Symbolic recording of signalling and cis-regulatory element activity to DNA.

Measurements of gene expression or signal transduction activity are conventionally performed using methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm in which such biological activities are stably recorded to the genome. Enhancer-driven genomic recording of transcriptional activity in multiplex (ENGRAM) is based on the signal-dependent production of prime editing guide RNAs that mediate the insertion of signal-specific barcodes (symbols) into a genomically encoded recording unit. We show how this strategy can be used for multiplex recording of the cell-type-specific activities of dozens to hundreds of cis-regulatory elements with high fidelity, sensitivity and reproducibility. Leveraging signal transduction pathway-responsive cis-regulatory elements, we also demonstrate time- and concentration-dependent genomic recording of WNT, NF-κB and Tet-On activities. By coupling ENGRAM to sequential genome editing via DNA Typewriter1, we stably record information about the temporal dynamics of two orthogonal signalling pathways to genomic DNA. Finally we apply ENGRAM to integratively record the transient activity of nearly 100 transcription factor consensus motifs across daily windows spanning the differentiation of mouse embryonic stem cells into gastruloids, an in vitro model of early mammalian development. Although these are proof-of-concept experiments and much work remains to fully realize the possibilities, the symbolic recording of biological signals or states within cells, to the genome and over time, has broad potential to complement contemporary paradigms for how we make measurements in biological systems.

CodonBERT large language model for mRNA vaccines.

mRNA-based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties, including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs, which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods, including on a new flu vaccine data set.

Representations of lipid nanoparticles using large language models for transfection efficiency prediction.

MOTIVATION: Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency. RESULTS: To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs. AVAILABILITY AND IMPLEMENTATION: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.

Coronary bypass surgery guided by computed tomography in a low-risk population.

BACKGROUND AND AIMS: In patients with three-vessel disease and/or left main disease, selecting revascularization strategy based on coronary computed tomography angiography (CCTA) has a high level of virtual agreement with treatment decisions based on invasive coronary angiography (ICA). METHODS: In this study, coronary artery bypass grafting (CABG) procedures were planned based on CCTA without knowledge of ICA. The CABG strategy was recommended by a central core laboratory assessing the anatomy and functionality of the coronary circulation. The primary feasibility endpoint was the percentage of operations performed without access to the ICA. The primary safety endpoint was graft patency on 30-day follow-up CCTA. Secondary endpoints included topographical adequacy of grafting, major adverse cardiac and cerebrovascular (MACCE), and major bleeding events at 30 days. The study was considered positive if the lower boundary of confidence intervals (CI) for feasibility was ≥75% (NCT04142021). RESULTS: The study enrolled 114 patients with a mean (standard deviation) anatomical SYNTAX score and Society of Thoracic Surgery score of 43.6 (15.3) and 0.81 (0.63), respectively. Unblinding ICA was required in one case yielding a feasibility of 99.1% (95% CI 95.2%-100%). The concordance and agreement in revascularization planning between the ICA- and CCTA-Heart Teams was 82.9% with a moderate kappa of 0.58 (95% CI 0.50-0.66) and between the CCTA-Heart Team and actual treatment was 83.7% with a substantial kappa of 0.61 (95% CI 0.53-0.68). The 30-day follow-up CCTA in 102 patients (91.9%) showed an anastomosis patency rate of 92.6%, whilst MACCE was 7.2% and major bleeding 2.7%. CONCLUSIONS: CABG guided by CCTA is feasible and has an acceptable safety profile in a selected population of complex coronary artery disease.

Analytical Characterization of Heterogeneities in mRNA-Lipid Nanoparticles Using Sucrose Density Gradient Ultracentrifugation.

Rational design and robust formulation processes are critical for optimal delivery of mRNA by lipid nanoparticles (LNPs). Varying degrees of heterogeneity in mRNA-LNPs can affect their biophysical and functional properties. Given the profound complexity of mRNA-LNPs, it is critical to develop comprehensive and orthogonal analytical techniques for a better understanding of these formulations. To this end, we developed a robust ultracentrifugation method for density-based separation of subpopulations of mRNA-LNPs. Four LNP formulations encapsulating human erythropoietin (hEPO) with varying functionalities were synthesized using two ionizable lipids, A and B, and two helper lipids, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) and 1,2-dierucoyl-sn-glycero-3-phosphoethanolamine (DEPE), along with cholesterol and DMG-PEG-2K. Upon ultracentrifugation on a sucrose gradient, a distinct pattern of "fractions" was observed across the gradient, from the less dense topmost fraction to the increasingly denser bottom fractions, which were harvested for comprehensive analyses. Parent LNPs, A-DOPE and B-DOPE, were resolved into three density-based fractions, each differing significantly in the hEPO expression following intravenous and intramuscular routes of administration. Parent B-DEPE LNPs resolved into two density-based fractions, with most of the payload and lipid content being attributed to the topmost fraction compared to the lower one, indicating some degree of heterogeneity, while parent A-DEPE LNPs showed remarkable homogeneity, as indicated by comparable in vivo potency, lipid numbers, and particle count among the three density-based fractions. This study is the first to demonstrate the application of density gradient-based ultracentrifugation (DGC) for a head-to-head comparison of heterogeneity as a function of biological performance and biophysical characteristics of parent mRNA-LNPs and their subpopulations.

How to assess nonresponsiveness to vasodilator stress.

Myocardial perfusion imaging (MPI) is a powerful tool for the functional assessment of ischemia in patients with suspected or known coronary artery disease (CAD). Given that the diagnostic accuracy and prognostic value of MPI and post-test management are highly dependent on achieving an adequate stress vasodilatory response, it is critical to identify those who may not have adequately responded to vasodilator pharmacological stress agents such as adenosine, dipyridamole, and regadenoson. Caffeine, a potent inhibitor of the adenosine receptor, is a compound that can affect vasodilatory hemodynamics, result in false negative studies, and potentially alter management in cases of inaccurate test results. Vasodilator non-responsiveness can be suspected by examining hemodynamics, quantitative positron emission tomography (PET) metrics such as myocardial flow reserve (MFR), and splenic response to stress. Quantitative MFR values of 1-1.2 should raise suspicion for nonresponsiveness in the setting of normal perfusion, along with the absence of a splenic switch off. Newer metrics, such as splenic response ratio, can be used to aid in the identification of potential nonresponders to pharmacologic vasodilators.

How DNA encodes the start of transcription.

A deep-learning model reveals the rules that define transcription initiation.

Changes in use of preventive medications after assessment of chest pain by coronary computed tomography angiography: A meta-analysis.

BACKGROUND: Coronary computed tomography angiogram (CCTA) is a crucial tool for diagnosing CAD, but its impact on altering preventive medications is not well-documented. This systematic review aimed to compare changes in aspirin and statin therapy following CCTA and functional stress testing in patients with suspected CAD, and in those underwent CCTA when stratified by the presence/absence of plaque. RESULTS: Eight studies involving 42,812 CCTA patients and 64,118 cardiac stress testing patients were analyzed. Compared to functional testing, CCTA led to 66 â€‹% more changes in statin therapy (pooled RR, 95 â€‹% CI [1.28-2.15]) and a 74 â€‹% increase in aspirin prescriptions (pooled RR, 95 â€‹% CI [1.34-2.26]). For medication modifications based on CCTA results, 13 studies (47,112 patients with statin data) and 11 studies (12,089 patients with aspirin data) were included. Patients with any plaque on CCTA were five times more likely to use or intensify statins compared to those without CAD (pooled RR, 5.40, 95 â€‹% CI [4.16-7.00]). Significant heterogeneity remained, which decreased when stratified by diabetes rates. Aspirin use increased eightfold after plaque detection (pooled RR, 8.94 [95 â€‹% CI, 4.21-19.01]), especially with obstructive plaque findings (pooled RR, 9.41, 95 â€‹% CI [2.80-39.02]). CONCLUSION: In conclusion, CCTA resulted in higher changes in statin and aspirin therapy compared to cardiac stress testing. Detection of plaque by CCTA significantly increased statin and aspirin therapy.

Predicting the translation efficiency of messenger RNA in mammalian cells.

The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess ~67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features (i.e., including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms.

Quantifying cardiac dysfunction and valvular heart disease associated with subretinal drusenoid deposits in age-related macular degeneration.

BACKGROUND/AIMS: Demonstrate through objective multidisciplinary imaging that subretinal drusenoid deposits (SDDs) in age-related macular degeneration (AMD) are linked to both coexistent valvular heart disease (VHD) and reduced systemic perfusion via cardiac index (CI). METHODS: Post-hoc analysis of cross-sectional study. 200 intermediate AMD (iAMD) subjects were assigned by masked readers to two groups: SDD (with or without drusen) and drusen (only) based on multimodal ophthalmic imaging. 65 transthoracic echocardiograms (TTEs) reports were available for cardiologist evaluation of VHD severity of the four cardiac valves and the presences of precursor lesions of aortic sclerosis (ASc) and mitral annular calcification (MAC). Necessary parameters to calculate CI were also obtained. Univariate testing was performed using Fisher's Exact test and t-test. RESULTS: 82.6% (19/23) of the iAMD subjects with at least one moderate/severe VHD had concurrent SDDs (p = 0.0040). All cases of aortic regurgitation (6/6, p = 0.0370) and mitral regurgitation (13/13, p = 0.0004) were found with coexisting SDDs. Stenotic VHD was not significantly associated with SDDs, however 70.7% of subjects with ASc (29/41, p = 0.0108) and 76.0% of subjects with MAC (19/25, 0.0377) had coexisting SDDs. CI was available in 48 subjects and was significantly below normal levels in the SDD cohort (mean CI SDD 1.95 ± 0.60 L/min/m2, non-SDD 2.71 ± 0.73 L/min/m2, p = 0.0004). CONCLUSIONS: Several specific VHDs have been found associated with the SDD form of AMD. Decreased systemic perfusion as measured by CI was also associated with SDDs, which supports a perfusion hypothesis of SDD pathogenesis. Further research is warranted to understand the relationship between cardiovascular disease and SDDs.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes.

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.