Discovery of deaminase functions by structure-based protein clustering.

The elucidation of protein function and its exploitation in bioengineering have greatly advanced the life sciences. Protein mining efforts generally rely on amino acid sequences rather than protein structures. We describe here the use of AlphaFold2 to predict and subsequently cluster an entire protein family based on predicted structure similarities. We selected deaminase proteins to analyze and identified many previously unknown properties. We were surprised to find that most proteins in the DddA-like clade were not double-stranded DNA deaminases. We engineered the smallest single-strand-specific cytidine deaminase, enabling efficient cytosine base editor (CBE) to be packaged into a single adeno-associated virus (AAV). Importantly, we profiled a deaminase from this clade that edits robustly in soybean plants, which previously was inaccessible to CBEs. These discovered deaminases, based on AI-assisted structural predictions, greatly expand the utility of base editors for therapeutic and agricultural applications.

Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases.

Genetic studies have revealed many variant loci that are associated with immune-mediated diseases. To elucidate the disease pathogenesis, it is essential to understand the function of these variants, especially under disease-associated conditions. Here, we performed a large-scale immune cell gene-expression analysis, together with whole-genome sequence analysis. Our dataset consists of 28 distinct immune cell subsets from 337 patients diagnosed with 10 categories of immune-mediated diseases and 79 healthy volunteers. Our dataset captured distinctive gene-expression profiles across immune cell types and diseases. Expression quantitative trait loci (eQTL) analysis revealed dynamic variations of eQTL effects in the context of immunological conditions, as well as cell types. These cell-type-specific and context-dependent eQTLs showed significant enrichment in immune disease-associated genetic variants, and they implicated the disease-relevant cell types, genes, and environment. This atlas deepens our understanding of the immunogenetic functions of disease-associated variants under in vivo disease conditions.

A Polarizing Issue: Diversity in the Mechanisms Underlying Apico-Basolateral Polarization In Vivo.

Polarization along an apico-basolateral axis is a hallmark of epithelial cells and is essential for their selective barrier and transporter functions, as well as for their ability to provide mechanical resiliency to organs. Loss of polarity along this axis perturbs development and is associated with a wide number of diseases. We describe three steps involved in polarization: symmetry breaking, polarity establishment, and polarity maintenance. While the proteins involved in these processes are highly conserved among epithelial tissues and species, the execution of these steps varies widely and is context dependent. We review both theoretical principles underlying these steps and recent work demonstrating how apico-basolateral polarity is established in vivo in different tissues, highlighting how developmental and physiological contexts play major roles in the execution of the epithelial polarity program.

The Computational and Neural Bases of Context-Dependent Learning.

Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

TRPS1 acts as a context-dependent regulator of mammary epithelial cell growth/differentiation and breast cancer development.

The GATA-type zinc finger transcription factor TRPS1 has been implicated in breast cancer. However, its precise role remains unclear, as both amplifications and inactivating mutations in TRPS1 have been reported. Here, we used in vitro and in vivo loss-of-function approaches to dissect the role of TRPS1 in mammary gland development and invasive lobular breast carcinoma, which is hallmarked by functional loss of E-cadherin. We show that TRPS1 is essential in mammary epithelial cells, since TRPS1-mediated suppression of interferon signaling promotes in vitro proliferation and lactogenic differentiation. Similarly, TRPS1 expression is indispensable for proliferation of mammary organoids and in vivo survival of luminal epithelial cells during mammary gland development. However, the consequences of TRPS1 loss are dependent on E-cadherin status, as combined inactivation of E-cadherin and TRPS1 causes persistent proliferation of mammary organoids and accelerated mammary tumor formation in mice. Together, our results demonstrate that TRPS1 can function as a context-dependent tumor suppressor in breast cancer, while being essential for growth and differentiation of normal mammary epithelial cells.

From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility.

Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

Where, When, and How: Context-Dependent Functions of RNA Methylation Writers, Readers, and Erasers.

