Attenuating ribosome load improves protein output from mRNA by limiting translation-dependent mRNA decay.

Developing an effective mRNA therapeutic often requires maximizing protein output per delivered mRNA molecule. We previously found that coding sequence (CDS) design can substantially affect protein output, with mRNA variants containing more optimal codons and higher secondary structure yielding the highest protein outputs due to their slow rates of mRNA decay. Here, we demonstrate that CDS-dependent differences in translation initiation and elongation rates lead to differences in translation- and deadenylation-dependent mRNA decay rates, thus explaining the effect of CDS on mRNA half-life. Surprisingly, the most stable and highest-expressing mRNAs in our test set have modest initiation/elongation rates and ribosome loads, leading to minimal translation-dependent mRNA decay. These findings are of potential interest for optimization of protein output from therapeutic mRNAs, which may be achieved by attenuating rather than maximizing ribosome load.

Intranasal mRNA-LNP vaccination protects hamsters from SARS-CoV-2 infection.

Intranasal vaccination represents a promising approach for preventing disease caused by respiratory pathogens by eliciting a mucosal immune response in the respiratory tract that may act as an early barrier to infection and transmission. This study investigated immunogenicity and protective efficacy of intranasally administered messenger RNA (mRNA)-lipid nanoparticle (LNP) encapsulated vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Syrian golden hamsters. Intranasal mRNA-LNP vaccination systemically induced spike-specific binding [immunoglobulin G (IgG) and IgA] and neutralizing antibodies. Intranasally vaccinated hamsters also had decreased viral loads in the respiratory tract, reduced lung pathology, and prevented weight loss after SARS-CoV-2 challenge. Together, this study demonstrates successful immunogenicity and protection against respiratory viral infection by an intranasally administered mRNA-LNP vaccine.

An optimized messenger RNA vaccine candidate protects non-human primates from Zika virus infection.

Zika virus (ZIKV), an arbovirus transmitted by mosquitoes, was identified as a cause of congenital disease during a major outbreak in the Americas in 2016. Vaccine design strategies relied on limited available isolate sequence information due to the rapid response necessary. The first-generation ZIKV mRNA vaccine, mRNA-1325, was initially generated and, as additional strain sequences became available, a second mRNA vaccine, mRNA-1893, was developed. Herein, we compared the immune responses following mRNA-1325 and mRNA-1893 vaccination and reported that mRNA-1893 generated comparable neutralizing antibody titers to mRNA-1325 at 1/20th of the dose and provided complete protection from ZIKV challenge in non-human primates. In-depth characterization of these vaccines indicated that the observed immunologic differences could be attributed to a single amino acid residue difference that compromised mRNA-1325 virus-like particle formation.

Altering the mRNA-1273 dosing interval impacts the kinetics, quality, and magnitude of immune responses in mice.

For a vaccine to achieve durable immunity and optimal efficacy, many require a multi-dose primary vaccination schedule that acts to first "prime" naive immune systems and then "boost" initial immune responses by repeated immunizations (ie, prime-boost regimens). In the context of the global coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2-dose primary vaccination regimens were often selected with short intervals between doses to provide rapid protection while still inducing robust immunity. However, emerging post-authorization evidence has suggested that longer intervals between doses 1 and 2 for SARS-CoV-2 vaccines may positively impact robustness and durability of immune responses. Here, the dosing interval for mRNA-1273, a messenger RNA based SARS-CoV-2 vaccine administered on a 2-dose primary schedule with 4 weeks between doses, was evaluated in mice by varying the dose interval between 1 and 8 weeks and examining immune responses through 24 weeks after dose 2. A dosing interval of 6 to 8 weeks generated the highest level of antigen-specific serum immunoglobulin G binding antibody titers. Differences in binding antibody titers between mRNA-1273 1 µg and 10 µg decreased over time for dosing intervals of ≥4 weeks, suggesting a potential dose-sparing effect. Longer intervals (≥4 weeks) also increased antibody-dependent cellular cytotoxicity activity and numbers of antibody-secreting cells (including long-lived plasma cells) after the second dose. An interval of 6 to 8 weeks elicited the strongest CD8+ T-cell responses, while an interval of 3 weeks elicited the strongest CD4+ T-cell response. Overall, these results suggest that in a non-pandemic setting, a longer interval (≥6 weeks) between the doses of the primary series for mRNA-1273 may induce more durable immune responses.

Selective activation and expansion of regulatory T cells using lipid encapsulated mRNA encoding a long-acting IL-2 mutein.

Interleukin-2 (IL-2) is critical for regulatory T cell (Treg) function and homeostasis. At low doses, IL-2 can suppress immune pathologies by expanding Tregs that constitutively express the high affinity IL-2Rα subunit. However, even low dose IL-2, signaling through the IL2-Rß/γ complex, may lead to the activation of proinflammatory, non-Treg T cells, so improving specificity toward Tregs may be desirable. Here we use messenger RNAs (mRNA) to encode a half-life-extended human IL-2 mutein (HSA-IL2m) with mutations promoting reliance on IL-2Rα. Our data show that IL-2 mutein subcutaneous delivery as lipid-encapsulated mRNA nanoparticles selectively activates and expands Tregs in mice and non-human primates, and also reduces disease severity in mouse models of acute graft versus host disease and experimental autoimmune encephalomyelitis. Single cell RNA-sequencing of mouse splenic CD4+ T cells identifies multiple Treg states with distinct response dynamics following IL-2 mutein treatment. Our results thus demonstrate the potential of mRNA-encoded HSA-IL2m immunotherapy to treat autoimmune diseases.

Impact of lipid nanoparticle size on mRNA vaccine immunogenicity.

Lipid nanoparticles (LNP) are effective delivery vehicles for messenger RNA (mRNA) and have shown promise for vaccine applications. Yet there are no published reports detailing how LNP biophysical properties can impact vaccine performance. In our hands, a retrospective analysis of mRNA LNP vaccine in vivo studies revealed a relationship between LNP particle size and immunogenicity in mice using LNPs of various compositions. To further investigate this, we designed a series of studies to systematically change LNP particle size without altering lipid composition and evaluated biophysical properties and immunogenicity of the resulting LNPs. While small diameter LNPs were substantially less immunogenic in mice, all particle sizes tested yielded a robust immune response in non-human primates (NHP).

Nasal Microbiota and Infectious Complications After Elective Surgical Procedures.

Importance: The association of the nasal microbiome with outcomes in surgical patients is poorly understood. Objective: To characterize the composition of nasal microbiota in patients undergoing clean elective surgical procedures and to examine the association between characteristics of preoperative nasal microbiota and occurrence of postoperative infection. Design, Setting, and Participants: Using a nested matched case-control design, 53 individuals who developed postoperative infection were matched (approximately 3:1 by age, sex, and surgical procedure) with 144 individuals who were not infected (ie, the control group). The 2 groups were selected from a prospective cohort of patients undergoing surgical procedures at 2 tertiary care university hospitals in Baltimore, Maryland, who were at high risk for postoperative infectious complications. Included individuals were aged 40 years or older; had no history of autoimmune disease, immunocompromised state, immune-modulating medication, or active infection; and were scheduled to undergo elective cardiac, vascular, spinal, or intracranial surgical procedure. Data were analyzed from October 2015 through September 2020. Exposures: Nasal microbiome cluster class served as the main exposure. An unsupervised clustering method (ie, grades of membership modeling) was used to classify nasal microbial samples into 2 groups based on features derived from 16S ribosomal RNA gene sequencing. The microbiome cluster groups were derived independently and agnostic of baseline clinical characteristics and infection status. Main Outcomes and Measures: Composite of surgical site infection, bacteremia, and pneumonia occurring within 6 months after surgical procedure. Results: Among 197 participants (mean [SD] age, 64.1 [10.6] years; 63 [37.7%] women), 553 bacterial taxa were identified from preoperative nasal swab samples. A 2-cluster model (with 167 patients in cluster 1 and 30 patients in cluster 2) accounted for the largest proportion of variance in microbial profiles using grades of membership modeling and was most parsimonious. After adjusting for potential confounders, the probability of assignment to cluster 2 was associated with 6-fold higher odds of infection after surgical procedure (odds ratio [OR], 6.18; 95% CI, 3.33-11.7; P < .001) independent of baseline clinical characteristics, including nasal carriage of Staphylococcus aureus. Intrasample (ie, α) diversity was inversely associated with infectious outcome in both clusters (OR, 0.57; 95% CI, 0.42-0.75; P < .001); however, probability of assignment to cluster 2 was associated with higher odds of infection independent of α diversity (OR, 4.61; 95% CI, 2.78-7.86; P < .001). Conclusions and Relevance: These findings suggest that the nasal microbiome was an independent risk factor associated with infectious outcomes among individuals who underwent elective surgical procedures and may serve as a biomarker associated with infection susceptibility in this population.

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis.

Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

A comparison of gene expression and DNA methylation patterns across tissues and species.

Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were often collected and analyzed using nonoptimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied in a comparative framework, individuals and tissues were confounded. We designed a multitissue comparative study of gene expression and DNA methylation in primates that minimizes confounding effects by using a balanced design with respect to species, tissues, and individuals. We also developed a comparative analysis pipeline that minimizes biases attributable to sequence divergence. Thus, we present the most comprehensive catalog of similarities and differences in gene expression and DNA methylation levels between livers, kidneys, hearts, and lungs, in humans, chimpanzees, and rhesus macaques. We estimate that overall, interspecies and inter-tissue differences in gene expression levels can only modestly be accounted for by corresponding differences in promoter DNA methylation. However, the expression pattern of genes with conserved inter-tissue expression differences can be explained by corresponding interspecies methylation changes more often. Finally, we show that genes whose tissue-specific regulatory patterns are consistent with the action of natural selection are highly connected in both gene regulatory and protein-protein interaction networks.

Reorganization of 3D genome structure may contribute to gene regulatory evolution in primates.

A growing body of evidence supports the notion that variation in gene regulation plays a crucial role in both speciation and adaptation. However, a comprehensive functional understanding of the mechanisms underlying regulatory evolution remains elusive. In primates, one of the crucial missing pieces of information towards a better understanding of regulatory evolution is a comparative annotation of interactions between distal regulatory elements and promoters. Chromatin conformation capture technologies have enabled genome-wide quantifications of such distal 3D interactions. However, relatively little comparative research in primates has been done using such technologies. To address this gap, we used Hi-C to characterize 3D chromatin interactions in induced pluripotent stem cells (iPSCs) from humans and chimpanzees. We also used RNA-seq to collect gene expression data from the same lines. We generally observed that lower-order, pairwise 3D genomic interactions are conserved in humans and chimpanzees, but higher order genomic structures, such as topologically associating domains (TADs), are not as conserved. Inter-species differences in 3D genomic interactions are often associated with gene expression differences between the species. To provide additional functional context to our observations, we considered previously published chromatin data from human stem cells. We found that inter-species differences in 3D genomic interactions, which are also associated with gene expression differences between the species, are enriched for both active and repressive marks. Overall, our data demonstrate that, as expected, an understanding of 3D genome reorganization is key to explaining regulatory evolution.

A comparative study of endoderm differentiation in humans and chimpanzees.

BACKGROUND: There is substantial interest in the evolutionary forces that shaped the regulatory framework in early human development. Progress in this area has been slow because it is difficult to obtain relevant biological samples. Induced pluripotent stem cells (iPSCs) may provide the ability to establish in vitro models of early human and non-human primate developmental stages. RESULTS: Using matched iPSC panels from humans and chimpanzees, we comparatively characterize gene regulatory changes through a four-day time course differentiation of iPSCs into primary streak, endoderm progenitors, and definitive endoderm. As might be expected, we find that differentiation stage is the major driver of variation in gene expression levels, followed by species. We identify thousands of differentially expressed genes between humans and chimpanzees in each differentiation stage. Yet, when we consider gene-specific dynamic regulatory trajectories throughout the time course, we find that at least 75% of genes, including nearly all known endoderm developmental markers, have similar trajectories in the two species. Interestingly, we observe a marked reduction of both intra- and inter-species variation in gene expression levels in primitive streak samples compared to the iPSCs, with a recovery of regulatory variation in endoderm progenitors. CONCLUSIONS: The reduction of variation in gene expression levels at a specific developmental stage, paired with overall high degree of conservation of temporal gene regulation, is consistent with the dynamics of a conserved developmental process.

A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes.

Phosphorylation of proteins on serine, threonine, and tyrosine residues is a ubiquitous post-translational modification that plays a key part of essentially every cell signaling process. It is reasonable to assume that inter-individual variation in protein phosphorylation may underlie phenotypic differences, as has been observed for practically any other molecular regulatory phenotype. However, we do not know much about the extent of inter-individual variation in phosphorylation because it is quite challenging to perform a quantitative high throughput study to assess inter-individual variation in any post-translational modification. To test our ability to address this challenge with SILAC-based mass spectrometry, we quantified phosphorylation levels for three genotyped human cell lines within a nested experimental framework, and found that genetic background is the primary determinant of phosphoproteome variation. We uncovered multiple functional, biophysical, and genetic associations with germline driven phosphopeptide variation. Variants affecting protein levels or structure were among these associations, with the latter presenting, on average, a stronger effect. Interestingly, we found evidence that is consistent with a phosphopeptide variability buffering effect endowed from properties enriched within longer proteins. Because the small sample size in this 'pilot' study may limit the applicability of our genetic observations, we also undertook a thorough technical assessment of our experimental workflow to aid further efforts. Taken together, these results provide the foundation for future work to characterize inter-individual variation in post-translational modification levels and reveal novel insights into the nature of inter-individual variation in phosphorylation.

Post-translational buffering leads to convergent protein expression levels between primates.

BACKGROUND: Differences in gene regulation between human and closely related species influence phenotypes that are distinctly human. While gene regulation is a multi-step process, the majority of research concerning divergence in gene regulation among primates has focused on transcription. RESULTS: To gain a comprehensive view of gene regulation, we surveyed genome-wide ribosome occupancy, which reflects levels of protein translation, in lymphoblastoid cell lines derived from human, chimpanzee, and rhesus macaque. We further integrated messenger RNA and protein level measurements collected from matching cell lines. We find that, in addition to transcriptional regulation, the major factor determining protein level divergence between human and closely related species is post-translational buffering. Inter-species divergence in transcription is generally propagated to the level of protein translation. In contrast, gene expression divergence is often attenuated post-translationally, potentially mediated through post-translational modifications. CONCLUSIONS: Results from our analysis indicate that post-translational buffering is a conserved mechanism that led to relaxation of selective constraint on transcript levels in humans.

Correction: Visualizing the structure of RNA-seq expression data using grade of membership models.

[This corrects the article DOI: 10.1371/journal.pgen.1006599.].

Visualizing the structure of RNA-seq expression data using grade of membership models.

Grade of membership models, also known as "admixture models", "topic models" or "Latent Dirichlet Allocation", are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple "populations", and in natural language processing to model documents having words from multiple "topics". Here we illustrate the potential for these models to cluster samples of RNA-seq gene expression data, measured on either bulk samples or single cells. We also provide methods to help interpret the clusters, by identifying genes that are distinctively expressed in each cluster. By applying these methods to several example RNA-seq applications we demonstrate their utility in identifying and summarizing structure and heterogeneity. Applied to data from the GTEx project on 53 human tissues, the approach highlights similarities among biologically-related tissues and identifies distinctively-expressed genes that recapitulate known biology. Applied to single-cell expression data from mouse preimplantation embryos, the approach highlights both discrete and continuous variation through early embryonic development stages, and highlights genes involved in a variety of relevant processes-from germ cell development, through compaction and morula formation, to the formation of inner cell mass and trophoblast at the blastocyst stage. The methods are implemented in the Bioconductor package CountClust.

Batch effects and the effective design of single-cell gene expression studies.

Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single-cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.