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Toxins Sep 2021is a clinically important pathogen that causes a wide range of human infections, from minor skin infections to severe tissue infection and sepsis. has a high level of... (Review)
Review
is a clinically important pathogen that causes a wide range of human infections, from minor skin infections to severe tissue infection and sepsis. has a high level of antibiotic resistance and is a common cause of infections in hospitals and the community. The rising prevalence of community-acquired methicillin-resistant (CA-MRSA), combined with the important severity of infections in general, has resulted in the frequent use of anti-staphylococcal antibiotics, leading to increasing resistance rates. Antibiotic-resistant continues to be a major health concern, necessitating the development of novel therapeutic strategies. uses a wide range of virulence factors, such as toxins, to develop an infection in the host. Recently, anti-virulence treatments that directly or indirectly neutralize toxins have showed promise. In this review, we provide an update on toxin pathogenic characteristics, as well as anti-toxin therapeutical strategies.
Topics: Anti-Bacterial Agents; Staphylococcus aureus; Toxins, Biological; Virulence; Virulence Factors
PubMed: 34678970
DOI: 10.3390/toxins13100677 -
Microbiology and Molecular Biology... Dec 2013Enteric pathogens such as Salmonella enterica cause significant morbidity and mortality. S. enterica serovars are a diverse group of pathogens that have evolved to... (Review)
Review
Enteric pathogens such as Salmonella enterica cause significant morbidity and mortality. S. enterica serovars are a diverse group of pathogens that have evolved to survive in a wide range of environments and across multiple hosts. S. enterica serovars such as S. Typhi, S. Dublin, and S. Gallinarum have a restricted host range, in which they are typically associated with one or a few host species, while S. Enteritidis and S. Typhimurium have broad host ranges. This review examines how S. enterica has evolved through adaptation to different host environments, especially as related to the chicken host, and continues to be an important human pathogen. Several factors impact host range, and these include the acquisition of genes via horizontal gene transfer with plasmids, transposons, and phages, which can potentially expand host range, and the loss of genes or their function, which would reduce the range of hosts that the organism can infect. S. Gallinarum, with a limited host range, has a large number of pseudogenes in its genome compared to broader-host-range serovars. S. enterica serovars such as S. Kentucky and S. Heidelberg also often have plasmids that may help them colonize poultry more efficiently. The ability to colonize different hosts also involves interactions with the host's immune system and commensal organisms that are present. Thus, the factors that impact the ability of Salmonella to colonize a particular host species, such as chickens, are complex and multifactorial, involving the host, the pathogen, and extrinsic pressures. It is the interplay of these factors which leads to the differences in host ranges that we observe today.
Topics: Animals; Chickens; Salmonella; Salmonella enterica; Virulence
PubMed: 24296573
DOI: 10.1128/MMBR.00015-13 -
International Journal of Molecular... Jul 2020Microbial virulence factors encompass a wide range of molecules produced by pathogenic microorganisms, enhancing their ability to evade their host defenses and cause...
Microbial virulence factors encompass a wide range of molecules produced by pathogenic microorganisms, enhancing their ability to evade their host defenses and cause disease [...].
Topics: Bacteria; Humans; Virulence; Virulence Factors
PubMed: 32727013
DOI: 10.3390/ijms21155320 -
Science (New York, N.Y.) Jun 2018Some pathogens and pests deliver small RNAs (sRNAs) into host cells to suppress host immunity. Conversely, hosts also transfer sRNAs into pathogens and pests to inhibit...
Some pathogens and pests deliver small RNAs (sRNAs) into host cells to suppress host immunity. Conversely, hosts also transfer sRNAs into pathogens and pests to inhibit their virulence. Although sRNA trafficking has been observed in a wide variety of interactions, how sRNAs are transferred, especially from hosts to pathogens and pests, is still unknown. Here, we show that host cells secrete exosome-like extracellular vesicles to deliver sRNAs into fungal pathogen These sRNA-containing vesicles accumulate at the infection sites and are taken up by the fungal cells. Transferred host sRNAs induce silencing of fungal genes critical for pathogenicity. Thus, has adapted exosome-mediated cross-kingdom RNA interference as part of its immune responses during the evolutionary arms race with the pathogen.
Topics: Arabidopsis; Botrytis; Extracellular Vesicles; Host-Pathogen Interactions; Plant Diseases; Plant Immunity; RNA Interference; RNA, Plant; RNA, Small Interfering; Virulence; Virulence Factors
PubMed: 29773668
DOI: 10.1126/science.aar4142 -
Molecular Oral Microbiology Dec 2012Recent advancements in the periodontal research field are consistent with a new model of pathogenesis according to which periodontitis is initiated by a synergistic and... (Review)
Review
Recent advancements in the periodontal research field are consistent with a new model of pathogenesis according to which periodontitis is initiated by a synergistic and dysbiotic microbial community rather than by select 'periopathogens', such as the 'red complex'. In this polymicrobial synergy, different members or specific gene combinations within the community fulfill distinct roles that converge to shape and stabilize a disease-provoking microbiota. One of the core requirements for a potentially pathogenic community to arise involves the capacity of certain species, termed 'keystone pathogens', to modulate the host response in ways that impair immune surveillance and tip the balance from homeostasis to dysbiosis. Keystone pathogens also elevate the virulence of the entire microbial community through interactive communication with accessory pathogens. Other important core functions for pathogenicity require the expression of diverse molecules (e.g. appropriate adhesins, cognate receptors, proteolytic enzymes and proinflammatory surface structures/ligands), which in combination act as community virulence factors to nutritionally sustain a heterotypic, compatible and proinflammatory microbial community that elicits a non-resolving and tissue-destructive host response. On the basis of the fundamental concepts underlying this model of periodontal pathogenesis, that is, polymicrobial synergy and dysbiosis, we term it the PSD model.
Topics: Bacteria; Host-Pathogen Interactions; Humans; Immune Evasion; Inflammation Mediators; Microbial Consortia; Periodontitis; Symbiosis; Virulence
PubMed: 23134607
DOI: 10.1111/j.2041-1014.2012.00663.x -
PeerJ 2022Obligate fungal pathogens (ascomycetes and basidiomycetes) and oomycetes are known to cause diseases in cereal crop plants. They feed on living cells and most of them... (Review)
Review
Obligate fungal pathogens (ascomycetes and basidiomycetes) and oomycetes are known to cause diseases in cereal crop plants. They feed on living cells and most of them have learned to bypass the host immune machinery. This paper discusses some of the factors that are associated with pathogenicity drawing examples from ascomycetes, basidiomycetes and oomycetes, with respect to their manifestation in crop plants. The comparisons have revealed a striking similarity in the three groups suggesting convergent pathways that have arisen from three lineages independently leading to an obligate lifestyle. This review has been written with the intent, that new information on adaptation strategies of biotrophs, modifications in pathogenicity strategies and population dynamics will improve current strategies for breeding with stable resistance.
Topics: Virulence; Plant Breeding; Oomycetes; Adaptation, Physiological; Ascomycota
PubMed: 36042858
DOI: 10.7717/peerj.13794 -
Nature Microbiology May 2022Fungal pathogens cause more than a billion human infections every year, resulting in more than 1.6 million deaths annually. Understanding the natural history and... (Review)
Review
Fungal pathogens cause more than a billion human infections every year, resulting in more than 1.6 million deaths annually. Understanding the natural history and evolutionary ecology of fungi is helping us understand how disease-relevant traits have repeatedly evolved. Different types and mechanisms of genetic variation have contributed to the evolution of fungal pathogenicity and specific genetic differences distinguish pathogens from non-pathogens. Insights into the traits, genetic elements, and genetic and ecological mechanisms that contribute to the evolution of fungal pathogenicity are crucial for developing strategies to both predict emergence of fungal pathogens and develop drugs to combat them.
Topics: Fungi; Humans; Life Style; Virulence
PubMed: 35508719
DOI: 10.1038/s41564-022-01112-0 -
Molecular Genetics and Genomics : MGG Nov 2022The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to...
The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.
Topics: Humans; Virulence; COVID-19; Oligonucleotides; DNA; Biomarkers
PubMed: 36125534
DOI: 10.1007/s00438-022-01951-w -
FEMS Microbiology Reviews Jan 2018Metals are essential for life, and they play a central role in the struggle between infecting microbes and their hosts. In fact, an important aspect of microbial... (Review)
Review
Metals are essential for life, and they play a central role in the struggle between infecting microbes and their hosts. In fact, an important aspect of microbial pathogenesis is the 'nutritional immunity', in which metals are actively restricted (or, in an extended definition of the term, locally enriched) by the host to hinder microbial growth and virulence. Consequently, fungi have evolved often complex regulatory networks, uptake and detoxification systems for essential metals such as iron, zinc, copper, nickel and manganese. These systems often differ fundamentally from their bacterial counterparts, but even within the fungal pathogens we can find common and unique solutions to maintain metal homeostasis. Thus, we here compare the common and species-specific mechanisms used for different metals among different fungal species-focusing on important human pathogens such as Candida albicans, Aspergillus fumigatus or Cryptococcus neoformans, but also looking at model fungi such as Saccharomyces cerevisiae or A. nidulans as well-studied examples for the underlying principles. These direct comparisons of our current knowledge reveal that we have a good understanding how model fungal pathogens take up iron or zinc, but that much is still to learn about other metals and specific adaptations of individual species-not the least to exploit this knowledge for new antifungal strategies.
Topics: Fungi; Homeostasis; Metals; Virulence
PubMed: 29069482
DOI: 10.1093/femsre/fux050 -
Proceedings of the National Academy of... Apr 2022Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the...
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.
Topics: Bacteria; Genome, Bacterial; Machine Learning; Phenotype; Virulence; Virulence Factors; Whole Genome Sequencing
PubMed: 35363569
DOI: 10.1073/pnas.2112886119