Managing curly top

Curly top of sugarbeets caused by Beet curly top virus (BCTV) was widespread from southeastern Oregon to southcentral Idaho in 2004. Curly top first became a serious threat to sugarbeet production in southern Idaho in 1919. By the time the first resistant variety was released in 1935, BCTV had almost eliminated the sugarbeet industry (2). Today this disease is largely managed through the use of resistant varieties. Early planting and the use of systemic insecticides (phorate, aldicarb, and imidicloprid) will also help limit curly top (1,2,4)

Clustered mutations in the GRIK2 kainate receptor subunit gene underlie diverse neurodevelopmental disorders

Kainate receptors (KARs) are glutamate-gated cation channels with diverse roles in the central nervous system. Bi-allelic loss of function of the KAR-encoding gene GRIK2 causes a nonsyndromic neurodevelopmental disorder (NDD) with intellectual disability and developmental delay as core features. The extent to which mono-allelic variants in GRIK2 also underlie NDDs is less understood because only a single individual has been reported previously. Here, we describe an additional eleven individuals with heterozygous de novo variants in GRIK2 causative for neurodevelopmental deficits that include intellectual disability. Five children harbored recurrent de novo variants (three encoding p.Thr660Lys and two p.Thr660Arg), and four children and one adult were homozygous for a previously reported variant (c.1969G>A [p.Ala657Thr]). Individuals with shared variants had some overlapping behavioral and neurological dysfunction, suggesting that the GRIK2 variants are likely pathogenic. Analogous mutations introduced into recombinant GluK2 KAR subunits at sites within the M3 transmembrane domain (encoding p.Ala657Thr, p.Thr660Lys, and p.Thr660Arg) and the M3-S2 linker domain (encoding p.Ile668Thr) had complex effects on functional properties and membrane localization of homomeric and heteromeric KARs. Both p.Thr660Lys and p.Thr660Arg mutant KARs exhibited markedly slowed gating kinetics, similar to p.Ala657Thr-containing receptors. Moreover, we observed emerging genotype-phenotype correlations, including the presence of severe epilepsy in individuals with the p.Thr660Lys variant and hypomyelination in individuals with either the p.Thr660Lys or p.Thr660Arg variant. Collectively, these results demonstrate that human GRIK2 variants predicted to alter channel function are causative for early childhood development disorders and further emphasize the importance of clarifying the role of KARs in early nervous system development.Genetics of disease, diagnosis and treatmen

Clustered mutations in the GRIK2 kainate receptor subunit gene underlie diverse neurodevelopmental disorders (vol 108, pg 1692, 2021)

Neurolog

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Thomson scattering diagnostic for the Microwave Tokamak Experiment

The Thomson-scattering diagnostic system (TSS) on the Microwave Tokamak Experiment (MTX) at LLNL routinely monitors electron temperature (T{sub e}) and density. Typical measured values at the plasma center under clean conditions are 900 {plus minus} 70 eV and 1 to 2 {times} 10{sup 14} ({plus minus}30%) cm{sup {minus}3}. The TSS apparatus is compact, with all elements mounted on one sturdy, two-level optics table. Because of this, we maintain with minimum effort the alignment of both the ruby-laser input optics and the scattered-light collecting optics. Undesired background signals, e.g., plasma light as well as ruby-laser light scattered off obstacles and walls, are generally small compared with the Thomson-scattered signals we normally detect. In the MTX T{sub e} region, the TSS data are definitely fitted better when relativistic effects are included in the equations. Besides determining the temperature of the Maxwellian electron distribution, the system is designed to detect electron heating from GW-level free-electron laser (FEL) pulses by measuring large wavelength shifts of the scattered laser photons. TSS data suggest that we may indeed by able to detect these electrons, which can have energies up to 10 keV, according to computer simulation. 7 refs., 4 figs