The HMGB Protein KlIxr1, a DNA Binding Regulator of Kluyveromyces lactis Gene Expression Involved in Oxidative Metabolism, Growth, and dNTP Synthesis

In the traditional fermentative model yeast Saccharomyces cerevisiae, ScIxr1 is an HMGB (High Mobility Group box B) protein that has been considered as an important regulator of gene transcription in response to external changes like oxygen, carbon source, or nutrient availability. Kluyveromyces lactis is also a useful eukaryotic model, more similar to many human cells due to its respiratory metabolism. We cloned and functionally characterized by different methodologies KlIXR1, which encodes a protein with only 34.4% amino acid sequence similarity to ScIxr1. Our data indicate that both proteins share common functions, including their involvement in the response to hypoxia or oxidative stress induced by hydrogen peroxide or metal treatments, as well as in the control of key regulators for maintenance of the dNTP (deoxyribonucleotide triphosphate) pool and ribosome synthesis. KlIxr1 is able to bind specific regulatory DNA sequences in the promoter of its target genes, which are well conserved between S. cerevisiae and K. lactis. Oppositely, we found important differences between ScIrx1 and KlIxr1 affecting cellular responses to cisplatin or cycloheximide in these yeasts, which could be dependent on specific and non-conserved domains present in these two proteins.

Defining kinetic roles of transcriptional activators in the early Drosophila embryo

SUMMARYMost animal transcription factors are categorized as activators or repressors without specifying their mechanisms of action. Defining their specific roles is critical for deciphering the logic of transcriptional regulation and predicting the function of regulatory sequences. Here, we define the kinetic roles of three activating transcription factors in the Drosophila embryo—Zelda, Bicoid and Stat92E—by introducing their binding sites into theeven skippedstripe 2 enhancer and measuring transcriptional output with live imaging. We find that these transcription factors act on different subsets of kinetic parameters, and these subsets can change over the course of nuclear cycle (NC) 14. These transcription factors all increase the fraction of active nuclei. Zelda dramatically shortens the time interval between the start of NC 14 and initial activation, and Stat92E increases the duration of active transcription intervals throughout NC 14. Zelda also decreases the time intervals between instances of active transcription early in NC 14, while Stat92E does so later. Different transcription factors therefore play distinct kinetic roles in activating transcription; this has consequences for understanding both regulatory DNA sequences as well as the biochemical function of transcription factors.

A comprehensive fitness landscape model reveals the evolutionary history and future evolvability of eukaryotic cis-regulatory DNA sequences

Mutations in non-coding cis-regulatory DNA sequences can alter gene expression, organismal phenotype, and fitness. Fitness landscapes, which map DNA sequence to organismal fitness, are a long-standing goal in biology, but have remained elusive because it is challenging to generalize accurately to the vast space of possible sequences using models built on measurements from a limited number of endogenous regulatory sequences. Here, we construct a sequence-to-expression model for such a landscape and use it to decipher principles of cis-regulatory evolution. Using tens of millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Sacccharomyces cerevisiae, we construct a deep transformer neural network model that generalizes with exceptional accuracy, and enables sequence design for gene expression engineering. Using our model, we predict and experimentally validate expression divergence under random genetic drift and strong selection weak mutation regimes, show that conflicting expression objectives in different environments constrain expression adaptation, and find that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for detecting selective constraint on gene expression using our model and natural sequence variation, and validate it using observed cis-regulatory diversity across 1,011 yeast strains, cross-species RNA-seq from three different clades, and measured expression-to-fitness curves. Finally, we develop a characterization of regulatory evolvability, use it to visualize fitness landscapes in two dimensions, discover evolvability archetypes, quantify the mutational robustness of individual sequences and highlight the mutational robustness of extant natural regulatory sequence populations. Our work provides a general framework that addresses key questions in the evolution of cis-regulatory sequences.

