Jae Wook Lee

The function of each renal tubule segment depends on the genes expressed therein. High-throughput methods used for global profiling of gene expression in unique cell types have shown low sensitivity and high false positivity, thereby limiting the usefulness of these methods in transcriptomic research. However, deep sequencing of RNA species (RNA-seq) achieves highly sensitive and quantitative transcriptomic profiling by sequencing RNAs in a massive, parallel manner. Here, we used RNA-seq coupled with classic renal tubule microdissection to comprehensively profile gene expression in each of 14 renal tubule segments from the proximal tubule through the inner medullary collecting duct of rat kidneys. Polyadenylated mRNAs were captured by oligo-dT primers and processed into adapter-ligated cDNA libraries that were sequenced using an Illumina platform. Transcriptomes were identified to a median depth of 8261 genes in microdissected renal tubule samples (105 replicates in total) and glomeruli (5 replicates). Manual microdissection allowed a high degree of sample purity, which was evidenced by the observed distributions of well established cell-specific markers. The main product of this work is an extensive database of gene expression along the nephron provided as a publicly accessible webpage (https://helixweb.nih.gov/ESBL/Database/NephronRNAseq/index.html). The data also provide genome-wide maps of alternative exon usage and polyadenylation sites in the kidney. We illustrate the use of the data by profiling transcription factor expression along the renal tubule and mapping metabolic pathways.

Significance A long-term goal in mammalian biology is to identify the genes expressed in every cell type of the body. In the kidney, the expressed genes (i.e., transcriptome) of all epithelial cell types have already been identified with the exception of the cells that make up the renal collecting duct, which is responsible for regulation of blood pressure and body fluid composition. Here, single-cell RNA-sequencing was used in mouse to identify transcriptomes for the major collecting duct cell types: type A intercalated cells, type B intercalated cells, and principal cells. The information was used to create a publicly accessible online resource. The data allowed identification of genes that are selectively expressed in each cell type, which is informative for cell-level understanding of physiology and pathophysiology.

The purpose of this study was to determine whether soluble CD14 (sCD14) in milk was affected by stage of lactation, milk somatic cell count (SCC), presence of bacteria, or lipopolysaccharide (LPS)-induced inflammation. Milk samples from 100 lactating cows (396 functional quarters) were assayed for sCD14 in milk to determine effects of stage of lactation, SCC, and intramammary infection. The concentration of sCD14 was highest in transitional milk (0 to 4 d postpartum) and in milk with high SCC (> 750,000 cells/ml). Most of the infected quarters (> 80%) were infected by coagulase-negative staphylococci and yeast. No difference was found between noninfected and infected quarters. One quarter of six healthy lactating cows was challenged with 100 microg LPS in order to study the kinetics of sCD14 during an LPS-induced inflammation. Milk samples were collected at various intervals until 72 h after injection. Rectal temperature, milk tumor necrosis factor-alpha, and interleukin-8 increased immediately after challenge. The increase in sCD14 paralleled the increase in SCC, peaked at 12 h, and started to decline after 24 h. Serum leakage, as characterized by the level of bovine serum albumin in milk, peaked at 4 h and then gradually decreased. All parameters remained at basal levels in control quarters throughout the study. In vitro experiments indicated that neutrophils released sCD14 in response to LPS in a dose-dependent manner. The results indicate that the concentration of sCD14 was significantly increased in milk after LPS challenge. The increase was not likely due to serum leakage. Instead, infiltrated neutrophils might be the main source of increased sCD14 in milk during inflammation.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs ( n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).