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Schmidt, E. M.

Publications and source records attributed to Schmidt, E. M..

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Two-photon imaging induces brain heating and calcium microdomain hyper-activity in cortical astrocytes

Unraveling how neural networks process and represent sensory information and how this cellular dynamics instructs behavioral output is a main goal in current neuroscience. Two-photon activation of optogenetic actuators and fluorescence calcium (Ca2+) imaging with genetically encoded Ca2+ indicators allow, respectively, the all-optical stimulation and readout of activity from genetically identified cell populations. However, these techniques expose the brain to high near-infrared light doses raising the concern of light-induced adverse effects on the biological phenomena being studied. Combing Ca2+ imaging of GCaMP6f-expressing cortical astrocytes as a sensitive readout for photodamage and an unbiased machine-based event detection, we demonstrate the subtle build-up of aberrant microdomain Ca2+ signals in fine astroglial processes. Illumination conditions routinely being used in biological two-photon microscopy (920-nm excitation, 100-fs regime, ten mW average power) increased the frequency of microdomain Ca2+ events, but left their amplitude, area and duration rather unchanged. This increase in local Ca2+ activity was followed by Ca2+ transients in the otherwise silent soma. Ca2+ hyperactivity occurred without overt morphological damage. Surprisingly, at the same average power, continuous-wave 920-nm illumination was as damaging as fs pulses, indicating a linear, heating-mediated (rather than a highly non-linear) damage mechanism. In an astrocyte-specific IP3-receptor knock-out mouse (IP3R2-KO), Near-infrared light-induced Ca2+ microdomains signals persisted in the small processes, underpinning their resemblance to physiological IP3R2-independent Ca2+ signals, while somatic activity was abolished. Contrary to what has generally been believed in the field, shorter pulses and lower average power are advantageous to alleviate photodamage and allow for longer useful recording windows.\n\nSIGNIFICANCE STATEMENTImaging the fine structure and function of the brain has become possible with two-photon microscopy that uses ultrashort-pulsed infrared laser light for better tissue penetration. The high peak energy of these light pulses has raised concerns about photodamage resulting from multi-photon processes. Here, we show that the time-averaged rather than the peak laser power matters. At wavelengths and with laser powers now commonly used in neuroscience brain damage occurs as a consequence of direct infrared light absorption, i.e., heating. To counteract brain heating we explore a strategy that uses even shorter, more energetic pulses but a lower time-averaged laser power to produce the same image quality while making two-photon microscopy less invasive.

neuroscience

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[&le;]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics

Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation

To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

genetics

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of \"exclusion PRS PheWAS\" was introduced. This approach led to phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This is the first comprehensive PheWAS study using PRS instead of a single variant.

genetics

A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS

The availability of electronic health record (EHR)-based phenotypes allows for genome-wide association analyses in thousands of traits, and has great potential to identify novel genetic variants associated with clinical phenotypes. We can interpret the phenome-wide association study (PheWAS) result for a single genetic variant by observing its association across a landscape of phenotypes. Since PheWAS can test 1000s of binary phenotypes, and most of them have unbalanced (case:control = 1:10) or often extremely unbalanced (case:control = 1:600) case-control ratios, existing methods cannot provide an accurate and scalable way to test for associations. Here we propose a computationally fast score test-based method that estimates the distribution of the test statistic using the saddlepoint approximation. Our method is much faster than the state of the art Firths test ([~] 100 times). It can also adjust for covariates and control type I error rates even when the case-control ratio is extremely unbalanced. Through application to PheWAS data from the Michigan Genomics Initiative, we show that the proposed method can control type I error rates while replicating previously known association signals even for traits with a very small number of cases and a large number of controls.

genomics