Biology subjects

Harper, N.

Publications and source records attributed to Harper, N..

9 recordsLinked to original sources

bioRxiv2026

Two IncHI1 megaplasmids in Klebsiella species reveal transposable-element-mediated blaIMP-1 mobilisation

Introduction. Carbapenemase-producing Enterobacterales (CPE) represent a major threat to hospitalised patients worldwide. The dissemination of carbapenemase genes, such as blaIMP, is frequently mediated by mobile genetic elements including plasmids. During a previously described multispecies, healthcare-associated outbreak of blaIMP-positive CPE in North West London, two unusual isolates, IMP47 (Klebsiella grimontii) and IMP76 (K. pneumoniae), recovered in 2019 from routinely collected rectal swabs of inpatients, were predicted to harbour blaIMP-1-carrying IncHI1 megaplasmids. Aims. This study aimed to determine complete genomic sequences of IMP47 and IMP76, resolve the genetic context of blaIMP-1, and assess the conjugative mobility of blaIMP-1-carrying megaplasmids. Methods. Genomic sequences of both isolates were recovered through hybrid assembly of Oxford Nanopore and Illumina sequencing reads. Complete plasmid sequences were characterised to determine replicons, conjugation machinery, and genes encoding resistance to antimicrobials or other stress factors. Integrons and transposable elements (TEs) within flanking regions of blaIMP-1 were resolved through genome annotation and search against public databases. Liquid-mating experiments were performed to assess the mobility of blaIMP-1-carrying plasmids. Results. Completed genome assemblies were generated from both isolates, confirming two blaIMP-1-carrying megaplasmids, pIMP47 (391 kbp) and pIMP76_1 (519 kbp), of the replicon type IncHI1A(pNDM-CIT)/IncHI1B(pNDM-CIT). The blaIMP-1 locus was carried by nearly identical class 1 integrons in both plasmids and a closely related IncHI1 megaplasmid pEB3_IMP1 (361 kbp) previously identified in South West England. Comparative analysis revealed conserved genetic structures linking blaIMP-1 to mercury-resistance genes and TEs Tn6025, TnAs3, IS26, and IS5075, suggesting a history of recombination and potential for TE-mediated mobilisation. Conjugation experiments confirmed transfer of pIMP76_1 into a recipient K. pneumoniae strain, resulting in acquisition of ertapenem resistance, whereas transfer of pIMP47 was not observed under the tested conditions. Conclusion. Two IMP-producing IncHI1 megaplasmids in gut-colonising Klebsiella species revealed TE-mediated blaIMP-1 mobilisation. The co-localisation of blaIMP-1 and metal-resistance genes in both plasmids highlights the potential for co-selection of blaIMP-1 in environments enriched with metal ions. Our findings underscore the importance of longitudinal genomic surveillance of carbapenemase-encoding megaplasmids in healthcare settings.

microbiology↗

bioRxiv2024

The developing Human Connectome Project fetal functional MRI release: Methods and data structures

Recent advances in fetal fMRI present a new opportunity for neuroscience to study functional human brain connectivity at the time of its emergence. Progress in the field however has been hampered by the lack of openly available datasets that can be exploited by researchers across disciplines to develop methods that would address the unique challenges associated with imaging and analysing functional brain in utero, such as unconstrained head motion, dynamically evolving geometric distortions, or inherently low signal-to-noise ratio. Here we describe the developing Human Connectome Projects release of the largest open access fetal fMRI dataset to date, containing 275 scans from 255 fetuses and spanning the period of 20.86 to 38.29 post-menstrual weeks. We present a systematic approach to its pre-processing, implementing multi-band soft SENSE reconstruction, dynamic distortion corrections via phase unwrapping method, slice-to-volume reconstruction and a tailored temporal filtering model, with attention to the prominent sources of structured noise in the in utero fMRI. The dataset is accompanied with an advanced registration infrastructure, enabling group-level data fusion, and contains outputs from the main intermediate processing steps. This allows for various levels of data exploration by the imaging and neuroscientific community, starting from the development of robust pipelines for anatomical and temporal corrections to methods for elucidating the development of functional connectivity in utero. By providing a high-quality template for further method development and benchmarking, the release of the dataset will help to advance fetal fMRI to its deserved and timely place at the forefront of the efforts to build a life-long connectome of the human brain.

neuroscience↗

bioRxiv2023

Molecular mechanism of α-synuclein aggregation on lipid membranes revealed.

The central hallmark of Parkinsons disease pathology is the aggregation of the -synuclein protein, which, in its healthy form, is associated with lipid membranes. Purified monomeric -synuclein is relatively stable in vitro, but its aggregation can be triggered by the presence of lipid vesicles. Despite this central importance of lipids in the context of -synuclein aggregation, their mechanistic role in this process has not been established to date. Here, we use chemical kinetics to develop a detailed mechanistic model that is able to globally describe the aggregation behaviour of -synuclein in the presence of DMPS lipid vesicles, across a range of lipid and protein concentrations. Through the application of our kinetic model to experimental data, we find that the reaction is a co-aggregation process involving both protein and lipids and that lipids promote aggregation predominantly by enabling the elongation process. Moreover, we find that the initial formation of aggregates, via primary nucleation, takes place not on the surface of lipid vesicles but at the interfaces present in vitro. Our model will enable mechanistic insights, also in other lipid-protein co-aggregation systems, which will be crucial in the rational design of drugs that inhibit aggregate formation and act at the key points in the -synuclein aggregation cascade.

biophysics↗

bioRxiv2023

Objective assessment of visual attention in toddlerhood

Visual attention is an important mechanism through which children learn about their environment, and individual differences could substantially shape later development. Eyetracking provides a sensitive and scalable tool for assessing visual attention that has potential for objective assessment of child development, but to date the majority of studies are small and replication attempts are rare. This study investigates the feasibility of a comprehensive eye-tracking assessment of visual attention and introduces a shared data resource for the scientific community. Data from eight eyetracking tasks were collected from 350 term-born (166 females) 18-month-olds recruited as neonates http://www.developingconnectome.org/). Analyses showed expected condition effects for seven of eight tasks (p-values from <.001 to .04), an important indication of replicability. Consistent with some theoretical models of visual attention, structural equation modelling indicated participants performance could be explained by two factors representing social and non-social attention. Comprehensive eye-tracking batteries can objectively measure individual differences in core components of visual attention in large-scale toddlerhood studies. This is the first large-scale comprehensive study to present high-quality normative eye-tracking data from a large task battery in toddlers and make them freely available to the scientific community.

neuroscience↗

bioRxiv2022

Clinical, socio-demographic, and parental correlates of early autism traits

BackgroundAutism traits emerge between the ages of 1 and 2. It is not known if experiences which increase the likelihood of childhood autism are related to early trait emergence, or if other exposures are more important. Identifying factors linked to toddler autism traits in the general population may improve our understanding of the mechanisms underlying atypical neurodevelopment. MethodsClinical, socio-demographic, and parental information was collected at birth from 536 toddlers in London, UK (gestational age at birth, sex, maternal body mass index, age, parental education level, parental first language, parental history of neurodevelopmental disorders) and at 18 months (parent cohabiting status, two measures of social deprivation, three measures of maternal parenting style, and a measure of maternal postnatal depression). General neurodevelopment was assessed with the Bayley Scales of Infant and Toddler Development, 3rd Edition (BSID-III), and autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT). Multivariable models were used to identify associations between variables and Q-CHAT. A model including BSID-III was used to identify factors associated with Q-CHAT independent of general neurodevelopment. Models were also evaluated addressing variable collinearity with principal component analysis (PCA). ResultsA multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). After adding general neurodevelopment into the model 36% of Q-CHAT variance was explained, with three individually significant variables (two measures of parenting style and one measure of language development). After addressing variable collinearity with PCA, parenting style and social deprivation were positively correlated with Q-CHAT score via a single principal component, independently of general neurodevelopment. Neither sex nor family history of autism were associated with Q-CHAT score. LimitationsThe Q-CHAT is parent rated and is therefore a subjective opinion rather than a clinical assessment. We measured Q-CHAT at a single timepoint, and to date no participant has been followed up in later childhood, so we are focused purely on emerging traits rather than clinical autism diagnoses. ConclusionsAutism traits are common at age 18 months, and greater emergence is specifically related to exposure to early life adversity.

neuroscience↗

bioRxiv2022

Development of neonatal brain functional centrality and alterations associated with preterm birth

Formation of the functional connectome in early life underpins future learning and behaviour. However, our understanding of how the functional organisation of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterised voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and decrease within motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive, and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

neuroscience↗

bioRxiv2021

Natural sound characteristics explain perceptual categorization

As we interact with our surroundings, we encounter the same or similar objects from different perspectives and are compelled to generalize. For example, we recognize dog barks as a distinct class of sound, despite the variety of individual barks. While we have some understanding of how generalization is done along a single stimulus dimension, such as frequency or color, natural stimuli are identifiable by a combination of dimensions. To understand perception, measuring the interaction across stimulus dimensions is essential. For example, when identifying a sound, does our brain focus on a specific dimension or a combination, such as its frequency and duration? Furthermore, does the relative relevance of each dimension reflect its contribution to the natural sensory environment? Using a 2- dimension discrimination task for mice we tested untrained generalization across several pairs of auditory dimensions in a naturalistic and automatized behavioral paradigm. We uncovered a perceptual hierarchy over the tested dimensions that was dominated by the sounds spectral composition. This hierarchy could reflect the relevance of the different dimensions in natural stimuli and their potentially associated differential shaping of neuronal tuning. Mice could learn to pay more attention to dimensions low in the hierarchy, but this learning was more rigid and did not generalize as flexibly. Stimuli are thus not perceived as a whole but as a combination of their features, each of which weights differently on the dentification of the stimulus according to an established hierarchy.

neuroscience↗

bioRxiv2021

Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies

Multiple studies have demonstrated less favourable childhood outcomes in infants born in early term (37-38 weeks gestation) compared to those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain and subsequent neurodevelopment in term-born babies. Here we characterise structural brain correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome. We used T2 and diffusion weighted Magnetic Resonance Images acquired in the neonatal period from a cohort (n=454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor based morphometry (TBM) and tract based spatial statistics (TBSS). Neurodevelopment was subsequently assessed at age 18 months using the Bayley-III Scales of Infant and Toddler Development, and the effects of gestational age at birth and related neuroimaging findings on outcome were analysed with linear regression. Infants born earlier had areas of higher relative ventricular volume, and lower relative brain volume in the basal ganglia, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Linear regression models predicting outcome from gestational age at birth were significantly improved by adding neuroimaging features associated with gestational age at birth. This work adds to the growing body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.

neuroscience↗

bioRxiv2020

Predicting age and clinical risk from the neonatal connectome

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and are complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible, and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to be predominantly thalamocortical. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome, and suggest that a neural substrate for later developmental outcome is detectable at term equivalent age.

neuroscience↗