Current Gene Therapy - Volume 18, Issue 5, 2018

Proteins with at least one carbohydrate recognition domain are lectins that can identify and reversibly interact with glycan moiety of glycoconjugates or a soluble carbohydrate. It has been proved that lectins can play various vital roles in mediating signal transduction, cell-cell recognition and interaction, immune defense, and so on. Most organisms can synthesize and secret lectins. A portion of lectins closely related to diverse cancers, called cancerlectins, are involved in tumor initiation, growth and recrudescence. Cancerlectins have been investigated for their applications in the laboratory study, clinical diagnosis and therapy, and drug delivery and targeting of cancers. The identification of cancerlectin genes from a lot of lectins is helpful for dissecting cancers. Several cancerlectin prediction tools based on machine learning approaches have been established and have become an excellent complement to experimental methods. In this review, we comprehensively summarize and expound the indispensable materials for implementing cancerlectin prediction models. We hope that this review will contribute to understanding cancerlectins and provide valuable clues for the study of cancerlectins. Novel systems for cancerlectin gene identification are expected to be developed for clinical applications and gene therapy.

Motivation: Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein and revealing the mechanism of many human diseases due to protein subcellular mislocalization, which is required before approaching gene therapy to treat a disease. In addition, it is well-known that the gene therapy is an effective way to overcome disease by targeting a gene therapy product to a specific subcellular compartment. Deep neural networks to predict protein function have become increasingly popular due to large increases in the available genomics data due to its strong superiority in the non-linear classification ability. However, they still have some drawbacks such as too many hyper-parameters and sufficient amount of labeled data. Results: We present a deep forest-based protein location algorithm relying on sequence information. The prediction model uses a random forest network with a multi-layered structure to identify the subcellular regions of protein. The model was trained and tested on a latest UniProt releases protein dataset, and we demonstrate that our deep forest predict the subcellular location of proteins given only the protein sequence with high accuracy, outperforming the current state-of-art algorithms. Meanwhile, unlike the deep neural networks, it has a significantly smaller number of parameters and is much easier to train.

Background: Polycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic subunit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are characterized by high tissue-specificity; however, little is known about the tissue profile of the EZH2- interacting lncRNAs. Objective: Here we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno- precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In addition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, majority of the lncRNAs were firstly reported to be associated with EZH2. Conclusion: Our findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation.

Background: Autophagy exists widely in various physiological and pathological conditions. Lots of investigations have verified that the autophagic activity is always related to the occurrence and the development of cancer. Endometriosis (EMs) is a disease that endometrium-like tissues abnormally grow outside the uterus and also considered to possess the characters of tumor because of its malignant biological behavior. Introduction: Recently, several studies have already revealed that autophagy may play a potential role in proliferative-phase EMs. However, the function of autophagic activity in secretory-phase EMs is still unclear. Methods: In our work, we explored autophagic activity between normal endometrium and EMs lesion endometrium during different menstrual phases and EMs stages. The clinical endometrium samples from 73 women were selected in this study, including 30 healthy individuals and 43 patients with EMs (endometrium samples include eutopic and its matched ectopic endometrium). All the participants were divided into two groups according to the menstrual cycle, namely proliferative-phase and secretive- phase group. Among the patients with EMs, 22 individuals in proliferative phase and the other 21 individuals in secretory phase were further classified into the groups of Stage I-II and Stage III-IV according to revised-American Fertility Society (r-AFS). Two autophagy-related proteins microtubuleassociated protein 1 light chain 3 beta-II (LC3B-II) and sequestosome protein (P62), which are believed to be the indicators of autophagy activity, were chosen in the study. Immunohistochemical (IHC) staining, Western blot assay and Real-Time quantitative Polymerase Chain Reaction (RTqPCR) were used to examine the expression of LC3B-II and P62 in protein and mRNA level accordingly. Result: It showed that the expression of LC3B-II both in protein and mRNA level decreased and that of P62 increased in secretory phase of the healthy group (P<0.05), but showed no significant difference in ectopic and its eutopic endometrium group during proliferative and secretory phase (P>0.05). In addition, the expression of LC3B-II in ectopic endometrium group was significantly lower than that of its eutopic endometrium group (P<0.05), and the expression of P62 was significantly higher accordingly (P<0.05). At the same time, both LC3B-II and P62 levels remained same between eutopic endometrium group and control group (P>0.05). Furthermore, compared to Stage I-II EMs group, the expression of LC3B-II was significantly lower (P<0.05) and P62 was significantly higher (P<0.05) in Stage III-IV EMs during secretory phase. Conclusion: Taken together, the periodicity-losing in EMs and the decreased autophagic activity in ectopic endometrium may exert a potential role in the pathogenesis of EMs. Down-regulated autophagy of ectopic endometrium in secretory phase may be related to the progression of EMs.

Background: In microbial communities, the keystone species have a greater impact on the performance and dynamics of ecosystem than that of other species, in which we can see from the results that losing gut microbiome causes some specific diseases. A number of ongoing studies aim at identifying links between microbial community structure and human diseases. Method: In this paper, we have introduced a valid keystone species identification method, in which a new Spread Intensity (SI) algorithm is used. Because the accuracies of current keystone species identification algorithms are difficult to evaluate for the high diversity and uncultivated status of microbial communities, we simulated cross-sectional data of microbial communities with known interactions and set up standard keystoneness rankings using Generalized Lotka-Volterra (GLV) model. Subsequently, we compared the SI algorithm with existing methods by using simulated data and obtained an obvious better performance of SI algorithm than other methods. Also, we applied this method to gut microbiota datasets and identified some microbes having the potential association with body weight. We first assembled three correlation metrics to calculate the interspecies correlation. Then we applied network deconvolution to remove indirect correlations. Finally, we used Molecular Ecological Network Analysis (MENA) to construct the co-occurrence network. According to experimental results, SI algorithm has an excellent performance in identifying highly correlated species in gut microbiome to body weight. Result: This result provides an effective indicator for modulating gut microbiota and thus enables the gene therapy and other gene-level treatments for losing-weight and other gut-associated diseases.

Introduction: Mesenchymal Stem Cell (MSC) therapy in recent years has gained significant attention. Though the functional outcomes following MSC therapy for neurodegenerative diseases are convincing, various mechanisms for the functional recovery are being debated. Nevertheless, recent studies convincingly demonstrated that recovery following MSC therapy could be reiterated with MSC secretome per se thereby shifting the dogma from cell therapy to cell “based” therapy. In addition to various functional proteins, stem cell secretome also includes extracellular membrane vesicles like exosomes. Exosomes which are of “Nano” size have attracted significant interest as they can pass through the bloodbrain barrier far easily than macro size cells or growth factors. Exosomes act as a cargo between cells to bring about significant alterations in target cells. As the importance of exosomes is getting unveil, it is imperial to carry out a comprehensive study to evaluate the neuroprotective potential of exosomes as compared to conventional co-culture or total condition medium treatments. Objective: Thus, the present study is designed to compare the neuroprotective potential of MSC derived exosomes with MSC-condition medium or neuron–MSC-co-culture system against kainic acid induced excitotoxicity in in vitro condition. The study also aims at comparing the neuroprotective efficacy of exosomes/condition medium/co-culture of two MSC viz., neural crest derived human Dental Pulp Stem Cells (hDPSC) and human Bone-Marrow Mesenchymal Stem Cells (hBM-MSC) to identify the appropriate MSC source for treating neurodegenerative diseases. Result: Our results demonstrated that neuroprotective efficacy of MSC-exosomes is as efficient as MSC-condition medium or neuron-MSC co-culture system and treating degenerating hippocampal neurons with all three MSC based approaches could up-regulate host’s endogenous growth factor expressions and prevent apoptosis by activating cell survival PI3K-B-cell lymphoma-2 (Bcl-2) pathway. Conclusion: Thus, the current study highlights the possibilities of treating neurodegenerative diseases with “Nano” size exosomes as opposed to transplanting billions of stem cells which inherit several disadvantages.