Automated Annotation Pipelines Research Articles

Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes.

Direct infusion-high-resolution mass spectrometry (DI-HRMS) allows for rapid profiling of complex mixtures of metabolites in blood, cerebrospinal fluid, tissue samples and cultured cells. Here, we present a DI-HRMS method suitable for the rapid determination of metabolic fluxes of isotopically labeled substrates in cultured cells and organoids. We adapted an automated annotation pipeline by selecting labeled adducts that best represent the majority of 13C and/or 15N-labeled glycolytic and tricarboxylic acid cycle intermediates as well as a number of their derivatives. Furthermore, valine, leucine and several of their degradation products were included. We show that DI-HRMS can determine anticipated and unanticipated alterations in metabolic fluxes along these pathways that result from the genetic alteration of single metabolic enzymes, including pyruvate dehydrogenase (PDHA1) and glutaminase (GLS). In addition, it can precisely pinpoint metabolic adaptations to the loss of methylmalonyl-CoA mutase in patient-derived liver organoids. Our results highlight the power of DI-HRMS in combination with stable isotopically labeled compounds as an efficient screening method for fluxomics.

QSP: An open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline

Quorum sensing (QS) has attracted great attention due to its important role in the bacterial interactions and its relevance to water management. With the development of high-throughput sequencing technology, a specific database for QS-related sequence annotation is urgently needed. Here, Hidden Markov Model (HMM) profiles for 38 types of QS-related proteins were built using a total of 4024 collected seed sequences. Based on both homolog search and keywords confirmation against the non-redundant database, we established a QS-related protein (QSP) database, that includes 809,721 protein sequences and 186,133 nucleotide sequences, downloaded available at: https://github.com/chunxiao-dcx/QSP. The entries were classified into 38 types and 315 subtypes among 91 bacterial phyla. Furthermore, an automatic annotation pipeline, named QSAP, was developed for rapid annotation, classification and abundance quantification of QSP-like sequences from sequencing data. This pipeline provided the two homolog alignment strategies offered by Diamond (Blastp) or HMMER (Hmmscan), as well as a data cleansing function for a subset or union set of the hits. The pipeline was tested using 14 metagenomic samples from various water environments, including activated sludge, deep-sea sediments, estuary water, and reservoir water. The QSAP pipeline is freely available for academic use in the code repository at: https://github.com/chunxiao-dcx/QSAP. The establishment of this database and pipeline, provides a useful tool for QS-related sequence annotation in a wide range of projects, and will increase our understanding of QS communication in aquatic environments.

TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes.

Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport.org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www.membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.

Embedding Weather Simulation in Auto-Labelling Pipelines Improves Vehicle Detection in Adverse Conditions.

The performance of deep learning-based detection methods has made them an attractive option for robotic perception. However, their training typically requires large volumes of data containing all the various situations the robots may potentially encounter during their routine operation. Thus, the workforce required for data collection and annotation is a significant bottleneck when deploying robots in the real world. This applies especially to outdoor deployments, where robots have to face various adverse weather conditions. We present a method that allows an independent car tansporter to train its neural networks for vehicle detection without human supervision or annotation. We provide the robot with a hand-coded algorithm for detecting cars in LiDAR scans in favourable weather conditions and complement this algorithm with a tracking method and a weather simulator. As the robot traverses its environment, it can collect data samples, which can be subsequently processed into training samples for the neural networks. As the tracking method is applied offline, it can exploit the detections made both before the currently processed scan and any subsequent future detections of the current scene, meaning the quality of annotations is in excess of those of the raw detections. Along with the acquisition of the labels, the weather simulator is able to alter the raw sensory data, which are then fed into the neural network together with the labels. We show how this pipeline, being run in an offline fashion, can exploit off-the-shelf weather simulation for the auto-labelling training scheme in a simulator-in-the-loop manner. We show how such a framework produces an effective detector and how the weather simulator-in-the-loop is beneficial for the robustness of the detector. Thus, our automatic data annotation pipeline significantly reduces not only the data annotation but also the data collection effort. This allows the integration of deep learning algorithms into existing robotic systems without the need for tedious data annotation and collection in all possible situations. Moreover, the method provides annotated datasets that can be used to develop other methods. To promote the reproducibility of our research, we provide our datasets, codes and models online.

Susceptibility Tests and Predictions of Transporter Profile in Serratia Species.

Disinfectants and biosecurity are critically important to control microbial diseases. Resistance to disinfectants compromises sectors such as agriculture and healthcare systems. Currently, efflux pumps are the most common mechanism of antimicrobial resistance. This study aimed to identify the efflux transporters responsible for disinfectant resistance in a multidrug-resistant isolate Serratia sp. HRI compared to a susceptible Serratia sp. type strain. An efflux system profile was generated using the Transporter Automatic Annotation Pipeline (TransAAP) for both isolates. Thereafter, the efflux pump inhibitors, reserpine (RSP) and carbonyl cyanide 3-chlorophenylhydrazone (CCCP) were used to reveal the role of efflux pumps in susceptibility to three disinfectants (Didecyldimethylammonium chloride, HyperCide®, and benzalkonium chloride). Interestingly, the resistant isolate had fewer efflux systems in total compared to the type strain and fewer efflux systems classified as resistance efflux pumps. After the addition of RSP, a significant reduction in resistance capabilities against all three antimicrobials was observed for both isolates. However, CCCP supplementation produced mixed results with some outcomes suggesting the involvement of the Eagle effect. This study provides evidence that efflux pumps are responsible for the disinfectant resistance phenotype of the Serratia species due to the increased susceptibility when efflux pump inhibitors are added.

Best genome sequencing strategies for annotation of complex immune gene families in wildlife.

The biodiversity crisis and increasing impact of wildlife disease on animal and human health provides impetus for studying immune genes in wildlife. Despite the recent boom in genomes for wildlife species, immune genes are poorly annotated in nonmodel species owing to their high level of polymorphism and complex genomic organisation. Our research over the past decade and a half on Tasmanian devils and koalas highlights the importance of genomics and accurate immune annotations to investigate disease in wildlife. Given this, we have increasingly been asked the minimum levels of genome quality required to effectively annotate immune genes in order to study immunogenetic diversity. Here we set out to answer this question by manually annotating immune genes in 5 marsupial genomes and 1 monotreme genome to determine the impact of sequencing data type, assembly quality, and automated annotation on accurate immune annotation. Genome quality is directly linked to our ability to annotate complex immune gene families, with long reads and scaffolding technologies required to reassemble immune gene clusters and elucidate evolution, organisation, and true gene content of the immune repertoire. Draft-quality genomes generated from short reads with HiC or 10× Chromium linked reads were unable to achieve this. Despite mammalian BUSCOv5 scores of up to 94.1% amongst the 6 genomes, automated annotation pipelines incorrectly annotated up to 59% of manually annotated immune genes regardless of assembly quality or method of automated annotation. Our results demonstrate that long reads and scaffolding technologies, alongside manual annotation, are required to accurately study the immune gene repertoire of wildlife species.

Ectopic germinal centers in the thymus accurately predict prognosis of myasthenia gravis after thymectomy

AbstractThe ability of thymic histopathology to predict the long-term impact of thymectomy in non-thymomatous myasthenia gravis (NTMG) is mainly uncharted. We applied digital pathology to quantitatively characterize differences of thymic histology between early-onset (EOMG) and late-onset MG (LOMG) and to investigate the role of thymic changes for thymectomy outcomes in MG. We analyzed 83 thymic H&E slides from thymectomized NTMG patients, of which 69 had EOMG and 14 LOMG, using digital pathology open-access software QuPath. We compared the results to the retrospectively assessed clinical outcome at two years after thymectomy and at the last follow-up visit where complete stable remission and minimal use of medication were primary outcomes. The automated annotation pipeline was an effective and reliable way to analyze thymic H&E samples compared to manual annotation with mean intraclass correlation of 0.80. The ratio of thymic tissue to stroma and fat was increased in EOMG compared to LOMG (p = 8.7e-07), whereas no difference was observed in the ratio of medulla to cortex between these subtypes. AChRAb seropositivity correlated with the number of ectopic germinal centers (eGC; p = 0.00067) but not with other histological areas. Patients with an increased number of eGCs had better post-thymectomy outcomes at two years after thymectomy (p = 0.0035) and at the last follow-up (p = 0.0267). ROC analysis showed that eGC area predicts thymectomy outcome in EOMG with an AUC of 0.79. Digital pathology can thus help in providing a predictive tool to the clinician, the eGC number, to guide the post-thymectomy treatment decisions in EOMG patients.

Abstract 1176: Leveraging the GENIE dataset to distinguish somatic cancer drivers from passenger events in routine oncology practice

Abstract Distinguishing genomic events that drive cancer (drivers) from inconsequential damage (passengers) is key to matching patients with biomarker-associated therapies and clinical trials. Whilst population level genomic analyses can estimate the proportion of driver events within a given gene, they cannot categorize individual variants. Reliable annotation remains a significant barrier to unlocking the full utility of cancer genomics. GENIE (Genomics Evidence Neoplasia Information Exchange) data was used to generate codon recurrence scores (CRS) for known cancer genes. GENIE was parsed to remove duplicates, sequencing artefact (recurrent variants reported by one institution, invariably associated with amplicon-based sequencing) and hypermutated samples (>15 mutations per megabase). For missense mutations, a codon recurrence of ≥10 was used to classify driver mutations. The table shows proportion of missense mutations classified as drivers by gene and cancer type, comparing population-based non-synonymous to synonymous mutation ratio; dN/dS (Martincorena 2017, PMID 29056346) with assessment of individual variants by CRS. This comparison demonstrates a high degree of concordance. For a few genes, CRS under called driver mutations (ie VHL in renal cancer) likely due to reduced power with small sample numbers. Importantly, CRS is able to assign driver status to individual missense mutations. Comparison with informatic analyses (PMIDs 28115009, 29247016, 30365005, 31034466) showed equivalent or superior performance of the GENIE approach. These findings demonstrate how a large (and expanding) real-world dataset can be used to predict the driver status of somatic missense mutations at the n=1 variant level. This process is amenable to implementation as a rules-based classification process for somatic missense mutations as part of an automated annotation pipeline. +corrected dN/dS ratio not significantly different from one. Pan- cancer Breast cancer Renal cancer Lung adenocarcinoma Gene dN/dS CRS dN/dS CRS dN/dS CRS dN/dS CRS BRAF 91% 76% <50% 34% <50% 33% 88% 74% PIK3CA 94% 89% 97% 95% 65%+ 84% 86% 76% KRAS 97% 98% 90% 95% 80%+ 100% 99% 99% IDH1 96% 81% <50% 16% <50% n/a <50% 47% EGFR 52% 51% <50% 7% <50% 17% 88% 81% TP53 96% 96% 99% 97% 82%+ 89% 96% 95% PTEN 94% 54% 90% 59% <50% 47% 70% 32% VHL 80% 22% <50% 8% 99% 33% <50% 0% CDKN2A 50% 51% <50% 43% <50% n/a <50% 46% APC <50% 1% <50% 1% <50% n/a <50% 1% RB1 <50% 6% <50% 6% <50% 50% <50% 5% ARID1A <50% 3% <50% 2% <50% 6% <50% 2% KMT2C <50% 0% <50% 0% <50% n/a <50% 0% Citation Format: Philip A. Beer, Susanna L. Cooke, Xuan Shirley Li, Andrew V. Biankin. Leveraging the GENIE dataset to distinguish somatic cancer drivers from passenger events in routine oncology practice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1176.

Genome and proteome analyses show the gaseous alkane degrader Desulfosarcina sp. strain BuS5 as an extreme metabolic specialist.

The metabolic potential of the sulfate-reducing bacterium Desulfosarcina sp. strain BuS5, currently the only pure culture able to oxidize the volatile alkanes propane and butane without oxygen, was investigated via genomics, proteomics and physiology assays. Complete genome sequencing revealed that strain BuS5 encodes a single alkyl-succinate synthase, an enzyme which apparently initiates oxidation of both propane and butane. The formed alkyl-succinates are oxidized to CO2 via beta oxidation and the oxidative Wood-Ljungdahl pathways as shown by proteogenomics analyses. Strain BuS5 conserves energy via the canonical sulfate reduction pathway and electron bifurcation. An ability to utilize long-chain fatty acids, mannose and oligopeptides, suggested by automated annotation pipelines, was not supported by physiology assays and in-depth analyses of the corresponding genetic systems. Consistently, comparative genomics revealed a streamlined BuS5 genome with a remarkable paucity of catabolic modules. These results establish strain BuS5 as an exceptional metabolic specialist, able to grow only with propane and butane, for which we propose the name Desulfosarcina aeriophaga BuS5. This highly restrictive lifestyle, most likely the result of habitat-driven evolutionary gene loss, may provide D. aeriophaga BuS5 a competitive edge in sediments impacted by natural gas seeps. Etymology: Desulfosarcina aeriophaga, aério (Greek): gas; phágos (Greek): eater; D. aeriophaga: a gas eating or gas feeding Desulfosarcina.

The widespread nature of Pack-TYPE transposons reveals their importance for plant genome evolution.

Pack-TYPE transposable elements (TEs) are a group of non-autonomous DNA transposons found in plants. These elements can efficiently capture and shuffle coding DNA across the host genome, accelerating the evolution of genes. Despite their relevance for plant genome plasticity, the detection and study of Pack-TYPE TEs are challenging due to the high similarity these elements have with genes. Here, we produced an automated annotation pipeline designed to study Pack-TYPE elements and used it to successfully annotate and analyse more than 10,000 new Pack-TYPE TEs in the rice and maize genomes. Our analysis indicates that Pack-TYPE TEs are an abundant and heterogeneous group of elements. We found that these elements are associated with all main superfamilies of Class II DNA transposons in plants and likely share a similar mechanism to capture new chromosomal DNA sequences. Furthermore, we report examples of the direct contribution of these TEs to coding genes, suggesting a generalised and extensive role of Pack-TYPE TEs in plant genome evolution.

Bioinformatic-Based Approaches for Disease-Resistance Gene Discovery in Plants

Pathogens are among the most limiting factors for crop success and expansion. Thus, finding the underlying genetic cause of pathogen resistance is the main goal for plant geneticists. The activation of a plant’s immune system is mediated by the presence of specific receptors known as disease-resistance genes (R genes). Typical R genes encode functional immune receptors with nucleotide-binding sites (NBS) and leucine-rich repeat (LRR) domains, making the NBS-LRRs the largest family of plant resistance genes. Establishing host resistance is crucial for plant growth and crop yield but also for reducing pesticide use. In this regard, pyramiding R genes is thought to be the most ecologically friendly way to enhance the durability of resistance. To accomplish this, researchers must first identify the related genes, or linked markers, within the genomes. However, the duplicated nature, with the presence of frequent paralogues, and clustered characteristic of NLRs make them difficult to predict with the classic automatic gene annotation pipelines. In the last several years, efforts have been made to develop new methods leading to a proliferation of reports on cloned genes. Herein, we review the bioinformatic tools to assist the discovery of R genes in plants, focusing on well-established pipelines with an important computer-based component.

Automated Annotator: Capturing Expert Knowledge for Free.

Deep learning enabled medical image analysis is heavily reliant on expert annotations which is costly. We present a simple yet effective automated annotation pipeline that uses autoencoder based heatmaps to exploit high level information that can be extracted from a histology viewer in an unobtrusive fashion. By predicting heatmaps on unseen images the model effectively acts like a robot annotator. The method is demonstrated in the context of coeliac disease histology images in this initial work, but the approach is task agnostic and may be used for other medical image annotation applications. The results are evaluated by a pathologist and also empirically using a deep network for coeliac disease classification. Initial results using this simple but effective approach are encouraging and merit further investigation, specially considering the possibility of scaling this up to a large number of users.

Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform

Today, there are excellent resources for the semantic annotation of biomedical text. These resources span from ontologies, tools for NLP, annotators, and web services. Most of these are available either in the form of open source components (i.e., MetaMap) or as web services that offer free access (i.e., Whatizit). In order to use these resources in automatic text annotation pipelines, researchers face significant technical challenges. For open-source tools, the challenges include the setting up of the computational environment, the resolution of dependencies, as well as the compilation and installation of the software. For web services, the challenge is implementing clients to undertake communication with the respective web APIs. Even resources that are available as Docker containers (i.e., NCBO annotator) require significant technical skills for installation and setup. This work deals with the task of creating ready-to-install and run Research Objects (ROs) for a large collection of components in biomedical text analysis. These components include (a) tools such as cTAKES, NOBLE Coder, MetaMap, NCBO annotator, BeCAS, and Neji; (b) ontologies from BioPortal, NCBI BioSystems, and Open Biomedical Ontologies; and (c) text corpora such as BC4GO, Mantra Gold Standard Corpus, and the COVID-19 Open Research Dataset. We make these resources available in OpenBio.eu, an open-science RO repository and workflow management system. All ROs can be searched, shared, edited, downloaded, commented on, and rated. We also demonstrate how one can easily connect these ROs to form a large variety of text annotation pipelines.

Simple, Reliable, and Time-Efficient Manual Annotation of Bacterial Genomes with MAISEN.

Over the last 15years, the costs of DNA sequencing have sharply fallen, effectively shifting the costs of DNA analysis from sequencing to bioinformatic curation and storage. A huge number of available DNA sequences (including genomes and metagenomes) resulted in the development of various tools for sequence annotation. While much effort has been invested into the development of automatic annotation pipelines, manual curation of their results is still necessary in order to obtain a reliable and strictly validated data. Unfortunately, due to its time-consuming nature, manual annotation is now rarely used.In this chapter, a protocol for efficient manual annotation of prokaryotic DNA sequences using a novel bioinformatic tool-MAISEN ( http://maisen.ddlemb.com ), is presented. MAISEN is a free, web-based tool designed to accelerate manual annotation, by providing the user with simple interface and precomputed alignments for each predicted feature. It was designed to be available for every scientist, regardless of their bioinformatic proficiency.

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

Streptococcus halichoeri: Comparative Genomics of an Emerging Pathogen.

Streptococcus halichoeri is an emerging pathogen with a variety of host species and zoonotic potential. It has been isolated from grey seals and other marine mammals as well as from human infections. Beginning in 2010, two concurrent epidemics were identified in Finland, in fur animals and domestic dogs, respectively. The fur animals suffered from a new disease fur animal epidemic necrotic pyoderma (FENP) and the dogs presented with ear infections with poor treatment response. S. halichoeri was isolated in both studies, albeit among other pathogens, indicating a possible role in the disease etiologies. The aim was to find a possible common origin of the fur animal and dog isolates and study the virulence factors to assess pathogenic potential. Isolates from seal, human, dogs, and fur animals were obtained for comparison. The whole genomes were sequenced from 20 different strains using the Illumina MiSeq platform and annotated using an automatic annotation pipeline RAST. The core and pangenomes were formed by comparing the genomes against each other in an all-against-all comparison. A phylogenetic tree was constructed using the genes of the core genome. Virulence factors were assessed using the Virulence Factor Database (VFDB) concentrating on the previously confirmed streptococcal factors. A core genome was formed which encompassed approximately half of the genes in Streptococcus halichoeri. The resulting core was nearly saturated and would not change significantly by adding more genomes. The remaining genes formed the pangenome which was highly variable and would still evolve after additional genomes. The results highlight the great adaptability of this bacterium possibly explaining the ease at which it switches hosts and environments. Virulence factors were also analyzed and were found primarily in the core genome. They represented many classes and functions, but the largest single category was adhesins which again supports the marine origin of this species.

A quick guide for student-driven community genome annotation.

High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the course of many years. The majority of gene models present in biological databases today have been identified in draft genome assemblies using automated annotation pipelines that are frequently based on orthologs from distantly related model organisms and usually have minor or major errors. Manual curation is time consuming and often requires substantial expertise, but is instrumental in improving gene model structure and identification. Manual annotation may seem to be a daunting and cost-prohibitive task for small research communities but involving undergraduates in community genome annotation consortiums can be mutually beneficial for both education and improved genomic resources. We outline a workflow for efficient manual annotation driven by a team of primarily undergraduate annotators. This model can be scaled to large teams and includes quality control processes through incremental evaluation. Moreover, it gives students an opportunity to increase their understanding of genome biology and to participate in scientific research in collaboration with peers and senior researchers at multiple institutions.

