Moshe Olshansky

A molecular threshold for effector CD8(+) T cell differentiation controlled by transcription factors Blimp-1 and T-bet.

T cell responses are guided by cytokines that induce transcriptional regulators, which ultimately control differentiation of effector and memory T cells. However, it is unknown how the activities of these molecular regulators are coordinated and integrated during the differentiation process. Using genetic approaches and transcriptional profiling of antigen-specific CD8+ T cells, we reveal a common program of effector differentiation that is regulated by IL-2 and IL-12 signaling and the combined activities of the transcriptional regulators Blimp-1 and T-bet. The loss of both T-bet and Blimp-1 leads to abrogated cytotoxic function and ectopic IL-17 production in CD8+ T cells. Overall, our data reveal two major overlapping pathways of effector differentiation governed by the availability of Blimp-1 and T-bet and suggest a model for cytokine-induced transcriptional changes that combine, quantitatively and qualitatively, to promote robust effector CD8+ T cell differentiation.

Id2-Mediated Inhibition of E2A Represses Memory CD8+ T Cell Differentiation

The transcription factor inhibitor of DNA binding (Id)2 modulates T cell fate decisions, but the molecular mechanism underpinning this regulation is unclear. In this study we show that loss of Id2 cripples effector differentiation and instead programs CD8+ T cells to adopt a memory fate with increased Eomesodermin and Tcf7 expression. We demonstrate that Id2 restrains CD8+ T cell memory differentiation by inhibiting E2A-mediated direct activation of Tcf7 and that Id2 expression level mirrors T cell memory recall capacity. As a result of the defective effector differentiation, Id2-deficient CD8+ T cells fail to induce sufficient Tbx21 expression to generate short-lived effector CD8+ T cells. Our findings reveal that the Id2/E2A axis orchestrates T cell differentiation through the induction or repression of downstream transcription factors essential for effector and memory T cell differentiation.

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

BackgroundGas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.ResultsPyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).ConclusionsPyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

Granulocyte macrophage colony-stimulating factor induces CCL17 production via IRF4 to mediate inflammation

Data from preclinical and clinical studies have demonstrated that granulocyte macrophage colony-stimulating factor (GM-CSF) can function as a key proinflammatory cytokine. However, therapies that directly target GM-CSF function could lead to undesirable side effects, creating a need to delineate downstream pathways and mediators. In this work, we provide evidence that GM-CSF drives CCL17 production by acting through an IFN regulatory factor 4-dependent (IRF4-dependent) pathway in human monocytes, murine macrophages, and mice in vivo. In murine models of arthritis and pain, IRF4 regulated the formation of CCL17, which mediated the proinflammatory and algesic actions of GM-CSF. Mechanistically, GM-CSF upregulated IRF4 expression by enhancing JMJD3 demethylase activity. We also determined that CCL17 has chemokine-independent functions in inflammatory arthritis and pain. These findings indicate that GM-CSF can mediate inflammation and pain by regulating IRF4-induced CCL17 production, providing insights into a pathway with potential therapeutic avenues for the treatment of inflammatory diseases and their associated pain.

Tissue-specific regulation of genes by estrogen receptors.

Estrogens are frequently used in reproductive medicine. The Womens Health Initiative trial found that the risks of menopausal hormone therapy (MHT) exceed the benefits. The estrogens in MHT, however, were introduced prior to our understanding of the mechanism of action of estrogens. Estrogen signaling is highly complex, involving various DNA regulatory elements to which estrogen receptors bind. Numerous transcription factors and co-regulatory proteins modify chromatin structure to further regulate gene transcription. With a greater understanding of estrogen action, the major problem with the current estrogens in MHT appears to be that they are nonselective. This produces beneficial effects in bone, brain, and adipose tissue but increases the risk of breast and endometrial cancer and thromboembolism. Resurrecting MHT for long-term therapy will require the development of more selective estrogens, such as estrogen receptor (ER)β-selective estrogens and tissue-selective ERα agonists. These compounds will offer the best prospects to expand the indications of MHT and thus prevent the chronic conditions associated with menopause.

Fibroid-Associated Heavy Menstrual Bleeding: Correlation Between Clinical Features, Doppler Ultrasound Assessment of Vasculature, and Tissue Gene Expression Profiles

