https://www.cell.com/cell-reports/pdf/S2211-1247(15)01101-8.pdf

Bactericidal Antibiotics Induce Toxic Metabolic Perturbations that Lead to Cellular Damage

The OmpA family of proteins: roles in bacterial pathogenesis and immunity.

Understanding how antibiotics impact bacterial metabolism may provide insight into their mechanisms of action and could lead to enhanced therapeutic methodologies. Here, we profiled the metabolome of Escherichia coli after treatment with three different classes of bactericidal antibiotics (?-lactams, aminoglycosides, quinolones). These treatments induced a similar set of metabolic changes after 30 min that then diverged into more distinct profiles at later time points. The most striking changes corresponded to elevated concentrations of central carbon metabolites, active breakdown of the nucleotide pool, reduced lipid levels, and evidence of an elevated redox state. We examined potential end-target consequences of these metabolic perturbations and found that antibiotic-treated cells exhibited cytotoxic changes indicative of oxidative stress, including higher levels of protein carbonylation, malondialdehyde adducts, nucleotide oxidation, and double-strand DNA breaks. This work shows that bactericidal antibiotics induce a complex set of metabolic changes that are correlated with the buildup of toxic metabolic by-products.

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

https://www.mdpi.com/1424-8220/20/10/2809/pdf

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.

Mollicutes is a class of parasitic bacteria that have evolved from a common Firmicutes ancestor mostly by massive genome reduction. With genomes under 1 Mbp in size, most Mollicutes species retain the capacity to replicate and grow autonomously. The major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in these bacteria. Using the experimentally validated genes from the model bacteria Escherichia coli and Bacillus subtilis as input, genes encoding proteins of the core translation machinery were predicted in 39 distinct Mollicutes species, 33 of which are culturable. The set of 260 input genes encodes proteins involved in ribosome biogenesis, tRNA maturation and aminoacylation, as well as proteins cofactors required for mRNA translation and RNA decay. A core set of 104 of these proteins is found in all species analyzed. Genes encoding proteins involved in post-translational modifications of ribosomal proteins and translation cofactors, post-transcriptional modifications of t+rRNA, in ribosome assembly and RNA degradation are the most frequently lost. As expected, genes coding for aminoacyl-tRNA synthetases, ribosomal proteins and initiation, elongation and termination factors are the most persistent (i.e. conserved in a majority of genomes). Enzymes introducing nucleotides modifications in the anticodon loop of tRNA, in helix 44 of 16S rRNA and in helices 69 and 80 of 23S rRNA, all essential for decoding and facilitating peptidyl transfer, are maintained in all species. Reconstruction of genome evolution in Mollicutes revealed that, beside many gene losses, occasional gains by horizontal gene transfer also occurred. This analysis not only showed that slightly different solutions for preserving a functional, albeit minimal, protein synthetizing machinery have emerged in these successive rounds of reductive evolution but also has broad implications in guiding the reconstruction of a minimal cell by synthetic biology approaches.

https://agritrop.cirad.fr/573293/1/document_573293.pdf

Predicting the Minimal Translation Apparatus: Lessons from the Reductive Evolution of Mollicutes

We have examined the level of 8-hydroxyguanosine (8-oxo-G), an oxidized form of guanosine, in RNA in Escherichia coli under normal and oxidative stress conditions. The level of 8-oxo-G in RNA rises rapidly and remains high for hours in response to hydrogen peroxide (H2O2) challenge in a dose-dependent manner. H2O2 induced elevation of 8-oxo-G content is much higher in RNA than that of 8-hydroxydeoxyguanosine (8-oxo-dG) in DNA. Under normal conditions, the 8-oxo-G level is low in RNA isolated from the ribosome and it is nearly three times higher in non-ribosomal RNAs. In contrast, 8-oxo-G generated by a short exposure to H2O2 is almost equally distributed in various RNA species, suggesting that although ribosomal RNAs are normally less oxidized, they are not protected against exogenous H2O2. Interestingly, highly folded RNA is not protected from oxidation because 8-oxo-G generated by H2O2 treatment in vitro increases to approximately the same levels in tRNA and rRNA in both native and denatured forms. Lastly, increased RNA oxidation is closely associated with cell death by oxidative stress. Our data suggests that RNA is a primary target for reactive oxygen species and RNA oxidation is part of the paradox that cells have to deal with under oxidative stress.

Characterization of RNA damage under oxidative stress in Escherichia coli.

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

https://www.mdpi.com/2072-6694/11/3/431/pdf

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

Oxidation is probably the most common type of damage that occurs in cellular RNA. Oxidized RNA may be dysfunctional and is implicated in the pathogenesis of age-related human diseases. Cellular mechanisms controlling oxidized RNA have begun to be revealed. Currently, a number of ribonucleases and RNA-binding proteins have been shown to reduce oxidized RNA and to protect cells under oxidative stress. Although information about how these factors work is still very limited, we suggest several mechanisms that can be used to minimize oxidized RNA in various organisms.

/pdf/battle-against-rna-oxidation-molecular-mechanisms-for-20so4xpaws.pdf

Battle against RNA oxidation: molecular mechanisms for reducing oxidized RNA to protect cells

The outer membrane protein A (OmpA) of Yersinia pestis promotes intracellular survival and virulence in mice.

Yersinia pestis, the causative agent of plague, possesses a number of virulence mechanisms that allows it to survive and proliferate during its interaction with the host. To discover additional infection-specific Y. pestis factors, a transposon site hybridization (TraSH)-based genome-wide screen was employed to identify genomic regions required for its survival during cellular infection. In addition to several well-characterized infection-specific genes, this screen identified three chromosomal genes (y3397, y3399, and y3400), located in an apparent operon, that promoted successful infection. Each of these genes is predicted to encode a leucine-rich repeat family protein with or without an associated ubiquitin E3 ligase domain. These genes were designated Yersinia leucine-rich repeat gene A (ylrA), B (ylrB), and C (ylrC). Engineered strains with deletions of y3397 (ylrC), y3399 (ylrB), or y3400 (ylrA), exhibited infection defects both in cultured cells and in the mouse. C-terminal FLAG-tagged YlrA, YlrB, and YlrC were secreted by Y. pestis in the absence but not the presence of extracellular calcium and deletions of the DNA sequences encoding the predicted N-terminal type III secretion signals of YlrA, YlrB, and YlrC prevented their secretion, indicating that these proteins are substrates of the type III secretion system (T3SS). Further strengthening the connection with the T3SS, YlrB was readily translocated into HeLa cells and expression of the YlrA and YlrC proteins in yeast inhibited yeast growth, indicating that these proteins may function as anti-host T3S effector proteins.

/pdf/chromosomally-encoded-yersinia-pestis-type-iii-secretion-2hqp4dh2sr.pdf

Zhongwei Li

Papers

Characterization of RNA damage under oxidative stress in Escherichia coli.

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

Battle against RNA oxidation: molecular mechanisms for reducing oxidized RNA to protect cells

The outer membrane protein A (OmpA) of Yersinia pestis promotes intracellular survival and virulence in mice.

Chromosomally-Encoded Yersinia pestis Type III Secretion Effector Proteins Promote Infection in Cells and in Mice