A comprehensive review of the genetics of juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is the most common chronic arthropathy of childhood which is believed to be influenced by both genetic and environmental factors. The progress in identifying genes underlying JIA susceptibility using candidate gene association studies has been slow. Several associations between JIA and variants in the genes encoding the human leukocyte antigens (HLA) have been confirmed and replicated in independent cohorts. However it is clear that genetic variants outside the HLA also influence susceptibility to JIA. While a large number of non-HLA candidate genes have been tested for associations, only a handful of reported associations such as PTPN22 have been validated. In this review we discuss the principles behind genetic studies of complex traits like JIA, and comprehensively catalogue non-HLA candidate-gene association studies performed in JIA to date and review several validated associations. Most candidate gene studies are underpowered and do not detect associations, and those that do are often not replicated. We also discuss the principles behind genome-wide association studies and discuss possible implications for identifying genes underlying JIA. Finally we discuss several genetic variants underlying multiple clinically distinct autoimmune phenotypes

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice

Honeycomb Core Permeability Under Mechanical Loads

A method for characterizing the air permeability of sandwich core materials as a function of applied shear stress was developed. The core material for the test specimens was either Hexcel HRP-3/16-8.0 and or DuPont Korex-1/8-4.5 and was nominally one-half inch thick and six inches square. The facesheets where made of Hercules' AS4/8552 graphite/epoxy (Gr/Ep) composites and were nominally 0.059-in. thick. Cytec's Metalbond 1515-3M epoxy film adhesive was used for co-curing the facesheets to the core. The permeability of the specimens during both static (tension) and dynamic (reversed and non-reversed) shear loads were measured. The permeability was measured as the rate of air flow through the core from a circular 1-in2 area of the core exposed to an air pressure of 10.0 psig. In both the static and dynamic testing, the Korex core experienced sudden increases in core permeability corresponding to a core catastrophic failure, while the URP core experienced a gradual increase in the permeability prior to core failure. The Korex core failed at lower loads than the HRP core both in the transverse and ribbon directions

Neurospora crassa transcriptomics reveals oxidative stress and plasma membrane homeostasis biology genes as key targets in response to chitosan

Chitosan is a natural polymer with antimicrobial activity. Chitosan causes plasma membrane permeabilization and induction of intracellular reactive oxygen species (ROS) in Neurospora crassa. We have determined the transcriptional profile of N. crassa to chitosan and identified the main gene targets involved in the cellular response to this compound. Global network analyses showed membrane, transport and oxidoreductase activity as key nodes affected by chitosan. Activation of oxidative metabolism indicates the importance of ROS and cell energy together with plasma membrane homeostasis in N. crassa response to chitosan. Deletion strain analysis of chitosan susceptibility pointed NCU03639 encoding a class 3 lipase, involved in plasma membrane repair by lipid replacement, and NCU04537 a MFS monosaccharide transporter related to assimilation of simple sugars, as main gene targets of chitosan. NCU10521, a glutathione S-transferase-4 involved in the generation of reducing power for scavenging intracellular ROS is also a determinant chitosan gene target. Ca2+ increased tolerance to chitosan in N. crassa. Growth of NCU10610 (fig 1 domain) and SYT1 (a synaptotagmin) deletion strains was significantly increased by Ca2+ in the presence of chitosan. Both genes play a determinant role in N. crassa membrane homeostasis. Our results are of paramount importance for developing chitosan as an antifungal.This work was supported by the National Institutes of Health (USA) grant GM060468 to NLG and Spanish Ministry of Economy and Competitiveness Grant AGL 2011-29297/AGR to LVLL

The regulatory and transcriptional landscape associated with carbon utilization in a filamentous fungus.

Filamentous fungi, such as Neurospora crassa, are very efficient in deconstructing plant biomass by the secretion of an arsenal of plant cell wall-degrading enzymes, by remodeling metabolism to accommodate production of secreted enzymes, and by enabling transport and intracellular utilization of plant biomass components. Although a number of enzymes and transcriptional regulators involved in plant biomass utilization have been identified, how filamentous fungi sense and integrate nutritional information encoded in the plant cell wall into a regulatory hierarchy for optimal utilization of complex carbon sources is not understood. Here, we performed transcriptional profiling of N. crassa on 40 different carbon sources, including plant biomass, to provide data on how fungi sense simple to complex carbohydrates. From these data, we identified regulatory factors in N. crassa and characterized one (PDR-2) associated with pectin utilization and one with pectin/hemicellulose utilization (ARA-1). Using in vitro DNA affinity purification sequencing (DAP-seq), we identified direct targets of transcription factors involved in regulating genes encoding plant cell wall-degrading enzymes. In particular, our data clarified the role of the transcription factor VIB-1 in the regulation of genes encoding plant cell wall-degrading enzymes and nutrient scavenging and revealed a major role of the carbon catabolite repressor CRE-1 in regulating the expression of major facilitator transporter genes. These data contribute to a more complete understanding of cross talk between transcription factors and their target genes, which are involved in regulating nutrient sensing and plant biomass utilization on a global level

Solution generating with perfect fluids

We apply a technique, due to Stephani, for generating solutions of the Einstein-perfect fluid equations. This technique is similar to the vacuum solution generating techniques of Ehlers, Harrison, Geroch and others. We start with a ``seed'' solution of the Einstein-perfect fluid equations with a Killing vector. The seed solution must either have (i) a spacelike Killing vector and equation of state P=rho or (ii) a timelike Killing vector and equation of state rho+3P=0. The new solution generated by this technique then has the same Killing vector and the same equation of state. We choose several simple seed solutions with these equations of state and where the Killing vector has no twist. The new solutions are twisting versions of the seed solutions

Association of the 5-aminoimidazole-4-carboxamide ribonucleotide transformylase gene with response to methotrexate in juvenile idiopathic arthritis

OBJECTIVES: Methotrexate (MTX) is the mainstay treatment for juvenile idiopathic arthritis (JIA), however approximately 30% of children will fail to respond to the drug. Identification of genetic predictors of response to MTX would be invaluable in developing optimal treatment strategies for JIA. Using a candidate gene approach, single nucleotide polymorphisms (SNPs) within genes in the metabolic pathway of MTX, were investigated for association with response to treatment in JIA cases. METHODS: Tagging SNPs were selected across 13 MTX metabolic pathway genes and were genotyped using Sequenom genotyping technology in subjects recruited from the Sparks Childhood Arthritis Response to Medication Study. Response to MTX was defined using the American College of Rheumatology (ACR) paediatric response criteria and SNP genotype frequencies were compared between the worst and best responders (ACR-Ped70) to MTX. An independent cohort of US JIA cases was available for validation of initial findings. RESULTS: One SNP within the inosine triphosphate pyrophosphatase gene (ITPA) and two SNPs within 5-aminoimidazole-4-carboxamide ribonucleotide transformylase gene (ATIC) were significantly associated with a poor response to MTX. One of the ATIC SNPs showed a trend towards association with MTX response in an independent cohort of US JIA cases. Meta-analysis of the two studies strengthened this association (combined p value=0.002). CONCLUSIONS: This study presents association of a SNP in the ATIC gene with response to MTX in JIA. There is now growing evidence to support a role of the ATIC gene with response to MTX treatment. These results could contribute towards a better understanding of and ability to predict MTX response in JIA