The Saturn-mass exoplanet WASP-39b has been the subject of extensive efforts to determine its atmospheric properties using transmission spectroscopy1-4. However, these efforts have been hampered by modelling degeneracies between composition and cloud properties that are caused by limited data quality5-9. Here, we present the transmission spectrum of WASP-39 b obtained using the SOSS mode of the NIRISS instrument on JWST. This spectrum spans 0.6-2.8𝝻m in wavelength and reveals multiple water absorption bands, the potassium resonance doublet, and signatures of clouds. The precision and broad wavelength coverage of NIRISS-SOSS allows us to break model degeneracies between cloud properties and the atmospheric composition of WASP-39b, favouring a heavy element enhancement ("metallicity") of ~10-30x the solar value, a sub-solar carbon-to-oxygen (C/O) ratio, and a solar-to-super-solar potassium-to-oxygen (K/O) ratio. The observations are also best explained by wavelength-dependent, non-gray clouds with inhomogeneous coverage of the planet's terminator.

Measuring the abundances of carbon and oxygen in exoplanet atmospheres is considered a crucial avenue for unlocking the formation and evolution of exoplanetary systems1,2. Access to an exoplanet's chemical inventory requires high precision observations, often inferred from individual molecular detections with low-resolution space-based3-5 and high-resolution ground-based6-8 facilities. Here we report the medium-resolution (R≈600) transmission spectrum of an exoplanet atmosphere between 3-5 μm covering multiple absorption features for the Saturn-mass exoplanet WASP-39b9, obtained with JWST NIRSpec G395H. Our observations achieve 1.46[Formula: see text] photon precision, providing an average transit depth uncertainty of 221 ppm per spectroscopic bin, and present minimal impacts from systematic effects. We detect significant absorption from CO2 (28.5[Formula: see text]) and H2O (21.5[Formula: see text]), and identify SO2 as the source of absorption at 4.1 [Formula: see text]m (4.8[Formula: see text]). Best-fit atmospheric models range between 3[Formula: see text] and 10[Formula: see text] solar metallicity, with sub-solar to solar C/O ratios. These results, including the detection of SO2, underscore the importance of characterising the chemistry in exoplanet atmospheres, and showcase NIRSpec G395H as an excellent mode for time series observations over this critical wavelength range10.

Measuring the metallicity and carbon-to-oxygen (C/O) ratio in exoplanet atmospheres is a fundamental step towards constraining the dominant chemical processes at work and, if in equilibrium, revealing planet formation histories. Transmission spectroscopye.g., 1,2 provides the necessary means by constraining the abundances of oxygen- and carbon-bearing species; however, this requires broad wavelength coverage, moderate spectral resolution, and high precision that, together, are not achievable with previous observatories. Now that JWST has commenced science operations, we are able to observe exoplanets at previously uncharted wavelengths and spectral resolutions. Here we report time-series observations of the transiting exoplanet WASP-39b using JWST's Near InfraRed Camera (NIRCam). The long-wavelength spectroscopic and short-wavelength photometric light curves span 2.0 - 4.0 µm, exhibit minimal systematics, and reveal well-defined molecular absorption features in the planet's spectrum. Specifically, we detect gaseous H2O in the atmosphere and place an upper limit on the abundance of CH4. The otherwise prominent CO2 feature at 2.8 µm is largely masked by H2O. The best-fit chemical equilibrium models favour an atmospheric metallicity of 1-100× solar (i.e., an enrichment of elements heavier than helium relative to the Sun) and a sub-stellar carbon-to-oxygen (C/O) ratio. The inferred high metallicity and low C/O ratio may indicate significant accretion of solid materials during planet formatione.g., 3,4 or disequilibrium processes in the upper atmospheree.g., 5,6.

Pyridines and related N-heteroarenes are commonly found in pharmaceuticals, agrochemicals, and other bioactive compounds1,2. Site-selective C-H functionalization would provide a direct way of making these medicinally-active products3,4,5. For example, nicotinic acid derivatives could be made by C-H carboxylation, but this remains an elusive transformation6,7,8. Here, we describe the development of an electrochemical strategy for the direct carboxylation of pyridines using CO2. The choice of electrolysis setup gives rise to divergent site selectivity: a divided electrochemical cell leads to C5-carboxylation, whereas an undivided cell promotes C4-carboxylation. The undivided cell reaction is proposed to operate via a paired electrolysis mechanism9,10, wherein both cathodic and anodic events play critical roles in altering the site selectivity. Specifically, anodically-generated iodine preferentially reacts with a key radical anion intermediate in the C4-carboxylation pathway via hydrogen-atom transfer, thus diverting the reaction selectivity via the Curtin-Hammett principle11. The scope of the transformation was expanded to a wide range of N-heteroarenes including bi- and terpyridines, pyrimidines, pyrazines and quinolines.

Author information: (1)Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA. (2)Graduate Program in Biological Sciences, University of Maryland, College Park, MD, USA. (3)NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA. (4)Division of Intramural Research, NIAID, NIH, Bethesda, MD, USA. (5)Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA. (6)Division of Viral Products, Center for Biologics Evaluation and Research (CBER), FDA, Silver Spring, MD, USA. (7)Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA. (8)Johns Hopkins University, Baltimore, MD, USA. (9)Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. (10)Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA. john.tsang@yale.edu. (11)NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA. john.tsang@yale.edu. (12)Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA. john.tsang@yale.edu. (13)Department of Biomedical Engineering, Yale University, New Haven, CT, USA. john.tsang@yale.edu. (#)Contributed equally

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.

Chloroplasts rely on the translocon complexes in the outer and inner envelope membranes (termed TOC and TIC, respectively) to import thousands of different nuclear-encoded proteins from the cytosol1-4. While previous studies indicated that the TOC and TIC complexes may assemble into larger supercomplexes5-7, the overall architectures of the TOC-TIC supercomplexes and the mechanism of preprotein translocation are elusive. Here we report the cryo-electron microscopy (cryo-EM) structure of the TOC-TIC supercomplex from Chlamydomonas reinhardtii at an overall resolution of 2.8 Å. The major subunits of the TOC complex (Toc75, Toc90 and Toc34) and TIC complex (Tic214, Tic20, Tic100 and Tic56), three chloroplast translocon-associated proteins (Ctap3, Ctap4 and Ctap5) and three newly-identified small inner-membrane proteins (Simp1-3) have been located in the supercomplex. As the largest protein, Tic214 traverses the inner membrane, the intermembrane space and the outer membrane, connecting the TOC complex with the TIC proteins. An inositol hexaphosphate (InsP6 or I6P) molecule is located at the Tic214-Toc90 interface and stabilizes their assembly. Moreover, four lipid molecules are located within or above an inner-membrane funnel formed by Tic214, Tic20, Simp1 and Ctap5. Furthermore, multiple potential pathways found in the TOC-TIC supercomplex may support translocation of different substrate preproteins into chloroplasts.

The transcription factor TFEB is a master regulator of lysosomal biogenesis and autophagy1. The phosphorylation of TFEB by the mechanistic target of rapamycin complex 1 (mTORC1)2-5 is unique in its mTORC1 substrate recruitment mechanism, which is strictly dependent on the amino acid-mediated activation of the RagC GTPase activating protein FLCN6,7. TFEB lacks the TOR signalling motif responsible for the recruitment of other mTORC1 substrates. We used cryogenic-electron microscopy to determine the structure of TFEB as presented to mTORC1 for phosphorylation, which we refer to as the 'megacomplex'. Two full Rag-Ragulator complexes present each molecule of TFEB to the mTOR active site. One Rag-Ragulator complex is bound to Raptor in the canonical mode seen previously in the absence of TFEB. A second Rag-Ragulator complex (non-canonical) docks onto the first through a RagC GDP-dependent contact with the second Ragulator complex. The non-canonical Rag dimer binds the first helix of TFEB with a RagCGDP-dependent aspartate clamp in the cleft between the Rag G domains. In cellulo mutation of the clamp drives TFEB constitutively into the nucleus while having no effect on mTORC1 localization. The remainder of the 108-amino acid TFEB docking domain winds around Raptor and then back to RagA. The double use of RagC GDP contacts in both Rag dimers explains the strong dependence of TFEB phosphorylation on FLCN and the RagC GDP state.

Author information: (1)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. moritz.gerstung@ebi.ac.uk. (2)European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. moritz.gerstung@ebi.ac.uk. (3)Wellcome Sanger Institute, Cambridge, UK. moritz.gerstung@ebi.ac.uk. (4)The Francis Crick Institute, London, UK. (5)Broad Institute of MIT and Harvard, Cambridge, MA, USA. (6)Wellcome Sanger Institute, Cambridge, UK. (7)Big Data Institute, University of Oxford, Oxford, UK. (8)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. (9)University of Cambridge, Cambridge, UK. (10)University of Toronto, Toronto, Ontario, Canada. (11)Vector Institute, Toronto, Ontario, Canada. (12)Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA. (13)The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (14)German Cancer Research Center (DKFZ), Heidelberg, Germany. (15)Heidelberg University, Heidelberg, Germany. (16)University of Ljubljana, Ljubljana, Slovenia. (17)NorthShore University HealthSystem, Evanston, IL, USA. (18)Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. (19)Simon Fraser University, Burnaby, British Columbia, Canada. (20)Vancouver Prostate Centre, Vancouver, British Columbia, Canada. (21)University of Melbourne, Melbourne, Victoria, Australia. (22)Walter and Eliza Hall Institute, Melbourne, Victoria, Australia. (23)University of Leuven, Leuven, Belgium. (24)Weill Cornell Medicine, New York, NY, USA. (25)New York Genome Center, New York, NY, USA. (26)University of California Santa Cruz, Santa Cruz, CA, USA. (27)Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (28)University of California, Los Angeles, CA, USA. (29)Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. (30)Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA. (31)Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. (32)Harvard Medical School, Boston, MA, USA. (33)Dana-Farber Cancer Institute, Boston, MA, USA. (34)Indiana University, Bloomington, IN, USA. (35)The University of Chicago, Chicago, IL, USA. (36)University of Cologne, Cologne, Germany. (37)University of Helsinki, Helsinki, Finland. (38)University of Glasgow, Glasgow, UK. (39)Oxford NIHR Biomedical Research Centre, Oxford, UK. (40)The Francis Crick Institute, London, UK. peter.vanloo@crick.ac.uk. (41)University of Leuven, Leuven, Belgium. peter.vanloo@crick.ac.uk. (#)Contributed equally

Author information: (1)European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK. (2)ETH Zurich, Zurich, Switzerland. (3)Memorial Sloan Kettering Cancer Center, New York, NY, USA. (4)Weill Cornell Medical College, New York, NY, USA. (5)SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. (6)University Hospital Zurich, Zurich, Switzerland. (7)National University of Singapore, Singapore, Singapore. (8)Genome Institute of Singapore, Singapore, Singapore. (9)Peking University, Beijing, China. (10)The University of Tokyo, Minato-ku, Japan. (11)University of California, Santa Cruz, Santa Cruz, CA, USA. (12)BGI-Shenzhen, Shenzhen, China. (13)China National GeneBank-Shenzhen, Shenzhen, China. (14)Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (15)University of California, San Francisco, San Francisco, CA, USA. (16)University of Glasgow, Glasgow, UK. (17)European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. (18)The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. (19)Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany. (20)University College London, London, UK. (21)Karolinska Institutet, Stockholm, Sweden. (22)Broad Institute, Cambridge, MA, USA. (23)Dana-Farber Cancer Institute, Boston, MA, USA. (24)Harvard Medical School, Boston, MA, USA. (25)Ulm University and Ulm University Medical Center, Ulm, Germany. (26)Duke-NUS Medical School, Singapore, Singapore. (27)University of Toronto, Toronto, Ontario, Canada. (28)Baylor College of Medicine, Houston, TX, USA. (29)European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK. brazma@ebi.ac.uk. (30)University of California, Santa Cruz, Santa Cruz, CA, USA. anbrooks@ucsc.edu. (31)Broad Institute, Cambridge, MA, USA. anbrooks@ucsc.edu. (32)Dana-Farber Cancer Institute, Boston, MA, USA. anbrooks@ucsc.edu. (33)National Cancer Centre Singapore, Singapore, Singapore. (34)ETH Zurich, Zurich, Switzerland. raetsch@inf.ethz.ch. (35)Memorial Sloan Kettering Cancer Center, New York, NY, USA. raetsch@inf.ethz.ch. (36)Weill Cornell Medical College, New York, NY, USA. raetsch@inf.ethz.ch. (37)SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. raetsch@inf.ethz.ch. (38)University Hospital Zurich, Zurich, Switzerland. raetsch@inf.ethz.ch. (39)German Cancer Consortium (DKTK), partner site Berlin, Germany. (40)German Cancer Research Center (DKFZ), Heidelberg, Germany. (#)Contributed equally

Author information: (1)Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA. (2)Broad Institute of MIT and Harvard, Cambridge, MA, USA. (3)Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA. (4)Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore. (5)Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore. (6)Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. (7)Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA. (8)Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA. (9)Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. (10)Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain. (11)Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain. (12)Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA. (13)Wellcome Sanger Institute, Hinxton, UK. (14)National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India. (15)Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. (16)Department of Computer Science, University of Helsinki, Helsinki, Finland. (17)Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland. (18)Institute of Biotechnology, University of Helsinki, Helsinki, Finland. (19)Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. (20)Harvard Medical School, Boston, MA, USA. (21)Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore. steverozen@gmail.com. (22)Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore. steverozen@gmail.com. (23)SingHealth, Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore, Singapore. steverozen@gmail.com. (24)Wellcome Sanger Institute, Hinxton, UK. mrs@sanger.ac.uk. (#)Contributed equally

Author information: (1)Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK. (2)Totient Inc, Cambridge, MA, USA. (3)The Broad Institute of Harvard and MIT, Cambridge, MA, USA. (4)Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA. (5)Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. (6)Weill Cornell Medical College, New York, NY, USA. (7)European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. (8)Department of Molecular Biology, Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA, USA. (9)New York Genome Center, New York, NY, USA. (10)Biotech Research & Innovation Centre (BRIC), The Finsen Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. joachim.weischenfeldt@bric.ku.dk. (11)The Broad Institute of Harvard and MIT, Cambridge, MA, USA. rameen_beroukhim@dfci.harvard.edu. (12)Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA. rameen_beroukhim@dfci.harvard.edu. (13)Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. rameen_beroukhim@dfci.harvard.edu. (14)Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK. pc8@sanger.ac.uk. (15)Department of Haematology, University of Cambridge, Cambridge, UK. pc8@sanger.ac.uk. (#)Contributed equally

