Network-Guided Discovery of Extensive Epistasis between Transcription Factors Involved in Aliphatic Glucosinolate Biosynthesis.

Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. These regulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasis within an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for 20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that control fitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership within the same or different phenotypic subnetworks. Extensive epistasis was observed between the transcription factors, regardless of subnetwork membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presence of a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue and environment and shifted between both antagonistic and synergistic forms. Transcriptional analysis showed that epistasis shifts depend on how the trait is measured. Because the 47 combinations described here represent a small sampling of the potential epistatic combinations in this genetic network, there is potential for significantly more epistasis. Additionally, the main effect of the individual gene was not predictive of the epistatic effects, suggesting that there is a need for further studies.

Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense.

Plants integrate internal and external signals to finely coordinate growth and defense for maximal fitness within a complex environment. A common model suggests that growth and defense show a trade-offs relationship driven by energy costs. However, recent studies suggest that the coordination of growth and defense likely involves more conditional and intricate connections than implied by the trade-off model. To explore how a transcription factor (TF) network may coordinate growth and defense, we used a high-throughput phenotyping approach to measure growth and flowering in a set of single and pairwise mutants previously linked to the aliphatic glucosinolate (GLS) defense pathway. Supporting a link between growth and defense, 17 of the 20 tested defense-associated TFs significantly influenced plant growth and/or flowering time. The TFs' effects were conditional upon the environment and age of the plant, and more critically varied across the growth and defense phenotypes for a given genotype. In support of the coordination model of growth and defense, the TF mutant's effects on short-chain aliphatic GLS and growth did not display a simple correlation. We propose that large TF networks integrate internal and external signals and separately modulate growth and the accumulation of the defensive aliphatic GLS.

Massively parallel interrogation and mining of natively paired human TCRαß repertoires.

T cells engineered to express antigen-specific T cell receptors (TCRs) are potent therapies for viral infections and cancer. However, efficient identification of clinical candidate TCRs is complicated by the size and complexity of T cell repertoires and the challenges of working with primary T cells. Here we present a high-throughput method to identify TCRs with high functional avidity from diverse human T cell repertoires. The approach used massively parallel microfluidics to generate libraries of natively paired, full-length TCRαß clones, from millions of primary T cells, which were then expressed in Jurkat cells. The TCRαß-Jurkat libraries enabled repeated screening and panning for antigen-reactive TCRs using peptide major histocompatibility complex binding and cellular activation. We captured more than 2.9 million natively paired TCRαß clonotypes from six healthy human donors and identified rare (<0.001% frequency) viral-antigen-reactive TCRs. We also mined a tumor-infiltrating lymphocyte sample from a patient with melanoma and identified several tumor-specific TCRs, which, after expression in primary T cells, led to tumor cell killing.

Preferential Identification of Agonistic OX40 Antibodies by Using Cell Lysate to Pan Natively Paired, Humanized Mouse-Derived Yeast Surface Display Libraries.

To discover therapeutically relevant antibody candidates, many groups use mouse immunization followed by hybridoma generation or B cell screening. One modern approach is to screen B cells by generating natively paired single chain variable fragment (scFv) display libraries in yeast. Such methods typically rely on soluble antigens for scFv library screening. However, many therapeutically relevant cell-surface targets are difficult to express in a soluble protein format, complicating discovery. In this study, we developed methods to screen humanized mouse-derived yeast scFv libraries using recombinant OX40 protein in cell lysate. We used deep sequencing to compare screening with cell lysate to screening with soluble OX40 protein, in the context of mouse immunizations using either soluble OX40 or OX40-expressing cells and OX40-encoding DNA vector. We found that all tested methods produce a unique diversity of scFv binders. However, when we reformatted forty-one of these scFv as full-length monoclonal antibodies (mAbs), we observed that mAbs identified using soluble antigen immunization with cell lysate sorting always bound cell surface OX40, whereas other methods had significant false positive rates. Antibodies identified using soluble antigen immunization and cell lysate sorting were also significantly more likely to activate OX40 in a cellular assay. Our data suggest that sorting with OX40 protein in cell lysate is more likely than other methods to retain the epitopes required for antibody-mediated OX40 agonism.