Annotation Terms Research Articles

On the Nature of Nucleobase Stacking in RNA: A Comprehensive Survey of Its Structural Variability and a Systematic Classification of Associated Interactions.

The astonishing diversity in folding patterns of RNA three-dimensional (3D) structures is crafted by myriads of noncovalent contacts, of which base pairing and stacking are the most prominent. A systematic and comprehensive classification and annotation of these interactions is necessary for a molecular-level understanding of their roles. However, unlike in the case of base pairing, where a widely accepted nomenclature and classification scheme exists in the public domain, currently available classification schemes for base-base stacking need major enhancements to comprehensively capture the necessary features underlying the rich stacking diversity in RNA. Here, we extend the previous stacking classification based on nucleobase interacting faces by introducing a structurally intuitive geometry-cum topology-based scheme. Specifically, a stack is first classified in terms of the geometry described by the relative orientation of the glycosidic bonds, which generates eight basic stacking geometric families for heterodimeric stacks and six of those for homodimeric stacks. Further annotation in terms of the identity of the bases and the region of involvement of purines (five-membered, six-membered, or both rings) leads to the enumeration of 384 distinct RNA base stacks. Based on our classification scheme, we present an algorithm for automated identification of stacks in RNA crystal structures and analyze the stacking context in selected RNA structures. Overall, the work described here is expected to greatly facilitate the structure-based RNA research.

Altered sphingolipid function in Alzheimer's disease; a gene regulatory network approach

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer's disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD.Subsequent gene regulatory network-based analysis identified 3 candidate genes, that is, SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

UniBioDicts: Unified access to Biological Dictionaries.

SummaryWe present a set of software packages that provide uniform access to diverse biological vocabulary resources that are instrumental for current biocuration efforts and tools. The Unified Biological Dictionaries (UniBioDicts or UBDs) provide a single query-interface for accessing the online API services of leading biological data providers. Given a search string, UBDs return a list of matching term, identifier and metadata units from databases (e.g. UniProt), controlled vocabularies (e.g. PSI-MI) and ontologies (e.g. GO, via BioPortal). This functionality can be connected to input fields (user-interface components) that offer autocomplete lookup for these dictionaries. UBDs create a unified gateway for accessing life science concepts, helping curators find annotation terms across resources (based on descriptive metadata and unambiguous identifiers), and helping data users search and retrieve the right query terms.Availability and implementationThe UBDs are available through npm and the code is available in the GitHub organisation UniBioDicts (https://github.com/UniBioDicts) under the Affero GPL license.Supplementary information Supplementary data are available at Bioinformatics online.

Differentiating (an)notation practices: an artist-scholar’s observation

ABSTRACT Video annotation is an emergent practice and not (yet) a common method in dance studies or research. Subsequently, there are limited accounts that detail the practice of using annotation in dance but those that are available point to how annotation serves diverse and particular purposes. However, a common understanding of what annotation is does not theoretically cohere. Furthermore, the tendency to use the terms annotation and notation synonymously conflates these practices and risks overlooking the significant contributions of each. In discussing my experience, reflections, and observations of working with four different approaches to annotation I offer an understanding of what it offers in analysing and transmitting ideas about dance from an artist-scholar's perspective. Crucially, drawing from Bernard Stiegler's philosophy of technology, I position annotations as technical memories created in dialogue with existing mnemotechnical forms, or technical objects. Such characterisation illuminates how annotation helps to overcome limitations of documentary forms and highlight information otherwise missing or previously unnoticed. To further emphasise annotation as a method of amplification I compare my experience of annotation and of Labanotation to highlight the similarities and differences between these distinctive methodological tools. While the examples primarily focus on dance the insight developed in this article is valuable for other fields working with time-based media.

FlyIT: Drosophila Embryogenesis Image Annotation based on Image Tiling and Convolutional Neural Networks.

