Hexbyte Glen Cove Machine learning predicts antibiotic resistance spread

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Genes aren’t only inherited through birth. Bacteria have the ability to pass genes to each other, or pick them up from their environment, through a process called horizonal gene transfer, which is a major culprit in the spread of antibiotic resistance.

Cornell researchers used machine learning to sort by their functions and use this information to predict with near-perfect accuracy how genes are transferred between them, an approach that could potentially be used to stop the spread of antibiotic resistance.

The team’s paper, “Functions Predict Horizontal Gene Transfer and the Emergence of Antibiotic Resistance,” published Oct. 22 in Science Advances. The lead author is doctoral student Hao Zhou.

“Organisms basically can acquire resistance genes from other organisms. And so it would help if we knew which organisms were exchanging with, and not only that, but we could figure out what are the driving factors that implicate organisms in this transfer,” said Ilana Brito, assistant professor and the Mong Family Sesquicentennial Faculty Fellow in Biomedical Engineering in the College of Engineering, and the paper’s senior author. “If we can figure out who is exchanging genes with who, then maybe it would give insight into how this actually happens and possibly even control these processes.”

Many novel traits are shared through gene transfer. But scientists haven’t been able to determine why some bacteria engage in gene transfer while others do not.

Instead of testing individual hypotheses, Brito’s team looked to bacteria genomes and their various functions—which can range from DNA replication to metabolizing carbohydrates—in order to identify signatures that indicate “who” were swapping genes and what was driving these networks of exchange.

Brito’s team used several , each of which teased out different phenomena embedded in the data. This enabled them to identify multiple networks of different antibiotic resistance genes, and across strains of the same organism.

For the study, the researchers focused on organisms associated with soil, plants and oceans, but their model is also well-suited to look at human-associated organisms and pathogens, such as Acinetobacter baumannii and E. coli, and within localized environments, such as an individual’s gut microbiome.

They found the machine-learning models were particularly effective when applied to antibiotic resistance genes.

“I think one of the big takeaways here is that the network of bacterial gene exchange—specifically for antibiotic resistance—is predictable,” Brito said. “We can understand it by looking at the data, and we can do better if we actually look at each organism’s genome. It’s not a .”

One of the most surprising findings was that the modeling predicted many possible antibiotic resistance transfers between human-associated bacteria and pathogens that haven’t yet been observed. These probable, yet undetected, transfer events were almost exclusive to human-associated bacteria in the or oral microbiome.

The research is emblematic of Cornell’s recently launched Center for Antimicrobial Resistance, according Brito, who serves on the center’s steering committee.

“One can imagine that if we can predict how these genes spread, we might be able to either intervene or choose a specific antibiotic, depending what we see in a patient’s gut,” Brito said. “More broadly, we may see where certain types of organisms are predicted to transfer with others in a certain environment. And we think there might be novel antibiotic targets in the data. For example, genes that could cripple these organisms, potentially, in terms of their ability to persist in certain environments or acquire these .”

Juan Felipe Beltrán, Ph.D. ’19, contributed to the research.

More information:
Hao Zhou et al, Functions predict horizontal gene transfer and the emergence of antibiotic resistance, Science Advances (2021). DOI: 10.1126/sciadv.abj5056. www.science.org/doi/10.1126/sciadv.abj5056

Machine learning predicts antibiotic resistance spread (2021, October 22)
retrieved 24 October 2021
from https://phys.org/news/2021-

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Hexbyte Glen Cove History of the spread of pepper (C. annuum) is an early example of global trade thumbnail

Hexbyte Glen Cove History of the spread of pepper (C. annuum) is an early example of global trade

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The researchers conducted a huge genomic scan of over ten thousand pepper (Capsicum spp.) samples from worldwide genebanks and used the data to investigate the history of this iconic staple. Credit: Ilan Paran

Genebanks collect vast collections of plants and detailed passport information, with the aim of preserving genetic diversity for conservation and breeding. Genetic characterisation of such collections has also the potential to elucidate the genetic histories of important crops, use marker-trait associations to identify loci controlling traits of interest, search for loci undergoing selection, and contribute to genebank management by identifying taxonomic misassignments and duplicates.

“We conducted a huge genomic scan of over ten thousand pepper (Capsicum spp.) samples from worldwide genebanks and used the data to investigate the history of this iconic staple,” says Dr. Pasquale Tripodi, researcher at the Italian research institute CREA and co-first author of the study.

The peppers originated from 130 countries across five continents, a feat made possible through collaboration among many genebanks. This allowed the researchers to assess aspects of genebank management such as sample duplication. Genomic data detected up to 1,618 duplicate accessions within and between genebanks. “This significant level of duplication should motivate the development of genetic pre-screening protocols to be used in genebanks for documenting the potential duplicate samples upon first acquisition,” says Prof. Dr. Nils Stein, head of the research group Genomics of Genetic Resources at IPK Leibniz Institute, holder of a joint professorship at the University of Göttingen and coordinator of this pepper study which was part of the larger effort of the EU H2020 funded project G2P-SOL.

At its heart, the project represents a in the exploitation and in-depth analysis of genetic data from collections to yield more and better information on expansion routes of the most economically important pepper species (Capsicum annuum); a species that has changed the face of culinary cultures worldwide. A method named ReMIXTURE—which uses genetic data to quantify the similarity between the complement of peppers from a focal region to those from other regions—was invented for the study and used to supplement more traditional population genetic analyses.

“The results reflect a vision of pepper as a highly desirable and tradable cultural commodity, spreading rapidly throughout the globe along major maritime and terrestrial trade routes,” says Dr. Mark Timothy Rabanus-Wallace from IPK Leibniz Institute, who co-led the study and who developed the ReMIXTURE method. “A large factor in pepper’s initial appeal was certainly its pungency, especially in nontropical Europe where hot spices were rare and imported black pepper could fetch good prices.”

The kinds of peppers collected in broad regions across the globe overlap considerably. In particular, peppers in Eurasian regions overlap with neighboring regions, a result of overland trade routes like the silk road. European and African peppers overlap a lot with peppers from the Americas, probably a result of transatlantic trade during the Age of Discovery. South/Mesoamerica, Eastern Europe, and Africa are all notable for large proportions of region-unique peppers.

The group also detected that regions of the genome affecting traits such as pungency were distributed non-uniformly across the globe, suggesting that human culture truly does exert a primary influence over how peppers spread throughout the globe. IPK scientist Dr. Mark Timothy Rabanus-Wallace hopes the study encourages broader enjoyment globally of these regions’ unique and beautiful .

The study is published in the Proceedings of the National Academy of Sciences.

More information:
Global range expansion history of pepper ( spp.) revealed by over 10,000 genebank accessions, Proceedings of the National Academy of Sciences, www.pnas.org/cgi/doi/10.1073/pnas.2104315118

Provided by
Leibniz Institute of Plant Genetics and Crop Plant Research

History of the spread of pepper (C. annuum) is an early example of global trade (2021, August 16)
retrieved 16 August 2021
from https://phys.org/news/2021-08-history-pepper-annuum-early-global.html

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