VIB-UGent Center for Inflammation Research

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Epithelial-mesenchymal transition (EMT) encompasses dynamic changes in cellular organization from epithelial to mesenchymal phenotypes, which leads to functional changes in cell migration and invasion. EMT occurs in a diverse range of physiological and pathological conditions and is driven by a conserved set of inducing signals, transcriptional regulators and downstream effectors. With over 5,700 publications indexed by Web of Science in 2019 alone, research on EMT is expanding rapidly. This growing interest warrants the need for a consensus among researchers when referring to and undertaking research on EMT. This Consensus Statement, mediated by 'the EMT International Association' (TEMTIA), is the outcome of a 2-year-long discussion among EMT researchers and aims to both clarify the nomenclature and provide definitions and guidelines for EMT research in future publications. We trust that these guidelines will help to reduce misunderstanding and misinterpretation of research data generated in various experimental models and to promote cross-disciplinary collaboration to identify and address key open questions in this research field. While recognizing the importance of maintaining diversity in experimental approaches and conceptual frameworks, we emphasize that lasting contributions of EMT research to increasing our understanding of developmental processes and combatting cancer and other diseases depend on the adoption of a unified terminology to describe EMT.

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.

Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline ( https://benchmark.dynverse.org ) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.

Tissue-resident macrophages can develop from circulating adult monocytes or from primitive yolk sac-derived macrophages. The precise ontogeny of alveolar macrophages (AMFs) is unknown. By performing BrdU labeling and parabiosis experiments in adult mice, we found that circulating monocytes contributed minimally to the steady-state AMF pool. Mature AMFs were undetectable before birth and only fully colonized the alveolar space by 3 d after birth. Before birth, F4/80(hi)CD11b(lo) primitive macrophages and Ly6C(hi)CD11b(hi) fetal monocytes sequentially colonized the developing lung around E12.5 and E16.5, respectively. The first signs of AMF differentiation appeared around the saccular stage of lung development (E18.5). Adoptive transfer identified fetal monocytes, and not primitive macrophages, as the main precursors of AMFs. Fetal monocytes transferred to the lung of neonatal mice acquired an AMF phenotype via defined developmental stages over the course of one week, and persisted for at least three months. Early AMF commitment from fetal monocytes was absent in GM-CSF-deficient mice, whereas short-term perinatal intrapulmonary GM-CSF therapy rescued AMF development for weeks, although the resulting AMFs displayed an immature phenotype. This demonstrates that tissue-resident macrophages can also develop from fetal monocytes that adopt a stable phenotype shortly after birth in response to instructive cytokines, and then self-maintain throughout life.

Cells succumbing to stress via regulated cell death (RCD) can initiate an adaptive immune response associated with immunological memory, provided they display sufficient antigenicity and adjuvanticity. Moreover, multiple intracellular and microenvironmental features determine the propensity of RCD to drive adaptive immunity. Here, we provide an updated operational definition of immunogenic cell death (ICD), discuss the key factors that dictate the ability of dying cells to drive an adaptive immune response, summarize experimental assays that are currently available for the assessment of ICD in vitro and in vivo, and formulate guidelines for their interpretation.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

OBJECTIVE: To update the European League Against Rheumatism (EULAR) recommendations for the pharmacological treatment of psoriatic arthritis (PsA). METHODS: According to the EULAR standardised operating procedures, a systematic literature review was followed by a consensus meeting to develop this update involving 28 international taskforce members in May 2019. Levels of evidence and strengths of recommendations were determined. RESULTS: The updated recommendations comprise 6 overarching principles and 12 recommendations. The overarching principles address the nature of PsA and diversity of both musculoskeletal and non-musculoskeletal manifestations; the need for collaborative management and shared decision-making is highlighted. The recommendations provide a treatment strategy for pharmacological therapies. Non-steroidal anti-inflammatory drugs and local glucocorticoid injections are proposed as initial therapy; for patients with arthritis and poor prognostic factors, such as polyarthritis or monoarthritis/oligoarthritis accompanied by factors such as dactylitis or joint damage, rapid initiation of conventional synthetic disease-modifying antirheumatic drugs is recommended. If the treatment target is not achieved with this strategy, a biological disease-modifying antirheumatic drugs (bDMARDs) targeting tumour necrosis factor (TNF), interleukin (IL)-17A or IL-12/23 should be initiated, taking into account skin involvement if relevant. If axial disease predominates, a TNF inhibitor or IL-17A inhibitor should be started as first-line disease-modifying antirheumatic drug. Use of Janus kinase inhibitors is addressed primarily after bDMARD failure. Phosphodiesterase-4 inhibition is proposed for patients in whom these other drugs are inappropriate, generally in the context of mild disease. Drug switches and tapering in sustained remission are addressed. CONCLUSION: These recommendations provide stakeholders with an updated consensus on the pharmacological management of PsA, based on a combination of evidence and expert opinion.

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis.

Peroxisome proliferator-activated receptor α (PPARα) is a nuclear receptor of clinical interest as a drug target in various metabolic disorders. PPARα also exhibits marked anti-inflammatory capacities. The first-generation PPARα agonists, the fibrates, have however been hampered by drug-drug interaction issues, statin drop-in, and ill-designed cardiovascular intervention trials. Notwithstanding, understanding the molecular mechanisms by which PPARα works will enable control of its activities as a drug target for metabolic diseases with an underlying inflammatory component. Given its role in reshaping the immune system, the full potential of this nuclear receptor subtype as a versatile drug target with high plasticity becomes increasingly clear, and a novel generation of agonists may pave the way for novel fields of applications.

Self-renewing tissue-resident macrophages are thought to be exclusively derived from embryonic progenitors. However, whether circulating monocytes can also give rise to such macrophages has not been formally investigated. Here we use a new model of diphtheria toxin-mediated depletion of liver-resident Kupffer cells to generate niche availability and show that circulating monocytes engraft in the liver, gradually adopt the transcriptional profile of their depleted counterparts and become long-lived self-renewing cells. Underlining the physiological relevance of our findings, circulating monocytes also contribute to the expanding pool of macrophages in the liver shortly after birth, when macrophage niches become available during normal organ growth. Thus, like embryonic precursors, monocytes can and do give rise to self-renewing tissue-resident macrophages if the niche is available to them.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

and to screen large chemical libraries for putative ICD inducers, based on a high-content, high-throughput platform that we recently developed. Such a platform allows for the detection of multiple DAMPs, like cell surface-exposed calreticulin, extracellular ATP and high mobility group box 1 (HMGB1), and/or the processes that underlie their emission, such as endoplasmic reticulum stress, autophagy and necrotic plasma membrane permeabilization. We surmise that this technology will facilitate the development of next-generation anticancer regimens, which kill malignant cells and simultaneously convert them into a cancer-specific therapeutic vaccine.

Although mixed lineage kinase domain-like (MLKL) protein has emerged as a specific and crucial protein for necroptosis induction, how MLKL transduces the death signal remains poorly understood. Here, we demonstrate that the full four-helical bundle domain (4HBD) in the N-terminal region of MLKL is required and sufficient to induce its oligomerization and trigger cell death. Moreover, we found that a patch of positively charged amino acids on the surface of the 4HBD binds to phosphatidylinositol phosphates (PIPs) and allows recruitment of MLKL to the plasma membrane. Importantly, we found that recombinant MLKL, but not a mutant lacking these positive charges, induces leakage of PIP-containing liposomes as potently as BAX, supporting a model in which MLKL induces necroptosis by directly permeabilizing the plasma membrane. Accordingly, we found that inhibiting the formation of PI(5)P and PI(4,5)P2 specifically inhibits tumor necrosis factor (TNF)-mediated necroptosis but not apoptosis.

Neuroinflammation and neurodegeneration often result from the aberrant deposition of aggregated host proteins, including amyloid-β, α-synuclein, and prions, that can activate inflammasomes. Inflammasomes function as intracellular sensors of both microbial pathogens and foreign as well as host-derived danger signals. Upon activation, they induce an innate immune response by secreting the inflammatory cytokines interleukin (IL)-1β and IL-18, and additionally by inducing pyroptosis, a lytic cell death mode that releases additional inflammatory mediators. Microglia are the prominent innate immune cells in the brain for inflammasome activation. However, additional CNS-resident cell types including astrocytes and neurons, as well as infiltrating myeloid cells from the periphery, express and activate inflammasomes. In this review, we will discuss current understanding of the role of inflammasomes in common degenerative diseases of the brain and highlight inflammasome-targeted strategies that may potentially treat these diseases.

Pattern Recognition Receptors (PRRs) are proteins capable of recognizing molecules frequently found in pathogens (the so-called Pathogen-Associated Molecular Patterns - PAMPs), or molecules released by damaged cells (the Damage-Associated Molecular Patterns - DAMPs). They emerged phylogenetically prior to the appearance of the adaptive immunity and, therefore, are considered part of the innate immune system. Signals derived from the engagement of PRRs on the immune cells activate microbicidal and pro-inflammatory responses required to eliminate or, at least, to contain infectious agents. Molecularly controlled forms of cell death are also part of a very ancestral mechanism involved in key aspects of the physiology of multicellular organism, including the elimination of unwanted, damaged or infected cells. Interestingly, each form of cell death has its particular effect on inflammation and on the development of innate and adaptive immune responses. In this mini-review article, we discuss some aspects of the molecular interplay between the cell death machinery and signals initiated by the activation of PRRs by PAMPs and DAMPs.