Despite the prevalence of uterine fibroids (Fs), few studies have investigated the links between clinical features and the cellular or molecular mechanisms that drive F growth and development. Such knowledge will ultimately help to differentiate symptomatic from asymptomatic Fs and could result in the development of more effective and individualized treatments. The aim of this study was to investigate the relationship between ultrasound appearance, blood flow, and angiogenic gene expression in F, perifibroid (PM), and distant myometrial (DM) tissues. We hypothesized that angiogenic gene expression would be increased in tissues and participants that showed increased blood flow by Doppler ultrasound. The study was performed using Doppler ultrasound to measure blood flow prior to hysterectomy, with subsequent tissue samples from the F, PM, and DM being investigated for angiogenic gene expression. Overall, PM blood flow (measured as peak systolic velocity [PSV]) was higher than F blood flow, although significant heterogeneity was seen in vascularity and blood flow between different Fs and their surrounding myometrium. We did not find any correlation between PSV and any other clinical or molecular parameter in this study. We identified 19 angiogenesis pathway-related genes with significant differences in expression between F and DM, and 2 genes, matrix metalloproteinase 9 (MMP9) and Neuropilin 2 (NRP2), that were significantly different between F and PM. These results are consistent with subtle differences between PM and DM. Understanding the differences between symptomatic versus asymptomatic Fs may eventually lead to more effective treatments that directly target the source of heavy menstrual bleeding.

Statistical Analysis of Metabolomics Data

Statistical matters form an integral part of a metabolomics experiment. In this chapter we describe several important aspects in the analysis of metabolomics data such as the removal of unwanted variation and the identification of differentially abundant metabolites, along with a number of other essential statistical considerations.

Transcriptional Analysis of T Cells Resident in Human Skin

Human skin contains various populations of memory T cells in permanent residence and in transit. Arguably, the best characterized of the skin subsets are the CD8+ permanently resident memory T cells (TRM) expressing the integrin subunit, CD103. In order to investigate the remaining skin T cells, we isolated skin-tropic (CLA+) helper T cells, regulatory T cells, and CD8+ CD103- T cells from skin and blood for RNA microarray analysis to compare the transcriptional profiles of these groups. We found that despite their common tropism, the T cells isolated from skin were transcriptionally distinct from blood-derived CLA+ T cells. A shared pool of genes contributed to the skin/blood discrepancy, with substantial overlap in differentially expressed genes between each T cell subset. Gene set enrichment analysis further showed that the differential gene profiles of each human skin T cell subset were significantly enriched for previously identified TRM core signature genes. Our results support the hypothesis that human skin may contain additional TRM or TRM-like populations.

Estrogenic Plant Extracts Reverse Weight Gain and Fat Accumulation without Causing Mammary Gland or Uterine Proliferation

Long-term estrogen deficiency increases the risk of obesity, diabetes and metabolic syndrome in postmenopausal women. Menopausal hormone therapy containing estrogens might prevent these conditions, but its prolonged use increases the risk of breast cancer, as wells as endometrial cancer if used without progestins. Animal studies indicate that beneficial effects of estrogens in adipose tissue and adverse effects on mammary gland and uterus are mediated by estrogen receptor alpha (ERα). One strategy to improve the safety of estrogens to prevent/treat obesity, diabetes and metabolic syndrome is to develop estrogens that act as agonists in adipose tissue, but not in mammary gland and uterus. We considered plant extracts, which have been the source of many pharmaceuticals, as a source of tissue selective estrogens. Extracts from two plants, Glycyrrhiza uralensis (RG) and Pueraria montana var. lobata (RP) bound to ERα, activated ERα responsive reporters, and reversed weight gain and fat accumulation comparable to estradiol in ovariectomized obese mice maintained on a high fat diet. Unlike estradiol, RG and RP did not induce proliferative effects on mammary gland and uterus. Gene expression profiling demonstrated that RG and RP induced estradiol-like regulation of genes in abdominal fat, but not in mammary gland and uterus. The compounds in extracts from RG and RP might constitute a new class of tissue selective estrogens to reverse weight gain, fat accumulation and metabolic syndrome in postmenopausal women.

Dynamic regulation of permissive histone modifications and GATA3 binding underpin acquisition of granzyme A expression by virus-specific CD8(+) T cells.

Numerous studies have focused on the molecular regulation of perforin (PFP) and granzyme B (GZMB) expression by activated cytotoxic T lymphocytes (CTLs), but little is known about the molecular factors that underpin granzyme A (GZMA) expression. In vitro activation of naïve CD8+ T cells, in the presence of IL‐4, enhanced STAT6‐dependent GZMA expression and was associated with GATA3 binding and enrichment of transcriptionally permissive histone posttranslational modifications (PTMs) across the Gzma gene locus. While GZMA expression by effector influenza A virus specific CTLs was also associated with a similar permissive epigenetic signature, memory CTL lacked enrichment of permissive histone PTMs at the Gzma locus, although this was restored within recalled secondary effector CTLs. Importantly, GZMA expression by virus‐specific CTLs was associated with GATA3 binding at the Gzma locus, and independent of STAT6‐mediated signaling. This suggests regulation of GZMA expression is underpinned by differentiation‐dependent regulation of chromatin composition at the Gzma locus and that, given GATA3 is key for CTL differentiation in response to infection, GATA3 expression is regulated by a distinct, IL‐4 independent, signaling pathway. Overall, this study provides insights into the molecular mechanisms that control transcription of Gzma during virus‐induced CD8+ T‐cell differentiation.