MicroRNAs comprise a group of non-coding small RNAs (17-25 nt) involved in post-transcriptional regulation that have been identified in various plants and animals. Studies have demonstrated that miRNAs are associated with stem cell self-renewal and differentiation and play a key role in controlling stem cell activities. However, the identification of specific miRNAs and their regulatory roles in the differentiation of multipotent mesenchymal stromal cells (MSCs) have so far been poorly defined. We isolated and cultured human MSCs and osteo-differentiated MSCs from four individual donors. miRNA expression in MSCs and osteo-differentiated MSCs was investigated using miRNA microarrays. miRNAs that were commonly expressed in all three MSC preparations and miRNAs that were differentially expressed between MSCs and osteo-differentiated MSCs were identified. Four underexpressed (hsa-miR-31, hsa-miR-106a, hsa-miR-148a, and hsa-miR-424) and three novel overexpressed miRNAs (hsa-miR-30c, hsa-miR-15b, and hsa-miR-130b) in osteo-differentiated MSCs were selected and their expression were verified in samples from the fourth individual donors. The putative targets of the miRNAs were predicted using bioinformatic analysis. The four miRNAs that were underexpressed in osteo-differentiated MSCs were predicted to target RUNX2, CBFB, and BMPs, which are involved in bone formation; while putative targets for miRNAs overexpressed in osteo-differentiated MSCs were MSC maker (e.g., CD44, ITGB1, and FLT1), stemness-maintaining factor (e.g., FGF2 and CXCL12), and genes related to cell differentiation (e.g., BMPER, CAMTA1, and GDF6). Finally, hsa-miR-31 was selected for target verification and function analysis. The results of this study provide an experimental basis for further research on miRNA functions during osteogenic differentiation of human MSCs.

Background: ERBB4 plays an important role in the etiology and progression of lung cancer. Results: miR-193a-3p suppressed proliferation and invasion and promoted apoptosis in lung cancer cells and xenograft mice by negatively regulating ERBB4. Conclusion: miR-193a-3p exerted an anti-tumor effect by negatively regulating ERBB4 in lung cancer. Significance:This study may open new avenues for future lung cancer therapies.

microRNAs (miRNAs) have emerged as major regulators of the initiation and progression of human cancers, including breast cancer. The aim of this study is to determine the expression pattern of miR-96 in breast cancer and to investigate its biological role during tumorigenesis. We showed that miR-96 was significantly upregulated in breast cancer. We then investigated its function and found that miR-96 significantly promoted cell proliferation, migration and invasion in vitro and enhanced tumor growth in vivo. Furthermore, we explored the molecular mechanisms by which miR-96 contributes to breast cancer progression and identified PTPN9 (protein tyrosine phosphatase, non-receptor type 9) as a direct target gene of miR-96. Finally, we showed that PTPN9 had opposite effects to those of miR-96 on breast cancer cells, suggesting that miR-96 may promote breast tumorigenesis by silencing PTPN9. Taken together, this study highlights an important role for miR-96 in the regulation of PTPN9 in breast cancer cells and may provide insight into the molecular mechanisms of breast carcinogenesis.

Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the postgenome era. In this article, we propose a new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which can adequately extract the raw data information. The proposed method has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers. The feature descriptors are then constructed by combining the molecular substructure fingerprint information, and fed into the rotation forest for accurate prediction. The experimental results of fivefold cross-validation indicate that the proposed method achieves superior performance on gold standard data sets (enzymes, ion channels, GPCRs [G-protein-coupled receptors], and nuclear receptors) with accuracy of 0.9414, 0.9116, 0.8669, and 0.8056, respectively. We further comprehensively explore the performance of the proposed method by comparing it with other feature extraction algorithms, state-of-the-art classifiers, and other excellent methods on the same data set. The excellent comparison results demonstrate that the proposed method is highly competitive when predicting drug-target interactions.

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Lysine acetylation in proteins is a dynamic and reversible PTM and plays an important role in diverse cellular processes. In this study, using lysine-acetylation (Kac) peptide enrichment coupled with nano HPLC/MS/MS, we initially identified the acetylome in the silkworms. Overall, a total of 342 acetylated proteins with 667 Kac sites were identified in silkworm. Sequence motifs analysis around Kac sites revealed an enrichment of Y, F, and H in the +1 position, and F was also enriched in the +2 and -2 positions, indicating the presences of preferred amino acids around Kac sites in the silkworm. Functional analysis showed the acetylated proteins were primarily involved in some specific biological processes. Furthermore, lots of nutrient-storage proteins, such as apolipophorin, vitellogenin, storage proteins, and 30 K proteins, were highly acetylated, indicating lysine acetylation may represent a common regulatory mechanism of nutrient utilization in the silkworm. Interestingly, Ser2 proteins, the coating proteins of larval silk, were found to contain many Kac sites, suggesting lysine acetylation may be involved in the regulation of larval silk synthesis. This study is the first to identify the acetylome in a lepidoptera insect, and expands greatly the catalog of lysine acetylation substrates and sites in insects.

Lung cancer remains the leading cause of cancer-related death worldwide, and non-small cell lung cancer (NSCLC) accounts for approximately 80% of lung cancer cases. Recently, microRNAs (miRNAs) have been consistently demonstrated to be involved in NSCLC and to act as either tumor oncogenes or tumor suppressors. In this study, we identified a specific binding site for miR-218-5p in the 3′-untranslated region of the epidermal growth factor receptor (EGFR). We further experimentally validated miR-218-5p as a direct regulator of EGFR. We also identified an inverse correlation between miR-218-5p and EGFR protein levels in NSCLC tissue samples. Moreover, we demonstrated that miR-218-5p plays a critical role in suppressing the proliferation and migration of lung cancer cells probably by binding to EGFR. Finally, we examined the function of miR-218-5p in vivo and revealed that miR-218-5p exerts an anti-tumor effect by negatively regulating EGFR in a xenograft mouse model. Taken together, the results of this study highlight an important role for miR-218-5p in the regulation of EGFR in NSCLC and may open new avenues for future lung cancer therapies.

Object segmentation and object tracking are fundamental research areas in the computer vision community. These two topics are difficult to handle some common challenges, such as occlusion, deformation, motion blur, scale variation, and more. The former contains heterogeneous object, interacting object, edge ambiguity, and shape complexity; the latter suffers from difficulties in handling fast motion, out-of-view, and real-time processing. Combining the two problems of Video Object Segmentation and Tracking (VOST) can overcome their respective difficulties and improve their performance. VOST can be widely applied to many practical applications such as video summarization, high definition video compression, human computer interaction, and autonomous vehicles. This survey aims to provide a comprehensive review of the state-of-the-art VOST methods, classify these methods into different categories, and identify new trends. First, we broadly categorize VOST methods into Video Object Segmentation (VOS) and Segmentation-based Object Tracking (SOT). Each category is further classified into various types based on the segmentation and tracking mechanism. Moreover, we present some representative VOS and SOT methods of each time node. Second, we provide a detailed discussion and overview of the technical characteristics of the different methods. Third, we summarize the characteristics of the related video dataset and provide a variety of evaluation metrics. Finally, we point out a set of interesting future works and draw our own conclusions.