Gut microbial GABAergic signaling improves stress-associated innate immunity to respiratory viral infection

Introduction: Epidemiological evidences reveal that populations with psychological stress have an increased likelihood of respiratory viral infection involving influenza A virus (IAV) and SARS-CoV-2. Objectives: This study aims to explore the potential correlation between psychological stress and increased susceptibility to respiratory viral infections and how this may contribute to a more severe disease progression. Methods: A chronic restraint stress (CRS) mouse model was used to infect IAV and estimate lung inflammation. Alveolar macrophages (AMs) were observed in the numbers, function and metabolic-epigenetic properties. To confirm the central importance of the gut microbiome in stress-exacerbated viral pneumonia, mice were conducted through microbiome depletion and gut microbiome transplantation. Results: Stress exposure induced a decline in Lactobacillaceae abundance and hence γ-aminobutyric acid (GABA) level in mice. Microbial-derived GABA was released in the peripheral and sensed by AMs via GABAAR, leading to enhanced mitochondrial metabolism and α-ketoglutarate (αKG) generation. The metabolic intermediator in turn served as the cofactor for the epigenetic regulator Tet2 to catalyze DNA hydroxymethylation and promoted the PPARγ-centered gene program underpinning survival, self-renewing, and immunoregulation of AMs. Thus, we uncover an unappreciated GABA/Tet2/PPARγ regulatory circuitry initiated by the gut microbiome to instruct distant immune cells through a metabolic-epigenetic program. Accordingly, reconstitution with GABA-producing probiotics, adoptive transferring of GABA-conditioned AMs, or resumption of pulmonary αKG level remarkably improved AMs homeostasis and alleviated severe pneumonia in stressed mice. Conclusion: Together, our study identifies microbiome-derived tonic signaling tuned by psychological stress to imprint resident immune cells and defensive response in the lungs. Further studies are warranted to translate these findings, basically from murine models, into the individuals with psychiatric stress during respiratory viral infection

Double-microcrack coupling stretchable neural electrode for electrophysiological communication

Developing neural electrodes with high stretchability and stable conductivity is a promising method to explore applications of them in biological medicine and electronic skin. However, considering the poor mechanical stretchability of typical conductive materials, maintaining the connection of electrode conductive paths under high stretching is still a challenge. Herein, for the first time, a double-microcrack coupling strategy for highly stretchable neural electrodes is proposed. Compared with single-layer stretchable microcrack electrodes, the design utilizes the complement between two gold microcrack films to contribute more conductive paths. It shows that the resistance change (R/R0) of the electrode under 100% strain is about 5.6 times, which is much lower than other electrodes and exhibits a high stretchability of ≈200%. Simultaneously, this design is an encapsulation-free design which avoids the electrode performance degradation caused by encapsulation. Furthermore, it is found that the adhesion strength between metal electrode and substrate is critical to the stretchability and stability of electrodes, so polydimethylsiloxane0.9-isophorone diisocyanate elastomer (PDMS0.9-IPDI), whose adhesion to gold electrode is 4.5 times higher than that of the commercial polydimethylsiloxane (PDMS), is synthesized. Finally, the electrophysiological communication between different organisms by electrodes is successfully demonstrated.The authors acknowledged funding support from the National Natural Science Foundation of China (Grant No. 52173237) and the Fundamental Research Funds for the Central Universities (Grant No. HIT.OCEF.2022018). Interdisciplinary Research Foundation of HIT (No. IR2021207); Project of National Center for International Research on Intelligent Nano-Materials and Detection Technology in Environmental Protection, Soochow University (No. SDGH2105); The Open Project Program (No. PEBM202107) of Key Laboratory for Photonic and Electric Bandgap Materials, Ministry of Education

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity

Technology roadmap for flexible sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.Agency for Science, Technology and Research (A*STAR)National Research Foundation (NRF)Submitted/Accepted versionY.L., Z.L., M.Z., and X.C. acknowledge the National Research Foundation, Singapore (NRF) under NRF’s Medium Sized Centre: Singapore Hybrid-Integrated Next-Generation μElectronics (SHINE) Centre funding programme, and AME programming funding scheme of Cyber Physiochemical Interface (CPI) project (no. A18A1b0045). Y.L. acknowledges National Natural Science Foundation of China (62201243). C.J. acknowledges funding support from the National Key R&D Program of China (no. 2019YFA0706100), the National Natural Science Foundation of China (82151305), Lingang Laboratory (LG-QS-202202-09). T.Q.T. and N.E.L. acknowledge support by the Basic Science Research Program (no. 2020R1A2C3013480) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. A.F. acknowledges the AFOSR (grant FA9550-22-1-0423). Y.L. and Y.Z. would like to acknowledge the NSF (award no. 2134664) and NIH (award no. R01HD108473) for financial support. X.F. acknowledges the support from the National Natural Science Foundation of China (grant no. U20A6001). L.Y. would like to thank the A*STAR Central Research Fund (CRF) and the AME Programmatic A18A1b0045 (Cyber Physiochemical Interfaces) for funding support. C.F.G. acknowledges the National Natural Science Foundation of China (no. T2225017). T.Q.T. acknowledges the Brain Pool Program (No. 2020H1D3A2A02111068) through the National Research Foundation (NRF) funded by the Ministry of Science. Z.L. acknowledges the support from RIE2020 AME Programmatic Grant funded by A*STAR-SERC, Singapore (Grant No. A18A1b0045). X.G. acknowledges funding support through the Shanghai Science and Technology Commission (grant no. 19JC1412400), the National Science Fund for Excellent Young Scholars (grant no. 61922057). C.D. acknowledges National Science Foundation CAREER: Conformable Piezoelectrics for Soft Tissue Imaging (grant no. 2044688) and MIT Media Lab Consortium funding. D.K. and O.G.S. acknowledge Leibniz Association and the German Research Foundation DFG (Gottfried Wilhelm Leibniz Program SCHM 1298/22-1, KA5051/1-1 and KA 5051/3-1), as well as the Leibniz association (Leibniz Transfer Program T62/2019). C.W. acknowledges the National Key Research and Development Program of China (grant no. 2021YFA1202600), National Natural Science Foundation of China (grant no. 62174082). A.V.-Y.T., E.Z., Y.Z., X.Z., and J.P. acknowledge the National Research Foundation, Singapore (NRF) under NRF’s Medium Sized Centre: Singapore Hybrid-Integrated Next-Generation μElectronics (SHINE) Centre funding programme, and AME programming funding scheme of Cyber Physiochemical Interface (CPI) project (no. A18A1b0045). R.Z. acknowledges National Natural Science Foundation of China (grant no. 51735007) and Beijing Natural Science Foundation (grant no. 3191001). N.M. acknowledges the support by JST PRESTO Grant Number JPMJPR20B7 and JST Adaptable and Seamless Technology transfer Program through Target-driven R&D (ASTEP) grant number JPMJTM22BK. C.P. acknowledges the Korean government (Ministry of Science and ICT, MSIT) (2022R1A4A3032923). M.W. acknowledges the National Key R&D Program of China under Grant (2021YFB3601200). X.Z. acknowledges National Natural Science Foundation of China (no. 62074029). S.X. acknowledges the 3M nontenured faculty award. T.-W.L. and D.-G.S. acknowledge the Pioneer Research Center Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (grant no. NRF-2022M3C1A3081211). C.T.L. would like to acknowledge support from the Institute for Health Innovation and Technology (iHealthtech), the MechanoBioEngineering Laboratory at the Department of Biomedical Engineering and the Institute for Functional Intelligent Materials (I-FIM) at the National University of Singapore (NUS). C.T.L. also acknowledges support from the National Research Foundation and A*STAR, under its RIE2020 Industry Alignment Fund − Industry Collaboration Projects (IAF-ICP) (grant no. I2001E0059) − SIA-NUS Digital Aviation Corp Lab and the NUS ARTIC Research (grant no. HFM-RP1). X.Y. acknowledges funding support by City University of Hong Kong (grant no. 9667221). T.X. and X.Z. acknowledge National Natural Science Foundation of China (22234006). B.C.K.T. acknowledges Cyber-Physiochemical Interfaces CPI, A*STAR A18A1b0045. H.G. acknowledges a research start-up grant (002479-00001) from Nanyang Technological University and the Agency for Science, Technology and Research (A*STAR) in Singapore. W.G. acknowledges National Science Foundation grant 2145802. D.J.L. acknowledges support from the US National Science Foundation grant number CBET-2223566. G.Y. acknowledges support from The Welch Foundation award F-1861, and Camille Dreyfus Teacher-Scholar Award. M.D.D. acknowledges funding support from NSF (grant no. EEC1160483). J.-H.A acknowledges the National Research Foundation of Korea (NRF-2015R1A3A2066337). J.C. acknowledges the Henry Samueli School of Engineering & Applied Science and the Department of Bioengineering at the University of California, Los Angeles for startup support and a Brain & Behavior Research Foundation Young Investigator Grant. K.T. acknowledges JST AIP Accelerated Program (no. JPMJCR21U1) and JSPS KAKENHI (grant no. JP22H00594). P.S.W. acknowledges the National Science Foundation (CMMI1636136) for support. A.M.A., M.C.H., and P.S.W. thank the National Institute on Drug Abuse (DA045550) for support. S.M. and X.C. appreciated the support from the Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation, Prime Minister’s Office, Singapore under its Campus of Research Excellence and Technological Enterprise (CREATE) programme