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

Modelling the Radiative Effects of Biomass Burning Aerosols on Carbon Fluxes in the Amazon Region

Every year, a dense smoke haze covers a large portion of South America originating from fires in the Amazon Basin and central parts of Brazil during the dry/biomass-burning season between August and October. Over a large portion of South America, the average aerosol optical depth at 550 nm exceeds 1.0 during the fire season while the background value during the rainy season is below 0.2. Biomass burning aerosol particles increase scattering and absorption of the incident solar radiation. The regional-scale aerosol layer reduces the amount of solar energy reaching the surface, cools the near surface air, and increases the diffuse radiation fraction over a large disturbed area of the Amazon rainforest. These factors affect the energy and CO2 fluxes at the surface. In this work, we applied a fully integrated atmospheric model to assess the impact of biomass burning aerosols in CO2 fluxes in the Amazon region during 2010. We address the effects of the attenuation of the global solar radiation and the enhancement of the diffuse solar radiation flux inside the vegetation canopy. Our results indicate that the biomass burning aerosols led to increases of about 27% of gross primary productivity of Amazonia, 10% of plant respiration and a decline in soil respiration of 3%. Consequently, in our model Amazonia, became a net carbon sink; net ecosystem exchange during September 2010 dropped from +101 to -104 TgC when the aerosol effects are considered, mainly due to the aerosol diffuse radiation effect. For the forest biome, our results point to a dominance of the diffuse radiation effect on CO2 fluxes, reaching a balance of 50% - 50% between the diffuse and direct aerosol effects for high aerosol loads. For C3 grasses and Savanna (cerrado), as expected, the contribution of the diffuse radiation effect is much lower, tending to zero with the increase of aerosol load. Taking all biomes together, our model shows the Amazon during the dry season, in the presence of high biomass burning aerosol loads, changing from being a source to being a sink of CO2 to the atmosphere

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 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

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

Direct photodegradation of carbamazepine followed by micellar electrokinetic chromatography and mass spectrometry

Carbamazepine, a widely consumed psychotropic pharmaceutical, is one of the most commonly detected drugs in the environment. To better assess the environmental persistence of carbamazepine in aqueous matrices, the effect of pH and dissolved oxygen on the direct photodegradation rate of this pharmaceutical was evaluated in this study, using simulated solar irradiation. In order to follow the degradation and the emergence of photoproducts, a micellar electrokinetic chromatography based method was developed, consisting on the use of a dynamically coated capillary column. The developed methodology showed good repeatability and efficiency in the separation of carbamazepine and photoirradiation products. Also, seven photodegradation products were identified by electrospray mass spectrometry (ESI-MS), including the known carcinogenic acridine that was produced under all the pH and oxygenation levels studied and one newly identified photoproduct. This paper gives new insights into the role of dissolved oxygen on the photodegradation rate of carbamazepine. The results indicate that acidic pH, combined with the absence of dissolved oxygen in the aqueous matrix, results in very high direct photodegradation rates. At basic pH, dissolved oxygen does not interfere with the process and very low rates were observed. At environmentally relevant conditions, carbamazepine was shown to persist in the environment from 4.5 to 25 days. (C) 2010 Elsevier Ltd. All rights reserved