SCAMP4 enhances the senescent cell secretome.

The senescence-associated secretory phenotype (SASP) is a major trait of senescent cells, but the molecular regulators of SASP factor secretion are poorly understood. Mass spectrometry analysis revealed that secretory carrier membrane protein 4 (SCAMP4) levels were strikingly elevated on the surface of senescent cells compared with proliferating cells. Interestingly, silencing SCAMP4 in senescent fibroblasts reduced the secretion of SASP factors, including interleukin 6 (IL6), IL8, growth differentiation factor 15 (GDF-15), C-X-C motif chemokine ligand 1 (CXCL1), and IL7, while, conversely, SCAMP4 overexpression in proliferating fibroblasts increased SASP factor secretion. Our results indicate that SCAMP4 accumulates on the surface of senescent cells, promotes SASP factor secretion, and critically enhances the SASP phenotype.

sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

LncRNA OIP5-AS1-directed miR-7 degradation promotes MYMX production during human myogenesis.

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) modulate gene expression programs in physiology and disease. Here, we report a noncoding RNA regulatory network that modulates myoblast fusion into multinucleated myotubes, a process that occurs during muscle development and muscle regeneration after injury. In early stages of human myogenesis, the levels of lncRNA OIP5-AS1 increased, while the levels of miR-7 decreased. Moreover, OIP5-AS1 bound and induced miR-7 decay via target RNA-directed miRNA decay; accordingly, loss of OIP5-AS1 attenuated, while antagonizing miR-7 accelerated, myotube formation. We found that the OIP5-AS1-mediated miR-7 degradation promoted myoblast fusion, as it derepressed the miR-7 target MYMX mRNA, which encodes the fusogenic protein myomixer (MYMX). Remarkably, an oligonucleotide site blocker interfered with the OIP5-AS1-directed miR-7 degradation, allowing miR-7 to accumulate, lowering MYMX production and suppressing myotube formation. These results highlight a mechanism whereby lncRNA OIP5-AS1-mediated miR-7 decay promotes myotube formation by stimulating a myogenic fusion program.

Enhanced myogenesis through lncFAM-mediated recruitment of HNRNPL to the MYBPC2 promoter.

The mammalian transcriptome comprises a vast family of long noncoding (lnc)RNAs implicated in physiologic processes such as myogenesis, through which muscle forms during embryonic development and regenerates in the adult. However, the specific molecular mechanisms by which lncRNAs regulate human myogenesis are poorly understood. Here, we identified a novel muscle-specific lncRNA, lncFAM71E1-2:2 (lncFAM), which increased robustly during early human myogenesis. Overexpression of lncFAM promoted differentiation of human myoblasts into myotubes, while silencing lncFAM suppressed this process. As lncFAM resides in the nucleus, chromatin isolation by RNA purification followed by mass spectrometry (ChIRP-MS) analysis was employed to identify the molecular mechanisms whereby it might promote myogenesis. Analysis of lncFAM-interacting proteins revealed that lncFAM recruited the RNA-binding protein HNRNPL to the promoter of MYBPC2, in turn increasing MYBPC2 mRNA transcription and enhancing production of the myogenic protein MYBPC2. These results highlight a mechanism whereby a novel ribonucleoprotein complex, lncFAM-HNRNPL, elevates MYBPC2 expression transcriptionally to promote myogenesis.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

LINC00162 regulates cell proliferation and apoptosis by sponging PAQR4-targeting miR-485-5p.

Growing evidence indicates that long intergenic noncoding RNAs play an important role in cancer progression by affecting gene regulation at the transcriptional and posttranscriptional levels. Recent studies have shown that long intergenic noncoding RNA functions as a competitive endogenous RNA, which can interact with and mitigate the function of microRNA. In this study, we investigated the molecular mechanism by which LINC00162 regulates cell proliferation and apoptotic cell death. By analyzing RNA sequencing data, LINC00162 was identified to be a target of heterogeneous nuclear ribonucleoprotein K (hnRNPK). HnRNPK positively regulated LINC00162 expression through p38 mitogen-activated protein kinase. Lowering the level of either hnRNPK or LINC00162 decreased proliferation and colony formation while it increased apoptotic cell death. Small RNA sequencing followed by the antisense oligonucleotide pulldown, revealed that LINC00162 interacts directly with miR-485-5p which exhibited tumor-suppressing effects by suppressing cell proliferation and colony formation, and increasing apoptotic cell death. Through the bioinformatic approaches, progestin and adipoQ receptor 4 (PAQR4) was selected as a common target of LINC00162 and miR-485-5p. miR-485-5p decreased the expression of PAQR4 by directly binding to the 3'-untranslated region of PAQR4 messenger RNA. Knockdown of hnRNPK and LINC00162 increased the level of functional miR-485-5p, indicating that LINC00162 may compete for miR-485-5p, thereby derepressing PAQR4 expression. Overexpression of either hnRNPK or LINC00162, or inhibition of miR-485-5p, protected cells against etoposide-induced apoptotic death. Our findings demonstrate that a regulatory paradigm implicating hnRNPK, LINC00162, miR-485-5p, and PAQR4 plays an important role in cell proliferation and apoptosis, and is a promising target for cancer therapeutics.

RNA-mediated immunotherapy regulating tumor immune microenvironment: next wave of cancer therapeutics.

Accumulating research suggests that the tumor immune microenvironment (TIME) plays an essential role in regulation of tumor growth and metastasis. The cellular and molecular nature of the TIME influences cancer progression and metastasis by altering the ratio of immune- suppressive versus cytotoxic responses in the vicinity of the tumor. Targeting or activating the TIME components show a promising therapeutic avenue to combat cancer. The success of immunotherapy is both astounding and unsatisfactory in the clinic. Advancements in RNA-based technology have improved understanding of the complexity and diversity of the TIME and its effects on therapy. TIME-related RNA or RNA regulators could be promising targets for anticancer immunotherapy. In this review, we discuss the available RNA-based cancer immunotherapies targeting the TIME. More importantly, we summarize the potential of various RNA-based therapeutics clinically available for cancer treatment. RNA-dependent targeting of the TIME, as monotherapy or combined with other evolving therapeutics, might be beneficial for cancer patients' treatment in the near future.

Interaction of OIP5-AS1 with MEF2C mRNA promotes myogenic gene expression.

Long noncoding (lnc)RNAs potently regulate gene expression programs in physiology and disease. Here, we describe a key function for lncRNA OIP5-AS1 in myogenesis, the process whereby myoblasts differentiate into myotubes during muscle development and muscle regeneration after injury. In human myoblasts, OIP5-AS1 levels increased robustly early in myogenesis, and its loss attenuated myogenic differentiation and potently reduced the levels of the myogenic transcription factor MEF2C. This effect relied upon the partial complementarity of OIP5-AS1 with MEF2C mRNA and the presence of HuR, an RNA-binding protein (RBP) with affinity for both transcripts. Remarkably, HuR binding to MEF2C mRNA, which stabilized MEF2C mRNA and increased MEF2C abundance, was lost after OIP5-AS1 silencing, suggesting that OIP5-AS1 might serve as a scaffold to enhance HuR binding to MEF2C mRNA, in turn increasing MEF2C production. These results highlight a mechanism whereby a lncRNA promotes myogenesis by enhancing the interaction of an RBP and a myogenic mRNA.

circSamd4 represses myogenic transcriptional activity of PUR proteins.

By interacting with proteins and nucleic acids, the vast family of mammalian circRNAs is proposed to influence many biological processes. Here, RNA sequencing analysis of circRNAs differentially expressed during myogenesis revealed that circSamd4 expression increased robustly in mouse C2C12 myoblasts differentiating into myotubes. Moreover, silencing circSamd4, which is conserved between human and mouse, delayed myogenesis and lowered the expression of myogenic markers in cultured myoblasts from both species. Affinity pulldown followed by mass spectrometry revealed that circSamd4 associated with PURA and PURB, two repressors of myogenesis that inhibit transcription of the myosin heavy chain (MHC) protein family. Supporting the hypothesis that circSamd4 might complex with PUR proteins and thereby prevent their interaction with DNA, silencing circSamd4 enhanced the association of PUR proteins with the Mhc promoter, while overexpressing circSamd4 interfered with the binding of PUR proteins to the Mhc promoter. These effects were abrogated when using a mutant circSamd4 lacking the PUR binding site. Our results indicate that the association of PUR proteins with circSamd4 enhances myogenesis by contributing to the derepression of MHC transcription.

Loss of RNA-binding protein GRSF1 activates mTOR to elicit a proinflammatory transcriptional program.

