Biology subjects

Cantini, G.

Publications and source records attributed to Cantini, G..

3 recordsLinked to original sources

bioRxiv2026

miRXplain: explainable isomiR-aware microRNA target prediction using CLIP-L experiments and hybrid attention transformers

MicroRNAs (miRNAs) are ~22 nt long noncoding RNAs that repress genes by base-pairing with complementary sequences in target mRNAs. Variants known as isomiRs arise from alternative hairpin processing and often shift the canonical seed region, thereby altering their target repertoire. Yet, the mRNA features that enable differential miRNA and isomiR target selection remain poorly understood, largely due to limited high-throughput assays that capture the exact miRNA bound to each target. Existing deep learning methods for miRNA-target prediction have neither leveraged such datasets nor investigated isomiR-specific interactions. To fill this gap, we developed miRXplain, an isomiR-aware transformer that predicts miRNA-mRNA interactions using miRNA and target sequences derived from CLIP-L data, directly linking precise miRNA variants to their target sites. These data however revealed a 5'-end nucleotide bias in target sites, which we corrected to generate high-quality, miRNA-target pairs for training, preserving isomiR-specific signal. miRXplain outperformed all benchmarked models, surpassing TEC-miTarget, in auROC and auPRC with 15x fewer parameters. Attention maps highlighted distinct sequence determinants for canonical versus isomiR interactions, and in silico saturation mutagenesis highlighted the importance of seed and 3' supplementary regions. miRXplain effectively prioritizes pathogenic single-nucleotide variants affecting mRNA-miRNA interactions and reveals isomiR targeting principles that deepen our understanding of miRNA biology.

bioinformatics↗

bioRxiv2023

A Systematic Benchmark of Machine Learning Methods for Protein-RNA Interaction Prediction

AO_SCPLOWBSTRACTC_SCPLOWRNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.

bioinformatics↗

bioRxiv2018

Altered function of the glutamate aspartate transporter GLAST in glioblastoma

In glioma patients, high levels of glutamate can cause brain edema and seizures. GLAST, a glutamate-aspartate transporter expressed by astrocytes with a role in glutamate uptake, is highly expressed on the plasma membrane of glioblastoma (GBM) cells, and its expression significantly correlates with shortened patient survival. Here, it was demonstrated that inhibition of GLAST expression limited the progression and invasion of GBM xenografts. Magnetic resonance spectroscopy was used to measure glutamate in GLAST-expressing gliomas showing that these tumors exhibit increased glutamate concentration. Despite their GLAST expression, GBM stem-like cells (GSCs) released rather than took up glutamate due to their lack of Na+/K+-ATPase. Overexpression of Na+/K+-ATPase in these cells restored glutamate uptake and induced apoptosis. The therapeutic relevance of targeting GLAST in gliomas was assessed using the inhibitor UCPH-101. In glioma-bearing mice, a single intratumoral injection of UCPH-101 significantly increased survival by decreasing GLAST expression and inducing apoptosis. Thus, GLAST has a novel role in GBM that appears to have crucial relevance in glutamate trafficking and may thus be a new therapeutic target.

cancer biology↗