Antibiotic resistance shaping multi-level population biology of bacteria

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

Download Full-text

Simulating multi-level dynamics of antimicrobial resistance in a membrane computing model

10.1101/306100 ◽

2018 ◽

Author(s):

Marcelino Campos ◽

Rafael Capilla ◽

Fernando Naya ◽

Ricardo Futami ◽

Teresa Coque ◽

...

Keyword(s):

Antibiotic Resistance ◽

Evolutionary Biology ◽

Membrane Computing ◽

Patient Flow ◽

P System ◽

Biological Organization ◽

Computing Model ◽

Computing Paradigm ◽

Cross Transmission ◽

Multi Level

AbstractMembrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested “membrane-surrounded entities” able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

Download Full-text

Accurate annotation of protein coding sequences with IDTAXA

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab080 ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Nicholas P Cooley ◽

Erik S Wright

Keyword(s):

Antibiotic Resistance ◽

R Package ◽

Protein Coding ◽

Common Error ◽

New Approach ◽

Coding Sequences ◽

Microbial Genomes ◽

Prokaryotic Genomes ◽

Multi Level ◽

Novel Associations

Abstract The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA’s utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

Download Full-text

Modeling antibiotic resistance in the microbiota using multi-level Petri Nets

BMC Systems Biology ◽

10.1186/s12918-018-0627-1 ◽

2018 ◽

Vol 12 (S6) ◽

Cited By ~ 2

Author(s):

Roberta Bardini ◽

Stefano Di Carlo ◽

Gianfranco Politano ◽

Alfredo Benso

Keyword(s):

Antibiotic Resistance ◽

Petri Nets ◽

Multi Level

Download Full-text

A One Health perspective on the issue of the antibiotic resistance

Parasite ◽

10.1051/parasite/2021006 ◽

2021 ◽

Vol 28 ◽

pp. 16

Author(s):

Estera Badau

Keyword(s):

Antibiotic Resistance ◽

One Health ◽

Animal Health ◽

International Health ◽

The United States ◽

Political Leaders ◽

Public Authorities ◽

Viable Solution ◽

Multi Level ◽

Different Levels

For a few years now, the One Health concept has appeared to go hand in hand with the issue of antibiotic resistance as the most comprehensive and global solution. As part of a study comparing the publicization process of the links between antibiotic resistance and food in France and in the United States, this paper retraces the One Health concept’s trajectory in terms of significations and (re)definitions, according to the actors adopting this approach as a viable solution. Furthermore, this paper questions the concept’s take over impact in antibiotic resistance reframing as well as its expansion in terms of functioning and applicability. Within social sciences research, interest in the issue of antibiotic resistance and the One Health approach has largely been established in recent years by a growing number of studies examining its different and multiple aspects. The specificity of this research lies in its two different levels of questioning the One Health concept. Firstly, the concept seems to be referred to by various formulas, from its oldest form, One Medicine-1984, to One World, One Health. Secondly, the concept is being redefined as links between a plurality of domains are recognized (human health, animal health, the environment, and food), following the emergence of international health and food crises and as their multi-level consequences are being addressed by various stakeholders, including public authorities, political leaders, and economic actors.

Download Full-text