Genetics Research Breakthroughs

Recently, a study published in Nature Immunology caught my eye. In it, the authors undertook an extensive study that charts generic variations influencing the tumour microenvironment (TME). The TME plays a crucial role in tumour progression and response to treatment. Understanding the genetic underpinnings of the TME could help pave the way for novel therapeutic approaches and enhanced treatment targeting. One of the study's most interesting aspects is its use of machine learning methods and advanced bioinformatic approaches to analyze and integrate large-scale datasets. The advanced computational methods used enabled identification of genetic variations that may have otherwise been overlooked, highlighting the power of computational biology in advancing our understanding of cancer. Leveraging these techniques, the researchers created a detailed atlas of genetic factors impacting the TME, which they refer to as immunity quantitative trait loci (immunQTLs), and showed that many of these genetic factors were likely co-localized with previously known expression quantitative trait loci. This observation suggests that the immunQTLs may contribute to the cellular heterogeneity observed within the TME by influencing the expression of genes modulating immune infiltration. Going beyond their initial discovery-driven computational work to further validate their findings, they mapped immunQTLs across >1,600 genes and 23 cancers that are associated with cancer pathogenesis and immune regulation. Diving even deeper, they went on to experimentally validate that one of the identified genes, CCL2, which is implicated in promoting colorectal carcinoma (CRC) progression by allowing tumour cells to evade immunity, may be a promising therapeutic target. This finding demonstrates the potential of the depth of the data set and how it might be used to identify and validate targets. This publication presents a significant amount of work that I have only scratched the surface of here. It offers new insights into the complexity of genetic factors influencing the TME, providing a comprehensive genetic map of the TME and its implications for cancer therapy. The authors have made their data available through a publicly accessible database to help propel further work by the research community. To me, an exciting aspect of this work is that it may help open the door to future combination therapeutic approaches that target both the tumour cells and their microenvironment. https://lnkd.in/ezRckvFh

Phenomenal #Proteomics #Biomarkers #TherapeuticTargets #Resource now breaking online at Cell Press | UK Biobank plasma yields comprehensive Open-Access #Proteome ↔ #Phenome #Atlas | #health | #disease | #diagnostics | Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here*, the authors provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://lnkd.in/dXGrTCbD) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets. *https://lnkd.in/dJuTcVMg Celentyx Ltd Professor Nicholas Barnes PhD, FBPhS Omar Qureshi Catherine Brady GRAPHICAL ABSTRACT

Biology is inherently multi-scale: understanding life requires models that can seamlessly reason across molecules, genes, cells, entire genomes, and clinical knowledge. Traditional deep learning models often struggle with these diverse scales - much like how LLMs struggle with very long contexts without specialized compression techniques. Excitingly, we’re witnessing the rise of powerful multi-scale models (note: multi-scale doesn’t necessarily mean multi-modal!). Some examples: 1. STATE (Arc Institute) uses learned single-cell embeddings to realistically simulate cellular responses to treatments at the population level. 2. AlphaGenome (Google DeepMind) predicts genomic structures at multiple resolutions, from base-pair level to chromatin contact maps. 3. Evo2 (Arc Institute) generalizes across diverse genomic domains, learning DNA representations applicable across multiple branches of life. 4. ModelGenerator (GenBio AI) enables integration of different biological modalities, unifying the gradient updates across those modalities. 5. Boltz 2 (Massachusetts Institute of Technology) democratizes biomolecular interaction modeling by efficiently predicting both protein structure and small molecule binding affinity. 6. Cell2Sentence (Google and Yale University) bridges single-cell biology with natural language, leveraging the vast clinical and biomedical knowledge encoded in text to enhance biological understanding and reasoning. and many more. Just as a picture is worth a thousand words, incorporating features or signals that naturally capture diverse biological scales will significantly enhance model understanding and reasoning. References: 1. https://lnkd.in/gHkBQ96K 2. https://lnkd.in/gtZithTf 3. https://lnkd.in/gU4rAqzC 4. https://lnkd.in/gvNvbAJB 5. https://lnkd.in/gQzsm3YY 6. https://lnkd.in/gHS_G4YS

