A Probabilistic Integrated Gene Network Model of Multiple Hematopoietic Lineage Specification

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4767-4767
Author(s):  
Hai Fang ◽  
Kankan Wang ◽  
Ji Zhang
Keyword(s):  
Gene Expression ◽  
Gene Network ◽  
Expression Patterns ◽  
Biological Data ◽  
Specific Gene ◽  
Hematopoietic Stem ◽  
Protein Protein Interaction ◽  
Proteomic Data ◽  
Hematopoietic Lineage ◽  
Regulatory Analysis

Abstract Hematopoietic stem cells and their progenitor hierarchy are highly controlled by the underlying gene regulatory network. In past decades, great progress has been made in elucidating lineage-restricted transcription factors and lineage-specific gene expression patterns. With accumulation of genome-wide biological data, it is of great value to expand beyond mere transcriptional regulatory analysis to the systemic understanding. Here, we utilized a probabilistic integrated gene network for integrating heterogeneous functional genomic and proteomic data sources into predictive model of hematopoietic lineage diversification. We first constructed a naïve Bayesian network by incorporating disparate biological data, including time-series gene expression during re-dedifferentiation of leukemia along alternative paths into granulocyte or monocyte upon the treatment of differentiation-inducing agents, computationally generated transcription factor regulatory sites and microRNA targets, well-curated physical protein-protein interaction, and functional annotation data. The resultant network, coupled with binomial-based statistical analysis of the interplay between node properties and the network topology, predicted the specific hematopoietic lineage, generating testable hypotheses regarding their unified reprogramming principles. This study demonstrates the utility of the growing biological data in detailed elucidations of the orchestration of distinct hematopoietic cell fates.

Molecular Phenotype of Malignant CD34+ Hematopoietic Stem and Progenitor Cells in Chronic Myelogenous Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2863-2863
Author(s):  
Ralf Kronenwett ◽  
Elena Diaz-Blanco ◽  
Thorsten Graef ◽  
Ulrich Steidl ◽  
Slawomir Kliszewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fatty Acid ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Leukemic Cell ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Hematopoietic Stem ◽  
Cd34 Cells

Abstract In this study, we examined gene expression profiles of immunomagnetically enriched CD34+ cells from bone marrow (BM) of 9 patients with untreated CML in chronic phase and from 8 healthy volunteers using Affymetrix GeneChips. Additionally, in 3 patients CD34+ from peripheral blood (PB) were compared with those from BM. Differential expression of 12 candidate genes was corroborated by quantitative real-time RT-PCR. Following hybridization of labelled cRNA to Affymetrix GeneChips covering 8793 genes we used the statistical scripting language “R” for data analysis. For normalization a method of variance stabilization transformations was used. To identify significantly differentially expressed genes we used the Significance Analysis of Microarrays (SAM) algorithm. The intraindividual comparison of CD34+ cells from BM and PB in CML showed no differentially expressed genes which is different to normal CD34+ cells which had distinct gene expression patterns comparing circulating and sedentary CD34+ cells (Steidl et al., Blood, 2002). Comparing malignant BM CD34+ cells from CML with normal BM CD34+ cells 792 genes were significantly differentially expressed (fold change: >1.3; q-value: <0.03). 735 genes had a higher and 57 genes a lower expression in CML. Gene expression patterns reflected BCR-ABL-induced functional alterations such as increased cell-cycle and proteasome activity as well as decreased apoptosis. Downregulation of several genes involved in DNA repair and detoxification in CML might be the basis for DNA instability and progression to blast crisis. An interesting finding was an upregulation of fetal hemoglobin (Hb) components such as Hb gamma A and G in leukemic progenitor cells whereas no difference in adult Hb expression was observed suggesting an induction of fetal Hb synthesis in CML. Looking at genes involved in stem cell maintenance we found an upregulation of GATA2 and a reduced expression of proteins from the Wnt signalling pathway suggesting an increased self-renewal of CML hematopoietic stem cells compared to the normal counterpart. Moreover, several genes playing a role in ubiquitin-dependent protein catabolism and in fatty acid biosynthesis such as fatty acid synthase (FAS) were stronger expressed in CML. The functional role of FAS for leukemic cell growth was assessed in cell culture experiments. Incubation of the leukemic cell line K562 with the FAS inhibitor cerulenin (10 μg/ml) for 3 days resulted in death of 99% of cells suggesting that survival of leukemic cells depends upon endogenous fatty acid synthesis. In an attempt to find a specific gene expression pattern associated with response to imatinib therapy we divided the patients included in this study into two groups: maximal reduction of BCR-ABL transcript level <3-log vs. >3-log (major molecular remission) during therapy. Comparing pretherapeutic gene expression profiles of both groups we could not identify a pattern predictive for major molecular response. In conclusion, malignant CD34+ cells in CML have a specific gene expression pattern which seems not to be predictive for response to imatinib therapy.

A Global Vista of the Epigenomic State of the Mouse Submandibular Gland

2021 ◽  
pp. 002203452110120
Author(s):  
C. Gluck ◽  
S. Min ◽  
A. Oyelakin ◽  
M. Che ◽  
E. Horeth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Submandibular Salivary Gland ◽  
Data Sets ◽  
Control Mechanisms ◽  
Chromatin Immunoprecipitation Sequencing

The parotid, submandibular, and sublingual glands represent a trio of oral secretory glands whose primary function is to produce saliva, facilitate digestion of food, provide protection against microbes, and maintain oral health. While recent studies have begun to shed light on the global gene expression patterns and profiles of salivary glands, particularly those of mice, relatively little is known about the location and identity of transcriptional control elements. Here we have established the epigenomic landscape of the mouse submandibular salivary gland (SMG) by performing chromatin immunoprecipitation sequencing experiments for 4 key histone marks. Our analysis of the comprehensive SMG data sets and comparisons with those from other adult organs have identified critical enhancers and super-enhancers of the mouse SMG. By further integrating these findings with complementary RNA-sequencing based gene expression data, we have unearthed a number of molecular regulators such as members of the Fox family of transcription factors that are enriched and likely to be functionally relevant for SMG biology. Overall, our studies provide a powerful atlas of cis-regulatory elements that can be leveraged for better understanding the transcriptional control mechanisms of the mouse SMG, discovery of novel genetic switches, and modulating tissue-specific gene expression in a targeted fashion.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lars Velten ◽  
Benjamin A. Story ◽  
Pablo Hernández-Malmierca ◽  
Simon Raffel ◽  
Daniel R. Leonce ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Single Cell ◽  
Lineage Tracing ◽  
Specific Gene ◽  
Leukemic Stem Cells ◽  
Hematopoietic Stem ◽  
Mutational Status

AbstractCancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

scholarly journals Regional and cell-specific gene expression patterns during petal development.

1992 ◽  
Vol 4 (11) ◽  
pp. 1383-1404 ◽  
Author(s):  
G N Drews ◽  
T P Beals ◽  
A Q Bui ◽  
R B Goldberg
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Petal Development

scholarly journals Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer

2019 ◽  
Vol 104 (11) ◽  
pp. 5225-5237 ◽  
Author(s):  
Mariam Haffa ◽  
Andreana N Holowatyj ◽  
Mario Kratz ◽  
Reka Toth ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Adipose Tissue ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Human Study ◽  
Subcutaneous Fat ◽  
Specific Gene ◽  
Adipose Tissue Depots

