A data-driven model of brain volume changes in progressive supranuclear palsy

Supplementary material: Supplementary material is available at Brain Communications online.Copyright © The Author(s) 2022. The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model’s staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.We thank the research participants for their contribution to the study. The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK and The Wolfson Foundation. This work was supported by the National Institute of Health Research UCLH Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute (DRI), which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. The Progressive Supranuclear Palsy-Cortico-Basal Syndrome-Multiple System Atrophy (PROSPECT) study is funded by the PSP Association and CBD Solutions. The 4-repeat tauopathy neuroimaging initiative (4RTNI) and frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) are funded by the National Institutes of Health Grant R01 AG038791 and through generous contributions from the Tau Research Consortium. Both are coordinated through the University of California, San Francisco, Memory and Aging Center. 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. W.J.S. is supported by a Wellcome Trust Clinical PhD fellowship (220582/Z/20/Z). M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517) and the UK Dementia Research Institute. N.P.O. is a UK Research and Innovation Future Leaders Fellow (MR/S03546X/1). D.C.A. is supported by the Engineering and Physical Sciences Research Council (EP/M020533/1); Medical Research Council (MR/T046422/1); Wellcome Trust (UNS113739). D.M.C. is supported by the UK Dementia Research Institute, as well as Alzheimer’s Research UK (ARUK-PG2017-1946) and the UCL/UCLH National Institute of Health Research Biomedical Research Centre. H.R.M. is supported by Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council, and the Michael J Fox Foundation. H.H. is supported by the National Institute of Health (R01AG038791, U19AG063911). L.V.V. is supported by National Institute of Health (R01AG038791). J.B.R. is supported by the Wellcome Trust (220258); National Institute of Health Research Cambridge Biomedical Research Centre (BRC-1215-20014); PSP Association; Evelyn Trust; Medical Research Council (SUAG051 R101400). A.B. is supported by National Institute of U19AG063911, R01AG038791, R01AG073482, U24AG057437, the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, the Alzheimer’s Association and the Association for Frontotemporal Degeneration. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and the National Institute of Health Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). P.A.W. is supported by a Medical Research Council Skills Development Fellowship (MR/T027770/1)

Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble

The North American Multi-Model Ensemble (NMME)-Phase II models are evaluated in terms of their retrospective seasonal forecast skill of the North Atlantic (NA) tropical cyclone (TC) activity, with a focus on TC frequency. The TC identification and tracking algorithm is modified to accommodate model data at daily resolution. It is also applied to three reanalysis products at the spatial and temporal resolution of the NMME-Phase II ensemble to allow for a more objective estimation of forecast skill. When used with the reanalysis data, the TC tracking generates realistic climatological distributions of the NA TC formation and tracks, and represents the interannual variability of the NA TC frequency quite well. Forecasts with the multi-model ensemble (MME) when initialized in April and later tend to have skill in predicting the NA seasonal TC counts (and TC days). At longer leads, the skill is low or marginal, although one of the models produces skillful forecasts when initialized as early as January and February. At short lead times, while demonstrating the highest skill levels the MME also tends to significantly outperform the individual models and attain skill comparable to the reanalysis. In addition, the short-lead MME forecasts are quite reliable. At regional scales, the skill is rather limited and mostly present in the western tropical NA and the Caribbean Sea. It is found that the overall MME forecast skill is limited by poor representation of the low-frequency variability in the predicted TC frequency, and large fluctuations in skill on decadal time scales. Addressing these deficiencies is thought to increase the value of the NMME ensemble in providing operational guidance

Detection of unsafety in families with parental and/or child developmental problems at the start of family support

Background Risk assessment is crucial in preventing child maltreatment as it can identify high-risk cases in need of child protection intervention. Despite this importance, there have been no validated risk assessment instruments available in the Netherlands for assessing the risk of child maltreatment. Therefore, the predictive validity of the California Family Risk Assessment (CFRA) was examined in Dutch families who received family support. In addition, the added value of a number of experimental items was examined. Finally, it was examined whether the predictive value of the instrument could be improved by modifying the scoring procedure. Methods Dutch families who experienced parenting and/or child developmental problems and were referred by the Centres for Youth and Family for family support between July 2009 and March 2011 were included. This led to a sample of 491 families. The predictive validity of the CFRA and the added value of the experimental items were examined by calculating AUC values. A CHAID analysis was performed to examine whether the scoring procedure could be improved. Results About half of the individual CFRA items were not related to future reports of child maltreatment. The predictive validity of the CFRA in predicting future reports of child maltreatment was found to be modest (AUC = .693). The addition of some of the experimental items and the modification of the scoring procedure by including only items that were significantly associated with future maltreatment reports resulted in a ‘high’ predictive validity (AUC = .795). Conclusions This new set of items might be a valuable instrument that also saves time because only variables that uniquely contribute to the prediction of future reports of child maltreatment are included. Furthermore, items that are perceived as difficult to assess by professionals, such as parental mental health problems or parents’ history of abuse/neglect, could be omitted without compromising predictive validity. However, it is important to examine the psychometric properties of this new set of items in a new dataset

Intestinal Epithelial Serum Amyloid A Modulates Bacterial Growth In Vitro and Pro-Inflammatory Responses in Mouse Experimental Colitis

<p>Abstract</p> <p>Background</p> <p>Serum Amyloid A (SAA) is a major acute phase protein of unknown function. SAA is mostly expressed in the liver, but also in other tissues including the intestinal epithelium. SAA reportedly has anti-bacterial effects, and because inflammatory bowel diseases (IBD) result from a breakdown in homeostatic interactions between intestinal epithelia and bacteria, we hypothesized that SAA is protective during experimental colitis.</p> <p>Methods</p> <p>Intestinal SAA expression was measured in mouse and human samples. Dextran sodium sulfate (DSS) colitis was induced in SAA 1/2 double knockout (DKO) mice and in wildtype controls. Anti-bacterial effects of SAA1/2 were tested in intestinal epithelial cell lines transduced with adenoviral vectors encoding the CE/J SAA isoform or control vectors prior to exposure to live <it>Escherichia coli</it>.</p> <p>Results</p> <p>Significant levels of SAA1/SAA2 RNA and SAA protein were detected by in situ hybridization and immunohistochemistry in mouse colonic epithelium. SAA3 expression was weaker, but similarly distributed. SAA1/2 RNA was present in the ileum and colon of conventional mice and in the colon of germfree mice. Expression of SAA3 was strongly regulated by bacterial lipopolysaccharides in cultured epithelial cell lines, whereas SAA1/2 expression was constitutive and not LPS inducible. Overexpression of SAA1/2 in cultured epithelial cell lines reduced the viability of co-cultured <it>E. coli</it>. This might partially explain the observed increase in susceptibility of DKO mice to DSS colitis. SAA1/2 expression was increased in colon samples obtained from Crohn's Disease patients compared to controls.</p> <p>Conclusions</p> <p>Intestinal epithelial SAA displays bactericidal properties in vitro and could play a protective role in experimental mouse colitis. Altered expression of SAA in intestinal biopsies from Crohn's Disease patients suggests that SAA is involved in the disease process..</p

Nerve growth factor induces neurite outgrowth of PC12 cells by promoting Gβγ-microtubule interaction

Background: Assembly and disassembly of microtubules (MTs) is critical for neurite outgrowth and differentiation. Evidence suggests that nerve growth factor (NGF) induces neurite outgrowth from PC12 cells by activating the receptor tyrosine kinase, TrkA. G protein-coupled receptors (GPCRs) as well as heterotrimeric G proteins are also involved in regulating neurite outgrowth. However, the possible connection between these pathways and how they might ultimately converge to regulate the assembly and organization of MTs during neurite outgrowth is not well understood. Results: Here, we report that Gβγ, an important component of the GPCR pathway, is critical for NGF-induced neuronal differentiation of PC12 cells. We have found that NGF promoted the interaction of Gβγ with MTs and stimulated MT assembly. While Gβγ-sequestering peptide GRK2i inhibited neurite formation, disrupted MTs, and induced neurite damage, the Gβγ activator mSIRK stimulated neurite outgrowth, which indicates the involvement of Gβγ in this process. Because we have shown earlier that prenylation and subsequent methylation/demethylation of γ subunits are required for the Gβγ-MTs interaction in vitro, small-molecule inhibitors (L-28 and L-23) targeting prenylated methylated protein methyl esterase (PMPMEase) were tested in the current study. We found that these inhibitors disrupted Gβγ and ΜΤ organization and affected cellular morphology and neurite outgrowth. In further support of a role of Gβγ-MT interaction in neuronal differentiation, it was observed that overexpression of Gβγ in PC12 cells induced neurite outgrowth in the absence of added NGF. Moreover, overexpressed Gβγ exhibited a pattern of association with MTs similar to that observed in NGF-differentiated cells. Conclusions: Altogether, our results demonstrate that βγ subunit of heterotrimeric G proteins play a critical role in neurite outgrowth and differentiation by interacting with MTs and modulating MT rearrangement. Electronic supplementary material The online version of this article (doi:10.1186/s12868-014-0132-4) contains supplementary material, which is available to authorized users

