Analysis of a transcriptional network involving PU.1, Notch, and Gata3 in the lymphomyeloid lineage decision during early T-cell development

Hematopoiesis is a well-established system used to study developmental choices amongst cells with multiple lineage potentials, as well as the transcription factor network interactions that drive these developmental paths. Multipotent progenitors travel from the bone marrow to the thymus where T-cell development is initiated and these early T-cell precursors retain lineage plasticity even after initiating a T-cell program. The development of these early cells is driven by Notch signaling and the combinatorial expression of many transcription factors, several of which are also involved in the development of other cell lineages. The ETS family transcription factor PU.1 is involved in the development of progenitor, myeloid, and lymphoid cells, and can divert progenitor T-cells from the T-lineage to a myeloid lineage. This diversion of early T-cells by PU.1 can be blocked by Notch signaling. The PU.1 and Notch interaction creates a switch wherein PU.1 in the presence of Notch promotes T-cell identity and PU.1 in the absence of Notch signaling promotes a myeloid identity. Here we characterized an early T-cell cell line, Scid.adh.2c2, as a good model system for studying the myeloid vs. lymphoid developmental choice dependent on PU.1 and Notch signaling. We then used the Scid.adh.2c2 system to identify mechanisms mediating PU.1 and Notch signaling interactions during early T-cell development. We show that the mechanism by which Notch signaling is protecting pro-T cells is neither degradation nor modification of the PU.1 protein. Instead we give evidence that Notch signaling is blocking the PU.1-driven inhibition of a key set of T-regulatory genes including Myb, Tcf7, and Gata3. We show that the protection of Gata3 from PU.1-mediated inhibition, by Notch signaling and Myb, is important for retaining a T-lineage identity. We also discuss a PU.1-driven mechanism involving E-protein inhibition that leads to the inhibition of Notch target genes. This is mechanism may be used as a lockdown mechanism in pro-T-cells that have made the decision to divert to the myeloid pathway.

The combinatorics of transcriptional regulation

The ability to regulate gene expression is of central importance for the adaptability of living organisms to changes in their internal and external environment. At the transcriptional level, binding of transcription factors (TFs) in the vicinity of promoters can modulate the rate at which transcripts are produced, and as such play an important role in gene regulation. TFs with regulatory action at multiple promoters is the rule rather than the exception, with examples ranging from TFs like the cAMP receptor protein (CRP) in E. coli that regulates hundreds of different genes, to situations involving multiple copies of the same gene, such as on plasmids, or viral DNA. When the number of TFs heavily exceeds the number of binding sites, TF binding to each promoter can be regarded as independent. However, when the number of TF molecules is comparable to the number of binding sites, TF titration will result in coupling ("entanglement") between transcription of different genes. The last few decades have seen rapid advances in our ability to quantitatively measure such effects, which calls for biophysical models to explain these data. Here we develop a statistical mechanical model which takes the TF titration effect into account and use it to predict both the level of gene expression and the resulting correlation in transcription rates for a general set of promoters. To test these predictions experimentally, we create genetic constructs with known TF copy number, binding site affinities, and gene copy number; hence avoiding the need to use free fit parameters. Our results clearly prove the TF titration effect and that the statistical mechanical model can accurately predict the fold change in gene expression for the studied cases. We also generalize these experimental efforts to cover systems with multiple different genes, using the method of mRNA fluorescence in situ hybridization (FISH). Interestingly, we can use the TF titration affect as a tool to measure the plasmid copy number at different points in the cell cycle, as well as the plasmid copy number variance. Finally, we investigate the strategies of transcriptional regulation used in a real organism by analyzing the thousands of known regulatory interactions in E. coli. We introduce a "random promoter architecture model" to identify overrepresented regulatory strategies, such as TF pairs which coregulate the same genes more frequently than would be expected by chance, indicating a related biological function. Furthermore, we investigate whether promoter architecture has a systematic effect on gene expression by linking the regulatory data of E. coli to genome-wide expression censuses.

