BacCapSeq: a Platform for Diagnosis and Characterization of Bacterial Infections. - Related Documents




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494101.0000BacCapSeq: a Platform for Diagnosis and Characterization of Bacterial Infections. We report a platform that increases the sensitivity of high-throughput sequencing for detection and characterization of bacteria, virulence determinants, and antimicrobial resistance (AMR) genes. The system uses a probe set comprised of 4.2 million oligonucleotides based on the Pathosystems Resource Integration Center (PATRIC) database, the Comprehensive Antibiotic Resistance Database (CARD), and the Virulence Factor Database (VFDB), representing 307 bacterial species that include all known human-pathogenic species, known antimicrobial resistance genes, and known virulence factors, respectively. The use of bacterial capture sequencing (BacCapSeq) resulted in an up to 1,000-fold increase in bacterial reads from blood samples and lowered the limit of detection by 1 to 2 orders of magnitude compared to conventional unbiased high-throughput sequencing, down to a level comparable to that of agent-specific real-time PCR with as few as 5 million total reads generated per sample. It detected not only the presence of AMR genes but also biomarkers for AMR that included both constitutive and differentially expressed transcripts.IMPORTANCE BacCapSeq is a method for differential diagnosis of bacterial infections and defining antimicrobial sensitivity profiles that has the potential to reduce morbidity and mortality, health care costs, and the inappropriate use of antibiotics that contributes to the development of antimicrobial resistance.201830352937
507610.9998Diagnostic microarray for human and animal bacterial diseases and their virulence and antimicrobial resistance genes. Rapid diagnosis and treatment of disease is often based on the identification and characterization of causative agents derived from phenotypic characteristics. Current methods can be laborious and time-consuming, often requiring many skilled personnel and a large amount of lab space. The objective of our study was to develop a spotted microarray for rapid identification and characterization of bacterial pathogens and their antimicrobial resistance genes. Our spotted microarray consists of 489 70mer probes that detect 40 bacterial pathogens of medical, veterinary and zoonotic importance (including 15 NIAID Category A, B and C pathogens); associated genes that encode resistance for antimicrobial and metal resistance; and DNA elements that are important for horizontal gene transfer among bacteria. High specificity and reliability of the microarray was achieved for bacterial pathogens of animal and human importance by validating MDR pathogenic bacteria as pure cultures or by following their inoculation in complex and highly organic sample matrices, such as soil and manure.201020035807
462820.9998Genomic Analysis of Molecular Bacterial Mechanisms of Resistance to Phage Infection. To optimize phage therapy, we need to understand how bacteria evolve against phage attacks. One of the main problems of phage therapy is the appearance of bacterial resistance variants. The use of genomics to track antimicrobial resistance is increasingly developed and used in clinical laboratories. For that reason, it is important to consider, in an emerging future with phage therapy, to detect and avoid phage-resistant strains that can be overcome by the analysis of metadata provided by whole-genome sequencing. Here, we identified genes associated with phage resistance in 18 Acinetobacter baumannii clinical strains belonging to the ST-2 clonal complex during a decade (Ab2000 vs. 2010): 9 from 2000 to 9 from 2010. The presence of genes putatively associated with phage resistance was detected. Genes detected were associated with an abortive infection system, restriction-modification system, genes predicted to be associated with defense systems but with unknown function, and CRISPR-Cas system. Between 118 and 171 genes were found in the 18 clinical strains. On average, 26% of these genes were detected inside genomic islands in the 2000 strains and 32% in the 2010 strains. Furthermore, 38 potential CRISPR arrays in 17 of 18 of the strains were found, as well as 705 proteins associated with CRISPR-Cas systems. A moderately higher presence of these genes in the strains of 2010 in comparison with those of 2000 was found, especially those related to the restriction-modification system and CRISPR-Cas system. The presence of these genes in genomic islands at a higher rate in the strains of 2010 compared with those of 2000 was also detected. Whole-genome sequencing and bioinformatics could be powerful tools to avoid drawbacks when a personalized therapy is applied. In this study, it allows us to take care of the phage resistance in A. baumannii clinical strains to prevent a failure in possible phage therapy.202135250902
462930.9998Screening and in silico characterization of prophages in Helicobacter pylori clinical strains. The increase of antibiotic resistance calls for alternatives to control Helicobacter pylori, a Gram-negative bacterium associated with various gastric diseases. Bacteriophages (phages) can be highly effective in the treatment of pathogenic bacteria. Here, we developed a method to identify prophages in H. pylori genomes aiming at their future use in therapy. A polymerase chain reaction (PCR)-based technique tested five primer pairs on 74 clinical H. pylori strains. After the PCR screening, 14 strains most likely to carry prophages were fully sequenced. After that, a more holistic approach was taken by studying the complete genome of the strains. This study allowed us to identify 12 intact prophage sequences, which were then characterized concerning their morphology, virulence, and antibiotic-resistance genes. To understand the variability of prophages, a phylogenetic analysis using the sequences of all H. pylori phages reported to date was performed. Overall, we increased the efficiency of identifying complete prophages to 54.1 %. Genes with homology to potential virulence factors were identified in some new prophages. Phylogenetic analysis revealed a close relationship among H. pylori-phages, although there are phages with different geographical origins. This study provides a deeper understanding of H. pylori-phages, providing valuable insights into their potential use in therapy.202539368610
511140.9998Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.201931597945
462350.9998Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes. Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome.201931611361
463160.9998Genome Analysis of an Enterococcal Prophage, Entfac.MY. BACKGROUND: Bacteriophages are bacterial parasites. Unlike lytic bacteriophages, lysogenic bacteriophages do not multiply immediately after entering the host cells and may integrate their genomes into the bacterial genomes as prophages. Prophages can include various phenotypic and genotypic effects on the host bacteria. Enterococcus spp. are Gram-positive bacteria that cause infections in humans and animals. In recent decades, these bacteria have become resistant to various antimicrobials, including vancomycin. The aim of this study was to analyze genome of an enterococcal prophage. METHODS: In this study, Enterococcus faecium EntfacYE was isolated from biological samples and its genome was analyzed using next-generation sequencing method. RESULTS: Overall, 254 prophage genes were identified in the bacterial genome. The prophage included 39 housekeeping, 41 replication and regulation, 80 structural and packaging, and 48 lysis genes. Moreover, 46 genes with unknown functions were identified. All genes were annotated in DNA Data Bank of Japan. CONCLUSION: In general, most prophage genes were linked to packaging and structure (31.5%) gene group. However, genes with unknown functions included a high proportion (18.11%), which indicated necessity of further analyses. Genomic analysis of the prophages can be effective in better understanding of their roles in development of bacterial resistance to antibiotics. Moreover, identification and study of prophages can help researchers develop genetic engineering tools and novel infection therapies.202236061127
494370.9997Targeted sequencing of Enterobacterales bacteria using CRISPR-Cas9 enrichment and Oxford Nanopore Technologies. Sequencing DNA directly from patient samples enables faster pathogen characterization compared to traditional culture-based approaches, but often yields insufficient sequence data for effective downstream analysis. CRISPR-Cas9 enrichment is designed to improve the yield of low abundance sequences but has not been thoroughly explored with Oxford Nanopore Technologies (ONT) for use in clinical bacterial epidemiology. We designed CRISPR-Cas9 guide RNAs to enrich the human pathogen Klebsiella pneumoniae, by targeting multi-locus sequence type (MLST) and transfer RNA (tRNA) genes, as well as common antimicrobial resistance (AMR) genes and the resistance-associated integron gene intI1. We validated enrichment performance in 20 K. pneumoniae isolates, finding that guides generated successful enrichment across all conserved sites except for one AMR gene in two isolates. Enrichment of MLST genes led to a correct allele call in all seven loci for 8 out of 10 isolates that had depth of 30× or more in these regions. We then compared enriched and unenriched sequencing of three human fecal samples spiked with K. pneumoniae at varying abundance. Enriched sequencing generated 56× and 11.3× the number of AMR and MLST reads, respectively, compared to unenriched sequencing, and required approximately one-third of the computational storage space. Targeting the intI1 gene often led to detection of 10-20 proximal resistance genes due to the long reads produced by ONT sequencing. We demonstrated that CRISPR-Cas9 enrichment combined with ONT sequencing enabled improved genomic characterization outcomes over unenriched sequencing of patient samples. This method could be used to inform infection control strategies by identifying patients colonized with high-risk strains. IMPORTANCE: Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen Klebsiella pneumoniae and improve recovery from human stool samples. We found our enrichment method to significantly outperform traditional methods, generating far more data originating from our target genes. Additionally, we developed new computational techniques to further enhance the analysis, providing a thorough method for characterizing pathogens from complex biological samples.202539772804