Abstract Context Adipose tissue inflammation and dysregulated energy homeostasis are key mechanisms linking obesity and cancer. Distinct adipose tissue depots strongly differ in their metabolic profiles; however, comprehensive studies of depot-specific perturbations among patients with cancer are lacking. Objective We compared transcriptome profiles of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) from patients with colorectal cancer and assessed the associations of different anthropometric measures with depot-specific gene expression. Design Whole transcriptomes of VAT and SAT were measured in 233 patients from the ColoCare Study, and visceral and subcutaneous fat area were quantified via CT. Results VAT compared with SAT showed elevated gene expression of cytokines, cell adhesion molecules, and key regulators of metabolic homeostasis. Increased fat area was associated with downregulated lipid and small molecule metabolism and upregulated inflammatory pathways in both compartments. Comparing these patterns between depots proved specific and more pronounced gene expression alterations in SAT and identified unique associations of integrins and lipid metabolism–related enzymes. VAT gene expression patterns that were associated with visceral fat area poorly overlapped with patterns associated with self-reported body mass index (BMI). However, subcutaneous fat area and BMI showed similar associations with SAT gene expression. Conclusions This large-scale human study demonstrates pronounced disparities between distinct adipose tissue depots and reveals that BMI poorly correlates with fat mass–associated changes in VAT. Taken together, these results provide crucial evidence for the necessity to differentiate between distinct adipose tissue depots for a correct characterization of gene expression profiles that may affect metabolic health of patients with colorectal cancer.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

scholarly journals The environmental stress response causes ribosome loss in aneuploid yeast cells

2020 ◽  
Vol 117 (29) ◽  
pp. 17031-17040 ◽  
Author(s):  
Allegra Terhorst ◽  
Arzu Sandikci ◽  
Abigail Keller ◽  
Charles A. Whittaker ◽  
Maitreya J. Dunham ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Stationary Phase ◽  
Environmental Stress ◽  
Budding Yeast ◽  
Expression Patterns ◽  
Yeast Cells ◽  
Specific Gene ◽  
Environmental Stress Response ◽  
Cellular Stresses

Aneuploidy, a condition characterized by whole chromosome gains and losses, is often associated with significant cellular stress and decreased fitness. However, how cells respond to the aneuploid state has remained controversial. In aneuploid budding yeast, two opposing gene-expression patterns have been reported: the “environmental stress response” (ESR) and the “common aneuploidy gene-expression” (CAGE) signature, in which many ESR genes are oppositely regulated. Here, we investigate this controversy. We show that the CAGE signature is not an aneuploidy-specific gene-expression signature but the result of normalizing the gene-expression profile of actively proliferating aneuploid cells to that of euploid cells grown into stationary phase. Because growth into stationary phase is among the strongest inducers of the ESR, the ESR in aneuploid cells was masked when stationary phase euploid cells were used for normalization in transcriptomic studies. When exponentially growing euploid cells are used in gene-expression comparisons with aneuploid cells, the CAGE signature is no longer evident in aneuploid cells. Instead, aneuploid cells exhibit the ESR. We further show that the ESR causes selective ribosome loss in aneuploid cells, providing an explanation for the decreased cellular density of aneuploid cells. We conclude that aneuploid budding yeast cells mount the ESR, rather than the CAGE signature, in response to aneuploidy-induced cellular stresses, resulting in selective ribosome loss. We propose that the ESR serves two purposes in aneuploid cells: protecting cells from aneuploidy-induced cellular stresses and preventing excessive cellular enlargement during slowed cell cycles by down-regulating translation capacity.

FOXA1 in breast cancer

2009 ◽  
Vol 11 ◽  
Author(s):  
Harikrishna Nakshatri ◽  
Sunil Badve
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Signature ◽  
Specific Gene ◽  
Breast Cancers ◽  
Transcription Factor Network ◽  
Histopathological Classification

Breast cancer is a heterogeneous disease and classification is important for clinical management. At least five subtypes can be identified based on unique gene expression patterns; this subtype classification is distinct from the histopathological classification. The transcription factor network(s) required for the specific gene expression signature in each of these subtypes is currently being elucidated. The transcription factor network composed of the oestrogen (estrogen) receptor α (ERα), FOXA1 and GATA3 may control the gene expression pattern in luminal subtype A breast cancers. Breast cancers that are dependent on this network correspond to well-differentiated and hormone-therapy-responsive tumours with good prognosis. In this review, we discuss the interplay between these transcription factors with a particular emphasis on FOXA1 structure and function, and its ability to control ERα function. Additionally, we discuss modulators of FOXA1 function, ERα–FOXA1–GATA3 downstream targets, and potential therapeutic agents that may increase differentiation through FOXA1.

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