Identification of a five gene signature to predict time to biochemical recurrence after radical prostatectomy.

BACKGROUND: Identification of novel biomarkers associated with high-risk prostate cancer or biochemical recurrence can drive improvement in detection, prognosis, and treatment. However, studies can be limited by small sample sizes and sparse clinical follow-up data. We utilized a large sample of prostate specimens to identify a predictive model of biochemical recurrence following radical prostatectomy and we validated this model in two external data sets. METHODS: We analyzed prostate specimens from patients undergoing radical prostatectomy at Hartford Hospital between 2008 and 2011. RNA isolated from formalin-fixed paraffin-embedded prostates was hybridized to a custom Affymetrix microarray. Regularized (least absolute shrinkage and selection operator [Lasso]) Cox regression was performed with cross-validation to identify a model that incorporated gene expression and clinical factors to predict biochemical recurrence, defined as postoperative prostate-specific antigen (PSA) > 0.2 ng/ml or receipt of triggered salvage treatment. Model performance was assessed using time-dependent receiver operating curve (ROC) curves and survival plots. RESULTS: A total of 606 prostate specimens with gene expression and both pre- and postoperative PSA data were available for analysis. We identified a model that included Gleason grade and stage as well as five genes (CNRIP1, endoplasmic reticulum protein 44 [ERP44], metaxin-2 [MTX2], Ras homolog family member U [RHOU], and OXR1). Using the Lasso method, we determined that the five gene model independently predicted biochemical recurrence better than a model that included Gleason grade and tumor stage alone. The time-dependent ROCAUC for the five gene signature including Gleason grade and tumor stage was 0.868 compared to an AUC of 0.767 when Gleason grade and tumor stage were included alone. Low and high-risk groups displayed significant differences in their recurrence-free survival curves. The predictive model was subsequently validated on two independent data sets identified through the Gene Expression Omnibus. The model included genes (RHOU, MTX2, and ERP44) that have previously been implicated in prostate cancer biology. CONCLUSIONS: Expression of a small number of genes is associated with an increased risk of biochemical recurrence independent of classical pathological hallmarks.

Neuronal calcineurin transcriptional targets parallel changes observed in Alzheimer disease brain.

Synaptic dysfunction and loss are core pathological features in Alzheimer disease (AD). In the vicinity of amyloid-ß plaques in animal models, synaptic toxicity occurs and is associated with chronic activation of the phosphatase calcineurin (CN). Indeed, pharmacological inhibition of CN blocks amyloid-ß synaptotoxicity. We therefore hypothesized that CN-mediated transcriptional changes may contribute to AD neuropathology and tested this by examining the impact of CN over-expression on neuronal gene expression in vivo. We found dramatic transcriptional down-regulation, especially of synaptic mRNAs, in neurons chronically exposed to CN activation. Importantly, the transcriptional profile parallels the changes in human AD tissue. Bioinformatics analyses suggest that both nuclear factor of activated T cells and numerous microRNAs may all be impacted by CN, and parallel findings are observed in AD. These data and analyses support the hypothesis that at least part of the synaptic failure characterizing AD may result from aberrant CN activation leading to down-regulation of synaptic genes, potentially via activation of specific transcription factors and expression of repressive microRNAs. OPEN PRACTICES: Open Science: This manuscript was awarded with the Open Materials Badge. For more information see: https://cos.io/our-services/open-science-badges/ Read the Editorial Highlight for this article on page 8.

Prediction of Incident Delirium Using a Random Forest classifier.

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.

Statin and Its Association With Delirium in the Medical ICU.

OBJECTIVES: To examine the association between statin use and the risk of delirium in hospitalized patients with an admission to the medical ICU. DESIGN: Retrospective propensity-matched cohort analysis with accrual from September 1, 2012, to September 30, 2015. SETTING: Hartford Hospital, Hartford, CT. PATIENTS: An initial population of patients with an admission to a medical ICU totaling 10,216 visits were screened for delirium by means of the Confusion Assessment Method. After exclusions, a population of 6,664 was used to match statin users and nonstatin users. The propensity-matched cohort resulted in a sample of 1,475 patients receiving statin matched 1:1 with control patients not using statin. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Delirium defined as a positive Confusion Assessment Method assessment was the primary end point. The prevalence of delirium was 22.3% in the unmatched cohort and 22.8% in the propensity-matched cohort. Statin use was associated with a significant decrease in the risk of delirium (odds ratio, 0.47; 95% CI, 0.38-0.56). Considering the type of statin used, atorvastatin (0.51; 0.41-0.64), pravastatin (0.40; 0.28-0.58), and simvastatin (0.33; 0.21-0.52) were all significantly associated with a reduced frequency of delirium. CONCLUSIONS: The use of statins was independently associated with a reduction in the risk of delirium in hospitalized patients. When considering types of statins used, this reduction was significant in patients using atorvastatin, pravastatin, and simvastatin. Randomized trials of various statin types in hospitalized patients prone to delirium should validate their use in protection from delirium.

The NMDA receptor co-agonists, D-serine and glycine, regulate neuronal dendritic architecture in the somatosensory cortex.

There is substantial evidence, both pharmacological and genetic, that hypofunction of the N-methyl-d-aspartate receptor (NMDAR) is a core pathophysiological feature of schizophrenia. There are morphological brain changes associated with schizophrenia, including perturbations in the dendritic morphology of cortical pyramidal neurons and reduction in cortical volume. Our experiments investigated whether these changes in dendritic morphology could be recapitulated in a genetic model of NMDAR hypofunction, the serine racemase knockout (SR-/-) mouse. Pyramidal neurons in primary somatosensory cortex (S1) of SR-/- mice had reductions in the complexity, total length, and spine density of apical and basal dendrites. In accordance with reduced cortical neuropil, SR-/- mice also had reduced cortical volume as compared to wild type mice. Analysis of S1 mRNA by DNA microarray and gene expression analysis revealed gene changes in SR-/- that are associated with psychiatric and neurologic disorders, as well as neurodevelopment. The microarray analysis also identified reduced expression of brain derived neurotrophic factor (BDNF) in SR-/- mice. Follow-up analysis by ELISA confirmed a reduction of BDNF protein levels in the S1 of SR-/- mice. Finally, S1 pyramidal neurons in glycine transporter heterozygote (GlyT1+/-) mutants, which display enhanced NMDAR function, had increased dendritic spine density. These results suggest that proper NMDAR function is important for the arborization and spine density of pyramidal neurons in cortex. Moreover, they suggest that NMDAR hypofunction might, in part, be contributing to the dendritic and synaptic changes observed in schizophrenia and highlight this signaling pathway as a potential target for therapeutic intervention.

Phospholipidosis as a function of basicity, lipophilicity, and volume of distribution of compounds.

Drug-induced phospholipidosis (PLD) is an adaptive histologic alteration that is seen with various marketed drugs and often encountered during drug development. Various in silico and in vitro cell-based methods have been developed to predict the PLD-inducing potential of compounds. These methods rely on the inherent physicochemical properties of the molecule and, as such, tend to overpredict compounds as PLD inducers. Recognizing that the distribution of compounds into tissues or tissue accumulation is likely a key factor in PLD induction, in addition to key physicochemical properties, we developed a model to predict PLD in vivo using the measures of basicity (pK(a)), lipophilicity (ClogP), and volume of distribution (V(d)). Using sets of PLD inducers and noninducers, we demonstrate improved concordance with this method. Furthermore, we propose a screening paradigm that includes a combination of various methods to predict the in vivo PLD-inducing potential of compounds, which may be especially useful in lead identification and optimization processes in drug discovery.

Delirium is an important predictor of mortality in elderly patients with ST-elevation myocardial infarction: insight from National Inpatient Sample database.

OBJECTIVE: Delirium is a frequently encountered clinical condition in hospitalized patients and is known to be associated with poor outcomes. This study aims to assess the impacts of delirium in elderly patients undergoing percutaneous coronary intervention (PCI) following ST-elevation myocardial infarction (STEMI). METHODS: We queried the National Inpatient Samples from 2010 to 2014 to identify all patients aged 65 and older, and admitted with a primary diagnosis of STEMI undergoing PCI by using the International Classification of Diseases-Ninth Edition-Clinical Modification diagnosis codes. The patients with delirium from this cohort were further evaluated. Multivariate regression model with SPSS Statistics 25.0 (IBM Corp., Armonk, New York, USA) was used to study the association between delirium and clinical outcomes including in-hospital mortality and length of stay (LOS). RESULTS: Out of weighted 42 980 patients aged ≥65 years with STEMI and PCI, delirium was present in 774 patients, accounting for 1.8% of this cohort. These patients were found to be older and had more underlying co-morbidities, compared to those without delirium [Median Charlson score 2 (1; 3) vs. 0 (0; 2); P < 0.001]. In-hospital mortality in STEMI patients with delirium was significantly higher than those without delirium [42.7% vs. 7.6%; unadjusted odds ratio (OR) 9.07; 95% confidence interval (CI) 6.55-12.57; P < 0.001; adjusted OR 1.86; 95% CI 1.13-3.04; P = 0.014]. CONCLUSION: Older age and comorbidities are known predisposing factors for delirium, which is in turn associated with higher in-hospital mortality and increased LOS in elderly patients with STEMI who undergo PCI. This study underscores the role of delirium and implicates the importance of further studies in recognition and targeted care of delirium.

Transcriptome Alterations in Prefrontal Pyramidal Cells Distinguish Schizophrenia From Bipolar and Major Depressive Disorders.

BACKGROUND: Impairments in certain cognitive processes (e.g., working memory) are typically most pronounced in schizophrenia (SZ), intermediate in bipolar disorder, and least in major depressive disorder. Given that working memory depends, in part, on neural circuitry that includes pyramidal cells in layer 3 (L3) and layer 5 (L5) of the dorsolateral prefrontal cortex (DLPFC), we sought to determine if transcriptome alterations in these neurons were shared or distinctive for each diagnosis. METHODS: Pools of L3 and L5 pyramidal cells in the DLPFC were individually captured by laser microdissection from 19 matched tetrads of unaffected comparison subjects and subjects with SZ, bipolar disorder, and major depressive disorder, and the messenger RNA was subjected to transcriptome profiling by microarray. RESULTS: In DLPFC L3 and L5 pyramidal cells, transcriptome alterations were numerous in subjects with SZ, but rare in subjects with bipolar disorder and major depressive disorder. The leading molecular pathways altered in subjects with SZ involved mitochondrial energy production and the regulation of protein translation. In addition, we did not find any significant transcriptome signatures related to psychosis or suicide. CONCLUSIONS: In concert, these findings suggest that molecular alterations in DLPFC L3 and L5 pyramidal cells might be characteristic of the disease processes operative in individuals diagnosed with SZ and thus might contribute to the circuitry alterations underlying cognitive dysfunction in individuals with SZ.

Analysis of multi-dimensional contemporaneous EHR data to refine delirium assessments.

Delirium is a potentially lethal condition of altered mental status, attention, and level of consciousness with an acute onset and fluctuating course. Its causes are multi-factorial, and its pathophysiology is not well understood; therefore clinical focus has been on prevention strategies and early detection. One patient evaluation technique in routine use is the Confusion Assessment Method (CAM): a relatively simple test resulting in 'positive', 'negative' or 'unable-to-assess' (UTA) ratings. Hartford Hospital nursing staff use the CAM regularly on all non-critical care units, and a high frequency of UTA was observed after reviewing several years of records. In addition, patients with UTA ratings displayed poor outcomes such as in-hospital mortality, longer lengths of stay, and discharge to acute and long term care facilities. We sought to better understand the use of UTA, especially outside of critical care environments, in order to improve delirium detection throughout the hospital. An unsupervised clustering approach was used with additional, concurrent assessment data available in the EHR to categorize patient visits with UTA CAMs. The results yielded insights into the most common situations in which the UTA rating was used (e.g. impaired verbal communication, dementia), suggesting potentially inappropriate ratings that could be refined with further evaluation and remedied with updated clinical training. Analysis of the patient clusters also suggested that unrecognized delirium may contribute to the poor outcomes associated with the use of UTA. This method of using temporally related high dimensional EHR data to illuminate a dynamic medical condition could have wider applicability.

Altered expression of CDC42 signaling pathway components in cortical layer 3 pyramidal cells in schizophrenia.

BACKGROUND: Cognitive dysfunction in schizophrenia is associated with a lower density of dendritic spines on deep layer 3 pyramidal cells in the dorsolateral prefrontal cortex (DLPFC). These alterations appear to reflect dysregulation of the actin cytoskeleton required for spine formation and maintenance. Consistent with this idea, altered expression of genes in the cell division cycle 42 (CDC42)-CDC42 effector protein (CDC42EP) signaling pathway, a key organizer of the actin cytoskeleton, was previously reported in DLPFC gray matter from subjects with schizophrenia. We examined the integrity of the CDC42-p21-activated serine/threonine protein kinases (PAK)-LIM domain-containing serine/threonine protein kinases (LIMK) signaling pathway in schizophrenia in a layer-specific and cell type-specific fashion in DLPFC deep layer 3. METHODS: Using laser microdissection, samples of DLPFC deep layer 3 were collected from 56 matched pairs of subjects with schizophrenia and comparison subjects, and levels of CDC42-PAK-LIMK pathway messenger RNAs were measured by quantitative polymerase chain reaction. These same transcripts also were quantified by microarray in samples of individually microdissected deep layer 3 pyramidal cells from a subset of the same subjects and from monkeys exposed to antipsychotics. RESULTS: Relative to comparison subjects, CDC42EP4, LIMK1, LIMK2, ARHGDIA, and PAK3 messenger RNA levels were significantly upregulated in subjects with schizophrenia in laminar and cellular samples. In contrast, CDC42 and PAK1 messenger RNA levels were significantly downregulated specifically in deep layer 3 pyramidal cells. These differences were not attributable to psychotropic medications or other comorbid factors. CONCLUSIONS: Findings from the present and prior studies converge on synergistic alterations in CDC42 signaling pathway that could destabilize actin dynamics and produce spine deficits preferentially in deep layer 3 pyramidal cells in schizophrenia.

Lower gene expression for KCNS3 potassium channel subunit in parvalbumin-containing neurons in the prefrontal cortex in schizophrenia.

OBJECTIVE: In schizophrenia, alterations in markers of cortical GABA neurotransmission are prominent in parvalbumin-containing neurons. Parvalbumin neurons selectively express KCNS3, the gene encoding the Kv9.3 potassium channel α-subunit. Kv9.3 subunits are present in voltage-gated potassium channels that contribute to the precise detection of coincident excitatory synaptic inputs to parvalbumin neurons. This distinctive feature of parvalbumin neurons appears important for the synchronization of cortical neural networks in γ-oscillations. Because impaired prefrontal cortical γ-oscillations are thought to underlie the cognitive impairments in schizophrenia, the authors investigated whether KCNS3 mRNA levels are altered in the prefrontal cortex of schizophrenia subjects. METHOD: KCNS3 mRNA expression was evaluated by in situ hybridization in 22 matched pairs of schizophrenia and comparison subjects and by microarray analyses of pooled samples of individually dissected neurons that were labeled with Vicia villosa agglutinin (VVA), a parvalbumin neuron-selective marker, in a separate cohort of 14 pairs. Effects of chronic antipsychotic treatments on KCNS3 expression were tested in the prefrontal cortex of antipsychotic-exposed monkeys. RESULTS: By in situ hybridization, KCNS3 mRNA levels were 23% lower in schizophrenia subjects. At the cellular level, both KCNS3 mRNA-expressing neuron density and KCNS3 mRNA level per neuron were significantly lower. By microarray, KCNS3 mRNA levels were lower by 40% in VVA-labeled neurons from schizophrenia subjects. KCNS3 mRNA levels were not altered in antipsychotic-exposed monkeys. CONCLUSIONS: These findings reveal lower KCNS3 expression in prefrontal cortical parvalbumin neurons in schizophrenia, providing a molecular basis for compromised detection of coincident synaptic inputs to parvalbumin neurons that could contribute to altered γ-oscillations and impaired cognition in schizophrenia.

Isolation of a novel rat neural progenitor clone that expresses Dlx family transcription factors and gives rise to functional GABAergic neurons in culture.

Gamma-aminobutyric acid (GABA) ergic interneurons are lost in conditions including epilepsy and central nervous system injury, but there are few culture models available to study their function. Toward the goal of obtaining renewable sources of GABAergic neurons, we used the molecular profile of a functionally incomplete GABAergic precursor clone to screen 17 new clones isolated from GFP(+) rat E14.5 cortex and ganglionic eminence (GE) that were generated by viral introduction of v-myc. The clones grow as neurospheres in medium with FGF2, and after withdrawal of FGF2, they exhibit varying patterns of differentiation. Transcriptional profiling and quantitative reverse transcriptase polymerase chain reaction (RT-PCR) indicated that one clone (GE6) expresses high levels of mRNAs encoding Dlx1, 2, 5, and 6, glutamate decarboxylases, and presynaptic proteins including neuropeptide Y and somatostatin. Protein expression confirmed that GE6 is a progenitor with restricted differentiation giving rise mostly to neurons with GABAergic markers. In cocultures with hippocampal neurons, GE6 neurons became electrically excitable and received both inhibitory and excitatory synapses. After withdrawal of FGF2 in cultures of GE6 alone, neurons matured to express ßIII-tubulin, and staining for synaptophysin and vesicular GABA transporter were robust after 1-2 weeks of differentiation. GE6 neurons also became electrically excitable and displayed synaptic activity, but synaptic currents were carried by chloride and were blocked by bicuculline. The results suggest that the GE6 clone, which is ventrally derived from the GE, resembles GABAergic interneuron progenitors that migrate into the developing forebrain. This is the first report of a relatively stable fetal clone that can be differentiated into GABAergic interneurons with functional synapses.

The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data.

We have developed an ontology to provide standardized nomenclature for anatomical terms in the postnatal mouse. The Adult Mouse Anatomical Dictionary is structured as a directed acyclic graph, and is organized hierarchically both spatially and functionally. The ontology will be used to annotate and integrate different types of data pertinent to anatomy, such as gene expression patterns and phenotype information, which will contribute to an integrated description of biological phenomena in the mouse.