Biology subjects

Coffey, E.

Publications and source records attributed to Coffey, E..

12 recordsLinked to original sources

bioRxiv2026

A unified framework for batch correction and missing data handling in large-scale and single-cell mass spectrometry proteomics

Large-scale mass spectrometry (MS)-based proteomics, including single-cell proteomics, is routinely affected by technical variation arising from discrete batch effects, inter-laboratory differences and continuous signal drift during data acquisition. Current correction strategies typically address these sources of unwanted variation independently and often require either removal of proteins with missing values or imputation before correction, both of which may lead to information loss and potential amplification of technical bias. Here we present NMFBatch, a unified statistical framework that simultaneously models discrete and continuous unwanted variation in bulk and single-cell proteomics data. NMFBatch integrates non-negative matrix factorization with generalized additive modelling and directly accommodates missing values, thereby enabling both on-the-fly imputation during correction and optional post-correction imputation. Benchmarking against six batch-correction methods using multi-laboratory reference datasets and a large plasma proteomics cohort, shows that NMFBatch consistently reduces batch-associated variation while preserving biological structure under both balanced and confounded experimental designs. Application to single-cell proteomics data further showed effective reduction of TMT- and acquisition-associated variation while retaining biologically meaningful clustering. Together, these results establish NMFBatch as a flexible framework for modelling unwanted variation in proteomics experiments, with potential applications in cross-cohort harmonization and integrative proteomics analysis. Graphical AbstractCreated in BioRender. Youssef, A. (2026) https://BioRender.com/c1q1yxt O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/726178v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@2b7cd1org.highwire.dtl.DTLVardef@10fada3org.highwire.dtl.DTLVardef@50e66corg.highwire.dtl.DTLVardef@147f81c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

FPmotion an Automated Signal Processing and Statistical Analysis Tool for Fiber Photometry Data

Fiber photometry measures neural activity in vivo from genetically encoded indicators. Recordings generate large datasets that require extensive preprocessing and robust statistical methods for meaningful interpretation. However, existing analysis tools demand programming expertise, limiting accessibility. Here we describe FPmotion, a comprehensive, user-friendly software platform for batchwise processing, integration, and statistical analysis of fiber photometry data with or without accompanying behavioral information. FPmotion performs filtering, isosbestic regression, {Delta}F/F computation, detrending, and z-scoring. It also extracts detailed peak properties at whole-file, block-level, and individual-peak resolution. When behavioral data is provided, FPmotion automatically identifies behavioral bouts, computes bout-level statistics, performs peri-event signal extraction, and supports multi-group comparisons through ANOVA- and LMEM-based statistical frameworks. All analyses generate publication-ready figures and structured CSV outputs suitable for downstream workflows. FPmotion also introduces a dedicated alignment module for peri-event signal analysis. This module applies dynamic time warping followed by barycenter averaging to realign peri-event traces while preserving the behavioral time anchor, producing cleaner and more temporally coherent peri-event motifs. We demonstrate FPmotions capabilities using the dlight1.1 sensor to measure dopamine responses from ventral striatum in behaving mice before and after amphetamine treatment. Together, FPmotion offers a fully automated framework for FP data analysis that improves interpretability and accessibility while reducing analysis time substantially.

neuroscience↗

bioRxiv2026

Environmental Toxin Rotenone Drives LRRK2-Mediated Microtubule, Cilia and Proteostasis Disruption in Parkinson's Disease Model

Exposure to the environmental toxin rotenone increases risk for Parkinsons disease (PD). However, protein phosphorylation changes induced by rotenone in neural-derived cells have not been reported. We examined the effect of rotenone on the proteome and phosphoproteome of cortex-derived cultures from wild-type and Lrrk2-/- mouse brains. We also analyzed the phosphoproteome of the PD cadaver brain using a previously unanalyzed dataset. Rotenone alters phosphorylation at 904 sites, most of which are unchanged in Lrrk2-/- cultures. Common targets include proteins that control microtubule stability, vesicular transport, and protein clearance via the autophagosomal/lysosomal pathway. Analysis of phosphosites with known function indicate that rotenone activates histone deacetylase-1 and represses the pro-survival transcriptional regulators Mef2C and Mef2D, while inhibiting translation by phosphorylating Eif2b5. These effects do not occur in Lrrk2-/- cultures. This study shows that Lrrk2 is required the earliest rotenone-triggered phospho-signalling events.

systems biology↗

bioRxiv2025

Hitting the right pitch: Cortical tracking of fundamental frequency changes across speech rates in auditory and sensorimotor regions

Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupling of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.

neuroscience↗

bioRxiv2025

LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics Data

MotivationDifferential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. ResultsIn this study, we developed LimROTS, a hybrid method integrating the linear model and empirical Bayes method from the limma framework with the Reproducibility-Optimized Statistics from ROTS, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. Availability and ImplementationLimROTS has been implemented as an R/Bioconductor package, available at https://bioconductor.org/packages/LimROTS/. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/636801v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1e6dd66org.highwire.dtl.DTLVardef@1d1589eorg.highwire.dtl.DTLVardef@110f73corg.highwire.dtl.DTLVardef@d78ef2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Identifying JNK-regulated phosphoproteome markers of anxiety-like behaviour in mouse hippocampus

JNKs mediate neuronal damage in neurodegenerative disease and inhibitors of JNK1 have shown anxiolytic and anti-depressive effects in mice. Here, we analyze the phosphoproteomes of hippocampus and nucleus accumbens from DJNKI-1 (JNK inhibitor)-infused mice. We correlate phospho-site changes with anxiety-like behaviours in the elevated plus maze and light-dark test and identify unique changes in responder mice. Among the DJNKI-1 regulated phosphosites, several lie within GSK3 motifs and are exclusively down-regulated. Consistent with this, GSK3{beta} is inhibited and AKT activated. Importantly, we detect multilevel regulation of glucose metabolism enzymes including increased PDPK1-S241 phosphorylation, and a 5-fold increase in pyruvate dehydrogenase (PDHA1)-S-293 phosphorylation, signifying its inhibition. This suggests that JNK inhibitor drives a metabolic transformation to the neuronal Warburg response. This and a range of identified synaptic and cytoskeletal protein phosphosite changes are discussed in the context of JNK-regulated anxiety responses. The annotated hippocampal and nucleus accumbens phosphoproteomes described here will support a mechanistic understanding of the JNK pathway for future studies of brain disorders.

neuroscience↗

bioRxiv2025

JNK regulates GABAAR expression at the cell surface via the receptor clustering protein GIT1 (ArfGAP1)

GABAA-type receptors (GABAARs) mediate fast and tonic inhibition and are essential for maintaining excitatory/inhibitory balance in neural circuits. Disrupted GABAergic signaling leads to maladaptive changes associated with neuropsychiatric disorders, yet the mechanisms that regulate GABAAR surface availability remain incompletely understood. We previously identified that the stress-activated kinase JNK1 drives anxiety-like behaviours in mice suggesting a potential link between stress signalling and GABAergic regulation. Here, we show that genetic deletion or inhibition of JNK1 increases surface expression of {beta}3-containing GABAARs at extrasynaptic sites and at excitatory synapses where they may participate in shunting inhibition. We identify the signalling scaffold GIT1 as a direct JNK1 substrate, phosphorylated at S371, and find that GIT1 accumulates in dendritic spines in Jnk1-/- neurons or following JNK inhibition, whereas the phosphomimetic GIT1-S371D is excluded. Moreover, GIT1 is required for JNK-dependent regulation of {beta}3-GABAAR trafficking. Accordingly, neurons from Jnk1-/- mice exhibit increased spontaneous inhibitory postsynaptic potentials and enhanced tonic inhibition. These results reveal a JNK1-GIT1 signalling axis that suppresses GABAAR stability and inhibitory tone, providing a mechanism by which the stress-activated JNK pathway modulates synaptic and extrasynaptic inhibition.

neuroscience↗

bioRxiv2024

JNK1 And Downstream Signalling Hubs Regulate Anxiety-Like Behaviours In A Zebrafish Larvae Phenotypic Screen

Current treatments for anxiety and depression show limited efficacy in many patients indicating that research into new underlying mechanisms is needed. Inhibition of JNK1 has been shown to evoke an anxiolytic-and antidepressant-like phenotype in mice however the downstream effectors that elicit these behavioural effects are unknown. Here we employ a zebrafish (D. Rerio) larvae behavioural assay to identify an antidepressant-/anxiolytic-like phenotype based on 2759 measured stereotypic responses to clinically proven antidepressant and anxiolytic (AA) drugs. Employing machine learning, we classify an AA phenotype from behavioural features measured during and after a startle battery in fish exposed to AA drugs (fluoxetine, imipramine, diazepam, lithium chloride, ketamine). We demonstrate that structurally independent JNK inhibitors replicate the AA classification with high accuracy, consistent with findings in mice. We go on to identify signalling hubs downstream from JNK1 by comparing phosphoproteome data from wildtype and Jnk1-/- mouse brains, and test these hubs as possible mediators of the AA phenotype in zebrafish larvae. Among these, we find that AKT, GSK-3, 14-3-3{zeta}/{varepsilon} and PKC{varepsilon}, when pharmacologically targeted, phenocopy clinically proven AA drugs. This assay shows promise as an early phase screening for compounds with anti-stress-axis/anxiolytic-like properties, and for mode of action analysis.

pharmacology and toxicology↗

bioRxiv2022

Loss of Rab8a in B cells leads to increased antibody responses and class-switch recombination

Rab8a is a small GTPase with a wide range of reported functions in different cell types, including vesicle recycling, vesicle traffic to cilia, cell ruffling, migration, neurite outgrowth, Toll-like receptor signalling and T cell receptor docking at the immune synapse. However, the role of Rab8a in B lymphocytes has not been described to date. Here, using a conditional B cell-specific Rab8a knockout mouse model, we investigate the role of Rab8a both in vivo and in vitro. Rab8a KO mice present enhanced antibody responses to both T-dependent and T-independent immunisations. Rab8a KO cells showed normal BCR trafficking and antigen processing and presentation but however, increased class-switch recombination. While the early BCR signalling responses, such as proximal kinase activation and calcium-flux, were normal, the signalling via AKT and ERK1/2 was decreased. We propose that the lack of Rab8a alters cellular signalling leading to enhanced antibody responses and increased class-switch recombination potentially via downmodulation of the PI3K/AKT/mTOR pathway.

immunology↗

bioRxiv2022

Regulators of proteostasis are translationally repressed in fibroblasts from sporadic and LRRK2-G2019S Parkinson's patients

Parkinsons disease (PD) is characterized by heterogeneous motor and non-motor symptoms. The majority of PD cases are sporadic, whereas the leucine-rich repeat kinase 2 (LRRK2) G2019S mutation is the most common monogenic cause of familial PD and symptomatically indistinguishable from the sporadic form. Deficits in protein synthesis have been reported in PD. However, it is not known which proteins are affected or if there are synthesis differences between patients with sporadic and LRRK2 G2019S PD. Here we used non-canonical amino acid labeling and mass-spectrometry based proteomics to perform a quantitative comparison of nascent proteins in fibroblasts from sporadic and G2019S mutation PD patients compared to healthy individuals. We found that several proteins were under-represented among nascent proteins in cells from PD patients. Among these were regulators endo-lysosomal sorting, mRNA processing and translation itself. We further validated which of these proteins were dysregulated at the static proteome level using targeted proteomics. Key downregulated proteins included the autophagy regulator ATG9A and translational stability regulator YTHDF3. Notably, 68% of the identified proteins were downregulated in both sporadic and LRRK2-G2019S forms of PD. We identify translational repression of key regulators of proteostasis in patient fibroblasts that are common to both sporadic and LRRK2-G2019S PD.

neuroscience↗

bioRxiv2021

PhosPiR: An automated phospho-proteomic pipeline in R

Large-scale phospho-proteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from this data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from largescale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phospho-site annotation and translation across species, multi-level enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

bioinformatics↗

bioRxiv2018

Antisense lncRNA transcription drives stochastic Protocadherin α promoter choice

Stochastic and combinatorial activation of clustered Protocadherin (Pcdh) , {beta}, and {gamma} gene promoters generates a cell-surface identity code in individual neurons that functions in neural circuit assembly. Here we show that Pcdh promoter choice requires transcription of a long noncoding RNA (lncRNA) initiated from newly identified promoters located in the protein coding sequence of each Pcdh exon. Antisense transcription of the lncRNA through the sense promoter results in its activation and in DNA demethylation of the binding sites for the CCCTC-binding protein, CTCF, located in close proximity to both sense and antisense promoters. Increased CTCF binding promotes the assembly of long-range DNA contacts between the activated promoter and a neuron-specific enhancer, thus locking in the epigenetic state of the stochastically chosen Pcdh promoter. Examination of this hierarchical molecular mechanism in differentiating olfactory sensory neurons, suggests that antisense Pcdh transcription is a key prerequisite for stochastic Pcdh promoter choice in vivo.

genomics↗