Quantitative proteomics and biological activity of extracellular vesicles engineered to express SARS-CoV-2 spike protein.

SARS-CoV-2 viral infection led to the devastating COVID-19 pandemic, where illness stemmed from interactions between virions and recipient host cells resulting in multi-layered pathological consequences. The role of the infection portal is now understood to be the cellular angiotensin converting enzyme-2 (ACE2) receptor, which binds to viral spike (S) protein initiating virion internalisation process. Since SARS-CoV-2 virions bear some resemblance to endogenously produced small extracellular vesicles (sEVs) we reasoned that EVs engineered to express S protein (viral mimics) may interfere with viral infection. Here, we report generation of HEK293T cells producing sEVs enriched for transmembrane S-protein tagged with green fluorescent protein (S/GFP). Strikingly, S protein drove the GFP tag to the membrane of sEVs, while GFP alone was not efficiently included in the sEV cargo. High-throughput quantitative proteomics revealed that S/GFP sEVs contained over 1000 proteins including canonical components of the exosomal pathway such as ALIX, syntenin-1, and tetraspanins (CD81, CD9), but depleted for calnexin and cytochrome c. We found that 84 sEV proteins were significantly altered by the presence of S/GFP. S protein expressing EVs efficiently adhered to target cells in an ACE2-dependent manner, but they were poorly internalised. Importantly, prolonged administration of S/GFP EV to K18-hACE2 mice provided a significant protection against SARS-CoV-2 infection. Thus, the generation of sEV containing S protein can be considered as a novel therapeutic approach in reducing the transmission of SARS-CoV-2.

Neonatal imprinting of alveolar macrophages via neutrophil-derived 12-HETE.

Resident-tissue macrophages (RTMs) arise from embryonic precursors1,2, yet the developmental signals that shape their longevity remain largely unknown. Here we demonstrate in mice genetically deficient in 12-lipoxygenase and 15-lipoxygenase (Alox15-/- mice) that neonatal neutrophil-derived 12-HETE is required for self-renewal and maintenance of alveolar macrophages (AMs) during lung development. Although the seeding and differentiation of AM progenitors remained intact, the absence of 12-HETE led to a significant reduction in AMs in adult lungs and enhanced senescence owing to increased prostaglandin E2 production. A compromised AM compartment resulted in increased susceptibility to acute lung injury induced by lipopolysaccharide and to pulmonary infections with influenza A virus or SARS-CoV-2. Our results highlight the complexity of prenatal RTM programming and reveal their dependency on in trans eicosanoid production by neutrophils for lifelong self-renewal.

Brain motor and fear circuits regulate leukocytes during acute stress.

The nervous and immune systems are intricately linked1. Although psychological stress is known to modulate immune function, mechanistic pathways linking stress networks in the brain to peripheral leukocytes remain poorly understood2. Here we show that distinct brain regions shape leukocyte distribution and function throughout the body during acute stress in mice. Using optogenetics and chemogenetics, we demonstrate that motor circuits induce rapid neutrophil mobilization from the bone marrow to peripheral tissues through skeletal-muscle-derived neutrophil-attracting chemokines. Conversely, the paraventricular hypothalamus controls monocyte and lymphocyte egress from secondary lymphoid organs and blood to the bone marrow through direct, cell-intrinsic glucocorticoid signalling. These stress-induced, counter-directional, population-wide leukocyte shifts are associated with altered disease susceptibility. On the one hand, acute stress changes innate immunity by reprogramming neutrophils and directing their recruitment to sites of injury. On the other hand, corticotropin-releasing hormone neuron-mediated leukocyte shifts protect against the acquisition of autoimmunity, but impair immunity to SARS-CoV-2 and influenza infection. Collectively, these data show that distinct brain regions differentially and rapidly tailor the leukocyte landscape during psychological stress, therefore calibrating the ability of the immune system to respond to physical threats.

Analysing the trends in breast surgery practice during COVID-19 pandemic: A comparative study with the Pre-COVID era.

BACKGROUND: The emergence of coronavirus disease 2019 (COVID-19) pandemic has crippled the healthcare systems all over the world. Cancer treatment is indispensable and disruption in its provision can lead to unanticipated consequences. No local data exists that has quantified the impact of COVID-19 pandemic on breast cancer surgery in a lower middle-income country (LMIC), therefore, the present retrospective comparative cohort study is directed to determine the trends in breast surgery operative volumes and its outcomes at our institution in Pakistan. MATERIALS AND METHODS: Data was collected retrospectively from Pre-COVID-19 and COVID-19 era to determine impact of the current pandemic on breast cancer management practices and outcomes. RESULTS: Cohort results showed a decline in the number of surgeries during COVID-19 era. A total 149 cases were operated during study period vs. 231 during same Pre-COVID-19 i.e. a 35.5% drop in cancer surgeries. In early COVID-19 time frame, only 4 patients had breast reconstruction, 12 out of 149 (8.05%) surgical candidates were identified having positive COVID-19 status preoperatively and one ASA class 3 patient caught COVID-19 post-surgery and succumbed to virus. CONCLUSION: Pandemic has a negative effect on cancer management in a LMIC with compromised access and care of cancer patients.

n-Gram based language processing using Twitter dataset to identify COVID-19 patients.

Due to the rapid growth of electronic documents, e.g., tweets, blogs, Facebook posts, snaps in different languages that use the same writing script, language categorization, and processing have great importance. For instance, to identify COVID-19 positive patients or people's emotions on COVID-19 pandemic from tweets written in 35 different languages faster and accurate, language categorization and processing of tweets is significantly essential. Among many language categorization and processing techniques, character and word n-gram based techniques are very popular and simple but very efficient for categorizing and processing both short and large documents. One of the fundamental problems of language processing is the efficient use of memory space in implementing a technique so that a vast collection of documents can be easily categorized and processed. In this paper, we introduce a framework that categorizes the language of tweets using n-gram based language categorization technique and further processes the tweets using the machine-learning approach, Linear Support Vector Machine (LSVM), that may be able to identify COVID-19 positive patients. We evaluate and compare the performance of the proposed framework in terms of language categorization accuracy, precession, recall, and F-measure over n-gram length. The proposed framework is scalable as many other applications that involve extracting features and classifying languages collected from social media, and different types of networks may use this framework. This proposed framework, also being a part of health monitoring and improvement, tends to achieve the goal of having a sustainable society.