Genomic Context Research Articles

Abstract B054: Using FRAGILE score to capture context-dependent cfDNA fragmentation patterns for inference on their tissues-of-origin

Abstract Cell-free nucleic acids circulating in blood and other body fluids are usually highly fragmented and derived from multiple different tissue types, which makes it challenging to obtain information about their cellular and contextual origin. Analyzing low coverage cell-free DNA whole genome sequencing data from 243 healthy individuals, we showed that cfDNA fragmentation and degradation patterns vary depending on their underlying genomic and epigenomic contexts. We developed a composite index called FRAGILE Score (FRagment of Atypical GC and Irregular Length Enrichment Score) to capture the structure, sequence, and fragmentation-related properties of cell-free, and showed that the score carried information about the epigenomic contexts of the cfDNA fragments. A deconvolution approach based on the score predicts the cell type contribution in serum cfDNA from healthy individuals and cancer patients. We collected blood and urine of a cohort of 30 bladder urothelial carcinoma patients, and found that cfDNA derived from blood and urine had very different fragmentation characteristics. An analysis of cfDNA from paired urine and blood samples from this showcased utility of the score in predicting clinical and pathological characteristics non-invasively for clinical management of the patients. Citation Format: Subhajyoti De. Using FRAGILE score to capture context-dependent cfDNA fragmentation patterns for inference on their tissues-of-origin [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B054.

The Limits of Our Explanation: A Case Study in Myxococcus xanthus Cooperation

AbstractIn this article, I demonstrate two ways in which our major theories of the evolution of cooperation may fail to capture particular social phenomena. The first shortcoming of our current major theories stems from the possibility of mischaracterizing the cooperative problem in game theory. The second shortcoming of our current major theories is the insensitivity of these explanatory models to ecological and genomic context. As a case study to illustrate these points, I will use the cooperative interaction of a species of myxobacteria called Myxococcus xanthus. M. xanthus cooperate in many areas of their life cycle—in quorum sensing, social motility, fruiting body formation, and predation. I focus in particular on predation as we have not yet discovered an adequate explanation of how they sustain cooperative predation in the face of developmental cheats. In explaining why we have not, I draw generalizable conclusions that shed light on our use of simplified models to explain real-world behaviors in a variety of organisms.

The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities

Abstract Determining the taxonomic composition (taxonomic profiling) is a fundamental task in studying environmental and host-associated microbial communities. However, genome-resolved microbial diversity on Earth remains undersampled, and accessing the genomic context of taxa detected during taxonomic profiling remains a challenging task. Here, we present the mOTUs online database (mOTUs-db), which is consistent with and interfaces with the mOTUs taxonomic profiling tool. It comprises 2.83 million metagenome-assembled genomes (MAGs) and 919 090 single-cell and isolate genomes from 124 295 species-level taxonomic units. In addition to being one of the largest prokaryotic genome resources to date, all MAGs in the mOTUs-db were reconstructed de novo in 117 902 individual samples by abundance correlation of scaffolds across multiple samples for improved quality metrics. The database complements the Genome Taxonomy Database, with over 50% of its species-level taxonomic groups being unique. It also offers interactive querying, enabling users to explore and download genomes at various taxonomic levels. The mOTUs-db is accessible at https://motus-db.org.

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes.

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

Unleashing the Power of Induced Pluripotent stem Cells in in vitro Modelling of Lesch-Nyhan Disease.

Lesch-Nyhan disease (LND) is a monogenic rare neurodevelopmental disorder caused by a deficiency in hypoxanthine-guanine phosphoribosyltransferase (HPRT), the key enzyme of the purines salvage pathway. Beyond its well-documented metabolic consequences, HPRT deficiency leads to a distinctive neurobehavioral syndrome characterized by motor disabilities, cognitive deficits, and self-injurious behavior. Although various cell and animal models have been developed to investigate LND pathology, none have adequately elucidated the underlying mechanisms of its neurological alterations. Recent advances in human pluripotent stem cell research and in vitro differentiation techniques have ushered in a new era in rare neurodevelopmental disorders research. Pluripotent stem cells, with their ability to propagate indefinitely and to differentiate into virtually any cell type, offer a valuable alternative for modeling rare diseases, allowing for the detection of pathological events from the earliest stages of neuronal network development. Furthermore, the generation of patient-derived induced pluripotent stem cells using reprogramming technology provides an opportunity to develop a disease-relevant model within the context of a patient-specific genome. In this review, we examine current stem cell-based models of LND and assess their potential as optimal models for exploring key pathological molecular events during neurogenesis and for the discovering novel treatment options. We also address the limitations, challenges, and future prospects for improving the use of iPSCs in LND research.

Development of compact transcriptional effectors using high-throughput measurements in diverse contexts.

Transcriptional effectors are protein domains known to activate or repress gene expression; however, a systematic understanding of which effector domains regulate transcription across genomic, cell type and DNA-binding domain (DBD) contexts is lacking. Here we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator called NFZ, by combining NCOA3, FOXO3 and ZNF473 domains, which enables efficient CRISPRa with better viral delivery and inducible control of chimeric antigen receptor T cells.

Development and evolution of Drosophila chromatin landscape in a 3D genome context

Little is known about how the epigenomic states change during development and evolution in a 3D genome context. Here we use Drosophila pseudoobscura with complex turnover of sex chromosomes as a model to address this, by collecting massive epigenomic and Hi-C data from five developmental stages and three adult tissues. We reveal that over 60% of the genes and transposable elements (TE) exhibit at least one developmental transition of chromatin state. Transitions on specific but not housekeeping enhancers are associated with specific chromatin loops and topologically associated domain borders (TABs). While evolutionarily young TEs are generally silenced, old TEs more often have been domesticated as interacting TABs or specific enhancers. But on the recently evolved X chromosome, young TEs are instead often active and recruited as TABs, due to acquisition of dosage compensation. Overall we characterize how Drosophila epigenomic landscapes change during development and in response to chromosome evolution, and highlight the important roles of TEs in genome organization and regulation.

Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs

Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools—Bandage, SPAligner, and GraphAligner—in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage’s potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread.

Assessing antimicrobial resistance in pasture-based dairy farms: a 15-month surveillance study in New Zealand.

Antimicrobial resistance is a global public and animal health concern. Antimicrobial resistance genes (ARGs) have been detected in dairy farm environments globally; however, few longitudinal studies have utilized shotgun metagenomics for ARG surveillance in pasture-based systems. This 15-month study aimed to undertake a baseline survey using shotgun metagenomics to assess the relative abundance and diversity of ARGs in two pasture-based dairy farm environments in New Zealand with different management practices. There was no statistically significant difference in overall ARG relative abundance between the two dairy farms (P = 0.321) during the study period. Compared with overseas data, the relative abundance of ARG copies per 16S rRNA gene in feces (0.08-0.17), effluent (0.03-0.37), soil (0.20-0.63), and bulk tank milk (0.0-0.12) samples was low. Models comparing the presence or absence of resistance classes found in >10% of all feces, effluent, and soil samples demonstrated no statistically significant associations (P > 0.05) with "season," and only multi-metal (P = 0.020) and tetracycline (P = 0.0003) resistance were significant at the "farm" level. Effluent samples harbored the most diverse ARGs, some with a recognized public health risk, whereas soil samples had the highest ARG relative abundance but without recognized health risks. This highlights the importance of considering the genomic context and risk of ARGs in metagenomic data sets. This study suggests that antimicrobial resistance on pasture-based dairy farms is low and provides essential baseline ARG surveillance data for such farming systems.IMPORTANCEAntimicrobial resistance is a global threat to human and animal health. Despite the detection of antimicrobial resistance genes (ARGs) in dairy farm environments globally, longitudinal surveillance in pasture-based systems remains limited. This study assessed the relative abundance and diversity of ARGs in two New Zealand dairy farms with different management practices and provided important baseline ARG surveillance data on pasture-based dairy farms. The overall ARG relative abundance on these two farms was low, which provides further evidence for consumers of the safety of New Zealand's export products. Effluent samples harbored the most diverse range of ARGs, some of which were classified with a recognized risk to public health, whereas soil samples had the highest ARG relative abundance; however, the soil ARGs were not classified with a recognized public health risk. This emphasizes the need to consider genomic context and risk as well as ARG relative abundance in resistome studies.

High-throughput transposon mutagenesis in the family Enterobacteriaceae reveals core essential genes and rapid turnover of essentiality.

