Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

A deep neural network to de-noise single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq), a powerful technique for investigating the transcriptome of individual cells, enables the discovery of heterogeneous cell populations, rare cell types, and transcriptional dynamics in separate cells. Yet, scRNA-seq data analysis is limited by the problem of measurement dropouts, i.e., genes displaying zero expression levels. We introduce ZiPo, a deep artificial neural network for rate estimation and library size prediction in scRNA-seq data which incorporates adjustable zero inflation in the distribution to capture the dropouts. ZiPo builds upon established concepts, including using deep autoencoders and adopting the Poisson and negative binomial distributions, by taking advantage of novel strategies, including library size prediction and residual connections, to improve the overall performance. A significant innovation of ZiPo is the introduction of a scale-invariant loss term, making the weights sparse and, hence, the model biologically more interpretable. ZiPo quickly handles vast singular and mixed datasets, with the processing time directly proportional to the number of cells. In this paper, we demonstrate the power of ZiPo on three datasets and show its advantages over other current techniques. The code used to produce the results in this manuscript is available at https://bitbucket.org/habilzare/alzheimer/src/master/code/deep/ZiPo/.

Investigating the Role of Hub Calcification Proteins in Atherosclerosis via Integrated Transcriptomics and Network-Based Approach

Atherosclerosis (AS) is a chronic inflammatory condition of the arteries, characterized by plaque formation that can restrict blood flow and lead to potentially fatal cardiovascular events. Given that AS is responsible for a quarter of global deaths, this study aimed to develop a systematic bioinformatics approach to identify biomarkers and regulatory targets involved in plaque development, with the goal of reducing cardiovascular disease risk. AS-specific mRNA expression profiles were retrieved from a publicly accessible database, followed by differentially expressed genes (DEGs) identification and AS-specific weighted gene co-expression network (WGCN) construction. Thereafter, calcification and atherosclerosis-specific (CASS) DEGs were utilized for protein-protein interaction network (PPIN) formation, followed by gene ontology (GO) term and pathway enrichment analyses. Lastly, AS-specific 3-node miRNA feed-forward loop (FFL) construction and analysis was performed. Microarray datasets GSE43292 and GSE28829 were obtained from gene expression omnibus (GEO). A total of 3785 and 6176 DEGs were obtained in case of GSE28829 and GSE43292; 3256 and 5962 module DEGs corresponding to GSE28829 and GSE43292 were obtained from WGCN. From a total of 54 vascular calcification (VC) genes, 20 and 29 CASS-DEGs corresponding to GSE28829 and GSE43292 were overlapped. As observed from FFL centrality measures, the highest-order subnetwork motif comprised one TF (SOX7), one miRNA (miR-484), and one mRNA (SPARC) in the case of GSE28829. Also, in the case of GSE43292, the highest-order subnetwork motif comprised one TF (ESR2), one miRNA (miR-214-3p), and one mRNA (MEF2C). These findings have important implications for developing new therapeutic strategies for AS. The identified TFs and miRNAs may serve as potential therapeutic targets for treating atherosclerotic plaques, offering insights into the molecular mechanisms underlying the pathogenesis and highlighting new avenues for research and treatment.

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A, DNMT3B) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Integrative multiomics and weighted network approach reveals the prognostic role of RPS7 in lung squamous cell carcinoma pathogenesis

Lung cancer is one of the most commonly occurring malignant cancers with the highest rate of mortality globally. Difference between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) and their treatment strategies according to genetic markers may be helpful in reducing the cancer progression and increasing the overall survival (OS) in patients. LUSC is known for comparatively less typical onco-drivers, target therapy resistance, marked genomic complexity, and a reasonably higher mutation rate. The mRNA-seq data and clinical information of LUAD and LUSC cohorts from UCSC Xena comprising 437 and 379 patient samples were extracted. Differential expression and weighted network analyses revealed 47 and 18 hub differentially expressed genes (DEGs) corresponding to LUAD and LUSC cohorts. These hub DEGs were further subjected to protein-protein interaction network (PPIN) and OS analyses. Lower mRNA expression levels of both RPS15A and RPS7 worsened the OS of LUSC patients. Additionally, both these prognostic biomarkers were validated via external sources such as UALCAN, cBioPortal, TIMER, and HPA. RPS7 had higher mutation frequency compared to RPS15A and showed significant negative correlations with infiltrating levels of CD4+ T cells, CD8+ T cells, neutrophils, and macrophages. Our findings provided novel insights into biomarker discovery and the critical role of ribosomal biogenesis especially smaller ribosomal subunit in pathogenesis of LUSC.