With the rise of image-based transcriptomics, spatial gene expression data has become increasingly important for understanding gene regulations from the tissue level down to the cell level. Especially, the gene expression images of Drosophila embryos provide a new data source in the study of Drosophila embryogenesis. It is imperative to develop automatic annotation tools since manual annotation is labor-intensive and requires professional knowledge. Although a lot of image annotation methods have been proposed in the computer vision field, they may not work well for gene expression images, due to the great difference between these two annotation tasks. Besides the apparent difference on images, the annotation is performed at the gene level rather than the image level, where the expression patterns of a gene are recorded in multiple images. Moreover, the annotation terms often correspond to local expression patterns of images, yet they are assigned collectively to groups of images and the relations between the terms and single images are unknown. In order to learn the spatial expression patterns comprehensively for genes, we propose a new method, called FlyIT (image annotation based on Image Tiling and convolutional neural networks for fruit Fly). We implement two versions of FlyIT, learning at image-level and gene-level, respectively. The gene-level version employs an image tiling strategy to get a combined image feature representation for each gene. FlyIT uses a pre-trained ResNet model to obtain feature representation and a new loss function to deal with the class imbalance problem. As the annotation of Drosophila images is a multi-label classification problem, the new loss function considers the difficulty levels for recognizing different labels of the same sample and adjusts the sample weights accordingly. The experimental results on the FlyExpress database show that both the image tiling strategy and the deep architecture lead to the great enhancement of the annotation performance. FlyIT outperforms the existing annotators by a large margin (over 9 percent on AUC and 12 percent on macro F1 for predicting the top 10 terms). It also shows advantages over other deep learning models, including both single-instance and multi-instance learning frameworks.

DFIPred: A Novel Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures

Possible food and drug interactivities could alter the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions is prone to the rise of drug-drug and drug-food/drug-nutrient interactions. The presence of these unfavorable interactions leads to other implications e.g., the decline in medicament’s effect, the withdrawals of various medications, and detrimental impacts on the patients’ health, etc. Yet the importance of drug-food/drug-nutrient interactions is remained underestimated, as the number of studies referring to these topics is constrained. Recently, state-of-the-art machine learning (ML) models have been created to unveil the hidden influences between drug and food compounds. However, there were still some limitations in terms of data mining, data input, and detailed annotations. In this study, we proposed a novel ML-based prediction model that could address most drawbacks of other models. A total of 4,384 food and 334 drug constituents were included in our training set, and two validation sets (containing 140,546 and 179 samples, respectively) were prepared for the model assessment. PyBioMed package has been used to obtain 3,780 chemical features, and then feature selection has been performed to select 25 most important features. The performance of our model was highly promising, as the accuracy score was 88.28% and 92.18% on the first and second validation set, respectively. More importantly, the DFIPred is capable of exhibiting the output recommendation about the interaction of any given pair of one drug and one food constituent based on their compound names. We believe that our model would contribute to the development of better and more powerful models used for the prediction of drug-food interactions in the future.

Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&A Site.

In recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.

Automatic Detection of Flavescence Dorée Symptoms Across White Grapevine Varieties Using Deep Learning.

Flavescence dorée (FD) is a grapevine disease caused by phytoplasmas and transmitted by leafhoppers that has been spreading in European vineyards despite significant efforts to control it. In this study, we aim to develop a model for the automatic detection of FD-like symptoms (which encompass other grapevine yellows symptoms). The concept is to detect likely FD-affected grapevines so that samples can be removed for FD laboratory identification, followed by uprooting if they test positive, all to be conducted quickly and without omission, thus avoiding further contamination in the fields. Developing FD-like symptoms detection models is not simple, as it requires dealing with the complexity of field conditions and FD symptoms’ expression. To address these challenges, we use deep learning, which has already been proven effective in similar contexts. More specifically, we train a Convolutional Neural Network on image patches, and convert it into a Fully Convolutional Network to perform inference. As a result, we obtain a coarse segmentation of the likely FD-affected areas while having only trained a classifier, which is less demanding in terms of annotations. We evaluate the performance of our model trained on a white grape variety, Chardonnay, across five other grape varieties with varying FD symptoms expressions. Of the two largest test datasets, the true positive rate for Chardonnay reaches 98.48% whereas for Ugni-Blanc it drops to 8.3%, underlining the need for a multi-varietal training dataset to capture the diversity of FD symptoms. To obtain more transparent results and to better understand the model’s sensitivity, we investigate its behavior using two visualization techniques, Guided Gradient-weighted Class Activation Mapping and the Uniform Manifold Approximation and Projection. Such techniques lead to a more comprehensive analysis with greater reliability, which is essential for in-field applications, and more broadly, for all applications impacting humans and the environment.

RNA-Seq profiling of microdissected glomeruli identifies potential biomarkers for human IgA nephropathy.

Few studies have examined gene expression changes occurring in the glomeruli of IgA nephropathy (IgAN) using a sensitive transcriptomic profiling method such as RNA sequencing (RNA-Seq). We collected glomeruli from biopsy specimens from patients with IgAN with relatively preserved kidney function (estimated glomerular filtration rate ≥ 60 mL·min-1·1.73 m-2 and urine protein-to-creatinine ratio < 3 g/g) and from normal kidney cortexes by hand microdissection and performed RNA-Seq. Differentially expressed genes were identified, and gene ontology term annotation and pathway analysis were performed. Immunohistochemical labeling and primary mesangial cell cultures were performed to confirm the findings of RNA-Seq analysis. Fourteen patients with IgAN and ten controls were included in this study. Glomerulus-specific genes were highly abundant. Principal component analysis showed clear separation between the IgAN and control groups. There were 2,497 differentially expressed genes, of which 1,380 were upregulated and 1,117 were downregulated (false discovery rate < 0.01). The enriched gene ontology terms included motility/migration, protein/vesicle transport, and immune system, and kinase binding was the molecular function overrepresented in IgAN. B cell signaling, chemokine signal transduction, and Fcγ receptor-mediated phagocytosis were the canonical pathways overrepresented. In vitro experiments confirmed that spleen tyrosine kinase (SYK), reported as upregulated in the IgAN transcriptome, was also upregulated in glomeruli from an independent set of patients with IgAN and that treatment with patient-derived IgA1 increased the expression of SYK in mesangial cells. In conclusion, transcriptomic profiling of the IgAN glomerulus provides insights in the intraglomerular pathophysiology of IgAN before it reaches profound kidney dysfunction. SYK may have a pathogenetic role in IgAN.

Associating Images with Sentences Using Recurrent Canonical Correlation Analysis

Associating images with sentences has drawn much attention recently. Existing methods commonly represent an image by indistinctively describing all its contents in an one-time static way, which ignores two facts that (1) the association analysis can only be effective for partial salient contents and the associated sentence, and (2) visual information acquisition is a dynamical rather than static process. To deal with this issue, we propose a recurrent canonical correlation analysis (RCCA) method for associating images with sentences. RCCA includes a contextual attention-based LSTM-RNN which can selectively attend to salient regions of an image at each time step, and then represent all the salient contents within a few steps. Different from existing attention-based models, our model focuses on the modelling of contextual visual attention mechanism for the task of association analysis. RCCA also includes a conventional LSTM-RNN for sentence representation learning. The resulting representations of images and sentences are fed into CCA to maximize linear correlation, where parameters of LSTM-RNNs and CCA are jointly learned. Due to the effective image representation learning, our model can well associate images with sentences with complex contents, and achieve better performance in terms of image annotation and retrieval.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.

A substrate-based ontology for human solute carriers.

Solute carriers (SLCs) are the largest family of transmembrane transporters in the human genome with more than 400 members. Despite the fact that SLCs mediate critical biological functions and several are important pharmacological targets, a large proportion of them is poorly characterized and present no assigned substrate. A major limitation to systems‐level de‐orphanization campaigns is the absence of a structured, language‐controlled chemical annotation. Here we describe a thorough manual annotation of SLCs based on literature. The annotation of substrates, transport mechanism, coupled ions, and subcellular localization for 446 human SLCs confirmed that ~30% of these were still functionally orphan and lacked known substrates. Application of a substrate‐based ontology to transcriptomic datasets identified SLC‐specific responses to external perturbations, while a machine‐learning approach based on the annotation allowed us to identify potential substrates for several orphan SLCs. The annotation is available at https://opendata.cemm.at/gsflab/slcontology/. Given the increasing availability of large biological datasets and the growing interest in transporters, we expect that the effort presented here will be critical to provide novel insights into the functions of SLCs.