Plasticity of the β-Trefoil Protein Fold in the Recognition and Control of Invertebrate Predators and Parasites by a Fungal Defence System

Discrimination between self and non-self is a prerequisite for any defence mechanism; in innate defence, this discrimination is often mediated by lectins recognizing non-self carbohydrate structures and so relies on an arsenal of host lectins with different specificities towards target organism carbohydrate structures. Recently, cytoplasmic lectins isolated from fungal fruiting bodies have been shown to play a role in the defence of multicellular fungi against predators and parasites. Here, we present a novel fruiting body lectin, CCL2, from the ink cap mushroom Coprinopsis cinerea. We demonstrate the toxicity of the lectin towards Caenorhabditis elegans and Drosophila melanogaster and present its NMR solution structure in complex with the trisaccharide, GlcNAcβ1,4[Fucα1,3]GlcNAc, to which it binds with high specificity and affinity in vitro. The structure reveals that the monomeric CCL2 adopts a β-trefoil fold and recognizes the trisaccharide by a single, topologically novel carbohydrate-binding site. Site-directed mutagenesis of CCL2 and identification of C. elegans mutants resistant to this lectin show that its nematotoxicity is mediated by binding to α1,3-fucosylated N-glycan core structures of nematode glycoproteins; feeding with fluorescently labeled CCL2 demonstrates that these target glycoproteins localize to the C. elegans intestine. Since the identified glycoepitope is characteristic for invertebrates but absent from fungi, our data show that the defence function of fruiting body lectins is based on the specific recognition of non-self carbohydrate structures. The trisaccharide specifically recognized by CCL2 is a key carbohydrate determinant of pollen and insect venom allergens implying this particular glycoepitope is targeted by both fungal defence and mammalian immune systems. In summary, our results demonstrate how the plasticity of a common protein fold can contribute to the recognition and control of antagonists by an innate defence mechanism, whereby the monovalency of the lectin for its ligand implies a novel mechanism of lectin-mediated toxicity

Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning

Data availability: Source data are not publicly available but non-commercial academic researcher requests may be made to the chief investigators of the seven source studies, subject to data access agreements and conditions that preserve participant anonymity. The underlying SuStaIn model code is publicly available at https://github.com/ucl-pond/pySuStaIn.68 .Supplementary data: available online at: https://academic.oup.com/braincomms/article/5/2/fcad048/7067775#398676040 .Copyright © The Author(s) 2023. To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy–Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy–Richardson, 52 with a progressive supranuclear palsy–cortical variant (progressive supranuclear palsy–frontal, progressive supranuclear palsy–speech/language, or progressive supranuclear palsy–corticobasal), and 17 with a progressive supranuclear palsy–subcortical variant (progressive supranuclear palsy–parkinsonism or progressive supranuclear palsy–progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a ‘subcortical’ subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a ‘cortical’ subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy–subcortical cases and 81% of progressive supranuclear palsy–Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy–cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the ‘subcortical’ subtype was associated with worse clinical severity scores compared to the ‘cortical subtype’ (progressive supranuclear palsy rating scale and Unified Parkinson’s Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.W.J.S. is supported by a Wellcome Trust Clinical PhD fellowship (220582/Z/20/Z). C.S. is supported by the UK Research and Innovation Medical Research Council (MR/S03546X/1). M.B. is supported by a fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517), and the UK Dementia Research Institute. D.M.C. is supported by the UK Dementia Research Institute, as well as Alzheimer’s Research UK (ARUK-PG2017-1946), and the University College London/University College London Hospitals, National Institute for Health and Care Research Biomedical Research Centre. H.H. is supported by the National Institutes of Health (R01AG038791, U19AG063911). A.L.Y. is supported by a Skills Development Fellowship from the Medical Research Council (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (MR/S03546X/1). L.V.V. is supported by the National Institutes of Health (R01AG038791, K23AG073514) and the Alzheimer’s Association. D.C.A. is supported by the Engineering and Physical Sciences Research Council (EP/M020533/1), Medical Research Council (MR/T046422/1), and Wellcome Trust (UNS113739). J.B.R. is supported by the Wellcome Trust (220258), National Institute for Health and Care Research Cambridge Biomedical Research Centre (BRC-1215-20014), PSP Association, Evelyn Trust, and Medical Research Council (SUAG051 R101400). H.R.M. is supported by Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council, and the Michael J Fox Foundation. A.L.B. is supported by the National Institutes of Health (U19AG063911, R01AG038791, R01AG073482, and U24AG057437), the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, and the Alzheimer’s Association and the Association for Frontotemporal Degeneration. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and the National Institute for Health and Care Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). P.A.W. is supported by a Medical Research Council Skills Development Fellowship (MR/T027770/1). The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. The PROSPECT study is funded by the PSP Association and CBD Solutions. The 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) and FTLDNI are funded by the National Institutes of Health Grant (R01 AG038791) and through generous contributions from the Tau Research Consortium. Both are coordinated through the University of California, San Francisco, Memory and Aging Center. 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

