Identification of phenotypically and functionally heterogeneous mouse mucosal-associated invariant T cells using MR1 tetramers

Studies on the biology of mucosal-associated invariant T cells (MAIT cells) in mice have been hampered by a lack of specific reagents. Using MR1-antigen (Ag) tetramers that specifically bind to the MR1-restricted MAIT T cell receptors (TCRs), we demonstrate that MAIT cells are detectable in a broad range of tissues in C57BL/6 and BALB/c mice. These cells include CD4(-)CD8(-), CD4(-)CD8(+), and CD4(+)CD8(-) subsets, and their frequency varies in a tissue- and strain-specific manner. Mouse MAIT cells have a CD44(hi)CD62L(lo) memory phenotype and produce high levels of IL-17A, whereas other cytokines, including IFN-gamma, IL-4, IL-10, IL-13, and GM-CSF, are produced at low to moderate levels. Consistent with high IL-17A production, most MAIT cells express high levels of retinoic acid-related orphan receptor gamma t (ROR gamma t), whereas ROR gamma t(lo) MAIT cells predominantly express T-bet and produce IFN-gamma. Most MAIT cells express the promyelocytic leukemia zinc finger (PLZF) transcription factor, and their development is largely PLZF dependent. These observations contrast with previous reports that MAIT cells from V alpha 19 TCR transgenic mice are PLZF(-) and express a naive CD44(lo) phenotype. Accordingly, MAIT cells from normal mice more closely resemble human MAIT cells than previously appreciated, and this provides the foundation for further investigations of these cells in health and disease

An Essential Role for Katanin p80 and Microtubule Severing in Male Gamete Production

Katanin is an evolutionarily conserved microtubule-severing complex implicated in multiple aspects of microtubule dynamics. Katanin consists of a p60 severing enzyme and a p80 regulatory subunit. The p80 subunit is thought to regulate complex targeting and severing activity, but its precise role remains elusive. In lower-order species, the katanin complex has been shown to modulate mitotic and female meiotic spindle dynamics and flagella development. The in vivo function of katanin p80 in mammals is unknown. Here we show that katanin p80 is essential for male fertility. Specifically, through an analysis of a mouse loss-of-function allele (the Taily line), we demonstrate that katanin p80, most likely in association with p60, has an essential role in male meiotic spindle assembly and dissolution and the removal of midbody microtubules and, thus, cytokinesis. Katanin p80 also controls the formation, function, and dissolution of a microtubule structure intimately involved in defining sperm head shaping and sperm tail formation, the manchette, and plays a role in the formation of axoneme microtubules. Perturbed katanin p80 function, as evidenced in the Taily mouse, results in male sterility characterized by decreased sperm production, sperm with abnormal head shape, and a virtual absence of progressive motility. Collectively these data demonstrate that katanin p80 serves an essential and evolutionarily conserved role in several aspects of male germ cell development

Reliably detecting clinically important variants requires both combined variant calls and optimized filtering strategies

A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality 'genome in a bottle' reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality

A novel mechanism for complement activation at the surface of B cells following antigen binding

Coligation of CD21 with BCR on the surface of B cells provides a costimulatory signal essential for efficient Ab responses to T-dependent Ags. To achieve this, Ag must be directly linked to C3 fragments, but bow this occurs in vivo is not fully understood. Using BCR transgenic mice, we demonstrated that C3 was deposited on the surface of B cells following both high- and moderate-affinity Ag binding. This was dependent on the specific binding of IgM to the BCR-bound Ag and can occur independently of soluble immune complex formation. Based on these data, we propose a novel model in which immune complexes can form directly on the surface of the B cell following Ag binding. This model has implications for our understanding of B lymphocyte activation. Copyrigh

Total SNV calls and tool-specific SNV calls for each aligner and variant caller run with and without filtering.

<p>The union of variant calls for each tool was calculated and false positive and false negative rates determined relative to high quality GIAB variants. Tool-specific calls were also calculated, defined as SNVs specific to a single tool.</p><p>^<b>a</b>From bases overlap ENSEMBL v75 canonical transcripts.</p><p>Total SNV calls and tool-specific SNV calls for each aligner and variant caller run with and without filtering.</p

Software concordance ROC curves.

<p>Merged variant calls for each tool were calculated and ROC curves generated using the genome quality score. Aligners are compared in row 1, variant callers without filtering in row 2, and variant callers with filtering in row 3.</p