Summary A major challenge in obtaining a full molecular description of evolutionary adaptation is to characterize how transcription factor (TF) DNA binding specificity can change. To identify mechanisms of TF diversification, we performed detailed comparisons of yeast C2H2 ZF proteins with identical canonical recognition residues that are expected to bind the same DNA sequences. Unexpectedly, we found that ZF proteins can adapt to recognize new binding sites in a modular fashion whereby binding to common core sites remains unaffected. We identified two distinct mechanisms, conserved across multiple Ascomycota species, by which this molecular adaptation occurred. Our results suggest a route for TF evolution that alleviates negative pleiotropic effects by modularly gaining new binding sites. These findings expand our current understanding of ZF DNA binding and provide evidence for paralogous ZFs utilizing alternate modes of DNA binding to recognize unique sets of noncanonical binding sites.

NF-κB transcription factors control a wide array of important cellular and organismal processes in eukaryotes. All NF-κB transcription factors bind to DNA target sites as dimers. In vertebrates, there are five NF-κB subunits, p50, p52, RelA (p65), c-Rel, and RelB, that can form almost all combinations of homodimers and heterodimers, which recognize distinct, but overlapping, target sequences. In this chapter, we describe the use of protein-binding microarrays (PBMs), a high-throughput method to measure the binding of proteins to different DNA sequences. PBM datasets allow for sensitive comparisons of NF-κB dimer DNA-binding differences and can aid in the computational and experimental prediction of NF-κB target genes.

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes.

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