scholarly journals Comparison of mammalian cell entry operons of mycobacteria: in silico analysis and expression profiling

2005 ◽  
Vol 43 (2) ◽  
pp. 185-195 ◽  
Author(s):  
Ashwani Kumar ◽  
Amita Chandolia ◽  
Uma Chaudhry ◽  
Vani Brahmachari ◽  
Mridula Bose
Keyword(s):  
Mammalian Cell ◽  
Expression Profiling ◽  
In Silico ◽  
In Silico Analysis ◽  
Cell Entry ◽  
Silico Analysis

scholarly journals Identification of Novel Hypothalamic MicroRNAs as Promising Therapeutics for SARS-CoV-2 by Regulating ACE2 and TMPRSS2 Expression: An In Silico Analysis

2020 ◽  
Vol 10 (10) ◽  
pp. 666
Author(s):  
Debasmita Mukhopadhyay ◽  
Bashair M. Mussa
Keyword(s):  
In Silico ◽  
Binding Capacity ◽  
Therapeutic Targets ◽  
In Silico Analysis ◽  
Neurological Impairment ◽  
Essential Elements ◽  
Cell Entry ◽  
Angiotensin Converting Enzyme 2 ◽  
Transmembrane Serine Protease ◽  
Silico Analysis

Background: Neuroinvasion of severe acute respiratory syndrome coronavirus (SARS-CoV) is well documented and, given the similarities between this virus and SARS-CoV-2, it seems that the neurological impairment that is associated with coronavirus disease 2019 (COVID-19) is due to SARS-CoV-2 neuroinvasion. Hypothalamic circuits are exposed to the entry of the virus via the olfactory bulb and interact centrally with crucial respiratory nuclei. Hypothalamic microRNAs are considered as potential biomarkers and modulators for various diseases and future therapeutic targets. The present study aims to investigate the microRNAs that regulate the expression of hypothalamic angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), essential elements for SARS-CoV-2 cell entry. Methods: To determine potential hypothalamic miRNAs that can directly bind to ACE2 and TMPRSS2, multiple target bioinformatics prediction algorithms were used, including miRBase, Target scan, and miRWalk2.029. Results: Our in silico analysis has revealed that, although there are over 5000 hypothalamic miRNAs, around 31 miRNAs and 29 miRNAs have shown binding sites and strong binding capacity against ACE2 and TMPRSS2, respectively. Conclusion: These novel potential hypothalamic miRNAs can be used to identify new therapeutic targets to treat neurological symptoms in COVID-19 patients via regulation of ACE2 and TMPRSS2 expression.

scholarly journals Analysis of Expression Profile of Mammalian Cell Entry (mce) Operons of Mycobacterium tuberculosis

2003 ◽  
Vol 71 (10) ◽  
pp. 6083-6087 ◽  
Author(s):  
Ashwani Kumar ◽  
Mridula Bose ◽  
Vani Brahmachari
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Expression Profile ◽  
In Silico ◽  
Infected Animal ◽  
In Silico Analysis ◽  
Cell Entry ◽  
Animal Tissues ◽  
Mce Operons ◽  
Silico Analysis

ABSTRACT The sequencing of the complete genome of M. tuberculosis H37Rv has resulted in the recognition of four mce operons in its genome by in silico analysis. In an attempt to understand the significance of the redundancy of mce operons, we analyzed the expression profile of mce operons after different periods of growth in culture as well as during in vivo infection. Our results strongly suggest that mce1 is expressed as a polycistronic message. In culture from day 8 to day 12, expression of only mce1 was observed, but as the cultures progress towards stationary phase the expression profile of mce operons was altered; the transcripts of the mce1 operon were barely detected while those of the mce4 operon were prominent. In an analysis of the expression of mce operons in tubercle material collected from infected animal tissues, we detected the expression of mce1, -3 and -4. Our results imply that mce operons other than mce1 are also expressed during infection and that it is necessary to examine their role in pathogenesis.

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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