Eliciting a single amino acid change by vaccination generates antibody protection against group 1 and group 2 influenza A viruses.

Broadly neutralizing antibodies (bnAbs) targeting the hemagglutinin (HA) stem of influenza A viruses (IAVs) tend to be effective against either group 1 or group 2 viral diversity. In rarer cases, intergroup protective bnAbs can be generated by human antibody paratopes that accommodate the conserved glycan differences between the group 1 and group 2 stems. We applied germline-engaging nanoparticle immunogens to elicit a class of cross-group bnAbs from physiological precursor frequency within a humanized mouse model. Cross-group protection depended on the presence of the human bnAb precursors within the B cell repertoire, and the vaccine-expanded antibodies enriched for an N55T substitution in the CDRH2 loop, a hallmark of the bnAb class. Structurally, this single mutation introduced a flexible fulcrum to accommodate glycosylation differences and could alone enable cross-group protection. Thus, broad IAV immunity can be expanded from the germline repertoire via minimal antigenic input and an exceptionally simple antibody development pathway.

Combinatorial and statistical prediction of gene expression from haplotype sequence.

MOTIVATION: Genome-wide association studies (GWAS) have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, expression quantitative trait loci studies have interpreted many of these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experimental investigation. RESULTS: In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that models haplotype sharing with a modified suffix tree data structure and computes expression groups by spectral clustering. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD with three state-of-the-art expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissues. HAPLEXD exhibits significantly higher classification accuracy overall; HAPLEXR shows higher prediction accuracy on approximately half of the genes tested and the largest number of best predicted genes (r2>0.1) among all methods. We show that variant and haplotype features selected by HAPLEXR are smaller in size than competing methods (and thus more interpretable) and are significantly enriched in functional annotations related to gene regulation. These results demonstrate the importance of explicitly modeling non-dosage dependent and intragenic epistatic effects when predicting expression. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at https://github.com/rapturous/HAPLEX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of selection stringency on the outcomes of directed evolution.

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

Comparison of TP53 Mutations in Myelodysplasia and Acute Leukemia Suggests Divergent Roles in Initiation and Progression.

TP53 mutation predicts adverse prognosis in many cancers, including myeloid neoplasms, but the mechanisms by which specific mutations impact disease biology, and whether they differ between disease categories, remain unknown. We analyzed TP53 mutations in four myeloid neoplasm subtypes (MDS, AML, AML with myelodysplasia-related changes (AML-MRC), and therapy-related acute myeloid leukemia (tAML)), and identified differences in mutation types, spectrum, and hotspots between disease categories and compared to solid tumors. Missense mutations in the DNA-binding domain were most common across all categories, whereas inactivating mutations and mutations outside the DNA binding domain were more common in AML-MRC compared to MDS. TP53 mutations in MDS were more likely to retain transcriptional activity, and co-mutation profiles were distinct between disease categories and mutation types. Our findings suggest that mutated TP53 contributes to initiation and progression of neoplasia via distinct mechanisms, and support the utility of specific identification of TP53 mutations in myeloid malignancies.

Evaluating molecular fingerprint-based models of drug side effects against a statistical control.

There are many machine learning models that use molecular fingerprints of drugs to predict side effects. Characterizing their skill is necessary for understanding their usefulness in pharmaceutical development. Here, we analyze a statistical control of side effect prediction skill, develop a pipeline for benchmarking models, and evaluate how well existing models predict side effects identified in pharmaceutical documentation. We demonstrate that molecular fingerprints are useful for ranking drugs by their likelihood to cause a given side effect. However, the predictions for one or more drugs overall benefit only marginally from molecular fingerprints when ranking the likelihoods of many possible side effects, and display at most modest overall skill at identifying the side effects that do and do not occur.