Genetic Technologies Invented to Combat Antibody Resistance in Bacteria

https://today.ucsd.edu/story/next-generation-genetics-technology-developed-to-counter-the-rise-of-antibiotic-resistance 

https://aspe.hhs.gov/collaborations-committees-advisory-groups/carb

One of the most prominent problems in modern medicine is the growth of bacteria that are resistant to current medical drugs or antibodies.  However due to a new breakthrough this could become a problem of the  past.  Researchers at the University of San Diego have invented a new technology that will be utilized to fight the growing threat of antibiotic resistance.  This system is known as the pPro-MobV and it uses CRISPR technologies to inhibit certain genes.  Genes that give bacteria resistance to antibodies and can be passed down to future generations can be targeted and disabled.  This new development offers scientists a new way to eliminate the problems of antibody resistance in different viruses.  

A Gene That May Predict Alzheimer’s Earlier Than Expected

 

  A recent study published by Alzheimer’s Research UK highlights how a specific gene, called APOE4, can strongly influence a person’s risk of developing Alzheimer’s disease. Researchers found that individuals who inherit two copies of this gene, one from each parent, are much more likely to develop Alzheimer’s and often at a younger age than others.

    

    Alzheimer’s disease is not usually caused by just one factor. Instead, it results from a combination of age, lifestyle, environment, and genetics. There are two main types of genes involved: faulty genes, which directly cause rare early-onset Alzheimer’s, and risk genes, which increase the likelihood of developing the disease. APOE4 is considered the most important risk gene discovered so far.

    In this study, scientists analyzed medical records and brain samples from over 10,000 people across the U.S. and Europe. They found that nearly all individuals with two copies of APOE4 showed early biological signs of Alzheimer’s, such as abnormal amyloid protein levels in the brain, by age 55–65. Symptoms of the disease in these individuals typically appeared around age 65, which is about 7–10 years earlier than people with other versions of the gene.

    However, it’s important to note that having the APOE4 gene does not guarantee someone will develop Alzheimer’s. Many other factors, such as diet, exercise, heart health, and overall lifestyle, can influence risk. Because of this uncertainty, genetic testing for APOE4 is not widely recommended outside of research settings.

    This study is important because it helps scientists better understand how genetics contributes to Alzheimer’s disease. By identifying high risk groups earlier, researchers may be able to improve early detection and develop more targeted treatments in the future.

Article link: https://www.alzheimersresearchuk.org/news/inheriting-two-copies-of-apoe4-linked-to-risk-of-alzheimers-at-a-younger-age-study-suggests/

Additional Resource: https://www.nih.gov/news-events/nih-research-matters/study-defines-major-genetic-form-alzheimers-disease

Can Tiny RNA Molecules Predict How Long We Live?

 A new study explores the role of small non-coding RNAs (smRNAs), including microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), in human aging and survival. These molecules don’t code for proteins but instead help regulate gene expression, meaning they can turn genes on or off. Researchers analyzed blood samples from over 1,200 older adults (age 71+) to see whether levels of these RNAs could predict survival. They found that certain smRNAs, especially piRNAs, were strongly associated with whether individuals lived longer, and models using these biomarkers were highly accurate in predicting short-term survival.

The study also showed that combining smRNA data with clinical factors (like physical function and health markers) improved prediction accuracy. Interestingly, several piRNAs were found at lower levels in longer-lived individuals, suggesting they may play a role in aging processes. These findings open the door to using smRNAs not only as biomarkers for predicting lifespan but also as potential targets for future therapies aimed at extending human longevity.

I think this research is really fascinating because it shifts the focus from just genetics to epigenetics, how gene expression is regulated over time. The idea that something as small as RNA circulating in your blood could help predict how long you live is honestly kind of crazy, but also really exciting. It shows how advanced medicine is becoming, especially with the use of machine learning to predict outcomes. At the same time, I think it’s important to remember that lifespan isn’t determined by biology alone, and that lifestyle, environment, and social factors still play a huge role. Overall, this study is a big step toward personalized medicine and could eventually help doctors better understand aging and even develop treatments to improve longevity.

