EMBnet.journal最新文献

{"title":"Exosomal Epigenetics","authors":"Eleni Papakonstantinou, Konstantina Dragoumani, G. Chrousos, Dimitrios Vlachakis","doi":"10.14806/ej.29.0.1049","DOIUrl":"https://doi.org/10.14806/ej.29.0.1049","url":null,"abstract":"Epigenetics is the study of heritable changes in gene expression that occur without changes to the underlying DNA sequence. Epigenetic modifications can include DNA methylation, histone modifications, and non-coding RNAs, among others. These modifications can influence the expression of genes by altering the way DNA is packaged and accessed by transcriptional machinery, thereby affecting cellular function and behavior. Epigenetic modifications can be influenced by a variety of factors, including environmental exposures, lifestyle factors, and aging, whilst abnormal epigenetic modifications have been implicated in a range of diseases, including cancer, neurodegenerative disorders, and cardiovascular disease. The study of epigenetics has the potential to provide new insights into the mechanisms of disease and could lead to the development of new diagnostic and therapeutic strategies. Exosomes can transfer epigenetic information to recipient cells, thereby influencing various physiological and pathological processes, and the identification of specific epigenetic modifications that are associated with a particular disease could lead to the development of targeted therapies that restore normal gene expression patterns. In recent years, the emerging role of exosomal epigenetics in human breast milk, highlighting its significance in infant nutrition and immune development. Milk exosomes are shown to carry epigenetic regulators, including miRNAs and long non-coding RNAs, which can modulate gene expression in recipient cells. These epigenetic modifications mediated by milk exosomal RNAs have implications for the development of the gastrointestinal tract, immune system, and metabolic processes in infants.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"68 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Milk exosomes and a new way of communication between mother and child","authors":"Eleni Papakonstantinou, Konstantina Dragoumani, Thanasis Mitsis, G. Chrousos, D. Vlachakis","doi":"10.14806/ej.29.0.1050","DOIUrl":"https://doi.org/10.14806/ej.29.0.1050","url":null,"abstract":"Extracellular vesicles (EVs) are a heterogeneous group of lipid-bound vesicles released by cells into the extracellular space. EVs are an important mediator of intercellular communications and carry a wide variety of molecules that exert a biological function, such as lipids, nucleic acids, proteins, ions, and adenosine triphosphate (ATP). Extracellular vesicles are classified into microvesicles, exosomes, and apoptotic bodies depending on their biogenesis and size. Exosomes are spherical lipid-bilayer vesicles with a diameter of about 40 to 100 nm. Exosomes originate from intracellular endosomal compartments, while microvesicles originated directly from a cell’s plasma membrane and apoptotic bodies originate from cells undergoing apoptosis and are released via outward blebbing and fragmentation of the plasma membrane. Specifically, exosomes have garnered great attention since they display great potential as both biomarkers and carriers of therapeutic molecules.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"57 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fingerprinting Breast Milk; insights into Milk Exosomics","authors":"Eleni Papakonstantinou, Konstantina Dragoumani, Antonia Mataragka, F. Bacopoulou, Christos Yapijakis, Nikolaos AA Balatsos, Katerina Pissaridi, Dimitris Ladikos, Aspasia Efthymiadou, George Katsaros, E. Gikas, Pantelis Hatzis, Martina Samiotaki, M. Aivaliotis, Vasileios Megalooikonomou, Antonis Giannakakis, C. Iliopoulos, Erik Bongcam-Rudloff, Sofia Kossida, Elias Eliopoulos, G. Chrousos, Dimitrios Vlachakis","doi":"10.14806/ej.29.0.1048","DOIUrl":"https://doi.org/10.14806/ej.29.0.1048","url":null,"abstract":"Breast milk, often referred to as \"liquid gold,\" is a complex biofluid that provides essential nutrients, immune factors, and developmental cues for newborns. Recent advancements in the field of exosome research have shed light on the critical role of exosomes in breast milk. Exosomes are nanosized vesicles that carry bioactive molecules, including proteins, lipids, nucleic acids, and miRNAs. These tiny messengers play a vital role in intercellular communication and are now being recognized as key players in infant health and development. This paper explores the emerging field of milk exosomics, emphasizing the potential of exosome fingerprinting to uncover valuable insights into the composition and function of breast milk. By deciphering the exosomal cargo, we can gain a deeper understanding of how breast milk influences neonatal health and may even pave the way for personalized nutrition strategies. ","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"43 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ds-Seq: An Integrated Pipeline for In Silico Small RNA Se-quence Analysis for Host-pathogen Interaction Studies","authors":"Temitayo A. Olagunju, Angela Uche Makolo, Andreas Gisel","doi":"10.14806/ej.29.0.1037","DOIUrl":"https://doi.org/10.14806/ej.29.0.1037","url":null,"abstract":"Plant-pathogen interactions activate molecular activities wherein the host defends the pathogen while the pathogen tries to suppress the plant response. Small RNAs (sRNAs) mediate major mechanisms, including post-transcriptional gene silencing, histone modification and DNA methylation by which plants respond to the presence of pathogens. Genome-wide profiling of host and pathogen sRNAs is therefore pivotal to uncovering the mechanisms underlying the host-pathogen interaction and mechanisms for host resistance. sRNA high throughput sequencing (HTS) data analysis often involves multiple stages/tools. Most necessary tools are accessible only through the command line, making it challenging for those without a high level of Unix/Linux skills. Furthermore, installation of some of these tools may become difficult due to dependencies and software version compatibility. We have developed an integrated open-source pipeline, Ds-Seq, for end-to-end in silico analysis of sRNA HTS data with improved reproducibility. The pipeline combines in-house scripts and public tools in a shell script, which can be invoked with a single command. The pipeline's usefulness has been demonstrated with testing on publicly available and published data from independent sRNA-seq datasets of host-pathogen interaction studies. Ds-Seq is available on GitHub, while a Docker image can be obtained from the Docker hub.Availability: Ds-Seq is freely available from the GitHub repository at https://github.com/CEPHAS-01/small-RNASeq.ngs and Docker hub with ID cephas/ds-seq (https://hub.docker.com/r/cephas/ds-seq).","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"19 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Intersection of Artificial Intelligence and Precision Endocrinology.","authors":"Dimitrios Vlachakis, Konstantina Dragoumani, Eleni Papakonstantinou, George P Chrousos","doi":"10.14806/ej.30.0.1052","DOIUrl":"https://doi.org/10.14806/ej.30.0.1052","url":null,"abstract":"<p><p>Bioinformatics and artificial intelligence (AI) have emerged as transformative tools in modern medicine, revolutionising the landscape of medical diagnosis and treatment. Herein, we provide an overview of the synergistic relationship between bioinformatics and AI, elucidating their pivotal roles in deciphering complex biological data and advancing precision medicine and, in particular, endocrinology. We explore various applications of bioinformatics and AI in medical research, including genomic analysis, drug discovery, disease diagnosis, and personalised treatment strategies. Additionally, we discuss challenges and future directions in leveraging these technologies to enhance healthcare outcomes.</p>","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"30 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of the planned behaviour theory and the transtheoretical behaviour model on physical activity. A systematic review.","authors":"Anastasia Dermatis, Flora Bacopoulou, Ioulia Kokka, Dimitrios Vlachakis, Georgios Lyrakos, Despina Menti, Christina Darviri","doi":"10.14806/ej.29.0.1046","DOIUrl":"https://doi.org/10.14806/ej.29.0.1046","url":null,"abstract":"Systematic physical activity (PA) is crucial in preventing illnesses that can become life-threatening, such as colon and breast cancer, heart disease and ischemic stroke, cardio-respiratory disease, type II diabetes, and depression. Many theory–based interventions have been applied to achieve positive outcomes in an individual's behavioural change and the ability to engage in systematic PA. This systematic review investigates the influence of the Transtheoretical model of behaviour (TTM) and the theory of planned behaviour (TPB) on PA. A substantial search in Science Direct, Wiley Online Library databases and PubMed was performed to obtain articles about the topic. Data exportation was possible after the reviewers applied exclusion–inclusion criteria to estimate evidence quality. Empirical evidence was assessed with the CONSORT checklist to appraise the risk of bias. The primary search identified 195 studies. Of those, ten original studies were comprised. All studies indicated a positive influence of TPB and TTM on physical activity in non–health and healthy populations. In particular, it was found to have an impact on energy expenditure, balance and body strength. Theory-based interventions are notably effective in promoting physical activity behaviour. Researchers and health professionals must select and utilise interventions based on the above mentioned theories and aim to enhance PA behavioural change on individual and interpersonal factors. Although the positive outcomes of theory-based interventions on PA behaviour, it is necessary for further research to be conducted.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136058816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EpiCass and CassavaNet4Dev Advanced Bioinformatics Workshop","authors":"A. Gisel, L. Stavolone, Temitayo A. Olagunju, Michael Landi, Renaud Van Damme, A. Niazi, L. Falquet, T. Shah, E. Bongcam-Rudloff","doi":"10.14806/ej.29.0.1045","DOIUrl":"https://doi.org/10.14806/ej.29.0.1045","url":null,"abstract":"EpiCass and CassavaNet4Dev are collaborative projects funded by the Swedish Research Council between the Swedish University of Agriculture (SLU) and the International Institute of Tropical Agriculture (IITA). The projects aim to investigate the influence of epigenetic changes on agricultural traits such as yield and virus resistance while also providing African students and researchers with advanced bioinformatics training and opportunities to participate in big data analysis events. The first advanced bioinformatics training workshop took place from May 16th to May 18th, 2022, followed by an online mini-symposium titled \"Epigenetics and crop improvement\" on May 19th. The symposium featured international speakers covering a wide range of topics related to plant epigenetics, cassava viral diseases, and cassava breeding strategies. A new online and on-site teaching concept was developed for the three-day workshop to ensure maximum student participation across Western, Eastern, and Southern Africa. Initially planned in Nigeria, Kenya, Ethiopia, Tanzania, and Zambia, the workshop ultimately focused on Nigeria, Kenya, and Ethiopia due to a lack of qualified candidates in the other countries. Each classroom hosted 20 to 25 students, with at least one bioinformatician present for support. The classrooms were connected via video conferencing, whereas teachers located in different places in Africa and Europe joined the video stream to conduct teaching sessions. The workshop was divided into theoretical classes and hands-on sessions, where participants could run data analysis with support from online teachers and local bioinformaticians. To enable participants to run guided, CPU and RAM-intensive data analysis workflows and overcome local computing and internet access restrictions, a system of virtual machines (VMs) hosted in the cloud was developed. The teaching platform provided teaching and exercise materials to support the use of the VMs. Although some students could not run heavy data analysis workflows due to unforeseen restrictions in the cloud, these issues were solved. All participants had the opportunity to run the analysis steps independently in the cloud using the protocols hosted on the teaching platform.