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Evolutionarily Diverse Organisms Switch Genes on Simply and Switch Them off Dynamically

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The signals that cells use to switch genes on have remained almost unchanged across two billion years of evolution, but the ones used to switch genes off vary dramatically from one branch of life to another, according to a new study by researchers at the Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST).

The findings result from the broadest comparative exercise to date of how different life forms regulate their genomes. The researchers carried out the first detailed analysis of chromatin, the protein scaffold that controls how DNA is read, in several major branches of life that have been largely absent from studies to date, including lineages such as discobans, rhizarians, ichtyosporeans, and cryptomonads.

The work helps understand how genomes evolved on Earth and could have implications for medical research into diseases involving faulty gene regulation. It also delivers a new method, developed at the CRG, which can help support international efforts to characterize life on Earth at the molecular level.

“The cell’s instructions for activating genes are essentially the same in a human, a sea anemone and a soil amoeba,” said Arnau Sebé-Pedrós, PhD, ICREA Research Professor and senior author of the team’s published paper in Nature Genetics. “But the instructions for silencing genes and other genomic elements like transposons have been continuously evolving since our last common eukaryotic ancestor. Different branches of life have developed different molecular toolkits to do the same thing.” The team’s report is titled “Diversity and evolution of chromatin regulatory states across eukaryotes.”

DNA is wrapped, inside every cell, around proteins called histones. Small chemical tags attached to these proteins tell the cell which stretches of DNA to read and which to ignore. The chemical tags are ancient, dating back roughly two billion years to a single-celled organism known as the last eukaryotic common ancestor, or LECA, the founder of all complex cellular life, from which every plant, animal, fungus and protist on Earth descends. The system, known as chromatin regulation, is what allows the same genome to produce a liver cell or a neuron, and a leaf or a root. Faults in the regulation of chromatin underpin many human diseases, including cancers.

“Histone post-translational modifications (hPTMs) are central to defining functional chromatin states,” the authors explained further. “These hPTMs are conserved across diverse eukaryotes, with dozens tracing back to the last eukaryotic common ancestor, which we confirmed by histone mass spectrometry.”

The enzymes that add and remove the tags are also broadly shared across plants, animals, fungi and microbial eukaryotes. Until now, however, almost all detailed knowledge of how these tags work has come from a handful of laboratory species such as humans, mice, fruit flies, yeast and the model plant Arabidopsis. The vast majority of life’s diversity has remained unexplored at this level.

The authors’ project began in 2017, when Sebé-Pedrós and David Lara-Astiaso, PhD, were using a technique called iChIP to study chromatin in comb jellies and placozoans, animals not traditionally studied in the lab. The researchers wondered whether the approach could be scaled up for use in other species in the eukaryotic tree of life.

“We wanted to map epigenetic states in scarce cell types in mice and humans,” recalls Lara-Astiaso, now at the Arc Institute in California. “Eventually, we managed to transform that precursor into a general method for mapping genome regulation across the tree of life—more streamlined, more sensitive, and finally able to handle the particularities of very different species.”

The new method, iChIP2 can label chromatin from many species with unique molecular barcodes and read them all in a single experiment. Using the technology helped profile twelve chemical tags, or histone modifications, across twelve phylogenetically diverse species, spanning amoebae, fungi, plants, algae, single-celled predators and animals.

Some organisms had never had their chromatin mapped before. “We initially hoped to build a completely universal protocol, but species differ too much for that,” noted co-first author Cristina Navarrete, PhD. Plants and algae have cell walls that require specialized preparation, for example. Once a lab has extracted chromatin from their favorite species, iChIP2 takes over robustly, and from very small amounts of material.”

The researchers found that the signature of an active gene, marked by the pattern of histone modifications clustered around its start and along its body, was nearly identical in every species the team examined. The signature of a silenced gene was not. The results indicated that different lineages used different combinations of modifications, in different patterns, to keep stretches of DNA silent. “Our analyses revealed highly conserved euchromatin states at active gene promoters and gene bodies,” the investigators stated. “In contrast, we observed diverse configurations of repressive heterochromatin states associated with silenced genes and transposable elements …”

In some species, one modification silenced transposable elements while a different tag silenced unused genes. In others, the same modifications piled up together on the same regions. In the soil amoeba Acanthamoeba, a chemical mark that signals gene activation in animals had been repurposed to switch genes off.

“We’ve established so many new rules from looking at such few species,” said study co-author Sean Montgomery, PhD, “It’s the power of looking at non-model organisms to see how evolution has brought about many differing solutions to the same problems.”

The researchers suggest the diversity reflects an ancient and ongoing conflict between genomes and the parasitic DNA within them, like transposable elements, also known as “jumping genes,” and endogenized viruses. Every genome carries within it stretches of jumping genes, sequences that copy and paste themselves into new locations, sometimes harmlessly, sometimes destructively. In a human genome, they account for roughly half of all DNA. In their paper the team wrote, “The diversity of repressive states across eukaryotes, compared with the highly conserved active states, reflects the history of genomic invasions by parasitic elements in different lineages and could also define the permissiveness of these genomes to future invasions.”

Keeping jumping genes silenced is a matter of survival, but they evolve. Their parasitic nature means they acquire new sequences and sometimes even fragments of the chromatin machinery itself to evade detection.

“If a species loses its repressive mechanisms completely, it can’t tolerate parasitic elements like transposable elements or endogenized viruses. The result is that it’s no longer there. It’s dead,” says Sebé-Pedrós.

Over hundreds of millions of years, the result is host and parasite adapting and a tree of life on which each branch has developed its own bespoke strategy to silence genes. Some of those strategies, the team suggests, were later borrowed for other purposes.

The work lands at an important moment for comparative genomics. International efforts such as the Earth BioGenome Project and the Wellcome Sanger Institute’s Tree of Life programme, with which Sebé-Pedrós is affiliated, are sequencing the genomes of life on Earth at unprecedented speed.

The data generated by the initiatives offer potential new insights into how life has evolved on Earth, but a genome sequence alone says little about how the genome is used. Methods like iChIP2 make it possible to ask how life forms regulate their genomes. “… our results exemplify the potential of biodiversity epigenomic profiling,” the team suggested. “As genome sequencing is rapidly advancing across the tree of life, this approach offers a valuable opportunity to similarly expand our understanding of eukaryotic genome function and regulation.”

The post Evolutionarily Diverse Organisms Switch Genes on Simply and Switch Them off Dynamically appeared first on GEN – Genetic Engineering and Biotechnology News.

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Ebola kills 1,700 in eastern Congo as the fastest-growing outbreak surges

BUNIA, Congo — Ebola has killed more than 1,700 people in eastern Congo in what has become the fastest-growing outbreak of the disease, according to data — spreading faster than health officials can track and with patient zero still unidentified.

As of Tuesday, 3,802 cases had been recorded, with 1,707 deaths, the latest government update showed.

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BUNIA, Congo — Ebola has killed more than 1,700 people in eastern Congo in what has become the fastest-growing outbreak of the disease, according to data — spreading faster than health officials can track and with patient zero still unidentified.

As of Tuesday, 3,802 cases had been recorded, with 1,707 deaths, the latest government update showed.

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Lisata axes 72% of workforce following failed merger with Kuva

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In addition to laying off employees, including a member of its management team, Lisata Therapeutics is also suing Kuva Labs, alleging the company breached a merger agreement.

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AI Uses MRIs to Generate Brain Aging Maps for Neurodegenerative Disease Research

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Researchers at the University of Southern California have developed an approach that uses artificial intelligence to generate detailed maps that highlight differences in how distinct parts of the brain age. The researchers, led by associate professor Andrei Irimia, PhD, at the USC Leonard Davis School of Gerontology, used magnetic resonance imaging (MRI) from nearly 15,000 cognitively healthy individuals to train a deep learning AI model. The data provided a baseline against which the model could measure local brain age (LBA), or how old specific regions of the brain appear.

While most studies of brain age measure this phenomenon using a single number, the new model provides a much richer picture of typical aging and neurodegeneration. Rather than assigning a single “brain age” (BA) to an individual, the approach generates a detailed map showing how old different parts of the brain appear relative to what is typical for someone of the same chronological age.

When the AI model was then used to analyze MRI images from people with mild cognitive impairment and Alzheimer’s disease (AD), it revealed distinct patterns of accelerated aging in brain regions known to be affected early in neurodegeneration.

“Not all brain regions age at the same rate,” Irimia said. “Some areas appear to be more resilient, while others are more vulnerable to aging and disease. By measuring local brain aging, we can identify where the brain is aging faster than expected and how those changes relate to cognitive function.”

In their in paper in PNAS, titled “Deep learning maps local brain aging in relation to cognition across human adulthood,” senior author Irimia and colleagues stated, “By providing spatially resolved measures of brain aging, this work enables more precise investigation of how neuroanatomic alterations and cognitive impairment affect brain anatomy, above and beyond global brain age measures.”

Aging is a prominent risk factor for the onset of brain diseases, including Alzheimer’s disease and related dementias, the authors wrote. “One of the most prominent biological features of brain aging is atrophy, i.e., brain volume decrease that often involves loss of brain cells and neural connectivity.”

The newly reported research builds on previous efforts to estimate BA, an emerging neuroimaging biomarker that compares a person’s brain structure to patterns seen in healthy people across the lifespan. But while human brain aging is not uniform across cortical regions, traditional methods typically reduce the brain to a single age estimate, which can obscure important regional differences. The new approach instead measures local brain age at the voxel level—the three-dimensional units that make up an MRI scan—producing a much more detailed picture of structural aging throughout the brain.

“This more nuanced understanding of how the brain ages could pave the way for earlier identification of dementia, a better understanding of what factors affect risk and new ideas for treatment approaches,” Irimia said.

To develop the model, the researchers trained a deep-learning neural network using MRI scans from 14,748 cognitively normal adults ages 19 years to 100 years, drawn from six large public datasets, including the UK Biobank, the Human Connectome Project and the Alzheimer’s Disease Neuroimaging Initiative. The team then tested the model using MRI scans from more than 1,900 additional participants in the Alzheimer’s Disease Neuroimaging Initiative, including cognitively normal adults, people with mild cognitive impairment and people with Alzheimer’s disease.

Across healthy adults, the model consistently found that the frontal and temporal lobes—regions involved in decision-making, memory and other higher cognitive functions—appeared biologically older than the parietal and occipital regions, which are involved in spatial awareness and sensory processing functions. “Our approach consistently reveals spatial patterns of aging, including relatively advanced aging in frontal and temporal regions, across both typical aging and Alzheimer’s disease,” the investigators noted. The researchers also found that the brain’s right hemisphere tended to show slightly more advanced aging than the left, a pattern that persisted regardless of whether participants were right- or left-handed.

As cognitive impairment progressed, the differences became even more pronounced. Compared with cognitively normal adults, participants with mild cognitive impairment (MCI) or Alzheimer’s disease showed significantly older local brain ages in structures that are among the first affected by Alzheimer’s pathology, including the hippocampus, amygdala and several deep brain regions involved in memory and cognitive processing.

The researchers also found that older local brain age was associated with poorer performance on cognitive assessments, strengthening the link between structural brain changes and real-world function. “Deviations from normative regional aging are significantly associated with cognitive performance supported by neural processes linked to those regions … thereby relating anatomic aging to functional outcomes,” they stated. The strongest relationships appeared in people with Alzheimer’s disease, suggesting that regional brain aging may become increasingly informative as neurodegeneration advances.

Because the model produces anatomically detailed maps, it could eventually help scientists better understand why some people experience faster decline in specific cognitive abilities than others. The approach may also prove useful for tracking disease progression or evaluating whether experimental therapies are slowing degeneration in targeted brain regions. “By providing spatially resolved measures of brain aging, this work enables more precise investigation of how neuroanatomic alterations and cognitive impairment affect brain anatomy, above and beyond global brain age measures,” they commented.

Although the findings are promising, Irimia emphasized that the method remains a research tool. The model was trained primarily on research-quality MRI data and will require additional validation using more diverse clinical datasets before it can be adopted in routine patient care. The study also relied largely on cross-sectional data, meaning that future longitudinal studies will be needed to determine whether local brain aging can reliably predict who will progress from healthy aging to mild cognitive impairment or Alzheimer’s disease.

Still, the researchers believe that moving beyond a single measure of brain age represents an important advance for neuroscience. “By quantifying the anatomy of brain aging and aligning it with cognition and disease stage, this work establishes a foundation for mechanistic inquiry and personalized intervention in neurodegeneration,” the authors stated. “This scalable framework paves the way for monitoring a broad spectrum of neurodegenerative and aging related disorders,” Irimia added, “Brain aging isn’t uniform. “By understanding how individual regions age, as well as how those patterns differ from person to person, we’re moving toward a much more precise understanding of healthy aging and neurodegenerative disease. Ultimately, that could help us identify people at risk earlier and develop more personalized approaches to preserving brain health.”

The post AI Uses MRIs to Generate Brain Aging Maps for Neurodegenerative Disease Research appeared first on GEN – Genetic Engineering and Biotechnology News.

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