Strain-level characterization of foodborne pathogens without culture enrichment for outbreak investigation using shotgun metagenomics facilitated with nanopore adaptive sampling

Introduction

Shotgun metagenomics has previously proven effective in the investigation of foodborne outbreaks by providing rapid and comprehensive insights into the microbial contaminant. However, culture enrichment of the sample has remained a prerequisite, despite the potential impact on pathogen detection resulting from the growth competition. To circumvent the need for culture enrichment, we explored the use of adaptive sampling using various databases for a targeted nanopore sequencing, compared to shotgun metagenomics alone.

Methods

The adaptive sampling method was first tested on DNA of mashed potatoes mixed with DNA of a Staphylococcus aureus strain previously associated with a foodborne outbreak. The selective sequencing was used to either deplete the potato sequencing reads or enrich for the pathogen sequencing reads, and compared to a shotgun sequencing. Then, living S. aureus were spiked at 105 CFU into 25 g of mashed potatoes. Three DNA extraction kits were tested, in combination with enrichment using adaptive sampling, following whole genome amplification. After data analysis, the possibility to characterize the contaminant with the different sequencing and extraction methods, without culture enrichment, was assessed.

Results

Overall, the adaptive sampling outperformed the shotgun sequencing. While the use of a host removal DNA extraction kit and targeted sequencing using a database of foodborne pathogens allowed rapid detection of the pathogen, the most complete characterization was achieved when using solely a database of S. aureus combined with a conventional DNA extraction kit, enabling accurate placement of the strain on a phylogenetic tree alongside outbreak cases.

Discussion

This method shows great potential for strain-level analysis of foodborne outbreaks without the need for culture enrichment, thereby enabling faster investigations and facilitating precise pathogen characterization. The integration of adaptive sampling with metagenomics presents a valuable strategy for more efficient and targeted analysis of microbial communities in foodborne outbreaks, contributing to improved food safety and public health.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance

Rapid, accurate and comprehensive diagnostics are essential for outbreak prevention and pathogen surveillance. Real-time, on-site metagenomics on miniaturized devices, such as Oxford Nanopore Technologies MinION sequencing, could provide a promising approach. However, current sample preparation protocols often require substantial equipment and dedicated laboratories, limiting their use. In this study, we developed a rapid on-site applicable DNA extraction and library preparation approach for nanopore sequencing, using portable devices. The optimized method consists of a portable mechanical lysis approach followed by magnetic bead-based DNA purification and automated sequencing library preparation, and resulted in a throughput comparable to a current optimal, laboratory-based protocol using enzymatic digestion to lyse cells. By using spike-in reference communities, we compared the on-site method with other workflows, and demonstrated reliable taxonomic profiling, despite method-specific biases. We also demonstrated the added value of long-read sequencing by recovering reads containing full-length antimicrobial resistance genes, and attributing them to a host species based on the additional genomic information they contain. Our method may provide a rapid, widely-applicable approach for microbial detection and surveillance in a variety of on-site settings.

Expression divergence of expansin genes drive the heteroblasty in Ceratopteris chingii

BACKGROUND

Sterile-fertile heteroblasty is a common phenomenon observed in ferns, where the leaf shape of a fern sporophyll, responsible for sporangium production, differs from that of a regular trophophyll. However, due to the large size and complexity of most fern genomes, the molecular mechanisms that regulate the formation of these functionally different heteroblasty have remained elusive. To shed light on these mechanisms, we generated a full-length transcriptome of Ceratopteris chingii with PacBio Iso-Seq from five tissue samples. By integrating Illumina-based sequencing short reads, we identified the genes exhibiting the most significant differential expression between sporophylls and trophophylls.

RESULTS

The long reads were assembled, resulting in a total of 24,024 gene models. The differential expressed genes between heteroblasty primarily involved reproduction and cell wall composition, with a particular focus on expansin genes. Reconstructing the phylogeny of expansin genes across 19 plant species, ranging from green algae to seed plants, we identified four ortholog groups for expansins. The observed high expression of expansin genes in the young sporophylls of C. chingii emphasizes their role in the development of heteroblastic leaves. Through gene coexpression analysis, we identified highly divergent expressions of expansin genes both within and between species.

CONCLUSIONS

The specific regulatory interactions and accompanying expression patterns of expansin genes are associated with variations in leaf shapes between sporophylls and trophophylls.

Competitive interactions facilitate resistance development against antimicrobials

While the evolution of antimicrobial resistance is well studied in free-living bacteria, information on resistance development in dense and diverse biofilm communities is largely lacking. Therefore, we explored how the social interactions in a duo-species biofilm composed of the brewery isolates Pseudomonas rhodesiae and Raoultella terrigena influence the adaptation to the broad-spectrum antimicrobial sulfathiazole. Previously, we showed that the competition between these brewery isolates enhances the antimicrobial tolerance of P. rhodesiae. Here, we found that this enhanced tolerance in duo-species biofilms is associated with a strongly increased antimicrobial resistance development in P. rhodesiae. Whereas P. rhodesiae was not able to evolve resistance against sulfathiazole in monospecies conditions, it rapidly evolved resistance in the majority of the duo-species communities. Although the initial presence of R. terrigena was thus required for P. rhodesiae to acquire resistance, the resistance mechanisms did not depend on the presence of R. terrigena. Whole genome sequencing of resistant P. rhodesiae clones showed no clear mutational hot spots. This indicates that the acquired resistance phenotype depends on complex interactions between low-frequency mutations in the genetic background of the strains. We hypothesize that the increased tolerance in duo-species conditions promotes resistance by enhancing the selection of partially resistant mutants and opening up novel evolutionary trajectories that enable such genetic interactions. This hypothesis is reinforced by experimentally excluding potential effects of increased initial population size, enhanced mutation rate, and horizontal gene transfer. Altogether, our observations suggest that the community mode of life and the social interactions therein strongly affect the accessible evolutionary pathways toward antimicrobial resistance.IMPORTANCEAntimicrobial resistance is one of the most studied bacterial properties due to its enormous clinical and industrial relevance; however, most research focuses on resistance development of a single species in isolation. In the present study, we showed that resistance evolution of brewery isolates can differ greatly between single- and mixed-species conditions. Specifically, we observed that the development of antimicrobial resistance in certain species can be significantly enhanced in co-culture as compared to the single-species conditions. Overall, the current study emphasizes the need of considering the within bacterial interactions in microbial communities when evaluating antimicrobial treatments and resistance evolution.

Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers.

SIGNIFICANCE

Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Introduction

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States.

Methods

In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed.

Results

We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated.

Discussion

This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

BACKGROUND

The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool.

RESULTS

We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively).

CONCLUSION

The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.

Using a combination of short- and long-read sequencing to investigate the diversity in plasmid- and chromosomally encoded extended-spectrum beta-lactamases (ESBLs) in clinical Shigella and Salmonella isolates in Belgium

For antimicrobial resistance (AMR) surveillance, it is important not only to detect AMR genes, but also to determine their plasmidic or chromosomal location, as this will impact their spread differently. Whole-genome sequencing (WGS) is increasingly used for AMR surveillance. However, determining the genetic context of AMR genes using only short-read sequencing is complicated. The combination with long-read sequencing offers a potential solution, as it allows hybrid assemblies. Nevertheless, its use in surveillance has so far been limited. This study aimed to demonstrate its added value for AMR surveillance based on a case study of extended-spectrum beta-lactamases (ESBLs). ESBL genes have been reported to occur also on plasmids. To gain insight into the diversity and genetic context of ESBL genes detected in clinical isolates received by the Belgian National Reference Center between 2013 and 2018, 100 ESBL-producing Shigella and 31 ESBL-producing Salmonella were sequenced with MiSeq and a representative selection of 20 Shigella and six Salmonella isolates additionally with MinION technology, allowing hybrid assembly. The bla _CTX-M-15 gene was found to be responsible for a rapid rise in the ESBL Shigella phenotype from 2017. This gene was mostly detected on multi-resistance-carrying IncFII plasmids. Based on clustering, these plasmids were determined to be distinct from the circulating plasmids before 2017. They were spread to different Shigella species and within Shigella sonnei between multiple genotypes. Another similar IncFII plasmid was detected after 2017 containing bla _CTX-M-27 for which only clonal expansion occurred. Matches of up to 99 % to plasmids of various bacterial hosts from all over the world were found, but global alignments indicated that direct or recent ESBL-plasmid transfers did not occur. It is most likely that travellers introduced these in Belgium and subsequently spread them domestically. However, a clear link to a specific country could not be made. Moreover, integration of bla _CTX-M in the chromosome of two Shigella isolates was determined for the first time, and shown to be related to ISEcp1. In contrast, in Salmonella, ESBL genes were only found on plasmids, of which bla _CTX-M-55 and IncHI2 were the most prevalent, respectively. No matching ESBL plasmids or cassettes were detected between clinical Shigella and Salmonella isolates. The hybrid assembly data allowed us to check the accuracy of plasmid prediction tools. MOB-suite showed the highest accuracy. However, these tools cannot replace the accuracy of long-read and hybrid assemblies. This study illustrates the added value of hybrid assemblies for AMR surveillance and shows that a strategy where even just representative isolates of a collection used for hybrid assemblies could improve international AMR surveillance as it allows plasmid tracking.

Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity

Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.

Evolution of isoform-level gene expression patterns across tissues during lotus species divergence

Both gene duplication and alternative splicing (AS) drive the functional diversity of gene products in plants, yet the relative contributions of the two key mechanisms to the evolution of gene function are largely unclear. Here, we studied AS in two closely related lotus plants, Nelumbo lutea and Nelumbo nucifera, and the outgroup Arabidopsis thaliana, for both single-copy and duplicated genes. We show that most splicing events evolved rapidly between orthologs and that the origin of lineage-specific splice variants or isoforms contributed to gene functional changes during species divergence within Nelumbo. Single-copy genes contain more isoforms, have more AS events conserved across species, and show more complex tissue-dependent expression patterns than their duplicated counterparts. This suggests that expression divergence through isoforms is a mechanism to extend the expression breadth of genes with low copy numbers. As compared to isoforms of local, small-scale duplicates, isoforms of whole-genome duplicates are less conserved and display a less conserved tissue bias, pointing towards their contribution to subfunctionalization. Through comparative analysis of isoform expression networks, we identified orthologous genes of which the expression of at least some of their isoforms displays a conserved tissue bias across species, indicating a strong selection pressure for maintaining a stable expression pattern of these isoforms. Overall, our study shows that both AS and gene duplication contributed to the diversity of gene function during the evolution of lotus.

Metagenomics to Detect and Characterize Viruses in Food Samples at Genome Level? Lessons Learnt from a Norovirus Study

In this proof-of-concept study on food contaminated with norovirus, we investigated the feasibility of metagenomics as a new method to obtain the whole genome sequence of the virus and perform strain level characterization but also relate to human cases in order to resolve foodborne outbreaks. We tested several preparation methods to determine if a more open sequencing approach, i.e., shotgun metagenomics, or a more targeted approach, including hybrid capture, was the most appropriate. The genetic material was sequenced using Oxford Nanopore technologies with or without adaptive sampling, and the data were analyzed with an in-house bioinformatics workflow. We showed that a viral genome sequence could be obtained for phylogenetic analysis with shotgun metagenomics if the contamination load was sufficiently high or after hybrid capture for lower contamination. Relatedness to human cases goes well beyond the results obtained with the current qPCR methods. This workflow was also tested on a publicly available dataset of food spiked with norovirus and hepatitis A virus. This allowed us to prove that we could detect even fewer genome copies and two viruses present in a sample using shotgun metagenomics. We share the lessons learnt on the satisfactory and unsatisfactory results in an attempt to advance the field.

BLSSpeller to discover novel regulatory motifs in maize

With the decreasing cost of sequencing and availability of larger numbers of sequenced genomes, comparative genomics is becoming increasingly attractive to complement experimental techniques for the task of transcription factor (TF) binding site identification. In this study, we redesigned BLSSpeller, a motif discovery algorithm, to cope with larger sequence datasets. BLSSpeller was used to identify novel motifs in Zea mays in a comparative genomics setting with 16 monocot lineages. We discovered 61 motifs of which 20 matched previously described motif models in Arabidopsis. In addition, novel, yet uncharacterized motifs were detected, several of which are supported by available sequence-based and/or functional data. Instances of the predicted motifs were enriched around transcription start sites and contained signatures of selection. Moreover, the enrichment of the predicted motif instances in open chromatin and TF binding sites indicates their functionality, supported by the fact that genes carrying instances of these motifs were often found to be co-expressed and/or enriched in similar GO functions. Overall, our study unveiled several novel candidate motifs that might help our understanding of the genotype to phenotype association in crops.

OMEN: Network-based Driver Gene Identification using Mutual Exclusivity

MOTIVATION

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed.

RESULTS

We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark data set derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.

AVAILABILITY

The source code is freely available for download at www.github.com/DriesVanDaele/OMEN The data set is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Extracting functional insights from loss-of-function screens using deep link prediction

We present deep link prediction (DLP), a method for the interpretation of loss-of-function screens. Our approach uses representation-based link prediction to reprioritize phenotypic readouts by integrating screening experiments with gene-gene interaction networks. We validate on 2 different loss-of-function technologies, RNAi and CRISPR, using datasets obtained from DepMap. Extensive benchmarking shows that DLP-DeepWalk outperforms other methods in recovering cell-specific dependencies, achieving an average precision well above 90% across 7 different cancer types and on both RNAi and CRISPR data. We show that the genes ranked highest by DLP-DeepWalk are appreciably more enriched in drug targets compared to the ranking based on original screening scores. Interestingly, this enrichment is more pronounced on RNAi data compared to CRISPR data, consistent with the greater inherent noise of RNAi screens. Finally, we demonstrate how DLP-DeepWalk can infer the molecular mechanism through which putative targets trigger cell line mortality.

Halvade somatic: Somatic variant calling with Apache Spark

Background

The accurate detection of somatic variants from sequencing data is of key importance for cancer treatment and research. Somatic variant calling requires a high sequencing depth of the tumor sample, especially when the detection of low-frequency variants is also desired. In turn, this leads to large volumes of raw sequencing data to process and hence, large computational requirements. For example, calling the somatic variants according to the GATK best practices guidelines requires days of computing time for a typical whole-genome sequencing sample.

Findings

We introduce Halvade Somatic, a framework for somatic variant calling from DNA sequencing data that takes advantage of multi-node and/or multi-core compute platforms to reduce runtime. It relies on Apache Spark to provide scalable I/O and to create and manage data streams that are processed on different CPU cores in parallel. Halvade Somatic contains all required steps to process the tumor and matched normal sample according to the GATK best practices recommendations: read alignment (BWA), sorting of reads, preprocessing steps such as marking duplicate reads and base quality score recalibration (GATK), and, finally, calling the somatic variants (Mutect2). Our approach reduces the runtime on a single 36-core node to 19.5 h compared to a runtime of 84.5 h for the original pipeline, a speedup of 4.3 times. Runtime can be further decreased by scaling to multiple nodes, e.g., we observe a runtime of 1.36 h using 16 nodes, an additional speedup of 14.4 times. Halvade Somatic supports variant calling from both whole-genome sequencing and whole-exome sequencing data and also supports Strelka2 as an alternative or complementary variant calling tool. We provide a Docker image to facilitate single-node deployment. Halvade Somatic can be executed on a variety of compute platforms, including Amazon EC2 and Google Cloud.

Conclusions

To our knowledge, Halvade Somatic is the first somatic variant calling pipeline that leverages Big Data processing platforms and provides reliable, scalable performance. Source code is freely available.

Whole-Genome Sequencing-Based Antimicrobial Resistance Characterization and Phylogenomic Investigation of 19 Multidrug-Resistant and Extended-Spectrum Beta-Lactamase-Positive Escherichia coli Strains Collected From Hospital Patients in Benin in 2019

The increasing worldwide prevalence of extended-spectrum beta-lactamase (ESBL) producing Escherichia coli constitutes a serious threat to global public health. Surgical site infections are associated with high morbidity and mortality rates in developing countries, fueled by the limited availability of effective antibiotics. We used whole-genome sequencing (WGS) to evaluate antimicrobial resistance and the phylogenomic relationships of 19 ESBL-positive E. coli isolates collected from surgical site infections in patients across public hospitals in Benin in 2019. Isolates were identified by MALDI-TOF mass spectrometry and phenotypically tested for susceptibility to 16 antibiotics. Core-genome multi-locus sequence typing and single-nucleotide polymorphism-based phylogenomic methods were used to investigate the relatedness between samples. The broader phylogenetic context was characterized through the inclusion of publicly available genome data. Among the 19 isolates, 13 different sequence types (STs) were observed, including ST131 (n = 2), ST38 (n = 2), ST410 (n = 2), ST405 (n = 2), ST617 (n = 2), and ST1193 (n = 2). The bla _CTX-M-15 gene encoding ESBL resistance was found in 15 isolates (78.9%), as well as other genes associated with ESBL, such as bla _OXA-1 (n = 14) and bla _TEM-1 (n = 9). Additionally, we frequently observed genes encoding resistance against aminoglycosides [aac-(6')-Ib-cr, n = 14], quinolones (qnrS₁ , n = 4), tetracyclines [tet(B), n = 14], sulfonamides (sul2, n = 14), and trimethoprim (dfrA17, n = 13). Nonsynonymous chromosomal mutations in the housekeeping genes parC and gyrA associated with resistance to fluoroquinolones were also detected in multiple isolates. Although the phylogenomic investigation did not reveal evidence of hospital-acquired transmissions, we observed two very similar strains collected from patients in different hospitals. By characterizing a set of multidrug-resistant isolates collected from a largely unexplored environment, this study highlights the added value for WGS as an effective early warning system for emerging pathogens and antimicrobial resistance.

Whole Genome Sequencing Provides an Added Value to the Investigation of Staphylococcal Food Poisoning Outbreaks

Through staphylococcal enterotoxin (SE) production, Staphylococcus aureus is a common cause of food poisoning. Detection of staphylococcal food poisoning (SFP) is mostly performed using immunoassays, which, however, only detect five of 27 SEs described to date. Polymerase chain reactions are, therefore, frequently used in complement to identify a bigger arsenal of SE at the gene level (se) but are labor-intensive. Complete se profiling of isolates from different sources, i.e., food and human cases, is, however, important to provide an indication of their potential link within foodborne outbreak investigation. In addition to complete se gene profiling, relatedness between isolates is determined with more certainty using pulsed-field gel electrophoresis, Staphylococcus protein A gene typing and other methods, but these are shown to lack resolution. We evaluated how whole genome sequencing (WGS) can offer a solution to these shortcomings. By WGS analysis of a selection of S. aureus isolates, including some belonging to a confirmed foodborne outbreak, its added value as the ultimate multiplexing method was demonstrated. In contrast to PCR-based se gene detection for which primers are sometimes shown to be non-specific, WGS enabled complete se gene profiling with high performance, provided that a database containing reference sequences for all se genes was constructed and employed. The custom compiled database and applied parameters were made publicly available in an online user-friendly interface. As an all-in-one approach with high resolution, WGS additionally allowed inferring correct isolate relationships. The different DNA extraction kits that were tested affected neither se gene profiling nor relatedness determination, which is interesting for data sharing during SFP outbreak investigation. Although confirming the production of enterotoxins remains important for SFP investigation, we delivered a proof-of-concept that WGS is a valid alternative and/or complementary tool for outbreak investigation.

Towards Real-Time and Affordable Strain-Level Metagenomics-Based Foodborne Outbreak Investigations Using Oxford Nanopore Sequencing Technologies

The current routine laboratory practices to investigate food samples in case of foodborne outbreaks still rely on attempts to isolate the pathogen in order to characterize it. We present in this study a proof of concept using Shiga toxin-producing Escherichia coli spiked food samples for a strain-level metagenomics foodborne outbreak investigation method using the MinION and Flongle flow cells from Oxford Nanopore Technologies, and we compared this to Illumina short-read-based metagenomics. After 12 h of MinION sequencing, strain-level characterization could be achieved, linking the food containing a pathogen to the related human isolate of the affected patient, by means of a single-nucleotide polymorphism (SNP)-based phylogeny. The inferred strain harbored the same virulence genes as the spiked isolate and could be serotyped. This was achieved by applying a bioinformatics method on the long reads using reference-based classification. The same result could be obtained after 24-h sequencing on the more recent lower output Flongle flow cell, on an extract treated with eukaryotic host DNA removal. Moreover, an alternative approach based on in silico DNA walking allowed to obtain rapid confirmation of the presence of a putative pathogen in the food sample. The DNA fragment harboring characteristic virulence genes could be matched to the E. coli genus after sequencing only 1 h with the MinION, 1 h with the Flongle if using a host DNA removal extraction, or 5 h with the Flongle with a classical DNA extraction. This paves the way towards the use of metagenomics as a rapid, simple, one-step method for foodborne pathogen detection and for fast outbreak investigation that can be implemented in routine laboratories on samples prepared with the current standard practices.

Evaluation of WGS performance for bacterial pathogen characterization with the Illumina technology optimized for time-critical situations

Whole genome sequencing (WGS) has become the reference standard for bacterial outbreak investigation and pathogen typing, providing a resolution unattainable with conventional molecular methods. Data generated with Illumina sequencers can however only be analysed after the sequencing run has finished, thereby losing valuable time during emergency situations. We evaluated both the effect of decreasing overall run time, and also a protocol to transfer and convert intermediary files generated by Illumina sequencers enabling real-time data analysis for multiple samples part of the same ongoing sequencing run, as soon as the forward reads have been sequenced. To facilitate implementation for laboratories operating under strict quality systems, extensive validation of several bioinformatics assays (16S rRNA species confirmation, gene detection against virulence factor and antimicrobial resistance databases, SNP-based antimicrobial resistance detection, serotype determination, and core genome multilocus sequence typing) for three bacterial pathogens (Mycobacterium tuberculosis , Neisseria meningitidis , and Shiga-toxin producing Escherichia coli ) was performed by evaluating performance in function of the two most critical sequencing parameters, i.e. read length and coverage. For the majority of evaluated bioinformatics assays, actionable results could be obtained between 14 and 22 h of sequencing, decreasing the overall sequencing-to-results time by more than half. This study aids in reducing the turn-around time of WGS analysis by facilitating a faster response in time-critical scenarios and provides recommendations for time-optimized WGS with respect to required read length and coverage to achieve a minimum level of performance for the considered bioinformatics assay(s), which can also be used to maximize the cost-effectiveness of routine surveillance sequencing when response time is not essential.

Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.

Dynamic partitioning of search patterns for approximate pattern matching using search schemes

Search schemes constitute a flexible and generic framework to describe how all approximate occurrences of a search pattern in a text can be found efficiently. We propose an algorithm for the dynamic partitioning of search patterns which can be universally applied to any kind of search scheme and demonstrate that this technique significantly reduces the search space. We present Columba, a software tool written in C++, in which a multitude of search schemes are implemented. We discuss implementation aspects such as memory interleaving of Burrows-Wheeler transform representations and the reduction of redundancy that is inherently associated with the edit distance metric. Ultimately, we demonstrate that Columba has superior performance to the state of the art. Using a single CPU core, Columba is able to retrieve all occurrences of 100,000 Illumina reads and their reverse complements within a maximum edit distance of four in the human genome in less than 3 min.

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Dynamic partitioning of search patterns reduces search space and runtime

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Memory interleaving of bit vector representations of the BWT reduces runtime

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Avoiding redundancy inherent to the edit distance metric reduces the search space

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Our software tool Columba outperforms the state of the art by a factor of 3.5

Genome-wide expression and network analyses of mutants in key brassinosteroid signaling genes

BACKGROUND

Brassinosteroid (BR) signaling regulates plant growth and development in concert with other signaling pathways. Although many genes have been identified that play a role in BR signaling, the biological and functional consequences of disrupting those key BR genes still require detailed investigation.

RESULTS

Here we performed phenotypic and transcriptomic comparisons of A. thaliana lines carrying a loss-of-function mutation in BRI1 gene, bri1-5, that exhibits a dwarf phenotype and its three activation-tag suppressor lines that were able to partially revert the bri1-5 mutant phenotype to a WS2 phenotype, namely bri1-5/bri1-1D, bri1-5/brs1-1D, and bri1-5/bak1-1D. From the three investigated bri1-5 suppressors, bri1-5/bak1-1D was the most effective suppressor at the transcriptional level. All three bri1-5 suppressors showed altered expression of the genes in the abscisic acid (ABA signaling) pathway, indicating that ABA likely contributes to the partial recovery of the wild-type phenotype in these bri1-5 suppressors. Network analysis revealed crosstalk between BR and other phytohormone signaling pathways, suggesting that interference with one hormone signaling pathway affects other hormone signaling pathways. In addition, differential expression analysis suggested the existence of a strong negative feedback from BR signaling on BR biosynthesis and also predicted that BRS1, rather than being directly involved in signaling, might be responsible for providing an optimal environment for the interaction between BRI1 and its ligand.

CONCLUSIONS

Our study provides insights into the molecular mechanisms and functions of key brassinosteroid (BR) signaling genes, especially BRS1.

A Bioinformatics Whole-Genome Sequencing Workflow for Clinical Mycobacterium tuberculosis Complex Isolate Analysis, Validated Using a Reference Collection Extensively Characterized with Conventional Methods and In Silico Approaches

The use of whole-genome sequencing (WGS) for routine typing of bacterial isolates has increased substantially in recent years. For Mycobacterium tuberculosis (MTB), in particular, WGS has the benefit of drastically reducing the time required to generate results compared to most conventional phenotypic methods. Consequently, a multitude of solutions for analyzing WGS MTB data have been developed, but their successful integration in clinical and national reference laboratories is hindered by the requirement for their validation, for which a consensus framework is still largely absent. We developed a bioinformatics workflow for (Illumina) WGS-based routine typing of MTB complex (MTBC) member isolates allowing complete characterization, including (sub)species confirmation and identification (16S, csb/RD, hsp65), single nucleotide polymorphism (SNP)-based antimicrobial resistance (AMR) prediction, and pathogen typing (spoligotyping, SNP barcoding, and core genome multilocus sequence typing). Workflow performance was validated on a per-assay basis using a collection of 238 in-house-sequenced MTBC isolates, extensively characterized with conventional molecular biology-based approaches supplemented with public data. For SNP-based AMR prediction, results from molecular genotyping methods were supplemented with in silico modified data sets, allowing us to greatly increase the set of evaluated mutations. The workflow demonstrated very high performance with performance metrics of >99% for all assays, except for spoligotyping, where sensitivity dropped to ∼90%. The validation framework for our WGS-based bioinformatics workflow can aid in the standardization of bioinformatics tools by the MTB community and other SNP-based applications regardless of the targeted pathogen(s). The bioinformatics workflow is available for academic and nonprofit use through the Galaxy instance of our institute at https://galaxy.sciensano.be.

DNA-SIP and repeated isolation corroborate Variovorax as a key organism in maintaining the genetic memory for linuron biodegradation in an agricultural soil

The frequent exposure of agricultural soils to pesticides can lead to microbial adaptation, including the development of dedicated microbial populations that utilize the pesticide compound as a carbon and energy source. Soil from an agricultural field in Halen (Belgium) with a history of linuron exposure has been studied for its linuron-degrading bacterial populations at two time points over the past decade and Variovorax was appointed as a key linuron degrader. Like most studies on pesticide degradation, these studies relied on isolates that were retrieved through bias-prone enrichment procedures and therefore might not represent the in situ active pesticide-degrading populations. In this study, we revisited the Halen field and applied, in addition to enrichment-based isolation, DNA stable isotope probing (DNA-SIP), to identify in situ linuron-degrading bacteria in linuron-exposed soil microcosms. Linuron dissipation was unambiguously linked to Variovorax and its linuron catabolic genes and might involve the synergistic cooperation between two species. Additionally, two novel linuron-mineralizing Variovorax isolates were obtained with high 16S rRNA gene sequence similarity to strains isolated from the same field a decade earlier. The results confirm Variovorax as a prime in situ degrader of linuron in the studied agricultural field soil and corroborate the genus as key for maintaining the genetic memory of linuron degradation functionality in that field.

Application of a strain-level shotgun metagenomics approach on food samples: resolution of the source of a Salmonella food-borne outbreak

Food-borne outbreak investigation currently relies on the time-consuming and challenging bacterial isolation from food, to be able to link food-derived strains to more easily obtained isolates from infected people. When no food isolate can be obtained, the source of the outbreak cannot be unambiguously determined. Shotgun metagenomics approaches applied to the food samples could circumvent this need for isolation from the suspected source, but require downstream strain-level data analysis to be able to accurately link to the human isolate. Until now, this approach has not yet been applied outside research settings to analyse real food-borne outbreak samples. In September 2019, a Salmonella outbreak occurred in a hotel school in Bruges, Belgium, affecting over 200 students and teachers. Following standard procedures, the Belgian National Reference Center for human salmonellosis and the National Reference Laboratory for Salmonella in food and feed used conventional analysis based on isolation, serotyping and MLVA (multilocus variable number tandem repeat analysis) comparison, followed by whole-genome sequencing, to confirm the source of the contamination over 2 weeks after receipt of the sample, which was freshly prepared tartar sauce in a meal cooked at the school. Our team used this outbreak as a case study to deliver a proof of concept for a short-read strain-level shotgun metagenomics approach for source tracking. We received two suspect food samples: the full meal and some freshly made tartar sauce served with this meal, requiring the use of raw eggs. After analysis, we could prove, without isolation, that Salmonella was present in both samples, and we obtained an inferred genome of a Salmonella enterica subsp. enterica serovar Enteritidis that could be linked back to the human isolates of the outbreak in a phylogenetic tree. These metagenomics-derived outbreak strains were separated from sporadic cases as well as from another outbreak circulating in Europe at the same time period. This is, to our knowledge, the first Salmonella food-borne outbreak investigation uniquely linking the food source using a metagenomics approach and this in a fast time frame.

Validation strategy of a bioinformatics whole genome sequencing workflow for Shiga toxin-producing Escherichia coli using a reference collection extensively characterized with conventional methods

Whole genome sequencing (WGS) enables complete characterization of bacterial pathogenic isolates at single nucleotide resolution, making it the ultimate tool for routine surveillance and outbreak investigation. The lack of standardization, and the variation regarding bioinformatics workflows and parameters, however, complicates interoperability among (inter)national laboratories. We present a validation strategy applied to a bioinformatics workflow for Illumina data that performs complete characterization of Shiga toxin-producing Escherichia coli (STEC) isolates including antimicrobial resistance prediction, virulence gene detection, serotype prediction, plasmid replicon detection and sequence typing. The workflow supports three commonly used bioinformatics approaches for the detection of genes and alleles: alignment with blast+, kmer-based read mapping with KMA, and direct read mapping with SRST2. A collection of 131 STEC isolates collected from food and human sources, extensively characterized with conventional molecular methods, was used as a validation dataset. Using a validation strategy specifically adopted to WGS, we demonstrated high performance with repeatability, reproducibility, accuracy, precision, sensitivity and specificity above 95 % for the majority of all assays. The WGS workflow is publicly available as a 'push-button' pipeline at https://galaxy.sciensano.be. Our validation strategy and accompanying reference dataset consisting of both conventional and WGS data can be used for characterizing the performance of various bioinformatics workflows and assays, facilitating interoperability between laboratories with different WGS and bioinformatics set-ups.

Low immunogenicity of common cancer hot spot mutations resulting in false immunogenic selection signals

Cancer is driven by somatic mutations that result in a cellular fitness advantage. This selective advantage is expected to be counterbalanced by the immune system when these driver mutations simultaneously lead to the generation of neoantigens, novel peptides that are presented at the cancer cell membrane via HLA molecules from the MHC complex. The presentability of these peptides is determined by a patient’s MHC genotype and it has been suggested that this results in MHC genotype-specific restrictions of the oncogenic mutational landscape. Here, we generated a set of virtual patients, each with an identical and prototypical MHC genotype, and show that the earlier reported HLA affinity differences between observed and unobserved mutations are unrelated to MHC genotype variation. We demonstrate how these differences are secondary to high frequencies of 13 hot spot driver mutations in 6 different genes. Several oncogenic mechanisms were identified that lower the peptides’ HLA affinity, including phospho-mimicking substitutions in BRAF, destabilizing tyrosine mutations in TP53 and glycine-rich mutational contexts in the GTP-binding KRAS domain. In line with our earlier findings, our results emphasize that HLA affinity predictions are easily misinterpreted when studying immunogenic selection processes.

The diagnosis of a malignant tumor is preceded by a long process of tumor evolution, characterized by the gradual accumulation of driver mutations, genomic alterations that give cancer cells their typical growth advantage. This process is controlled by the immune system and understanding tumor-immune interactions is critical for the development of new anti-cancer therapies. Immune cells mainly respond to neoantigens, small mutated peptides that are presented at the cancer cell membrane by binding to the Major Histocompatibility Complex (MHC). MHC genes are highly variable between individuals and it was recently suggested that an individual’s MHC genotype determines cancer susceptibility. In this study we used a computational approach and simulated a set of virtual cancer patients, based on the expected driver mutation frequencies and each with a similar prototypical MHC genotype. Using these simulations, we show that the earlier perceived signals are unrelated to the underlying MHC genotypes, but rather secondary to high frequencies of 13 driver mutations in 6 cancer genes.

First detection of a plasmid-encoded New-Delhi metallo-beta-lactamase-1 (NDM-1) producing Acinetobacter baumannii using whole genome sequencing, isolated in a clinical setting in Benin

BACKGROUND

Carbapenem-resistant Acinetobacter baumannii is considered a top priority pathogen by the World Health Organization for combatting increasing antibiotic resistance and development of new drugs. Since it was originally reported in Klebsiella pneumoniae in 2009, the quick spread of the bla_NDM-1 gene encoding a New-Delhi metallo-beta-lactamase-1 (NDM-1) is increasingly recognized as a serious threat. This gene is usually carried by large plasmids and has already been documented in diverse bacterial species, including A. baumannii. Here, we report the first detection of a NDM-1-producing A. baumannii strain isolated in Benin.

CASE PRESENTATION

A 31-year-old woman was admitted to a surgical unit with a diagnosis of post-cesarean hematoma. An extensively-drug resistant A. baumannii strain solely susceptible to amikacin, colistin and ciprofloxacin, and resistant to several other antibiotics including ceftazidime, imipenem, meropenem, gentamicin, tobramycin, ceftazidime/avibactam, and sulfamethoxazole-trimethoprim, was isolated from the wound. Production of NDM-1 was demonstrated by immunochromatographic testing. Whole genome sequencing of the isolate confirmed the presence of bla_NDM-1, but also antibiotic resistance genes against multiple beta-lactamases and other classes of antibiotics, in addition to several virulence genes. Moreover, the bla_NDM-1 gene was found to be present in a Tn125 transposon integrated on a plasmid.

CONCLUSIONS

The discovery of this extensively-drug resistant A. baumannii strain carrying bla_NDM-1 in Benin is worrying, especially because of its high potential risk of horizontal gene transfer due to being integrated into a transposon located on a plasmid. Strict control and prevention measures should be taken, once NDM-1 positive A. baumannii has been identified to prevent transfer of this resistance gene to other Enterobacterales. Capacity building is required by governmental agencies to provide suitable antibiotic treatment options and strategies, in combination with strengthening laboratory services for detection and surveillance of this pathogen.

Impact of DNA extraction on whole genome sequencing analysis for characterization and relatedness of Shiga toxin-producing Escherichia coli isolates

Whole genome sequencing (WGS) has proven to be the ultimate tool for bacterial isolate characterization and relatedness determination. However, standardized and harmonized workflows, e.g. for DNA extraction, are required to ensure robust and exchangeable WGS data. Data sharing between (inter)national laboratories is essential to support foodborne pathogen control, including outbreak investigation. This study evaluated eight commercial DNA preparation kits for their potential influence on: (i) DNA quality for Nextera XT library preparation; (ii) MiSeq sequencing (data quality, read mapping against plasmid and chromosome references); and (iii) WGS data analysis, i.e. isolate characterization (serotyping, virulence and antimicrobial resistance genotyping) and phylogenetic relatedness (core genome multilocus sequence typing and single nucleotide polymorphism analysis). Shiga toxin-producing Escherichia coli (STEC) was selected as a case study. Overall, data quality and inferred phylogenetic relationships between isolates were not affected by the DNA extraction kit choice, irrespective of the presence of confounding factors such as EDTA in DNA solution buffers. Nevertheless, completeness of STEC characterization was, although not substantially, influenced by the plasmid extraction performance of the kits, especially when using Nextera XT library preparation. This study contributes to addressing the WGS challenges of standardizing protocols to support data portability and to enable full exploitation of its potential.

Development of an NGS-Based Workflow for Improved Monitoring of Circulating Plasmids in Support of Risk Assessment of Antimicrobial Resistance Gene Dissemination

Antimicrobial resistance (AMR) is one of the most prominent public health threats. AMR genes localized on plasmids can be easily transferred between bacterial isolates by horizontal gene transfer, thereby contributing to the spread of AMR. Next-generation sequencing (NGS) technologies are ideal for the detection of AMR genes; however, reliable reconstruction of plasmids is still a challenge due to large repetitive regions. This study proposes a workflow to reconstruct plasmids with NGS data in view of AMR gene localization, i.e., chromosomal or on a plasmid. Whole-genome and plasmid DNA extraction methods were compared, as were assemblies consisting of short reads (Illumina MiSeq), long reads (Oxford Nanopore Technologies) and a combination of both (hybrid). Furthermore, the added value of conjugation of a plasmid to a known host was evaluated. As a case study, an isolate harboring a large, low-copy mcr-1-carrying plasmid (>200 kb) was used. Hybrid assemblies of NGS data obtained from whole-genome DNA extractions of the original isolates resulted in the most complete reconstruction of plasmids. The optimal workflow was successfully applied to multidrug-resistant Salmonella Kentucky isolates, where the transfer of an ESBL-gene-containing fragment from a plasmid to the chromosome was detected. This study highlights a strategy including wet and dry lab parameters that allows accurate plasmid reconstruction, which will contribute to an improved monitoring of circulating plasmids and the assessment of their risk of transfer.

Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.

A Practical Method to Implement Strain-Level Metagenomics-Based Foodborne Outbreak Investigation and Source Tracking in Routine

The management of a foodborne outbreak depends on the rapid and accurate identification of the responsible food source. Conventional methods based on isolation of the pathogen from the food matrix and target-specific real-time polymerase chain reactions (qPCRs) are used in routine. In recent years, the use of whole genome sequencing (WGS) of bacterial isolates has proven its value to collect relevant information for strain characterization as well as tracing the origin of the contamination by linking the food isolate with the patient's isolate with high resolution. However, the isolation of a bacterial pathogen from food matrices is often time-consuming and not always successful. Therefore, we aimed to improve outbreak investigation by developing a method that can be implemented in reference laboratories to characterize the pathogen in the food vehicle without its prior isolation and link it back to human cases. We tested and validated a shotgun metagenomics approach by spiking food pathogens in specific food matrices using the Shiga toxin-producing Escherichia coli (STEC) as a case study. Different DNA extraction kits and enrichment procedures were investigated to obtain the most practical workflow. We demonstrated the feasibility of shotgun metagenomics to obtain the same information as in ISO/TS 13136:2012 and WGS of the isolate in parallel by inferring the genome of the contaminant and characterizing it in a shorter timeframe. This was achieved in food samples containing different E. coli strains, including a combination of different STEC strains. For the first time, we also managed to link individual strains from a food product to isolates from human cases, demonstrating the power of shotgun metagenomics for rapid outbreak investigation and source tracking.

Culture-Independent Analysis of Linuron-Mineralizing Microbiota and Functions in on-Farm Biopurification Systems via DNA-Stable Isotope Probing: Comparison with Enrichment Culture

Our understanding of the microorganisms involved in in situ biodegradation of xenobiotics, like pesticides, in natural and engineered environments is poor. On-farm biopurification systems (BPSs) treat farm-produced pesticide-contaminated wastewater to reduce surface water pollution. BPSs are a labor and cost-efficient technology but are still mainly operated as black box systems. We used DNA-stable isotope probing (DNA-SIP) and classical enrichment to be informed about the organisms responsible for in situ degradation of the phenylurea herbicide linuron in a BPS matrix. DNA-SIP identified Ramlibacter, Variovorax, and an unknown Comamonadaceae genus as the dominant linuron assimilators. While linuron-degrading Variovorax strains have been isolated repeatedly, Ramlibacter has never been associated before with linuron degradation. Genes and mobile genetic elements (MGEs) previously linked to linuron catabolism were enriched in the heavy DNA-SIP fractions, suggesting their involvement in in situ linuron assimilation. BPS material free cultivation of linuron degraders from the same BPS matrix resulted in a community dominated by Variovorax, while Ramlibacter was not observed. Our study provides evidence for the role of Variovorax in in situ linuron biodegradation in a BPS, alongside other organisms like Ramlibacter, and further shows that cultivation results in a biased representation of the in situ linuron-assimilating bacterial populations.

Distinct Expression and Methylation Patterns for Genes with Different Fates following a Single Whole-Genome Duplication in Flowering Plants

For most sequenced flowering plants, multiple whole-genome duplications (WGDs) are found. Duplicated genes following WGD often have different fates that can quickly disappear again, be retained for long(er) periods, or subsequently undergo small-scale duplications. However, how different expression, epigenetic regulation, and functional constraints are associated with these different gene fates following a WGD still requires further investigation due to successive WGDs in angiosperms complicating the gene trajectories. In this study, we investigate lotus (Nelumbo nucifera), an angiosperm with a single WGD during the K-pg boundary. Based on improved intraspecific-synteny identification by a chromosome-level assembly, transcriptome, and bisulfite sequencing, we explore not only the fundamental distinctions in genomic features, expression, and methylation patterns of genes with different fates after a WGD but also the factors that shape post-WGD expression divergence and expression bias between duplicates. We found that after a WGD genes that returned to single copies show the highest levels and breadth of expression, gene body methylation, and intron numbers, whereas the long-retained duplicates exhibit the highest degrees of protein-protein interactions and protein lengths and the lowest methylation in gene flanking regions. For those long-retained duplicate pairs, the degree of expression divergence correlates with their sequence divergence, degree in protein-protein interactions, and expression level, whereas their biases in expression level reflecting subgenome dominance are associated with the bias of subgenome fractionation. Overall, our study on the paleopolyploid nature of lotus highlights the impact of different functional constraints on gene fate and duplicate divergence following a single WGD in plant.

The Benefits of Whole Genome Sequencing for Foodborne Outbreak Investigation from the Perspective of a National Reference Laboratory in a Smaller Country

Gradually, conventional methods for foodborne pathogen typing are replaced by whole genome sequencing (WGS). Despite studies describing the overall benefits, National Reference Laboratories of smaller countries often show slower uptake of WGS, mainly because of significant investments required to generate and analyze data of a limited amount of samples. To facilitate this process and incite policy makers to support its implementation, a Shiga toxin-producing Escherichia coli (STEC) O157:H7 (stx1+, stx2+, eae+) outbreak (2012) and a STEC O157:H7 (stx2+, eae+) outbreak (2013) were retrospectively analyzed using WGS and compared with their conventional investigations. The corresponding results were obtained, with WGS delivering even more information, e.g., on virulence and antimicrobial resistance genotypes. Besides a universal, all-in-one workflow with less hands-on-time (five versus seven actual working days for WGS versus conventional), WGS-based cgMLST-typing demonstrated increased resolution. This enabled an accurate cluster definition, which remained unsolved for the 2013 outbreak, partly due to scarce epidemiological linking with the suspect source. Moreover, it allowed detecting two and one earlier circulating STEC O157:H7 (stx1+, stx2+, eae+) and STEC O157:H7 (stx2+, eae+) strains as closely related to the 2012 and 2013 outbreaks, respectively, which might have further directed epidemiological investigation initially. Although some bottlenecks concerning centralized data-sharing, sampling strategies, and perceived costs should be considered, we delivered a proof-of-concept that even in smaller countries, WGS offers benefits for outbreak investigation, if a sufficient budget is available to ensure its implementation in surveillance. Indeed, applying a database with background isolates is critical in interpreting isolate relationships to outbreaks, and leveraging the true benefit of WGS in outbreak investigation and/or prevention.

Comparative analysis of somatic variant calling on matched FF and FFPE WGS samples

Background

Research grade Fresh Frozen (FF) DNA material is not yet routinely collected in clinical practice. Many hospitals, however, collect and store Formalin Fixed Paraffin Embedded (FFPE) tumor samples. Consequently, the sample size of whole genome cancer cohort studies could be increased tremendously by including FFPE samples, although the presence of artefacts might obfuscate the variant calling. To assess whether FFPE material can be used for cohort studies, we performed an in-depth comparison of somatic SNVs called on matching FF and FFPE Whole Genome Sequence (WGS) samples extracted from the same tumor.

Methods

Four variant callers (i.e. Strelka2, Mutect2, VarScan2 and Shimmer) were used to call somatic variants on matching FF and FFPE WGS samples from a metastatic prostate tumor. Using the variants identified by these callers, we developed a heuristic to maximize the overlap between the FF and its FFPE counterpart in terms of sensitivity and precision. The proposed variant calling approach was then validated on nine matched primary samples. Finally, we assessed what fraction of the discrepancy could be attributed to intra-tumor heterogeneity (ITH), by comparing the overlap in clonal and subclonal somatic variants.

Results

We first compared variants between an FF and an FFPE sample from a metastatic prostate tumor, showing that on average 50% of the calls in the FF are recovered in the FFPE sample, with notable differences between callers. Combining the variants of the different callers using a simple heuristic, increases both the precision and the sensitivity of the variant calling. Validating the heuristic on nine additional matched FF-FFPE samples, resulted in an average F1-score of 0.58 and an outperformance of any of the individual callers. In addition, we could show that part of the discrepancy between the FF and the FFPE samples can be attributed to ITH.

Conclusion

This study illustrates that when using the correct variant calling strategy, the majority of clonal SNVs can be recovered in an FFPE sample with high precision and sensitivity. These results suggest that somatic variants derived from WGS of FFPE material can be used in cohort studies.

First detection of a plasmid located carbapenem resistant blaVIM-1 gene in E. coli isolated from meat products at retail in Belgium in 2015

Carbapenemase-producing Enterobacteriaceae (CPE) confer resistance to antibiotics that are of critical importance to human medicine. There have only been a few reported cases of CPEs in the European food chain. We report the first detection of a carbapenemase-producing Escherichia coli (ST 5869) in the Belgian food chain. Our aim was to characterize the origin of the carbapenem resistance in the E. coli isolate. The isolate was detected during the screening of 178 minced pork samples and was shown to contain the carbapenemase gene bla_VIM-1 by PCR and Sanger sequencing. Whole genome short and long read sequencing (MiSeq and MinION) was performed to characterize the isolate. With a hybrid assembly we reconstructed a 190,205 bp IncA/C2 plasmid containing bla_VIM-1 (S15FP06257_p), in addition to other critically important resistance genes. This plasmid showed only low similarity to plasmids containing bla_VIM-1 previously reported in Germany. Moreover, no sequences existed in the NCBI nucleotide database that completely covered S15FP06257_p. Analysis of the bla_VIM-1 gene cassette demonstrated that it likely originated from an integron of a Klebsiella plasmid reported previously in a clinical isolate in Europe, suggesting that the meat could have been contaminated by human handling in one of the steps of the food chain. This study shows the relevance of fully reconstructing plasmids to characterize their genetic content and to allow source attribution. This is especially important in view of the potential risk of antimicrobial resistance gene transmission through mobile elements as was reported here for the of public health concern bla_VIM-1.

Image-Based Dynamic Phenotyping Reveals Genetic Determinants of Filamentation-Mediated β-Lactam Tolerance

Antibiotic tolerance characterized by slow killing of bacteria in response to a drug can lead to treatment failure and promote the emergence of resistance. β-lactam antibiotics inhibit cell wall growth in bacteria and many of them cause filamentation followed by cell lysis. Hence delayed cell lysis can lead to β-lactam tolerance. Systematic discovery of genetic factors that affect β-lactam killing kinetics has not been performed before due to challenges in high-throughput, dynamic analysis of viability of filamented cells during bactericidal action. We implemented a high-throughput time-resolved microscopy approach in a gene deletion library of Escherichia coli to monitor the response of mutants to the β-lactam cephalexin. Changes in frequency of lysed and intact cells due to the antibiotic action uncovered several strains with atypical lysis kinetics. Filamentation confers tolerance because antibiotic removal before lysis leads to recovery through numerous concurrent divisions of filamented cells. Filamentation-mediated tolerance was not associated with resistance, and therefore this phenotype is not discernible through most antibiotic susceptibility methods. We find that deletion of Tol-Pal proteins TolQ, TolR, or Pal but not TolA, TolB, or CpoB leads to rapid killing by β-lactams. We also show that the timing of cell wall degradation determines the lysis and killing kinetics after β-lactam treatment. Altogether, this study uncovers numerous genetic determinants of hitherto unappreciated filamentation-mediated β-lactam tolerance and support the growing call for considering antibiotic tolerance in clinical evaluation of pathogens. More generally, the microscopy screening methodology described here can easily be adapted to study lysis in large numbers of strains.

Combining short and long read sequencing to characterize antimicrobial resistance genes on plasmids applied to an unauthorized genetically modified Bacillus

Antimicrobial resistance (AMR) is a major public health threat. Plasmids are able to transfer AMR genes among bacterial isolates. Whole genome sequencing (WGS) is a powerful tool to monitor AMR determinants. However, plasmids are difficult to reconstruct from WGS data. This study aimed to improve the characterization, including the localization of AMR genes using short and long read WGS strategies. We used a genetically modified (GM) Bacillus subtilis isolated as unexpected contamination in a feed additive, and therefore considered unauthorized (RASFF 2014.1249), as a case study. In GM organisms, AMR genes are used as selection markers. Because of the concern of spread of these AMR genes when present on mobile genetic elements, it is crucial to characterize their location. Our approach resulted in an assembly of one chromosome and one plasmid, each with several AMR determinants of which five are against critically important antibiotics. Interestingly, we found several plasmids, containing AMR genes, integrated in the chromosome in a repetitive region of at least 53 kb. Our findings would have been impossible using short reads only. We illustrated the added value of long read sequencing in addressing the challenges of plasmid reconstruction within the context of evaluating the risk of AMR spread.

Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing: Neisseria meningitidis as a Proof of Concept

Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context.

A multiplex oligonucleotide ligation-PCR method for the genoserotyping of common Salmonella using a liquid bead suspension assay

Salmonella is a major pathogen having a public health and economic impact in both humans and animals. Six serotypes of the Salmonella genus are mentioned in the Belgian and European regulation as to be rapidly excluded from the food chain (EU regulation N°2160/2003, Belgian royal decree 27/04/2017). The reference method for Salmonella serotyping, including slide-agglutination and biochemical tests, is time-consuming, expensive, not always objective, and therefore does not match the fast identification criteria required by the legislation. In this study, a molecular method, using genetic markers detected by Multiplex Oligonucleotide Ligation - PCR and Luminex technology, was developed for the identification of the 6 Salmonella serotypes and their variants subjected to an official control. The resulting method was validated with the analysis of 971 Salmonella isolated from different matrixes (human, animal, food or environment) and 33 non-Salmonella strains. The results were compared with the reference identifications, achieving an accuracy of 99.7%. The cost-effective high-throughput genoserotyping assay is performed in 1 day and generates objective results, thanks to the automatic interpretation of raw data using a barcode system. In conclusion, it is fully adapted to the implementation in first line laboratories and meets the requirements of the regulation.

Managing evidence in food safety and nutrition

Evidence (‘data’) is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on ‘Managing evidence’ of EFSA's third Scientific Conference ‘Science, Food, Society’, current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it. This paper reports on presentations given and discussions held during the session, which were centred around the following three main topics: (1) (big) data availability and (big) data connection, (2) problem formulation and (3) evidence integration.

IAMBEE: a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement.