New Human Gene Cluster Discovery Sheds Light on “Genetic Dark Matter”

                            

    Scientists led by Ali Shilatifard have discovered a new repeat gene cluster in the human genome that is unique to humans and other primates. Published in Science Advances, the study reveals a previously hidden region of DNA that could reshape how we understand gene regulation and evolution.

    The newly identified cluster produces a protein called ELOA3, related to transcription machinery that controls how genes are expressed. Unlike typical genes, which appear once or in small numbers, this cluster contains multiple repeated copies of the same gene located together. Interestingly, the number of copies varies widely between individuals, adding another layer of genetic diversity.

    For years, large portions of the genome made up of repetitive DNA were considered “genetic dark matter” because older sequencing methods couldn’t properly analyze them. With newer long-read sequencing technologies, researchers can now explore these regions and uncover their biological significance. The team found that ELOA3 plays a role in regulating RNA polymerase II transcription, but through distinct mechanisms compared to related proteins. This suggests the gene cluster may contribute to subtle differences in gene expression between individuals and across primate species.

    Beyond basic genetics, this discovery has important implications. Because gene regulation is tightly linked to diseases like cancer, understanding how ELOA3 works could eventually support targeted drug development. It may also help explain aspects of human evolution, since the gene cluster appears conserved across primates but varies in copy number.

Article Link: https://news.feinberg.northwestern.edu/2023/11/22/new-human-gene-cluster-sequence-discovered/

Additional Resource: https://www.science.org/doi/10.1126/sciadv.adj1261

AI tool reveals DNA exists in partially open states

                    

    Scientists from the Gladstone Institutes and Arc Institute used artificial intelligence to discover that DNA is often stored in partially open states, rather than being fully closed or fully accessible as previously believed.

    DNA is wrapped around protein structures called nucleosomes, which help package over six feet of DNA inside each cell. For years, researchers thought genes were either “on” or “off” depending on whether DNA was tightly wrapped. However, this study found the genome behaves more like a volume dial, allowing different levels of gene activity.

    Using an AI tool called IDLI, researchers identified 14 distinct nucleosome structural states. More than 85% of nucleosomes showed some distortion, meaning sections of DNA were partially exposed and potentially available for gene regulation. This creates a more flexible system for controlling how genes function.

    These findings are important in genetics because subtle gene expression changes are linked to diseases like cancer, aging, and neurodegenerative disorders. Understanding this hidden DNA “grammar” could help scientists design future treatments that adjust gene activity more precisely.

Article Link: https://www.drugtargetreview.com/ai-tool-reveals-dna-exists-in-partially-open-states/2135350.article

Additional Resource: https://distilinfo.com/2026/04/30/ai-reveals-dna-is-far-more-accessible/

Orthrus: A New AI Model Advancing RNA Genetics and Gene Regulation Prediction

    A recent publication introduces Orthrus, a cutting edge RNA foundation model designed to improve how scientists predict RNA behavior and gene regulation. Despite the massive amount of genomic data available today, understanding the “RNA regulatory code” remains a major challenge in genetics. Traditional experimental methods like eCLIP and ribosome profiling are accurate but expensive and time-consuming, creating a need for faster computational alternatives.

    Orthrus addresses this gap by using a machine learning approach called contrastive learning, combined with a Mamba-based encoder optimized for long RNA sequences. Unlike older models that rely on generic text based training methods, Orthrus is trained using biologically meaningful relationships, specifically RNA splice variants and evolutionary similarities across species. The model learns by comparing related RNA sequences from over 400 mammalian species, allowing it to better capture functional genetic relationships.

    This approach significantly improves performance in predicting key RNA properties such as RNA half-life, ribosome load, protein localization, and gene function classification. Importantly, Orthrus performs well even in low-data environments, reducing the need for large labeled datasets, which is a major limitation in genetics research.

    Overall, Orthrus represents a shift toward more biologically informed artificial intelligence models in genomics. By integrating evolutionary biology with machine learning, it improves our ability to interpret RNA function and gene regulation more accurately than previous self-supervised models.

Article link: https://www.marktechpost.com/2024/10/15/orthrus-a-contrastive-learning-approach-for-enhanced-rna-representation-and-property-prediction/

Additional resource: https://www.biorxiv.org/content/10.1101/2024.10.10.617658v1.full.pdf

How Shifting Genes Helped Humans Stand Upright

   

    A new study in Nature the Journal is giving scientists a clearer picture of how our ancestors became upright walkers. By examining embryos from humans, mice, and a wide range of primates, researchers discovered that although all mammals rely on the same basic genes to build the ilium; the major hip bone; humans use those genes in a very different way. Instead of forming along the spine like it does in other species, the human ilium starts out perpendicular to it. That early shift completely changes how the pelvis grows, ultimately creating the shape and muscle support needed for bipedal movement.

     The team also found that the human ilium takes much longer to turn from cartilage into bone compared with the rest of the skeleton. They think this delay emerged as early humans developed larger brains; a slower-forming ilium likely helped widen the birth canal enough to deliver bigger-headed babies. Together, these findings suggest that our evolution didn’t hinge on inventing new genes, but on changing when and where old ones switched on, small developmental tweaks that ended up reshaping how we move and how we’re born.

Restoring Healthy Gene Expression with Programmable Therapeutics

 Zach Winn's recent article in MIT News discusses how CAMP4 Therapeutics targets regulatory RNA. Many diseases are caused by dysfunctional gene expression, which produces too much or too little protein. The CAMP4 approach is to edit genes by inserting new genetic snippets into cells.

MIT professor Young and the CAMP4 co-founder have found that the regulatory RNA controls how much of the gene is expressed when molecules interact with transcription factors. This treatment could help metabolic diseases, heart conditions, and neurological disorders. They found that targeting RNA regulators could offer more precise treatments to target these conditions. The first CAMP lead drug candidate targets genetic defects in the metabolism of excreting ammonia. As the company plans to expand the study for preclinical safety, they hope to next look into seizure disorders. This can lead to learning about multiple conditions and opening up collaboration to make an important impact.