Enhancing the anticancer effect of androgen deprivation therapy by monocarboxylate transporter 1 inhibitor in prostate cancer cells.

BACKGROUND: Tumor initiation and progression necessitate a metabolic shift in cancer cells. Consequently, the progression of prostate cancer (PCa), a leading cause of cancer-related deaths in males globally, involves a shift from lipogenic to glycolytic metabolism. Androgen deprivation therapy (ADT) serves as the standard treatment for advanced-stage PCa. However, despite initial patient responses, castrate resistance emerges ultimately, necessitating novel therapeutic approaches. Therefore, in this study, we aimed to investigate the role of monocarboxylate transporters (MCTs) in PCa post-ADT and evaluate their potential as therapeutic targets. METHODS: PCa cells (LNCaP and C4-2 cell line), which has high prostate-specific membrane antigen (PSMA) and androgen receptor (AR) expression among PCa cell lines, was used in this study. We assessed the expression of MCT1 in PCa cells subjected to ADT using charcoal-stripped bovine serum (CSS)-containing medium or enzalutamide (ENZ). Furthermore, we evaluated the synergistic anticancer effects of combined treatment with ENZ and SR13800, an MCT1 inhibitor. RESULTS: Short-term ADT led to a significant upregulation in folate hydrolase 1 (FOLH1) and solute carrier family 16 member 1 (SLC16A1) gene levels, with elevated PSMA and MCT1 protein levels. Long-term ADT induced notable changes in cell morphology with further upregulation of FOLH1/PSMA and SLC16A1/MCT1 levels. Treatment with ENZ, a nonsteroidal anti-androgen, also increased PSMA and MCT1 expression. However, combined therapy with ENZ and SR13800 led to reduced PSMA level, decreased cell viability, and suppressed expression of cancer stem cell markers and migration indicators. Additionally, analysis of human PCa tissues revealed a positive correlation between PSMA and MCT1 expression in tumor regions. CONCLUSIONS: Our results demonstrate that ADT led to a significant upregulation in MCT1 levels. However, the combination of ENZ and SR13800 demonstrated a promising synergistic anticancer effect, highlighting a potential therapeutic significance for patients with PCa undergoing ADT.

Impact of glucose metabolism on PD-L1 expression in sorafenib-resistant hepatocellular carcinoma cells.

Hepatocellular carcinoma (HCC) is the fifth leading cause of cancer-related mortality worldwide. Programmed cell death ligand-1 (PD-L1) is an immune checkpoint protein that binds to programmed cell death-1 (PD-1), which is expressed in activated T cells and other immune cells and has been employed in cancer therapy, including HCC. Recently, PD-L1 overexpression has been documented in treatment-resistant cancer cells. Sorafenib is a multikinase inhibitor and the only FDA-approved treatment for advanced HCC. However, several patients exhibit resistance to sorafenib during treatment. This study aimed to assess the effect of glucose deprivation on PD-L1 expression in HCC cells. We used PD-L1-overexpressing HepG2 cells and IFN-γ-treated SK-Hep1 cells to explore the impact of glycolysis on PD-L1 expression. To validate the correlation between PD-L1 expression and glycolysis, we analyzed data from The Cancer Genome Atlas (TCGA) and used immunostaining for HCC tissue analysis. Furthermore, to modulate PD-L1 expression, we treated HepG2, SK-Hep1, and sorafenib-resistant SK-Hep1R cells with rapamycin. Here, we found that glucose deprivation reduced PD-L1 expression in HCC cells. Additionally, TCGA data and immunostaining analyses confirmed a positive correlation between the expression of hexokinase II (HK2), which plays a key role in glucose metabolism, and PD-L1. Notably, rapamycin treatment  decreased the expression of PD-L1 and HK2 in both high PD-L1-expressing HCC cells and sorafenib-resistant cells. Our results suggest that the modulation of PD-L1 expression by glucose deprivation may represent a strategy to overcome PD-L1 upregulation in patients with sorafenib-resistant HCC.

Screening of Pancreatic Ribonuclease A Inhibitors from an Autodisplayed Fv-Antibody Library.

Pancreatic ribonuclease A (RNase A) inhibitors were screened from an autodisplayed Fv-antibody library, which was prepared by randomizing amino acid sequences of the third complementary-determining region (CDR3) within the heavy chain variable region (VH region) of immunoglobulin G (called "Fv-antibody" comprising three CDRs and four frame regions (FRs)) through site-directed mutagenesis. The library was autodisplayed on the outer membrane of Escherichia coli. Target Fv-variants (clones) with specific binding affinity for RNase A were screened using fluorescein-labeled RNase A and flow cytometry. Three Fv variants (clones) were screened, and CDR3 amino acid sequences were analyzed. The screened Fv-antibodies were expressed as soluble proteins, and CDR3 was synthesized into peptides (11 residues). The binding affinity constants (KD) of the expressed Fv-antibodies and synthesized peptides to RNase A were estimated using surface plasmon resonance. Fitting analysis based on the adsorption model showed that KD values of the three expressed Fv-antibodies were estimated to be 17.5 ± 4.1, 28.8 ± 9.7, and 33.9 ± 8.9 nM (n = 3), and those of the three synthesized peptides were 1.3 ± 0.1, 1.3 ± 0.3, and 3.7 ± 1.3 µM (n = 3). From the RNase activity assay with an RNA probe labeled with fluorophore and quencher, inhibition constants (IC50) of the three expressed Fv-antibodies were estimated to be 90.2, 65.3, and 98.8 nM (n = 3), and those of the three synthesized peptides were 8.1, 3.6, and 0.4 µM (n = 3). The activity of RNase inhibitors constituting the expressed Fv-antibodies and synthesized peptides was demonstrated via an RNA cleavage test using the total RNA from HeLa cells.

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted. METHODS: In this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis. RESULTS: We found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM10, NO2, CO, and pollen had a significant impact on asthma exacerbation. In addition, the week of the year and the number of holidays per week were an important factor to model the seasonality of the number of asthma patients and the effect of holiday clinic closures, respectively. CONCLUSION: LSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation.

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).

Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6-era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002-2015 (pre-Euro 6-era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016-2019 were lower than the concentrations predicted using RNN by - 1.2%, - 3.4%, and - 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6.

Impact of Exogenous Treatment with Histidine on Hepatocellular Carcinoma Cells.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Sorafenib, a multi-kinase inhibitor, is the first-line therapy for advanced HCC. However, long-term exposure to sorafenib often results in reduced sensitivity and the development of resistance. Although various amino acids have been shown to contribute to cancer initiation and progression, little is known about the effects of histidine, a dietary essential amino acid that is partially taken up via histidine/large neutral amino acid transporter (LAT1), on cancer cells. In this study, we evaluated the effects of histidine on HCC cells and sensitivity to sorafenib. Remarkably, we found that exogenous histidine treatment induced a reduction in the expression of tumor markers related to glycolysis (GLUT1 and HK2), inflammation (STAT3), angiogenesis (VEGFB and VEGFC), and stem cells (CD133). In addition, LAT1 expression was downregulated in HCC tumor regions with high expression of GLUT1, CD133, and pSTAT3, which are known to induce sorafenib resistance. Finally, we demonstrated that combined treatment with sorafenib and histidine could be a novel therapeutic strategy to enhance the sensitivity to sorafenib, thereby improving long-term survival in HCC.

Impacts of COVID-19 on Air Quality through Traffic Reduction.

In 2020, the first case of COVID-19 was confirmed in Korea, and social distancing was implemented to prevent its spread. This reduced the movement of people, and changes in air quality were expected owing to reduced emissions. In the present paper, the impact of traffic volume change caused by COVID-19 on air quality in Seoul, Korea, is examined. Two regression analyses were performed using the generalized additive model (GAM), assuming a Gaussian distribution; the relationships between (1) the number of confirmed COVID-19 cases in 2020-2021 and the rate of change in the traffic volume in Seoul, and (2) the traffic volume and the rate of change in the air quality in Seoul from 2016 to 2019 were analyzed. The regression results show that traffic decreased by 0.00431% per COVID-19 case; when traffic fell by 1%, the PM10, PM2.5, CO, NO2, O3, and SO2 concentrations fell by 0.48%, 0.94%, 0.39%, 0.74%, 0.16%, and -0.01%, respectively. This mechanism accounts for air quality improvements in PM10, PM2.5, CO, NO2, and O3 in Seoul during 2020-2021. From these results, the majority of the reduction in pollutant concentrations in 2020-2021 appears to be the result of a long-term declining trend rather than COVID-19.

A reversibly mechanochromic conjugated polymer.

A reversibly mechanochromic conjugated polymer has been developed. The polymer, polydiacetylene (PDA), obtained by thermal polymerization of a diphenyldisulfide-containing bisdiacetylene undergoes a blue-to-green color change upon grinding. The original blue color of the PDA is regenerated via a metastable red-color state by utilizing a thermochromic (heating-cooling) process.

Chemical Genetics Approach Reveals Importance of cAMP and MAP Kinase Signaling to Lipid and Carotenoid Biosynthesis in Microalgae.

In this study, we attempted to understand signaling pathways behind lipid biosynthesis by employing a chemical genetics approach based on small molecule inhibitors. Specific signaling inhibitors of MAP kinase or modulators of cAMP signaling were selected to evaluate the functional roles of each of the key signaling pathways in three different microalgal species: Chlamydomonas reinhardtii, Chlorella vulgaris, and Haematococcus pluvialis. Our results clearly indicate that cAMP signaling pathways are indeed positively associated with microalgal lipid biosynthesis. In contrast, MAP kinase pathways in three microalgal species are all negatively implicated in both lipid and carotenoid biosynthesis.

Comparative proteomics using lipid over-producing or less-producing mutants unravels lipid metabolisms in Chlamydomonas reinhardtii.

In this study, proteomic approach was employed to advance our understanding of genes associated with microalgal lipid production. We first generated random mutants of Chlamydomonas reinhardtii, which displayed increased or decreased lipid content, compared to that in the wild-type. Mutants were designated as CR12 and CR48. Then, the changes of protein expression associated with the mutations of Chlamydomonas reinhardtii were analyzed. We could identify proteins that were significantly up-regulated or down-regulated either in the wild-type or CR12 or CR48. Our results will help understand additional genes or pathways directly or indirectly linked to microalgal lipid production.

Organic phototransistors with nanoscale phase-separated polymer/polymer bulk heterojunction layers.

Low-cost detectors for sensing photons at a low light intensity are of crucial importance in modern science. Phototransistors can deliver better signals of low-intensity light by electrical amplification, but conventional inorganic phototransistors have a limitation owing to their high temperature processes in vacuum. In this work, we demonstrate organic phototransistors with polymer/polymer bulk heterojunction blend films (mixtures of p-type and n-type semiconducting polymers), which can be fabricated by inexpensive solution processes at room temperature. The key idea here is to effectively exploit hole charges (from p-type polymer) as major signaling carriers by employing p-type transistor geometry, while the n-type polymer helps efficient charge separation from excitons generated by incoming photons. Results showed that the present organic transistors exhibited proper functions as p-type phototransistors with ∼4.3 A W(-1) responsivity at a low light intensity (1 µW cm(-2)), which supports their encouraging potential to replace conventional cooled charge coupled devices (CCD) for low-intensity light detection applications.

Thermal annealing time effect on the performance of ambipolar organic light-emitting transistors based on conjugated polymer blends.

Here we report the effect of thermal annealing time on the performance of ambipolar organic light-emitting transistors (OLETs) made using conjugated polymer blends. Regioregular poly(3-hexylthiophene) (P3HT) and poly(9,9-dioctylfluorenyl-2,3-diyl-co-1,4-benzo-2,1',3-thiadiazole) (F8BT) were chosen as a p-type and a n-type component, respectively. As a gate insulator, poly(4,4'-oxydiphenylene-pyromellitimide) (PMDA-ODA PI) was employed due to its high solvent resistance and thermal stability. Results showed that the present OLETs exhibited ambipolar characteristics even after thermal annealing. All devices showed almost identical field-effect mobility for both holes and electrons. The highest field-effect mobility was achieved for the OLET annealed at 130 degrees C for 60 min, which was assigned to the improved polymer-metal contact by thermal annealing leading to better charge injection.

E2F1-directed activation of Bcl-2 is correlated with lactoferrin-induced apoptosis in Jurkat leukemia T lymphocytes.

Lactoferrin (Lf) has been shown to control the proliferation of a variety of mammalian cells. Recently, we reported that human Lf induces apoptosis via a c-Jun N-terminal kinases (JNK)-associated Bcl-2 pathway that stimulates programmed cell death. In order to gain insight into the mechanism underlying Lf-triggered apoptotic features, we attempted to determine the mechanisms whereby the Lf-induced Bcl-2 family proteins exert their pro- or anti-apoptotic effects in Jurkat leukemia T lymphocytes. Treatment of the cells with high concentrations of Lf resulted in a significant reduction in in vitro growth and cell viability. As the levels of Lf increased, greater quantities of CDK6 and hyper-phosphorylated retinoblastoma protein were produced, resulting in the induction of E2F1-dependent apoptosis. Simultaneously, PARP and caspases were efficiently cleaved during Lf-induced apoptosis. The E2F1-induced apoptotic process occurred preferentially in p53-deficient Jurkat leukemia cells. Therefore, we attempted to determine whether E2F1-regulated Bcl-2 family proteins involved in the apoptotic process were relevant to Lf-induced apoptosis. We found that Lf increased the interaction of Bcl-2 with the pro-apoptotic protein Bad, whereas the total protein levels did not change significantly. Our results, collectively, suggest that Lf exploits the control mechanism of E2F1-regulated target genes or Bcl-2 family gene networks involved in the apoptotic process in Jurkat human leukemia T lymphocytes.