Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer.

Identifying gene regulatory networks (GRNs) at the resolution of single cells has long been a great challenge, and the advent of single-cell multi-omics data provides unprecedented opportunities to construct GRNs. Here, we propose a novel strategy to integrate omics datasets of single-cell ribonucleic acid sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing, and using an unsupervised learning neural network to divide the samples with high copy number variation scores, which are used to infer the GRN in each gene block. Accuracy validation of proposed strategy shows that approximately 80% of transcription factors are directly associated with cancer, colorectal cancer, malignancy and disease by TRRUST; and most transcription factors are prone to produce multiple transcript variants and lead to tumorigenesis by RegNetwork database, respectively. The source code access are available at: https://github.com/Cuily-v/Colorectal_cancer.

Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data.

Recent advances in single-cell RNA sequencing (scRNA-seq) provide exciting opportunities for transcriptome analysis at single-cell resolution. Clustering individual cells is a key step to reveal cell subtypes and infer cell lineage in scRNA-seq analysis. Although many dedicated algorithms have been proposed, clustering quality remains a computational challenge for scRNA-seq data, which is exacerbated by inflated zero counts due to various technical noise. To address this challenge, we assess the combinations of nine popular dropout imputation methods and eight clustering methods on a collection of 10 well-annotated scRNA-seq datasets with different sample sizes. Our results show that (i) imputation algorithms do typically improve the performance of clustering methods, and the quality of data visualization using t-Distributed Stochastic Neighbor Embedding; and (ii) the performance of a particular combination of imputation and clustering methods varies with dataset size. For example, the combination of single-cell analysis via expression recovery and Sparse Subspace Clustering (SSC) methods usually works well on smaller datasets, while the combination of adaptively-thresholded low-rank approximation and single-cell interpretation via multikernel learning (SIMLR) usually achieves the best performance on larger datasets.

A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module Levels.

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

Silibinin ameliorates STZ-induced impairment of memory and learning by up- regulating insulin signaling pathway and attenuating apoptosis.

Alzheimer's disease (AD) is a neurodegenerative disease, mainly characterized by cognitive dysfunction and memory impairment. Due to its pathological similarities to type 2 diabetes mellitus (T2DM), such as ß-amyloid deposition, oxidative stress, inflammation, disordered glucose metabolism, impaired signaling pathways of insulin and insulin-like growth factor-1 (IGF-1), we speculate that AD is another form of brain diabetes. Clarifying the relationship between T2DM and AD is important for us to better understand the exact pathological mechanisms of AD. Silibinin, a polyphenolic flavonoid extracted from the seeds of Silybum marianum, exerts hepatoprotective, anti- diabetic and neuroprotective effects. Streptozotocin (STZ), which is used to disrupt the insulin signal transduction pathway, could well mimic the sporadic AD models by intracerebroventricular (ICV) injection. Therefore, we selected ICV injection of STZ (ICV-STZ) to investigate the neuroprotective effects of silibinin in rats and to make a foundation for further exploring the relationship between AD and T2DM. ICV-STZ obviously caused memory damage, sharply reduced the number of nissl bodies and destroyed morphological structure of hippocampal neuronal cells, while silibinin attenuated the damages. Moreover, silibinin significantly decreased STZ-induced tau hyperphosphorylation (ser404) in hippocampus and cerebral cortex, markedly inhibited apoptosis of neurons induced by STZ, and up-regulated insulin signal transduction pathway. Silibinin exerts neuroprotective effect in STZ-treated rats, indicating the potential of silibinin for the treatment of AD patients with T2DM in future.

Comparison of multi-tissue aging between human and mouse.

With the rapid growth of the aging population, exploring the biological basis of aging and related molecular mechanisms has become an important topic in modern scientific research. Aging can cause multiple organ function attenuations, leading to the occurrence and development of various age-related metabolic, nervous system, and cardiovascular diseases. In addition, aging is closely related to the occurrence and development of tumors. Although a number of studies have used various mouse models to study aging, further research is needed to associate mouse and human aging at the molecular level. In this paper, we systematically assessed the relationship between human and mouse aging by comparing multi-tissue age-related gene expression sets. We compared 18 human and mouse tissues, and found 9 significantly correlated tissue pairs. Functional analysis also revealed some terms related to aging in human and mouse. And we performed a crosswise comparison of homologous age-related genes with 18 tissues in human and mouse respectively, and found that human Brain_Cortex was significantly correlated with Brain_Hippocampus, which was also found in mouse. In addition, we focused on comparing four brain-related tissues in human and mouse, and found a gene-GFAP-related to aging in both human and mouse.

Estrogen Receptors Are Involved in the Neuroprotective Effect of Silibinin in Aß1-42-Treated Rats.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that is characterized by a cascade of pathologic changes. A widely discussed theory indicates that amyloid ß (Aß) peptides are the causative agents of AD. Silibinin, a flavonoid derived from milk thistle, is well known for its hepato-protective activities and we have reported the neuroprotective effects of silibinin. In this study, we investigated the role of estrogen receptors (ERs) in silibinin's neuroprotective effect on Aß1-42-injected rats. Results of Morris water maze and novel object-recognition tests demonstrated that silibinin significantly attenuated Aß1-42-induced memory impairment. Silibinin attenuated ERs and PI3K-Akt pathways, as well as modulated mitogen-activated protein kinases in the hippocampus of Aß1-42-injected rats. Taken together, silibinin is a potential candidate in the treatment of Alzheimer's disease.

Effects of maternal exposure to bisphenol AF on emotional behaviors in adolescent mice offspring.

Exposure to bisphenol A (BPA), one kind of environmental endocrine disruptors (EEDs), exerted significantly detrimental effects on neuro-endocrinological system and related disorders, such as memory dysfunction and depression. Bisphenol AF (BPAF),a newly introduced chemical structurally related to BPA, is used extensively. BPAF has stronger estrogenic activities than BPA. However, the potential neurotoxicological effects of BPAF are still elusive. The present study aimed to investigate the potential effects of maternal BPAF exposure during pregnancy on emotional behaviors of adolescent mice offspring. In male adolescent offspring, maternal exposure to BPAF (0.4, 4.0 mg kg-1, intragastrically administration) induced significant anxiety- and depressive-like behaviors, assessed by open field test (OFT), novelty-suppressed feeding test (NSF), sucrose preference test (SPT), tail suspension test (TST) and forced swimming test (FST). In female adolescent offspring, BPAF exposure at 0.4 mg kg-1 dose reduced the latency to feeding in the NSF test, while increased the floating time in the FST. Maternal BPAF exposure decreased the recognition index in the long term memory (LTM) test in both sexes, while only decreased the freezing time of male offspring in the contextual fear conditioning (CFC) task. These results indicate that maternal exposure to BPAF significantly affect emotion-related behaviors in adolescent mice offspring, and the male offspring with a higher probability to develop symptoms of anxiety and depression and to suffer memory impairment after maternal exposure to BPAF.

Silibinin ameliorates anxiety/depression-like behaviors in amyloid ß-treated rats by upregulating BDNF/TrkB pathway and attenuating autophagy in hippocampus.

Depression is one of the most frequent psychiatric disorders of Alzheimer's disease (AD). Depression and anxiety are associated with increased risk of developing AD. Silibinin, a flavonoid derived from milk thistle (Silybum marianum), has been used as a hepato-protectant in the clinical treatment of liver diseases. In this study, the effect of silibinin on Aß-induced anxiety/depression-like behaviors in rats was investigated. Silibinin significantly attenuated anxiety/depression-like behaviors caused by Aß1-42-treatment as shown in tail suspension test (TST), elevated plus maze (EPM) and forced swimming tests (FST). Moreover, silibinin was able to attenuate the neuronal damage in the hippocampus of Aß1-42-injected rats. Silibinin-treatment up-regulated the function through BDNF/TrkB pathway and attenuated autophagy in the hippocampus. Our study provides a new insight into the protective effects of silibinin in the treatment of anxiety/depression.

Silibinin ameliorates Aß25-35-induced memory deficits in rats by modulating autophagy and attenuating neuroinflammation as well as oxidative stress.

Alzheimer's disease (AD) is a progressive, neurodegenerative disease. Accumulating evidence suggests that inflammatory response, oxidative stress and autophagy are involved in amyloid ß (Aß)-induced memory deficits. Silibinin (silybin), a flavonoid derived from the herb milk thistle, is well known for its hepatoprotective activities. In this study, we investigated the neuroprotective effect of silibinin on Aß25-35-injected rats. Results demonstrated that silibinin significantly attenuated Aß25-35-induced memory deficits in Morris water maze and novel object-recognition tests. Silibinin exerted anxiolytic effect in Aß25-35-injected rats as determined in elevated plus maze test. Silibinin attenuated the inflammatory responses, increased glutathione (GSH) levels and decreased malondialdehyde (MDA) levels, and upregulated autophagy levels in the Aß25-35-injected rats. In conclusion, silibinin is a potential candidate for AD treatment because of its anti-inflammatory, antioxidant and autophagy regulating activities.