Unraveling the Genetic Basis for the Rapid Diversification of Male Genitalia between Drosophila Species

Abstract In the last 240,000 years, males of the Drosophila simulans species clade have evolved striking differences in the morphology of their epandrial posterior lobes and claspers (surstyli). These appendages are used for grasping the female during mating and so their divergence is most likely driven by sexual selection. Mapping studies indicate a highly polygenic and generally additive genetic basis for these morphological differences. However, we have limited understanding of the gene regulatory networks that control the development of genital structures and how they evolved to result in this rapid phenotypic diversification. Here, we used new D. simulans/D. mauritiana introgression lines on chromosome arm 3L to generate higher resolution maps of posterior lobe and clasper differences between these species. We then carried out RNA-seq on the developing genitalia of both species to identify the expressed genes and those that are differentially expressed between the two species. This allowed us to test the function of expressed positional candidates during genital development in D. melanogaster. We identified several new genes involved in the development and possibly the evolution of these genital structures, including the transcription factors Hairy and Grunge. Furthermore, we discovered that during clasper development Hairy negatively regulates tartan (trn), a gene known to contribute to divergence in clasper morphology. Taken together, our results provide new insights into the regulation of genital development and how this has evolved between species.

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Unravelling the genetic basis for the rapid diversification of male genitalia between Drosophila species

10.1101/2020.06.19.161018 ◽

2020 ◽

Author(s):

Joanna F. D. Hagen ◽

Cláudia C. Mendes ◽

Shamma R. Booth ◽

Javier Figueras Jimenez ◽

Kentaro M. Tanaka ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Genetic Basis ◽

Posterior Lobe ◽

Rna Seq ◽

New Genes ◽

Genital Development ◽

Rapid Diversification ◽

Gene Regulatory ◽

Phenotypic Diversification

AbstractIn the last 240,000 years, males of the Drosophila simulans species clade have evolved striking differences in the morphology of their epandrial posterior lobes and claspers (surstyli). These changes have most likely been driven by sexual selection and mapping studies indicate a highly polygenic and generally additive genetic basis. However, we have limited understanding of the gene regulatory networks that control the development of genital structures and how they evolved to result in this rapid phenotypic diversification. Here, we used new D. simulans / D. mauritiana introgression lines on chromosome 3L to generate higher resolution maps of posterior lobe and clasper differences between these species. We then carried out RNA-seq on the developing genitalia of both species to identify the genes expressed during this process and those that are differentially expressed between the two species. This allowed us to test the function of expressed positional candidates during genital development in D. melanogaster. We identified several new genes involved in the development and possibly the evolution of these genital structures, including the transcription factors Hairy and Grunge. Furthermore, we discovered that during clasper development Hairy negatively regulates tartan, a gene known to contribute to divergence in clasper morphology. Taken together our results provide new insights into the regulation of genital development and how this evolves between species.

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Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽

10.1016/j.patter.2021.100332 ◽

2021 ◽

Vol 2 (9) ◽

pp. 100332

Author(s):

N. Alexia Raharinirina ◽

Felix Peppert ◽

Max von Kleist ◽

Christof Schütte ◽

Vikram Sunkara

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Higher Order ◽

Rna Seq ◽

Higher Order Moments ◽

Gene Regulatory

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Characterization of rare spindle and root cell transcriptional profiles in the stria vascularis of the adult mouse cochlea

Scientific Reports ◽

10.1038/s41598-020-75238-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Shoujun Gu ◽

Rafal Olszewski ◽

Ian Taukulis ◽

Zheng Wei ◽

Daniel Martin ◽

...

Keyword(s):

Hearing Loss ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Adult Mouse ◽

Stria Vascularis ◽

Cell Types ◽

Rna Seq ◽

Root Cells ◽

Transcriptional Profiles ◽

Gene Regulatory

Abstract The stria vascularis (SV) in the cochlea generates and maintains the endocochlear potential, thereby playing a pivotal role in normal hearing. Knowing transcriptional profiles and gene regulatory networks of SV cell types establishes a basis for studying the mechanism underlying SV-related hearing loss. While we have previously characterized the expression profiles of major SV cell types in the adult mouse, transcriptional profiles of rare SV cell types remained elusive due to the limitation of cell capture in single-cell RNA-Seq. The role of these rare cell types in the homeostatic function of the adult SV remain largely undefined. In this study, we performed single-nucleus RNA-Seq on the adult mouse SV in conjunction with sample preservation treatments during the isolation steps. We distinguish rare SV cell types, including spindle cells and root cells, from other cell types, and characterize their transcriptional profiles. Furthermore, we also identify and validate novel specific markers for these rare SV cell types. Finally, we identify homeostatic gene regulatory networks within spindle and root cells, establishing a basis for understanding the functional roles of these cells in hearing. These novel findings will provide new insights for future work in SV-related hearing loss and hearing fluctuation.

