IRX3 plays an important role in the pathogenesis of metabolic-associated fatty liver disease by regulating hepatic lipid metabolism

Metabolic-associated fatty liver disease (MAFLD) affects approximately a quarter of the global population. Identification of the key genes and pathways involved in hepatic lipid metabolism is of the utmost importance for the diagnosis, treatment, and prevention of MAFLD. In this study, differentially expressed genes were identified through whole-genome transcriptional analysis of liver tissue from MAFLD patients and healthy controls, and a series of lipid metabolism-related molecules and pathways were obtained through pathway analysis. Subsequently, we focused on Iroquois homeobox protein 3 (IRX3), one of 13 transcription factors that were screened from the 331 differentially expressed genes. The transcription factor IRX3 was significantly decreased in the liver tissue of patients with MAFLD when compared with healthy controls. Pearson’s correlation analysis showed that the expression levels of IRX3 in liver tissue were negatively correlated with serum total cholesterol, triglycerides, low-density lipoprotein cholesterol, and uric acid levels. The overexpression and interference of IRX3 induced the increased and decreased lipid droplet accumulation in vitro, respectively. Moreover, interference of IRX3 expression increased mitochondrial fragmentation and reduced the activity of the mitochondrial respiratory chain complex IV. In summary, the study demonstrated that IRX3 regulated hepatic lipid metabolism of MAFLD, and also revealed the effect of IRX3 on mitochondria might be an important mechanism by which IRX3 regulated hepatic lipid metabolism of MAFLD

An enzyme mimic ammonium polymer as a single catalyst for glucose dehydration to 5-hydroxymethylfurfural

An ammonium resin (PBnNH3Cl) was used as a single catalyst for the glucose and polysaccharide dehydration to 5-hydroxymethylfurfural (HMF) in high selectivity (>80%) in a DMSO or biphasic reaction system. The isomerization mechanism was also studied by using the DFT computational method.ASTAR (Agency for Sci., Tech. and Research, S’pore

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors

Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n = 33), benign pulmonary diseases (n = 40), lung cancer (n = 33), bladder cancer (n = 17), cervical cancer (n = 25), colorectal cancer (n = 22), esophageal cancer (n = 14), and gastric cancer (n = 47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n = 46) and the test sets (n = 14–47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection. Keywords: Lung cancer, Machine learning, Urinary biomarker

Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers

As next-generation sequencing (NGS) is leaping forward, more than 160 covalent RNA modification processes have been reported, and they are widely present in every organism and overall RNA type. Many modification processes of RNA introduce a new layer to the gene regulation process, resulting in novel RNA epigenetics. The commonest RNA modification includes pseudouridine (Ψ), N7-methylguanosine (m7G), 5-hydroxymethylcytosine (hm5C), 5-methylcytosine (m5C), N1-methyladenosine (m1A), N6-methyladenosine (m6A), and others. In this study, we focus on non-coding RNAs (ncRNAs) to summarize the epigenetic consequences of RNA modifications, and the pathogenesis of cancer, as diagnostic markers and therapeutic targets for cancer, as well as the mechanisms affecting the immune environment of cancer. In addition, we summarize the current status of epigenetic drugs for tumor therapy based on ncRNA modifications and the progress of bioinformatics methods in elucidating RNA modifications in recent years

Strong red-emitting near-infrared-to-visible upconversion fluorescent nanoparticles

10.1021/cm103175sChemistry of Materials23112729-2734CMAT

Proof-of-Concept Workflow for Establishing Reference Intervals of Human Urine Proteome for Monitoring Physiological and Pathological Changes

Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening