HUN-REN Research Centre for Natural Sciences

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Characterization of unannotated and uncharacterized protein segments is expected to lead to the discovery of novel functions as well as provide important insights into existing biological processes. In addition, it is likely to shed new light on molecular mechanisms of diseases that are not yet fully understood. The classical concept implies that protein sequence defines structure, which in turn determines function; that is, function can be inferred from the sequence and its structure. Even when protein sequences diverge during evolution, for example, after gene duplication, the overall fold of their structures remains roughly the same. Therefore, structural similarity between proteins can reveal distant evolutionary relationships that are not easily detectable using sequence-based methods.

BACKGROUND: Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation. OBJECTIVE: Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies. METHODS: We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables. RESULTS: We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data. CONCLUSIONS: This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

BACKGROUND: Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. RESULTS: A supplier database (https://mcule.com/) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). CONCLUSIONS: This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. Graphical AbstractA visual summary of the comparison of similarity metrics with sum of ranking differences (SRD).

Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.

A recent innovation in mass spectrometry is the ability to record mass spectra on ordinary samples, in their native environment, without sample preparation or preseparation by creating ions outside the instrument. In desorption electrospray ionization (DESI), the principal method described here, electrically charged droplets are directed at the ambient object of interest; they release ions from the surface, which are then vacuumed through the air into a conventional mass spectrometer. Extremely rapid analysis is coupled with high sensitivity and high chemical specificity. These characteristics are advantageously applied to high-throughput metabolomics, explosives detection, natural products discovery, and biological tissue imaging, among other applications. Future possible uses of DESI for in vivo clinical analysis and its adaptation to portable mass spectrometers are described.

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or RNA-seq from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories. The altered expression within different platforms was analyzed separately. Statistical significance was computed using Mann-Whitney or Kruskal-Wallis tests. False Discovery Rate (FDR) was computed using the Benjamini-Hochberg method. The entire database contains 56,938 samples, including 33,520 samples from 3180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3691 normal samples), 11,010 samples from TCGA (394 metastatic, 9886 tumorous and 730 normal), 1193 samples from TARGET (1 metastatic, 1180 tumorous and 12 normal) and 11,215 normal samples from GTEx. The most consistently upregulated genes across multiple tumor types were TOP2A (FC = 7.8), SPP1 (FC = 7.0) and CENPA (FC = 6.03), and the most consistently downregulated gene was ADH1B (FC = 0.15). Validation of differential expression using equally sized training and test sets confirmed the reliability of the database in breast, colon, and lung cancer at an FDR below 10%. The online analysis platform enables unrestricted mining of the database and is accessible at TNMplot.com.

// A. Marcell Szász 1, 2 , András Lánczky 1 , Ádám Nagy 1 , Susann Förster 3 , Kim Hark 4 , Jeffrey E. Green 4 , Alex Boussioutas 5, 6, 7 , Rita Busuttil 5, 6, 7 , András Szabó 8 , Balázs Győrffy 1, 8 1 MTA-TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary 2 2nd Department of Pathology, Semmelweis University, Budapest, Hungary 3 Max Delbrück Center for Molecular Medicine, Berlin, Germany 4 Transgenic Oncogenesis and Genomics Section, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, Maryland, USA 5 Cancer Genetics and Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Australia 6 Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia 7 Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia 8 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary Correspondence to: Balázs Győrffy, email: gyorffy.balazs@ttk.mta.hu Keywords: gastric cancer, survival, meta-analysis Received: April 07, 2016      Accepted: June 13, 2016      Published: June 30, 2016 ABSTRACT Introduction: Multiple gene expression based prognostic biomarkers have been repeatedly identified in gastric carcinoma. However, without confirmation in an independent validation study, their clinical utility is limited. Our goal was to establish a robust database enabling the swift validation of previous and future gastric cancer survival biomarker candidates. Results: The entire database incorporates 1,065 gastric carcinoma samples, gene expression data. Out of 29 established markers, higher expression of BECN1 (HR = 0.68, p = 1.5E-05), CASP3 (HR = 0.5, p = 6E-14), COX2 (HR = 0.72, p = 0.0013), CTGF (HR = 0.72, p = 0.00051), CTNNB1 (HR = 0.47, p = 4.3E-15), MET (HR = 0.63, p = 1.3E-05), and SIRT1 (HR = 0.64, p = 2.2E-07) correlated to longer OS. Higher expression of BIRC5 (HR = 1.45, p = 1E-04), CNTN1 (HR = 1.44, p = 3.5E- 05), EGFR (HR = 1.86, p = 8.5E-11), ERCC1 (HR = 1.36, p = 0.0012), HER2 (HR = 1.41, p = 0.00011), MMP2 (HR = 1.78, p = 2.6E-09), PFKB4 (HR = 1.56, p = 3.2E-07), SPHK1 (HR = 1.61, p = 3.1E-06), SP1 (HR = 1.45, p = 1.6E-05), TIMP1 (HR = 1.92, p = 2.2E- 10) and VEGF (HR = 1.53, p = 5.7E-06) were predictive for poor OS. Materials and Methods: We integrated samples of three major cancer research centers (Berlin, Bethesda and Melbourne datasets) and publicly available datasets with available follow-up data to form a single integrated database. Subsequently, we performed a literature search for prognostic markers in gastric carcinomas (PubMed, 2012–2015) and re-validated their findings predicting first progression (FP) and overall survival (OS) using uni- and multivariate Cox proportional hazards regression analysis. Conclusions: The major advantage of our analysis is that we evaluated all genes in the same set of patients thereby making direct comparison of the markers feasible. The best performing genes include BIRC5, CASP3, CTNNB1, TIMP-1, MMP-2, SIRT, and VEGF.

Corrosion inhibition efficiencies of heterocyclic, unsaturated (aromatic and nonaromatic) compounds (pyrimidines, benzothiazole derivatives, amino acids containing an aromatic part, pyridines, and quinolines) were correlated with quantum chemical indices of the respective molecules. Inhibition efficiencies were determined in acidic solutions containing 0.001 M or 0.01 M of the inhibitor. Quantum chemical calculations were made by using the Hückel method. The difference (Δ) between energy of the highest occupied and the lowest unoccupied molecular orbital was related to the inhibition efficiency (E) of the molecules. At values Δ < 1.3 beta, efficiency increased with increasing values of Δ. At values Δ > 1.3 beta, efficiency (in 0.001 M concentration) tended to decrease with increasing values of Δ. The optimal value of index Δ may have been lower in solutions containing 0.01 M of the inhibitor. Results indicated that Δ alone is insufficient to account for all variations in the experimental inhibition efficiency.

Interactions among living organisms, from bacteria colonies to human
\nsocieties, are inherently more complex than interactions among particles
\nand non-living matter. Group interactions are a particularly important and
\nwidespread class, representative of which is the public goods game. In
\naddition, methods of statistical physics have proved valuable for studying
\npattern formation, equilibrium selection and self-organization in evolution-
\nary games. Here, we review recent advances in the study of evolutionary
\ndynamics of group interactions on top of structured populations, including
\nlattices, complex networks and coevolutionary models. We also compare
\nthese results with those obtained on well-mixed populations. The review
\nparticularly highlights that the study of the dynamics of group interactions,
\nlike several other important equilibrium and non-equilibrium dynamical
\nprocesses in biological, economical and social sciences, benefits from the
\nsynergy between statistical physics, network science and evolutionary
\ngame theory.

International audience

We present the Database of Disordered Protein Prediction (D(2)P(2)), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D(2)P(2) will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.

Sample preparation is an essential step in analysis, greatly influencing the reliability and accuracy of resulted the time and cost of analysis. Solid-Phase Microextraction (SPME) is a very simple and efficient, solventless sample preparation method, invented by Pawliszyn in 1989. SPME has been widely used in different fields of analytical chemistry since its first applications to environmental and food analysis and is ideally suited for coupling with mass spectrometry (MS). All steps of the conventional liquid-liquid extraction (LLE) such as extraction, concentration, (derivatization) and transfer to the chromatograph are integrated into one step and one device, considerably simplifying the sample preparation procedure. It uses a fused-silica fibre that is coated on the outside with an appropriate stationary phase. The analytes in the sample are directly extracted to the fibre coating. The SPME technique can be routinely used in combination with gas chromatography, high-performance liquid chromatography and capillary electrophoresis and places no restriction on MS. SPME reduces the time necessary for sample preparation, decreases purchase and disposal costs of solvents and can improve detection limits. The SPME technique is ideally suited for MS applications, combining a simple and efficient sample preparation with versatile and sensitive detection. This review summarizes analytical characteristics and variants of the SPME technique and its applications in combination with MS.

The production and use of biopolymers increases continuously with a very high rate thus all information on these materials is very important. This feature article first defines the terms used in the area then discusses the distinction between degradation and biodegradation as well as their importance for practice. Biopolymers often have inferior properties compared to commodity polymers. Modification is a way to improve properties and achieve property combinations required for specific applications. One technique is blending which allows considerable improvement in the impact resistance of brittle polymers. However, further study is needed on the miscibility–structure–property relationships of these materials to utilize all potentials of the approach. The chemical structure of biopolymers opens up possibilities to their reactive modification. Copolymerization, grafting, trans-esterification, the use of reactive coupling agents have all been utilized with success to achieve polymers and blends with improved properties. Several examples are shown for the various approaches and their outcome. Biopolymers and their blends are applied successfully in several areas from agriculture to consumer goods, packaging and automotive.

The paper accounts for the author's activity in developing bond order and valence indices since the early 80s. These indices represent an important conceptual link between the physical description of molecules as systems of electrons and nuclei and the chemical picture of molecules consisting of atoms kept together by bonds. They are also useful for a systematization and interpretation of the results obtained in the quantum chemical calculations, by permitting to extract from the wave function different pieces of information that may be assigned chemical significance. In some cases they can have some predictive power, too. Historically, the prototypes of such indices were introduced in the semiempirical quantum chemistry; the most important developments were Coulson's charge-bond order matrix in the simple Hückel theory and the Wiberg index in the CNDO framework. (Valence indices were also introduced in the semiempirical theory.) The definition of the ab initio bond order index emerged from the asymptotic term of the exchange energy component of the partitioning performed in the framework of the author's so-called "chemical Hamiltonian approach" using a "mixed" second quantization formalism for overlapping basis sets. They can also be introduced by studying the exchange part of the two-particle density (or of the second-order density matrix). Some properties of the bond order indices are discussed and the author's (until now unpublished) proof is also presented, showing the sufficient conditions under which the bond order index of a homonuclear diatomics is equal to the "chemist's bond order," i.e., the half of the difference between the number of electrons occupying bonding and antibonding orbitals. The ab initio valence indices are also introduced and discussed, and it is stressed that for correlated wave function the same "exchange only" definition of the bond order and valence indices should be used, which was introduced for the SCF case. The recent concept of the "atomic decomposition of identity" is also discussed and it is utilized for introducing bond orders and valences in the framework of the "3D analysis," when atoms are defined not by their basis orbitals but as regions of the three-dimensional (3D) physical space. Two versions of the 3D analysis are considered--the AIM (atoms in molecules)-type decomposing the space into disjunct atomic domains and the "fuzzy atoms" scheme in which there are no sharp boundaries between the atoms but they exhibit a continuous transition from one to another.

Phase separation of multivalent protein and RNA molecules underlies the biogenesis of biomolecular condensates such as membraneless organelles. In vivo, these condensates encompass hundreds of distinct types of molecules that typically organize into multilayered structures supporting the differential partitioning of molecules into distinct regions with distinct material properties. The interplay between driven (active) versus spontaneous (passive) processes that are required for enabling the formation of condensates with coexisting layers of distinct material properties remains unclear. Here, we deploy systematic experiments and simulations based on coarse-grained models to show that the collective interactions among the simplest, biologically relevant proteins and archetypal RNA molecules are sufficient for driving the spontaneous emergence of multilayered condensates with distinct material properties. These studies yield a set of rules regarding homotypic and heterotypic interactions that are likely to be relevant for understanding the interplay between active and passive processes that control the formation of functional biomolecular condensates.

Circulating extracellular vesicles have emerged as potential new biomarkers in a wide variety of diseases. Despite the increasing interest, their isolation and purification from body fluids remains challenging. Here we studied human pre-prandial and 4 hours postprandial platelet-free blood plasma samples as well as human platelet concentrates. Using flow cytometry, we found that the majority of circulating particles within the size range of extracellular vesicles lacked common vesicular markers. We identified most of these particles as lipoproteins (predominantly low-density lipoprotein, LDL) which mimicked the characteristics of extracellular vesicles and also co-purified with them. Based on biophysical properties of LDL this finding was highly unexpected. Current state-of-the-art extracellular vesicle isolation and purification methods did not result in lipoprotein-free vesicle preparations from blood plasma or from platelet concentrates. Furthermore, transmission electron microscopy showed an association of LDL with isolated vesicles upon in vitro mixing. This is the first study to show co-purification and in vitro association of LDL with extracellular vesicles and its interference with vesicle analysis. Our data point to the importance of careful study design and data interpretation in studies using blood-derived extracellular vesicles with special focus on potentially co-purified LDL.

Background: Potential prognostic biomarker candidates for hepatocellular carcinoma (HCC) are abundant, but their generalizability is unexplored. We cross-validated markers of overall survival (OS) and vascular invasion in independent datasets. Methods: The literature search yielded 318 genes related to survival and 52 related to vascular invasion. Validation was performed in three datasets (RNA-seq, n = 371; Affymetrix arrays, n = 91; Illumina gene chips, n = 135) by uni- and multivariate Cox regression and Mann–Whitney U -test, separately for Asian and Caucasian patients. Results: One hundred and eighty biomarkers remained significant in Asian and 128 in Caucasian subjects at p < 0.05. After multiple testing correction BIRC5 ( p = 1.9 × 10 −10 ), CDC20 ( p = 2.5 × 10 −9 ) and PLK1 ( p = 3 × 10 −9 ) endured as best performing genes in Asian patients; however, none remained significant in the Caucasian cohort. In a multivariate analysis, significance was reached by stage ( p = 0.0018) and expression of CENPH ( p = 0.0038) and CDK4 ( p = 0.038). KIF18A was the only gene predicting vascular invasion in the Affymetrix and Illumina cohorts ( p = 0.003 and p = 0.025, respectively). Conclusion: Overall, about half of biomarker candidates failed to retain prognostic value and none were better than stage predicting OS. Impact: Our results help to eliminate biomarkers with limited capability to predict OS and/or vascular invasion.

COVID-19, caused by SARS-CoV-2, lacks effective therapeutics. Additionally, no antiviral drugs or vaccines were developed against the closely related coronavirus, SARS-CoV-1 or MERS-CoV, despite previous zoonotic outbreaks. To identify starting points for such therapeutics, we performed a large-scale screen of electrophile and non-covalent fragments through a combined mass spectrometry and X-ray approach against the SARS-CoV-2 main protease, one of two cysteine viral proteases essential for viral replication. Our crystallographic screen identified 71 hits that span the entire active site, as well as 3 hits at the dimer interface. These structures reveal routes to rapidly develop more potent inhibitors through merging of covalent and non-covalent fragment hits; one series of low-reactivity, tractable covalent fragments were progressed to discover improved binders. These combined hits offer unprecedented structural and reactivity information for on-going structure-based drug design against SARS-CoV-2 main protease.

Rock is wrapped by paper, paper is cut by scissors and scissors are crushed by rock. This simple game is popular among children and adults to decide on trivial disputes that have no obvious winner, but cyclic dominance is also at the heart of predator-prey interactions, the mating strategy of side-blotched lizards, the overgrowth of marine sessile organisms and competition in microbial populations. Cyclical interactions also emerge spontaneously in evolutionary games entailing volunteering, reward, punishment, and in fact are common when the competing strategies are three or more, regardless of the particularities of the game. Here, we review recent advances on the rock-paper-scissors (RPS) and related evolutionary games, focusing, in particular, on pattern formation, the impact of mobility and the spontaneous emergence of cyclic dominance. We also review mean-field and zero-dimensional RPS models and the application of the complex Ginzburg-Landau equation, and we highlight the importance and usefulness of statistical physics for the successful study of large-scale ecological systems. Directions for future research, related, for example, to dynamical effects of coevolutionary rules and invasion reversals owing to multi-point interactions, are also outlined.