The Evolution and Expression Profiles of EC1 Gene Family during Development in Cotton

Fertilization is essential to sexual reproduction of flowering plants. EC1 (EGG CELL 1) proteins have a conserved cysteine spacer characteristic and play a crucial role in double fertilization process in many plant species. However, to date, the role of EC1 gene family in cotton is fully unknown. Hence, detailed bioinformatics analysis was explored to elucidate the biological mechanisms of EC1 gene family in cotton. In this study, we identified 66 genes in 10 plant species in which a total of 39 EC1 genes were detected from cotton genome. Phylogenetic analysis clustered the identified EC1 genes into three families (I-III) and all of them contain Prolamin-like domains. A good collinearity was observed in the synteny analysis of the orthologs from cotton genomes. Whole-genome duplication was determined to be one of the major impetuses for the expansion of the EC1 gene family during the process of evolution. qRT-PCR analysis showed that EC1 genes were highly expressed in reproductive tissues under multiple stresses, signifying their potential role in enhancing stress tolerance or responses. Additionally, gene interaction networks showed that EC1 genes may be involved in cell stress and response transcriptional regulator in the synergid cells and activate the expression of genes required for pollen tube guidance. Our results provide novel functional insights into the evolution and functional elucidation of EC1 gene family in cotton.

Brain and Pituitary Transcriptome Analyses Reveal the Differential Regulation of Reproduction-Related LncRNAs and mRNAs in Cynoglossus semilaevis

Long noncoding RNAs (lncRNAs) have been identified to be involved in half-smooth tongue sole (Cynoglossus semilaevis) reproduction. However, studies of their roles in reproduction have focused mainly on the ovary, and their expression patterns and potential roles in the brain and pituitary are unclear. Thus, to explore the mRNAs and lncRNAs that are closely associated with reproduction in the brain and pituitary, we collected tongue sole brain and pituitary tissues at three stages for RNA sequencing (RNA-seq), the 5,135 and 5,630 differentially expressed (DE) mRNAs and 378 and 532 DE lncRNAs were identified in the brain and pituitary, respectively. The RNA-seq results were verified by RT-qPCR. Moreover, enrichment analyses were performed to analyze the functions of DE mRNAs and lncRNAs. Interestingly, their involvement in pathways related to metabolism, signal transduction and endocrine signaling was revealed. LncRNA-target gene interaction networks were constructed based on antisense, cis and trans regulatory mechanisms. Moreover, we constructed competing endogenous RNA (ceRNA) networks. In summary, this study provides mRNA and lncRNA expression profiles in the brain and pituitary to understand the molecular mechanisms regulating tongue sole reproduction.

Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks

Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.

The structure balance of gene-gene networks beyond pairwise interactions

Despite its high and direct impact on nearly all biological processes, the underlying structure of gene-gene interaction networks is investigated so far according to pair connections. To address this, we explore the gene interaction networks of the yeast Saccharomyces cerevisiae beyond pairwise interaction using the structural balance theory (SBT). Specifically, we ask whether essential and nonessential gene interaction networks are structurally balanced. We study triadic interactions in the weighted signed undirected gene networks and observe that balanced and unbalanced triads are over and underrepresented in both networks, thus beautifully in line with the strong notion of balance. Moreover, we note that the energy distribution of triads is significantly different in both essential and nonessential networks compared with the shuffled networks. Yet, this difference is greater in the essential network regarding the frequency as well as the energy of triads. Additionally, results demonstrate that triads in the essential gene network are more interconnected through sharing common links, while in the nonessential network they tend to be isolated. Last but not least, we investigate the contribution of all-length signed walks and its impact on the degree of balance. Our findings reveal that interestingly when considering longer cycles the nonessential gene network is more balanced compared to the essential network.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulation of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 13.7 million single-cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

MCT4 Upregulates PD-L1 Expression and Defines Novel Composite Therapeutic Targets for Triple-Negative Breast Cancer

Immune checkpoint blocking therapy targeting the PD-1/PD-L1 axis has shown promising availability for triple-negative breast cancer (TNBC). Nevertheless, in some cases, targeting efficiency is low and efficient gene interaction networks need to be sought, which inspired the exploration that MCT4 and PD-L1 co-expression network analysis and potential regulatory mechanism research. In the paper, bioinformatics, Western blot, qRT-PCR, flow cytometry, biochemical analysis, multiple immunohistochemistry, CRISPR/Cas9 and lentiviral transduction for MCT4 knockout (sgMCT4/231 KO) or overexpression (pEGFP-N1-MCT4/231) were adopted. Analysis of database basis showed MCT4 (SLC16A3) and PD-L1 (CD274) were functionally correlated and highly expressed in TNBC cells, further MCT4 and PD-L1 were co-expressed (more than 50% PD-L1+MCT4+ cells) in tissue section of TNBC patients. The expression of PD-L1 in TNBC cell lines MDA-MB-231, MDA-MB-468 and BT-549 was sensitive to lactate concentration, and lowering MCT4 expression could downregulate PD-L1 expression through affecting the lactate concentration. These data suggests that MCT4 is positively associated with PD-L1 and the co-targeted therapy for TNBC may be a promising clinical treatment strategy.

Single-cell classification using graph convolutional networks

Background Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. Results In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. Conclusions Results indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.

Identifying functional modules using energy minimization with graph cuts

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Method: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.

Single-Cell Classification Using Graph Convolutional Networks

BackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods.ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.