MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets

Abstract

Background: Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of metagenomic studies is to identify specific functional adaptations of microbial communities to their habitats. The functional profile and the abundances for a sample can be estimated by mapping metagenomic sequences to the global metabolic network consisting of thousands of molecular reactions. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge.

Methods: First, we introduce a scoring function for an arbitrary subnetwork and find the max-weight subnetwork in the global network by a greedy search algorithm. Then we compute two p values (pabund and pstruct) using nonparametric approaches to answer two different statistical questions: (1) is this subnetwork differentically abundant? (2) What is the probability of finding such good subnetworks by chance given the data and network structure? Finally, significant metabolic subnetworks are discovered based on these two p values.

Results: In order to validate our methods, we have designed a simulated metabolic pathways dataset and show that MetaPath outperforms other commonly used approaches. We also demonstrate the power of our methods in analyzing two publicly available metagenomic datasets, and show that the subnetworks identified by MetaPath provide valuable insights into the biological activities of the microbiome.

Conclusions: We have introduced a statistical method for finding significant metabolic subnetworks from metagenomic datasets. Compared with previous methods, results from MetaPath are more robust against noise in the data, and have significantly higher sensitivity and specificity (when tested on simulated datasets). When applied to two publicly available metagenomic datasets, the output of MetaPath is consistent with previous observations and also provides several new insights into the metabolic activity of the gut microbiome. The software is freely available at http://metapath.cbcb.umd.edu.

Figure 1

Schematic diagram of the MetaPath methods. Sequences from each sample are annotated against KEGG genes database and mapped to reactions in metabolic networks, resulting an abundance matrix where the rows are reactions and columns are samples. Then p values are computed for all reactions using Metastats [9], then converted into Z values, and greedy search is performed on the edge-weighted graph to find max-weight subnetworks. Finally, we calculate the p_abund and p_struct significance values of the max-weight subnetwork.

Figure 2

Significant subnetworks that are caused by structural biases. On the left side, both of the two pathways have equal weight, indicating equal significance of differential abundance. The high weight of the second pathway, however, mainly come from the middle fat edge that has weight 7. On the right side, in a densely connected network, any random high-weight edges will form a subnetwork with high weight (correlated noise).

Figure 3

Comparison of statistical methods for discovering significant reactions in simulated datasets. Four methods are evaluated: discovering active subnetworks using simulated annealing (Anneal) and greedy search (Greedy) [13], discovering significant individual reactions using Metastats [9], finding differentially abundant KEGGdefined pathways (KEGGPath), and MetaPath. Four datasets are created by varying the number of significant reactions n and their significances.

Figure 4

p values distributions from comparing individual metabolic reactions by Metastats and from comparing metabolic networks by MetaPath. The top histogram is the distribution of the p values of individual metabolic reactions calculated by Metastats. The Bottom histogram is the distribution of the p_abund values of the subnetworks calculated by MetaPath.

Figure 5

9 statistically significant subnetworks are found in the comparison of the gut microbiome from the obese and lean subjects. All these subnetworks are enriched in the obese subjects. p_abund and p_struct significance values are shown above each subnetwork. p values for each reaction are shown with the KEGG reaction number. Five pathways (a)-(e) belong to the Fatty Acid Metabolism pathway in KEGG. Four pathways (f)-(i) contain the L-Homocysteine molecules.

Figure 6

10 statistically significant subpathways are found in the infant and adult individuals dataset. 6 subpathways are enriched in the infant subjects (Fig. 4a-4f), and 4 subpathways are enriched in the adult subjects (Fig. 4g-4j). p_abund and p_struct significance values are shown above each pathway. p values for each reaction are shown with the KEGG reaction number.