Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Systems Biology 5 Suppl 2 (2011): S15, doi:10.1186/1752-0509-5-S2-S15.The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsaThis research is partially supported by the National Science Foundation (NSF) DMS-1043075 and OCE 1136818

Disentangling environmental effects in microbial association networks

Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstrac

Initial soil community drives heathland fungal community trajectory over multiple years through altered plant-soil interactions

•Dispersal limitation, biotic interactions and environmental filters interact to drive plant and fungal community assembly, but their combined effects are rarely investigated. •This study examines how different heathland plant and fungal colonization scenarios realized via three biotic treatments ‐ addition of mature heathland derived sod, addition of hay and no additions ‐ affect soil fungal community development over six years along a manipulated pH gradient in a large‐scale experiment starting from an agricultural, topsoil removed state. •Our results show that both biotic and abiotic (pH) treatments had a persistent influence on the development of fungal communities, but that sod additions diminished the effect of abiotic treatments through time. Analysis of correlation networks between soil fungi and plants suggests that the reduced effect of pH in the sod treatment, where both soil and plant propagules were added, might be due to plant‐fungal interactions since the sod additions caused stronger, more specific, and more consistent connections compared to no addition treatment. •Based on these results, we suggest that the initial availability of heathland fungal and plant taxa, that reinforce each other, can significantly steer further fungal community development to an alternative configuration, overriding otherwise prominent effect of abiotic (pH) conditions

Christensenella minuta interacts with multiple gut bacteria

IntroductionGut microbes form complex networks that significantly influence host health and disease treatment. Interventions with the probiotic bacteria on the gut microbiota have been demonstrated to improve host well-being. As a representative of next-generation probiotics, Christensenella minuta (C. minuta) plays a critical role in regulating energy balance and metabolic homeostasis in human bodies, showing potential in treating metabolic disorders and reducing inflammation. However, interactions of C. minuta with the members of the networked gut microbiota have rarely been explored.MethodsIn this study, we investigated the impact of C. minuta on fecal microbiota via metagenomic sequencing, focusing on retrieving bacterial strains and coculture assays of C. minuta with associated microbial partners.ResultsOur results showed that C. minuta intervention significantly reduced the diversity of fecal microorganisms, but specifically enhanced some groups of bacteria, such as Lactobacillaceae. C. minuta selectively enriched bacterial pathways that compensated for its metabolic defects on vitamin B1, B12, serine, and glutamate synthesis. Meanwhile, C. minuta cross-feeds Faecalibacterium prausnitzii and other bacteria via the production of arginine, branched-chain amino acids, fumaric acids and short-chain fatty acids (SCFAs), such as acetic. Both metagenomic data analysis and culture experiments revealed that C. minuta negatively correlated with Klebsiella pneumoniae and 14 other bacterial taxa, while positively correlated with F. prausnitzii. Our results advance our comprehension of C. minuta’s in modulating the gut microbial network.ConclusionsC. minuta disrupts the composition of the fecal microbiota. This disturbance is manifested through cross-feeding, nutritional competition, and supplementation of its own metabolic deficiencies, resulting in the specific enrichment or inhibition of the growth of certain bacteria. This study will shed light on the application of C. minuta as a probiotic for effective interventions on gut microbiomes and improvement of host health

Impact of Terrestrial Input on Deep-Sea Benthic Archaeal Community Structure in South China Sea Sediments

