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Article ; Online: A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases.

Pepe, Gerardo / Appierdo, Romina / Ausiello, Gabriele / Helmer-Citterich, Manuela / Gherardini, Pier Federico

International journal of molecular sciences

2024 Volume 25, Issue 8

Abstract: Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing ... ...

Abstract	Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure. By integrating data from multiple independent studies, our approach unveiled crucial regulatory associations between transcription factors (TFs) and structural genes, emphasizing their pivotal roles in regulating metabolic pathways, such as fatty acid metabolism, oxidative stress response, epithelial-to-mesenchymal transition, and coagulation. In addition to known associations, our analysis also identified novel regulators, including the identification of TFs FPM315 and OVOL2, which are implicated in dilated cardiomyopathies, and TEAD1 and TEAD2 in both dilated and ischemic cardiomyopathies. Moreover, we uncovered alterations in adipogenesis and oxidative phosphorylation pathways in hypertrophic cardiomyopathy and discovered a role for IL2 STAT5 signaling in heart failure. Our findings underscore the importance of TF activity in the initiation and progression of cardiac disease, highlighting their potential as pharmacological targets.
MeSH term(s)	Humans ; Gene Regulatory Networks ; Cardiovascular Diseases/genetics ; Cardiovascular Diseases/metabolism ; Transcription Factors/metabolism ; Transcription Factors/genetics ; Gene Expression Regulation ; Algorithms ; Heart Failure/genetics ; Heart Failure/metabolism
Chemical Substances	Transcription Factors
Language	English
Publishing date	2024-04-11
Publishing country	Switzerland
Document type	Journal Article ; Meta-Analysis
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25084224
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Article: Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.

Ballarino, Monica / Pepe, Gerardo / Helmer-Citterich, Manuela / Palma, Alessandro

Computational and structural biotechnology journal

2023 Volume 21, Page(s) 4706–4716

Abstract: In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent ... ...

Abstract	In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.
Language	English
Publishing date	2023-09-29
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.09.041
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Article ; Online: Looking for Plant microRNAs in Human Blood Samples: Bioinformatics Evidence and Perspectives.

Olmi, Lorenzo / Pepe, Gerardo / Helmer-Citterich, Manuela / Canini, Antonella / Gismondi, Angelo

Plant foods for human nutrition (Dordrecht, Netherlands)

2023 Volume 78, Issue 2, Page(s) 399–406

Abstract: Literature has proposed the existence of a cross kingdom regulation (CRK) between human and plants. In this context, microRNAs present in edible plants would be acquired through diet by the consumer's organism and transported via bloodstream to tissues, ... ...

Abstract	Literature has proposed the existence of a cross kingdom regulation (CRK) between human and plants. In this context, microRNAs present in edible plants would be acquired through diet by the consumer's organism and transported via bloodstream to tissues, where they would modulate gene expression. However, the validity of this phenomenon is strongly debated; indeed, some scholars have discussed both the methodologies and the results obtained in previous works. To date, only one study has performed a bioinformatics analysis on small RNA-sequencing data for checking the presence of plant miRNAs (pmiRNAs) in human plasma. For that investigation, the lack of reliable controls, which led to the misidentification of human RNAs as pmiRNAs, has been deeply criticized. Thus, in the present contribution, we aim to demonstrate the existence of pmiRNAs in human blood, adopting a bioinformatics approach characterized by more stringent conditions and filtering. The information obtained from 380 experiments produced in 5 different next generation sequencing (NGS) projects was examined, revealing the presence of 350 circulating pmiRNAs across the analysed data set. Although one of the NGS projects shows results likely to be attributed to sample contamination, the others appear to provide reliable support for the acquisition of pmiRNAs through diet. To infer the potential biological activity of the identified pmiRNAs, we predicted their putative human mRNA targets, finding with great surprise that they appear to be mainly involved in neurogenesis and nervous system development. Unfortunately, no consensus was identified within the sequences of detected pmiRNAs, in order to justify their stability or capability to be preserved in human plasma. We believe that the issue regarding CKR still needs further clarifications, even if the present findings would offer a solid support that this hypothesis is not impossible.
MeSH term(s)	Humans ; MicroRNAs/genetics ; Diet ; Plants, Edible/genetics ; Computational Biology ; RNA, Plant/genetics ; High-Throughput Nucleotide Sequencing ; Gene Expression Regulation, Plant
Chemical Substances	MicroRNAs ; RNA, Plant
Language	English
Publishing date	2023-05-31
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	188869-9
ISSN	1573-9104 ; 0377-3205
ISSN (online)	1573-9104
ISSN	0377-3205
DOI	10.1007/s11130-023-01063-9
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Article ; Online: Dissecting the Genome for Drug Response Prediction.

