Combined use of the Ab105-2фΔCI lytic mutant phage and different antibiotics in clinical isolates of multiresistant Acinetobacter baumannii

Phage therapy is an abandoned antimicrobial therapy that has been resumed in recent years. In this study, we mutated a lysogenic phage from Acinetobacter baumannii into a lytic phage (Ab105-2phiΔCI) showing antimicrobial activity against A.baumannii clinical strains(such as Ab177_GEIH-2000 which showed MICs to meropenem and imipenem of 32 μg/ml and 16 μg/ml, respectively as well as belonging to GEIH-REIPI Spanish MulticenterA. baumannii Study II 2000/2010, Umbrella Genbank Bioproject PRJNA422585).We observed in vitro, an antimicrobial synergistic effect(from 4 log to 7 log CFU/ml) with meropenem plus lytic phage in all combinations analysed(0.1, 1 and 10 MOI of Ab105-2phiΔCI mutant as well as 1/4 and 1/8 MIC of meropenem). Moreover, we had a decrease in bacterial growth of 8 log CFU/ml for the combination of imipenem at 1/4 MIC plus lytic phage(Ab105-2phiΔCI mutant) and of 4 log CFU/ml for the combination of imipenem at 1/8 MIC plus lytic phage (Ab105-2phiΔCI mutant) in both MOI 1 and 10.These results were confirmed in in vivo(G. mellonella) obtaining a higher effectiveness in thecombination of imipenem and Ab105-2phiΔCI mutant(P<0.05). This approach could help to reducethe emergence of phage resistant bacteria and restore sensitivity to the antibiotics when used tocombat multiresistant strains of Acinetobacter baumannii

Relationship Between the Quorum Network (Sensing/Quenching) and Clinical Features of Pneumonia and Bacteraemia Caused by A. baumannii

Acinetobacter baumannii (Ab) is one of the most important pathogens associated with nosocomial infections, especially pneumonia. Interest in the Quorum network, i.e., Quorum Sensing (QS)/Quorum Quenching (QQ), in this pathogen has grown in recent years. The Quorum network plays an important role in regulating diverse virulence factors such as surface motility and bacterial competition through the type VI secretion system (T6SS), which is associated with bacterial invasiveness. In the present study, we investigated 30 clinical strains of A. baumannii isolated in the “II Spanish Study of A. baumannii GEIH-REIPI 2000-2010” (Genbank Umbrella Bioproject PRJNA422585), a multicentre study describing the relationship between the Quorum network in A. baumannii and the development of pneumonia and associated bacteraemia. Expression of the aidA gene (encoding the AidA protein, QQ enzyme) was lower (P < 0.001) in strains of A. baumannii isolated from patients with bacteraemic pneumonia than in strains isolated from patients with non-bacteraemic pneumonia. Moreover, aidA expression in the first type of strain was not regulated in the presence of environmental stress factors such as the 3-oxo-C12-HSL molecule (substrate of AidA protein, QQ activation) or H2O2 (inhibitor of AidA protein, QS activation). However, in the A. baumannii strains isolated from patients with non-bacteraemic pneumonia, aidA gene expression was regulated by stressors such as 3-oxo-C12-HSL and H2O2. In an in vivo Galleria mellonella model of A. baumannii infection, the A. baumannii ATCC 17978 strain was associated with higher mortality (100% at 24 h) than the mutant, abaI-deficient, strain (carrying a synthetase enzyme of Acyl homoserine lactone molecules) (70% at 24 h). These data suggest that the QS (abaR and abaI genes)/QQ (aidA gene) network affects the development of secondary bacteraemia in pneumonia patients and also the virulence of A. baumannii.National Plan for Scientific ResearchTechnological Development and Innovation PI16/01163ISCIII-Deputy General Directorate for Evaluation and Promotion of Research-European Regional Development Fund A way of Making EuropeInstituto de Salud Carlos IIIMiguel Servet Research Programme SERGAS and ISCIIIXunta de Galicia (GAIN, Axencia de Innovación

Development of an epigenetic age predictor for costal cartilage with a simultaneous somatic tissue differentiation system

Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual's lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19–74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19–75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.</p

Mechanisms of Tolerance and Resistance to Chlorhexidine in Clinical Strains of Klebsiella pneumoniae Producers of Carbapenemase: Role of New Type II Toxin-Antitoxin System, PemIK

Although the failure of antibiotic treatment is normally attributed to resistance, tolerance and persistence display a significant role in the lack of response to antibiotics. Due to the fact that several nosocomial pathogens show a high level of tolerance and/or resistance to chlorhexidine, in this study we analyzed the molecular mechanisms associated with chlorhexidine adaptation in two clinical strains of Klebsiella pneumoniae by phenotypic and transcriptomic studies. These two strains belong to ST258-KPC3 (high-risk clone carrying β-lactamase KPC3) and ST846-OXA48 (low-risk clone carrying β-lactamase OXA48). Our results showed that the K. pneumoniae ST258-KPC3CA and ST846-OXA48CA strains exhibited a different behavior under chlorhexidine (CHLX) pressure, adapting to this biocide through resistance and tolerance mechanisms, respectively. Furthermore, the appearance of cross-resistance to colistin was observed in the ST846-OXA48CA strain (tolerant to CHLX), using the broth microdilution method. Interestingly, this ST846-OXA48CA isolate contained a plasmid that encodes a novel type II toxin/antitoxin (TA) system, PemI/PemK. We characterized this PemI/PemK TA system by cloning both genes into the IPTG-inducible pCA24N plasmid, and found their role in persistence and biofilm formation. Accordingly, the ST846-OXA48CA strain showed a persistence biphasic curve in the presence of a chlorhexidine-imipenem combination, and these results were confirmed by the enzymatic assay (WST-1).The State Plan for R+D+I 2013–2016 National Plan for Scientific Research, Technological Development and Innovation 2008–2011 PI16/01163 and PI19/00878ISCIII-Deputy General Directorate for Evaluation and Promotion of Research - European Regional Development Fund “A way of Making Europe” and Instituto de Salud Carlos III FEDER, Spanish Network for the Research in Infectious Diseases REIPI, RD16/0016/0001, RD16/CIII/0004/0002 and RD16/0016/0006The Study Group on Mechanisms of Action and Resistance to Antimicrobials, GEMAR

Relationship Between Quorum Sensing and Secretion Systems

Quorum sensing (QS) is a communication mechanism between bacteria that allows specific processes to be controlled, such as biofilm formation, virulence factor expression, production of secondary metabolites and stress adaptation mechanisms such as bacterial competition systems including secretion systems (SS). These SS have an important role in bacterial communication. SS are ubiquitous; they are present in both Gram-negative and Gram-positive bacteria and in Mycobacterium sp. To date, 8 types of SS have been described (T1SS, T2SS, T3SS, T4SS, T5SS, T6SS, T7SS, and T9SS). They have global functions such as the transport of proteases, lipases, adhesins, heme-binding proteins, and amidases, and specific functions such as the synthesis of proteins in host cells, adaptation to the environment, the secretion of effectors to establish an infectious niche, transfer, absorption and release of DNA, translocation of effector proteins or DNA and autotransporter secretion. All of these functions can contribute to virulence and pathogenesis. In this review, we describe the known types of SS and discuss the ones that have been shown to be regulated by QS. Due to the large amount of information about this topic in some pathogens, we focus mainly on Pseudomonas aeruginosa and Vibrio spp

Quorum and light signals modulate acetoin/Butanediol catabolism in acinetobacterspp

Copyright © 2019 Tuttobene, Fernández-García, Blasco, Cribb, Ambroa, Müller, Fernández-Cuenca, Bleriot, Rodríguez, Barbosa, Lopez-Rojas, Trastoy, López, Bou, Tomás and Mussi.Acinetobacter spp. are found in all environments on Earth due to their extraordinary capacity to survive in the presence of physical and chemical stressors. In this study, we analyzed global gene expression in airborne Acinetobacter sp. strain 5-2Ac02 isolated from hospital environment in response to quorum network modulators and found that they induced the expression of genes of the acetoin/butanediol catabolism, volatile compounds shown to mediate interkingdom interactions. Interestingly, the acoN gene, annotated as a putative transcriptional regulator, was truncated in the downstream regulatory region of the induced acetoin/butanediol cluster in Acinetobacter sp. strain 5-2Ac02, and its functioning as a negative regulator of this cluster integrating quorum signals was confirmed in Acinetobacter baumannii ATCC 17978. Moreover, we show that the acetoin catabolism is also induced by light and provide insights into the light transduction mechanism by showing that the photoreceptor BlsA interacts with and antagonizes the functioning of AcoN in A. baumannii, integrating also a temperature signal. The data support a model in which BlsA interacts with and likely sequesters AcoN at this condition, relieving acetoin catabolic genes from repression, and leading to better growth under blue light. This photoregulation depends on temperature, occurring at 23°C but not at 30°C. BlsA is thus a dual regulator, modulating different transcriptional regulators in the dark but also under blue light, representing thus a novel concept. The overall data show that quorum modulators as well as light regulate the acetoin catabolic cluster, providing a better understanding of environmental as well as clinical bacteria.This study was funded by grant PI16/01163 awarded to MT within the State Plan for R+D+I 2013–2016 (National Plan for Scientific Research, Technological Development and Innovation 2008–2011) and co-financed by the ISCIII-Deputy General Directorate for Evaluation and Promotion of Research – European Regional Development Fund “A way of Making Europe” and Instituto de Salud Carlos III FEDER, Spanish Network for the Research in Infectious Diseases (REIPI, RD16/0016/0001, RD16/0016/0006, and RD16/0016/0008) and by the Study Group on Mechanisms of Action and Resistance to Antimicrobials, GEMARA (SEIMC, http://www.seimc.org/). MT was financially supported by the Miguel Servet Research Program (SERGAS and ISCIII). RT and LF-G were financially supported by, respectively, a SEIMC grant and predoctoral fellowship from the Xunta de Galicia (GAIN, Axencia de Innovación). BB was financially supported by CAPES, Process: PDSE 99999.001069/2014-04. This work was also supported by grants from the Agencia Nacional de Promoción Científica y Tecnológica (PICT 2014-1161) and ASaCTeI (Ministerio de Ciencia, Tecnología e Innovación Productiva de la Provincia de Santa Fe) 2010-147-16 to MAM. PC, GLM, and MAM are career investigators of CONICET. MRT is a fellow from the same institution

A common epigenetic clock from childhood to old age

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual's lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of +/-3-4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER((R)), pyrosequencing, MiSeq, and SNaPshot(TM). The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER((R)), pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshot(TM) gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation