The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Landmarks in the history of selective sweeps

Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.

The risk of drug resistance during long-acting antimicrobial therapy

The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.

Antimicrobial Resistance in the COVID-19 Landscape: Is There an Opportunity for Anti-Infective Antibodies and Antimicrobial Peptides?

Although COVID-19 has captured most of the public health attention, antimicrobial resistance (AMR) has not disappeared. To prevent the escape of resistant microorganisms in animals or environmental reservoirs a "one health approach" is desirable. In this context of COVID-19, AMR has probably been affected by the inappropriate or over-use of antibiotics. The increased use of antimicrobials and biocides for disinfection may have enhanced the prevalence of AMR. Antibiotics have been used empirically in patients with COVID-19 to avoid or prevent bacterial coinfection or superinfections. On the other hand, the measures to prevent the transmission of COVID-19 could have reduced the risk of the emergence of multidrug-resistant microorganisms. Since we do not currently have a sterilizing vaccine against SARS-CoV-2, the virus may still multiply in the organism and new mutations may occur. As a consequence, there is a risk of the appearance of new variants. Nature-derived anti-infective agents, such as antibodies and antimicrobial peptides (AMPs), are very promising in the fight against infectious diseases, because they are less likely to develop resistance, even though further investigation is still required.

Antibacterial activity and mechanism of essential oils in combination with medium-chain fatty acids against predominant bovine mastitis pathogens

Bovine mastitis has become a significant economic importance for dairy industry. Concerns regarding poor milk quality, and emergence of bacterial resistance has necessitated to develop alternative therapeutic approach to antibiotics for treatment of mastitis. Saturated medium-chain fatty acids (MCFAs) and essential oils (EOs) are known natural antimicrobials, but their combined effect has not been investigated extensively. The objective of the present investigation was to examine the bactericidal effect of various combined treatments of eight EOs and three saturated MCFAs to inactivate predominant mastitis pathogens, including Staphylococcus aureus ATCC 29213; Escherichia coli ATCC 25922; Klebsiella pneumoniae ATCC 27736; and Streptococcus agalactiae ATCC 27956. The minimum inhibition concentration (MIC) values confirmed that all the tested pathogens were variably susceptible to both EOs and saturated MCFAs. Among essential oils, carvacrol (CAR), trans-cinnamaldehyde (TC), and thymol (TM) showed highest inhibitory activity at concentration 0.38-1.32 mg/mL. Carvacrol exhibited effective additive antibacterial activity in combined treatment with octanoic acid (OA) in terms of its fractional inhibitory index (0.63-0.88) and time-kill effect in reducing about 6 log CFU/mL bacterial cells in less than 5 min. Effort was also made to elucidate mechanism of antibacterial action of CAR and OA against selected mastitis pathogens by observing changes in cell microstructure, permeability and integrity of cell membrane and their membrane potential. After adding CAR and OA at MIC level, there were obvious changes in cell morphology, leakage of small electrolytes, and macromolecules at the initial few hours of treatment i.e. within 1-2 h were observed. Our results indicated that CAR and OA could be evaluated as alternatives or adjuncts to antibiotics as intramammary infusion or topical application to treat bovine mastitis, significantly improving the microbiological safety of milk.

Efficacy and mechanism of carvacrol with octanoic acid against mastitis causing multi-drug-resistant pathogens

In the present investigation, we determined the in vitro antimicrobial activity of eight essential oils (EOs) and three medium-chain fatty acids (MCFAs) alone and in combination against Staphylococcus aureus ATCC 700698, Klebsiella pneumoniae ATCC 700603, and E. coli FcW5. The interactions between EOs and MCFAs were determined by fractional inhibitory concentration indices. Moreover, mode of action of selected bioactive components was studied by changes in bacterial surface charge, morphology, and membrane integrity assays. Among EOs, carvacrol (CAR), trans-cinnamaldehyde (TC), and thymol (TM) showed strong antimicrobial activity. In combination study, CAR+OA (octanoic acid), CAR+DA (decanoic acid), and TM+OA were observed as the most significant (P≤0.05) which were also confirmed through time-kill plots. Based on these results, CAR+OA were found to be most efficacious in terms of killing time (P≤0.05). Changes in the surface charge, morphology, and membrane integrity upon the combined treatment of CAR+OA were also observed, which ultimately leads to cell death. Results suggest that CAR+OA when used in combination offer a significant (P≤0.05) additive antimicrobial activity against the selected pathogenic bacteria. Therefore, these natural bioactive molecules could be interesting alternatives to conventional therapy for the control of mastitis caused by multi-drug-resistant pathogens in bovine animals to ensure the milk safety.

