Salta al contenuto principale

Laboratory of Computational Network Biology and Large-Scale Transcriptomic Integration

Aurora Savino P.I.

AS

Biosketch

Tenure Track Researcher – 10/2025 – present
Molecular Biotechnology Department, University of Turin (Italy)

Group Members

Group Members

Dalia Magdy Ibrahim Ibrahim Shaban, Master Student

Ana Coroi, Master Student

Research Activity

Fields of interest

The Laboratory of Computational Network Biology and Transcriptomic Integration investigates how biological systems are organized through interactions — between genes, between cell types, and between tissues.

Although the laboratory operates in two biologically distinct domains, cancer biology and neuroscience, the unifying core of the research is methodological. We develop computational frameworks to model biological states as structured interaction systems using large-scale transcriptomic data.

Our work combines bulk RNA sequencing, single-cell transcriptomics, and spatial transcriptomics with network modeling and large-scale data integration. A defining feature of the laboratory is the systematic construction of harmonized transcriptomic databases that enable reproducible cross-study and cross-scale analysis. 

Cancer Biology: Regulatory Architecture and Tumor Ecosystems

Cancer progression reflects changes in regulatory organization and cellular interactions rather than isolated molecular alterations. Our objective is to characterize tumors as coordinated interaction systems operating across multiple biological scales. 

We investigate:

  • Reorganization of gene regulatory networks across tumor subtypes
  • Emergence of regulatory hubs under selective pressure
  • Interactions between tumor cells and stromal compartments
  • Reproducibility of network-level patterns across large patient cohorts

Within this framework, we have developed crossWGCNA, a co-expression-based computational method to identify inter-tissue molecular interactions, which we have applied to identify signals exchanged between tumor cells and microenvironment in breast cancer.

A major component of this work is the construction of large, harmonized cancer transcriptomic compendia integrating thousands of publicly available samples. These databases include standardized clinical metadata and allow systematic cross-dataset comparison.

Neuroscience: Brain Regions Coordination and Psychedelic-Induced Plasticity

In neuroscience, the laboratory investigates how transcriptional states are coordinated across brain regions, cortical layers, and cell classes, and how this coordination changes in disease and under pharmacological perturbation.

We examine:

  • Cross-brain-region transcriptional coordination
  • Cell-cell communication patterns inferred from ligand-receptor and co-expression analyses
  • Disease-associated shifts in inter-regional molecular coupling

Psychedelic compounds are investigated as controlled perturbations that provide a unique experimental window into how the brain generates and maintain coherent representations of reality. By transiently altering system-level coordination, these compounds allow us to study the organizational principles underlying human perceptual and cognitive functioning.

To support this work, we have generated multi-layered datasets comprising bulk RNA sequencing, single-cell, and spatial transcriptomic profiles across multiple mouse brain regions following treatment with lysergic acid diethylamide (LSD).

Computational Drug Repurposing

A major translational extension of our integrative framework is computational drug repurposing. Using large transcriptomic compendia, we develop strategies to:

  • Compare disease-associated transcriptional states with perturbation signatures
  • Identify compounds predicted to reverse or modulate specific biological programs
  • Map drug signatures onto heterogeneous subgroups evaluating context-dependent repurposing hypotheses

Drug repurposing is approached quantitatively, leveraging structured databases and cross-dataset integration.

Large-Scale Data Integration and Database Construction

A defining strength of the laboratory is its emphasis on data infrastructure.

Public transcriptomic repositories contain vast information but are often fragmented and inconsistently annotated. We develop computational pipelines for metadata harmonization and structured database construction.

These infrastructures transform dispersed datasets into coherent biological reference spaces. They enable systematic meta-analysis, the development of robust predictors and allow new transcriptomes to be quantitatively positioned and interpreted within broader interaction landscapes.

Database construction is therefore not auxiliary to research — it is a core scientific contribution.

Over the next years, the laboratory will consolidate and expand its research along two interconnected axes: quantitative modeling of system reorganization under perturbation, and large-scale transcriptomic infrastructure development for disease heterogeneity analysis.

A primary objective in cancer biology is to characterize network rewiring associated with drug resistance. We will extend our current modeling frameworks to systematically quantify how regulatory architecture reorganizes during the emergence of therapeutic resistance.

This effort will be supported by the expansion of what will become the largest harmonized transcriptomic resource of breast cancer. By integrating thousands of patient-derived transcriptomes with functional data from cancer cell lines we will develop computational strategies to infer drug response from tumor-level transcriptional organization.

In neuroscience, a major focus will be the impact of LSD on brain transcriptional networks. By integrating bulk, single-cell, and spatial transcriptomic datasets, we aim to characterize how psychedelic exposure reshapes brain networks at the molecular level. These analyses will be embedded within a broader framework assessing whether LSD-induced transcriptional states oppose or modulate transcriptional signatures associated with psychiatric diseases and neurodegenerative conditions, indicating potential repurposing avenues. 

  1. Savino A.#, Iannuzzi R.M., Avalle L., Lobascio A., Iorio F., Provero P., Poli V.#.  Cross-tissue gene expression interactions from bulk, single cell and spatial transcriptomics with crossWGCNA. BMC Genomics, 26, 583 (2025). https://doi.org/10.1186/s12864-025-11747-y
  2. Ponzone L., Audrito V., Landi C., Moiso E., Levra Levron C., Ferrua S., Savino A., Vitale N., Gasparrini M., Avalle L., Lorenza V., Shaba E., Tassone B., Saoncella S., Orso F., Viavattene D., Marina E., Fiorilla I., Burrone G., Abili Y., Altruda F., Bini L., Deaglio S., Defilippi P., Menga A., Poli V., Porporato P. E., Provero P., Raffaelli N., Riganti C., Taverna D., Cavallo F., Calautti E. (2024). RICTOR/mTORC2 downregulation in BRAFV600E melanoma cells promotes resistance to BRAF/MEK inhibition. Molecular Cancer, vol. 23, ISSN: 1476-4598, doi: 10.1186/s12943-024-02010-1
  3. Lanciano T.*, Savino A.*, Porcu F., Cittaro D., Bonchi F., Provero P. (2023). Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data. Gigascience, vol. 12, p. 1-10, ISSN: 2047-217X, doi: 10.1093/gigascience/giad010
  4. Avalle L.*, Raggi L.*, Monteleone E.*, Savino A.*, Viavattene D., Statello L., Camperi A., Stabile S. A., Salemme V., De Marzo N., Marino F., Guglielmi C., Lobascio A., Zanini C., Forni M., Incarnato D., Defilippi P., Oliviero S., Poli V. (2022). STAT3 induces breast cancer growth via ANGPTL4, MMP13 and STC1 secretion by cancer associated fibroblasts. Oncogene, vol. 41, p. 1456-1467, ISSN: 0950-9232, doi: 10.1038/s41388-021-02172-y
  5. Savino A.#, De Marzo N., Provero P., Poli V#. (2021). Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis. Cancers, vol. 13, ISSN: 2072-6694, doi: https://doi.org/10.3390/cancers13133371
  6. Savino A.#, Nichols C. D.# (2021). Lysergic acid diethylamide induces increased signalling entropy in rats’ prefrontal cortex. Journal of Neurochemistry, p. 1, ISSN: 0022-3042, doi: 10.1111/jnc.15534
  7. Savino A.#, Provero P., Poli V.# (2020). Differential co-expression analyses allow the identification of critical signalling pathways altered during tumour transformation and progression. International Journal of Molecular Sciences, vol. 21, p. 1-23, ISSN: 1661-6596, doi: 10.3390/ijms21249461
  8. Messmer T., von Meyenn F., Savino A., Santos F., Mohammed H., Lun A. T. L., Marioni J. C., Reik W. (2019). Transcriptional Heterogeneity in Naive and Primed Human Pluripotent Stem Cells at Single-Cell Resolution. Cell Reports, vol. 26, p. 815-824, ISSN: 2211-1247, doi: 10.1016/j.celrep.2018.12.099
  9. Mohammed H.*, Hernando-Herraez I.*, Savino A.*, Scialdone A., Macaulay I., Mulas C., Chandra T., Voet T., Dean W., Nichols J., Marioni J. C., Reik W. (2017). Single-Cell Landscape of Transcriptional Heterogeneity and Cell Fate Decisions during Mouse Early Gastrulation. Cell Reports, vol. 20, p. 1215- 1228, ISSN: 2211-1247, doi: 10.1016/j.celrep.2017.07.009
  10. Terzibasi Tozzini E.*, Savino A.*, Ripa R., Battistoni G., Baumgart M., Cellerino A. (2014). Regulation of microRNA expression in the neuronal stem cell niches during aging of the short-lived annual fish Nothobranchius furzeri. Frontiers in Cellular Neuroscience, ISSN: 1662-5102, doi: https://doi.org/10.3389/fncel.2014.00051

*Co-first author; #Co-corresponding author

Ultimo aggiornamento: