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Abstract Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Abstract Background Brain-derived neurotrophic factor (BDNF) is a major signaling molecule that the brain uses to control a vast network of intracellular cascades fundamental to properties of learning and memory, and cognition. While much is known about BDNF signaling in the healthy nervous system where it controls the mitogen activated protein kinase (MAPK) and cyclic-AMP pathways, less is known about its role in multiple brain disorders where it contributes to the dysregulated neuroplasticity seen in epilepsy and traumatic brain injury (TBI). We previously found that neurons respond to prolonged BDNF exposure (both in vivo (in models of epilepsy and TBI) and in vitro (in BDNF treated primary neuronal cultures)) by activating the Janus Kinase/Signal Transducer and Activator of Transcription (JAK/STAT) signaling pathway. This pathway is best known for its association with inflammatory cytokines in non-neuronal cells. Results Here, using deep RNA-sequencing of neurons exposed to BDNF in the presence and absence of well characterized JAK/STAT inhibitors, and without non-neuronal cells, we determine the BDNF transcriptome that is specifically regulated by agents that inhibit JAK/STAT signaling. Surprisingly, the BDNF-induced JAK/STAT transcriptome contains ion channels and neurotransmitter receptors coming from all the major classes expressed in the brain, along with key modulators of synaptic plasticity, neurogenesis, and axonal remodeling. Analysis of this dataset has revealed a unique non-canonical mechanism of JAK/STATs in neurons as differential gene expression mediated by STAT3 is not solely dependent upon phosphorylation at residue 705 and may involve a BDNF-induced interaction of STAT3 with Heterochromatin Protein 1 alpha (HP1α). Conclusions These findings suggest that the neuronal BDNF-induced JAK/STAT pathway involves more than STAT3 phosphorylation at 705, providing the first evidence for a non-canonical mechanism that may involve HP1α. Our analysis reveals that JAK/STAT signaling regulates many of the genes associated with epilepsy syndromes where BDNF levels are markedly elevated. Uncovering the mechanism of this novel form of BDNF signaling in the brain may provide a new direction for epilepsy therapeutics and open a window into the complex mechanisms of STAT3 transcriptional regulation in neurological disease.

While the exact role of β1 subunit-containing GABA-A receptors (GABARs) in brain function is not well understood, altered expression of the β1 subunit gene (GABRB1) is associated with neurological and neuropsychiatric disorders. In particular, down-regulation of β1 subunit levels is observed in brains of patients with epilepsy, autism, bipolar disorder and schizophrenia. A pathophysiological feature of these disease states is imbalance in energy metabolism and mitochondrial dysfunction. The transcription factor, nuclear respiratory factor 1 (NRF-1), has been shown to be a key mediator of genes involved in oxidative phosphorylation and mitochondrial biogenesis. Using a variety of molecular approaches (including mobility shift, promoter/reporter assays, and overexpression of dominant negative NRF-1), we now report that NRF-1 regulates transcription of GABRB1 and that its core promoter contains a conserved canonical NRF-1 element responsible for sequence specific binding and transcriptional activation. Our identification of GABRB1 as a new target for NRF-1 in neurons suggests that genes coding for inhibitory neurotransmission may be coupled to cellular metabolism. This is especially meaningful as binding of NRF-1 to its element is sensitive to the kind of epigenetic changes that occur in multiple disorders associated with altered brain inhibition.