Cellular RNAs are naturally decorated with a variety of chemical modifications. The structural diversity of the modified nucleosides provides regulatory potential to sort groups of RNAs for organized metabolism and functions, thus affecting gene expression. Recent years have witnessed a burst of interest in and understanding of RNA modification biology, thanks to the emerging transcriptome-wide sequencing methods for mapping modified sites, highly sensitive mass spectrometry for precise modification detection and quantification, and extensive characterization of the modification "effectors," including enzymes ("writers" and "erasers") that alter the modification level and binding proteins ("readers") that recognize the chemical marks. However, challenges remain due to the vast heterogeneity in expression abundance of different RNA species, further complicated by divergent cell-type-specific and tissue-specific expression and localization of the effectors as well as modifications. In this review, we highlight recent progress in understanding the function of N6-methyladenosine (m6A), the most abundant internal mark on eukaryotic mRNA, in light of the specific biological contexts of m6A effectors. We emphasize the importance of context for RNA modification regulation and function.

Dynamic electrical synapses rewire brain networks for persistent oscillations and epileptogenesis.

One of the very fundamental attributes for telencephalic neural computation in mammals involves network activities oscillating beyond the initial trigger. The continuing and automated processing of transient inputs shall constitute the basis of cognition and intelligence but may lead to neuropsychiatric disorders such as epileptic seizures if carried so far as to engross part of or the whole telencephalic system. From a conventional view of the basic design of the telencephalic local circuitry, the GABAergic interneurons (INs) and glutamatergic pyramidal neurons (PNs) make negative feedback loops which would regulate the neural activities back to the original state. The drive for the most intriguing self-perpetuating telencephalic activities, then, has not been posed and characterized. We found activity-dependent deployment and delineated functional consequences of the electrical synapses directly linking INs and PNs in the amygdala, a prototypical telencephalic circuitry. These electrical synapses endow INs dual (a faster excitatory and a slower inhibitory) actions on PNs, providing a network-intrinsic excitatory drive that fuels the IN-PN interconnected circuitries and enables persistent oscillations with preservation of GABAergic negative feedback. Moreover, the entities of electrical synapses between INs and PNs are engaged in and disengaged from functioning in a highly dynamic way according to neural activities, which then determine the spatiotemporal scale of recruited oscillating networks. This study uncovers a special wide-range and context-dependent plasticity for wiring/rewiring of brain networks. Epileptogenesis or a wide spectrum of clinical disorders may ensue, however, from different scales of pathological extension of this unique form of telencephalic plasticity.

Intrinsic and extrinsic regulators of Aux/IAA protein degradation dynamics.

The plant hormone auxin acts through regulated degradation of Auxin/INDOLE-3-ACETIC ACID (Aux/IAA) proteins to regulate transcriptional events. In this review, we examine the composition and function of each Aux/IAA structural motif. We then focus on recent characterization of Aux/IAA N-terminal disordered regions, formation of secondary structure within these disordered regions, and post-translational modifications (PTMs) that affect Aux/IAA function and stability. We propose how structural variations between Aux/IAA family members may be tuned for differential transcriptional repression and degradation dynamics.

SRC homology 3 domains: multifaceted binding modules.

The assembly of complexes following the detection of extracellular signals is often controlled by signaling proteins comprising multiple peptide binding modules. The SRC homology (SH)3 family represents the archetypical modular protein interaction module, with ~300 annotated SH3 domains in humans that regulate an impressive array of signaling processes. We review recent findings regarding the allosteric contributions of SH3 domains host protein context, their phosphoregulation, and their roles in phase separation that challenge the simple model in which SH3s are considered to be portable domains binding to specific proline-rich peptide motifs.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data.

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity toward a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, however, we observed extensive preferences for contextual features distinct from short linear RNA motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. Our results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif.

Hippocampal activity predicts contextual misattribution of false memories.

Failure of contextual retrieval can lead to false recall, wherein people retrieve an item or experience that occurred in a different context or did not occur at all. Whereas the hippocampus is thought to play a crucial role in memory retrieval, we lack understanding of how the hippocampus supports retrieval of items related to a target context while disregarding related but irrelevant information. Using direct electrical recordings from the human hippocampus, we investigate the neural process underlying contextual misattribution of false memories. In two large datasets, we characterize key physiological differences between correct and false recalls that emerge immediately prior to vocalization. By differentiating between false recalls that share high or low contextual similarity with the target context, we show that low-frequency activity (6 to 18 Hz) in the hippocampus tracks similarity between the current and retrieved context. Applying multivariate decoding methods, we were able to reliably predict the contextual source of the to-be-recalled item. Our findings elucidate one of the hallmark features of episodic memory: our ability to distinguish between memories that were formed on different occasions.

Evolution of acoustic signals associated with cooperative parental behavior in a poison frog.

The emergence of complex social interactions is predicted to be an important selective force in the diversification of communication systems. Parental care presents a key social context in which to study the evolution of novel signals, as care often requires communication and behavioral coordination between parents and is an evolutionary stepping-stone toward increasingly complex social systems. Anuran amphibians (frogs and toads) are a classic model of acoustic communication and the vocal repertoires of many species have been characterized in the contexts of advertisement, courtship, and aggression, yet quantitative descriptions of calls elicited in the context of parental care are lacking. The biparental poison frog, Ranitomeya imitator, exhibits a remarkable parenting behavior in which females, cued by the calls of their male partners, feed tadpoles unfertilized eggs. Here, we characterized and compared calls across three social contexts, for the first time including a parental care context. We found that egg-feeding calls share some properties with both advertisement and courtship calls but also had unique properties. Multivariate analysis revealed high classification success for advertisement and courtship calls but misclassified nearly half of egg feeding calls as either advertisement or courtship calls. Egg feeding and courtship calls both contained less identity information than advertisement calls, as expected for signals used in close-range communication where uncertainty about identity is low and additional signal modalities may be used. Taken together, egg-feeding calls likely borrowed and recombined elements of both ancestral call types to solicit a novel, context-dependent parenting response.

What can machine learning teach us about habit formation? Evidence from exercise and hygiene.

We apply a machine learning technique to characterize habit formation in two large panel data sets with objective measures of 1) gym attendance (over 12 million observations) and 2) hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identifies context variables that best predict behavior for each individual. This approach also creates a time series of overall predictability for each individual. These time series predictability values are used to trace a habit formation curve for each individual, operationalizing the time of habit formation as the asymptotic limit of when behavior becomes highly predictable. Contrary to the popular belief in a "magic number" of days to develop a habit, we find that it typically takes months to form the habit of going to the gym but weeks to develop the habit of handwashing in the hospital. Furthermore, we find that gymgoers who are more predictable are less responsive to an intervention designed to promote more gym attendance, consistent with past experiments showing that habit formation generates insensitivity to reward devaluation.

In-context operator learning with data prompts for differential equation problems.

This paper introduces the paradigm of "in-context operator learning" and the corresponding model "In-Context Operator Networks" to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update. Existing methods are limited to using a neural network to approximate a specific equation solution or a specific operator, requiring retraining when switching to a new problem with different equations. By training a single neural network as an operator learner, rather than a solution/operator approximator, we can not only get rid of retraining (even fine-tuning) the neural network for new problems but also leverage the commonalities shared across operators so that only a few examples in the prompt are needed when learning a new operator. Our numerical results show the capability of a single neural network as a few-shot operator learner for a diversified type of differential equation problems, including forward and inverse problems of ordinary differential equations, partial differential equations, and mean-field control problems, and also show that it can generalize its learning capability to operators beyond the training distribution.

STAT3 is a master regulator of epithelial identity and KRAS-driven tumorigenesis.

A dichotomy exists regarding the role of signal transducer and activator of transcription 3 (STAT3) in cancer. Functional and genetic studies demonstrate either an intrinsic requirement for STAT3 or a suppressive effect on common types of cancer. These contrasting actions of STAT3 imply context dependency. To examine mechanisms that underlie STAT3 function in cancer, we evaluated the impact of STAT3 activity in KRAS-driven lung and pancreatic cancer. Our study defines a fundamental and previously unrecognized function of STAT3 in the maintenance of epithelial cell identity and differentiation. Loss of STAT3 preferentially associates with the acquisition of mesenchymal-like phenotypes and more aggressive tumor behavior. In contrast, persistent STAT3 activation through Tyr705 phosphorylation confers a differentiated epithelial morphology that impacts tumorigenic potential. Our results imply a mechanism in which quantitative differences of STAT3 Tyr705 phosphorylation, as compared with other activation modes, direct discrete outcomes in tumor progression.