David J. Stone

Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.

The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimers disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

Effect of a multiple-site intensive care unit telemedicine program on clinical and economic outcomes: An alternative paradigm for intensivist staffing*

ObjectiveTo examine whether a supplemental remote intensive care unit (ICU) care program, implemented by an integrated delivery network using a commercial telemedicine and information technology system, can improve clinical and economic performance across multiple ICUs. DesignBefore-and-after trial to assess the effect of adding the supplemental remote ICU telemedicine program. SettingTwo adult ICUs of a large tertiary care hospital. PatientsA total of 2,140 patients receiving ICU care between 1999 and 2001. InterventionsThe remote care program used intensivists and physician extenders to provide supplemental monitoring and management of ICU patients for 19 hrs/day (noon to 7 am) from a centralized, off-site facility (eICU). Supporting software, including electronic data display, physician note- and order-writing applications, and a computer-based decision-support tool, were available both in the ICU and at the remote site. Clinical and economic performance during 6 months of the remote intensivist program was compared with performance before the intervention. Measurements and Main ResultsHospital mortality for ICU patients was lower during the period of remote ICU care (9.4% vs. 12.9%; relative risk, 0.73; 95% confidence interval [CI], 0.55–0.95), and ICU length of stay was shorter (3.63 days [95% CI, 3.21–4.04] vs. 4.35 days [95% CI, 3.93–4.78]). Lower variable costs per case and higher hospital revenues (from increased case volumes) generated financial benefits in excess of program costs. ConclusionsThe addition of a supplemental, telemedicine-based, remote intensivist program was associated with improved clinical outcomes and hospital financial performance. The magnitude of the improvements was similar to those reported in studies examining the impact of implementing on-site dedicated intensivist staffing models; however, factors other than the introduction of off-site intensivist staffing may have contributed to the observed results, including the introduction of computer-based tools and the increased focus on ICU performance. Although further studies are needed, the apparent success of this on-going multiple-site program, implemented with commercially available equipment, suggests that telemedicine may provide a means for hospitals to achieve quality improvements associated with intensivist care using fewer intensivists.

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro

Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general “efficacy profiles” of biomarker gene expression that correlate with anti-depressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.

Transcriptional Architecture of the Primate Neocortex

Genome-wide transcriptional profiling was used to characterize the molecular underpinnings of neocortical organization in rhesus macaque, including cortical areal specialization and laminar cell-type diversity. Microarray analysis of individual cortical layers across sensorimotor and association cortices identified robust and specific molecular signatures for individual cortical layers and areas, prominently involving genes associated with specialized neuronal function. Overall, transcriptome-based relationships were related to spatial proximity, being strongest between neighboring cortical areas and between proximal layers. Primary visual cortex (V1) displayed the most distinctive gene expression compared to other cortical regions in rhesus and human, both in the specialized layer 4 as well as other layers. Laminar patterns were more similar between macaque and human compared to mouse, as was the unique V1 profile that was not observed in mouse. These data provide a unique resource detailing neocortical transcription patterns in a nonhuman primate with great similarity in gene expression to human.

Inhibition of Calcineurin-mediated Endocytosis and α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid (AMPA) Receptors Prevents Amyloid β Oligomer-induced Synaptic Disruption

Synaptic degeneration, including impairment of synaptic plasticity and loss of synapses, is an important feature of Alzheimer disease pathogenesis. Increasing evidence suggests that these degenerative synaptic changes are associated with an accumulation of soluble oligomeric assemblies of amyloid β (Aβ) known as ADDLs. In primary hippocampal cultures ADDLs bind to a subpopulation of neurons. However the molecular basis of this cell type-selective interaction is not understood. Here, using siRNA screening technology, we identified α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunits and calcineurin as candidate genes potentially involved in ADDL-neuron interactions. Immunocolocalization experiments confirmed that ADDL binding occurs in dendritic spines that express surface AMPA receptors, particularly the calcium-impermeable type II AMPA receptor subunit (GluR2). Pharmacological removal of the surface AMPA receptors or inhibition of AMPA receptors with antagonists reduces ADDL binding. Furthermore, using co-immunoprecipitation and photoreactive amino acid cross-linking, we found that ADDLs interact preferentially with GluR2-containing complexes. We demonstrate that calcineurin mediates an endocytotic process that is responsible for the rapid internalization of bound ADDLs along with surface AMPA receptor subunits, which then both colocalize with cpg2, a molecule localized specifically at the postsynaptic endocytic zone of excitatory synapses that plays an important role in activity-dependent glutamate receptor endocytosis. Both AMPA receptor and calcineurin inhibitors prevent oligomer-induced surface AMPAR and spine loss. These results support a model of disease pathogenesis in which Aβ oligomers interact selectively with neurotransmission pathways at excitatory synapses, resulting in synaptic loss via facilitated endocytosis. Validation of this model in human disease would identify therapeutic targets for Alzheimer disease.

