Antitrypanosomal Activity of Anthriscus Nemorosa Essential Oils and Combinations of Their Main Constituents

This study aimed to investigate the susceptibility of Trypanosoma brucei to the Anthriscus nemorosa essential oils (EOs), isolated compounds from these oils, and artificial mixtures of the isolated compounds in their conventional and nanoencapsulated forms. The chemical composition of the essential oils from the aerial parts and roots of Anthriscus nemorosa, obtained from a wild population growing in central Italy, were analyzed by gas chromatography/mass spectrometry (GC/MS). In both cases, the predominant class of compounds was monoterpene hydrocarbons, which were more abundant in the EOs from the roots (81.5%) than the aerial parts (74.0%). The overall results of this work have shed light on the biological properties of A. nemorosa EO from aerial parts (EC50 = 1.17 μg/mL), farnesene (EC50 = 0.84 μg/mL), and artificial mixtures (Mix 3–5, EC50 in the range of 1.27 to 1.58 μg/mL) as relevant sources of antiprotozoal substances. Furthermore, the pool measurements of ADP (adenosine diphosphate) and NTPs (nucleoside triphosphates) in the cultivated bloodstream form of trypanosomes exposed to different concentrations of EOs showed a disturbed energy metabolism, as indicated by increased pools of ADP in comparison to ATP (adenosine triphosphate) and other NTPs. Ultimately, this study highlights the significant efficacy of A. nemorosa EO to develop long-lasting and effective antiprotozoal formulations, including nanoemulsions.

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ naïve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.

High-frequency absorbance measurements for sediment source apportionment – laboratory and field assessment

<p>To manage effectively excessive sediment inputs to rivers and streams, it is crucial to have detailed and reliable information on key sediment sources. Such evidence is important for implementing targeted measures for improving ecosystem functioning and meeting environmental objectives. Although sediment fingerprinting is increasingly adopted worldwide to provide such evidence, current procedures do not provide detailed information on how sediment sources can change over both short (e.g., events and in between events) and long (e.g., over seasons or years) time scales. These limitations are mainly due to the conventional methods used for target sediment sampling and the high workloads and costs associated with laboratory analyses for tracers, which limit both high-frequency and longer duration sampling campaigns. To address this issue, we propose the use of a submersible spectrophotometer, which measures absorbance in the UV-VIS range in situ and at high frequency (e.g., minutes) to trace suspended sediment sources. In our proof of concept investigation, the approach was first tested in a laboratory setting, using soil samples and artificial mixtures with known proportions of two, three and four soil source samples in an experimental water tank. A total of six soil samples were collected, which were sieved to different fractions to investigate the influence of particle size on the sensor absorbance readings. Both soil samples and artificial mixtures were suspended in the laboratory tank set-up at different concentrations to investigate the effects on: (i) absorbance, and; (ii) un-mixing accuracy. The results showed that absorbance was linearly additive and could be used to predict dominant samples in the artificial mixtures correctly using a Bayesian tracer un-mixing model, largely regardless of particle size and of the concentration inside the experimental tank. This approach is currently being tested in a field experiment in the Attert River Basin (Luxembourg) to investigate if the results found in the laboratory experiments hold under natural field conditions. Our preliminary insights into the use of absorbance for sediment source apportionment in the field will be presented.</p>

Geochemical fingerprints for tracing sediments in the sub-catchment Durazno del Medio, Argentina

<p>Soil erosion is one of the most serious environmental problems caused by land-use changes in semi-arid regions of central Argentina. Hence, to understand the erosive dynamics in these regions becomes fundamental. </p><p>Sub-catchment Durazno del Medio (6.56 km<sup>2</sup>) is located 21 km northeast of San Luis City (S 33º 08’ 16” – W 66º 09’ 18”; S 33º 11’ 44” – W 66º 08’ 06”), in the central region of Argentina. The average annual temperature is 17 ºC. Annual rainfall ranges from 600 to 800 mm, with a tendency to increase in the last years. Rainfall varies seasonally, with a dry season from May to October and a rainy season from November to April. This agricultural catchment has been researched to identify critical hot spots of land degradation by applying sediment source fingerprinting techniques. </p><p>In the studied area, exotic tree plantations in protected areas (in a state of youth development), native woodland, roads (dirt and paved), agricultural fields, and channel banks were identified as sources of sediments. Most of the sources were found on quaternary deposits (loessoid deposits), except native forests and some roads, which were found in gneiss and migmatites. The sub-catchment has a drainage network formed by two water courses that converge into the main one. Channel sediments (mixtures) were collected at the end of the main channel.</p><p>The energy dispersive X-ray fluorescence (EDXRF) analytical technique and the MixSIAR unmixing model were implemented to estimate the contribution of sediment sources in mixtures. The sources and mixtures (tablets) were analyzed in triplicate using a Si-Li detector SDD (resolution 145 keV, Kalpha Mn) with a X ray tube of 50 W and 50 kV. The tracers were analyzed using a fundamental parameters method. Since the selection of correct fingerprints has been proven to be an essential stage in the analysis, before unmixing the natural sediment samples, two artificial mixtures were made using known quantities of soil collected in the identified sources to test the precision and robustness of the tracers selection procedure. These analyses showed that the use of most of the tracers (25 elements) increases the exactitude of calculated proportions. The first mixture was made using two sources, exotic tree plantation in protected areas (35.6 %) and agricultural fields (64.4 %). The second mixture was created using different proportions of 4 different sources including native woodland (30 %), exotic tree plantation in protected areas (20 %), agricultural fields (5 %) and channel banks (45 %). A low mean absolute error (MAE) of 2 % and 4 %, respectively, was obtained when reconstructing the 2 artificial mixtures. This outcome indicates that the selection process was effective.</p><p>Once the tracers were properly selected, the natural sediment samples were analyzed. Hence, for the catchment mixture, the main sources of sediments were exotic tree plantation in protected areas (96.7 %) and roads (1.8 %). The application of the fingerprint technique highlighted that forests are one of the largest contributors of sediment, followed by dirt roads.</p>

Comparing different error structures in MixSIAR analysis using artificial mixtures from real sediment sources

<p>Within a catchment context, statistical models are widely used to predict the load of pollutants (i.e. fine sediments, chemicals compounds) from potential sources around it, into a main channel (mixture). MixSIAR is a Bayesian mixing model framework that has been used in many environmental studies. As with other models, it presents some assumptions that might be assessed before its use. In this study, a set of artificial mixtures (from real sources) were created using four different catchment sediment sources (Channel Bank; Cultivated land; Pasture and Road Material). The material collected from each source was sieved (<63um) then analysed via WD-XRF for elemental composition. The data collected from this analysis was used to test and assess the main model parameters within an experimental context. A simple range test was performed to initially select tracers that were potentially good predictors. In the end, the model was structured with 43 tracers (elements) using the mean and standard deviation among 10 replicates. Furthermore, it was run using 10^6 iterations (length of the chain) and two different error structures to be compared (residual vs multiplicative error). The results demonstrated the accuracy of the MixSIAR approach to get the real composition in different mixture combinations using a large number of tracers, although in some mixtures a statistically different value was observed where the source term with highest internal variability was present in larger proportion (frequently when %CB >10%). The most precise and reliable results based on convergence were those using the “Residual error” structure, where the value of each mixture was closer to the real and model convergence was achieved more easily. On the other hand, “Multiplicative error” structure led to longer model run times (due to its complexity) and in most cases the model did not converge as for the “Residual error” structure when using the full set of tracers. To mitigate this problem, a posterior tracer selection based on diagnostic information was devised which made it  possible to increase dramatically the convergence of the predicted parameters without a significant difference in the result. Although the “Residual error” structure showed to be the most convenient for further analysis, the technique applied for “Multiplicative error” structure can be used as a potential solution to achieve model convergence while reducing model runtime.</p>

Estimating relative biomasses of organisms in microbiota using “phylopeptidomics”

Background There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. Results Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called “phylopeptidomics” principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models. Conclusions This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization.