Stephanie Portelli

Cancer progression is driven by epigenetic reprogramming, where promoter hypermethylation of tumour-suppressor genes and global hypomethylation reshape gene regulation and cellular phenotypes, promoting oncogenesis and disease advancement. We previously introduced the Methylscape, a cancer-specific DNA methylation landscape characterized by clustered promoter hypermethylation and gene body hypomethylation that enhances DNA’s physical affinity for gold surfaces. Here, we demonstrate that Methylscape can be leveraged to monitor cancer progression. In a TGF-β-induced breast cancer epithelial-mesenchymal transition (EMT) model, we observe increased Methylscape enrichment of mesenchymal-state DNA, indicating that this method can sensitively detect subtle epigenetic remodelling linked to tumour progression. Using a gold-based DNA desorption enrichment strategy coupled with methylation sequencing and qPCR, we show that hypermethylated regions are preferentially enriched on gold surface. Finally, we developed a low-cost, disposable screen-printed electrode platform for stage-specific breast cancer monitoring. Together, these findings establish Methylscape as a promising biophysical biomarker for non-invasive, real-time monitoring of cancer progression, advancing its potential for clinical translation.

Missense mutations in TERT , the gene encoding the human telomerase catalytic subunit hTERT, are associated with Telomere Biology Disorders (TBDs). Experimentally elucidating the effects of all possible missense variants would be time-consuming and technically challenging. Moreover, current computational predictors are not hTERT-specific and primarily rely on sequence information, failing to capture the complex biological and structural context of the telomerase enzyme. In this work, we developed three machine learning models integrating both sequence- and structure-based features to account for the biological mechanisms of hTERT. Compared to state-of-the-art methods, our best-performing models achieved a higher Matthew’s Correlation Coefficient of 0.88 on ClinVar and gnomAD curated variants and demonstrated robust sensitivity (0.75) on a dataset curated according to guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Feature interpretation highlighted hTERT residue conservation and changes in hydrophobic and weak polar interactions as critical determinants of pathogenicity. Finally, in silico saturation mutagenesis was performed to present a mutational landscape of TERT , available in a user-friendly web server, CharacTERT, which could offer valuable insights into the molecular mechanisms driving TBDs, aid in early diagnosis, as well as guide personalized treatment strategies. CharacTERT is freely available at https://biosig.lab.uq.edu.au/charactert/ .

Receptor tyrosine kinases (RTKs) are key regulators of cellular functions, such as differentiation, migration and proliferation. Dysregulated RTK activity contributes to various diseases, including neurological disorders and cancer, for which small molecule inhibitors are often used as therapeutic drugs to manage conditions involving constitutively active RTKs. Preclinical development of these inhibitors, unfortunately, often faces uncertainty, high costs, and time constraints, primarily due to the extensive in vitro and in vivo testing of numerous chemical compounds. To tackle the challenge of shortlisting potential drug candidates, we developed kinCSM-RTK, which incorporates two machine learning models to predict small molecule pK_i and pIC₅₀ values, estimating the inhibitory potency of small molecules against RTKs. Our proposed machine learning models have shown a robust and generalizable predictive performance. Specifically, the models achieved Pearson's correlation coefficients of 0.773 and 0.762 for pK_i prediction, under 10-fold cross-validation (CV) and an independent blind test, respectively. Similarly, for pIC₅₀ prediction, the models yielded coefficients of 0.773 in 10-fold CV and 0.768 in the independent blind test. In addition, we aimed to understand these results through post hoc explanation analyses. In our explanation analyses, we observed that the proximity and quantity of aromatic interactions have correlated with stronger RTK inhibition, providing important insights for drug design targeting RTKs. Accordingly, we made our models publicly accessible on a user-friendly web server at https://biosig.lab.uq.edu.au/kincsm_rtk/.

The past few years have revolutionised the utility of protein structural information in assessing disease risk. Such efforts have been pioneered by the DeepMind and Meta teams, who initially developed accurate protein prediction tools AlphaFold and ESMFold, respectively. More recently, both have been readapted to create AlphaMissene and ESM1b, through which, the pathogenicity potential of all possible missense mutations within the human proteome has been predicted. Similarly, VESPA was created as an alignment-agnostic predictor, which infers conservation from protein-language model embeddings to assign an effect for each variant. To facilitate their utility, we present a unified dashboard, MissenseViewer, which permits direct predictor comparison, and visualisation within the protein 3-dimensional structural context. Our interactive resource is freely available at: https://biosig.lab.uq.edu.au/missenseviewer/ .

