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Article ; Online: Filling the gaps in the global prevalence map of clinical antimicrobial resistance.

Oldenkamp, Rik / Schultsz, Constance / Mancini, Emiliano / Cappuccio, Antonio

Proceedings of the National Academy of Sciences of the United States of America

2021 Volume 118, Issue 1

Abstract: Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are ... ...

Abstract	Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are limited. While socioeconomic characteristics correlate with clinical AMR prevalence, this correlation has not yet been used to estimate AMR prevalence in countries lacking surveillance. We captured the statistical relationship between AMR prevalence and socioeconomic characteristics in a suite of beta-binomial principal component regression models for nine pathogens resistant to 19 (classes of) antibiotics. Prevalence data from ResistanceMap were combined with socioeconomic profiles constructed from 5,595 World Bank indicators. Cross-validated models were used to estimate clinical AMR prevalence and temporal trends for countries lacking data. Our approach provides robust estimates of clinical AMR prevalence in LMICs for most priority pathogens (cross-validated
MeSH term(s)	Acinetobacter baumannii/drug effects ; Anti-Bacterial Agents/pharmacology ; Anti-Infective Agents/pharmacology ; Bacterial Infections/drug therapy ; Bacterial Infections/epidemiology ; Carbapenems/pharmacology ; Drug Resistance, Bacterial/drug effects ; Drug Resistance, Microbial/drug effects ; Drug Resistance, Multiple, Bacterial/drug effects ; Epidemiological Monitoring ; Escherichia coli/drug effects ; Humans ; Klebsiella pneumoniae/drug effects ; Prevalence
Chemical Substances	Anti-Bacterial Agents ; Anti-Infective Agents ; Carbapenems
Language	English
Publishing date	2021-01-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2013515118
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Article ; Online: Advances in the computational landscape for repurposed drugs against COVID-19.

Aronskyy, Illya / Masoudi-Sobhanzadeh, Yosef / Cappuccio, Antonio / Zaslavsky, Elena

Drug discovery today

2021 Volume 26, Issue 12, Page(s) 2800–2815

Abstract: The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, ... ...

Abstract	The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
MeSH term(s)	Antiviral Agents/therapeutic use ; Computational Biology ; Computer Simulation ; Databases, Factual ; Drug Repositioning/methods ; Drug Repositioning/trends ; Humans ; Machine Learning ; Molecular Docking Simulation ; COVID-19 Drug Treatment
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2021-07-30
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	1324988-5
ISSN	1878-5832 ; 1359-6446
ISSN (online)	1878-5832
ISSN	1359-6446
DOI	10.1016/j.drudis.2021.07.026
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Article ; Online: Benchmarking transcriptional host response signatures for infection diagnosis.

Chawla, Daniel G / Cappuccio, Antonio / Tamminga, Andrea / Sealfon, Stuart C / Zaslavsky, Elena / Kleinstein, Steven H

Cell systems

2022 Volume 13, Issue 12, Page(s) 974–988.e7

Abstract: Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To ... ...

Abstract	Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.
MeSH term(s)	Humans ; Transcriptome/genetics ; Benchmarking ; Bacterial Infections
Language	English
Publishing date	2022-12-22
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Comment
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2022.11.007
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Article ; Online: Earlier detection of SARS-CoV-2 infection by blood RNA signature microfluidics assay.

Cappuccio, Antonio / Geis, Jennifer / Ge, Yongchao / Nair, Venugopalan D / Ramalingam, Naveen / Mao, Weiguang / Chikina, Maria / Letizia, Andrew G / Sealfon, Stuart C

Clinical and translational discovery

2022 Volume 2, Issue 3, Page(s) e47

Language	English
Publishing date	2022-07-10
Publishing country	United States
Document type	Journal Article
ISSN	2768-0622
ISSN (online)	2768-0622
DOI	10.1002/ctd2.47
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Article: Automated single-cell omics end-to-end framework with data-driven batch inference.

Wang, Yuan / Thistlethwaite, William / Tadych, Alicja / Ruf-Zamojski, Frederique / Bernard, Daniel J / Cappuccio, Antonio / Zaslavsky, Elena / Chen, Xi / Sealfon, Stuart C / Troyanskaya, Olga G

bioRxiv : the preprint server for biology

2023

Abstract: To facilitate single cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. ... ...

Abstract	To facilitate single cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.
Language	English
Publishing date	2023-11-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.01.564815
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Article ; Online: Deciphering the combinatorial landscape of immunity.

Cappuccio, Antonio / Jensen, Shane T / Hartmann, Boris M / Sealfon, Stuart C / Soumelis, Vassili / Zaslavsky, Elena

eLife

2020 Volume 9

Abstract: From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial ... ...

Abstract	From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNβ controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.
MeSH term(s)	Animals ; Databases as Topic ; Dendritic Cells/drug effects ; Humans ; Immune Checkpoint Inhibitors/pharmacology ; Immunity/drug effects ; Immunity/immunology ; Immunity/physiology ; Immunologic Factors/pharmacology ; Interferon-beta/metabolism ; Interleukins/metabolism ; Machine Learning ; Mice ; Software ; T-Lymphocytes/drug effects ; T-Lymphocytes/metabolism ; Tumor Necrosis Factor-alpha/metabolism
Chemical Substances	Immune Checkpoint Inhibitors ; Immunologic Factors ; Interleukins ; Tumor Necrosis Factor-alpha ; Interferon-beta (77238-31-4)
Language	English
Publishing date	2020-11-23
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.62148
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Article ; Online: Multiscale modelling in immunology: a review.

Cappuccio, Antonio / Tieri, Paolo / Castiglione, Filippo

Briefings in bioinformatics

2016 Volume 17, Issue 3, Page(s) 408–418

Abstract: One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, ... ...

Abstract	One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
Language	English
Publishing date	2016-05
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbv012
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Article: Multiscale modelling in immunology: a review

Cappuccio, Antonio / Tieri, Paolo / Castiglione, Filippo

Briefings in bioinformatics. 2016 May, v. 17, no. 3

2016

Abstract	One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host–virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
Keywords	angiogenesis ; host-pathogen relationships ; immunology ; medicine ; models ; neoplasms ; neurophysiology ; therapeutics
Language	English
Dates of publication	2016-05
Size	p. 408-418.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbv012
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Systems approaches to unravel innate immune cell diversity, environmental plasticity and functional specialization.

Soumelis, Vassili / Pattarini, Lucia / Michea, Paula / Cappuccio, Antonio

Current opinion in immunology

2015 Volume 32, Page(s) 42–47

Abstract: Innate immune cells are generated through central and peripheral differentiation pathways, and receive multiple signals from tissue microenvironment. The complex interplay between immune cell state and environmental signals is crucial for the adaptation ... ...

Abstract	Innate immune cells are generated through central and peripheral differentiation pathways, and receive multiple signals from tissue microenvironment. The complex interplay between immune cell state and environmental signals is crucial for the adaptation and efficient response to pathogenic threats. Here, we discuss how systems biology approaches have brought global view and high resolution to the characterization of (1) immune cell diversity, (2) phenotypic, transcriptional and functional changes in response to environmental signals, (3) integration of multiple stimuli. We will mostly focus on systems level studies in dendritic cells and macrophages. Generalization of these approaches should elucidate innate immune cell diversity and plasticity, and may be used in the human to generate hypothesis on cell filiation and novel strategies for immunotherapy.
MeSH term(s)	Animals ; Dendritic Cells/immunology ; Dendritic Cells/metabolism ; Humans ; Immune System/cytology ; Immune System/physiology ; Immunity, Innate ; Macrophages/immunology ; Macrophages/metabolism
Language	English
Publishing date	2015-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1035767-1
ISSN	1879-0372 ; 0952-7915
ISSN (online)	1879-0372
ISSN	0952-7915
DOI	10.1016/j.coi.2014.12.007
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Article ; Online: Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

Cappuccio, Antonio / Chawla, Daniel G / Chen, Xi / Rubenstein, Aliza B / Cheng, Wan Sze / Mao, Weiguang / Burke, Thomas W / Tsalik, Ephraim L / Petzold, Elizabeth / Henao, Ricardo / McClain, Micah T / Woods, Christopher W / Chikina, Maria / Troyanskaya, Olga G / Sealfon, Stuart C / Kleinstein, Steven H / Zaslavsky, Elena

Cell systems

2022 Volume 13, Issue 12, Page(s) 989–1001.e8

Abstract: The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, ... ...

Abstract	The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.
MeSH term(s)	Humans ; COVID-19 ; SARS-CoV-2 ; Virus Diseases
Language	English
Publishing date	2022-10-18
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2022.11.008
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