Molecular and spatial transcriptomic classification of midbrain dopamine neurons and their alterations in a LRRK2G2019S model of Parkinson's disease.

Several studies have revealed that midbrain dopamine (DA) neurons, even within a single neuroanatomical area, display heterogeneous properties. In parallel, studies using single cell profiling techniques have begun to cluster DA neurons into subtypes based on their molecular signatures. Recent work has shown that molecularly defined DA subtypes within the substantia nigra (SNc) display distinctive anatomic and functional properties, and differential vulnerability in Parkinson's disease (PD). Based on these provocative results, a granular understanding of these putative subtypes and their alterations in PD models, is imperative. We developed an optimized pipeline for single-nuclear RNA sequencing (snRNA-seq) and generated a high-resolution hierarchically organized map revealing 20 molecularly distinct DA neuron subtypes belonging to three main families. We integrated this data with spatial MERFISH technology to map, with high definition, the location of these subtypes in the mouse midbrain, revealing heterogeneity even within neuroanatomical sub-structures. Finally, we demonstrate that in the preclinical LRRK2G2019S knock-in mouse model of PD, subtype organization and proportions are preserved. Transcriptional alterations occur in many subtypes including those localized to the ventral tier SNc, where differential expression is observed in synaptic pathways, which might account for previously described DA release deficits in this model. Our work provides an advancement of current taxonomic schemes of the mouse midbrain DA neuron subtypes, a high-resolution view of their spatial locations, and their alterations in a prodromal mouse model of PD.

Autocatalytic-protection for an unknown locus CRISPR-Cas countermeasure for undesired mutagenic chain reactions.

The mutagenic chain reaction (MCR) is a genetic tool to use a CRISPR-Cas construct to introduce a homing endonuclease, allowing gene drive to influence whole populations in a minimal number of generations (Esvelt et al., 2014; Gantz and Bier, 2015; Gantz and Bier, 2016). The question arises: if an active genetic terror event is released into a population, could we prevent the total spread of the undesired allele (Gantz, et al., 2015; Webber et al., 2015)? Thus far, effective protection methods require knowledge of the terror locus (Grunwald et al., 2019). Here we introduce a novel approach, an autocatalytic-Protection for an Unknown Locus (a-PUL), whose aim is to spread through a population and arrest and decrease an active terror event's spread without any prior knowledge of the terror-modified locus, thus allowing later natural selection and ERACR drives to restore the normal locus (Hammond et al., 2017). a-PUL, using a mutagenic chain reaction, includes (i) a segment encoding a non-Cas9 endonuclease capable of homology-directed repair suggested as Type II endonuclease Cpf1 (Cas12a), (ii) a ubiquitously-expressed gene encoding a gRNA (gRNA1) with a U4AU4 3'-overhang specific to Cpf1 and with crRNA specific to some desired genomic sequence of non-coding DNA, (iii) a ubiquitously-expressed gene encoding two gRNAs (gRNA2/gRNA3) both with tracrRNA specific to Cas9 and crRNA specific to two distinct sites of the Cas9 locus, and (iv) homology arms flanking the Cpf1/gRNA1/gRNA2/gRNA3 cassette that are identical to the region surrounding the target cut directed by gRNA1 (Khan, 2016; Zetsche et al., 2015). We demonstrate the proof-of-concept and efficacy of our protection construct through a Graphical Markov model and computer simulation.

On the relation of gene essentiality to intron structure: a computational and deep learning approach.

Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3' end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.

Lateralized Deficits in Motor, Sensory, and Olfactory Domains in Dementia.

BACKGROUND: There exist functional deficits in motor, sensory, and olfactory abilities in dementias. Measures of these deficits have been discussed as potential clinical markers. OBJECTIVE: We measured the deficit of motor, sensory, and olfactory functions on both the left and right body side, to study potential body lateralizations. METHODS: This IRB-approved study (N = 84) performed left/right clinical tests of gross motor (dynamometer test), sensory (Von Frey test), and olfactory (peppermint oil test) ability. The Mini-Mental Status Exam was administered to determine level of dementia; medical and laboratory data were collected. RESULTS: Sensory and olfactory deficits lateralized to the left side of the body, while motor deficits lateralized to the right side. We found clinical correlates of motor lateralization: female, depression, MMSE <15, and diabetes. While clinical correlates of sensory lateralization: use of psychotherapeutic agent, age ≥85, MMSE <15, and male. Lastly, clinical correlates of olfactory lateralization: age <85, number of medications >10, and male. CONCLUSION: These lateralized deficits in body function can act as early clinical markers for improved diagnosis and treatment. Future research should identify correlates and corresponding therapies to strengthen at-risk areas.

Deep learning applied to polysomnography to predict blood pressure in obstructive sleep apnea and obesity hypoventilation: a proof-of-concept study.

STUDY OBJECTIVES: Nocturnal blood pressure (BP) profile shows characteristic abnormalities in OSA, namely acute postapnea BP surges and nondipping BP. These abnormal BP profiles provide prognostic clues indicating increased cardiovascular disease risk. We developed a deep neural network model to perform computerized analysis of polysomnography data and predict nocturnal BP profile. METHODS: We analyzed concurrently performed polysomnography and noninvasive beat-to-beat BP measurement with a deep neural network model to predict nocturnal BP profiles from polysomnography data in 13 patients with severe OSA. RESULTS: A good correlation was noted between measured and predicted postapnea systolic and diastolic BP (Pearson r ≥ .75). Moreover, Bland-Altman analyses showed good agreement between the 2 values. Continuous systolic and diastolic BP prediction by the deep neural network model was also well correlated with measured BP values (Pearson r ≥ .83). CONCLUSIONS: We developed a deep neural network model to predict nocturnal BP profile from clinical polysomnography signals and provide a potential prognostic tool in OSA. Validation of the model in larger samples and examination of its utility in predicting CVD risk in future studies is warranted.