Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH ‘Therapeutic Use’ classes, can be predicted from only chemical structure. We evaluated two chemical structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used drug-induced transcriptomic changes as chemical representations. This suggests that a chemical’s structure contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Further, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug repurposing opportunities, and demonstrate the effectiveness of this modeling strategy for multi-label classification.

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

SUMMARY: We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

Genome mining has become an increasingly powerful, scalable, and economically accessible tool for the study of natural product biosynthesis and drug discovery. However, there remain important biological and practical problems that can complicate or obscure biosynthetic analysis in genomic and metagenomic sequencing projects. Here, we focus on limitations of available technology as well as computational and experimental strategies to overcome them. We review the unique challenges and approaches in the study of symbiotic and uncultured systems, as well as those associated with biosynthetic gene cluster (BGC) assembly and product prediction. Finally, to explore sequencing parameters that affect the recovery and contiguity of large and repetitive BGCs assembled de novo, we simulate Illumina and PacBio sequencing of the Salinispora tropica genome focusing on assembly of the salinilactam (slm) BGC.

DNA sequencing of a large collection of bacterial genomes reveals a wealth of orphan biosynthetic gene clusters (BGCs) with no identifiable products. BGC silencing, for those orphan clusters that are truly silent, rather than those whose products have simply evaded detection and cluster correlation, is postulated to result from transcriptional inactivation of these clusters under standard laboratory conditions. Here, we employ a multi-omics approach to demonstrate how interspecies interactions modulate the keyicin producing kyc cluster at the transcriptome level in co-cultures of kyc-bearing Micromonospora sp. and a Rhodococcus sp. We further correlate co-culture dependent changes in keyicin production to changes in transcriptomic and proteomic profiles and show that these changes are attributable to small molecule signaling consistent with a quorum sensing pathway. In piecing together the various elements underlying keyicin production in co-culture, this study highlights how omics technologies can expedite future efforts to understand and exploit silent BGCs.

The in vivo reflectance spectra of Caucasian skin, coated with preparations containing sunscreen vehicle, vehicle with olive oil and vehicle with the UVB and UVA absorbers 2-ethylhexyl-4-methoxycinnamate and 4-t-butyl-4'-methoxydibenzoylmethane were determined. All preparations reduced the reflectance of skin throughout the UVA spectral range (320 to 400 nm), with the sunscreen preparations containing the UVB and UVB plus UVA absorbers reducing the reflectance more than the sunscreen vehicle alone. This phenomenon, which facilitates the penetration of UV radiation to the lower epidermis and dermal layers of skin and therefore lessens sunscreen efficacy, is attributed to optical coupling mediated by refractive index matching of the sunscreen to the upper epidermis. The greater reduction in skin diffuse reflectance caused by sunscreens containing methoxycinnamate is associated with this compound's high refractive index. Also, by determining the excitation spectra of the autofluorescence originating from the dermal layer of skin, the transmission spectra of the various components of sunscreen on skin were established, and these were in good general agreement with previously published spectra.