Supplementary material: Supplementary material is available at Brain online: https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/brain/145/5/10.1093_brain_awab382/1/awab382_supplementary_data.zip?Expires=1665139578&Signature=C7VStQxldRqnpcchAWh4igaKwveciF~gaQCbInqMnI1YkIFV0euPXlI-0ZlRZ26hbRum6myjm88d3KzOM-wqVG~H7JO9TTUXoyi-n3hRRd1a4Vw0Hay9ykagca92gMqWij5ax4WzsEGlv~dKGSKKivH02pflzQyDAwF6xjjObYRYe29grdOZQ5h8orT6XNAdK5YFqpiX7L6mpVaNs7AOgNDdxtwshaa4kq1xxCgojTgAaIR3WFTFDpHkJ6wnhncxuteykTzq5~w1RCoDIfKQSA9C42i~iWryOeOvjv-P6j-R0tSkDGzFKcI3kUo3lUT9GiPG-vDwAO5EsLkUikJLOw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA.GENFI consortium members Full details are available in the Supplementary material. Sónia Afonso, Maria Rosario Almeida, Sarah Anderl-Straub, Christin Andersson, Anna Antonell, Silvana Archetti, Andrea Arighi, Mircea Balasa, Myriam Barandiaran, Nuria Bargalló, Robart Bartha, Benjamin Bender, Alberto Benussi, Luisa Benussi, Valentina Bessi, Giuliano Binetti, Sandra Black, Martina Bocchetta, Sergi Borrego-Ecija, Jose Bras, Rose Bruffaerts, Marta Cañada, Valentina Cantoni, Paola Caroppo, David Cash, Miguel Castelo-Branco, Rhian Convery, Thomas Cope, Giuseppe Di Fede, Alina Díez, Diana Duro, Chiara Fenoglio, Camilla Ferrari, Catarina B. Ferreira, Nick Fox, Morris Freedman, Giorgio Fumagalli, Alazne Gabilondo, Roberto Gasparotti, Serge Gauthier, Stefano Gazzina, Giorgio Giaccone, Ana Gorostidi, Caroline Greaves, Rita Guerreiro, Tobias Hoegen, Begoña Indakoetxea, Vesna Jelic, Hans-Otto Karnath, Ron Keren, Tobias Langheinrich, Maria João Leitão, Albert Lladó, Gemma Lombardi, Sandra Loosli, Carolina Maruta, Simon Mead, Gabriel Miltenberger, Rick van Minkelen, Sara Mitchell, Katrina Moore, Benedetta Nacmias, Jennifer Nicholas, Linn Öijerstedt, Jaume Olives, Sebastien Ourselin, Alessandro Padovani, Georgia Peakman, Michela Pievani, Yolande Pijnenburg, Cristina Polito, Enrico Premi, Sara Prioni, Catharina Prix, Rosa Rademakers, Veronica Redaelli, Tim Rittman, Ekaterina Rogaeva, Pedro Rosa-Neto, Giacomina Rossi, Martin Rosser, Beatriz Santiago, Elio Scarpini, Sonja Schönecker, Elisa Semler, Rachelle Shafei, Christen Shoesmith, Miguel Tábuas-Pereira, Mikel Tainta, Ricardo Taipa, David Tang-Wai, David L Thomas, Paul Thompson, Hakan Thonberg, Carolyn Timberlake, Pietro Tiraboschi, Emily Todd, Philip Van Damme, Mathieu Vandenbulcke, Michele Veldsman, Ana Verdelho, Jorge Villanua, Jason Warren, Ione Woollacott, Elisabeth Wlasich, Miren Zulaica.Copyright © The Author(s) 2021. Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.Deltaplan Dementie (The Netherlands Organisation for Health Research and Development and Alzheimer Nederland; grant numbers 733050813,733050103 and 733050513), the Bluefield Project to Cure Frontotemporal Dementia, the Dioraphte founda tion (grant number 1402 1300), the European Joint Programme— Neurodegenerative Disease Research and the Netherlands Organisation for Health Research and Development (PreFrontALS: 733051042, RiMod-FTD: 733051024); V.V. and S.K. have received funding from the European Union’s Horizon 2020 research and in novation programme under grant agreement no. 666992 (EuroPOND). E.B. was supported by the Hartstichting (PPP Allowance, 2018B011); in Belgium by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie; in the UK by the MRC UK GENFI grant (MR/M023664/1); J.D.R. is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH); I.J.S. is supported by the Alzheimer’s Association; J.B.R. is supported by the Wellcome Trust (103838); in Spain by the Fundacio´ Marato´ de TV3 (20143810 to R.S.V.); in Germany by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198) and by grant 779357 ‘Solve-RD’ from the Horizon 2020 Research and Innovation Programme (to MS); in Sweden by grants from the Swedish FTD Initiative funded by the Scho¨rling Foundation, grants from JPND PreFrontALS Swedish Research Council (VR) 529–2014-7504, Swedish Research Council (VR) 2015–02926, Swedish Research Council (VR) 2018–02754, Swedish Brain Foundation, Swedish Alzheimer Foundation, Stockholm County Council ALF, Swedish Demensfonden, Stohnes foundation, Gamla Tja¨narinnor, Karolinska Institutet Doctoral Funding and StratNeuro. H.Z. is a Wallenberg Scholar

