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

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When the history of our ancestry is written, the fact that we've interbred with some of our closest relatives, the Neanderthals and Denisovans, will have a central role. And it will be tempting to write it as a very tidy story: Once we got genomes from these other groups, it was possible to identify the sequences in our genomes that we shared with them.

But in reality, as scientists started poking large enough collections of data, there were regular hints of some strange ancestry in our genomes. It was hard to pin down, though, at least in part because the 2 percent on average of Neanderthal DNA found in many populations does not guarantee that any two individuals will have the same 2 percent. So having the genomes of those two groups made sense of some things researchers had already been seeing.

But knowing what we do about Neanderthal and Denisovan DNA is now allowing researchers to answer a somewhat different question: Is there anything else? Using recently developed analytical techniques, they find evidence of a third lineage that we apparently interbred with before any modern humans left Africa. Again, there were hints of this earlier, but so far, there's been no genome from a modern human relative to help us understand the details—the source of this DNA remains a "ghost lineage."

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When the history of our ancestry is written, the fact that we've interbred with some of our closest relatives, the Neanderthals and Denisovans, will have a central role. And it will be tempting to write it as a very tidy story: Once we got genomes from these other groups, it was possible to identify the sequences in our genomes that we shared with them.

But in reality, as scientists started poking large enough collections of data, there were regular hints of some strange ancestry in our genomes. It was hard to pin down, though, at least in part because the 2 percent on average of Neanderthal DNA found in many populations does not guarantee that any two individuals will have the same 2 percent. So having the genomes of those two groups made sense of some things researchers had already been seeing.

But knowing what we do about Neanderthal and Denisovan DNA is now allowing researchers to answer a somewhat different question: Is there anything else? Using recently developed analytical techniques, they find evidence of a third lineage that we apparently interbred with before any modern humans left Africa. Again, there were hints of this earlier, but so far, there has been no genome from a modern human relative to help us understand the details—the source of this DNA remains a "ghost lineage."

Read full article

Comments

© BSIP

A new technique reveals human evolution was rife with interbreeding

The post You’re Carrying “Ghost DNA” From Extinct Hominins appeared first on Nautilus.

In recent years, advances in ancient DNA sequencing have redefined our understanding of human evolution, revealing that our species did not emerge in isolation but repeatedly interbred with close evolutionary relatives like Neanderthals and Denisovans.

Yet one of the field’s greatest mysteries has remained unanswered: What if some of our ancestors also encountered extinct human groups whose DNA has never been recovered from fossils? Now, a recent study published in Science suggests researchers may finally have found a way to detect those long-lost relatives.

Instead of relying on ancient bones or preserved genomes, researchers developed a powerful computational approach capable of identifying genetic traces of previously unknown archaic humans using only the genomes of living people.

Applying the technique to modern populations around the world, the researchers uncovered evidence that an unidentified “ghost” hominin contributed DNA to the ancestors of all living humans before the great migration out of Africa.

Researchers also found new evidence providing evidence for the existence of an even older “super-archaic” human lineage whose genetic legacy may survive today through Denisovan ancestry in Oceania.

“Admixture between modern humans and extinct hominins has shaped the genomes of present-day individuals, but reconstructing this history has been constrained by the scarcity of archaic samples and unadmixed outgroup populations,” researchers write. “We introduce TRACE, a reference- and outgroup-free approach that uses features of ancestral recombination graphs to identify archaic ancestry.”

Past research has sequenced only a handful of high-quality archaic human genomes, all from Eurasia. While those genomes have revealed that most people outside Africa carry roughly 1 to 2 percent Neanderthal DNA and that many Asian and Oceanian populations also inherited Denisovan ancestry, scientists have long suspected those known groups represented only part of humanity’s evolutionary history.

Previous genetic studies had hinted that modern humans, especially African populations, contained fragments of DNA from unidentified archaic relatives. However, because no fossil genomes exist for those populations, proving their existence has been extraordinarily difficult.

The new method, called TRACE, short for “TRacking Archaic Contributions via ARG Estimation,” takes a fundamentally different approach. Rather than comparing modern DNA against known Neanderthal or Denisovan genomes, it reconstructs ancestral recombination graphs. These enormous evolutionary family trees trace how pieces of DNA have been inherited over hundreds of thousands of years.

These ancestral graphs preserve signatures left behind when deeply divergent human lineages interbred. Such events create unusually long branches and distinctive genetic patterns that can persist in descendants for hundreds of thousands of years. By searching for those signatures, TRACE can identify archaic ancestry even when no reference genome exists.

To test the system, the researchers first ran extensive computer simulations modeling ancient human populations and known episodes of Neanderthal interbreeding. The method consistently revealed high accuracy while producing very low false-positive rates.

The team then applied TRACE to genomes from the 1000 Genomes Project, encompassing hundreds of individuals representing populations across Africa, Europe, Asia, and elsewhere.

The program successfully recovered well-established Neanderthal ancestry in Europeans and Asians, along with Denisovan ancestry concentrated primarily in Asian populations. More intriguing, however, was the discovery of an additional category of DNA that matched neither Neanderthals nor Denisovans.

TRACE identified between roughly 0.5 and 1.1 percent “ghost” ancestry across all modern populations examined. The genetic evidence suggests these segments originated from an unknown archaic human lineage that diverged from our ancestors roughly 830,000 years ago, before later contributing DNA to the ancestors of modern humans.

Perhaps most surprising was where that ancestry appeared.

Rather than being confined to African populations, as several earlier hypotheses proposed, the researchers found remarkably similar ghost ancestry in both Africans and non-Africans.

Most of the segments detected outside Africa were shared with African populations, suggesting the interbreeding event occurred before modern humans started dispersing across the globe roughly 50,000 to 70,000 years ago.

The findings point toward a previously unrecognized chapter of human evolution in which an unidentified human population mixed with the common ancestors of everyone alive today.

The researchers also explored where these ancient genetic fragments survive in the modern genome.

They found ghost ancestry spread across more than 70 percent of the accessible human genome, although it was far from evenly distributed. Some regions contained unusually high concentrations of ghost DNA, including genes involved in immune function and metabolism.

Even more unexpectedly, ghost ancestry persisted inside genomic regions previously believed to be largely devoid of archaic DNA.

These regions, known as “Neanderthal and Denisovan ancestry deserts,” have often been interpreted as places where natural selection eliminated archaic genetic variants because uniquely modern human versions proved advantageous.

The presence of ghost ancestry inside many of those same regions suggests the story may be more complicated. Rather than rejecting all archaic DNA equally, natural selection may have acted differently depending on which ancient human lineage contributed the genes.

The study also ventured even farther back in evolutionary time.

Focusing on genomes from Papua New Guinea, Vanuatu, and the Santa Cruz Islands, the researchers searched for evidence of “super-archaic” ancestry—genetic contributions from a lineage that diverged long before Neanderthals, Denisovans, and modern humans separated.

Previous studies had suggested Denisovans themselves may have inherited DNA from such an ancient population. TRACE now provides additional support for that hypothesis.

The team identified deeply divergent genetic segments embedded explicitly within Denisovan-derived DNA in Oceanian populations. Based on their analyses, those ancient lineages may have split from the ancestors of modern humans approximately 1.77 million years ago.

While the researchers stress this represents a conservative estimate of surviving super-archaic ancestry, the findings strengthen the idea that modern humans indirectly inherited DNA from a much older human lineage through Denisovan interbreeding.

The identity of both the ghost lineage and the super-archaic population remains unknown.

The ghost population may represent Middle Pleistocene human groups or African populations of Homo heidelbergensis that encountered the ancestors of modern humans before the Out-of-Africa migration. For the older super-archaic lineage, the researchers note that Homo erectus remains one plausible candidate given its estimated divergence time and broad geographic distribution.

Although the new method cannot yet identify specific fossil species, it provides researchers with an entirely new tool for investigating chapters of human evolution that may never yield recoverable ancient DNA.

Because DNA older than roughly one million years is extraordinarily unlikely to survive outside permanently frozen environments, computational approaches like TRACE are becoming increasingly important for reconstructing humanity’s deepest evolutionary history.

“We find evidence for at least one ghost introgression predating the OOA expansion that contributed ancestry to all present-day populations,” researchers write. “These results point to multiple layers of introgression—both direct and mediated through other archaic groups—shaping modern human genomes.”

The study, “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” was published in the journal Science.

Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan.  Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com