Interactions between HIV-1 and the Cell-Autonomous Innate Immune System

HIV-1 was recognized as the cause of AIDS in humans in 1984. Despite 30 years of intensive research, we are still unraveling the molecular details of the host-pathogen interactions that enable this virus to escape immune clearance and cause immunodeficiency. Here we explore a series of recent studies that consider how HIV-1 interacts with the cell-autonomous innate immune system as it navigates its way in and out of host cells. We discuss how these studies improve our knowledge of HIV-1 and host biology as well as increase our understanding of transmission, persistence, and immunodeficiency and the potential for therapeutic or prophylactic interventions

Toward a more generalizable blood RNA signature for bacterial and viral infections

Host-response profiles can discriminate different infections. A new 8-gene blood RNA signature to discriminate bacterial and viral infections extends our focus hitherto on the case mix from the US and Europe to include that of low- and middle-income countries.1 Challenges remain

Streptococcus pneumoniae meningitis and the CNS barriers

Streptococcus pneumoniae (SPN) is a globally significant cause of meningitis, the pathophysiology of which involves damage to the brain by both bacterial virulence factors and the host inflammatory response. In most cases of SPN meningitis bacteria translocate from the blood into the central nervous system (CNS). The principal site of SPN translocation into the CNS is not known, with possible portals of entry proposed to be the cerebral or meningeal blood vessels or the choroid plexus. All require SPN to bind to and translocate across the vascular endothelial barrier, and subsequently the basement membrane and perivascular structures, including an additional epithelial barrier in the case of the blood-CSF barrier. The presence of SPN in the CNS is highly inflammatory resulting in marked neutrophilic infiltration. The secretion of toxic inflammatory mediators by activated neutrophils within the CNS damages pathogen and host alike, including the non-replicative neurons which drives morbidity and mortality. As with the translocation of SPN, the recruitment of neutrophils into the CNS in SPN meningitis necessitates the translocation of neutrophils from the circulation across the vascular barrier, a process that is tightly regulated under basal conditions - a feature of the 'immune specialization' of the CNS. The brain barriers are therefore central to SPN meningitis, both through a failure to exclude bacteria and maintain CNS sterility, and subsequently through the active recruitment and/or failure to exclude circulating leukocytes. The interactions of SPN with these barriers, barrier inflammatory responses, along with their therapeutic implications, are explored in this review

Streptococcus pneumoniae interactions with the complement system

Host innate and adaptive immunity to infection with Streptococcus pneumoniae is critically dependent on the complement system, demonstrated by the high incidence of invasive S. pneumoniae infection in people with inherited deficiency of complement components. The complement system is activated by S. pneumoniae through multiple mechanisms. The classical complement pathway is activated by recognition of S. pneumoniae by C-reactive protein, serum amyloid P, C1q, SIGN-R1, or natural or acquired antibody. Some S. pneumoniae strains are also recognised by ficolins to activate the mannose binding lectin (MBL) activation pathway. Complement activation is then amplified by the alternative complement pathway, which can also be activated by S. pneumoniae directly. Complement activation results in covalent linkage of the opsonic complement factors C3b and iC3b to the S. pneumoniae surface which promote phagocytic clearance, along with complement-mediated immune adherence to erythrocytes, thereby protecting against septicaemia. The role of complement for mucosal immunity to S. pneumoniae is less clear. Given the major role of complement in controlling infection with S. pneumoniae, it is perhaps unsurprising that S. pneumoniae has evolved multiple mechanisms of complement evasion, including the capsule, multiple surface proteins, and the toxin pneumolysin. There is considerable variation between S. pneumoniae capsular serotypes and genotypes with regards to sensitivity to complement which correlates with ability to cause invasive infections. However, at present we only have a limited understanding of the main mechanisms causing variations in complement sensitivity between S. pneumoniae strains and to non-pathogenic streptococci

