scholarly journals Phylogenetic origin and sequence features of MreB from the wall-less swimming bacteria Spiroplasma

2020 ◽  
Vol 533 (4) ◽  
pp. 638-644
Author(s):  
Daichi Takahashi ◽  
Ikuko Fujiwara ◽  
Makoto Miyata
Keyword(s):  
Sequence Features ◽  
Swimming Bacteria ◽  
Phylogenetic Origin

scholarly journals Phylogenetic origin and sequence features of MreB from the wall-less swimming bacteria Spiroplasma

2020 ◽  
Author(s):  
Daichi Takahashi ◽  
Ikuko Fujiwara ◽  
Makoto Miyata
Keyword(s):  
Maximum Likelihood Method ◽  
Bone Structure ◽  
Likelihood Method ◽  
Flagellar Motility ◽  
Sequence Comparisons ◽  
Sequence Features ◽  
Protein Protein Interaction ◽  
Swimming Bacteria ◽  
Phylogenetic Origin ◽  
Unique Mechanism

ABSTRACTSpiroplasma are wall-less bacteria which belong to the phylum Tenericutes that evolved from Firmicutes including Bacillus subtilis. Spiroplasma swim by a mechanism unrelated to widespread bacterial motilities, such as flagellar motility, and caused by helicity switching with kinks traveling along the helical cell body. The swimming force is likely generated by five classes of bacterial actin homolog MreBs (SMreBs 1-5) involved in the helical bone structure. We analyzed sequences of SMreBs to clarify their phylogeny and sequence features. The maximum likelihood method based on around 5,000 MreB sequences showed that the phylogenetic tree was divided into several radiations. SMreBs formed a clade adjacent to the radiation of MreBH, an MreB isoform of Firmicutes. Sequence comparisons of SMreBs and Bacillus MreBs were also performed to clarify the features of SMreB. Catalytic glutamic acid and threonine were substituted to aspartic acid and lysine, respectively, in SMreB3. In SMreBs 2 and 4, amino acids involved in inter- and intra-protofilament interactions were significantly different from those in Bacillus MreBs. A membrane-binding region was not identified in most SMreBs 1 and 4 unlike many walled-bacterial MreBs. SMreB5 had a significantly longer C-terminal region than the other MreBs, which possibly forms a protein-protein interaction. These features may support the functions responsible for the unique mechanism of Spiroplasma swimming.

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu
Keyword(s):  
Model Building ◽  
De Novo ◽  
Cpg Islands ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Global Methylation ◽  
Sequence Features ◽  
A Genome ◽  
Combined Features ◽  
Epigenetic Repression

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

scholarly journals Piper nigrum CYP719A37 Catalyzes the Decisive Methylenedioxy Bridge Formation in Piperine Biosynthesis

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 128
Author(s):  
Arianne Schnabel ◽  
Fernando Cotinguiba ◽  
Benedikt Athmer ◽  
Thomas Vogt
Keyword(s):  
Ferulic Acid ◽  
Functional Expression ◽  
Black Pepper ◽  
Amide Bond ◽  
Chain Elongation ◽  
Piper Nigrum ◽  
Yeast Saccharomyces Cerevisiae ◽  
Bridge Formation ◽  
Phylogenetic Origin ◽  
Catalyzed Reactions

Black pepper (Piper nigrum) is among the world’s most popular spices. Its pungent principle, piperine, has already been identified 200 years ago, yet the biosynthesis of piperine in black pepper remains largely enigmatic. In this report we analyzed the characteristic methylenedioxy bridge formation of the aromatic part of piperine by a combination of RNA-sequencing, functional expression in yeast, and LC-MS based analysis of substrate and product profiles. We identified a single cytochrome P450 transcript, specifically expressed in black pepper immature fruits. The corresponding gene was functionally expressed in yeast (Saccharomyces cerevisiae) and characterized for substrate specificity with a series of putative aromatic precursors with an aromatic vanilloid structure. Methylenedioxy bridge formation was only detected when feruperic acid (5-(4-hydroxy-3-methoxyphenyl)-2,4-pentadienoic acid) was used as a substrate, and the corresponding product was identified as piperic acid. Two alternative precursors, ferulic acid and feruperine, were not accepted. Our data provide experimental evidence that formation of the piperine methylenedioxy bridge takes place in young black pepper fruits after a currently hypothetical chain elongation of ferulic acid and before the formation of the amide bond. The partially characterized enzyme was classified as CYP719A37 and is discussed in terms of specificity, storage, and phylogenetic origin of CYP719 catalyzed reactions in magnoliids and eudicots.

scholarly journals MAINTENANCE OF SKIN XENOGRAFTS OF WIDELY DIVERGENT PHYLOGENETIC ORIGIN ON CONGENITALLY ATHYMIC (NUDE) MICE

1973 ◽  
Vol 138 (2) ◽  
pp. 488-494 ◽  
Author(s):  
Dean D. Manning ◽  
Norman D. Reed ◽  
Charles F. Shaffer
Keyword(s):  
Nude Mice ◽  
Tree Frog ◽  
Skin Grafts ◽  
Intact Skin ◽  
Athymic Nude Mice ◽  
Phylogenetic Origin ◽  
Definitive Role

Congenitally athymic (nude) mice accepted for their lifetime intact skin grafts from distantly related mammals (cat, human) and birds (chicken). They also failed to immunologically reject skin grafts from reptiles (lizards) and amphibians (tree frog), although the skin in these grafts underwent varying degrees of disorganization. A definitive role for the thymic defect in this failure to reject xenografts was established by showing that thymus implantation into nude mice enabled them to reject such foreign skin.

scholarly journals Modelling and analysis of bacterial tracks suggest an active reorientation mechanism in Rhodobacter sphaeroides

2014 ◽  
Vol 11 (97) ◽  
pp. 20140320 ◽  
Author(s):  
Gabriel Rosser ◽  
Ruth E. Baker ◽  
Judith P. Armitage ◽  
Alexander G. Fletcher
Keyword(s):  
Rhodobacter Sphaeroides ◽  
Computational Algorithm ◽  
Rotational Diffusion ◽  
Particle Model ◽  
Cell Geometry ◽  
Brownian Rotation ◽  
Swimming Bacteria ◽  
Run And Tumble ◽  
Open Question ◽  
Straight Lines

Most free-swimming bacteria move in approximately straight lines, interspersed with random reorientation phases. A key open question concerns varying mechanisms by which reorientation occurs. We combine mathematical modelling with analysis of a large tracking dataset to study the poorly understood reorientation mechanism in the monoflagellate species Rhodobacter sphaeroides . The flagellum on this species rotates counterclockwise to propel the bacterium, periodically ceasing rotation to enable reorientation. When rotation restarts the cell body usually points in a new direction. It has been assumed that the new direction is simply the result of Brownian rotation. We consider three variants of a self-propelled particle model of bacterial motility. The first considers rotational diffusion only, corresponding to a non-chemotactic mutant strain. Two further models incorporate stochastic reorientations, describing ‘run-and-tumble’ motility. We derive expressions for key summary statistics and simulate each model using a stochastic computational algorithm. We also discuss the effect of cell geometry on rotational diffusion. Working with a previously published tracking dataset, we compare predictions of the models with data on individual stopping events in R. sphaeroides . This provides strong evidence that this species undergoes some form of active reorientation rather than simple reorientation by Brownian rotation.

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