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.

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.

Detecting horizontal gene transfer with metagenomics co-barcoding sequencing

Horizontal gene transfer (HGT) is the process through which genetic information is transferred between different genomes and that played a crucial role in bacterial evolution. HGT can enable bacteria to rapidly acquire antibiotic resistance and bacteria that have acquired resistance is spreading within the microbiome. Conventional methods of characterizing HGT patterns include short-read metagenomic sequencing (short-reads mNGS), long-read sequencing, and single-cell sequencing. These approaches present several limitations, such as short-read fragments, high amounts of input DNA, and sequencing costs, respectively. Here, we attempt to circumvent present limitations to detect HGT by developing a metagenomics co-barcode sequencing workflow (MECOS) and applying it to the human and mouse gut microbiomes. In addition to that, we have over 10-fold increased contig length compared to short-reads mNGS; we also obtained exceeding 30 million paired reads with co-barcode information. Applying the novel bioinformatic pipeline, we integrated this co-barcoding information and the context information from long reads, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Specifically, we detected approximately 3,000 HGT blocks in individual samples, encompassing ~6,000 genes and ~100 taxonomic groups, including loci conferring tetracycline resistance through ribosomal protection. MECOS provides a valuable tool for investigating HGT and advance our understanding on the evolution of natural microbial communities within hosts.IMPORTANCEIn this study, to better identify horizontal gene transfer (HGT) in individual samples, we introduce a new co-barcoding sequencing system called metagenomics co-barcoding sequencing (MECOS), which has three significant improvements: (i) long DNA fragment extraction, (ii) a special transposome insertion, (iii) hybridization of DNA to barcode beads, and (4) an integrated bioinformatic pipeline. Using our approach, we have over 10-fold increased contig length compared to short-reads mNGS, and observed over 50-fold HGT events after we corrected the potential wrong HGT events. Our results indicate the presence of approximately 3,000 HGT blocks, involving roughly 6,000 genes and 100 taxonomic groups in individual samples. Notably, these HGT events are predominantly enriched in genes that confer tetracycline resistance via ribosomal protection. MECOS is a useful tool for investigating HGT and the evolution of natural microbial communities within hosts, thereby advancing our understanding of microbial ecology and evolution.

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.

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.

Helicobacter pylori serology is associated with worse overall survival in patients with melanoma treated with immune checkpoint inhibitors

The microbiome is now regarded as one of the hallmarks of cancer and several strategies to modify the gut microbiota to improve immune checkpoint inhibitor (ICI) activity are being evaluated in clinical trials. Preliminary data regarding the upper gastro-intestinal microbiota indicated that Helicobacter pylori seropositivity was associated with a negative prognosis in patients amenable to ICI. In 97 patients with advanced melanoma treated with ICI, we assessed the impact of H. pylori on outcomes and microbiome composition. We performed H. pylori serology and profiled the fecal microbiome with metagenomics sequencing. Among the 97 patients, 22% were H. pylori positive (Pos). H. pylori Pos patients had a significantly shorter overall survival (p = .02) compared to H. pylori negative (Neg) patients. In addition, objective response rate and progression-free survival were decreased in H. pylori Pos patients. Metagenomics sequencing did not reveal any difference in diversity indexes between the H. pylori groups. At the taxa level, Eubacterium ventriosum, Mediterraneibacter (Ruminococcus) torques, and Dorea formicigenerans were increased in the H. pylori Pos group, while Alistipes finegoldii, Hungatella hathewayi and Blautia producta were over-represented in the H. pylori Neg group. In a second independent cohort of patients with NSCLC, diversity indexes were similar in both groups and Bacteroides xylanisolvens was increased in H. pylori Neg patients. Our results demonstrated that the negative impact of H. pylori on outcomes seem to be independent from the fecal microbiome composition. These findings warrant further validation and development of therapeutic strategies to eradicate H. pylori in immuno-oncology arena.

Current Methods for Recombination Detection in Bacteria

The role of genetic exchanges, i.e., homologous recombination (HR) and horizontal gene transfer (HGT), in bacteria cannot be overestimated for it is a pivotal mechanism leading to their evolution and adaptation, thus, tracking the signs of recombination and HGT events is importance both for fundamental and applied science. To date, dozens of bioinformatics tools for revealing recombination signals are available, however, their pros and cons as well as the spectra of solvable tasks have not yet been systematically reviewed. Moreover, there are two major groups of software. One aims to infer evidence of HR, while the other only deals with horizontal gene transfer (HGT). However, despite seemingly different goals, all the methods use similar algorithmic approaches, and the processes are interconnected in terms of genomic evolution influencing each other. In this review, we propose a classification of novel instruments for both HR and HGT detection based on the genomic consequences of recombination. In this context, we summarize available methodologies paying particular attention to the type of traceable events for which a certain program has been designed.

Expansion and persistence of antibiotic-specific resistance genes following antibiotic treatment

Oral antibiotics are commonly prescribed to non-hospitalized adults. However, antibiotic-induced changes in the human gut microbiome are often investigated in cohorts with preexisting health conditions and/or concomitant medication, leaving the effects of antibiotics not completely understood. We used a combination of omic approaches to comprehensively assess the effects of antibiotics on the gut microbiota and particularly the gut resistome of a small cohort of healthy adults. We observed that 3 to 19 species per individual proliferated during antibiotic treatment and Gram-negative species expanded significantly in relative abundance. While the overall relative abundance of antibiotic resistance gene homologs did not significantly change, antibiotic-specific gene homologs with presumed resistance toward the administered antibiotics were common in proliferating species and significantly increased in relative abundance. Virome sequencing and plasmid analysis showed an expansion of antibiotic-specific resistance gene homologs even 3 months after antibiotic administration, while paired-end read analysis suggested their dissemination among different species. These results suggest that antibiotic treatment can lead to a persistent expansion of antibiotic resistance genes in the human gut microbiota and provide further data in support of good antibiotic stewardship.Abbreviation: ARG - Antibiotic resistance gene homolog; AsRG - Antibiotic-specific resistance gene homolog; AZY - Azithromycin; CFX - Cefuroxime; CIP - Ciprofloxacin; DOX - Doxycycline; FDR - False discovery rate; GRiD - Growth rate index value; HGT - Horizontal gene transfer; NMDS - Non-metric multidimensional scaling; qPCR - Quantitative polymerase chain reaction; RPM - Reads per million mapped reads; TA - Transcriptional activity; TE - Transposable element; TPM - Transcripts per million mapped reads.

Biocide resistance in Acinetobacter baumannii: appraising the mechanisms

A global upsurge in antibiotic-resistant Acinetobacter baumannii requires supervised selection of biocides and disinfectants to avert nosocomial infections by reducing its spread. Moreover, inadequate and improper biocides have been reported as a contributing factor in antimicrobial resistance. Regardless of the manner of administration, a biocidal concentration that does not kill the target bacteria creates a stress response, propagating the resistance mechanisms. This is an essential aspect of the disinfection programme and the overall bio-contamination management plan. Knowing the mechanisms of action of biocides and resistance modalities may open new avenues to discover novel agents. This review describes the mechanisms of action of some biocides, resistance mechanisms, and approaches to study susceptibility/resistance to these agents.

Deep learning-based real-time detection of novel pathogens during sequencing

Novel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state of the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens but require relatively long input sequences and processed data from a finished sequencing run. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats.

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.

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

Understanding Horizontal Gene Transfer network in human gut microbiota

Background

Horizontal Gene Transfer (HGT) is the process of transferring genetic materials between species. Through sharing genetic materials, microorganisms in the human microbiota form a network. The network can provide insights into understanding the microbiota. Here, we constructed the HGT networks from the gut microbiota sequencing data and performed network analysis to characterize the HGT networks of gut microbiota.

Results

We constructed the HGT network and perform the network analysis to two typical gut microbiota datasets, a 283-sample dataset of Mother-to-Child and a 148-sample dataset of longitudinal inflammatory bowel disease (IBD) metagenome. The results indicated that (1) the HGT networks are scale-free. (2) The networks expand their complexities, sizes, and edge numbers, accompanying the early stage of lives; and microbiota established in children shared high similarity as their mother (p-value = 0.0138), supporting the transmission of microbiota from mother to child. (3) Groups harbor group-specific network edges, and network communities, which can potentially serve as biomarkers. For instances, IBD patient group harbors highly abundant communities of Proteobacteria (p-value = 0.0194) and Actinobacteria (p-value = 0.0316); children host highly abundant communities of Proteobacteria (p-value = 2.8785\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e^{-5}$$\end{document}e-5) and Actinobacteria (p-value = 0.0015), and the mothers host highly abundant communities of Firmicutes (p-value = 8.0091\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e^{-7}$$\end{document}e-7). IBD patient networks contain more HGT edges in pathogenic genus, including Mycobacterium, Sutterella, and Pseudomonas. Children’s networks contain more edges from Bifidobacterium and Escherichia.

Conclusion

Hence, we proposed the HGT network constructions from the gut microbiota sequencing data. The HGT networks capture the host state and the response of microbiota to the environmental and host changes, and they are essential to understand the human microbiota.

Comprehensive analysis of chromosomal mobile genetic elements in the gut microbiome reveals phylum-level niche-adaptive gene pools

Mobile genetic elements (MGEs) drive extensive horizontal transfer in the gut microbiome. This transfer could benefit human health by conferring new metabolic capabilities to commensal microbes, or it could threaten human health by spreading antibiotic resistance genes to pathogens. Despite their biological importance and medical relevance, MGEs from the gut microbiome have not been systematically characterized. Here, we present a comprehensive analysis of chromosomal MGEs in the gut microbiome using a method that enables the identification of the mobilizable unit of MGEs. We curated a database of 5,219 putative MGEs encompassing seven MGE classes called ImmeDB. We observed that many MGEs carry genes that could confer an adaptive advantage to the gut environment including gene families involved in antibiotic resistance, bile salt detoxification, mucus degradation, capsular polysaccharide biosynthesis, polysaccharide utilization, and sporulation. We find that antibiotic resistance genes are more likely to be spread by conjugation via integrative conjugative elements or integrative mobilizable elements than transduction via prophages. Horizontal transfer of MGEs is extensive within phyla but rare across phyla, supporting phylum level niche-adaptive gene pools in the gut microbiome. ImmeDB will be a valuable resource for future studies on the gut microbiome and MGE communities.

Molecular basis of bacterial disinfectant resistance

Antibiotic resistance could accelerate humanity towards an already fast-approaching post-antibiotic era, where disinfectants and effective biosecurity measures will be critically important to control microbial diseases. Disinfectant resistance has the potential to change our way of life from compromising food security to threatening our medical health systems. Resistance to antimicrobial agents occurs through either intrinsic or acquired resistance mechanisms. Acquired resistance occurs through the efficient transfer of mobile genetic elements, which can carry single, or multiple resistance determinants. Drug resistance genes may form part of integrons, transposons and insertions sequences which are capable of intracellular transfer onto plasmids or gene cassettes. Thereafter, resistance plasmids and gene cassettes mobilize by self-transmission between bacteria, increasing the prevalence of drug resistance determinants in a bacterial population. An accumulation of drug resistance genes through these mechanisms gives rise to multidrug resistant (MDR) bacteria. The study of this mobility is integral to safeguard current antibiotics, disinfectants and other antimicrobials. Literature evidence, however, indicates that knowledge regarding disinfectant resistance is severly limited. Genome engineering such as the CRISPR-Cas system, has identified disinfectant resistance genes, and reversed resistance altogether in certain prokaryotes. Demonstrating that these techniques could prove invaluable in the combat against disinfectant resistance by uncovering the secrets of MDR bacteria.

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

Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. The tool Daisy offers the first HGT detection approach based on read mapping that provides complementary evidence compared to existing methods. However, Daisy relies on the acceptor and donor organism involved in the HGT being known. We introduce DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor reference candidates of an HGT event based on sequencing reads. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods cannot be directly applied. DaisyGPS uses MicrobeGPS, a metagenomic profiling tool tailored towards estimating the genomic distance between organisms in the sample and the reference database. We enhance the underlying scoring system of MicrobeGPS to account for the sequence patterns in terms of mapping coverage of an acceptor and donor involved in an HGT event, and report a ranked list of reference candidates. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of an outbreak involving Methicillin-resistant Staphylococcus aureus data.

The End of a 60-year Riddle: Identification and Genomic Characterization of an Iridovirus, the Causative Agent of White Fat Cell Disease in Zooplankton

The planktonic freshwater crustacean of the genus Daphnia are a model system for biomedical research and, in particular, invertebrate-parasite interactions. Up until now, no virus has been characterized for this system. Here we report the discovery of an iridovirus as the causative agent of White Fat Cell Disease (WFCD) in Daphnia WFCD is a highly virulent disease of Daphnia that can easily be cultured under laboratory conditions. Although it has been studied from sites across Eurasia for more than 60 years, its causative agent had not been described, nor had an iridovirus been connected to WFCD before now. Here we find that an iridovirus-the Daphnia iridescent virus 1 (DIV-1)-is the causative agent of WFCD. DIV-1 has a genome sequence of about 288 kbp, with 39% G+C content and encodes 367 predicted open reading frames. DIV-1 clusters together with other invertebrate iridoviruses but has by far the largest genome among all sequenced iridoviruses. Comparative genomics reveal that DIV-1 has apparently recently lost a substantial number of unique genes but has also gained genes by horizontal gene transfer from its crustacean host. DIV-1 represents the first invertebrate iridovirus that encodes proteins to purportedly cap RNA, and it contains unique genes for a DnaJ-like protein, a membrane glycoprotein and protein of the immunoglobulin superfamily, which may mediate host-pathogen interactions and pathogenicity. Our findings end a 60-year search for the causative agent of WFCD and add to our knowledge of iridovirus genomics and invertebrate-virus interactions.

HgtSIM: a simulator for horizontal gene transfer (HGT) in microbial communities

The development and application of metagenomic approaches have provided an opportunity to study and define horizontal gene transfer (HGT) on the level of microbial communities. However, no current metagenomic data simulation tools offers the option to introduce defined HGT within a microbial community. Here, we present HgtSIM, a pipeline to simulate HGT event among microbial community members with user-defined mutation levels. It was developed for testing and benchmarking pipelines for recovering HGTs from complex microbial datasets. HgtSIM is implemented in Python3 and is freely available at: https://github.com/songweizhi/HgtSIM.