The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the model organism Escherichia coli, as well as major human pathogens including Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, with the Keio E. coli single-gene deletion library often regarded as a gold standard. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of 13 sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first assess several gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations, we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently acquired essentiality multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival and identifies several instances of essential stress responses masked by redundancy in free-living bacteria.IMPORTANCEThe essential genome, that is the set of genes absolutely required to sustain life, is a core concept in genetics. Essential genes in bacteria serve as drug targets, put constraints on the engineering of biological chassis for technological or industrial purposes, and are key to constructing synthetic life. Despite decades of study, relatively little is known about how gene essentiality varies across related bacteria. In this study, we have collected gene essentiality data for 13 bacteria related to the model organism Escherichia coli, including several human pathogens, and investigated the conservation of essentiality. We find that approximately a third of the genes essential in any particular strain are non-essential in another related strain. Surprisingly, we do not find evidence for essential genes unique to specific genera; rather it appears a substantial fraction of the essential genome rapidly gains or loses essentiality during evolution. This suggests that essentiality is not an immutable characteristic but depends crucially on the genomic context. We illustrate this through a comparison of our essential genes in free-living bacteria to genes conserved in 34 insect endosymbionts with naturally reduced genomes, finding several cases where genes generally regarded as being important for specific stress responses appear to have become essential in endosymbionts due to a loss of functional redundancy in the genome.

Discovery, characterization, and structure of a cofactor-independent histidine racemase from the oral pathogen Fusobacterium nucleatum

Fusobacterium nucleatum is an oral commensal bacterium that can act as an opportunistic pathogen, and is implicated in diseases such as periodontitis, adverse pregnancy outcomes, colorectal cancer, and Alzheimer’s disease. F. nucleatum synthesizes lanthionine for its peptidoglycan, rather than meso-2,6-diaminopimelic acid (DAP) used by most Gram-negative bacteria. Despite lacking the biosynthetic pathway for DAP, the genome of F. nucleatum ATCC 25586 encodes a predicted DAP epimerase. A recent study hypothesized that this enzyme may act as a lanthionine epimerase, but the authors found a very low turnover rate, suggesting that this enzyme likely has another more favored substrate. Here, we characterize this enzyme as a histidine racemase (HisR), and found that catalytic turnover is ∼10,000× faster with L-histidine than with L,L-lanthionine. Kinetic experiments suggest that HisR functions as a cofactor-independent racemase and that turnover is specific for histidine, while crystal structures of catalytic cysteine to serine mutants (C67S or C209S) reveal this enzyme in its substrate-unbound, open conformation. Currently, the only other reported cofactor-independent histidine racemase is CntK from Staphylococcus aureus, which is used in the biosynthesis of staphylopine, a broad-spectrum metallophore that increases virulence of S. aureus. However, CntK shares only 28% sequence identity with HisR, and their genes exist in different genomic contexts. Knock-out of hisR in F. nucleatum results in a small but reproducible lag in growth compared to wild-type during exponential phase, suggesting that HisR may play a role in growth of this periodontal pathogen.

2-Deoxy-4-epi-scyllo-inosose (DEI) is the Product of EboD, a Highly Conserved Dehydroquinate Synthase-like Enzyme in Bacteria and Eustigmatophyte Algae.

A cryptic cluster of genes, known as the ebo cluster, has been found in a variety of genomic contexts among bacteria and algae. In Pseudomonas fluorescens NZI7, the ebo cluster (a.k.a. EDB cluster) is involved in the bacterial repellent mechanism against nematode grazing. In cyanobacteria, the cluster plays a role in the transport of the scytonemin monomer from the cytosol to the periplasm. Despite their broad distribution and interesting phenotypes, neither the pathway nor the functions of the enzymes are known. Here we show that EboD proteins from the ebo clusters in Nostoc punctiforme and Sporocytophaga myxococcoides catalyze the cyclization of mannose 6-phosphate to a novel cyclitol, 2-deoxy-4-epi-scyllo-inosose. The enzyme product is postulated to be a precursor of a signaling molecule or a transporter in the organisms. This study sheds the first light onto ebo/EDB pathways and established a functionally distinct enzyme that extends the diversity of sugar phosphate cyclases.

Metagenomic assemblies tend to break around antibiotic resistance genes

BackgroundAssembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies.ResultsThe results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample.ConclusionsOur study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database should be performed as a complementary strategy to get accurate ARG abundance and diversity measures.

DNA methylation of transposons pattern aging differences across a diverse cohort of dogs from the Dog Aging Project.

Within a species, larger individuals often have shorter lives and higher rates of age-related disease. Despite this well-known link, we still know little about underlying age-related epigenetic differences, which could help us better understand inter-individual variation in aging and the etiology, onset, and progression of age-associated disease. Dogs exhibit this negative correlation between size, health, and longevity and thus represent an excellent system in which to test the underlying mechanisms. Here, we quantified genome-wide DNA methylation in a cohort of 864 dogs in the Dog Aging Project. Age strongly patterned the dog epigenome, with the majority (66% of age-associated loci) of regions associating age-related loss of methylation. These age effects were non-randomly distributed in the genome and differed depending on genomic context. We found the LINE1 (long interspersed elements) class of TEs (transposable elements) were the most frequently hypomethylated with age (FDR < 0.05, 40% of all LINE1 regions). This LINE1 pattern differed in magnitude across breeds of different sizes- the largest dogs lost 0.26% more LINE1 methylation per year than the smallest dogs. This suggests that epigenetic regulation of TEs, particularly LINE1s, may contribute to accelerated age and disease phenotypes within a species. Since our study focused on the methylome of immune cells, we looked at LINE1 methylation changes in golden retrievers, a breed highly susceptible to hematopoietic cancers, and found they have accelerated age-related LINE1 hypomethylation compared to other breeds. We also found many of the LINE1s hypomethylated with age are located on the X chromosome and are, when considering X chromosome inactivation, counter-intuitively more methylated in males. These results have revealed the demethylation of LINE1 transposons as a potential driver of inter-species, demographic-dependent aging variation.

StratoMod: predicting sequencing and variant calling errors with interpretable machine learning

Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their application. Currently, assessing such tradeoffs relies on intuition about how a certain pipeline will perform in a given genomic context. We present StratoMod, which addresses this problem using an interpretable machine-learning classifier to predict germline variant calling errors in a data-driven manner. We show StratoMod can precisely predict recall using Hifi or Illumina and leverage StratoMod’s interpretability to measure contributions from difficult-to-map and homopolymer regions for each respective outcome. Furthermore, we use Statomod to assess the effect of mismapping on predicted recall using linear vs. graph-based references, and identify the hard-to-map regions where graph-based methods excelled and by how much. For these we utilize our draft benchmark based on the Q100 HG002 assembly, which contains previously-inaccessible difficult regions. Furthermore, StratoMod presents a new method of predicting clinically relevant variants likely to be missed, which is an improvement over current pipelines which only filter variants likely to be false. We anticipate this being useful for performing precise risk-reward analyses when designing variant calling pipelines.

Helicase-assisted continuous editing for programmable mutagenesis of endogenous genomes.

Deciphering the context-specific relationship between sequence and function is a major challenge in genomics. Existing tools for inducing locus-specific hypermutation and evolution in the native genome context are limited. Here we present a programmable platform for long-range, locus-specific hypermutation called helicase-assisted continuous editing (HACE). HACE leverages CRISPR-Cas9 to target a processive helicase-deaminase fusion that incurs mutations across large (>1000-base pair) genomic intervals. We applied HACE to identify mutations in mitogen-activated protein kinase kinase 1 (MEK1) that confer kinase inhibitor resistance, to dissect the impact of individual variants in splicing factor 3B subunit 1 (SF3B1)-dependent missplicing, and to evaluate noncoding variants in a stimulation-dependent immune enhancer of CD69. HACE provides a powerful tool for investigating coding and noncoding variants, uncovering combinatorial sequence-to-function relationships, and evolving new biological functions.

Fast Context-Aware Analysis of Genome Annotation Colocalization.

An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, sequence repeats, regions with a particular epigenetic state, and copy number variants. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing random unrelated annotations. To incorporate more background information into such analyses, we propose a new null model based on a Markov chain that differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistic and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings.

Gene editing of NCF1 loci is associated with homologous recombination and chromosomal rearrangements

CRISPR-based genome editing of pseudogene-associated disorders, such as p47phox-deficient chronic granulomatous disease (p47 CGD), is challenged by chromosomal rearrangements due to presence of multiple targets. We report that interactions between highly homologous sequences that are localized on the same chromosome contribute substantially to post-editing chromosomal rearrangements. We successfully employed editing approaches at the NCF1 gene and its pseudogenes, NCF1B and NCF1C, in a human cell line model of p47 CGD and in patient-derived human hematopoietic stem and progenitor cells. Upon genetic engineering, a droplet digital PCR-based method identified cells with altered copy numbers, spanning megabases from the edited loci. We attributed the high aberration frequency to the interaction between repetitive sequences and their predisposition to recombination events. Our findings emphasize the need for careful evaluation of the target-specific genomic context, such as the presence of homologous regions, whose instability can constitute a risk factor for chromosomal rearrangements upon genome editing.

Resolved genomes of wastewater ESBL-producing Escherichia coli and metagenomic analysis of source wastewater samples.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli pose a serious threat to human health because of their resistance to the most commonly prescribed antibiotics: penicillins and cephalosporins. In this study, we provide a genomic and metagenomic context for the determinant beta-lactam resistance genes of ESBL-positive E. coli isolated from various wastewater treatment utilities in Oregon, USA. Class A beta-lactamase genes on chromosomes (blaCTX-M, blaTEM) were clustered with antibiotic resistance genes associated with other classes of antibiotics (sulfonamides and aminoglycosides) along with insertional elements. ESBL genes such as blaCTX-M, blaTEM, and blaSHV were also detected on conjugable plasmids of IncF and IncI incompatibility types. One novel IncF plasmid (pSHV2A_ESBLF) was identified, which carried a multidrug resistance genotype (blaSHV-2A, aadA22, aac3, aph6, tetA, and sul1) in addition to a mer (mercury resistance) operon, colicin, and aerobactin genes. Shotgun metagenomic analysis of the ESBL-producing E. coli-originating wastewater samples showed the presence of class A beta-lactamases; however, the ESBL genes identified in the E. coli genomes were below the detection limits. Other ESBL-associated genes (i.e., blaOXA.11, blaFOX.7, and blaGES.17) were identified in the wastewater samples, and their occurrences were correlated with the core microbial genera (e.g., Paraprevotella). In the E. coli genomes and wastewater samples, tetracycline, aminoglycoside, and beta-lactam resistance determinants frequently co-occurred. The combination of whole-genome and metagenomic analysis provides a holistic description of ESBL-producing organisms and genes in wastewater systems.IMPORTANCEUsing a hybrid sequencing and assembly strategy (short- and long-read sequencing), we identified the distribution of ARGs and virulence factors harbored on plasmids and chromosomes. We further characterized plasmids' incompatibility types and the co-occurrences of ARGs and virulence factors on plasmids and chromosomes. We investigated the transferability of plasmid-mediated beta-lactams via conjugation. Finally, using shotgun metagenomic analysis of the ESBL-producing Escherichia coli-originated wastewater samples, we described the microbial community, the resistome composition, and the potential associations with plasmid-mediated beta-lactam genes and other ARGs.

Developmental and housekeeping transcriptional programs display distinct modes of enhancer-enhancer cooperativity in Drosophila

Genomic enhancers are key transcriptional regulators which, upon the binding of sequence-specific transcription factors, activate their cognate target promoters. Although enhancers have been extensively studied in isolation, a substantial number of genes have more than one simultaneously active enhancer, and it remains unclear how these cooperate to regulate transcription. Using Drosophila melanogaster S2 cells as a model, we assay the activities of more than a thousand individual enhancers and about a million enhancer pairs toward housekeeping and developmental core promoters with STARR-seq. We report that housekeeping and developmental enhancers show distinct modes of enhancer-enhancer cooperativity: while housekeeping enhancers are additive such that their combined activity mirrors the sum of their individual activities, developmental enhancers are super-additive and combine multiplicatively. Super-additivity between developmental enhancers is promiscuous and neither depends on the enhancers’ endogenous genomic contexts nor on specific transcription factor motif signatures. However, it can be further boosted by Twist and Trl motifs and saturates for the highest levels of enhancer activity. These results have important implications for our understanding of gene regulation in complex multi-enhancer developmental loci and genomically clustered housekeeping genes, providing a rationale to interpret the transcriptional impact of non-coding mutations at different loci.