Census-based rapid and accurate metagenome taxonomic profiling

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet

Background

Recently, studies have suggested a role for the gut microbiota in epilepsy. Gut microbial changes during ketogenic diet (KD) treatment of drug-resistant epilepsy have been described. Inflammation is associated with certain types of epilepsy and specific inflammation markers decrease during KD. The gut microbiota plays an important role in the regulation of the immune system and inflammation.

Methods

28 children with drug-resistant epilepsy treated with the ketogenic diet were followed in this observational study. Fecal and serum samples were collected at baseline and three months after dietary intervention.

Findings

We identified both gut microbial and inflammatory changes during treatment. KD had a general anti-inflammatory effect. Novel bioinformatics and machine learning approaches identified signatures of specific Bifidobacteria and TNF (tumor necrosis factor) associated with responders before starting KD. During KD, taxonomic and inflammatory profiles between responders and non-responders were more similar than at baseline.

Interpretation

Our results suggest that children with drug-resistant epilepsy are more likely to benefit from KD treatment when specific Bifidobacteria and TNF are elevated. We here present a novel signature of interaction of the gut microbiota and the immune system associated with anti-epileptic response to KD treatment. This signature could be used as a prognostic biomarker to identify potential responders to KD before starting treatment. Our findings may also contribute to the development of new anti-seizure therapies by targeting specific components of the gut microbiota.

Funding

This study was supported by the Swedish Brain Foundation, Margarethahemmet Society, Stiftelsen Sunnerdahls Handikappfond, Linnea & Josef Carlssons Foundation, and The McCormick Genomic & Proteomic Center.

Analyzing the Role of Gut Microbiota on the Onset of Autoimmune Diseases Using TNFΔARE Murine Model

Very little is known about disease transmission via the gut microbiome. We hypothesized that certain inflammatory features could be transmitted via the gut microbiome and tested this hypothesis using an animal model of inflammatory diseases. Twelve-week-old healthy C57 Bl/6 and Germ-Free (GF) female and male mice were fecal matter transplanted (FMT) under anaerobic conditions with TNFΔARE-/+ donors exhibiting spontaneous Rheumatoid Arthritis (RA) and Inflammatory Bowel Disease (IBD) or with conventional healthy mice control donors. The gut microbiome analysis was performed using 16S rRNA sequencing amplification and bioinformatics analysis with the HIVE bioinformatics platform. Histology, immunohistochemistry, ELISA Multiplex analysis, and flow cytometry were conducted to confirm the inflammatory transmission status. We observed RA and IBD features transmitted in the GF mice cohort, with gut tissue disruption, cartilage alteration, elevated inflammatory mediators in the tissues, activation of CD4/CD8+ T cells, and colonization and transmission of the gut microbiome similar to the donors' profile. We did not observe a change or transmission when conventional healthy mice were FMT with TNFΔARE-/+ donors, suggesting that a healthy microbiome might withstand an unhealthy transplant. These findings show the potential involvement of the gut microbiome in inflammatory diseases. We identified a cluster of bacteria playing a role in this mechanism.

Mice with dysfunctional TGF-β signaling develop altered intestinal microbiome and colorectal cancer resistant to 5FU

Emerging data show a rise in colorectal cancer (CRC) incidence in young men and women that is often chemoresistant. One potential risk factor is an alteration in the microbiome. Here, we investigated the role of TGF-β signaling on the intestinal microbiome and the efficacy of chemotherapy for CRC induced by azoxymethane and dextran sodium sulfate in mice. We used two genotypes of TGF-β-signaling-deficient mice (Smad4^+/- and Smad4^+/-Sptbn1^+/-), which developed CRC with similar phenotypes and had similar alterations in the intestinal microbiome. Using these mice, we evaluated the intestinal microbiome and determined the effect of dysfunctional TGF-β signaling on the response to the chemotherapeutic agent 5-Fluoro-uracil (5FU) after induction of CRC. Using shotgun metagenomic sequencing, we determined gut microbiota composition in mice with CRC and found reduced amounts of beneficial species of Bacteroides and Parabacteroides in the mutants compared to the wild-type (WT) mice. Furthermore, the mutant mice with CRC were resistant to 5FU. Whereas the abundances of E. boltae, B.dorei, Lachnoclostridium sp., and Mordavella sp. were significantly reduced in mice with CRC, these species only recovered to basal amounts after 5FU treatment in WT mice, suggesting that the alterations in the intestinal microbiome resulting from compromised TGF-β signaling impaired the response to 5FU. These findings could have implications for inhibiting the TGF-β pathway in the treatment of CRC or other cancers.

Bioinformatics tools developed to support BioCompute Objects

Developments in high-throughput sequencing (HTS) result in an exponential increase in the amount of data generated by sequencing experiments, an increase in the complexity of bioinformatics analysis reporting and an increase in the types of data generated. These increases in volume, diversity and complexity of the data generated and their analysis expose the necessity of a structured and standardized reporting template. BioCompute Objects (BCOs) provide the requisite support for communication of HTS data analysis that includes support for workflow, as well as data, curation, accessibility and reproducibility of communication. BCOs standardize how researchers report provenance and the established verification and validation protocols used in workflows while also being robust enough to convey content integration or curation in knowledge bases. BCOs that encapsulate tools, platforms, datasets and workflows are FAIR (findable, accessible, interoperable and reusable) compliant. Providing operational workflow and data information facilitates interoperability between platforms and incorporation of future dataset within an HTS analysis for use within industrial, academic and regulatory settings. Cloud-based platforms, including High-performance Integrated Virtual Environment (HIVE), Cancer Genomics Cloud (CGC) and Galaxy, support BCO generation for users. Given the 100K+ userbase between these platforms, BioCompute can be leveraged for workflow documentation. In this paper, we report the availability of platform-dependent and platform-independent BCO tools: HIVE BCO App, CGC BCO App, Galaxy BCO API Extension and BCO Portal. Community engagement was utilized to evaluate tool efficacy. We demonstrate that these tools further advance BCO creation from text editing approaches used in earlier releases of the standard. Moreover, we demonstrate that integrating BCO generation within existing analysis platforms greatly streamlines BCO creation while capturing granular workflow details. We also demonstrate that the BCO tools described in the paper provide an approach to solve the long-standing challenge of standardizing workflow descriptions that are both human and machine readable while accommodating manual and automated curation with evidence tagging. Database URL:  https://www.biocomputeobject.org/resources.

Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application

The scientific community currently defines the human microbiome as all the bacteria, viruses, fungi, archaea, and eukaryotes that occupy the human body. When considering the variable locations, composition, diversity, and abundance of our microbial symbionts, the sheer volume of microorganisms reaches hundreds of trillions. With the onset of next generation sequencing (NGS), also known as high-throughput sequencing (HTS) technologies, the barriers to studying the human microbiome lowered significantly, making in-depth microbiome research accessible. Certain locations on the human body, such as the gastrointestinal, oral, nasal, and skin microbiomes have been heavily studied through community-focused projects like the Human Microbiome Project (HMP). In particular, the gastrointestinal microbiome (GM) has received significant attention due to links to neurological, immunological, and metabolic diseases, as well as cancer. Though HTS technologies allow deeper exploration of the GM, data informing the functional characteristics of microbiota and resulting effects on human function or disease are still sparse. This void is compounded by microbiome variability observed among humans through factors like genetics, environment, diet, metabolic activity, and even exercise; making GM research inherently difficult to study. This chapter describes an interdisciplinary approach to GM research with the goal of mitigating the hindrances of translating findings into a clinical setting. By applying tools and knowledge from microbiology, metagenomics, bioinformatics, machine learning, predictive modeling, and clinical study data from children with treatment-resistant epilepsy, we describe a proof-of-concept approach to clinical translation and precision application of GM research.

Mutated CEACAMs Disrupt Transforming Growth Factor Beta Signaling and Alter the Intestinal Microbiome to Promote Colorectal Carcinogenesis

BACKGROUND & AIMS

We studied interactions among proteins of the carcinoembryonic antigen-related cell adhesion molecule (CEACAM) family, which interact with microbes, and transforming growth factor beta (TGFB) signaling pathway, which is often altered in colorectal cancer cells. We investigated mechanisms by which CEACAM proteins inhibit TGFB signaling and alter the intestinal microbiome to promote colorectal carcinogenesis.

METHODS

We collected data on DNA sequences, messenger RNA expression levels, and patient survival times from 456 colorectal adenocarcinoma cases, and a separate set of 594 samples of colorectal adenocarcinomas, in The Cancer Genome Atlas. We performed shotgun metagenomic sequencing analyses of feces from wild-type mice and mice with defects in TGFB signaling (Sptbn1+/- and Smad4+/-/Sptbn1+/-) to identify changes in microbiota composition before development of colon tumors. CEACAM protein and its mutants were overexpressed in SW480 and HCT116 colorectal cancer cell lines, which were analyzed by immunoblotting and proliferation and colony formation assays.

RESULTS

In colorectal adenocarcinomas, high expression levels of genes encoding CEACAM proteins, especially CEACAM5, were associated with reduced survival times of patients. There was an inverse correlation between expression of CEACAM genes and expression of TGFB pathway genes (TGFBR1, TGFBR2, and SMAD3). In colorectal adenocarcinomas, we also found an inverse correlation between expression of genes in the TGFB signaling pathway and genes that regulate stem cell features of cells. We found mutations encoding L640I and A643T in the B3 domain of human CEACAM5 in colorectal adenocarcinomas; structural studies indicated that these mutations would alter the interaction between CEACAM5 and TGFBR1. Overexpression of these mutants in SW480 and HCT116 colorectal cancer cell lines increased their anchorage-independent growth and inhibited TGFB signaling to a greater extent than overexpression of wild-type CEACAM5, indicating that they are gain-of-function mutations. Compared with feces from wild-type mice, feces from mice with defects in TGFB signaling had increased abundance of bacterial species that have been associated with the development of colon tumors, including Clostridium septicum, and decreased amounts of beneficial bacteria, such as Bacteroides vulgatus and Parabacteroides distasonis.

CONCLUSION

We found expression of CEACAMs and genes that regulate stem cell features of cells to be increased in colorectal adenocarcinomas and inversely correlated with expression of TGFB pathway genes. We found colorectal adenocarcinomas to express mutant forms of CEACAM5 that inhibit TGFB signaling and increase proliferation and colony formation. We propose that CEACAM proteins disrupt TGFB signaling, which alters the composition of the intestinal microbiome to promote colorectal carcinogenesis.

Baseline human gut microbiota profile in healthy people and standard reporting template

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual’s microbiome to the growing knowledgebase of “normal” microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI’s Short Read Archive.

Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.

Whole genome sequencing of live attenuated Leishmania donovani parasites reveals novel biomarkers of attenuation and enables product characterization

No licensed human vaccines are currently available against leishmaniasis. Several anti-leishmanial vaccines are currently undergoing testing, including genetically modified live-attenuated parasite vaccines. Studies with live attenuated Leishmania vaccines such as centrin deleted Leishmania donovani parasites (LdCen^−/−) showed protective immunity in animal models. Such studies typically examined the biomarkers of protective immunity however the biomarkers of attenuation in the parasite preparations have not received adequate attention. As several candidate vaccines enter clinical trials, a more complete product characterization to enable maintenance of product quality will help meet regulatory requirements. Towards this goal, we have determined the complete genome sequence of LdCen^−/− and its parent strain Ld1S-2D (LdWT) and characterized the LdCen^−/− vaccine strain using bioinformatics tools. Results showed that the LdCen^−/− parasites, in addition to loss of the centrin gene, have additional deletions ranging from 350 bp to 6900 bp in non-contiguous loci on several chromosomes, most commonly in untranslated regions. We have experimentally verified a subset of these adventitious deletions that had no impact on the attenuation of the LdCen^−/− parasites. Our results identified hitherto unknown features of attenuation of virulence that could be used as markers of product quality in production lots and highlight the importance of product characterization in parasitic vaccines.

Advancing Public Health Using Regulatory Science to Enhance Development and Regulation of Medical Products: Food and Drug Administration Research at the Center for Biologics Evaluation and Research

Center for Biologics Evaluation and Research enhances and supports regulatory decision-making and policy development. This work contributes to our regulatory mission, advances medical product development, and supports Food and Drug Administration’s regulatory response to public health crises. This review presents some examples of our diverse scientific work undertaken in recent years to support our regulatory and public health mission.

MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data

MOTIVATION

High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features.

RESULTS

We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from novel environmental niches.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download at https://github.com/ctlab/metafast The code is written in Java and is platform independent (tested on Linux and Windows x86_64).

CONTACT

ulyantsev@rain.ifmo.ru

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.

The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed.

Database URL: https://hive.biochemistry.gwu.edu

Purification of mumps virus particles of high viability

Mumps is a highly infectious viral disease of humans with a wide array of clinical manifestations ranging from painful swelling of the salivary glands to meningitis and encephalitis. Despite the clinical importance of mumps virus, most of what is known of its biological properties comes from studies using supernatants from virus infected cell cultures, which contain substantial levels of host cell derived debris and biologically active substances such as cytokines, transcription factors and secreted virus proteins. These contaminants complicate interpretation of studies of virus replication, virus-host interactions and in vivo virulence. Here we describe a protocol for concentration of the virus from cell culture supernatants followed by gradient purification, resulting in attainment of high titer live virus of high purity.

Development of a candidate reference material for adventitious virus detection in vaccine and biologicals manufacturing by deep sequencing

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Deep sequencing has potential as an improved adventitious virus screening method.

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15 laboratories sequenced a common reagent containing 25 target viruses.

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6 viruses were detected by all lab, the remainder were detected by 4–14 labs.

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A wide range of sample preparation and bioinformatics methods is currently used.

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A common reference material is essential to enable results to be compared.

Background

Unbiased deep sequencing offers the potential for improved adventitious virus screening in vaccines and biotherapeutics. Successful implementation of such assays will require appropriate control materials to confirm assay performance and sensitivity.

Methods

A common reference material containing 25 target viruses was produced and 16 laboratories were invited to process it using their preferred adventitious virus detection assay.

Results

Fifteen laboratories returned results, obtained using a wide range of wet-lab and informatics methods. Six of 25 target viruses were detected by all laboratories, with the remaining viruses detected by 4–14 laboratories. Six non-target viruses were detected by three or more laboratories.

Conclusion

The study demonstrated that a wide range of methods are currently used for adventitious virus detection screening in biological products by deep sequencing and that they can yield significantly different results. This underscores the need for common reference materials to ensure satisfactory assay performance and enable comparisons between laboratories.