New insights into the variability of lactic acid production in Lachancea thermotolerans at the phenotypic and genomic level

Non-conventional yeasts are increasingly applied in fermented beverage industry to obtain distinctive products with improved quality. Among these yeasts, Lachancea thermotolerans has multiple features of industrial relevance, especially the production of l(+)-lactic acid (LA), useful for the biological acidification of wine and beer. Since few information is available on this peculiar activity, the current study aimed to explore the physiological and genetic variability among L. thermotolerans strains. From a strain collection, mostly isolated from wine, a huge phenotypic diversity was acknowledged and allowed the selection of a high (SOL13) and a low (COLC27) LA producer. Comparative whole-genome sequencing of these two selected strains and the type strain CBS 6340T showed a high similarity in terms of gene content and functional annotation. Notwithstanding, target gene-based analysis revealed variations between high and low producers in the key gene sequences related to LA accumulation. More in-depth investigation of the core promoters and expression analysis of the genes ldh, encoding lactate dehydrogenase, indicated the transcriptional regulation may be the principal cause behind phenotypic differences. These findings highlighted the usefulness of whole-genome sequencing coupled with expression analysis. They provided crucial genetic insights for a deeper investigation of the intraspecific variability in LA production pathway.

Source-sink modifications affect leaf senescence and grain mass in wheat as revealed by proteomic analysis

BackgroundThe grain yield of cereals is determined by the synergistic interaction between source activity and sink capacity. However, source-sink interactions are far from being fully understood. Therefore, a field experiment was performed in wheat to investigate the responses of flag leaves and grains to sink/source manipulations.ResultsHalf-degraining delayed but partial defoliation enhanced leaf senescence. Sink/source manipulations influenced the content of reactive oxygen species in the flag leaf and the concentration of phytohormones, including cytokinins, indoleacetic 3-acid and jasmonic acid, in the flag leaves (LDef) and grains (GDef) in defoliated plants and flag leaves (LDG) and grain (GDG) in de-grained plants. Isobaric tag for relative and absolute quantitation (iTRAQ)-based quantitative proteomic analysis indicated that at 16 days after manipulation, a total of 97 and 59 differentially expressed proteins (DEPs) from various functional categories were observed in the LDG and LDef groups, respectively, compared with the control, and 115 and 121 DEPs were observed in the GDG and GDef groups, respectively. The gene ontology annotation terms of the DEPs mainly included carbon fixation, hydrogen peroxide catabolic process, chloroplast and cytoplasm, oxidoreductase activity and glutamate synthase activity in the flag leaves of manipulated plants and organonitrogen compound metabolic process, cytoplasm, vacuolar membrane, CoA carboxylase activity, starch synthase activity and nutrient reservoir activity in the grains of manipulated plants. KEGG pathway enrichment analysis revealed that photosynthesis, carbon, nitrogen and pyruvate metabolism and glycolysis/gluconeogenesis were the processes most affected by sink/source manipulations. Sink/source manipulations affected the activities of amylase and proteinases and, ultimately, changed the mass per grain.ConclusionsManipulations to change the sink/source ratio affect hormone levels; hydrolytic enzyme activities; metabolism of carbon, nitrogen and other main compounds; stress resistance; and leaf senescence and thus influence grain mass.

Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis.

A central theme in learning from image data is to develop appropriate representations for the specific task at hand. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in Drosophila, texture features were particularly effective for determining the developmental stages from in situ hybridization images. Such image representation is however not suitable for controlled vocabulary term annotation. Here, we developed feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain. To account for the differences between the source and target domains, we proposed a partial transfer learning scheme in which only part of the source model is transferred. We employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images. Results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges.

