Topical Steroid Withdrawal is a Targetable Excess of Mitochondrial NAD

Background

Topical corticosteroids (TCS) are first-line therapies for numerous skin conditions. Topical Steroid Withdrawal (TSW) is a controversial diagnosis advocated by patients with prolonged TCS exposure who report severe systemic reactions upon treatment cessation. However, to date there have been no systematic clinical or mechanistic studies to distinguish TSW from other eczematous disorders.

Methods

A re-analysis of a previous survey with eczematous skin disease was performed to evaluate potential TSW distinguishing symptoms. We subsequently conducted a pilot study of 16 patients fitting the proposed diagnostic criteria. We then performed: tissue metabolomics, transcriptomics, and immunostaining on skin biopsies; serum metabolomics and cytokine assessments; shotgun metagenomics on microbiome skin swabs; genome sequencing; followed by functional, mechanistic studies using human skin cell lines and mice.

Results

Clinically distinct TSW symptoms included burning, flushing, and thermodysregulation. Metabolomics and transcriptomics both implicated elevated NAD+ oxidation stemming from increased expression of mitochondrial complex I and conversion of tryptophan into kynurenine metabolites. These abnormalities were induced by glucocorticoid exposure both in vitro and in a cohort of healthy controls (N=19) exposed to TCS. Targeting complex I via either metformin or the herbal compound berberine improved outcomes in both cell culture and in an open-label case series for patients with TSW.

Conclusion

Taken together, our results suggest that TSW has a distinct dermatopathology. While future studies are needed to validate these results in larger cohorts, this work provides the first mechanistic evaluation into TSW pathology, and offers insights into clinical identification, pharmacogenomic candidates, and directed therapeutic strategies.

hcHCR: High-Throughput Single-Cell Imaging of RNA in Human Primary Immune Cells

Functional genomics and chemical screens can identify and characterize novel cellular factors regulating signaling networks and chemical tools to modulate their function for the treatment of disease. Screening methods have relied primarily on immortalized and/or transformed cancer cell lines, which can limit the generalization of results to more physiologically relevant systems. Most have also relied on immunofluorescence, or on stably expressed recombinant fluorescent proteins, to detect specific protein markers using high-content imaging readouts. In comparison, high-throughput methods to visualize and measure RNA species have been less explored. To address this, we have adapted an isothermal signal amplification chemistry for RNA FISH known as hybridization chain reaction (HCR) to an automated, high-content imaging assay format. We present a detailed protocol for this technique, which we have named high-content HCR (hcHCR). The protocol focuses on the measurement of changes in mRNA abundance at the single-cell level in human primary cells, but it can be applied to a variety of primary cell types and perturbing agents. We anticipate that hcHCR will be most suitable for low- to medium-throughput screening experiments in which changes in transcript abundance are the desired output measure.

High-throughput imaging of mRNA at the single-cell level in human primary immune cells

Measurement of gene expression at the single-cell level has advanced the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for high-content imaging (HCI)-based quantification of relative changes in transcript abundance at the single-cell level in human primary immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, named hcHCR, combines the sensitivity of the hybridization chain reaction (HCR) for the visualization of RNA in single cells, with the speed, scalability, and reproducibility of HCI. We first tested eight cell attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in 384-well format and documented the ability of the method to detect changes in transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of three transcripts per cell at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be suitable for low- to medium-throughput chemical or functional genomics screens in primary human cells, with the possibility of performing screens on cells obtained from patients with a specific disease.

Single-Cell Analysis Reveals the Range of Transcriptional States of Circulating Human Neutrophils

Neutrophils are the most abundant leukocytes in human blood and are essential components of innate immunity. Until recently, neutrophils were considered homogeneous and transcriptionally inactive cells, but both concepts are being challenged. Single-cell RNA sequencing (scRNA-seq) offers an unbiased view of cells along a continuum of transcriptional states. However, the use of scRNA-seq to characterize neutrophils has proven technically difficult, explaining in part the paucity of published single-cell data on neutrophils. We have found that modifications to the data analysis pipeline, rather than to the existing scRNA-seq chemistries, can significantly increase the detection of human neutrophils in scRNA-seq. We have then applied a modified pipeline to the study of human peripheral blood neutrophils. Our findings indicate that circulating human neutrophils are transcriptionally heterogeneous cells, which can be classified into one of four transcriptional clusters that are reproducible among healthy human subjects. We demonstrate that peripheral blood neutrophils shift from relatively immature (Nh0) cells, through a transitional phenotype (Nh1), into one of two end points defined by either relative transcriptional inactivity (Nh2) or high expression of type I IFN-inducible genes (Nh3). Transitions among states are characterized by the expression of specific transcription factors. By simultaneously measuring surface proteins and intracellular transcripts at the single-cell level, we show that these transcriptional subsets are independent of the canonical surface proteins that are commonly used to define and characterize human neutrophils. These findings provide a new view of human neutrophil heterogeneity, with potential implications for the characterization of neutrophils in health and disease.

GCgx: transcriptome-wide exploration of the response to glucocorticoids

Glucocorticoids are the cornerstone of immunosuppressive and anti-inflammatory therapy in humans, yet the mechanisms of glucocorticoid immunoregulation and toxicity remain unclear. The response to glucocorticoids is highly cell type-dependent, so translating results from different experimental systems into a better understanding of glucocorticoid effects in humans would benefit from rapid access to high-quality data on the response to glucocorticoids by different cell types. We introduce GCgx, a web application that allows investigators to quickly visualize changes in transcript abundance in response to glucocorticoids in a variety of cells and species. The tool is designed to grow by the addition of datasets based on input from the user community. GCgx is implemented in R and HTML and packaged as a Docker image. The tool and its source code are publicly available.

A Deep Learning Pipeline for Nucleus Segmentation

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.

Immune regulation by glucocorticoids can be linked to cell type-dependent transcriptional responses

A functional genomics approach uncovers previously undescribed cell type–dependent responses that can be linked to the immunoregulatory actions of glucocorticoids in humans.

Glucocorticoids remain the most widely used immunosuppressive and anti-inflammatory drugs, yet substantial gaps exist in our understanding of glucocorticoid-mediated immunoregulation. To address this, we generated a pathway-level map of the transcriptional effects of glucocorticoids on nine primary human cell types. This analysis revealed that the response to glucocorticoids is highly cell type dependent, in terms of the individual genes and pathways affected, as well as the magnitude and direction of transcriptional regulation. Based on these data and given their importance in autoimmunity, we conducted functional studies with B cells. We found that glucocorticoids impair upstream B cell receptor and Toll-like receptor 7 signaling, reduce transcriptional output from the three immunoglobulin loci, and promote significant up-regulation of the genes encoding the immunomodulatory cytokine IL-10 and the terminal-differentiation factor BLIMP-1. These findings provide new mechanistic understanding of glucocorticoid action and emphasize the multifactorial, cell-specific effects of these drugs, with potential implications for designing more selective immunoregulatory therapies.

Transcript- and protein-level analyses of the response of human eosinophils to glucocorticoids

Glucocorticoids are first-line agents for the treatment of many eosinophil-associated disorders; however, their effects on human eosinophils remain poorly understood. To gain an unbiased, genome-wide view of the early transcriptional effects of glucocorticoids on human eosinophils in vivo, RNA sequencing was performed on purified blood eosinophils obtained before and 30, 60, and 120 minutes after administration of a single dose of oral prednisone (1 mg/kg) to three unrelated healthy subjects with hypereosinophilia of unknown significance. The resulting dataset is of high quality and suitable for differential expression analysis. Flow cytometry and qPCR were then performed on three additional cohorts of human subjects, to validate the key findings at the transcript and protein levels. The resulting datasets provide a resource for understanding the response of circulating human eosinophils to glucocorticoid administration.

Immune regulation by glucocorticoids can be linked to cell-lineage-specific transcriptional responses

Glucocorticoids remain the most widely used class of immunosuppressive and anti-inflammatory drugs, yet significant gaps exist in our understanding of glucocorticoid-mediated immunoregulation. We have studied the transcriptional response to glucocorticoids, by total RNA sequencing and small-RNA sequencing, in nine human primary cell populations: B cells, CD4+ T cells, monocytes, neutrophils, endothelial cells, myoblasts, osteoblasts, fibroblasts, and preadipocytes. Our results suggest that the human response to glucocorticoids displays cell-lineage dependency, in terms of the individual genes and pathways that are differentially affected, as well as the magnitude and direction of transcriptional regulation. While decreased expression of genes that function in critical immune pathways is widespread, increased expression of negative regulators of immune activation is also common. Despite the critical role of B cells in many diseases for which glucocorticoids are used, glucocorticoid effects on human B cells are particularly poorly understood. We have identified significant transcriptional and functional changes in key molecules involved in B cell receptor signaling, intracellular Toll-like receptor signaling, B cell activation, differentiation, and antibody production. We have validated these results at the RNA and protein levels in vivo, by assaying B cells isolated from healthy human volunteers given a single intravenous dose of a glucocorticoid. Overall, our results illustrate the ability of an unbiased, genome-wide approach to uncover previously undescribed, cell-type-dependent transcriptional responses that can be linked to the immunoregulatory actions of glucocorticoids in humans.

Targeted p16(Ink4a) epimutation causes tumorigenesis and reduces survival in mice

Cancer has long been viewed as a genetic disease; however, epigenetic silencing as the result of aberrant promoter DNA methylation is frequently associated with cancer development, suggesting an epigenetic component to the disease. Nonetheless, it has remained unclear whether an epimutation (an aberrant change in epigenetic regulation) can induce tumorigenesis. Here, we exploited a functionally validated cis-acting regulatory element and devised a strategy to induce developmentally regulated genomic targeting of DNA methylation. We used this system to target DNA methylation within the p16(Ink4a) promoter in mice in vivo. Engineered p16(Ink4a) promoter hypermethylation led to transcriptional suppression in somatic tissues during aging and increased the incidence of spontaneous cancers in these mice. Further, mice carrying a germline p16(Ink4a) mutation in one allele and a somatic epimutation in the other had accelerated tumor onset and substantially shortened tumor-free survival. Taken together, these results provide direct functional evidence that p16(Ink4a) epimutation drives tumor formation and malignant progression and validate a targeted methylation approach to epigenetic engineering.

Hyperhomocysteinemia and elevated methylmalonic acid indicate a high prevalence of cobalamin deficiency in Asian Indians

BACKGROUND

In India, most people adhere to a vegetarian diet, which may lead to cobalamin deficiency.

OBJECTIVE

The objective was to examine indicators of cobalamin status in Asian Indians.

DESIGN

The study population included 204 men and women aged 27-55 y from Pune, Maharashtra, India, categorized into 4 groups: patients with cardiovascular disease (CVD) and diabetes, patients with CVD but no diabetes, patients with diabetes but no CVD, and healthy subjects. Data on medical history, lifestyle, and diet were obtained by interviews and questionnaires. Blood samples were collected for measurement of serum or plasma total cobalamin, holotranscobalamin (holoTC), methylmalonic acid (MMA), and total homocysteine (tHcy) and hemetologic indexes.

RESULTS

MMA, tHcy, total cobalamin, and holoTC did not differ significantly among the 4 groups; therefore, the data were pooled. Total cobalamin showed a strong inverse correlation with tHcy (r = -0.59) and MMA (r = -0.54). Forty-seven percent of the subjects had cobalamin deficiency (total cobalamin <150 pmol/L), 73% had low holoTC (<35 pmol/L), 77% had hyperhomocysteinemia (tHcy >15 micromol/L), and 73% had elevated serum MMA (>0.26 micromol/L). These indicators of impaired cobalamin status were observed in both vegetarians and nonvegetarians. Folate deficiency was rare and only 2.5% of the subjects were homozygous for the MTHFR 677C-->T polymorphism.

CONCLUSIONS

About 75% of the subjects had metabolic signs of cobalamin deficiency, which was only partly explained by the vegetarian diet. If impaired cobalamin status is confirmed in other parts of India, it may have important health implications.