Higher- or lower-affinity germinal center (GC) B cells are directed either to plasma cell or GC recycling, respectively; however, how commitment to the plasma cell fate takes place is unclear. We found that a population of light zone (LZ) GC cells, Bcl6CD69 expressing a transcription factor IRF4 and higher-affinity B cell receptors (BCRs) or Bcl6CD69 with lower-affinity BCRs, favored the plasma cell or recycling GC cell fate, respectively. Mechanistically, CD40 acted as a dose-dependent regulator for Bcl6CD69 cell formation. Furthermore, we found that expression of intercellular adhesion molecule 1 (ICAM-1) and signaling lymphocytic activation molecule (SLAM) in Bcl6CD69 cells was higher than in Bcl6CD69 cells, thereby affording more stable T follicular helper (Tfh)-GC B cell contacts. These data support a model whereby commitment to the plasma cell begins in the GC and suggest that stability of Tfh-GC B cell contacts is key for plasma cell-prone GC cell formation.

RNA sequencing (RNA-Seq) is a powerful tool for transcriptome profiling, but is hampered by sequence-dependent bias and inaccuracy at low copy numbers intrinsic to exponential PCR amplification. We developed a simple strategy for mitigating these complications, allowing truly digital RNA-Seq. Following reverse transcription, a large set of barcode sequences is added in excess, and nearly every cDNA molecule is uniquely labeled by random attachment of barcode sequences to both ends. After PCR, we applied paired-end deep sequencing to read the two barcodes and cDNA sequences. Rather than counting the number of reads, RNA abundance is measured based on the number of unique barcode sequences observed for a given cDNA sequence. We optimized the barcodes to be unambiguously identifiable, even in the presence of multiple sequencing errors. This method allows counting with single-copy resolution despite sequence-dependent bias and PCR-amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome. We demonstrated transcriptome profiling of Escherichia coli with more accurate and reproducible quantification than conventional RNA-Seq.

In our previously published study, we provided high-resolution measurements of RNA synthesis and degradation lifetimes and proposed a model to explain the coordination of gene expression in Escherichia coli given the measured constraints. As part of that study, we reported 847 RNA lifetimes (Supplementary Table S4) measured in exponentially growing bacteria, and 1,259 RNA lifetimes (Supplementary Table S7) measured in stationary phase bacteria. For an exponential process, lifetime is related to half-life as follows: half-life = (ln2) × lifetime. To report the distribution of the lifetimes, we described the data using average and standard deviation, which we reported as 2.5 min (s.d. 2.5 min) and 4.5 min (s.d. 2.5 min) for exponential and stationary phase, respectively, in Figures 3 and 4, as well as in the main text.Since the publication of the above article, we have realized that the average lifetimes and standard deviations were miscalculated, although the reported dataset and histograms are correct. The new average lifetimes and standard deviations are 4.1 min (s.d. 6.2 min) and 7.8 min (s.d. 48.1 min) for exponentially growing bacteria and stationary phase bacteria, respectively. Additionally, we now report that the median lifetime in exponential phase is 2.8 min and the median lifetime in stationary phase is 4.6 min. We regret that this error occurred; however, it does not substantively change our interpretation of the data. Because of the change in the spread of the distributions, we can no longer make the point that stationary phase distribution of RNA lifetime is relatively narrow compared to that of RNA lifetimes measured in exponentially growing E. coli cells, which was a minor point in our article. The more important claim that the average lifetimes measured with correction for RNA polymerase elongation is shorter than previously published microarray data (average lifetime~8 min) still holds. Moreover, our kinetic model of RNA synthesis and degradation does not depend on the reported average lifetimes and standard deviations.Given that the average lifetime is of interest to the general scientific community, we thought it is important to correct the average lifetimes in our report. The updated figures are presented below, where the only changes concern the number of average lifetime. The dataset does not require any changes.We also noticed that the synthesis rates and degradation rates shown in Figure 4C and D were calculated using an older dataset.We have corrected the calculations and provide here the updated figure panels. The conclusion that RNA abundance is better explained by synthesis rather than degradation still stands, although the R 2 values (the degree to which RNA abundance can be explained) are slightly different. Additionally, we would like to correct a number from a reference. In the 2 nd paragraph under the section "Global behavior of RNA degradation", we mentioned that the average RNA lifetime in E.coli reported by previous papers is 6 min. This is incorrect; the average half-life, n...

Small, soluble metabolites not only are essential intermediates in intracellular biochemical processes, but can also influence neighbouring cells when released into the extracellular milieu1–3. Here we identify the metabolite and neurotransmitter GABA as a candidate signalling molecule synthesized and secreted by activated B cells and plasma cells. We show that B cell-derived GABA promotes monocyte differentiation into anti-inflammatory macrophages that secrete interleukin-10 and inhibit CD8+ T cell killer function. In mice, B cell deficiency or B cell-specific inactivation of the GABA-generating enzyme GAD67 enhances anti-tumour responses. Our study reveals that, in addition to cytokines and membrane proteins, small metabolites derived from B-lineage cells have immunoregulatory functions, which may be pharmaceutical targets allowing fine-tuning of immune responses.

Group 2 innate lymphoid cells (ILC2s) have tissue-resident competence and contribute to the pathogenesis of allergic diseases. However, the mechanisms regulating prolonged ILC2-mediated TH2 cytokine production under chronic inflammatory conditions are unclear. Here we show that, at homeostasis, Runx deficiency induces excessive ILC2 activation due to overly active GATA-3 functions. By contrast, during allergic inflammation, the absence of Runx impairs the ability of ILC2s to proliferate and produce effector TH2 cytokines and chemokines. Instead, functional deletion of Runx induces the expression of exhaustion markers, such as IL-10 and TIGIT, on ILC2s. Finally, these ‘exhausted-like’ ILC2s are unable to induce type 2 immune responses to repeated allergen exposures. Thus, Runx confers competence for sustained ILC2 activity at the mucosa, and contributes to allergic pathogenesis.