Stochastic principles governing alternative splicing of RNA

<div><p>The dominance of the major transcript isoform relative to other isoforms from the same gene generated by alternative splicing (AS) is essential to the maintenance of normal cellular physiology. However, the underlying principles that determine such dominance remain unknown. Here, we analyzed the physical AS process and found that it can be modeled by a stochastic minimization process, which causes the scaled expression levels of all transcript isoforms to follow the same Weibull extreme value distribution. Surprisingly, we also found a simple equation to describe the median frequency of transcript isoforms of different dominance. This two-parameter Weibull model provides the statistical distribution of all isoforms of all transcribed genes, and reveals that previously unexplained observations concerning relative isoform expression derive from these principles.</p></div

A model of alternative splicing.

<p>(A) Splicing factor U1 and U2AF search the 5’ GU and 3’ AG splicing sites by 3D and 1D Brownian motion. Multiple candidate splice sites compete for the binding of U1 and U2AF. The binding is ATP-independent and reversible. (B) The binding of U1 and U2AF to the splice sites becomes stable only after the ATP-dependent binding of U2 snRNP. The identification of each intron is equivalent to a minimization process that U1 and U2AF dynamically search their global or local minimal energy sites on the pre-mRNA segment presented for AS. (C) The scaled expression level of transcript isoform follows type III extreme value distribution—a Weibull distribution. The approximate values of parameters <i>a (0</i>.<i>44)</i> and <i>b (0</i>.<i>6)</i> are estimated by curve fitting. Black curve represents the distribution of scaled expression level from experimental data. Red curve represent the Weibull distribution produced by curve fitting.</p

The frequency distribution of the <i>k</i>th dominant transcript isoform.

<p>(A) <i>k</i> = 1. (B) <i>k</i> = 2. <i>k</i> is the rank of a transcript isoform. <i>M</i> is the number of transcript isoforms for a gene. Black curves represent frequency distribution of the experimental RNA-seq data. Red curves represent the frequency distribution of the simulated data from Weibull distribution <i>W(0</i>.<i>39)</i>. KLd is the Kullback-Leibler divergence between the two distributions.</p

Transcript isoform expression pattern of two genes in different conditions.

<p>(A) BRD4. (B) SRSF7. Among 11 transcript isoforms of BRD4 and 12 transcript isoforms of SRSF7, ENST00000371835 and ENST00000409276 are the most dominant isoforms in all four activated conditions, ENST00000263377 and ENST00000477635 are the most dominant isoforms in all four resting conditions, respectively. This result indicates the major transcript isoform can be regulated by single external signal.</p

Loss of Circulating CD4 T Cells with B Cell Helper Function during Chronic HIV Infection

<div><p>The interaction between follicular T helper cells (T_FH) and B cells in the lymph nodes and spleen has a major impact on the development of antigen-specific B cell responses during infection or vaccination. Recent studies described a functional equivalent of these cells among circulating CD4 T cells, referred to as peripheral T_FH cells. Here, we characterize the phenotype and in vitro B cell helper activity of peripheral T_FH populations, as well as the effect of HIV infection on these populations. In co-culture experiments we confirmed CXCR5+ cells from HIV-uninfected donors provide help to B cells and more specifically, we identified a CCR7^highCXCR5^highCCR6^highPD-1^high CD4 T cell population that secretes IL-21 and enhances isotype-switched immunoglobulin production. This population is significantly decreased in treatment-naïve, HIV-infected individuals and can be recovered after anti-retroviral therapy. We found impaired immunoglobulin production in co-cultures from HIV-infected individuals and found no correlation between the frequency of peripheral T_FH cells and memory B cells, or with neutralization activity in untreated HIV infection in our cohort. Furthermore, we found that within the peripheral T_FH population, the expression level of T_FH-associated genes more closely resembles a memory, non-T_FH population, as opposed to a T_FH population. Overall, our data identify a heterogeneous population of circulating CD4 T cells that provides <i>in vitro</i> help to B cells, and challenges the origin of these cells as memory T_FH cells.</p></div

Characterization of peripheral T_FH cells.

<p>(<b>A</b>) Left: Representative flow cytometry plots from HIV-uninfected PBMC showing the gating scheme for isolating T cell subsets for the T cell/B cell coculture assay. Isolated populations include naïve cells (brown), CM CCR7^low (pink), CM CCR7^highCXCR5^low (orange), CM CCR7^highCXCR5^highCCR6^lowPD-1^high (green), CM CCR7^highCXCR5^highCCR6^highPD-1^low (blue) and CCR7^highCXCR5^highCCR6^highPD-1^high (red). Before gating on CCR6 and PD-1, cells were first gated on CD150^high. Right: Scatter plot indicating the frequency of each population in HIV-uninfected subjects (<i>n</i> = 13). Cells were not gated on CD150 for phenotypic analysis. (<b>B</b>) Indicated CD4 T cell populations were cultured with autologous naïve B cells (CD19^highCD27^lowIgD⁻) in the presence of SEB for 12 days and Ig concentrations were measured from supernatants (n = 6). (<b>C</b>) Indicated CD4 T cell populations were cultured with autologous naïve B cells in the presence of SEB for 2 days and cytokine concentrations were measured from supernatants (n = 6). Horizontal lines indicate limit of detection. Significant differences were determined using the Friedman test with Dunn's multiple comparison post-test. *p<0.05; **p<0.01.</p

Functional characteristics of pT_FH cells and the impact of HIV.

<p>(<b>A</b>) Representative flow cytometry plots showing CM, CD154-positive, cytokine-positive cells after SEB stimulation. CD154-positive, cytokine-positive CD4 T cells, shown by contour plots (blue: HIV-uninfected; red: HIV-infected), are overlaid onto 2 dimensional density plots for CM CD4 T cells plotted against CCR7 and CD3, and CXCR5 and CCR6. (<b>B</b>) Bar graphs showing the frequency of SEB-stimulated CD154-positive, cytokine-positive cells that express CCR7, CXCR5 and CCR6 (Blue: uninfected; n = 5; Red: HIV-infected; n = 24). (<b>C</b>) Left: Gag-specific CD4+ T cells (CD154-positive, cytokine-positive) shown as red contour plots are overlaid onto 2 dimensional density plots for CM cells CD4 T cells plotted against CCR7 and CD3, and CXCR5 and CCR6. Right: Bar graphs showing the frequency of Gag-specific CD154-positive, cytokine-positive cells that express CCR7, CXCR5 and CCR6 (n = 14). *p<0.05.</p

Relationship between pT_FH cells and T_FH cells in human tonsil.

<p>(<b>A</b>) Representative flow cytometry plots from HIV-uninfected, pediatric tonsils showing the gating scheme for determining the frequency of CCR6^high cells in T_FH (CXCR5^highPD-1^high) and non-T_FH populations. (<b>B</b>) Bar graphs showing the frequency of CCR6^high cells in T_FH and non-T_FH populations in human tonsils (n = 5). (<b>C</b>) Heatmap analysis of selected genes from RNA-seq data comparing pT_FH cells (CXCR5^highCCR6^highPD-1^high) from HIV-uninfected donors, pT_FH cells from HIV-infected donors, non-T_FH CD4 memory tonsil cells (CM CD57^lowPD-1^dimCCR7^highCCR5^lowCXCR4^low), non-germinal center T_FH tonsil cells (CM CD57^lowPD-1^highCCR7^lowCXCR5^high) and germinal center T_FH tonsil cells (CM PD-1^highCD57^high) from HIV-uninfected donors. (<b>D</b>) Top: Comparison of MAF expression on CD4 T cells from blood or tonsil. Bottom: Geometric mean (MFI) of MAF expression in the indicated populations of central memory CD4 T cells normalized to MAF MFI in naïve CD4 T cells.</p

Progressive loss of pT_FH cells in HIV infection.

<p>(<b>A</b>) Pooled data showing the frequency (%) of CXCR5^high, CXCR5^highCCR6^high and CXCR5^highCCR6^highPD-1^high populations in total CD4 cells from PBMC from HIV uninfected (open circles; n = 13), HIV-infected (treatment-naïve), CD4 count >200 (light gray circles; n = 44), and HIV-infected (treatment-naïve), CD4 count <200 (black circles; n = 22). Significant differences between HIV-uninfected and HIV-infected subjects were determined using the Mann-Whitney U test. ***p<0.001; **p<0.01; *p<0.05. (<b>B</b>) Longitudinal analysis showing the frequency (%) of CXCR5^high, CXCR5^highCCR6^high and CXCR5^highCCR6^highPD-1^high populations in total CD4 cells or indicated populations in CXCR5-expressing cells (bottom row) from HIV-infected (treatment naïve) subjects (n = 10) over 36–48 months. No significant correlations were found. (<b>C</b>) Pooled data showing the frequency (%) of CXCR5^high, CXCR5^highCCR6^high and CXCR5^highCCR6^highPD-1^high populations in total CD4 cells from PBMC from HIV-uninfected subjects (open circles; n = 13) and HIV-infected subjects before (n = 14, week 0; black circles) and after ART (week 24, dark gray circles; week 48, light gray circles). Significant differences between HIV-uninfected and HIV-infected subjects were determined using the Mann-Whitney U test. Significant differences between subjects before and after ART were determined using the Wilcoxon matched-pairs signed rank test. ***p<0.001; **p<0.01; *p<0.05.</p

Impaired B cell help by pT_FH cells in HIV infection.

<p>(<b>A</b>) CCR7^highCXCR5^low and CCR7^highCXCR5^highCCR6^high CM CD4 T cells isolated from PBMCs were cultured with autologous naïve B cells (CD19^highCD27^lowIgD⁻) in the presence of SEB for 12 days and Ig concentrations were measured from supernatants (HIV-uninfected, n = 8; HIV-infected (non-viremic), n = 5–7, HIV-infected (viremic), n = 1–2). Significant differences were determined using the Wilcoxon paired t-test or the Mann-Whitney test. *p<0.05; **p<0.01. (<b>B</b>) Top: HIV-uninfected PBMCs were incubated with indicated concentrations of CXCL-13 for 1 hour at 37°C (red) or 4°C (black). Bottom: Healthy PBMCs were incubated with 1 µg/mL CXCL13 for 10, 30, 60 or 120 minutes at 37°C (red) or 4°C (black). The frequency of CXCR5-positve CD4 T cells was calculated and normalized to time 0. (n = 3). (<b>C</b>) Top: Correlative analysis showing the frequency of CM CXCR5-positive CD4 T cells versus viral load (n = 27; r = −0.4036, P = 0.0368). Bottom: Correlative analysis showing the concentration of CXCL-13 in plasma or sera versus viral load (n = 27; r = 0.4414, P = 0.0165). Correlations were analyzed using the nonparametric Spearman test.</p