Nucleolar HEAT Repeat Containing 1 Up-regulated by the Mechanistic Target of Rapamycin Complex 1 Signaling Promotes Hepatocellular Carcinoma Growth by Dominating Ribosome Biogenesis and Proteome Homeostasis.

BACKGROUND & AIMS: Hyperactivation of ribosome biogenesis leads to hepatocyte transformation and plays pivotal roles in hepatocellular carcinoma (HCC) development. We aimed to identify critical ribosome biogenesis proteins that are overexpressed and crucial in HCC progression. METHODS: HEAT repeat containing 1 (HEATR1) expression and clinical correlations were analyzed using The Cancer Genome Atlas and Gene Expression Omnibus databases and further evaluated by immunohistochemical analysis of an HCC tissue microarray. Gene expression was knocked down by small interfering RNA. HEATR1-knockdown cells were subjected to viability, cell cycle, and apoptosis assays and used to establish subcutaneous and orthotopic tumor models. Chromatin immunoprecipitation and quantitative polymerase chain reaction were performed to detect the association of candidate proteins with specific DNA sequences. Endogenous coimmunoprecipitation combined with mass spectrometry was used to identify protein interactions. We performed immunoblot and immunofluorescence assays to detect and localize proteins in cells. The nucleolus ultrastructure was detected by transmission electron microscopy. Click-iT (Thermo Fisher Scientific) RNA imaging and puromycin incorporation assays were used to measure nascent ribosomal RNA and protein synthesis, respectively. Proteasome activity, 20S proteasome foci formation, and protein stability were evaluated in HEATR1-knockdown HCC cells. RESULTS: HEATR1 was the most up-regulated gene in a set of ribosome biogenesis mediators in HCC samples. High expression of HEATR1 was associated with poor survival and malignant clinicopathologic features in patients with HCC and contributed to HCC growth in vitro and in vivo. HEATR1 expression was regulated by the transcription factor specificity protein 1, which can be activated by insulin-like growth factor 1-mammalian target of rapamycin complex 1 signaling in HCC cells. HEATR1 localized predominantly in the nucleolus, bound to ribosomal DNA, and was associated with RNA polymerase I transcription/processing factors. Knockdown of HEATR1 disrupted ribosomal RNA biogenesis and impaired nascent protein synthesis, leading to reduced cytoplasmic proteasome activity and inhibitory-κB/nuclear factor-κB signaling. Moreover, HEATR1 knockdown induced nucleolar stress with increased nuclear proteasome activity and inactivation of the nucleophosmin 1-MYC axis. CONCLUSIONS: Our study revealed that HEATR1 is up-regulated by insulin-like growth factor 1-mammalian target of rapamycin complex 1-specificity protein 1 signaling in HCC and functions as a crucial regulator of ribosome biogenesis and proteome homeostasis to promote HCC development.

Optimizing Iodine Enrichment through Induced-Fit Transformations in a Flexible Ag(I)-Organic Framework: From Accelerated Adsorption Kinetics to Record-High Storage Density.

Efficient capture and storage of radioactive I2 is a prerequisite for developing nuclear power but remains a challenge. Here, two flexible Ag-MOFs (FJI-H39 and 40) with similar active sites but different pore sizes and flexibility are prepared; both of them can capture I2 with excellent removal efficiencies and high adsorption capacities. Due to the more flexible pores, FJI-H39 not only possesses the record-high I2 storage density among all the reported MOFs but also displays a very fast adsorption kinetic (124 times faster than FJI-H40), while their desorption kinetics are comparable. Mechanistic studies show that FJI-H39 can undergo induced-fit transformations continuously (first contraction then expansion), making the adsorbed iodine species enrich near the Ag(I) nodes quickly and orderly, from discrete I- anion to the dense packing of various iodine species, achieving the very fast adsorption kinetic and the record-high storage density simultaneously. However, no significant structural transformations caused by the adsorbed iodine are observed in FJI-H40. In addition, FJI-H39 has excellent stability/recyclability/obtainability, making it a practical adsorbent for radioactive I2. This work provides a useful method for synthesizing practical radioactive I2 adsorbents.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC. METHODS: RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8). RESULTS: The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups. CONCLUSION: The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. RESULTS: To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. AVAILABILITY AND IMPLEMENTATION: The code is available in Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MolTrans: Molecular Interaction Transformer for drug-target interaction prediction.

MOTIVATION: Drug-target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data. RESULTS: We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real-world data and show it improved DTI prediction performance compared to state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The model scripts are available at https://github.com/kexinhuang12345/moltrans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepPurpose: a deep learning library for drug-target interaction prediction.

SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/kexinhuang12345/DeepPurpose. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization.

The goal of molecular optimization is to generate molecules similar to a target molecule but with better chemical properties. Deep generative models have shown great success in molecule optimization. However, due to the iterative local generation process of deep generative models, the resulting molecules can significantly deviate from the input in molecular similarity and size, leading to poor chemical properties. The key issue here is that the existing deep generative models restrict their attention on substructure-level generation without considering the entire molecule as a whole. To address this challenge, we propose Molecule-Level Reward functions (MOLER) to encourage (1) the input and the generated molecule to be similar, and to ensure (2) the generated molecule has a similar size to the input. The proposed method can be combined with various deep generative models. Policy gradient technique is introduced to optimize reward-based objectives with small computational overhead. Empirical studies show that MOLER achieves up to 20.2% relative improvement in success rate over the best baseline method on several properties, including QED, DRD2 and LogP.

CHEER: Rich Model Helps Poor Model via Knowledge Infusion.

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a poor-data environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such poor-data environment by leveraging knowledge extracted from existing models trained using rich data in a related environment? To address this question, we develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations, which can be incorporated into the poor model to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.

High-dynamic-range blood flow rate measurement in a large-diameter vessel.

We propose a new, to the best of our knowledge, compound technique to measure high-dynamic-range blood flow rate in a large-diameter vessel, which combines the dynamic scattering light (DLS) and the laser speckle contrast imaging (LSCI) methods, possessing the advantages of the high temporal resolution of DLS and the robust property of LSCI. By controlling the second-order spatial correlations of the laser speckle through two imaging systems, the speckle temporal intensity autocorrelation function g2(t) and the decorrelation time τc are directly measured using a high-speed camera. It turns out the enhanced spatial second-order correlation helps to measure the blood flow with higher dynamic range and that the measured parameter ß and the blood flow dynamics n were accurately determined. For further improvement the dynamic range, the modified LSCI method was adopted, and the decorrelation time as a function of blood flow rate was constructed. It reveals the feasibility of measuring the high flow rate in large-diameter vessels and provides significant guidance for the future biomedical study of the myocardial perfusion in coronary artery bypass grafting, ghost imaging, and ghost cytometry.

Introduction of Flexibility into a Metal-Organic Framework to Promote Hg(II) Capture through Adaptive Deformation.

The development of a practical Hg(II) adsorbent is highly important for both environmental protection and public health. Herein, an adaptive metal-organic framework (MOF; FJI-H30) has been prepared from a highly flexible ligand [tris(pyridin-4-ylmethyl)amine] and Co(SCN)2 with cheap Hg(II) adsorption sites (SCN- groups) that not only has excellent chemical stability but also can capture Hg(II) from aqueous solution with high adsorption capacity (705 mg g-1). Moreover, it also has good anti-interference ability and can be used repeatedly and large-scale prepared. Further researches demonstrate that the relatively high Hg(II) adsorption capacity originates from the adsorbed Hg(II)-induced deformation of FJI-H30, and such an adaptive deformation will reduce the potential repulsive forces between the adsorbed Hg(II) ions, enabling almost all Hg(II) absorption sites to adsorb Hg(II) ions. Finally, how to induce the deformation of FJI-H30 by adsorbed Hg(II) also has been studied in detail. Our work not only provides a practical Hg(II) adsorbent for wastewater treatment but also offers a novel strategy for the design of novel MOFs for efficient heavy-metal-ion removal.

Identification and Gene Expression Analysis of the Pheromone Biosynthesis Activating Neuropeptide Receptor (PBANR) From the Ostrinia furnacalis (Lepidoptera: Pyralidae).

Pheromonal communication is important in insect mate finding and reproduction. Identifying components of pest insect pheromone system is a first step to disrupt pest insect reproduction. In this study, we identified and cloned the pheromone biosynthesis activating neuropeptide receptor (PBANR) from the Asian corn borer, Ostrinia furnacalis (Guenée) (Lepidoptera: Pyralidae), which is one of the most damaging pests of corn and other crops in parts of Asia and Australia. The O. furnacalis PBANR (OstfuPBANR) gene has an ORF of 1,086 bp and encoded 362 amino acids with seven transmembrane domains and had a high sequence identity to known lepidopteran PBANRs. Expression analysis showed that OstfuPBANR was highly expressed in the pheromone glands compared with other tissues, consistent with other studies. Interestingly, OstfuPBANR was expressed higher in the larval stages compared to the pupal or adult stages, suggesting that OstfuPBANR may have broad functions in larva beyond adult pheromone synthesis.

Generation and Role of Oscillatory Contractions in Mouse Airway Smooth Muscle.

BACKGROUND/AIMS: Tetraethylammonium chloride (TEA) induces oscillatory contractions in mouse airway smooth muscle (ASM); however, the generation and maintenance of oscillatory contractions and their role in ASM are unclear. METHODS: In this study, oscillations of ASM contraction and intracellular Ca2+ were measured using force measuring and Ca2+ imaging technique, respectively. TEA, nifedipine, niflumic acid, acetylcholine chloride, lithium chloride, KB-R7943, ouabain, 2-Aminoethoxydiphenyl borate, thapsigargin, tetrodotoxin, and ryanodine were used to assess the mechanism of oscillatory contractions. RESULTS: TEA induced depolarization, resulting in activation of L-type voltage-dependent Ca2+ channels (LVDCCs) and voltage-dependent Na+ (VNa) channels. The former mediated Ca2+ influx to trigger a contraction and the latter mediated Na+ entry to enhance the contraction via activating LVDCCs. Meanwhile, increased Ca2+-activated Cl- channels, inducing depolarization that resulted in contraction through LVDCCs. In addition, the contraction was enhanced by intracellular Ca2+ release from Ca2+ stores mediated by inositol (1,4,5)-trisphosphate receptors (IP3Rs). These pathways together produce the contractile phase of the oscillatory contractions. Furthermore, the increased Ca2+ activated the Na+-Ca2+ exchanger (NCX), which transferred Ca2+ out of and Na+ into the cells. The former induced relaxation and the latter activated Na+/K+-ATPase that induced hypopolarization to inactivate LVDCCs causing further relaxation. This can also explain the relaxant phase of the oscillatory contractions. Moreover, the depolarization induced by VNa channels and NCX might be greater than the hypopolarization caused by Na+/K+-ATPase alone, inducing LVDCC activation and resulting in further contraction. CONCLUSIONS: These data indicate that the TEA-induced oscillatory contractions were cooperatively produced by LVDCCs, VNa channels, Ca2+-activated Cl- channels, NCX, Na+/K+ ATPase, IP3Rs-mediated Ca2+ release, and extracellular Ca2+.

Hypertonic saline inhibits airway smooth muscle contraction by inhibiting Ca2+ sensitization.

The effects of hypertonic solution on airway smooth muscle (ASM) contraction and the underlying mechanisms are largely unknown. We found that hypertonic saline (HS) inhibited acetylcholine (ACh)-induced contraction of ASM from the mouse trachea and human bronchi. In single mouse ASM cells (ASMCs), ACh induced an increase in intracellular Ca2+ that was further enhanced by 5% NaCl, indicating that the HS-induced inhibition of ASM contraction was not mediated by a decrease in cytosolic Ca2+ . The Rho-associated kinase (ROCK) inhibitor Y-27632 relaxed ACh-induced precontraction of mouse tracheal rings. However, such inhibition was not observed after the relaxation induced by 5% NaCl. Moreover, the incubation of mouse tracheal rings with 5% NaCl decreased ACh-induced phosphorylation of myosin light chain 20 and myosin phosphatase target subunit 1. These data indicate that HS inhibits the contraction of ASM by inhibiting Ca2+ sensitization, not by decreasing intracellular Ca2+ .

Genetic and environmental effects on the bone development of the hand and wrist in Chinese young twins.

We assessed genetic and environmental effects on bone development of the hand and wrist, and on key anthropometric measures in Chinese young twins. In total, 139 monozygotic and 95 dizygotic twin pairs aged from 5 to 18 years were recruited. The twin correlations of total hand and wrist scores for monozygotic (MZ) and dizygotic (DZ) twins were 0.71 and 0.36, respectively. Bivariate model analysis showed moderate genetic correlations only for total skeletal maturity vs. weight and total skeletal maturity vs. waist circumference (r, 0.51 and 0.46, respectively). Our findings demonstrated that genetic factors played important roles in bone development of the hand and wrist in Chinese young twins, and that these genetic effects might be distinct from those influencing anthropometric measures.

Expression prolife and role of miRNAs in prostate cancer.

miRNAs are a class of noncoding RNA molecules about 18-25 nt in length. As key gene expression regulatory factors, they may inhibit or degrade mRNA by incompletely or completely complement to mRNA. Their abnormal expressions are closely related with the carcinogenesis of prostate cancer (PCa). Some miRNAs trigger cancer, while others inhibit the malignant changes. In this article we summarize the latest information on miRNAs expression prolifing and potential biomarkers,in particular the roles of miRNAs in PCa.

FRAMM: Fair ranking with missing modalities for clinical trial site selection.

The underrepresentation of gender, racial, and ethnic minorities in clinical trials is a problem undermining the efficacy of treatments on minorities and preventing precise estimates of the effects within these subgroups. We propose FRAMM, a deep reinforcement learning framework for fair trial site selection to help address this problem. We focus on two real-world challenges: the data modalities used to guide selection are often incomplete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity. To address the missing data challenge, FRAMM has a modality encoder with a masked cross-attention mechanism for bypassing missing data. To make efficient trade-offs, FRAMM uses deep reinforcement learning with a reward function designed to simultaneously optimize for both enrollment and fairness. We evaluate FRAMM using real-world historical clinical trials and show that it outperforms the leading baseline in enrollment-only settings while also greatly improving diversity.

Water-stable metal-organic frameworks (MOFs): rational construction and carbon dioxide capture.

Metal-organic frameworks (MOFs) are considered to be a promising porous material due to their excellent porosity and chemical tailorability. However, due to the relatively weak strength of coordination bonds, the stability (e.g., water stability) of MOFs is usually poor, which severely inhibits their practical applications. To prepare water-stable MOFs, several important strategies such as increasing the bonding strength of building units and introducing hydrophobic units have been proposed, and many MOFs with excellent water stability have been prepared. Carbon dioxide not only causes a range of climate and health problems but also is a by-product of some important chemicals (e.g., natural gas). Due to their excellent adsorption performances, MOFs are considered as a promising adsorbent that can capture carbon dioxide efficiently and energetically, and many water-stable MOFs have been used to capture carbon dioxide in various scenarios, including flue gas decarbonization, direct air capture, and purified crude natural gas. In this review, we first introduce the design and synthesis of water-stable MOFs and then describe their applications in carbon dioxide capture, and finally provide some personal comments on the challenges facing these areas.

Minimally modified LDL upregulates endothelin type B receptors in rat coronary artery via ERK1/2 MAPK and NF-κB pathways.

Minimally modified low density lipoprotein (mmLDL) is a well-known risk factor for coronary artery disease. Upregulation of vascular endothelin type B (ET(B)) receptors on the vascular smooth muscle cells is predicted to be the molecular mechanism that leads to cardiovascular pathogenesis. The objective of the present study was to examine the hypothesis that mmLDL upregulates ET(B) receptors in rat coronary artery. The contractile responses to sarafotoxin 6c (ET(B) receptor agonist) were studied using a sensitive myograph. ET(B) receptor mRNA and protein expression was determined using real-time PCR and Western blot analysis. The results showed that organ culture increased the contractile responses induced by sarafotoxin 6c and the levels of ET(B) receptor mRNA and protein. This increase was further enhanced by the addition of mmLDL (10µg/mL). Specific ERK1/2 inhibitors (SB386023 and U0126) and an NF-κB inhibitor (wedelolactone) attenuated the mmLDL-increased ET(B) receptor-mediated contraction and ET(B) receptor mRNA and protein levels. Wedelolactone significantly attenuated the mmLDL-decreased IκB(α) protein expression. Consistent with this result, IκB(α) protein expression was significantly decreased by culture with mmLDL compared to the level of expression in the organ culture group. However, the JNK inhibitor, SP600125 or p38 pathway inhibitor, SB203580 did not inhibit mmLDL-enhanced effects. The PKC inhibitor, staurosporine attenuated only culture-alone-increased effects. In conclusion, mmLDL upregulates the ET(B) receptors in rat coronary arterial smooth muscle cells, mainly via activation of the ERK1/2 MAPK and the downstream transcriptional factor NF-κB.

[Study on chemical constituents of triterpenoids from Potentilla discolor].

OBJECTIVE: To study the triterpenoids constituents in Potentilla discolor. METHODS: The compounds were isolated by silica gel chromatography, macroporous resins and polyamide column chromatography from the 70% ethanol extract, and their structures were identified by spectral analysis. RESULTS: Six compounds were obtained and their structures were identified as 3-O-beta-D-glucopyranosyl-(1 --> 2) -beta-D-xylopyranosyl-19a-hydroxyurs-12-en-28-acid (1), 2alpha, 3/beta, 19alpha-trihydroxyurs-12-en-28-acid (2), 3beta, 19alpha-trihydroxyurs-12-en-24, 28-acid (3), 2alpha, 3beta-dihydroxyolean-12-en-28-acid (4), 2alpha, 3alpha, 19alpha-trihydroxyurs-12-en-28-acid (5), beta-sitosterol (6). CONCLUSION: Compounds 1 and 3 are obtained from this plant for the fisrt time.