Epithelial HVEM maintains intraepithelial T cell survival and contributes to host protection.

Intraepithelial T cells (IETs) are in close contact with intestinal epithelial cells and the underlying basement membrane, and they detect invasive pathogens. How intestinal epithelial cells and basement membrane influence IET survival and function, at steady state or after infection, is unclear. The herpes virus entry mediator (HVEM), a member of the TNF receptor superfamily, is constitutively expressed by intestinal epithelial cells and is important for protection from pathogenic bacteria. Here, we showed that at steady-state LIGHT, an HVEM ligand, binding to epithelial HVEM promoted the survival of small intestine IETs. RNA-seq and addition of HVEM ligands to epithelial organoids indicated that HVEM increased epithelial synthesis of basement membrane proteins, including collagen IV, which bound to ß1 integrins expressed by IETs. Therefore, we proposed that IET survival depended on ß1 integrin binding to collagen IV and showed that ß1 integrin-collagen IV interactions supported IET survival in vitro. Moreover, the absence of ß1 integrin expression by T lymphocytes decreased TCR αß+ IETs in vivo. Intravital microscopy showed that the patrolling movement of IETs was reduced without epithelial HVEM. As likely consequences of decreased number and movement, protective responses to Salmonella enterica were reduced in mice lacking either epithelial HVEM, HVEM ligands, or ß1 integrins. Therefore, IETs, at steady state and after infection, depended on HVEM expressed by epithelial cells for the synthesis of collagen IV by epithelial cells. Collagen IV engaged ß1 integrins on IETs that were important for their maintenance and for their protective function in mucosal immunity.

Transcriptional analysis of peripheral memory T cells reveals Parkinson's disease-specific gene signatures.

Parkinson's disease (PD) is a multi-stage neurodegenerative disorder with largely unknown etiology. Recent findings have identified PD-associated autoimmune features including roles for T cells. To further characterize the role of T cells in PD, we performed RNA sequencing on PBMC and peripheral CD4 and CD8 memory T cell subsets derived from PD patients and age-matched healthy controls. When the groups were stratified by their T cell responsiveness to alpha-synuclein (α-syn) as a proxy for an ongoing inflammatory autoimmune response, the study revealed a broad differential gene expression profile in memory T cell subsets and a specific PD associated gene signature. We identified significant enrichment of transcriptomic signatures previously associated with PD, including for oxidative stress, phosphorylation, autophagy of mitochondria, cholesterol metabolism and inflammation, and the chemokine signaling proteins CX3CR1, CCR5, and CCR1. In addition, we identified genes in these peripheral cells that have previously been shown to be involved in PD pathogenesis and expressed in neurons, such as LRRK2, LAMP3, and aquaporin. Together, these findings suggest that features of circulating T cells with α-syn-specific responses in PD patients provide insights into the interactive processes that occur during PD pathogenesis and suggest potential intervention targets.

Transcriptome and chromatin landscape of iNKT cells are shaped by subset differentiation and antigen exposure.

Invariant natural killer T cells (iNKT cells) differentiate into thymic and peripheral NKT1, NKT2 and NKT17 subsets. Here we use RNA-seq and ATAC-seq analyses and show iNKT subsets are similar, regardless of tissue location. Lung iNKT cell subsets possess the most distinct location-specific features, shared with other innate lymphocytes in the lung, possibly consistent with increased activation. Following antigenic stimulation, iNKT cells undergo chromatin and transcriptional changes delineating two populations: one similar to follicular helper T cells and the other NK or effector like. Phenotypic analysis indicates these changes are observed long-term, suggesting that iNKT cells gene programs are not fixed, but they are capable of chromatin remodeling after antigen to give rise to additional subsets.

Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data.

RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount .