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Fournier, I.

Publications and source records attributed to Fournier, I..

12 recordsLinked to original sources

SpacePath enables pathway and molecular-family analysis beyond annotation ambiguity in spatial metabolomics and lipidomics

Mass spectrometry imaging (MSI) maps metabolites and lipids in tissue with spatial resolution, but molecular identity remains partly resolved: single ions match multiple candidate compounds, and most detected metabolites and lipids fall outside canonical biochemical pathways. We developed SpacePath, a computational framework that retains ion-to-compound ambiguity, scores canonical pathways across tissue locations, and groups assignments that were unmapped to pathways into de novo molecular families by chemical similarity. Pathway mappings covered on average only 1% of molecular assignments, whereas the combined SpacePath representation covered 24% of pooled assignments. Benchmarking on four public spatial lipidomics datasets (healthy mouse brain, cerebellum, a demyelination model, and human bladder cancer) recovered previously reported spatial patterns. In newly acquired glioblastoma xenograft datasets, SpacePath identified lipid programs specific to tumor and surrounding brain, and SpiderMass-MS/MS independently confirmed four tumor-associated features from a de novo family, refining the initial MS1 assignments. SpacePath extends the spatial interpretation of MSI data beyond canonical pathways while preserving annotation uncertainty.

bioinformatics↗

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

Establishment and characterization of a tumoroid biobank derived from dog patients' mammary tumors for translational research

Breast cancer is the most frequent cancer among women causing the greatest number of cancer-related deaths. Cancer heterogeneity is a main obstacle to therapies. Around 96% of the drugs fail from discovery to the clinical trial phase probably because of the current unreliable preclinical models. New models emerge such as companion dogs who develop spontaneous mammary tumors resembling human breast cancer in many clinical and molecular aspects. The present work aimed at developing a robust canine mammary tumor model in the form of tumoroids which recapitulate the tumor diversity and heterogeneity. We conducted a complete characterization of these canine mammary tumoroids through histologic, molecular and proteomic analysis, demonstrating their strong similarity to the primary tumor. We demonstrated that these tumoroids can be used as a drug screening model. In fact, we showed that Paclitaxel, a human chemotherapeutic, could killed canine tumoroids with the same efficacy as human tumoroids with 0.1 to 1 M of drug needed to kill 50% of the cells. Due to easy tissue availability, canine tumoroids can be produced at larger scale and cryopreserved to constitute a biobank. We have demonstrated that cryopreserved tumoroids keep the same histologic and molecular features (ER, PR and HER2 expression) as fresh tumoroids. Two techniques of cryopreservation were compared demonstrating that tumoroids made from frozen tumor material allowed to maintain a higher molecular diversity. These findings revealed that canine mammary tumoroids can be easily generated at large scale and can represent a more reliable preclinical model to investigate tumorigenesis mechanisms and develop new treatments for both veterinary and human medicine.

cancer biology↗

Towards high spatially resolved microproteomics using expansion microscopy

Expansion microscopy is an emerging approach for morphological examination of biological specimens at nanoscale resolution using conventional optical microscopy. To achieve physical separation of cell structures, tissues are embedded in a swellable polymer and expanded several folds in an isotropic manner. This work shows the development and optimization of physical tissue expansion as a new method for spatially resolved large scale proteomics. Herein, we established a novel method to enlarge the tissue section to be compatible with manual microdissection on regions of interest and to carry out MS-based proteomic analysis. A major issue in the Expansion microscopy is the loss of proteins information during the mechanical homogenization phase due to the use of Proteinase K. For isotropic expansion, different homogenization agents are investigated, both to maximize protein identification and to minimize protein diffusion. Better results are obtained with SDS. From a tissue section enlarge more than 3-fold, we have been able to manually cut out regions of 1mm in size, equivalent to 300{micro}m in their real size. We identified up to 655 proteins from a region corresponding to an average of 940 cells. This approach can be performed easily without any expensive sampling instrument. We demonstrated the compatibility of sample preparation for expansion microscopy and proteomic study in a spatial context. Abstract graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/433765v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@14cc519org.highwire.dtl.DTLVardef@1201e36org.highwire.dtl.DTLVardef@19fa853org.highwire.dtl.DTLVardef@e4112a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Robot-Assisted SpiderMass for in vivo Real-Time Topography Mass Spectrometry Imaging

Mass Spectrometry Imaging (MSI) has shown to bring invaluable information for biological and clinical applications. However, conventional MSI is generally performed ex vivo from tissue sections. Here, we develop a novel MS-based method for in vivo mass spectrometry imaging. By coupling the SpiderMass technology - that provides in vivo minimally invasive analysis - to a robotic arm of high accuracy, we demonstrate that images can be acquired from any surface by moving the laser probe above the surface. By equipping the robotic arm with a sensor, we are also able to both get the topography image of the sample surface and the molecular distribution, and then and plot back the molecular data, directly to the 3D topographical image without the need for image fusion. This is shown for the first time with the 3D topographic MS-Based whole-body imaging of a mouse. Enabling fast in vivo MSI bridged to topography pave the way for surgical applications to excision margins.

biochemistry↗

Overall patient's survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study

Molecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patients stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patients survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development. SignificanceGlioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathways i.e. the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision. HighlightsO_LIA novel stratification of glioblastoma based on mass spectrometry was established. C_LIO_LIThree groups with different molecular features and survival were identified. C_LIO_LIThis new classification could improve prognostication and may help therapeutic options. C_LIO_LI8 prognosis markers for oncologist therapeutic decision have been validated. C_LI

cancer biology↗

Preneoplastic lesions fimbria pan-proteomic studies establish the fimbriectomy benefit for BRCA1/2 patients and identify early diagnosis markers of HGSC

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide; however, the origin of ovarian tumors, particularly for high-grade serous carcinoma (HGSC), is still debated. Accumulated evidence converges towards the involvement of the preneoplastic lesions observed in the fimbriated end of the fallopian tubes. In this study, we propose to carry out an in-depth proteomics analysis of these epithelial lesions (p53 signature, serous tubal intraepithelial carcinoma-STIC and serous tubal intraepithelial lesions-STIL) based on spatially resolved proteomic guided by IHC technique. We identified specific clusters related to each preneoplastic lesions, specific protein mutations based on Cosmic database and a Ghost proteome translated from non-coding RNAs and alternative ORFs, using the OpenProt database. Protein networks have been constructed from each cluster utilizing systems biology platform. Generated data were used to confirm the potentially dormant character of the STIL lesion and the more aggressive profile of the STIC which appears closer to HGSC than other lesions. In summary, our results established the chronological mechanisms and genesis of different ovarian cancer phenotypes but also identified the early diagnostic markers of HCSC guiding an adapted therapy and a better patient care.

cancer biology↗

On-tissue spatially-resolved glycoproteomics guided by N-glycan imaging reveal global dysregulation of canine glioma glycoproteomic landscape

Here we present an approach to identify N-linked glycoproteins and deduce their spatial localization using a combination of MALDI mass spectrometry N-glycan imaging and spatially-resolved glycoproteomic strategies. We subjected formalin-fixed, paraffin-embedded glioma biopsies to on-tissue PNGaseF digestion and MALDI imaging and found that the glycan HexNAc4-Hex5-NeuAc2 was found to be predominantly expressed in necrotic regions of high-grade canine gliomas, whereas high mannose HexNAc2-Hex5 was predominantly found in benign regions. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were subjected to microdigestion and the extracts were analyzed by LC-MS/MS without further glycopeptide enrichment. Results identified haptoglobin, which is involved in iron scavenging that presents aberrant fucosylation/sialylation in various cancers, as the protein associated with HexNAc4-Hex5-NeuAc2. Additionally, we identified several high-mannose (Hex2-HexNAc5) glycopeptides enriched in benign regions. To the best of our knowledge, this is the first report that directly links glycan imaging with intact glycopeptide identification. In total, our spatially-resolved glycoproteomics technique identified over 400 N-glycosylated, O-GalNAcylated, O-mannosylated, and S- and O-GlcNAcylated glycopeptides from over 30 proteins, demonstrating the diverse array of glycosylation present on the tissue slides and the sensitivity of our technique. While N-glycosylation and O-mannosylation were similar between benign and tumor/necrotic sections, S- and O-GlcNAc glycopeptides were significantly deceased in tumor/necrotic sections, whereas sialylated O-GalNAc glycopeptides were significantly upregulated. Ultimately, this proof-of-principle work demonstrates the capability of spatially-resolved glycoproteomics to complement MALDI-imaging technologies in understanding dysregulated glycosylation in cancer.

cancer biology↗

Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

systems biology↗

Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning

Rapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology...) where training samples are scarce.

bioinformatics↗

Meropenem vs standard of care for treatment of neonatal late onset sepsis (NeoMero1): a randomised controlled trial

BackgroundThe early use of broad-spectrum antibiotics remains the cornerstone for the treatment of neonatal late onset sepsis (LOS). However, which antibiotics should be used is still debatable, as relevant studies were conducted more than 20 years ago, were single centre or country, insufficiently powered, evaluated antibiotics not in clinical use anymore and had variable inclusion/exclusion criteria and outcome measures. Moreover, antibiotic-resistant bacteria have become a major problem in many countries worldwide. We hypothesized that efficacy of meropenem as a broad spectrum antibiotic is superior to standard of care regimen (SOC) in empiric treatment of LOS and thus aimed to compare the efficacy and safety of meropenem to SOC in infants aged <90 days with LOS.\n\nMethods and findingsNeoMero-1 was a randomized, open-label, phase III superiority trial conducted in 18 neonatal units in 6 countries. Infants with post-menstrual age (PMA) of [&le;]44 weeks with positive blood culture and one, or those with negative culture and at least with two predefined clinical and laboratory signs suggestive of LOS, or those with PMA >44 weeks meeting the Goldstein criteria of sepsis, were randomized in a 1:1 ratio to receive meropenem or SOC (ampicillin+gentamicin or cefotaxime+gentamicin) for 8-14 days. The primary outcome was treatment success (survival, no modification of allocated therapy, resolution/improvement of clinical and laboratory markers, no need of additional antibiotics and presumed/confirmed eradication of pathogens) at test-of-cure visit (TOC) in full analysis set. Stool samples were tested at baseline and day 28 for meropenem-resistant Gram-negative organisms (CRGNO).\n\nThe primary analysis was performed in all randomised patients (full analysis set) and in patients with culture confirmed LOS. Proportions of participants with successful outcome were compared by using a logistic regression model adjusted for the stratification factors.\n\nFrom September 3rd 2012 to November 30th 2014, in total 136 patients in each arm were randomized; 140 (52%) were culture positive. Success at TOC was achieved in 44/136 (32%) in the meropenem arm vs. 31/135 (23%) in the SOC arm (p=0.087); 17/63 (27%) vs. 10/77 (13%) in patients with positive cultures (p=0.022). The main reason of failure was modification of allocated therapy. Adverse events occurred in 72% and serious adverse events in 17% of patients, the mortality rate was 6% with no differences between study arms. Cumulative acquisition of CRGNO by day 28 occurred in 4% in the meropenem and 12% in the SOC arm (p=0.052).\n\nConclusionsMeropenem was not superior to SOC in terms of success at TOC, short term hearing disturbances, safety or mortality and did not outselect colonization with CRGNOs. Meropenem as broad-spectrum antibiotic should be reserved for neonates who are more likely to have Gram-negative LOS, especially in NICUs where microorganisms producing ESBL and AmpC beta-lactamases are circulating.

clinical trials↗

Deep transcriptome annotation suggests that small and large proteins encoded in the same genes often cooperate

Recent studies in eukaryotes have demonstrated the translation of alternative open reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and evolutionary patterns indicate that altORFs are particularly constrained in CDSs that evolve slowly. Thousands of predicted alternative proteins are detected in proteomic datasets by reanalysis with a database containing predicted alternative proteins. Protein domains and co-conservation analyses suggest potential functional cooperation or shared function between small and large proteins encoded in the same genes. This is illustrated with specific examples, including altMID51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many coding genes code for more than one protein that are often functionally related.

biochemistry↗