Publications by Elena Pianfetti

Shigellosis is an acute small intestine infection caused by different species of Shigella. Worldwide, the emergence of antibiotic-resistant strains aggravates the impact of Shigella infections. In this context, human monoclonal antibodies (mAbs) offer an alternative to traditional antimicrobials. However, identifying a potent candidate mAb requires intense and meticulous efforts. Here, we show the potential of Deep Learning to screen mAbs rapidly. We measured the phagocytosis-promoting activity of mAbs by analyzing images collected with a high-throughput and high-content confocal fluorescence microscope. We acquired images of S. sonnei and S. flexneri infecting THP-1-derived macrophages and evaluated the effect of different mAbs and of a wide selection of Deep Learning tools. We found that our model can generalize on strains and mAbs not encountered in training. Importantly, our approach enables the screening and characterization of multiple anti-Shigella mAbs at the same time, facilitating the identification of potent antibacterial candidates. Our code is available on the GitHub repository vOPA_Shigella.

Gene fusions are common in high-grade serous ovarian cancer (HGSC). Such genetic lesions may promote tumorigenesis, but the pathogenic mechanisms are currently poorly understood. Here, we investigated the role of a PIK3R1-CCDC178 fusion identified from a patient with advanced HGSC. We show that the fusion induces HGSC cell migration by regulating ERK1/2 and increases resistance to platinum treatment. Platinum resistance was associated with rod and ring-like cellular structure formation. These structures contained, in addition to the fusion protein, CIN85, a key regulator of PI3K-AKT-mTOR signaling. Our data suggest that the fusion-driven structure formation induces a previously unrecognized cell survival and resistance mechanism, which depends on ERK1/2-activation.

Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs’ half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels.