Biology subjects

Wood, J. A.

Publications and source records attributed to Wood, J. A..

3 recordsLinked to original sources

bioRxiv2021

The production of 4,182 mouse lines identifies experimental and biological variables impacting Cas9-mediated mutant mouse line production

The International Mouse Phenotyping Consortium (IMPC) systematically produces and phenotypes mouse lines with presumptive null mutations to provide insight into gene function. The IMPC now uses the programmable RNA-guided nuclease Cas9 for its increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of 3,313 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo gene engineering with Cas9. Mouse line production has two critical steps - generation of founders with the desired allele and germline transmission (GLT) of that allele from founders to offspring. A systematic evaluation of the variables impacting success rates identified gene essentiality as the primary factor influencing successful production of null alleles. Collectively, our findings provide best practice recommendations for using Cas9 to generate alleles in mouse essential genes, many of which are orthologs of genes linked to human disease.

genetics↗

bioRxiv2021

3D printed lung on a chip device with a stretchable nanofibrous membrane for modeling ventilator induced lung injury

Mechanical ventilation is often required in patients with pulmonary disease to maintain adequate gas exchange. Despite improved knowledge regarding the risks of over ventilating the lung, ventilator induced lung injury (VILI) remains a major clinical problem due to inhomogeneities within the diseased lung itself as well as the need to increase pressure or volume of oxygen to the lung as a life-saving measure. VILI is characterized by increased physical forces exerted within the lung, which results in cell death, inflammation and long-term fibrotic remodeling. Animal models can be used to study VILI, but it is challenging to distinguish the contributions of individual cell types in such a setup. In vitro models, which allow for controlled stretching of specific lung cell types have emerged as a potential option, but these models and the membranes used in them are unable to recapitulate some key features of the lung such as the 3D nanofibrous structure of the alveolar basement membrane while also allowing for cells to be cultured at an air liquid interface (ALI) and undergo increased mechanical stretch that mimics VILI. Here we develop a lung on a chip device with a nanofibrous synthetic membrane to provide ALI conditions and controllable stretching, including injurious stretching mimicking VILI. The lung on a chip device consists of a thin (i.e. [~]20 {micro}m) stretchable poly(caprolactone) (PCL) nanofibrous membrane placed between two channels fabricated in polydimethylsiloxane (PDMS) using 3D printed molds. We demonstrate that this lung on a chip device can be used to induce mechanotrauma in lung epithelial cells due to cyclic pathophysiologic stretch ([~]25%) that mimics clinical VILI. Pathophysiologic stretch induces cell injury and subsequently cell death, which results in loss of the epithelial monolayer, a feature mimicking the early stages of VILI. We also validate the potential of our lung on a chip device to be used to explore cellular pathways known to be altered with mechanical stretch and show that pathophysiologic stretch of lung epithelial cells causes nuclear translocation of the mechanotransducers YAP/TAZ. In conclusion, we show that a breathable lung on a chip device with a nanofibrous membrane can be easily fabricated using 3D printing of the lung on a chip molds and that this model can be used to explore pathomechanisms in mechanically induced lung injury.

bioengineering↗

bioRxiv2020

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with high-throughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequence-activity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases. One sentence summaryDeep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus.

bioinformatics↗