A wake-active locomotion circuit depolarizes a sleep-active neuron to switch on sleep

Sleep-active neurons depolarize during sleep to suppress wakefulness circuits. Wake-active wake-promoting neurons in turn shut down sleep-active neurons, thus forming a bipartite flip-flop switch. However, how sleep is switched on is unclear because it is not known how wakefulness is translated into sleep-active neuron depolarization when the system is set to sleep. Using optogenetics in Caenorhabditis elegans, we solved the presynaptic circuit for depolarization of the sleep-active RIS neuron during developmentally regulated sleep, also known as lethargus. Surprisingly, we found that RIS activation requires neurons that have known roles in wakefulness and locomotion behavior. The RIM interneurons-which are active during and can induce reverse locomotion-play a complex role and can act as inhibitors of RIS when they are strongly depolarized and as activators of RIS when they are modestly depolarized. The PVC command interneurons, which are known to promote forward locomotion during wakefulness, act as major activators of RIS. The properties of these locomotion neurons are modulated during lethargus. The RIMs become less excitable. The PVCs become resistant to inhibition and have an increased capacity to activate RIS. Separate activation of neither the PVCs nor the RIMs appears to be sufficient for sleep induction; instead, our data suggest that they act in concert to activate RIS. Forward and reverse circuit activity is normally mutually exclusive. Our data suggest that RIS may be activated at the transition between forward and reverse locomotion states, perhaps when both forward (PVC) and reverse (including RIM) circuit activity overlap. While RIS is not strongly activated outside of lethargus, altered activity of the locomotion interneurons during lethargus favors strong RIS activation and thus sleep. The control of sleep-active neurons by locomotion circuits suggests that sleep control may have evolved from locomotion control. The flip-flop sleep switch in C. elegans thus requires an additional component, wake-active sleep-promoting neurons that translate wakefulness into the depolarization of a sleep-active neuron when the worm is sleepy. Wake-active sleep-promoting circuits may also be required for sleep state switching in other animals, including in mammals