IL-6-targeted therapies to block the cytokine or its receptor drive distinct alterations in T cell function.

Therapeutics that inhibit IL-6 at different points in its signaling pathway are in clinical use, yet whether the immunological effects of these interventions differ based on their molecular target is unknown. We performed short-term interventions in individuals with type 1 diabetes using anti-IL-6 (siltuximab) or anti-IL-6 receptor (IL-6R; tocilizumab) therapies and investigated the impact of this in vivo blockade on T cell fate and function. Immune outcomes were influenced by the target of the therapeutic intervention (IL-6 versus IL-6R) and by peak drug concentration. Tocilizumab reduced ICOS expression on T follicular helper cell populations and T cell receptor-driven (TCR-driven) STAT3 phosphorylation. Siltuximab reversed resistance to Treg-mediated suppression and increased TCR-driven phosphorylated STAT3 and production of IL-10, IL-21, and IL-27 by T effectors. Together, these findings indicate that the context of IL-6 blockade in vivo drives distinct T cell-intrinsic changes that may influence therapeutic outcomes.

A composite immune signature parallels disease progression across T1D subjects.

At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting ß cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.

Stochastic Kinetics of Nascent RNA.

The stochastic kinetics of transcription is typically inferred from the distribution of RNA numbers in individual cells. However, cellular RNA reflects additional processes downstream of transcription, hampering this analysis. In contrast, nascent (actively transcribed) RNA closely reflects the kinetics of transcription. We present a theoretical model for the stochastic kinetics of nascent RNA, which we solve to obtain the probability distribution of nascent RNA per gene. The model allows us to evaluate the kinetic parameters of transcription from single-cell measurements of nascent RNA. The model also predicts surprising discontinuities in the distribution of nascent RNA, a feature which we verify experimentally.

Single-cell analysis of transcription kinetics across the cell cycle.

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.

The spliceosome is a therapeutic vulnerability in MYC-driven cancer.

MYC (also known as c-MYC) overexpression or hyperactivation is one of the most common drivers of human cancer. Despite intensive study, the MYC oncogene remains recalcitrant to therapeutic inhibition. MYC is a transcription factor, and many of its pro-tumorigenic functions have been attributed to its ability to regulate gene expression programs. Notably, oncogenic MYC activation has also been shown to increase total RNA and protein production in many tissue and disease contexts. While such increases in RNA and protein production may endow cancer cells with pro-tumour hallmarks, this increase in synthesis may also generate new or heightened burden on MYC-driven cancer cells to process these macromolecules properly. Here we discover that the spliceosome is a new target of oncogenic stress in MYC-driven cancers. We identify BUD31 as a MYC-synthetic lethal gene in human mammary epithelial cells, and demonstrate that BUD31 is a component of the core spliceosome required for its assembly and catalytic activity. Core spliceosomal factors (such as SF3B1 and U2AF1) associated with BUD31 are also required to tolerate oncogenic MYC. Notably, MYC hyperactivation induces an increase in total precursor messenger RNA synthesis, suggesting an increased burden on the core spliceosome to process pre-mRNA. In contrast to normal cells, partial inhibition of the spliceosome in MYC-hyperactivated cells leads to global intron retention, widespread defects in pre-mRNA maturation, and deregulation of many essential cell processes. Notably, genetic or pharmacological inhibition of the spliceosome in vivo impairs survival, tumorigenicity and metastatic proclivity of MYC-dependent breast cancers. Collectively, these data suggest that oncogenic MYC confers a collateral stress on splicing, and that components of the spliceosome may be therapeutic entry points for aggressive MYC-driven cancers.

Measuring mRNA copy number in individual Escherichia coli cells using single-molecule fluorescent in situ hybridization.

We present a protocol for measuring the absolute number of mRNA molecules from a gene of interest in individual, chemically fixed Escherichia coli cells. A set of fluorescently labeled oligonucleotide probes is hybridized to the target mRNA, such that each mRNA molecule is decorated by a known number of fluorescent dyes. Cells are then imaged using fluorescence microscopy. The copy number of the target mRNA is estimated from the total intensity of fluorescent foci in the cell, rather than from counting discrete 'spots' as in other currently available protocols. Image analysis is performed using an automated algorithm. The measured mRNA copy number distribution obtained from many individual cells can be used to extract the parameters of stochastic gene activity, namely the frequency and size of transcription bursts from the gene of interest. The experimental procedure takes 2 d, with another 2-3 d typically required for image and data analysis.

A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis.

Myc is an oncogenic transcription factor frequently dysregulated in human cancer. To identify pathways supporting the Myc oncogenic program, we used a genome-wide RNA interference screen to search for Myc-synthetic lethal genes and uncovered a role for the SUMO-activating enzyme (SAE1/2). Loss of SAE1/2 enzymatic activity drives synthetic lethality with Myc. Inactivation of SAE2 leads to mitotic catastrophe and cell death upon Myc hyperactivation. Mechanistically, SAE2 inhibition switches a transcriptional subprogram of Myc from activated to repressed. A subset of these SUMOylation-dependent Myc switchers (SMS genes) is required for mitotic spindle function and to support the Myc oncogenic program. SAE2 is required for growth of Myc-dependent tumors in mice, and gene expression analyses of Myc-high human breast cancers suggest that low SAE1 and SAE2 abundance in the tumors correlates with longer metastasis-free survival of the patients. Thus, inhibition of SUMOylation may merit investigation as a possible therapy for Myc-driven human cancers.

Single-virus tracking reveals a spatial receptor-dependent search mechanism.

Viral infection begins with the binding of a virus to a specific target on the surface of the host cell, followed by viral genome delivery into the host and a continuation of the infection process. Before binding occurs, the virus must first find its receptor by a process whose details are largely unknown. We applied high-resolution fluorescence microscopy and single-particle tracking to elucidate the target-finding process in bacteriophage λ as it infects an Escherichia coli cell. By monitoring the motion of individual viruses through the early stages of infection, we identified a unique spatial focusing process that allows a virus to arrive from its initial random landing site to its destination at the cell pole. The search process is governed by the interaction between the virus and the LamB receptors, and by the spatial organization of the receptor network on the cell surface. Our findings allowed us to develop a theoretical model for the target-finding process that reproduces the key features observed in experiment. We discuss the possible implications of our findings for the process of viral receptor-finding in higher systems.

Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene.

The ability of living cells to maintain an inheritable memory of their gene-expression state is key to cellular differentiation. Bacterial lysogeny serves as a simple paradigm for long-term cellular memory. In this study, we address the following question: in the absence of external perturbation, how long will a cell stay in the lysogenic state before spontaneously switching away from that state? We show by direct measurement that lysogen stability exhibits a simple exponential dependence on the frequency of activity bursts from the fate-determining gene, cI. We quantify these gene-activity bursts using single-molecule-resolution mRNA measurements in individual cells, analyzed using a stochastic mathematical model of the gene-network kinetics. The quantitative relation between stability and gene activity is independent of the fine details of gene regulation, suggesting that a quantitative prediction of cell-state stability may also be possible in more complex systems.

Decision making at a subcellular level determines the outcome of bacteriophage infection.

When the process of cell-fate determination is examined at single-cell resolution, it is often observed that individual cells undergo different fates even when subject to identical conditions. This "noisy" phenotype is usually attributed to the inherent stochasticity of chemical reactions in the cell. Here we demonstrate how the observed single-cell heterogeneity can be explained by a cascade of decisions occurring at the subcellular level. We follow the postinfection decision in bacteriophage lambda at single-virus resolution, and show that a choice between lysis and lysogeny is first made at the level of the individual virus. The decisions by all viruses infecting a single cell are then integrated in a precise (noise-free) way, such that only a unanimous vote by all viruses leads to the establishment of lysogeny. By detecting and integrating over the subcellular "hidden variables," we are able to predict the level of noise measured at the single-cell level.