A new study shows that a father's stress leaves lasting marks on his sperm — influencing the development of his offspring. This new research, published in the journal Molecular Psychiatry, delves into the field of epigenetics, which explores how environmental factors can alter gene expression without changing the underlying DNA sequence. These epigenetic changes can act as molecular switches, turning genes on or off and influencing various biological processes. Researchers analyzed sperm samples from 58 men, most in their late 30s to early 40s. The study revealed that men who reported high levels of childhood stress had different epigenetic profiles in their sperm compared to those who reported lower stress. These differences persisted even after accounting for other factors like smoking and drinking, suggesting that childhood experiences can leave lasting epigenetic marks. The researchers also found differences in a specific small noncoding RNA molecule previously linked to brain development in mice, as well as variations in DNA methylation patterns near genes involved in early brain development. While these findings suggest a potential link between childhood stress and epigenetic changes in sperm that could influence offspring development, it's crucial to emphasize that this research is still preliminary. It's not yet confirmed whether these epigenetic changes are passed down to children or what their ultimate impact might be. Further research is needed to determine the extent to which these epigenetic modifications in sperm can affect the health and development of future generations.

💬 "If you don't have celiac disease, you don't need to avoid gluten." If only you had a dime for every time a healthcare provider said this. It turns out that celiac disease isn't the only autoimmune condition that can be caused or exacerbated by gluten consumption. 🧬 Celiac disease is considered a hereditary genetic condition, characterized by the presence of certain genetic alleles such as HLA-DQ2 and HLA-DQ8. But several other autoimmune diseases are also linked to gluten sensitivity, including type 1 diabetes, rheumatoid arthritis, and Sjögren's syndrome. 🍏 Nutrigenomics provides a genetic understanding for how common dietary components, such as gluten, affect our health and disease status. Let’s examine the interplay between gluten and various genes associated with autoimmune conditions. The genes implicated in these conditions fall into three main categories: 1️⃣ HLA genes (in pink): These are part of our immune system and play a crucial role in how our bodies recognize and respond to foreign substances, including gluten. Examples include: 📌 HLA-DQ2 and HLA-DQ8: Strongly associated with celiac disease 📌 HLA-DR3 and HLA-DR5: Linked to autoimmune thyroid diseases 2️⃣ Non-HLA genes (in blue): These include genes involved in immune regulation, intestinal barrier function, and cellular processes that can influence autoimmune responses. Examples include: 📌 IL-2 and IL-21: Involved in regulating immune responses 📌 INS: Insulin gene, associated with type 1 diabetes 📌 FOXP3: Important for the function of regulatory T cells 3️⃣ Shared genes (in orange): These genes are associated with multiple autoimmune conditions, suggesting common pathways in autoimmune dysfunction. Examples include: 📌 CTLA4: Regulates T cell responses & linked to several autoimmune diseases 📌 STAT4: Involved in immune cell signaling & associated with multiple autoimmune conditions 📌 MYO9B: Affects intestinal permeability & linked to celiac disease, rheumatoid arthritis, and lupus While not everyone with these genes will develop gluten sensitivity or an autoimmune condition, this research is a reminder that nutrition isn't one-size-fits-all, and some individuals might benefit from reducing gluten intake even in the absence of celiac diagnosis. Always consult with a healthcare professional before making significant dietary changes, but don't be afraid to advocate for yourself if you suspect gluten sensitivity. Your body's response to food is unique, and recognizing that is key to improving health outcomes.

