Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.

High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes are high-dimensional, unbiased approaches to detect and visualize the changes in phenotypes are still needed. Here, we suggest that changes in cellular phenotype can be visualized in reduced dimensionality representations of the image feature space. We describe a freely available analysis pipeline to visualize changes in protein localization in feature spaces obtained from deep learning. As an example, we use the pipeline to identify changes in subcellular localization after the yeast GFP collection was treated with hydroxyurea.

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

MOTIVATION: Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it has been suggested that the genome contains insufficient sequence variation to train models with suitable complexity. Data augmentation is a widely used approach to increase the data variation available for model training, however current data augmentation methods for genomic sequence data are limited. RESULTS: Inspired by the success of comparative genomics, we show that augmenting genomic sequences with evolutionarily related sequences from other species, which we term phylogenetic augmentation, improves the performance of deep learning models trained on regulatory genomic sequences to predict high-throughput functional assay measurements. Additionally, we show that phylogenetic augmentation can rescue model performance when the training set is down-sampled and permits deep learning on a real-world small dataset, demonstrating that this approach improves data efficiency. Overall, this data augmentation method represents a solution for improving model performance that is applicable to many supervised deep-learning problems in genomics. AVAILABILITY AND IMPLEMENTATION: The open-source GitHub repository agduncan94/phylogenetic_augmentation_paper includes the code for rerunning the analyses here and recreating the figures.

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2.

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.

A New Phase of Networking: The Molecular Composition and Regulatory Dynamics of Mammalian Stress Granules.

Stress granules (SGs) are cytosolic biomolecular condensates that form in response to cellular stress. Weak, multivalent interactions between their protein and RNA constituents drive their rapid, dynamic assembly through phase separation coupled to percolation. Though a consensus model of SG function has yet to be determined, their perceived implication in cytoprotective processes (e.g., antiviral responses and inhibition of apoptosis) and possible role in the pathogenesis of various neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and frontotemporal dementia) have drawn great interest. Consequently, new studies using numerous cell biological, genetic, and proteomic methods have been performed to unravel the mechanisms underlying SG formation, organization, and function and, with them, a more clearly defined SG proteome. Here, we provide a consensus SG proteome through literature curation and an update of the user-friendly database RNAgranuleDB to version 2.0 (http://rnagranuledb.lunenfeld.ca/). With this updated SG proteome, we use next-generation phase separation prediction tools to assess the predisposition of SG proteins for phase separation and aggregation. Next, we analyze the primary sequence features of intrinsically disordered regions (IDRs) within SG-resident proteins. Finally, we review the protein- and RNA-level determinants, including post-translational modifications (PTMs), that regulate SG composition and assembly/disassembly dynamics.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

Developmental dynamics of RNA translation in the human brain.

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity and cognitive function. Although several studies have profiled transcription in the developing human brain, there is a gap in understanding of accompanying translational regulation. In this study, we performed ribosome profiling on 73 human prenatal and adult cortex samples. We characterized the translational regulation of annotated open reading frames (ORFs) and identified thousands of previously unknown translation events, including small ORFs that give rise to human-specific and/or brain-specific microproteins, many of which we independently verified using proteomics. Ribosome profiling in stem-cell-derived human neuronal cultures corroborated these findings and revealed that several neuronal activity-induced non-coding RNAs encode previously undescribed microproteins. Physicochemical analysis of brain microproteins identified a class of proteins that contain arginine-glycine-glycine (RGG) repeats and, thus, may be regulators of RNA metabolism. This resource expands the known translational landscape of the human brain and illuminates previously unknown brain-specific protein products.

Evolution of short linear motifs and disordered proteins Topic: yeast as model system to study evolution.

Evolutionary preservation of protein structure had a major influence on the field of molecular evolution: changes in individual amino acids that did not disrupt protein folding would either have no effect or subtly change the 'lock' so that it could fit a new 'key'. Homology of individual amino acids could be confidently assigned through sequence alignments, and models of evolution could be tested. This view of molecular evolution excluded large regions of proteins that could not be confidently aligned, such as intrinsically disordered regions (IDRs) that do not fold into stable structures. In the last decade, major progress has been made in understanding the evolution of IDRs, much of it facilitated by new experimental and computational approaches in yeast. Here, we review this progress as well as several still outstanding questions.

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.

A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

Stochastic models for single-cell data: Current challenges and the way forward.

Although the quantity and quality of single-cell data have progressed rapidly, making quantitative predictions with single-cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single-cell data: (a) because variability in single-cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single-cell data can lead to violations of the assumption of symmetric errors in least-squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single-cell data.

A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling.

Stochastic signaling dynamics expand living cells' information processing capabilities. An increasing number of studies report that regulators encode information in their pulsatile dynamics. The evolutionary mechanisms that lead to complex signaling dynamics remain uncharacterized, perhaps because key interactions of signaling proteins are encoded in intrinsically disordered regions (IDRs), whose evolution is difficult to analyze. Here we focused on the IDR that controls the stochastic pulsing dynamics of Crz1, a transcription factor in fungi downstream of the widely conserved calcium signaling pathway. We find that Crz1 IDRs from anciently diverged fungi can all respond transiently to calcium stress; however, only Crz1 IDRs from the Saccharomyces clade support pulsatility, encode extra information, and rescue fitness in competition assays, while the Crz1 IDRs from distantly related fungi do none of the three. On the other hand, we find that Crz1 pulsing is conserved in the distantly related fungi, consistent with the evolutionary model of stabilizing selection on the signaling phenotype. Further, we show that a calcineurin docking site in a specific part of the IDRs appears to be sufficient for pulsing and show evidence for a beneficial increase in the relative calcineurin affinity of this docking site. We propose that evolutionary flexibility of functionally divergent IDRs underlies the conservation of stochastic signaling by stabilizing selection.

