Where did you come from, where did you go: Refining metagenomic analysis tools for horizontal gene transfer characterisation

Current state and future prospects of Horizontal Gene Transfer detection

Artificial intelligence (AI) has been shown to be beneficial in a wide range of bioinformatics applications. Horizontal Gene Transfer (HGT) is a driving force of evolutionary changes in prokaryotes. It is widely recognized that it contributes to the emergence of antimicrobial resistance (AMR), which poses a particularly serious threat to public health. Many computational approaches have been developed to study and detect HGT. However, the application of AI in this field has not been investigated. In this work, we conducted a review to provide information on the current trend of existing computational approaches for detecting HGT and to decipher the use of AI in this field. Here, we show a growing interest in HGT detection, characterized by a surge in the number of computational approaches, including AI-based approaches, in recent years. We organize existing computational approaches into a hierarchical structure of computational groups based on their computational methods and show how each computational group evolved. We make recommendations and discuss the challenges of HGT detection in general and the adoption of AI in particular. Moreover, we provide future directions for the field of HGT detection.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Profiling lateral gene transfer events in the human microbiome using WAAFLE

Lateral gene transfer (LGT), also known as horizontal gene transfer, facilitates genomic diversification in microbial populations. While previous work has surveyed LGT in human-associated microbial isolate genomes, the landscape of LGT arising in personal microbiomes is not well understood, as there are no widely adopted methods to characterize LGT from complex communities. Here we developed, benchmarked and validated a computational algorithm (WAAFLE or Workflow to Annotate Assemblies and Find LGT Events) to profile LGT from assembled metagenomes. WAAFLE prioritizes specificity while maintaining high sensitivity for intergenus LGT. Applying WAAFLE to >2,000 human metagenomes from diverse body sites, we identified >100,000 high-confidence previously uncharacterized LGT (~2 per microbial genome-equivalent). These were enriched for mobile elements, as well as restriction-modification functions associated with the destruction of foreign DNA. LGT frequency was influenced by biogeography, phylogenetic similarity of involved pairs (for example, Fusobacterium periodonticum and F. nucleatum) and donor abundance. These forces manifest as networks in which hub taxa donate unequally with phylogenetic neighbours. Our findings suggest that human microbiome LGT may be more ubiquitous than previously described.

Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome

Horizontal gene transfer (HGT) phenomena pervade the gut microbiome and significantly impact human health. Yet, no current method can accurately identify complete HGT events, including the transferred sequence and the associated deletion and insertion breakpoints from shotgun metagenomic data. Here, we develop LocalHGT, which facilitates the reliable and swift detection of complete HGT events from shotgun metagenomic data, delivering an accuracy of 99.4%-verified by Nanopore data-across 200 gut microbiome samples, and achieving an average F1 score of 0.99 on 100 simulated data. LocalHGT enables a systematic characterization of HGT events within the human gut microbiome across 2098 samples, revealing that multiple recipient genome sites can become targets of a transferred sequence, microhomology is enriched in HGT breakpoint junctions (P-value = 3.3e-58), and HGTs can function as host-specific fingerprints indicated by the significantly higher HGT similarity of intra-personal temporal samples than inter-personal samples (P-value = 4.3e-303). Crucially, HGTs showed potential contributions to colorectal cancer (CRC) and acute diarrhoea, as evidenced by the enrichment of the butyrate metabolism pathway (P-value = 3.8e-17) and the shigellosis pathway (P-value = 5.9e-13) in the respective associated HGTs. Furthermore, differential HGTs demonstrated promise as biomarkers for predicting various diseases. Integrating HGTs into a CRC prediction model achieved an AUC of 0.87.

Profiling novel lateral gene transfer events in the human microbiome

Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial populations, including the human microbiome. While prior work has surveyed LGT events in human-associated microbial isolate genomes, the scope and dynamics of novel LGT events arising in personal microbiomes are not well understood, as there are no widely adopted computational methods to detect, quantify, and characterize LGT from complex microbial communities. We addressed this by developing, benchmarking, and experimentally validating a computational method (WAAFLE) to profile novel LGT events from assembled metagenomes. Applying WAAFLE to >2K human metagenomes from diverse body sites, we identified >100K putative high-confidence but previously uncharacterized LGT events (~2 per assembled microbial genome-equivalent). These events were enriched for mobile elements (as expected), as well as restriction-modification and transport functions typically associated with the destruction of foreign DNA. LGT frequency was quantifiably influenced by biogeography, the phylogenetic similarity of the involved taxa, and the ecological abundance of the donor taxon. These forces manifest as LGT networks in which hub species abundant in a community type donate unequally with their close phylogenetic neighbors. Our findings suggest that LGT may be a more ubiquitous process in the human microbiome than previously described. The open-source WAAFLE implementation, documentation, and data from this work are available at http://huttenhower.sph.harvard.edu/waafle.

Uncovering Bacterial Hosts of Class 1 Integrons in an Urban Coastal Aquatic Environment with a Single-Cell Fusion-Polymerase Chain Reaction Technology

Horizontal gene transfer (HGT) is a key driver of bacterial evolution via transmission of genetic materials across taxa. Class 1 integrons are genetic elements that correlate strongly with anthropogenic pollution and contribute to the spread of antimicrobial resistance (AMR) genes via HGT. Despite their significance to human health, there is a shortage of robust, culture-free surveillance technologies for identifying uncultivated environmental taxa that harbor class 1 integrons. We developed a modified version of epicPCR (emulsion, paired isolation, and concatenation polymerase chain reaction (PCR)) that links class 1 integrons amplified from single bacterial cells to taxonomic markers from the same cells in emulsified aqueous droplets. Using this single-cell genomic approach and Nanopore sequencing, we successfully assigned class 1 integron gene cassette arrays containing mostly AMR genes to their hosts in coastal water samples that were affected by pollution. Our work presents the first application of epicPCR for targeting variable, multigene loci of interest. We also identified the Rhizobacter genus as novel hosts of class 1 integrons. These findings establish epicPCR as a powerful tool for linking taxa to class 1 integrons in environmental bacterial communities and offer the potential to direct mitigation efforts toward hotspots of class 1 integron-mediated dissemination of AMR.

hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer

Horizontal gene transfer (HGT) is well described in prokaryotes: it plays a crucial role in evolution, and has functional consequences in insects and plants. However, less is known about HGT in humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known human sequencing projects. Few of the existing tools for investigating HGT are highly automated. Thanks to the adoption of Nextflow for life sciences workflows, and to the standards and best practices curated by communities such as nf-core, fully automated, portable, and scalable pipelines can now be developed. Here we present nf-core/hgtseq to facilitate the analysis of HGT from sequencing data in different organisms. We showcase its performance by analysing six exome datasets from five mammals. Hgtseq can be run seamlessly in any computing environment and accepts data generated by existing exome and whole-genome sequencing projects; this will enable researchers to expand their analyses into this area. Fundamental questions are still open about the mechanisms and the extent or role of horizontal gene transfer: by releasing hgtseq we provide a standardised tool which will enable a systematic investigation of this phenomenon, thus paving the way for a better understanding of HGT.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Examining horizontal gene transfer in microbial communities

Bacteria acquire novel DNA through horizontal gene transfer (HGT), a process that enables an organism to rapidly adapt to changing environmental conditions, provides a competitive edge and potentially alters its relationship with its host. Although the HGT process is routinely exploited in laboratories, there is a surprising disconnect between what we know from laboratory experiments and what we know from natural environments, such as the human gut microbiome. Owing to a suite of newly available computational algorithms and experimental approaches, we have a broader understanding of the genes that are being transferred and are starting to understand the ecology of HGT in natural microbial communities. This Review focuses on these new technologies, the questions they can address and their limitations. As these methods are applied more broadly, we are beginning to recognize the full extent of HGT possible within a microbiome and the punctuated dynamics of HGT, specifically in response to external stimuli. Furthermore, we are better characterizing the complex selective pressures on mobile genetic elements and the mechanisms by which they interact with the bacterial host genome.

Diversity within species: interpreting strains in microbiomes

Studying within-species variation has traditionally been limited to culturable bacterial isolates and low-resolution microbial community fingerprinting. Metagenomic sequencing and technical advances have enabled culture-free, high-resolution strain and subspecies analyses at high throughput and in complex environments. This holds great scientific promise but has also led to an overwhelming number of methods and terms to describe infraspecific variation. This Review aims to clarify these advances by focusing on the diversity within bacterial and archaeal species in the context of microbiomics. We cover foundational microevolutionary concepts relevant to population genetics and summarize how within-species variation can be studied and stratified directly within microbial communities with a focus on metagenomics. Finally, we describe how common applications of within-species variation can be achieved using metagenomic data. We aim to guide the selection of appropriate terms and analytical approaches to facilitate researchers in benefiting from the increasing availability of large, high-resolution microbiome genetic sequencing data.

Experimental approaches to tracking mobile genetic elements in microbial communities

Horizontal gene transfer is an important mechanism of microbial evolution and is often driven by the movement of mobile genetic elements between cells. Due to the fact that microbes live within communities, various mechanisms of horizontal gene transfer and types of mobile elements can co-occur. However, the ways in which horizontal gene transfer impacts and is impacted by communities containing diverse mobile elements has been challenging to address. Thus, the field would benefit from incorporating community-level information and novel approaches alongside existing methods. Emerging technologies for tracking mobile elements and assigning them to host organisms provide promise for understanding the web of potential DNA transfers in diverse microbial communities more comprehensively. Compared to existing experimental approaches, chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts. We also briefly discuss how fermented food microbiomes, given their experimental tractability and moderate species complexity, make ideal models to which to apply the techniques discussed herein and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities.