Biology subjects

Raymond, W.

Publications and source records attributed to Raymond, W..

5 recordsLinked to original sources

bioRxiv2023

Identification of potential riboswitch elements in Homo Sapiens mRNA 5'UTR sequences using Positive-Unlabeled Machine learning

Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and prokaryotes, with additional examples also being found in fungi, plants, and yeast. To date, no purely RNA-small molecule riboswitch has been discovered in Homo Sapiens. Several analogous riboswitch-like mechanisms have been described within the H. Sapiens translatome within the past decade, prompting the question: Is there a H. Sapiens riboswitch dependent on only small molecule ligands? In this work, we set out to train positive unlabeled machine learning classifiers on known riboswitch sequences and apply the classifiers to H. Sapiens mRNA 5UTR sequences found in the 5UTR database, UTRdb, in the hope of identifying a set of mRNAs to investigate for riboswitch functionality. 67,683 riboswitch sequences were obtained from RNAcentral and sorted for ligand type and used as positive examples and 48,031 5UTR sequences were used as unlabeled, unknown examples. Positive examples were sorted by ligand, and 20 positive-unlabeled classifiers were trained on sequence and secondary structure features while withholding one or two ligand classes. Cross validation was then performed on the withheld ligand sets to obtain a validation accuracy range of 75%-99%. The joint sets of 5UTRs identified as potential riboswitches by the 20 classifiers were then analyzed. 15333 sequences were identified as a riboswitch by one or more classifier(s) and 436 of the H. Sapiens 5UTRs were labeled as harboring potential riboswitch elements by all 20 classifiers. These 436 sequences were mapped back to the most similar riboswitches within the positive data and examined. An online database of identified and ranked 5UTRs, their features, and their most similar matches to known riboswitches, is provided to guide future experimental efforts to identify H. Sapiens riboswitches. Author summaryRiboswitches are an important regulatory element mostly found in bacteria that have not been described in Homo Sapiens. However, if human riboswitches exist and if they can be found, they could have vast implications on human disease. We apply positive-unlabeled machine learning to on known riboswitch sequences to search H. Sapiens 5UTR sequences for potential riboswitches. We analyze our ensemble predictions for likely H. Sapiens 5UTR riboswitches using GO analysis to determine their potential functional roles, and we rank and display our predicted sequences next to the most similar known riboswitches. We expect these analyses to be helpful to the scientific community in planning future experiments for laboratory discovery and validation. 0.1 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/568398v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@87c27org.highwire.dtl.DTLVardef@10f085corg.highwire.dtl.DTLVardef@1edfcaborg.highwire.dtl.DTLVardef@1674ce0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Binding of SARS-CoV-2 spike protein to ACE2 is disabled by thiol-based drugs; evidence from in vitro SARS-CoV-2 infection studies.

Neutrophil-induced oxidative stress is a mechanism of lung injury in COVID-19, and drugs with a functional thiol group ("thiol drugs"), especially cysteamine, have anti-oxidant and anti-inflammatory properties that could limit this injury. Thiol drugs may also alter the redox status of the cysteine-rich SARS-CoV-2 spike glycoprotein (SARS-2-S) and thereby disrupt ACE2 binding. Using ACE2 binding assay, reporter virus pseudotyped with SARS-CoV-2 spikes (ancestral and variants) and authentic SARS-CoV-2 (Wuhan-1), we find that multiple thiol drugs inhibit SARS-2-S binding to ACE2 and virus entry into cells. Pseudoviruses carrying variant spikes were less efficiently inhibited as compared to pseudotypes bearing an ancestral spike, but the most potent drugs still inhibited the Delta variant in the low millimolar range. IC50 values followed the order of their cystine cleavage rates and lower thiol pKa values. In hamsters infected with SARS-CoV-2, intraperitoneal (IP) cysteamine decreased neutrophilic inflammation and alveolar hemorrhage in the lungs but did not decrease viral infection, most likely because IP delivery could not achieve millimolar concentrations in the airways. These data show that thiol drugs inhibit SARS-CoV-2 infection in vitro and reduce SARS-CoV-2-related lung injury in vivo and provide strong rationale for trials of systemically delivered thiol drugs as COVID-19 treatments. We propose that antiviral effects of thiol drugs in vivo will require delivery directly to the airways to ensure millimolar drug concentrations and that thiol drugs with lower thiol pKa values are most likely to be effective. One Sentence SummaryThe effect of cysteamine to decrease SARS-CoV-2 pneumonia in vivo and of multiple thiol drugs to inhibit SARS-CoV-2 infection in vitro provides rationale for clinical trials of thiol drugs in COVID-19.

microbiology↗

bioRxiv2020

Live-cell imaging reveals the spatiotemporal organization of endogenous RNA polymerase II phosphorylation at a single gene

The carboxyl-terminal domain of RNA polymerase II is dynamically phosphorylated during transcription in eukaryotic cells. While residue-specific phosphorylation has been mapped with exquisite spatial resolution along the 1D genome in a population of fixed cells using immunoprecipitation-based assays, the timing, kinetics, and spatial organization of phosphorylation along a single-copy gene have not yet been measured in living cells. Here, we achieve this by combining multi-color, single-molecule microscopy with fluorescent antibody-based probes that specifically bind to unphosphorylated and phosphorylated forms of endogenous RNAP2 in living cells. Applying this methodology to a single-copy HIV-1 reporter gene provides live-cell evidence for heterogeneity in the distribution of RNAP2 along the length of the gene as well as clusters of Serine 5 phosphorylated RNAP2 that form around active genes and are separated in both space and time from nascent mRNA synthesis. Computational models fit to our data determine that 5 to 40 RNAP2 cluster around the promoter of a gene during typical transcriptional bursts. Rapid imaging demonstrates nearly all RNAP2 in the cluster acquire Serine 5 phosphorylation within 3-6 seconds of arrival. Transcription from the cluster appears to be highly efficient, with nearly half of the clustered RNAP2 ultimately escaping the promoter in 1.5 minutes on average to elongate a full-length mRNA in approximately five minutes. The highly dynamic and spatially organized concentrations of RNAP2 we observe support the notion of highly efficient transcription clusters that form around promoters and contain high concentrations of RNAP2 phosphorylated at Serine 5.

molecular biology↗

bioRxiv2019

Computational design and interpretation of live-cell, single-RNA translation experiments

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, {beta}-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (O_SCPCAPRC_SCPCAPSNAPO_SCPCAPSIMC_SCPCAP), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. O_SCPCAPRC_SCPCAPSNAPO_SCPCAPSIMC_SCPCAP is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.\n\nAuthor summaryTranslation is an essential step in which ribosomes decipher mRNA sequences to manufacture proteins. Recent advances in time-lapse fluorescence microscopy allow live-cell quantification of translation dynamics at the resolution of single mRNA molecules. Here, we develop a flexible computational framework to reproduce and interpret such experiments. We use this framework to explore how well different single-mRNA translation experiment designs would perform to estimate key translation parameters. We then integrate experimental data from the most flexible design with our stochastic model framework to reproduce the statistics and temporal dynamics of nascent protein elongation for three different human genes. Our validated computational method is packaged with a simple graphical user interface that (1) starts with mRNA sequences, (2) generates discrete, codon-dependent translation models, (3) provides visualization of ribosome movement as trajectories or kymographs, and (4) allows the user to estimate how optical single-mRNA translation experiments would be affected by different genetic alterations (e.g., codon substitutions) or environmental perturbations (e.g., tRNA titrations or drug treatments).

systems biology↗

bioRxiv2017

Identification of Gene Regulation Models from Single-Cell Data

In quantitative analyses of biological processes, one may use many different scales of models (e.g., spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g., model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MO_SCPLOWATLABC_SCPLOW and PO_SCPLOWYTHONC_SCPLOW software to consider a time-dependent input signal (e.g., a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g., deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast [1] and human cells [2]{ddagger}.\n\nPACS numbers: 87.10.+e, 87.15.Aa, 05.10.Gg, 05.40.Ca,02.50.-r\n\nSubmitted to: Phys. Biol.

systems biology↗