Synthetic biology is - quite literally - our future. A goundbreaking new biological foundation model Evo2 achieves state-of-the-art prediction of genetic variation impacts and generates coherent genome sequences, spanning all domains of life. A diverse team from leading research institutions including Arc Institute Stanford University NVIDIA University of California, Berkeley trained the model on 9.3 trillion DNA base pairs and has fully shared all code, parameters, and data. A few highlights from the paper (link in comments) 🔬 Zero-shot prediction achieves state-of-the-art accuracy in genetic variant interpretation. Evo 2 can predict the functional consequences of genetic mutations across all domains of life without specialized training. It surpasses existing models in assessing the pathogenicity of both coding and noncoding variants, including BRCA1 cancer-linked mutations. This generalist capability suggests Evo 2 could revolutionize genetic disease research, reducing reliance on expensive, manually curated datasets. 🛠 Genome-scale generation paves the way for synthetic life design. Evo 2 can generate full-length genome sequences with realistic structure and function, including mitochondrial genomes, bacterial chromosomes, and yeast DNA. Unlike prior models, Evo 2 ensures natural sequence coherence, improving synthetic biology applications like engineered microbes or artificial organelles. This sets the stage for programmable biology at an unprecedented scale. 🧬 Unprecedented long-context understanding revolutionizes genomic analysis. Evo 2 operates with a context window of up to 1 million nucleotides—far beyond the capabilities of previous models—allowing it to analyze genomic features across vast distances. This ability enables it to accurately identify regulatory elements, exon-intron boundaries, and structural components critical for understanding genome function. Its long-context recall is a major breakthrough for interpreting complex biological sequences. 🎛 Inference-time search enables controllable epigenomic design. Evo 2’s generative abilities extend beyond raw DNA sequence to epigenomic features, allowing researchers to design sequences with specific chromatin accessibility patterns. This approach successfully encoded Morse code messages into synthetic epigenomes, demonstrating a new method for controlling gene regulation via AI. This could lead to breakthroughs in gene therapy and epigenetic engineering. 🔮 Future potential: Toward AI-driven biological design and virtual cell modeling. Evo 2 represents a major leap toward AI-powered genomic engineering. Future iterations could integrate additional biological layers—such as transcriptomics and proteomics—to create virtual cell models that simulate complex cellular behaviors. This could revolutionize drug discovery, genetic therapy, and even synthetic life creation.

Not every shiny ML algorithm belongs in Bioinformatics. Bioinformatics doesn’t just need AI. It needs Bio-aware AI. In the rush to apply the latest AI/ML models to every problem, there’s a reality check many overlook: 👉 Bioinformatics ≠ generic tabular data. 👉 Bioinformatics ≠ simple image recognition. 👉 Bioinformatics ≠ “just another dataset.” Genomics, proteomics, structural biology, and systems biology produce data with unique statistical distributions, noise profiles, and biological constraints. - Sequence data isn’t like stock market data. - Protein structures don’t behave like social network graphs. - Gene expression matrices are not regular spreadsheets. This is why some ML models that dominate in other fields (finance, NLP, recommender systems) break down in bioinformatics unless carefully adapted. In Bioinformatics, success comes when: Algorithms are tuned for biological priors. Models respect the physics & chemistry of life. Data preprocessing mirrors the complexity of biology, not just math. The best ML algorithm is not the “newest” one, it’s the one that truly understands biological data. Here are the top ML/LLM models in 2025: - AlphaGenome (June 2025): Gene regulation & variant impact from long DNA sequences - AlphaFold 3 (Launched 2024; widely adopted by 2025): Protein complex, ligand, DNA/RNA structure prediction - SonicParanoid2 (2024): Fast orthologous gene inference using ML & LMs - NuFold (2025): RNA 3D prediction using AlphaFold 2 architecture - trRosettaRNA (Recent): Transformer-based RNA tertiary structure modeling - esmGFP / ESM3-derived protein design (Published Jan 2025): AI-designed protein simulating evolutionary processes - Generative AI Models: DNABERT, DNAGPT, GENA LM: DNA sequence modeling and classification with LLMs - EMitool (2025): Explainable multi-omics integration for cancer subtyping - DeepGO-SE and TAWFN (2025): Enhanced protein function inference via embeddings and GNNs - Graph Neural Networks (GNNs) (Growing relevance by 2025): Modeling biological networks and spatial gene expression - Quantum-Inspired Algorithms: QSVM, QNN, VQE, QFT: Experimental bioinformatics acceleration via quantum algorithms - BioMaster (2025): Automated bioinformatics pipeline management with LLM agents Models like AlphaGenome or DeepGO-SE are purpose built for biology they understand sequence context, structure, or biological ontologies. AlphaGenome handles million-base pair sequences; ESM3 was trained on hundreds of billions of protein. NuFold, AlphaFold 3, and trRosettaRNA capture 3D structure; GNNs model networks and tissue spatial contexts. Tools like EMitool and BioMaster support interpretability and autonomous workflows. Quantum-inspired algorithms and LLM agents (e.g., BioMaster) point toward the next wave of bioinformatics automation and acceleration.

🔬 Out today! We report a new compendium of human gut microbiomes with more than 168,000 samples. https://lnkd.in/gWUWfkPp We uniformly processed essentially all public human gut microbiome 16S samples, and analyzed this massive dataset to uncover patterns of microbiome variation across the globe. Key findings: - Microbiome composition varies dramatically across world regions, and every taxon tested is different between at least two world regions - We can predict a sample's geographic origin based solely on microbiome composition - Technical factors like DNA extraction methods significantly impact microbiome composition We hope that the Human Microbiome Compendium will be a resource for the research community, enabling scientists worldwide to conduct more sophisticated microbiome studies and unlock new insights into human health. We've made all the data and code available, and also have a website and R package to help explore the data: - Website: https://microbiomap.org/ - Paper: https://lnkd.in/gWUWfkPp - Data: https://lnkd.in/gm-GnRpg - R package: https://lnkd.in/gyEGyduX - Code: https://lnkd.in/g4zG-tg5 The work was led by Richard Abdill and Samantha Graham in my lab, with help from our wonderful collaborators Casey Greene, Arjun Krishnan, Sean Davis, and many others. This project was supported by generous funding from the The National Institutes of Health. #Microbiome #Genomics #OpenScience

❓ ONT, Illumina & MGI – What’s the Difference? 🔬 Next-Generation Sequencing (NGS) allows scientists to read genetic code by sequencing millions (or billions) of DNA fragments in parallel. Let’s explore some key platforms: 1️⃣ Illumina 1) Sample & Library Preparation: DNA/RNA is purified, fragmented, and ligated with adapters containing cluster recognition sites (bind to specific spots on the flow cell), index sequences (identify the sample), and primer binding sites. NEBNext UltraExpress® FS DNA Library Prep Kit https://lnkd.in/dMjcZphg is widely used for high-quality library preparation. 2) Cluster Generation: The flow cell has oligonucleotides complementary to the adapters, allowing fragments to bind. A PCR-like process (bridge amplification) forms clusters. Multiple copies of the strand ensure that the fluorescent signal during sequencing will be strong enough. 3) Sequencing: Fluorescently labeled nucleotides (G,C,A,T) with terminators bind one at a time to all single strands in the cluster (at any given moment, only one type of nucleotide binds, emitting a specific color). A camera records fluorescence to identify nucleotides. Terminator groups are cleaved to allow the next cycle. 4) Reverse Strand Sequencing: Index sequences are read, the reverse strand is synthesized and sequencing is repeated. 5) Data Analysis: Low-quality reads are filtered, and sequences are aligned. 2️⃣ MGI 1) Sample & Library Preparation: DNA is fragmented, ligated with adapters, and circularized into ssCirDNA. NEBNext® FS DNA Library Prep Kit for MGI® https://lnkd.in/dQMtgbNd provides a reliable solution for generating high-complexity libraries with optimized workflow. 2) DNB Generation by Rolling Circle Amplification: ssCirDNA acts as a template for continuous amplification, forming dense DNA Nanoballs (DNBs) with multiple copies of the sequence. 3) Loading DNBs: DNBs bind to specific spots on the flow cell. 4) Sequencing: Fluorescently labeled nucleotides with terminators bind one at a time to all sequences in the DNBs simultaneously. A camera records fluorescence to identify each nucleotide. Terminators are cleaved to allow the next cycle. 3️⃣ Oxford Nanopore Technologies 1) Sample & Library Preparation: DNA/RNA is extracted, purified, and ligated with motor protein adapters. 2) Loading the Flow Cell: The library is added to a flow cell containing thousands of nanopores. 3) Sequencing: The motor protein unzips the DNA, guiding it through the nanopore one base at a time. Each nucleotide disrupts the ionic current in a unique way, producing a signal used to determine the sequence. 4) Base Calling & Data Analysis: Signals are converted into nucleotide sequences, followed by read alignment and error correction. #NGS #Sequencing #Genomics #Bioinformatics #Illumina #Nanopore #MGI #Biotech