Bacteria, Volume 1, Issue 1 (March 2022) – 6 articles

A recent publication described the construction and utility of a comprehensive “Chromatic Bacteria” toolbox containing a set of genetic tools that allows for fluorescently tagging a variety of Proteobacteria. In an effort to expand the range of bacteria taggable with the Chromatic Bacteria toolbox, a series of Himar1 transposon vectors was constructed to mediate insertion of fluorescent protein and antibiotic resistant genes. The Himar1 transposon was chosen as it is known to function in a wide range of bacterial species. To test the suitability of the new Himar1 Chromatic Bacteria plasmid derivatives, conjugations were attempted on recently isolated non-model organisms. Although we were unsuccessful in delivering the plasmids into Gram-positive bacterial isolates, we successfully modified previously recalcitrant isolates to the first set of the Chromatic Bacteria toolbox, such as Sphingomonas sp. Leaf357 and Acidovorax sp. Leaf84. This manuscript reports on the currently available plasmids and transposition success in different bacteria. Full article

There is little evidence on the role of prior infection in patients with external ventricular drains (EVDs) and lumbar drains (LDs). In this study, our aim is to investigate whether previous bacteremia is a risk factor for cerebrospinal fluid drain infection (CSFDI) in patients with EVDs and LDs and to describe the microorganisms implicated. We designed a retrospective, single-center cohort study. We recorded patients’ demographic and clinical characteristics, as well as microbiology laboratory data. We used non-parametric statistical methods to identify possible risk factors for CSFDI. We found 799 neurosurgical admissions during the study period, 70 of which fulfilled the inclusion criteria. Acinetobacter baumannii was the most frequent single pathogen isolated in the cerebrospinal fluid (CSF). Acinetobacter baumannii bacteremia was more common in patients with Acinetobacter baumannii CSFDI (p = 0.01). The distribution of the pathogens in the CSF differed from that of the pathogens isolated in blood (p = 0.001). In the univariate analysis, prior bacteremia was more common in patients with CSFDI (p = 0.027), but, in the multivariate model, prior bacteremia was not identified as an independent risk factor (OR = 0.456, CI: 0.138–1.512, p = 0.2). In an ICU population, the most frequently isolated pathogens were Gram-negative Enterobacteriaceae and Acinetobacter baumannii. Previous bacteremia was significantly more probable among patients with EVDs or LDs who developed a CSFDI, and its role warrants further investigation. Full article

Asinibacterium spp. (Family Chitinophagaceae, Phylum Bacteroidota) are abundant in environments contaminated with heavy metals. We characterized the physiology and genome of two Asinibacterium species to elucidate their ability to survive and grow at ambient conditions in the uranium-contaminated environments. Both strains were able to grow at pH 4.5 or 50 mM nitrate under aerobic conditions and did not grow with alternative electron acceptors under anaerobic conditions. Asinibacterium sp. OR53 grew in medium with uranium concentrations up to 300 µM uranium while Asinibacterium sp. OR43 could not grow at uranium concentrations > 200 µM. Elemental mapping using energy dispersive X-ray spectroscopy indicate that uranium co-localized with phosphorus-containing compounds on the cell surface. Genes potentially encoding resistance mechanisms to a variety of heavy metals were detected in the genomes of both strains. The localization of uranium and missing acidic and alkaline phosphatase genes in the genome suggest that biosorption of uranium to the lipopolysaccharide layer might be the mechanism of uranium resistance. In summary, Asinibacterium spp. OR43 and OR53 are physiologically similar to closely related strains within the Chitinophagaceae family but are uniquely acclimated to the presence of uranium and other heavy metals prevalent in the subsurface at Oak Ridge, Tennessee. Full article

We used machine-learning algorithms to evaluate demographic and clinical data in an administrative data set to identify relevant predictors of mortality due to Listeria monocytogenes infection. We used the Spanish Minimum Basic Data Set at Hospitalization (MBDS-H) to estimate the impacts of several predictors on mortality. The MBDS-H is a mandatory registry of clinical discharge reports. Data were coded with International Classification of Diseases, either Ninth or Tenth Revisions, codes. Diagnoses and clinical conditions were defined using recorded data from these codes or a combination of them. We used two different statistical approaches to produce two predictive models. The first was logistic regression, a classic statistical approach that uses data science to preprocess data and measure performance. The second was a random forest algorithm, a strategy based on machine learning and feature selection. We compared the performance of the two models using predictive accuracy and the area under the curve. Between 2001 and 2016, a total of 5603 hospitalized patients were identified as having any clinical form of listeriosis. Most patients were adults (94.9%). Among all hospitalized individuals, there were 2318 women (41.4%). We recorded 301 pregnant women and 287 newborns with listeriosis. The mortality rate was 0.13 patients per 100,000 population. The performance of the model produced by logistic regression after intense preprocessing was similar to that of the model produced by the random forest algorithm. Predictive accuracy was 0.83, and the area under the receiver operating characteristic curve was 0.74 in both models. Sepsis, age, and malignancy were the most relevant features related to mortality. Our combined use of data science, preprocessing, conventional statistics, and machine learning provides insights into mortality due to Listeria-related infection. These methods are not mutually exclusive. The combined use of several methods would allow researchers to better explain results and understand data related to Listeria monocytogenes infection. Full article

Listeriosis is an uncommon and potentially severe zoonotic bacterial infection that usually occurs in outbreaks instead of isolated cases. In recent years, there has been an increase in the incidence of this disease. One of the most severe of its complications involves the central nervous system (CNS) in a condition known as neurolisteriosis. Here, we describe the demographic and clinical features of patients presenting with neurolisteriosis between 2001 and 2015 using administrative data and attempt to identify potential predictors for mortality. We used the Spanish Minimum Basic Data Set at Hospitalization, a compulsory registry that collects data from clinical discharge reports. Up to 2015, data were coded based on the International Classification of Diseases, 9th Revision, so we used diagnoses and clinical conditions based on these codes. Age, sex, clinical presentation, mortality, and involvement of the CNS were identified. Using algorithms to aggregate data, variables such as immunosuppression and malignant disease were obtained. We analyzed correlations among clinical features and identified risk factors for morbidity and mortality. Between 2001 and 2015 we identified 5180 individuals, with a hospitalization rate of 0.76 per 100,000 population. Most (94%) were adults, and only 5.4% were pregnant women. The average age was 66 years. Neurological involvement was present in 2313 patients (44.7%), mostly meningitis (90.4%). Global mortality was 17%, but mortality in CNS infections was 19.2%. Age, severe sepsis, chronic liver disease, chronic kidney disease, and malignancy were the main risk factors for mortality in patients with CNS infections by Listeria monocytogenes. Although it is uncommon, neurolisteriosis can be a severe condition, associated with a high rate of mortality. Health care providers should be aware of potential sources of infection so that appropriate measures can be taken to prevent it. Full article