© Copyright © 2020 Lai, Hedlund, Xie, Liu, Phelps, Zhang and Wang. Archaea are widespread in marine sediments and play important roles in the cycling of sedimentary organic carbon. However, factors controlling the distribution of archaea in marine sediments are not well understood. Here we investigated benthic archaeal communities over glacial-interglacial cycles in the northern South China Sea and evaluated their responses to sediment organic matter sources and inter-species interactions. Archaea in sediments deposited during the interglacial period Marine Isotope Stage (MIS) 1 (Holocene) were significantly different from those in sediments deposited in MIS 2 and MIS 3 of the Last Glacial Period when terrestrial input to the South China Sea was enhanced based on analysis of the long-chain n-alkane C31. The absolute archaeal 16S rRNA gene abundance in subsurface sediments was highest in MIS 2, coincident with high sedimentation rates and high concentrations of total organic carbon. Soil Crenarchaeotic Group (SCG; Nitrososphaerales) species, the most abundant ammonia-oxidizing archaea in soils, increased dramatically during MIS 2, likely reflecting transport of terrestrial archaea during glacial periods with high sedimentation rates. Co-occurrence network analyses indicated significant association of SCG archaea with benthic deep-sea microbes such as Bathyarchaeota and Thermoprofundales in MIS 2 and MIS 3, suggesting potential interactions among these archaeal groups. Meanwhile, Thermoprofundales abundance was positively correlated with total organic carbon (TOC), along with n-alkane C31 and sedimentation rate, indicating that Thermoprofundales may be particularly important in processing of organic carbon in deep-sea sediments. Collectively, these results demonstrate that the composition of heterotrophic benthic archaea in the South China Sea may be influenced by terrestrial organic input in tune with glacial-interglacial cycles, suggesting a plausible link between global climate change and microbial population dynamics in deep-sea marine sediments

Machine learning for microbial ecology: predicting interactions and identifying their putative mechanisms

Microbial communities are key components of Earth’s ecosystems and they play important roles in human health and industrial processes. These communities and their functions can strongly depend on the diverse interactions between constituent species, posing the question of how such interactions can be predicted, measured and controlled. This challenge is particularly relevant for the many practical applications enabled by the rising field of synthetic microbial ecology, which includes the design of microbiome therapies for human diseases. Advances in sequencing technologies and genomic databases provide valuable datasets and tools for studying inter-microbial interactions, but the capacity to characterize the strength and mechanisms of interactions between species in large consortia is still an unsolved challenge. In this thesis, I show how machine learning methods can be used to help address these questions. The first portion of my thesis work was focused on predicting the outcome of pairwise interactions between microbial species. By integrating genomic information and observed experimental data, I used machine learning algorithms to explore the predictive relationship between single-species traits and inter-species interaction phenotypes. I found that organismal traits (e.g. annotated functions of genomic elements) are sufficient to predict the qualitative outcome of interactions between microbes. I also found that the relative fraction of possible experiments needed to build acceptable models drastically shrinks as the combinatorial space grows. In the second part of my thesis work, I developed an algorithmic method for identifying putative interaction mechanisms by scoring combinations of variables that random forest uses in order to predict interaction outcomes. I applied this method to a study of the human microbiome and identified a previously unreported combination of microbes that are strongly associated with Crohn’s disease. In the last part of my thesis, I utilized a regression approach to first identify and then quantify interactions between microbial species relevant to community function. The work I present in this dissertation provides a general framework for understanding the myriad interactions that occur in natural and synthetic microbial consortia

Food Microbiol

Next Generation Sequencing (NGS) combined with powerful bioinformatic approaches are revolutionising food microbiology. Whole genome sequencing (WGS) of single isolates allows the most detailed comparison possible hitherto of individual strains. The two principle approaches for strain discrimination, single nucleotide polymorphism (SNP) analysis and genomic multi-locus sequence typing (MLST) are showing concordant results for phylogenetic clustering and are complementary to each other. Metabarcoding and metagenomics, applied to total DNA isolated from either food materials or the production environment, allows the identification of complete microbial populations. Metagenomics identifies the entire gene content and when coupled to transcriptomics or proteomics, allows the identification of functional capacity and biochemical activity of microbial populations. The focus of this review is on the recent use and future potential of NGS in food microbiology and on current challenges. Guidance is provided for new users, such as public health departments and the food industry, on the implementation of NGS and how to critically interpret results and place them in a broader context. The review aims to promote the broader application of NGS technologies within the food industry as well as highlight knowledge gaps and novel applications of NGS with the aim of driving future research and increasing food safety outputs from its wider use.CC999999/Intramural CDC HHS/United States2019-06-01T00:00:00Z30621881PMC64922637184vault:3458