Experimental Evolution of Copper Resistance in Escherichia coli Produces Evolutionary Trade-Offs in the Antibiotics Chloramphenicol, Bacitracin, and Sulfonamide.

The rise in antimicrobial resistant bacteria have prompted the need for antibiotic alternatives. To address this problem, significant attention has been given to the antimicrobial use and novel applications of copper. As novel applications of antimicrobial copper increase, it is important to investigate how bacteria may adapt to copper over time. Here, we used experimental evolution with re-sequencing (EER-seq) and RNA-sequencing to study the evolution of copper resistance in Escherichia coli. Subsequently, we tested whether copper resistance led to rifampicin, chloramphenicol, bacitracin, and/or sulfonamide resistance. Our results demonstrate that E. coli is capable of rapidly evolving resistance to CuSO4 after 37 days of selection. We also identified multiple de novo mutations and differential gene expression patterns associated with copper, most notably those mutations identified in the cpx gene. Furthermore, we found that the copper resistant bacteria had decreased sensitivity when compared to the ancestors in the presence of chloramphenicol, bacitracin, and sulfonamide. Our data suggest that the selection of copper resistance may inhibit growth in the antimicrobials tested, resulting in evolutionary trade-offs. The results of our study may have important implications as we consider the antimicrobial use of copper and how bacteria may respond to increased use over time.

TFIDF-Random Forest: Prediction of Aptamer-Protein Interacting Pairs.

Aptamers are short, single-stranded oligonucleotides or peptides generated from in vitro selection to selectively bind with various molecules. Due to their molecular recognition capability for proteins, aptamers are becoming promising reagents in new drug development. Aptamers can fold into specific spatial configuration that bind to certain targets with extremely high specificity. The ability of aptamers to reversibly bind proteins has generated increasing interest in using them to facilitate controlled release of therapeutic biomolecules. In-vitro selection experiments to produce the aptamer-protein binding pairs is very complex and MD/MM in-silico experiments can be computationally expensive. In this study, we introduce a natural language processing approach for data-driven computational selection. We compared our method to the sequential model with the embedding layer, applied in the literature. We transformed the DNA/RNA and protein sequences into text format using a sliding window approach. This methodology showed that efficiency was notably higher than those observed from the literature. This indicates that our preliminary model has marked improvement over previous models which brings us closer to a data-driven computational selection method.

Influence of Hydroxyproline on Mechanical Behavior of Collagen Mimetic Proteins Under Fraying Deformation-Molecular Dynamics Investigations.

Molecular dynamics modeling is used to simulate, model, and analyze mechanical deformation behavior and predictive properties of three different synthetic collagen proteins obtained from RSC-PDB, 1BKV, 3A08, and 2CUO, with varying concentrations of hydroxyproline (HYP). Hydroxyproline is credited with providing structural support for the collagen protein molecules. Hydroxyproline's influence on these three synthetic collagen proteins' mechanical deformation behavior and predictive properties is investigated in this paper. A detailed study and inference of the protein's mechanical characteristics associated with HYP content are investigated through fraying deformation behavior. A calculated Gibbs free energy value (ΔG) of each polypeptide α chain that corresponds with a complete unfolding of a single polypeptide α-chain from a triple-helical protein is obtained with umbrella sampling. The force needed for complete separation of the polypeptide α-chain from the triple-helical protein is analyzed for proteins to understand the influence of HYP concentration and is discussed in this paper. Along with a difference in ΔG, different unfolding pathways for the molecule and individual chains are observed. The correlation between the fraying deformation mechanical characteristics and the collagen proteins' hydroxyproline content is provided in this study via the three collagen proteins' resulting binding energies.

Too much of a good thing: Adaption to iron (II) intoxication in Escherichia coli.

BACKGROUND: There has been an increased usage of metallic antimicrobial materials to control pathogenic and multi-drug resistant bacteria. Yet, there is a corresponding need to know if this usage leads to genetic adaptations that could produce more harmful strains. METHODOLOGY: Experimental evolution was used to adapt Escherichia coli K-12 MG1655 to excess iron (II) with subsequent genomic analysis. Phenotypic assays and gene expression studies were conducted to demonstrate pleiotropic effects associated with this adaptation and to elucidate potential cellular responses. RESULTS: After 200 days of adaptation, populations cultured in excess iron (II), showed a significant increase in 24-h optical densities compared to controls. Furthermore, these populations showed increased resistance toward other metals [iron (III) and gallium (III)] and to traditional antibiotics (bacitracin, rifampin, chloramphenicol and sulfanilamide). Genomic analysis identified selective sweeps in three genes; fecA, ptsP and ilvG unique to the iron (II) resistant populations, and gene expression studies demonstrated that their cellular response may be to downregulate genes involved in iron transport (cirA and fecA) while increasing the oxidative stress response (oxyR, soxS and soxR) prior to FeSO4 exposure. CONCLUSIONS AND IMPLICATIONS: Together, this indicates that the selected populations can quickly adapt to stressful levels of iron (II). This study is unique in that it demonstrates that E. coli can adapt to environments that contain excess levels of an essential micronutrient while also demonstrating the genomic foundations of the response and the pleiotropic consequences. The fact that adaptation to excess iron also causes increases in general antibiotic resistance is a serious concern. Lay summary: The evolution of iron resistance in E. coli leads to multi-drug and general metal resistance through the acquisition of mutations in three genes (fecA, ptsP and ilvG) while also initiating cellular defenses as part of their normal growth process.

Experimental evolution of gallium resistance in Escherichia coli.

BACKGROUND AND OBJECTIVES: Metallic antimicrobial materials are of growing interest due to their potential to control pathogenic and multidrug-resistant bacteria. Yet we do not know if utilizing these materials can lead to genetic adaptations that produce even more dangerous bacterial varieties. METHODOLOGY: Here we utilize experimental evolution to produce strains of Escherichia coli K-12 MG1655 resistant to, the iron analog, gallium nitrate (Ga(NO3)3). Whole genome sequencing was utilized to determine genomic changes associated with gallium resistance. Computational modeling was utilized to propose potential molecular mechanisms of resistance. RESULTS: By day 10 of evolution, increased gallium resistance was evident in populations cultured in medium containing a sublethal concentration of gallium. Furthermore, these populations showed increased resistance to ionic silver and iron (III), but not iron (II) and no increase in traditional antibiotic resistance compared with controls and the ancestral strain. In contrast, the control populations showed increased resistance to rifampicin relative to the gallium-resistant and ancestral population. Genomic analysis identified hard selective sweeps of mutations in several genes in the gallium (III)-resistant lines including: fecA (iron citrate outer membrane transporter), insl1 (IS30 tranposase) one intergenic mutations arsC →/→ yhiS; (arsenate reductase/pseudogene) and in one pseudogene yedN ←; (iapH/yopM family). Two additional significant intergenic polymorphisms were found at frequencies > 0.500 in fepD ←/→ entS (iron-enterobactin transporter subunit/enterobactin exporter, iron-regulated) and yfgF ←/→ yfgG (cyclic-di-GMP phosphodiesterase, anaerobic/uncharacterized protein). The control populations displayed mutations in the rpoB gene, a gene associated with rifampicin resistance. CONCLUSIONS: This study corroborates recent results observed in experiments utilizing pathogenic Pseudomonas strains that also showed that Gram-negative bacteria can rapidly evolve resistance to an atom that mimics an essential micronutrient and shows the pleiotropic consequences associated with this adaptation. LAY SUMMARY: We utilize experimental evolution to produce strains of Escherichia coli K-12 MG1655 resistant to, the iron analog, gallium nitrate (Ga(NO3)3). Whole genome sequencing was utilized to determine genomic changes associated with gallium resistance. Computational modeling was utilized to propose potential molecular mechanisms of resistance.

Molecular modeling and SPRi investigations of interleukin 6 (IL6) protein and DNA aptamers.

Interleukin 6 (IL6), an inflammatory response protein has major implications in immune-related inflammatory diseases. Identification of aptamers for the IL6 protein aids in diagnostic, therapeutic, and theranostic applications. Three different DNA aptamers and their interactions with IL6 protein were extensively investigated in a phosphate buffed saline (PBS) solution. Molecular-level modeling through molecular dynamics provided insights of structural, conformational changes and specific binding domains of these protein-aptamer complexes. Multiple simulations reveal consistent binding region for all protein-aptamer complexes. Conformational changes coupled with quantitative analysis of center of mass (COM) distance, radius of gyration (Rg), and number of intermolecular hydrogen bonds in each IL6 protein-aptamer complex was used to determine their binding performance strength and obtain molecular configurations with strong binding. A similarity comparison of the molecular configurations with strong binding from molecular-level modeling concurred with Surface Plasmon Resonance imaging (SPRi) for these three aptamer complexes, thus corroborating molecular modeling analysis findings. Insights from the natural progression of IL6 protein-aptamer binding modeled in this work has identified key features such as the orientation and location of the aptamer in the binding event. These key features are not readily feasible from wet lab experiments and impact the efficacy of the aptamers in diagnostic and theranostic applications.

Molecular Dynamics Simulation Analysis of Anti-MUC1 Aptamer and Mucin 1 Peptide Binding.

Aptasensors utilize aptamers as bioreceptors. Aptamers are highly efficient, have a high specificity and are reusable. Within the biosensor the aptamers are immobilized to maximize their access to target molecules. Knowledge of the orientation and location of the aptamer and peptide during binding could be gained through computational modeling. Experimentally, the aptamer (anti-MUC1 S2.2) has been identified as a bioreceptor for breast cancer biomarker mucin 1 (MUC1) protein. However, within this protein lie several peptide variants with the common sequence APDTRPAP that are targeted by the aptamer. Understanding orientation and location of the binding region for a peptide-aptamer complex is critical in their biosensor applicability. In this study, we investigate through computational modeling how this peptide sequence and its minor variants affect the peptide-aptamer complex binding. We use molecular dynamics simulations to study multiple peptide-aptamer systems consisting of MUC1 (APDTRPAP) and MUC1-G (APDTRPAPG) peptides with the anti-MUC1 aptamer under similar physiological conditions reported experimentally. Multiple simulations of the MUC1 peptide and aptamer reveal that the peptide interacts between 3' and 5' ends of the aptamer but does not fully bind. Multiple simulations of the MUC1-G peptide indicate consistent binding with the thymine loop of the aptamer, initiated by the arginine residue of the peptide. We find that the binding event induces structural changes in the aptamer by altering the number of hydrogen bonds within the aptamer and establishes a stable peptide-aptamer complex. In all MUC1-G cases the occurrence of binding was confirmed by systematically studying the distance distributions between peptide and aptamers. These results are found to corroborate well with experimental study reported in the literature that indicated a strong binding in the case of MUC1-G peptide and anti-MUC1 aptamer. Present MD simulations highlight the role of the arginine residue of MUC1-G peptide in initiating the binding. The addition of the glycine residue to the peptide, as in the case of MUC1-G, is shown to yield a stable binding. Our study clearly demonstrates the ability of MD simulations to obtain molecular insights for peptide-aptamer binding, and to provide details on the orientation and location of binding between the peptide-aptamer that can be instrumental in biosensor development.

Computational modeling of peptide-aptamer binding.

Evolution is the progressive process that holds each living creature in its grasp. From strands of DNA evolution shapes life with response to our ever-changing environment and time. It is the continued study of this most primitive process that has led to the advancement of modern biology. The success and failure in the reading, processing, replication, and expression of genetic code and its resulting biomolecules keep the delicate balance of life. Investigations into these fundamental processes continue to make headlines as science continues to explore smaller scale interactions with increasing complexity. New applications and advanced understanding of DNA, RNA, peptides, and proteins are pushing technology and science forward and together. Today the addition of computers and advances in science has led to the fields of computational biology and chemistry. Through these computational advances it is now possible not only to quantify the end results but also visualize, analyze, and fully understand mechanisms by gaining deeper insights. The biomolecular motion that exists governing the physical and chemical phenomena can now be analyzed with the advent of computational modeling. Ever-increasing computational power combined with efficient algorithms and components are further expanding the fidelity and scope of such modeling and simulations. This chapter discusses computational methods that apply biological processes, in particular computational modeling of peptide-aptamer binding.