Mirtrons are non-canonical microRNAs encoded in introns the biogenesis of which starts with splicing. They are not processed by Drosha and enter the canonical pathway at the Exportin-5 level. Mirtrons are much less evolutionary conserved than canonical miRNAs. Due to the differences, canonical miRNA predictors are not applicable to mirtron prediction. Identification of differences is important for designing mirtron prediction algorithms and may help to improve the understanding of mirtron functioning. So far, only simple, single-feature comparisons were reported. These are insensitive to complex feature relations. We quantified miRNAs with 25 features and showed that it is impossible to distinguish the two miRNA species using simple thresholds on any single feature. However, when using the Principal Component Analysis mirtrons and canonical miRNAs are grouped separately. Moreover, several methodologically diverse machine learning classifiers delivered high classification performance. Using feature selection algorithms we found features (e.g. bulges in the stem region), previously reported divergent in two classes, that did not contribute to improving classification accuracy, which suggests that they are not biologically meaningful. Finally, we proposed a combination of the most important features (including Guanine content, hairpin free energy and hairpin length) which convey a specific pattern, crucial for identifying mirtrons.
Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed. Their comparison is difficult due to different methodologies used for validation. In this work, a Contact Map-to-Structure pipeline (C2S_pipeline) for contact-based protein structure reconstruction is designed and validated. The C2S_pipeline can be used to reconstruct monomeric and multimeric proteins. The median RMSD of structures obtained during validation on a representative set of protein structures, equaled 5.27 Å, and the best structure was reconstructed with RMSD of 1.59 Å. The validation is followed by a detailed case study on the KcsA ion channel. Models of KcsA are reconstructed based on different portions of contact site information. Structural feature analysis of acquired KcsA models is supported by a thorough analysis of electrostatic potential distributions inside the channels. The study shows that electrostatic parameters are correlated with structural quality of models. Therefore, they can be used to discriminate between high and low quality structures. We show that 30 % of contact information is needed to obtain accurate structures of KcsA, if contacts are selected randomly. This number increases to 70 % in case of erroneous maps in which the remaining contacts or non-contacts are changed to the opposite. Furthermore, the study reveals that local reconstruction accuracy is correlated with the number of contacts in which amino acid are involved. This results in higher reconstruction accuracy in the structure core than peripheral regions.
IntroductionIn acromegaly, chronic exposure to impaired GH and IGF-I levels leads to the development of typical acromegaly symptoms, and multiple systemic complications as cardiovascular, metabolic, respiratory, endocrine, and bone disorders. Acromegaly comorbidities contribute to decreased life quality and premature mortality. The aim of our study was to assess the frequency of acromegaly complications and to evaluate diagnostic methods performed toward recognition of them.Materials and MethodsIt was a retrospective study and we analyzed data of 179 patients hospitalized in the Department of Endocrinology, Diabetes and Isotope Therapy in Wroclaw Medical University (Poland) in 1976 to 2018 to create a database for statistical analysis.ResultsThe study group comprised of 119 women (66%) and 60 men (34%). The median age of acromegaly diagnosis was 50.5 years old for women (age range 20–78) and 46 for men (range 24–76). Metabolic disorders (hyperlipidemia, diabetes, and prediabetes) were the most frequently diagnosed complications in our study, followed by cardiovascular diseases and endocrine disorders (goiter, pituitary insufficiency, osteoporosis). BP measurement, ECG, lipid profile, fasting glucose or OGTT were performed the most often, while colonoscopy and echocardiogram were the least frequent.ConclusionsIn our population we observed female predominance. We revealed a decrease in the number of patients with active acromegaly and an increase in the number of well-controlled patients. More than 50% of patients demonstrated a coexistence of cardiac, metabolic and endocrine disturbances and only 5% of patients did not suffer from any disease from those main groups.
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