"iNETgrate": integrating DNA methylation and gene expression data in a single gene network

Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Investigating the link between miR-34a-5p and TLR6 signaling in sepsis-induced ARDS

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) are lung complications diagnosed by impaired gaseous exchanges leading to mortality. From the diverse etiologies, sepsis is a prominent contributor to ALI/ARDS. In the present study, we retrieved sepsis-induced ARDS mRNA expression profile and identified 883 differentially expressed genes (DEGs). Next, we established an ARDS-specific weighted gene co-expression network (WGCN) and picked the blue module as our hub module based on highly correlated network properties. Later we subjected all hub module DEGs to form an ARDS-specific 3-node feed-forward loop (FFL) whose highest-order subnetwork motif revealed one TF (STAT6), one miRNA (miR-34a-5p), and one mRNA (TLR6). Thereafter, we screened a natural product library and identified three lead molecules that showed promising binding affinity against TLR6. We then performed molecular dynamics simulations to evaluate the stability and binding free energy of the TLR6-lead molecule complexes. Our results suggest these lead molecules may be potential therapeutic candidates for treating sepsis-induced ALI/ARDS. In-silico studies on clinical datasets for sepsis-induced ARDS indicate a possible positive interaction between miR-34a and TLR6 and an antagonizing effect on STAT6 to promote inflammation. Also, the translational study on septic mice lungs by IHC staining reveals a hike in the expression of TLR6. We report here that miR-34a actively augments the effect of sepsis on lung epithelial cell apoptosis. This study suggests that miR-34a promotes TLR6 to heighten inflammation in sepsis-induced ALI/ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03700-1.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

BACKGROUND

Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.

OBJECTIVE

To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.

METHODS

Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).

RESULTS

Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.

CONCLUSIONS

Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

Inference of epigenetic subnetworks by Bayesian regression with the incorporation of prior information

Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation. However, the knowledge about the degree to which epigenetic changes might cause differences in gene expression in cancer is currently lacking. By considering the change of gene expression as the response of altered DNA methylation, we introduce a novel analytical framework to identify epigenetic subnetworks in which the methylation status of a set of highly correlated genes is predictive of a set of gene expression. By detecting highly correlated modules as representatives of the regulatory scenario underling the gene expression and DNA methylation, the dependency between DNA methylation and gene expression is explored by a Bayesian regression model with the incorporation of g-prior followed by a strategy of an optimal predictor subset selection. The subsequent network analysis indicates that the detected epigenetic subnetworks are highly biologically relevant and contain many verified epigenetic causal mechanisms. Moreover, a survival analysis indicates that they might be effective prognostic factors associated with patient survival time.

CBNplot: Bayesian network plots for enrichment analysis

SUMMARY

When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot, which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot, including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets.

AVAILABILITY AND IMPLEMENTATION

The library, documentation and web server are available at https://github.com/noriakis/CBNplot.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying large scale interaction atlases using probabilistic graphs and external knowledge

Introduction:

Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.

Methods:

We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.

Results:

We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.

Conclusions:

We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

Molecular characterization of depression trait and state

Major depressive disorder (MDD) is a brain disorder often characterized by recurrent episode and remission phases. The molecular correlates of MDD have been investigated in case-control comparisons, but the biological alterations associated with illness trait (regardless of clinical phase) or current state (symptomatic and remitted phases) remain largely unknown, limiting targeted drug discovery. To characterize MDD trait- and state-dependent changes, in single or recurrent depressive episode or remission, we generated transcriptomic profiles of subgenual anterior cingulate cortex of postmortem subjects in first MDD episode (n = 20), in remission after a single episode (n = 15), in recurrent episode (n = 20), in remission after recurring episodes (n = 15) and control subject (n = 20). We analyzed the data at the gene, biological pathway, and cell-specific molecular levels, investigated putative causal events and therapeutic leads. MDD-trait was associated with genes involved in inflammation, immune activation, and reduced bioenergetics (q < 0.05) whereas MDD-states were associated with altered neuronal structure and reduced neurotransmission (q < 0.05). Cell-level deconvolution of transcriptomic data showed significant change in density of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin, or vasoactive-intestinal peptide (p < 3 × 10^-3). A probabilistic Bayesian-network approach showed causal roles of immune-system-activation (q < 8.67 × 10^-3), cytokine-response (q < 4.79 × 10^-27) and oxidative-stress (q < 2.05 × 10^-3) across MDD-phases. Gene-sets associated with these putative causal changes show inverse associations with the transcriptomic effects of dopaminergic and monoaminergic ligands. The study provides first insights into distinct cellular and molecular pathologies associated with trait- and state-MDD, on plasticity mechanisms linking the two pathologies, and on a method of drug discovery focused on putative disease-causing pathways.

Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology

Senescent cells contribute to pathology and dysfunction in animal models1. Their sparse distribution and heterogenous phenotype have presented challenges for detecting them in human tissues. We developed a senescence eigengene approach to identify these rare cells within large, diverse populations of postmortem human brain cells. Eigengenes are useful when no single gene reliably captures a phenotype, like senescence; they also help to reduce noise, which is important in large transcriptomic datasets where subtle signals from low-expressing genes can be lost. Each of our eigengenes detected ~2% senescent cells from a population of ~140,000 single nuclei derived from 76 postmortem human brains with various levels of Alzheimer's disease (AD) pathology. More than 97% of the senescent cells were excitatory neurons and overlapped with tau-containing neurofibrillary tangles (NFTs). Cyclin dependent kinase inhibitor 2D (CDKN2D/p19) was predicted as the most significant contributor to the primary senescence eigengene. RNAscope and immunofluorescence confirmed its elevated expression in AD brain tissue whereby p19-expressing neurons had 1.8-fold larger nuclei and significantly more cells with lipofuscin than p19-negative neurons. These hallmark senescence phenotypes were further elevated in the presence of NFTs. Collectively, CDKN2D/p19-expressing neurons with NFTs represent a unique cellular population in human AD with a senescence phenotype. The eigengenes developed may be useful in future senescence profiling studies as they accurately identified senescent cells in snRNASeq datasets and predicted biomarkers for histological investigation.

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

DNA methylation analysis improves the prognostication of acute myeloid leukemia

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups. The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10-11). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis. These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.

An Infection-Tolerant Mammalian Reservoir for Several Zoonotic Agents Broadly Counters the Inflammatory Effects of Endotoxin

Animals that are competent reservoirs of zoonotic pathogens commonly suffer little morbidity from the infections. To investigate mechanisms of this tolerance of infection, we used single-dose lipopolysaccharide (LPS) as an experimental model of inflammation and compared the responses of two rodents: Peromyscus leucopus, the white-footed deermouse and reservoir for the agents of Lyme disease and other zoonoses, and the house mouse Mus musculus Four hours after injection with LPS or saline, blood, spleen, and liver samples were collected and subjected to transcriptome sequencing (RNA-seq), metabolomics, and specific reverse transcriptase quantitative PCR (RT-qPCR). Differential expression analysis was at the gene, pathway, and network levels. LPS-treated deermice showed signs of sickness similar to those of exposed mice and had similar increases in corticosterone levels and expression of interleukin 6 (IL-6), tumor necrosis factor, IL-1β, and C-reactive protein. By network analysis, the M. musculus response to LPS was characterized as cytokine associated, while the P. leucopus response was dominated by neutrophil activity terms. In addition, dichotomies in the expression levels of arginase 1 and nitric oxide synthase 2 and of IL-10 and IL-12 were consistent with type M1 macrophage responses in mice and type M2 responses in deermice. Analysis of metabolites in plasma and RNA in organs revealed species differences in tryptophan metabolism. Two genes in particular signified the different phenotypes of deermice and mice: the Slpi and Ibsp genes. Key RNA-seq findings for P. leucopus were replicated in older animals, in a systemic bacterial infection, and with cultivated fibroblasts. The findings indicate that P. leucopus possesses several adaptive traits to moderate inflammation in its balancing of infection resistance and tolerance.IMPORTANCE Animals that are natural carriers of pathogens that cause human diseases commonly manifest little or no sickness as a consequence of infection. Examples include the deermouse, Peromyscus leucopus, which is a reservoir for Lyme disease and several other disease agents in North America, and some types of bats, which are carriers of viruses with pathogenicity for humans. Mechanisms of this phenomenon of infection tolerance and entailed trade-off costs are poorly understood. Using a single injection of lipopolysaccharide (LPS) endotoxin as a proxy for infection, we found that deermice differed from the mouse (Mus musculus) in responses to LPS in several diverse pathways, including innate immunity, oxidative stress, and metabolism. Features distinguishing the deermice cumulatively would moderate downstream ill effects of LPS. Insights gained from the P. leucopus model in the laboratory have implications for studying infection tolerance in other important reservoir species, including bats and other types of wildlife.

Altered microRNA expression links IL6 and TNF-induced inflammaging with myeloid malignancy in humans and mice

Aging is associated with significant changes in the hematopoietic system, including increased inflammation, impaired hematopoietic stem cell (HSC) function, and increased incidence of myeloid malignancy. Inflammation of aging ("inflammaging") has been proposed as a driver of age-related changes in HSC function and myeloid malignancy, but mechanisms linking these phenomena remain poorly defined. We identified loss of miR-146a as driving aging-associated inflammation in AML patients. miR-146a expression declined in old wild-type mice, and loss of miR-146a promoted premature HSC aging and inflammation in young miR-146a-null mice, preceding development of aging-associated myeloid malignancy. Using single-cell assays of HSC quiescence, stemness, differentiation potential, and epigenetic state to probe HSC function and population structure, we found that loss of miR-146a depleted a subpopulation of primitive, quiescent HSCs. DNA methylation and transcriptome profiling implicated NF-κB, IL6, and TNF as potential drivers of HSC dysfunction, activating an inflammatory signaling relay promoting IL6 and TNF secretion from mature miR-146a-/- myeloid and lymphoid cells. Reducing inflammation by targeting Il6 or Tnf was sufficient to restore single-cell measures of miR-146a-/- HSC function and subpopulation structure and reduced the incidence of hematological malignancy in miR-146a-/- mice. miR-146a-/- HSCs exhibited enhanced sensitivity to IL6 stimulation, indicating that loss of miR-146a affects HSC function via both cell-extrinsic inflammatory signals and increased cell-intrinsic sensitivity to inflammation. Thus, loss of miR-146a regulates cell-extrinsic and -intrinsic mechanisms linking HSC inflammaging to the development of myeloid malignancy.

miRNA-mRNA crosstalk in myocardial ischemia induced by calcified aortic valve stenosis

Aortic valve stenosis is the most common cause of morbidity and mortality in valvular heart disease in aged people. Both microRNA (miRNA) and mRNA are potential targets for the diagnosis and therapeutic intervention of myocardial ischemia induced by calcified aortic valve stenosis (CAVS), with unclear mechanisms. Here, 3 gene expression profiles of 47 male participants were applied to generate shared differentially expressed genes (DEGs) with significant major biological functions. Moreover, 20 hub genes were generated by a Weighted Genes Co-Expression Network Analysis (WGCNA) and were cross-linked to miRNA based on miRanda/miRwalk2 databases. Integrated miRNA/mRNA analysis identified several novel miRNAs and targeted genes as diagnostic/prognostic biomarkers or therapeutic targets in CAVS patients. In addition, the clinical data suggested that myocardial hypertrophy and myocardial ischemia in CAVS patients are likely associated with hub genes and the upstream regulatory miRNAs. Together, our data provide evidence that miRNAs and their targeted genes play an important role in the pathogenesis of myocardial hypertrophy and ischemia in patients with CAVS.

Shannon entropy approach reveals relevant genes in Alzheimer's disease

Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer’s disease.

Using R and Bioconductor in Clinical Genomics and Transcriptomics

Bioinformatics pipelines are essential in the analysis of genomic and transcriptomic data generated by next-generation sequencing (NGS). Recent guidelines emphasize the need for rigorous validation and assessment of robustness, reproducibility, and quality of NGS analytic pipelines intended for clinical use. Software tools written in the R statistical language and, in particular, the set of tools available in the Bioconductor repository are widely used in research bioinformatics; and these frameworks offer several advantages for use in clinical bioinformatics, including the breath of available tools, modular nature of software packages, ease of installation, enforcement of interoperability, version control, and short learning curve. This review provides an introduction to R and Bioconductor software, its advantages and limitations for clinical bioinformatics, and illustrative examples of tools that can be used in various steps of NGS analysis.

Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer

In patients with head and neck cancer (HNC), lymph node (N) metastases are associated with cancer aggressiveness and poor prognosis. Identifying meaningful gene modules and representative biomarkers relevant to the N stage helps predict prognosis and reveal mechanisms underlying tumor progression. The present study used a step-wise approach for weighted gene co-expression network analysis (WGCNA). Dataset GSE65858 was subjected to WGCNA. RNA sequencing data of HNC downloaded from the Cancer Genome Atlas (TCGA) and dataset GSE39366 were utilized to validate the results. Following data preprocessing, 4,295 genes were screened, and blue and black modules associated with the N stage of HNC were identified. A total of 16 genes [keratinocyte differentiation associated protein, suprabasin, cornifelin (CNFN), small proline rich protein 1B, desmoglein 1 (DSG1), chromosome 10 open reading frame 99, keratin 16 pseudogene 3, gap junction protein β2, dermokine, LY6/PLAUR domain containing 3, transmembrane protein 79, phospholipase A2 group IVE, transglutaminase 5, potassium two pore domain channel subfamily K member 6, involucrin, kallikrein related peptidase 8] that had a negative association with the N-stage in the blue module, and two genes (structural maintenance of chromosomes 4 and mutS homolog 6) that had a positive association in the black module, were identified to be candidate hub genes. Following further validation in TCGA and dataset GSE65858, it was identified that CNFN and DSG1 were associated with the clinical stage of HNC. Survival analysis of CNFN and DSG1 was subsequently performed. Patients with increased expression of CNFN displayed better survival probability in dataset GSE65858 and TCGA. Therefore, CNFN was selected as the hub gene for further verification in the Gene Expression Profiling Interactive Analysis database. Finally, functional enrichment and gene set enrichment analyses were performed using datasets GSE65858 and GSE39366. Three gene sets, namely ‘P53 pathway’, ‘estrogen response early’ and ‘estrogen response late’, were enriched in the two datasets. In conclusion, CNFN, identified via the WGCNA algorithm, may contribute to the prediction of lymph node metastases and prognosis, probably by regulating the pathways associated with P53, and the early and late estrogen response.

C3HeB/FeJ Mice mimic many aspects of gene expression and pathobiological features of human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a deadly cancer, underscoring the need for relevant preclinical models. Male C3HeB/FeJ mice model spontaneous HCC with some hepatocarcinogenesis susceptibility loci corresponding to syntenic regions of human chromosomes altered in HCC. We tested other properties of C3HeB/FeJ tumors for similarity to human HCC. C3HeB/FeJ tumors were grossly visible at 4 months of age, with prevalence and size increasing until about 11 months of age. Histologic features shared with human HCC include hepatosteatosis, tumor progression from dysplasia to poorly differentiated, vascular invasion, and trabecular, oncocytic, vacuolar, and clear cell variants. More tumor cells displayed cytoplasmic APE1 staining versus normal liver. Ultrasound effectively detected and monitored tumors, with 85.7% sensitivity. Over 5000 genes were differentially expressed based on the GSE62232 and GSE63898 human HCC datasets. Of these, 158 and 198 genes, respectively, were also differentially expressed in C3HeB/FeJ. Common cancer pathways, cell cycle, p53 signaling and other molecular aspects, were shared between human and mouse differentially expressed genes. We established eigengenes that distinguish HCC from normal liver in the C3HeB/FeJ model and a subset of human HCC. These features extend the relevance and improve the utility of the C3HeB/FeJ line for HCC studies.

Pathogenic tau-induced piRNA depletion promotes neuronal death through transposable element dysregulation in neurodegenerative tauopathies

Transposable elements, known colloquially as “jumping genes,” constitute approximately 45% of the human genome. Cells utilize epigenetic defenses to limit transposable element jumping, including formation of silencing heterochromatin and generation of piwi-interacting RNAs (piRNAs), small RNAs that facilitate clearance of transposable element transcripts. Here we identify transposable element dysregulation as a key mediator of neuronal death in tauopathies, a group of neurodegenerative disorders that are pathologically characterized by deposits of tau protein in the brain. Mechanistically, we find that heterochromatin decondensation and reduction of piwi/piRNAs drive transposable element dysregulation in tauopathy. We further report a significant increase in transcripts of the endogenous retrovirus class of transposable elements in human Alzheimer’s disease and progressive supranuclear palsy, suggesting that transposable element dysregulation is conserved in human tauopathy. Taken together, our data identify heterochromatin decondensation, piwi/piRNA depletion and consequent transposable element dysregulation as a novel, pharmacologically targetable, mechanistic driver of neurodegeneration in tauopathy.

Applications of Bayesian network models in predicting types of hematological malignancies

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

Oncogenomic analysis identifies novel biomarkers for tumor stage mycosis fungoides

Patients with mycosis fungoides (MF) developing tumors or extracutaneous lesions usually have a poor prognosis with no cure has so far been available. To identify potential novel biomarkers for MF at the tumor stage, a genomic mapping of 41 cutaneous lymphoma biopsies was used to explore for significant genes.The gene expression profiling datasets of MF were obtained from Gene Expression Omnibus database (GEO). Gene modules were simulated using Weighted Gene Co-expression Network Analysis (WGCNA) and the top soft-connected genes (hub genes) were filtrated with a threshold (0.5). Subsequently, module eigengenes were calculated and significant biological pathways were enriched based on the KEGG database.Four genetic modules were simulated with 3263 genes collected from the whole genomic profile based on cutoff values. Significant diseases genetic terminologies associated with tumor stage MF were found in black module. Subsequently, 13 hub genes including CFLAR, GCNT2, IFNG, IL17A, IL22, MIP, PLCG1, PTH, PTPN6, REG1A, SNAP25, SUPT7L, and TP63 were shown to be related to cutaneous T-cell lymphoma (CTCL) and adult T-cell lymphoma/leukemia (ATLL).In summary, in addition to the reported genes (IL17F, PLCG1, IFNG, and PTH) in CTCL/ATLL, the other high instable genes may serve as novel biomarkers for the regulation of the biological processes and molecular mechanisms of CTLT (MF/SS).

The bone-marrow niche in MDS and MGUS: implications for AML and MM

Several haematological malignancies, including multiple myeloma (MM) and acute myeloid leukaemia (AML), have well-defined precursor states that precede the development of overt cancer. MM is almost always preceded by monoclonal gammopathy of undetermined significance (MGUS), and at least a quarter of all patients with myelodysplastic syndromes (MDS) have disease that evolves into AML. In turn, MDS are frequently anteceded by clonal haematopoiesis of indeterminate potential (CHIP). The acquisition of additional genetic and epigenetic alterations over time clearly influences the increasingly unstable and aggressive behaviour of neoplastic haematopoietic clones; however, perturbations in the bone-marrow microenvironment are increasingly recognized to have key roles in initiating and supporting oncogenesis. In this Review, we focus on the concept that the haematopoietic neoplasia-microenvironment relationship is an intimate rapport between two partners, provide an overview of the evidence supporting a role for the bone-marrow niche in promoting neoplasia, and discuss the potential for niche-specific therapeutic targets.

Potential for subsets of wt-NPM1 primary AML blasts to respond to retinoic acid treatment

Acute myeloid leukemia (AML) has high mortality rates, perhaps reflecting a lack of understanding of the molecular diversity in various subtypes and a lack of known actionable targets. There are currently 12 open clinical trials for AML using combination therapeutic modalities including all-trans retinoic acid (RA). Mutant nucleophosmin-1, proposed as a possible marker for RA response, is the criterion for recruiting patients in three active RA phase 3 clinical trials. We tested the ability of RA alone or in combination with either bosutinib (B) or 6-formylindolo(3,2-b) carbazole (F) to induce conversion of 12 de novo AML samples toward a more differentiated phenotype. We assessed levels of expression of cell surface markers associated with differentiation, aldehyde dehydrogenase activity, and glucose uptake activity. Colony formation capacity was reduced with the combined treatment of RA and B or F, and correlated with modulation of a c-Cbl/Lyn/c-Raf-centered signalsome. Combination treatment was in most cases more effective than RA alone. Based on their responses to the treatments, some primary leukemic samples cluster closer to HL-60 cells than to other primary samples, suggesting that they may represent a hitherto undefined AML subtype that is potentially responsive to RA in a combination differentiation therapy.

Expression of a specific extracellular matrix signature is a favorable prognostic factor in acute myeloid leukemia

Relapse of acute myeloid leukemia (AML) is still dramatically frequent, imposing the need for early markers to quantify such risk. Recent evidence point to a prominent role for extracellular matrix (ECM) in AML, but its prognostic value has not yet been investigated. Here we have investigated whether the expression of a 15-ECM gene signature could be applied to clinical AML research evaluating a retrospective cohort of 61 AML patients and 12 healthy donors. Results show that patients whose ECM signature expression is at least twice as that of healthy donors have considerably longer relapse-free survival, with further stage-specific therapy outcomes.

•

Extracellular matrix (ECM) expression in acute myeloid leukemia predicts relapse.

•

The ECM-signature is small, with only 15 genes.

•

High ECM-signature expression indicates an overall favorable outcome.

•

High ECM-signature expression predicts therapeutic stage-specific outcome.

•

ECM-signature expression works in RT-qPCR and microarrays.

Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

Background

Marker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles.

Methods

We first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, "mRMR" is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, "RANWAR" to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel.

Results

The novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated).

Conclusion

The identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.