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The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse‐like pattern and returned to values close to pre‐treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes. Synopsis The contribution of mRNA and protein level regulation in the mammalian endoplasmic reticulum stress response is deconvoluted by analyzing time‐series protein and matching mRNA concentrations with a new statistical tool. Protein and mRNA concentrations are quantified at different time points, generating a high‐confidence dataset of 1,237 genes/mRNAs. A new statistical tool quantifies the contribution of regulatory processes and shows that mRNA and protein level regulation play similarly important roles. mRNA and protein level regulation have different dynamics: mRNA concentrations spike in their change and return to pre‐perturbation levels, while protein concentrations switch in their behavior and reach a new steady‐state. Hypotheses on modes of regulation for several groups of genes are presented. Graphical Abstract The contribution of mRNA and protein level regulation in the mammalian endoplasmic reticulum stress response is deconvoluted by analyzing time‐series protein and matching mRNA concentrations with a new statistical tool.

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73–98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data. BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding.

Low-affinity transcription factor (TF) motifs are an important element of the cis-regulatory code, yet they are notoriously difficult to map and mechanistically incompletely understood, limiting our ability to interpret non-coding variation in development, evolution, and disease. Here we investigate their role in pioneering and leverage sequence-to-profile models of chromatin accessibility in mouse embryonic stem cells to reliably map and interpret low-affinity motifs across the genome. We find that low-affinity motifs have outsized effects by cooperating with nearby motifs through intra-nucleosomal soft syntax. By modeling nucleosome-mediated cooperativity with a kinetic model, we discover and validate that pioneer cooperativity makes a motif operate at higher pioneering ranges across changing TF concentrations, thereby raising the regulatory potential. These results show that low-affinity motifs can be accurately mapped, shape the properties of developmental enhancers and likely play a widespread role in fine-tuning enhancers during evolution.

The response to signaling pathways is highly context-specific, and identifying the transcription factors and mechanisms that are responsible is very challenging. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show here that this information is encoded in cis-regulatory sequences and can be learned from high-resolution binding data of signaling transcription factors. Using interpretable deep learning, we show that the binding levels of TEAD4 and YAP1 are enhanced in a distance-dependent manner by cell type-specific transcription factors, including TFAP2C. We also discovered that strictly spaced Tead double motifs are widespread highly active canonical response elements that mediate cooperativity by promoting labile TEAD4 protein-protein interactions on DNA. These syntax rules and mechanisms apply genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways.Competing Interest StatementThe authors have declared no competing interest.

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in the Drosophila embryo as a model, we generated high-resolution TF binding and chromatin accessibility data, analyzed the data with interpretable deep learning, and performed genetic experiments for validation. We uncover a clear hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, while patterning TFs augment chromatin accessibility in sequence contexts in which they mediate enhancer activation. We conclude that chromatin accessibility occurs in two phases: one through pioneering, which makes enhancers accessible but not necessarily active, and a second when the correct combination of transcription factors leads to enhancer activation.Competing Interest StatementThe authors have declared no competing interest.

The arrangement of transcription factor (TF) binding motifs (syntax) is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution ChIP-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using CRISPR-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data. The neural network BPNet accurately predicts TF binding data at base-resolution. Model interpretation discovers TF motifs and TF interactions dependent on soft syntax. Motifs for Nanog and partners are preferentially spaced at ∼10.5 bp periodicity. Directional cooperativity is validated: Sox2 enhances Nanog binding, but not vice versa.

Genes are regulated through enhancer sequences, in which transcription factor binding motifs and their specific arrangements (syntax) form a cis-regulatory code. To understand the relationship between motif syntax and transcription factor binding, we train a deep learning model that uses DNA sequence to predict base-resolution binding profiles of four pluripotency transcription factors Oct4, Sox2, Nanog, and Klf4. We interpret the model to accurately map hundreds of thousands of motifs in the genome, learn novel motif representations and identify rules by which motifs and syntax influence transcription factor binding. We find that instances of strict motif spacing are largely due to retrotransposons, but that soft motif syntax influences motif interactions at protein and nucleosome range. Most strikingly, Nanog binding is driven by motifs with a strong preference for ~10.5 bp spacings corresponding to helical periodicity. Interpreting deep learning models applied to high-resolution binding data is a powerful and versatile approach to uncover the motifs and syntax of cis-regulatory sequences.

The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped the proteomics and transcriptomics changes in mammalian cells responding to stress induced by dithiothreitol over 30 hours. Specifically, we estimated the kinetic parameters for synthesis and degradation of RNA and proteins, and deconvoluted response patterns common and unique to each regulatory level using a new statistical tool. Overall, both regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. Further, mRNA concentrations were regulated in a transient, spike-like pattern and returned to values close to pre-treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for example groups of genes.

Laser texturing is an emerging technology for generating surface functionalities on basis of optical, mechanical, or chemical properties. Taking benefit of laser sources with ultrashort (fs) pulse durations features outstanding precision of machining and negligible rims or burrs surrounding the laser-irradiation zone. Consequently, additional mechanical or chemical post-processing steps are usually not required for fs-laser surface texturing (fs-LST). This work aimed to provide a bridge between research in the field of tribology and laser materials processing. The paper reviews the current state-of-the-art in fs-LST, with a focus on the tribological performance (friction and wear) of specific self-organized surface structures (so-called ripples, grooves, and spikes) on steel and titanium alloys. On the titanium alloy, specific sickle-shaped hybrid micro-nanostructures were also observed and tribologically tested. Care is taken to identify accompanying effects affecting the materials hardness, superficial oxidation, nano- and microscale topographies, and the role of additives contained in lubricants, such as commercial engine oil.