Repeated mild traumatic brain injury triggers pathology in asymptomatic C9ORF72 transgenic mice.

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are fatal neurodegenerative diseases that represent ends of the spectrum of a single disease. The most common genetic cause of FTD and ALS is a hexanucleotide repeat expansion in the C9orf72 gene. Although epidemiological data suggest that traumatic brain injury (TBI) represents a risk factor for FTD and ALS, its role in exacerbating disease onset and course remains unclear. To explore the interplay between traumatic brain injury and genetic risk in the induction of FTD/ALS pathology we combined a mild repetitive traumatic brain injury paradigm with an established bacterial artificial chromosome transgenic C9orf72 (C9BAC) mouse model without an overt motor phenotype or neurodegeneration. We assessed 8-10 week-old littermate C9BACtg/tg (n = 21), C9BACtg/- (n = 20) and non-transgenic (n = 21) mice of both sexes for the presence of behavioural deficits and cerebral histopathology at 12 months after repetitive TBI. Repetitive TBI did not affect body weight gain, general neurological deficit severity, nor survival over the 12-month observation period and there was no difference in rotarod performance, object recognition, social interaction and acoustic characteristics of ultrasonic vocalizations of C9BAC mice subjected to repetitive TBI versus sham injury. However, we found that repetitive TBI increased the time to the return of the righting reflex, reduced grip force, altered sociability behaviours and attenuated ultrasonic call emissions during social interactions in C9BAC mice. Strikingly, we found that repetitive TBI caused widespread microglial activation and reduced neuronal density that was associated with loss of histological markers of axonal and synaptic integrity as well as profound neuronal transactive response DNA binding protein 43 kDa mislocalization in the cerebral cortex of C9BAC mice at 12 months; this was not observed in non-transgenic repetitive TBI and C9BAC sham mice. Our data indicate that repetitive TBI can be an environmental risk factor that is sufficient to trigger FTD/ALS-associated neuropathology and behavioural deficits, but not paralysis, in mice carrying a C9orf72 hexanucleotide repeat expansion.

Positive maternal affect during mother-litter interaction is reduced in new mother rats exhibiting a depression-like phenotype.

The experience of positive affect during new motherhood is considered essential for a healthy mother-infant relationship, with life-long consequences for both mother and child. Affective availability and contingent responsiveness are often compromised in mothers experiencing postpartum depression, yet how maternal affect impacts parenting is not fully understood. In this study, we used the Wistar-Kyoto (WKY) rat model of depression and ultrasonic vocalizations to examine the relationship between maternal affect and parenting. We examined the affective and behavioral response of WKY and control new mother rats during social interactions with their offspring. Our results show that WKY mothers displayed altered USV signaling accompanying substantial disturbances in their maternal caregiving. In addition, WKY mothers failed to adjust vocal frequency in coordination with offspring proximity and interaction compared to control mothers. A follow up experiment demonstrated that the administration of the adenosine A2A receptor antagonist MSX-3 ameliorated both maternal behavioral deficits and low positive affect in WKY mothers. Together, our results highlight the importance of maternal positive affect in the dyad relationship and suggest a role for the striatopallidal pathway in the affective processing of parenting.

Plasma biomarkers for diagnosis of Alzheimer's disease and prediction of cognitive decline in individuals with mild cognitive impairment.

Background: The last few years have seen major advances in blood biomarkers for Alzheimer's Disease (AD) with the development of ultrasensitive immunoassays, promising to transform how we diagnose, prognose, and track progression of neurodegenerative dementias. Methods: We evaluated a panel of four novel ultrasensitive electrochemiluminescence (ECL) immunoassays against presumed CNS derived proteins of interest in AD in plasma [phosphorylated-Tau181 (pTau181), total Tau (tTau), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)]. Two sets of banked plasma samples from the Massachusetts Alzheimer's Disease Research Center's longitudinal cohort study were examined: A longitudinal prognostic sample (n = 85) consisting of individuals with mild cognitive impairment (MCI) and 4 years of follow-up and a cross-sectional sample (n = 238) consisting of individuals with AD, other neurodegenerative diseases (OND), and normal cognition (CN). Results: Participants with MCI who progressed to dementia due to probable AD during follow-up had higher baseline plasma concentrations of pTau181, NfL, and GFAP compared to non-progressors. The best prognostic discrimination was observed with pTau181 (AUC = 0.83, 1.7-fold increase) and GFAP (AUC = 0.83, 1.6-fold increase). Participants with autopsy- and/or biomarker verified AD had higher plasma levels of pTau181, tTau and GFAP compared to CN and OND, while NfL was elevated in AD and further increased in OND. The best diagnostic discrimination was observed with pTau181 (AD vs CN: AUC = 0.90, 2-fold increase; AD vs. OND: AUC = 0.84, 1.5-fold increase) but tTau, NfL, and GFAP also showed good discrimination between AD and CN (AUC = 0.81-0.85; 1.5-2.2 fold increase). Conclusions: These new ultrasensitive ECL plasma assays for pTau181, tTau, NfL, and GFAP demonstrated diagnostic utility for detection of AD. Moreover, the absolute baseline plasma levels of pTau181 and GFAP reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.

Health phenome of Parkinson's patients reveals prominent mood-sleep cluster.

Background: Associations between phenotypic traits, environmental exposures, and Parkinson's disease have largely been evaluated one-by-one, piecemeal, and pre-selections. A comprehensive picture of comorbidities, phenotypes, exposures, and polypharmacy characterizing the complexity and heterogeneity of real-world patients presenting to academic movement disorders clinics in the US is missing. Objectives: To portrait the complexity of features associated with patients with Parkinson's disease in a study of 933 cases and 291 controls enrolled in the Harvard Biomarkers Study. Methods: The primary analysis evaluated 64 health features for associations with Parkinson's using logistic regression adjusting for age and sex. We adjusted for multiple testing using the false discovery rate (FDR) with £ 0.05 indicating statistical significance. Exploratory analyses examined feature correlation clusters and feature combinations. Results: Depression (OR = 3.11, 95% CI 2.1 to 4.71), anxiety (OR = 3.31, 95% CI 2.01-5.75), sleep apnea (OR 2.58, 95% CI 1.47-4.92), and restless leg syndrome (RLS; OR 4.12, 95% CI 1.81-12.1) were significantly more common in patients with Parkinson's than in controls adjusting for age and sex with FDR £ 0.05. The prevalence of depression, anxiety, sleep apnea, and RLS were correlated, and these diseases formed part of a larger cluster of mood traits and sleep traits linked to PD. Exposures to pesticides (OR 1.87, 95% CI 1.37-2.6), head trauma (OR 2.33, 95% CI 1.51-3.73), and smoking (OR 0.57, 95% CI 0.43-0.75) were significantly associated with the disease consistent with previous studies. Vitamin supplementation with cholecalciferol (OR 2.18, 95% CI 1.4-3.45) and coenzyme Q10 (OR 2.98, 95% CI 1.89-4.92) was more commonly used by patients than controls. Cumulatively, 43% (398 of 933) of Parkinson's patients had at least one psychiatric or sleep disorder, compared to 21% (60 of 291) of healthy controls. Conclusions: 43% of Parkinson's patients seen at Harvard-affiliated teaching hospitals have depression, anxiety, and disordered sleep. This syndromic cluster of mood and sleep traits may be pathophysiologically linked and clinically important.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson's disease.

Parkinson's disease (PD) is a growing burden worldwide, and there is no reliable biomarker used in clinical routines to date. Cerebrospinal fluid (CSF) is routinely collected in patients with neurological symptoms and should closely reflect alterations in PD patients' brains. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomics workflow for CSF proteome profiling. From two independent cohorts with over 200 individuals, our workflow reproducibly quantifies over 1,700 proteins from minimal CSF amounts. Machine learning determines OMD, CD44, VGF, PRL, and MAN2B1 to be altered in PD patients or to significantly correlate with clinical scores. We also uncover signatures of enhanced neuroinflammation in LRRK2 G2019S carriers, as indicated by increased levels of CTSS, PLD4, and HLA proteins. A comparison with our previously acquired urinary proteomes reveals a large overlap in PD-associated changes, including lysosomal proteins, opening up new avenues to improve our understanding of PD pathogenesis.

Glucosylceramide in cerebrospinal fluid of patients with GBA-associated and idiopathic Parkinson's disease enrolled in PPMI.