Author information: (1)The Broad Institute of MIT and Harvard, Cambridge, MA, USA. (2)Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA. (3)Harvard Medical School, Boston, MA, USA. (4)Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark. (5)Wellcome Trust Sanger Institute, Hinxton, UK. (6)Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA. (7)Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. (8)Harvard University, Cambridge, MA, USA. (9)Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (10)Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. (11)Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain. (12)Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (13)Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (14)Department for BioMedical Research, University of Bern, Bern, Switzerland. (15)Graduate School of Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland. (16)Department of Medical Oncology, Bern University Hospital, University of Bern, Bern, Switzerland. (17)Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (18)Bioquant Center, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany. (19)Department of Cell Biology, Harvard Medical School, Boston, MA, USA. (20)cBio Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. (21)Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA. (22)Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX, USA. (23)Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. (24)Department of Genitourinary Medical Oncology - Research, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA. (25)Department of Urology, Icahn school of Medicine at Mount Sinai, New York, NY, USA. (26)Faculty of Biosciences, Heidelberg University, Heidelberg, Germany. (27)Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA. (28)Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA. (29)Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA. (30)Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. (31)European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, UK. (32)Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark. (33)Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA. (34)Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. (35)Division of Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. (36)Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland. (37)Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. (38)Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland. (39)Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. (40)SBGD Inc, Cambridge, MA, USA. (41)Department of Haematology, University of Cambridge, Cambridge, UK. (42)Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA. (43)Biotech Research & Innovation Centre (BRIC), The Finsen Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. (44)Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. (45)Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (46)Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (47)The Biomedical Research Centre (CINBIO), Universidade de Vigo, Vigo, Spain. (48)Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden. (49)Genetics and Genome Biology Program, SickKids Research Institute, Toronto, Ontario, Canada. (50)Department of Gene Technology, Tallinn University of Technology, Tallinn, Estonia. (51)Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. (52)New York Genome Center, New York, NY, USA. (53)Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. (54)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (55)Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA, USA. (56)Department of Pathology and Laboratory Medicine, and Englander Institute for Precision Medicine, and Institute for Computational Biomedicine, and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. (57)MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA. (58)Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan. (59)Department of Computer Science, Princeton University, Princeton, NJ, USA. (60)Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA. (61)Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. (62)Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. (63)Center for Biomolecular Science and Engineering, University of California at Santa Cruz, Santa Cruz, CA, USA. (64)Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. (65)Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan. (66)Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. (67)Department of Computer Science, Yale University, New Haven, CT, USA. (68)Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain. (69)Biotech Research & Innovation Centre (BRIC), The Finsen Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. joachim.weischenfeldt@bric.ku.dk. (70)Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. joachim.weischenfeldt@bric.ku.dk. (71)The Broad Institute of MIT and Harvard, Cambridge, MA, USA. rameen@broadinstitute.org. (72)Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA. rameen@broadinstitute.org. (73)Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. rameen@broadinstitute.org. (74)Wellcome Trust Sanger Institute, Hinxton, UK. im3@sanger.ac.uk. (75)Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark. jakob.skou@clin.au.dk. (76)Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark. jakob.skou@clin.au.dk. (77)The Broad Institute of MIT and Harvard, Cambridge, MA, USA. gadgetz@broadinstitute.org. (78)Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA. gadgetz@broadinstitute.org. (79)Harvard Medical School, Boston, MA, USA. gadgetz@broadinstitute.org. (80)Department of Pathology, Massachusetts General Hospital, Boston, MA, USA. gadgetz@broadinstitute.org. (#)Contributed equally

Indirect development with an intermediate larva exists in all major animal lineages1, which makes larvae central to most scenarios of animal evolution2-11. Yet how larvae evolved remains disputed. Here we show that temporal shifts (that is, heterochronies) in trunk formation underpin the diversification of larvae and bilaterian life cycles. We performed chromosome-scale genome sequencing in the annelid Owenia fusiformis with transcriptomic and epigenomic profiling during the life cycles of this and two other annelids. We found that trunk development is deferred to pre-metamorphic stages in the feeding larva of O. fusiformis but starts after gastrulation in the non-feeding larva with gradual metamorphosis of Capitella teleta and the direct developing embryo of Dimorphilus gyrociliatus. Accordingly, the embryos of O. fusiformis develop first into an enlarged anterior domain that forms larval tissues and the adult head12. Notably, this also occurs in the so-called 'head larvae' of other bilaterians13-17, with which the O. fusiformis larva shows extensive transcriptomic similarities. Together, our findings suggest that the temporal decoupling of head and trunk formation, as maximally observed in head larvae, facilitated larval evolution in Bilateria. This diverges from prevailing scenarios that propose either co-option9,10 or innovation11 of gene regulatory programmes to explain larva and adult origins.

Differentiated somatic mammalian cells putatively exhibit species-specific division limits that impede cancer but may constrain lifespans1-3. To provide immunity, transiently stimulated CD8+ T cells undergo unusually rapid bursts of numerous cell divisions, and then form quiescent long-lived memory cells that remain poised to reproliferate following subsequent immunological challenges. Here we addressed whether T cells are intrinsically constrained by chronological or cell-division limits. We activated mouse T cells in vivo using acute heterologous prime-boost-boost vaccinations4, transferred expanded cells to new mice, and then repeated this process iteratively. Over 10 years (greatly exceeding the mouse lifespan)5 and 51 successive immunizations, T cells remained competent to respond to vaccination. Cells required sufficient rest between stimulation events. Despite demonstrating the potential to expand the starting population at least 1040-fold, cells did not show loss of proliferation control and results were not due to contamination with young cells. Persistent stimulation by chronic infections or cancer can cause T cell proliferative senescence, functional exhaustion and death6. We found that although iterative acute stimulations also induced sustained expression and epigenetic remodelling of common exhaustion markers (including PD1, which is also known as PDCD1, and TOX) in the cells, they could still proliferate, execute antimicrobial functions and form quiescent memory cells. These observations provide a model to better understand memory cell differentiation, exhaustion, cancer and ageing, and show that functionally competent T cells can retain the potential for extraordinary population expansion and longevity well beyond their organismal lifespan.

