Dataset Information

Topological analysis reveals state transitions in human gut and marine bacterial communities.

ABSTRACT: Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making large-scale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes. We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological features of the data density. We find a reproducible two-state succession during recovery from cholera in the gut microbiomes of multiple patients, evidence of dynamic stability in the gut microbiome of a healthy human after experiencing diarrhea during travel, and periodic state transitions in a marine Prochlorococcus community driven by water column cycling. Our approach bridges small-scale fluctuations in microbiome composition and large-scale changes in phenotype without details of underlying mechanisms, and provides an assessment of microbiome stability and its relation to human and environmental health.

SUBMITTER: Chang WK

PROVIDER: S-EPMC7560872 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Topological analysis reveals state transitions in human gut and marine bacterial communities.

Chang William K WK VanInsberghe David D Kelly Libusha L

NPJ biofilms and microbiomes 20201014 1

Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making large-scale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes. We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological featu ...[more]

PMID: 33057043

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Project description:BACKGROUND: Environmental shotgun sequencing (metagenomics) provides a new way to study communities in microbial ecology. We here use sequence data from the Global Ocean Sampling (GOS) expedition to investigate toxicant selection pressures revealed by the presence of detoxification genes in marine bacteria. To capture a broad range of potential toxicants we selected detoxification protein families representing systems protecting microorganisms from a variety of stressors, such as metals, organic compounds, antibiotics and oxygen radicals. RESULTS: Using a bioinformatics procedure based on comparative analysis to finished bacterial genomes we found that the amount of detoxification genes present in marine microorganisms seems surprisingly small. The underrepresentation is particularly evident for toxicant transporters and proteins involved in detoxifying metals. Exceptions are enzymes involved in oxidative stress defense where peroxidase enzymes are more abundant in marine bacteria compared to bacteria in general. In contrast, catalases are almost completely absent from the open ocean environment, suggesting that peroxidases and peroxiredoxins constitute a core line of defense against reactive oxygen species (ROS) in the marine milieu. CONCLUSIONS: We found no indication that detoxification systems would be generally more abundant close to the coast compared to the open ocean. On the contrary, for several of the protein families that displayed a significant geographical distribution, like peroxidase, penicillin binding transpeptidase and divalent ion transport protein, the open ocean samples showed the highest abundance. Along the same lines, the abundance of most detoxification proteins did not increase with estimated pollution. The low level of detoxification systems in marine bacteria indicate that the majority of marine bacteria have a low capacity to adapt to increased pollution. Our study exemplifies the use of metagenomics data in ecotoxicology, and in particular how anthropogenic consequences on life in the sea can be examined.

Project description:Aquatic systems worldwide can exist in multiple ecosystem states (i.e., a recurring collection of biological and chemical attributes), and effectively characterizing multidimensionality will aid protection of desirable states and guide rehabilitation. The Upper Mississippi River System is composed of a large floodplain river system spanning 2200 km and multiple federal, state, tribal and local governmental units. Multiple ecosystem states may occur within the system, and characterization of the variables that define these ecosystem states could guide river rehabilitation. We coupled a long-term (30-year) highly dimensional water quality monitoring dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years in the river to guide conservation. Across the entire system, TDA identified five ecosystem states. State 1 was characterized by exceptionally clear, clean, and cold-water conditions typical of winter (i.e., a clear-water state); State 2 had the greatest range of environmental conditions and contained most the data (i.e., a status-quo state); and States 3, 4, and 5 had extremely high concentrations of suspended solids (i.e., turbid states, with State 5 as the most turbid). The TDA mapped clear patterns of the ecosystem states across several riverine navigation reaches and seasons that furthered ecological understanding. State variables were identified as suspended solids, chlorophyll a, and total phosphorus, which are also state variables of shallow lakes worldwide. The TDA change detection function showed short-term state transitions based on seasonality and episodic events, and provided evidence of gradual, long-term changes due to water quality improvements over three decades. These results can inform decision making and guide actions for regulatory and restoration agencies by assessing the status and trends of this important river and provide quantitative targets for state variables. The TDA change detection function may serve as a new tool for predicting the vulnerability to undesirable state transitions in this system and other ecosystems with sufficient data. Coupling ecosystem state concepts and TDA tools can be transferred to any ecosystem with large data to help classify states and understand their vulnerability to state transitions.

Dataset Information

Topological analysis reveals state transitions in human gut and marine bacterial communities.

Publications

Topological analysis reveals state transitions in human gut and marine bacterial communities.

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