Biology subjects

Hartley, C.

Publications and source records attributed to Hartley, C..

11 recordsLinked to original sources

bioRxiv2025

Spatial Conformation of Pancreatic Cancer Is Associated with Disease Recurrence after Curative-Intent Total Neoadjuvant Therapy

Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after total neoadjuvant therapy (TNT) and curative-intent resection. Traditional histopathologic response assessments demonstrate that major pathologic response (<5-10% viable residual cancer) is associated with favorable outcomes. However, most TNT cases achieve only a minor response. We investigated whether the spatial organization of residual PDAC encodes clinically meaningful biology beyond residual tumor burden. In a retrospective cohort of 203 resected PDAC patients, all with minor pathologic response, H&E whole-slide images were segmented into cancer and stroma to subsequently quantify spatial composition (e.g., patch size/density, edge density) and configuration (e.g., patch complexity/compactness, spatial intermixing) to model treatment-resistant tumor architecture. Non-response was associated with a more fragmented interface-rich ecology, higher edge density and diversity, and reduced homotypic aggregation, independent of conventional clinicopathologic features. This demonstrates a link between emergent tissue architecture of treated PDAC and therapeutic resistance. Further, two multivariable, spatial risk models were independently associated with disease-free survival (DFS): (1) cancer mean shape index and stromal shape-index variability (high-risk median DFS 7.23 vs 11.57 months; adjusted HR 1.75, p=0.007) and (2) mean stromal area with edge density (high- vs low-risk adjusted HR 1.94, p=0.006), outperforming traditional treatment response assessments. Quantifying residual cancer-stroma topology thus yields independent, prognostic signals in post-TNT PDAC and motivates prospective, spatially informed adjuvant strategies and mechanistic studies of edge habitats and mixing as therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

PYEAST - Python Enabled Automated Strain Transformaiton

Saccharomyces cerevisiae is a widely used biotechnological workhorse in both academic and industrial settings. One reason for its continued popularity is the extensive legacy of genetic tools, developed over its long history of use, that enable precise manipulation of the S. cerevisiae genome. These tools have enabled extensive genetic characterisation and dramatic re-programming efforts for applications ranging from fundamental research to industrial chemical production. Here we present a digital toolkit called PYEAST (Python Enabled Automated Strain Transformation) that encodes some of the most widely used methods for working with S. cerevisiae and modernizes them to leverage advances in DNA synthesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/655004v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@4584feorg.highwire.dtl.DTLVardef@1e65caorg.highwire.dtl.DTLVardef@1acbe2borg.highwire.dtl.DTLVardef@1f91abc_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Cortical motor activity modulates respiration and reduces apnoea in neonates

Respiration is governed by a widespread network of cortical and subcortical structures. This complex communication between the brain and lungs is altered in pathological conditions. Apnoea - the cessation of respiration - is a common condition in infants, particularly those born prematurely. Apnoea in infants is believed to relate to immaturity of brainstem respiratory centres; involvement of the cortex in respiration in infants has yet to be explored. We investigated if there was any evidence for cortical coupling with respiration in newborn humans and whether it relates to apnoea. Using simultaneous electroencephalography (EEG) and impedance pneumography we investigated interactions between cortical and respiratory activity (known as cortico-respiratory coupling) using phase-amplitude coupling. We show that cortico-respiratory coupling is present in premature and term newborns (104 recordings from 68 infants; 34.5 {+/-} 2.6 weeks post-menstrual age), identifying an interplay between breathing phase and EEG amplitude. We further shed light on the biological meaning by revealing that the strongest coupling occurs during inspiration and that cortical activity precedes respiration, with coupling strongest over frontocentral regions. Whilst our study was limited in spatial resolution, and determining causality is challenging, we believe these findings support the notion that the cortico-respiratory coupling observed here constitutes communication between cortical motor areas and lung effectors. Moreover, we show that cortico-respiratory coupling is negatively correlated with the rate of apnoea, revealing novel insight into this common and potentially life-threatening neonatal pathology.

neuroscience↗

bioRxiv2024

Apnoea suppresses brain activity in infants

Apnoea - the cessation of breathing - is commonly observed in premature infants. These events can reduce cerebral oxygenation and are associated with poorer neurodevelopmental outcomes. However, relatively little is known about how apnoea and shorter pauses in breathing impact brain function in infants, which will provide greater mechanistic understanding of how apnoea affects brain development. We analysed simultaneous recordings of respiration, electroencephalography (EEG), heart rate, and peripheral oxygen saturation in 124 recordings from 118 infants (post-menstrual age: 38.6 {+/-} 2.7 weeks [mean {+/-} standard deviation]) during apnoeas (pauses in breathing greater than 15 seconds) and shorter pauses in breathing between 5 and 15 seconds. EEG amplitude significantly decreased during both apnoeas and shorter pauses in breathing compared with normal breathing periods. Change in EEG amplitude was significantly associated with change in heart rate during apnoea and breathing pauses and, during apnoeas only, with oxygen saturation change. No associations were found between EEG amplitude and pause duration or post-menstrual age. The decrease in EEG amplitude may be a result of the changing metabolism and/or homeostasis following changes in oxygen and carbon dioxide concentrations, which alters the release of neurotransmitters. As apnoeas often occur in premature infants, frequent disruption to brain activity may impact neural development and result in long-term neurodevelopmental consequences.