Comprehensive analysis of long noncoding RNA expression in circulation of breast cancer patients on neoadjuvant therapy.

e12644 Background: Though long considered “nonfunctional”, recent evidence is growing that non-coding RNAs, including long non-coding RNAs (lncRNAs), have an active role in tumor biology, mainly as gene expression regulators. Breast cancer is heterogeneous in nature and locally advanced cases are distinct entity in terms of curability. Being borderline between early and metastatic disease, long-term outcome here strongly depends on the efficacy of systemic therapy. To the best of our knowledge, no study has yet investigated global changes of circulating lncRNAs in this patient population during preoperative (neoadjuvant) treatment. Methods: We conducted a small transcriptomic trial on 10 locally advanced breast cancer patients, assessing lncRNA and messenger RNA (mRNA) expression in plasma samples before (S1) and after (S2) initiation of neoadjuvant therapy. Next-generation sequencing was performed with differential gene expression analysis between S1 and S2 groups. We assessed co-expression between lncRNAs and mRNAs, identifying mRNAs whose transcription was potentially regulated by lncRNAs, i.e. “lncRNA target genes”. In order to elucidate biological roles of these target mRNAs, we performed gene ontology (GO) and pathway analysis (Kyoto Encyclopedia of Genes and Genomes, KEGG). Results: 394 lncRNAs and 1085 mRNAs demonstrated statistically significant difference in expression between pretreatment and posttreatment samples. Co-expression analysis revealed positive correlation between 25 lncRNAs and 25 mRNAs located in cis, while potential trans interactions exceeded 105. GO evaluation of lncRNA target genes was significant for 44 terms: 28 for biological process (BP), 9 for cellular component (CC) and 7 for molecular function (MF). The most annotated terms for BP, MF and CC were respectively biosynthetic process, DNA-binding transcription factor activity and nucleus. KEGG analysis showed that 105 of 201 analyzed pathways were statistically significant with most prominent being pathways in cancer and transcriptional misregulation in cancer. Conclusions: Despite limited in size, present study provides broad view on transcriptional landscape in blood circulation, interrogating in vivo dynamics of systemic gene expression during neoadjuvant breast cancer treatment. It demonstrates that substantial number of circulating lncRNAs could be up- or downregulated in the course of therapy and this has the potential to control protein-coding genes that are tightly implicated in cancer biology.

Reaudit of transvaginal ultrasound practice in a general gynecology clinic.

A recent pilot audit found that the quality of transvaginal ultrasound practice in a teaching hospital did not reflect current guidelines. This was a concern given the frequency and importance of ultrasound examinations in approaching a diagnosis. Interventional measures involved ad hoc training sessions coincident with installation of updated equipment. A reaudit was performed to assess any changes in the standard of ultrasound practice. Data were collected by random direct observation. To minimize the Hawthorne effect, staffs were not made aware of when active data collection took place. Observations of both patients and end user were compared with current guidelines. Data collected maintained the anonymity of users throughout. Practice was graded as: compliant, partially-compliant, or noncompliant. Descriptive statistics were used to demonstrate any change post-intervention. Observations (n = 48) of completed gynecology ultrasound practice were recorded, and results described as percentage frequencies (%). Image optimization noncompliant rates declined in the reaudit from 65.1% to 41.67% (P = .07) and noncompliant rates of global examination of gynecology decreased from 60.47% to 14.58% (P < .05). Substantial improvements were seen in terms of image annotations (initial audit, 41.9% vs reaudit, 66.67%), and end user examination of bladder, vagina and cervix when indicated (initial audit, 25.6% vs reaudit, 77.08%). Regular audit, ad hoc ultrasound training sessions and updated ultrasound equipment resulted in considerably improved compliance of transvaginal ultrasound practice in gynecology.

Maternal Protein Restriction Altered Insulin Resistance and Inflammation-Associated Gene Expression in Adipose Tissue of Young Adult Mouse Offspring in Response to a High-Fat Diet.

Maternal protein restriction is associated with increased risk of insulin resistance and inflammation in adulthood offspring. Here, we investigated whether maternal protein restriction could alter the risk of metabolic syndrome in postweaning high-fat (HF)-diet-challenged offspring, with focus on epididymal adipose tissue gene expression profile. Female ICR mice were fed a control (C) or a low-protein (LP) diet for two weeks before mating and throughout gestation and lactation, and their male offspring were fed an HF diet for 22 weeks (C/HF and LP/HF groups). A subset of offspring of control dams was fed a low-fat control diet (C/C group). In response to postweaning HF diet, serum insulin level and the homeostasis model assessment of insulin resistance (HOMA-IR) were increased in control offspring. Maternal LP diet decreased HOMA-IR and adipose tissue inflammation, and increased serum adiponectin level in the HF-diet-challenged offspring. Accordingly, functional analysis revealed that differentially expressed genes (DEGs) enriched in cytokine production were downregulated in the LP/HF group compared to the C/HF group. We also observed the several annotated gene ontology terms associated with innate immunity and phagocytosis in down-regulated DEGs between LP/HF and C/C groups. In conclusion, maternal protein restriction alleviated insulin resistance and inflammation in young offspring mice fed a HF diet but may impair development of immune system in offspring.

