Biology subjects

Giorgi, F. M.

Publications and source records attributed to Giorgi, F. M..

16 recordsLinked to original sources

bioRxiv2025

Gene network inference and master regulator analysis identifies the estrogen-related receptor gamma (ERRγ) as a therapeutic target for alcohol use disorder (AUD).

Differential gene expression is often inadequate to predict the activity of transcription factors and their contribution to the phenotypes associated with specific gene expression states. Here we used a systems biology approach based on gene network inference and master regulator analysis (MRA) to identify candidate drivers of the gene network dysregulations in the prefrontal cortex (PFC) of human subjects with a history of alcohol dependence. The estrogen-related receptor gamma (ERR{gamma}) gene ESRRG, an orphan nuclear receptor protein that acts as a transcription activator, emerged as a high-ranking Master Regulator (MR) based on the expression of its targets and was selected for functional validation due to its translational and druggability potential. The ERR{gamma} agonist, GSK4716, reduced alcohol drinking in the mouse binge drinking paradigm of drinking in the dark (DID) and in both non-dependent mice as well as in mice made dependent by chronic intermittent vapor exposure (CIE). GSK4716 also prevented alcohol-conditioned place preference without affecting saccharin intake or mouse locomotion. Similarly, in rats, GSK4716 reduced operant oral alcohol self-administration in non-dependent and dependent (by CIE) rats under fixed and progressive ratio schedules of reinforcement. Overall, these results support the efficacy of transcriptome-wide gene regulatory network approaches for the identification of key druggable regulators of long-term transcriptional adaptations that sustain the molecular and behavioral pathology of alcohol dependence and identify ERR{gamma} as a regulator of excessive alcohol drinking and seeking, and a therapeutic target for AUD.

neuroscience↗

bioRxiv2023

Direct and indirect responses of the Arabidopsis transcriptome to an induced increase in trehalose 6-phosphate

Trehalose 6-phosphate (Tre6P) is an essential signal metabolite that reports and regulates the level of sucrose, linking growth and development to the metabolic status. We hypothesized that Tre6P plays a role in mediating the regulation of gene expression by sucrose. To test this, we performed transcriptomic profiling on Arabidopsis plants that expressed a bacterial trehalose-6-phosphate synthase (TPS) under the control of an ethanol-inducible promoter. Induction led to a 4-fold rise in Tre6P levels, a concomitant decrease in sucrose, and significant changes of over 13,000 transcripts and two-fold or larger changes of over 5000 transcripts. Comparison with nine published responses to sugar availability allowed some of these changes to be linked to the rise in Tre6P, while others were probably due to lower sucrose or other indirect effects. Changes linked to Tre6P included repression of photosynthesis and induction of many growth-related processes including ribosome biogenesis. About 500 starvation-related genes are known to be induced by SUCROSE-NON-FERMENTING-1-RELATED KINASE 1 (SnRK1). They were largely repressed by Tre6P in a manner consistent with Tre6P acting to inhibit SnRK1. SnRK1 also represses many genes that are involved in biosynthesis and growth. These responded to Tre6P in a more complex manner, pointing to Tre6P also interacting with further C-signaling pathways. In addition, elevated Tre6P modified expression of genes encoding regulatory subunits of the SnRK1 complex and TPS class II and FLZ proteins that are thought to modulate SnRK1 function, and genes involved in the circadian clock and in TOR, light, abscisic acid and other hormone signaling. One sentence summaryAn induced increase in trehalose 6-phosphate levels has direct effects on gene expression via inhibition of SUCROSE-NON-FERMENTING-1-RELATED KINASE 1 and interactions with light, circadian clock and phytohormone signaling, and widespread indirect effects on gene expression from reciprocal changes in sucrose levels.

plant biology↗

bioRxiv2022

Copper chelation inhibits TGF-β pathways and suppresses epithelial-mesenchymal transition in cancer

Copper is a trace element essential to cellular function with elevated levels implicated in cancer progression. Clinical trials using copper chelators are associated with improved patient survival, however, the molecular mechanisms by which copper depletion inhibits tumor progression are poorly understood. This remains a major hurdle to the clinical translation of copper chelators. Epithelial-mesenchymal transition (EMT) is often exploited by malignant cells to promote growth and metastasis. Transforming growth factor (TGF)-{beta} is a master regulator of EMT and facilitates cancer progression through changes in the tumor and its microenvironment. Herein, we report that a reduction of copper with the chelating agent tetraethylenepentamine (TEPA) inhibited EMT in vitro in three diverse cancer cell types; human triple-negative breast cancer (TNBC), neuroblastoma (NB), and diffuse intrinsic pontine glioma (DIPG) cell lines. Single-molecule imaging demonstrated EMT markers including Vimentin, {beta}-catenin, ZEB1, and p-SMAD2 had increased expression with copper treatment and this pro-mesenchymal shift was rescued by the addition of TEPA. Moreover, SNAI1, ZEB1, and p-SMAD2 demonstrated increased accumulation in the cytoplasm after treating with TEPA. Transcriptomic analyses revealed a significant downregulation of the EMT pathway, including canonical (TGF-{beta}/SMAD2&3) and non-canonical (TGF-{beta}/PI3K/AKT and TGF-{beta}/RAS/RAF/MEK/ERK) TGF signaling pathways. Matrix metalloproteinases MMP-9 and MMP-14 proteins which activate latent TGF-{beta} complexes were also downregulated by TEPA treatment. These molecular changes are consistent with reduced plasma levels of TGF-{beta} we observed in cancer models treated with TEPA. Importantly, copper chelation reduced metastasis to the lung in a TNBC orthotopic syngeneic mouse model. Our studies suggest copper chelation therapy can be used to inhibit EMT-induced metastasis by targeting TGF-{beta} signalling. Because on-target anti-TGF-{beta} therapies are failing in the clinic, copper chelation presents itself as a potential therapy for targeting TGF-{beta} in cancer.

cancer biology↗

bioRxiv2022

Genomic characterization of the recent monkeypox outbreak

The Monkeypox virus is the etiological cause of a recent multi-country outbreak, with nearly one hundred distinct cases detected outside the endemic areas of Africa in May 2022. In this article, we analyze the sequences of two full genomes of Monkeypox virus from Portugal and Belgium, and compare them with all available Monkeypox sequences, annotated by year and geographic origin, as well as related Cowpox and Variola (smallpox) virus sequences. Our results show that the recent outbreak is most likely originating from the West African clade of Monkeypox, with >99% sequence identity with sequences derived from historical and recent cases, dating from 1971 to 2017. We analyze specific mutations occurring in viral proteins A42R (for which a crystal structure is available) and H3L (an important epitope in host immune recognition), highlighting specific amino acids varying between the current outbreak, previous Monkeypox and Cowpox sequences and the historical Variola virus. Genome-wide sequence analysis of the recent outbreak and other monkeypox/cowpox/variola viruses shows a very high conservation, with 97.9% (protein-based) and 97.8% (nucleotide-based) sequence identity.

bioinformatics↗

bioRxiv2021

Preliminary report on SARS-CoV-2 Spike mutation T478K

Several SARS-CoV-2 variants have emerged, posing a renewed threat to COVID-19 containment and to vaccine and drug efficacy. In this study, we analyzed more than 1,000,000 SARS-CoV-2 genomic sequences deposited up to April 27, 2021 on the GISAID public repository, and identified a novel T478K mutation located on the SARS-CoV-2 Spike protein. The mutation is structurally located in the region of interaction with human receptor ACE2 and was detected in 11,435 distinct cases. We show that T478K has appeared and risen in frequency since January 2021, predominantly in Mexico and USA, but we could also detect it in several European countries.

evolutionary biology↗

bioRxiv2020

Structural Genetics of circulating variants affecting the SARS CoV-2 Spike / human ACE2 complex

SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V. Significance StatementWe developed a method to identify key amino acids responsible for the initial interaction between SARS-CoV-2 (the COVID-19 virus) and human cells, through the analysis of Spike/ACE2 complexes. We further identified which of these amino acids show variants in the viral and human populations. Our results will facilitate scientists and clinicians alike in identifying the possible role of present and future Spike and ACE2 sequence variants in cell entry and general susceptibility to infection.

bioinformatics↗

bioRxiv2020

coronapp: a Web Application to Annotate and Monitor SARS-CoV-2 Mutations

The avalanche of genomic data generated from the SARS-CoV-2 virus requires the development of tools to detect and monitor its mutations across the world. Here, we present a webtool, coronapp, dedicated to easily processing user-provided SARS-CoV-2 genomic sequences and visualizing current worldwide status of SARS-CoV-2 mutations. The webtool allows users to highlight mutations and categorize them by frequency, country, genomic location and effect on protein sequences, and to monitor their presence in the population over time. The tool is available at http://giorgilab.unibo.it/coronapp/ for the worldwide dataset and at http://giorgilab.unibo.it/coronannotator/ for the annotation of user-provided sequences. The full code is freely shared at https://github.com/federicogiorgi/giorgilab/tree/master/coronapp Data Availability StatementThe data that support the findings of this study derive from the GISAID consortium and are openly available in Github, in Rdata format for the R environment, in files results.rda and metadata.rda, at the following link: https://github.com/federicogiorgi/giorgilab/tree/master/coronapp/data

bioinformatics↗

bioRxiv2020

Master Regulator Analysis of the SARS-CoV-2/Human interactome

the recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.

bioinformatics↗

bioRxiv2020

Differential submergence tolerance between juvenile and adult Arabidopsis plants involves the ANAC017 transcription factor

Plants need to attune stress responses to the ongoing developmental programs to maximize their efficacy. For instance, successful submergence adaptation is often associated to a delicate poise between saving resources and their expenditure to activate measures that allow stress avoidance or attenuation. We observed a significant decrease in submergence tolerance associated with aging in Arabidopsis thaliana, with a critical step between two and three weeks of post-germination development. This sensitization to flooding was concomitant with the transition from juvenility to adulthood. Transcriptomic analyses indicated that a group of genes related to ABA and oxidative stress response was more expressed in juvenile plants than in adult ones. These genes are induced by endomembrane tethered ANAC factors that were in turn activated by submergence-associated oxidative stress. A combination of molecular, biochemical and genetic analyses showed that these genes are located in genomic regions that move towards a heterochromatic state with adulthood, as marked by lysine 4 dimethylation of histone H3. We concluded that, while the mechanism of flooding stress perception and signal transduction were unaltered between juvenile and adult phases, the sensitivity that these mechanisms set into action is integrated, via epigenetic regulation, into the developmental programme of the plant.

plant biology↗

bioRxiv2020

corto: a lightweight R package for Gene NetworkInference and Master Regulator Analysis

MotivationGene Network Inference and Master Regulator Analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses, but most require a computer cluster or large amounts of RAM to be executed. ResultsWe developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for Copy Number Variations (CNVs) and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets. AvailabilityCross-platform and multi-threaded R package on CRAN (stable version) https://cran.rproject.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto. Contactfederico.giorgi@unibo.it

bioinformatics↗

bioRxiv2020

Phylogenomic analysis of the 2019-nCoV coronavirus

There is rising global concern for the recently emerged novel Coronavirus (2019-nCov). Full genomic sequences have been released by the worldwide scientific community in the last few weeks in order to understand the evolutionary origin and molecular characteristics of this virus. Taking advantage of all the genomic information currently available, we constructed a phylogenetic tree including also representatives of other coronaviridae, such as Bat coronavirus (BCoV) and SARS. We confirm high sequence similarity (>99%) between all sequenced 2019-nCoVs genomes available, with the closest BCoV sequence sharing 96.2% sequence identity, confirming the notion of a zoonotic origin of 2019-nCoV. Despite the low heterogeneity of the 2019-nCoV genomes, we could identify at least two hyper-variable genomic hotspots, one of which is responsible for a Serine/Leucine variation in the viral ORF8-encoded protein. Finally, we perform a full proteomic comparison with other coronaviridae, identifying key aminoacidic differences to be considered for antiviral strategies deriving from previous anti-coronavirus approaches.

bioinformatics↗

bioRxiv2019

ZMIZ1 - A novel Estrogen Receptor co-activator that enhances the growth of ER+ breast cancer

The Estrogen Receptor (ER) drives 75% of breast cancers. On activation, the ER recruits co-factors to form a transcriptionally active complex. These co-factors can modulate tumour growth, and understanding their roles can help to identify new therapeutic targets. Here, we present the discovery of an ER-ZMIZ1 interaction by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating that targeting ZMIZ1 results in the reduction of ER transcriptional activity at estrogen response elements and a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 hours. GSEA analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell-cycle genes. Integration of ENCODE data with our RNA-seq results identified ER and ZMIZ1 binding at the promoter of E2F2. We therefore propose that ER and ZMIZ1 co-regulate an important subset of cell cycle genes via a novel ER-ZMIZ1-E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA, METABRIC, and the proteins are co-localised within the nuclei of tumours cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER-ZMIZ1 interaction ER+ patient tumours, supporting potential clinical relevance.

cancer biology↗

bioRxiv2019

A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionally-distinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highly-specific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

bioinformatics↗

bioRxiv2018

Pan-Cancer modelling of genomic alterations through gene expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single cell sequencing. Author SummaryIn this article we show that transcript abundance can be used to predict the presence or absence of the majority of genomic alterations present in human cancer. We also show how these predictions can be improved by aggregating genes into small networks to counteract the effects of transcript measurement noise.

systems biology↗

bioRxiv2018

Systematic Elucidation and Validation of OncoProtein-Centric Molecular Interaction Maps

The largely incomplete and tissue-independent nature of cancer pathways represents a key limitation to the ability to elucidate mechanistic determinants of cancer phenotypes and to predict adaptive response to targeted therapy. To address these challenges, we propose replacing canonical cancer pathways with a more accurate, comprehensive, and context-specific architecture - dubbed a Protein-Centric molecular interaction Map (PC-Map) - representing modulators, effectors, and cognate binding-partners of any oncoprotein of interest. To reconstruct these complex molecular architectures de novo, we introduce a novel OncoSig algorithm. Validation of a lung adenocarcinoma specific (LUAD) KRAS-centric PC-Map recapitulated known KRAS biology and, more critically, identified a novel repertoire of proteins eliciting synthetic lethality in KRASG12D LUAD organoid cultures. Showing the generalizable nature of the algorithm, we elucidated PC-Maps for ten recurrently mutated oncoproteins, including KRAS, in distinct tumor contexts. This revealed a highly context-specific nature of cancers regulatory and signaling architectures to an unprecedented degree of resolution.

systems biology↗

bioRxiv2018

Network Dynamics of ER-α activation reveals reprogramming of GRHL2

VULCAN infers regulatory interactions of transcription factors by overlaying networks generated from tumor expression data onto ChIP-seq data. VULCAN analysis of estrogen receptor (ER) activation in breast cancer highlighted key components of the ER complex alongside a novel interaction with GRHL2. We demonstrate that GRHL2 is recruited to a subset of ER binding sites and regulates the transcriptional output of ER, as evidenced by: changes in ER-associated eRNA expression; and stronger ER binding at active enhancers (H3K27ac sites) after GRHL2 knockdown. Our findings provide new insight into ER signaling and demonstrate VULCAN, available from Bioconductor, as a powerful predictive tool.

cancer biology↗