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Article ; Online: Deciphering the code of viral-host adaptation through maximum entropy models

Di Gioacchino, Andrea / Greenbaum, Benjamin D / Monasson, Remi / Cocco, Simona

bioRxiv

Abstract: ... single, di- and tri- nucleotide usage, which can be trained from viral sequences that infect a given host ...

Abstract	Understanding how the genome of a virus evolves depending on the host it infects is an important question that challenges our knowledge about several mechanisms of host-pathogen interactions, including mutational signatures, innate immunity, and codon optimization. A key facet of this general topic is the study of viral genome evolution after a host-jumping event, a topic which has experienced a surge in interest due to the fight against emerging pathogens such as SARS-CoV-2. In this work, we tackle this question by introducing a new method to learn Maximum Entropy Nucleotide Bias models (MENB) reflecting single, di- and tri- nucleotide usage, which can be trained from viral sequences that infect a given host. We show that both the viral family and the host leave a fingerprint in nucleotide usages which MENB models decode. When the task is to classify both the host and the viral family for a sequence of unknown viral origin MENB models outperform state of the art methods based on deep neural networks. We further demonstrate the generative properties of the proposed framework, presenting an example where we change the nucleotide composition of the 1918 H1N1 Influenza A sequence without changing its protein sequence, while manipulating the nucleotide usage, by diminishing its CpG content. Finally we consider two well-known cases of zoonotic jumps, for the H1N1 Influenza A and for the SARS-CoV-2 viruses, and show that our method can be used to track the adaptation to the new host and to shed light on the more relevant selective pressures which have acted on motif usage during this process. Our work has wide-ranging applications, including integration into metagenomic studies to identify hosts for diverse viruses, surveillance of emerging pathogens, prediction of synonymous mutations that effect immunogenicity during viral evolution in a new host, and the estimation of putative evolutionary ages for viral sequences in similar scenarios. Additionally, the computational framework introduced here can be used to assist vaccine design by tuning motif usage with fine-grained control.
Keywords	covid19
Language	English
Publishing date	2023-10-30
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.10.28.564530
Database	COVID19

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Article ; Online: A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.

Bravi, Barbara / Di Gioacchino, Andrea / Fernandez-de-Cossio-Diaz, Jorge / Walczak, Aleksandra M / Mora, Thierry / Cocco, Simona / Monasson, Rémi

eLife

2023 Volume 12

Abstract: Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build ... ...

Abstract	Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
MeSH term(s)	T-Cell Antigen Receptor Specificity ; Learning ; Amino Acids ; Cell Membrane ; Mitochondrial Membranes
Chemical Substances	Amino Acids
Language	English
Publishing date	2023-09-08
Publishing country	England
Document type	Journal Article ; 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.85126
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Retroelement decay by the exonuclease XRN1 is a viral mimicry dependency in cancer.

Hosseini, Amir / Lindholm, Håvard T / Chen, Raymond / Mehdipour, Parinaz / Marhon, Sajid A / Ishak, Charles A / Moore, Paul C / Classon, Marie / Di Gioacchino, Andrea / Greenbaum, Benjamin / De Carvalho, Daniel D

Cell reports

2024 Volume 43, Issue 2, Page(s) 113684

Abstract: Viral mimicry describes the immune response induced by endogenous stimuli such as double-stranded RNA (dsRNA) from endogenous retroelements. Activation of viral mimicry has the potential to kill cancer cells or augment anti-tumor immune responses. Here, ... ...

Abstract	Viral mimicry describes the immune response induced by endogenous stimuli such as double-stranded RNA (dsRNA) from endogenous retroelements. Activation of viral mimicry has the potential to kill cancer cells or augment anti-tumor immune responses. Here, we systematically identify mechanisms of viral mimicry adaptation associated with cancer cell dependencies. Among the top hits is the RNA decay protein XRN1 as an essential gene for the survival of a subset of cancer cell lines. XRN1 dependency is mediated by mitochondrial antiviral signaling protein and protein kinase R activation and is associated with higher levels of cytosolic dsRNA, higher levels of a subset of Alus capable of forming dsRNA, and higher interferon-stimulated gene expression, indicating that cells die due to induction of viral mimicry. Furthermore, dsRNA-inducing drugs such as 5-aza-2'-deoxycytidine and palbociclib can generate a synthetic dependency on XRN1 in cells initially resistant to XRN1 knockout. These results indicate that XRN1 is a promising target for future cancer therapeutics.
MeSH term(s)	Humans ; Retroelements ; Cell Line ; Cytosol ; Decitabine ; Exonucleases ; Neoplasms/genetics ; RNA, Double-Stranded ; Exoribonucleases ; Microtubule-Associated Proteins
Chemical Substances	Retroelements ; Decitabine (776B62CQ27) ; Exonucleases (EC 3.1.-) ; RNA, Double-Stranded ; XRN1 protein, human (EC 3.1.13.1) ; Exoribonucleases (EC 3.1.-) ; Microtubule-Associated Proteins
Language	English
Publishing date	2024-01-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2024.113684
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Article: Inflammatory status in pediatric sickle cell disease: Unravelling the role of immune cell subsets.

Marchesani, Silvio / Bertaina, Valentina / Marini, Olivia / Cossutta, Matilde / Di Mauro, Margherita / Rotulo, Gioacchino Andrea / Palma, Paolo / Sabatini, Letizia / Petrone, Maria Isabella / Frati, Giacomo / Monteleone, Giulia / Palumbo, Giuseppe / Ceglie, Giulia

Frontiers in molecular biosciences

2023 Volume 9, Page(s) 1075686

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2023-01-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2022.1075686
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Article ; Online: The Heterogeneous Landscape and Early Evolution of Pathogen-Associated CpG Dinucleotides in SARS-CoV-2.

Di Gioacchino, Andrea / Šulc, Petr / Komarova, Anastassia V / Greenbaum, Benjamin D / Monasson, Rémi / Cocco, Simona

Molecular biology and evolution

2021 Volume 38, Issue 6, Page(s) 2428–2445

Abstract: COVID-19 can lead to acute respiratory syndrome, which can be due to dysregulated immune signaling. We analyze the distribution of CpG dinucleotides, a pathogen-associated molecular pattern, in the SARS-CoV-2 genome. We characterize CpG content by a CpG ... ...

Abstract	COVID-19 can lead to acute respiratory syndrome, which can be due to dysregulated immune signaling. We analyze the distribution of CpG dinucleotides, a pathogen-associated molecular pattern, in the SARS-CoV-2 genome. We characterize CpG content by a CpG force that accounts for statistical constraints acting on the genome at the nucleotidic and amino acid levels. The CpG force, as the CpG content, is overall low compared with other pathogenic betacoronaviruses; however, it widely fluctuates along the genome, with a particularly low value, comparable with the circulating seasonal HKU1, in the spike coding region and a greater value, comparable with SARS and MERS, in the highly expressed nucleocapside coding region (N ORF), whose transcripts are relatively abundant in the cytoplasm of infected cells and present in the 3'UTRs of all subgenomic RNA. This dual nature of CpG content could confer to SARS-CoV-2 the ability to avoid triggering pattern recognition receptors upon entry, while eliciting a stronger response during replication. We then investigate the evolution of synonymous mutations since the outbreak of the COVID-19 pandemic, finding a signature of CpG loss in regions with a greater CpG force. Sequence motifs preceding the CpG-loss-associated loci in the N ORF match recently identified binding patterns of the zinc finger antiviral protein. Using a model of the viral gene evolution under human host pressure, we find that synonymous mutations seem driven in the SARS-CoV-2 genome, and particularly in the N ORF, by the viral codon bias, the transition-transversion bias, and the pressure to lower CpG content.
MeSH term(s)	COVID-19/genetics ; CpG Islands ; Evolution, Molecular ; Genome, Viral ; Humans ; RNA, Viral/genetics ; SARS-CoV-2/genetics ; SARS-CoV-2/pathogenicity
Chemical Substances	RNA, Viral
Language	English
Publishing date	2021-02-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msab036
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Explaining neural activity in human listeners with deep learning via natural language processing of narrative text

Andrea G. Russo / Assunta Ciarlo / Sara Ponticorvo / Francesco Di Salle / Gioacchino Tedeschi / Fabrizio Esposito

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 9

Abstract: Abstract Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural ... ...

Abstract	Abstract Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction.
Keywords	Medicine ; R ; Science ; Q
Subject code	401
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

Di Gioacchino, Andrea / Procyk, Jonah / Molari, Marco / Schreck, John S / Zhou, Yu / Liu, Yan / Monasson, Rémi / Cocco, Simona / Šulc, Petr

PLoS computational biology

2022 Volume 18, Issue 9, Page(s) e1010561

Abstract: Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann ... ...

Abstract	Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM's performance with different supervised learning approaches that include random forests and several deep neural network architectures.
MeSH term(s)	Machine Learning ; Neural Networks, Computer ; Thrombin
Chemical Substances	Thrombin (EC 3.4.21.5)
Language	English
Publishing date	2022-09-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010561
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Article ; Online: A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Barbara Bravi / Andrea Di Gioacchino / Jorge Fernandez-de-Cossio-Diaz / Aleksandra M Walczak / Thierry Mora / Simona Cocco / Rémi Monasson

eLife, Vol

2023 Volume 12

Abstract	Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen’s probability of triggering a response, and on the other hand the T-cell receptor’s ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
Keywords	machine learning ; immune response ; immunogenicity ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Explaining neural activity in human listeners with deep learning via natural language processing of narrative text.

Russo, Andrea G / Ciarlo, Assunta / Ponticorvo, Sara / Di Salle, Francesco / Tedeschi, Gioacchino / Esposito, Fabrizio

Scientific reports

2022 Volume 12, Issue 1, Page(s) 17838

Abstract: Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings ... ...

Abstract	Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction.
MeSH term(s)	Humans ; Brain Mapping ; Comprehension ; Brain/diagnostic imaging ; Natural Language Processing ; Deep Learning ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2022-10-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-21782-4
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Article ; Online: Assessment of body composition: Intrinsic methodological limitations and statistical pitfalls.

Barone, Michele / Losurdo, Giuseppe / Iannone, Andrea / Leandro, Gioacchino / Di Leo, Alfredo / Trerotoli, Paolo

Nutrition (Burbank, Los Angeles County, Calif.)

2022 Volume 102, Page(s) 111736

Abstract: Evaluation of body composition (BC) is crucial for an adequate assessment of nutritional status and its alterations, to ensure the optimal tailoring of nutritional therapies during several pathologic conditions. The need for feasible and reliable methods ...

Abstract	Evaluation of body composition (BC) is crucial for an adequate assessment of nutritional status and its alterations, to ensure the optimal tailoring of nutritional therapies during several pathologic conditions. The need for feasible and reliable methods for BC measurement, which could be applied either in healthcare across the lifespan as well as in clinical research and epidemiologic studies, has led to the development of various techniques. Unfortunately, they have not always produced equivalent results due to the fact that they are based on completely different principles or suffer intrinsic biases related to specific conditions. Furthermore, different population and clinical settings (ethnicity, age, type of disease) may interfere, thereby leading to dissimilar results. Finally, the need to compare the data obtained by new techniques to a reference standard has produced a further bias, due to a systematic misinterpretation of the statistical methods in the attempt to correlate the various techniques. In this context, the most used statistical methods for the comparison between different techniques have been Pearson's correlation test, the more recent intraclass correlation coefficient, Lin's concordance correlation coefficient method, and the Bland-Altman analysis. The aim of this review was to offer a summary of the methods that are mostly used in clinical practice to measure BC with the intent to give appropriate suggestions when statistical methods are used to interpret data, and underline pitfalls and limitations.
MeSH term(s)	Absorptiometry, Photon/methods ; Body Composition ; Body Mass Index ; Electric Impedance ; Ethnicity ; Humans ; Nutritional Status
Language	English
Publishing date	2022-05-14
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	639259-3
ISSN	1873-1244 ; 0899-9007
ISSN (online)	1873-1244
ISSN	0899-9007
DOI	10.1016/j.nut.2022.111736
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