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1.
Life Sci ; 244: 117305, 2020 Mar 01.
Artículo en Inglés | MEDLINE | ID: mdl-31953161

RESUMEN

Diabetes mellitus (DM) is a complex metabolic disorder involving multiple deleterious molecular pathways and cellular defects leading to disturbance in the biologic milieu. It is currently a global health concern with growing incidence, especially among younger adults. There is an unmet need to find new therapeutic targets for the management of diabetes. Vitamin D is a promising target in the pathophysiology of DM, especially since vitamin D deficiency is common in patients with diabetes compared to people without diabetes. Evidence suggests that it can play significant roles in improving peripheral insulin sensitivity and glucose metabolism, however, the exact pathophysiological mechanism is not clarified yet. In this current study, we have reviewed the evidence on the effect of vitamin D in improving insulin resistance via distinct molecular pathways.


Asunto(s)
Intolerancia a la Glucosa/prevención & control , Homeostasis , Deficiencia de Vitamina D/complicaciones , Vitamina D/administración & dosificación , Vitaminas/administración & dosificación , Animales , Intolerancia a la Glucosa/etiología , Humanos
2.
Life Sci ; 240: 117090, 2020 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-31765648

RESUMEN

Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are a relatively newer class of anti-hyperglycemic medications that reduce blood glucose by inhibition of renal glucose re-uptake, thereby increasing urinary glucose excretion. Although glycosuria is the primary mechanism of action of these agents, there is some evidence suggesting they can reduce insulin resistance and induce peripheral insulin sensitivity. Identifying the molecular mechanisms by which these medications improve glucose homeostasis can help us to develop newer forms of SGLT2i with lesser side effects. We have reviewed the molecular mechanisms and signaling pathways by which SGLT2i therapy improve insulin sensitivity and ameliorates insulin resistance.


Asunto(s)
Diabetes Mellitus/tratamiento farmacológico , Diabetes Mellitus/metabolismo , Hipoglucemiantes/farmacología , Resistencia a la Insulina , Inhibidores del Cotransportador de Sodio-Glucosa 2/uso terapéutico , Transportador 2 de Sodio-Glucosa/metabolismo , Animales , Humanos , Hipoglucemiantes/uso terapéutico
3.
Life Sci ; 241: 117152, 2020 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-31837333

RESUMEN

GLP-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase 4 inhibitors (DPP-4i) are two classes of antidiabetic agents used in the management of diabetes based on incretin hormones. There is emerging evidence that they have anti-inflammatory effects. Since most long-term complications of diabetes have a background of chronic inflammation, these agents may be beneficial against diabetic complications not only due to their hypoglycemic potential but also via their anti-inflammatory effects. However, the exact molecular mechanisms by which GLP-1RAs and DPP-4i exert their anti-inflammatory effects are not clearly understood. In this review, we discuss the potential molecular pathways by which these incretin-based therapies exert their anti-inflammatory effects.


Asunto(s)
Antiinflamatorios/uso terapéutico , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Hipoglucemiantes/uso terapéutico , Incretinas/uso terapéutico , Inflamación/prevención & control , Diabetes Mellitus Tipo 2/inmunología , Humanos
4.
Life Sci ; 237: 116950, 2019 Nov 15.
Artículo en Inglés | MEDLINE | ID: mdl-31605709

RESUMEN

C-peptide is a small peptide connecting two chains of proinsulin molecule and is dissociated before the release of insulin. It is secreted in an equimolar amount to insulin from the pancreatic beta-cells into the circulation. Recent evidence demonstrates that it has other physiologic activities beyond its structural function. C-peptide modulates intracellular signaling pathways in various pathophysiologic states and, could potentially be a new therapeutic target for different disorders including diabetic complications. There is growing evidence that c-peptide has modulatory effects on the molecular mechanisms involved in the development of diabetic nephropathy. Although we have little direct evidence, pharmacological properties of c-peptide suggest that it can provide potent renoprotective effects especially, in a c-peptide deficient milieu as in type 1 diabetes mellitus. In this review, we describe possible molecular mechanisms by which c-peptide may improve renal efficiency in a diabetic milieu.


Asunto(s)
Péptido C/uso terapéutico , Complicaciones de la Diabetes/prevención & control , Diabetes Mellitus Tipo 1/complicaciones , Nefropatías Diabéticas/prevención & control , Animales , Complicaciones de la Diabetes/etiología , Nefropatías Diabéticas/etiología , Humanos
5.
BMC Bioinformatics ; 19(Suppl 14): 410, 2018 Nov 20.
Artículo en Inglés | MEDLINE | ID: mdl-30453876

RESUMEN

BACKGROUND: The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. RESULTS: We propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). CONCLUSIONS: Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding proteins.


Asunto(s)
Proteínas de Unión a Calmodulina/química , Biología Computacional/métodos , Secuencias de Aminoácidos , Secuencia de Aminoácidos , Teorema de Bayes , Calcio/metabolismo , Humanos , Probabilidad , Estructura Cuaternaria de Proteína , Reproducibilidad de los Resultados , Máquina de Vectores de Soporte
6.
Proteome Sci ; 11(Suppl 1): S11, 2013 Nov 07.
Artículo en Inglés | MEDLINE | ID: mdl-24564955

RESUMEN

BACKGROUND: Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types. RESULTS: We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction. CONCLUSIONS: Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

7.
Proteomics ; 11(19): 3802-10, 2011 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-21789780

RESUMEN

Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.


Asunto(s)
Mapeo de Interacción de Proteínas/métodos , Proteínas/metabolismo , Proteómica/métodos , Bases de Datos de Proteínas , Modelos Biológicos , Modelos Moleculares , Máquina de Vectores de Soporte
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