Extended local similarity analysis (eLSA) reveals unique associations between bacterial community structure and odor emission during pig carcasses decomposition

Slaughterhouse by-products composting: can microorganisms inoculum addition mitigate final compost odor emission?

Small slaughterhouses generate biowaste, which for economic reasons, is generally destined for composting. Inoculating appropriate microorganisms can improve biodegradation efficiency and mitigate odor generation during the composting process and can give rise to composts with neutral or pleasant odors. Therefore, the aim of this study was to compare the odor intensity reduction of compost generated with and without a formulated inoculum (Saccharomyces cerevisiae, Bacillus subtilis, and Rhodopseudomonas palustris). A set of experimental data was collected and analyzed according to the German "Verein Deutscher Ingenieure" odor protocol. The results showed that adding microorganisms was effective in reducing unpleasant odors in all three composts generated from swine, cattle, and poultry slaughterhouse by-products during both summer and winter seasons. Additionally, soil odor was predominant in composts that were inoculated in the two tested seasons (i.e., summer and winter). On the other hand, composts without inoculation had odors similar to peat for swine compost, ammonia for cattle compost, and manure for poultry compost, regardless of the season tested. Overall, composting process with appropriate inoculum can help in the correct disposal of slaughterhouse wastes by transforming organic matter into composts, which can have economic and environmental value as a soil conditioner and/or fertilizer.

Identifying local associations in biological time series: algorithms, statistical significance, and applications

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Correlation of microbial dynamics to odor production and emission in full-scale sewage sludge composting

Odor is inevitably produced during sewage sludge composting, and the subsequent pollution hinders the further development of composting technologies. Third-generation high-throughput sequencing was used to analyze microbial community succession, and the correlations between odor and microbial communities were evaluated. Hydrogen sulfide (47.5-87.9 %) and ammonia (9.4-49.9 %) contributed majorly to odor emissions, accounting for 93.7-98.5 % of the emissions. Volatile sulfur compounds were mainly produced in the mesophilic and pre-thermophilic phases (43.0-83.4 %), whereas ammonia was mainly produced in the thermophilic phase (52.1-59.4 %). Microorganisms dominant in the mesophilic and thermophilic phases correlated positively with odor production in the following order: Rhodocyclaceae > Clostridiaceae_1 > Hyphomicrobiaceae > Acidimicrobiales > Family_XI, whereas those dominant in the cooling phase showed negative correlations with odor production in the following order: Bacillus > Sphingobacteriaceae > Pseudomonadaceae > DSSF69 > Chitinophagaceae. The back mixing of mature compost is expected to serve as an economical measure for controlling odor during sewage sludge composting.

Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time

Microbial time-series analysis, typically, examines the abundances of individual taxa over time and attempts to assign etiology to observed patterns. This approach assumes homogeneous groups in terms of profiles and response to external effectors. These assumptions are not always fulfilled, especially in complex natural systems, like the microbiome of the human gut. It is actually established that humans with otherwise the same demographic or dietary backgrounds can have distinct microbial profiles. We suggest an alternative approach to the analysis of microbial time-series, based on the following premises: 1) microbial communities are organized in distinct clusters of similar composition at any time point, 2) these intrinsic subsets of communities could have different responses to the same external effects, and 3) the fate of the communities is largely deterministic given the same external conditions. Therefore, tracking the transition of communities, rather than individual taxa, across these states, can enhance our understanding of the ecological processes and allow the prediction of future states, by incorporating applied effects. We implement these ideas into Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table (e.g., OTU table), their phylogenetic relations as a tree, and the associated metadata. Cronos detects the intrinsic microbial profile clusters on all time points, describes them in terms of composition, and records the transitions between them. Cluster assignments, combined with the provided metadata, are used to model the transitions and predict samples’ fate under various effects. We applied Cronos to available data from growing infants’ gut microbiomes, and we observe two distinct trajectories corresponding to breastfed and formula-fed infants that eventually converge to profiles resembling those of mature individuals. Cronos is freely available at https://github.com/Lagkouvardos/Cronos.

Metagenomic analysis of microbial community structure and function in a improved biofilter with odorous gases

Biofilters have been broadly applied to degrade the odorous gases from industrial emissions. A industrial scale biofilter was set up to treat the odorous gases. To explore biofilter potentials, the microbial community structure and function must be well defined. Using of improved biofilter, the differences in microbial community structures and functions in biofilters before and after treatment were investigated by metagenomic analysis. Odorous gases have the potential to alter the microbial community structure in the sludge of biofilter. A total of 90,016 genes assigned into various functional metabolic pathways were identified. In the improved biofilter, the dominant phyla were Proteobacteria, Planctomycetes, and Chloroflexi, and the dominant genera were Thioalkalivibrio, Thauera, and Pseudomonas. Several xenobiotic biodegradation-related pathways showed significant changes during the treatment process. Compared with the original biofilter, Thermotogae and Crenarchaeota phyla were significantly enriched in the improved biofilter, suggesting their important role in nitrogen-fixing. Furthermore, several nitrogen metabolic pathway-related genes, such as nirA and nifA, and sulfur metabolic pathway-related genes, such as fccB and phsA, were considered to be efficient genes that were involved in removing odorous gases. Our findings can be used for improving the efficiency of biofilter and helping the industrial enterprises to reduce the emission of waste gases.

Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls

The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.

Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

IMPACT STATEMENT

This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

Odor mitigation and bacterial community dynamics in on-site biocovers at a sanitary landfill in South Korea

Unpleasant odors emitted from landfills have been caused environmental and societal problems. For odor abatement, two pilot-scale biocovers were installed at a sanitary landfill site in South Korea. Biocovers PBC1 and PBC2 comprised a soil mixture with different ratios of earthworm casts as an inoculum source and were operated for 240 days. Their odor removal efficiencies were evaluated, and their bacterial community structures were characterized using pyrosequencing. In addition, the correlation between odor removability and bacterial community dynamics was assessed using network analysis. The removal efficiency of complex odor intensity in the two biocovers ranged from 81.1% to 97.8%. Removal efficiencies of sulfur-containing odors (hydrogen sulfide, methanethiol, dimethyl sulfide, and dimethyl disulfide), which contributed most to complex odor intensity, were greater than 91% in both biocovers. Despite the fluctuations in ambient temperature (-8.2 to 31.3 °C) and inlet complex odor intensity (10,000-42,748 of odor dilution ratio), biocovers PBC1 and PBC2 displayed stable deodorizing performance. A high ratio of earthworm casts as an inoculum source led to high odor removability during the first 25 days of operation, but different mixing ratios of earthworm casts did not significantly affect overall odor removability. A bacterial community analysis showed that Methylobacter, Arthrobacter, Acinetobacter, Rhodanobacter, and Pedobacter were the dominant genera in both biocovers. Network analysis results indicated that Steroidobacter, Cystobacter, Methylosarcina, Solirubrobacter, and Pseudoxanthomonas increased in relative abundance with time and were major contributors to odor removal, although these bacteria had a relatively low abundance compared to the overall bacterial community. These data contribute to a more comprehensive understanding of the relationship between bacterial community dynamics and deodorizing performance in biocovers.