OpenProt 2.0 builds a path to the functional characterization of alternative proteins.

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Shedding Light on the Ghost Proteome.

Conventionally, eukaryotic mRNAs were thought to be monocistronic, leading to the translation of a single protein. However, large-scale proteomics has led to the identification of proteins translated from alternative open reading frames (AltORFs) in mRNAs. AltORFs are found in addition to predicted reference ORFs and noncoding RNA. Alternative proteins are not represented in the conventional protein databases, and this 'Ghost proteome' was not considered until recently. Some of these proteins are functional, and there is growing evidence that they are involved in central functions in physiological and physiopathological contexts. Here, we review how this Ghost proteome fills the gap in our understanding of signaling pathways, establishes new markers of pathologies, and highlights therapeutic targets.

Neuronal A2A receptor exacerbates synapse loss and memory deficits in APP/PS1 mice.

Early pathological upregulation of adenosine A2A receptors (A2ARs), one of the caffeine targets, by neurons is thought to be involved in the development of synaptic and memory deficits in Alzheimer's disease (AD) but mechanisms remain ill-defined. To tackle this question, we promoted a neuronal upregulation of A2AR in the hippocampus of APP/PS1 mice developing AD-like amyloidogenesis. Our findings revealed that the early upregulation of A2AR in the presence of an ongoing amyloid pathology exacerbates memory impairments of APP/PS1 mice. These behavioural changes were not linked to major change in the development of amyloid pathology but rather associated with increased phosphorylated tau at neuritic plaques. Moreover, proteomic and transcriptomic analyses coupled with quantitative immunofluorescence studies indicated that neuronal upregulation of the receptor promoted both neuronal and non-neuronal autonomous alterations, i.e. enhanced neuroinflammatory response but also loss of excitatory synapses and impaired neuronal mitochondrial function, presumably accounting for the detrimental effect on memory. Overall, our results provide compelling evidence that neuronal A2AR dysfunction, as seen in the brain of patients, contributes to amyloid-related pathogenesis and underscores the potential of A2AR as a relevant therapeutic target for mitigating cognitive impairments in this neurodegenerative disorder.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

In Vivo and Real-Time Metabolic Profiling of Plant-Microbe Interactions in Leaves, Stems, and Roots of Bacterially Inoculated Chardonnay Plantlets using SpiderMass.

There is growing interest in limiting the use of fungicides and implementing innovative, environmentally friendly strategies, such as the use of beneficial bacteria-triggered immunity, to protect grapevines from natural pathogens. Therefore, we need rapid and innovative ways to translate the knowledge of the molecular mechanisms underlying the activation of grapevine defenses against pathogens to induced resistance. Here, we have implemented an in vivo minimally invasive approach to study the interaction between plants and beneficial bacteria based on metabolic signatures. Paraburkholderia phytofirmans strain PsJN and PsJN-grapevine were used as bacterial and plant-bacterium interaction models, respectively. Using an innovative tool, SpiderMass, based on water-assisted laser desorption ionization with an IR microsampling probe, we simultaneously detect metabolic and lipidomic species. A metabolomic spectrum was thus generated, which was used to build a library and identify the most variable and discriminative peaks between the two conditions. We then showed that caftaric acid (m/z 311.04), caftaric acid dimer (m/z 623.09), derived caftaric acid (m/z 653.15), and quercetin-O-glucuronide tended to accumulate in grapevine leaves after root bacterization with PsJN. In addition, together with these phenolic messengers, we identified lipid biomarkers such as palmitic acid, linoleic acid, and α-linoleic acid as important messengers of enhanced defense mechanisms in Chardonnay plantlets. Taken together, SpiderMass is the next-generation methodology for studying plant-microorganism metabolic interactions with the prospect of in vivo real-time analysis in viticulture.

Neurotrauma investigation through spatial omics guided by mass spectrometry imaging: Target identification and clinical applications.

Traumatic brain injury (TBI) represents one of the major public health concerns worldwide due to the increase in TBI incidence as a result of injuries from daily life accidents such as sports and motor vehicle transportation as well as military-related practices. This type of central nervous system trauma is known to predispose patients to several neurological disorders such as Parkinson's disease, Alzheimer's disease, chronic trauamatic encephalopathy, and age-related Dementia. Recently, several proteomic and lipidomic platforms have been applied on different TBI studies to investigate TBI-related mechanisms that have broadened our understanding of its distinct neuropathological complications. In this study, we provide an updated comprehensive overview of the current knowledge and novel perspectives of the spatially resolved microproteomics and microlipidomics approaches guided by mass spectrometry imaging used in TBI studies and its applications in the neurotrauma field. In this regard, we will discuss the use of the spatially resolved microproteomics and assess the different microproteomic sampling methods such as laser capture microdissection, parafilm assisted microdissection, and liquid microjunction extraction as accurate and precise techniques in the field of neuroproteomics. Additionally, we will highlight lipid profiling applications and their prospective potentials in characterizing molecular processes involved in the field of TBI. Specifically, we will discuss the phospholipid metabolism acting as a precursor for proinflammatory molecules such as eicosanoids. Finally, we will survey the current state of spatial neuroproteomics and microproteomics applications and present the various studies highlighting their findings in these fields.

Deciphering genetic and nongenetic factors underlying tumour dormancy: insights from multiomics analysis of two syngeneic MRD models of melanoma and leukemia.

BACKGROUND: Tumour dormancy, a resistance mechanism employed by cancer cells, is a significant challenge in cancer treatment, contributing to minimal residual disease (MRD) and potential relapse. Despite its clinical importance, the mechanisms underlying tumour dormancy and MRD remain unclear. In this study, we employed two syngeneic murine models of myeloid leukemia and melanoma to investigate the genetic, epigenetic, transcriptomic and protein signatures associated with tumour dormancy. We used a multiomics approach to elucidate the molecular mechanisms driving MRD and identify potential therapeutic targets. RESULTS: We conducted an in-depth omics analysis encompassing whole-exome sequencing (WES), copy number variation (CNV) analysis, chromatin immunoprecipitation followed by sequencing (ChIP-seq), transcriptome and proteome investigations. WES analysis revealed a modest overlap of gene mutations between melanoma and leukemia dormancy models, with a significant number of mutated genes found exclusively in dormant cells. These exclusive genetic signatures suggest selective pressure during MRD, potentially conferring resistance to the microenvironment or therapies. CNV, histone marks and transcriptomic gene expression signatures combined with Gene Ontology (GO) enrichment analysis highlighted the potential functional roles of the mutated genes, providing insights into the pathways associated with MRD. In addition, we compared "murine MRD genes" profiles to the corresponding human disease through public datasets and highlighted common features according to disease progression. Proteomic analysis combined with multi-omics genetic investigations, revealed a dysregulated proteins signature in dormant cells with minimal genetic mechanism involvement. Pathway enrichment analysis revealed the metabolic, differentiation and cytoskeletal remodeling processes involved in MRD. Finally, we identified 11 common proteins differentially expressed in dormant cells from both pathologies. CONCLUSIONS: Our study underscores the complexity of tumour dormancy, implicating both genetic and nongenetic factors. By comparing genomic, transcriptomic, proteomic, and epigenomic datasets, our study provides a comprehensive understanding of the molecular landscape of minimal residual disease. These results provide a robust foundation for forthcoming investigations and offer potential avenues for the advancement of targeted MRD therapies in leukemia and melanoma patients, emphasizing the importance of considering both genetic and nongenetic factors in treatment strategies.

Establishment and characterization of canine mammary tumoroids for translational research.

BACKGROUND: Cancer heterogeneity is a main obstacle for the development of effective therapies, as its replication in in vitro preclinical models is challenging. Around 96% of developed drugs are estimated to fail from discovery to the clinical trial phase probably because of the unsuitability and unreliability of current preclinical models (Front Pharmacol 9:6, 2018; Nat Rev Cancer 8: 147-56, 2008) in replicating the overall biology of tumors, for instance the tumor microenvironment. Breast cancer is the most frequent cancer among women causing the greatest number of cancer-related deaths. Breast cancer can typically be modeled in vitro through the use of tumoroids; however, current approaches using mouse tumoroids fail to reproduce crucial aspect of human breast cancer, while access to human cells is limited and the focus of ethical concerns. New models of breast cancer, such as companion dogs, have emerged given the resemblance of developed spontaneous mammary tumors to human breast cancer in many clinical and molecular aspects; however, they have so far failed to replicate the tumor microenvironment. The present work aimed at developing a robust canine mammary tumor model in the form of tumoroids which recapitulate the tumor diversity and heterogeneity. RESULTS: We conducted a complete characterization of 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 kill canine tumoroids with the same efficacy as human tumoroids with 0.1 to 1 µM 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. Furthermore, two cryopreservation techniques were compared from a proteomic point of view which showed that tumoroids made from frozen material allowed to maintain the same molecular diversity as from freshly dissociated tumor. CONCLUSIONS: These findings revealed that canine mammary tumoroids can be easily generated and may provide an adequate and more reliable preclinical model to investigate tumorigenesis mechanisms and develop new treatments for both veterinary and human medicine.

Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.

Comparing MS imaging of lipids by WALDI and MALDI: two technologies for evaluating a common ground truth in MS imaging.

In this study, we conducted a direct comparison of water-assisted laser desorption ionization (WALDI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, with MALDI serving as the benchmark for label-free molecular tissue analysis in biomedical research. Specifically, we investigated the lipidomic profiles of several biological samples and calculated the similarity of detected peaks and Pearson's correlation of spectral profile intensities between the two techniques. We show that, overall, MALDI MS and WALDI MS present very close lipidomic analyses and that the highest similarity is obtained for the norharmane MALDI matrix. Indeed, for norharmane in negative ion mode, the lipidomic spectra revealed 100% similarity of detected peaks and over 0.90 intensity correlation between both technologies for five samples. The MALDI-MSI positive ion lipid spectra displayed more than 83% similarity of detected peaks compared to those of WALDI-MSI. However, we observed a lower percentage (77%) of detected peaks when comparing WALDI-MSI with MALDI-MSI due to the rich WALDI-MSI lipid spectra. Despite this difference, the global lipidomic spectra showed high consistency between the two technologies, indicating that they are governed by similar processes. Thanks to this similarity, we can increase datasets by including data from both modalities to either co-train classification models or obtain cross-interrogation.

OpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes.

OpenProt (www.openprot.org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets re-analysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein. Finally, to foster access to deeper ORF annotation independently of one's bioinformatics skills or computational resources, OpenProt now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.

The NANOTUMOR consortium - Towards the Tumor Cell Atlas.

Cancer is a multi-step disease where an initial tumour progresses through critical steps shaping, in most cases, life-threatening secondary foci called metastases. The oncogenic cascade involves genetic, epigenetic, signalling pathways, intracellular trafficking and/or metabolic alterations within cancer cells. In addition, pre-malignant and malignant cells orchestrate complex and dynamic interactions with non-malignant cells and acellular matricial components or secreted factors within the tumour microenvironment that is instrumental in the progression of the disease. As our aptitude to effectively treat cancer mostly depends on our ability to decipher, properly diagnose and impede cancer progression and metastasis formation, full characterisation of molecular complexes and cellular processes at play along the metastasis cascade is crucial. For many years, the scientific community lacked adapted imaging and molecular technologies to accurately dissect, at the highest resolution possible, tumour and stromal cells behaviour within their natural microenvironment. In that context, the NANOTUMOR consortium is a French national multi-disciplinary workforce which aims at a providing a multi-scale characterisation of the oncogenic cascade, from the atomic level to the dynamic organisation of the cell in response to genetic mutations, environmental changes or epigenetic modifications. Ultimately, this program aims at identifying new therapeutic targets using innovative drug design.

Alternative proteins are functional regulators in cell reprogramming by PKA activation.

It has been recently shown that many proteins are lacking from reference databases used in mass spectrometry analysis, due to their translation templated on alternative open reading frames. This questions our current understanding of gene annotation and drastically expands the theoretical proteome complexity. The functions of these alternative proteins (AltProts) still remain largely unknown. We have developed a large-scale and unsupervised approach based on cross-linking mass spectrometry (XL-MS) followed by shotgun proteomics to gather information on the functional role of AltProts by mapping them back into known signalling pathways through the identification of their reference protein (RefProt) interactors. We have identified and profiled AltProts in a cancer cell reprogramming system: NCH82 human glioma cells after 0, 16, 24 and 48 h Forskolin stimulation. Forskolin is a protein kinase A activator inducing cell differentiation and epithelial-mesenchymal transition. Our data show that AltMAP2, AltTRNAU1AP and AltEPHA5 interactions with tropomyosin 4 are downregulated under Forskolin treatment. In a wider perspective, Gene Ontology and pathway enrichment analysis (STRING) revealed that RefProts associated with AltProts are enriched in cellular mobility and transfer RNA regulation. This study strongly suggests novel roles of AltProts in multiple essential cellular functions and supports the importance of considering them in future biological studies.

Systematic review of international guidelines for head and neck oncology management in COVID-19 patients.

PURPOSE: The coronavirus pandemic has redefined the practice of head and neck surgeons in the management of oncology patients. Several countries have issued practice recommendations in that context. This review is a collaboration of the YO-IFOS (Young Otolaryngologists of the International Federation of Otolaryngological Societies) group in order to summarize, in a systematic way, all available guidelines and provide clear guidelines for the management of head and neck cancer patients in the COVID-19 pandemic. METHODS: This systematic review was performed according to the PRISMA statements. Inclusion criteria for the systematic review were based on the population, intervention, comparison, and outcomes according to (PICO) framework. The AGREE II (Appraisal of Guidelines for Research and Evaluation II) instrument was used to assess quality of all practice guidelines included in this review. RESULTS: Recommendations include adjustments regarding new patients' referral such as performing a pre-appointment triage and working in telemedicine when possible. Surgical prioritization must be adjusted in order to respect pandemic requirements. High-grade malignancies should, howeve,r not be delayed, due to potential serious consequences. Many head and neck interventions being aerosol-generating procedures, COVID-19 testing prior to a surgery and adequate PPE precautions are essential in operating rooms. CONCLUSION: These recommendations for head and neck oncology patients serve as a guide for physicians in the pandemic. Adjustments and updates are necessary as the pandemic evolves.

Toward High Spatially Resolved Proteomics Using Expansion Microscopy.

Expansion microscopy (EM) 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 fold 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 MS-based proteomic analysis. A major issue in expansion microscopy is the loss of protein information during the mechanical homogenization phase due to the use of proteinase K. For isotropic expansion, different homogenization agents were investigated, both to maximize protein identification and to minimize protein diffusion. Best results were obtained with SDS for homogenization. Using our modified protocol, we were able to enlarge a tissue section more than 3-fold and identified up to 655 proteins from 1 mm in size after expansion, equivalent to 330 µm in their real size corresponding thus to an average of 260 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.

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 developed 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 paves the way for surgical applications to excision margins.

Direct Water-Assisted Laser Desorption/Ionization Mass Spectrometry Lipidomic Analysis and Classification of Formalin-Fixed Paraffin-Embedded Sarcoma Tissues without Dewaxing.

BACKGROUND: Formalin-fixed paraffin-embedded (FFPE) tissue has been the gold standard for routine pathology for general and cancer postoperative diagnostics. Despite robust histopathology, immunohistochemistry, and molecular methods, accurate diagnosis remains difficult for certain cases. Overall, the entire process can be time consuming, labor intensive, and does not reach over 90% diagnostic sensitivity and specificity. There is a growing need in onco-pathology for adjunct novel rapid, accurate, reliable, diagnostically sensitive, and specific methods for high-throughput biomolecular identification. Lipids have long been considered only as building blocks of cell membranes or signaling molecules, but have recently been introduced as central players in cancer. Due to sample processing, which limits their detection, lipid analysis directly from unprocessed FFPE tissues has never been reported. METHODS: We present a proof-of-concept with direct analysis of tissue-lipidomic signatures from FFPE tissues without dewaxing and minimal sample preparation using water-assisted laser desorption ionization mass spectrometry and deep-learning. RESULTS: On a cohort of difficult canine and human sarcoma cases, classification for canine sarcoma subtyping was possible with 99.1% accuracy using "5-fold" and 98.5% using "leave-one-patient out," and 91.2% accuracy for human sarcoma using 5-fold and 73.8% using leave-one-patient out. The developed classification model enabled stratification of blind samples in <5 min and showed >95% probability for discriminating 2 human sarcoma blind samples. CONCLUSION: It is possible to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.

Mapping Spatiotemporal Microproteomics Landscape in Experimental Model of Traumatic Brain Injury Unveils a link to Parkinson's Disease.

Traumatic brain injury (TBI) represents a major health concerns with no clinically-approved FDA drug available for therapeutic intervention. Several genomics and neuroproteomics studies have been employed to decipher the underlying pathological mechanisms involved that can serve as potential neurotherapeutic targets and unveil a possible underlying relation of TBI to other secondary neurological disorders. In this work, we present a novel high throughput systems biology approach using a spatially resolved microproteomics platform conducted on different brain regions in an experimental rat model of moderate of controlled cortical injury (CCI) at a temporal pattern postinjury (1 day, 3 days, 7 days, and 10 days). Mapping the spatiotemporal landscape of signature markers in TBI revealed an overexpression of major protein families known to be implicated in Parkinson's disease (PD) such as GPR158, HGMB1, synaptotagmin and glutamate decarboxylase in the ipsilateral substantia nigra. In silico bioinformatics docking experiments indicated the potential correlation between TBI and PD through alpha-synuclein. In an in vitro model, stimulation with palmitoylcarnitine triggered an inflammatory response in macrophages and a regeneration processes in astrocytes which also further confirmed the in vivo TBI proteomics data. Taken together, this is the first study to assess the microproteomics landscape in TBI, mainly in the substantia nigra, thus revealing a potential predisposition for PD or Parkinsonism post-TBI.

Canine Bone Marrow-derived Mesenchymal Stem Cells: Genomics, Proteomics and Functional Analyses of Paracrine Factors.

Adult stem cells have become prominent candidates for treating various diseases in veterinary practice. The main goal of our study was therefore to provide a comprehensive study of canine bone marrow-derived mesenchymal stem cells (BMMSC) and conditioned media, isolated from healthy adult dogs of different breeds. Under well-defined standardized isolation protocols, the multipotent differentiation and specific surface markers of BMMSC were supplemented with their gene expression, proteomic profile, and their biological function. The presented data confirm that canine BMMSC express important genes for differentiation toward osteo-, chondro-, and tendo-genic directions, but also genes associated with angiogenic, neurotrophic, and immunomodulatory properties. Furthermore, using proteome profiling, we identify for the first time the dynamic release of various bioactive molecules, such as transcription and translation factors and osteogenic, growth, angiogenic, and neurotrophic factors from canine BMMSC conditioned medium. Importantly, the relevant genes were linked to their proteins as detected in the conditioned medium and further associated with angiogenic activity in chorioallantoic membrane (CAM) assay. In this way, we show that the canine BMMSC release a variety of bioactive molecules, revealing a strong paracrine component that may possess therapeutic potential in various pathologies. However, extensive experimental or preclinical trials testing canine sources need to be performed in order to better understand their paracrine action, which may lead to novel therapeutic strategies in veterinary medicine.

Learning versus reasoning to use tools in children.

Tool behavior might be based on two strategies associated with specific cognitive mechanisms: cued-learning and technical-reasoning strategies. We aimed to explore whether these strategies coexist in young children and whether they are manifest differently through development. We presented 216 3- to 9-year-olds with a vertical maze task consisting in moving a ball from the top to the bottom of a maze. Two tool-use/mechanical actions were possible: rotating action and sliding action. Three conditions were tested, each focused on a different strategy. In the Opaque-Cue condition (cued-learning strategy), children could not see the mechanical action of each tool. Nevertheless, a cue was provided according to the tool needed to solve the problem. In the Transparent-No Cue condition (technical-reasoning strategy), no cue was presented. However, children could see the mechanical actions associated with each tool. In the Transparent-Cue condition (cued-learning and/or technical-reasoning strategies) children saw both the mechanical actions and the cues. Results indicated that the Opaque-Cue and Transparent-Cue conditions were easier than the Transparent-No-Cue condition in all children. These findings stress that children can use either cued learning or technical reasoning to use tools, according to the available information. The behavioral pattern observed in the Transparent-Cue condition suggests that children might be inclined to use technical reasoning even when the task can be solved through cued learning.