Sequence-function relationships in E. coli transcriptional regulation

Understanding how transcriptional regulatory sequence maps to regulatory function remains a difficult problem in regulatory biology. Given a particular DNA sequence for a bacterial promoter region, we would like to be able to say which transcription factors bind there, how strongly they bind, and whether they interact with each other and/or RNA polymerase, with the ultimate objective of integrating knowledge of these parameters into a prediction of gene expression levels. The theoretical framework of statistical thermodynamics provides a useful framework for doing so, enabling us to predict how gene expression levels depend on transcription factor binding energies and concentrations. We used thermodynamic models, coupled with models of the sequence-dependent binding energies of transcription factors and RNAP, to construct a genotype to phenotype map for the level of repression exhibited by the lac promoter, and tested it experimentally using a set of promoter variants from E. coli strains isolated from different natural environments. For this work, we sought to ``reverse engineer'' naturally occurring promoter sequences to understand how variations in promoter sequence affects gene expression. The natural inverse of this approach is to ``forward engineer'' promoter sequences to obtain targeted levels of gene expression. We used a high precision model of RNAP-DNA sequence dependent binding energy, coupled with a thermodynamic model relating binding energy to gene expression, to predictively design and verify a suite of synthetic E. coli promoters whose expression varied over nearly three orders of magnitude.

However, although thermodynamic models enable predictions of mean levels of gene expression, it has become evident that cell-to-cell variability or ``noise'' in gene expression can also play a biologically important role. In order to address this aspect of gene regulation, we developed models based on the chemical master equation framework and used them to explore the noise properties of a number of common E. coli regulatory motifs; these properties included the dependence of the noise on parameters such as transcription factor binding strength and copy number. We then performed experiments in which these parameters were systematically varied and measured the level of variability using mRNA FISH. The results showed a clear dependence of the noise on these parameters, in accord with model predictions.

Finally, one shortcoming of the preceding modeling frameworks is that their applicability is largely limited to systems that are already well-characterized, such as the lac promoter. Motivated by this fact, we used a high throughput promoter mutagenesis assay called Sort-Seq to explore the completely uncharacterized transcriptional regulatory DNA of the E. coli mechanosensitive channel of large conductance (MscL). We identified several candidate transcription factor binding sites, and work is continuing to identify the associated proteins.

A Detailed Analysis of Transcriptional Regulators Affecting the Saccharomyces cerevisiae Heat-shock Gene SSA1

The yeast Saccharomyces cerevisiae contains a family of hsp70 related genes. One member of this family, SSA1, encodes a 70kD heat-shock protein which in addition to its heat inducible expression has a significant basal level of expression. The first 500 bp upstream of the SSA1 start point of transcription was examined by DNAse I protection analysis. The results reveal the presence of at least 14 factor binding sites throughout the upstream promoter region. The function of these binding sites has been examined using a series of 5' promoter deletions fused to the recorder gene lacZ in a centromere-containing yeast shuttle vector. The following sites have been identified in the promoter and their activity in yeast determined individually with a centromere-based recorder plasmid containing a truncated CYC1 /lacZ fusion: a heat-shock element or HSE which is sufficient to convey heat-shock response on the recorder plasmid; a homology to the SV40 'core' sequence which can repress the GCN4 recognition element (GCRE) and the yAP1 recognition element (ARE), and has been designated a upstream repression element or URE; a 'G'-rich region named G-box which can also convey heatshock response on the recorder plasmid; and a purine-pyrimidine alternating sequence name GT-box which is an activator of transcription. A series of fusion constructs were made to identify a putative silencer-like element upstream of SSA1. This element is position dependent and has been localized to a region containing both an ABF1 binding site and a RAP1 binding site. Five site-specific DNA-binding factors are identified and their purification is presented: the heat-shock transcription factor or HSTF, which recognizes the HSE; the G-box binding factor or GBF; the URE recognition factor or URF; the GT-box binding factor; and the GC-box binding factor or yeast Sp1.

