Biology subjects

Green, J. R.

Publications and source records attributed to Green, J. R..

11 recordsLinked to original sources

bioRxiv2026

Inefficient autophagosome formation limits the temporal dynamics of OPTN-mediated mitophagy in neurons

Mitophagy is an essential quality control mechanism that maintains neuronal health by selectively removing damaged mitochondria via autophagosomes. In neurons, mitophagy is mainly driven by Optineurin (OPTN), a selective autophagy receptor that is recruited to damaged mitochondria. Consistent with its role in maintaining mitochondrial integrity, OPTN-mediated mitophagy is upregulated in response to mild oxidative stress. However, many mechanistic studies of mitophagy have relied on non-neuronal systems and acute mitochondrial damage paradigms. Thus, it remains unclear how well these findings translate to physiological stress conditions in neurons. Here, we investigated the temporal dynamics of neuronal mitophagy under mild oxidative stress using live-cell imaging in primary rat hippocampal neurons. Surprisingly, we found that in neurons, autophagosomes failed to readily engulf OPTN-positive (OPTN+) mitochondria, revealing a novel rate-limiting step in neuronal mitophagy. Interestingly, this inefficient engulfment was specific to OPTN+ mitochondria at mitophagy events. Given the slow progression of mitophagy, we extended our time course to define the timescale of OPTN-regulated mitophagy. Using a pulse-chase assay to monitor long-term mitochondrial turnover, we found that OPTN+ mitochondria colocalized with acidified lysosomes over a timescale significantly longer than reported in non-neuronal cells and acute neuronal models. Since inefficient autophagosome engulfment appeared to limit mitophagy, we stimulated autophagosome formation via nutrient deprivation, which increased lysosomal colocalization with damaged mitochondria and enhanced mitophagy flux. Together, these findings indicate that mitophagy proceeds relatively slowly in neurons, a characteristic that may contribute to neuronal vulnerability in neurodegenerative disease by promoting the accumulation of dysfunctional mitochondria.

cell biology↗

bioRxiv2025

Leveraging learned representations and multitask learning for lysine methylation site discovery

Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role of this modification in epigenetics and gene regulation is relatively well understood, but our understanding of the extent and the role of lysine methylation of non-histone substrates remains fairly limited. Several lysine methyltransferases which methylate non-histone substrates are overexpressed in a number of cancers and are believed to be key drivers of cancer progression. There is great incentive to identify the lysine methylome, as this is a key step in identifying drug targets. While numerous computational models have been developed in the last decade to identify novel lysine methylation sites, the accuracy of these model has been modest, leaving much room for improvement. In this work, we leverage the most recent advancements in deep learning and present a transformer-based model for lysine methylation site prediction which achieves state-of-the-art accuracy. In addition, we show that other post-translational modifications of lysine are informative and that multitask learning is an effective way to integrate this prior knowledge into our lysine methylation site predictor, MethylSight 2.0. Finally, we validate our model by means of mass spectrometry experiments and identify 68 novel lysine methylation sites. This work constitutes another contribution towards the completion of a comprehensive map of the lysine methylome.

bioinformatics↗

bioRxiv2023

Can a Sparse 29 x 29 Pixel Chaos Game Representation Predict Protein Binding Sites using Fine-Tuned State-of-the Art Deep Learning Semantic Segmentation Models?

No. While our experiments ultimately failed, this work was motivated by the seemingly reasonable hypothesis that encoding protein sequences as a fractal-based image in combination with a binary mask identifying those pixels representative of the protein binding interface could effectively be used to fine-tune a semantic segmentation model. We were wrong. Despite the shortcomings of this work, a number of insights were drawn, inspiring discussion about how this fractal-based space may be exploited to generate effective protein binding site predictors in the future. Furthermore, these realizations promise to orient complimentary studies leveraging fractal-based representations, whether in the field of bioinformatics, or more broadly within disparate fields leveraging sequence-type data, such as Natural Language Processing. In a non-traditional way, this work presents the experimental design undertaken and interleaves various insights and limitations. It is the hope of this work that those interested in leveraging fractal-based representations and deep learning architectures as part of their work will benefit from the insights arising from this work.

bioinformatics↗

bioRxiv2023

Role of Articulatory Motor Networks in Perceptual Categorization of Speech Signals: A 7 T fMRI Study

BACKGROUNDThe association between brain regions involved in speech production and those that play a role in speech perception is not yet fully understood. We compared speech production related brain activity with activations resulting from perceptual categorization of syllables using high field 7 Tesla functional magnetic resonance imaging (fMRI) at 1-mm isotropic voxel resolution, enabling high localization accuracy compared to previous studies. METHODSBlood oxygenation level dependent (BOLD) signals were obtained in 20 normal hearing subjects using a simultaneous multi-slice (SMS) 7T echo-planar imaging (EPI) acquisition with whole-head coverage and 1 mm isotropic resolution. In a speech production localizer task, subjects were asked to produce a silent lip-round vowel /u/ in response to the visual cue "U" or purse their lips when they saw the cue "P". In a phoneme discrimination task, subjects were presented with pairs of syllables, which were equiprobably identical or different along an 8-step continuum between the prototypic /ba/ and /da/ sounds. After the presentation of each stimulus pair, the subjects were asked to indicate whether the two syllables they heard were identical or different by pressing one of two buttons. In a phoneme classification task, the subjects heard only one syllable and asked to indicate whether it was /ba/ or /da/. RESULTSUnivariate fMRI analyses using a parametric modulation approach suggested that left motor, premotor, and frontal cortex BOLD activations correlate with phoneme category variability in the /ba/-/da/ discrimination task. In contrast, the variability related to acoustic features of the phonemes were the highest in the right primary auditory cortex. Our multivariate pattern analysis (MVPA) suggested that left precentral/inferior frontal cortex areas, which were associated with speech production according to the localizer task, play a role also in perceptual categorization of the syllables. CONCLUSIONSThe results support the hypothesis that articulatory motor networks in the left hemisphere that are activated during speech production could also have a role in perceptual categorization of syllables. Importantly, high voxel-resolution combined with advanced coil technology allowed us to pinpoint the exact brain regions involved in both perception and production tasks.

neuroscience↗

bioRxiv2021

Computational design of peptide therapeutics: how useful are sequence-based protein-protein interaction predictors?

Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.

bioinformatics↗

bioRxiv2021

Do examinations prepare students for higher education? A lesson from the Covid-19 lockdown.

The COVID-19 pandemic caused severe disruption to education in the UK in 2020, with most of the school teaching moving online and national school examinations being cancelled. This was particularly disruptive for those taking end of school examinations in preparation for higher education. Biological science courses require students to absorb a lot of new vocabulary and concepts, with examinations traditionally focusing on content recall rather than reasoning. Students who had entered university in September 2019 were compared with those arriving in September 2020 with respect to their knowledge of bioscience vocabulary and understanding of key concepts. Results showed no significant difference between those who had gone through the examination process in 2019 relative to those who had not, in 2020. This suggests the cramming of information for examinations has no detectable effect on the knowledge and understanding of biology that students take with them to university.

scientific communication and education↗

bioRxiv2020

Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. Given the known interaction between the human ACE2 ("Angiotensin-converting enzyme 2") protein and the SARS-CoV virus (responsible for the coronavirus outbreak circa. 2003), considerable focus has been directed towards the putative interaction between the SARS-CoV-2 Spike protein and ACE2. However, a more holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises additional putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. To that end, we leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Of these, we identify the high-scoring subset of human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins by both methods, comprising 279 high-confidence putative interactions involving 225 human proteins. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors, corroborating existing evidence for this PPI. Furthermore, the PIPE-Sites algorithm was used to predict the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. We hereby publicly release the comprehensive set of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: 10.5683/SP2/JZ77XA. All data and metadata are released under a CC-BY 4.0 licence. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

bioinformatics↗

bioRxiv2019

Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data

MotivationHigh-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximises analytic power while minimising noise from unspecified environmental factors.\n\nResultsHere we introduce \"soft windowing\", a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant p-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft windowed and non-windowed approaches, respectively, from a set of 2,082 mutant mouse lines. Our method is generalisable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.\n\nAvailability and ImplementationThe method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

bioinformatics↗

bioRxiv2018

miPIE: NGS-based Prediction of miRNA Using Integrated Evidence

Methods for the de novo identification of microRNA (miRNA) have been developed using a range of sequence-based features. With the increasing availability of next generation sequencing (NGS) transcriptome data, there is a need for miRNA identification that integrates both NGS transcript expression-based patterns as well as advanced genomic sequence-based methods. While miRDeep2 does examine the predicted secondary structure of putative miRNA sequences, it does not leverage many of the sequence-based features used in state-of-the-art de novo methods. Meanwhile, other NGS-based methods, such as miRanalyzer, place an emphasis on sequence-based features without leveraging advanced expression-based features reflecting miRNA biosynthesis. This represents an opportunity to combine the strengths of NGS-based analysis with recent advances in de novo sequence-based miRNA prediction. We here develop a method, microRNA Prediction using Integrated Evidence (miPIE), which integrates both expression-based and sequence-based features to achieve significantly improved miRNA prediction performance. Feature selection identifies the 20 most discriminative features, 3 of which reflect strictly expression-based information. Evaluation using precision-recall curves, for six NGS data sets representing six diverse species, demonstrates substantial improvements in prediction performance compared to miRDeep2 and miRanalyzer. The individual contributions of expression-based and sequence-based features are also examined and we demonstrate that their combination is more effective than either alone.

bioinformatics↗

bioRxiv2018

MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteome

Protein Lys methylation plays a critical role in numerous cellular processes, yet it has been challenging to identify Lys methylation in a systematic manner. We present here an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We have developed MethylSight, a program that predicts Kme events solely on physicochemical and biochemical properties of putative methylation sites, which can then be validated by targeted MS. Using this approach, we have identified 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts ~50,000 Kme sites in non-histone proteins with high confidence, suggesting that Lys methylation is a prevalent post-translational modification. Our work provides a useful resource for systematic exploration of the role of Lys methylation in human health and disease.

biochemistry↗

bioRxiv2017

Designing Anti-Zika Virus Peptides Derived from Predicted Human-Zika Virus Protein-Protein Interactions

The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.

synthetic biology↗