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Category: genetics

What changes happen in the aging brain?

A new study from the Salk Institute maps how the aging brain changes at the epigenetic level — cell type by cell type.

The researchers created one of the most detailed single-cell atlases yet of the aging mouse brain, spanning 8 brain regions, 36 cell types, and hundreds of thousands of cells. They found major age-related changes in DNA methylation, chromatin structure, and gene activity, with some of the strongest changes appearing in non-neuronal cells.

This kind of work matters because it moves brain aging closer to mechanism — not just describing decline, but identifying the molecular regulatory shifts that may drive vulnerability to neurodegenerative disease.

Highlights Salk researchers create epigenetic atlas of cell type-specific changes in the aging mouse brain The atlas represents eight different brain regions and 36 different cell types, and shows clear epigenetic differences associated with different ages The new resource—available publicly on Amazon Web services—can be used to unravel age-related contributions to neurodegenerative diseases like Alzheimer’s, Parkinson’s, and ALS LA JOLLA—Neurodegenerative diseases affect more than 57 million people globally. The incidence of these diseases, from Alzheimer’s to Parkinson’s to ALS and beyond, is expected to double every 20 years. Though scientists know aging is a major risk factor for neurodegenerative diseases, the full mechanisms behind aging’s impact remain unclear.

DNA shape explains crucial gene-therapy challenges

CRISPR is a powerful DNA-editing tool that has underpinned huge advancements in human health care in the last decade. It is a precision tool, but is not perfect, and misplaced DNA edits can compromise safety and efficacy, costing billions each year. Researchers at the MRC Laboratory of Medical Sciences (LMS), Imperial College London and the University of Sheffield have published research in Nature showing that the physical twisting of DNA plays an important role in these mistakes. Using a newly developed platform of tiny (nanometer-sized) DNA circles, called DNA minicircles, the team captured never-before-seen interactions between CRISPR and DNA, providing insights that could help eradicate errors altogether.

CRISPR-Cas9 has transformed biology by giving scientists a programmable way to cut and edit DNA. Its ever-growing impact includes groundbreaking therapies for genetic diseases such as sickle cell anemia and an increasing role in personalized cancer treatment and rapid diagnostics. But even carefully designed CRISPR systems can sometimes cut DNA sequences that were not the intended targets.

“It’s a tool that is not perfect and can introduce errors and make edits where it shouldn’t make them,” says Professor David Rueda, head of the Single Molecule Imaging group at the LMS and Chair in Molecular and Cellular Biophysics at Imperial College London. “And it’s an important problem for the industry. It’s been estimated to be $0.3 to $0.9 billions per year in industry costs, in profiling off-targets, redesigning guides and delays.”

Characterization of a Splice Variant in FLNA Associated With Periventricular Nodular Heterotopia

This study broadens the phenotypic and genetic spectrum of PNH, demonstrating a dual PNH phenotype associated with a bi‑transcript mechanism and mosaic inheritance, including tissue‑specific mosaicism.

PNH is a neurodevelopmental brain malformation characterized by failure of the gray matter to properly migrate to the cerebral cortex during embryonic development. This results in ectopic localization around the ventricular ependyma.1 MRI serves as the primary diagnostic tool, showing bilateral periventricular gray matter nodules with a signal intensity similar to that of normal cortical gray matter.2,3 Its primary clinical manifestation is epilepsy, which is often accompanied by intellectual disabilities and learning difficulties.2,3 PNH is genetically heterogeneous and is linked to variants in multiple genes, including ARFGEF2, ERMARD, NEDD4L, ARF1, and MAP1B, as well as abnormalities in chromosome 5. Among these, pathogenic variants in FLNA are the most common genetic causes.4

FLNA is located at Xq28 and comprises 47 exons,5 encoding a 280 kDa actin binding protein, called filamin A. The N-terminal region contains an actin binding domain (ABD) and a rod-like structure composed of 24 immunoglobulin-like repeats. ABD interacts with actin to stabilize the cytoskeletal architecture and plays crucial roles in maintaining cell shape, migration, and transmitting mechanical force. FLNA regulates cellular migration and extension processes via interactions with several signaling proteins, including small GTPases Rac/Rho, TRAF2, integrins, and BRCA2.6–8 This gene possesses at least 2 transcription initiation sites (ENST00000369850.8 and ENST00000610817.4) that use distinct promoters and demonstrate tissue-specific expression.6–8 Rat FLNA-knockdown models exhibit impaired neuronal migration and elevated epileptic susceptibility.

Hearing research traces evolution of key inner ear protein

In the intricate machinery of the inner ear, hearing begins with a protein that moves a few billionths of a meter up to 100,000 times per second. That protein, called TMC1, sits at the tips of sensory hair cells deep in the snail-shaped cochlea. When sound waves move these microscopic hairs, TMC1 acts as a channel, opening and allowing charged particles to flow into the cell and trigger an electrical signal to the brain.

Without TMC1, that signal never starts. Mutations in the TMC1 gene are a well-known cause of hereditary hearing loss in humans. Because of this central role, TMC1 is an attractive target for researchers designing gene therapies aimed at restoring hearing. Several groups are testing ways to supply working copies of the gene or fix harmful mutations.

For these efforts to be safe and effective, scientists need to know in detail how TMC1 is built, how it opens, and which parts of the protein are most sensitive to change. However, the hair-cell system that includes TMC1 is so complex, sensitive, and hard to access that it is notoriously difficult to take apart and study directly.

Why no individual is like another when epigenetics come into play

Why do animals behave differently, and what are the consequences of this? A research team from the Collaborative Research Center NC³ at Bielefeld University and the University of Münster now provides a new explanation: epigenetic processes—chemical markings on DNA—may play a key role. The study, published in the journal Trends in Ecology & Evolution, links individuality, environmental adaptation, genetics, ecology, and evolution in a novel way.

“With our study, we propose that individuality and epigenetic variation influence each other,” explains Dr. Denis Meuthen, an evolutionary biologist at Bielefeld University, who is one of the study’s main authors. “This bidirectionality—this mutual interaction—helps us to better understand ecological and evolutionary processes.”

RNA-guided CRISPR system activates gene expression

In back-to-back studies published in Nature, researchers from Purdue University and Columbia University report a naturally evolved gene-editing system that can activate genes, offering an advantage over existing CRISPR gene-editing systems that merely find and cut DNA. The research includes two complementary studies, one examining the biological function of the system and the other revealing the molecular mechanism that enables it.

The team’s research on a variant of the CRISPR—Clustered Regularly Interspaced Short Palindromic Repeats—system broadens understanding of CRISPR’s natural diversity and provides a foundation for new gene-regulation technologies. Because this CRISPR variant activates genes without cutting DNA, it could be adapted for precise gene control applications, including research tools and potential therapeutic strategies that turn on genes without permanently altering the genome.

One study shows that this CRISPR system, using a strand of RNA as a guide, finds specific sections of DNA, known as genes, and attracts the cell’s own gene expression machinery to the location to activate the gene. The second study explains how the molecular complex performs this task, revealing how its structure allows it to recruit RNA polymerase—the enzyme responsible for transcribing DNA into RNA—to initiate gene expression.

Protein modification discovery opens cancer therapy possibilities

A research team led by Purdue University’s W. Andy Tao has discovered a new type of protein modification related to cellular mutation that impairs a crucial enzyme’s ability to help drive energy processes. Their discovery, published in Nature Chemistry, opens a new route to therapeutic cancer intervention.

“Mutation is considered the driving mechanism leading to cancer. Many mutations are hidden and harmless, but the mutation of enzymes like kinases can lead to the uncontrolled growth of cancer cells,” said Tao, a professor of biochemistry in Purdue’s College of Agriculture.

The study wades into the interactive dynamic complexity of the human genome (containing 20,000 to 25,000 genes) and the human proteome (containing more than 1 million proteins). The researchers identified a new modification on proteins because of the mutation in the isocitrate dehydrogenase (IDH) enzyme, which affects how kinase enzymes control protein function.

Early Clinical and EEG Association of Genotype and Outcome in Genetic EpilepsiesA Cohort Study and Hierarchical Clustering Analysis

This study analyzed a large cohort of patients with genetic epilepsies using hierarchical clustering analysis to identify homogeneous subgroups defined by specific genetic causes, each showing distinct clinical and EEG patterns.

We included 277 patients (52.3% female; median age at last follow-up 8.1 years, range 0–40). Drug resistance occurred in 58.8% and severe DD/ID in 35.4% of patients. EEG data at onset were available for 107 individuals. Neonatal onset was associated with a higher rate of drug resistance (71.4%; odds ratio [OR] 2.0, 95% CI 1.05–3.77), movement disorders (60.7%; OR 3.7, 95% CI 2.02–6.82), and severe DD/ID (71.4%; OR 7.0, 95% CI 3.66–13.49). Slow EEG background activity and multifocal epileptiform discharges were associated with both drug resistance and severe DD/ID. HCA identified genotype-phenotype groupings, including clusters involving SCN1A, PRRT2, STXBP1, KCNQ2, SCN2A, CHD2, SYNGAP1, and MECP2, each linked to specific clinical and EEG features.

New computational biology for genome sequencing analysis

To improve the ability of metapipeline-DNA to determine where changes in the genome have occurred, the scientists worked with the Genome in a Bottle Consortium led by the U.S. Department of Commerce’s National Institute of Standards and Technology. By incorporating this public-private-academic consortium’s meticulously validated resources, the researchers reduced the rate of false positives without reducing the tool’s precision in finding true genetic variants.

The researchers also produced two case studies demonstrating the pipeline’s capabilities for cancer research. The investigators used metapipeline-DNA to analyze sequencing data from five patients that donated both normal tissue and tumor samples, as well as another five from The Cancer Genome Atlas.

The next step is to get metapipeline-DNA into more labs to accelerate discoveries, and to continue improving the resource with more user feedback. ScienceMission sciencenewshighlights.

In a single experiment, scientists can decipher the entire genomes of many patient samples, animal models or cultured cells. To fully realize the potential to study biology at this unprecedented scale, researchers must be equipped to analyze the titanic troves of data generated by these new methods.

Scientists published findings in Cell Reports Methods discussing building and testing a new computational tool for tackling massive and complex sequencing datasets. The new resource, named metapipeline-DNA, may also make sequencing data analysis more standardized across different research labs.

The sequence of a single human genome represents about 100 gigabytes of raw data, the rough equivalent of 20,000 smartphone photos. The sheer scale of experimental data increases significantly as tens or hundreds of genomes are added into the mix.

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