Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Occurrence and Influencing Factors of Antibiotics and Antibiotic Resistance Genes in Sediments of the Largest Multi-habitat Lakes in Northern China

Baiyangdian Lake is a typical and largest multi-habitat lake in the North plain of China. To understand the generation and transmission of antibiotics resistance genes (ARGs) in multi-habitat lakes, the contents of nutrients (TC, TOC, TN, TP and TS), heavy metals (Zn, Cr, Ni, Cu, Pb, As, Cd and Hg), 22 antibiotics, 16S-rRNA(16S), Class I integron (intI1) and 20 ARGs were determined. Samples were taken from the Fuhe river, river estuaries, reed marshes, living area, fish poods and open water of Baiyangdian Lake. The results showed that quinolones (QNs) were the main pollutants, and the content range was ND-104.94 ng/g. Thereinto, aac (6') -IB, blaTEM-1, ermF, qnrA, qnrD, tetG, sul1, sul2 and tetM were detected in 100%. The absolute abundance of sul1 was the highest (5.25×105copies/g-6.21×107 copies/g), which was the dominant ARGs. In these different habitats, the abundance of antibiotics and ARGs in river estuary was the highest, and that in reed marshes was the lowest. There was a significant positive correlation between the abundance of heavy metals (Cu, Pb, Zn, Ni, Cd, Hg) and the absolute abundance of 11 ARGs (P<0.01). Redundancy analysis showed that Cu, Zn, intI1, TP and macrolides (MLs) were the important factors affecting the distribution of ARGs. Our findings provides a more likely driving and influencing factor for the transmission of ARGs in lakes with complex and diverse habitats.

Metagenomic Analysis of Bacterial Communities and Antibiotic Resistance Genes in Penaeus monodon Biofloc-Based Aquaculture Environments

Biofloc technology (BFT) is one of the most promising technologies in global aquaculture for the purpose of improving water quality, waste treatment, and disease prevention in intensive aquaculture systems. However, characterization of the microbial species and antibiotic resistance potentially present in biofloc-based aquaculture environments is needed. In this study, we used high-throughput sequencing technology to comprehensively compare the bacterial communities in mariculture ponds of Penaeus monodon (P. monodon), by testing of water, biofloc, and intestine of P. monodon. Operational taxonomic units (OTUs) cluster analysis showed that the nine samples tested divided into 45 phyla and 457 genera. Proteobacteria was the dominant bacteria in water, biofloc and prawn intestine. In biofloc and intestine, the Ruegeria (2.23–6.31%) genus represented the largest proportion of bacteria, with Marivita (14.01–20.94%) the largest group in water. Microbial functional annotation revealed that in all the samples, genes encoding metabolism were predominant. The antibiotic resistance gene annotation showed the highest absolute abundance of patB, adeF, OXA-243, and Brucella_suis_mprF from Proteobacteria. PatB (11.33–15.01%), adeF (15.79–18.16%), OXA-243 (35.65%), and Brucella_suis_mprF (10.03%) showed the highest absolute abundance of antibiotic resistance genes in water, biofloc, and intestines, respectively. These findings may greatly increase our understanding of the characteristics of the microbiota of shrimp biofloc-based aquaculture systems and the complex interactions among shrimp, ambient microflora, and environmental variables. It provides a reference basis for policy on breeding, environmental safety, and maintaining food safety in the production of P. monodon.

Comparison of Different Label-free Techniques for the Semi-absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the &ldquo;gold standard&rdquo; for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Anthropogenic disturbances consistently favor the high-yield strategists of soil bacterial community in the Eurasian steppe

Purpose Multiple anthropogenic disturbances, such as climate warming and nitrogen deposition are affecting terrestrial ecosystems. Different disturbances may have some consistent effects on the soil microbial community, which remains largely unexplored. Methods We mimicked 16 anthropogenic disturbances in a steppe ecosystem, and measured the absolute abundance and taxonomic composition of soil bacterial communities with qPCR and amplicon sequencing, respectively. Results We found that while the absolute abundance of each of the four dominant bacterial phyla did not show a consistent response to these disturbances, that of the five subdominant phyla showed a consistent increase. Meanwhile, these disturbances consistently stimulated the relative abundances of metabolic functions for high-growth-yield, including the transport/metabolism of amino acids and carbohydrates. Stochastic processes (e.g., random birth) played more critical roles in structuring the subdominant than dominant phyla, and the disturbances promoted the stochastic processes. Conclusions Overall, the high-yield traits and stochasticity of subdominant phyla led to their positive responses to disturbances. Furthermore, our findings indicate that the intensifying human activities are likely to cause a high-yield-strategies-toward shift in soil microbial composition in the Eurasian steppe ecosystem.

The spatial distribution characteristics of typical pathogens and nitrogen and phosphorus in the sediments of Shahe Reservoir and their relationships

Using samples collected in Shahe Reservoir in the upper North Canal in China, this research analyzes the structure of a microorganism group in sediment and the absolute abundance of two typical pathogenic bacteria (Escherichia coli and Enterococcus), and their relationship with environmental factors including total nitrogen (TN) and total phosphorus (TP). The study of samples collected from the surface (0–20 cm) and sediment cores shows that the absolute abundance of E. coli in horizontal distribution in the sediment is highest in downstream of the reservoir and point source pollution area. In vertical distribution, the absolute gene expression level of the two pathogenic bacteria in the sediment tends to decrease with increasing depth, although its highest value at 10–30 cm depth. The relative abundance the two pathogenic bacteria is much greater in the sediment of Shahe Reservoir with the structure of horizontal groups including Clortridium sensu stricto, unclassified Anaeroineaceae, and Povalibacter, while Anaeroineaceae is much more abundant in the group structure of the vertical distribution. Pearson correlation analysis suggests positive correlation in horizontal distribution for E. coli and TN and TP (P < 0.05) and for Enterococcus and TP (P < 0.05). The results clearly show that the amount of pathogenic bacteria in the sediment in Shahe Reservoir is most likely due to water eutrophication.

Quantitative sequencing clarifies the role of disruptor taxa, oral microbiota, and strict anaerobes in the human small-intestine microbiome

Background Upper gastrointestinal (GI) disorders and abdominal pain afflict between 12 and 30% of the worldwide population and research suggests these conditions are linked to the gut microbiome. Although large-intestine microbiota have been linked to several GI diseases, the microbiota of the human small intestine and its relation to human disease has been understudied. The small intestine is the major site for immune surveillance in the gut, and compared with the large intestine, it has greater than 100 times the surface area and a thinner and more permeable mucus layer. Results Using quantitative sequencing, we evaluated total and taxon-specific absolute microbial loads from 250 duodenal-aspirate samples and 21 paired duodenum-saliva samples from participants in the REIMAGINE study. Log-transformed total microbial loads spanned 5 logs and were normally distributed. Paired saliva-duodenum samples suggested potential transmission of oral microbes to the duodenum, including organisms from the HACEK group. Several taxa, including Klebsiella, Escherichia, Enterococcus, and Clostridium, seemed to displace strict anaerobes common in the duodenum, so we refer to these taxa as disruptors. Disruptor taxa were enriched in samples with high total microbial loads and in individuals with small intestinal bacterial overgrowth (SIBO). Absolute loads of disruptors were associated with more severe GI symptoms, highlighting the value of absolute taxon quantification when studying small-intestine health and function. Conclusion This study provides the largest dataset of the absolute abundance of microbiota from the human duodenum to date. The results reveal a clear relationship between the oral microbiota and the duodenal microbiota and suggest an association between the absolute abundance of disruptor taxa, SIBO, and the prevalence of severe GI symptoms.

Quantitative insights into effects of intrapartum antibiotics and birth mode on infant gut microbiota in relation to well-being during the first year of life

Background and aims Caesarean section (CS)-birth and maternally administered intrapartum antibiotics (IP) affect colonization of the neonate. We compared the effects of CS delivery and IP antibiotics on infant gut microbiota development and wellbeing over the first year. To understand the developing community dynamics, we focused on absolute bacterial abundance estimates over relative abundances. Methods We studied 144 healthy infants born between gestational weeks 37-42 vaginally without antibiotics (N=58), with IP penicillin (N=25) or cephalosporin (N=13), or by CS with IP cephalosporin (N=34) or other antibiotics (N=14). Gut microbiota composition and temporal development was analysed at 5-7 time points during the first year of life using 16S rRNA gene amplicon sequencing, complemented with qPCR to obtain absolute abundance estimates in 92 infants. A mediation analysis was carried out to identify taxa linked to gastrointestinal function and discomfort (crying, defecation frequency and signs of gastrointestinal symptoms) and birth interventions. Results Based on absolute abundance estimates, depletion of Bacteroides spp. was specific to CS birth while decreased bifidobacteria and increased Bacilli were common to CS birth and exposure to IP antibiotics in vaginal delivery. Abundance of numerous taxa differed between the birth modes among cephalosporin-exposed infants. Penicillin had a milder impact on the infant gut microbiota than cephalosporin. The effects of both CS birth and IP antibiotics on infant gut microbiota associated with increased gastrointestinal symptoms during the first months. Conclusion CS birth and maternal IP antibiotics have both specific and overlapping effects on infant gut microbiota development. The resulting microbiota deviations were found to associate with gastrointestinal symptoms in infancy.