Membrane contact sites between organelles are critical for the transfer of biomolecules. Lipid droplets store fatty acids and form contacts with mitochondria, which regulate fatty acid oxidation and adenosine triphosphate production. Protein compartmentalization at lipid droplet-mitochondria contact sites and their effects on biological processes are poorly described. Using proximity-dependent biotinylation methods, we identify 71 proteins at lipid droplet-mitochondria contact sites, including a multimeric complex containing extended synaptotagmin (ESYT) 1, ESYT2, and VAMP Associated Protein B and C (VAPB). High resolution imaging confirms localization of this complex at the interface of lipid droplet-mitochondria-endoplasmic reticulum where it likely transfers fatty acids to enable β-oxidation. Deletion of ESYT1, ESYT2 or VAPB limits lipid droplet-derived fatty acid oxidation, resulting in depletion of tricarboxylic acid cycle metabolites, remodeling of the cellular lipidome, and induction of lipotoxic stress. These findings were recapitulated in Esyt1 and Esyt2 deficient mice. Our study uncovers a fundamental mechanism that is required for lipid droplet-derived fatty acid oxidation and cellular lipid homeostasis, with implications for metabolic diseases and survival.

Multidrug-resistant bacterial pathogens like vancomycin-resistant Enterococcus faecium (VREfm) are a critical threat to human health¹. Daptomycin is a last-resort antibiotic for VREfm infections with a novel mode of action², but for which resistance has been widely reported but is unexplained. Here we show that rifaximin, an unrelated antibiotic used prophylactically to prevent hepatic encephalopathy in patients with liver disease³, causes cross-resistance to daptomycin in VREfm. Amino acid changes arising within the bacterial RNA polymerase in response to rifaximin exposure cause upregulation of a previously uncharacterized operon (prdRAB) that leads to cell membrane remodelling and cross-resistance to daptomycin through reduced binding of the antibiotic. VREfm with these mutations are spread globally, making this a major mechanism of resistance. Rifaximin has been considered ‘low risk’ for the development of antibiotic resistance. Our study shows that this assumption is flawed and that widespread rifaximin use, particularly in patients with liver cirrhosis, may be compromising the clinical use of daptomycin, a major last-resort intervention for multidrug-resistant pathogens. These findings demonstrate how unanticipated antibiotic cross-resistance can undermine global strategies designed to preserve the clinical use of critical antibiotics.

Nicotinamide adenine dinucleotide (NAD) is essential for embryonic development. To date, biallelic loss-of-function variants in 3 genes encoding nonredundant enzymes of the NAD de novo synthesis pathway — KYNU, HAAO, and NADSYN1 — have been identified in humans with congenital malformations defined as congenital NAD deficiency disorder (CNDD). Here, we identified 13 further individuals with biallelic NADSYN1 variants predicted to be damaging, and phenotypes ranging from multiple severe malformations to the complete absence of malformation. Enzymatic assessment of variant deleteriousness in vitro revealed protein domain–specific perturbation, complemented by protein structure modeling in silico. We reproduced NADSYN1-dependent CNDD in mice and assessed various maternal NAD precursor supplementation strategies to prevent adverse pregnancy outcomes. While for Nadsyn1^+/– mothers, any B₃ vitamer was suitable to raise NAD, preventing embryo loss and malformation, Nadsyn1^–/– mothers required supplementation with amidated NAD precursors (nicotinamide or nicotinamide mononucleotide) bypassing their metabolic block. The circulatory NAD metabolome in mice and humans before and after NAD precursor supplementation revealed a consistent metabolic signature with utility for patient identification. Our data collectively improve clinical diagnostics of NADSYN1-dependent CNDD, provide guidance for the therapeutic prevention of CNDD, and suggest an ongoing need to maintain NAD levels via amidated NAD precursor supplementation after birth.

Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.

Background: Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints. Methods: To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation. Results: Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew’s Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches. Conclusion: This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline. Key words Computational biology Drug development Machine learning In silico Pharmacokinetics Toxicity