scholarly journals Technical considerations when designing a gene expression panel for renal transplant diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
F. Toulza ◽  
K. Dominy ◽  
T. Cook ◽  
J. Galliford ◽  
J. Beadle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Transplant ◽  
Biological Significance ◽  
Tissue Sampling ◽  
Individual Gene ◽  
Antibody Mediated Rejection ◽  
Gene Level ◽  
Diagnostic Panel ◽  
The Individual ◽  
Gene Panels

Abstract Gene expression analysis is emerging as a new diagnostic tool in transplant pathology, in particular for the diagnosis of antibody-mediated rejection. Diagnostic gene expression panels are defined on the basis of their pathophysiological relevance, but also need to be tested for their robustness across different preservatives and analysis platforms. The aim of this study is the investigate the effect of tissue sampling and preservation on candidate genes included in a renal transplant diagnostic panel. Using the NanoString platform, we compared the expression of 219 genes in 51 samples, split for formalin-fixation and paraffin-embedding (FFPE) and RNAlater preservation (RNAlater). We found that overall, gene expression significantly correlated between FFPE and RNAlater samples. However, at the individual gene level, 46 of the 219 genes did not correlate across the 51 matched FFPE and RNAlater samples. Comparing gene expression results using NanoString and qRT-PCR for 18 genes in the same pool of RNA (RNAlater), we found a significant correlation in 17/18 genes. Our study indicates that, in samples from the same routine diagnostic renal transplant biopsy procedure split for FFPE and RNAlater, 21% of 219 genes of potential biological significance do not correlate in expression. Whether this is due to fixatives or tissue sampling, selection of gene panels for routine diagnosis should take this information into consideration.

scholarly journals Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing He ◽  
Ping Chen ◽  
Sonia Zambrano ◽  
Dina Dabaghie ◽  
Yizhou Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Kidney ◽  
Cell Types ◽  
Model Organisms ◽  
Mural Cell ◽  
Glomerular Cell ◽  
Individual Gene ◽  
The Individual

AbstractMolecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), are missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we use Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis reveals genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells. Importantly, we detect heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observe remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

scholarly journals Deep transcriptomic profiling reveals the similarity between endothelial cells cultured under static and oscillatory shear stress conditions

2016 ◽  
Vol 48 (9) ◽  
pp. 660-666 ◽  
Author(s):  
Congzhen Qiao ◽  
Fan Meng ◽  
Inhwan Jang ◽  
Hanjoong Jo ◽  
Y. Eugene Chen ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Shear Stress ◽  
Endothelial Function ◽  
Stress Conditions ◽  
Individual Gene ◽  
Gene Level ◽  
The Individual ◽  
Oscillatory Shear Stress ◽  
Whole Transcriptome ◽  
Transcriptome Level

Atherosclerosis is a multifactorial disease that preferentially develops in specific regions in the arterial tree. This characteristic is mainly attributed to the unique pattern of hemodynamic shear stress in vivo. High laminar shear stress (LS) found in straight lumen exerts athero-protective effects. Low or oscillatory shear stress (OS) present in regions of lesser curvature and arterial bifurcations predisposes arterial intima to atherosclerosis. Shear stress-regulated endothelial function plays an important role in the process of atherosclerosis. Most in vitro research studies focusing on the molecular mechanisms of endothelial function are performed in endothelial cells (ECs) under cultured static (ST) condition. Some findings, however, are not recapitulated in subsequent translational studies, mostly likely due to the missing biomechanical milieu. Here, we profiled the whole transcriptome of primary human coronary arterial endothelial cells (HCAECs) under different shear stress conditions with RNA sequencing. Among 16,313 well-expressed genes, we detected 8,177 that were differentially expressed in OS vs. LS conditions and 9,369 in ST vs. LS conditions. Notably, only 1,618 were differentially expressed in OS vs. ST conditions. Hierarchical clustering of ECs demonstrated a strong similarity between ECs under OS and ST conditions at the transcriptome level. Subsequent pairwise heat mapping and principal component analysis gave further weight to the similarity. At the individual gene level, expressional analysis of representative well-known genes as well as novel genes showed a comparable amount at mRNA and protein levels in ECs under ST and OS conditions. In conclusion, the present work compared the whole transcriptome of HCAECs under different shear stress conditions at the transcriptome level as well as at the individual gene level. We found that cultured ECs are significantly different from those under LS conditions. Thus using cells under ST conditions is unlikely to elucidate endothelial physiology. Given the revealed high similarities of the endothelial transcriptome under OS and ST conditions, it may be helpful to understand the underlying mechanisms of OS-induced endothelial dysfunction from static cultured endothelial studies.

