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El nuevo borrador de un tratado sobre la contaminación por plásticos, un proyecto de la ONU con graves dificultades para ser aprobado, ha sido objeto de críticas desde su divulgación por supuesta falta de ambición.
Scientists in Arkansas have developed a machine-learning approach that can identify potentially disease-causing strains of a poultry bacterium by analyzing not only which genes are present, but also how those genes are arranged across the genome. The method focuses on the “genetic neighborhoods” surrounding genes, offering a new way to distinguish harmful strains of Enterococcus cecorum from closely related strains that do not cause disease.
E. cecorum is commonly found in poultry environments, and many of its strains are harmless. However, pathogenic strains can cause arthritis, bone infections and lameness, creating serious animal-welfare problems and economic losses for poultry producers. Because pathogenic and nonpathogenic strains can share many of the same genes, identifying the genetic features associated with disease has been challenging.
The research team, led by Aranyak Goswami of the Arkansas Agricultural Experiment Station’s Center for Agricultural Data Analytics, examined the organization of genes within genomic islands. These are segments of DNA that bacteria often acquire from other microorganisms through horizontal gene transfer. Genomic islands can carry genes involved in antibiotic resistance, bacterial survival, movement of DNA and virulence, making them important regions for understanding how pathogens emerge.
Rather than treating the bacterial genome as a simple list of genetic components, the researchers analyzed it as a structured map. They investigated which genes occurred near one another, the order in which they appeared and the way groups of neighboring genes formed recurring units known as genomic-island cassettes. This architecture provided information that could be missed when genes are examined individually.
“The machine-learning model recognizes patterns in gene order much like it recognizes patterns in language,” Goswami said. In the same way that a language model can learn that certain words frequently appear together, the computational system learned that particular combinations and arrangements of bacterial genes were associated with pathogenic strains. The approach therefore captures relationships among genes rather than relying only on the presence or absence of individual DNA sequences.
For the proof-of-concept study, the researchers analyzed the genomes of 145 E. cecorum strains isolated from poultry. The dataset included 95 nonpathogenic strains and 50 pathogenic strains capable of causing illness. The analysis found that disease-associated strains were more likely to contain genomic islands enriched in genes linked to antimicrobial resistance and the transfer of genetic material between bacteria.
Those findings do not mean that every bacterium carrying such genes will necessarily cause disease. Instead, the researchers describe the system as an exploratory classification and research tool. It identifies patterns that may help scientists investigate why some bacterial lineages become harmful, while also providing clues about how resistance and virulence traits move through bacterial populations.
Current monitoring methods for E. cecorum often depend on culturing the bacterium or screening for specific genes. Such techniques remain valuable, but they may overlook broader genomic relationships. The new pipeline could complement these approaches by evaluating the wider context in which genes occur. Additional testing with larger and more geographically diverse datasets will be necessary before the method can be used for routine flock surveillance or diagnostic decision-making.
The researchers believe the strategy could eventually be adapted to other bacterial species affecting animals, humans, wildlife and plants. Goswami’s team is beginning to examine Enterococcus faecalis, a close relative of E. cecorum and a frequent cause of hospital-acquired infections in humans. They also plan to apply the pipeline to Escherichia coli and other bacteria to study how nonpathogenic lineages acquire the genomic configurations associated with disease. The study, published in Frontiers in Microbiology, demonstrates how machine learning and comparative genomics can reveal hidden signals in bacterial DNA and may help researchers track the evolution of emerging pathogens.
Subject of Research: Animals
Article Title: Genomic-island cassette architecture provides interpretable signal for exploratory classification of poultry-associated Enterococcus cecorum lineages
News Publication Date: 7-Jul-2026
Web References: https://doi.org/10.3389/fmicb.2026.1882753
References: Frontiers in Microbiology, DOI: 10.3389/fmicb.2026.1882753
Image Credits: UA University Relations photo by Chieko Hara
Keywords: artificial intelligence, machine learning, deep learning, computational biology, bacterial pathogens, Enterococcus cecorum, genomic islands, genomic architecture, antibiotic resistance, poultry disease, pathogen surveillance, comparative genomics
Around 11,000 years ago, communities in southwest Asia began one of the most consequential experiments in human history: growing their own food. Wild cereals, legumes and fruit-bearing plants were gradually brought under human management, setting in motion the transition from mobile hunter-gatherer lifeways to farming societies. For decades, archaeologists have assumed that one of the earliest signs of this transformation was an increase in cereal grain size. A new study, however, suggests that some of the biggest grains found at the dawn of agriculture may not have been genetically altered at all. They may simply have grown in wetter conditions.
The findings, published in the Proceedings of the National Academy of Sciences, challenge a central assumption about how cereal domestication began. Researchers studying charred barley and emmer wheat grains from the Early Neolithic sites of el-Hemmeh and Sharara in the southern Levant found that the plants still possessed fully wild characteristics during the earliest phases of the Pre-Pottery Neolithic. Their ears had brittle rachises, the structures that naturally break apart when mature, allowing seeds to disperse in the wild. There was no evidence of the tough, non-shattering rachis that later became one of the defining features of domesticated cereals.
The absence of non-shattering forms is significant because a tough rachis makes harvesting far more efficient. In wild cereals, the rachis fractures as the plant ripens, scattering seeds before people can collect them. A mutation that keeps the grains attached would have offered major advantages to human harvesters and could then have spread through repeated cultivation and replanting. Researchers have therefore often treated the appearance of non-shattering cereals as a key marker of domestication. Grain size, by contrast, has frequently been interpreted as an earlier signal that people were already selecting plants through cultivation.
At el-Hemmeh and Sharara, however, grain size told a more complicated story. Barley and emmer wheat remained morphologically wild, yet their grains varied dramatically. Some were as small as typical wild cereals, while others fell within the size range commonly associated with domesticated plants. If large grains were the product of genetic selection caused by systematic tillage, the researchers expected to find other evidence of intensive cultivation. Instead, the ecological evidence pointed toward relatively low-disturbance environments.
The team examined the weed species preserved alongside the cereal remains. Weeds are valuable archaeological indicators because different plants thrive under different environmental conditions. Species associated with heavily tilled or frequently disturbed soils were not dominant in the assemblages from these early sites. This weakened the argument that repeated ploughing or intensive soil preparation had already created strong selection for larger seeds. The researchers began looking for another explanation for the striking variation in grain size.
Their answer came from stable carbon isotope analysis. Carbon exists in several naturally occurring forms, or isotopes, and plants incorporate these isotopes in ways that are influenced by their growing conditions. In cereals, the stable carbon isotopic composition of a grain can reflect the balance between carbon uptake and water loss during photosynthesis. When plants experience water stress, changes in gas exchange through their leaves alter the carbon isotope signal recorded in their tissues. By measuring this signal in ancient grains, scientists can reconstruct aspects of the moisture conditions under which the plants grew.
The results showed a strong relationship between grain size and water availability. Barley grains that reached domestic-sized dimensions had carbon isotope values indicating that they developed under wetter conditions. Smaller, wild-sized grains carried signatures consistent with considerably drier growth environments. The difference suggests that environmental moisture, rather than inherited genetic change, was responsible for much of the early increase in cereal grain size. In other words, the same wild plant population could produce grains of very different sizes depending on the conditions it experienced during development.
This phenomenon is known as developmental plasticity: the ability of a single genotype to produce different physical traits in response to the environment. It is widespread in plants and can affect characteristics such as height, leaf area, flowering time and seed production. In the Early Holocene southern Levant, developmental plasticity may have created the visual impression that cereals were already becoming domesticated. People collecting and replanting the largest grains might eventually have generated genetic selection, but the initial size increase itself appears to have been ecological rather than evolutionary.
“Environmental conditions provided a much better explanation for the observed variation in grain size than genetic selection under tillage,” said Dr Jade Whitlam of the University of Oxford, who led the study. The researchers propose that genetic selection for larger grains may have occurred later than previously thought, perhaps after selection for non-shattering rachises had already begun. This reverses the sequence often assumed in models of domestication and places greater emphasis on the complex interaction between climate, plant biology and human harvesting practices.
The study also reframes the origins of agriculture as a gradual process of niche construction rather than a sudden decision to domesticate plants. Early communities altered their relationship with wild vegetation through harvesting, protection, transport and repeated use of particular landscapes. These practices may have changed plant populations long before crops displayed unmistakable domestication traits. By combining archaeobotany, weed ecology and stable isotope evidence, the researchers argue that the first steps toward agriculture were not necessarily driven by deliberate genetic engineering of crops, but by prolonged human management operating within a variable environment. The result is a more nuanced picture of farming’s origins—one in which climate could temporarily make wild cereals look domesticated before their genomes caught up.
Subject of Research: The environmental and biological processes that influenced cereal grain size and plant domestication in the Early Holocene southern Levant.
Article Title: Developmental plasticity under human management shaped cereal evolution prior to domestication in the Early Holocene southern Levant
News Publication Date: 4-Aug-2026
Web References: https://www.eurekalert.org/multimedia/1146086
References: Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2535274123
Image Credits: Jerome Poulalier, www.jerome-poulalier-photography.com
Keywords: Archaeology, archaeobotany, cereal domestication, agriculture, Neolithic, Early Holocene, southern Levant, barley, emmer wheat, stable carbon isotopes, developmental plasticity, plant evolution, climate, hunter-gatherers
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