scholarly journals The evolution of ovary-specific gene expression in Hawaiian Drosophilidae

2021 ◽  
Author(s):  
Samuel H Church ◽  
Catriona Munro ◽  
Casey Dunn ◽  
Cassandra G. Extavour
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Model Organisms ◽  
Laboratory Model ◽  
Developmental Genetics ◽  
Specific Gene ◽  
Ancestral State ◽  
Phylogenetic Distance ◽  
Evolutionary Changes

As detailed data on gene expression become accessible from more species, we have an opportunity to test the extent to which our understanding of developmental genetics from model organisms helps predict expression patterns across species. Central to this is the question: how much variation in gene expression do we expect to observe between species? Here we provide an answer by comparing RNAseq data between twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that there exists a cohort of ovary-specific genes that is stable across species, and that largely corresponds to described expression patterns from laboratory model Drosophila species. However, our results also show that, as phylogenetic distance increases, variation between species overwhelms variation between tissues. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression profiles, and use this to identify gains and losses of ovarian expression across these twelve species. We then use this distribution to calculate the correlation in expression evolution between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that have similar evolutionary trajectories, and we provide these as a dataset of novel testable hypotheses about genetic roles and interactions.

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.

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.

scholarly journals Tumor-specific and Proliferation-specific Gene Expression Typifies Murine Transgenic B Cell Lymphomagenesis

2006 ◽  
Vol 282 (7) ◽  
pp. 4803-4811 ◽  
Author(s):  
Marc E. Lenburg ◽  
Anupama Sinha ◽  
Douglas V. Faller ◽  
Gerald V. Denis
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
B Cell ◽  
Cell Cycle Progression ◽  
Cellular Proliferation ◽  
Expression Profiles ◽  
Expression Patterns ◽  
B Cell Lymphoma ◽  
Specific Pattern ◽  
Specific Gene

The dual bromodomain protein Brd2 is closely related to the basal transcription factor TAFII250, which is essential for cyclin A transactivation and mammalian cell cycle progression. In transgenic mice, constitutive lymphoid expression of Brd2 causes a malignancy most similar to human diffuse large B cell lymphoma. We compare the genome-wide transcriptional expression profiles of these lymphomas with those of proliferating and resting normal B cells. Transgenic tumors reproducibly show differential expression of a large number of genes important for cell cycle control and lymphocyte biology; expression patterns are either tumor-specific or proliferation-specific. Several of their human orthologs have been implicated in human lymphomagenesis. Others correlate with human disease survival time. BRD2 is underexpressed in some subtypes of human lymphoma and these subtypes display a number of similarities to the BRD2-mediated murine tumors. We illustrate with a high degree of detail that cancer is more than rampant cellular proliferation, but involves the additional transcriptional mobilization of many genes, some of them poorly characterized, which show a tumor-specific pattern of gene expression.

scholarly journals Imprints of independent allopolyploid formations on patterns of gene expression in two sibling yarrow species (Achillea, Asteraceae)

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Duo Chen ◽  
Peng-Cheng Yan ◽  
Yan-Ping Guo
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Specific Gene ◽  
Differential Adaptation ◽  
Expression Bias ◽  
Parental Gene ◽  
Species Specific ◽  
Go Terms ◽  
Allotetraploid Species

Abstract Background Polyploid species often originate recurrently. While this is well known, there is little information on the extent to which distinct allotetraploid species formed from the same parent species differ in gene expression. The tetraploid yarrow species Achillea alpina and A. wilsoniana arose independently from allopolyploidization between diploid A. acuminata and A. asiatica. The genetics and geography of these origins are clear from previous studies, providing a solid basis for comparing gene expression patterns of sibling allopolyploid species that arose independently. Results We conducted comparative RNA-sequencing analyses on the two Achillea tetraploid species and their diploid progenitors to evaluate: 1) species-specific gene expression and coexpression across the four species; 2) patterns of inheritance of parental gene expression; 3) parental contributions to gene expression in the allotetraploid species, and homeolog expression bias. Diploid A. asiatica showed a higher contribution than diploid A. acuminata to the transcriptomes of both tetraploids and also greater homeolog bias in these transcriptomes, possibly reflecting a maternal effect. Comparing expressed genes in the two allotetraploids, we found expression of ca. 30% genes were species-specific in each, which were most enriched for GO terms pertaining to “defense response”. Despite species-specific and differentially expressed genes between the two allotetraploids, they display similar transcriptome changes in comparison to their diploid progenitors. Conclusion Two independently originated Achillea allotetraploid species exhibited difference in gene expression, some of which must be related to differential adaptation during their post-speciation evolution. On the other hand, they showed similar expression profiles when compared to their progenitors. This similarity might be expected when pairs of merged diploid genomes in tetraploids are similar, as is the case in these two particular allotetraploids.

scholarly journals Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

2021 ◽  
Author(s):  
Ying-xue Zhang ◽  
Feng-xia Sun ◽  
Xiao-ling Li ◽  
Qing-hua Liu ◽  
Zi-meng Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Cirrhosis ◽  
Expression Analysis ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Molecular Characteristics ◽  
Specific Gene ◽  
Subgroup Ii

Abstract Background: Cirrhosis is a common clinical chronic progressive liver disease and has become one of the main causes of death worldwide. The condition of liver cirrhosis is complex and there is also clinical heterogeneity. Identifying liver cirrhosis based on molecular characteristics has become a challenge.Methods: To reveal the potential molecular characteristics of different types of cirrhosis, we divided 79 patients with cirrhosis into 4 subgroups based on gene expression profiles. These gene expression profiles were retrieved from the mprehensive gene expression database. In addition, these subgroups showed different expression patterns. To reveal the differences between subgroups, we used weighted gene co-expression analysis and identified six subgroup-specific gene co-expression analysis modules.Results: The characteristics ofWCGNAmodules indicate that TGF - β signaling pathway,viral protein interaction with cytokines and cytokine receptors, including a variety of chemokines and inflammatory factors, are upregulated in subgroup I, indicating that subjects in subgroup I may show inflammatory characteristics; fatty acid metabolism, biosynthesis of cofactors, carbon metabolism and protein processing pathway in endoplasmic reticulum were significantly enriched in subgroup II, which indicated that the subjects in subgroup II might have the characteristics of active metabolism; arrhythmogenic right ventricular cardiomyopathy and Neuroactive ligand−receptor interaction are significantly enriched in subgroup IV; we did not find a significant upregulation pathway in the third subgroup.Conclusion: The subgroups classification of liver cirrhosis cases shows that patients from different subgroups may have unique gene expression patterns, which indicates that patients in each subgroup should receive more personalized treatment.

scholarly journals Meta-Analysis of RNA-Seq Studies Reveals Genes With Dominant Functions During Flower Bud Endo- To Eco-Dormancy Transition in Prunus Species

2021 ◽  
Author(s):  
Monica Canton ◽  
Cristian Forestan ◽  
Claudio Bonghi ◽  
Serena Varotto
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Fruit Trees ◽  
Day Length ◽  
Specific Gene ◽  
Flower Buds ◽  
Flower Bud

Abstract In deciduous fruit trees, entrance into dormancy occurs in later summer/fall, concomitantly with the shortening of day length and decrease in temperature. Dormancy can be divided into endodormancy, ecodormancy and paradormancy. In Prunus species flower buds, entrance into the dormant stage occurs when the apical meristem is partially differentiated; during dormancy, flower verticils continue their growth and differentiation. Each species and/or cultivar requires exposure to low winter temperature followed by warm temperatures, quantified as chilling and heat requirements, to remove the physiological blocks that inhibit budburst. A comprehensive meta-analysis of transcriptomic studies on flower buds of sweet cherry, apricot and peach was conducted, by investigating the gene expression profiles during bud endo- to ecodormancy transition in genotypes differing in chilling requirements. Conserved and distinctive expression patterns were observed, allowing the identification of gene specifically associated with endodormancy or ecodormancy. In addition to the MADS-box transcription factor family, hormone-related genes, chromatin modifiers, macro- and micro-gametogenesis related genes and environmental integrators, were identified as novel biomarker candidates for flower bud development during winter in stone fruits. In parallel, flower bud differentiation processes were associated to dormancy progression and termination and to environmental factors triggering dormancy phase-specific gene expression.

The prospective MammaPrint MINT (Multi-Institutional Neo-adjuvant Therapy) study.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. TPS11122-TPS11122
Author(s):  
Charles E. Cox ◽  
Peter William Blumencranz ◽  
Ruben A. Saez ◽  
Robert Wesolowski ◽  
Lisette Stork ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Adjuvant Chemotherapy ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Locally Advanced ◽  
Specific Gene ◽  
Axillary Lymph ◽  
Long Term Outcome ◽  
Neo Adjuvant Chemotherapy

TPS11122 Background: Patients with locally advanced breast cancer (LABC) are often treated with neo-adjuvant chemotherapy to reduce the size of the tumor before definitive surgery. Complete pathologic Response (pCR) predicts better long term outcome. Genomics assays that measure specific gene expression patterns in a patient's primary tumor have become important prognostic and predictive tools for early breast cancer. This study is designed to test the ability of molecular profiling, as well as traditional pathologic and clinical prognostic factors to predict responsiveness to neo-adjuvant chemotherapy in patients with LABC. Methods: Women ≥ 18 yrs with histologically-proven invasive breast cancer T2(≥3.5cm)-T4,N0M0 or T2-T4N1M0, with measurable disease, adequate bone marrow reserves and normal renal and hepatic function who signed informed consent are enrolled. Axillary lymph nodes will be staged according to protocol. MammaPrint risk profile, BluePrint molecular subtyping profile, TargetPrint ER, PR and HER2 single gene readout, and the 56-gene TheraPrint Research Gene Panel will be analysed using the whole genome expression array. Patients will receive neo-adjuvant chemotherapy treatment according to protocol. Response will be measured by centrally assessed Residual Cancer Burden (RCB). Objectives are: (1) To determine the predictive power of MammaPrint and BluePrint for sensitivity to neo-adjuvant chemotherapy as measured by pCR. (2) To identify and/or validate predictive gene expression profiles of clinical response or resistance to neo-adjuvant chemotherapy. (3) To compare TargetPrint ER, PR and HER2 with local and centralized IHC and/or CISH/FISH assessment. (4) To identify correlations between TheraPrint and response to neo-adjuvant chemotherapy. (5) To compare BluePrint molecular subtype with IHC-based subtype classification. To achieve a difference of 20% in chemotherapy sensitivity for patients stratified by MammaPrint, a total of 226 samples is needed (significance level 0.05 and power of 0.90). So far 45 patients have been enrolled from multiple institutions. Clinical trial information: NCT01501487.

scholarly journals Identifying Transcriptomic Correlates of Histology using Deep Learning

2020 ◽  
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242858
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Cell-type diversity and regionalized gene expression in the planarian intestine revealed by laser-capture microdissection transcriptome profiling

2019 ◽  
Author(s):  
David J. Forsthoefel ◽  
Nicholas I. Cejda ◽  
Umair W. Khan ◽  
Phillip A. Newmark
Keyword(s):  
Gene Expression ◽  
Laser Capture Microdissection ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cell Types ◽  
Goblet Cells ◽  
Intestinal Cell ◽  
Specific Gene ◽  
Cell Type ◽  
Laser Capture

AbstractOrgan regeneration requires precise coordination of new cell differentiation and remodeling of uninjured tissue to faithfully re-establish organ morphology and function. An atlas of gene expression and cell types in the uninjured state is therefore an essential pre-requisite for understanding how damage is repaired. Here, we use laser-capture microdissection (LCM) and RNA-Seq to define the transcriptome of the intestine of Schmidtea mediterranea, a planarian flatworm with exceptional regenerative capacity. Bioinformatic analysis of 1,844 intestine-enriched transcripts suggests extensive conservation of digestive physiology with other animals, including humans. Comparison of the intestinal transcriptome to purified absorptive intestinal cell (phagocyte) and published single-cell expression profiles confirms the identities of known intestinal cell types, and also identifies hundreds of additional transcripts with previously undetected intestinal enrichment. Furthermore, by assessing the expression patterns of 143 transcripts in situ, we discover unappreciated mediolateral regionalization of gene expression and cell-type diversity, especially among goblet cells. Demonstrating the utility of the intestinal transcriptome, we identify 22 intestine-enriched transcription factors, and find that several have distinct functional roles in the regeneration and maintenance of goblet cells. Furthermore, depletion of goblet cells inhibits planarian feeding and reduces viability. Altogether, our results show that LCM is a viable approach for assessing tissue-specific gene expression in planarians, and provide a new resource for further investigation of digestive tract regeneration, the physiological roles of intestinal cell types, and axial polarity.

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