Biology subjects

Wong, E. M.

Publications and source records attributed to Wong, E. M..

7 recordsLinked to original sources

bioRxiv2025

Interactive electrical behaviour in mormyrid weakly electric fish: Jamming avoidance response or social interaction?

Weakly electric fish emit electric organ discharges (EODs) for both active electrolocation and electrocommunication. In African mormyrids, pulse-type EODs are produced at highly variable inter-discharge intervals (IDIs), forming sequences that can convey contextual and behavioural information to conspecifics. Neighbouring fish frequently engage in time-locked interactive behaviours, such as fixed-latency echo responses and EOD synchronisation. These behaviours have been proposed to function either as a jamming avoidance response (JAR), preventing simultaneous discharges and interference with electrolocation, or as a form of social signal. To test these hypotheses, we analysed interactions between pairs of Mormyrus rume proboscirostris, quantifying EOD synchronisations, echo events, and instances of jamming. Our results show that jamming is rare in this species. We found no correlation between jamming frequency and echoing behaviour, nor evidence that synchronisation and echo events are direct responses to recent jams. Instead, these interactive behaviours were associated with specific movement patterns and social contexts. Synchronisations were mainly initiated by stationary hovering individuals, while echo responses were more frequent in smaller fish actively following a social partner, suggesting a role in leader-follower dynamics. Pairs with more frequent echoing also exhibited reduced inter-individual distances, indicating that interactive electrical signalling promotes social cohesion. Rather than mitigating jamming, interactive electrical behaviours in mormyrids likely serve as communicative strategies to maintain group coherence or allocate social attention. These findings highlight the role of electrocommunication in structuring social interactions. Investigating the rules of these behaviours could eventually decipher which specific IDI patterns signal social intent and help to understand the underlying mechanisms of electrocommunication. HighlightsO_LIMormyrid fish frequently perform interactive signalling during electrocommunication. C_LIO_LIInteractive electrical signalling is associated with specific locomotor behaviours. C_LIO_LIEcho responses and signal synchronisations are not linked to jamming. C_LIO_LISignal synchronisations may support mutual assessment upon encounters. C_LIO_LIInteractive signalling promotes social cohesion and leader-follower dynamics. C_LI

animal behavior and cognition↗

bioRxiv2024

Control of OPC proliferation and repopulation by the intellectual disability gene PAK1 under homeostatic and demyelinating conditions

Appropriate proliferation and repopulation of oligodendrocyte progenitor cells (OPCs) determine successful (re)myelination in homeostatic and demyelinating brains. Activating mutations in p21-activated kinase 1 (PAK1) cause intellectual disability, neurodevelopmental abnormality, and white matter anomaly in children. It remains unclear if and how PAK1 regulates oligodendroglial development. Here, we report that PAK1 controls proliferation and regeneration of OPCs. Unlike differentiating oligodendrocytes, OPCs display high PAK1 activity which maintains them in a proliferative state by modulating PDGFRa-mediated mitogenic signaling. PAK1-deficient or kinase-inhibited OPCs reduce their proliferation capacity and population expansion. Mice carrying OPC-specific PAK1 deletion or kinase inhibition are populated with fewer OPCs in the homeostatic and demyelinated CNS than control mice. Together, our findings suggest that kinase-activating PAK1 mutations stall OPCs in a progenitor state, impacting timely oligodendroglial differentiation in the CNS of affected children and that PAK1 is a potential molecular target for replenishing OPCs in demyelinating lesions.

neuroscience↗

bioRxiv2023

RFIDeep: Unfolding the Potential of Deep Learning forRadio-Frequency Identification

O_LIAutomatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding, and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. C_LIO_LIWe present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and exits. C_LIO_LITo determine individual breeding status and phenology and for each breeding season, we first developed a one-dimensional convolution neural network (1D-CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a "black box" step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%; phenological accuracy = 86.9%; breeding success accuracy = 97.3%. C_LIO_LIRFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analyzing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology. C_LI

ecology↗

bioRxiv2019

Smoking and blood DNA methylation: novel associations, replication of previous findings and assessment of reversibility

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N=3,327) and former (N=172) smoking. A comprehensive smoking index accounting for the bioactivity of smoking and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. We identified 4,496 cross-sectional associations at P<10-7, including 3,296 that were novel. We replicated the majority (90%) of previously reported associations for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR=63-86%). Consistent with this, we found wide-ranging estimates for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study provides evidence of many novel associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and indicates wide-ranging reversibility rates for smoking-induced methylation changes.

genomics↗

bioRxiv2018

Genome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures

Age- and body mass index (BMI)-adjusted mammographie density is one the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie interindividual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, non-dense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant(P<10-7)association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all P>0.05/299 = 1.7 x 10-4). In summary, our study did not detect associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.

epidemiology↗

bioRxiv2018

Associations between environmental breast cancer risk factors and DNA methylation-based risk-predicting measures

BackgroundGenome-wide average DNA methylation (GWAM) and epigenetic age acceleration have been suggested to predict breast cancer risk. We aimed to investigate the relationships between these putative risk-predicting measures and environmental breast cancer risk factors.\n\nMethodsUsing the Illumina HumanMethylation450K assay methylation data, we calculated GWAM and epigenetic age acceleration for 132 female twin pairs and their 215 sisters. Linear regression was used to estimate associations between these risk-predicting measures and multiple breast cancer risk factors. Within-pair analysis was performed for the 132 twin pairs.\n\nResultsGWAM was negatively associated with number of live births, and positively with age at first live birth (both P<0.05). Epigenetic age acceleration was positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche, and negatively with age at first live birth (all P<0.05), and the associations with BMI, alcohol drinking and age at first live birth remained in the within-pair analysis.\n\nConclusionsThis exploratory study shows that lifestyle and hormone-related breast cancer risk factors are associated with DNA methylation-based measures that could predict breast cancer risk. The associations of epigenetic age acceleration with BMI, alcohol drinking and age at first live birth are unlikely to be due to familial confounding.

epidemiology↗

bioRxiv2017

Inference about causation between body mass index and DNA methylation in blood from a twin family study

BackgroundSeveral studies have reported DNA methylation in blood to be associated with body mass index (BMI), but only a few have investigated causal aspects of the association. We used a twin family design to assess this association at two life points and applied a novel analytical approach to investigate the evidence for causality.\n\nMethodsThe methylation profile of DNA from peripheral blood was measured for 479 Australian women (mean age 56 years) from 130 twin families. Linear regression was used to estimate the associations of methylation at ~410 000 cytosine-guanine dinucleotides (CpG), and of the average methylation at ~20 000 genes, with current BMI, BMI at age 18-21 years, and the change between the two (BMI change). A novel regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess causation.\n\nResultsAt 5% false discovery rate, nine, six and 12 CpGs at 24 loci were associated with current BMI, BMI at age 18-21 years and BMI change, respectively. The average methylation of BHLHE40 and SOCS3 loci was associated with current BMI, and of PHGDH locus was associated with BMI change. From the ICE FALCON analyses with BMI as the predictor and methylation as the outcome, a womans methylation level was associated with her co-twins BMI, and the association disappeared conditioning on her own BMI, consistent with BMI causing methylation. To the contrary, using methylation as the predictor and BMI as the outcome, a womans BMI was not associated with her co-twins methylation level, consistent with methylation not causing BMI.\n\nConclusionFor middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with current BMI, BMI at age 18-21 years and BMI change. Our study suggests that BMI has a causal effect on peripheral blood DNA methylation.

epidemiology↗