IL-17-producing γδ T cells protect against Clostridium difficile infection

Colitis caused by Clostridium difficile infection is a growing cause of human morbidity and mortality, especially after antibiotic use in health care settings. The natural immunity of newborn infants and protective host immune mediators against C. difficile infection are not fully understood, with data suggesting that inflammation can be either protective or pathogenic. Here, we show an essential role for IL-17A produced by γδ T cells in host defense against C. difficile infection. Fecal extracts from children with C. difficile infection showed increased IL-17A and T cell receptor γ chain expression, and IL-17 production by intestinal γδ T cells was efficiently induced after infection in mice. C. difficile-induced tissue inflammation and mortality were markedly increased in mice deficient in IL-17A or γδ T cells. Neonatal mice, with naturally expanded RORγt+ γδ T cells poised for IL-17 production were resistant to C. difficile infection, whereas elimination of γδ T cells or IL-17A each efficiently overturned neonatal resistance against infection. These results reveal an expanded role for IL-17-producing γδ T cells in neonatal host defense against infection and provide a mechanistic explanation for the clinically observed resistance of infants to C. difficile colitis

Mapk-activated protein kinase 2 contributes to Clostridium difficile-associated inflammation

Clostridium difficile infection (CDI) results in toxin-induced epithelial injury and marked intestinal inflammation. Fecal markers of intestinal inflammation correlate with CDI disease severity, but regulation of the inflammatory response is poorly understood. Previous studies demonstrated that C. difficile toxin TcdA activates p38 kinase in tissue culture cells and mouse ilium, resulting in interleukin-8 (IL-8) release. Here, we investigated the role of phosphorylated mitogen-activated protein kinase (MAPK)-activated protein kinase (MK2 kinase, pMK2), a key mediator of p38-dependent inflammation, in CDI. Exposure of cultured intestinal epithelial cells to the C. difficile toxins TcdA and TcdB resulted in p38-dependent MK2 activation. Toxin-induced IL-8 and GROα release required MK2 activity. We found that p38 and MK2 are activated in response to other actin-disrupting agents, suggesting that toxin-induced cytoskeleton disruption is the trigger for kinase-dependent cytokine response. Phosphorylated MK2 was detected in the intestines of C. difficile-infected hamsters and mice, demonstrating for the first time that the pathway is activated in infected animals. Furthermore, we found that elevated pMK2 correlated with the presence of toxigenic C. difficile among 100 patient stool samples submitted for C. difficile testing. In conclusion, we find that MK2 kinase is activated by TcdA and TcdB and regulates the expression of proinflammatory cytokines. Activation of p38-MK2 in infected animals and humans suggests that this pathway is a key driver of intestinal inflammation in patients with CDI

Age Estimates from Name Characters

Traditionally, we have been attempting to extract useful features from the massive amount of data generated daily. However, following the legal constraints regarding personal data protection and the challenges of potential data biases and manipulation, artificial intelligence that relies less on big data and more on reasoning ability has become an emerging trend. This paper demonstrates how to estimate age and gender using names only. The proposed two-layer comparative model was trained on Taiwanese names, and its generalizability was further examined on bilingual and cross-border names. By considering additional features of the contextual environment, the model achieves high accuracy in age and gender prediction on Taiwanese and bilingual names. However, the prediction results for ethnic-Chinese Malaysian names (in English) do not reach the same level. This is due to the linguistic differences among Chinese dialects; the features trained on Taiwanese names cannot be directly applied to English names in Malaysia. This study illustrates a path for accomplishing prediction tasks using minimal data and highlights a future possibility for further research

Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting

Cancer immunoediting, the process by which the immune system controls tumour outgrowth and shapes tumour immunogenicity, is comprised of three phases: elimination, equilibrium and escape. Although many immune components that participate in this process are known, its underlying mechanisms remain poorly defined. A central tenet of cancer immunoediting is that T-cell recognition of tumour antigens drives the immunological destruction or sculpting of a developing cancer. However, our current understanding of tumour antigens comes largely from analyses of cancers that develop in immunocompetent hosts and thus may have already been edited. Little is known about the antigens expressed in nascent tumour cells, whether they are sufficient to induce protective antitumour immune responses or whether their expression is modulated by the immune system. Here, using massively parallel sequencing, we characterize expressed mutations in highly immunogenic methylcholanthrene-induced sarcomas derived from immunodeficient Rag2 mice that phenotypically resemble nascent primary tumour cells. Using class I prediction algorithms, we identify mutant spectrin-β2 as a potential rejection antigen of the d42m1 sarcoma and validate this prediction by conventional antigen expression cloning and detection. We also demonstrate that cancer immunoediting of d42m1 occurs via a T-cell-dependent immunoselection process that promotes outgrowth of pre-existing tumour cell clones lacking highly antigenic mutant spectrin-β2 and other potential strong antigens. These results demonstrate that the strong immunogenicity of an unedited tumour can be ascribed to expression of highly antigenic mutant proteins and show that outgrowth of tumour cells that lack these strong antigens via a T-cell-dependent immunoselection process represents one mechanism of cancer immunoediting

Noninvasive and point-of-care surface-enhanced Raman scattering (SERS)-based breathalyzer for mass screening of coronavirus disease 2019 (COVID-19) under 5 min

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityNational Medical Research Council (NMRC)Submitted/Accepted versionThis research is supported by National Medical Research Council, Singapore under COVID-19 Research Fund (MOH-COVID19RF-0007 and MOH-COVID19RF-0012), A*STAR Singapore, AME Individual Research Grant (A20E5c0082) and Max Planck Institute-Nanyang Technological University Joint Lab. S.X.L and L.B.T.N. acknowledge Nanyang Presidential scholarship support from Nanyang Technological University, Singapore