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"51 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72622641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On potential limitations of differential expression analysis with non-linear machine learning models","authors":"G. Sabbatini, L. Manganaro","doi":"10.14806/ej.28.0.1035","DOIUrl":"https://doi.org/10.14806/ej.28.0.1035","url":null,"abstract":"Recently, there has been a growing interest in bioinformatics toward the adoption of increasingly complex machine learning models for the analysis of next-generation sequencing data with the goal of disease subtyping (i.e., patient stratification based on molecular features) or risk-based classification for specific endpoints, such as survival. With gene-expression data, a common approach consists in characterising the emerging groups by exploiting a differential expression analysis, which selects relevant gene sets coupled with pathway enrichment analysis, providing an insight into the underlying biological processes. However, when non-linear machine learning models are involved, differential expression analysis could be limiting since patient groupings identified by the model could be based on a set of genes that are hidden to differential expression due to its linear nature, affecting subsequent biological characterisation and validation. The aim of this study is to provide a proof-of-concept example demonstrating such a limitation. Moreover, we suggest that this issue could be overcome by the adoption of the innovative paradigm of eXplainable Artificial Intelligence, which consists in building an additional explainer to get a trustworthy interpretation of the model outputs and building a reliable set of genes characterising each group, preserving also non-linear relations, to be used for downstream analysis and validation.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83939695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"qualign: solving sequence alignment based on quadratic unconstrained binary optimisation","authors":"Yuki Matsumoto, Shota Nakamura","doi":"10.14806/ej.28.0.1020","DOIUrl":"https://doi.org/10.14806/ej.28.0.1020","url":null,"abstract":"Bioinformatics has, among others, the issue of solving complex computational problems with vast amounts of sequencing data. Recently, a new computing architecture, the annealing machine, has emerged that applies to actual problems and is available for practical use. This novel architecture can solve discrete optimisation problems by replacing algorithms designed under the von Neumann architecture. To perform computations on the annealing machine, quadratic unconstrained binary optimisation (QUBO) formulations should be constructed and optimised according to the application. In this study, we developed an algorithm under the annealing machine architecture to solve sequence alignment problems, a known fundamental process widely used in genetic analysis, such as mutation detection and genome assembly. We constructed a QUBO formulation based on dynamic programming to solve a pairwise sequence alignment and derived its general form. We compared with conventional methods to solve 40 bp of pairwise alignment problem. Our implementation, named qualign, solved sequence alignment problems with accuracy comparable to that of conventional methods. Although a small pairwise alignment was solved owing to the limited memory size of this method, this is the first step of the application of annealing machines. We showed that our QUBO formulation solved the sequencing alignment problem. In the future, increasing the memory size of annealing machine will allow annealing machines to impact a wide range of bioinformatics applications positively.Availability: the source code of qualign is available at https://github.com/ymatsumoto/qualign","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136179358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability and validity of the Dyadic Coping Inventory for Financial Stress in Greek couples.","authors":"Joanne Maria Velegraki,&nbsp;Flora Bacopoulou,&nbsp;George P Chrousos,&nbsp;Marilena Panagiotou,&nbsp;Orsalia Gerakini,&nbsp;Maria Charalampopoulou,&nbsp;Dimitrios Vlachakis,&nbsp;Christina Darviri","doi":"10.14806/ej.28.0.1018","DOIUrl":"https://doi.org/10.14806/ej.28.0.1018","url":null,"abstract":"<p><p>Financial stress can negatively affect a couple's relationship. The Dyadic Coping Inventory for Financial Stress (DCIFS) instrument assesses the way couples cope with financial stress. This study sought to validate the Dyadic Coping Inventory for Financial Stress (DCIFS) in Greek. The sample included 152 Greek couples (mean age: 42.82 ± 11.94). Confirmatory factor analyses provided support for delegated dyadic coping and evaluation of dyadic coping. Confirmatory Factor Analysis results supported a 33-item version consisting of the following subscales for both men and women: Stress Communication by Oneself and by Partner, Emotion and Problem-Focused Supportive Dyadic Coping (DC) by Oneself and by Partner, Negative DC by Oneself and by Partner, Emotion and Problem-Focused Common DC, and Evaluation of DC. The Dyadic Coping Inventory questionnaire and Perceived Stress Scale were used to assess the criterion validity of DCIFS.</p>","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"28 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065463/pdf/nihms-1883430.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9283066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}