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Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called K D -seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions. Protein–ligand binding affinity is predicted quantitatively from sequencing data.

Protein tyrosine kinase and phosphatase domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue on their substrates. Although the preferred recognition motifs of many kinase and phosphatase domains have been characterized, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences and predict phosphorylation efficiencies of substrates. We then screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. Finally, we have made progress towards extending this platform to the analysis of tyrosine phosphatase domains, by optimizing methods to produce tyrosine-phosphorylated bacterial display libraries and implementing methods to detect peptide dephosphorylation on the cell surface. Collectively, these experiments demonstrate the utility of our platform for rapid profiling of sequence specificity by tyrosine kinases and will shed new light on phosphotyrosine signaling.

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.Competing Interest StatementThe authors have declared no competing interest.Footnotes* This revision includes new experiments to further characterize various aspects of substrate recognition by kinases: (1) experiments to examine mutational effects in the context of a full-length substrate, (2) experiments to validate context-dependent substrate sequence preferences, (3) experiments to assess the effects of lysine acetylation on phosphorylation rates. This revision also includes new analyses, data visualizations, and a more complete set of source data files.* https://datadryad.org/stash/dataset/doi:10.5061/dryad.0zpc86727

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions. Competing Interest Statement H.J.B., C.R., and H.T.R. have filed a patent application describing the design, composition and function of ProBound

Anxiety disorders are complex diseases, and often co-occur with depression. It is as yet unclear if a common neural circuit controls anxiety-related behaviors in both anxiety-alone and comorbid conditions. Here, utilizing the chronic social defeat stress (CSDS) paradigm that induces singular or combined anxiety- and depressive-like phenotypes in mice, we show that a ventral tegmental area (VTA) dopamine circuit projecting to the basolateral amygdala (BLA) selectively controls anxiety- but not depression-like behaviors. Using circuit-dissecting ex vivo electrophysiology and in vivo fiber photometry approaches, we establish that expression of anxiety-like, but not depressive-like, phenotypes are negatively correlated with VTA → BLA dopamine neuron activity. Further, our optogenetic studies demonstrate a causal link between such neuronal activity and anxiety-like behaviors. Overall, these data establish a functional role for VTA → BLA dopamine neurons in bi-directionally controlling anxiety-related behaviors not only in anxiety-alone, but also in anxiety-depressive comorbid conditions in mice. Anxiety and depression are highly comorbid, yet the distinct or shared neurobiological correlates of anxiety remain elusive. Here, Morel et al. define that the midbrain projection to the basolateral amygdala control anxiety but not depression.

Tissue damage elicits cell fate switching through a process called metaplasia, but how the starting cell fate is silenced and the new cell fate is activated has not been investigated in animals. In cell culture, pioneer transcription factors mediate “reprogramming” by opening new chromatin sites for expression that can attract transcription factors from the starting cell’s enhancers. Here we report that SOX4 is sufficient to initiate hepatobiliary metaplasia in the adult mouse liver, closely mimicking metaplasia initiated by toxic damage to the liver. In lineage-traced cells, we assessed the timing of SOX4-mediated opening of enhancer chromatin versus enhancer decommissioning. Initially, SOX4 directly binds to and closes hepatocyte regulatory sequences via an overlapping motif with HNF4A, a hepatocyte master regulatory transcription factor. Subsequently, SOX4 exerts pioneer factor activity to open biliary regulatory sequences. The results delineate a hierarchy by which gene networks become reprogrammed under physiological conditions, providing deeper insight into the basis for cell fate transitions in animals. Upon physiological injury, hepatocytes transdifferentiate into biliary epithelial cells, a process involving molecular rewiring. Here, authors show that Sox4 organizes the early steps, acting as a pioneer factor to decommission hepatocyte enhancers and open chromatin around biliary genes.

Typical therapies try to reverse pathogenic mechanisms. Here, we describe treatment effects achieved by enhancing depression-causing mechanisms in ventral tegmental area (VTA) dopamine (DA) neurons. In a social defeat stress model of depression, depressed (susceptible) mice display hyperactivity of VTA DA neurons, caused by an up-regulated hyperpolarization-activated current (Ih). Mice resilient to social defeat stress, however, exhibit stable normal firing of these neurons. Unexpectedly, resilient mice had an even larger Ih, which was observed in parallel with increased potassium (K+) channel currents. Experimentally further enhancing Ih or optogenetically increasing the hyperactivity of VTA DA neurons in susceptible mice completely reversed depression-related behaviors, an antidepressant effect achieved through resilience-like, projection-specific homeostatic plasticity. These results indicate a potential therapeutic path of promoting natural resilience for depression treatment.

Background Defining the ever-evolving correlates of protection against symptomatic infection is critical in light of the widespread deployment of mRNA vaccines in children. Since immune maturation and exposure histories differ between children and adults, immune kinetics and correlates of protection identified in adult cohorts may not directly generalize to pediatric vaccine recipients. Methods A prospective cohort of 5–12-year-old children ( N  = 70) was monitored longitudinally for 12 months after SARS-CoV-2 mRNA (BNT162b2) vaccination. Four immunological biomarkers were assessed: anti-Spike immunoglobulin G (anti-S IgG), neutralizing antibodies (nAbs), Spike-specific memory B cells (S + MBCs), and S-reactive T cell response. We utilized mathematical models to reconstruct time-varying biomarker trajectories, while accounting for multiple immunity-conferring events (i.e., primary vaccination, breakthrough infection, and booster vaccination). Using the biomarker levels as time-varying covariates in survival analyses, we evaluated the circulating correlates of protection against symptomatic breakthrough SARS-CoV-2 infection across three post-primary vaccination periods (30 days to 3 months, 3 to 6 months, and 6 to 12 months), as an interpretable summary of early, intermediate, and late phases. We also repeated this evaluation in a global analysis without considering specific time windows. Results Serological markers were best described by the exponential model, while S + MBC and T cell responses followed the inactivation model. While early protection was associated with antibodies, multivariable analyses identified T cell responses as the primary correlate of protection from 3 months onward, and this association was strongest in the presence of hybrid immunity. Under our prespecified threshold for the T cell response, the model-projected protection duration under hybrid immunity was estimated to be approximately 500 days after primary vaccination; however, empirical validation in independent cohorts will be required. Importantly, we obtained similar conclusions on the identity and direction of the correlates of protection in several sensitivity analyses adjusting for model structure, accounting for age and sex, and including asymptomatic infections. Conclusions Our findings highlight the time-dependent nature of correlates of protection, emphasizing the need for employing mathematical models to reinforce the accuracy and reliability of clinical analysis so as to prevent overreliance on single-timepoint measurements of immune parameters.

Analysis of the increasing wealth of metagenomic data collected from diverse environments can lead to the discovery of novel branches on the tree of life. Here we analyse 5.2 Tb of metagenomic data collected globally to discover a novel bacterial phylum (‘ Candidatus Kryptonia’) found exclusively in high-temperature pH-neutral geothermal springs. This lineage had remained hidden as a taxonomic ‘blind spot’ because of mismatches in the primers commonly used for ribosomal gene surveys. Genome reconstruction from metagenomic data combined with single-cell genomics results in several high-quality genomes representing four genera from the new phylum. Metabolic reconstruction indicates a heterotrophic lifestyle with conspicuous nutritional deficiencies, suggesting the need for metabolic complementarity with other microbes. Co-occurrence patterns identifies a number of putative partners, including an uncultured Armatimonadetes lineage. The discovery of Kryptonia within previously studied geothermal springs underscores the importance of globally sampled metagenomic data in detection of microbial novelty, and highlights the extraordinary diversity of microbial life still awaiting discovery. The analysis of existing metagenomic data can lead to discovery of new microorganisms. Here, Eloe-Fadrosh et al . perform a large-scale analysis of global metagenomic data, followed by genome reconstruction and single-cell genomics, to describe a new bacterial phylum that inhabits geothermal springs.