A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurements that only paint a macroscopic picture, gene measurements at the cell level aid researchers in studying different tissues and organs at various stages. However, accurate clustering methods for such high-dimensional data remain exiguous and a persistent challenge in this domain. Of late, several methods and techniques have been promulgated to address this issue. In this article, we propose a novel framework for clustering large-scale single-cell data and subsequently identifying the rare-cell sub-populations. To handle such sparse, high-dimensional data, we leverage PaCMAP (Pairwise Controlled Manifold Approximation), a feature extraction algorithm that preserves both the local and the global structures of the data and Gaussian Mixture Model to cluster single-cell data. Subsequently, we exploit Edited Nearest Neighbours sampling and Isolation Forest/One-class Support Vector Machine to identify rare-cell sub-populations. The performance of the proposed method is validated using the publicly available datasets with varying degrees of cell types and rare-cell sub-populations. On several benchmark datasets, the proposed method outperforms the existing state-of-the-art methods. The proposed method successfully identifies cell types that constitute populations ranging from 0.1 to 8% with F1-scores of 0.91 0.09. The source code is available at https://github.com/scrab017/RarPG.

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data.

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single-cell data are susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantages of $R{\prime}{e}nyi$ and $Tsallis$ entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter ($q$), $R{\prime}{e}nyi$ and $Tsallis$ entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to determine the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data. Availability: The sc-REnF is available at https://github.com/Snehalikalall/sc-REnF.

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell-cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space.

IV-GNN : interval valued data handling using graph neural network.

Interval-valued data is an effective way to represent complex information where uncertainty, inaccuracy etc. are involved in the data space and they are worthy of taking into account. Interval analysis together with neural network has proven to work well on Euclidean data. However, in real-life scenarios, data follows a much more complex structure and is often represented as graphs, which is non-Euclidean in nature. Graph Neural Network is a powerful tool to handle graph like data with countable feature space. So, there is a research gap between the interval-valued data handling approaches and existing GNN model. No model in GNN literature can handle a graph with interval-valued features and, on the other hand, Multi Layer Perceptron (MLP) based on interval mathematics can not process the same due to non-Euclidean structure behind the graph. This article proposes an Interval-Valued Graph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable without compromising the time complexity of the best performing GNN model in the literature. Our model is much more general than existing models as any countable set is always a subset of the universal set ℝ n , which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification task by comparing its performance with those of the state-of-the-art models on several benchmark and synthetic network datasets.

RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We formulate an objective function by adding l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop is able to annotate the unknown cells with high accuracy.

S-conLSH: alignment-free gapped mapping of noisy long reads.

BACKGROUND: The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. RESULTS: We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. CONCLUSIONS: S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.

Improved dropClust R package with integrative analysis support for scRNA-seq data.

SUMMARY: DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. AVAILABILITY AND IMPLEMENTATION: dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Leprosy drug clofazimine activates peroxisome proliferator-activated receptor-γ and synergizes with imatinib to inhibit chronic myeloid leukemia cells.

Leukemia stem cells contribute to drug-resistance and relapse in chronic myeloid leukemia (CML) and BCR-ABL1 inhibitor monotherapy fails to eliminate these cells, thereby necessitating alternate therapeutic strategies for patients CML. The peroxisome proliferator-activated receptor-γ (PPARγ) agonist pioglitazone downregulates signal transducer and activator of transcription 5 (STAT5) and in combination with imatinib induces complete molecular response in imatinib-refractory patients by eroding leukemia stem cells. Thiazolidinediones such as pioglitazone are, however, associated with severe side effects. To identify alternate therapeutic strategies for CML we screened Food and Drug Administration-approved drugs in K562 cells and identified the leprosy drug clofazimine as an inhibitor of viability of these cells. Here we show that clofazimine induced apoptosis of blood mononuclear cells derived from patients with CML, with a particularly robust effect in imatinib-resistant cells. Clofazimine also induced apoptosis of CD34+38- progenitors and quiescent CD34+ cells from CML patients but not of hematopoietic progenitor cells from healthy donors. Mechanistic evaluation revealed that clofazimine, via physical interaction with PPARγ, induced nuclear factor kB-p65 proteasomal degradation, which led to sequential myeloblastoma oncoprotein and peroxiredoxin 1 downregulation and concomitant induction of reactive oxygen species-mediated apoptosis. Clofazimine also suppressed STAT5 expression and consequently downregulated stem cell maintenance factors hypoxia-inducible factor-1α and -2α and Cbp/P300 interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 (CITED2). Combining imatinib with clofazimine caused a far superior synergy than that with pioglitazone, with clofazimine reducing the half maximal inhibitory concentration (IC50) of imatinib by >4 logs and remarkably eroding quiescent CD34+ cells. In a K562 xenograft study clofazimine and imatinib co-treatment showed more robust efficacy than the individual treatments. We propose clinical evaluation of clofazimine in imatinib-refractory CML.

dropClust: efficient clustering of ultra-large scRNA-seq data.

Droplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Locality Sensitive Hashing, an approximate nearest neighbour search technique to develop a de novo clustering algorithm for large-scale single cell data. On a number of real datasets, dropClust outperformed the existing best practice methods in terms of execution time, clustering accuracy and detectability of minor cell sub-types.

Rosiglitazone up-regulates glial fibrillary acidic protein via HB-EGF secreted from astrocytes and neurons through PPARγ pathway and reduces apoptosis in high-fat diet-fed mice.

The anti-diabetic drug and peroxisome proliferator-activated receptor-gamma (PPARγ) agonist, rosiglitazone, alters astrocyte activation; however, its mechanism remains less-known. We hypothesized participation of epidermal growth factor receptor (EGFR), known to control astrocyte reactivity. We first detected that rosiglitazone promoted glial fibrillary acidic protein (GFAP) expression in primary astrocytes as well as the mouse cerebral cortex, associated with increased EGFR activation. Screening for EGFR ligands revealed a rosiglitazone-mediated increase of heparin-binding epidermal growth factor (HB-EGF) in astrocytes, resulting in HB-EGF release into culture medium and mouse cerebrospinal fluid too. Treatment with HB-EGF-siRNA and EGFR inhibitors showed that the rosiglitazone-induced HB-EGF and p-EFGR were interdependent, which participated in GFAP increase. Interestingly, we observed that rosiglitazone could induce cellular and secreted-HB-EGF in neurons also, contributing toward the activated EGFR-induced GFAP in astrocytes. Probing whether these effects of rosiglitazone were PPARγ-linked, revealed potential PPARγ-responsive elements within HB-EGF gene. Moreover, gel-shift, site-directed mutagenesis, chromatin-immunoprecipitation and luciferase-reporter assays demonstrated a PPARγ-dependent HB-EGF transactivation. Subsequently, we examined effects of rosiglitazone in a high-fat diet-fed diabetes mouse model, and supporting observations in the normal cortical cells, identified a rosiglitazone-induced GFAP, astrocyte and neuronal HB-EGF and secreted-HB-EGF in the cerebral cortex of diabetic mice. Moreover, assessing relevance of increased HB-EGF and GFAP revealed an anti-apoptotic role of rosiglitazone in the cerebral cortex, supported by a GFAP-siRNA as well as HB-EGF-siRNA-mediated increase in cleaved-caspase 3 and 9 levels in the rosiglitazone-treated astrocyte-neuron coculture. Overall, our study indicates that rosiglitazone may protect the brain, via a PPARγ-dependent HB-EGF/EGFR signaling and increased GFAP.

Detecting TF-miRNA-gene network based modules for 5hmC and 5mC brain samples: a intra- and inter-species case-study between human and rhesus.

BACKGROUND: Study of epigenetics is currently a high-impact research topic. Multi stage methylation is also an area of high-dimensional prospect. In this article, we provide a new study (intra and inter-species study) on brain tissue between human and rhesus on two methylation cytosine variants based data-profiles (viz., 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) samples) through TF-miRNA-gene network based module detection. RESULTS: First of all, we determine differentially 5hmC methylated genes for human as well as rhesus for intra-species analysis, and differentially multi-stage methylated genes for inter-species analysis. Thereafter, we utilize weighted topological overlap matrix (TOM) measure and average linkage clustering consecutively on these genesets for intra- and inter-species study.We identify co-methylated and multi-stage co-methylated gene modules by using dynamic tree cut, for intra-and inter-species cases, respectively. Each module is represented by individual color in the dendrogram. Gene Ontology and KEGG pathway based analysis are then performed to identify biological functionalities of the identified modules. Finally, top ten regulator TFs and targeter miRNAs that are associated with the maximum number of gene modules, are determined for both intra-and inter-species analysis. CONCLUSIONS: The novel TFs and miRNAs obtained from the analysis are: MYST3 and ZNF771 as TFs (for human intra-species analysis), BAZ2B, RCOR3 and ATF1 as TFs (for rhesus intra-species analysis), and mml-miR-768-3p and mml-miR-561 as miRs (for rhesus intra-species analysis); and MYST3 and ZNF771 as miRs(for inter-species study). Furthermore, the genes/TFs/miRNAs that are already found to be liable for several brain-related dreadful diseases as well as rare neglected diseases (e.g., wolf Hirschhorn syndrome, Joubarts Syndrome, Huntington's disease, Simian Immunodeficiency Virus(SIV) mediated enchaphilits, Parkinsons Disease, Bipolar disorder and Schizophenia etc.) are mentioned.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease.

BACKGROUND: Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease. RESULT: We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD. CONCLUSION: Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

Docosahexaenoic acid up-regulates both PI3K/AKT-dependent FABP7-PPARγ interaction and MKP3 that enhance GFAP in developing rat brain astrocytes.

The astrocyte marker, glial fibrillary acidic protein (GFAP), has essential functions in the brain, but may trigger astroglial scarring when expressed in excess. Docosahexaenoic acid (DHA) is an n-3 fatty acid that is protective during brain development. However, the effect of DHA on GFAP levels of developing brain remains unexplored. Here, we detected that treating developing rats with DHA-enriched fish-oil caused dose-dependent GFAP augmentation. We investigated the mechanism promoting GFAP, hypothesizing the participation of fatty acid-binding protein-7 (FABP7), known to bind DHA. We identified that DHA stimulated FABP7 expression in astrocytes, and FABP7-silencing suppressed DHA-induced GFAP, indicating FABP7-mediated GFAP increase. Further investigation proved FABP7 expression to be phosphatidylinositide 3-kinases (PI3K)/AKT and nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARγ)-dependent. We found that PI3K/AKT activated PPARγ that triggered FABP7 expression via PPARγ-responsive elements within its gene. Towards identifying FABP7-downstream pathways, we considered our previous report that demonstrated cyclin-dependent kinase-5 (CDK5)-PPARγ-protein-protein complex to suppress GFAP. We found that the DHA-induced FABP7 underwent protein-protein interaction with PPARγ, which impeded CDK5-PPARγ formation. Hence, it appeared that enhanced FABP7-PPARγ in lieu of CDK5-PPARγ resulted in increased GFAP. PI3K/AKT not only stimulated formation of FABP7-PPARγ protein-protein complex, but also up-regulated a FABP7-independent MAP-kinase-phosphatase-3 pathway that inactivated CDK5 and hence attenuated CDK5-PPARγ. Overall, our data reveal that via the proximal PI3K/AKT, DHA induces FABP7-PPARγ, through genomic and non-genomic mechanisms, and MAP-kinase-phosphatase-3 that converged at attenuated CDK5-PPARγ and therefore, enhanced GFAP. Accordingly, our study demonstrates a DHA-mediated astroglial hyperactivation, pointing toward a probable injurious role of DHA in brain development.

Concordant dysregulation of miR-5p and miR-3p arms of the same precursor microRNA may be a mechanism in inducing cell proliferation and tumorigenesis: a lung cancer study.

A precursor microRNA (miRNA) has two arms: miR-5p and miR-3p (miR-5p/-3p). Depending on the tissue or cell types, both arms can become functional. However, little is known about their coregulatory mechanisms during the tumorigenic process. Here, by using the large-scale miRNA expression profiles of five cancer types, we revealed that several of miR-5p/-3p arms were concordantly dysregulated in each cancer. To explore possible coregulatory mechanisms of concordantly dysregulated miR-5p/-3p pairs, we developed a robust computational framework and applied it to lung cancer data. The framework deciphers miR-5p/-3p coregulated protein interaction networks critical to lung cancer development. As a novel part in the method, we uniquely applied the second-order partial correlation to minimize false-positive regulations. Using 279 matched miRNA and mRNA expression profiles extracted from tumor and normal lung tissue samples, we identified 17 aberrantly expressed miR-5p/-3p pairs that potentially modulate the gene expression of 35 protein complexes. Functional analyses revealed that these complexes are associated with cancer-related biological processes, suggesting the oncogenic potential of the reported miR-5p/-3p pairs. Specifically, we revealed that the reduced expression of miR-145-5p/-3p pair potentially contributes to elevated expression of genes in the "FOXM1 transcription factor network" pathway, which may consequently lead to uncontrolled cell proliferation. Subsequently, the regulation of miR-145-5p/-3p in the FOXM1signaling pathway was validated by a cohort of 104 matched miRNA and protein (reverse-phase protein array) expression profiles in lung cancer. In summary, our computational framework provides a novel tool to study miR-5p/-3p coregulatory mechanisms in cancer and other diseases.

