A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence

Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies

Pseudomonas aeruginosa MifS-MifR Two-Component System Is Specific for alpha-Ketoglutarate Utilization

Pseudomonas aeruginosa is a Gram-negative, metabolically versatile opportunistic pathogen that elaborates a multitude of virulence factors, and is extraordinarily resistant to a gamut of clinically significant antibiotics. This ability, in part, is mediated by two-component regulatory systems (TCS) that play a crucial role in modulating virulence mechanisms and metabolism. MifS (PA5512) and MifR (PA5511) form one such TCS implicated in biofilm formation. MifS is a sensor kinase whereas MifR belongs to the NtrC superfamily of transcriptional regulators that interact with RpoN (sigma(54)). In this study we demonstrate that the mifS and mifR genes form a two-gene operon. The close proximity of mifSR operon to poxB (PA5514) encoding a beta-lactamase hinted at the role of MifSR TCS in regulating antibiotic resistance. To better understand this TCS, clean in-frame deletions were made in P. aeruginosa PAO1 creating PAO Delta mifS, PAO Delta mifR and PAO Delta mifSR. The loss of mifSR had no effect on the antibiotic resistance profile. Phenotypic microarray (BioLOG) analyses of PAO Delta mifS and PAO Delta mifR revealed that these mutants were unable to utilize C-5-dicarboxylate alpha-ketoglutarate (alpha-KG), a key tricarboxylic acid cycle intermediate. This finding was confirmed using growth analyses, and the defect can be rescued by mifR or mifSR expressed in trans. These mifSR mutants were able to utilize all the other TCA cycle intermediates (citrate, succinate, fumarate, oxaloacetate or malate) and sugars (glucose or sucrose) except alpha-KG as the sole carbon source. We confirmed that the mifSR mutants have functional dehydrogenase complex suggesting a possible defect in alpha-KG transport. The inability of the mutants to utilize alpha-KG was rescued by expressing PA5530, encoding C-5-dicarboxylate transporter, under a regulatable promoter. In addition, we demonstrate that besides MifSR and PA5530, alpha-KG utilization requires functional RpoN. These data clearly suggests that P. aeruginosa MifSR TCS is involved in sensing a-KG and regulating its transport and subsequentmetabolism

Deep sequencing analyses expands the Pseudomonas aeruginosa AmpR regulon to include small RNA-mediated regulation of iron acquisition, heat shock and oxidative stress response

Pathogenicity of Pseudomonas aeruginosa, a major cause of many acute and chronic human infections, is determined by tightly regulated expression of multiple virulence factors. Quorum sensing (QS) controls expression of many of these pathogenic determinants. Previous microarray studies have shown that the AmpC β-lactamase regulator AmpR, a member of the LysR family of transcription factors, also controls non-β-lactam resistance and multiple virulence mechanisms. Using RNA-Seq and complementary assays, this study further expands the AmpR regulon to include diverse processes such as oxidative stress, heat shock and iron uptake. Importantly, AmpR affects many of these phenotypes, in part, by regulating expression of non-coding RNAs such as rgP32, asRgsA, asPrrF1 and rgRsmZ. AmpR positively regulates expression of the major QS regulators LasR, RhlR and MvfR, and genes of the Pseudomonas quinolone system. Chromatin immunoprecipitation (ChIP)-Seq and ChIP–quantitative real-time polymerase chain reaction studies show that AmpR binds to the ampC promoter both in the absence and presence of β-lactams. In addition, AmpR directly binds the lasR promoter, encoding the QS master regulator. Comparison of the AmpR-binding sequences from the transcriptome and ChIP-Seq analyses identified an AT-rich consensus-binding motif. This study further attests to the role of AmpR in regulating virulence and physiological processes in P. aeruginosa

Otopathogenic Pseudomonas aeruginosa Enters and Survives Inside Macrophages

Otitis media (OM) is a broad term describing a group of infectious and inflammatory disorders of the middle ear. Despite antibiotic therapy, acute OM can progress to chronic suppurative otitis media (CSOM) characterized by ear drum perforation and purulent discharge. Pseudomonas aeruginosa is the most common pathogen associated with CSOM. Although, macrophages play an important role in innate immune responses but their role in the pathogenesis of P. aeruginosa-induced CSOM is not known. The objective of this study is to examine the interaction of P. aeruginosa with primary macrophages. We observed that P. aeruginosa enters and multiplies inside human and mouse primary macrophages. This bacterial entry in macrophages requires both microtubule and actin dependent processes. Transmission electron microscopy demonstrated that P. aeruginosa was present in membrane bound vesicles inside macrophages. Interestingly, deletion of oprF expression in P. aeruginosa abrogates its ability to survive inside macrophages. Our results suggest that otopathogenic P. aeruginosa entry and survival inside macrophages is OprF-dependent. The survival of bacteria inside macrophages will lead to evasion of killing and this lack of pathogen clearance by phagocytes contributes to the persistence of infection in CSOM. Understanding host–pathogen interaction will provide novel avenues to design effective treatment modalities against OM

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Pseudomonas aeruginosa AmpR: an acute–chronic switch regulator

Pseudomonas aeruginosa is one of the most intractable human pathogens that pose serious clinical challenge due to extensive prevalence of multidrug-resistant clinical isolates. Armed with abundant virulence and antibiotic resistance mechanisms, it is a major etiologic agent in a number of acute and chronic infections. A complex and intricate network of regulators dictates the expression of pathogenicity factors in P. aeruginosa. Some proteins within the network play key roles and control multiple pathways. This review discusses the role of one such protein, AmpR, which was initially recognized for its role in antibiotic resistance by regulating AmpC β-lactamase. Recent genomic, proteomic and phenotypic analyses demonstrate that AmpR regulates expression of hundreds of genes that are involved in diverse pathways such as β-lactam and non-β-lactam resistance, quorum sensing and associated virulence phenotypes, protein phosphorylation, and physiological processes. Finally, ampR mutations in clinical isolates are reviewed to shed light on important residues required for its function in antibiotic resistance. The prevalence and evolutionary implications of AmpR in pathogenic and nonpathogenic proteobacteria are also discussed. A comprehensive understanding of proteins at nodal positions in the P. aeruginosa regulatory network is crucial in understanding, and ultimately targeting, the pathogenic stratagems of this organism

A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence

Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies