UBC researchers have created a topical CRISPR-based therapy that could offer a lasting cure for a range of skin disorders.
Category: genetics
Different Autism Mutations Can Lead to Similar Brain Changes
The shared pathways were linked to neuron maturation, synapse formation, and the control of gene activity. Further analysis pointed to a group of genes involved in organizing DNA and regulating which genes are switched on or off. These genes sit high in the regulatory chain, influencing many downstream processes previously linked to autism.
To test whether this network played an active role, the team reduced the activity of several key regulators using CRISPR-based methods in neural cells. This led to downstream changes similar to those seen in the autism models.
However, organoids from individuals with idiopathic autism showed less consistent changes, likely reflecting the complex and distributed genetic risk seen in most autism cases.
CLN3 mediates chloride efflux from lysosomes
Lysosomes degrade damaged organelles and macromolecules to recycle nutrient components. Lysosomal storage diseases (LSDs) are linked to mutations of genes encoding lysosomal proteins and may lead to age-related disorders, including neurodegenerative diseases. But, how lysosomal dysfunction contributes to neurodegenerative diseases is not clear yet…
The researchers identify CLN3 (ceroid lipofuscinosis, neuronal 3), linked to Batten disease as a conserved lysosomal protein that regulates lysosomal chloride homeostasis, pH, and protein degradation.
Curcumin analog C1 is a natural compound with anti-inflammatory properties could enhance CLN3 activity and improve lysosomal function by activating TFEB. sciencenewshighlights ScienceMission https://sciencemission.com/CLN3-n-chloride-efflux-n-lysosomes
Wang et al. identify CLN3 as a conserved lysosomal protein that regulates lysosomal chloride homeostasis, pH, and protein degradation. Transcription factor EB (TFEB) activation enhances CLN3 function, revealing the TFEB-CLN3 signaling axis as a promising therapeutic target for lysosomal storage disorders.
Advancing regulatory variant effect prediction with AlphaGenome
What makes it special is its versatility. Where older models might only predict how a mutation affects gene activity, AlphaGenome forecasts thousands of biological outcomes simultaneously—whether a variant will alter how DNA folds, change how proteins dock onto genes, disrupt the splicing machinery that edits genetic messages, or modify histone “spools” that package DNA. It’s essentially a universal translator for genetic regulatory language.
AlphaGenome is a deep learning model designed to learn the sequence basis of diverse molecular phenotypes from human and mouse DNA (Fig. 1a). It simultaneously predicts 5,930 human or 1,128 mouse genome tracks across 11 modalities covering gene expression (RNA-seq, CAGE and PRO-cap), detailed splicing patterns (splice sites, splice site usage and splice junctions), chromatin state (DNase, ATAC-seq, histone modifications and transcription factor binding) and chromatin contact maps. These span a variety of biological contexts, such as different tissue types, cell types and cell lines (see Supplementary Table 1 for the summary and Supplementary Table 2 for the complete metadata). These predictions are made on the basis of 1-Mb of DNA sequence, a context length designed to encompass a substantial portion of the relevant distal regulatory landscape. For instance, 99% (465 of 471) of validated enhancer–gene pairs fall within 1 Mb (ref. 12).
AlphaGenome uses a U-Net-inspired2,13 backbone architecture (Fig. 1a and Extended Data Fig. 1a) to efficiently process input sequences into two types of sequence representations: one-dimensional embeddings (at 1-bp and 128-bp resolutions), which correspond to representations of the linear genome, and two-dimensional embeddings (2,048-bp resolution), which correspond to representations of spatial interactions between genomic segments. The one-dimensional embeddings serve as the basis for genomic track predictions, whereas the two-dimensional embeddings are the basis for predicting pairwise interactions (contact maps). Within the architecture, convolutional layers model local sequence patterns necessary for fine-grained predictions, whereas transformer blocks model coarser but longer-range dependencies in the sequence, such as enhancer–promoter interactions.
Deep-learning algorithms enhance mutation detection in cancer and RNA sequencing
Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic mutation detection in cancer diagnostics and RNA-based genomic studies.
The pioneering research team, led by Professor Ruibang Luo from the School of Computing and Data Science, Faculty of Engineering, has unveiled two groundbreaking deep-learning algorithms—ClairS-TO and Clair3-RNA—set to revolutionize genetic analysis in both clinical and research settings.
Leveraging long-read sequencing technologies, these tools significantly improve the accuracy of detecting genetic mutations in complex samples, opening new horizons for precision medicine and genomic discovery. Both research articles have been published in Nature Communications.
Brewing possibilities: Using caffeine to edit gene expression
What if a cup of coffee could help treat cancer? Researchers at the Texas A&M Health Institute of Biosciences and Technology believe it’s possible. By combining caffeine with the use of CRISPR—a gene-editing tool known as clustered regularly interspaced short palindromic repeats—scientists are unlocking new treatments for long-term diseases, like cancer and diabetes, using a strategy known as chemogenetics.
The work is published in the journal Chemical Science.
Yubin Zhou, professor and director of the Center for Translational Cancer Research at the Institute of Biosciences and Technology, specializes in utilizing groundbreaking tools and technology to study medicine at the cellular, epigenetic and genetic levels. Throughout his career and over 180 publications, he has sought answers to medical questions by using highly advanced tools like CRISPR and chemogenetic control systems.
Several Psychiatric Disorders Share The Same Root Cause, Study Suggests
Researchers have discovered that eight different psychiatric conditions share a common genetic basis.
A study published in early 2025 pinpointed specific variants among those shared genes, showing how they behave during brain development.
The US team found many of these variants remain active for extended periods, potentially influencing multiple developmental stages – and offering new targets for treatments that could address several disorders at once.
How your life story leaves epigenetic fingerprints on your immune cells
The COVID-19 pandemic gave us tremendous perspective on how wildly symptoms and outcomes can vary between patients experiencing the same infection. How can two people infected by the same pathogen have such different responses? It largely comes down to variability in genetics (the genes you inherit) and life experience (your environmental, infection, and vaccination history).
These two influences are imprinted on our cells through small molecular alterations called epigenetic changes, which shape cell identity and function by controlling whether genes are turned “on” or “off.”
Salk Institute researchers are debuting a new epigenetic catalog that reveals the distinct effects of genetic inheritance and life experience on various types of immune cells. The new cell type-specific database, published in Nature Genetics, helps explain individual differences in immune responses and may serve as the foundation for more effective and personalized therapeutics.