Function-based classification of hazardous biological sequences: Demonstration of a new paradigm for biohazard assessments.

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous "fingerprints" based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

Dynamic control of gene regulatory logic by seemingly redundant transcription factors.

Many transcription factors co-express with their homologs to regulate identical target genes, however the advantages of such redundancies remain elusive. Using single-cell imaging and microfluidics, we study the yeast general stress response transcription factor Msn2 and its seemingly redundant homolog Msn4. We find that gene regulation by these two factors is analogous to logic gate systems. Target genes with fast activation kinetics can be fully induced by either factor, behaving as an 'OR' gate. In contrast, target genes with slow activation kinetics behave as an 'AND' gate, requiring distinct contributions from both factors, upon transient stimulation. Furthermore, such genes become an 'OR' gate when the input duration is prolonged, suggesting that the logic gate scheme is not static but rather dependent on the input dynamics. Therefore, Msn2 and Msn4 enable a time-based mode of combinatorial gene regulation that might be applicable to homologous transcription factors in other organisms.

Mutual information and the fidelity of response of gene regulatory models.

We investigate cellular response to extracellular signals by using information theory techniques motivated by recent experiments. We present results for the steady state of the following gene regulatory models found in both prokaryotic and eukaryotic cells: a linear transcription-translation model and a positive or negative auto-regulatory model. We calculate both the information capacity and the mutual information exactly for simple models and approximately for the full model. We find that (1) small changes in mutual information can lead to potentially important changes in cellular response and (2) there are diminishing returns in the fidelity of response as the mutual information increases. We calculate the information capacity using Gillespie simulations of a model for the TNF-α-NF-κB network and find good agreement with the measured value for an experimental realization of this network. Our results provide a quantitative understanding of the differences in cellular response when comparing experimentally measured mutual information values of different gene regulatory models. Our calculations demonstrate that Gillespie simulations can be used to compute the mutual information of more complex gene regulatory models, providing a potentially useful tool in synthetic biology.

Noise propagation through extracellular signaling leads to fluctuations in gene expression.

BACKGROUND: Cell-to-cell variability in mRNA and proteins has been observed in many biological systems, including the human innate immune response to viral infection. Most of these studies have focused on variability that arises from (a) intrinsic stochastic fluctuations in gene expression and (b) extrinsic sources (e.g. fluctuations in transcription factors). The main focus of our study is the effect of extracellular signaling on enhancing intrinsic stochastic fluctuations. As a new source of noise, the communication between cells with fluctuating numbers of components has received little attention. We use agent-based modeling to study this contribution to noise in a system of human dendritic cells responding to viral infection. RESULTS: Our results, validated by single-cell experiments, show that in the transient state cell-to-cell variability in an interferon-stimulated gene (DDX58) arises from the interplay between the spatial randomness of the cellular sources of the interferon and the temporal stochasticity of its own production. The numerical simulations give insight into the time scales on which autocrine and paracrine signaling act in a heterogeneous population of dendritic cells upon viral infection. We study the effect of different factors that influence the magnitude of the cell-to-cell-variability of the induced gene, including the cell density, multiplicity of infection, and the time scale over which the cellular sources begin producing the cytokine. CONCLUSIONS: We propose a mechanism of noise propagation through extracellular communication and establish conditions under which the mechanism is operative. The cellular stochasticity of gene induction, which we investigate, is not limited to the specific interferon-induced gene we have studied; a broad distribution of copy numbers across cells is to be expected for other interferon-stimulated genes. This can lead to functional consequences for the system-level response to a viral challenge.

A quantitative model of nucleosome dynamics.

The expression, replication and repair of eukaryotic genomes require the fundamental organizing unit of chromatin, the nucleosome, to be unwrapped and disassembled. We have developed a quantitative model of nucleosome dynamics which provides a fundamental understanding of these DNA processes. We calibrated this model using results from high precision single molecule nucleosome unzipping experiments, and then tested its predictions for experiments in which nucleosomes are disassembled by the DNA mismatch recognition complex hMSH2-hMSH6. We found that this calibrated model quantitatively describes hMSH2-hMSH6 induced disassembly rates of nucleosomes with two separate DNA sequences and four distinct histone modification states. In addition, this model provides mechanistic insight into nucleosome disassembly by hMSH2-hMSH6 and the influence of histone modifications on this disassembly reaction. This model's precise agreement with current experiments suggests that it can be applied more generally to provide important mechanistic understanding of the numerous nucleosome alterations that occur during DNA processing.