Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis.

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.

Identification of shed proteins from Chinese hamster ovary cells: application of statistical confidence using human and mouse protein databases.

The shedding process releases ligands, receptors, and other proteins from the surface of the cell and is a mechanism whereby cells communicate. Even though altered regulation of this process has been implicated in several diseases, global approaches to evaluate shed proteins have not been developed. A goal of this study was to identify global changes in shed proteins in media taken from cells exposed to low-doses of radiation to develop a fundamental understanding of the bystander response. Chinese hamster ovary cells were chosen because they have been widely used for radiation studies and are reported to respond to radiation by releasing factors into the media that cause genomic instability and cytotoxicity in unexposed cells, i.e., a bystander effect. Media samples taken for irradiated cells were evaluated using a combination of tandem- and Fourier transform-ion cyclotron resonance (FT-ICR)-mass spectrometry (MS) analyses. Since the hamster genome has not been sequenced, MS data was searched against the mouse and human protein databases. Nearly 150 proteins identified by tandem mass spectrometry were confirmed by FT-ICR. When both types of MS data were evaluated, using a new confidence scoring tool based on discriminant analyses, about 500 proteins were identified. Approximately 20% of these identifications were either integral membrane proteins or membrane associated proteins, suggesting that they were derived from the cell surface and, hence were likely shed. However, estimates of quantitative changes, based on two independent MS approaches, did not identify any protein abundance changes attributable to the bystander effect. Results from this study demonstrate the feasibility of global evaluation of shed proteins using MS in conjunction with cross-species protein databases and that significant improvement in peptide/protein identifications is provided by the confidence scoring tool.

The influence of the full moon on seizure frequency: myth or reality?

To investigate a possible relationship between seizure frequency and the lunar cycle, we reviewed the occurrence of seizures recorded in our epilepsy monitoring unit over a 3-year period. Analysis of the total number of seizures (epileptic plus nonepileptic) showed no significant association. A separate analysis revealed that for nonepileptic seizures, there was an increase at the full moon, and for epileptic seizures, an increase in the last quarter. We conclude that there is no "full moon" effect on seizures as a whole, although there is a possible effect on nonepileptic seizures.