Biology subjects

Hoffman, G. E.

Publications and source records attributed to Hoffman, G. E..

6 recordsLinked to original sources

bioRxiv2018

Biology and Bias in Cell Type-Specific RNAseq of Nucleus Accumbens Medium Spiny Neurons

Isolation of cell populations is untangling complex biological interactions, but studies comparing methodologies lack in vivo complexity and draw limited conclusions about the types of transcripts identified by each technique. Furthermore, few studies compare FACS-based techniques to ribosomal affinity purification, and none do so genome-wide. We addressed this gap by systematically comparing nuclear-FACS, whole cell-FACS, and RiboTag affinity purification in the context of D1 or D2 dopamine receptor-expressing medium spiny neuron (MSN) subtypes of the nucleus accumbens (NAc), a key brain reward region. We find that nuclear-FACS-seq generates a substantially longer list of differentially expressed genes between these cell types, and a significantly larger number of neuropsychiatric GWAS hits than the other two methods. RiboTag-seq has much lower coverage of the transcriptome than the other methods, but very efficiently distinguishes D1- and D2-MSNs. We also demonstrate differences between D1- and D2-MSNs with respect to RNA localization, suggesting fundamental cell type differences in mechanisms of transcriptional regulation and subcellular transport of RNAs. Together, these findings guide the field in selecting the RNAseq method that best suits the scientific questions under investigation.

neuroscience↗

bioRxiv2018

dream: Powerful differential expression analysis for repeated measures designs

Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false positive findings. Dream is available within the variancePartition Bioconductor package (http://bioconductor.org/packages/variancePartition).

genomics↗

bioRxiv2018

A model of repetitive mild brain injury without symptoms – risk for Parkinson’s disease with aging?

Objectives: To test the hypothesis that repetitive mild traumatic brain injury in early life may be a potential risk factor for Parkinson's disease. Methods: A closed-head momentum exchange model was used to produce one or three mild concussions in young adult male rats as compare to non-injured, age and weight-matched controls. Six-seven weeks post-injury, rats were studied for deficits in cognitive and motor function Changes in brain anatomy and function were evaluated through analysis of resting state functional connectivity, diffusion weighted imaging with quantitative anisotropy and immunohistochemistry for neuroinflammation. Results: Head injuries occurred without skull fracture or signs of intracranial bleeding or contusion. There were no significant differences in cognitive or motors behaviors between experimental groups. Rats concussed three times showed altered diffusivity in white matter tracts, basal ganglia, central amygdala, brainstem, and cerebellum. With a single concussion, the affected areas were limited to the caudate/putamen and central amygdala. Disruption of functional connectivity was most pronounced with three concussions as the midbrain dopamine system, hippocampus and brainstem/cerebellum showed hypoconnectivity. The suprachiasmatic nucleus was isolated from all functional connections. Interestingly, rats exposed to one concussion showed enhanced functional connectivity (or hyperconnectivity) across brain sites, particularly between the olfactory system and the cerebellum. Immunostaining for microglia activation showed inflammation in striatum and substantia nigra with three concussions but not with one. Interpretation: Neuroradiological and immunohistochemical evidence of altered brain structure and function, particularly in striatal and midbrain dopaminergic areas, persists long after mild repetitive head injury. These changes may be long lasting and serve as early biomarkers of neurodegeneration and risk for Parkinson's disease with aging.

neuroscience↗

bioRxiv2018

Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome

Identifying causal variants underling disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequences for 438 million variants. These impact scores are assay-specific, are predictive of allele-specific transcription factor binding and are enriched for variants associated with gene expression and disease risk. Nucleotide-level functional consequence scores for non-coding variants can refine the mechanism of known causal variants, identify novel risk variants and prioritize downstream experiments.

genomics↗

bioRxiv2017

Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with signatures from post mortem adult brains

Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. In developing a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons, we identified and accounted for a variety of technical and biological sources of variation. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal in hiPSC-based models and increased the concordance with post mortem datasets. Because this concordance was strongest in hiPSC-neurons, it suggests that this cell type may better model genetic risk for SZ. We predict a growing convergence between hiPSC and post mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones.

genetics↗

bioRxiv2017

A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

systems biology↗