scholarly journals Conserved Mechanisms, Novel Anatomies: The Developmental Basis of Fin Evolution and the Origin of Limbs

Diversity ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 384
Author(s):  
Amanda N. Cass ◽  
Ashley Elias ◽  
Madeline L. Fudala ◽  
Benjamin D. Knick ◽  
Marcus C. Davis
Keyword(s):  
Gene Expression ◽  
Linear Connection ◽  
Expression Data ◽  
Evolutionary Novelty ◽  
Model Species ◽  
Animal Evolution ◽  
Molecular Approaches ◽  
Key Aspects ◽  
Insight Into ◽  
Appendage Patterning

The transformation of paired fins into tetrapod limbs is one of the most intensively scrutinized events in animal evolution. Early anatomical and embryological datasets identified distinctive morphological regions within the appendage and posed hypotheses about how the loss, gain, and transformation of these regions could explain the observed patterns of both extant and fossil appendage diversity. These hypotheses have been put to the test by our growing understanding of patterning mechanisms that regulate formation of the appendage axes, comparisons of gene expression data from an array of phylogenetically informative taxa, and increasingly sophisticated and elegant experiments leveraging the latest molecular approaches. Together, these data demonstrate the remarkable conservation of developmental mechanisms, even across phylogenetically and morphologically disparate taxa, as well as raising new questions about the way we view homology, evolutionary novelty, and the often non-linear connection between morphology and gene expression. In this review, we present historical hypotheses regarding paired fin evolution and limb origins, summarize key aspects of central appendage patterning mechanisms in model and non-model species, address how modern comparative developmental data interface with our understanding of appendage anatomy, and highlight new approaches that promise to provide new insight into these well-traveled questions.

scholarly journals Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

2020 ◽  
Author(s):  
Cynthia Ma ◽  
Michael R. Brent
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Target Genes ◽  
Activity Levels ◽  
Expression Data ◽  
Performance Constraints ◽  
Perturbation Data ◽  
Carry Over ◽  
Insight Into

ABSTRACTBackgroundThe activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.ResultsUsing a new dataset, we systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. These approaches require a TF network map, which specifies the target genes of each TF, as input. We evaluate different approaches to building the network map and deriving constraints on the matrices. We find that such constraints are essential for good performance. Constraints can be obtained from expression data in which the activities of individual TFs have been perturbed, and we find that such data are both necessary and sufficient for obtaining good performance. Remaining uncertainty about whether a TF activates or represses a target is a major source of error. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions. As a result, the control strength matrices derived here can be used for other applications. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of four yeast TFs: Gcr2, Gln3, Gcn4, and Msn2. Evaluation code and data available at https://github.com/BrentLab/TFA-evaluationConclusionsWhen a high-quality network map, constraints, and perturbation-response data are available, inferring TF activity levels by factoring gene expression matrices is effective. Furthermore, it provides insight into regulators of TF activity.

scholarly journals ReCodLiver0.9: Overcoming challenges in genome-scale metabolic reconstruction of a non-model species

2020 ◽  
Author(s):  
Eileen Marie Hanna ◽  
Xiaokang Zhang ◽  
Marta Eide ◽  
Shirin Fallahi ◽  
Tomasz Furmanek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gadus Morhua ◽  
Metabolic Response ◽  
Atlantic Cod ◽  
Biological Data ◽  
Environmental Toxicants ◽  
Expression Data ◽  
Model Species ◽  
Genome Scale

AbstractThe availability of genome sequences, annotations and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.Author summaryGenome-scale metabolic models (GEMs) are constructed based upon reconstructed networks that are carried out by an organism. The underlying biochemical knowledge in such networks can be transformed into mathematical models that could serve as a platform to answer biological questions. The availability of high-throughput biological data, including genomics, proteomics, and metabolomics data, supports the generation of such models for a large number of organisms. Nevertheless, challenges arise for non-model species which are typically less annotated. In this paper, we discuss these challenges and possible solutions in the context of generation of a draft liver reconstruction of Atlantic cod (Gadus morhua). We also show how experimental data, here gene expression data, can be mapped to the resulting model to understand the metabolic response of cod liver to environmental toxicants.

NCMP-04. BRAIN-UMAP: THE GENETIC INTERSECTION BETWEEN NEUROSCIENCE, NEUROLOGY, PSYCHIATRY, AND ONCOLOGY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi148-vi148
Author(s):  
Sonali Arora ◽  
Nicholas Nuechterlein ◽  
Siobhan Pattwell ◽  
Eric Holland
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Brain Regions ◽  
Human Diseases ◽  
Next Generation Sequencing Data ◽  
Normal Brain ◽  
Expression Data ◽  
Sequencing Data ◽  
Insight Into ◽  
Genome Atlas

Abstract Whole transcriptome sequencing (RNA-seq) is an important tool for understanding genetic mechanisms underlying human diseases and gaining a better insight into complex human diseases. Several ground-breaking projects have uniformly processed RNASeq data from publicly available studies to enable cross-comparison. One noteworthy study is the recount2 pipeline, which in 2017, has reprocessed ~70,0000 samples from Short Read Archive(SRA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx). This vast dataset also includes gene expression data for GTEx-defined brain regions, neurological and psychiatric disorders (such as Parkinson's, Alzheimer’s, Huntington’s) and gliomas (such as TCGA, Chinese Glioma Genome Atlas (CGGA)). We apply uniform manifold approximation and projection (UMAP), a non-linear dimension reduction tool, to bulk gene expression data from brain-related diseases to build a BRAIN-UMAP, which allows for visualization of gene expression profiles across datasets. This UMAP shows that while gliomas form a distinct cluster, the neurological and psychiatric diseases are similar to GTEX-defined normal brain regions which exhibit tissue-specific profiles and patterns. Incorporating gliomas from various publicly available datasets also allows for the ability to observe unique clustering of particular subtypes, which can increase our genetic understanding of the disease. We also present a resource where researchers interested in mechanisms, can easily compare, and contrast the expression of a given gene and/or pathway of interest across various diseases, gliomas, and normal brain regions. Our current study, focusing on brain related diseases, offers insight into what may be possible for the broader neuroscientific community if we continually reprocess newly available brain related RNASeq samples using recount2. Additionally, if we build similar uniformly processing pipelines for other kinds of next-generation sequencing data, we would be able to use multi-omic sequencing data to find novel associations between biological entities and increase our mechanistic knowledge of the disease.

scholarly journals Novel Insight into the Aetiology of Autism Spectrum Disorder Gained by Integrating Expression Data with Genome-wide Association Statistics

2018 ◽  
Author(s):  
Oliver Pain ◽  
Andrew J. Pocklington ◽  
Peter A. Holmans ◽  
Nicholas J. Bray ◽  
Heath E. O’Brian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Expression Data ◽  
Genome Wide ◽  
Insight Into

AbstractBackgroundA recent genome-wide association study (GWAS) of autism spectrum disorders (ASD) (Ncases=18,381, Ncontrols=27,969) has provided novel opportunities for investigating the aetiology of ASD. Here, we integrate the ASD GWAS summary statistics with summary-level gene expression data to infer differential gene expression in ASD, an approach called transcriptome-wide association study (TWAS).MethodsUsing FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brain. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways, specific tissues, and at different stages of brain development. The proportion of ASD heritability explained by predicted expression of genes in the TWAS was estimated using stratified linkage disequilibrium-score regression.ResultsThis study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into functional consequence of this associated locus. One novel transcriptome-wide significant association from this study is the downregulation of PDIA6, which showed minimal evidence of association in the GWAS, and in gene-based analysis using MAGMA. Predicted gene expression in this study accounted for 13.0% of the total ASD SNP-heritability.ConclusionThis study has implicated several genes as significantly up-/down-regulated in ASD providing novel and useful information for subsequent functional studies. This study also explores the utility of TWAS-based enrichment analysis and compares TWAS results with a functionally agnostic approach.

Phylogeny of actin and tubulin gene homologs in diverse eukaryotic species

2019 ◽  
Vol 79 (01S) ◽  
Author(s):  
Pawan Kumar Jayaswal ◽  
Asheesh Shanker ◽  
Nagendra Kumar Singh
Keyword(s):  
Evolutionary Relationship ◽  
Genomic Data ◽  
Gene Clusters ◽  
Species Trees ◽  
Model Species ◽  
Tubulin Gene ◽  
Tubulin Genes ◽  
Eukaryotic Species ◽  
Relationship Of ◽  
Insight Into

Actin and tubulin are cytoskeleton proteins, which are important components of the celland are conserved across species. Despite their crucial significance in cell motility and cell division the distribution and phylogeny of actin and tubulin genes across taxa is poorly understood. Here we used publicly available genomic data of 49 model species of plants, animals, fungi and Protista for further understanding the distribution of these genes among diverse eukaryotic species using rice as reference. The highest numbers of rice actin and tubulin gene homologs were present in plants followed by animals, fungi and Protista species, whereas ten actin and nine tubulin genes were conserved in all 49 species. Phylogenetic analysis of 19 actin and 18 tubulin genes clustered them into four major groups each. One each of the actin and tubulin gene clusters was conserved across eukaryotic species. Species trees based on the conserved actin and tubulin genes showed evolutionary relationship of 49 different taxa clustered into plants, animals, fungi and Protista. This study provides a phylogenetic insight into the evolution of actin and tubulin genes in diverse eukaryotic species.

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