Analysis tools to quantify dissemination of pathology in zebrafish larvae

We describe new open source software called QuantiFish for rapid quantitation of fluorescent foci in zebrafish larvae, to support infection research in this animal model. QuantiFish extends the conventional measurements of bacterial load and number of bacterial foci to include measures for dissemination of infection. These are represented by the proportions of bacteria between foci and their spatial distribution. We showcase these measures by comparison of intravenous and hindbrain routes of Mycobacterium marinum infection, which are indistinguishable by measurement of bacterial load and not consistently differentiated by the number of bacterial foci. The intravenous route showed dose dependent dissemination of infection, reflected by increased spatial dispersion of bacteria and lower proportions of bacteria distributed across many foci. In contrast, hindbrain infection resulted in localised disease, limited to a smaller area and higher proportions of bacteria distributed across fewer foci. The application of QuantiFish may extend beyond models of infection, to study other pathologies such as metastatic cancer

Tumor Necrosis Factor (TNF) Bioactivity at the Site of an Acute Cell-Mediated Immune Response Is Preserved in Rheumatoid Arthritis Patients Responding to Anti-TNF Therapy

The impact of anti-tumor necrosis factor (TNF) therapies on inducible TNF-dependent activity in humans has never been evaluated in vivo. We aimed to test the hypothesis that patients responding to anti-TNF treatments exhibit attenuated TNF-dependent immune responses at the site of an immune challenge. We developed and validated four context-specific TNF-inducible transcriptional signatures to quantify TNF bioactivity in transcriptomic data. In anti-TNF treated rheumatoid arthritis (RA) patients, we measured the expression of these biosignatures in blood, and in skin biopsies from the site of tuberculin skin tests (TSTs) as a human experimental model of multivariate cell-mediated immune responses. In blood, anti-TNF therapies attenuated TNF bioactivity following ex vivo stimulation. However, at the site of the TST, TNF-inducible gene expression and genome-wide transcriptional changes associated with cell-mediated immune responses were comparable to that of RA patients receiving methotrexate only. These data demonstrate that anti-TNF agents in RA patients do not inhibit inducible TNF activity at the site of an acute inflammatory challenge in vivo, as modeled by the TST. We hypothesize instead that their therapeutic effects are limited to regulating TNF activity in chronic inflammation or by alternative non-canonical pathways

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells

HIV gp120 in the lungs of antiretroviral therapy–treated Individuals impairs alveolar macrophage responses to pneumococci

Rationale People living with HIV (PLWH) are at significantly increased risk of invasive pneumococcal disease, despite long-term antiretroviral therapy (ART). The mechanism explaining this observation remains undefined. Objectives We hypothesized apoptosis-associated microbicidal mechanisms, required to clear intracellular pneumococci that survive initial phagolysosomal killing, are perturbed. Methods Alveolar macrophages (AM) were obtained by bronchoalveolar lavage (BAL) from healthy donors or HIV-1-seropositive donors on long-term ART with undetectable plasma viral load. Monocyte-derived macrophages (MDM) were obtained from healthy donors and infected with HIV-1BaL or treated with gp120. Macrophages were challenged with opsonized serotype 2 Streptococcus pneumoniae and assessed for apoptosis, bactericidal activity, protein expression and mitochondrial reactive oxygen species (mROS). AM phenotyping, ultra-sensitive HIV-1 RNA quantification and gp120 measurement were also performed in BAL. Measurements and Main Results HIV-1BaL infection impaired apoptosis, induction of mROS and pneumococcal killing by MDM. Apoptosis-associated pneumococcal killing was also reduced in AM from ART treated HIV-1-seropositive donors. BAL fluid from these individuals demonstrated persistent lung CD8+ T-cell lymphocytosis, and gp120 or HIV-1 RNA was also detected. Despite this, transcriptional activity in AM freshly isolated from PLWH was broadly similar to healthy volunteers. Instead, gp120 phenocopied the defect in pneumococcal killing in healthy MDM through post-translational modification of Mcl-1, preventing apoptosis induction, caspase activation and increased mROS generation. Moreover gp120 also inhibited mROS dependent pneumococcal killing in MDM. Conclusions. Despite ART, HIV-1, via gp120, drives persisting innate immune defects in AM microbicidal mechanisms, enhancing susceptibility to pneumococcal disease

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK

Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study.

The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.We independently externally validated the performance of candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at the time of admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictors in univariable analyses.We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78, 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78, 95% CI 0.74-0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76, 95% CI 0.71-0.81), and age for in-hospital mortality (AUROC 0.76, 95% CI 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors