Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity

Dendritic cells (DCs) play a critical role in orchestrating adaptive immune responses due to their unique ability to initiate T cell responses and direct their differentiation into effector lineages. Classical DCs have been divided into two subsets, cDC1 and cDC2, based on phenotypic markers and their distinct abilities to prime CD8 and CD4 T cells. While the transcriptional regulation of the cDC1 subset has been well characterized, cDC2 development and function remain poorly understood. By combining transcriptional and chromatin analyses with genetic reporter expression, we identified two principal cDC2 lineages defined by distinct developmental pathways and transcriptional regulators, including T-bet and RORgt, two key transcription factors known to define innate and adaptive lymphocyte subsets. These novel cDC2 lineages were characterized by distinct metabolic and functional programs. Extending our findings to humans revealed conserved DC heterogeneity and the presence of the newly defined cDC2 subsets in human cancer

Chronic inflammation permanently reshapes tissue-resident immunity in celiac disease

Tissue-resident lymphocytes play a key role in immune surveillance, but it remains unclear how these inherently stable cell populations respond to chronic inflammation. In the setting of celiac disease (CeD), where exposure to dietary antigen can be controlled, gluten-induced inflammation triggered a profound depletion of naturally occurring Vγ4+/Vδ1+ intraepithelial lymphocytes (IELs) with innate cytolytic properties and specificity for the butyrophilin-like (BTNL) molecules BTNL3/BTNL8. Creation of a new niche with reduced expression of BTNL8 and loss of Vγ4+/Vδ1+ IELs was accompanied by the expansion of gluten-sensitive, interferon-γ-producing Vδ1+ IELs bearing T cell receptors (TCRs) with a shared non-germline-encoded motif that failed to recognize BTNL3/BTNL8. Exclusion of dietary gluten restored BTNL8 expression but was insufficient to reconstitute the physiological Vγ4+/Vδ1+ subset among TCRγδ+ IELs. Collectively, these data show that chronic inflammation permanently reconfigures the tissue-resident TCRγδ+ IEL compartment in CeD

Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.</p

Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

<div><p>Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.</p></div

AUROCs for simulated data with 50% noise (standard deviation of Gaussian noise as a percent of amplitude).

<p>An AUROC value of 1 represents perfect discrimination between rhythmic and arrhythmic time series, and a value of 0.5 corresponds to random guessing. In each panel, the number of replicates increases from 1 to 4 replicates from left to right, and the number of sampled points per period is indicated by color. AUROC for single-replicate ANOVA (for which the method is undefined) is set at 0.5 exactly. Imp: impulse waveform, Cyclo: cyclohedron test, Address: address reduction, Stable: stable persistence, JTK: original JTK_CYCLE with Bonferroni correction, JTK_BH: JTK_CYCLE with Benjamini-Hochberg correction with symmetric triangle reference, eJTK: empirical JTK_CYCLE with symmetric triangle reference, JTK_BH_aby2: JTK_CYCLE with Benjamini-Hochberg correction and triangle references with asymmetries from 2 to 22 h by 2 h, eJTK_aby2: empirical JTK_CYCLE with triangle references with asymmetries from 2 to 22 h by 2 h.</p

Z-score normalization allows combining of time series from different datasets into smooth time series.

<p><i>Pdp1</i> gene expression from metadata before (A) and after (B) Z-score normalization. Light gray crosses indicate individual replicates, and the black curve is the mean.</p

Empirical <b>p</b>-values are uniformly distributed for the null model of JTK_CYCLE.

<p><i>P</i>-values versus their ranks from lowest (most significant) to highest (least significant) for JTK_CYCLE testing phases at 2 h intervals (green line) or phases and asymmetries at 2 h intervals (blue line) with time series consisting of Gaussian noise. Unbiased estimates should follow the black line (see text). (A) “Initial” <i>p</i>-values from JTK_CYCLE with multiple hypothesis testing underestimate the true <i>p</i>-values. (B) The Bonferroni correction results in <i>p</i>-values that are too high (less significant). (C) The Benjamini-Hochberg correction performs better than the Bonferroni correction but still results in <i>p</i>-values that are generally too high. (D) Empirical <i>p</i>-values that we calculate by permutation are close to uniformly distributed, as desired; their correspondence to the null model improves as the number of hypotheses tested increases.</p

Annotation terms identified by DAVID as enriched for rhythmic genes.

<p>Rhythmic genes shown are those that are identified with eJTK_aby4 with a Benjamini-Hochberg adjusted <i>p</i>-value less than 0.05. The terms shown are those identified by the DAVID web tool [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004094#pcbi.1004094.ref051" target="_blank">51</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004094#pcbi.1004094.ref052" target="_blank">52</a>] as enriched with a Benjamini-Hochberg adjusted <i>p</i>-value less than 0.05. (A) The individual annotation terms are shown with their adjusted <i>p</i>-values and phase distributions. The red numbers refer to the number of genes in that annotation term with that phase. The horizontal axis of A is the same as that of B. (B) Total phase distribution of the cycling genes. (C) Total asymmetry distribution of the cycling genes.</p

JTK_CYCLE compares all possible pair relations for a time series to those for a reference waveform.

<p>(A) JTK_CYCLE tests for pairwise agreement between a reference (blue) and a signal (cyan) time series. Three discordant pairwise relationships are indicated by red lines. (B) The previous implementation compared a time series to a set of phase-shifted cosines. (C) We add a set of asymmetric waveforms to the reference. An example is shown here with the same phases as in A.</p

Examples of simulated data.

<p>(A) Different waveforms simulated with a 24 h period. From left to right, cosine, ramp, step, and impulse (width at half-max is 2 h). Waveforms in figure may not be to scale. (B) Cosine in black, with Gaussian noise with standard deviation of 25% (blue) or 50% (green) of amplitude.</p