Long-Read Annotation: Automated Eukaryotic Genome Annotation Based on Long-Read cDNA Sequencing.

Single-molecule full-length complementary DNA (cDNA) sequencing can aid genome annotation by revealing transcript structure and alternative splice forms, yet current annotation pipelines do not incorporate such information. Here we present long-read annotation (LoReAn) software, an automated annotation pipeline utilizing short- and long-read cDNA sequencing, protein evidence, and ab initio prediction to generate accurate genome annotations. Based on annotations of two fungal genomes (Verticillium dahliae and Plicaturopsis crispa) and two plant genomes (Arabidopsis [Arabidopsis thaliana] and Oryza sativa), we show that LoReAn outperforms popular annotation pipelines by integrating single-molecule cDNA-sequencing data generated from either the Pacific Biosciences or MinION sequencing platforms, correctly predicting gene structure, and capturing genes missed by other annotation pipelines.

Omics approaches to understand sourdough fermentation processes

The application of omics methodologies helps to further unravel sourdough fermentation processes. Of all methodologies applied, metagenetics is the most used one to analyse sourdoughs, allowing to elucidate their microbial structure, albeit that it is based on the sequencing of a very small part of whole-community DNA. Although shotgun metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics are very promising to analyse sourdough fermentations and the terminology is often used in a confusing way, they have not been fully used sensu stricto yet. For instance, up to now, metatranscriptomics is restricted to the use of a functional gene microarray for lactic acid bacteria. Further, meta-metabolomics often deals with metabolite target analysis of sourdough fermentation samples to determine the actual concentrations of residual substrates and metabolites produced as well as to list their volatilome solely. In contrast, genomics has been applied several times, albeit that the whole-genome sequence of only one yeast strain and of 41 lactic acid bacterial strains, originally isolated from sourdoughs, is available. However, the genomics data accessible in public databases should be considered with caution because of inaccurate gene annotations, due to automated annotation pipelines, thereby possibly overruling original, high-quality, well-curated gene annotations.

The putative drug efflux systems of the Bacillus cereus group.

The Bacillus cereus group of bacteria includes seven closely related species, three of which, B. anthracis, B. cereus and B. thuringiensis, are pathogens of humans, animals and/or insects. Preliminary investigations into the transport capabilities of different bacterial lineages suggested that genes encoding putative efflux systems were unusually abundant in the B. cereus group compared to other bacteria. To explore the drug efflux potential of the B. cereus group all putative efflux systems were identified in the genomes of prototypical strains of B. cereus, B. anthracis and B. thuringiensis using our Transporter Automated Annotation Pipeline. More than 90 putative drug efflux systems were found within each of these strains, accounting for up to 2.7% of their protein coding potential. Comparative analyses demonstrated that the efflux systems are highly conserved between these species; 70–80% of the putative efflux pumps were shared between all three strains studied. Furthermore, 82% of the putative efflux system proteins encoded by the prototypical B. cereus strain ATCC 14579 (type strain) were found to be conserved in at least 80% of 169 B. cereus group strains that have high quality genome sequences available. However, only a handful of these efflux pumps have been functionally characterized. Deletion of individual efflux pump genes from B. cereus typically had little impact to drug resistance phenotypes or the general fitness of the strains, possibly because of the large numbers of alternative efflux systems that may have overlapping substrate specificities. Therefore, to gain insight into the possible transport functions of efflux systems in B. cereus, we undertook large-scale qRT-PCR analyses of efflux pump gene expression following drug shocks and other stress treatments. Clustering of gene expression changes identified several groups of similarly regulated systems that may have overlapping drug resistance functions. In this article we review current knowledge of the small molecule efflux pumps encoded by the B. cereus group and suggest the likely functions of numerous uncharacterised pumps.