scholarly journals Auxin-induced expression divergence between Arabidopsis species likely originates within the TIR1/AFB-AUX/IAA-ARF module

2016 ◽  
Author(s):  
Jana Trenner ◽  
Yvonne Poeschl ◽  
Jan Grau ◽  
Andreas Gogol-Döring ◽  
Marcel Quint ◽  
...  
Keyword(s):  
Gene Expression ◽  
Motif Discovery ◽  
Natural Variation ◽  
De Novo ◽  
Gene Clusters ◽  
Auxin Signaling ◽  
Expression Divergence ◽  
Sources Of Variation ◽  
Species Comparisons ◽  
Signaling Components

HighlightTIR1/AFB, AUX/IAA, and ARF proteins show interspecies expression variation correlating with variation in downstream responses which indicates a source for natural variation within this conserved signaling module.AbstractAuxin is an essential regulator of plant growth and development and auxin signaling components are conserved among land plants. Yet, a remarkable degree of natural variation in physiological and transcriptional auxin responses has been described among Arabidopsis thaliana accessions. As intra-species comparisons offer only limited genetic variation, we here inspect the variation of auxin responses between A. thaliana and A. lyrata. This approach allowed the identification of conserved auxin response genes including novel genes with potential relevance for auxin biology. Furthermore, promoter divergences were analyzed for putative sources of variation. De novo motif discovery identified novel and variants of known elements with potential relevance for auxin responses, emphasizing the complex, and yet elusive, code of element combinations accounting for the diversity in transcriptional auxin responses. Furthermore, network analysis revealed correlations of inter-species differences in the expression of AUX/IAA gene clusters and classic auxin-related genes. We conclude that variation in general transcriptional and physiological auxin responses may originate substantially from functional or transcriptional variations in the TIR1/AFB, AUX/IAA, and ARF signaling network. In that respect, AUX/IAA gene expression divergence potentially reflects differences in the manner in which different species transduce identical auxin signals into gene expression responses.

De Novo Motif Prediction Using the Fireworks Algorithm

Biotechnology ◽  
2019 ◽  
pp. 1069-1085
Author(s):  
Andrei Lihu ◽  
Ștefan Holban
Keyword(s):  
Gene Expression ◽  
Motif Discovery ◽  
De Novo ◽  
Fireworks Algorithm ◽  
Regulatory Processes ◽  
Motif Prediction ◽  
Leibler Divergence ◽  
De Novo Motif Discovery ◽  
Low Performance ◽  
Motif Finding Algorithm

De novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Evolutionary methods, including swarm intelligence algorithms, have been applied with limited success for motif prediction. However, recently developed methods, such as the Fireworks Algorithm (FWA) which simulates the explosion process of fireworks, may show better prospects. This paper describes a motif finding algorithm based on FWA that maximizes the Kullback-Leibler divergence between candidate solutions and the background noise. Following the terminology of FWA's framework, the candidate motifs are fireworks that generate additional sparks (i.e. derived motifs) in their neighborhood. During the iterations, better sparks can replace the fireworks, as the Fireworks Motif Finder (FW-MF) assumes a one occurrence per sequence mode. The results obtained on a standard benchmark for promoter analysis show that our proof of concept is promising.

DISPOM: A DISCRIMINATIVE DE-NOVO MOTIF DISCOVERY TOOL BASED ON THE JSTACS LIBRARY

2013 ◽  
Vol 11 (01) ◽  
pp. 1340006 ◽  
Author(s):  
JAN GRAU ◽  
JENS KEILWAGEN ◽  
ANDRÉ GOHR ◽  
IVAN A. PAPONOV ◽  
STEFAN POSCH ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Binding ◽  
Binding Sites ◽  
Motif Discovery ◽  
Binding Proteins ◽  
Target Genes ◽  
De Novo ◽  
Specific Binding ◽  
Dna Binding Proteins ◽  
De Novo Motif Discovery

DNA-binding proteins are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in target regions of genomic DNA. However, de-novo discovery of these binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not yet been solved satisfactorily. Here, we present a detailed description and analysis of the de-novo motif discovery tool Dispom, which has been developed for finding binding sites of DNA-binding proteins that are differentially abundant in a set of target regions compared to a set of control regions. Two additional features of Dispom are its capability of modeling positional preferences of binding sites and adjusting the length of the motif in the learning process. Dispom yields an increased prediction accuracy compared to existing tools for de-novo motif discovery, suggesting that the combination of searching for differentially abundant motifs, inferring their positional distributions, and adjusting the motif lengths is beneficial for de-novo motif discovery. When applying Dispom to promoters of auxin-responsive genes and those of ABI3 target genes from Arabidopsis thaliana, we identify relevant binding motifs with pronounced positional distributions. These results suggest that learning motifs, their positional distributions, and their lengths by a discriminative learning principle may aid motif discovery from ChIP-chip and gene expression data. We make Dispom freely available as part of Jstacs, an open-source Java library that is tailored to statistical sequence analysis. To facilitate extensions of Dispom, we describe its implementation using Jstacs in this manuscript. In addition, we provide a stand-alone application of Dispom at http://www.jstacs.de/index.php/Dispom for instant use.

scholarly journals Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

2016 ◽  
Author(s):  
Shirley Pepke ◽  
Greg Ver Steeg
Keyword(s):  
Gene Expression ◽  
Drug Targets ◽  
De Novo ◽  
Epithelial Mesenchymal Transition ◽  
Learning Algorithm ◽  
Gene Clusters ◽  
Rna Seq ◽  
Latent Factors ◽  
Mesenchymal Transition ◽  
Therapeutic Implications

BackgroundDe novoinference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations (co-expression) that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.MethodsIn this work we adapt a recently developed machine learning algorithm, CorEx, that efficiently optimizes over multivariate mutual information for sensitive detection of complex gene relationships. The algorithm can be iteratively applied to generate a hierarchy of latent factors. Patients are stratified relative to each factor and combinatoric survival analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies.ResultsAnalysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a potentially druggable microRNA. Further, combinations of factors lead to a synergistic survival advantage in some cases.ConclusionsIn contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are both found to have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.

scholarly journals EPEN-41. C11orf95-RELA FUSION REGULATES ABERRANT GENE EXPRESSION THROUGH THE UNIQUE GENOMIC BINDING SITES FOR EPENDYMOMA FORMATION

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii316-iii316
Author(s):  
Tatsuya Ozawa ◽  
Syuzo Kaneko ◽  
Mutsumi Takadera ◽  
Eric Holland ◽  
Ryuji Hamamoto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Binding ◽  
Binding Sites ◽  
Motif Discovery ◽  
Target Genes ◽  
Cultured Cells ◽  
De Novo ◽  
Binding Motif ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

Abstract A majority of supratentorial ependymoma is associated with recurrent C11orf95-RELA fusion (RELAFUS). The presence of RELA as one component of the RELAFUS leads to the suggestion that NF-kB activity is involved in the ependymoma formation, thus being a viable therapeutic target in these tumors. However, the oncogenic role of another C11orf95 component in the tumorigenesis is not still determined. In this study, to clarify the molecular mechanism underlying tumorigenesis of RELAFUS, we performed RELAFUS-ChIP-Seq analysis in cultured cells expressing the RELAFUS protein. Genomic profiling of RELAFUS binding sites pinpointed the transcriptional target genes directly regulated by RELAFUS. We then identified a unique DNA binding motif of the RELAFUS different from the canonical NF-kB motif in de novo motif discovery analysis. Significant responsiveness of RELAFUS but not RELA to the motif was confirmed in the reporter assay. An N-terminal portion of C11orf95 was sufficient to localize in the nucleus and recognizes the unique motif. Interestingly, the RELAFUS peaks concomitant with the unique motif were identified around the transcription start site in the RELAFUS target genes as previously reported. These observations suggested that C11orf95 might have served as a key determinant for the DNA binding sites of RELAFUS, thereby induced aberrant gene expression necessary for ependymoma formation. Our results will give insights into the development of new ependymoma therapy.

scholarly journals SArKS: de novo discovery of gene expression regulatory motifs and domains by suffix array kernel smoothing

2017 ◽  
Author(s):  
Dennis Wylie ◽  
Hans A. Hofmann ◽  
Boris V. Zemelman
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Kernel Smoothing ◽  
De Novo ◽  
Supplementary Information ◽  
Spatial Proximity ◽  
Regulatory Sequences ◽  
Expression Data

AbstractMotivationWe set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score—fold-change, test-statistic, p-value—comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies nonparametric kernel smoothing to uncover promoter motifs that correlate with elevated differential expression scores. SArKS detects motifs by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.ResultsWe applied SArKS to published gene expression data representing distinct neocortical neuron classes in M. musculus and interneuron developmental states in H. sapiens. When benchmarked against several existing algorithms for correlative motif discovery using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.Availabilityhttps://github.com/denniscwylie/[email protected] informationappended to document.

scholarly journals Alternative splicing during mammalian organ development

2021 ◽  
Author(s):  
Pavel V. Mazin ◽  
Philipp Khaitovich ◽  
Margarida Cardoso-Moreira ◽  
Henrik Kaessmann
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Regulatory Networks ◽  
Organ Development ◽  
Functional Diversification ◽  
Mammalian Genomes ◽  
Species Comparisons ◽  
Time Specific ◽  
The Brain ◽  
Gene Expression Levels

AbstractAlternative splicing (AS) is pervasive in mammalian genomes, yet cross-species comparisons have been largely restricted to adult tissues and the functionality of most AS events remains unclear. We assessed AS patterns across pre- and postnatal development of seven organs in six mammals and a bird. Our analyses revealed that developmentally dynamic AS events, which are especially prevalent in the brain, are substantially more conserved than nondynamic ones. Cassette exons with increasing inclusion frequencies during development show the strongest signals of conserved and regulated AS. Newly emerged cassette exons are typically incorporated late in testis development, but those retained during evolution are predominantly brain specific. Our work suggests that an intricate interplay of programs controlling gene expression levels and AS is fundamental to organ development, especially for the brain and heart. In these regulatory networks, AS affords substantial functional diversification of genes through the generation of tissue- and time-specific isoforms from broadly expressed genes.

scholarly journals AAV-Mediated CRISPRi and RNAi Based Gene Silencing in Mouse Hippocampal Neurons

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 324
Author(s):  
Matthias Deutsch ◽  
Anne Günther ◽  
Rodrigo Lerchundi ◽  
Christine R. Rose ◽  
Sabine Balfanz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Initiation ◽  
Hippocampal Neurons ◽  
De Novo ◽  
Protein Biosynthesis ◽  
Physiological Role ◽  
High Specificity ◽  
Hcn Channels ◽  
Proliferating Cells ◽  
Neuronal Tissues

Uncovering the physiological role of individual proteins that are part of the intricate process of cellular signaling is often a complex and challenging task. A straightforward strategy of studying a protein’s function is by manipulating the expression rate of its gene. In recent years, the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9-based technology was established as a powerful gene-editing tool for generating sequence specific changes in proliferating cells. However, obtaining homogeneous populations of transgenic post-mitotic neurons by CRISPR/Cas9 turned out to be challenging. These constraints can be partially overcome by CRISPR interference (CRISPRi), which mediates the inhibition of gene expression by competing with the transcription machinery for promoter binding and, thus, transcription initiation. Notably, CRISPR/Cas is only one of several described approaches for the manipulation of gene expression. Here, we targeted neurons with recombinant Adeno-associated viruses to induce either CRISPRi or RNA interference (RNAi), a well-established method for impairing de novo protein biosynthesis by using cellular regulatory mechanisms that induce the degradation of pre-existing mRNA. We specifically targeted hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels, which are widely expressed in neuronal tissues and play essential physiological roles in maintaining biophysical characteristics in neurons. Both of the strategies reduced the expression levels of three HCN isoforms (HCN1, 2, and 4) with high specificity. Furthermore, detailed analysis revealed that the knock-down of just a single HCN isoform (HCN4) in hippocampal neurons did not affect basic electrical parameters of transduced neurons, whereas substantial changes emerged in HCN-current specific properties.

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