scholarly journals Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization

2020 ◽  
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Regulatory Networks ◽  
Sampling Methods ◽  
Streptomyces Coelicolor ◽  
System Level ◽  
Valuable Insight ◽  
Regulatory Interactions ◽  
Network Analyses ◽  
Daunting Task ◽  
Novel Model ◽  
Different Sources

AbstractSome organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species (Mycobacterium tuberculosis, Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pyogenes, Streptococcus pneumoniae, and Streptomyces coelicolor), containing 8,459 regulons and 4,335 modules.Database URLhttps://abasy.ccg.unam.mx/

scholarly journals Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

2018 ◽  
Author(s):  
Adrian I. Campos-González ◽  
Julio A. Freyre-González
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Clustering Coefficient ◽  
Valuable Insight ◽  
Large Set ◽  
Final Size ◽  
Scale Free ◽  
Daunting Task ◽  
Complete Networks

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network is currently complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random. Therefore, attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.

scholarly journals Reconstruction of a global gene regulatory network for Streptomyces coelicolor: Curation, inference, and assessment

2021 ◽  
Author(s):  
Andrea Zorro-Aranda ◽  
Juan Miguel Escorcia-Rodriguez ◽  
Jose Kenyi Gonzalez-Kise ◽  
Julio Augusto Freyre-Gonzalez
Keyword(s):  
Gene Regulatory Network ◽  
Dna Sequences ◽  
Regulatory Network ◽  
Sequence Data ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
System Level ◽  
Upstream Region ◽  
Regulatory Interactions ◽  
Gene Regulatory

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. We found a low level of direct experimental validation for the regulatory interactions reported in the literature and curated in this work. Only ~6% (533/9687) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called "strong" evidence. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions' reliability, finding the inference from DNA sequence data to be the most trustworthy. Finally, we show two possible applications of the inferred and the curated network. The inferred one allowed us to identify putative novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated one allows us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

scholarly journals AB0080 A SYSTEM-LEVEL APPROACH IDENTIFIES A CRITICAL REGULATOR OF CHONDROSARCOMA PROGRESSION

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1340.3-1340
Author(s):  
H. Kim ◽  
Y. Cho ◽  
J. H. Kim
Keyword(s):  
Histological Grade ◽  
System Level ◽  
Clinical Approach ◽  
Cancer Therapies ◽  
Functional Roles ◽  
Network Analyses ◽  
Molecular Events ◽  
Anti Cancer ◽  
Tumor Growth And Metastasis ◽  
Chemotherapy Agents

Background:Chondrosarcomas are cartilaginous tumors that constitute one-third of skeletal system cancers. Chondrosarcomas are capable of transitioning to highly metastatic and treatment-refractory states, resulting in significant patient mortality. However, the molecular events accompanying this behavior remain unknown.Objectives:We aimed to uncover the molecular pathway underlying such tumor progression that confers a higher malignancy to chondrosarcoma.Methods:We conducted unsupervised gene co-expression network analyses using transcriptomes of patients with chondrosarcoma and extracted a characteristic transcription network underlying chondrosarcoma malignancy. By implementing a system-level upstream analysis of this gene network, we identified the transcriptional factor as a key regulator governing chondrosarcoma progression. We unraveled the functional roles of the identified factor in promoting tumor growth and metastasis of chondrosarcomas in the context of their unique microenvironments.Results:By conducting system-level upstream analysis, we identified a factor as a transcriptional regulator that governs the malignancy gene module. The identified factor was upregulated in chondrosarcoma biopsies associated with a high histological grade and conferred chondrosarcoma cells invasiveness and tumor-initiating capacity. In an orthotopic xenograft mouse model, the identified factor modulated local outgrowth and pulmonary metastasis of chondrosarcoma. Pharmacological inhibition of the identified factor in conjunction with the chemotherapy agents such as cisplatin or doxorubicin synergistically enhanced chondrosarcoma cell apoptosis and abolished malignant phenotypes of chondrosarcoma in mice.Conclusion:Our study provides a proof of concept evidence that inhibiting the identified factor suppresses progression of chondrosarcoma and improves the efficacy of chemotherapy in cellular and pre-clinical levels. Taken together, we believe that our findings provide novel molecular insights for the development of new anti-cancer therapies to target chondrosarcomas.References:[1]Gelderblom H, et al. The clinical approach towards chondrosarcoma. Oncologist 13, 320-329 (2008)Disclosure of Interests:None declared

scholarly journals A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data

2021 ◽  
Vol 4 (1) ◽  
pp. 21
Author(s):  
Austin Bow
Keyword(s):  
Rna Sequencing ◽  
Pathway Analysis ◽  
Valuable Insight ◽  
Sequencing Data ◽  
Analytical Technique ◽  
Mapping Software ◽  
Formidable Challenge ◽  
Daunting Task ◽  
Software Platforms ◽  
Time Required

The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhanwei Xuan ◽  
Xiang Feng ◽  
Jingwen Yu ◽  
Pengyao Ping ◽  
Haochen Zhao ◽  
...  
Keyword(s):  
Higher Order ◽  
System Level ◽  
Cell Proliferation And Differentiation ◽  
Proliferation And Differentiation ◽  
Great Insight ◽  
Novel Method ◽  
Global And Local ◽  
Novel Model ◽  
Insight Into ◽  
Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

Ventilatory and metabolic responses to cold and CO2 in intact and carotid body-denervated awake rats

1993 ◽  
Vol 75 (6) ◽  
pp. 2570-2579 ◽  
Author(s):  
H. Gautier ◽  
M. Bonora ◽  
H. C. Trinh
Keyword(s):  
Carotid Body ◽  
System Level ◽  
Nonshivering Thermogenesis ◽  
Ventilatory Control ◽  
Pco2 Level ◽  
Ambient Temperatures ◽  
The Central Nervous System ◽  
Central Actions ◽  
Colonic Temperature ◽  
Different Sources

We investigated in conscious rats the characteristics and modes of action of CO2 on thermoregulation and ventilatory control during cold stress. In a group of 10 rats studied intact and after carotid body denervation, measurements of metabolic rate (VO2), ventilation (V), shivering, and colonic temperature (Tc) were made at controlled ambient temperatures (Ta) of 25, 20, 15, 10, and 5 degrees C. Animals were exposed on different days to 1) normoxia, 2) normoxia and 4% CO2, 3) 12% hypoxia, or 4) 10.8% hypoxia and 4% CO2. The following results were obtained. 1) During CO2 exposure in normoxia or hypoxia, VO2 is increased at Ta of 25 degrees C and decreased for lower Ta. These effects are partly mediated by carotid body afferents. 2) Shivering and nonshivering thermogenesis and therefore Tc regulation are affected by CO2 exposure as shown by relationships between VO2-Tc and VO2-shivering intensity. 3) V is controlled by PO2 and PCO2 directly through their peripheral and central actions but also indirectly through their effects on VO2. Our conclusions are as follows. 1) Control of Tc is markedly dependent on PCO2 level. Carotid body afferents play a role, but direct central effects acting on the different sources of thermogenesis and possibly on thermolysis are most prominent. 2) As far as control of V is concerned, during hypercapnia in normoxia or hypoxia, several analogies may be formed between exposure to cold and muscular exercise, both of which increase VO2 and V, suggesting common integrative mechanisms at the central nervous system level.

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