Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer's Disease Pathology

BACKGROUND: Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown. OBJECTIVE: We aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes. METHODS: We first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677). RESULTS: We identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T-N-, A+T+N-, A+T-N+, and A+T+N+) from no AD pathology (A-T-N-) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts. CONCLUSIONS: Our results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo

Inflammatory biomarkers in Alzheimer's disease plasma.

Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers. A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation

Inflammatory biomarkers in Alzheimer's disease plasma

Introduction: Plasma biomarkers for Alzheimer's disease (AD)diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers. Methods: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262)subjects from AddNeuroMed. Results: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1)that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1)that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH)plus age predicted MCI progression to AD (AUC: 0.71). Discussion: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation

Determining the molecular pathways underlying the protective effect of non-steroidal anti-inflammatory drugs for Alzheimer's disease: A bioinformatics approach

Alzheimer's disease (AD) represents a substantial unmet need, due to increasing prevalence in an ageing society and the absence of a disease modifying therapy. Epidemiological evidence shows a protective effect of non steroidal anti inflammatory (NSAID) drugs, and genome wide association studies (GWAS) show consistent linkage to inflammatory pathways; both observations suggesting anti-inflammatory compounds might be effective in AD therapy although clinical trials to date have not been positive. In this study, we use pathway enrichment and fuzzy logic to identify pathways (KEGG database) simultaneously affected in both AD and by NSAIDs (Sulindac, Piroxicam, Paracetamol, Naproxen, Nabumetone, Ketoprofen, Diclofenac and Aspirin). Gene expression signatures were derived for disease from both blood (n = 344) and post-mortem brain (n = 690), and for drugs from immortalised human cell lines exposed to drugs of interest as part of the Connectivity Map platform. Using this novel approach to combine datasets we find striking overlap between AD gene expression in blood and NSAID induced changes in KEGG pathways of Ribosome and Oxidative Phosphorylation. No overlap was found in non NSAID comparison drugs. In brain we find little such overlap, although Oxidative Phosphorylation approaches our pre-specified significance level. These findings suggest that NSAIDs might have a mode of action beyond inflammation and moreover that their therapeutic effects might be mediated in particular by alteration of Oxidative Phosphorylation and possibly the Ribosome pathway. Mining of such datasets might prove increasingly productive as they increase in size and richness

Effective connectivity of the subthalamic nucleus-globus pallidus network during Parkinsonian oscillations

In Parkinsonism, subthalamic nucleus (STN) neurons and two types of external globus pallidus (GP) neuron inappropriately synchronise their firing in time with slow (∼1 Hz) or beta (13-30 Hz) oscillations in cortex. We recorded the activities of STN, Type-I GP (GP-TI) and Type-A GP (GP-TA) neurons in anaesthetised Parkinsonian rats during such oscillations to constrain a series of computational models that systematically explored the effective connections and physiological parameters underlying neuronal rhythmic firing and phase preferences in vivo. The best candidate model, identified with a genetic algorithm optimising accuracy/complexity measures, faithfully reproduced experimental data and predicted that the effective connections of GP-TI and GP-TA neurons are quantitatively different. Estimated inhibitory connections from striatum were much stronger to GP-TI neurons than to GP-TA neurons, whereas excitatory connections from thalamus were much stronger to GP-TA and STN neurons than to GP-TI neurons. Reciprocal connections between GP-TI and STN neurons were matched in weight, but those between GP-TA and STN neurons were not; only GP-TI neurons sent substantial connections back to STN. Different connection weights between and within the two types of GP neuron were also evident. Adding to connection differences, GP-TA and GP-TI neurons were predicted to have disparate intrinsic physiological properties, reflected in distinct autonomous firing rates. Our results elucidate potential substrates of GP functional dichotomy, and emphasise that rhythmic inputs from striatum, thalamus and cortex are important for setting activity in the STN-GP network during Parkinsonian beta oscillations, suggesting they arise from interactions between most nodes of basal ganglia-thalamocortical circuits. © 2013 The Authors. The Journal of Physiology published by John Wiley and Sons Ltd on behalf of The Physiological Society

Effective connectivity of the subthalamic nucleus-globus pallidus network during Parkinsonian oscillations

In Parkinsonism, subthalamic nucleus (STN) neurons and two types of external globus pallidus (GP) neuron inappropriately synchronise their firing in time with slow (∼1 Hz) or beta (13-30 Hz) oscillations in cortex. We recorded the activities of STN, Type-I GP (GP-TI) and Type-A GP (GP-TA) neurons in anaesthetised Parkinsonian rats during such oscillations to constrain a series of computational models that systematically explored the effective connections and physiological parameters underlying neuronal rhythmic firing and phase preferences in vivo. The best candidate model, identified with a genetic algorithm optimising accuracy/complexity measures, faithfully reproduced experimental data and predicted that the effective connections of GP-TI and GP-TA neurons are quantitatively different. Estimated inhibitory connections from striatum were much stronger to GP-TI neurons than to GP-TA neurons, whereas excitatory connections from thalamus were much stronger to GP-TA and STN neurons than to GP-TI neurons. Reciprocal connections between GP-TI and STN neurons were matched in weight, but those between GP-TA and STN neurons were not; only GP-TI neurons sent substantial connections back to STN. Different connection weights between and within the two types of GP neuron were also evident. Adding to connection differences, GP-TA and GP-TI neurons were predicted to have disparate intrinsic physiological properties, reflected in distinct autonomous firing rates. Our results elucidate potential substrates of GP functional dichotomy, and emphasise that rhythmic inputs from striatum, thalamus and cortex are important for setting activity in the STN-GP network during Parkinsonian beta oscillations, suggesting they arise from interactions between most nodes of basal ganglia-thalamocortical circuits. © 2013 The Authors. The Journal of Physiology published by John Wiley and Sons Ltd on behalf of The Physiological Society

Effective connectivity of the subthalamic nucleus-globus pallidus network during Parkinsonian oscillations.

In Parkinsonism, subthalamic nucleus (STN) neurons and two types of external globus pallidus (GP) neuron inappropriately synchronise their firing in time with slow (∼1 Hz) or beta (13-30 Hz) oscillations in cortex. We recorded the activities of STN, Type-I GP (GP-TI) and Type-A GP (GP-TA) neurons in anaesthetised Parkinsonian rats during such oscillations to constrain a series of computational models that systematically explored the effective connections and physiological parameters underlying neuronal rhythmic firing and phase preferences in vivo. The best candidate model, identified with a genetic algorithm optimising accuracy/complexity measures, faithfully reproduced experimental data and predicted that the effective connections of GP-TI and GP-TA neurons are quantitatively different. Estimated inhibitory connections from striatum were much stronger to GP-TI neurons than to GP-TA neurons, whereas excitatory connections from thalamus were much stronger to GP-TA and STN neurons than to GP-TI neurons. Reciprocal connections between GP-TI and STN neurons were matched in weight, but those between GP-TA and STN neurons were not; only GP-TI neurons sent substantial connections back to STN. Different connection weights between and within the two types of GP neuron were also evident. Adding to connection differences, GP-TA and GP-TI neurons were predicted to have disparate intrinsic physiological properties, reflected in distinct autonomous firing rates. Our results elucidate potential substrates of GP functional dichotomy, and emphasise that rhythmic inputs from striatum, thalamus and cortex are important for setting activity in the STN-GP network during Parkinsonian beta oscillations, suggesting they arise from interactions between most nodes of basal ganglia-thalamocortical circuits