Marco Del Giudice's research while affiliated with Italian Institute for Genomic Medicine and other places

Publications (22)

Article
Full-text available
Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer....
Article
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Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impac...
Article
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Although subcellular positioning of endosomes significantly impacts on their functions, the molecular mechanisms governing the different steady-state distribution of early endosomes (EEs) and late endosomes (LEs)/lysosomes (LYs) in peripheral and perinuclear eukaryotic cell areas, respectively, are still unsolved. We unveil that such differences ar...
Article
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Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in pa...
Article
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Heterogeneity is a fundamental feature of complex phenotypes. So far, genomic screenings have profiled thousands of samples providing insights into the transcriptome of the cell. However, disentangling the heterogeneity of these transcriptomic Big Data to identify defective biological processes remains challenging. Here we present GSECA, a method e...
Preprint
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Cell-to-cell variation in gene expression is a common feature of developmental processes. Yet, it remains unclear whether molecular mediators can generate variation and how this process is coordinated across loci to allow the emergence of new cell states. Using embryonic stem cells (ESCs) as a model of development, we found interconverting cell sta...
Chapter
Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA–RNA interaction networks. Within such networks, competition to bind...
Preprint
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Prostate cancer (PCa) is genomically driven by dysregulation of transcriptional networks involving the transcriptional factors (TFs) FOXA1, ERG, AR, and HOXB13. However, the role of these specific TFs in the regulation of alternative pre-mRNA splicing (AS), which is a proven therapeutic vulnerability for cancers driven by the TF MYC, is not describ...
Preprint
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Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind...
Article
Full-text available
Directional transport of recycling cargo from early endosomes (EE) to the endocytic recycling compartment (ERC) relies on phosphatidylinositol 3-phosphate (PtdIns(3)P) hydrolysis and activation of the small GTPase Rab11. However, how these events are coordinated is yet unclear. By using a novel genetically-encoded FRET biosensor for Rab11, we repor...
Article
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Many biological processes are known to be based on molecular sequestration. This kind of dynamics involves two types of molecular species, namely targets and sequestrants, that bind to form a complex. In the simple framework of mass-action law, key features of these systems appear to be threshold-like profiles of the amounts of free molecules as a...
Data
The role of the offset. (A) Example of two average mRNA profiles, for a regulated (orange) and an unregulated (blue) mRNA. (B) Same profile as panel A but with the curves shifted upwards by an arbitrary offset of 10 mRNA molecules. (C) Fold repression (ratio of blue to orange curve from panel A) without the offset. (D) Fold repression (ratio of blu...
Data
Analytical prediction for the coefficient of variation. Analytical predictions for the target coefficient of variation in case of one (A) or two (B) targets. In (A) the parameters are k¯S=1.2×10-3nMmin-1, σ = 4.8 × 10−4 nM min−1, gS = 1.2 × 10−2 min−1, gR = 2.4 × 10−2 min−1, g = 1.2 × 102 nM−1 min−1, kP = 6.0 min−1, gP = 1.2 × 10−2 min−1, α = 0.5....
Data
Details on different approximation methods, supplementary analysis, and simulations. (PDF)
Data
Comparison between Van Kampen and Gaussian approximations. In (A-C) mRNA and protein distributions for unstable and stable proteins are shown together with the two approximations. In (D) the mean number of mRNA molecules as a function of the miRNA transcription rate is shown, the blue line corresponds to numerical simulations, while the red and bla...
Data
Bimodality phase diagram. The plot shows the bimodality phase diagram for the mRNA 1 in a system with two targets competing for the same miRNA. The parameters here used are the following: k¯S=1.2×10-3 nM min−1, σS = 2.4 × 10−4 nM min−1, g1 = 1.2 × 102 nM−1 min−1, kR1 and kR2 range from 0 nM min−1 to 5.1 × 10−3 nM min−1, gS = 1.2 × 10−2 min−1, gR1 =...
Data
Comparison between the bimodal mRNA noisy distribution and the weighted superposition of distributions obtained without noise for different miRNA transcription rates. The parameters are the following: kR = 3.1 × 10−3 nM min−1, gS = 1.2 × 10−2 min−1, gR = 2.4 × 10−2 min−1, g = 1.2 × 102 nM−1 min−1, kP = 6.0 min−1, gP = 1.2 × 10−2 min−1 and α = 0.5....
Data
Bimodality amplitude phase diagram. Phase diagram of the bimodality amplitude of the mRNA distribution as a function of the mRNA transcription rate kR and of the extrinsic noise level. The parameters here used are the following: gS = 1.2 × 10−2 min−1, gR = 2.4 × 10−2 min−1, g = 3.0 × 102 nM−1 min−1, kP = 6.0 min−1, gP = 1.2 × 10−2 min−1, α = 0.5. T...
Article
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We document the initial-density dependence of the growth rate achieved by Jurkat cell cultures in a standard growth medium with fixed carrying capacity. As the density $N_0$ of the inoculum varies over 4 orders of magnitude, three distinct growth regimes appear. At small $N_0$, the growth rate $\lambda$ is roughly constant and displays small sample...
Article
Full-text available
Several studies pointed out the relevance of extrinsic noise in molecular networks in shaping cell decision making and differentiation. Interestingly, bimodal distributions of gene expression levels, that may be a feature of phenotypic differentiation, are a common phenomenon in gene expression data. The modes of the distribution often correspond t...
Article
Full-text available
In view of the relation between information and thermodynamics we investigate how much information about an external protocol can be stored in the memory of a stochastic measurement device given an energy budget. We consider a layered device with a memory component storing information about the external environment by monitoring the history of a se...

Citations

... Auslander et al. reviewed machine learning/deep learning approaches incorporated to establish bioinformatics and computational biology frameworks in the areas of molecular evolution, protein structure analysis, systems biology, and disease genomics [19]. Del Giudice et al. comprehensively reviewed machine learning/deep learning solutions for computational problems in bulk and single-cell RNA-sequencing data analysis [20]. Banegas-Luna et al. discussed the interpretability of machine learning/deep learning methods in cancer research [21]. ...
... As a second application of TOLOMEO, we discuss the case of biological images, where the progression of microscopy and multiplexed fluorescence imaging techniques allows one to take snapshots with enough resolution to distinguish cell populations [31][32][33][34] or even cellular compounds [35,36] and their respective spatial organization [37]. ...
... Another open question is which kinesins are required for the transport of Hrs and STAM1 under basal conditions, as KIF13A knockdown does not seem to impair this process. Several kinesins in addition to KIF13A and KIF13B, including KIF5A/B/ C, KIFC1, and KIF16B, have been implicated in endosomal trafficking (Bonifacino & Neefjes, 2017;Li et al, 2020;Villari et al, 2020) and may also have roles in the axonal transport of ESCRT-0 proteins. Clearly, additional work is needed to identify other cargoes of KIF13A, as well as the specific motor proteins responsible for anterograde and retrograde transport of Hrs+ vesicles. ...
... These variations have functional consequences such as altered differentiation potentials of stem cells and drug resistance of cancer cells (1)(2)(3)6,7). For progenitor cells that exhibit multimodal expression patterns, a small subpopulation with a relatively homogenous expression profile recovers the parental population's heterogeneity of individual gene products after several days or longer (2,(8)(9)(10) (Figure 1 box). Although stochasticity in transcriptional activities can cause expression variation and associated cell state changes (3,11), this type of noise influences expression at a much faster timescale (minutes) than the fluctuations required to achieve observed cell state transitions (days) (1,2,8,12,13). ...
... The development of AI industry enables easier and more visually appealing solutions for SCS technology. For example, AI can be widely exploited in all aspects of the SCS workflow, such as batch correction for technical heterogeneity [133,134], feature extraction [135,136], data distribution transformation [137,138], classification of cancer subtypes [139,140], and biomarker identification [141][142][143]. Most notably, SCS in combination with AI is also widely used to identify and analyze CTCs, a class of cells that can be used for searching therapeutic targets for tumor metastasis [133][134][135][136][137][138][139][140][141][142][143][144]. ...
... (23)(24)(25) in the limit of small perturbations. [100] The above program has been carried out starting in 2013 over a series of papers by various authors, covering aspects ranging from the off-equilibrium dynamics of small miRNA-mediated circuits to the typical system-level properties of crosstalk in the human transcriptome (see Ref. [101] for a thorough review). For sakes of clarity, we focus here on two sets of results with high biological significance, concerning the roles of (a) transcriptional heterogeneities and (b) topological heterogeneities in shaping the emergent network-scale expression profiles in the human miRNA interactome mapped via the CLASH protocol (Crosslinking, Ligation And Sequencing of Hybrids), accounting for O(10 7 ) potential miRNA-mediated cross-regulatory interactions, each quantifiable by the value of χ ij . ...
... A recent study based on a combination of theory and experiments [23] showed that selective pressure might even increase expression noise and the positively selected genes with elevated noise are also those highly regulated by transcription factors. On the same idea that cells do not necessarily buffer noise, a recent work showed that introduction of extrinsic noise in microRNA-mediated regulatory networks, i.e., increased variability in gene expression, can instead favour cell differentiation [24][25][26][27]. These recent studies arouse the possibility that cells do not only buffer noise but they rather take advantage of stochasticity to optimise specific needs, e.g., cell-to-cell variability, protein number precision, information flow [28,29], etc. ...
... Class III PI3K, Vps34, is required for GPCRs to be recycled from the endosomal compartments to the plasma membrane during resensitization ( 93 ). PtdIns3P activates Rab11, a master regulator of endocytic recycling ( 94 ), and recruits the 3-phosphatase MTM1, which dephosphorylates PtdIns3P in the sorting of endosomes ( 95 ) ( Figure 4 ). Some GPCRs can recruit 14-3-3 proteins after endocytosis, which are scaffold proteins that direct GPCRs from recycling endosomes to the cell membrane ( 96 ) ( Figure 4 ). ...
... Few previous studies have searched for bimodality in large-scale gene expression data (Bessarabova et al., 2010;Mason et al., 2011;Shalek et al., 2013) and causes for such bimodality have been discussed, including: i) differential action of transcription factors (Ochab-Marcinek and Tabaka;, ii) regulation by microRNAs (Bosia et al., 2017;Del Giudice et al., 2018); iii) regulation by circular RNA (Hu and Zhou;) and even iv) stochastic events (Samoilov et al., 2005). For an extensive review of the different methods developed for detection of bimodality, please see Moody et al. (2019). ...
... For any A ⊆ N I define K x x (A; t) := i∈A K x x (i; t). As a concrete illustration, consider the scenario investigated in [33,42], in which receptors in the wall of a cell sense the concentration of a ligand in the intercellular medium, and those receptors are in turn observed by a "memory" subsystem inside the cell. Modify this scenario by introducing a second cell, which is observing the same external medium as the first cell. ...