scholarly journals The good, the bad, and the ugly: Evolutionary and pathological aspects of gene dosage alterations

PLoS Genetics ◽  
2021 ◽  
Vol 17 (12) ◽  
pp. e1009906
Author(s):  
M. Felicia Basilicata ◽  
Claudia Isabelle Keller Valsecchi
Keyword(s):  
Developmental Disorders ◽  
Common Ground ◽  
Gene Dosage ◽  
Paternal Genome ◽  
Individual Gene ◽  
Developmental Progression ◽  
Gene Level ◽  
Heteromorphic Sex Chromosomes ◽  
Genetic Perturbations ◽  
The Individual

Diploid organisms contain a maternal and a paternal genome complement that is thought to provide robustness and allow developmental progression despite genetic perturbations that occur in heterozygosity. However, changes affecting gene dosage from the chromosome down to the individual gene level possess a significant pathological potential and can lead to developmental disorders (DDs). This indicates that expression from a balanced gene complement is highly relevant for proper cellular and organismal function in eukaryotes. Paradoxically, gene and whole chromosome duplications are a principal driver of evolution, while heteromorphic sex chromosomes (XY and ZW) are naturally occurring aneuploidies important for sex determination. Here, we provide an overview of the biology of gene dosage at the crossroads between evolutionary benefit and pathogenicity during disease. We describe the buffering mechanisms and cellular responses to alterations, which could provide a common ground for the understanding of DDs caused by copy number alterations.

scholarly journals Perturbations of Ribosomal Protein Expression Reveal Overlapping Gene Networks between Drosophila Minutes and Human Cancer

2020 ◽  
Author(s):  
Jai A Denton ◽  
Mariana Velasque ◽  
Floyd A Reed
Keyword(s):  
Ribosomal Proteins ◽  
Gene Networks ◽  
Ribosome Biogenesis ◽  
Human Cancer ◽  
Whole Body ◽  
Individual Gene ◽  
Gene Level ◽  
The Individual ◽  
Time Specific

AbstractRibosomal proteins (RPs) are critical to all cellular operations through their key roles in ribosome biogenesis and translation, as well as their extra-ribosomal functions. Although highly tissue- and time-specific in expression, little is known about the macro-level roles of RPs in shaping transcriptomes. A wealth of RP mutants exist, including the Drosophila melanogaster Minutes, with RP encoding genes that vary from greatly under-expressed to greatly over-expressed. Leveraging a subset of these mutants and using whole-body RNA sequencing, we identified the RP macro transcriptome and then sought to compare it with transcriptomes of pathologies associated with failures of ribosomal function. Gene-based analysis revealed highly variable transcriptomes of RP mutations with little overlap in genes that were differentially expressed. In contrast, weighted gene co-expression network analysis (WGCNA) revealed a highly conserved pattern across all RP mutants studied. When we compared network changes in RP mutants, we observed similarities to transcriptome alterations in human cancer, and thus confirming the oncogenic role of RPs. Therefore, what may appear stochastic at the individual gene level, forms clearly predictable patterns when viewed as a whole.

scholarly journals MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

2020 ◽  
Author(s):  
Justin Williams ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Andrew Thrasher ◽  
Chunliang Li ◽  
...  
Keyword(s):  
Promoter Activity ◽  
Malignant Neoplasms ◽  
Clinical Value ◽  
Individual Gene ◽  
Learning Framework ◽  
Genome Wide ◽  
Gene Level ◽  
Biological Impacts ◽  
The Individual ◽  
Methylation Patterns

AbstractAlthough genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities (H3K4me3 and H3K27ac enrichment) from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

scholarly journals An interaction-based model for neuropsychiatric features of copy-number variants

2018 ◽  
Author(s):  
Matthew Jensen ◽  
Santhosh Girirajan
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Copy Number Variants ◽  
Model Organisms ◽  
Expression Data ◽  
Individual Gene ◽  
Functional Assays ◽  
Genetic Complexity ◽  
The Individual ◽  
Lines Of Evidence

ABSTRACTVariably expressive copy-number variants (CNVs) are characterized by extensive phenotypic heterogeneity of neuropsychiatric phenotypes. Approaches to identify single causative genes for these phenotypes within each CNV have not been successful. Here, we posit using multiple lines of evidence, including pathogenicity metrics, functional assays of model organisms, and gene expression data, that multiple genes within each CNV region are likely responsible for the observed phenotypes. We propose that candidate genes within each region likely interact with each other through shared pathways to modulate the individual gene phenotypes, emphasizing the genetic complexity of CNV-associated neuropsychiatric features.

scholarly journals Extrinsic noise and heavy-tailed laws in gene expression

2019 ◽  
Author(s):  
Lucy Ham ◽  
Rowan D. Brackston ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Extrinsic Noise ◽  
Individual Gene ◽  
Copy Numbers ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Intrinsic Stochasticity ◽  
The Individual ◽  
Noise In Gene Expression

AbstractNoise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise – encompassing factors external to the transcription of the individual gene – and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

scholarly journals Understanding and using quantitative genetic variation

2010 ◽  
Vol 365 (1537) ◽  
pp. 73-85 ◽  
Author(s):  
William G. Hill
Keyword(s):  
Genetic Variation ◽  
Complex Traits ◽  
Natural Populations ◽  
Multiple Traits ◽  
Individual Gene ◽  
Term Selection ◽  
Laboratory Selection ◽  
Gene Level ◽  
Breeding Programmes ◽  
The Individual

Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet.

scholarly journals Characterizing Xenopus tropicalis endurance capacities with multilevel transcriptomics

2016 ◽  
Author(s):  
Adam J. Richards ◽  
Anthony Herrel ◽  
Mathieu Videlier ◽  
Konrad Paszkiewicz ◽  
Nicolas Pollet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Xenopus Tropicalis ◽  
Endurance Capacity ◽  
Functional Modules ◽  
Gene Set ◽  
Individual Level ◽  
Gene Level ◽  
Genetic Mechanisms ◽  
Ideal Tool ◽  
The Individual

AbstractVertebrate endurance capacity is a phenotype with considerable genetic heterogeneity. RNA-Seq technologies are an ideal tool to investigate the involved genes and processes, but several challenges exist when the phenotype of interest has a complex genetic background. Difficulties manifest at the level of results interpretation because commonly used statistical methods are designed to identify strongly associated genes. If an observed phenotype can be achieved though multiple distinct genetic mechanisms then typical gene-centric methods come with the attached risk that signal may be lost or misconstrued.Gene set analysis (GSA) methods are now widely accepted as a means to address some of the shortcomings of gene-by-gene analysis methods. We carry out both gene level and gene set level analyses on Xenopus tropicalis to identify the genetic factors that contribute to endurance heterogeneity. A typical workflow might consider gene level and pathway level analyses, but in this work we propose an additional focus at the intermediate level of functional modules. We generate functional modules for GSA testing in order to be explicit in how ontology information is used with respect to the functional genomics of Xenopus. Additionally, we make use of multiple assemblies to corroborate implicated genes and processes.We identified 42 core genes, 10 functional modules, and 14 pathways based on gene expression differences between endurant and non-endurant frogs. The majority of the genes and processes are readily associated with muscle contraction or catabolism. A substantial number of these genes are involved in lipid metabolic processes, suggesting an important role in frog endurance heterogeneity. Unsurprisingly, many of the gene expression differences between endurant and non-endurant frogs can be distilled down to the capacity to utilize substrate for energy, but at the individual level frogs appear to make use of diverse machinery to achieve these differences.

scholarly journals Analysis of gene expression and connectivity on hippocampus of Alzheimer’s disease by a new comprehensive approach

2020 ◽  
Author(s):  
Shan Jiang
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
R Package ◽  
Biological Pathways ◽  
Limited Attention ◽  
Functional Protein ◽  
Tissue Samples ◽  
Individual Gene ◽  
The Individual

AbstractGene expression and gene connectivity describe two different functional aspects of a gene. These two different measures reveal different information about the involvement of genes in disorders. Previous case-control gene expression studies have often focused on expression level of individual genes. Correlated expression relationships among genes, measured as gene connectivity, have obtained limited attention. We developed a comprehensive method, TRIple Differentiation (TRID), to assess these two measures, both separately and jointly. We applied TRID to gene expression data in hippocampus tissue samples from three Alzheimer’s disease (AD) microarray datasets. Following TRID, comparisons among the three datasets showed poor consistency for disease-associated individual genes but reproducible changes of disease-associated biological pathways annotated for functional protein-protein interaction (PPI) modules identified from network analysis. Our results suggest that changes of gene expression in hippocampus of AD patients are highly heterogeneous at the individual gene level, while biological pathways annotated for PPI modules identified based on TRID weights demonstrate consistency among the three datasets. The R package TRID can be accessed from GitHub (https://github.com/shannjiang/TRID).

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