Improving knowledge on the activation of bone marrow fibroblasts in MGUS and MM disease through the automatic extraction of genes via a nonnegative matrix factorization approach on gene expression profiles

Simplicity within biological complexity

Motivation

Heterogeneous, interconnected, systems-level, molecular (multi-omic) data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs.

Results

In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods (also called graph representation learning) map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications, and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics, focusing on precision medicine and personalized drug discovery. It will lead to a paradigm shift in the computational and biomedical understanding of data and diseases that will open up ways to solve some of the major bottlenecks in precision medicine and other domains.

A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data

Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma. Non-negative Matrix Factorization (NMF) has been widely used in numerous transcriptome data analyses in order to identify subtypes and biomarkers of different diseases; however, its application in glaucoma biomarker discovery has not been previously reported. Our study applied NMF to extract latent representations of RNA-seq data from BXD mouse strains and sorted the genes based on a novel gene scoring method. The enrichment ratio of the glaucoma-reference genes, extracted from multiple relevant resources, was compared using both the classical differentially expressed gene (DEG) analysis and NMF methods. The complete pipeline was validated using an independent RNA-seq dataset. Findings showed our NMF method significantly improved the enrichment detection of glaucoma genes. The application of NMF with the scoring method showed great promise in the identification of marker genes for glaucoma.

Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization

Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using non-negative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multi-omics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM.

Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme

One of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.

Colorectal cancer in Crohn's disease evaluated with genes belonging to fibroblasts of the intestinal mucosa selected by NMF

Crohn's disease (CD) is a type of chronic, inflammatory bowel disease (IBD) which affects any part of the gastrointestinal tract. This study aims to understand the mechanism which activate mucosal fibroblasts in the microenvironment of the colon in CD and colorectal carcinomas and to extract fibroblasts phenotypes via a novel framework based on non-negative factorization of matrix (NMF). The results identify a fibroblast phenotype characterized by intense pro-inflammatory activity ensured by the presence of genes belonging to the APOBEC1 family, such as APOBEC3F and APOBEC3G. These results demonstrated that there is a difference in fibroblast response in producing a pro-tumorigenic effect in CD. The different activation mechanisms could represent useful biomarkers in controlling CD development without generalizing its significance as IBD.

Analysis of fibroblast genes selected by NMF to reveal the potential crosstalk between ulcerative colitis and colorectal cancer

Patients with ulcerative colitis (UC) have an increased risk of developing colorectal cancer (CRC). The CRC risk extent raises with increasing age, duration of symptoms, severity of inflammation and dysplasia. CRC is a complex multi-stage process and associated with UC represents 2% of all colon cancers. With the aim of clarifying some aspects of the evolution of UC towards CRC, we characterized the phenotype of fibroblasts present in the mucosa of subjects affected by UC to verify whether they can contribute to the genesis of a microenvironment favorable to tumor transformation. The fibroblast phenotype was obtained with the help of transcriptome analysis adopting a novel framework based on Nonnegative Matrix Factorization (NMF) which automatically extracts a limited number of genes from fibroblast gene expression profiles of patients with UC and CRC. These genes may be considered possible candidates in generating a permissive microenvironment for the evolution of disease under study.

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.

A Comprehensive Biological and Clinical Perspective Can Drive a Patient-Tailored Approach to Multiple Myeloma: Bridging the Gaps between the Plasma Cell and the Neoplastic Niche

There is a broad spectrum of diseases labeled as multiple myeloma (MM). This is due not only to the composite prognostic risk factors leading to different clinical outcomes and responses to treatments but also to the composite tumor microenvironment that is involved in a vicious cycle with the MM plasma cells. New therapeutic strategies have improved MM patients' chances of survival. Nevertheless, certain patients' subgroups have a particularly unfavorable prognosis. Biological stratification can be subdivided into patient, disease, or therapy-related factors. Alternatively, the biological signature of aggressive disease and dismal therapeutic response can promote a dynamic, comprehensive strategic approach, better tailoring the clinical management of high-risk profiles and refractoriness to therapy and taking into account the role played by the MM milieu. By means of an extensive literature search, we have reviewed the state-of-the-art pathophysiological insights obtained from translational investigations of the MM-bone marrow microenvironment. A good knowledge of the MM niche pathophysiological dissection is crucial to tailor personalized approaches in a bench-bedside fashion. The discussion in this review pinpoints two main aspects that appear fundamental in order to gain novel and definitive results from the biology of MM. A systematic knowledge of the plasma cell disorder, along with greater efforts to face the unmet needs present in MM evolution, promises to open a new therapeutic window looking out onto the plethora of scientific evidence about the myeloma and the bystander cells.

An NMF-Based Methodology for Selecting Biomarkers in the Landscape of Genes of Heterogeneous Cancer-Associated Fibroblast Populations

The rapid development of high-performance technologies has greatly promoted studies of molecular oncology producing large amounts of data. Even if these data are publicly available, they need to be processed and studied to extract information useful to better understand mechanisms of pathogenesis of complex diseases, such as tumors. In this article, we illustrated a procedure for mining biologically meaningful biomarkers from microarray datasets of different tumor histotypes. The proposed methodology allows to automatically identify a subset of potentially informative genes from microarray data matrices, which differs either in the number of rows (genes) and of columns (patients). The methodology integrates nonnegative matrix factorization method, a functional enrichment analysis web tool with a properly designed gene extraction procedure to allow the analysis of omics input data with different row size. The proposed methodology has been used to mine microarray of solid tumors of different embryonic origin to verify the presence of common genes characterizing the heterogeneity of cancer-associated fibroblasts. These automatically extracted biomarkers could be used to suggest appropriate therapies to inactivate the state of active fibroblasts, thus avoiding their action on tumor progression.

Bone marrow fibroblasts overexpress miR-27b and miR-214 in step with multiple myeloma progression, dependent on tumour cell-derived exosomes

Aberrant microRNA (miR) expression has an important role in tumour progression, but its involvement in bone marrow fibroblasts of multiple myeloma patients remains undefined. We demonstrate that a specific miR profile in bone marrow fibroblasts parallels the transition from monoclonal gammopathy of undetermined significance (MGUS) to myeloma. Overexpression of miR-27b-3p and miR-214-3p triggers proliferation and apoptosis resistance in myeloma fibroblasts via the FBXW7 and PTEN/AKT/GSK3 pathways, respectively. Transient transfection of miR-27b-3p and miR-214-3p inhibitors demonstrates a cooperation between these two miRNAs in the expression of the anti-apoptotic factor MCL1, suggesting that miR-27b-3p and miR-214-3p negatively regulate myeloma fibroblast apoptosis. Furthermore, myeloma cells modulate miR-27b-3p and miR-214-3p expression in fibroblasts through the release of exosomes. Indeed, tumour cell-derived exosomes induce an overexpression of both miRNAs in MGUS fibroblasts not through a simple transfer mechanism but by de novo synthesis triggered by the transfer of exosomal WWC2 protein that regulates the Hippo pathway. Increased levels of miR-27b-3p and miR-214-3p in MGUS fibroblasts co-cultured with myeloma cell-derived exosomes enhance the expression of fibroblast activation markers αSMA and FAP. These data show that the MGUS-to-myeloma transition entails an aberrant miRNA profile in marrow fibroblasts and highlight a key role of myeloma cells in modifying the bone marrow microenvironment by reprogramming the marrow fibroblasts' behaviour. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines

Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. Since the number of genes in DNA is huge, they are usually high dimensional, therefore they require dimensionality reduction and clustering techniques to extract useful information. In this paper we use NMF, non-negative matrix factorization, to analyze microarray data, and also develop “intelligent” results visualization with the aim to facilitate the analysis of the domain experts. For this purpose, a case study based on the analysis of the gene expression profiles (GEPs), representative of the human multiple myeloma diseases, was investigated in 40 human myeloma cell lines (HMCLs). The aim of the experiments was to study the genes involved in arachidonic acid metabolism in order to detect gene patterns that possibly could be connected to the different gene expression profiles of multiple myeloma. NMF results have been verified by western blotting analysis in six HMCLs of proteins expressed by some of the most abundantly expressed genes. The experiments showed the effectiveness of NMF in intelligently analyzing microarray data.

Orthogonal joint sparse NMF for microarray data analysis

The 3D microarrays, generally known as gene-sample-time microarrays, couple the information on different time points collected by 2D microarrays that measure gene expression levels among different samples. Their analysis is useful in several biomedical applications, like monitoring dose or drug treatment responses of patients over time in pharmacogenomics studies. Many statistical and data analysis tools have been used to extract useful information. In particular, nonnegative matrix factorization (NMF), with its natural nonnegativity constraints, has demonstrated its ability to extract from 2D microarrays relevant information on specific genes involved in the particular biological process. In this paper, we propose a new NMF model, namely Orthogonal Joint Sparse NMF, to extract relevant information from 3D microarrays containing the time evolution of a 2D microarray, by adding additional constraints to enforce important biological proprieties useful for further biological analysis. We develop multiplicative updates rules that decrease the objective function monotonically, and compare our approach to state-of-the-art NMF algorithms on both synthetic and real data sets.