Brain resident memory T cells rapidly expand and initiate neuroinflammatory responses following CNS viral infection.

The contribution of circulating verses tissue resident memory T cells (TRMs) to clinical neuropathology is an enduring question due to a lack of mechanistic insights. The prevailing view is TRMs are protective against pathogens in the brain. However, the extent to which antigen-specific TRMs induce neuropathology upon reactivation is understudied. Using the described phenotype of TRMs, we found that brains of naïve mice harbor populations of CD69+ CD103- T cells. Notably, numbers of CD69+ CD103- TRMs rapidly increase following neurological insults of various origins. This TRM expansion precedes infiltration of virus antigen-specific CD8 T cells and is due to proliferation of T cells within the brain. We next evaluated the capacity of antigen-specific TRMs in the brain to induce significant neuroinflammation post virus clearance, including infiltration of inflammatory myeloid cells, activation of T cells in the brain, microglial activation, and significant blood brain barrier disruption. These neuroinflammatory events were induced by TRMs, as depletion of peripheral T cells or blocking T cell trafficking using FTY720 did not change the neuroinflammatory course. Depletion of all CD8 T cells, however, completely abrogated the neuroinflammatory response. Reactivation of antigen-specific TRMs in the brain also induced profound lymphopenia within the blood compartment. We have therefore determined that antigen-specific TRMs can induce significant neuroinflammation, neuropathology, and peripheral immunosuppression. The use of cognate antigen to reactivate CD8 TRMs enables us to isolate the neuropathologic effects induced by this cell type independently of other branches of immunological memory, differentiating this work from studies employing whole pathogen re-challenge. This study also demonstrates the capacity for CD8 TRMs to contribute to pathology associated with neurodegenerative disorders and long-term complications associated with viral infections. Understanding functions of brain TRMs is crucial in investigating their role in neurodegenerative disorders including MS, CNS cancers, and long-term complications associated with viral infections including COVID-19.

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures.

Background: Low temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear. Methods: This study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed. Results: Experimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze-thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability. Conclusion: Our studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment.

Faecal microbiome-based machine learning for multi-class disease diagnosis.

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.