An mRNA-based platform for the delivery of pathogen-specific IgA into mucosal secretions.

Colonization of the gut and airways by pathogenic bacteria can lead to local tissue destruction and life-threatening systemic infections, especially in immunologically compromised individuals. Here, we describe an mRNA-based platform enabling delivery of pathogen-specific immunoglobulin A (IgA) monoclonal antibodies into mucosal secretions. The platform consists of synthetic mRNA encoding IgA heavy, light, and joining (J) chains, packaged in lipid nanoparticles (LNPs) that express glycosylated, dimeric IgA with functional activity in vitro and in vivo. Importantly, mRNA-derived IgA had a significantly greater serum half-life and a more native glycosylation profile in mice than did a recombinantly produced IgA. Expression of an mRNA encoded Salmonella-specific IgA in mice resulted in intestinal localization and limited Peyer's patch invasion. The same mRNA-LNP technology was used to express a Pseudomonas-specific IgA that protected from a lung challenge. Leveraging the mRNA antibody technology as a means to intercept bacterial pathogens at mucosal surfaces opens up avenues for prophylactic and therapeutic interventions.

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.

CLIMB: High-dimensional association detection in large scale genomic data.

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

Learning the properties of adaptive regions with functional data analysis.

Identifying regions of positive selection in genomic data remains a challenge in population genetics. Most current approaches rely on comparing values of summary statistics calculated in windows. We present an approach termed SURFDAWave, which translates measures of genetic diversity calculated in genomic windows to functional data. By transforming our discrete data points to be outputs of continuous functions defined over genomic space, we are able to learn the features of these functions that signify selection. This enables us to confidently identify complex modes of natural selection, including adaptive introgression. We are also able to predict important selection parameters that are responsible for shaping the inferred selection events. By applying our model to human population-genomic data, we recapitulate previously identified regions of selective sweeps, such as OCA2 in Europeans, and predict that its beneficial mutation reached a frequency of 0.02 before it swept 1,802 generations ago, a time when humans were relatively new to Europe. In addition, we identify BNC2 in Europeans as a target of adaptive introgression, and predict that it harbors a beneficial mutation that arose in an archaic human population that split from modern humans within the hypothesized modern human-Neanderthal divergence range.

Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure.

Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI's performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.

powerTCR: A model-based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire.

Sequencing of the T cell receptor (TCR) repertoire is a powerful tool for deeper study of immune response, but the unique structure of this type of data makes its meaningful quantification challenging. We introduce a new method, the Gamma-GPD spliced threshold model, to address this difficulty. This biologically interpretable model captures the distribution of the TCR repertoire, demonstrates stability across varying sequencing depths, and permits comparative analysis across any number of sampled individuals. We apply our method to several datasets and obtain insights regarding the differentiating features in the T cell receptor repertoire among sampled individuals across conditions. We have implemented our method in the open-source R package powerTCR.

Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.