Microbial metaproteomics--From sample processing to data acquisition and analysis

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

ABSTRACTA silica sands-based method has been developed to isolate high quality genomic DNAs from cells of animals, plants and microorganisms, such as Hemisalanx prognathus, Spinacia oleracea, Pichia pastoris, Bacillus licheniformis and Escherichia coli. To the best of our knowledge, no DNA isolation method has so wide application until now. In addition, this method and a commercially available kit were compared in analysis of microbial communities using high-throughput 16s rDNA sequencing. As a result, the silica sands-based method was found to be even more efficient in isolating genomic DNA from gram-positive bacteria than the kit, indicating that it would become a very valuable choice to faithfully reflect the composition of microbial communities.

Microbial Community Composition and Predicted Functional Attributes of Antarctic Lithobionts Using Targeted Next-Generation Sequencing and Bioinformatics Tools

Molasses is a highly dense and refined byproduct produced in the sugarcane industry, and it contains high amounts of degradable compounds. Through bioconversion, these compounds can be transformed into renewable products. However, the involved biological process is negatively influenced by the high chemical oxygen demand (COD) of molasses and ion concentration. The co-digestion of molasses with rice-alcohol wastewater (RAW) was compared with its mono-digestion at an increasing organic loading rate (OLR). Both processes were assessed by detecting the COD removal rate, the methane contents of biogas, and the structure and composition of microbial communities at different stages. Results showed that the co-digestion is stable up to a maximum OLR of 16 g COD L−1 d−1, whereas after the acclimatization phase, the mono-digestion process was disturbed two times, which occurred at a maximum OLR of 9 and 10 g COD L−1 d−1. The volatile fatty acids (VFAs) observed were 2059.66 mg/L and 1896.9 mg/L, which in mono-digestion causes the inhibition at maximum OLRs. In the co-digestion process, the concomitant COD removal rates and methane content recorded was 90.72 ± 0.63% 64.47% ± 0.59% correspondingly. While in the mono-digestion process, high COD removal rate and methane contents observed were 89.29 ± 0.094% and 61.37 ± 1.06% respectively. From the analysis of microbial communities, it has been observed that both the bacterial and archaeal communities respond differently at unlike stages. However, in both processes, Propionibacteriaceae was the most abundant family in the bacterial communities, whereas Methanosaetaceae was abundant in the archaeal communities. From the current study, it has been concluded that that rice-alcohol wastewater could be a good co-substrate for the anaerobic digestion of molasses in terms of COD removal rate and methane contents production, that could integrate molasses into progressive biogas production with high OLR.

Elevation explains variation in soil microbial diversity and community composition under experimental warming and fertilization treatments in mountain meadows

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Using landscape and depth gradients to decouple the impact of correlated environmental variables on soil microbial community composition

Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

It is assumed that microbial communities involved in the biological treatment of different wastewaters having a different chemical composition harbor different microbial populations which are specifically adapted to the environmental stresses encountered in these systems. Yet, little is known about the composition of these microbial communities. Therefore, the aim of this study was to assess the microbial community composition over two seasons (winter and summer) in activated sludge from well‐operating textile wastewater treatment plants (WWTPs) in comparison with municipal WWTPs, and to explain observed differences by environmental variables. 454‐pyrosequencing generated 160 archaeal and 1645 bacterial species‐level Operational Taxonomic Units (OTUs), with lower observed richness in activated sludge from textile WWTPs compared to municipal WWTPs. The bacterial phyla Planctomycetes, Chloroflexi, Chlorobi, and Acidobacteria were more abundant in activated sludge samples from textile WWTPs, together with archaeal members of Thaumarchaeota. Nonmetric multidimensional scaling analysis of the microbial communities showed that microbial communities from textile and municipal WWTPs were significantly different, with a seasonal effect on archaea. Nitrifying and denitrifying bacteria as well as phosphate‐accumulation bacteria were more abundant in municipal WWTPs, while sulfate‐reducing bacteria were almost only detected in textile WWTPs. Additionally, microbial communities from textile WWTPs were more dissimilar than those of municipal WWTPs, possibly due to a wider diversity in environmental stresses to which microbial communities in textile WWTPs are subjected to. High salinity, high organic loads, and a higher water temperature were important potential variables driving the microbial community composition in textile WWTPs. This study provides a general view on the composition of microbial communities in activated sludge of textile WWTPs, and may provide novel insights for identifying key players performing important functions in the purification of textile wastewaters.

Arsenic-rich stalactites from abandoned mines: Mineralogy and biogeochemistry

Currently, characterization of soil microbial communities relies heavily on the use of molecular approaches. Independently of the approach used, soil DNA extraction is a crucial step, and success of downstream procedures will depend on how well DNA extraction was performed. Often, studies describing and comparing soil microbial communities are based on a single DNA extraction, which may not lead to a representative recovery of DNA from all organisms present in the soil. The use of successive DNA extractions might improve soil microbial characterization, but the benefit of this approach has only been limitedly studied. To determine whether successive DNA extractions of the same soil sample would lead to different observations in terms of microbial abundance and community composition, we performed three successive extractions, with two widely used commercial kits, on a range of clay and sandy soils. Successive extractions increased DNA yield considerably (1–374%), as well as total bacterial and fungal abundances in most of the soil samples. Analysis of the 16S and 18S ribosomal RNA genes using 454-pyrosequencing, revealed that microbial community composition (taxonomic groups) observed in the successive DNA extractions were similar. However, successive DNA extractions did reveal several additional microbial groups. For some soil samples, shifts in microbial community composition were observed, mainly due to shifts in relative abundance of a number of microbial groups. Our results highlight that performing successive DNA extractions optimize DNA yield, and can lead to a better picture of overall community composition.

To investigate the retention and removal of the fish pathogenic bacterium Yersinia ruckeri in biological sand filters and effects on the microbial community composition. Sand filter columns were loaded (70 mm day(-1)) with fish farm wastewater and a suspension (10(8) CFU ml(-1)) of Y. ruckeri. Bacterial numbers and protozoan numbers were determined by plate counts and epifluorescence microscopy, respectively, and microbial biomass and community composition were assessed by phospholipid fatty acids (PLFA) analysis. Concentrations of Y. ruckeri in the filter effluent decreased from 10(8) to 10(3)-10(5) CFU ml(-1) during the experiment. Numbers of Y. ruckeri in the sand decreased from 10(6) CFU g(-1) dry weight (DW) sand to 10(4) CFU g(-1) DW sand. In contrast, microbial biomass determined with plate counts and total PLFA increased during the whole experiment. Principal component analysis (PCA) revealed a change in microbial community composition with time, with the most pronounced change in surface layers and towards the end of the experiment. Protozoan numbers increased from ca 0-600 cells g(-1) DW sand, indicating the establishment of a moderate population of bacterial grazers. The removal of Y. ruckeri improved during the experiment. Introduction of Y. ruckeri to the sand filter columns stimulated growth of other micro-organisms, which in turn caused a shift in the microbial community composition in the sand. This study increases the understanding of the dynamics of sand filters subjected to a high loading of a pathogenic bacterium and can therefore be used in future work were the overall aim is to provide a more reliable and efficient removal of pathogenic bacteria in biological sand filter systems.

Recent advances in mass-spectrometry based proteomics software, tools and databases

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.