scholarly journals Differential gene regulatory pathways and co-expression networks associated with fire blight infection in apple (Malus × domestica)

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Katchen Julliany Pereira Silva ◽  
Jugpreet Singh ◽  
Ryland Bednarek ◽  
Zhangjun Fei ◽  
Awais Khan
Keyword(s):  
Malus Domestica ◽  
Fire Blight ◽  
Regulatory Pathways ◽  
Differential Gene ◽  
Gene Regulatory

scholarly journals Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

2020 ◽  
Vol 21 (24) ◽  
pp. 9461
Author(s):  
Aurora Savino ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Cancer Biology ◽  
Regulatory Networks ◽  
Tumour Progression ◽  
External Perturbation ◽  
Data Types ◽  
Differential Gene ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Definition Of ◽  
Future Work

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

scholarly journals Synaptic and Gene Regulatory Mechanisms in Schizophrenia, Autism, and 22q11.2 CNV Mediated Risk for Neuropsychiatric Disorders

2019 ◽  
Author(s):  
Jennifer K. Forsyth ◽  
Daniel Nachun ◽  
Michael J. Gandal ◽  
Daniel H. Geschwind ◽  
Ariana E. Anderson ◽  
...  
Keyword(s):  
Gene Networks ◽  
Disease Risk ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Synaptic Function ◽  
Protein Interaction Data ◽  
Regulatory Pathways ◽  
Polygenic Risk ◽  
Risk Variants ◽  
Gene Regulatory

AbstractBackground22q11.2 copy number variants (CNVs) are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear.MethodsUsing a functional genomics approach, we integrated transcriptomic data from the developing human brain, genome-wide association findings for SCZ and ASD, protein interaction data, and pathophysiological signatures of SCZ and ASD to: 1) organize genes into the developmental cellular and molecular systems within which they operate; 2) identify neurodevelopmental processes associated with polygenic risk for SCZ and ASD across the allelic frequency spectrum; and 3) elucidate pathways and individual genes through which 22q11.2 CNVs may confer risk for each disorder.ResultsPolygenic risk for SCZ and ASD converged on partially overlapping gene networks involved in synaptic function and transcriptional regulation, with ASD risk variants additionally enriched for networks involved in neuronal differentiation during fetal development. The 22q11.2 locus formed a large protein network that disproportionately affected SCZ- and ASD-associated neurodevelopmental networks, including loading highly onto synaptic and gene regulatory pathways. SEPT5, PI4KA, and SNAP29 genes are candidate drivers of 22q11.2 synaptic pathology relevant to SCZ and ASD, and DGCR8 and HIRA are candidate drivers of disease-relevant alterations in gene regulation.ConclusionsThe current approach provides a powerful framework to identify neurodevelopmental processes affected by diverse risk variants for SCZ and ASD, and elucidate the mechanisms through which highly penetrant multi-gene CNVs contribute to disease risk.

Prenet: Predictive network from ATAC-SEQ data

2020 ◽  
Vol 18 (01) ◽  
pp. 2040003 ◽  
Author(s):  
Nazmus Salehin ◽  
Patrick P. L. Tam ◽  
Pierre Osteil
Keyword(s):  
Regulatory Network ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Novel Approach ◽  
Multiple Cell ◽  
Bona Fide ◽  
Gene Regulatory ◽  
Accessible Chromatin

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips in sequencing peaks can be attributed to the binding of transcription factors. These dips, also known as footprints, can be used to identify trans-regulating interactions leading to gene expression. Current protocols used to identify footprints (e.g. pyDNAse and HINT-ATAC) have shown limitations resulting in the discovery of many false positive footprints. We generated a novel approach to identify genuine footprints within any given ATAC-seq dataset. Herein, we developed a new pipeline embedding DACs together with bona fide footprints resulting in the generation of a Predictive gene regulatory Network (PreNet) simply from ATAC-seq data. We further demonstrated that PreNet can be used to unveil meaningful molecular regulatory pathways in a given cell type.

scholarly journals When Viruses Cross Developmental Pathways

2021 ◽  
Vol 9 ◽  
Author(s):  
Pankaj Trivedi ◽  
Sandesh Kumar Patel ◽  
Diana Bellavia ◽  
Elena Messina ◽  
Rocco Palermo ◽  
...  
Keyword(s):  
Human Tumor ◽  
Lytic Cycle ◽  
Developmental Pathways ◽  
Oncogenic Viruses ◽  
Regulatory Pathways ◽  
Antiviral Therapies ◽  
Viral Immune Evasion ◽  
Tumor Viruses ◽  
Metastatic Behavior ◽  
Gene Regulatory

Aberrant regulation of developmental pathways plays a key role in tumorigenesis. Tumor cells differ from normal cells in their sustained proliferation, replicative immortality, resistance to cell death and growth inhibition, angiogenesis, and metastatic behavior. Often they acquire these features as a consequence of dysregulated Hedgehog, Notch, or WNT signaling pathways. Human tumor viruses affect the cancer cell hallmarks by encoding oncogenic proteins, and/or by modifying the microenvironment, as well as by conveying genomic instability to accelerate cancer development. In addition, viral immune evasion mechanisms may compromise developmental pathways to accelerate tumor growth. Viruses achieve this by influencing both coding and non-coding gene regulatory pathways. Elucidating how oncogenic viruses intersect with and modulate developmental pathways is crucial to understanding viral tumorigenesis. Many currently available antiviral therapies target viral lytic cycle replication but with low efficacy and severe side effects. A greater understanding of the cross-signaling between oncogenic viruses and developmental pathways will improve the efficacy of next-generation inhibitors and pave the way to more targeted antiviral therapies.

scholarly journals Compensatory mutation can drive gene regulatory network evolution

2019 ◽  
Author(s):  
Yifei Wang ◽  
Marios Richards ◽  
Steve Dorus ◽  
Nicholas K. Priest ◽  
Joanna J. Bryson
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Evolution ◽  
Regulatory Function ◽  
Compensatory Mutation ◽  
Relaxed Selection ◽  
Mutation Site ◽  
Regulatory Pathways ◽  
Gene Regulatory

AbstractGene regulatory networks underlie every aspect of life; better understanding their assembly would better our understanding of evolution more generally. For example, evolutionary theory typically assumed that low-fitness intermediary pathways are not a significant factor in evolution, yet there is substantial empirical evidence of compensatory mutation. Here we revise theoretical assumptions to explore the possibility that compensatory mutation may drive rapid evolutionary recovery. Using a well-established in silico model of gene regulatory networks, we show that assuming only that deleterious mutations are not fatal, compensatory mutation is surprisingly frequent. Further, we find that it entails biases that drive the evolution of regulatory pathways. In our simulations, we find compensatory mutation to be common during periods of relaxed selection, with 8-15% of degraded networks having regulatory function restored by a single randomly-generated additional mutation. Though this process reduces average robustness, proportionally higher robustness is found in networks where compensatory mutations occur close to the deleterious mutation site, or where the compensatory mutation results in a large regulatory effect size. This location- and size-specific robustness systematically biases which networks are purged by selection for network stability, producing emergent changes to the population of regulatory networks. We show that over time, large-effect and co-located mutations accumulate, assuming only that episodes of relaxed selection occur, even very rarely. This accumulation results in an increase in regulatory complexity. Our findings help explain a process by which large-effect mutations structure complex regulatory networks, and may account for the speed and pervasiveness of observed occurrence of compensatory mutation, for example in the context of antibiotic resistance, which we discuss. If sustained by in vitro experiments, these results promise a significant breakthrough in the understanding of evolutionary and regulatory processes.

Sign in / Sign up

Export Citation Format

Share Document