Alveolar epithelial type 1 (AT1) cells are necessary to transfer oxygen and carbon dioxide between the blood and air. Alveolar epithelial type 2 (AT2) cells serve as a partially committed stem cell population, producing AT1 cells during postnatal alveolar development and repair after influenza A and SARS-CoV-2 pneumonia^1–6. Little is known about the metabolic regulation of the fate of lung epithelial cells. Here we report that deleting the mitochondrial electron transport chain complex I subunit Ndufs2 in lung epithelial cells during mouse gestation led to death during postnatal alveolar development. Affected mice displayed hypertrophic cells with AT2 and AT1 cell features, known as transitional cells. Mammalian mitochondrial complex I, comprising 45 subunits, regenerates NAD⁺ and pumps protons. Conditional expression of yeast NADH dehydrogenase (NDI1) protein that regenerates NAD⁺ without proton pumping^7,8 was sufficient to correct abnormal alveolar development and avert lethality. Single-cell RNA sequencing revealed enrichment of integrated stress response (ISR) genes in transitional cells. Administering an ISR inhibitor^9,10 or NAD⁺ precursor reduced ISR gene signatures in epithelial cells and partially rescued lethality in the absence of mitochondrial complex I function. Notably, lung epithelial-specific loss of mitochondrial electron transport chain complex II subunit Sdhd, which maintains NAD⁺ regeneration, did not trigger high ISR activation or lethality. These findings highlight an unanticipated requirement for mitochondrial complex I-dependent NAD⁺ regeneration in directing cell fate during postnatal alveolar development by preventing pathological ISR induction.

Ulcerative colitis (UC), a type of inflammatory bowel disease (IBD), is a chronic inflammatory disorder of the colon. Although UC is generally treated with anti-inflammatory drugs or immunosuppressants, most of these treatments often prove to be inadequate. Rosmarinic acid (RA) is a phenolic ester included in various medicinal herbs such as Salvia miltiorrhiz and Perilla frutescens. Although RA has many biological and pharmacological activities, the anti-inflammatory effect of RA in colonic tissue remains unclear. In this study, we investigated the anti-inflammatory effects and underlying molecular mechanism of RA in mice with dextran sulphate sodium (DSS)-induced colitis. In the DSS-induced colitis model, RA significantly reduced the severity of colitis, as assessed by disease activity index (DAI) scores, colonic damage, and colon length. In addition, RA resulted in the reduction of the inflammatory-related cytokines, such as IL-6, IL-1β, and IL-22, and protein levels of COX-2 and iNOS in mice with DSS-induced colitis. Furthermore, RA effectively and pleiotropically inhibited nuclear factor-kappa B and signal transducer and activator of transcription 3 activation, and subsequently reduced the activity of pro-survival genes that depend on these transcription factors. These results demonstrate that RA has an ameliorative effect on colonic inflammation and thus a potential therapeutic role in colitis.

Previously, we found that rosmarinic acid (RA) exerted anti-inflammatory activities in a dextran sulfate sodium (DSS)-induced colitis model. Here, we investigated the anti-tumor effects of RA on colitis-associated colon cancer (CAC) and the underlying molecular mechanisms. We established an azoxymethane (AOM)/DSS-induced CAC murine model for in vivo studies and used a conditioned media (CM) culture system in vitro. H&E staining, immunohistochemistry, western blot assay, enzyme-linked immunosorbent assay, molecular docking, co-immunoprecipitation, and immunofluorescence assay were utilized to investigate how RA prevented colorectal cancer. In the AOM/DSS-induced CAC murine model, RA significantly reduced colitis severity, inflammation-related protein expression, tumor incidence, and colorectal adenoma development. It significantly modulated toll-like receptor-4 (TLR4)-mediated nuclear factor-kappa B (NF-κB) and signal transducer and activator of transcription 3 (STAT3) activation, thus attenuating the expression of anti-apoptotic factors, which mediate transcription factor-dependent tumor growth. In vitro, RA inhibited CM-induced TLR4 overexpression and competitively inhibited TLR4-myeloid differentiation factor 2 complex in an inflammatory microenvironment. Thus, RA suppressed NF-κB and STAT3 activation in colon cancer cells in an inflammatory microenvironment. Therefore, RA suppressed colitis-associated tumorigenesis in the AOM/DSS-induced CAC murine model and abrogated human colon cancer progression in an inflammatory microenvironment by propitiating TLR4-mediated NF-κB and STAT3 activation, pleiotropically.

The identification of target molecules is important for understanding the mechanism of “target deconvolution” in phenotypic screening and “polypharmacology” of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets. We developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr . The target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand–target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.

Reverse docking approaches have been explored in previous studies on drug discovery to overcome some problems in traditional virtual screening. However, current reverse docking approaches are problematic in that the target spaces of those studies were rather small, and their applications were limited to identifying new drug targets. In this study, we expanded the scope of target space to a set of all protein structures currently available and developed several new applications of reverse docking method. We generated 2D Matrix of docking scores among all the possible protein structures in yeast and human and 35 famous drugs. By clustering the docking profile data and then comparing them with fingerprint-based clustering of drugs, we first showed that our data contained accurate information on their chemical properties. Next, we showed that our method could be used to predict the druggability of target proteins. We also showed that a combination of sequence similarity and docking profile similarity could predict the enzyme EC numbers more accurately than sequence similarity alone. In two case studies, 5-fluorouracil and cycloheximide, we showed that our method can successfully find identifying target proteins. By using a large number of protein structures, we improved the sensitivity of reverse docking and showed that using as many protein structure as possible was important in finding real binding targets.