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Article ; Online: Mucosal T-cell responses to chronic viral infections: Implications for vaccine design.

Al-Talib, Mohammed / Dimonte, Sandra / Humphreys, Ian R

2024

Abstract: Mucosal surfaces that line the respiratory, gastrointestinal and genitourinary tracts are the major interfaces between the immune system and the environment. Their unique immunological landscape is characterized by the necessity of balancing tolerance to ...

Abstract	Mucosal surfaces that line the respiratory, gastrointestinal and genitourinary tracts are the major interfaces between the immune system and the environment. Their unique immunological landscape is characterized by the necessity of balancing tolerance to commensal microorganisms and other innocuous exposures against protection from pathogenic threats such as viruses. Numerous pathogenic viruses, including herpesviruses and retroviruses, exploit this environment to establish chronic infection. Effector and regulatory T-cell populations, including effector and resident memory T cells, play instrumental roles in mediating the transition from acute to chronic infection, where a degree of viral replication is tolerated to minimize immunopathology. Persistent antigen exposure during chronic viral infection leads to the evolution and divergence of these responses. In this review, we discuss advances in the understanding of mucosal T-cell immunity during chronic viral infections and how features of T-cell responses develop in different chronic viral infections of the mucosa. We consider how insights into T-cell immunity at mucosal surfaces could inform vaccine strategies: not only to protect hosts from chronic viral infections but also to exploit viruses that can persist within mucosal surfaces as vaccine vectors.
Language	English
Publishing date	2024-03-08
Publishing country	China
Document type	Journal Article ; Review
ZDB-ID	2435097-7
ISSN	2042-0226 ; 1672-7681
ISSN (online)	2042-0226
ISSN	1672-7681
DOI	10.1038/s41423-024-01140-2
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Article ; Online: Genetic influences on viral-induced cytokine responses in the lung.

Forbester, Jessica L / Humphreys, Ian R

Mucosal immunology

2020 Volume 14, Issue 1, Page(s) 14–25

Abstract: Infection with respiratory viruses such as influenza, respiratory syncytial virus and coronavirus provides a difficult immunological challenge for the host, where a balance must be established between controlling viral replication and limiting damage to ... ...

Abstract	Infection with respiratory viruses such as influenza, respiratory syncytial virus and coronavirus provides a difficult immunological challenge for the host, where a balance must be established between controlling viral replication and limiting damage to the delicate lung structure. Although the genetic architecture of host responses to respiratory viral infections is not yet understood, it is clear there is underlying heritability that influences pathogenesis. Immune control of virus replication is essential in respiratory infections, but overt activation can enhance inflammation and disease severity. Cytokines initiate antiviral immune responses but are implicated in viral pathogenesis. Here, we discuss how host genetic variation may influence cytokine responses to respiratory viral infections and, based on our current understanding of the role that cytokines play in viral pathogenesis, how this may influence disease severity. We also discuss how induced pluripotent stem cells may be utilised to probe the mechanistic implications of allelic variation in genes in virus-induced inflammatory responses. Ultimately, this could help to design better immune modulators, stratify high risk patients and tailor anti-inflammatory treatments, potentially expanding the ability to treat respiratory virus outbreaks in the future.
MeSH term(s)	COVID-19/immunology ; COVID-19/pathology ; Cytokines/blood ; Cytokines/genetics ; Genetic Variation/genetics ; Genetic Variation/immunology ; Humans ; Induced Pluripotent Stem Cells ; Inflammation/genetics ; Inflammation/pathology ; Influenza A virus/immunology ; Influenza, Human/immunology ; Lung/pathology ; Lung/virology ; Respiratory Syncytial Virus Infections/immunology ; Respiratory Syncytial Viruses/immunology ; SARS-CoV-2/immunology
Chemical Substances	Cytokines
Keywords	covid19
Language	English
Publishing date	2020-11-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2411370-0
ISSN	1935-3456 ; 1933-0219
ISSN (online)	1935-3456
ISSN	1933-0219
DOI	10.1038/s41385-020-00355-6
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Article ; Online: Elevated interleukin-6, interleukin-10 and neutrophil : lymphocyte ratio as identifiers of severe coronavirus disease 2019.

Godkin, Andrew / Humphreys, Ian R

Immunology

2020 Volume 160, Issue 3, Page(s) 221–222

MeSH term(s)	Betacoronavirus ; COVID-19 ; China ; Coronavirus ; Coronavirus Infections ; Humans ; Interleukin-10 ; Interleukin-6 ; Lymphocytes ; Neutrophils ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2
Chemical Substances	Interleukin-6 ; Interleukin-10 (130068-27-8)
Keywords	covid19
Language	English
Publishing date	2020-07-02
Publishing country	England
Document type	Editorial ; Comment
ZDB-ID	80124-0
ISSN	1365-2567 ; 0019-2805 ; 0953-4954
ISSN (online)	1365-2567
ISSN	0019-2805 ; 0953-4954
DOI	10.1111/imm.13225
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Article ; Online: Recent advances in predicting and modeling protein–protein interactions

Durham, Jesse / Zhang, Jing / Humphreys, Ian R. / Pei, Jimin / Cong, Qian

Trends in Biochemical Sciences. 2023 Apr. 14,

2023

Abstract: Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial ... ...

Abstract	Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
Keywords	artificial intelligence ; nucleotide sequences ; prediction ; protein–protein interaction (PPI) ; coevolution ; homology ; multiple sequence alignment (MSA) ; protein–protein docking ; machine learning ; interactome
Language	English
Dates of publication	2023-0414
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Pre-press version
ZDB-ID	194220-7
ISSN	0968-0004 ; 0376-5067
ISSN	0968-0004 ; 0376-5067
DOI	10.1016/j.tibs.2023.03.003
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Cytokine-Mediated Induction and Regulation of Tissue Damage During Cytomegalovirus Infection.

Clement, Mathew / Humphreys, Ian R

Frontiers in immunology

2019 Volume 10, Page(s) 78

Abstract: Human cytomegalovirus (HCMV) is a β-herpesvirus with high sero-prevalence within the human population. Primary HCMV infection and life-long carriage are typically asymptomatic. However, HCMV is implicated in exacerbation of chronic conditions and ... ...

Abstract	Human cytomegalovirus (HCMV) is a β-herpesvirus with high sero-prevalence within the human population. Primary HCMV infection and life-long carriage are typically asymptomatic. However, HCMV is implicated in exacerbation of chronic conditions and associated damage in individuals with intact immune systems. Furthermore, HCMV is a significant cause of morbidity and mortality in the immunologically immature and immune-compromised where disease is associated with tissue damage. Infection-induced inflammation, including robust cytokine responses, is a key component of pathologies associated with many viruses. Despite encoding a large number of immune-evasion genes, HCMV also triggers the induction of inflammatory cytokine responses during infection. Thus, understanding how cytokines contribute to CMV-induced pathologies and the mechanisms through which they are regulated may inform clinical management of disease. Herein, we discuss our current understanding based on clinical observation and
MeSH term(s)	Animals ; Cytokines/immunology ; Cytomegalovirus Infections/immunology ; Humans
Chemical Substances	Cytokines
Language	English
Publishing date	2019-01-29
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2019.00078
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Article: Recent advances in predicting and modeling protein-protein interactions.

Durham, Jesse / Zhang, Jing / Humphreys, Ian R / Pei, Jimin / Cong, Qian

Trends in biochemical sciences

2023 Volume 48, Issue 6, Page(s) 527–538

Abstract: Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial ... ...

Abstract	Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
MeSH term(s)	Artificial Intelligence ; Protein Interaction Mapping/methods ; Algorithms ; Machine Learning ; Proteome ; Computational Biology/methods
Chemical Substances	Proteome
Language	English
Publishing date	2023-04-14
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	194216-5
ISSN	1362-4326 ; 0968-0004 ; 0376-5067
ISSN (online)	1362-4326
ISSN	0968-0004 ; 0376-5067
DOI	10.1016/j.tibs.2023.03.003
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Article: Elevated interleukin-6, interleukin-10 and neutrophil : lymphocyte ratio as identifiers of severe coronavirus disease 2019

Godkin, Andrew / Humphreys, Ian R

Immunology

Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #635318
Database	COVID19

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Article ; Online: Novel viral vectors in infectious diseases.

Humphreys, Ian R / Sebastian, Sarah

Immunology

2017 Volume 153, Issue 1, Page(s) 1–9

Abstract: Since the development of vaccinia virus as a vaccine vector in 1984, the utility of numerous viruses in vaccination strategies has been explored. In recent years, key improvements to existing vectors such as those based on adenovirus have led to ... ...

Abstract	Since the development of vaccinia virus as a vaccine vector in 1984, the utility of numerous viruses in vaccination strategies has been explored. In recent years, key improvements to existing vectors such as those based on adenovirus have led to significant improvements in immunogenicity and efficacy. Furthermore, exciting new vectors that exploit viruses such as cytomegalovirus (CMV) and vesicular stomatitis virus (VSV) have emerged. Herein, we summarize these recent developments in viral vector technologies, focusing on novel vectors based on CMV, VSV, measles and modified adenovirus. We discuss the potential utility of these exciting approaches in eliciting protection against infectious diseases.
MeSH term(s)	Adaptive Immunity ; Animals ; Communicable Disease Control/methods ; Communicable Diseases/immunology ; Genetic Vectors/genetics ; Genetic Vectors/immunology ; Humans ; Vaccination ; Vaccines, Synthetic/genetics ; Vaccines, Synthetic/immunology ; Viral Vaccines/genetics ; Viral Vaccines/immunology ; Viruses/classification ; Viruses/genetics ; Viruses/immunology
Chemical Substances	Vaccines, Synthetic ; Viral Vaccines
Language	English
Publishing date	2017-09-26
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	80124-0
ISSN	1365-2567 ; 0019-2805 ; 0953-4954
ISSN (online)	1365-2567
ISSN	0019-2805 ; 0953-4954
DOI	10.1111/imm.12829
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Article ; Online: COVID-19 and X-linked agammaglobulinemia (XLA) - insights from a monogenic antibody deficiency.

Ponsford, Mark J / Shillitoe, Benjamin M J / Humphreys, Ian R / Gennery, Andrew R / Jolles, Stephen

Current opinion in allergy and clinical immunology

2021 Volume 21, Issue 6, Page(s) 525–534

Abstract: Purpose of review: The clinical outcomes from COVID-19 in monogenic causes of predominant antibody deficiency have pivotal implications for our understanding of the antiviral contribution of humoral immunity. This review summarizes the lessons learned ... ...

Abstract	Purpose of review: The clinical outcomes from COVID-19 in monogenic causes of predominant antibody deficiency have pivotal implications for our understanding of the antiviral contribution of humoral immunity. This review summarizes the lessons learned from COVID-19 infection in X-linked agammaglobulinemia (XLA) due to genetic defects in Bruton's tyrosine kinase (BTK). Recent findings: Key molecular pathways underlying the development of severe COVID-19 are emerging, highlighting the possible contribution of BTK to hyperinflammation. SARS-CoV-2 specific T-cell responses and complement activation appear insufficient to achieve viral clearance in some B-cell deficient individuals. Whilst appearing efficacious in this group, use of convalescent plasma has been recently associated with the evolution of viral escape variants. Early data suggests individuals with XLA can mount a viral-specific T-cell vaccine response, however, the clinical significance of this is still emerging. Summary: In contrast to reports made early in the pandemic, we show XLA patients remain susceptible to severe disease. Persistent infection was common and is likely to carry a significant symptom burden and risk of novel variant evolution. COVID-19 infection in this vulnerable, antibody deficient group due to genetic, therapeutic or disease causes may require prompt and specific intervention for both patient and societal benefit.
MeSH term(s)	Agammaglobulinaemia Tyrosine Kinase/genetics ; Agammaglobulinemia/complications ; Agammaglobulinemia/genetics ; Agammaglobulinemia/immunology ; COVID-19/diagnosis ; COVID-19/immunology ; COVID-19/virology ; Evolution, Molecular ; Genetic Diseases, X-Linked/complications ; Genetic Diseases, X-Linked/genetics ; Genetic Diseases, X-Linked/immunology ; Humans ; SARS-CoV-2/genetics ; SARS-CoV-2/immunology ; SARS-CoV-2/pathogenicity ; Severity of Illness Index
Chemical Substances	Agammaglobulinaemia Tyrosine Kinase (EC 2.7.10.2)
Language	English
Publishing date	2021-10-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2088710-3
ISSN	1473-6322 ; 1528-4050
ISSN (online)	1473-6322
ISSN	1528-4050
DOI	10.1097/ACI.0000000000000792
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Article ; Online: Protein oligomer modeling guided by predicted interchain contacts in CASP14.

Baek, Minkyung / Anishchenko, Ivan / Park, Hahnbeom / Humphreys, Ian R / Baker, David

Proteins

2021 Volume 89, Issue 12, Page(s) 1824–1833

Abstract: For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes ... ...

Abstract	For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient-based fold-and-dock method with template-based and ab initio docking approaches using deep learning-based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z-scores 5.5 units higher than the next best group, with the fold-and-dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM-score of 0.71 (average oligomer TM-score of the next best group: 0.64), and explicit modeling of inter-subunit interactions improved modeling of six out of 40 individual domains (ΔGDT-TS > 2.0).
MeSH term(s)	Computational Biology ; Databases, Protein ; Deep Learning ; Models, Molecular ; Protein Binding ; Protein Conformation ; Protein Subunits/chemistry ; Protein Subunits/metabolism ; Proteins/chemistry ; Proteins/metabolism ; Sequence Analysis, Protein ; Software
Chemical Substances	Protein Subunits ; Proteins
Language	English
Publishing date	2021-08-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.26197
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