Minimum drag profiles in infinite cavity flows

The problem considered is that of minimizing the drag of a symmetric plate in infinite cavity flow under the constraints of fixed arclength and fixed chord. The flow is assumed to be steady, irrotational, and incompressible. The effects of gravity and viscosity are ignored.

Using complex variables, expressions for the drag, arclength, and chord, are derived in terms of two hodograph variables, Γ (the logarithm of the speed) and β (the flow angle), and two real parameters, a magnification factor and a parameter which determines how much of the plate is a free-streamline.

Two methods are employed for optimization:

(1) The parameter method. Γ and β are expanded in finite orthogonal series of N terms. Optimization is performed with respect to the N coefficients in these series and the magnification and free-streamline parameters. This method is carried out for the case N = 1 and minimum drag profiles and drag coefficients are found for all values of the ratio of arclength to chord.

(2) The variational method. A variational calculus method for minimizing integral functionals of a function and its finite Hilbert transform is introduced, This method is applied to functionals of quadratic form and a necessary condition for the existence of a minimum solution is derived. The variational method is applied to the minimum drag problem and a nonlinear integral equation is derived but not solved.

Estimation of catalyst activity profiles and deactivation parameters from reactor operating data with applications to naphtha reforming

Techniques are developed for estimating activity profiles in fixed bed reactors and catalyst deactivation parameters from operating reactor data. These techniques are applicable, in general, to most industrial catalytic processes. The catalytic reforming of naphthas is taken as a broad example to illustrate the estimation schemes and to signify the physical meaning of the kinetic parameters of the estimation equations. The work is described in two parts. Part I deals with the modeling of kinetic rate expressions and the derivation of the working equations for estimation. Part II concentrates on developing various estimation techniques.

Part I: The reactions used to describe naphtha reforming are dehydrogenation and dehydroisomerization of cycloparaffins; isomerization, dehydrocyclization and hydrocracking of paraffins; and the catalyst deactivation reactions, namely coking on alumina sites and sintering of platinum crystallites. The rate expressions for the above reactions are formulated, and the effects of transport limitations on the overall reaction rates are discussed in the appendices. Moreover, various types of interaction between the metallic and acidic active centers of reforming catalysts are discussed as characterizing the different types of reforming reactions.

Part II: In catalytic reactor operation, the activity distribution along the reactor determines the kinetics of the main reaction and is needed for predicting the effect of changes in the feed state and the operating conditions on the reactor output. In the case of a monofunctional catalyst and of bifunctional catalysts in limiting conditions, the cumulative activity is sufficient for predicting steady reactor output. The estimation of this cumulative activity can be carried out easily from measurements at the reactor exit. For a general bifunctional catalytic system, the detailed activity distribution is needed for describing the reactor operation, and some approximation must be made to obtain practicable estimation schemes. This is accomplished by parametrization techniques using measurements at a few points along the reactor. Such parametrization techniques are illustrated numerically with a simplified model of naphtha reforming.

To determine long term catalyst utilization and regeneration policies, it is necessary to estimate catalyst deactivation parameters from the the current operating data. For a first order deactivation model with a monofunctional catalyst or with a bifunctional catalyst in special limiting circumstances, analytical techniques are presented to transform the partial differential equations to ordinary differential equations which admit more feasible estimation schemes. Numerical examples include the catalytic oxidation of butene to butadiene and a simplified model of naphtha reforming. For a general bifunctional system or in the case of a monofunctional catalyst subject to general power law deactivation, the estimation can only be accomplished approximately. The basic feature of an appropriate estimation scheme involves approximating the activity profile by certain polynomials and then estimating the deactivation parameters from the integrated form of the deactivation equation by regression techniques. Different bifunctional systems must be treated by different estimation algorithms, which are illustrated by several cases of naphtha reforming with different feed or catalyst composition.