Characterization of carotenoids in soil bacteria and investigation of their photodegradation by UVA radiation via resonance Raman spectroscopy.

A soil habitat consists of an enormous number of pigmented bacteria with the pigments mainly composed of diverse carotenoids. Most of the pigmented bacteria in the top layer of the soil are photoprotected from exposure to huge amounts of UVA radiation on a daily basis by these carotenoids. The photostability of these carotenoids depends heavily on the presence of specific features like a carbonyl group or an ionone ring system on its overall structure. Resonance Raman spectroscopy is one of the most sensitive and powerful techniques to detect and characterize these carotenoids and also monitor processes associated with them in their native system at a single cell resolution. However, most of the resonance Raman profiles of carotenoids have very minute differences, thereby making it extremely difficult to confirm if these differences are attributed to the presence of different carotenoids or if it is a consequence of their interaction with other cellular components. In this study, we devised a method to overcome this problem by monitoring also the photodegradation of the carotenoids in question by UVA radiation wherein a differential photodegradation response will confirm the presence of different carotenoids irrespective of the proximities in their resonance Raman profiles. Using this method, the detection and characterization of carotenoids in pure cultures of five species of pigmented coccoid soil bacteria is achieved. We also shed light on the influence of the structure of the carotenoid on its photodegradation which can be exploited for use in the characterization of carotenoids via resonance Raman spectroscopy.

Raman spectroscopic differentiation of planktonic bacteria and biofilms.

Both biofilm formations as well as planktonic cells of water bacteria such as diverse species of the Legionella genus as well as Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli were examined in detail by Raman microspectroscopy. Production of various molecules involved in biofilm formation of tested species in nutrient-deficient media such as tap water was observed and was particularly evident in the biofilms formed by six Legionella species. Biofilms of selected species of the Legionella genus differ significantly from the planktonic cells of the same organisms in their lipid amount. Also, all Legionella species have formed biofilms that differ significantly from the biofilms of the other tested genera in the amount of lipids they produced. We believe that the significant increase in the synthesis of this molecular species may be associated with the ability of Legionella species to form biofilms. In addition, a combination of Raman microspectroscopy with chemometric approaches can distinguish between both planktonic form and biofilms of diverse bacteria and could be used to identify samples which were unknown to the identification model. Our results provide valuable data for the development of fast and reliable analytic methods based on Raman microspectroscopy, which can be applied to the analysis of tap water-adapted microorganisms without any cultivation step.

Culture independent Raman spectroscopic identification of urinary tract infection pathogens: a proof of principle study.

Urinary tract infection (UTI) is a very common infection. Up to every second woman will experience at least one UTI episode during her lifetime. The gold standard for identifying the infectious microorganisms is the urine culture. However, culture methods are time-consuming and need at least 24 h until the results are available. Here, we report about a culture independent identification procedure by using Raman microspectroscopy in combination with innovative chemometrics. We investigated, for the first time directly, urine samples by Raman microspectroscopy on a single-cell level. In a first step, a database of eleven important UTI bacterial species, which were grown in sterile filtered urine, was built up. A support vector machine (SVM) was used to generate a statistical model, which allows a classification of this data set with an accuracy of 92% on a species level. This model was afterward used to identify infected urine samples of ten patients directly without a preceding culture step. Thereby, we were able to determine the predominant bacterial species (seven Escherichia coli and three Enterococcus faecalis ) for all ten patient samples. These results demonstrate that Raman microspectroscopy in combination with support vector machines allow an identification of important UTI bacteria within two hours without the need of a culture step.

Microbial community functioning during plant litter decomposition.

Microbial life in soil is fueled by dissolved organic matter (DOM) that leaches from the litter layer. It is well known that decomposer communities adapt to the available litter source, but it remains unclear if they functionally compete or synergistically address different litter types. Therefore, we decomposed beech, oak, pine and grass litter from two geologically distinct sites in a lab-scale decomposition experiment. We performed a correlative network analysis on the results of direct infusion HR-MS DOM analysis and cross-validated functional predictions from 16S rRNA gene amplicon sequencing and with DOM and metaproteomic analyses. Here we show that many functions are redundantly distributed within decomposer communities and that their relative expression is rapidly optimized to address litter-specific properties. However, community changes are likely forced by antagonistic mechanisms as we identified several natural antibiotics in DOM. As a consequence, the decomposer community is specializing towards the litter source and the state of decomposition (community divergence) but showing similar litter metabolomes (metabolome convergence). Our multi-omics-based results highlight that DOM not only fuels microbial life, but it additionally holds meta-metabolomic information on the functioning of ecosystems.

Raman spectroscopic study of crystallization from solutions containing MgSO4 and Na2SO4: Raman spectra of double salts.

Mg(2+), Na(+), and SO(4)(2-) are common ions in natural systems, and they are usually found in water bodies. Precipitation processes have great importance in environmental studies because they may be part of complex natural cycles; natural formation of atmospheric particulate matter is just one case. In this work, Na(2)Mg(SO(4))(2)·5H(2)O (konyaite), Na(6)Mg(SO(4))(4) (vanthoffite), and Na(12)Mg(7)(SO(4))(13)·15H(2)O (loeweite) were synthesized and their Raman spectra reported. By slow vaporization (at 20 °C and relative humidity of 60-70%), crystallization experiments were performed within small droplets (diameter ≤ 1-2 mm) of solutions containing MgSO(4) and Na(2)SO(4), and crystal formations were studied by Raman spectroscopy. Crystallization of Na(2)Mg(SO(4))(2)·4H(2)O (bloedite) was observed, and the formation of salt mixtures was confirmed by Raman spectra. Bloedite, konyaite, and loeweite, as well as Na(2)SO(4) and MgSO(4)·6H(2)O, were the components found to occur in different proportions. No crystallization of Na(6)Mg(SO(4))(4) (vanthoffite) was observed under the crystallization condition used in this study.

Environmental selection shapes the formation of near-surface groundwater microbiomes.

Hydrodynamics drives both stochastic and deterministic community assembly in aquatic habitats, by translocating microbes across geographic barriers and generating changes in selective pressures. Thus, heterogeneity of hydrogeological settings and episodic surface inputs from recharge areas might play important roles in shaping and maintaining groundwater microbial communities. Here we took advantage of the Hainich Critical Zone Exploratory to disentangle mechanisms of groundwater microbiome differentiation via a three-year observation in a setting of mixed carbonate-siliciclastic alternations along a hillslope transect. Variation partitioning of all data elucidated significant roles of hydrochemistry (35.0%) and spatial distance (18.6%) but not of time in shaping groundwater microbiomes. Groundwater was dominated by rare species (99.6% of OTUs), accounting for 25.9% of total reads, whereas only 26 OTUs were identified as core species. The proximity to the recharge area gave prominence to high microbial diversity coinciding with high surface inputs. In downstream direction, the abundance of rare OTUs decreased whereas core OTUs abundance increased up to 47% suggesting increasing selection stress with a higher competition cost for colonization. In general, environmental selection was the key mechanism driving the spatial differentiation of groundwater microbiomes, with N-compounds and dissolved oxygen as the major determinants, but it was more prominent in the upper aquifer with low flow velocity. Across the lower aquifer with higher flow velocity, stochastic processes appeared to be additionally important for community assembly. Overall, this study highlights the impact of surface and subsurface conditions, as well as flow regime and related habitat accessibility, on groundwater microbiomes assembly.

Distinction of Ecuadorian varieties of fermented cocoa beans using Raman spectroscopy.

Cocoa (Theobroma cacao) is a crop of economic importance. In Ecuador, there are two predominant cocoa varieties: National and CCN-51. The National variety is the most demanded, since its cocoa beans are used to produce the finest chocolates. Raman measurements of fermented, dried and unpeeled cocoa beans were performed using a handheld spectrometer. Samples of the National and CCN-51 varieties were collected from different provinces and studied in this work. For each sample, 25 cocoa beans were considered and each bean was measured at 4 different spots. The most important Raman features of the spectra were assigned and discussed. The spectroscopic data were processed using chemometrics, resulting in a distinction of varieties with 91.8% of total accuracy. Differences in the average Raman spectra of cocoa beans from different sites but within the same variety can be attributed to environmental factors affecting the cocoa beans during the fermentation and drying processes.

Classification and identification of pigmented cocci bacteria relevant to the soil environment via Raman spectroscopy.

A soil habitat consists of a significant number of bacteria that cannot be cultivated by conventional means, thereby posing obvious difficulties in their classification and identification. This difficulty necessitates the need for advanced techniques wherein a well-compiled biomolecular database consisting of the already cultivable bacteria can be used as a reference in an attempt to link the noncultivable bacteria to their closest phylogenetic groups. Raman spectroscopy has been successfully applied to taxonomic studies of many systems like bacteria, fungi, and plants relying on spectral differences contributed by the variation in their overall biomolecular composition. However, these spectral differences can be obscured due to Raman signatures from photosensitive microbial pigments like carotenoids that show enormous variation in signal intensity hindering taxonomic investigations. In this study, we have applied laser-induced photobleaching to expel the carotenoid signatures from pigmented cocci bacteria. Using this method, we have investigated 12 species of pigmented bacteria abundant in soil habitats belonging to three genera mainly Micrococcus, Deinococcus, and Kocuria based on their Raman spectra with the assistance of a chemometric tool known as the radial kernel support vector machine (SVM). Our results demonstrate the potential of Raman spectroscopy as a minimally invasive taxonomic tool to identify pigmented cocci soil bacteria at a single-cell level.

Identification of water pathogens by Raman microspectroscopy.

Legionella species can be found living in water mostly in a viable but nonculturable state or associated with protozoa and complex biofilm formations. Isolation and afterwards identification of these pathogens from environmental samples by using common identification procedures based on cultivation are extremely difficult and prolonged. The development of fast and sensitive method based on the cultivation free identification of bacteria is necessary. In this study Raman microspectroscopy combined with multiclass support vector machines have been used to discriminate between Legionella and other common aquatic bacteria, to distinguish among clinically relevant Legionella species and to classify unknown Raman spectra for a fast and reliable identification. Recorded Raman spectra of the twenty-two Legionella species as well as the Raman spectra of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa were utilized to build the classification model. Afterwards, independent Raman spectra of eleven species were used to identify them on the basis of the classification model that was created. The present study shows that Raman microspectroscopy can be used as a rapid and reliable method to distinguish between Legionella species recognized as human pathogens and to identify samples which are unknown to the model based on multiclass support vector machines (MC-SVM).

Insights into the mechanism of polymer coating self-healing using Raman spectroscopy.

Self-healing polymer coatings are an emerging class of smart materials. Upon mechanical damage the material properties may be restored by self-healing, which can be triggered externally, e.g., by an increased temperature. An alternative approach relies on embedding capsules with repair agents into the polymers, the rupture of which is induced by the mechanical damage, and the release of the repair agents triggers the self-repair reaction. The work at hand presents in situ Raman spectroscopic investigations on the reaction dynamics in such self-healing polymer coatings. Analysis of the Raman spectra allows one to assign specific Raman bands characteristic for the progress of the self-healing reaction.

Inorganic salts in atmospheric particulate matter: Raman spectroscopy as an analytical tool.

Atmospheric particulate matter is composed of inorganic and organic components of natural and anthropogenic origin. Wind-transport is probably the most important process responsible for the emission of solid particulate matter into the troposphere, but there are also important contributions from chemical reactions due to the interaction of different atmospheric components in presence of water and solar radiation. Sulfate, nitrate and carbonate salts can be both reactants and products in this complex dynamic system, and there is no doubt about their important impact on the climate. Both simple and mixed salts can be produced in atmosphere by dissolution-crystallization processes. The Raman spectra of 45 representative salts of the atmospheric environment were recorded and the bands assigned. The chemometric analysis of the spectroscopic data of these 45 salts demonstrates the suitability of Raman spectroscopy to classify and identify sulfate, nitrate and carbonate salts of atmospheric importance. Salts were classified into three groups: "sulfates", "nitrates or carbonates" and "sulfate-nitrates or sulfate-carbonate". This kind of information is relevant in atmospheric studies because specific characteristics of the salts can provide valuable information about the origin of the salts, the atmospheric chemistry and climate forcing, thus contributing to the evaluation of environmental impacts.