Quantifying the regulatory role of individual transcription factors in Escherichia coli

Transcription factors (TFs) modulate gene expression by binding to regulatory DNA sequences surrounding target genes. To isolate the fundamental regulatory interactions of E. coli TFs, we measure regulation of TFs acting on synthetic target genes that are designed to isolate the individual TF regulatory effect. This data is interpreted through a thermodynamic model that decouples the role of TF copy number and TF binding affinity from the interactions of the TF on RNA polymerase through two distinct mechanisms: (de)stabilization of the polymerase and (de)acceleration of transcription initiation. We find the contribution of each mechanism towards the observed regulation depends on TF identity and binding location; for the set of TFs studied here, regulation immediately downstream of the promoter is not sensitive to TF identity, however these same TFs regulate through distinct mechanisms at an upstream binding site. Furthermore, depending on binding location, these two mechanisms of regulation can act coherently, to reinforce the observed regulatory role (activation or repression), or incoherently, where the TF regulates two distinct steps with opposing effect.

Maternal pluripotency factors initiate extensive chromatin remodelling to predefine first response to inductive signals

Embryonic development yields many different cell types in response to just a few families of inductive signals. The property of signal-receiving cells that determines how they respond to inductive signals is known as competence, and it differs in different cell types. Here, we explore the ways in which maternal factors modify chromatin to specify initial competence in the frog Xenopus tropicalis. We identify early-engaged regulatory DNA sequences, and infer from them critical activators of the zygotic genome. Of these, we show that the pioneering activity of the maternal pluripotency factors Pou5f3 and Sox3 determines competence for germ layer formation by extensively remodelling compacted chromatin before the onset of inductive signalling. This remodelling includes the opening and marking of thousands of regulatory elements, extensive chromatin looping, and the co-recruitment of signal-mediating transcription factors. Our work identifies significant developmental principles that inform our understanding of how pluripotent stem cells interpret inductive signals.

Targeted optimization of regulatory DNA sequences with neural editing architectures

Targeted optimizing of existing DNA sequences for useful properties, has the potential to enable several synthetic biology applications from modifying DNA to treat genetic disorders to designing regulatory elements to fine tune context-specific gene expression. Current approaches for targeted genome editing are largely based on prior biological knowledge or ad-hoc rules. Few if any machine learning approaches exist for targeted optimization of regulatory DNA sequences.Here, we propose a novel generative neural network architecture for targeted DNA sequence editing – the EDA architecture – consisting of an encoder, decoder, and analyzer. We showcase the use of EDA to optimize regulatory DNA sequences to bind to the transcription factor SPI1. Compared to other state-of-the-art approaches such as a textual variational autoencoder and rule-based editing, EDA significantly improves predicted binding of SPI1 of genomic sequences with the minimal set of edits. We also use EDA to design regulatory elements with optimized grammars of CREB1 binding sites that can tune reporter expression levels as measured by massively parallel reporter assays (MPRA). We analyze the properties of the binding sites in the edited sequences and find patterns that are consistent with previously reported grammatical rules which tie gene expression to CRE binding site density, spacing and affinity.

Projection layers improve deep learning models of regulatory DNA function

With the increasing application of deep learning methods to the modelling of regulatory DNA sequences has come an interest in exploring what types of architecture are best suited to the domain. Networks designed to predict many functional characteristics of noncoding DNA in a multitask framework have to recognise a large number of motifs and as a result benefit from large numbers of convolutional filters in the first layer. The use of large first layers in turn motivates an exploration of strategies for addressing the sparsity of output and possibility for overfitting that result. To this end we propose the use of a dimensionality-reducing linear projection layer after the initial motif-recognising convolutions. In experiments with a reduced version of the DeepSEA dataset we find that inserting this layer in combination with dropout into convolutional and convolutional-recurrent architectures can improve predictive performance across a range of first layer sizes. We further validate our approach by incorporating the projection layer into a new convolutional-recurrent architecture which achieves state of the art performance on the full DeepSEA dataset. Analysis of the learned projection weights shows that the inclusion of this layer simplifies the network’s internal representation of the occurrence of motifs, notably by projecting features representing forward and reverse-complement motifs to similar positions in the lower dimensional feature space output by the layer.