A flexible repertoire of transcription factor binding sites and a diversity threshold determines enhancer activity in embryonic stem cells.

Transcriptional enhancers are critical for development and phenotype evolution and are often mutated in disease contexts; however, even in well-studied cell types, the sequence code conferring enhancer activity remains unknown. To examine the enhancer regulatory code for pluripotent stem cells, we identified genomic regions with conserved binding of multiple transcription factors in mouse and human embryonic stem cells (ESCs). Examination of these regions revealed that they contain on average 12.6 conserved transcription factor binding site (TFBS) sequences. Enriched TFBSs are a diverse repertoire of 70 different sequences representing the binding sequences of both known and novel ESC regulators. Using a diverse set of TFBSs from this repertoire was sufficient to construct short synthetic enhancers with activity comparable to native enhancers. Site-directed mutagenesis of conserved TFBSs in endogenous enhancers or TFBS deletion from synthetic sequences revealed a requirement for 10 or more different TFBSs. Furthermore, specific TFBSs, including the POU5F1:SOX2 comotif, are dispensable, despite cobinding the POU5F1 (also known as OCT4), SOX2, and NANOG master regulators of pluripotency. These findings reveal that a TFBS sequence diversity threshold overrides the need for optimized regulatory grammar and individual TFBSs that recruit specific master regulators.

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions.

In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al., 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations.

It is unclear how disease mutations impact intrinsically disordered protein regions (IDRs), which lack a stable folded structure. These mutations, while prevalent in disease, are frequently neglected or annotated as variants of unknown significance. Biomolecular phase separation, a physical process often mediated by IDRs, has increasingly appreciated roles in cellular organization and regulation. We find that autism spectrum disorder (ASD)- and cancer-associated proteins are enriched for predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular processes. More generally, we hypothesize that combinations of small-effect IDR mutations perturb phase separation, potentially contributing to "missing heritability" in complex disease susceptibility.

Whence Blobs? Phylogenetics of functional protein condensates.

What do we know about the molecular evolution of functional protein condensation? The capacity of proteins to form biomolecular condensates (compact, protein-rich states, not bound by membranes, but still separated from the rest of the contents of the cell) appears in many cases to be bestowed by weak, transient interactions within one or between proteins. Natural selection is expected to remove or fix amino acid changes, insertions or deletions that preserve and change this condensation capacity when doing so is beneficial to the cell. A few recent studies have begun to explore this frontier of phylogenetics at the intersection of biophysics and cell biology.

Condensation of Ded1p Promotes a Translational Switch from Housekeeping to Stress Protein Production.

Cells sense elevated temperatures and mount an adaptive heat shock response that involves changes in gene expression, but the underlying mechanisms, particularly on the level of translation, remain unknown. Here we report that, in budding yeast, the essential translation initiation factor Ded1p undergoes heat-induced phase separation into gel-like condensates. Using ribosome profiling and an in vitro translation assay, we reveal that condensate formation inactivates Ded1p and represses translation of housekeeping mRNAs while promoting translation of stress mRNAs. Testing a variant of Ded1p with altered phase behavior as well as Ded1p homologs from diverse species, we demonstrate that Ded1p condensation is adaptive and fine-tuned to the maximum growth temperature of the respective organism. We conclude that Ded1p condensation is an integral part of an extended heat shock response that selectively represses translation of housekeeping mRNAs to promote survival under conditions of severe heat stress.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

Proteome-wide signatures of function in highly diverged intrinsically disordered regions.

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as 'evolutionary signatures' of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

SUMMARY: We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. AVAILABILITY AND IMPLEMENTATION: YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers.

MOTIVATION: Mammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed; however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm. RESULTS: We implemented a Dirichlet Process Heterogeneous Mixture model that infers Gaussian, Bernoulli and Poisson distributions over features. We derived a novel variational inference algorithm to handle semi-supervised learning tasks where certain observations are forced to cluster together. We applied this model to enhancer candidates in mouse heart tissues based on heterogeneous features. We constrained a small number of known active enhancers to appear in the same cluster, and 47 additional regions clustered with them. Many of these are located near heart-specific genes. The model also predicted 1176 active promoters, suggesting that it can discover new enhancers and promoters. AVAILABILITY AND IMPLEMENTATION: We created the 'dphmix' Python package: https://pypi.org/project/dphmix/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Noisy Analog-to-Digital Converter Connects Cytosolic Calcium Bursts to Transcription Factor Nuclear Localization Pulses in Yeast.

Several examples of transcription factors that show stochastic, unsynchronized pulses of nuclear localization have been described. Here we show that under constant calcium stress, nuclear localization pulses of the transcription factor Crz1 follow stochastic variations in cytosolic calcium concentration. We find that the size of the stochastic calcium bursts is positively correlated with the number of subsequent Crz1 pulses. Based on our observations, we propose a simple stochastic model of how the signaling pathway converts a constant external calcium concentration into a digital number of Crz1 pulses in the nucleus, due to the time delay from nuclear transport and the stochastic decoherence of individual Crz1 molecule dynamics. We find support for several additional predictions of the model and suggest that stochastic input to nuclear transport may produce noisy digital responses to analog signals in other signaling systems.