The human "contaminome": bacterial, viral, and computational contamination in whole genome sequences from 1000 families

Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Central Nervous System Infections: Advances and Challenges

Central nervous system (CNS) infections carry a substantial burden of morbidity and mortality worldwide, and accurate and timely diagnosis is required to optimize management. Metagenomic next-generation sequencing (mNGS) has proven to be a valuable tool in detecting pathogens in patients with suspected CNS infection. By sequencing microbial nucleic acids present in a patient's cerebrospinal fluid, brain tissue, or samples collected outside of the CNS, such as plasma, mNGS can detect a wide range of pathogens, including rare, unexpected, and/or fastidious organisms. Furthermore, its target-agnostic approach allows for the identification of both known and novel pathogens. This is particularly useful in cases where conventional diagnostic methods fail to provide an answer. In addition, mNGS can detect multiple microorganisms simultaneously, which is crucial in cases of mixed infections without a clear predominant pathogen. Overall, clinical mNGS testing can help expedite the diagnostic process for CNS infections, guide appropriate management decisions, and ultimately improve clinical outcomes. However, there are key challenges surrounding its use that need to be considered to fully leverage its clinical impact. For example, only a few specialized laboratories offer clinical mNGS due to the complexity of both the laboratory methods and analysis pipelines. Clinicians interpreting mNGS results must be aware of both false negatives-as mNGS is a direct detection modality and requires a sufficient amount of microbial nucleic acid to be present in the sample tested-and false positives-as mNGS detects environmental microbes and their nucleic acids, despite best practices to minimize contamination. Additionally, current costs and turnaround times limit broader implementation of clinical mNGS. Finally, there is uncertainty regarding the best practices for clinical utilization of mNGS, and further work is needed to define the optimal patient population(s), syndrome(s), and time of testing to implement clinical mNGS.

Increased expression of human endogenous retrovirus K in endomyocardial biopsies from patients with cardiomyopathy - a transcriptomics meta-analysis

Most studied, investigating transcriptional changes in myocardial biopsies focus on human genes. However, the presence and potential consequence of persistent expression of viral genes within the myocardium is unclear. The aim of the study was to analyze viral gene expression in RNAseq data from endomyocardial biopsies. The NCBI Bioproject library was screened for published projects that included bulk RNA sequencing data from endomyocardial biopsies from both healthy and diseased patients with a sample size greater than 20. Diseased patients with hypertrophic, dilated, and ischemic cardiomyopathies were included. A total of 507 patients with 507 samples from 6 bioprojects were included and mapped to the human genome (hg38). Unmappable sequences were extracted and mapped to an artificial 'super-virus' genome comprising 12,182 curated viral reference genomes. Subsequently, the sequences were reiteratively permutated and mapped again to account for randomness. In total, sequences from 68 distinct viruses were found, all of which were potentially human pathogenic. No increase in cardiotropic viruses was found in patients with dilated cardiomyopathy. However, the expression levels of the particle forming human endogenous retrovirus K were significantly increased (q < 0.0003, ANOVA). Higher expression levels were associated with increased expression in mitochondrial pathways such as oxidative phosphorylation (p < 0.0001). In Conclusion, expression of human endogenous retrovirus K is significantly increased in patients with dilated cardiomyopathy, which in turn was associated with transcriptional alterations in major cellular pathways.

Intratumoral microbiome of adenoid cystic carcinomas and comparison with other head and neck cancers

