Bioinformatics for Drug Discovery

Genome-wide CRISPR screen in human T cells reveals regulators of FOXP3 The RBPJ–NCOR repressor complex is identified as a negative regulator of FOXP3 expression through modulation of histone acetylation in induced regulatory T cells. - Regulatory T (Treg) cells, which specifically express the master transcription factor FOXP3, have a pivotal role in maintaining immunological tolerance and homeostasis and have the potential to revolutionize cell therapies for autoimmune diseases. - A systematic approach towards understanding the regulatory networks that dictate Treg differentiation could lead to more effective iTreg cell-based therapies. - Here the authors performed a genome-wide CRISPR loss-of-function screen to catalogue gene regulatory determinants of FOXP3 induction in primary human T cells and characterized their effects at single-cell resolution using Perturb-icCITE-seq. - The authors identify the RBPJ–NCOR repressor complex as a novel negative regulator of FOXP3 expression. RBPJ-targeted knockout enhanced iTreg differentiation and function, independent of canonical Notch signalling. - Overexpression of RBPJ potently suppressed FOXP3 induction through direct modulation of FOXP3 histone acetylation by HDAC3. - RBPJ-ablated human iTreg cells more effectively suppressed xenogeneic graft-versus-host disease than control iTreg cells in a humanized mouse model. - These findings are instrumental towards advancing our understanding of the molecular basis of Treg differentiation and the development of iTreg-based therapy for autoimmune and other inflammatory diseases. https://lnkd.in/efyWaZFt #immunology #Tregs #autoimmunity #celltherapy #autoimmunedisease

Most genomics pipelines move in a straight line. FASTQ → QC → Alignment → DEGs → Plot → Done. But biology doesn't work like that. One of the most underused tools in bioinformatics? Systems Thinking. Instead of just analyzing mutations or differentially expressed genes in isolation, ask: 1)-> How does this change affect the larger biological system? 2) -> What signaling pathway does this gene influence? 3) -> How does that impact immune response or cell cycle regulation? 4) -> What expression ripple effects happen elsewhere in the genome? Because cancer isn't caused by one mutation. And resistance doesn't emerge from a single gene. It's not a list of hits..... it's a network of effects. What's actually in a systems network? >> Core Elements: Genes, transcripts, and regulatory sequences • Proteins (enzymes, transcription factors, signaling molecules) • Metabolites and small molecules >> Relationship Types: • Regulatory interactions (gene → protein, TF → target) • Physical interactions (protein binding, enzyme reactions) • Functional relationships (pathway membership, co-expression) >> Biological Context: • Metabolic pathways (glycolysis, TCA cycle) • Signaling cascades (p53, Wnt, MAPK) • Cellular processes (cell cycle, apoptosis) • Spatial organization (nucleus, mitochondria, membrane) • Temporal dynamics (expression timing, feedback loops) >> Data Integration: • Genomics + Transcriptomics + Proteomics + Metabolomics + Epigenomics 💡 Systems thinking helps you: ✅ See beyond the "top 20" DEGs ✅ Connect molecular data to clinical outcomes ✅ Build biological insight, not just computational output #Bioinformatics #SystemsThinking #ComputationalBiology #CancerGenomics #NGS #PathwayAnalysis #Omics #PrecisionMedicine #ScientificThinking #ResearchMindset #NetworkBiology #SystemsBiology

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.

#SpatialOrganisation of the human #healthy and #fibrotic liver at single-cell resolution using multiplexed error-robust fluorescence in situ hybridization (#MERFISH) and single-nucleus RNA sequencing (#snRNA-seq) This innovative approach allows us to: > Visualize the zonal distribution of hepatocytes, confirming the classic organization into zones 1, 2, and 3, and revealing a continuous gradient of gene expression rather than strict zones. > Spatially resolve macrophage and hepatic stellate cell (HSC) subpopulations, identifying distinct distributions and gene expression patterns. >Investigate the impact of hepatocyte ploidy and find that while multinucleated cells are larger and contain more RNA, they do not show differences in relative gene expression. >Identify key receptor-ligand interactions between hepatocytes and other cell types, highlighting potential communication pathways within the liver. >Analyze fibrotic liver samples, discovering two new hepatocyte populations that expand with injury and do not adhere to the typical zonal patterns, plus other disease-associated changes in gene expression. https://lnkd.in/eJWQG5ke https://lnkd.in/eHJ7wR5f #TheScienceCircuit #TranslationalResearch #MultiOmics #liver #spatialtranscriptomics #singlecell #MERFISH #snRNAseq 

Decoding Fungal Mysteries: AI Unlocks the Secrets of Gene Response Researchers at the University of Illinois have developed a groundbreaking machine-learning model called FUN-PROSE. This model excels in predicting how genes in fungi respond to different environmental conditions. It specifically focuses on three fungal species: Neurospora crassa, Saccharomyces cerevisiae (baker's yeast), and Issatchenkia orientalis. FUN-PROSE analyzes gene expression by considering the role of mRNA and transcription factors. Transcription factors attach to DNA sequences, impacting mRNA formation, which in turn influences protein production in cells. This process is vital for cells to adapt to changes in their environment. The challenge in genomic studies has been understanding how transcription factors interact with gene promoters under various conditions. With FUN-PROSE, researchers can better predict these interactions. The model was trained on promoter sequences and transcription factors of the three fungi, learning to recognize specific motifs in different environmental settings. The accuracy of FUN-PROSE was verified against RNA-seq data, showcasing its effectiveness in predicting gene expression changes. Despite some limitations with novel promoters, the model opens new avenues for understanding gene regulation in fungi and potentially other organisms. This research, published in PLOS Computational Biology, marks a significant step in genomic studies using artificial intelligence. #access #genomics #ai Amit Saxena Ajay Nandgaonkar Sanju Senthil Kumar Suchitaa Paatil Taruna Anand read more : https://lnkd.in/gicu96xD

Machine learning (ML) is revolutionizing genomics, but common pitfalls can lead to misleading results. Here's a thread on how to avoid them 🧵 1/ Pitfall 1: Distributional Differences Genomic data often exhibits inherent biological structure, leading to distributional differences. This can impact model performance when training & test sets have different distributions than the prediction set. Example: Models trained on in vitro data often perform poorly on in vivo data, as seen in transcription factor binding site prediction. This highlights the need to carefully consider the context in which a model will be applied. 2/ Pitfall 2: Dependent Examples Genomic data is often interconnected, violating the independence assumption of many ML models. This can inflate performance estimates during cross-validation. Example: When predicting protein-protein interactions, pairs sharing a protein are correlated. This can be mitigated by employing group k-fold cross-validation, ensuring dependent examples don't cross the train-test divide. 3/ Pitfall 3: Confounding Unmeasured variables can create or mask associations, leading to misinterpretations. Example: In GWAS, population structure can confound genotype-phenotype relationships. The infamous autism spectrum disorder prediction model initially seemed successful but failed to replicate after accounting for population structure. 4/ Pitfall 4: Leaky Preprocessing Data processing can inadvertently leak information from the test set into the training set, resulting in over-optimistic performance estimates. Example: Feature selection based on the entire dataset before cross-validation, common in DNA methylation analysis, introduces leakage. (this is probably one of the most common mistakes I see...) hold a test dataset that you never touch until the final step. Pitfall 5: Unbalanced Classes Datasets with uneven class distribution can lead to models overfitting the majority class. Example: Predicting enhancers is challenging due to the small proportion of positive examples. Resampling techniques and choosing appropriate performance metrics like auPR can help address this. Also use PR not ROC to evaluate your model https://lnkd.in/eh9JHmxc Key Takeaways: • Genomic data has unique characteristics that require careful consideration when applying ML. • Thoroughly inspect your data, considering potential dependencies, confounders, & class imbalance. • Employ appropriate techniques like group k-fold cross-validation, balancing methods, & robust performance metrics. By understanding these pitfalls and taking steps to mitigate them, we can ensure that ML applications in genomics yield reliable and insightful results. 💪 dive deep into the paper https://lnkd.in/eDFWnW9U I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter https://lnkd.in/erw83Svn

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

⚠️Unraveling #Gene-#Diet Interactions in #Colorectal #Cancer Risk❗️ 📍They identified 2 loci associated with fibre and fruit intake that also modify the association of these dietary factors with CRC risk. ❇️Recent genome-wide association studies (GWAS) have pinpointed over 200 independent loci associated with colorectal cancer (CRC), explaining approximately 35% of heritability based on twin studies. ❇️Gene-environment interactions (G × E) may further elucidate missing heritability. ❇️Despite earlier limited findings, this large-scale GWAS analysis of nearly 70,000 participants has identified significant interactions between dietary factors and genetic variants, offering new insights into CRC risk. ❗️Key Findings: ❇️Dietary Fibre and the #SLC26A3 Gene: 👉🏻SNP rs4730274, located near the SLC26A3 gene, showed the strongest interaction with dietary fibre intake in relation to CRC risk. ❇️SLC26A3 plays a pivotal role in regulating chloride absorption and secretion in the digestive tract, and its downregulation has been linked to CRC. ❇️This SNP has previously been associated with inflammatory bowel disease (#IBD), which increases CRC risk. ❇️Dietary fibre's protective role may be stronger in individuals with genetic susceptibility to IBD, supporting the notion that fibre consumption promotes SLC26A3 expression and beneficial gut bacteria, which produce short-chain fatty acids (#SCFAs) with anti-inflammatory properties. 💠Fruits and the #NEGR1 Gene: 🔹An interaction was observed between fruit intake and SNP rs1620977 in the NEGR1 gene. 🔹This variant has been linked to BMI and fruit consumption in previous GWAS, although our sensitivity analyses ruled out BMI as a confounder. 🔹NEGR1 plays a role in neuronal development and intercellular adhesion, potentially influencing both dietary choices and cancer progression. 🔹While further investigation is needed, its downregulation in various cancers, including CRC, suggests a tumor suppressive role. 📍Mechanistic Insights: ♦️The SLC26A3 gene appears to function as a tumor suppressor, with potential mechanisms involving reduced intracellular alkalinization, which limits cell proliferation. ♦️Its role in SCFA absorption further strengthens the connection between high-fibre diets and reduced CRC risk. ♦️The NEGR1 gene may contribute to CRC through its role in cellular adhesion and possibly dietary preferences, hinting at a complex interplay between diet, genetics, and cancer. ❇️This large G × E study used advanced methods like the 3-DF joint test and data harmonization to improve detection of gene-diet interactions and reliability of results. 📌To sum up, their findings reveal key gene-diet interactions, particularly between fiber, fruits, and variants in SLC26A3 and NEGR1, offering new insights into CRC risk and future research directions. 🖇Plz read more on the topic here👉🏻: https://lnkd.in/dw7d39-w

The genetics of neurodegenerative diseases is the genetics of age-related damage clearance failure This commentary published in Mol Psychiatry: https://lnkd.in/e3S6PDKB, reviews genome-wide association studies (GWAS) for Alzheimer's, Parkinson's, and tauopathies, concluding that risk loci in each disease primarily involve the clearance of deposited proteins: microglial Aβ removal in Alzheimer's, lysosomal synuclein clearance in Parkinson's, and ubiquitin-proteasome tau removal in tauopathies. The authors argue that most GWAS loci reflect failures in removing age-related damage rather than being strictly pathogenic. They discuss these findings in the context of co-pathologies in elderly individuals, polygenic risk score analysis for disease prediction across ages, and future analytical approaches needed for adequately sized case-control studies in white populations. #genetics #genomics #precisionmedicine #genomicmedicine #brain #neurology #neurodegeneration #neuroscience #neuroinflammation #alzheimer #parkinson #taupathology #aging #longevity #cognition #disease #metabolism #biomarkers #gwas #therapeutics #lysosomalstoragedisorder #lysosome #biotechnology #therapeutics #innovation #research #science #sciencecommunication