GLE, DPE-L1, DPE-L2), located between +1 and +32 bp, regulate both constitutive and activated MHC class I gene expression by selectively increasing usage of transcription start sites clustered at the 5' end of the core promoter upstream of +1 bp. Results indicate that the downstream elements preferentially regulate TAF1-dependent, relative to TAF1-independent, transcription

Human activity learning for assistive robotics using a classifier ensemble

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K-nearest neighbour and random forest - is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Detecting phenological changes in plant functional types over West African savannah dominated landscape

Phenology is an important component in the climate system, and play a key role in controlling many feedbacks of vegetation to the climate systems. Differences in phenology of plant functional types (PFTs) due to the variation in seasonal cycles (e.g. changes in weather variability), the impact from land-use activities (e.g. fire) and their mechanisms for adaptation (e.g. climate change, regrowth/post-fire regeneration) have major implications in conservation planning and monitoring actions. Detecting phenological variations in PFTs is therefore of great importance to quantitative remote sensing applications, especially in a biome as diverse and complex as savannah. In this study, we implement Savitzky–Golay filtering and the Breaks For Additive Seasonal and Trend (BFAST) algorithms to detect changes in PFTs based on their phenological events using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data from 2001 to 2018. In this region, PFTs present distinct seasonal, annual and interannual variability. Woody phenology presents early green-up dates with a prolonged senescence period and invariably observed the longest growing season length (GSL). The relationship between the start of season (SOS) or end of season (EOS) was assessed for each PFT to find out the extent to which they determine GSL for different PFTs. GSL is mostly determined by the SOS in woody savannah (coefficient of determination (R²) =0.41, p <0.01), open shrubs and (R² =0.79, p <0.001), grassland (R² =0.35, p <0.01) while EOS determined the GSL for both dryland crops (R² =0.75, p <0.001) and paddy rice (R² =0.69, p <0.001). We compared the interannual variability of woody savannah and other PFTs by measuring the differences of their phenological indicators using Welch’s t-test. All PFTs show statistically significant difference with the GSL of woody savannah except open shrubs (p = 0.23). The abrupt vegetation changes estimated with BFAST varied by PFT. Some PFTs are more resilient to harsh environmental conditions than others. While woody species present a few abrupt changes, grass phenology is more vulnerable to disturbance, seasonally as well as in the trend components (large number of abrupt changes) with browning trend following an abrupt negative change. In all PFTs, breakpoint (disturbance) assessed using BFAST negatively correlate with precipitation data which means the magnitude of disturbance decreases with increasing precipitation. Woody species had an r (correlation coefficient) value of -0.5 while grassland had r = -0.7 which is a further indication that grass phenology responds more strongly to annual precipitation than the woody species. These results show that MODIS NDVI time-series data can be used to distinguish the phenological events of different PFTs in West African savannah dominated landscape