The Degenerate Tale of Ascidian Tails

Abstract Ascidians are invertebrate chordates, with swimming chordate tadpole larvae that have distinct heads and tails. The head contains the small brain, sensory organs, including the ocellus (light) and otolith (gravity) and the presumptive endoderm, while the tail has a notochord surrounded by muscle cells and a dorsal nerve cord. One of the chordate features is a post-anal tail. Ascidian tadpoles are nonfeeding, but their tail is critical for larval locomotion. After hatching the larvae swim up towards light and are carried by the tide and ocean currents. When competent to settle, ascidian tadpole larvae swim down, away from light, to settle and metamorphose into a sessile adult. Tunicates are classified as chordates because of their chordate tadpole larvae; in contrast, the sessile adult has a U-shaped gut and very derived body plan, looking nothing like a chordate. There is one group of ascidians, the Molgulidae, where many species are known to have tailless larvae. The Swalla Lab has been studying the evolution of tailless ascidian larvae in this clade for over thirty years and has shown that tailless larvae have evolved independently several times in this clade. Comparison of the genomes of two closely related species, the tailed Molgula oculata and tailless Molgula occulta reveals much synteny, but there have been multiple insertions and deletions that have disrupted larval genes in the tailless species. Genomics and transcriptomics have previously shown that there are expressed pseudogenes in the tailless embryos, suggesting that the partial rescue of tailed features in their hybrid larvae is due to the expression of intact genes from the tailed parent. Yet surprisingly, we find that the notochord gene regulatory network is mostly intact in the tailless M. occulta, although the notochord does not converge and extend and remains as an aggregate of cells we call the “notoball”. We expect that eventually many of the larval gene networks will be become evolutionarily lost in tailless ascidians and the larval body plan abandoned, with eggs developing directly into an adult. Here we review the current evolutionary and developmental evidence on how the molgulids lost their tails.

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BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

10.1101/032383 ◽

2015 ◽

Author(s):

Aurélie Pirayre ◽

Camille Couprie ◽

Frédérique Bidard ◽

Laurent Duval ◽

Jean-Christophe Pesquet

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Networks ◽

Network Inference ◽

State Of The Art ◽

A Priori ◽

Graph Cuts ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Inference Methods

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions. Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6% to 11%). On a real Escherichia coli compendium, an improvement of 11.8% compared to CLR and 3% compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the-art GRN inference methods. It is applicable as a generic network inference post-processing, due its computational efficiency.

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scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference

10.1101/2021.07.08.451697 ◽

2021 ◽

Author(s):

Abdullah Karaaslanli ◽

Satabdi Saha ◽

Selin Aviyente ◽

Tapabrata Maiti

Keyword(s):

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Networks ◽

Signed Graph ◽

Reconstruction Algorithms ◽

Gene Regulatory Network Inference ◽

Graph Learning ◽

Gene Regulatory ◽

Cell Gene

Elucidating the topology of gene regulatory networks (GRN) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a characteristic feature of GRNs, as they account for both activating and inhibitory relationships between genes. To this end, we propose a novel signed GL approach, scSGL, that incorporates the similarity and dissimilarity between observed gene expression data to construct gene networks. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. In our experiments on simulated and real single cell datasets, scSGL compares favorably with other single cell gene regulatory network reconstruction algorithms.

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Combinatorial analysis of gene regulatory network reveals the causal genetic basis of breast cancer and gene-specific personalized drug treatments

10.1101/2020.04.10.035998 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mohammad Kawsar Sharif Siam ◽

Mohammad Umer Sharif Shohan ◽

Easin Uddin Syed

Keyword(s):

Breast Cancer ◽

Gene Regulatory Network ◽

Biological System ◽

Regulatory Network ◽

Gene Networks ◽

Genetic Basis ◽

Interaction Model ◽

Proteomic Data ◽

Incidence And Mortality ◽

Gene Regulatory

AbstractCancer is the major burden of diseases around the world. The incidence and mortality rate of cancers is mounting up with the passage of days. Breast cancer is the most demoralizing cause of death, where both diseases interlocked with each other due to some genetic, biological and behavioral motives. The molecular mechanism of breast cancer through which they crop up and manifest together remains questionable. The genetic basis of protein-protein interactions and gene networks has elucidated a group of gene regulatory systems in Breast cancer. Thus, the extraction of all genomic and proteomic data has enabled unprecedented views of gene-protein co-expression, co-regulation, and interactions in the biological system. This study explored the biological system to develop a gene-disease interaction model by implementing the extracted genomic and proteomic data of Breast cancer. The disease-specific and correlated genes were pulled out and their cabling studied by PPI, disease pathway and drug-disease interaction data to articulate their role in disease development. By analyzing mined genes that are related to breast cancer, a network model is also proposed. Exploration of all the correlated genes, Hub and common genes have given some promising pieces of evidence surrounding the genetic networking models. The result of this prospective study disclosed breast cancer mediated crosslinking or possible metastatic relation on a genetic basis. Moreover, other diseases like prostate, colorectal and ovarian cancers are at the same risk and might count into consideration. The finding provides a narrative broad approach for understanding the genetic basis of these fatal diseases by the pathway analysis with gene regulatory network evaluation.

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