Endothelium-dependent Effects of Halothane, Enflurane, and Isoflurane on Isolated Rat Aortic Vascular Rings

To determine the endothelium-dependent vascular effects of halothane, enflurane, and isoflurane, isometric tension was recorded in isolated ring preparations of rat thoracic aorta suspended in Krebs buffer and aerated with 95% O2/5% CO2. One set of the rings had intact endothelium and the other set of the rings had the endothelium mechanically denuded. The rings were precontracted with phenylephrine (1 x 10(-6) M). Halothane, enflurane, or isoflurane gas was bubbled through the baths at increasing concentrations (0.5-5.0%) in preparations with and without indomethacin (28 microM). Endothelium-intact rings demonstrated significant (P less than 0.05) vasoconstriction at low concentrations of both isoflurane and enflurane followed by vasodilation at higher concentrations. Halothane also induced vasoconstriction in some rings, but its mean effect was not significantly different from control. After discontinuation of the anesthetic gas, endothelium-intact rings demonstrated a rebound vasoconstriction above previous maximal levels for all three anesthetics. When indomethacin was added to the baths, the vasoconstriction was potentiated and was statistically significant for all three anesthetics. These results suggest that at low concentrations, enflurane and isoflurane cause vasoconstriction through inhibition of basal EDRF production and/or stimulation of the release of an endothelium-derived constricting factor. At higher anesthetic concentrations, a direct vasodilating effect of the anesthetic predominates. The potentiation of vasoconstriction in the presence of indomethacin suggests that these volatile anesthetics stimulate the release of a vasodilating prostanoid from endothelium.

LRRTM3 promotes processing of amyloid-precursor protein by BACE1 and is a positional candidate gene for late-onset Alzheimer's disease

Rare familial forms of Alzheimers disease (AD) are thought to be caused by elevated proteolytic production of the Aβ42 peptide from the β-amyloid-precursor protein (APP). Although the pathogenesis of the more common late-onset AD (LOAD) is not understood, BACE1, the protease that cleaves APP to generate the N terminus of Aβ42, is more active in patients with LOAD, suggesting that increased amyloid production processing might also contribute to the sporadic disease. Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Aβ42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Aβ40, Aβ42, and sAPPβ, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Aβ secretion. LRRTM3 is expressed nearly exclusively in the nervous system, including regions affected during AD, such as the dentate gyrus. Furthermore, LRRTM3 maps to a region of chromosome 10 linked to both LOAD and elevated plasma Aβ42, and is structurally similar to a family of neuronal receptors that includes the NOGO receptor, an inhibitor of neuronal regeneration and APP processing. Thus, LRRTM3 is a functional and positional candidate gene for AD, and, given its receptor-like structure and restricted expression, a potential therapeutic target.

“Big Data” in the Intensive Care Unit. Closing the Data Loop

The data generated in the process of medical care has historically not just been underused, it has been wasted. This was due in part to the difficulty of accessing, organizing, and using data entered on paper charts, but notable variability in clinical documentation methods and quality made the problem even more challenging. In the absence of a practical way to systematically capture, analyze, and integrate the information contained in the massive amount of data generated during patient care, medicine has remained a highly empirical process in which the disconnected application of individual experiences and subjective preferences continues to thwart continuous improvement and consistent delivery of best practices to all patients.

A genome wide analysis of ubiquitin ligases in APP processing identifies a novel regulator of BACE1 mRNA levels

Proteolysis of beta-amyloid precursor protein (APP) into amyloid beta peptide (Abeta) by beta- and gamma-secretases is a critical step in the pathogenesis of Alzheimers Disease (AD), but the pathways regulating secretases are not fully characterized. Ubiquitinylation, which is dysregulated in AD, may affect APP processing. Here, we describe a screen for APP processing modulators using an siRNA library targeting 532 predicted ubiquitin ligases. Seven siRNA pools diminished Abeta production. Of these, siRNAs targeting PPIL2 (hCyp-60) suppressed beta-site cleavage. Knockdown of PPIL2 mRNA decreased BACE1 mRNA, while overexpression of PPIL2 cDNA enhanced BACE1 mRNA levels. Microarray analysis of PPIL2 or BACE1 knockdown indicated that genes affected by BACE1 knockdown are a subset of those dependent upon PPIL2; suggesting that BACE1 expression is downstream of PPIL2. The association of PPIL2 with BACE expression and its requirement for Abeta production suggests new approaches to discover disease modifying agents for AD.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

BACKGROUND Accurate diagnosis and early detection of complex diseases, such as Parkinsons disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinsons disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. METHODS We developed a model for disease classification using data from the Parkinsons Progression Marker Initiative (PPMI) study for 367 patients with Parkinsons disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinsons disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinsons disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinsons Disease Biomarkers Program (PDBP), the Parkinsons Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinsons Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). FINDINGS In the population from PPMI, our initial model correctly distinguished patients with Parkinsons disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinsons disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinsons disease converted to Parkinsons disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinsons disease underwent conversion (test of proportions, p=0·003). INTERPRETATION Our model provides a potential new approach to distinguish participants with Parkinsons disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinsons disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. FUNDING National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.