Importance: Clinical trials assessing the efficacy of IL-6 antagonists in patients hospitalized for COVID-19 have variously reported benefit, no effect, and harm. Objective: To estimate the association between administration of IL-6 antagonists compared with usual care or placebo and 28-day all-cause mortality and other outcomes. Data Sources: Trials were identified through systematic searches of electronic databases between October 2020 and January 2021. Searches were not restricted by trial status or language. Additional trials were identified through contact with experts. Study Selection: Eligible trials randomly assigned patients hospitalized for COVID-19 to a group in whom IL-6 antagonists were administered and to a group in whom neither IL-6 antagonists nor any other immunomodulators except corticosteroids were administered. Among 72 potentially eligible trials, 27 (37.5%) met study selection criteria. Data Extraction and Synthesis: In this prospective meta-analysis, risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool. Inconsistency among trial results was assessed using the I2 statistic. The primary analysis was an inverse variance-weighted fixed-effects meta-analysis of odds ratios (ORs) for 28-day all-cause mortality. Main Outcomes and Measures: The primary outcome measure was all-cause mortality at 28 days after randomization. There were 9 secondary outcomes including progression to invasive mechanical ventilation or death and risk of secondary infection by 28 days. Results: A total of 10 930 patients (median age, 61 years [range of medians, 52-68 years]; 3560 [33%] were women) participating in 27 trials were included. By 28 days, there were 1407 deaths among 6449 patients randomized to IL-6 antagonists and 1158 deaths among 4481 patients randomized to usual care or placebo (summary OR, 0.86 [95% CI, 0.79-0.95]; P = .003 based on a fixed-effects meta-analysis). This corresponds to an absolute mortality risk of 22% for IL-6 antagonists compared with an assumed mortality risk of 25% for usual care or placebo. The corresponding summary ORs were 0.83 (95% CI, 0.74-0.92; P < .001) for tocilizumab and 1.08 (95% CI, 0.86-1.36; P = .52) for sarilumab. The summary ORs for the association with mortality compared with usual care or placebo in those receiving corticosteroids were 0.77 (95% CI, 0.68-0.87) for tocilizumab and 0.92 (95% CI, 0.61-1.38) for sarilumab. The ORs for the association with progression to invasive mechanical ventilation or death, compared with usual care or placebo, were 0.77 (95% CI, 0.70-0.85) for all IL-6 antagonists, 0.74 (95% CI, 0.66-0.82) for tocilizumab, and 1.00 (95% CI, 0.74-1.34) for sarilumab. Secondary infections by 28 days occurred in 21.9% of patients treated with IL-6 antagonists vs 17.6% of patients treated with usual care or placebo (OR accounting for trial sample sizes, 0.99; 95% CI, 0.85-1.16). Conclusions and Relevance: In this prospective meta-analysis of clinical trials of patients hospitalized for COVID-19, administration of IL-6 antagonists, compared with usual care or placebo, was associated with lower 28-day all-cause mortality. Trial Registration: PROSPERO Identifier: CRD42021230155.

Thymus development, cell by cell The human thymus is the organ responsible for the maturation of many types of T cells, which are immune cells that protect us from infection. However, it is not well known how these cells develop with a full immune complement that contains the necessary variation to protect us from a variety of pathogens. By performing single-cell RNA sequencing on more than 250,000 cells, Park et al. examined the changes that occur in the thymus over the course of a human life. They found that development occurs in a coordinated manner among immune cells and with their developmental microenvironment. These data allowed for the creation of models of how T cells with different specific immune functions develop in humans. Science , this issue p. eaay3224

Autophagy and apoptosis are two important and interconnected stress-response mechanisms. However, the molecular interplay between these two pathways is not fully understood. To study the fate and function of autophagic proteins at the onset of apoptosis, we used a cellular model system in which autophagy precedes apoptosis. IL-3 depletion of Ba/F3 cells caused caspase (casp)-mediated cleavage of Beclin-1 and PI3KC3, two crucial components of the autophagy-inducing complex. We identified two casp cleavage sites in Beclin-1, TDVD(133) and DQLD(149), cleavage at which yields fragments lacking the autophagy-inducing capacity. Noteworthy, the C-terminal fragment, Beclin-1-C, localized predominantly at the mitochondria and sensitized the cells to apoptosis. Moreover, on isolated mitochondria, recombinant Beclin-1-C was able to induce the release of proapoptotic factors. These findings point to a mechanism by which casp-dependent generation of Beclin-1-C creates an amplifying loop enhancing apoptosis upon growth factor withdrawal.