Rapid screening and identification of viral pathogens in metagenomic data

Background Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. Results In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). Conclusions Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity

Background Obesity and related comorbidities are major health concerns among many US immigrant populations. Emerging evidence suggests a potential involvement of the gut microbiome. Here, we evaluated gut microbiome features and their associations with immigration, dietary intake, and obesity in 2640 individuals from a population-based study of US Hispanics/Latinos. Results The fecal shotgun metagenomics data indicate that greater US exposure is associated with reduced ɑ-diversity, reduced functions of fiber degradation, and alterations in individual taxa, potentially related to a westernized diet. However, a majority of gut bacterial genera show paradoxical associations, being reduced with US exposure and increased with fiber intake, but increased with obesity. The observed paradoxical associations are not explained by host characteristics or variation in bacterial species but might be related to potential microbial co-occurrence, as seen by positive correlations among Roseburia, Prevotella, Dorea, and Coprococcus. In the conditional analysis with mutual adjustment, including all genera associated with both obesity and US exposure in the same model, the positive associations of Roseburia and Prevotella with obesity did not persist, suggesting that their positive associations with obesity might be due to their co-occurrence and correlations with obesity-related taxa, such as Dorea and Coprococcus. Conclusions Among US Hispanics/Latinos, US exposure is associated with unfavorable gut microbiome profiles for obesity risk, potentially related to westernized diet during acculturation. Microbial co-occurrence could be an important factor to consider in future studies relating individual gut microbiome taxa to environmental factors and host health and disease.

Scalable estimation of microbial co-occurrence networks with Variational Autoencoders

Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.

Metagenomics-informed soil biogeochemical models projected less carbon loss in tropical soils in response to climate warming

A major challenge of quantifying feedback between microbial communities and climate is the vast diversity of microbial communities and the intricacy of soil biogeochemical processes they mediate. We overcome this challenge by simplifying the representation of diverse enzyme functions from metagenomics data. We developed a dynamic allocation scheme for enzyme functional classes (EFCs) based on the premise that microbial communities act to maximize acquisition of limiting resources while minimizing energy expenditure for acquiring unlimited resources. We incorporated this scheme into a biogeochemical model to explicitly represent microbial functional diversity and simulate responses of microbially-mediated soil biogeochemical processes to varying environmental and nutrient conditions. Representing microbial functional diversity and environmental acclimation improved predictions of the stoichiometry of microbial biomass and mitigated the sensitivity of soil organic carbon to warming in nutrient-deficient regions. Our results indicate the importance of microbial functional diversity and environmental acclimation for projecting climate feedbacks of nutrient-limited soils.

Acetobase: database update and reanalysis of formyltetrahydrofolate synthetase amplicon sequencing data from the anaerobic digesters

AcetoBase is a public repository and database published in 2019, for the formyltetrahydrofolate synthetase (FTHFS) sequences. It is the first systematic collection of bacterial formyltetrahydrofolate nucleotide and protein sequences from the genomes and metagenome assembled genomes (MAGs), as well as sequences generated by clone library sequencing. In addition, AcetoBase was first to establish connection between FTHFS gene with the Wood-Ljungdahl pathway and 16S rRNA genes. Since the publication of AcetoBase, significant improvements were seen in the taxonomy of many bacterial lineages and accessibility/availability of public genomics and metagenomics data. Thus, an update to the AcetoBase database with new sequence data and taxonomy has been made along with improvements in web-functionality and user interface. The update in AcetoBase reference database version 2 was furthermore evaluated by reanalysis of publicly accessible FTHFS amplicon sequencing data previously analysed with AcetoBase version 1. The latest database update showed significant improvements in the taxonomic assignments of FTHFS sequences. AcetoBase with its enhancements in functionality and content is publicly accessible at https://acetobase.molbio.slu.se.

Metagenomics-informed soil biogeochemical models projected less carbon loss in tropical soils in response to climate warming

A major challenge of quantifying feedback between microbial communities and climate is the vast diversity of microbial communities and the intricacy of soil biogeochemical processes they mediate. We overcome this challenge by simplifying the representation of diverse enzyme functions from metagenomics data. We developed a dynamic allocation scheme for enzyme functional classes (EFCs) based on the premise that microbial communities act to maximize acquisition of limiting resources while minimizing energy expenditure for acquiring unlimited resources. We incorporated this scheme into a biogeochemical model to explicitly represent microbial functional diversity and simulate responses of microbially-mediated soil biogeochemical processes to varying environmental and nutrient conditions. Representing microbial functional diversity and environmental acclimation improved predictions of the stoichiometry of microbial biomass and mitigated the sensitivity of soil organic carbon to warming in nutrient-deficient regions. Our results indicate the importance of microbial functional diversity and environmental acclimation for projecting climate feedbacks of nutrient-limited soils.

Microbiome-Gut-Brain-Axis Communication Influences Metabolic Switch in the Mosquito Anopheles Culicifacies

Periodic ingestion of a protein-rich blood meal by adult female mosquitoes causes a drastic metabolic change in their innate physiological status, which is referred to as ‘metabolic switch. Although the down-regulation of olfactory factors is key to restrain host-attraction, how the gut ‘metabolic switch’ modulates brain functions, and resilience physiological homeostasis remains unexplored. Here we demonstrate that the protein-rich diet induces the expression of brain transcripts related to mitochondrial function and energy metabolism, possibly to cause a shift of the brain’s engagement to manage organismal homeostasis. A dynamic expression pattern of neuro-signalling and neuro-modulatory genes in both gut and brain, establishes an active brain-distant organ communication. Disruption of this comunication through decapitation, does not affect the modulation of the neuro-modulator receptor genes in the gut. In parallel, an unusual and paramount shift in the level of the Neurotransmitters (NTs), from the brain to the gut after blood feeding, further supports the idea of the gut’s ability to serve as a ‘second brain’. Finally, a comparative metagenomics evaluation of gut microbiome population dynamics, highlighted that blood-feeding not only suppresses Enterobacteriaceae family member by 50%, but favors rapid proliferation of Pseudomonadales to 46% of the total community. Notable obesrvation of a rapid proliferation of Pseudomonas bacterial sp. in the gut correlates a possible cause for the suppression of appetite after blood-feeding. Additionally, an altered NTs dynamics of naïve and aseptic mosquitoes provide the initial evidence that gut-endosymbionts are key modulators for the synthesis of major neuroactive molecules. Conclusion: Our data establish a new conceptual understanding of microbiome-gut-brain-axis communication in mosquitoes.Data deposition: Mosquito Brain RNAseq data are accessible under Accession IDs: SRR9853884 (Ac-Br-SF); SRR9853885 (Ac-Br-BF-30Min), SRR9853883 (Ac-Br-BF-30hrs) at NCBI repository. Mosquito Gut metagenomics data are accessible under accession IDs: SRR12579422 (Ac-MG-SF); SRR12622557 (Ac-MG-BF) at NCBI repository.