Metabolic reprogramming based on RNA sequencing of gemcitabine-resistant cells reveals the FASN gene as a therapeutic for bladder cancer.

Bladder cancer (BLCA) is the most frequent malignant tumor of the genitourinary system. Postoperative chemotherapy drug perfusion and chemotherapy are important means for the treatment of BLCA. However, once drug resistance occurs, BLCA develops rapidly after recurrence. BLCA cells rely on unique metabolic rewriting to maintain their growth and proliferation. However, the relationship between the metabolic pattern changes and drug resistance in BLCA is unclear. At present, this problem lacks systematic research. In our research, we identified and analyzed resistance- and metabolism-related differentially expressed genes (RM-DEGs) based on RNA sequencing of a gemcitabine-resistant BLCA cell line and metabolic-related genes (MRGs). Then, we established a drug resistance- and metabolism-related model (RM-RM) through regression analysis to predict the overall survival of BLCA. We also confirmed that RM-RM had a significant correlation with tumor metabolism, gene mutations, tumor microenvironment, and adverse drug reactions. Patients with a high drug resistance- and metabolism-related risk score (RM-RS) showed more active lipid synthesis than those with a low RM-RS. Further in vitro and in vivo studies were implemented using Fatty Acid Synthase (FASN), a representative gene, which promotes gemcitabine resistance, and its inhibitor (TVB-3166) that can reverse this resistance effect.

Cellular senescence and metabolic reprogramming model based on bulk/single-cell RNA sequencing reveals PTGER4 as a therapeutic target for ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the prevailing histological subtype of renal cell carcinoma and has unique metabolic reprogramming during its occurrence and development. Cell senescence is one of the newly identified tumor characteristics. However, there is a dearth of methodical and all-encompassing investigations regarding the correlation between the broad-ranging alterations in metabolic processes associated with aging and ccRCC. We utilized a range of analytical methodologies, such as protein‒protein interaction network analysis and least absolute shrinkage and selection operator (LASSO) regression analysis, to form and validate a risk score model known as the senescence-metabolism-related risk model (SeMRM). Our study demonstrated that SeMRM could more precisely predict the OS of ccRCC patients than the clinical prognostic markers in use. By utilizing two distinct datasets of ccRCC, ICGC-KIRC (the International Cancer Genome Consortium) and GSE29609, as well as a single-cell dataset (GSE156632) and real patient clinical information, and further confirmed the relationship between the senescence-metabolism-related risk score (SeMRS) and ccRCC patient progression. It is worth noting that patients who were classified into different subgroups based on the SeMRS exhibited notable variations in metabolic activity, immune microenvironment, immune cell type transformation, mutant landscape, and drug responsiveness. We also demonstrated that PTGER4, a key gene in SeMRM, regulated ccRCC cell proliferation, lipid levels and the cell cycle in vivo and in vitro. Together, the utilization of SeMRM has the potential to function as a dependable clinical characteristic to increase the accuracy of prognostic assessment for patients diagnosed with ccRCC, thereby facilitating the selection of suitable treatment strategies.

Exploring factors influencing delayed first antibiotic treatment for suspected early-onset sepsis in preterm newborns: a study before quality improvement initiative.

BACKGROUND: Early-onset sepsis (EOS) is a serious illness that affects preterm newborns, and delayed antibiotic initiation may increase the risk of adverse outcomes. PURPOSE: The objective of this study was to examine the present time of antibiotic administration in preterm infants with suspected EOS and the factors that contribute to delayed antibiotic initiation. METHODS: In this retrospective study in China, a total of 82 early preterm infants with suspected EOS between December 2021 and March 2023 were included. The study utilized a linear regression analytical approach to identify independent factors that contribute to delayed antibiotic administration. RESULTS: The mean gestational age and birth weight of the study population were 29.1 ± 1.4 weeks and 1265.7 ± 176.8 g, respectively. The median time of initial antibiotic administration was 3.8 (3.1-5.0) hours. Linear regression revealed that severe respiratory distress syndrome (RDS) (ß = 0.07, P = 0.013), penicillin skin test (PST) timing (ß = 0.06, P < 0.001) and medical order timing (ß = 0.04, P = 0.017) were significantly associated with the initial timing of antibiotic administration. CONCLUSIONS: There is an evident delay in antibiotic administration in preterm infants with suspected EOS in our unit. Severe RDS, PST postponement and delayed medical orders were found to be associated with the delayed use of antibiotics, which will be helpful for quality improvement efforts in the neonatal intensive care unit (NICU).

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

m6A-Mediated Induction of 7-Dehydrocholesterol Reductase Stimulates Cholesterol Synthesis and cAMP Signaling to Promote Bladder Cancer Metastasis.

Dysregulation of cholesterol homeostasis occurs in multiple types of tumors and promotes cancer progression. Investigating the specific processes that induce abnormal cholesterol metabolism could identify therapeutic targets to improve cancer treatment. In this investigation, we observed upregulation of 7-dehydrocholesterol reductase (DHCR7), a vital enzyme involved in the synthesis of cholesterol, within bladder cancer (BC) tissues in comparison to normal tissues, which was correlated with increased BC metastasis. Increased expression of DHCR7 in BC was attributed to decreased mRNA degradation mediated by YTHDF2. Loss or inhibition of DHCR7 reduced BC cell invasion in vitro and metastasis in vivo. Mechanistically, DHCR7 promoted BC metastasis by activating the cAMP/PKA/FAK pathway. Specifically, DHCR7 increased cAMP levels by elevating cholesterol content in lipid rafts, thereby facilitating the transduction of signaling pathways mediated by cAMP receptors. DHCR7 additionally enhanced the cAMP signaling pathway by reducing the concentration of 7-DHC and promoting the transcription of the G protein-coupled receptor GIPR. Overall, these findings demonstrate that DHCR7 plays an important role in BC invasion and metastasis by modulating cholesterol synthesis and cAMP signaling. Furthermore, inhibition of DHCR7 shows promise as a viable therapeutic strategy for suppressing BC invasion and metastasis.