Genetics is a vast field with thousands of teams working on uncovering the secrets behind DNA, RNA and proteins. Research in genetics has also been given a boost with AI technologies. Here’s a look at some of the latest research in this area.

AI-Driven Genomics – Bentham Science

Cracking the Code of Disease Variants with AI for Better Health

Scientists have made a groundbreaking advancement in utilizing artificial intelligence (AI) to identify disease-causing genetic variants in human DNA. The new AI-powered tool, named PrimateAI-3D, has been tested on a biomedical database of over 450,000 individuals in the UK and demonstrates a significant step towards harnessing the full potential of the genome for medical purposes. PrimateAI-3D offers a promising solution to a long-standing challenge faced by doctors: pinpointing which genetic variants contribute to health issues among the 4 million variants present in a person's genetic blueprint.

The PrimateAI-3D algorithm was created by an international team of researchers, who trained it using the genetic blueprints of 233 primate species, including humans. By comparing sequences across primate genomes, the algorithm identifies potentially harmful variants with greater accuracy. The tool scans about 70 million genetic variants, dwarfing the selection available in the conventional archive, ClinVar, by over 1,000 times. Additionally, the algorithm's focus on three-dimensional protein structures aids in distinguishing mutations that disrupt protein function, leading to diseases.

The implementation of PrimateAI-3D in hospitals could have significant implications for genomic medicine. It enables doctors to prioritize investigating specific disease-related variants, helping to overcome the challenge of identifying variants of unknown significance. The new tool also promises to assist pharmaceutical companies in their drug discovery efforts, reducing the failure rate of clinical trials by accurately targeting disease-causing genes.

Through testing on patient genomes, the algorithm predicts that 97 percent of the general population carries a rare variant significantly affecting health, such as cholesterol levels or risks for cardiovascular disease and diabetes. While the algorithm cannot account for environmental factors, it offers valuable insights into the genetic basis of health risks.

With the staggering amount of genomic data being generated annually, AI appears to be an ideal tool for interpreting and extracting relevant information from this vast genetic haystack. The integration of AI and genomics provides a promising avenue for advancing medicine and improving patient outcomes.

Understanding Human Skeletal Proportions with the help of AI

Researchers from The University of Texas at Austin and New York Genome Center have utilized artificial intelligence (AI) to study the genes responsible for shaping human skeletons. By combining data from full-body x-ray images and genomic information from over 30,000 UK Biobank participants, the researchers shed light on the genetic basis of various skeletal proportions, such as shoulder width and leg length.

These findings help us to understand the evolution of human skeletal form. Knowledge from the study also has the potential to aid doctors in predicting patients' risks of developing musculoskeletal conditions like back pain or arthritis later in life.

Read more from Bentham Science on AI models in genomics: Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Unsense Mutations: Redefining the paradigms for mRNA Substitution | Bentham Science

A synonymous mutation is a type of genetic mutation that occurs when there is a change in the DNA nucleotide sequence, without altering the amino acid sequence of the resulting protein. This is because the genetic code is degenerate, meaning that multiple codons can code for the same amino acid. Since the amino acid sequence remains unchanged, the resulting protein's structure and function remain the same as the unmutated version.

Unlike missense mutations, which change the amino acid sequence and may impact the protein's function, synonymous mutations do not lead to any detectable changes in the protein's properties. As a result, many synonymous mutations are considered "silent" because they do not cause any observable effect on the organism's phenotype or health.

However, while synonymous mutations themselves may not have significant direct effects, they can still play a role in gene regulation and mRNA stability, affecting protein expression levels or the efficiency of translation. Additionally, some synonymous mutations, if they occur within certain functional regions of the mRNA, can influence splicing or protein folding processes. In these situations, the synonymous variant behaves like a non-synonymous variant. This has led to the creation of a new term for mutations, called unsense mutations. In a recent Current Genomics article, geneticist Mauno Vihinen from the University of Lund, Sweden, has proposed a new schematic that describes the different types of mRNA substitutions. The author suggests that prediction methods should account for unsense mutations, and published results on genetic variations and evolutionary biology should be reevaluated.

Blooming Secrets: Unraveling Asteraceae's Genetic Success with Nitrogen-Carbon Balance

The Asteraceae family, commonly known as the daisy family is one of the most genetically diverse and adaptable plant species known to us. Researchers from China have attempted to find the secret behind these traits. They compared the genomes of 29 terrestrial plant species, including two chromosome-scale genome assemblies for stem lettuce (an Asteraceae member) and Scaevola taccada (a member of Goodeniaceae, one of the closest outgroups of Asteraceae), the study sheds light on the evolutionary history and distinctive characteristics of the Asteraceae family.

The findings suggest that Asteraceae originated approximately 80 million years ago and underwent repeated paleopolyploidization, a process in which their genomes duplicated multiple times. Notably, the study reveals that PII, a crucial regulator of nitrogen-carbon (N-C) assimilation found in many life forms, has been conspicuously lost across Asteraceae.

Instead, Asteraceae have developed an upgraded N-C balance system through paleopolyploidization events and tandem duplications of key metabolic genes. This genetic enhancement facilitated improved nitrogen uptake and fatty acid biosynthesis in the family, contributing to their ecological success and adaptability.

The research not only provides insights into the molecular basis of Asteraceae's unique characteristics but also presents a potential crop improvement strategy based on their distinctive N-C balance system.

Psychic Cellular Chatter: Extracellular Messages May Trigger Mental Disorders

In a recent study, scientists have made a significant discovery regarding Extracellular Genomic Materials (EGMs) and their role in psychiatric disorders. EGMs are nucleic acids released by cells in response to internal or external triggers and can be found in biological fluids. These "cellular messages" carry essential characteristics of their cell of origin and can travel to distant organs, even crossing the blood-brain barrier, allowing the transfer of crucial information that can trigger changes in the brain that adversely affect mental health.

The study's findings suggest that EGMs could serve as potential diagnostic biomarkers and play a vital role in treatment approaches for conditions like schizophrenia spectrum disorders, bipolar disorder, depressive disorders, and autism spectrum disorders. The ability to utilize these "cellular messages" in clinical settings offers a promising tool for prognosis and diagnosis, bringing hope for improved outcomes in psychiatric research and treatment.

Interested in more research in genomics? Here is a selection of articles on genomics for this issue.

For further reading, explore latest articles in some of the leading genetics journals from Bentham Science:

Current Genomics
Current Gene Therapy
Current Molecular Medicine
Current Pharmacogenomics and Personalized Medicine
MicroRNA

Also read our Oncology Article Collection..