510680.9997Metagenomic diagnostics for the simultaneous detection of multiple pathogens in human stool specimens from Côte d'Ivoire: a proof-of-concept study. BACKGROUND: The intestinal microbiome is a complex community and its role in influencing human health is poorly understood. While conventional microbiology commonly attributes digestive disorders to a single microorganism, a metagenomic approach can detect multiple pathogens simultaneously and might elucidate the role of microbial communities in the pathogenesis of intestinal diseases. We present a proof-of-concept that a shotgun metagenomic approach provides useful information on the diverse composition of intestinal pathogens and antimicrobial resistance profiles in human stool samples. METHODS: In October 2012, we obtained stool specimens from patients with persistent diarrhea in south Côte d'Ivoire. Four stool samples were purposefully selected and subjected to microscopy, multiplex polymerase chain reaction (PCR), and a metagenomic approach. For the latter, we employed the National Center for Biotechnology Information nucleotide database and screened for 36 pathogenic organisms (bacteria, helminths, intestinal protozoa, and viruses) that may cause digestive disorders. We further characterized the bacterial population and the prevailing resistance patterns by comparing our metagenomic datasets with a genome-specific marker database and with a comprehensive antibiotic resistance database. RESULTS: In the four patients, the metagenomic approach identified between eight and 11 pathogen classes that potentially cause digestive disorders. For bacterial pathogens, the diagnostic agreement between multiplex PCR and metagenomics was high; yet, metagenomics diagnosed several bacteria not detected by multiplex PCR. In contrast, some of the helminth and intestinal protozoa infections detected by microscopy were missed by metagenomics. The antimicrobial resistance analysis revealed the presence of genes conferring resistance to several commonly used antibiotics. CONCLUSIONS: A metagenomic approach provides detailed information on the presence and diversity of pathogenic organisms in human stool samples. Metagenomic studies allow for in-depth molecular characterization such as the antimicrobial resistance status, which may be useful to develop setting-specific treatment algorithms. While metagenomic approaches remain challenging, the benefits of gaining new insights into intestinal microbial communities call for a broader application in epidemiologic studies. TRIAL REGISTRATION: ISRCTN86951400.201626391184
494290.9997Nanopore-based enrichment of antimicrobial resistance genes - a case-based study. Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we used real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compared its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then modelled the influence of several variables on the enrichment results and predicted the large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we showed how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.202336949817
4936100.9997A New Tool for Analyses of Whole Genome Sequences Reveals Dissemination of Specific Strains of Vancomycin-Resistant Enterococcus faecium in a Hospital. A new easy-to-use online bioinformatic tool analyzing whole genome sequences of healthcare associated bacteria was used by a local infection control unit to retrospectively map genetic relationship of isolates of E. faecium carrying resistance genes to vancomycin in a hospital. Three clusters of isolates were detected over a period of 5 years, suggesting transmission between patients. Individual relatedness between isolates within each cluster was established by SNP analyses provided by the system. Genetic antimicrobial resistance mechanisms to antibiotics other than vancomycin were identified. The results suggest that the system is suited for hospital surveillance of E. faecium carrying resistance genes to vancomycin in settings with access to next Generation Sequencing without bioinformatic expertise for interpretation of the genome sequences.202134778297
4630110.9997Genome Analysis of the Enterococcus faecium Entfac.YE Prophage. BACKGROUND: Bacteriophages are viruses that infect bacteria. Bacteriophages are widely distributed in various environments. The prevalence of bacteriophages in water sources, especially wastewaters, is naturally high. These viruses affect evolution of most bacterial species. Bacteriophages are able to integrate their genomes into the chromosomes of their hosts as prophages and hence transfer resistance genes to the bacterial genomes. Enterococci are commensal bacteria that show high resistance to common antibiotics. For example, prevalence of vancomycin-resistant enterococci has increased within the last decades. METHODS: Enterococcal isolates were isolated from clinical samples and morphological, phenotypical, biochemical, and molecular methods were used to identify and confirm their identity. Bacteriophages extracted from water sources were then applied to isolated Enterococcus faecium (E. faecium). In the next step, the bacterial genome was completely sequenced and the existing prophage genome in the bacterial genome was analyzed. RESULTS: In this study, E. faecium EntfacYE was isolated from a clinical sample. The EntfacYE genome was analyzed and 88 prophage genes were identified. The prophage content included four housekeeping genes, 29 genes in the group of genes related to replication and regulation, 25 genes in the group of genes related to structure and packaging, and four genes belonging to the group of genes associated with lysis. Moreover, 26 genes were identified with unknown functions. CONCLUSION: In conclusion, genome analysis of prophages can lead to a better understanding of their roles in the rapid evolution of bacteria.202235509366
