Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis

Early activation of inflammatory pathways in UBA1-mutated hematopoietic stem and progenitor cells in VEXAS

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome is a pleiotropic, severe autoinflammatory disease caused by somatic mutations in the ubiquitin-like modifier activating enzyme 1 (UBA1) gene. To elucidate VEXAS pathophysiology, we performed transcriptome sequencing of single bone marrow mononuclear cells and hematopoietic stem and progenitor cells (HSPCs) from VEXAS patients. HSPCs are biased toward myeloid (granulocytic) differentiation, and against lymphoid differentiation in VEXAS. Activation of multiple inflammatory pathways (interferons and tumor necrosis factor alpha) occurs ontogenically early in primitive hematopoietic cells and particularly in the myeloid lineage in VEXAS, and inflammation is prominent in UBA1-mutated cells. Dysregulation in protein degradation likely leads to higher stress response in VEXAS HSPCs, which positively correlates with inflammation. TCR usage is restricted and there are increased cytotoxicity and IFN-γ signaling in T cells. In VEXAS syndrome, both aberrant inflammation and myeloid predominance appear intrinsic to hematopoietic stem cells mutated in UBA1.

Single-cell profiling of T lymphocytes in deficiency of adenosine deaminase 2

Deficiency of adenosine deaminase 2 (DADA2) is a monogenic vasculitis syndrome caused by autosomal-recessive loss-of-function mutations in the ADA2 gene (previously known as CECR1). Vasculitis, vasculopathy, and inflammation are dominant clinical features of this disease; the spectrum of manifestations includes immunodeficiency and lymphoproliferation as well as hematologic manifestations. ADA2 is primarily secreted by stimulated monocytes and macrophages. Aberrant monocyte differentiation to macrophages and neutrophils are important in the pathogenesis of DADA2, but little is known about T lymphocytes in this disease. We performed combined single-cell RNA sequencing and single-cell TCR sequencing in order to profile T cell repertoires in 10 patients with DADA2. Although there were no significant alterations of T cell subsets, we observed activation of both CD8⁺ and CD4⁺ T cells. There was no clonal expansion of T cells: most TCRs were expressed at basal levels in patients and healthy donors. TCR usage was private to individual patients and not disease specific, indicating as unlikely a common pathogenic background or predisposition to a common pathogen. We recognized activation of IFN pathways as a signature of T cells and STAT1 as a hub gene in the gene network of T cell activation and cytotoxicity. Overall, T cells in DADA2 patients showed distinct cell-cell interactions with monocytes, as compared with healthy donors, and many of these ligand-receptor interactions likely drove up-regulation of STAT1 in both T cells and other immune cells in patients. Our analysis reveals previously undercharacterized cell characteristics in DADA2.

Sequencing of RNA in single cells reveals a distinct transcriptome signature of hematopoiesis in GATA2 deficiency

Constitutional GATA2 deficiency caused by heterozygous germline GATA2 mutations has a broad spectrum of clinical phenotypes, including systemic infections, lymphedema, cytopenias, and myeloid neoplasms. Genotype-phenotype correlation is not well understood mechanistically in GATA2 deficiency. We performed whole transcriptome sequencing of single hematopoietic stem and progenitor cells from 8 patients, who had pathogenic GATA2 mutations and myelodysplasia. Mapping patients' cells onto normal hematopoiesis, we observed deficiency in lymphoid/myeloid progenitors, also evident from highly constrained gene correlations. HSPCs of patients exhibited distinct patterns of gene expression and coexpression compared with counterparts from healthy donors. Distinct lineages showed differently altered transcriptional profiles. Stem cells in patients had dysregulated gene expression related to apoptosis, cell cycle, and quiescence; increased expression of erythroid/megakaryocytic priming genes; and decreased lymphoid priming genes. The prominent deficiency in lympho-myeloid lineages in GATA2 deficiency appeared at least partly due to the expression of aberrant gene programs in stem cells prior to lineage commitment. We computationally imputed cells with chromosomal abnormalities and determined their gene expression; DNA repair genes were downregulated in trisomy 8 cells, potentially rendering these cells vulnerable to second-hit somatic mutations and additional chromosomal abnormalities. Cells with complex cytogenetic abnormalities showed defects in genes related to multilineage differentiation and cell cycle. Single-cell RNA sequencing is powerful in resolving transcriptomes of cell subpopulations despite a paucity of cells in marrow failure. Our study discloses previously uncharacterized transcriptome signatures of stem cells and progenitors in GATA2 deficiency, providing a broad perspective of potential mechanisms by which germline mutations modulate early hematopoiesis in a human disease. This trial was registered at www.clinicaltrials.gov as NCT01905826, NCT01861106, and NCT00001620.

WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

Background

In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes.

Method

We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models.

Results

Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation.

Conclusion

We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks.

Availability

The free WIPER API is described at discovery.informatics.uab.edu/wiper/.

Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts

Accumulating evidence suggests that dysregulation of hypoxia-regulated transcriptional mechanisms is involved in development of chronic kidney diseases (CKD). However, it remains unclear how hypoxia-induced transcription factors (HIFs) and subsequent biological processes contribute to CKD development and progression. In our study, genome-wide expression profiles of more than 200 renal biopsies from patients with different CKD stages revealed significant correlation of HIF-target genes with eGFR in glomeruli and tubulointerstitium. These correlations were positive and negative and in part compartment-specific. Microarrays of proximal tubular cells and podocytes with stable HIF1α and/or HIF2α suppression displayed cell type-specific HIF1/HIF2-dependencies as well as dysregulation of several pathways. WGCNA analysis identified gene sets that were highly coregulated within modules. Characterization of the modules revealed common as well as cell group- and condition-specific pathways, GO-Terms and transcription factors. Gene expression analysis of the hypoxia-interconnected pathways in patients with different CKD stages revealed an increased dysregulation with loss of renal function. In conclusion, our data clearly point to a compartment- and cell type-specific dysregulation of hypoxia-associated gene transcripts and might help to improve the understanding of hypoxia, HIF dysregulation, and transcriptional program response in CKD.

Microarray‑based screening of differentially expressed genes in glucocorticoid‑induced avascular necrosis

The underlying mechanisms of glucocorticoid (GC)-induced avascular necrosis of the femoral head (ANFH) have yet to be fully understood, in particular the mechanisms associated with the change of gene expression pattern. The present study aimed to identify key genes with a differential expression pattern in GC-induced ANFH. E-MEXP-2751 microarray data were downloaded from the ArrayExpress database. Differentially expressed genes (DEGs) were identified in 5 femoral head samples of steroid-induced ANFH rats compared with 5 placebo-treated rat samples. Gene Ontology (GO) and pathway enrichment analyses were performed upon these DEGs. A total 93 DEGs (46 upregulated and 47 downregulated genes) were identified in GC-induced ANFH samples. These DEGs were enriched in different GO terms and pathways, including chondrocyte differentiation and detection of chemical stimuli. The enrichment map revealed that skeletal system development was interconnected with several other GO terms by gene overlap. The literature mined network analysis revealed that 5 upregulated genes were associated with femoral necrosis, including parathyroid hormone receptor 1 (PTHR1), vitamin D (1,25-Dihydroxyvitamin D3) receptor (VDR), collagen, type II, α1, proprotein convertase subtilisin/kexin type 6 and zinc finger protein 354C (ZFP354C). In addition, ZFP354C and VDR were identified to transcription factors. Furthermore, PTHR1 was revealed to interact with VDR, and α-2-macroglobulin (A2M) interacted with fibronectin 1 (FN1) in the PPI network. PTHR1 may be involved in GC-induced ANFH via interacting with VDR. A2M may also be involved in the development of GC-induced ANFH through interacting with FN1. An improved understanding of the molecular mechanisms underlying GC-induced ANFH may provide novel targets for diagnostics and therapeutic treatment.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Hidden heterogeneity of transcription factor binding sites: A case study of SF-1

Steroidogenic factor 1 (SF-1) belongs to a small group of the transcription factors that bind DNA only as a monomer. Three different approaches-Sitecon, SiteGA, and oPWM-constructed using the same training sample of experimentally confirmed SF-1 binding sites have been used to recognize these sites. The appropriate prediction thresholds for recognition models have been selected. Namely, the thresholds concordant by false positive or negative rates for various methods were used to optimize the discrimination of steroidogenic gene promoters from the datasets of non-specific promoters. After experimental verification, the models were used to analyze the ChIP-seq data for SF-1. It has been shown that the sets of sites recognized by different models overlap only partially and that an integration of these models allows for identification of SF-1 sites in up to 80% of the ChIP-seq loci. The structures of the sites detected using the three recognition models in the ChIP-seq peaks falling within the [-5000, +5000] region relative to the transcription start sites (TSS) extracted from the FANTOM5 project have been analyzed. The MATLIGN classified the frequency matrices for the sites predicted by oPWM, Sitecon, and SiteGA into two groups. The first group is described by oPWM/Sitecon and the second, by SiteGA. Gene ontology (GO) analysis has been used to clarify the differences between the sets of genes carrying different variants of SF-1 binding sites. Although this analysis in general revealed a considerable overlap in GO terms for the genes carrying the binding sites predicted by oPWM, Sitecon, or SiteGA, only the last method elicited notable trend to terms related to negative regulation and apoptosis. The results suggest that the SF-1 binding sites are different in both their structure and the functional annotation of the set of target genes correspond to the predictions by oPWM+Sitecon and SiteGA. Further application of Homer software for de novo identification of enriched motifs in ChIP-Seq data for SF-1ChIP-seq dataset gave the data similar to oPWM+Sitecon.