neuroscience↗

bioRxiv2023

Sensory event-related potential morphology predicts age in premature infants

Preterm infants undergo substantial neurosensory development in the first weeks after birth. Infants born prematurely are more likely to have long-term adverse neurological outcomes and early detection of abnormal brain development is essential for timely interventions. We investigated whether sensory-evoked cortical potentials could be used to accurately estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment by comparing the brain age to the infants postmenstrual age (PMA). Infants aged between 28- and 40-weeks PMA from a training and test sample (consisting of 101 and 65 recording sessions in 82 and 14 infants, respectively) received trains of approximately 10 visual and 10 tactile stimuli (interstimulus interval approximately 10 seconds). PMA could be predicted accurately from the magnitude of the evoked responses (training set mean absolute error (MAE and 95% confidence intervals): 1.41 [1.14; 1.74] weeks, p = 0.0001; test set MAE: 1.55 [1.21; 1.95] weeks, p = 0.0002. Moreover, we show with two examples that brain age, and the deviations between brain age and PMA, may be biologically and clinically meaningful. By firstly demonstrating that brain age is correlated with a measure known to relate to maturity of the nervous system (based on animal and human literature, the magnitude of reflex withdrawal is used) and secondly by linking brain age to long-term neurological outcomes, we show that brain age deviations are related to biologically meaningful individual differences in the rate of functional nervous system maturation rather than noise generated by the model. In summary, we demonstrate that sensory-evoked potentials are predictive of age in premature infants. It takes less than 5 minutes to collect the stimulus electroencephalographic data required for our model, hence, increasing its potential utility in the busy neonatal care unit. This model could be used to detect abnormal development of infants response to sensory stimuli in their environment and may be predictive of later life abnormal neurodevelopmental outcome.

neuroscience↗

bioRxiv2023

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonates brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/- 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically >2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning models brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the models generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks). HighlightsO_LIPreterm stress exposure leads to long-term neurodevelopmental deficits C_LIO_LIDeficits are quantifiable using EEG-based brain age prediction errors C_LIO_LIOur deep-learning solution for brain age prediction outperforms previous approaches C_LIO_LIPredictions are achieved with only 20 mins EEG and a single bipolar channel C_LIO_LIPrediction errors correlate with long-term Bayley scale neurodevelopmental outcomes C_LI

neuroscience↗

bioRxiv2021

Rapid selection of P323L in the SARS-CoV-2 polymerase (NSP12) in humans and non-human primate models and confers a large plaque phenotype

The mutational landscape of SARS-CoV-2 varies at both the dominant viral genome sequence and minor genomic variant population. An early change associated with transmissibility was the D614G substitution in the spike protein. This appeared to be accompanied by a P323L substitution in the viral polymerase (NSP12), but this latter change was not under strong selective pressure. Investigation of P323L/D614G changes in the human population showed rapid emergence during the containment phase and early surge phase of wave 1 in the UK. This rapid substitution was from minor genomic variants to become part of the dominant viral genome sequence. A rapid emergence of 323L but not 614G was observed in a non-human primate model of COVID-19 using a starting virus with P323 and D614 in the dominant genome sequence and 323L and 614G in the minor variant population. In cell culture, a recombinant virus with 323L in NSP12 had a larger plaque size than the same recombinant virus with P323. These data suggest that it may be possible to predict the emergence of a new variant based on tracking the distribution and frequency of minor variant genomes at a population level, rather than just focusing on providing information on the dominant viral genome sequence e.g., consensus level reporting. The ability to predict an emerging variant of SARS-CoV-2 in the global landscape may aid in the evaluation of medical countermeasures and non-pharmaceutical interventions.

microbiology↗

bioRxiv2021

Using Oxford Nanopore Technology direct RNA sequencing to identify depurination events induced by ricin and other ribosome inactivating proteins

Depurination is a frequent modification to both DNA and RNA, in DNA causing point mutations through misincorporation, in RNA, disabling ribosomes and halting protein synthesis. Some modifications of nucleic acids can be determined by direct sequencing using Oxford Nanopore Technologies (ONT). However, the identification of modifications is often limited by noise and their variety and number. Ricin is a toxin which enters cells and depurinates an adenine base in the sarcin-ricin loop of the large ribosomal subunit. This leaves only a ribose backbone, thus inhibiting protein translation. In humans, biological threat agents and ribosome inactivating proteins, such as ricin and saporin, depurinate base 4605 on the 28S rRNA providing a single defined target to try and identify. We postulated that the depurination event could be detected using ONT direct RNA sequencing through a change in charge in the ricin loop. A software tool was developed, RIPpore, that quantified the adenine modification from direct RNA sequencing data of ribosomal RNA purified from respiratory epithelial cells exposed to ricin. This provided a novel method of directly identifying ricin exposure and a basis for ONTs utility in detecting lesions in nucleic acids caused by depurination events.

pharmacology and toxicology↗

bioRxiv2021

Sequence analysis of SARS-CoV-2 in nasopharyngeal samples from patients with COVID-19 illustrates population variation and diverse phenotypes, placing the in vitro growth properties of B.1.1.7 and B.1.351 lineage viruses in context.

New variants of SARS-CoV-2 are continuing to emerge and dominate the regional and global sequence landscapes. Several variants have been labelled as Variants of Concern (VOCs) because of perceptions or evidence that these may have a transmission advantage, increased risk of morbidly and/or mortality or immune evasion in the context of prior infection or vaccination. Placing the VOCs in context and also the underlying variability of SARS-CoV-2 is essential in understanding virus evolution and selection pressures. Sequences of SARS-CoV-2 in nasopharyngeal swabs from hospitalised patients in the UK were determined and virus isolated. The data indicated the virus existed as a population with a consensus level and non-synonymous changes at a minor variant. For example, viruses containing the nsp12 P323L variation from the Wuhan reference sequence, contained minor variants at the position including P and F and other amino acids. These populations were generally preserved when isolates were amplified in cell culture. In order to place VOCs B.1.1.7 (the UK Kent variant) and B.1.351 (the South African variant) in context their growth was compared to a spread of other clinical isolates. The data indicated that the growth in cell culture of the B.1.1.7 VOC was no different from other variants, suggesting that its apparent transmission advantage was not down to replicating more quickly. Growth of B.1.351 was towards the higher end of the variants. Overall, the study suggested that studying the biology of SARS-CoV-2 is complicated by population dynamics and that these need to be considered with new variants. ImportanceSARS-CoV-2 is the causative agent of COVID-19. The virus has spread across the planet causing a global pandemic. In common with other coronaviruses, SARS-CoV-2 genetic material (genomes) can become quite diverse as a consequence of replicating inside cells. This has given rise to multiple variants from the original virus that infected humans. These variants may have different properties and in the context of a widespread vaccination program may render vaccines less ineffective. Our research confirms the degree of genetic diversity of SARS-CoV-2 in patients. By isolating viruses from these patients, we show that there is a 100-fold range in growth of even normal variants. Interestingly, by comparing this to the pattern seen with two Variants of Concern (UK and South African variants), we show that at least in cells the ability of the B.1.1.7 variant to grow is not substantially different to many of the previous variants.

microbiology↗

bioRxiv2020

A retrospective analysis of gender parity in scientific authorship in a biomedical research centre

ObjectiveScientific authorship is a vital marker of success in academic careers and gender equity is a key performance metric in research. However, there is little understanding of gender equity in publications in biomedical research centres funded by the National Institute for Health Research (NIHR). This study assesses the gender parity in scientific authorship of biomedical research. DesignA retrospective descriptive study. SettingNIHR Oxford Biomedical Research Centre. Data2409 publications accepted or published from 1 April 2012 to 31 March 2017. Main outcome measuresGender of authors, defined as a binary variable comprising either male or female categories, in six authorship categories: first author, joint first authors, first corresponding author, joint corresponding authors, last author and joint last authors. ResultsPublications comprised clinical research (39%, n=939), basic research (27%, n=643), and other types of research (34%, n=827). The proportion of female authors as first author (41%), first corresponding authors (34%) and last author (23%) was statistically significantly lower than male authors in these authorship categories. Of total joint first authors (n=458), joint corresponding authors (n=169), and joint last authors (n=229), female only authors comprised statistically significant smaller proportions i.e. 15% (n=69), 29% (n=49) and 10% (n=23) respectively, compared to male only authors in these joint authorship categories. There was a statistically significant association between gender of the last author(s) with gender of the first author(s) ({chi} 2 33.742, P < 0.001), corresponding author(s) ({chi}2 540.774, P < 0.001) and joint last author(s) ({chi} 2 91.291, P < 0.001). ConclusionsAlthough there are increasing trends of female authors as first authors (41%) and last authors (23%), female authors are underrepresented compared to male authors in all six categories of scientific authorship in biomedical research. Further research is needed to encourage gender parity in different categories of scientific authorship. Strengths and limitations of this studyO_LIThis is the first study to investigate gender parity in six categories of scientific authorship: first authors, first corresponding authors, last authors and three joint authorship categories i.e. joint first authors, joint corresponding authors and joint last authors in biomedical research. C_LIO_LIThis study provides an important benchmark on gender equity in scientific authorship for other NIHR funded centres and organisations in England. C_LIO_LIThe generalisability of the findings of this study may be limited due to differences in medical specialities, research areas, institutional cultures, and levels of support to individual researchers. C_LIO_LIUsing secondary sources for determining the gender of authors may have limitations, which could be avoided by seeking relevant information from original authors and institution affiliation at the time of submission. C_LI

scientific communication and education↗

bioRxiv2018

Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure - characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain.

neuroscience↗