Justin B. Kinney

Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare >27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-β enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.

Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains

CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases. Current screening strategies target CRISPR-Cas9–induced mutations to the 5′ exons of candidate genes, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies. Here we overcome this limitation by targeting CRISPR-Cas9 mutagenesis to exons encoding functional protein domains. This generates a higher proportion of null mutations and substantially increases the potency of negative selection. We also show that the magnitude of negative selection can be used to infer the functional importance of individual protein domains of interest. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies. A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting.

Evaluation of methods for modeling transcription factor sequence specificity

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a proteins DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites.

We present a genomewide cross-species analysis of regulation for broad-acting transcription factors in yeast. Our model for binding site evolution is founded on biophysics: the binding energy between transcription factor and site is a quantitative phenotype of regulatory function, and selection is given by a fitness landscape that depends on this phenotype. The model quantifies conservation, as well as loss and gain, of functional binding sites in a coherent way. Its predictions are supported by direct cross-species comparison between four yeast species. We find ubiquitous compensatory mutations within functional sites, such that the energy phenotype and the function of a site evolve in a significantly more constrained way than does its sequence. We also find evidence for substantial evolution of regulatory function involving point mutations as well as sequence insertions and deletions within binding sites. Genes lose their regulatory link to a given transcription factor at a rate similar to the neutral point mutation rate, from which we infer a moderate average fitness advantage of functional over nonfunctional sites. In a wider context, this study provides an example of inference of selection acting on a quantitative molecular trait.

Equitability, mutual information, and the maximal information coefficient

Significance Attention has recently focused on a basic yet unresolved problem in statistics: How can one quantify the strength of a statistical association between two variables without bias for relationships of a specific form? Here we propose a way of mathematically formalizing this “equitability” criterion, using core concepts from information theory. This criterion is naturally satisfied by a fundamental information-theoretic measure of dependence called “mutual information.” By contrast, a recently introduced dependence measure called the “maximal information coefficient” is seen to violate equitability. We conclude that estimating mutual information provides a natural and practical method for equitably quantifying associations in large datasets. How should one quantify the strength of association between two random variables without bias for relationships of a specific form? Despite its conceptual simplicity, this notion of statistical “equitability” has yet to receive a definitive mathematical formalization. Here we argue that equitability is properly formalized by a self-consistency condition closely related to Data Processing Inequality. Mutual information, a fundamental quantity in information theory, is shown to satisfy this equitability criterion. These findings are at odds with the recent work of Reshef et al. [Reshef DN, et al. (2011) Science 334(6062):1518–1524], which proposed an alternative definition of equitability and introduced a new statistic, the “maximal information coefficient” (MIC), said to satisfy equitability in contradistinction to mutual information. These conclusions, however, were supported only with limited simulation evidence, not with mathematical arguments. Upon revisiting these claims, we prove that the mathematical definition of equitability proposed by Reshef et al. cannot be satisfied by any (nontrivial) dependence measure. We also identify artifacts in the reported simulation evidence. When these artifacts are removed, estimates of mutual information are found to be more equitable than estimates of MIC. Mutual information is also observed to have consistently higher statistical power than MIC. We conclude that estimating mutual information provides a natural (and often practical) way to equitably quantify statistical associations in large datasets.

Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence

Cells use protein-DNA and protein-protein interactions to regulate transcription. A biophysical understanding of this process has, however, been limited by the lack of methods for quantitatively characterizing the interactions that occur at specific promoters and enhancers in living cells. Here we show how such biophysical information can be revealed by a simple experiment in which a library of partially mutated regulatory sequences are partitioned according to their in vivo transcriptional activities and then sequenced en masse. Computational analysis of the sequence data produced by this experiment can provide precise quantitative information about how the regulatory proteins at a specific arrangement of binding sites work together to regulate transcription. This ability to reliably extract precise information about regulatory biophysics in the face of experimental noise is made possible by a recently identified relationship between likelihood and mutual information. Applying our experimental and computational techniques to the Escherichia coli lac promoter, we demonstrate the ability to identify regulatory protein binding sites de novo, determine the sequence-dependent binding energy of the proteins that bind these sites, and, importantly, measure the in vivo interaction energy between RNA polymerase and a DNA-bound transcription factor. Our approach provides a generally applicable method for characterizing the biophysical basis of transcriptional regulation by a specified regulatory sequence. The principles of our method can also be applied to a wide range of other problems in molecular biology.

Precise physical models of protein-DNA interaction from high-throughput data

A cells ability to regulate gene transcription depends in large part on the energy with which transcription factors (TFs) bind their DNA regulatory sites. Obtaining accurate models of this binding energy is therefore an important goal for quantitative biology. In this article, we present a principled likelihood-based approach for inferring physical models of TF–DNA binding energy from the data produced by modern high-throughput binding assays. Central to our analysis is the ability to assess the relative likelihood of different model parameters given experimental observations. We take a unique approach to this problem and show how to compute likelihood without any explicit assumptions about the noise that inevitably corrupts such measurements. Sampling possible choices for model parameters according to this likelihood function, we can then make probabilistic predictions for the identities of binding sites and their physical binding energies. Applying this procedure to previously published data on the Saccharomyces cerevisiae TF Abf1p, we find models of TF binding whose parameters are determined with remarkable precision. Evidence for the accuracy of these models is provided by an astonishing level of phylogenetic conservation in the predicted energies of putative binding sites. Results from in vivo and in vitro experiments also provide highly consistent characterizations of Abf1p, a result that contrasts with a previous analysis of the same data.

