Upper respiratory tract mucosal immunity for SARS-CoV-2 vaccines

SARS-CoV-2 vaccination significantly reduces morbidity and mortality, but has less impact on viral transmission rates, thus aiding viral evolution; and the longevity of vaccine-induced immunity rapidly declines. Immune responses in respiratory tract mucosal tissues are crucial for early control of infection, and can generate long-term antigen-specific protection with prompt recall responses. However, currently approved SARS-CoV-2 vaccines are not amenable to adequate respiratory mucosal delivery, particularly in the upper airways, which could account for the high vaccine breakthrough infection rates and limited duration of vaccine-mediated protection. In view of these drawbacks, we outline a strategy that has the potential to enhance both the efficacy and durability of existing SARS-CoV-2 vaccines, by inducing robust memory responses in the upper respiratory tract mucosa

The identification of markers of macrophage differentiation in PMA-stimulated THP-1 Cells and monocyte-derived macrophages

Differentiated macrophages are the resident tissue phagocytes and sentinel cells of the innate immune response. The phenotype of mature tissue macrophages represents the composite of environmental and differentiation-dependent imprinting. Phorbol-12-myristate-13-acetate (PMA) and 1,25-dihydroxyvitamin D3 (VD3) are stimuli commonly used to induce macrophage differentiation in monocytic cell lines but the extent of differentiation in comparison to primary tissue macrophages is unclear. We have compared the phenotype of the promonocytic THP-1 cell line after various protocols of differentiation utilising VD3 and PMA in comparison to primary human monocytes or monocyte-derived macrophages (MDM). Both stimuli induced changes in cell morphology indicative of differentiation but neither showed differentiation comparable to MDM. In contrast, PMA treatment followed by 5 days resting in culture without PMA (PMAr) increased cytoplasmic to nuclear ratio, increased mitochondrial and lysosomal numbers and altered differentiation-dependent cell surface markers in a pattern similar to MDM. Moreover, PMAr cells showed relative resistance to apoptotic stimuli and maintained levels of the differentiation-dependent anti-apoptotic protein Mcl-1 similar to MDM. PMAr cells retained a high phagocytic capacity for latex beads, and expressed a cytokine profile that resembled MDM in response to TLR ligands, in particular with marked TLR2 responses. Moreover, both MDM and PMAr retained marked plasticity to stimulus-directed polarization. These findings suggest a modified PMA differentiation protocol can enhance macrophage differentiation of THP-1 cells and identify increased numbers of mitochondria and lysosomes, resistance to apoptosis and the potency of TLR2 responses as important discriminators of the level of macrophage differentiation for transformed cells

Does autonomous macrophage-driven inflammation promote alveolar damage in COVID-19?

SARS-CoV-2 has caused devastating effects with over 550 million infections by July 2022 and approximately 6.4 million deaths [1]. Societal and economic impacts will reverberate for years, with continuous evolution of SARS-CoV-2 as it persistently spreads through the human population as exemplified by reduced activity of vaccines and monoclonals against Omicron BA.4 or BA.5 subvariants [2]. A greater understanding of pathogenesis and more tailored therapeutic approaches are therefore essential

Hypoxaemia prevalence and its adverse clinical outcomes among children hospitalised with WHO-defined severe pneumonia in Bangladesh

BACKGROUND: With an estimated 1 million cases per year, pneumonia accounts for 15% of all under-five deaths globally, and hypoxaemia is one of the strongest predictors of mortality. Most of these deaths are preventable and occur in low- and middle-income countries. Bangladesh is among the six high burden countries with an estimated 4 million pneumonia episodes annually. There is a gap in updated evidence on the prevalence of hypoxaemia among children with severe pneumonia in high burden countries, including Bangladesh. METHODS: We conducted a secondary analysis of data obtained from icddr,b-Dhaka Hospital, a secondary level referral hospital located in Dhaka, Bangladesh. We included 2646 children aged 2-59 months admitted with WHO-defined severe pneumonia during 2014-17. The primary outcome of interest was hypoxaemia, defined as SpO(2) < 90% on admission. The secondary outcome of interest was adverse clinical outcomes defined as deaths during hospital stay or referral to higher-level facilities due to clinical deterioration. RESULTS: On admission, the prevalence of hypoxaemia among children hospitalised with severe pneumonia was 40%. The odds of hypoxaemia were higher among females (adjusted Odds ratio AOR = 1.44; 95% confidence interval CI = 1.22-1.71) and those with a history of cough or difficulty in breathing for 0-48 hours before admission (AOR = 1.61; 95% CI = 1.28-2.02). Among all children with severe pneumonia, 6% died during the hospital stay, and 9% were referred to higher-level facilities due to clinical deterioration. Hypoxaemia was the strongest predictor of mortality (AOR = 11.08; 95% CI = 7.28-16.87) and referral (AOR = 5.94; 95% CI = 4.31-17) among other factors such as age, sex, history of fever and cough or difficulty in breathing, and severe acute malnutrition. Among those who survived, the median duration of hospital stay was 7 (IQR = 4-11) days in the hypoxaemic group and 6 (IQR = 4-9) days in the non-hypoxaemic group, and the difference was significant at P < 0.001. CONCLUSIONS: The high burden of hypoxaemia and its clinical outcomes call for urgent attention to promote oxygen security in low resource settings like Bangladesh. The availability of pulse oximetry for rapid identification and an effective oxygen delivery system for immediate correction should be ensured for averting many preventable deaths

Systematic comparison of ranking aggregation methods for gene lists in experimental results

MOTIVATION: A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS: In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION: The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online