Identification of immune-related proteins of Dreissena polymorpha hemocytes and plasma involved in host-microbe interactions by differential proteomics

Biological responses of zebra mussel Dreissena polymorpha are investigated to assess the impact of contaminants on aquatic organisms and ecosystems. In addition to concentrate chemical contaminants in their tissues, zebra mussels accumulate several microorganisms such as viruses, protozoa and bacteria. In order to understand the molecular mechanisms involved in the defence against microorganisms this study aims at identifying immune proteins from D. polymorpha hemolymph involved in defence against protozoa and viruses. For this purpose, hemolymph were exposed ex vivo to Cryptosporidium parvum and RNA poly I:C. Differential proteomics on both hemocytes and plasma revealed immune proteins modulated under exposures. Different patterns of response were observed after C. parvum and RNA poly I:C exposures. The number of modulated proteins per hemolymphatic compartments suggest that C. parvum is managed in cells while RNA poly I:C is managed in plasma after 4 h exposure. BLAST annotation and GO terms enrichment analysis revealed further characteristics of immune mechanisms. Results showed that many proteins involved in the recognition and destruction of microorganisms were modulated in both exposure conditions, while proteins related to phagocytosis and apoptosis were exclusively modulated by C. parvum. This differential proteomic analysis highlights in zebra mussels modulated proteins involved in the response to microorganisms, which reflect a broad range of immune mechanisms such as recognition, internalization and destruction of microorganisms. This study paves the way for the identification of new markers of immune processes that can be used to assess the impact of both chemical and biological contaminations on the health status of aquatic organisms.

Plasma Extracellular Vesicles May Regulate Inflammation and Hemodynamic Changes in Brain-Dead Donors

Brain death causes hemodynamic instability and systemic inflammatory responses. However, the consequences of brain death on donor organs, and in particular on the microvascular endothelium, remains unknown. Extracellular vesicles (EV) are involved in cell-to-cell communication by delivering their functional cargo to target cells. EVs are able to cross the blood-brain barrier and may affect the function of the cells of donor organs and immune cells. To get insight into the molecular mechanisms of brain death, we sequenced and analyzed plasma EV RNA from brain-dead solid organ donors and healthy controls. We analyzed circulating EV RNA samples of 30 brain-dead (BD) donors and 9 healthy controls. The identity of the isolated plasma EVs was confirmed on electron microscopy and nanoparticle tracking analysis. The quality of the purified RNA was analyzed on a bioanalyzer, followed by analysis with next-generation RNA sequencing. The sequencing data were used for differentially expressed gene analysis, gene ontology enrichment analysis, and pathway analysis. Transcripts with p-values less than 0.05 and a log2 fold change larger than 1.5 were considered to be significant. Preliminary results of sequencing data analyses from 17 BD donors revealed that brain death clearly affects the transcriptomic profiles of plasma EV. In BD donors, 2165 genes were differentially expressed, of which 1873 were upregulated and 292 transcripts were downregulated compared to the controls. Based on the gene annotation terms, the identified differentially expressed transcripts were related to inflammatory responses and coagulation. Moreover, pathway analysis indicated that the genes are related to cell survival, cytokine production, and regulation of gene expression, and are involved in PI3K-Akt-, NF-kB-, and PPAR- signaling pathways. Our results demonstrate that brain death significantly affects the plasma EV transcriptome profiles. Thus, plasma EVs released after brain death may accelerate inflammation and hemodynamic changes. Further studies are required to elucidate the origin of EVs and their effects on microvascular dysfunction in potential donor organs to develop targeted therapy for donor treatment to prevent ischemia-reperfusion injury.