See Zekeridou and Lennon (doi: 10.1093/aww213 ) for a scientific commentary on this article . Anti- N -methyl-D-aspartate receptor (NMDAR) encephalitis is a recently discovered autoimmune syndrome associated with psychosis, dyskinesias, and seizures. Little is known about the cerebrospinal fluid autoantibody repertoire. Antibodies against the NR1 subunit of the NMDAR are thought to be pathogenic; however, direct proof is lacking as previous experiments could not distinguish the contribution of further anti-neuronal antibodies. Using single cell cloning of full-length immunoglobulin heavy and light chain genes, we generated a panel of recombinant monoclonal NR1 antibodies from cerebrospinal fluid memory B cells and antibody secreting cells of NMDAR encephalitis patients. Cells typically carried somatically mutated immunoglobulin genes and had undergone class-switching to immunoglobulin G, clonally expanded cells carried identical somatic hypermutation patterns. A fraction of NR1 antibodies were non-mutated, thus resembling ‘naturally occurring antibodies’ and indicating that tolerance induction against NMDAR was incomplete and somatic hypermutation not essential for functional antibodies. However, only a small percentage of cerebrospinal fluid-derived antibodies reacted against NR1. Instead, nearly all further antibodies bound specifically to diverse brain-expressed epitopes including neuronal surfaces, suggesting that a broad repertoire of antibody-secreting cells enrich in the central nervous system during encephalitis. Our functional data using primary hippocampal neurons indicate that human cerebrospinal fluid-derived monoclonal NR1 antibodies alone are sufficient to cause neuronal surface receptor downregulation and subsequent impairment of NMDAR-mediated currents, thus providing ultimate proof of antibody pathogenicity. The observed formation of immunological memory might be relevant for clinical relapses.

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

Objective: Maternal autoantibodies are a risk factor for impaired brain development in offspring. Antibodies (ABs) against the NR1 (GluN1) subunit of the N‐methyl‐d‐aspartate receptor (NMDAR) are among the most frequently diagnosed anti‐neuronal surface ABs, yet little is known about effects on fetal development during pregnancy.Methods: We established a murine model of in utero exposure to human recombinant NR1 and isotype‐matched nonreactive control ABs. Pregnant C57BL/6J mice were intraperitoneally injected on embryonic days 13 and 17 each with 240μg of human monoclonal ABs. Offspring were investigated for acute and chronic effects on NMDAR function, brain development, and behavior.Results: Transferred NR1 ABs enriched in the fetus and bound to synaptic structures in the fetal brain. Density of NMDAR was considerably reduced (up to −49.2%) and electrophysiological properties were altered, reflected by decreased amplitudes of spontaneous excitatory postsynaptic currents in young neonates (−34.4%). NR1 AB‐treated animals displayed increased early postnatal mortality (+27.2%), impaired neurodevelopmental reflexes, altered blood pH, and reduced bodyweight. During adolescence and adulthood, animals showed hyperactivity (+27.8% median activity over 14 days), lower anxiety, and impaired sensorimotor gating. NR1 ABs caused long‐lasting neuropathological effects also in aged mice (10 months), such as reduced volumes of cerebellum, midbrain, and brainstem.Interpretation: The data collectively support a model in which asymptomatic mothers can harbor low‐level pathogenic human NR1 ABs that are diaplacentally transferred, causing neurotoxic effects on neonatal development. Thus, AB‐mediated network changes may represent a potentially treatable neurodevelopmental congenital brain disorder contributing to lifelong neuropsychiatric morbidity in affected children. ANN NEUROL 2019;86:656–670

Anti–N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis related to autoantibody‐mediated synaptic dysfunction. Cerebrospinal fluid–derived human monoclonal NR1 autoantibodies showed low numbers of somatic hypermutations or were unmutated. These unexpected germline‐configured antibodies showed weaker binding to the NMDAR than matured antibodies from the same patient. In primary hippocampal neurons, germline NR1 autoantibodies strongly and specifically reduced total and synaptic NMDAR currents in a dose‐ and time‐dependent manner. The findings suggest that functional NMDAR antibodies are part of the human naïve B cell repertoire. Given their effects on synaptic function, they might contribute to a broad spectrum of neuropsychiatric symptoms. Ann Neurol 2019;85:771–776

Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink.