Biology subjects

Zuckerman, B.

Publications and source records attributed to Zuckerman, B..

5 recordsLinked to original sources

bioRxiv2024

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for [~]90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule--amplifying noise without altering mean-expression levels--which could enable investigations of the physiological impacts of transcriptional noise. MOTIVATIONCell-to-cell variability in isogenic populations is predominantly attributed to the stochastic fluctuations (i.e., noise) in transcription. However, the quantification of this noise, particularly on a genome-wide scale, remains an open question. To address this general question, here we utilize a small-molecule perturbation reported to amplify transcriptional noise. Previous single-cell RNA-sequencing (scRNA-seq) analysis indicated that the pyrimidine nucleobase 5'-iodo-2'-deoxyuridine (IdU) amplifies noise but technical drawbacks of scRNA-seq may have obscured the penetrance and degree of noise amplification. Consequently, here we assess numerous scRNA-seq algorithms, on two different scRNA-seq datasets for a human and mouse cell type, for their ability to quantify noise amplification and then compare the results to noise quantification from single-molecule RNA FISH (smFISH) imaging for a panel of representative genes. The specific questions addressed are whether IdU amplifies noise in a globally or partially penetrant manner, and what scRNA-seq algorithm is most appropriate for quantifying transcriptional noise. O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/607289v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f9b17aorg.highwire.dtl.DTLVardef@1f2d410org.highwire.dtl.DTLVardef@1407cfaorg.highwire.dtl.DTLVardef@cd2bc9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

systems biology↗

bioRxiv2023

Comparative analysis between RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5-iodo-2-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

systems biology↗

bioRxiv2021

Cap-independent translation and a precisely localized RNA sequence enable SARS-CoV-2 to control host translation and escape anti-viral response

Translation of SARS-CoV-2-encoded mRNAs by the host ribosomes is essential for its propagation. Following infection, the early expressed viral protein NSP1 binds the ribosome, represseses translation and induces mRNA degradation, while the host elicits anti-viral response. The mechanisms enabling viral mRNAs to escape this multifaceted repression remain obscure. Here we show that expression of NSP1 leads to destabilization of multi-exon cellular mRNAs, while intron-less transcripts, such as viral mRNAs and anti-viral interferon genes, remain relatively stable. We identified a conserved and precisely located cap-proximal RNA element devoid of guanosines that confers resistance to NSP1-meidated translation inhibition. Importantly, the primary sequence rather than the secondary structure is critical for protection. We further show that the genomic 5UTR of SARS-CoV-2 exhibits an IRES-like activity and promotes expression of NSP1 in an eIF4E-independent and Torin-1 resistant manner. Upon expression, NSP1 enhances cap-independent translation. However, the sub-genomic 5UTRs are highly sensitive to eIF4E availability, rendering viral propagation partially sensitive to Torin-1. The combined NSP1-mediated degradation of spliced mRNAs and translation inhibition of single-exon genes, along with the unique features present in the viral 5UTRs, ensure robust expression of viral mRNAs. These features can be exploited as potential therapeutic targets.

molecular biology↗

bioRxiv2020

SARS-CoV-2 utilizes a multipronged strategy to suppress host protein synthesis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 19 (COVID-19) pandemic. Despite the urgent need, we still do not fully understand the molecular basis of SARS-CoV-2 pathogenesis and its ability to antagonize innate immune responses. Here, we use RNA-sequencing and ribosome profiling along SARS-CoV-2 infection and comprehensively define the mechanisms that are utilized by SARS-CoV-2 to shutoff cellular protein synthesis. We show SARS-CoV-2 infection leads to a global reduction in translation but that viral transcripts are not preferentially translated. Instead, we reveal that infection leads to accelerated degradation of cytosolic cellular mRNAs which facilitates viral takeover of the mRNA pool in infected cells. Moreover, we show that the translation of transcripts whose expression is induced in response to infection, including innate immune genes, is impaired, implying infection prevents newly transcribed cellular mRNAs from accessing the ribosomes. Overall, our results uncover the multipronged strategy employed by SARS-CoV-2 to commandeer the translation machinery and to suppress host defenses.

microbiology↗

bioRxiv2019

Integrating Quality of Life and Survival Outcomes Cardiovascular Clinical Trials: Results from the PARTNER Trial

BackgroundSurvival and health status (e.g., symptoms and quality of life) are key outcomes in clinical trials of heart failure treatment. However, health status can only be recorded on survivors, potentially biasing treatment effect estimates when there is differential survival across treatment groups. Joint modeling of survival and health status can address this bias.\n\nMethods and ResultsWe analyzed patient-level data from the PARTNER 1B trial of transcatheter aortic valve replacement (TAVR) versus standard care. Health status was quantified with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at randomization, 1, 6, and 12 months. We compared hazard ratios for survival and mean differences in KCCQ scores at 12 months using several models: the original growth curve model for KCCQ scores (ignoring death), separate Bayesian models for survival and KCCQ scores, and a Bayesian joint longitudinal-survival model fit to either 12 or 30 months of survival follow-up. The benefit of TAVR on 12-month KCCQ scores was greatest in the joint model fit to all survival data (mean difference = 33.7 points; 95% CrI: 24.2, 42.4), followed by the joint model fit to 12 months of survival follow-up (32.3 points; 95% CrI: 22.5, 41.5), a Bayesian model without integrating death (30.4 points; 95% CrI: 21.4, 39.3), and the original growth curve model (26.0 points; 95% CI: 18.7, 33.3). At 12 months, the survival benefit of TAVR was also greater in the joint model (hazard ratio = 0.50; 95% CrI: 0.32, 0.73) than in the non-joint Bayesian model (0.54; 95% CrI: 0.37, 0.75) or the original Kaplan-Meier estimate (0.55; 95% CI: 0.40, 0.74).\n\nConclusionsIn patients with severe symptomatic aortic stenosis and prohibitive surgical risk, the estimated benefits of TAVR on survival and health status compared with standard care were greater in joint Bayesian models than other approaches.

clinical trials↗