Article: https://onlinelibrary.wiley.com/doi/10.1111/acel.70403?msockid=35b54d1c864f6a6e068559f287c96b83

Additional website: https://pmc.ncbi.nlm.nih.gov/articles/PMC4609956/

Gene Activity in Male vs. Female Brains

 

Hsiao-Ying Wey/Science Translational Medicine

Past studies revealed that the risks of developing neurological disorders vary depending on a person’s sex. Males have a higher chance of developing Parkinson’s disease, ALS, or neurodevelopmental disorders such as autism or ADHD. Females have higher percentages of Alzheimer's disease or other dementias, as well as mood-related disorders such as depression or bipolar disorder. A recent study involving the effect of sex on gene expression explains why.

The study found that sex chromosomes and hormonal influences on cell-type gene expression might explain sex differences in susceptibility to neurodevelopmental, psychiatric, and neurodegenerative diseases. The study analyzed samples of transcriptomic cell types of the brain’s cortex from 15 adult males and 15 adult females across six different areas of the brain. They found that more than 3000 genes showed sex biased gene expression, and over 100 of these genes were consistent across the different regions and cell types. Furthermore, most of these genes with sex-biased expression are not located on sex chromosomes but are autosomal and can be activated by sex hormones. Overall, this data can be used to link the differences we see between the sexes in hormone regulation, cortical structure, and susceptibility to brain-related disorders.

​Source:

https://www.science.org/doi/10.1126/science.aea9063 

Additional: 

https://www.nature.com/articles/d41586-026-01227-y

The Genetic Component to Mirror Movement Disorder

In a recent study regarding mirror movement disorder, researchers may have discovered the gene that is responsible. 

Mirror movement disorder is a condition where any time the affected person moves a body part, the body part on their opposite side moves as well. This condition arises due to irregularities in structures such as the corpus callosum, an area of the brain that is responsible for sending signals across cranial hemispheres. This makes tasks requiring different motions of opposite limbs painful, extremely debilitating and, in many cases, almost impossible. 

In order to determine what caused the condition, a family with autosomal dominant mirror movement disorder underwent genetic analysis. Researchers found a common mutation in the ARHGEF7 gene, which binds to Dcc. Dcc acts as a receptor for the guidance of axons across the middle of the brain, and if mis-regulated, provides an explanation for mirror movement disorder.

Additionally, analysis was done on mice containing the ARHGEF7 gene. Mice that were heterozygous for the mutation had mirror movements while walking, further cementing the idea that a mutation in the ARHGEF7 gene is one of the causes for mirror movement disorder.

Mirror movement disorder is very rare, which makes this research all the more important. By identifying the genetic component responsible for this condition, further research can be done towards medical advances that would help those afflicted.

Sources:

https://www.science.org/doi/10.1126/sciadv.add5501

https://pubmed.ncbi.nlm.nih.gov/21633904/

25 years of the Human Genome project

 

An international group of scientists had published their first working draft of the human genome in 2001 as part of the Human Genome Project (HGP). The project was completed in 2003 and the entire human genome has since been mapped. With the publicized genomic database, specific genes associated with disease have been able to be identified. In partnership with other publicly available resources, the HGP has enabled more accurate diagnostic and therapeutic capabilities. Over the past 25 years, the HGP has revolutionized the field of forensics, medicine, and family planning. 

On the 25th anniversary of the Human Genome Project, esteemed scientists have reflected on the efforts that went into the publication of the human genome and its impact across specialties. From wide-scale comparative genomics to the identification of specific polymorphisms, most modern advancements in genetics and biotechnology would not be possible without the HGP.

Source: 

https://web.ub.edu/en/web/actualitat/w/25th-anniversary-human-genome

Additional link:

https://www.britannica.com/event/Human-Genome-Project/Advances-based-on-the-HGP

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

A Hybrid Honeybee Population Has Evolved Natural Mite Resistance

 

A hybrid honeybee population in Southern California was studied over a 4 year period, and was found to have lower mite infestation rates than colonies with queens from commercial stock. Varroa mites feed on the fat body tissue of honeybees, which weakens their immune system and ultimately shortens their lives. The California hybrid honeybees were not completely immune, they were found to have about 68% fewer Varroa mites on average. The differences showed even at the larval stage, suggesting the resistance must be more than learned behavior, but genetically built into the bees. One explanation for the difference between the hybrid honeybees and the managed honeybees they were compared to, is that continued chemical treatments in managed honeybee populations reduces natural selection for host resistance.

Sources:

https://www.nature.com/articles/s41598-026-45759-9

https://www.sci.news/biology/californian-hybrid-honeybees-varroa-mite-resistance-14689.html