Aptamer Applications in Emerging Viral Diseases

Aptamers are single-stranded DNA or RNA molecules which are submitted to a process denominated SELEX. SELEX uses reiterative screening of a random oligonucleotide library to identify high-affinity binders to a chosen target, which may be a peptide, protein, or entire cells or viral particles. Aptamers can rival antibodies in target recognition, and benefit from their non-proteic nature, ease of modification, increased stability, and pharmacokinetic properties. This turns them into ideal candidates for diagnostic as well as therapeutic applications. Here, we review the recent accomplishments in the development of aptamers targeting emerging viral diseases, with emphasis on recent findings of aptamers binding to coronaviruses. We focus on aptamer development for diagnosis, including biosensors, in addition to aptamer modifications for stabilization in body fluids and tissue penetration. Such aptamers are aimed at in vivo diagnosis and treatment, such as quantification of viral load and blocking host cell invasion, virus assembly, or replication, respectively. Although there are currently no in vivo applications of aptamers in combating viral diseases, such strategies are promising for therapy development in the future.

Antimalarial Drug Resistance and Implications for the WHO Global Technical Strategy

PURPOSE OF REVIEW

Five years have passed since the World Health Organization released its Global Technical Strategy for Malaria (GTS). In that time, progress against malaria has plateaued. This review focuses on the implications of antimalarial drug resistance for the GTS and how interim progress in parasite genomics and antimalarial pharmacology offer a bulwark against it.

RECENT FINDINGS

For the first time, drug resistance-conferring genes have been identified and validated before their global expansion in malaria parasite populations. More efficient methods for their detection and elaboration have been developed, although low-density infections and polyclonality remain a nuisance to be solved. Clinical trials of alternative regimens for multidrug-resistant malaria have delivered promising results. New agents continue down the development pipeline, while a nascent infrastructure in sub-Saharan Africa for conducting phase I trials and trials of transmission-blocking agents has come to fruition after years of preparation.

SUMMARY

These and other developments can help inform the GTS as the world looks ahead to the next two decades of its implementation. To remain ahead of the threat that drug resistance poses, wider application of genomic-based surveillance and optimization of existing and forthcoming antimalarial drugs are essential.

Elevated rates of positive selection drive the evolution of pestiferousness in the Colorado potato beetle (Leptinotarsa decemlineata, Say)

Contextualizing evolutionary history and identifying genomic features of an insect that might contribute to its pest status is important in developing early detection and control tactics. In order to understand the evolution of pestiferousness, which we define as the accumulation of traits that contribute to an insect population's success in an agroecosystem, we tested the importance of known genomic properties associated with rapid adaptation in the Colorado potato beetle (CPB), Leptinotarsa decemlineata Say. Within the leaf beetle genus Leptinotarsa, only CPB, and a few populations therein, has risen to pest status on cultivated nightshades, Solanum. Using whole genomes from ten closely related Leptinotarsa species native to the United States, we reconstructed a high-quality species tree and used this phylogenetic framework to assess evolutionary patterns in four genomic features of rapid adaptation: standing genetic variation, gene family expansion and contraction, transposable element abundance and location, and positive selection at protein-coding genes. Throughout approximately 20 million years of history, Leptinotarsa species show little evidence of gene family turnover and transposable element variation. However, there is a clear pattern of CPB experiencing higher rates of positive selection on protein-coding genes. We determine that these rates are associated with greater standing genetic variation due to larger effective population size, which supports the theory that the demographic history contributes to rates of protein evolution. Furthermore, we identify a suite of coding genes under positive selection that are putatively associated with pestiferousness in the Colorado potato beetle lineage. They are involved in the biological processes of xenobiotic detoxification, chemosensation and hormone function.

Insertion and deletion evolution reflects antibiotics selection pressure in a Mycobacterium tuberculosis outbreak

In genome evolution, genetic variants are the source of diversity, which natural selection acts upon. Treatment of human tuberculosis (TB) induces a strong selection pressure for the emergence of antibiotic resistance-conferring variants in the infecting Mycobacterium tuberculosis (MTB) strains. MTB evolution in response to treatment has been intensively studied and mainly attributed to point substitutions. However, the frequency and contribution of insertions and deletions (indels) to MTB genome evolution remains poorly understood. Here, we analyzed a multi-drug resistant MTB outbreak for the presence of high-quality indels and substitutions. We find that indels are significantly enriched in genes conferring antibiotic resistance. Furthermore, we show that indels are inherited during the outbreak and follow a molecular clock with an evolutionary rate of 5.37e-9 indels/site/year, which is 23 times lower than the substitution rate. Inherited indels may co-occur with substitutions in genes along related biological pathways; examples are iron storage and resistance to second-line antibiotics. This suggests that epistatic interactions between indels and substitutions affect antibiotic resistance and compensatory evolution in MTB.

Whole-genome analyses reveal gene content differences between nontypeable Haemophilus influenzae isolates from chronic obstructive pulmonary disease compared to other clinical phenotypes

Nontypeable Haemophilus influenzae (NTHi) colonizes human upper respiratory airways and plays a key role in the course and pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (COPD). Currently, it is not possible to distinguish COPD isolates of NTHi from other clinical isolates of NTHi using conventional genotyping methods. Here, we analysed the core and accessory genome of 568 NTHi isolates, including 40 newly sequenced isolates, to look for genetic distinctions between NTHi isolates from COPD with respect to other illnesses, including otitis media, meningitis and pneumonia. Phylogenies based on polymorphic sites in the core-genome did not show discrimination between NTHi strains collected from different clinical phenotypes. However, pan-genome-wide association studies identified 79 unique NTHi accessory genes that were significantly associated with COPD. Furthermore, many of the COPD-related NTHi genes have known or predicted roles in virulence, transmembrane transport of metal ions and nutrients, cellular respiration and maintenance of redox homeostasis. This indicates that specific genes may be required by NTHi for its survival or virulence in the COPD lung. These results advance our understanding of the pathogenesis of NTHi infection in COPD lungs.

Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

Background

Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms.

Methods and Findings

We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets.

Conclusion

Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.

Cancer biology as revealed by the research autopsy

A research autopsy is a post-mortem medical procedure performed on a deceased individual with the primary goal of collecting tissue to support basic and translational research. This approach has increasingly been used to investigate the pathophysiological mechanisms of cancer evolution, metastasis and treatment resistance. In this Review, we discuss the rationale for the use of research autopsies in cancer research and provide an evidence-based discussion of the quality of post-mortem tissues compared with other types of biospecimens. We also discuss the advantages of using post-mortem tissues over other types of biospecimens, including the large amounts of tissue that can be obtained and the extent of multiregion sampling that is achievable, which is not otherwise possible in living patients. We highlight how the research autopsy has supported the identification of the clonal origins and modes of spread among metastases, the extent that selective pressures imposed by treatments cause bottlenecks leading to parallel and convergent tumour evolution, and the creation of rare tissue banks and patient-derived model systems. Finally, we comment on the future of the research autopsy as an integral component of precision medicine strategies.

Looking for New Antifungal Drugs from Flavonoids: Impact of the Genetic Diversity of Candida albicans on the in-vitro Response

BACKGROUND

In an era in which antimicrobial resistance is increasing at an alarming pace, it is very important to find new antimicrobial agents effective against pathogenic microrganisms resistant to traditional treatments. Among the notable breakthroughs in the past years of research in natural-drug discovery, there is the identification and testing of flavonoids, a group of plant-derived substances capable of promoting many beneficial effects on humans. These compounds show different biological activities such as inhibition of neuroinflammation and tumor growth as well as antimicrobial activity against many microbial pathogens.

METHODS

We undertook a review of protocols and standard strains used in studies reporting the inhibitory effects of flavonoids against Candida albicans by focusing our attention on genetic characterization of the strains examined. Moreover, using the C. albicans MLST-database, we performed a phylogenetic analysis showing the genetic variation occurring in this species.

RESULTS

Today, we have enough information to estimate genetic diversity within microbial species and recent data revealed that most of fungal pathogens show complex population structures in which not a single isolate can be designated as representative of the entire taxon. This is especially true for the highly divergent fungal pathogen C. albicans, in which the assumption that one or few "standard strains" can represent the whole species is overly unrealistic and should be laid to rest.

CONCLUSION

The goal of this article is to shed light on the extent of genetic variation in C. albicans and how this phenomenon can largely influence the activity of flavonoids against this species.

Evolution of infectious bronchitis virus in the field after homologous vaccination introduction

Despite the fact that vaccine resistance has been typically considered a rare phenomenon, some episodes of vaccine failure have been reported with increasing frequency in intensively-raised livestock. Infectious bronchitis virus (IBV) is a widespread avian coronavirus, whose control relies mainly on extensive vaccine administration. Unfortunately, the continuous emergence of new vaccine-immunity escaping variants prompts the development of new vaccines. In the present work, a molecular epidemiology study was performed to evaluate the potential role of homologous vaccination in driving IBV evolution. This was undertaken by assessing IBV viral RNA sequences from the ORF encoding the S1 portion of viral surface glycoprotein (S) before and after the introduction of a new live vaccine on broiler farms in northern-Italy. The results of several biostatistics analyses consistently demonstrate the presence of a higher pressure in the post-vaccination period. Natural selection was detected essentially on sites located on the protein surface, within or nearby domains involved in viral attachment or related functions. This evidence strongly supports the action of vaccine-induced immunity in conditioning viral evolution, potentially leading to the emergence of new vaccine-escape variants. The great plasticity of rapidly-evolving RNA-viruses in response to human intervention, which extends beyond the poultry industry, is demonstrated, claiming further attention due to their relevance for animal and especially human health.

How to increase our belief in discovered statistical interactions via large-scale association studies?

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.

Comparative Genome Analysis of an Extensively Drug-Resistant Isolate of Avian Sequence Type 167 Escherichia coli Strain Sanji with Novel In Silico Serotype O89b:H9

E. coli strain Sanji is the first sequenced and analyzed genome of the recently emerged pathogenic XDR strains with sequence type ST167 and novel in silico serotype O89b:H9. Comparison of the genomes of Sanji with other ST167 strains revealed distinct sets of different plasmids, mobile IS elements, and antibiotic resistance genes in each genome, indicating that there exist multiple paths toward achieving XDR. The emergence of these pathogenic ST167 E. coli strains with diverse XDR capabilities highlights the difficulty of preventing or mitigating the development of XDR properties in bacteria and points to the importance of better understanding of the shared underlying virulence mechanisms and physiology of pathogenic bacteria.

Extensive drug resistance (XDR) is an escalating global problem. Escherichia coli strain Sanji was isolated from an outbreak of pheasant colibacillosis in Fujian province, China, in 2011. This strain has XDR properties, exhibiting sensitivity to carbapenems but no other classes of known antibiotics. Whole-genome sequencing revealed a total of 32 known antibiotic resistance genes, many associated with insertion sequence 26 (IS26) elements. These were found on the Sanji chromosome and 2 of its 6 plasmids, pSJ_255 and pSJ_82. The Sanji chromosome also harbors a type 2 secretion system (T2SS), a type 3 secretion system (T3SS), a type 6 secretion system (T6SS), and several putative prophages. Sanji and other ST167 strains have a previously uncharacterized O-antigen (O89b) that is most closely related to serotype O89 as determined on the basis of analysis of the wzm-wzt genes and in silico serotyping. This O89b-antigen gene cluster was also found in the genomes of a few other pathogenic sequence type 617 (ST617) and ST10 complex strains. A time-scaled phylogeny inferred from comparative single nucleotide variant analysis indicated that development of these O89b-containing lineages emerged about 30 years ago. Comparative sequence analysis revealed that the core genome of Sanji is nearly identical to that of several recently sequenced strains of pathogenic XDR E. coli belonging to the ST167 group. Comparison of the mobile elements among the different ST167 genomes revealed that each genome carries a distinct set of multidrug resistance genes on different types of plasmids, indicating that there are multiple paths toward the emergence of XDR in E. coli.

IMPORTANCEE. coli strain Sanji is the first sequenced and analyzed genome of the recently emerged pathogenic XDR strains with sequence type ST167 and novel in silico serotype O89b:H9. Comparison of the genomes of Sanji with other ST167 strains revealed distinct sets of different plasmids, mobile IS elements, and antibiotic resistance genes in each genome, indicating that there exist multiple paths toward achieving XDR. The emergence of these pathogenic ST167 E. coli strains with diverse XDR capabilities highlights the difficulty of preventing or mitigating the development of XDR properties in bacteria and points to the importance of better understanding of the shared underlying virulence mechanisms and physiology of pathogenic bacteria.

Author Video: An author video summary of this article is available.

Evaluation of a gene-by-gene approach for prospective whole-genome sequencing-based surveillance of multidrug resistant Mycobacterium tuberculosis

Whole-genome sequencing (WGS) offers unprecedented resolution for tracking Mycobacterium tuberculosis transmission and antibiotic-resistance spread. Still, the establishment of standardized WGS-based pipelines and the definition of epidemiological clusters based on genetic relatedness are under discussion. We aimed to implement a dynamic gene-by-gene approach, fully relying on freely available software, for prospective WGS-based tuberculosis surveillance, demonstrating its application for detecting transmission chains by retrospectively analysing all M/XDR strains isolated in 2013-2017 in Portugal. We observed a good correlation between genetic relatedness and epidemiological links, with strongly epilinked clusters displaying mean pairwise allele differences (AD) always below 0.3% (ratio of mean AD over the total number of shared loci between same-cluster strains). This data parallels the genetic distances acquired by the core-SNV analysis, while providing higher resolution and epidemiological concordance than MIRU-VNTR genotyping. The dynamic analysis of strain sub-sets (i.e., increasing the number of shared loci within each sub-set) also strengthens the confidence in detecting epilinked clusters. This gene-by-gene strategy also offers several practical benefits (e.g., reliance on freely-available software, scalability and low computational requirements) that further consolidated its suitability for a timely and robust prospective WGS-based laboratory surveillance of M/XDR-TB cases.

Cinnamaldehyde Inhibits Staphylococcus aureus Virulence Factors and Protects against Infection in a Galleria mellonella Model

Bacterial resistance to the available marketed drugs has prompted the search of novel therapies; especially in regards of anti-virulence strategies that aim to make bacteria less pathogenic and/or decrease their probability to become resistant to therapy. Cinnamaldehyde is widely known for its antibacterial properties through mechanisms that include the interaction of this compound with bacterial cell walls. However, only a handful of studies have addressed its effects on bacterial virulence, especially when tested at sub-inhibitory concentrations. Herein, we show for the first time that cinnamaldehyde is bactericidal against Staphylococcus aureus and Enterococcus faecalis multidrug resistant strains and does not promote bacterial tolerance. Cinnamaldehyde actions were stronger on S. aureus as it was able to inhibit its hemolytic activity on human erythrocytes and reduce its adherence to latex. Furthermore, cinnamaldehyde enhanced the serum-dependent lysis of S. aureus. In vivo testing of cinnamaldehyde in Galleria mellonella larvae infected with S. aureus, showed this compound improves larvae survival whilst diminishing bacterial load in their hemolymph. We suggest that cinnamaldehyde may represent an alternative therapy to control S. aureus-induced bacterial infections as it presents the ability to reduce bacterial virulence/survival without promoting an adaptive phenotype.

The prediction of virus mutation using neural networks and rough set techniques

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.