The RNA-binding protein GRSF1 (G-rich RNA sequence-binding factor 1) critically maintains mitochondrial homeostasis. Accordingly, loss of GRSF1 impaired mitochondrial respiration and increased the levels of reactive oxygen species (ROS), triggering DNA damage, growth suppression, and a senescent phenotype characterized by elevated production and secretion of interleukin (IL)6. Here, we characterize the pathways that govern IL6 production in response to mitochondrial dysfunction in GRSF1-depleted cells. We report that loss of GRSF1 broadly altered protein expression programs, impairing the function of respiratory complexes I and IV. The rise in oxidative stress led to increased DNA damage and activation of mTOR, which in turn activated NF-κB to induce IL6 gene transcription and orchestrate a pro-inflammatory program. Collectively, our results indicate that GRSF1 helps preserve mitochondrial homeostasis, in turn preventing oxidative DNA damage and the activation of mTOR and NF-κB, and suppressing a transcriptional pro-inflammatory program leading to increased IL6 production.

Translation of insulin granule proteins are regulated by PDI and PABP.

Glucose mediated insulin biosynthesis is tightly regulated and shared between insulin granule proteins such as its processing enzymes, prohormone convertases, PC1/3 and PC2. However, the molecular players involved in the co-ordinated translation remain elusive. The trans-acting factors like PABP (Poly A Binding Protein) and PDI (Protein Disulphide Isomerize) binds to a conserved sequence in the 5'UTR of insulin mRNA and regulates its translation. Here, we demonstrate that 5'UTR of PC1/3 and PC2 also associate with PDI and PABP. We show that a' and RRM 3-4 domains of PDI and PABP respectively, are necessary for RNA binding activity to the 5'UTRs of insulin and its processing enzymes.

RPAD (RNase R treatment, polyadenylation, and poly(A)+ RNA depletion) method to isolate highly pure circular RNA.

Recent developments in high-throughput RNA sequencing methods coupled with innovative bioinformatic tools have uncovered thousands of circular (circ)RNAs. CircRNAs have emerged as a vast and novel class of regulatory RNAs with potential to modulate gene expression by acting as sponges for microRNAs (miRNAs) and RNA-binding proteins (RBPs). The biochemical enrichment of circRNAs by exoribonuclease treatment or by depletion of polyadenylated RNAs coupled with deep-sequencing is widely used for the systematic identification of circRNAs. Although these methods enrich circRNAs substantially, they do not eliminate efficiently non-polyadenylated and highly-structured RNAs. Here, we describe a method we termed RPAD, based on initial RNase R treatment followed by Polyadenylation and poly(A)+ RNA Depletion. These joint interventions drastically depleted linear RNAs leading to isolation of highly pure circRNAs from total RNA pools. By facilitating the isolation of highly pure circRNAs, RPAD enables the elucidation of circRNA biogenesis, sequence, and function.

Elucidating the role of key structural motifs in antifreeze glycoproteins.

Antifreeze glycoproteins (AFGPs) are distinctively riveting class of bio-macromolecules, which endows the survival of organisms inhabiting polar and subpolar regions. These proteins are believed to hinder microscopic freezing by interacting with embryonic ice crystals and precluding their further growth. The underlying molecular mechanism by which AFGPs bind to ice has remained elusive due to insufficient structural characterization, with conflicting hypotheses on the possible binding mode of AFGPs - either via the hydrophobic peptide backbone or via the hydrophilic carbohydrate side chains - when interacting with ice. Chemical synthesis has allowed researchers to access synthetic variants of natural AFGPs. These studies revealed that AFGPs exhibit huge variations in their thermal hysteresis and ice shaping behavior with only slight structural variations, especially to the carbohydrate side chains. Four key structural motifs were identified as crucial to AFGP activity: the presence of a threonine γ-methyl group, an α-glycosidic carbohydrate-protein linkage, an acetylamide group (-NHCOCH3) at the C2 position of the carbohydrate linked to the protein, and the presence of carbohydrate hydroxyl groups. In this study, we use molecular dynamics (MD) simulations to probe the microscopic properties of water accompanying these structural variations of AFGPs. We find that these variations primarily influence the conformation space of AFGPs and also crucially control their hydration dynamics. Owing to the disordered nature of AFGPs we use Markov-state modeling to identify the conformational preferences of AFGPs. The simulations reveal the importance of steric bulk, intra-molecular carbohydrate-protein H-bonds and conformational preferences (α- vs. ß-linkages) in controlling the spatial segregation of the hydrophilic and hydrophobic regions of AFGPs. We hypothesize that the hydrophobic component of AFGPs is crucial to their binding to ice, which determines the ice shaping ability of AFGPs. However, the hydrophilic carbohydrate hydroxyl groups and their ability to form water bridges control the subsequent hydration dynamics, which is key to the antifreeze properties. Investigating the tetrahedral order parameter of water molecules around the carbohydrates revealed competition between solute- and bulk-influenced solvent structures, with maximum restructuring being observed in the interfacial region 2.5-4.5 Å away from the AFGPs.

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to gain insight into CPP activity. Experiments can provide detailed insight into the cell-penetration property of CPPs. However, the synthesis and identification of CPPs through wet-lab experiments is both resource- and time-expensive. Therefore, the development of an efficient prediction tool is essential for the identification of unique CPP prior to experiments. To this end, we developed a kernel extreme learning machine (KELM) based CPP prediction model called KELM-CPPpred. The main data set used in this study consists of 408 CPPs and an equal number of non-CPPs. The input features, used to train the proposed prediction model, include amino acid composition, dipeptide amino acid composition, pseudo amino acid composition, and the motif-based hybrid features. We further used an independent data set to validate the proposed model. In addition, we have also tested the prediction accuracy of KELM-CPPpred models with the existing artificial neural network (ANN), random forest (RF), and support vector machine (SVM) approaches on respective benchmark data sets used in the previous studies. Empirical tests showed that KELM-CPPpred outperformed existing prediction approaches based on SVM, RF, and ANN. We developed a web interface named KELM-CPPpred, which is freely available at http://sairam.people.iitgn.ac.in/KELM-CPPpred.html.

Prolonged exposure to insulin with insufficient glucose leads to impaired Glut4 translocation.

Insulin maintains glucose homeostasis by stimulating glucose uptake from extracellular environment to adipose and muscle tissue through glucose transporter (GLUT4). Insulin resistance plays a significant role in pathologies associated with type2 diabetes. It has been previously shown that hyperinsulinemia can lead to insulin resistance. In these studies very high levels of insulin was used to achieve insulin resistance. We hypothesized that one of the causes of type 2 diabetes could be insulin synthesis in the absence of glucose stimulation. We used CHO cell line, stably expressing Myc-GLUT4-GFP along with human insulin receptor to study the effect of hyperinsulinemia in the presence of low glucose (6.5 mM) or high glucose (20 mM). The insulin responsiveness of these cells was assessed by FRAP, FACS and subcellular fractionation. The results suggest that exposure of cells to insulin in low glucose conditions made these cells insulin resistant within 10 passages, while the same level of insulin in the presence of high glucose did not result in insulin resistance. These results clearly suggest that hyperinsulinemia combined with hypoglycaemia may lead to insulin resistance and may be one of the causes for the typ2 diabetes.

LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells.

The contribution of antitumor immunity to metastatic dormancy is poorly understood. Here we show that the long noncoding RNA Malat1 is required for tumor initiation and metastatic reactivation in mouse models of breast cancer and other tumor types. Malat1 localizes to nuclear speckles to couple transcription, splicing and mRNA maturation. In metastatic cells, Malat1 induces WNT ligands, autocrine loops to promote self-renewal and the expression of Serpin protease inhibitors. Through inhibition of caspase-1 and cathepsin G, SERPINB6B prevents gasdermin D-mediated induction of pyroptosis. In this way, SERPINB6B suppresses immunogenic cell death and confers evasion of T cell-mediated tumor lysis of incipient metastatic cells. On-target inhibition of Malat1 using therapeutic antisense nucleotides suppresses metastasis in a SERPINB6B-dependent manner. These results suggest that Malat1-induced expression of SERPINB6B can titrate pyroptosis and immune recognition at metastatic sites. Thus, Malat1 is at the nexus of tumor initiation, reactivation and immune evasion and represents a tractable and clinically relevant drug target.

Salicylic acid alleviates aluminum toxicity in rice seedlings better than magnesium and calcium by reducing aluminum uptake, suppressing oxidative damage and increasing antioxidative defense.

Aluminum toxicity is a major constraint to crop production in acid soils. The present study was undertaken to examine the comparative ameliorating effects of salicylic acid, Ca and Mg on Al toxicity in rice (Oryza sativa L.) seedlings grown in hydroponics. Al treatment (0.5 mM AlCl3) caused decrease in plant vigour, loss of root plasma membrane integrity, increased contents of O 2 (∙-) , H2O2, lipid peroxidation, protein carbonyls and decline in the level of protein thiol. Al treatment caused significant changes in activity of antioxidative enzymes in rice seedlings. Exogenously added salicylic acid (60 µM), Ca (1 mM) and Mg (0.25 mM) significantly alleviated Al toxicity effects in the seedlings marked by restoration of growth, suppression of Al uptake, restoration of root plasma membrane integrity and decline in O 2 (∙-) , H2O2, lipid peroxidation and protein carbonyl contents. Salicylic acid, Ca and Mg suppressed Al-induced increase in SOD, GPX and APX activities while it elevated Al-induced decline in CAT activity. By histochemical staining of O 2 (∙-) using NBT and H2O2 using DAB, it was further confirmed that added salicylic acid, Ca or Mg decreased Al-induced accumulation of O 2 (∙-) and H2O2 in the leaf tissues. Results indicate that exogenously added salicylic acid, Ca or Mg alleviates Al toxicity in rice seedlings by suppressing Al uptake, restoring root membrane integrity, reducing ROS level and ROS induced oxidative damage and regulating the level of antioxidative enzyme activities. Further salicylic appears to be superior to Mg and Ca in alleviating Al toxicity effects in rice plants.

Seasonal trends of PM2.5 and PM10 in ambient air and their correlation in ambient air of Lucknow city, India.

The PM(10) concentration (µg/m(3)) in Lucknow city at 4 locations in three different seasons ranged between 148.6-210.8 (avg. 187.2 ± 17.1) during summer, 111.8-187.6 (avg. 155.7 ± 22.7) during monsoon and 199.3-308.8 (avg. 269.3 ± 42.9) during winter while PM(2.5) ranged between 32.4-67.2 (avg. 45.6 ± 10.9), 25.6-68.9 (avg. 39.8 ± 4.6) and 99.3-299.3 (avg. 212.4 ± 55.0) during respective seasons. The mass fraction ratio of PM(2.5) ranged between 0.22-0.92 (avg. 0.42 ± 0.26) and was significantly high during winter season indicating their composition.

Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field.

The Drude polarizable force field (FF) captures electronic polarization effects via auxiliary Drude particles that are attached to non-hydrogen atoms, distinguishing it from commonly used additive FFs that rely on fixed charges. The Drude FF currently includes parameters for biomolecules such as proteins, nucleic acids, lipids, and carbohydrates and small-molecule representative of those classes of molecules as well as a range of atomic ions. Extension of the Drude FF to novel small druglike molecules is challenging as it requires the assignment of partial charges, atomic polarizabilities, and Thole scaling factors. In the present article, deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizabilities. Training of the DNN model used a collection of 39 421 molecules with molecular weights up to 200 Da and containing H, C, N, O, P, S, F, Cl, Br, or I atoms. The DNN model utilizes bond connectivity, including 1,2, 1,3, 1,4, and 1,5 terms and distances of Drude FF atom types as the feature vector to build the model, allowing it to capture both local and nonlocal effects in the molecules. Novel methods have been developed to determine restrained electrostatic potential (RESP) charges on atoms and external points representing lone pairs and to determine Thole scale factors, which have no QM analogue. A penalty scheme is devised as a performance predictor of the trained model. Validation studies show that these DNN models can precisely predict molecular dipole and polarizabilities of Food and Drug Administration (FDA)-approved drugs compared to reference MP2 calculations. The availability of the DNN model allowing for the rapid estimation of the Drude electrostatic parameters will facilitate its applicability to a wider range of molecular species.

Uptake and accumulation of potentially toxic metals (Zn, Cu and Pb) in soils and plants of Durgapur industrial belt.

Uptake and accumulation of metals in crops may cause possible health risks through food chain. A field survey was conducted to investigate the accumulation of potentially toxic metals contamination in soil and plants irrigated with complexed industrial effluents. Concentration of Zn, Cu and Pb was 205-255,101-130,118-177 microg g(-1) in rhizosphere soils and 116-223, 57-102 and 63-95 microg g(-1) d. wt. in root and 95-186, 44-75 and 27-58 microg g(-1) d. wt. in shoot, respectively. The trend in Cu and Pb was in the order: soil > root > shoot > seed while in Zn it was soil > root > seed > shoot. Roots accumulated a larger fraction of soil Cu (70%) > Zn (67%) > Pb (54%). Bioaccumulation coefficient of soil to root ranged from 51-98 for Zn, 54-85 for Cu and 43-63 for Pb.Analysis of variance showed marginal change in bioaccumulation coefficient, noticed between plants (p > 0.05) while it varied significantly (p < 0.01) between tissues and metals. It increased from root to seed/fruit (root > shoot > seed/fruit) while decreased between metals from Zn to Pb (Zn > Cu > Pb). Out of the three, two Cu and Pb accumulated to phyotoxic levels while Zn was within threshold limit of phytotoxicity.