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A Novel Micropeptide, \u3cem\u3eSlitharin\u3c/em\u3e, Exerts Cardioprotective Effects in Myocardial Infarction
Get PDFPurpose: Micropeptides are an emerging class of proteins that play critical roles in cell signaling. Here, we describe the discovery of a novel micropeptide, dubbed slitharin (Slt), in conditioned media from Cardiosphere-derived cells (CDCs), a therapeutic cardiac stromal cell type.
Experimental design: We performed mass spectrometry of peptide-enriched fractions from the conditioned media of CDCs and a therapeutically inert cell type (human dermal fibrobasts). We then evaluated the therapeutic capacity of the candidate peptide using an in vitro model of cardiomyocyte injury and a rat model of myocardial infarction.
Results: We identified a novel 24-amino acid micropeptide (dubbed Slitharin [Slt]) with a non-canonical leucine start codon, arising from long intergenic non-coding (LINC) RNA 2099. Neonatal rat ventricular myocytes (NRVMs) exposed to Slt were protected from hypoxic injury in vitro compared to a vehicle or scrambled control. Transcriptomic analysis of cardiomyocytes exposed to Slt reveals cytoprotective capacity, putatively through regulation of stress-induced MAPK-ERK. Slt also exerted cardioprotective effects in rats with myocardial infarction as shown by reduced infarct size 48 h post-injury.
Conclusions and clinical relavance: Thus, Slt is a non-coding RNA-derived micropeptide, identified in the extracellular space, with a potential cardioprotective function
How to Create an Inclusive Campus for Neurodivergent Students
No full textNeurodivergent people are not a monolith, and we must consider that there will be times that the best ways to ensure inclusion and access for one person may not be the best for another. For instance, we recognise the various views of the neurodivergent community, such as a preference for the use of person-first language (using terms such as \u27person with a disability\u27) or identity-first language (\u27disabled person\u27). Being flexible and accommodating to the varied student needs on campus shows recognition and support for diverse communities. By implementing accessible resources and supports such as alternative learning spaces, sensory spaces, comprehensive mental health support and accessible modalities to learning, universities can support neurodivergent students to succeed
Development of Yoghurt Incorporated with Beetroot Puree and Its Effect on the Physicochemical Properties and Consumer Acceptance
Get PDFBackground Yoghurt is one of the most popularly consumed fermented products which provides several nutritional benefits. Yoghurt products often include flavour and colour additives however, growing awareness of the risks associated with synthetic food additives has necessitated the need to explore more natural colour and flavour as food additives. Methods This study evaluated the effect of beetroot puree as flavouring and colourant in yoghurt production and quality. To develop the yoghurt product, incubation time and proportion of beetroot puree were optimized based on the resulting pH, titratable acidity, colour, and viscosity using response surface methods. Results Optimum yoghurt formulations were obtained in products containing 2 %, 2.03 % and 8 % beetroot puree following an incubation of 2.5h. Increasing beetroot puree did not affect the pH and titratable acidity of the yoghurt samples but slightly influenced the viscosity of the yoghurt. The colour of yoghurt was mainly affected by the puree concentration. A consumer acceptance test was conducted on the optimized products compared to a control sample without beetroot. Yoghurt incorporated with 8 % beetroot puree was the least preferred with a mean score of 6.08, whereas yoghurt incorporated with 2.03 % beetroot puree was the most preferred (7.42), with a higher acceptance than the control (7.28). Conclusion /Implications for industry: Findings from the study provide evidence for exploration of beetroot yoghurt as a natural product alternative to the use of synthetic flavour and colour additives in yoghurt
Predisposing Deleterious Variants in the Cancer-Associated Human Kinases in the Global Populations
Get PDFHuman kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in \u3e65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management
A Deep Learning-Based Theoretical Protocol to Identify Potentially Isoform-Selective PI3Kα Inhibitors
No full textPhosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition
