Brf1 post-transcriptionally regulates pluripotency and differentiation responses downstream of Erk MAP Kinase

FGF/Erk MAP Kinase Signaling is a central regulator of mouse embryonic stem cell (mESC) self-renewal, pluripotency and differentiation. However, the mechanistic connection between this signaling pathway activity and the gene circuits stabilizing mESCs in vitro remain unclear. Here we show that FGF signaling post-transcriptionally regulates the mESC transcription factor network by controlling the expression of Brf1 (zfp36l1), an AU-rich element mRNA binding protein. Changes in Brf1 level disrupts the expression of core pluripotency-associated genes and attenuates mESC self-renewal without inducing differentiation. These regulatory effects are mediated by rapid and direct destabilization of Brf1 targets, such as Nanog mRNA. Interestingly, enhancing Brf1 expression does not compromise mESC pluripotency, but does preferentially regulate differentiation to mesendoderm by accelerating the expression of primitive streak markers. Together, these studies demonstrate that FGF signals utilize targeted mRNA degradation by Brf1 to enable rapid post-transcriptional control of gene expression.

Molecular neurogenetics of eye development in Drosophila

The compound eye of Drosophila melanogaster begins to differentiate during the late third larval instar in the eye-antennal imaginal disc. A wave of morphogenesis crosses the disc from posterior to anterior, leaving behind precisely patterned clusters of photoreceptor cells and accessory cells that will constitute the adult ommatidia of the retina. By the analysis of genetically mosaic eyes, it appears that any cell in the eye disc can adopt the characteristics of any one of the different cell types found in the mature eye, including photoreceptor cells and non-neuronal accessory cells such as cone cells. Therefore, cells within the prospective retinal epithelium assume different fates presumably via information present in the environment. The sevenless^+ (sev^+) gene appears to play a role in the expression of one of the possible fates, since the mutant phenotype is the lack of one of the pattern elements, namely, photoreceptor cell R7. The sev^+ gene product had been shown to be required during development of the eye, and had also been shown in genetic mosaics to be autonomous to presumptive R7. As a means of better understanding the pathway instructing the differentiation R7, the gene and its protein product were characterized.

The sev+ gene was cloned by P-element transposon tagging, and was found to encode an 8.2 kb transcript expressed in developing eye discs and adult heads. By raising monoclonal antibodies (MAbs) against a sev^+- β-galactosidase fusion protein, the expression of the protein in the eye disc was localized by immuno-electronmicroscopy. The protein localizes to the apical cell membranes and microvilli of cells in the eye disc epithelium. It appears during development at a time coincident with the initial formation of clusters, and in all the developing photoreceptors and accessory cone cells at a time prior to the overt differentiation of R7. This result is consistent with the pluripotency of cells in the eye disc. Its localization in the membranes suggests that it may receive information directing the development of R7. Its localization in the apical membranes and microvilli is away from the bulk of the cell contacts, which have been cited as a likely regions for information presentation and processing. Biochemical characterization of the sev^+ protein will be necessary to describe further its role in development.

Other mutations in Drosophila have eye phenotypes. These were analyzed to find which ones affected the initial patterning of cells in the eye disc, in order to identify other genes, like sev, whose gene products may be involved in generating the pattern. The adult eye phenotypes ranged from severe reduction of the eye, to variable numbers of photoreceptor cells per ommatidium, to sub de defects in the organization of the supporting cells. Developing eye discs from the different strains were screened using a panel of MAbs, which highlight various developmental stages. Two identified matrix elements in and anterior to the furrow, while others identified the developing ommatidia themselves, like the anti-sev MAb. Mutation phenotypes were shown to appear at many stages of development. Some mutations seem to affect the precursor cells, others, the setting up of the pattern, and still others, the maintenance of the pattern. Thus, additional genes have now been identified that may function to support the development of a complex pattern.

Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.