Synthetic transcription factor engineering for cell and gene therapy.

Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic.

Partial loss of CFIm25 causes learning deficits and aberrant neuronal alternative polyadenylation.

We previously showed that NUDT21-spanning copy-number variations (CNVs) are associated with intellectual disability (Gennarino et al., 2015). However, the patients' CNVs also included other genes. To determine if reduced NUDT21 function alone can cause disease, we generated Nudt21+/- mice to mimic NUDT21-deletion patients. We found that although these mice have 50% reduced Nudt21 mRNA, they only have 30% less of its cognate protein, CFIm25. Despite this partial protein-level compensation, the Nudt21+/- mice have learning deficits, cortical hyperexcitability, and misregulated alternative polyadenylation (APA) in their hippocampi. Further, to determine the mediators driving neural dysfunction in humans, we partially inhibited NUDT21 in human stem cell-derived neurons to reduce CFIm25 by 30%. This induced APA and protein level misregulation in hundreds of genes, a number of which cause intellectual disability when mutated. Altogether, these results show that disruption of NUDT21-regulated APA events in the brain can cause intellectual disability.

Identification of Pathogenic Bacteria from Public Libraries via Proteomics Analysis.

Hazardous organisms may thrive on surfaces that are often exposed to human contact, including children's library books. In this study, swab samples were taken from 42 children's books collected from four public libraries in Texas and California. Samples were then cultivated in brain⁻heart infusion (BHI) medium and then in Luria broth (LB) medium containing either ampicillin or kanamycin. All 42 samples (100%) were positive for bacterial growth in normal BHI medium. Furthermore, 35 samples (83.3%) and 20 samples (47.6%) in total were positive in LB medium containing ampicillin or kanamycin, respectively. Bacterial populations were then identified in samples using an Orbitrap Fusion™ Tribrid ™ mass spectrometer, a state-of-the-art proteomic analysis tool. Identified bacterial species grown in ampicillin included Bacillus, Acinetobacter, Pseudomonas, Staphylococcus, Enterobacter, Klebsiella, Serratia, Streptococcus, Escherichia, Salmonella, and Enterococcus. In contrast, identified bacteria grown in kanamycin included Staphylococcus, Streptococcus, Enterococcus, and Bacillus. The presences of pathogenic bacteria species were also confirmed. The results of this study warrant follow up studies to assess the potential health risks of identified pathogens. This study demonstrates the utility of proteomics in identifying environmental pathogenic bacteria for specific public health risk evaluations.

gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data.

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.