Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

Diurnal oscillations of amino acids dynamically associate with microbiota and resistome in the colon of pigs

BACKGROUND

Nutrients are one of the key determinants of gut microbiota variation. However, the intricate associations between the amino acid (AA) profile and the dynamic fluctuations in the gut microbiota and resistome remain incompletely elucidated. Herein, we investigated the temporal dynamics of AA profile and gut microbiota in the colon of pigs over a 24-hour period, and further explored the dynamic interrelationships among AA profile, microbiota, and resistome using metagenomics and metabolomics approaches.

RESULTS

JTK_circle analysis revealed that both the AA profile and the gut microbiota exhibited rhythmic fluctuations. With respect to the feed intake, all AAs except L-homoserine (PAdj = 0.553) demonstrated significant fluctuations. Over 50% of Lactobacillaceae, Ruminococcaceae, Clostridiaceae, and Eubacteriaceae species reached their peaks during T15 ∼ T21 when 50% of Lachnospiraceae species experienced a trough. The eLSA results showed that most AAs positively correlated with Prevotellaceae species but negatively correlated with Lactobacillaceae and Lachnospiraceae species. Moreover, most of the AAs negatively correlated with the mobile genetic elements Tn916 and istA group but positively correlated with plasmids. Further partial least squares structural equation model analysis indicated that AAs affected the antibiotic resistance gene dynamics through mobile genetic elements and the gut microbiota.

CONCLUSIONS

Taken together, the AA profile and the gut microbiota exhibit robust fluctuations over a day. The AA profile can affect the gut microbiota and resistome in a direct or indirect manner. These findings may provide new insights into a potential strategy for manipulating the gut microbiota and resistome.

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.

iNAP: An integrated network analysis pipeline for microbiome studies

Integrated network analysis pipeline (iNAP) is an online analysis pipeline for generating and analyzing comprehensive ecological networks in microbiome studies. It is implemented in two sections, that is, network construction and network analysis, and integrates many open-access tools. Network construction contains multiple feasible alternatives, including correlation-based approaches (Pearson's correlation and Spearman's rank correlation along with random matrix theory, and sparse correlations for compositional data) and conditional dependence-based methods (extended local similarity analysis and sparse inverse covariance estimation for ecological association inference), while network analysis provides topological structures at different levels and the potential effects of environmental factors on network structures. Considering the full workflow, from microbiome data set to network result, iNAP contains the molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline (IDENAP), which correspond to the intradomain and interdomain associations of microbial species at multiple taxonomic levels. Here, we describe the detailed workflow by taking IDENAP as an example and show the comprehensive steps to assist researchers to conduct the relevant analyses using their own data sets. Afterwards, some auxiliary tools facilitating the pipeline are introduced to effectively aid in the switch from local analysis to online operations. Therefore, iNAP, as an easy-to-use platform that provides multiple network-associated tools and approaches, can enable researchers to better understand the organization of microbial communities. iNAP is available at http://mem.rcees.ac.cn:8081 with free registration.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

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 abstract.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data

Interactions between biological molecules in a cell are tightly coordinated and often highly dynamic. As a result of these varying signaling activities, changes in gene coexpression patterns could often be observed. The advancements in next‐generation sequencing technologies bring new statistical challenges for studying these dynamic changes of gene coexpression. In recent years, methods have been developed to examine genomic information from individual cells. Single‐cell RNA sequencing (scRNA‐seq) data are count‐based, and often exhibit characteristics such as overdispersion and zero inflation. To explore the dynamic dependence structure in scRNA‐seq data and other zero‐inflated count data, new approaches are needed. In this paper, we consider overdispersion and zero inflation in count outcomes and propose a ZEro‐inflated negative binomial dynamic COrrelation model (ZENCO). The observed count data are modeled as a mixture of two components: success amplifications and dropout events in ZENCO. A latent variable is incorporated into ZENCO to model the covariate‐dependent correlation structure. We conduct simulation studies to evaluate the performance of our proposed method and to compare it with existing approaches. We also illustrate the implementation of our proposed approach using scRNA‐seq data from a study of minimal residual disease in melanoma.

Environment dependent microbial co-occurrences across a cyanobacterial bloom in a freshwater lake

Microbial taxon-taxon co-occurrences may directly or indirectly reflect the potential relationships between the members within a microbial community. However, to what extent and the specificity by which these co-occurrences are influenced by environmental factors remains unclear. In this report, we evaluated how the dynamics of microbial taxon-taxon co-occurrence is associated with the changes of environmental factors in Nan Lake at Wuhan city, China with a Modified Liquid Association method. We were able to detect more than 1000 taxon-taxon co-occurrences highly correlated with one or more environmental factors across a phytoplankton bloom using 16S rRNA gene amplicon community profiles. These co-occurrences, referred to as environment dependent co-occurrences (ED_co-occurrences), delineate a unique network in which a taxon-taxon pair exhibits specific, and potentially dynamic correlations with an environmental parameter, while the individual relative abundance of each may not. Microcystis involved ED_co-occurrences are in important topological positions in the network, suggesting relationships between the bloom dominant species and other taxa could play a role in the interplay of microbial community and environment across various bloom stages. Our results may broaden our understanding of the response of a microbial community to the environment, particularly at the level of microbe-microbe associations.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.