Adenoid cystic carcinoma (ACC) is a rare, usually slow-growing yet aggressive head and neck malignancy. Despite its clinical significance, our understanding of the cellular evolution and microenvironment in ACC remains limited. We investigated the intratumoral microbiomes of 50 ACC tumor tissues and 33 adjacent normal tissues using 16S rRNA gene sequencing. This allowed us to characterize the bacterial communities within the ACC and explore potential associations between the bacterial community structure, patient clinical characteristics, and tumor molecular features obtained through RNA sequencing. The bacterial composition in the ACC was significantly different from that in adjacent normal salivary tissue, and the ACC exhibited diverse levels of species richness. We identified two main microbial subtypes within the ACC: oral-like and gut-like. Oral-like microbiomes, characterized by increased diversity and abundance of Neisseria, Leptotrichia, Actinomyces, Streptococcus, Rothia, and Veillonella (commonly found in healthy oral cavities), were associated with a less aggressive ACC-II molecular subtype and improved patient outcomes. Notably, we identified the same oral genera in oral cancer and head and neck squamous cell carcinomas. In both cancers, they were part of shared oral communities associated with a more diverse microbiome, less aggressive tumor phenotype, and better survival that reveal the genera as potential pancancer biomarkers for favorable microbiomes in ACC and other head and neck cancers. Conversely, gut-like intratumoral microbiomes, which feature low diversity and colonization by gut mucus layer-degrading species, such as Bacteroides, Akkermansia, Blautia, Bifidobacterium, and Enterococcus, were associated with poorer outcomes. Elevated levels of Bacteroides thetaiotaomicron were independently associated with significantly worse survival and positively correlated with tumor cell biosynthesis of glycan-based cell membrane components.

Developmental validation of the ForenSeq® Kintelligence kit, MiSeq FGx® sequencing system and ForenSeq Universal Analysis Software

Forensic Investigative Genetic Genealogy, a recent sub discipline of forensic genomics, leverages the high throughput and sensitivity of detection of next generation sequencing and established genetic and genealogical approaches to support the identification of human remains from missing persons investigations and investigative lead generation in violent crimes. To facilitate forensic DNA evidence analysis, the ForenSeq® Kintelligence multiplex, consisting of 10,230 SNPs, was developed. Design of the ForenSeq Kintelligence Kit, the MiSeq FGx® Sequencing System and the ForenSeq Universal Analysis Software is described. Developmental validation in accordance with SWGDAM guidelines and forensic quality assurance standards, using single source samples, is reported for the end-to-end workflow from library preparation to data interpretation. Performance metrics support the conclusion that more genetic information can be obtained from challenging samples compared to other commercially available forensic targeted DNA assays developed for capillary electrophoresis (CE) or other current next generation sequencing (NGS) kits due to the higher number of markers, the overall shorter amplicon sizes (97.8% <150 bp), and kit design. Data indicate that the multiplex is robust and fit for purpose for a wide range of quantity and quality samples. The ForenSeq Kintelligence Kit and the Universal Analysis Software allow transfer of the genetic component of forensic investigative genetic genealogy to the operational forensic laboratory.

Status check: next-generation sequencing for infectious-disease diagnostics

Next-generation sequencing (NGS) applications for the diagnostics of infectious diseases has demonstrated great potential with three distinct approaches: whole-genome sequencing (WGS), targeted NGS (tNGS), and metagenomic NGS (mNGS, also known as clinical metagenomics). These approaches provide several advantages over traditional microbiologic methods, though challenges still exist.

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

BACKGROUND

Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response.

CONTENT

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes.

SUMMARY

The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.

Association between gut microbiota and CpG island methylator phenotype in colorectal cancer

The intestinal microbiota is an important environmental factor implicated in CRC development. Intriguingly, modulation of DNA methylation by gut microbiota has been reported in preclinical models, although the relationship between tumor-infiltrating bacteria and CIMP status is currently unexplored. In this study, we investigated tumor-associated bacteria in 203 CRC tumor cases and validated the findings using The Cancer Genome Atlas datasets. We assessed the abundance of Bacteroides fragilis, Escherichia coli, Fusobacterium nucleatum, and Klebsiella pneumoniae through qPCR analysis and observed enrichment of all four bacterial species in CRC samples. Notably, except for E. coli, all exhibited significant enrichment in cases of CIMP. This enrichment was primarily driven by a subset of cases distinguished by high levels of these bacteria, which we labeled as "Superhigh". The bacterial Superhigh status showed a significant association with CIMP (odds ratio 3.1, p-value = 0.013) and with MLH1 methylation (odds ratio 4.2, p-value = 0.0025). In TCGA CRC cases (393 tumor and 45 adj. normal), bacterial taxa information was extracted from non-human whole exome sequencing reads, and the bacterial Superhigh status was similarly associated with CIMP (odds ratio 2.9, p < 0.001) and MLH1 methylation (odds ratio 3.5, p < 0.001). Finally, 16S ribosomal RNA gene sequencing revealed high enrichment of Bergeyella spp. C. concisus, and F. canifelinum in CIMP-Positive tumor cases. Our findings highlight that specific bacterial taxa may influence DNA methylation, particularly in CpG islands, and contribute to the development and progression of CIMP in colorectal cancer.

aKmerBroom: Ancient oral DNA decontamination using Bloom filters on k-mer sets

Dental calculus samples are modeled as a mixture of DNA coming from dental plaque and contaminants. Current computational decontamination methods such as Recentrifuge and DeconSeq require either a reference database or sequenced negative controls, and therefore have limited use cases. We present a reference-free decontamination tool tailored for the removal of contaminant DNA of ancient oral sample called aKmerBroom. Our tool builds a Bloom filter of known ancient and modern oral k-mers, then scans an input set of ancient metagenomic reads using multiple passes to iteratively retain reads likely to be of oral origin. On synthetic data, aKmerBroom achieves over 89.53 % sensitivity and 94.00 % specificity. On real datasets, aKmerBroom shows higher read retainment (+ 60 % on average) than other methods. We anticipate aKmerBroom will be a valuable tool for the processing of ancient oral samples as it will prevent contaminated datasets from being completely discarded in downstream analyses.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

Localizing unmapped sequences with families to validate the Telomere-to-Telomere assembly and identify new hotspots for genetic diversity

Although it is ubiquitous in genomics, the current human reference genome (GRCh38) is incomplete: It is missing large sections of heterochromatic sequence, and as a singular, linear reference genome, it does not represent the full spectrum of human genetic diversity. To characterize gaps in GRCh38 and human genetic diversity, we developed an algorithm for sequence location approximation using nuclear families (ASLAN) to identify the region of origin of reads that do not align to GRCh38. Using unmapped reads and variant calls from whole-genome sequences (WGSs), ASLAN uses a maximum likelihood model to identify the most likely region of the genome that a subsequence belongs to given the distribution of the subsequence in the unmapped reads and phasings of families. Validating ASLAN on synthetic data and on reads from the alternative haplotypes in the decoy genome, ASLAN localizes >90% of 100-bp sequences with >92% accuracy and ∼1 Mb of resolution. We then ran ASLAN on 100-mers from unmapped reads from WGS from more than 700 families, and compared ASLAN localizations to alignment of the 100-mers to the recently released T2T-CHM13 assembly. We found that many unmapped reads in GRCh38 originate from telomeres and centromeres that are gaps in GRCh38. ASLAN localizations are in high concordance with T2T-CHM13 alignments, except in the centromeres of the acrocentric chromosomes. Comparing ASLAN localizations and T2T-CHM13 alignments, we identified sequences missing from T2T-CHM13 or sequences with high divergence from their aligned region in T2T-CHM13, highlighting new hotspots for genetic diversity.

The complete sequence of a human Y chromosome

The human Y chromosome has been notoriously difficult to sequence and assemble because of its complex repeat structure that includes long palindromes, tandem repeats and segmental duplications1-3. As a result, more than half of the Y chromosome is missing from the GRCh38 reference sequence and it remains the last human chromosome to be finished4,5. Here, the Telomere-to-Telomere (T2T) consortium presents the complete 62,460,029-base-pair sequence of a human Y chromosome from the HG002 genome (T2T-Y) that corrects multiple errors in GRCh38-Y and adds over 30 million base pairs of sequence to the reference, showing the complete ampliconic structures of gene families TSPY, DAZ and RBMY; 41 additional protein-coding genes, mostly from the TSPY family; and an alternating pattern of human satellite 1 and 3 blocks in the heterochromatic Yq12 region. We have combined T2T-Y with a previous assembly of the CHM13 genome4 and mapped available population variation, clinical variants and functional genomics data to produce a complete and comprehensive reference sequence for all 24 human chromosomes.

Improving diagnosis of non-malarial fevers in Senegal: Borrelia and the contribution of tick-borne bacteria

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata from febrile patients and healthy controls in a low malaria burden area. Using 16S and unbiased sequencing, we detected viral, bacterial, or eukaryotic pathogens in 29% of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15% and 3.7% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model to distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs. These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

The role of microbiome-host interactions in the development of Alzheimer´s disease

Alzheimer`s disease (AD) is the most prevalent cause of dementia. It is often assumed that AD is caused by an aggregation of extracellular beta-amyloid and intracellular tau-protein, supported by a recent study showing reduced brain amyloid levels and reduced cognitive decline under treatment with a beta-amyloid-binding antibody. Confirmation of the importance of amyloid as a therapeutic target notwithstanding, the underlying causes of beta-amyloid aggregation in the human brain, however, remain to be elucidated. Multiple lines of evidence point towards an important role of infectious agents and/or inflammatory conditions in the etiology of AD. Various microorganisms have been detected in the cerebrospinal fluid and brains of AD-patients and have thus been hypothesized to be linked to the development of AD, including Porphyromonas gingivalis (PG) and Spirochaetes. Intriguingly, these microorganisms are also found in the oral cavity under normal physiological conditions, which is often affected by multiple pathologies like caries or tooth loss in AD patients. Oral cavity pathologies are mostly accompanied by a compositional shift in the community of oral microbiota, mainly affecting commensal microorganisms and referred to as 'dysbiosis'. Oral dysbiosis seems to be at least partly mediated by key pathogens such as PG, and it is associated with a pro-inflammatory state that promotes the destruction of connective tissue in the mouth, possibly enabling the translocation of pathogenic microbiota from the oral cavity to the nervous system. It has therefore been hypothesized that dysbiosis of the oral microbiome may contribute to the development of AD. In this review, we discuss the infectious hypothesis of AD in the light of the oral microbiome and microbiome-host interactions, which may contribute to or even cause the development of AD. We discuss technical challenges relating to the detection of microorganisms in relevant body fluids and approaches for avoiding false-positives, and introduce the antibacterial protein lactoferrin as a potential link between the dysbiotic microbiome and the host inflammatory reaction.

Inadvertent human genomic bycatch and intentional capture raise beneficial applications and ethical concerns with environmental DNA

The field of environmental DNA (eDNA) is advancing rapidly, yet human eDNA applications remain underutilized and underconsidered. Broader adoption of eDNA analysis will produce many well-recognized benefits for pathogen surveillance, biodiversity monitoring, endangered and invasive species detection, and population genetics. Here we show that deep-sequencing-based eDNA approaches capture genomic information from humans (Homo sapiens) just as readily as that from the intended target species. We term this phenomenon human genetic bycatch (HGB). Additionally, high-quality human eDNA could be intentionally recovered from environmental substrates (water, sand and air), holding promise for beneficial medical, forensic and environmental applications. However, this also raises ethical dilemmas, from consent, privacy and surveillance to data ownership, requiring further consideration and potentially novel regulation. We present evidence that human eDNA is readily detectable from 'wildlife' environmental samples as human genetic bycatch, demonstrate that identifiable human DNA can be intentionally recovered from human-focused environmental sampling and discuss the translational and ethical implications of such findings.

