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Objective: To develop comprehensive recommendations for the treatment of the various clinical manifestations of psoriatic arthritis (PsA) based on evidence obtained from a systematic review of the literature and from consensus opinion. Methods: Formal literature reviews of treatment for the most significant discrete clinical manifestations of PsA (skin and nails, peripheral arthritis, axial disease, dactylitis and enthesitis) were performed and published by members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). Treatment recommendations were drafted for each of the clinical manifestations by rheumatologists, dermatologists and PsA patients based on the literature reviews and consensus opinion. The level of agreement for the individual treatment recommendations among GRAPPA members was assessed with an online questionnaire. Results: Treatment recommendations were developed for peripheral arthritis, axial disease, psoriasis, nail disease, dactylitis and enthesitis in the setting of PsA. In rotal, 19 recommendations were drafted, and over 80% agreement was obtained on 16 of them. In addition, a grid that factors disease severity into each of the different disease manifestations was developed to help the clinician with treatment decisions for the individual patient from an evidenced-based perspective. Conclusions: Treatment recommendations for the cardinal physical manifestations of PsA were developed based on a literature review and consensus between rheumatologists and dermatologists. In addition, a grid was established to assist in therapeutic reasoning and decision making for individual patients. It is anticipated that periodic updates will take place using this framework as new data become available.
Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations
(2019)
The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.
A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented, and used with simulations from the HadCM3 and FAMOUS climate models to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Global modelled (BIOME4) biome distributions over time generally agree well with those inferred from pollen data. The two climate models show good agreement in global net primary productivity (NPP). NPP is strongly influenced by atmospheric carbon dioxide (CO2) concentrations through CO2 fertilization. The combined effects of modelled changes in vegetation and (via a simple model) soil carbon result in a global terrestrial carbon storage at the Last Glacial Maximum that is 210–470 Pg C less than in pre-industrial time. Without the contribution from exposed glacial continental shelves the reduction would be larger, 330–960 Pg C. Other intervals of low terrestrial carbon storage include stadial intervals at 108 and 85 kaBP, and between 60 and 65 kaBP during Marine Isotope Stage 4. Terrestrial carbon storage, determined by the balance of global NPP and decomposition, influences the stable carbon isotope composition (δ 13C) of seawater because terrestrial organic carbon is depleted in 13C. Using a simple carbon-isotope mass balance equation we find agreement in trends between modelled ocean δ 13C based on modelled land carbon storage, and palaeo-archives of ocean δ 13C, confirming that terrestrial carbon storage variations may be important drivers of ocean δ 13 C changes.
A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented and used with simulations from the HadCM3 and FAMOUS climate models and the BIOME4 vegetation model to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Simulated biome distributions using BIOME4 driven by HadCM3 and FAMOUS at the global scale over time generally agree well with those inferred from pollen data. Global average areas of grassland and dry shrubland, desert, and tundra biomes show large-scale increases during the Last Glacial Maximum, between ca. 64 and 74 ka BP and cool substages of Marine Isotope Stage 5, at the expense of the tropical forest, warm-temperate forest, and temperate forest biomes. These changes are reflected in BIOME4 simulations of global net primary productivity, showing good agreement between the two models. Such changes are likely to affect terrestrial carbon storage, which in turn influences the stable carbon isotopic composition of seawater as terrestrial carbon is depleted in 13C.
Biallelic pathogenic variants in CLPP, encoding mitochondrial matrix peptidase ClpP, cause a rare autosomal recessive condition, Perrault syndrome type 3 (PRLTS3). It is characterized by primary ovarian insufficiency and early sensorineural hearing loss, often associated with progressive neurological deficits. Mouse models showed that accumulations of (i) its main protein interactor, the substrate-selecting AAA+ ATPase ClpX, (ii) mitoribosomes, and (iii) mtDNA nucleoids are the main cellular consequences of ClpP absence. However, the sequence of these events and their validity in human remain unclear. Here, we studied global proteome profiles to define ClpP substrates among mitochondrial ClpX interactors, which accumulated consistently in ClpP-null mouse embryonal fibroblasts and brains. Validation work included novel ClpP-mutant patient fibroblast proteomics. ClpX co-accumulated in mitochondria with the nucleoid component POLDIP2, the mitochondrial poly(A) mRNA granule element LRPPRC, and tRNA processing factor GFM1 (in mouse, also GRSF1). Only in mouse did accumulated ClpX, GFM1, and GRSF1 appear in nuclear fractions. Mitoribosomal accumulation was minor. Consistent accumulations in murine and human fibroblasts also affected multimerizing factors not known as ClpX interactors, namely, OAT, ASS1, ACADVL, STOM, PRDX3, PC, MUT, ALDH2, PMPCB, UQCRC2, and ACADSB, but the impact on downstream metabolites was marginal. Our data demonstrate the primary impact of ClpXP on the assembly of proteins with nucleic acids and show nucleoid enlargement in human as a key consequence.
Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data.
While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.
Virus-infected cells are eliminated by cytotoxic T lymphocytes, which recognize viral epitopes displayed on major histocompatibility complex class I molecules at the cell surface. Herpesviruses have evolved sophisticated strategies to escape this immune surveillance. During the lytic phase of EBV infection, the viral factor BNLF2a interferes with antigen processing by preventing peptide loading of major histocompatibility complex class I molecules. Here we reveal details of the inhibition mechanism of this EBV protein. We demonstrate that BNLF2a acts as a tail-anchored protein, exploiting the mammalian Asna-1/WRB (Get3/Get1) machinery for posttranslational insertion into the endoplasmic reticulum membrane, where it subsequently blocks antigen translocation by the transporter associated with antigen processing (TAP). BNLF2a binds directly to the core TAP complex arresting the ATP-binding cassette transporter in a transport-incompetent conformation. The inhibition mechanism of EBV BNLF2a is distinct and mutually exclusive of other viral TAP inhibitors.
The Kinase Chemogenomic Set (KCGS): an open science resource for kinase vulnerability identification
(2021)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification
(2019)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.