Sphingomyelins Prevent Propagation of Lipid Peroxidation—LC-MS/MS Evaluation of Inhibition Mechanisms

Free radical driven lipid peroxidation is a chain reaction which can lead to oxidative degradation of biological membranes. Propagation vs. termination rates of peroxidation in biological membranes are determined by a variety of factors including fatty acyl chain composition, presence of antioxidants, as well as biophysical properties of mono- or bilayers. Sphingomyelins (SMs), a class of sphingophospholipids, were previously described to inhibit lipid oxidation most probably via the formation of H-bond network within membranes. To address the “antioxidant” potential of SMs, we performed LC-MS/MS analysis of model SM/glycerophosphatidylcholine (PC) liposomes with different SM fraction after induction of radical driven lipid peroxidation. Increasing SM fraction led to a strong suppression of lipid peroxidation. Electrochemical oxidation of non-liposomal SMs eliminated the observed effect, indicating the importance of membrane structure for inhibition of peroxidation propagation. High resolution MS analysis of lipid peroxidation products (LPPs) observed in in vitro oxidized SM/PC liposomes allowed to identify and relatively quantify SM- and PC-derived LPPs. Moreover, mapping quantified LPPs to the known pathways of lipid peroxidation allowed to demonstrate significant decrease in mono-hydroxy(epoxy) LPPs relative to mono-keto derivatives in SM-rich liposomes. The results presented here illustrate an important property of SMs in biological membranes, acting as “biophysical antioxidant”. Furthermore, a ratio between mono-keto/mono-hydroxy(epoxy) oxidized species can be used as a marker of lipid peroxidation propagation in the presence of different antioxidants

Structure of Hierridin C, Synthesis of Hierridins B and C, and Evidence for Prevalent Alkylresorcinol Biosynthesis in Picocyanobacteria

Small, single-celled planktonic cyanobacteria are ubiquitous in the world's oceans yet tend not to be perceived as secondary metabolite-rich organisms. Here we report the isolation and structure elucidation of hierridin C, a minor metabolite obtained from the cultured picocyanobacterium Cyanobium sp. LEGE 06113. We describe a simple, straightforward synthetic route to the scarcely produced hierridins that relies on a key regioselective halogenation step. In addition, we show that these compounds originate from a type III PKS pathway and that similar biosynthetic gene clusters are found in a variety of bacterial genomes, most notably those of the globally distributed picocyanobacteria genera Prochlorococcus, Cyanobium and Synechococcus.info:eu-repo/semantics/publishedVersio

CD20+ T cells in monoclonal B cell lymphocytosis and chronic lymphocytic leukemia: frequency, phenotype and association with disease progression

Introduction: In monoclonal B cell lymphocytosis (MBL) and chronic lymphocytic leukemia (CLL), the expansion of malignant B cells disrupts the normal homeostasis and interactions between B cells and T cells, leading to immune dysregulation. CD20+ T cells are a subpopulation of T cells that appear to be involved in autoimmune diseases and cancer. Methods: Here, we quantified and phenotypically characterized CD20+ T cells from MBL subjects and CLL patients using flow cytometry and correlated our findings with the B-cell receptor mutational status and other features of the disease. Results and discussion: CD20+ T cells were more represented within the CD8+T cell compartment and they showed a predominant memory Tc1 phenotype. CD20+ T cells were less represented in MBL and CLL patients vs healthy controls, particularly among those with unmutated IGVH gene. The expansion of malignant B cells was accompanied by phenotypic and functional changes in CD20+ T cells, including an increase in follicular helper CD4+ CD20+ T cells and CD20+ Tc1 cells, in addition to the expansion of the TCR Vb 5.1 in CD4+ CD20+T cells in CLL.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pan-European Network in Lipidomics and EpiLipidomics

Lipids represent a wide variety of molecules that play different biological roles such as energy resources, structural components or signaling molecules that regulate metabolic homeostasis. Most notably, lipids and oxidatively modified lipids have been found to be involved in regulating important mechanisms mediating tissue injury, inflammation, and related non-communicable diseases, which are responsible for near 70% of all deaths in developed countries. Lipidomics and Epilipidomics are the most promising strategies for the progress in the knowledge of lipids, aiming at biomarker discovery for the prevention, early diagnosis, monitoring, evaluation of diseases therapeutics. These approaches involve the use of complex protocols, different instrumentation and processing huge amounts of data. Effectiveness, while reducing the high costs associated with these technologies, requires a harmonized multidisciplinary approach involving coordinated actions from pan-European centres of lipidomics investigation. This will avoid unnecessary redundancy, improving reproducibility and ensuring efficient and productive research. EpiLipidNET aims to build and maintain a multidisciplinary pan-European network of researchers, clinicians and enterprises working in the field of lipidomics and epilipidomics to boost a hub of research excellence, advanced knowledge and technology transfer, to promote high level of training for young researches and facilitate clinical translation. EpiLipidNet will include five interactive Working Groups covering analytical methods and computational approaches in (epi)Lipidomics, clinical significance and applications, lipid signaling and mechanisms of action, dissemination and outreach

Polar Lipids Composition, Antioxidant and Anti-Inflammatory Activities of the Atlantic Red Seaweed Grateloupia turuturu

Grateloupia turuturu Yamada, 1941, is a red seaweed widely used for food in Japan and Korea which was recorded on the Atlantic Coast of Europe about twenty years ago. This seaweed presents eicosapentaenoic acid (EPA) and other polyunsaturated fatty acids (PUFAs) in its lipid fraction, a feature that sparked the interest on its potential applications. In seaweeds, PUFAs are mostly esterified to polar lipids, emerging as healthy phytochemicals. However, to date, these biomolecules are still unknown for G. turuturu. The present work aimed to identify the polar lipid profile of G. turuturu, using modern lipidomics approaches based on high performance liquid chromatography coupled to high resolution mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS). The health benefits of polar lipids were identified by health lipid indices and the assessment of antioxidant and anti-inflammatory activities. The polar lipids profile identified from G. turuturu included 205 lipid species distributed over glycolipids, phospholipids, betaine lipids and phosphosphingolipids, which featured a high number of lipid species with EPA and PUFAs. The nutritional value of G. turuturu has been shown by its protein content, fatty acyl composition and health lipid indices, thus confirming G. turuturu as an alternative source of protein and lipids. Some of the lipid species assigned were associated to biological activity, as polar lipid extracts showed antioxidant activity evidenced by free radical scavenging potential for the 2,2 '-azino-bis-3-ethyl benzothiazoline-6-sulfonic acid (ABTS(?)(+)) radical (IC50 ca. 130.4 mu g mL(-1)) and for the 2,2-diphenyl-1-picrylhydrazyl (DPPH?) radical (IC25 ca. 129.1 mu g mL(-1)) and anti-inflammatory activity by inhibition of the COX-2 enzyme (IC50 ca. 33 mu g mL(-1)). Both antioxidant and anti-inflammatory activities were detected using a low concentration of extracts. This integrative approach contributes to increase the knowledge of G. turuturu as a species capable of providing nutrients and bioactive molecules with potential applications in the nutraceutical, pharmaceutical and cosmeceutical industries