Secreted frizzled related proteins modulate pathfinding and fasciculation of mouse retina ganglion cell axons by direct and indirect mechanisms

Retina ganglion cell (RGC) axons grow along a stereotyped pathway undergoing coordinated rounds of fasciculation and defasciculation, which are critical to establishing proper eye– brain connections. How this coordination is achieved is poorly understood, but shedding of guidance cues by metalloproteinases is emerging as a relevant mechanism. Secreted Frizzled Related Proteins (Sfrps) are multifunctional proteins, which, among others, reorient RGC growth cones by regulating intracellular second messengers, and interact with Tolloid and ADAM metalloproteinases, thereby repressing their activity. Here, we show that the combination of these two functions well explain the axon guidance phenotype observed in Sfrp1 and Sfrp2 single and compound mouse mutant embryos, in whichRGCaxons make subtle but significant mistakes during their intraretinal growth and inappropriately defasciculate along their pathway. The distribution of Sfrp1 and Sfrp2 in the eye is consistent with the idea that Sfrp1/2 normally constrain axon growth into the fiber layer and the optic disc. Disheveled axon growth instead seems linked to Sfrp-mediated modulation of metalloproteinase activity. Indeed, retinal explants from embryos with different Sfrp-null alleles or explants overexpressing ADAM10 extend axons with a disheveled appearance, which is reverted by the addition of Sfrp1 or an ADAM10-specific inhibitor. This mode of growth is associated with an abnormal proteolytic processing of L1 and N-cadherin, two ADAM10 substrates previously implicated in axon guidance.Wethus propose that Sfrps contribute to coordinate visual axon growth with a dual mechanism: by directly signaling at the growth cone and by regulating the processing of other relevant cuesThis work was supported by the Spanish MINECO (Grants BFU2010-16031 and BFU2013-43213-P), Comunidad Autónoma de Madrid (Grant S2010/BMD-2315) Cost Action BM1001 Brain ECM in Health and Disease, an institutional grant from the Fundación Ramón Areces and Centro de Investigación Biomédica en Red de Enfermedades Raras (P.B.). F.N.-L. and M.J.C. were supported by a FPU and FPI fellowship from the Spanish Government, respectively. We thank F. Murakami, L. Erskine, A. Chedotal, A. Ludwig, and V.P. Lemmon for reagent

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. METHODS: Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R2, Akaike's information criterion, sensitivity, specificity, C-statistic and calibration. RESULTS: Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05. CONCLUSIONS: Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account

Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study

BACKGROUND: Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia. METHODS: Obesity (body mass index ≥ 30 kg/m2) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression. RESULTS: Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results. CONCLUSIONS: Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70 years

Change in lipids before onset of dementia, coronary heart disease, and mortality: A 28-year follow-up Whitehall II prospective cohort study

INTRODUCTION: The association of lipids with dementia remains a subject of debate. Using data from 7,672 participants of the Whitehall II prospective cohort study, we examined whether timing of exposure, length of follow-up, or sex modifies this association. METHODS: Twelve markers of lipid levels were measured from fasting blood and eight among them a further five times. We performed time-to-event as well as trajectory analyses. RESULTS: No associations were observed in men; in women most lipids were associated with the risk of dementia, but only for events occurring after the first 20 years of follow-up. Differences in lipid trajectories in men emerged only in the years immediately before diagnosis whereas in women total cholesterol (TC), LDL-cholesterol (LDL-C), non-HDL-cholesterol (non-HDL-C), TC/HDL-C, and LDL-C/HDL-C were higher in midlife among dementia cases before declining progressively. DISCUSSION: Abnormal lipid levels in midlife seem to be associated with a higher risk of dementia in women

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Tubulin Tyrosination Is Required for the Proper Organization and Pathfinding of the Growth Cone

International audienceBACKGROUND: During development, neuronal growth cones integrate diffusible and contact guidance cues that are conveyed to both actin and microtubule (MT) cytoskeletons and ensure axon outgrowth and pathfinding. Although several post-translational modifications of tubulin have been identified and despite their strong conservation among species, their physiological roles during development, especially in the nervous sytem, are still poorly understood. METHODOLOGY/FINDINGS: Here, we have dissected the role of a post-translational modification of the last amino acid of the alpha-tubulin on axonal growth by analyzing the phenotype of precerebellar neurons in Tubulin tyrosin ligase knock-out mice (TTL(-/-)) through in vivo, ex vivo and in vitro analyses. TTL(-/-) neurons are devoid of tyrosinated tubulin. Their pathway shows defects in vivo, ex vivo, in hindbrains open-book preparations or in vitro, in a collagen matrix. Their axons still orient toward tropic cues, but they emit supernumerary branches and their growth cones are enlarged and exhibit an emission of mis-oriented filopodia. Further analysis of the TTL(-/-) growth cone intracellular organization also reveals that the respective localization of actin and MT filaments is disturbed, with a decrease in the distal accumulation of Myosin IIB, as well as a concomitant Rac1 over-activation in the hindbrain. Pharmacological inhibition of Rac1 over-activation in TTL(-/-) neurons can rescue Myosin IIB localization. CONCLUSIONS/SIGNIFICANCE: In the growth cone, we propose that tubulin tyrosination takes part in the relative arrangement of actin and MT cytoskeletons, in the regulation of small GTPases activity, and consequently, in the proper morphogenesis, organization and pathfinding of the growth cone during development

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe

Solving unsolved rare neurological diseases-a Solve-RD viewpoint.

Funder: Durch Princess Beatrix Muscle Fund Durch Speeren voor Spieren Muscle FundFunder: University of Tübingen Medical Faculty PATE programFunder: European Reference Network for Rare Neurological Diseases | 739510Funder: European Joint Program on Rare Diseases (EJP-RD COFUND-EJP) | 44140962

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques