Christine Nardini

National Research Council

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Claudia Sala

University of Bologna

Pietro Di Lena

University of Bologna

Darong Lai

Southeast University

Luca Benini

ETH Zurich

Paolo Garagnani

University of Bologna

Xiaoyuan Zhou

University of California, San Francisco

Claudio Franceschi

Institute of Neurological Sciences

Jennifer E. Dent

Shanghai Institutes for Biological Sciences

Maria Giulia Bacalini

University of Bologna

Andrea Prodi

Institute of Organic Synthesis and Photoreactivity

Cooperative Institutions

University of Bologna

Istituti di Ricovero e Cura a Carattere Scientifico

Istituto delle Scienze Neurologiche di Bologna

Chinese Academy of Sciences

Biomedical Research Networking Center on Neurodegenerative Diseases

University College London

National Research Council

Instituto de Biomedicina de Sevilla

Universidad de Sevilla

Hospital Universitario Virgen del Rocío

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Editorial: Computational methods for analysis of DNA methylation data, volume II

Frontiers in Bioinformatics (2024)

Pietro Di Lena Christine Nardini Matteo Pellegrini

DNA methylation stands out as one of the most extensively investigated epigenetic modifications 14 within the realm of eukaryotic biology. DNA methylation-based predictive models for chronological 15 age, referred to as epigenetic clocks, serve as widely used tools for investigating age-related pathologies 16 and physiological alterations. Discrepancies between predicted and actual chronological ages are 17 frequently interpreted as manifestations of biological age acceleration, a phenomenon linked to the 18 onset of various disorders. A plethora of epigenetic clocks have been developed in the literature (Di 19 Lena et al., 2021), and several studies have demonstrated associations between epigenetic age 20 acceleration and pathological conditions (Horvath, Raj, 2018). This active area of research is currently 21 engaged in endeavors to enhance the predictive capabilities of epigenetic clocks and facilitate the 22 translation of their applications into the realm of predictive medicine. The most significant conclusion drawn from the first and second volumes of this research topic is that 73 although DNA methylation analysis shows great potential, there is a pressing need for further 74 investigation and refinement of methodologies to fully harness its predictive power and translate its 75 findings into actionable insights for clinical practice.

10.3389/fbinf.2024.1415791

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Additional file 1: Table S1. of A method for automated pathogenic content estimation with application to rheumatoid arthritis

Figshare (2016)

Xiaoyuan Zhou Christine Nardini

Human bacteria pathogens in a Genus-Species table collected from six public database and manual searching, this table is integral to the eudysbiome package, reported here for convenience. (XLSX 73 kb)

Table (database)

Content (measure theory)

Table of contents

File format

10.6084/m9.figshare.c.3608747_d1

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A Non-Invasive Approach for Molecular Characterization of Glioblastoma Multiforme: Magnetic Resonance Imaging Correlation with cDNA Microarray Expression Profiles

Christine Nardini Sangwon Cha D. Wang Maximilian Diehn Luca Benini

Gene chip analysis

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Systemic evaluation of cellular reprogramming processes exploiting a novel R-tool: eegc

Bioinformatics (2017)

Xiaoyuan Zhou Guofeng Meng Christine Nardini Hongkang Mei

Cells derived by cellular engineering, i.e. differentiation of induced pluripotent stem cells and direct lineage reprogramming, carry a tremendous potential for medical applications and in particular for regenerative therapies. These approaches consist in the definition of lineage-specific experimental protocols that, by manipulation of a limited number of biological cues-niche mimicking factors, (in)activation of transcription factors, to name a few-enforce the final expression of cell-specific (marker) molecules. To date, given the intricate complexity of biological pathways, these approaches still present imperfect reprogramming fidelity, with uncertain consequences on the functional properties of the resulting cells.We propose a novel tool eegc to evaluate cellular engineering processes, in a systemic rather than marker-based fashion, by integrating transcriptome profiling and functional analysis. Our method clusters genes into categories representing different states of (trans)differentiation and further performs functional and gene regulatory network analyses for each of the categories of the engineered cells, thus offering practical indications on the potential lack of the reprogramming protocol.eegc R package is released under the GNU General Public License within the Bioconductor project, freely available at https://bioconductor.org/packages/eegc/.christine.nardini.rsrc@gmail.com or hongkang.k.mei@gsk.com.Supplementary data are available at Bioinformatics online.

Reprogramming

Bioconductor

Gene regulatory network

10.1093/bioinformatics/btx205

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[Non-specific spondylodiscitis in children: report of a case in an infant. Review of the literature].

PubMed (1991)

Christine Nardini S Zanchetta

A case of non-specific spondylitis in an infant has prompted us to an ample review of the literature. The disease is acknowledged as characteristically benign in children. However some publications seem to imply that in the very young child it would display a remarkable seriousness. From our case and half a score of others that we found in the literature, it appears that in the first year of life the nonspecific spondylitis has the same features as in the older children, namely a mild course and a good prognosis. The above publications probably refer to peculiar cases, which give rise to an as yet unsolved nosological problem. The characteristics of the disease, with special emphasis on its clinical and radiological presentation and evolution, are exposed.

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Spondylodiscitis

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Spondylitis

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The emerging role of ECM crosslinking in T cell mobility as a hallmark of immunosenescence in humans

Ageing Research Reviews (2016)

Jean-François Moreau Thomas Pradeu Andrea Grignolio Christine Nardini Filippo Castiglione

Immunosenescence is thought to result from cellular aging and to reflect exposure to environmental stressors and antigens, including cytomegalovirus (CMV). However, not all of the features of immunosenescence are consistent with this view, and this has led to the emergence of the sister theory of "inflammaging". The recently discovered diffuse tissue distribution of resident memory T cells (TRM) which don't recirculate, calls these theories into question. These cells account for most T cells residing in barrier epithelia which sit in and travel through the extracellular matrix (ECM). With almost all studies to date carried out on peripheral blood, the age-related changes of the ECM and their consequences for T cell mobility, which is crucial for the function of these cells, have been largely ignored. We propose an update of the theoretical framework of immunosenescence, based on a novel hypothesis: the increasing stiffness and cross-linking of the senescent ECM lead to a progressive immunodeficiency due to an age-related decrease in T cell mobility and eventually the death of these cells. A key element of this mechanism is the mechanical stress to which the cell cytoplasm and nucleus are subjected during passage through the ECM. This hypothesis is based on an "evo-devo" perspective bringing together some major characteristics of aging, to create a single interpretive framework for immunosenescence.

Immunosenescence

10.1016/j.arr.2016.11.005

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Citations (58)

Dissection of the module network implementation “LemonTree”: enhancements towards applications in metagenomics and translation in autoimmune maladies

Molecular BioSystems (2017)

Youtao Lu Xiaoyuan Zhou Christine Nardini

Under the current deluge of omics, module networks distinctively emerge as methods capable of not only identifying inherently coherent groups (modules), thus reducing dimensionality, but also hypothesizing cause-effect relationships between modules and their regulators. Module networks were first designed in the transcriptomic era and further exploited in the multi-omic context to assess (for example) miRNA regulation of gene expression. Despite a number of available implementations, expansion of module networks to other omics is constrained by a limited characterization of the solutions' (modules plus regulators) accuracy and stability - an immediate need for the better characterization of molecular biology complexity in silico. We hence carefully assessed for LemonTree - a popular and open source module network implementation - the dependency of the software performances (sensitivity, specificity, false discovery rate, solutions' stability) on the input parameters and on the data quality (sample size, expression noise) based on synthetic and real data. In the process, we uncovered and fixed an issue in the code for the regulator assignment procedure. We concluded this evaluation with a table of recommended parameter settings. Finally, we applied these recommended settings to gut-intestinal metagenomic data from rheumatoid arthritis patients, to characterize the evolution of the gut-intestinal microbiome under different pharmaceutical regimens (methotrexate and prednisone) and we inferred innovative clinical recommendations with therapeutic potential, based on the computed module network.

10.1039/c7mb00248c

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Citations (4)

An S-System Parameter Estimation Method (SPEM) for Biological Networks

Journal of Computational Biology (2012)

Xinyi Yang Jennifer E. Dent Christine Nardini

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.

Reverse engineering

Gene regulatory network

10.1089/cmb.2011.0269

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Citations (24)

Additional file 3 of Methylation data imputation performances under different representations and missingness patterns

Zenodo (CERN European Organization for Nuclear Research) (2020)

Pietro Di Lena Claudia Sala Andrea Prodi Christine Nardini

Additional file 3 Performance comparison between complete (450k) and restricted (21k) datasets. Additional file 3 of Methylation data imputation performances under different representations and missingness patterns (figshare.com)

Imputation (statistics)

10.5281/zenodo.5869127

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Systemic Wound Healing Associated with local sub-Cutaneous Mechanical Stimulation

Scientific Reports (2016)

Christine Nardini Valentina Devescovi Yuanhua Liu Xiaoyuan Zhou Youtao Lu

Abstract Degeneration is a hallmark of autoimmune diseases, whose incidence grows worldwide. Current therapies attempt to control the immune response to limit degeneration, commonly promoting immunodepression. Differently, mechanical stimulation is known to trigger healing (regeneration) and it has recently been proposed locally for its therapeutic potential on severely injured areas. As the early stages of healing consist of altered intra- and inter-cellular fluxes of soluble molecules, we explored the potential of this early signal to spread, over time, beyond the stimulation district and become systemic, to impact on distributed or otherwise unreachable injured areas. We report in a model of arthritis in rats how stimulations delivered in the subcutaneous dorsal tissue result, over time, in the control and healing of the degeneration of the paws’ joints, concomitantly with the systemic activation of wound healing phenomena in blood and in correlation with a more eubiotic microbiome in the gut intestinal district.

10.1038/srep39043

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Citations (19)