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Smoking has been associated with diseases of the lung, pulmonary airways and oral cavity. Cytologic, genomic and transcriptomic changes in oral mucosa correlate with oral pre-neoplasia, cancer and inflammation (e.g. periodontitis). Alteration of smoking-related gene expression changes in oral epithelial cells is similar to that in bronchial and nasal epithelial cells. Using a systems toxicology approach, we have previously assessed the impact of cigarette smoke (CS) seen as perturbations of biological processes in human nasal and bronchial organotypic epithelial culture models. Here, we report our further assessment using in vitro human oral organotypic epithelium models. We exposed the buccal and gingival organotypic epithelial tissue cultures to CS at the air-liquid interface. CS exposure was associated with increased secretion of inflammatory mediators, induction of cytochrome P450s activity and overall weak toxicity in both tissues. Using microarray technology, gene-set analysis and a novel computational modeling approach leveraging causal biological network models, we identified CS impact on xenobiotic metabolism-related pathways accompanied by a more subtle alteration in inflammatory processes. Gene-set analysis further indicated that the CS-induced pathways in the in vitro buccal tissue models resembled those in the in vivo buccal biopsies of smokers from a published dataset. These findings support the translatability of systems responses from in vitro to in vivo and demonstrate the applicability of oral organotypical tissue models for an impact assessment of CS on various tissues exposed during smoking, as well as for impact assessment of reduced-risk products. [ABSTRACT FROM AUTHOR]

Characterizing libraries of mutant proteins is a challenging task, but can lead to detailed functional insights on a specific protein, and general insights for families of proteins such as transcription factors. Challenges in mutant protein screening consist in synthesizing the necessary expression-ready DNA constructs and transforming them into a suitable host for protein expression. Protein purification and characterization are also non-trivial tasks that are not easily scalable to hundreds or thousands of protein variants. Here we describe a method based on a high-throughput microfluidic platform to screen and characterize the binding profile of hundreds of transcription factor variants. DNA constructs are synthesized by a rapid two-step PCR approach without the need of cloning or transformation steps. All transcription factor mutants are expressed on-chip followed by characterization of their binding specificities against 64 different DNA target sequences. The current microfluidic platform can synthesize and characterize up to 2,400 protein-DNA pairs in parallel. The platform method is also generally applicable, allowing high-throughput functional studies of proteins.

Gene regulatory networks (GRNs) consist of transcription factors (TFs) that determine the level of gene expression by binding to specific DNA sequences. Mapping all TF-DNA interactions and elucidating their dynamics is a major goal to generate comprehensive models of GRNs. Measuring quantitative binding affinities of large sets of TF-DNA interactions requires the application of novel tools and methods. These tools need to cope with the difficulties related to the facts that TFs tend to be expressed at low levels in vivo, and often form only transient interactions with both DNA and their protein partners. Our approach describes a high-throughput microfluidic platform with a novel detection principle based on the mechanically induced trapping of molecular interactions (MITOMI). MITOMI allows the detection of transient and low-affinity TF-DNA interactions in high-throughput.