Pepe, Gerardo / Carrino, Chiara / Parca, Luca / Helmer-Citterich, Manuela

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2449, Page(s) 187–196

Abstract: The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep ... ...

Abstract	The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.
MeSH term(s)	Algorithms ; Biological Phenomena ; Cell Line, Tumor ; Humans ; Machine Learning ; Pharmacogenetics ; Precision Medicine
Language	English
Publishing date	2022-05-04
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2095-3_7
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Article ; Online: Motif Discovery from CLIP Experiments.

Pietrosanto, Marco / Ausiello, Gabriele / Helmer-Citterich, Manuela

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2284, Page(s) 43–50

Abstract: RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient ... ...

Abstract	RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient discovery of interaction motifs. Recent advances have increased the amount of data produced by experimental assays and there is no available method suitable for the analysis of all type of results. Here we present a simple workflow to help choosing the more appropriate method, depending on the starting situation, among the three algorithms that best cover the landscape of approaches. A detailed analysis is presented to highlight the need for different algorithms in different working settings. In conclusion, the proposed workflow depends on the nature of the starting data and on the availability of RNA annotations.
MeSH term(s)	Algorithms ; Animals ; Binding Sites/genetics ; Computational Biology/methods ; Datasets as Topic ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Nucleic Acid Conformation ; Protein Binding/genetics ; RNA/chemistry ; RNA/genetics ; RNA/metabolism ; RNA-Binding Proteins/metabolism ; Sequence Analysis, RNA/methods ; Software
Chemical Substances	RNA-Binding Proteins ; RNA (63231-63-0)
Language	English
Publishing date	2021-04-09
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1307-8_3
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Article ; Online: Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types.

Pepe, Gerardo / Parca, Luca / Viviani, Lorenzo / Ausiello, Gabriele / Helmer-Citterich, Manuela

Non-coding RNA research

2022 Volume 7, Issue 2, Page(s) 98–105

Abstract: Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3'UTR, there are relatively few studies examining the changes in ... ...

Abstract	Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3'UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types. We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples. We show how more than 90% of these miRNA-mRNA interactions show a loss of regulation in the tumoral samples compared with their healthy counterparts. As expected, we found shared miRNA-mRNA dysregulated pairs among different tumors of the same tissue. However, anatomically different cancers also share multiple dysregulated interactions, suggesting that some cancer-related mechanisms are not tumor-specific. 2865 unique miRNA-mRNA pairs were identified across 13 cancer types, ≈ 40% of these pairs showed a loss of correlation in the tumoral samples in at least 2 out of the 13 analyzed cancers. Specifically, miR-200 family, miR-155 and miR-1 were identified, based on the computational analysis described below, as the miRNAs that potentially lose the highest number of interactions across different samples (only literature-based interactions were used for this analysis). Moreover, the miR-34a/ALDH2 and miR-9/MTHFD2 pairs show a switch in their correlation between healthy and tumor kidney samples suggesting a possible change in the regulation exerted by the miRNAs. Interestingly, the expression of these mRNAs is also associated with the overall survival. The disruption of miRNA regulation on its target, therefore, suggests the possible involvement of these pairs in cell malignant functions. The analysis reported here shows how the regulation of miRNA-mRNA interactions strongly differs between healthy and tumoral cells, based on the strong correlation variation between miRNA and its target that we obtained by analyzing the expression data of healthy and tumor tissue in highly reliable miRNA-target pairs. Finally, a go term enrichment analysis shows that the critical pairs identified are involved in cellular adhesion, proliferation, and migration.
Language	English
Publishing date	2022-03-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-0540
ISSN (online)	2468-0540
DOI	10.1016/j.ncrna.2022.03.003
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Article ; Online: COTAN: scRNA-seq data analysis based on gene co-expression.

Galfrè, Silvia Giulia / Morandin, Francesco / Pietrosanto, Marco / Cremisi, Federico / Helmer-Citterich, Manuela

NAR genomics and bioinformatics

2021 Volume 3, Issue 3, Page(s) lqab072

Abstract: Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We ... ...

Abstract	Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate
Language	English
Publishing date	2021-08-11
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqab072
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Article ; Online: Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction.

Latini, Sara / Venafra, Veronica / Massacci, Giorgia / Bica, Valeria / Graziosi, Simone / Pugliese, Giusj Monia / Iannuccelli, Marta / Frioni, Filippo / Minnella, Gessica / Marra, John Donald / Chiusolo, Patrizia / Pepe, Gerardo / Helmer Citterich, Manuela / Mougiakakos, Dimitros / Böttcher, Martin / Fischer, Thomas / Perfetto, Livia / Sacco, Francesca

eLife

2024 Volume 12

Abstract: Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical ... ...

Abstract	Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
MeSH term(s)	Humans ; Signal Transduction ; Leukemia, Myeloid, Acute/drug therapy ; Leukemia, Myeloid, Acute/genetics ; MAP Kinase Signaling System ; Cell Line ; Drug Resistance ; fms-Like Tyrosine Kinase 3/genetics
Chemical Substances	FLT3 protein, human (EC 2.7.10.1) ; fms-Like Tyrosine Kinase 3 (EC 2.7.10.1)
Language	English
Publishing date	2024-04-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.90532
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Article ; Online: Large-scale DNA sequencing identifies rare variants associated with Systemic Lupus Erythematosus susceptibility in known risk genes.

Latini, Andrea / Borgiani, Paola / De Benedittis, Giada / Ciccacci, Cinzia / Novelli, Lucia / Pepe, Gerardo / Helmer-Citterich, Manuela / Baldini, Isabella / Perricone, Carlo / Ceccarelli, Fulvia / Conti, Fabrizio / Ianniciello, Generoso / Caceres, Juan / Ottalevi, Riccardo / Capulli, Mattia / Novelli, Giuseppe

Gene

2024 Volume 907, Page(s) 148279

Abstract: The identification of rare genetic variants associated to Systemic Lupus Erythematosus (SLE) could also help to understand the pathogenic mechanisms at the basis of the disease. In this study we have analyzed a cohort of 200 Italian SLE patients in order ...

Abstract	The identification of rare genetic variants associated to Systemic Lupus Erythematosus (SLE) could also help to understand the pathogenic mechanisms at the basis of the disease. In this study we have analyzed a cohort of 200 Italian SLE patients in order to explore the rare protein-coding variants in five genes (TNFAIP3, STAT4, IL10, TRAF3IP2, and HCP5) already investigated for commons variants found associated in our previous studies. Genomic DNA of 200 SLE patients was sequenced by whole exome sequencing. The identified variants were filtered by frequency and evaluated by in silico predictions. Allelic association analysis was performed with standard Fisher's exact test. Introducing a cutoff at MAF < 0.01, a total of 19 rare variants were identified. Seven of these variants were ultra-rare (MAF < 0.001) and six were absent in the GnomAD database. For TNFAIP3 gene, the variant c.A1939C was observed in 4 SLE patients and it is located in a region enriched in phosphorylation sites and affects the predict affinity of specific kinases. In TRAF3IP2 gene, we observed 5 different rare variants, including the novel variant c.G410A, located in the region that mediates interaction with TRAF6, and therefore a possible risk factor for SLE development. In STAT4 gene, we identified 6 different rare variants. Among these, three missense variants decrease the stability of this protein. Moreover, 3 novel rare variants were detected in 3 SLE patients. In particular, c.A767T variant was predicted as damaging by six prediction tools. Concluding, we have observed that even in genes whose common variability is associated with SLE susceptibility, it is possible to identify rare variants that could have a strong effect in the disease development and could therefore allow a better understanding of the functional domain involved.
MeSH term(s)	Humans ; Genetic Predisposition to Disease ; Lupus Erythematosus, Systemic/genetics ; Alleles ; DNA ; Sequence Analysis, DNA ; Polymorphism, Single Nucleotide
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2024-02-13
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2024.148279
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Article ; Online: Bioinformatics in Italy

Romano Paolo / Helmer-Citterich Manuela

BMC Bioinformatics, Vol 13, Iss Suppl 4, p I

BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

2012 Volume 1

Abstract: Abstract The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and ... ...

Abstract	Abstract The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research. This preface provides a brief overview of the meeting and introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2012-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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