Domain within the helicase subunit Mcm4 integrates multiple kinase signals to control DNA replication initiation and fork progression

Significance During each cell-division cycle, eukaryotic cells initiate DNA synthesis from multiple replication origins on chromosomes to duplicate the entire genome once and only once. Spatial and temporal control of initiation and subsequent DNA synthesis at replication forks is important for maintaining genome integrity. Here we present a comprehensive analysis of patterns of origin activation, replication fork progression, and checkpoint responses in cells under replication stress. Our studies showed that a domain intrinsic to the replicative helicase, which unwinds DNA during replication, integrates multiple kinase-signaling pathways to control various aspects of the genome duplication process. Our work suggests a mechanism by which eukaryotic cells modulate the pattern of replication in response to environmental conditions through the replicative helicase. Eukaryotic DNA synthesis initiates from multiple replication origins and progresses through bidirectional replication forks to ensure efficient duplication of the genome. Temporal control of initiation from origins and regulation of replication fork functions are important aspects for maintaining genome stability. Multiple kinase-signaling pathways are involved in these processes. The Dbf4-dependent Cdc7 kinase (DDK), cyclin-dependent kinase (CDK), and Mec1, the yeast Ataxia telangiectasia mutated/Ataxia telangiectasia mutated Rad3-related checkpoint regulator, all target the structurally disordered N-terminal serine/threonine-rich domain (NSD) of mini-chromosome maintenance subunit 4 (Mcm4), a subunit of the mini-chromosome maintenance (MCM) replicative helicase complex. Using whole-genome replication profile analysis and single-molecule DNA fiber analysis, we show that under replication stress the temporal pattern of origin activation and DNA replication fork progression are altered in cells with mutations within two separate segments of the Mcm4 NSD. The proximal segment of the NSD residing next to the DDK-docking domain mediates repression of late-origin firing by checkpoint signals because in its absence late origins become active despite an elevated DNA damage-checkpoint response. In contrast, the distal segment of the NSD at the N terminus plays no role in the temporal pattern of origin firing but has a strong influence on replication fork progression and on checkpoint signaling. Both fork progression and checkpoint response are regulated by the phosphorylation of the canonical CDK sites at the distal NSD. Together, our data suggest that the eukaryotic MCM helicase contains an intrinsic regulatory domain that integrates multiple signals to coordinate origin activation and replication fork progression under stress conditions.

r-modes in accreting neutron stars with magnetoviscous boundary layers

We explore the dynamics of the r-modes in accreting neutron stars in two ways. First, we explore how dissipation in the magnetoviscous boundary layer (MVBL) at the crust-core interface governs the damping of r-mode perturbations in the fluid interior. Two models are considered: one assuming an ordinary-fluid interior, the other taking the core to consist of superfluid neutrons, type II superconducting protons, and normal electrons. We show, within our approximations, that no solution to the magnetohydrodynamic equations exists in the superfluid model when both the neutron and proton vortices are pinned. However, if just one species of vortex is pinned, we can find solutions. When the neutron vortices are pinned and the proton vortices are unpinned there is much more dissipation than in the ordinary-fluid model, unless the pinning is weak. When the proton vortices are pinned and the neutron vortices are unpinned the dissipation is comparable or slightly less than that for the ordinary-fluid model, even when the pinning is strong. We also find in the superfluid model that relatively weak radial magnetic fields ∼109G(108K/T)2 greatly affect the MVBL, though the effects of mutual friction tend to counteract the magnetic effects. Second, we evolve our two models in time, accounting for accretion, and explore how the magnetic field strength, the r-mode saturation amplitude, and the accretion rate affect the cyclic evolution of these stars. If the r-modes control the spin cycles of accreting neutron stars we find that magnetic fields can affect the clustering of the spin frequencies of low mass x-ray binaries and the fraction of these that are currently emitting gravitational waves.

The transcriptional cofactor TRIM33 prevents apoptosis in B lymphoblastic leukemia by deactivating a single enhancer

Most mammalian transcription factors (TFs) and cofactors occupy thousands of genomic sites and modulate the expression of large gene networks to implement their biological functions. In this study, we describe an exception to this paradigm. TRIM33 is identified here as a lineage dependency in B cell neoplasms and is shown to perform this essential function by associating with a single cis element. ChIP-seq analysis of TRIM33 in murine B cell leukemia revealed a preferential association with two lineage-specific enhancers that harbor an exceptional density of motifs recognized by the PU.1 TF. TRIM33 is recruited to these elements by PU.1, yet acts to antagonize PU.1 function. One of the PU.1/TRIM33 co-occupied enhancers is upstream of the pro-apoptotic gene Bim, and deleting this enhancer renders TRIM33 dispensable for leukemia cell survival. These findings reveal an essential role for TRIM33 in preventing apoptosis in B lymphoblastic leukemia by interfering with enhancer-mediated Bim activation. DOI: http://dx.doi.org/10.7554/eLife.06377.001