scholarly journals The Tiger Rattlesnake genome reveals a complex genotype underlying a simple venom phenotype

2021 ◽  
Vol 118 (4) ◽  
pp. e2014634118
Author(s):  
Mark J. Margres ◽  
Rhett M. Rautsaw ◽  
Jason L. Strickland ◽  
Andrew J. Mason ◽  
Tristan D. Schramer ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Complex Traits ◽  
Gene Loss ◽  
Chromatin Accessibility ◽  
Model System ◽  
Complete Characterization ◽  
Regulatory Mechanisms ◽  
Snake Venoms ◽  
Gene Regulatory

Variation in gene regulation is ubiquitous, yet identifying the mechanisms producing such variation, especially for complex traits, is challenging. Snake venoms provide a model system for studying the phenotypic impacts of regulatory variation in complex traits because of their genetic tractability. Here, we sequence the genome of the Tiger Rattlesnake, which possesses the simplest and most toxic venom of any rattlesnake species, to determine whether the simple venom phenotype is the result of a simple genotype through gene loss or a complex genotype mediated through regulatory mechanisms. We generate the most contiguous snake-genome assembly to date and use this genome to show that gene loss, chromatin accessibility, and methylation levels all contribute to the production of the simplest, most toxic rattlesnake venom. We provide the most complete characterization of the venom gene-regulatory network to date and identify key mechanisms mediating phenotypic variation across a polygenic regulatory network.

scholarly journals Chromatin Accessibility-Based Characterization of the Gene Regulatory Network Underlying Plasmodium falciparum Blood-Stage Development

2018 ◽  
Vol 23 (4) ◽  
pp. 557-569.e9 ◽  
Author(s):  
Christa Geeke Toenhake ◽  
Sabine Anne-Kristin Fraschka ◽  
Mahalingam Shanmugiah Vijayabaskar ◽  
David Robert Westhead ◽  
Simon Jan van Heeringen ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Chromatin Accessibility ◽  
Blood Stage ◽  
Stage Development ◽  
Gene Regulatory

scholarly journals Interpreting ruminant specific conserved non-coding elements by developmental gene regulatory network

2021 ◽  
Author(s):  
Xiangyu Pan ◽  
Zhaoxia Ma ◽  
Xinqi Sun ◽  
Hui Li ◽  
Tingting Zhang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Systematic Approach ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Developmental Gene ◽  
Gene Regulatory ◽  
Evo Devo ◽  
Interpretation Method

Biologists long recognized that the genetic information encoded in DNA leads to trait innovation via gene regulatory network (GRN) in development. Here, we generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep and revealed 1,601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we developed a Conserved Non-coding Element interpretation method by gene Regulatory network (CNEReg) to define toolkit transcription factors (TTF) and model its regulation on rumen specific gene via batteries of active-RSCNEs during development. Our developmental GRN reveals 18 TTFs and 313 active-RSCNEs regulating the functional modules of the rumen and identifies OTX1, SOX21, HOXC8, SOX2, TP63, PPARG and 16 active-RSCNEs that functionally distinguish the rumen from the esophagus. We argue that CNEReg is an attractive systematic approach to integrate evo-devo concepts with omics data to understand how gene regulation evolves and shapes complex traits.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Active enhancer and chromatin accessibility landscapes chart the regulatory network of primary multiple myeloma

Blood ◽  
2018 ◽  
Vol 131 (19) ◽  
pp. 2138-2150 ◽  
Author(s):  
Yi Jin ◽  
Kenian Chen ◽  
Ayla De Paepe ◽  
Eva Hellqvist ◽  
Aleksandra D. Krstic ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Regulatory Network ◽  
Chromatin Accessibility ◽  
Epigenetic Changes ◽  
Active Enhancer ◽  
Key Points ◽  
Gene Regulatory

Key Points Gene regulatory features in MM patients reveal a key regulatory network and epigenetic changes that underpin the disease.

Characterization of a gene regulatory network underlying astringency loss in persimmon fruit

Planta ◽  
2017 ◽  
Vol 247 (3) ◽  
pp. 733-743 ◽  
Author(s):  
Soichiro Nishiyama ◽  
Noriyuki Onoue ◽  
Atsushi Kono ◽  
Akihiko Sato ◽  
Keizo Yonemori ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Persimmon Fruit ◽  
Gene Regulatory

scholarly journals Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

2021 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Multiple Testing ◽  
Critical Role ◽  
Supplementary Information ◽  
Effective Sample Size ◽  
Eqtl Mapping ◽  
Gene Regulatory ◽  
Cis And Trans

Abstract Motivation Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. Results In this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further employed to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. Availability R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The transcription factor Zic4 acts as a transdifferentiation switch

2021 ◽  
Author(s):  
Matthias Christian Vogg ◽  
Jaroslav Ferenc ◽  
Wanda Christa Buzgariu ◽  
Chrystelle Perruchoud ◽  
Panagiotis Papasaikas ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Transcription Factor ◽  
Gene Regulatory Network ◽  
Cell Fate ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Model System ◽  
Cell Identity ◽  
Transcriptional Switch ◽  
Gene Regulatory

The molecular mechanisms that maintain cell identities and prevent transdifferentiation remain mysterious. Interestingly, both dedifferentiation and transdifferentiation are transiently reshuffled during regeneration. Therefore, organisms that regenerate readily offer a fruitful paradigm to investigate the regulation of cell fate stability. Here, we used Hydra as a model system and show that Zic4 silencing is sufficient to induce transdifferentiation of tentacle into foot cells. We identified a Wnt-controlled Gene Regulatory Network that controls a transcriptional switch of cell identity. Furthermore, we show that this switch also controls the re-entry into the cell cycle. Our data indicate that maintenance of cell fate by a Wnt-controlled GRN is a key mechanism during both homeostasis and regeneration.

scholarly journals Experimental approaches for gene regulatory network construction: The chick as a model system

genesis ◽  
2012 ◽  
Vol 51 (5) ◽  
pp. 296-310 ◽  
Author(s):  
Andrea Streit ◽  
Monica Tambalo ◽  
Jingchen Chen ◽  
Timothy Grocott ◽  
Maryam Anwar ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Model System ◽  
Network Construction ◽  
Experimental Approaches ◽  
Gene Regulatory
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