Biology subjects

Kinney, J. B.

Publications and source records attributed to Kinney, J. B..

27 records · Page 2Linked to original sources

bioRxiv2020

Evolution of DNA Replication Origin Specification and Gene Silencing Mechanisms

DNA replication in eukaryotic cells initiates from chromosomal locations, called replication origins, that bind the Origin Recognition Complex (ORC) prior to S phase. Origin establishment is guided by well-defined DNA sequence motifs in Saccharomyces cerevisiae and some other budding yeasts, but most eukaryotes lack sequence-specific origins. At present, the mechanistic and evolutionary reasons for this difference are unclear. A 3.9 Å structure of S. cerevisiae ORC-Cdc6-Cdt1-Mcm2-7 (OCCM) bound to origin DNA revealed, among other things, that a loop within Orc2 inserts into a DNA minor groove and an α-helix within Orc4 inserts into a DNA major groove1. We show that this Orc4 α-helix mediates the sequence-specificity of origins in S. cerevisiae. Specifically, mutations were identified within this α-helix that alter the sequence-dependent activity of individual origins as well as change global genomic origin firing patterns. This was accomplished using a massively parallel origin selection assay analyzed using a custom mutual-information-based modeling approach and a separate analysis of whole-genome replication profiling and statistics. Interestingly, the sequence specificity of DNA replication initiation, as mediated by the Orc4 α-helix, has evolved in close conjunction with the gain of ORC-Sir4-mediated gene silencing and the loss of RNA interference.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2020

Deciphering the regulatory genome of Escherichiacoli, one hundred promoters at a time

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for {approx} 65% of the promoters we remain completely ignorant of their regulation. Until we have cracked this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method (Reg-Seq) linking a massively-parallel reporter assay and mass spectrometry to produce a base pair resolution dissection of more than 100 promoters in E. coli in 12 different growth conditions. First, we show that our method recapitulates regulatory information from known sequences. Then, we examine the regulatory architectures for more than 80 promoters in the E. coli genome which previously had no known regulation. In many cases, we also identify which transcription factors mediate their regulation. The method introduced here clears a path for fully characterizing the regulatory genome of model organisms, with the potential of moving on to an array of other microbes of ecological and medical relevance.

biophysics↗

bioRxiv2019

Biophysical models of cis-regulation as interpretable neural networks

The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.

bioinformatics↗

bioRxiv2019

Logomaker: beautiful sequence logos in Python

Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA, and protein sequences, yet it is currently difficult to generate such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from any matrix-like array of numbers. Logos are rendered as vector graphics that are easy to stylize using standard matplotlib functions. Methods for creating logos from multiple-sequence alignments are also included.\n\nAvailability and ImplementationLogomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Source code is available at http://github.com/jbkinney/logomaker.\n\nSupplemental InformationDocumentation is provided at http://logomaker.readthedocs.io.\n\nContactjkinney@cshl.edu.

bioinformatics↗

bioRxiv2018

Precision measurement of cis-regulatory energetics in living cells

Gene expression in all organisms is controlled by cooperative interactions between DNA-bound transcription factors (TFs), but quantitatively measuring TF-DNA and TF-TF interactions remains difficult. Here we introduce a strategy for precisely measuring the Gibbs free energy of such interactions in living cells. This strategy centers on the measurement and modeling of \"allelic manifolds\", a multidimensional generalization of the classical genetics concept of allelic series. Allelic manifolds are measured using reporter assays performed on strategically designed cis-regulatory sequences. Quantitative biophysical models are then fit to the resulting data. We used this strategy to study regulation by two Escherichia coli TFs, CRP and {sigma}70 RNA polymerase. Doing so, we consistently obtained energetic measurements precise to ~ 0.1 kcal/mol. We also obtained multiple results that deviate from the prior literature. Our strategy is compatible with massively parallel reporter assays in both prokaryotes and eukaryotes, and should therefore be highly scalable and broadly applicable.

biophysics↗

bioRxiv2018

Selection for protein stability enriches for epistatic interactions

A now classical argument for the marginal thermodynamic stability of proteins explains the distribution of observed protein stabilities as a consequence of an entropic pull in protein sequence space. In particular, most sequences that are sufficiently stable to fold will have stabilities near the folding threshold. Here we extend this argument to consider its predictions for epistatic interactions for the effects of mutations on the free energy of folding. Although there is abundant evidence to indicate that the effects of mutations on the free energy of folding are nearly additive and conserved over evolutionary time, we show that these observations are compatible with the hypothesis that a non-additive contribution to the folding free energy is essential for observed proteins to maintain their native structure. In particular through both simulations and analytical results, we show that even very small departures from additivity are sufficient to drive this effect.

evolutionary biology↗

bioRxiv2018

Mapping DNA sequence to transcription factor binding energy in vivo

Despite the central importance of transcriptional regulation in systems biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to analyze a promoter sequence and identify the locations, regulatory roles, and energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for interpreting transcriptional regulatory sequences using in vivo methods (i.e. the massively parallel reporter assay Sort-Seq) to formulate quantitative models that map a transcription factor binding sites DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factors sequence specificity.

biophysics↗

bioRxiv2017

A systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria

Gene regulation is one of the most ubiquitous processes in biology. But while the catalog of bacterial genomes continues to expand rapidly, we remain ignorant about how almost all of the genes in these genomes are regulated. At present, characterizing the molecular mechanisms by which individual regulatory sequences operate requires focused efforts using low-throughput methods. Here we show how a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling can be used to dissect bacterial promoters in a systematic and scalable way. We demonstrate this method on both well-studied and previously uncharacterized promoters in the enteric bacterium Escherichia coli. In all cases we recover nucleotide-resolution models of promoter mechanism. For some promoters, including previously unannotated ones, the approach allowed us to further extract quantitative biophysical models describing input-output relationships. This method opens up the possibility of exhaustively dissecting the mechanisms of promoter function in E. coli and a wide range of other bacteria.

biophysics↗

bioRxiv2017

Physical epistatic landscape of antibody binding affinity

Affinity maturation produces antibodies that bind antigens with high specificity by accumulating mutations in the antibody sequence. Mapping out the antibody-antigen affinity landscape can give us insight into the accessible paths during this rapid evolutionary process. By developing a carefully controlled null model for noninteracting mutations, we characterized epistasis in affinity measurements of a large library of antibody variants obtained by Tite-Seq, a recently introduced Deep Mutational Scan method yielding physical values of the binding constant. We show that representing affinity as the binding free energy minimizes epistasis. Yet, we find that epistatically interacting sites contribute substantially to binding. In addition to negative epistasis, we report a large amount of beneficial epistasis, enlarging the space of high-affinity antibodies as well as their mutational accessibility. These properties suggest that the degeneracy of antibody sequences that can bind a given antigen is enhanced by epistasis -- an important property for vaccine design.

biophysics↗