Transcriptional and Position Effect Contributions to rAAV-Mediated Gene Targeting
CELL PRESS. 2020: 290
View details for Web of Science ID 000530089301198
Transcriptomic signatures across human tissues identify functional rare genetic variation.
Science (New York, N.Y.)
2020; 369 (6509)
Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.
View details for DOI 10.1126/science.aaz5900
View details for PubMedID 32913073
Transcriptional and Position Effect Contributions to rAAV-Mediated Gene Targeting
CELL PRESS. 2019: 294
View details for Web of Science ID 000464381003086
Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.
It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.
View details for DOI 10.1038/s41591-019-0457-8
View details for PubMedID 31160820
Transcriptome analysis of IL-10-stimulated (M2c) macrophages by next-generation sequencing
2017; 222 (7): 847–56
Alternatively activated "M2" macrophages are believed to function during late stages of wound healing, behaving in an anti-inflammatory manner to mediate the resolution of the pro-inflammatory response caused by "M1" macrophages. However, the differences between two main subtypes of M2 macrophages, namely interleukin-4 (IL-4)-stimulated "M2a" macrophages and IL-10-stimulated "M2c" macrophages, are not well understood. M2a macrophages are characterized by their ability to inhibit inflammation and contribute to the stabilization of angiogenesis. However, the role and temporal profile of M2c macrophages in wound healing are not known. Therefore, we performed next generation sequencing (RNA-seq) to identify biological functions and gene expression signatures of macrophages polarized in vitro with IL-10 to the M2c phenotype in comparison to M1 and M2a macrophages and an unactivated control (M0). We then explored the expression of these gene signatures in a publicly available data set of human wound healing. RNA-seq analysis showed that hundreds of genes were upregulated in M2c macrophages compared to the M0 control, with thousands of alternative splicing events. Following validation by Nanostring, 39 genes were found to be upregulated by M2c macrophages compared to the M0 control, and 17 genes were significantly upregulated relative to the M0, M1, and M2a phenotypes (using an adjusted p-value cutoff of 0.05 and fold change cutoff of 1.5). Many of the identified M2c-specific genes are associated with angiogenesis, matrix remodeling, and phagocytosis, including CD163, MMP8, TIMP1, VCAN, SERPINA1, MARCO, PLOD2, PCOCLE2 and F5. Analysis of the macrophage-conditioned media for secretion of matrix-remodeling proteins showed that M2c macrophages secreted higher levels of MMP7, MMP8, and TIMP1 compared to the other phenotypes. Interestingly, temporal gene expression analysis of a publicly available microarray data set of human wound healing showed that M2c-related genes were upregulated at early times after injury, similar to M1-related genes, while M2a-related genes appeared at later stages or were downregulated after injury. While further studies are required to confirm the timing and role of M2c macrophages in vivo, these results suggest that M2c macrophages may function at early stages of wound healing. Identification of markers of the M2c phenotype will allow more detailed investigations into the role of M2c macrophages in vivo.
View details for DOI 10.1016/j.imbio.2017.02.006
View details for Web of Science ID 000405444700003
View details for PubMedID 28318799
View details for PubMedCentralID PMC5719494
Deconvolution of heterogeneous wound tissue samples into relative macrophage phenotype composition via models based on gene expression
2017; 9 (4): 328–38
Macrophages, the primary cell of the innate immune system, act on a spectrum of phenotypes that correspond to diverse functions. Dysregulation of macrophage phenotype is associated with many diseases. In particular, defective transition from pro-inflammatory (M1) to anti-inflammatory (M2) behavior has been implicated as a potential source of sustained inflammation that prevents healing of chronic wounds such as diabetic ulcers. In order to design effective treatments, an understanding of the relative presence of macrophage phenotypes during tissue repair is necessary. Inferring the relative phenotype composition is currently challenging due to the heterogeneous nature of the macrophages themselves and also of tissue samples. We propose here a method to deconvolute gene expression from heterogeneous tissue samples into the composition of two primary macrophage phenotypes (M1 and M2). Our final method uses gene expression signatures for each phenotype cultivated in vitro as input to a predictive model that infers sample composition with an average error of 0.16, and whose predictions fit known compositions prepared in vitro with an R2 value of 0.90. Finally, we apply this model to describe macrophage behavior in human diabetic ulcer healing using clinically isolated ulcer tissue samples. The model predicted that non-healing diabetic ulcers contained higher proportions of M1 macrophages compared to healing diabetic ulcers, in agreement with numerous studies that have implicated a dysfunctional M1-to-M2 transition in the impaired healing of diabetic ulcers. These results show proof of concept that the model holds utility in making predictions regarding macrophage behavior in heterogeneous samples, with potential application as a wound healing diagnostic.
View details for DOI 10.1039/c7ib00018a
View details for Web of Science ID 000399687400005
View details for PubMedID 28290581
View details for PubMedCentralID PMC5719501
Macrophage Transcriptional Profile Identifies Lipid Catabolic Pathways That Can Be Therapeutically Targeted after Spinal Cord Injury
JOURNAL OF NEUROSCIENCE
2017; 37 (9): 2362–76
Although infiltrating macrophages influence many pathological processes after spinal cord injury (SCI), the intrinsic molecular mechanisms that regulate their function are poorly understood. A major hurdle has been dissecting macrophage-specific functions from those in other cell types as well as understanding how their functions change over time. Therefore, we used the RiboTag method to obtain macrophage-specific mRNA directly from the injured spinal cord in mice and performed RNA sequencing to investigate their transcriptional profile. Our data show that at 7 d after SCI, macrophages are best described as foam cells, with lipid catabolism representing the main biological process, and canonical nuclear receptor pathways as their potential mediators. Genetic deletion of a lipoprotein receptor, CD36, reduces macrophage lipid content and improves lesion size and locomotor recovery. Therefore, we report the first macrophage-specific transcriptional profile after SCI and highlight the lipid catabolic pathway as an important macrophage function that can be therapeutically targeted after SCI.SIGNIFICANCE STATEMENT The intrinsic molecular mechanisms that regulate macrophage function after spinal cord injury (SCI) are poorly understood. We obtained macrophage-specific mRNA directly from the injured spinal cord and performed RNA sequencing to investigate their transcriptional profile. Our data show that at 7 d after SCI, macrophages are best described as foam cells, with lipid catabolism representing the main biological process and canonical nuclear receptor pathways as their potential mediators. Genetic deletion of a lipoprotein receptor, CD36, reduces macrophage lipid content and improves lesion size and locomotor recovery. Therefore, we report the first macrophage-specific transcriptional profile after SCI and highlight the lipid catabolic pathway as an important macrophage function that can be therapeutically targeted after SCI.
View details for DOI 10.1523/JNEUROSCI.2751-16.2017
View details for Web of Science ID 000397189100012
View details for PubMedID 28130359
View details for PubMedCentralID PMC5354348
- On the Use of Electronic Documentation Systems in Fast-Paced, Time-Critical Medical Settings INTERACTING WITH COMPUTERS 2017; 29 (2): 203–19
Capacity Planning for Maternal-Fetal Medicine Using Discrete Event Simulation
AMERICAN JOURNAL OF PERINATOLOGY
2015; 32 (8): 761–69
Maternal-fetal medicine is a rapidly growing field requiring collaboration from many subspecialties. We provide an evidence-based estimate of capacity needs for our clinic, as well as demonstrate how simulation can aid in capacity planning in similar environments.A Discrete Event Simulation of the Center for Fetal Diagnosis and Treatment and Special Delivery Unit at The Children's Hospital of Philadelphia was designed and validated. This model was then used to determine the time until demand overwhelms inpatient bed availability under increasing capacity.No significant deviation was found between historical inpatient censuses and simulated censuses for the validation phase (p = 0.889). Prospectively increasing capacity was found to delay time to balk (the inability of the center to provide bed space for a patient in need of admission). With current capacity, the model predicts mean time to balk of 276 days. Adding three beds delays mean time to first balk to 762 days; an additional six beds to 1,335 days.Providing sufficient access is a patient safety issue, and good planning is crucial for targeting infrastructure investments appropriately. Computer-simulated analysis can provide an evidence base for both medical and administrative decision making in a complex clinical environment.
View details for DOI 10.1055/s-0034-1396074
View details for Web of Science ID 000356992300007
View details for PubMedID 25519198