Genome Bins Research Articles

Evaluating the potential of assembler-binner combinations in recovering low-abundance and strain-resolved genomes from human metagenomes.

Human-associated microbial communities are a complex mixture of bacterial species and diverse strains prevalent at varying abundances. Due to the inherent limitations of metagenomic assemblers and genome binning tools in recovering low-abundance species (<1%) and strains, we lack comprehensive insight into these communities. Although many bioinformatics approaches are available for recovering metagenome-assembled genomes, their effectiveness in recovering low-abundance species and strains is often questioned. Moreover, each tool has its trade-offs, making selecting the right tools challenging. In this study, we investigated the combinatory effect of various assemblers and binning tools on the recovery of low-abundance species and strain-resolved genomes from real and simulated human metagenomes. We evaluated the performance of nine combinations of metagenome assemblers and genome binning tools for their potential to recover genomes of useable quality. Our results revealed that the metaSPAdes-MetaBAT2 combination is highly effective in recovering low-abundance species, while MEGAHIT-MetaBAT2 excels in recovering strain-resolved genomes. These findings highlight the significant variation in the performance of different combinations, even when aiming for the same objective. This suggests the profound impact of selecting the right assembler-binner combination for metagenome analyses. We believe this study will be a cornerstone for the scientific community, guiding the choice of tools by highlighting their complementary effects. Furthermore, it underscores the potential of existing tools to address the current challenges in the field improving the recovery of information from metagenomes.

Comprehensive lung microbial gene and genome catalogs assist the mechanism survey of Mesomycoplasma hyopneumoniae strains causing pig lung lesions

AbstractUnderstanding the community structure of the lower respiratory tract microbiome is crucial for elucidating its roles in respiratory tract diseases. However, there are few studies about this topic due to the difficulty in obtaining microbial samples from both healthy and disease individuals. Here, using 744 high‐depth metagenomic sequencing data of lower respiratory tract microbial samples from 675 well‐phenotyped pigs, we constructed a lung microbial gene catalog containing the largest scale of 10,031,593 nonredundant genes to date, 44.8% of which are novel. We obtained 356 metagenome‐assembled genomes (MAGs) which were further clustered into 256 species‐level genome bins with 41.8% being first reported in the current databases. Based on these data sets and through integrated analysis of the isolation of the related bacterial strains, in vitro infection, and RNA sequencing, we identified and confirmed that Mesomycoplasma hyopneumoniae (M. hyopneumoniae) MAG_47 and its adhesion‐related virulence factors (VFs) were associated with lung lesions in pigs. Differential expression levels of adhesion‐ and immunomodulation‐related VFs likely determined the heterogenicity of adhesion and pathogenicity among M. hyopneumoniae strains. M. hyopneumoniae adhesion activated several pathways, including nuclear factor kappa‐light‐chain‐enhancer of activated B, mitogen‐activated protein kinase, cell apoptosis, T helper 1 and T helper 2 cell differentiation, tumor necrosis factor signaling, interleukin‐6/janus kinase 2/signal transducer and activator of transcription signaling, and response to reactive oxygen species, leading to cilium loss, epithelial cell‒cell barrier disruption, and lung tissue lesions. Finally, we observed the similar phylogenetic compositions of the lung microbiome between humans with Mycoplasma pneumoniae and pigs infected with M. hyopneumoniae. The results provided important insights into pig lower respiratory tract microbiome and its relationship with lung health.

Large-scale metagenomic assembly provide new insights into the genetic evolution of gut microbiomes in plateau ungulates

Trillions of microbes colonize the ungulate gastrointestinal tract, playing a pivotal role in enhancing host nutrient utilization by breaking down cellulose and hemicellulose present in plants. Here, through large-scale metagenomic assembly, we established a catalog of 131,416 metagenome-assembled genomes (MAGs) and 11,175 high-quality species-level genome bins (SGBs) from 17 species of ungulates in China. Our study revealed the convergent evolution of high relative abundances of carbohydrate-active enzymes (CAZymes) in the gut microbiomes of plateau-dwelling ungulates. Notably, two significant factors contribute to this phenotype: structural variations in their gut microbiome genomes, which contain more CAZymes, and the presence of novel gut microbiota species, particularly those in the genus Cryptobacteroides, which are undergoing independent rapid evolution and speciation and have higher gene densities of CAZymes. Furthermore, these enrichment CAZymes in the gut microbiomes are highly enrichment in known metabolic pathways for short-chain fatty acid (SCFA) production. Our findings not only provide a valuable genomic resource for understanding the gut microbiomes of ungulates but also offer fresh insights into the interaction between gut microbiomes and their hosts, as well as the co-adaptation of hosts and their gut microbiomes to their environments.

Characterization of gut microbiota dynamics in an Alzheimer’s disease mouse model through clade-specific marker-based analysis of shotgun metagenomic data

Alzheimer’s disease (AD) is a complex neurodegenerative disorder significantly impairing cognitive faculties, memory, and physical abilities. To characterize the modulation of the gut microbiota in an in vivo AD model, we performed shotgun metagenomics sequencing on 3xTgAD mice at key time points (i.e., 2, 6, and 12 months) of AD progression. Fecal samples from both 3xTgAD and wild-type mice were collected, DNA extracted, and sequenced. Quantitative taxon abundance assessment using MetaPhlAn 4 ensured precise microbial community representation. The analysis focused on species-level genome bins (SGBs) including both known and unknown SGBs (kSGBs and uSGBs, respectively) and also comprised higher taxonomic categories such as family-level genome bins (FGBs), class-level genome bins (CGBs), and order-level genome bins (OGBs). Our bioinformatic results pinpointed the presence of extensive gut microbial diversity in AD mice and showed that the largest proportion of AD- and aging-associated microbiome changes in 3xTgAD mice concern SGBs that belong to the Bacteroidota and Firmicutes phyla, along with a large set of uncharacterized SGBs. Our findings emphasize the need for further advanced bioinformatic studies for accurate classification and functional analysis of these elusive microbial species in relation to their potential bridging role in the gut-brain axis and AD pathogenesis.

V- and VL-Scores Uncover Viral Signatures and Origins of Protein Families.

Viruses are key drivers of microbial diversity, nutrient cycling, and co-evolution in ecosystems, yet their study is hindered due to challenges in culturing. Traditional gene-centric methods, which focus on a few hallmark genes like for capsids, miss much of the viral genome, leaving key viral proteins and functions undiscovered. Here, we introduce two powerful annotation-free metrics, V-score and VL-score, designed to quantify the "virus-likeness" of protein families and genomes and create an open-access searchable database, 'V-Score-Search'. By applying V- and VL-scores to public databases (KEGG, Pfam, and eggNOG), we link 38-77% of protein families with viruses, a 9-16x increase over current estimates. These metrics outperform existing approaches, enabling precise detection of viral genomes, prophages, and host-derived auxiliary viral genes (AVGs) from fragmented sequences, and significantly improving genome binning. Remarkably, we identify up to 17x more AVGs, dominated by non-metabolic proteins of unknown function. This innovation unlocks new insights into virus signatures and host interactions, with wide-ranging implications from genomics to biotechnology.

Deciphering defense system modulating bacteria-mobile genetic elements symbiosis in microbial aggregates under elevated hydraulic stress

Bacterial defense systems are under strong evolutionary pressures to defend against mobile genetic elements (MGEs), yet their distribution in microbial aggregates in engineered systems remains largely unexplored. Herein, we investigated the bacterial defensome and MGEs within activated sludge flocs (AS) and membrane-attached biofilm (MF) in a full-scale membrane bioreactor. Similar distribution pattern of bacterial defense systems (63 types) was observed in prokaryotic genome in AS and MF, including RM system (∼40 %), Cas system (∼18 %) and TA-Abi system (∼28 %), exhibiting a dependency on the genome size and bacterial taxonomy in microbial aggregates under elevated hydraulic stress (MF). In contrast to plasmid and provirus, which carried defense systems (22 types) similar to their associated hosts, virome (61 %) carried novel defense systems (40 types) absent in their associated hosts. With 54 % of which involved in MGEs geneflow network, 69 % of high quality bacterial genome bins were associated with horizontal gene transfer (HGT), facilitating the exchange of mobile core functional genes. This potentially conferred competitive advantages to hosts through habitat-specific payload genes related to biotic defense, antibiotic resistance, and nitrogen metabolism. The longer growth cycle and varied defense gene density suggested the potential defense redundancy and trade-off of metabolic expense and immunity in bacterial host-MGE symbionts. Furthermore, enhanced cooperative network modules of cross-feeding and defense were observed in the MF, potentially helped the symbiotic microbial communities in coping with hostile conditions under elevated hydraulic stress. These findings shed light on the dynamics of bacterial defense systems in host-MGE coevolution and provide new perspectives of microbial aggregates manipulation for ecological and engineering application.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

Exploring microbial diversity and biosynthetic potential in zoo and wildlife animal microbiomes

Understanding human, animal, and environmental microbiota is essential for advancing global health and combating antimicrobial resistance (AMR). We investigate the oral and gut microbiota of 48 animal species in captivity, comparing them to those of wildlife animals. Specifically, we characterize the microbiota composition, metabolic pathways, AMR genes, and biosynthetic gene clusters (BGCs) encoding the production of specialized metabolites. Our results reveal a high diversity of microbiota, with 585 novel species-level genome bins (SGBs) and 484 complete BGCs identified. Functional gene analysis of microbiomes shows diet-dependent variations. Furthermore, by comparing our findings to wildlife-derived microbiomes, we observe the impact of captivity on the animal microbiome, including examples of converging microbiome compositions. Importantly, our study identifies AMR genes against commonly used veterinary antibiotics, as well as resistance to vancomycin, a critical antibiotic in human medicine. These findings underscore the importance of the ‘One Health’ approach and the potential for zoonotic transmission of pathogenic bacteria and AMR. Overall, our study contributes to a better understanding of the complexity of the animal microbiome and highlights its BGC diversity relevant to the discovery of novel antimicrobial compounds.

Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics

The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Multi-omics illuminates the functional significance of previously unknown species in a full-scale landfill leachate treatment plant

Landfill leachate treatment plants (LLTPs) harbor a vast reservoir of uncultured microbes, yet limited studies have systematically unraveled their functional potentials within LLTPs. Combining 36 metagenomic and 18 metatranscriptomic datasets from a full-scale LLTP, we unveiled a double-edged sword role of unknown species in leachate biotreatment and environmental implication. We identified 655 species-level genome bins (SGBs) spanning 47 bacterial and 3 archaeal phyla, with 75.9 % unassigned to any known species. Over 90 % of up-regulated functional genes in biotreatment units, compared to the leachate influent, were carried by unknown species and actively participated in carbon, nitrogen, and sulfur cycles. Approximately 79 % of the 37,366 carbohydrate active enzymes (CAZymes), with ∼90 % novelty and high expression, were encoded by unknown species, exhibiting great potential in biodegrading carbohydrate compounds linked to human meat-rich diets. Unknown species offered a valuable genetic resource of thousands of versatile, abundant, and actively expressed metabolic gene clusters (MGCs) and biosynthetic gene clusters (BGCs) for enhancing leachate treatment. However, unknown species may contribute to the emission of hazardous N2O/H2S and represented significant reservoirs for antibiotic-resistant pathogens that posed environmental safety risks. This study highlighted the significance of considering both positive and adverse effects of LLTP microbes to optimize LLTP performance.

Copper and cadmium co-contamination increases the risk of nitrogen loss in red paddy soils

The microbiome plays a crucial role in soil nitrogen (N) cycling and in regulating its bioavailability. However, the functional and genomic information of microorganisms encoding N cycling in response to copper (Cu) and cadmium (Cd) contamination is largely unknown. Here, metagenomics and genome binning were used to examine microbial N cycling in Cu and Cd co-contaminated red paddy soils collected from a polluted watershed in southern China. The results showed that soil Cu and Cd concentrations induced more drastic changes in microbial N functional and taxonomic traits than soil general properties. Soil Cu and Cd co-contamination stimulated microbial nitrification, denitrification, and dissimilatory nitrate reduction processes mainly by increasing the abundance of Nitrospira (phylum Nitrospirota), while inhibiting N fixation by decreasing the abundance of Desulfobacca. These contrasting changes in microbial N cycling processes suggested a potential risk of N loss in paddy soils. A high-quality genome was identified as belonging to Nitrospirota with the highest abundance in heavily contaminated soils. This novel Nitrospirota strain possessed metabolic capacities for N transformation and metal resistance. These findings elucidate the genetic mechanisms underlying soil N bioavailability under long-term Cu and Cd contamination, which is essential for maintaining agricultural productivity and controlling heavy metal pollution.

Construction of high-quality genomes and gene catalogue for culturable microbes of sugarcane (Saccharum spp.)

Microbes living inside or around sugarcane (Saccharum spp.) are crucial for their resistance to abiotic and biotic stress, growth, and development. Sequences of microbial genomes and genes are helpful to understand the function of these microbes. However, there is currently a lack of such knowledge in sugarcane. Here, we combined Nanopore and Illumina sequencing technologies to successfully construct the first high-quality metagenome-assembled genomes (MAGs) and gene catalogues of sugarcane culturable microbes (GCSCMs), which contained 175 species-level genome bins (SGBs), and 7,771,501 non-redundant genes. The SGBs included 79 novel culturable bacteria genomes, and 3 bacterial genomes with nitrogen-fixing gene clusters. Four single scaffold near-complete circular MAGs (cMAGs) with 0% contamination were obtained from Nanopore sequencing data. In conclusion, we have filled a research gap in the genomes and gene catalogues of culturable microbes of sugarcane, providing a vital data resource for further understanding the genetic basis and functions of these microbes. In addition, our methodology and results can provide guidance and reference for other plant microbial genome and gene catalogue studies.

A Diverging Species within the Stewartia gemmata (Theaceae) Complex Revealed by RAD-Seq Data.

Informed species delimitation is crucial in diverse biological fields; however, it can be problematic for species complexes. Showing a peripatric distribution pattern, Stewartia gemmata and S. acutisepala (the S. gemmata complex) provide us with an opportunity to study species boundaries among taxa undergoing nascent speciation. Here, we generated genomic data from representative individuals across the natural distribution ranges of the S. gemmata complex using restriction site-associated DNA sequencing (RAD-seq). Based on the DNA sequence of assembled loci containing 41,436 single-nucleotide polymorphisms (SNPs) and invariant sites, the phylogenetic analysis suggested strong monophyly of both the S. gemmata complex and S. acutisepala, and the latter was nested within the former. Among S. gemmata individuals, the one sampled from Mt. Tianmu (Zhejiang) showed the closest evolutionary affinity with S. acutisepala (which is endemic to southern Zhejiang). Estimated from 2996 high-quality SNPs, the genetic divergence between S. gemmata and S. acutisepala was relatively low (an Fst of 0.073 on a per-site basis). Nevertheless, we observed a proportion of genomic regions showing relatively high genetic differentiation on a windowed basis. Up to 1037 genomic bins showed an Fst value greater than 0.25, accounting for 8.31% of the total. After SNPs subject to linkage disequilibrium were pruned, the principal component analysis (PCA) showed that S. acutisepala diverged from S. gemmata along the first and the second PCs to some extent. By applying phylogenomic analysis, the present study determines that S. acutisepala is a variety of S. gemmata and is diverging from S. gemmata, providing empirical insights into the nascent speciation within a species complex.

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes.

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CATpack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

Dispersion of antimicrobial resistant bacteria in pig farms and in the surrounding environment

BackgroundAntimicrobial resistance has been identified as a major threat to global health. The pig food chain is considered an important source of antimicrobial resistance genes (ARGs). However, there is still a lack of knowledge on the dispersion of ARGs in pig production system, including the external environment.ResultsIn the present study, we longitudinally followed one swine farm located in Italy from the weaning phase to the slaughterhouse to comprehensively assess the diversity of ARGs, their diffusion, and the bacteria associated with them. We obtained shotgun metagenomic sequences from 294 samples, including pig feces, farm environment, soil around the farm, wastewater, and slaughterhouse environment. We identified a total of 530 species-level genome bins (SGBs), which allowed us to assess the dispersion of microorganisms and their associated ARGs in the farm system. We identified 309 SGBs being shared between the animals gut microbiome, the internal and external farm environments. Specifically, these SGBs were characterized by a diverse and complex resistome, with ARGs active against 18 different classes of antibiotic compounds, well matching antibiotic use in the pig food chain in Europe.ConclusionsCollectively, our results highlight the urgency to implement more effective countermeasures to limit the dispersion of ARGs in the pig food systems and the relevance of metagenomics-based approaches to monitor the spread of ARGs for the safety of the farm working environment and the surrounding ecosystems.

Abstract 965: Urine cell-free DNA multi-omics combining copy number analysis with fragmentomics to detect minimal residual disease in bladder cancer patients

Abstract Background: Standard-of-care for patients with localized muscle-invasive bladder cancer (BC) is neoadjuvant chemotherapy followed by radical cystectomy (RC). Bladder-sparing treatments are limited by our current inability to sensitively detect minimal residual disease (MRD). Here, we analyzed urine, a noninvasive and proximal biofluid, from patients with bladder cancer and utilized an approach that incorporates copy number-derived tumor fraction as well as fragmentomics to sensitively detect MRD and predict pathologic complete response (pCR). Methods: We acquired urine preoperatively from 51 BC patients (80% muscle-invasive) on the day of standard-of-care RC, and after neoadjuvant chemotherapy in 57% of patients. We performed whole genome sequencing (WGS) of urine cfDNA (median depth: 10X) from all 51 BC patients and 13 healthy adults. Tumor fraction level based on genome-wide copy number alterations was estimated using ichorCNA. We inferred fragment size and genomic coverage of urine cfDNA using Picard Tools. Then, we computed the short-to-long (S/L) fragment ratio for genomic bins in each sample. The S/L ratio per bin was derived by partitioning the genome into 100 kb bins and evaluating the ratio of GC-corrected short fragments (50-150 bp) to long fragments (151-250 bp) within each bin. A normalized genome-wide S/L score was calculated by averaging across all bins. Results: In our cohort of 51 BC patients, 45% of patients achieved pCR (n = 23) while 55% had residual disease detected in their surgical sample (no pCR; n = 28). Patients with no pCR had significantly higher copy number-derived tumor fractions in urine compared to patients with pCR (median 10.6% vs 1.9%, p = 0.0002) and healthy adults (n = 12) (median 10.6% vs 1.8%, p = 0.003). Tumor fraction achieved an AUC of 0.89 for classification of No pCR from pCR with sensitivity of 82% and specificity of 92%. Presumed cancer-free patients (healthy and pCR) had significantly longer urine cfDNA median fragment sizes than patients with no pCR (median 177 bp vs 156 bp, p = 0.002). Presumed cancer-free patients (healthy and pCR) also had significantly lower S/L ratio scores than no pCR patients (mean .98 vs 1.5, p = 0.007). The top 100 bins with maximally differential S/L ratio between these two patient groups were enriched for genes involved in KEGG pathways implicated in cancer and immune signaling (autophagy, transcriptional misregulation in cancer, Th1 differentiation) indicating biological relevance. Conclusion: Copy number alteration-derived tumor fraction inference combined with fragmentomic analysis of urine cell-free DNA can enable sensitive detection of MRD to predict pCR and reveal potentially onco-relevant pathways. Citation Format: Arpit Panda, Irfan Alahi, Pradeep S. Chauhan, John Sheng, Nathan Colon, Cayce Nawaf, Alexander Shiang, Peter K. Harris, Faridi Qaium, Eric H. Kim, Melissa A. Reimers, Woodson Smelser, Zachary L. Smith, Aadel A. Chaudhuri. Urine cell-free DNA multi-omics combining copy number analysis with fragmentomics to detect minimal residual disease in bladder cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 965.

Abstract 2860: High-resolution mapping of oncogenic structural changes in osteosarcoma

Abstract We have leveraged long-read and spatial sequencing modalities to identify cancer-specific oncogenic alterations for treatment of osteosarcomas. Osteosarcoma is archetypal of cancers driven by aneuploidy and structural rearrangement rather than point mutations. The functional consequences of many SVs stem from their effect on multidimensional genome organization. SVs can enable enhancer hijacking, alter boundaries of topologically associated domains (TADs), and move gene loci to different regulatory compartments to affect gene expression. SVs can also lead to the formation of extrachromosomal DNA amplicons (ecDNA), which can be megabases in size and can incorporate both genes and regulatory elements to result in substantial increases in gene transcription and intratumor heterogeneity. Complex SVs may have profound implications for prognosis and treatment in OS and other cancers yet they remain poorly understood. By incorporating long-read optical genome mapping (OGM) and chromatin conformation capture sequencing (HiC), we studied SVs in osteosarcoma with the goal of elucidating their contribution to cancer development. We used a unique panel of 11 OS patient-derived xenograft (PDX) cell lines which have been previously characterized regarding response to targeted therapies as well as genomically via whole genome (WGS), RNA (RNA-seq), and chromatin accessibility (ATAC-seq) sequencing. To understand the complex genomic reorganization that occurs in osteosarcoma, we integrated OGM and WGS data for all 11 cell lines to describe detailed genome-wide SVs including ecDNA formation. PDX lines assayed using OGM exhibited between 106 and 704 translocations each (versus zero detected in germline controls) and we identified dozens of potential ecDNA amplicons, including one amplicon which may explain exceptionally high expression of YAP1 and BIRC3 transcripts. Further, we used HiC to describe the topological environment resulting from these SVs and observed potential enhancer hijacking events affecting dozens of known oncogenes including MYC and CDK4. HiC interaction maps among six cell lines reveal genomic compartments and TAD boundaries in detail, with 4,167 unique TAD boundaries identified among six cell lines using 50kb genomic bins. Despite the observed complexity of these genomes, we note that 71% of TAD boundaries are consistent in at least four of the six HiC assayed cell lines, and 44% are present in all six lines. Changing TAD boundaries are associated with numerous SVs and/or copy number discrepancies, and are associated with genomic regions that contain genes with dysregulated RNA transcript levels. Overall, OGM and HiC modalities add considerably to our ability to detect oncogenic SVs and the genes they affect. Citation Format: Andrew Scott Clugston, Stan Leung, Eunice Fuentes, Leanne Sayles, Megan Ostrowski, Coco Wu, Christina Curtis, Zhicheng Ma, Yanding Zhao, Georgi K. Marinov, Vijay Ramani, Marcus Breese, Alejandro Sweet-Cordero. High-resolution mapping of oncogenic structural changes in osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2860.

Robust chromatin state annotation.

With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.

Donor-recipient intermicrobial interactions impact transfer of subspecies and fecal microbiota transplantation outcome

Studies on fecal microbiota transplantation (FMT) have reported inconsistent connections between clinical outcomes and donor strain engraftment. Analyses of subspecies-level crosstalk and its influences on lineage transfer in metagenomic FMT datasets have proved challenging, as single-nucleotide polymorphisms (SNPs) are generally not linked and are often absent. Here, we utilized species genome bin (SGB), which employs co-abundance binning, to investigate subspecies-level microbiome dynamics in patients with autism spectrum disorder (ASD) who had gastrointestinal comorbidities and underwent encapsulated FMT (Chinese Clinical Trial: 2100043906). We found that interactions between donor and recipient microbes, which were overwhelmingly phylogenetically divergent, were important for subspecies transfer and positive clinical outcomes. Additionally, a donor-recipient SGB match was indicative of a high likelihood of strain transfer. Importantly, these ecodynamics were shared across FMT datasets encompassing multiple diseases. Collectively, these findings provide detailed insight into specific microbial interactions and dynamics that determine FMT success.