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DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

10.1101/2021.02.03.429484 ◽

2021 ◽

Author(s):

Jiaxing Chen ◽

Chinwang Cheong ◽

Liang Lan ◽

Xin Zhou ◽

Jiming Liu ◽

...

Keyword(s):

Neural Network ◽

Single Cell ◽

Regulatory Networks ◽

Deep Neural Network ◽

Neighborhood Context ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Rna Seq ◽

Cell Type Specific ◽

Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

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ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

10.7287/peerj.preprints.3328 ◽

2017 ◽

Author(s):

Mikhail Pachkov ◽

Piotr J Balwierz ◽

Phil Arnold ◽

Andreas J Gruber ◽

Mihaela Zavolan ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Regulatory Networks ◽

Target Genes ◽

Chromatin State ◽

Response Analysis ◽

Rna Seq ◽

High Throughput Data ◽

Micro Rnas ◽

Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

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EchinoDB: An update to the web-based application for genomic and transcriptomic data on Echinoderms.

10.1101/2022.01.03.474134 ◽

2022 ◽

Author(s):

Varnika Mittal ◽

Robert W. Reid ◽

Denis Jacob Machado ◽

Vladimir Mashanov ◽

Dan A Janies

Keyword(s):

Regulatory Networks ◽

Sequence Similarity ◽

Lytechinus Variegatus ◽

Rna Seq ◽

Web Based ◽

Transcriptomic Data ◽

Green Sea Urchin ◽

R Shiny ◽

Gene Regulatory ◽

Keyword Searches

Here we release a new version of EchinoDB (https://echinodb.uncc.edu). EchinoDB is a database of genomic and transcriptomic data on echinoderms. The initial database consisted of groups of 749,397 orthologous and paralogous transcripts arranged in orthoclusters by sequence similarity. The new version of EchinoDB includes RNA-seq data of the brittle star Ophioderma brevispinum and high-quality genomic assembly data of the green sea urchin Lytechinus variegatus. In addition, we enabled keyword searches for annotated data and installed an updated version of Sequenceserver to allow BLAST searches. The data are downloadable in FASTA format. The first version of EchinoDB appeared in 2016 and was implemented in GO on a local server. The new version has been updated using R Shiny to include new features and improvements in the application. Furthermore, EchinoDB now runs entirely in the cloud for increased reliability and scaling. EchinoDB enjoys a user base drawn from the fields of phylogenetics, developmental biology, genomics, physiology, neurobiology, and regeneration. As use cases, we illustrate how EchinoDB is used in discovering pathways and gene regulatory networks involved in the tissue regeneration process.

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Identifying progressive gene network perturbation from single-cell RNA-seq data

10.1101/297275 ◽

2018 ◽

Author(s):

Sumit Mukherjee ◽

Alberto Carignano ◽

Georg Seelig ◽

Su-In Lee

Keyword(s):

Single Cell ◽

Gene Networks ◽

Regulatory Networks ◽

Simulated Data ◽

Alternative Methods ◽

Rna Seq ◽

Differential Network Analysis ◽

Master Regulators ◽

Differential Network ◽

Gene Regulatory

AbstractIdentifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.

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A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets

Bioinformation ◽

10.6026/97320630012340 ◽

2016 ◽

Vol 12 (06) ◽

pp. 340-341 ◽

Cited By ~ 6

Author(s):

Fereshteh Izadi ◽

◽

Hamid Najafi Zarrini ◽

Nadali Babaeian Jelodar ◽

◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Rna Seq ◽

Gene Regulatory ◽

Analytical Assay

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PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

10.1101/2020.12.18.423482 ◽

2020 ◽

Author(s):

Ming Wu ◽

Tim Kacprowski ◽

Dietmar Zehn

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Cellular Level ◽

Rna Seq ◽

Analysis Workflow ◽

Gene Regulatory ◽

Cell Trajectory ◽

User Friendly ◽

Molecular Expression ◽

Marker Detection

AbstractSummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way.AvailabilitySource code and documentation are available: https://github.com/MingBit/[email protected]

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Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data

10.1101/2021.01.20.427499 ◽

2021 ◽

Author(s):

Klebea Carvalho ◽

Elisabeth Rebboah ◽

Camden Jansen ◽

Katherine Williams ◽

Andrew Dowey ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Course ◽

Macrophage Polarization ◽

Cell Polarization ◽

Rna Seq ◽

Gene Regulatory ◽

Cell Subpopulations ◽

Cell Data

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

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