Erratum in Nature. 2023 Feb 6;:

The protective efficacy of serum antibody results from the interplay of antigen-specific B cell clones of different affinities and specificities. These cellular dynamics underlie serum-level phenomena such as "Original Antigenic Sin" (OAS), a proposed propensity of the immune system to rely repeatedly on the first cohort of B cells engaged by an antigenic stimulus when encountering related antigens, in detriment of inducing de novo responses1-5. OAS-type suppression of new, variant-specific antibodies may pose a barrier to vaccination against rapidly evolving viruses such as influenza and SARS-CoV-26,7. Precise measurement of OAS-type suppression is challenging because cellular and temporal origins cannot readily be ascribed to antibodies in circulation; thus, its impact on subsequent antibody responses remains unclear5,8. Here, we introduce a molecular fate-mapping approach in which serum antibodies derived from specific cohorts of B cells can be differentially detected. We show that serum responses to sequential homologous boosting derive overwhelmingly from primary cohort B cells, while later induction of new antibody responses from naïve B cells is strongly suppressed. Such "primary addiction" decreases sharply as a function of antigenic distance, allowing reimmunization with divergent viral glycoproteins to produce de novo antibody responses targeting epitopes absent from the priming variant. Our findings have implications for the understanding of OAS and for the design and testing of vaccines against evolving pathogens.

Despite the success of PD-1 blockade in melanoma and other cancers, effective treatment strategies to overcome resistance to cancer immunotherapy are lacking1,2. We identified the innate immune kinase TANK-binding kinase 1 (TBK1)3 as a candidate immune evasion gene in a pooled genetic screen4. Using a suite of genetic and pharmacologic tools across multiple experimental model systems, we confirm a role for TBK1 as an immune evasion gene. Targeting TBK1 enhances response to PD-1 blockade by lowering the cytotoxicity threshold to effector cytokines (TNFα/IFNγ). TBK1 inhibition in combination with PD-1 blockade also demonstrated efficacy using patient-derived tumour models, with concordant findings in matched patient-derived organotypic tumour spheroids (PDOTS) and matched patient-derived organoids (PDOs). Tumour cells lacking TBK1 are primed to undergo RIPK- and caspase-dependent cell death in response to TNFα/IFNγ in a JAK/STAT-dependent manner. Taken together, our results demonstrate that targeting TBK1 is a novel and effective strategy to overcome resistance to cancer immunotherapy.

The SARS-CoV-2 Omicron variant is more immune-evasive and less virulent than other major viral variants recognized to date1-12. Omicron spike (S), with an unusually large number of mutations, is considered the major driver of these phenotypes. We generated chimeric recombinant SARS-CoV-2 encoding the S gene of Omicron (BA.1 lineage) in the backbone of an ancestral SARS-CoV-2 isolate and compared this virus with the naturally circulating Omicron variant. The Omicron S-bearing virus robustly escaped vaccine-induced humoral immunity, mainly due to mutations in the receptor-binding motif (RBM), yet unlike naturally occurring Omicron, efficiently replicated in cell lines and primary-like distal lung cells. Similarly, in K18-hACE2 mice, although Omicron S-carrying virus caused less severe disease compared to the ancestral virus, it failed to achieve the attenuation level of Omicron. Further investigation showed that mutating nsp6 in addition to S was sufficient to recapitulate the attenuated phenotype of Omicron. This indicates that while the vaccine escape of Omicron is driven by mutations in S, the pathogenicity of Omicron is determined by mutations both in and outside of S.