Biology subjects

Lavallee-Adam, M.

Publications and source records attributed to Lavallee-Adam, M..

15 recordsLinked to original sources

bioRxiv2026

Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

bioinformatics↗

bioRxiv2026

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

bioinformatics↗

bioRxiv2025

Identification of polyphosphate-binding proteins in E. coli uncovers targets involved in translation control and ribosome biogenesis

In many bacteria, polyphosphate kinase (PPK) enzymes use ATP to synthesize polyphosphate (polyP) in response to cellular stress. These chains of inorganic phosphates are joined by high-energy bonds and can reach hundreds of residues in length. PolyP plays diverse functions in helping bacteria adjust to changing environmental conditions. However, the molecular mechanisms underlying these functions are poorly understood. In eukaryotic cells, polyacidic serine- and lysine-rich (PASK) motifs of proteins can mediate binding to polyP chains. Whereas PASK motifs are relatively common in yeast and human cells, we report that these sequences are rare in bacteria commonly used for polyP research. Thus, to identify novel polyP-binding proteins in Escherichia coli, we carried out an untargeted screen and identified 7 novel targets with links to translation control and ribosome biogenesis. For two targets, the GTPase activating protein YihI and the ribonuclease Rnr, we mapped the regions of polyP interaction to non-PASK sequences and identified lysine residues critical for binding. We found that deletion of rnr suppressed the slow growth phenotype of {Delta}ppk mutants grown on minimal media. Conversely, ppk deletion resulted in decreased Rnr protein expression. These phenotypes were dependent on the polyP binding region of Rnr but independent of polyP binding itself, suggesting a complex interplay between PPK and Rnr function in E. coli. Overall, our work provides new insights into the scope of polyP binding proteins and extends the connections between polyP and the regulation of protein translation in E. coli.

molecular biology↗

bioRxiv2023

Proteomics analysis reveals a role for E. coli polyphosphate kinase in membrane structure and polymyxin resistance during starvation

Polyphosphates (polyP) are chains of inorganic phosphates that can reach over 1000 residues in length. In Escherichia coli, polyP is produced by the polyP kinase (PPK) and is thought to play a protective role during the response to cellular stress. However, the molecular pathways impacted by PPK activity and polyP accumulation remain poorly characterized. In this work we used label-free mass spectrometry to study the response of bacteria that cannot produce polyP ({Delta}ppk) during starvation to identify novel pathways regulated by PPK. In response to starvation, we found 92 proteins significantly differentially expressed between wild-type and {Delta}ppk mutant cells. Wild-type cells were enriched for proteins related to amino acid biosynthesis and transport, while{Delta} ppk mutants were enriched for proteins related to translation and ribosome biogenesis, suggesting that without PPK, cells remain inappropriately primed for growth even in the absence of required building blocks. From our dataset, we were particularly interested in Arn and EptA proteins, which were downregulated in {Delta}ppk mutants compared to wild-type controls, because they play a role in lipid A modifications linked to polymyxin resistance. Using western blotting, we confirm differential expression of these and related proteins, and provide evidence that this mis-regulation in {Delta}ppk cells stems from a failure to induce the BasS/BasR two-component system during starvation. We also show that {Delta}ppk mutants unable to upregulate Arn and EptA expression lack the respective L-Ara4N and pEtN modifications on lipid A. In line with this observation, loss of ppk restores polymyxin sensitivity in resistant strains carrying a constitutively active basR allele. Overall, we show a new role for PPK in lipid A modification during starvation and provide a rationale for targeting PPK to sensitize bacteria towards polymyxin treatment. We further anticipate that our proteomics work will provide an important resource for researchers interested in the diverse pathways impacted by PPK.

molecular biology↗

bioRxiv2022

METAbolomics data Balancing with Over-sampling Al-gorithms (META-BOA): an online resource for addressing class imbalance

MotivationClass imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. ResultsMETAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the users choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN), or Random Over-Sampling Examples (ROSE). META-BOA further displays both principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their analyses. Availability and implementationMETA-BOA is available at https://complimet.ca/meta-boa. Supplementary InformationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Ddp1 cooperates with Ppx1 to counter a stress response initiated by non-vacuolar polyphosphate

In diverse cells from bacterial to mammalian species, inorganic phosphate is stored in long chains called polyphosphates (polyP). These near universal polymers, ranging from 3 to thousands of phosphate moieties in length, are associated with molecular functions including energy homeostasis, protein folding, and cell signaling. In many cell types, polyphosphate is concentrated in subcellular compartments or organelles. In the budding yeast S. cerevisiae, polyP synthesis by the membrane-bound VTC complex is coupled to its translocation into the lumen of the vacuole, a lysosome-related organelle, where it is stored at high concentrations. In contrast, ectopic expression of bacterial polyphosphate kinase, PPK, results in the toxic accumulation of polyP outside of the vacuole. In this study, we used label-free mass spectrometry to investigate the mechanisms underlying this toxicity. We find that PPK expression results in the activation of a stress response mediated in part by the Hog1 and Yak1 kinases, and Msn2/Msn4 transcription factors. This response is countered by the combined action of the Ddp1 and Ppx1 polyphosphatases that function together to counter polyP accumulation and downstream toxicity. In contrast, ectopic expression of previously proposed mammalian polyphosphatases did not impact PPK-mediated toxicity in this model, suggesting either that these enzymes do not function directly as polyphosphatases in vivo or that they require co-factors unique to higher eukaryotes. Our work provides a mechanistic explanation for why polyP accumulation outside of lysosome-related organelles is toxic. Further, it serves as a resource for exploring how polyP may impact conserved biological processes at a molecular level.

molecular biology↗

bioRxiv2021

MetaProClust-MS1: A tool for clustering metaproteomes using rapid MS1 profiling

Metaproteomics is used to explore the composition, dynamics and function of microbial communities. How-ever, acquiring data by tandem mass spectrometry is time consuming and resource intensive. To mediate this challenge, we present MetaProClust-MS1, a computational framework for microbiome screening developed to reduce the time required for data acquisition by mass spectrometry. In this proof-of-concept study, we tested MetaProClust-MS1 on data acquired using short 15 minute MS1-only mass spectrometry gradients and compared the results to those produced using data acquired by a traditional tandem mass spectrometry approach. MetaProClust-MS1 identified robust microbiome shifts caused by xenobiotics in both datasets. Cluster topologies were also significantly correlated. We demonstrate that MetaProClust-MS1 is able to rapidly screen microbiomes using only short MS1 profiles. This approach can be used to prioritize samples for deep metaproteomic analysis and will be especially useful in large-scale metaproteomic screens or in clinical settings where rapid results are required.

bioinformatics↗

bioRxiv2021

PIGNON: A protein-protein interaction-guided functional enrichment analysis for quantitative proteomics

BackgroundQuantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline heavily relies on arbitrary thresholds of significance. Indeed, a functional annotation may be dysregulated in a given experimental condition, while none or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches. ResultsHerein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of individual genes, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across two breast cancer subtypes. PIGNON identified 168 breast cancer pathways dysregulated and clustered within the network between the HER2+ and triple negative subtypes, 203 breast cancer pathways shared by HER2+ and hormone receptor positive subtypes, 19 breast cancer pathways shared by hormone receptor positive and triple negative breast that are not detected by standard approaches. PIGNON identifies functional annotations that have been previously associated with specific breast cancer subtypes as well as novel annotations that may be implicated in the diseases. ConclusionPIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different conditions.

bioinformatics↗

bioRxiv2020

Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

bioinformatics↗

bioRxiv2020

MealTime-MS: A Machine Learning-Guided Real-Time Mass SpectrometryAnalysis for Protein Identification and Efficient DynamicExclusion

Mass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

bioinformatics↗

bioRxiv2020

A broad response to intracellular long-chain polyphosphate in human cells

Polyphosphates (PolyP) are composed of long chains of inorganic phosphates linked together by phosphoanhydride bonds. They are found in all kingdoms of life, playing roles in cell growth, infection, and blood coagulation. A resurgence in interest in polyP has shown links to diverse aspects of human disease. However, unlike in bacteria and lower eukaryotes, the mammalian enzymes responsible for polyP metabolism are not known. Many studies have resorted to adding polyP to cell culture media, but it is not clear if externally applied polyP enters the cell to impact signaling events or whether their effect is mediated exclusively by extracellular receptors. For the first time, we use RNA-seq and mass spectrometry to define a broad impact of polyP produced inside of mammalian cells via ectopic expression of the E. coli polyP synthetase Ppk1. RNA-seq demonstrates that Ppk1 expression impacts expression of over 350 genes enriched for processes related to transcription and cell motility. Analysis of proteins via label-free mass spectrometry identified over 100 changes with functional enrichment in cell migration. Follow up work suggests a role for internally-synthesized polyP in promoting activation of mTOR and ERK1/2-EGR1 signaling pathways implicated in cell growth and stress. Finally, fractionation analysis shows that polyP accumulated in multiple cellular compartments and was associated with the relocalization several nuclear/cytoskeleton proteins, including chromatin bound proteins DEK, TAF10, GTF2I and translation initiation factor eIF5b. Our work is the first to demonstrate that internally produced polyP can activate diverse signaling pathways in human cells. Significance StatementFor many years following its discovery in 1890, polyphosphates (polyP) were dismissed as evolutionary fossils. Best understood for its role in bacteria and yeast, our understanding of polyP in mammals remains rudimentary because the enzymes that synthesize and degrade polyP in mammalian systems are currently unknown. In our work, we carried out large-scale transcriptome and proteome approaches on human cells designed to accumulate internally produced polyP via ectopic expression of a bacterial polyP synthetase. Our work is the first to systematically assess the impact of increased intracellular polyP.

molecular biology↗

bioRxiv2020

Metaproteomic responses of in vitro gut microbiomes to resistant starches: the role of resistant starch type and inter-individual variations

Resistant starches (RS) are dietary compounds processed by the gut microbiota into metabolites, such as butyrate, that are beneficial to the host. The production of butyrate by the microbiome appears to be affected by the plant source and type of RS as well as the individuals microbiota. In this study, we used in vitro culture and metaproteomic methods to explore the consistency and variations in individual microbiomes functional responses to three types of RS - RS2(Hi Maize 260), RS3(Novelose 330) and RS4(Fibersym RW). Results showed that RS2 and RS3 significantly altered the levels of protein expression in the individual gut microbiomes, while RS4 did not result in significant protein changes. Significantly elevated protein groups were enriched in carbohydrate metabolism and transport functions of families Eubacteriaceae, Lachnospiraceae and Ruminococcaceae. In addition, Bifidobacteriaceae was significantly increased in response to RS3. We also observed taxon-specific enrichments of starch metabolism and pentose phosphate pathways corresponding to this family. Functions related to starch utilization, ABC transporters and pyruvate metabolism pathways were consistently increased in the individual microbiomes in response to RS2 and RS3; in contrast, the downstream butyrate producing pathway response varied. Our study confirm that different types of RS have markedly variable functional effects on the human gut microbiome, and also found considerable inter-individual differences in microbiome pathway responses.

microbiology↗

bioRxiv2019

pepFunk, a tool for peptide-centric functional analysis in metaproteomic human gut microbiome studies

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. To combat this challenge, we developed a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with KEGG terms and a pathway enrichment analysis adapted for peptide level data. Analysis using our peptide-centric workflow is fast and identifies more enriched KEGG pathways than protein-centric analysis. Our workflow is open source and available as a web application or source code to be run locally.

bioinformatics↗

bioRxiv2019

TargetSeeker-MS: A computational method for drug target discovery using protein separation coupled to mass spectrometry

When coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug target predictions made by these methods have remained rudimentary and significantly differ depending on the protocol used to produce the data. To identify drug targets in datasets produced using different EBPS-MS techniques, we have developed a novel flexible computational approach named TargetSeeker-MS. We showed that TargetSeeker-MS reproducibly identifies known and novel drug targets in C. elegans and HEK293 samples that were treated with the fungicide benomyl and processed using two different EBPS techniques. We also validated a novel benomyl target in vitro. TargetSeeker-MS, which is available online, allows for the confident identification of targets of a drug on a proteome scale, thereby facilitating the evaluation of its clinical viability.

bioinformatics↗

bioRxiv2017

Amyloid accumulation drives proteome-wide alterations in mouse models of Alzheimers disease like pathology

Amyloid beta (A{beta}) peptides impair multiple cellular pathways in the brain and play a causative role in Alzheimers disease (AD) pathology, but how the brain proteome is remodeled during this process is unknown. To identify new protein networks associated with AD-like pathology, we performed global quantitative proteomic analysis in three mouse models at pre- and post-symptomatic ages. Our analysis revealed a robust and consistent increase in Apolipoprotein E (ApoE) levels in nearly all transgenic brain regions with increased A{beta} levels. Taken together with prior findings on ApoE driving A{beta} accumulation, this analysis points to a pathological dysregulation of the ApoE-A{beta} axis. We also found dysregulation of protein networks involved in excitatory synaptic transmission consistent with AD pathophysiology. Targeted analysis of the AMPA receptor complex revealed a specific loss of TARP{gamma}-2, a key AMPA receptor trafficking protein. Expression of TARP{gamma}-2 in vivo in hAPP transgenic mice led to a restoration of AMPA currents. This database of proteome alterations represents a unique resource for the identification of protein alterations responsible for AD.\n\nHighlightsO_LIProteomic analysis of mouse brains with AD-like pathology reveals stark remodeling\nC_LIO_LIProteomic evidence points to a dysregulation of ApoE levels associated with A{beta} clearance rather than production\nC_LIO_LICo-expression analysis found distinctly impaired synapse and mitochondria modules\nC_LIO_LIIn-depth analyses of AMPAR complex points to loss of TARP{gamma}-2, which may compromise synapses in AD\nC_LI\n\neTOC BlurbProteome-wide profiling of brain tissue from three mouse models of AD-like pathology reveals A{beta}, brain region, and age dependent alterations of protein levels. This resource provides a new global protein expression atlas for the Alzheimers disease research community.

neuroscience↗