Protein-coding variants in the GBA gene modulate susceptibility and progression in ~10% of patients with Parkinson's disease (PD). GBA encodes the ß-glucocerebrosidase enzyme that hydrolyzes glucosylceramide. We hypothesized that GBA mutations will lead to glucosylceramide accumulation in cerebrospinal fluid (CSF). Glucosylceramide, ceramide, sphingomyelin, and lactosylceramide levels were measured by liquid chromatography-tandem mass spectrometry in CSF of 411 participants from the Parkinson's Progression Markers Initiative (PPMI) cohort, including early stage, de novo PD patients with abnormal dopamine transporter neuroimaging and healthy controls. Forty-four PD patients carried protein-coding GBA variants (GBA-PD) and 227 carried wild-type alleles (idiopathic PD). The glucosylceramide fraction was increased (P = 0.0001), and the sphingomyelin fraction (a downstream metabolite) was reduced (P = 0.0001) in CSF of GBA-PD patients compared to healthy controls. The ceramide fraction was unchanged, and lactosylceramide was below detection limits. We then used the ratio of glucosylceramide to sphingomyelin (the GlcCer/SM ratio) to explore whether these two sphingolipid fractions altered in GBA-PD were useful for stratifying idiopathic PD patients. Idiopathic PD patients in the top quartile of GlcCer/SM ratios at baseline showed a more rapid decline in Montreal Cognitive Assessment scores during longitudinal follow-up compared to those in the lowest quartile with a P-value of 0.036. The GlcCer/SM ratio was negatively associated with α-synuclein levels in CSF of PD patients. This study highlights glucosylceramide as a pathway biomarker for GBA-PD patients and the GlcCer/SM ratio as a potential stratification tool for clinical trials of idiopathic PD patients. Our sphingolipids data together with the clinical, imaging, omics, and genetic characterization of PPMI will contribute a useful resource for multi-modal biomarkers development.

Integrating multi-network topology for gene function prediction using deep neural networks.

MOTIVATION: The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods do not consider the shared information among different networks during the feature learning process. RESULTS: Taking the correlation among the networks into account, we design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human datasets and compare with three state-of-the-art methods. The results demonstrate the superior performance of our method. We not only provide a comprehensive analysis of the performance of the newly proposed algorithm but also provide a tool for extracting features of genes based on multiple networks, which can be used in the downstream machine learning task. AVAILABILITY: DeepMNE-CNN is freely available at https://github.com/xuehansheng/DeepMNE-CNN. CONTACT: jiajiepeng@nwpu.edu.cn; shang@nwpu.edu.cn; jianye.hao@tju.edu.cn.

ß-Glucocerebrosidase activity in GBA-linked Parkinson disease: The type of mutation matters.

OBJECTIVE: To test the relationship between clinically relevant types of GBA mutations (none, risk variants, mild mutations, severe mutations) and ß-glucocerebrosidase activity in patients with Parkinson disease (PD) in cross-sectional and longitudinal case-control studies. METHODS: A total of 481 participants from the Harvard Biomarkers Study (HBS) and the NIH Parkinson's Disease Biomarkers Program (PDBP) were analyzed, including 47 patients with PD carrying GBA variants (GBA-PD), 247 without a GBA variant (idiopathic PD), and 187 healthy controls. Longitudinal analysis comprised 195 participants with 548 longitudinal measurements over a median follow-up period of 2.0 years (interquartile range, 1-2 years). RESULTS: ß-Glucocerebrosidase activity was low in blood of patients with GBA-PD compared to healthy controls and patients with idiopathic PD, respectively, in HBS (p < 0.001) and PDBP (p < 0.05) in multivariate analyses adjusting for age, sex, blood storage time, and batch. Enzyme activity in patients with idiopathic PD was unchanged. Innovative enzymatic quantitative trait locus (xQTL) analysis revealed a negative linear association between residual ß-glucocerebrosidase activity and mutation type with p < 0.0001. For each increment in the severity of mutation type, a reduction of mean ß-glucocerebrosidase activity by 0.85 µmol/L/h (95% confidence interval, -1.17, -0.54) was predicted. In a first longitudinal analysis, increasing mutation severity types were prospectively associated with steeper declines in ß-glucocerebrosidase activity during a median 2 years of follow-up (p = 0.02). CONCLUSIONS: Residual activity of the ß-glucocerebrosidase enzyme measured in blood inversely correlates with clinical severity types of GBA mutations in PD. ß-Glucocerebrosidase activity is a quantitative endophenotype that can be monitored noninvasively and targeted therapeutically.