A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions.

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.

A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.

BACKGROUND: Predicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information. RESULTS: In this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions. CONCLUSIONS: Here, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as 'hubs' and 'bottlenecks' in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant.

Chronic cerebral hypoperfusion-induced impairment of Aß clearance requires HB-EGF-dependent sequential activation of HIF1α and MMP9.

Chronic cerebral hypoperfusion (CCH) manifests Alzheimer's Disease (AD) neuropathology, marked by increased amyloid beta (Aß). Besides, hypoxia stimulates Heparin-binding EGF-like growth factor (HB-EGF) mRNA expression in the hippocampus. However, involvement of HB-EGF in CCH-induced Aß pathology remains unidentified. Here, using Bilateral Common Carotid Artery Occlusion mouse model, we explored the mechanism of HB-EGF regulated Aß induction in CCH. We found that HB-EGF inhibition suppressed, while exogenous-HB-EGF triggered hippocampal Aß, proving HB-EGF-dependent Aß increase. We also detected that HB-EGF affected the expression of primary Aß transporters, receptor for advanced glycation end-products (RAGE) and lipoprotein receptor-related protein-1 (LRP-1), indicating impaired Aß clearance across the blood-brain barrier (BBB). An HB-EGF-dependent loss in BBB integrity supported impaired Aß clearance. The effect of HB-EGF on Amyloid Precursor Protein pathway was relatively insignificant, suggesting a lesser effect on Aß generation. Delving into BBB disruption mechanism demonstrated HB-EGF-mediated stimulation of Matrix metalloprotease-9 (MMP9), which affected BBB via HB-EGF-ectodomain shedding and epidermal growth factor receptor activation. Examining the intersection of HB-EGF-regulated pathway and hypoxia revealed HB-EGF-dependent increase in transcription factor, Hypoxia-inducible factor-1alpha (HIF1α). Further, via binding to hypoxia-responsive elements in MMP9 gene, HIF1α stimulated MMP9 expression, and therefore appeared as a prominent intermediary in HB-EGF-induced BBB damage. Overall, our study reveals the essential role of HB-EGF in triggering CCH-mediated Aß accumulation. The proposed mechanism involves an HB-EGF-dependent HIF1α increase, generating MMP9 that stimulates soluble-HB-EGF/EGFR-induced BBB disintegration. Consequently, CCH-mediated hippocampal RAGE and LRP-1 deregulation together with BBB damage impair Aß transport and clearance where HB-EGF plays a pivotal role.

Identifying significant microRNA-mRNA pairs associated with breast cancer subtypes.

MicroRNAs (miRNAs) are small non-coding RNAs that help in post-transcriptional gene silencing. These endogenous RNAs develop a post-transcriptional gene-regulatory network by binding to complementary sequences of target mRNAs and essentially degrade them. Cancer is a class of diseases that is caused by the uncontrolled cell growth, thereby resulting into a gradual degradation of cell structure. Earlier researches have shown that miRNAs have significant biological involvement in cancer. Prolonged research in this genre has led to the identification of the functions of numerous miRNAs in cancer development. Studying the differential expression profiles of miRNAs and mRNAs together could help us in recognizing the significant miRNA-mRNA pairs from cancer samples. In this paper, we have analyzed the simultaneous over-expression of miRNAs and under-expression of mRNAs and vice versa to establish their association with cancer. This study focuses on breast tumor samples and the miRNA-mRNA target pairs that have a visible signature in such breast tumor samples. We have been able to identify the differentially expressed miRNAs and mRNAs, and further established relations between them to extract the miRNA-mRNA pairs that might be significant in the breast cancer types. This gives us the clue about the potential biomarkers for the breast cancer subtypes that can further help in understanding the progression of each of the subtypes separately. This might be helpful for the joint miRNA-mRNA biomarker identification.