Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.

An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

Tomato brown rugose fruit virus (ToBRFV) is an emerging pathogen that causes severe disease in tomato (Solanum lycopersicum L.) crops. The first ToBRFV outbreak in Italy occurred in 2018 in several Sicilian provinces, representing a serious threat for tomato production. In the present work, the spatiotemporal displacement of ToBRFV in Sicily was evaluated, analyzing a total of 590 lots of tomato seed, 982 lots of plantlets from nurseries and 100 commercial greenhouses. Furthermore, we investigated the ToBRFV spreading dynamic in a greenhouse under experimental conditions. Results showed several aspects related to ToBRFV dispersion in protected tomato crops. In detail, an important decrease of the ToBRFV-infected seed and plantlet lots was detected. Regarding the examined commercial greenhouses, ToBRFV still appears to be present in Sicily, although there has been a decrease during monitoring. In experimental conditions, it was demonstrated that the presence of few infected plants are sufficient to damage the entire crop in a short time, reaching almost 100% of infection.

Osteolytic bone disease is the major complication associated with the progression of multiple myeloma (MM). Recently, extracellular vesicles (EVs) have emerged as mediators of MM-associated bone disease by inhibiting the osteogenic differentiation of human mesenchymal stem cells (hMSCs). Here, we investigated a correlation between the EV-mediated osteogenic inhibition and MM vesicle content, focusing on miRNAs. By the use of a MicroRNA Card, we identified a pool of miRNAs, highly expressed in EVs, from MM cell line (MM1.S EVs), expression of which was confirmed in EVs from bone marrow (BM) plasma of patients affected by smoldering myeloma (SMM) and MM. Notably,we found that miR-129-5p, which targets different osteoblast (OBs) differentiation markers, is enriched in MM-EVs compared to SMM-EVs, thus suggesting a selective packaging correlated with pathological grade. We found that miR-129-5p can be transported to hMSCs by MM-EVs and, by the use of miRNA mimics, we investigated its role in recipient cells. Our data demonstrated that the increase of miR-129-5p levels in hMSCs under osteoblastic differentiation stimuli inhibited the expression of the transcription factor Sp1, previously described as a positive modulator of osteoblastic differentiation, and of its target the Alkaline phosphatase (ALPL), thus identifying miR-129-5p among the players of vesicle-mediated bone disease.

The catechins derived from green tea possess antioxidant activity and may have a potentially anticancer effect. PTP1B is tyrosine phosphatase that is oxidative stress regulated and is involved with prooncogenic pathways leading to the formation of a.o. breast cancer. Here, we present the effect of selected green tea catechins on enzymatic activity of PTP1B phosphatase and viability of MCF-7 breast cancer cells. We showed also the computational analysis of the most effective catechin binding with a PTP1B molecule. We observed that epigallocatechin, epigallocatechin gallate, epicatechin, and epicatechin gallate may decrease enzymatic activity of PTP1B phosphatase and viability of MCF-7 cells. Conclusions: From the tested compounds, epigallocatechin and epigallocatechin gallate were the most effective inhibitors of the MCF-7 cell viability. Moreover, epigallocatechin was also the strongest inhibitor of PTP1B activity. Computational analysis allows us also to conclude that epigallocatechin is able to interact and bind to PTP1B. Our results suggest also the most predicted binding site to epigallocatechin binding to PTP1B.