BackgroundMicroRNAs (miRNAs) are small non-protein-coding RNAs. miRNA genes need several biogenesis steps to form function miRNAs. However, the precise mechanism and biology involved in the mature miRNA molecules are not clearly investigated. In this study, we conducted in-depth analyses to examine the arm selection and isomiRs using NGS platform.MethodsWe sequenced small RNAs from one pair of normal and gastric tumor tissues with Solexa platform. By analyzing the NGS data, we quantified the expression profiles of miRNAs and isomiRs in gastric tissues. Then, we measured the expression ratios of 5p arm to 3p arm of the same pre-miRNAs. And, we used Kolmogorov-Smirnov (KS) test to examine isomiR pattern difference between tissues.ResultsOur result showed the 5p arm and 3p arm miRNA derived from the same pre-miRNAs have different tissue expression preference, one preferred normal tissue and the other preferred tumor tissue, which strongly implied that there could be other mechanism controlling mature miRNA selection in addition to the known hydrogen-bonding selection rule. Furthermore, by using the KS test, we demonstrated that some isomiR types preferentially occur in normal gastric tissue but other types prefer tumor gastric tissue.ConclusionsArm selections and isomiR patterns are significantly varied in human cancers by using deep sequencing NGS data. Our results provided a novel research topic in miRNA regulation study. With advanced bioinformatics and molecular biology studies, more robust conclusions and insight into miRNA regulation can be achieved in the near future.
Feathers are hallmark avian integument appendages, although they were also present on theropods. They are composed of flexible corneous materials made of α- and β-keratins, but their genomic organization and their functional roles in feathers have not been well studied. First, we made an exhaustive search of α- and β-keratin genes in the new chicken genome assembly (Galgal4). Then, using transcriptomic analysis, we studied α- and β-keratin gene expression patterns in five types of feather epidermis. The expression patterns of β-keratin genes were different in different feather types, whereas those of α-keratin genes were less variable. In addition, we obtained extensive α- and β-keratin mRNA in situ hybridization data, showing that α-keratins and β-keratins are preferentially expressed in different parts of the feather components. Together, our data suggest that feather morphological and structural diversity can largely be attributed to differential combinations of α- and β-keratin genes in different intrafeather regions and/or feather types from different body parts. The expression profiles provide new insights into the evolutionary origin and diversification of feathers. Finally, functional analysis using mutant chicken keratin forms based on those found in the human α-keratin mutation database led to abnormal phenotypes. This demonstrates that the chicken can be a convenient model for studying the molecular biology of human keratin-based diseases.
Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model’s generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m
2
. A Pearson correlation coefficient of 0.741 indicated the strong relationship between artificial intelligence (AI)- and creatinine-based GFR estimations. Overall CKD status classification accuracy of our model was 85.6% —higher than that of experienced nephrologists (60.3%–80.1%). Our model is the first fundamental step toward realizing the potential of transforming kidney ultrasound imaging into an effective, real-time, distant screening tool. AI-GFR estimation offers the possibility of noninvasive assessment of kidney function, a key goal of AI-powered functional automation in clinical practice.
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