Recovery of metagenomic data from the Aedes aegypti microbiome using a reproducible snakemake pipeline: MINUUR

Background: Ongoing research of the mosquito microbiome aims to uncover novel strategies to reduce pathogen transmission. Sequencing costs, especially for metagenomics, are however still significant. A resource that is increasingly used to gain insights into host-associated microbiomes is the large amount of publicly available genomic data based on whole organisms like mosquitoes, which includes sequencing reads of the host-associated microbes and provides the opportunity to gain additional value from these initially host-focused sequencing projects. Methods: To analyse non-host reads from existing genomic data, we developed a snakemake workflow called MINUUR (Microbial INsights Using Unmapped Reads). Within MINUUR, reads derived from the host-associated microbiome were extracted and characterised using taxonomic classifications and metagenome assembly followed by binning and quality assessment. We applied this pipeline to five publicly available Aedes aegypti genomic datasets, consisting of 62 samples with a broad range of sequencing depths. Results: We demonstrate that MINUUR recovers previously identified phyla and genera and is able to extract bacterial metagenome assembled genomes (MAGs) associated to the microbiome. Of these MAGS, 42 are high-quality representatives with >90% completeness and <5% contamination. These MAGs improve the genomic representation of the mosquito microbiome and can be used to facilitate genomic investigation of key genes of interest. Furthermore, we show that samples with a high number of KRAKEN2 assigned reads produce more MAGs. Conclusions: Our metagenomics workflow, MINUUR, was applied to a range of Aedes aegypti genomic samples to characterise microbiome-associated reads. We confirm the presence of key mosquito-associated symbionts that have previously been identified in other studies and recovered high-quality bacterial MAGs. In addition, MINUUR and its associated documentation are freely available on GitHub and provide researchers with a convenient workflow to investigate microbiome data included in the sequencing data for any applicable host genome of interest.

The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges

Blood is conventionally thought to be sterile. However, emerging evidence on the blood microbiome has started to challenge this notion. Recent reports have revealed the presence of genetic materials of microbes or pathogens in the blood circulation, leading to the conceptualization of a blood microbiome that is vital for physical wellbeing. Dysbiosis of the blood microbial profile has been implicated in a wide range of health conditions. Our review aims to consolidate recent findings about the blood microbiome in human health and to highlight the existing controversies, prospects, and challenges around this topic. Current evidence does not seem to support the presence of a core healthy blood microbiome. Common microbial taxa have been identified in some diseases, for instance, Legionella and Devosia in kidney impairment, Bacteroides in cirrhosis, Escherichia/Shigella and Staphylococcus in inflammatory diseases, and Janthinobacterium in mood disorders. While the presence of culturable blood microbes remains debatable, their genetic materials in the blood could potentially be exploited to improve precision medicine for cancers, pregnancy-related complications, and asthma by augmenting patient stratification. Key controversies in blood microbiome research are the susceptibility of low-biomass samples to exogenous contamination and undetermined microbial viability from NGS-based microbial profiling, however, ongoing initiatives are attempting to mitigate these issues. We also envisage future blood microbiome research to adopt more robust and standardized approaches, to delve into the origins of these multibiome genetic materials and to focus on host–microbe interactions through the elaboration of causative and mechanistic relationships with the aid of more accurate and powerful analytical tools.

Transmission dynamics of human herpesvirus 6A, 6B and 7 from whole genome sequences of families

While hundreds of thousands of human whole genome sequences (WGS) have been collected in the effort to better understand genetic determinants of disease, these whole genome sequences have less frequently been used to study another major determinant of human health: the human virome. Using the unmapped reads from WGS of over 1000 families, we present insights into the human blood DNA virome, focusing particularly on human herpesvirus (HHV) 6A, 6B, and 7. In addition to extensively cataloguing the viruses detected in WGS of human whole blood and lymphoblastoid cell lines, we use the family structure of our dataset to show that household drives transmission of several viruses, and identify the Mendelian inheritance patterns characteristic of inherited chromsomally integrated human herpesvirus 6 (iciHHV-6). Consistent with prior studies, we find that 0.6% of our dataset's population has iciHHV, and we locate candidate integration sequences for these cases. We document genetic diversity within exogenous and integrated HHV species and within integration sites of HHV-6. Finally, in the first observation of its kind, we present evidence that suggests widespread de novo HHV-6B integration and HHV-7 integration and reactivation in lymphoblastoid cell lines. These findings show that the unmapped read space of WGS is a promising source of data for virology research.

De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee

Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.