High prevalence of NMDA receptor IgA/IgM antibodies in different dementia types

OBJECTIVE: To retrospectively determine the frequency of N-Methyl-D-Aspartate (NMDA) receptor (NMDAR) autoantibodies in patients with different forms of dementia. METHODS: Clinical characterization of 660 patients with dementia, neurodegenerative disease without dementia, other neurological disorders and age-matched healthy controls combined with retrospective analysis of serum or cerebrospinal fluid (CSF) for the presence of NMDAR antibodies. Antibody binding to receptor mutants and the effect of immunotherapy were determined in a subgroup of patients. RESULTS: Serum NMDAR antibodies of IgM, IgA, or IgG subtypes were detected in 16.1% of 286 dementia patients (9.5% IgM, 4.9% IgA, and 1.7% IgG) and in 2.8% of 217 cognitively healthy controls (1.9% IgM and 0.9% IgA). Antibodies were rarely found in CSF. The highest prevalence of serum antibodies was detected in patients with “unclassified dementia” followed by progressive supranuclear palsy, corticobasal syndrome, Parkinson’s disease-related dementia, and primary progressive aphasia. Among the unclassified dementia group, 60% of 20 patients had NMDAR antibodies, accompanied by higher frequency of CSF abnormalities, and subacute or fluctuating disease progression. Immunotherapy in selected prospective cases resulted in clinical stabilization, loss of antibodies, and improvement of functional imaging parameters. Epitope mapping showed varied determinants in patients with NMDAR IgA-associated cognitive decline. INTERPRETATION: Serum IgA/IgM NMDAR antibodies occur in a significant number of patients with dementia. Whether these antibodies result from or contribute to the neurodegenerative disorder remains unknown, but our findings reveal a subgroup of patients with high antibody levels who can potentially benefit from immunotherapy

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Geographic Differences in Genetic Susceptibility to IgA Nephropathy: GWAS Replication Study and Geospatial Risk Analysis

Peer reviewe

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM