Enzymatic properties and clinical associations of serum alpha-galactosidase A in Parkinson's disease.

OBJECTIVE: Recent studies have revealed an association between Parkinson's disease (PD) and Fabry disease, a lysosomal storage disorder; however, the underlying mechanisms remain to be elucidated. This study aimed to investigate the enzymatic properties of serum alpha-galactosidase A (GLA) and compared them with the clinical parameters of PD. METHODS: The study participants consisted of 66 sporadic PD patients and 52 controls. We measured serum GLA activity and calculated the apparent Michaelis constant (Km ) and maximal velocity (Vmax ) by Lineweaver-Burk plot analysis. Serum GLA protein concentration was measured by enzyme-linked immunosorbent assay. We examined the potential correlations between serum GLA activity and GLA protein concentration and clinical features and the plasma neurofilament light chain (NfL) level. RESULTS: Compared to controls, PD patients showed significantly lower serum GLA activity (P < 0.0001) and apparent Vmax (P = 0.0131), but no change in the apparent Km value. Serum GLA protein concentration was lower in the PD group (P = 0.0168) and was positively associated with GLA activity. Serum GLA activity and GLA protein concentration in the PD group showed a negative correlation with age. Additionally, serum GLA activity was negatively correlated with the motor severity score and the level of plasma NfL, and was positively correlated with the score of frontal assessment battery. INTERPRETATION: This study highlights that the lower serum GLA activity in PD is the result of a quantitative decrement of GLA protein in the serum and that it may serve as a biomarker of disease severity.

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

Background: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge. Methods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms. Results: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit. Conclusion: We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

KANPHOS: A Database of Kinase-Associated Neural Protein Phosphorylation in the Brain.

Protein phosphorylation plays critical roles in a variety of intracellular signaling pathways and physiological functions that are controlled by neurotransmitters and neuromodulators in the brain. Dysregulation of these signaling pathways has been implicated in neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder and schizophrenia. While recent advances in mass spectrometry-based proteomics have allowed us to identify approximately 280,000 phosphorylation sites, it remains largely unknown which sites are phosphorylated by which kinases. To overcome this issue, previously, we developed methods for comprehensive screening of the target substrates of given kinases, such as PKA and Rho-kinase, upon stimulation by extracellular signals and identified many candidate substrates for specific kinases and their phosphorylation sites. Here, we developed a novel online database to provide information about the phosphorylation signals identified by our methods, as well as those previously reported in the literature. The "KANPHOS" (Kinase-Associated Neural Phospho-Signaling) database and its web portal were built based on a next-generation XooNIps neuroinformatics tool. To explore the functionality of the KANPHOS database, we obtained phosphoproteomics data for adenosine-A2A-receptor signaling and its downstream MAPK-mediated signaling in the striatum/nucleus accumbens, registered them in KANPHOS, and analyzed the related pathways.

Prediction of daily happiness using supervised learning of multimodal lifelog data / Predição da felicidade diária usando aprendizado supervisionado de registro de dados multimodais de vida / Predicción de la felicidad diaria mediante el aprendizaje supervisado de datos lifelog multimodales

Developing an approach to predict happiness based on individual conditions and actions could enable us to select daily behaviors for enhancing well-being in life. Therefore, we propose a novel approach of applying machine learning, a branch of the field of artificial intelligence, to a variety of information concerning people's lives (i.e., a lifelog). We asked a participant (a healthy young man) to record 55 lifelog items (e.g., positive mood, negative events, sleep time etc.) in his daily life for about eight months using smartphone apps and a smartwatch. We then constructed a predictor to estimate the degree of happiness from the multimodal lifelog data using a support vector machine, which achieved 82.6% prediction accuracy. This suggests that our approach can predict the behaviors that increase individuals' happiness in their daily lives, thereby contributing to improvement in their happiness. Future studies examining the usability and clinical applicability of this approach would benefit from a larger and more diverse sample size.

Desenvolver uma abordagem para prever a felicidade com base em condições e ações individuais pode nos permitir selecionar comportamentos diários para melhorar o bem-estar na vida. Portanto, propomos uma nova abordagem de aplicação da aprendizagem de máquina, um ramo do campo da inteligência artificial, para uma variedade de informações sobre a vida das pessoas (ou seja, um lifelog). Pedimos a um participante (um jovem saudável) que registrasse 55 itens de vida útil (por exemplo, humor positivo, eventos negativos, tempo de sono etc.) em sua vida diária por cerca de oito meses usando aplicativos de smartphones e um relógio inteligente. Em seguida, construímos um preditor para estimar o grau de felicidade dos dados de vida multimodal usando uma máquina de vetores de suporte, que atingiu 82,6% de precisão de previsão. Isso sugere que nossa abordagem pode prever os comportamentos que aumentam a felicidade dos indivíduos em suas vidas diárias, contribuindo para uma melhoria em sua felicidade. Estudos futuros examinando a usabilidade e a aplicabilidade clínica dessa abordagem se beneficiariam de um tamanho de amostra maior e mais diversificado.

El desarrollar un enfoque para predecir la felicidad, basado en las condiciones y acciones individuales, nos permitiría seleccionar comportamientos habituales para mejorar el bienestar en la vida. Por lo tanto, proponemos un novedoso enfoque de aplicación del aprendizaje automático, una rama del campo de la Inteligencia Artificial, a una variedad de información de la vida de las personas (es decir, un lifelog). Se le pidió a un participante (un sujeto joven sano) que registrara 55 elementos de lifelog (por ejemplo, humor positivo, eventos negativos, tiempo de sueño etc.) en su vida diaria, durante aproximadamente ocho meses, usando aplicaciones de teléfonos inteligentes, y un reloj inteligente. Posteriormente, construimos un predictor para estimar el grado de felicidad, a partir de los datos lifelog multimodales, utilizando un equipo de vectores de soporte, que logró una precisión de predicción del 82.6%. Estos datos sugieren que nuestro enfoque, puede predecir los comportamientos que incrementan la felicidad de las personas en su vida diaria, contribuyendo así, a una mejora en su felicidad. Los futuros estudios que examinen la usabilidad, y la aplicabilidad clínica de este enfoque, se beneficiarían al analizar un tamaño de muestra más grande, y más diversa.

Balance between dopamine and adenosine signals regulates the PKA/Rap1 pathway in striatal medium spiny neurons.

Medium spiny neurons (MSNs) expressing dopamine D1 receptor (D1R) or D2 receptor (D2R) are major components of the striatum. Stimulation of D1R activates protein kinase A (PKA) through Golf to increase neuronal activity, while D2R stimulation inhibits PKA through Gi. Adenosine A2A receptor (A2AR) coupled to Golf is highly expressed in D2R-MSNs within the striatum. However, how dopamine and adenosine co-operatively regulate PKA activity remains largely unknown. Here, we measured Rap1gap serine 563 phosphorylation to monitor PKA activity and examined dopamine and adenosine signals in MSNs. We found that a D1R agonist increased Rap1gap phosphorylation in striatal slices and in D1R-MSNs in vivo. A2AR agonist CGS21680 increased Rap1gap phosphorylation, and pretreatment with the D2R agonist quinpirole blocked this effect in striatal slices. D2R antagonist eticlopride increased Rap1gap phosphorylation in D2R-MSNs in vivo, and the effect of eticlopride was blocked by the pretreatment with the A2AR antagonist SCH58261. These results suggest that adenosine positively regulates PKA in D2R-MSNs through A2AR, while this effect is blocked by basal dopamine in vivo. Incorporating computational model analysis, we propose that the shift from D1R-MSNs to D2R-MSNs or vice versa appears to depend predominantly on a change in dopamine concentration.

Phosphoproteomics of the Dopamine Pathway Enables Discovery of Rap1 Activation as a Reward Signal In Vivo.

Dopamine (DA) type 1 receptor (D1R) signaling in the striatum presumably regulates neuronal excitability and reward-related behaviors through PKA. However, whether and how D1Rs and PKA regulate neuronal excitability and behavior remain largely unknown. Here, we developed a phosphoproteomic analysis method to identify known and novel PKA substrates downstream of the D1R and obtained more than 100 candidate substrates, including Rap1 GEF (Rasgrp2). We found that PKA phosphorylation of Rasgrp2 activated its guanine nucleotide-exchange activity on Rap1. Cocaine exposure activated Rap1 in the nucleus accumbens in mice. The expression of constitutively active PKA or Rap1 in accumbal D1R-expressing medium spiny neurons (D1R-MSNs) enhanced neuronal firing rates and behavioral responses to cocaine exposure through MAPK. Knockout of Rap1 in the accumbal D1R-MSNs was sufficient to decrease these phenotypes. These findings demonstrate a novel DA-PKA-Rap1-MAPK intracellular signaling mechanism in D1R-MSNs that increases neuronal excitability to enhance reward-related behaviors.

A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity.

Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra. While cortical stimulation alone results in long-term depression (LTD), the combination with dopamine switches LTD to long-term potentiation (LTP), which is known as dopamine-dependent plasticity. LTP is also induced by cortical stimulation in magnesium-free solution, which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity. Signaling cascades in the corticostriatal spines are currently under investigation. However, because of the existence of multiple excitatory and inhibitory pathways with loops, the mechanisms regulating the two types of plasticity remain poorly understood. A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. Calcium input modulated this loop through the PP2B (phosphatase 2B)-CK1 (casein kinase 1)-Cdk5 (cyclin-dependent kinase 5)-Thr75 pathway and PP2A, whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP (cAMP). The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters. Increased basal dopamine levels disrupted this dopamine-dependent plasticity. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction.