Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

Abstract Ribosomal genes produce the constituents of the ribosome, one of the most conserved subcellular structures of all cells, from bacteria to eukaryotes, including animals. There are notions that some protein-coding ribosomal genes vary in their roles across species, particularly vertebrates, through the involvement of some in a number of genetic diseases. Based on extensive sequence comparisons and systematic curation, we establish a reference set for ribosomal proteins (RPs) in eleven vertebrate species and quantify their sequence conservation levels. Moreover, we correlate their coordinated gene expression patterns within up to 33 tissues and assess the exceptional role of paralogs in tissue specificity. Importantly, our analysis supported by the development and use of machine learning models strongly proposes that the variation in the observed tissue-specific gene expression of RPs is rather species-related and not due to tissue-based evolutionary processes. The data obtained suggest that RPs exhibit a complex relationship between their structure and function that broadly maintains a consistent expression landscape across tissues, while most of the variation arises from species idiosyncrasies. The latter may be due to evolutionary change and adaptation, rather than functional constraints at the tissue level throughout the vertebrate lineage.

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

Scientific Reports ◽

10.1038/s41598-019-53251-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 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.

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A genome-wide transcriptional analysis of morphology determination inCandida albicans

Molecular Biology of the Cell ◽

10.1091/mbc.e12-01-0065 ◽

2013 ◽

Vol 24 (3) ◽

pp. 246-260 ◽

Cited By ~ 31

Author(s):

Patricia L. Carlisle ◽

David Kadosh

Keyword(s):

Gene Expression ◽

Fungal Infections ◽

Transcriptional Profiling ◽

Expression Patterns ◽

Global Gene Expression ◽

Transcriptional Analysis ◽

Specific Gene ◽

Genome Wide ◽

A Genome ◽

Genetically Manipulated

Candida albicans, the most common cause of human fungal infections, undergoes a reversible morphological transition from yeast to pseudohyphal and hyphal filaments, which is required for virulence. For many years, the relationship among global gene expression patterns associated with determination of specific C. albicans morphologies has remained obscure. Using a strain that can be genetically manipulated to sequentially transition from yeast to pseudohyphae to hyphae in the absence of complex environmental cues and upstream signaling pathways, we demonstrate by whole-genome transcriptional profiling that genes associated with pseudohyphae represent a subset of those associated with hyphae and are generally expressed at lower levels. Our results also strongly suggest that in addition to dosage, extended duration of filament-specific gene expression is sufficient to drive the C. albicans yeast-pseudohyphal-hyphal transition. Finally, we describe the first transcriptional profile of the C. albicans reverse hyphal-pseudohyphal-yeast transition and demonstrate that this transition involves not only down-regulation of known hyphal-specific, genes but also differential expression of additional genes that have not previously been associated with the forward transition, including many involved in protein synthesis. These findings provide new insight into genome-wide expression patterns important for determining fungal morphology and suggest that in addition to similarities, there are also fundamental differences in global gene expression as pathogenic filamentous fungi undergo forward and reverse morphological transitions.

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

Journal of Dental Research ◽

10.1177/00220345211012000 ◽

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.

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

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab131.001 ◽

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.

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Use of serial analysis of gene expression to generate kidney expression libraries

AJP Renal Physiology ◽

10.1152/ajprenal.2000.279.2.f383 ◽

2000 ◽

Vol 279 (2) ◽

pp. F383-F392 ◽

Cited By ~ 16

Author(s):

M. Ashraf El-Meanawy ◽

Jeffrey R. Schelling ◽

Fatima Pozuelo ◽

Matthew M. Churpek ◽

Eckhard K. Ficker ◽

...

Keyword(s):

Gene Expression ◽

Renal Disease ◽

Ribosomal Proteins ◽

Expressed Sequence Tag ◽

Chronic Renal Disease ◽

Expression Patterns ◽

Expression Library ◽

Parathyroid Hormone Receptor ◽

Homology Searching ◽

Genome Wide

Chronic renal disease initiation and progression remain incompletely understood. Genome-wide expression monitoring should clarify mechanisms that cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling, which permits simultaneous, comparative, and quantitative analysis of gene-specific, 9- to 13-bp sequence tags. Using SAGE, we have constructed a tag expression library from ROP-+/+ mouse kidney. Tag sequences were sorted by abundance, and identity was determined by sequence homology searching. Analyses of 3,868 tags yielded 1,453 unique kidney transcripts. Forty-two percent of these transcripts matched mRNA sequence entries with known function, 35% of the transcripts corresponded to expressed sequence tag (EST) entries or cloned genes, whose function has not been established, and 23% represented unidentified genes. Previously characterized transcripts were clustered into functional groups, and those encoding metabolic enzymes, plasma membrane proteins (transporters/receptors), and ribosomal proteins were most abundant (39, 14, and 12% of known transcripts, respectively). The most common, kidney-specific transcripts were kidney androgen-regulated protein (4% of all transcripts), sodium-phosphate cotransporter (0.3%), renal cytochrome P-450 (0.3%), parathyroid hormone receptor (0.1%), and kidney-specific cadherin (0.1%). Comprehensively characterizing and contrasting gene expression patterns in normal and diseased kidneys will provide an alternative strategy to identify candidate pathways, which regulate nephropathy susceptibility and progression, and novel targets for therapeutic intervention.

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

The Plant Cell ◽

10.1105/tpc.4.11.1383 ◽

1992 ◽

Vol 4 (11) ◽

pp. 1383-1404 ◽

Cited By ~ 25

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

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

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2019-00461 ◽

2019 ◽

Vol 104 (11) ◽

pp. 5225-5237 ◽

Cited By ~ 3

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.

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The environmental stress response causes ribosome loss in aneuploid yeast cells

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2005648117 ◽

2020 ◽

Vol 117 (29) ◽

pp. 17031-17040 ◽

Cited By ~ 1

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.

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FOXA1 in breast cancer

Expert Reviews in Molecular Medicine ◽

10.1017/s1462399409001008 ◽

2009 ◽

Vol 11 ◽

Cited By ~ 39

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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