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Article ; Online: The Cellular Response to Complex DNA Damage Induced by Ionising Radiation

Beth Wilkinson / Mark A. Hill / Jason L. Parsons

International Journal of Molecular Sciences, Vol 24, Iss 4920, p

2023 Volume 4920

Abstract: Radiotherapy (ionising radiation; IR) is utilised in the treatment of ~50% of all human cancers, and where the therapeutic effect is largely achieved through DNA damage induction. In particular, complex DNA damage (CDD) containing two or more lesions ... ...

Abstract	Radiotherapy (ionising radiation; IR) is utilised in the treatment of ~50% of all human cancers, and where the therapeutic effect is largely achieved through DNA damage induction. In particular, complex DNA damage (CDD) containing two or more lesions within one to two helical turns of the DNA is a signature of IR and contributes significantly to the cell killing effects due to the difficult nature of its repair by the cellular DNA repair machinery. The levels and complexity of CDD increase with increasing ionisation density (linear energy transfer, LET) of the IR, such that photon (X-ray) radiotherapy is deemed low-LET whereas some particle ions (such as carbon ions) are high-LET radiotherapy. Despite this knowledge, there are challenges in the detection and quantitative measurement of IR-induced CDD in cells and tissues. Furthermore, there are biological uncertainties with the specific DNA repair proteins and pathways, including components of DNA single and double strand break mechanisms, that are engaged in CDD repair, which very much depends on the radiation type and associated LET. However, there are promising signs that advancements are being made in these areas and which will enhance our understanding of the cellular response to CDD induced by IR. There is also evidence that targeting CDD repair, particularly through inhibitors against selected DNA repair enzymes, can exacerbate the impact of higher LET, which could be explored further in a translational context.
Keywords	carbon ions ; complex DNA damage ; DNA repair ; ionising radiation ; linear energy transfer ; proton beam therapy ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	612
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Leveraging machine learning for taxonomic classification of emerging astroviruses

Fatemeh Alipour / Connor Holmes / Yang Young Lu / Kathleen A. Hill / Lila Kari

Frontiers in Molecular Biosciences, Vol

2024 Volume 10

Abstract: Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, ... ...

Abstract	Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method’s pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.
Keywords	machine learning ; viral classification and clustering ; family Astroviridae ; Avastrovirus ; Mamastrovirus ; alignment-free classification ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: N6‐methyladenosine‐RNA methylation promotes expression of solute carrier family 7 member 11, an uptake transporter of cystine for lipid reactive oxygen species scavenger glutathione synthesis, leading to hepatoblastoma ferroptosis resistance

Ronald A. Hill / Yong‐Yu Liu

Clinical and Translational Medicine, Vol 12, Iss 5, Pp n/a-n/a (2022)

2022

Keywords	cystine ; ferroptosis ; hepatoblastoma ; METTL3 ; RNA methylation ; SLC7A11 transporter ; Medicine (General) ; R5-920
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Role of Adenylyl Cyclase Type 7 in Functions of BV-2 Microglia

Yawen Hu / Rebecca A. Hill / Masami Yoshimura

International Journal of Molecular Sciences, Vol 24, Iss 1, p

2022 Volume 347

Abstract: To assess the role of adenylyl cyclase type 7 (AC7) in microglia’s immune function, we generated AC7 gene knockout (AC7 KO) clones from a mouse microglial cell line, BV-2, using the CRISPR-Cas9 gene editing system. The ability of BV-2 cells to generate ... ...

Abstract	To assess the role of adenylyl cyclase type 7 (AC7) in microglia’s immune function, we generated AC7 gene knockout (AC7 KO) clones from a mouse microglial cell line, BV-2, using the CRISPR-Cas9 gene editing system. The ability of BV-2 cells to generate cAMP and their innate immune functions were examined in the presence or absence of ethanol. The parental BV-2 cells showed robust cAMP production when stimulated with prostaglandin-E 1 (PGE 1 ) and ethanol increased cAMP production in a dose-dependent manner. AC7 KO clones of BV-2 cells showed diminished and ethanol-insensitive cAMP production. The phagocytic activity of the parental BV-2 cells was inhibited in the presence of PGE 1 AC7 KO BV-2 cells showed lower and PGE 1 -insensitive phagocytic activity. Innate immune activities of the parental BV-2 cells, including bacterial killing, nitric oxide synthesis, and expression of arginase 1 and interleukin 10 were activated as expected with small effects of ethanol. However, the innate immune activities of AC7 KO cells were either drastically diminished or not detected. The data presented suggest that AC7 has an important role in the innate immune functions of microglial cells. AC7’s involvement in ethanol’s effects on immune functions remains unclear. Further studies are needed.
Keywords	microglia ; adenylyl cyclase type 7 ; CRISPR-Cas9 gene editing ; macrophage activation ; alcohol ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	570
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Camera trapping with photos and videos

Sian E. Green / Philip A. Stephens / Mark J. Whittingham / Russell A. Hill

Remote Sensing in Ecology and Conservation, Vol 9, Iss 2, Pp 268-

implications for ecology and citizen science

2023 Volume 283

Abstract: Abstract Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high‐quality video, the ... ...

Abstract	Abstract Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high‐quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera‐trapping projects.
Keywords	activity level ; camera traps ; citizen science ; occupancy ; species diversity ; Technology ; T ; Ecology ; QH540-549.5
Subject code	020
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Stratospheric Aerosol and Gas Experiment (SAGE) from SAGE III on the ISS to a Free Flying SAGE IV Cubesat

John P. Leckey / Robert Damadeo / Charles A. Hill

Remote Sensing, Vol 13, Iss 4664, p

2021 Volume 4664

Abstract: The Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) is widely accepted as a stable source for high-quality stratospheric ozone, aerosol, and water vapor measurements since it was installed on the ISS in ... ...

Abstract	The Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) is widely accepted as a stable source for high-quality stratospheric ozone, aerosol, and water vapor measurements since it was installed on the ISS in 2017. The ISS is a unique platform that provides access for hosted payloads while furnishing infrastructure for power, uplink, downlink, etc. for instrument operations. The opportunities, risks, and challenges from operating on the ISS are described in addition to comprehensive lessons learned. In addition, SAGE IV is presented as an option for the future of the SAGE lineage where the lessons learned from SAGE III and technological advances have enabled the instrument to fit into a 6U CubeSat yielding a significantly smaller and cheaper form-factor to preserve the continuity of critical atmospheric measurements.
Keywords	stratosphere ; ozone ; aerosol ; ISS ; SAGE III ; SAGE IV ; Science ; Q
Subject code	070
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Influence of ionizing radiation and cell density on the kinetics of autocrine destruction and intercellular induction of apoptosis in precancerous cells

Abdelrazek B. Abdelrazzak / Peter O’Neill / Mark A. Hill

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

2022 Volume 12

Abstract: Abstract Intercellular induction of apoptosis (IIA) represents a well-defined signaling model by which precancerous cells are selectively eradicated through reactive oxygen/nitrogen species and cytokine signaling from neighbour normal cells. Previously, ... ...

Abstract	Abstract Intercellular induction of apoptosis (IIA) represents a well-defined signaling model by which precancerous cells are selectively eradicated through reactive oxygen/nitrogen species and cytokine signaling from neighbour normal cells. Previously, we demonstrated that the IIA process could be enhanced by exposure of normal cells to very low doses of ionizing radiation as a result of perturbing the intercellular signaling. In this study, we investigate the kinetic behaviour of both autocrine destruction (AD) and IIA as a function of cell density of both precancerous and normal cells using an insert co-culture system and how exposure of normal cells to ionizing radiation influence the kinetics of apoptosis induction in precancerous cells. Increasing the seeding density of transformed cells shifts the kinetics of AD towards earlier times with the response plateauing only at high seeding densities. Likewise, when co-culturing precancerous cells with normal cells, increasing the seeding density of either normal or precancerous cells also shifts the kinetics of IIA response towards earlier times and plateau only at higher seeding densities. Irradiation of normal cells prior to co-culture further enhances the kinetics of IIA response, with the degree of enhancement dependent on the relative cell densities. These results demonstrate the pivotal role of the cell seeding density of normal and precancerous cells in modulating both AD and IIA. These results further support the proposition that ionizing radiation could result in an enhancement in the rate of removal of precancerous cells through the IIA process.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Intolerant baboons avoid observer proximity, creating biased inter-individual association patterns

Andrew T. L. Allan / Amy F. White / Russell A. Hill

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

2022 Volume 14

Abstract: Abstract Social network analysis is an increasingly popular tool for behavioural ecologists exploring the social organisation of animal populations. Such analyses require data on inter-individual association patterns, which in wild populations are often ... ...

Abstract	Abstract Social network analysis is an increasingly popular tool for behavioural ecologists exploring the social organisation of animal populations. Such analyses require data on inter-individual association patterns, which in wild populations are often collected using direct observations of habituated animals. This assumes observers have no influence on animal behaviour; however, our previous work showed that individuals in a habituated group of chacma baboons (Papio ursinus griseipes) displayed consistent and individually distinct responses to observer approaches. We explored the implications of our previous findings by measuring the inter-individual association patterns of the same group of chacma baboons at different observer distances. We found a strong positive association between individual tolerance levels (towards observers) and how often an animal appeared as a neighbour to focal animals when observers were nearer, and a neutral relationship between the same variables when the observer was further away. Additionally, association matrices constructed from different observation distances were not comparable within any proximity buffer, and neither were the individual network metrics generated from these matrices. This appears to be the first empirical evidence that observer presence and behaviour can influence the association patterns of habituated animals and thus have potentially significant impacts on measured social networks.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: DeLUCS

Pablo Millán Arias / Fatemeh Alipour / Kathleen A. Hill / Lila Kari

PLoS ONE, Vol 17, Iss

Deep learning for unsupervised clustering of DNA sequences

2022 Volume 1

Abstract: We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary ... ...

Abstract	We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates “mimic” sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.
Keywords	Medicine ; R ; Science ; Q
Subject code	612 ; 006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: DeLUCS

Pablo Millán Arias / Fatemeh Alipour / Kathleen A Hill / Lila Kari

PLoS ONE, Vol 17, Iss 1, p e

Deep learning for unsupervised clustering of DNA sequences.

2022 Volume 0261531

Abstract	We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates "mimic" sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.
Keywords	Medicine ; R ; Science ; Q
Subject code	612 ; 006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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