Biology subjects

Skinnider, M. A.

Publications and source records attributed to Skinnider, M. A..

14 recordsLinked to original sources

bioRxiv2026

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

bioinformatics↗

bioRxiv2026

Confronting spurious evaluations of computational methods in small molecule mass spectrometry

Mass spectrometry-based metabolomics detects thousands of small molecule-associated signals in biological samples, but the vast majority cannot be structurally identified. Mounting interest in this metabolomic "dark matter" has spurred the development of dozens of machine-learning models for structural annotation of small molecules from their MS/MS spectra. Here, we expose a fundamental flaw in the longstanding paradigm by which these models have been evaluated. We show that a trivial machine-learning model can achieve strong performance on existing benchmarks despite entirely discarding the information contained within MS/MS spectra themselves, and without using any other auxiliary information. This performance arises because compounds with reference MS/MS spectra are structurally distinct from those found in generic chemical databases, and machine-learning models can exploit this dissimilarity by learning to predict whether a compound is likely to have been measured by MS/MS. However, we show that this confound can be overcome by using a generative model to sample decoy structures that are chemically indistinguishable from compounds in reference MS/MS libraries. The resulting benchmark cannot be solved without learning from MS/MS spectra themselves. We leverage this benchmark to compare 17 published machine-learning models for MS/MS annotation, and find that many of these models fail to outperform simple baselines and may learn little about MS/MS itself. In contrast, a subset of models show convincing evidence of generalization. Our work provides a sound foundation for developing and evaluating computational methods for small molecule MS/MS.

bioinformatics↗

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2024

Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases

We introduce Vespucci, a machine-learning method to identify perturbation-responsive regions, genes and gene programs within comparative spatial transcriptomics atlases. We validate Vespucci on simulated and published datasets and show that it outperforms 19 published computational methods for spatial transcriptomics. We apply Vespucci to expose the spatial organization of gene programs activated by therapies that guide repair of the injured spinal cord.

bioinformatics↗

bioRxiv2024

THE NEURONAL ARCHITECTURE OF AUTONOMIC DYSREFLEXIA

Autonomic dysreflexia is a life-threatening medical condition characterized by episodes of uncontrolled hypertension that occur in response to sensory stimuli after spinal cord injury (SCI)1-7. The fragmented understanding of the mechanisms underlying autonomic dysreflexia hampers the development of therapeutic strategies to manage this condition, leaving people with SCI at daily risk of heart attack and stroke8-18. Here, we expose the complete de novo neuronal architecture that develops after SCI and causes autonomic dysreflexia. In parallel, we uncover a competing, yet overlapping neuronal architecture activated by epidural electrical stimulation of the spinal cord that safely regulates blood pressure after SCI. The discovery that these adversarial neuronal architectures converge onto a single neuronal subpopulation provided a blueprint for the design of a mechanism-based intervention that reversed autonomic dysreflexia in mice, rats, and humans with SCI. These results establish a path for the effective treatment of autonomic dysreflexia in people with SCI.

neuroscience↗

bioRxiv2023

The Tabulae Paralytica: Multimodal single-cell and spatial atlases of spinal cord injury

Here, we introduce the Tabulae Paralytica--a compilation of four atlases of spinal cord injury (SCI) comprising a single-nucleus transcriptome atlas of half a million cells; a multiome atlas pairing transcriptomic and epigenomic measurements within the same nuclei; and two spatial transcriptomic atlases of the injured spinal cord spanning four spatial and temporal dimensions. We integrated these atlases into a common framework to dissect the molecular logic that governs the responses to injury within the spinal cord. The Tabulae Paralytica exposed new biological principles that dictate the consequences of SCI, including conserved and divergent neuronal responses to injury; the priming of specific neuronal subpopulations to become circuit-reorganizing neurons after injury; an inherent trade-off between neuronal stress responses and the activation of circuit reorganization programs; the necessity of reestablishing a tripartite neuroprotective barrier between immune-privileged and extra-neural environments after SCI; and a catastrophic failure to form this barrier in old mice. We leveraged the Tabulae Paralytica to develop a rejuvenative gene therapy that reestablished this tripartite barrier, and restored the natural recovery of walking after paralysis in old mice. The Tabulae Paralytica provides an unprecedented window into the pathobiology of SCI, while establishing a framework for integrating multimodal, genome-scale measurements in four dimensions to study biology and medicine.

genomics↗

bioRxiv2021

Confronting false discoveries in single-cell differential expression

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

bioinformatics↗

bioRxiv2021

Proteomic portraits reveal evolutionarily conserved and divergent responses to spinal cord injury

Despite the emergence of promising therapeutic approaches in preclinical studies, the failure of large-scale clinical trials leaves clinicians without effective treatments for acute spinal cord injury (SCI). These trials are hindered by their reliance on detailed neurological examinations to establish outcomes, which inflate the time and resources required for completion. Moreover, therapeutic development takes place in animal models whose relevance to human injury remains unclear. Here, we address these challenges through targeted proteomic analyses of CSF and serum samples from 111 acute SCI patients and, in parallel, a large animal (porcine) model of SCI. We develop protein biomarkers of injury severity and recovery, including a prognostic model of neurological improvement at six months with an AUC of 0.91, and validate these in an independent cohort. Through cross-species proteomic analyses, we dissect evolutionarily conserved and divergent aspects of the SCI response, and establish the CSF abundance of glial fibrillary acidic protein (GFAP) as a biochemical outcome measure in both humans and pigs. Our work opens up new avenues to catalyze translation by facilitating the evaluation of novel SCI therapies, while also providing a resource from which to direct future preclinical efforts.

neuroscience↗

bioRxiv2020

On the robustness of graph-based clustering to random network alterations

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multi-member protein-complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially interactomes that infer interactions using high-throughput biochemical assays. Therefore, robustness to network-level variability is an important criterion for any clustering algorithm that aims to generate robust, reproducible clusters. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of network-level noise, including algorithms common across domains and those specific to protein networks. We found that the results of all clustering algorithms measured were profoundly sensitive to injected network noise. Randomly rewiring 1% of network edges yielded up to a 57% change in clustering results, indicating that clustering markedly amplified network-level noise. However, the impact of network noise on individual clusters was not uniform. We found that some clusters were consistently robust to injected network noise while others were not. Therefore, we developed the clust.perturb R package and Shiny web application, which measures the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that quantifying the robustness of a cluster to network noise, as implemented in clust.perturb, provides a powerful tool for ranking the reproducibility of clusters, and separating stable protein complexes from spurious associations.

bioinformatics↗

bioRxiv2019

Cell type prioritization in single-cell data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

genomics↗

bioRxiv2019

Dynamic rewiring of the human interactome by interferon signalling

BackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.\n\nResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.\n\nConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

systems biology↗

bioRxiv2018

An atlas of protein-protein interactions across mammalian tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

systems biology↗

bioRxiv2018

Conditional interactions in literature-curated protein interaction databases

Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study. Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate. We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to experimental conditions whenever possible.

bioinformatics↗

bioRxiv2017

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

BackgroundAn organisms protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.\n\nResultsHere we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, better performance, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2015b). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.\n\nConclusionsPrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics proficiency to analyze high-throughput co-elution datasets.

bioinformatics↗