Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV

Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method—maximum relevance and minimum redundancy (mRMR)—was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV

Designing single guide RNAs for CRISPR/Cas9

Researchers have been working towards development of tools to facilitate regular use genome engineering techniques. In recent years, the focus of these efforts has been the Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)/CRISPR associated(Cas) systems. These systems, while found naturally in bacteria and archaea as an immunity mechanism, can be used for genome engineering in eukaryotes. There are three major computational challenges associated with the use of CRISPR/Cas9 in genome engineering for mammals - identification of CRISPR arrays, single guide RNA design and minimizing off-target effects. This project attempts to solve the problem of single guide RNA design using a novel approach. Researchers have been trying to solve the problem by using different machine learning classification algorithms. The algorithms have been trained to use the sequential and structural properties of single guide RNAs (sgRNAs). This project explores the use of a neural network based approach to solve the sgRNA design problem. A form of the Recurrent Neural Network (RNN) called the Long Short Term Memory (LSTM) model can be used as feature-less classification model to differentiate between functional and non-functional single guide RNAs. The project covers different experiments conducted using Support Vector Machine and Random Forest classifiers using sequential and structural features to identify the most potent sgRNAs in a given set of input sgRNAs. It also summarizes the implementation of the LSTM model and its results, along with the cross-validation results for each of these models. Through these results, it has been observed that LSTMs perform better than existing models such as Random Forest Classifiers and Support Vector Machines and give results comparable to existing tools

Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles

Primary tumor site specificity is preserved in patient-derived tumor xenograft models

Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients

Tissue Expression Difference between mRNAs and lncRNAs

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups

Optimizing sgRNA to Improve CRISPR/Cas9 Knockout Efficiency: Special Focus on Human and Animal Cell

During recent years, clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) technologies have been noticed as a rapidly evolving tool to deliver a possibility for modifying target sequence expression and function. The CRISPR/Cas9 tool is currently being used to treat a myriad of human disorders, ranging from genetic diseases and infections to cancers. Preliminary reports have shown that CRISPR technology could result in valued consequences for the treatment of Duchenne muscular dystrophy (DMD), cystic fibrosis (CF), β-thalassemia, Huntington’s diseases (HD), etc. Nonetheless, high rates of off-target effects may hinder its application in clinics. Thereby, recent studies have focused on the finding of the novel strategies to ameliorate these off-target effects and thereby lead to a high rate of fidelity and accuracy in human, animals, prokaryotes, and also plants. Meanwhile, there is clear evidence indicating that the design of the specific sgRNA with high efficiency is of paramount importance. Correspondingly, elucidation of the principal parameters that contributed to determining the sgRNA efficiencies is a prerequisite. Herein, we will deliver an overview regarding the therapeutic application of CRISPR technology to treat human disorders. More importantly, we will discuss the potent influential parameters (e.g., sgRNA structure and feature) implicated in affecting the sgRNA efficacy in CRISPR/Cas9 technology, with special concentration on human and animal studies

Gating of small RNA mobility in plant stem cell niches

Signalling in plants is crucial for the proper spatio-temporal coordination of development. Small RNAs (sRNAs), in addition to classical signalling molecules like phytohormones, peptides and small mobile proteins, have emerged as potent signals that can convey positional information over space. Mobile sRNAs play important roles in differentiation of plant tissues. In the leaf, an opposing gradient of tasiARF and miR166 creates a sharp on-off switch of ad- and abaxial cell identity. In the root, miR166 has been shown to migrate from the endodermis into the central stele regulating HD-ZIPIII expression resulting in the differentiation of proto- and metaxylem. Moreover, in the developing embryo, the non-cell autonomous miR394 is involved in the initiation of the future shoot apical meristem. Despite its importance, very little is known about the mobility of sRNAs. Using a highly sensitive GFP-based synthetic miRNA sensor system, we showed that miRNA mobility involves a finely regulated mechanism based on gatekeepers polarised at select cell-cell interfaces creating directionality between neighbouring cells. In stem cell niches, we showed that sRNA mobility is highly restrictive although plasmodesmata (PD), cell-cell connections crucial for sRNA mobility, are abundant. The restrictive behaviour of mobility in stem cell niches is exclusive for sRNAs and independent from protein movement whether via passive diffusion or active transport. The idea of an independent mechanism controlling sRNA mobility is conceivable, however, identifying such a mechanism is a herculean task. We designed a reverse genetic candidate approach based on a high-throughput CRISPR/Cas9 system aiming to uncover facilitators of miRNA mobility. We introduced mutations in more than 50 candidate genes and identified the PD-localised receptor-like kinases BARELY ANY MERISTEM 1 and BARELY ANY MERISTEM 2 as facilitators of miRNA mobility in the root. To identify components of the negative regulation mechanism based on gatekeepers at specific cell-cell interfaces, we designed a forward genetic morphology-based screen. For this we utilised single-cell RNA sequencing data to setup a screening platform for quick and reliable identification of putative mutants involved in the gatekeeping mechanism. In this study we provided major insight into the regulation of sRNA mobility in stem cell niches and setup genetic screens that will contribute to the identification of sRNA mobility mechanisms