4653120.9997Modelling the effectiveness of surveillance based on metagenomics in detecting, monitoring, and forecasting antimicrobial resistance in livestock production under economic constraints. Current surveillance of antimicrobial resistance (AMR) is mostly based on testing indicator bacteria using minimum inhibitory concentration (MIC) panels. Metagenomics has the potential to identify all known antimicrobial resistant genes (ARGs) in complex samples and thereby detect changes in the occurrence earlier. Here, we simulate the results of an AMR surveillance program based on metagenomics in the Danish pig population. We modelled both an increase in the occurrence of ARGs and an introduction of a new ARG in a few farms and the subsequent spread to the entire population. To make the simulation realistic, the total cost of the surveillance was constrained, and the sampling schedule was set at one pool per month with 5, 20, 50, or 100 samples. Our simulations demonstrate that a pool of 20-50 samples and a sequencing depth of 250 million fragments resulted in the shortest time to detection in both scenarios, with a time delay to detection of change of [Formula: see text]15 months in all scenarios. Compared with culture-based surveillance, our simulation indicates that there are neither significant reductions nor increases in time to detect a change using metagenomics. The benefit of metagenomics is that it is possible to monitor all known resistance in one sampling and laboratory procedure in contrast to the current monitoring that is based on the phenotypic characterisation of selected indicator bacterial species. Therefore, overall changes in AMR in a population will be detected earlier using metagenomics due to the fact that the resistance gene does not have to be transferred to and expressed by an indicator bacteria before it is possible to detect.202337990114
5002130.9997Genomic Diversity of Hospital-Acquired Infections Revealed through Prospective Whole-Genome Sequencing-Based Surveillance. Healthcare-associated infections (HAIs) cause mortality, morbidity, and waste of health care resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAIs using prospective whole-genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here, we describe the diversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of bacterial isolate genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 groups of related species. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal-component analysis of accessory genes (PCA-A). Core genome genetic distances and rates of evolution varied among species, which has practical implications for defining shared ancestry during outbreaks and for our broader understanding of the origins of bacterial strains and species. Finally, antimicrobial resistance genes and putative mobile genetic elements were frequently observed, and our systematic analysis revealed patterns of occurrence across the different species sampled from our hospital. Overall, this study shows how understanding the population structure of diverse pathogens circulating in a single health care setting can improve the discriminatory power of genomic epidemiology studies and can help define the processes leading to strain and species differentiation. IMPORTANCE Hospitalized patients are at increased risk of becoming infected with antibiotic-resistant organisms. We used whole-genome sequencing to survey and compare over 3,000 clinical bacterial isolates collected from hospitalized patients at a large medical center over a 2-year period. We identified nearly 100 different bacterial species, which we divided into 14 different groups of related species. When we examined how genetic relatedness differed between species, we found that different species were likely evolving at different rates within our hospital. This is significant because the identification of bacterial outbreaks in the hospital currently relies on genetic similarity cutoffs, which are often applied uniformly across organisms. Finally, we found that antibiotic resistance genes and mobile genetic elements were abundant and were shared among the bacterial isolates we sampled. Overall, this study provides an in-depth view of the genomic diversity and evolutionary processes of bacteria sampled from hospitalized patients, as well as genetic similarity estimates that can inform hospital outbreak detection and prevention efforts.202235695507
3405140.9997Practical implications of erythromycin resistance gene diversity on surveillance and monitoring of resistance. Use of antibiotics in human and animal medicine has applied selective pressure for the global dissemination of antibiotic-resistant bacteria. Therefore, it is of interest to develop strategies to mitigate the continued amplification and transmission of resistance genes in environmental reservoirs such as farms, hospitals and watersheds. However, the efficacy of mitigation strategies is difficult to evaluate because it is unclear which resistance genes are important to monitor, and which primers to use to detect those genes. Here, we evaluated the diversity of one type of macrolide antibiotic resistance gene (erm) in one type of environment (manure) to determine which primers would be most informative to use in a mitigation study of that environment. We analyzed all known erm genes and assessed the ability of previously published erm primers to detect the diversity. The results showed that all known erm resistance genes group into 66 clusters, and 25 of these clusters (40%) can be targeted with primers found in the literature. These primers can target 74%-85% of the erm gene diversity in the manures analyzed.201829346541
5746150.9997Identification of a Novel Plasmid-Borne Gentamicin Resistance Gene in Nontyphoidal Salmonella Isolated from Retail Turkey. The spread of antibiotic-resistant bacteria presents a global health challenge. Efficient surveillance of bacteria harboring antibiotic resistance genes (ARGs) is a critical aspect to controlling the spread. Increased access to microbial genomic data from many diverse populations informs this surveillance but only when functional ARGs are identifiable within the data set. Current, homology-based approaches are effective at identifying the majority of ARGs within given clinical and nonclinical data sets for several pathogens, yet there are still some whose identities remain elusive. By coupling phenotypic profiling with genotypic data, these unknown ARGs can be identified to strengthen homology-based searches. To prove the efficacy and feasibility of this approach, a published data set from the U.S. National Antimicrobial Resistance Monitoring System (NARMS), for which the phenotypic and genotypic data of 640 Salmonella isolates are available, was subjected to this analysis. Six isolates recovered from the NARMS retail meat program between 2011 and 2013 were identified previously as phenotypically resistant to gentamicin but contained no known gentamicin resistance gene. Using the phenotypic and genotypic data, a comparative genomics approach was employed to identify the gene responsible for the observed resistance in all six of the isolates. This gene, grdA, is harbored on a 9,016-bp plasmid that is transferrable to Escherichia coli, confers gentamicin resistance to E. coli, and has never before been reported to confer gentamicin resistance. Bioinformatic analysis of the encoded protein suggests an ATP binding motif. This work demonstrates the advantages associated with coupling genomics technologies with phenotypic data for novel ARG identification.202032816720
5694160.9997Multiplex characterization of human pathogens including species and antibiotic-resistance gene identification. The efficient medical treatment of infections requires detailed information about the pathogens involved and potential antibiotic-resistance mechanisms. The dramatically increasing incidence of multidrug-resistant bacteria especially highlights the importance of sophisticated diagnostic tests enabling a fast patient-customized therapy. However, the current molecular detection methods are limited to either the detection of species or only a few antibiotic-resistance genes.In this work, we present a human pathogen characterization assay using a rRNA gene microarray identifying 75 species comprising bacteria and fungi. A statistical classifier was developed to facilitate the automated species identification. Additionally, the clinically most important β-lactamases were identified simultaneously in a 100-plex reaction using padlock probes and the same microarray. The specificity and sensitivity of the combined assay was determined using clinical isolates. The detection limit was 10(5) c.f.u. ml(-1), recovering 89 % of the detectable β-lactamase-encoding genes specifically. The total assay time was less than 7 hand the modular character of the antibiotic-resistance detection allows the easy integration of further genetic targets. In summary, we present a fast, highly specific and sensitive multiplex pathogen characterization assay.201626489938
5079170.9997Development of a Rapid, Culture-Free, Universal Microbial Identification System Using Internal Transcribed Spacer Targeting Primers. The indiscriminate administration of broad-spectrum antibiotics is a primary contributor to the increasing prevalence of antibiotic resistance. Unfortunately, culture, the gold standard for bacterial identification is a time intensive process. Due to this extended diagnostic period, broad-spectrum antibiotics are generally prescribed to prevent poor outcomes. To overcome the deficits of culture-based methods, we have developed a rapid universal bacterial identification system. The platform uses a unique universal polymerase chain reaction primer set that targets the internal transcribed spacer regions between conserved bacterial genes, creating a distinguishable amplicon signature for every bacterial species. Bioinformatic simulation demonstrates that nearly every bacteria in a set of 45 commonly isolated pathogenic species can be uniquely identified using this approach. We experimentally confirmed these predictions on a representative set of pathogenic bacterial species. We further showed that the system can determine the corresponding concentration of each pathogen. Finally, we validated performance in clinical urinary tract infection samples.202539503259
4644180.9997Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes. Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically-important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs and that the relative abundance of many individual AMR genes/variants increased over time and may be connected to antibiotic use during the COVID-19 pandemic. Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.202235982656
4934190.9997Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates. Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.201931100356