scholarly journals An Interactive Regulatory Network Controls Stress Response in Bifidobacterium breve UCC2003

2009 ◽  
Vol 191 (22) ◽  
pp. 7039-7049 ◽  
Author(s):  
Aldert Zomer ◽  
Matilde Fernandez ◽  
Breda Kearney ◽  
Gerald F. Fitzgerald ◽  
Marco Ventura ◽  
...  
Keyword(s):  
Stress Response ◽  
Protein Interactions ◽  
Regulatory Network ◽  
In Silico Analysis ◽  
Sos Response ◽  
Probiotic Properties ◽  
Adaptive Responses ◽  
Functional Ingredients ◽  
Gram Positive Bacteria ◽  
Silico Analysis

ABSTRACT Members of the genus Bifidobacterium are gram-positive bacteria that commonly are found in the gastrointestinal tract (GIT) of mammals, including humans. Because of their perceived probiotic properties, they frequently are incorporated as functional ingredients in food products. From probiotic production to storage and GIT delivery, bifidobacteria encounter a plethora of stresses. To cope with these environmental challenges, they need to protect themselves through stress-induced adaptive responses. We have determined the response of B. breve UCC2003 to various stresses (heat, osmotic, and solvent) using transcriptome analysis, DNA-protein interactions, and GusA reporter fusions, and we combined these with results from an in silico analysis. The integration of these results allowed the formulation of a model for an interacting regulatory network for stress response in B. breve UCC2003 where HspR controls the SOS response and the ClgR regulon, which in turn regulates and is regulated by HrcA. This model of an interacting regulatory network is believed to represent the paradigm for stress adaptation in bifidobacteria.

scholarly journals Bioinformatic Analysis of a “Functional Cluster” Probably Related to Retinitis Pigmentosa

2018 ◽  
Vol 11 (1) ◽  
pp. 89-105
Author(s):  
Luigi Donato ◽  
Lucia Denaro
Keyword(s):  
Retinitis Pigmentosa ◽  
Protein Interactions ◽  
In Silico ◽  
Dark Adaptation ◽  
In Silico Analysis ◽  
Bioinformatic Analysis ◽  
Hereditary Disease ◽  
Functional Cluster ◽  
Starting Point ◽  
Silico Analysis

Background: Retinitis pigmentosa is an eye hereditary disease caused by photoreceptor death. One of the biggest problem is represented by its genetic heterogeneity, which has not yet allowed us to found all causative genes and how known ones could influence each other, leading to retinitis etiopathogenesis. Objective: To propose the possible relation between the “functional cluster” of vision dark adaptation, made of five phototransductional genes (RCVRN, GNB1, GNGT1, GRK7 and ARRB1), and retinitis pigmentosa onset. Methods: A bioinformatic approach was exploited: the starting point was searching through online database as PubMed and EMBASE to acquire information about the state of art of these gene. This step was followed by an in-silico analysis, performed by softwares as Cytoscape and Genecards Suite Plus, articulated in three phases: I) identification of common pathways and genes involved in; II) collection of previously detected genes; III) deep analysis of intersected genes and implication into etiopathogenesis of analzyed disease. Results: The whole in-silico analysis showed that all five gene products cooperate during phototransductional activation, expecially in the dark adaptation. Interestingly, the most exciting aspect regards the direct relation with several known retinitis pigmentosa causative genes, in form of protein interactions or other pathway correlations. Conclusion: Pathway analysis permitted us to hypothesize a possible role of analyzed genes in retinitis pigmentosa etiopathogenesis, also considering the key activity of their encoded proteins. Next step will be validating our hypotesis with functional assays to ensure the real meaning of this possible association, leading to new potential retinitis pigmentosa causative genes.

scholarly journals In-silico analysis: common biomarkers of NDs

2021 ◽  
Author(s):  
Deepanjan Sarkar ◽  
Souvik Chakraborty ◽  
Tarasankar Maiti ◽  
Sushmita Bhowmick
Keyword(s):  
Protein Interactions ◽  
Neurological Disorders ◽  
Biological Process ◽  
Kegg Pathway ◽  
In Silico Analysis ◽  
Molecular Function ◽  
Hub Genes ◽  
Parkinsons Disease ◽  
Online Tool ◽  
Silico Analysis

Neurodegenerative disorders (NDs) are a class of rapidly rising devastating diseases and the reason behind are might be an improper function of related genes or a mutation in a particular gene or even could be autoimmune also. Parkinsons disease (PD), Multiple sclerosis (MS), Huntingtons disease (HD) are some of the NDs, and still, incurable fully. Apart from the similarities in symptoms, there are common genes that express somehow a differential manner in patients of PDs, MSs, and HDs. A total of 1197 differentially expressed genes (DEGs) are obtained by analyzing the chosen datasets. The protein interactions by STRING online tool and degree sorted hubs obtained through a plug-in in Cytoscape; Cyto-Hubba. Among the sorted hubs KRAS, CREB1, PIK3CA, JAK2 are the ones that are not only common to all the studied datasets of NDs but also in other neurological disorders like Alzheimers. The enriched pathways with biological process, molecular function, cellular component, and KEGG pathway details are obtained and analyzed using Enricher. This paper frames that the obtained hub genes could be potential biomarkers also and a need for further drug design for finding a possible cure.

scholarly journals Target related in silico analysis of Bergenin and tuberculosis management

Biomedicine ◽  
2021 ◽  
Vol 40 (4) ◽  
pp. 474-481
Author(s):  
Virupaksha A. Bastikar ◽  
Alpana Bastikar ◽  
Pramodkumar P. Gupta ◽  
Sandeep R. Pai ◽  
Santosh S. Chhajed
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Biological Process ◽  
In Silico Analysis ◽  
Enrichment Analysis ◽  
Binding Mode ◽  
Health Concern ◽  
Silico Analysis

Introduction and Aim: Tuberculosis (TB) is a global health concern, claiming two million lives every year. Although an oldest known human infectious disease, researcher is falling short of giving out an effective and reliable vaccine or therapy. The current antimycobacterial drugs include Isoniazid, Ethambutol, Rifampicin and Pyrazinemamide available in market, but most of these are known to have certain adverse effects. Hence there is an increase in demand for natural products with anti-tuberculosis activity with no or limited side effects. Indian traditional systems of medicine have a plethora of promising plants for treatment of tuberculosis, of which Bergenin is the most well established and extensively used compound. The main aim of this research was to investigate the role of Bergenin as an anti-tuberculosis agent with the help of in-silico analysis and protein interaction studies. Materials and Methods: In the present study 04 known 3-dimensional crystallized anti-tubercular drug target is considered and retrieved from PDB. Drug Isoniazid, Ethambutol, Rifampicin, Pyrazineamide and phytochemical Bergenin were retrieved, sketched and geometrically optimized. Molecular docking is carried to understand the binding mode and its core interactions. ADMET properties were calculated in assessment of the toxicity. Protein-protein interactions and enrichment analysis is carried out to understand the biological process involved with rpsA protein. Results: In the present study other than Rifampicin, Bergenin reported with better binding energy and similar pharmacophoric interaction pattern as compared to all the 04 indigenous inhibitors. The PPI network and enrichment analysis predicts the plausible biological process involved with rpsA protein and can be further targeted in treatment of tuberculosis. Conclusion: The results showed that Bergenin was better than and competent with the existing drugs and can be used as an anti-tuberculosis agent if studied in-vitro and in-vivo for its activity.

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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