Genome-Wide Assessment of Stress-Associated Genes in Bifidobacteria.

Over the last decade, the genomes of several Bifidobacterium strains have been sequenced, delivering valuable insights into their genetic makeup. However, bifidobacterial genomes have not yet been systematically mined for genes associated with stress response functions and their regulation. In this work, a list of 76 genes related to stress response in bifidobacteria was compiled from previous studies. The prevalence of the genes was evaluated among the genome sequences of 171 Bifidobacterium strains. Although genes of the protein quality control and DNA repair systems appeared to be highly conserved, genome-wide in silico screening for consensus sequences of putative regulators suggested that the regulation of these systems differs among phylogenetic groups. Homologs of multiple oxidative stress-associated genes are shared across species, albeit at low sequence similarity. Bee isolates were confirmed to harbor unique genetic features linked to oxygen tolerance. Moreover, most studied Bifidobacterium adolescentis and all Bifidobacterium angulatum strains lacked a set of reactive oxygen species-detoxifying enzymes, which might explain their high sensitivity to oxygen. Furthermore, the presence of some putative transcriptional regulators of stress responses was found to vary across species and strains, indicating that different regulation strategies of stress-associated gene transcription contribute to the diverse stress tolerance. The presented stress response gene profiles of Bifidobacterium strains provide a valuable knowledge base for guiding future studies by enabling hypothesis generation and the identification of key genes for further analyses. IMPORTANCE Bifidobacteria are Gram-positive bacteria that naturally inhabit diverse ecological niches, including the gastrointestinal tract of humans and animals. Strains of the genus Bifidobacterium are widely used as probiotics, since they have been associated with health benefits. In the course of their production and administration, probiotic bifidobacteria are exposed to several stressors that can challenge their survival. The stress tolerance of probiotic bifidobacteria is, therefore, an important selection criterion for their commercial application, since strains must maintain their viability to exert their beneficial health effects. As the ability to cope with stressors varies among Bifidobacterium strains, comprehensive understanding of the underlying stress physiology is required for enabling knowledge-driven strain selection and optimization of industrial-scale production processes.

Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis.

Lactococcus lactis strains are important components in industrial starter cultures for cheese manufacturing. They have many strain-dependent properties, which affect the final product. Here, we explored the use of machine learning to create systematic, high-throughput screening methods for these properties. Fast acidification of milk is such a strain-dependent property. To predict the maximum hourly acidification rate (Vmax), we trained Random Forest (RF) models on four different genomic representations: Presence/absence of gene families, counts of Pfam domains, the 8 nucleotide long subsequences of their DNA (8-mers), and the 9 nucleotide long subsequences of their DNA (9-mers). Vmax was measured at different temperatures, volumes, and in the presence or absence of yeast extract. These conditions were added as features in each RF model. The four models were trained on 257 strains, and the correlation between the measured Vmax and the predicted Vmax was evaluated with Pearson Correlation Coefficients (PC) on a separate dataset of 85 strains. The models all had high PC scores: 0.83 (gene presence/absence model), 0.84 (Pfam domain model), 0.76 (8-mer model), and 0.85 (9-mer model). The models all based their predictions on relevant genetic features and showed consensus on systems for lactose metabolism, degradation of casein, and pH stress response. Each model also predicted a set of features not found by the other models.

A comparative genomics study of 23 Aspergillus species from section Flavi.

Section Flavi encompasses both harmful and beneficial Aspergillus species, such as Aspergillus oryzae, used in food fermentation and enzyme production, and Aspergillus flavus, food spoiler and mycotoxin producer. Here, we sequence 19 genomes spanning section Flavi and compare 31 fungal genomes including 23 Flavi species. We reassess their phylogenetic relationships and show that the closest relative of A. oryzae is not A. flavus, but A. minisclerotigenes or A. aflatoxiformans and identify high genome diversity, especially in sub-telomeric regions. We predict abundant CAZymes (598 per species) and prolific secondary metabolite gene clusters (73 per species) in section Flavi. However, the observed phenotypes (growth characteristics, polysaccharide degradation) do not necessarily correlate with inferences made from the predicted CAZyme content. Our work, including genomic analyses, phenotypic assays, and identification of secondary metabolites, highlights the genetic and metabolic diversity within section Flavi.

Genus level analysis of PKS-NRPS and NRPS-PKS hybrids reveals their origin in Aspergilli.

BACKGROUND: Filamentous fungi produce a vast amount of bioactive secondary metabolites (SMs) synthesized by e.g. hybrid polyketide synthase-nonribosomal peptide synthetase enzymes (PKS-NRPS; NRPS-PKS). While their domain structure suggests a common ancestor with other SM proteins, their evolutionary origin and dynamics in fungi are still unclear. Recent rational engineering approaches highlighted the possibility to reassemble hybrids into chimeras - suggesting molecular recombination as diversifying mechanism. RESULTS: Phylogenetic analysis of hybrids in 37 species - spanning 9 sections of Aspergillus and Penicillium chrysogenum - let us describe their dynamics throughout the genus Aspergillus. The tree topology indicates that three groups of PKS-NRPS as well as one group of NRPS-PKS hybrids developed independently from each other. Comparison to other SM genes lead to the conclusion that hybrids in Aspergilli have several PKS ancestors; in contrast, hybrids are monophyletic when compared to available NRPS genes - with the exception of a small group of NRPSs. Our analysis also revealed that certain NRPS-likes are derived from NRPSs, suggesting that the NRPS/NRPS-like relationship is dynamic and proteins can diverge from one function to another. An extended phylogenetic analysis including bacterial and fungal taxa revealed multiple ancestors of hybrids. Homologous hybrids are present in all sections which suggests frequent horizontal gene transfer between genera and a finite number of hybrids in fungi. CONCLUSION: Phylogenetic distances between hybrids provide us with evidence for their evolution: Large inter-group distances indicate multiple independent events leading to the generation of hybrids, while short intra-group distances of hybrids from different taxonomic sections indicate frequent horizontal gene transfer. Our results are further supported by adding bacterial and fungal genera. Presence of related hybrid genes in all Ascomycetes suggests a frequent horizontal gene transfer between genera and a finite diversity of hybrids - also explaining their scarcity. The provided insights into relations of hybrids and other SM genes will serve in rational design of new hybrid enzymes.

Strategies to establish the link between biosynthetic gene clusters and secondary metabolites.

Filamentous fungi produce a vast number of bioactive secondary metabolites (SMs), some of which have found applications in the pharmaceutical industry including as antibiotics and immunosuppressants. As more and more species are whole genome sequenced the number of predicted clusters of genes for SM biosynthesis is ever increasing - holding a promise of novel useful bioactive SMs. To be able to fully utilize the potential of novel SMs, it is necessary to link the SM and the genes responsible for producing it. This can be challenging, but many strategies and tools have been developed for this purpose. Here we provide an overview of the methods used to establish the link between SM and biosynthetic gene cluster (BGC) and vice versa, along with the challenges and advantages of each of the methods. Part I of the review, associating BCG with SM, is divided into gene manipulations native strain and heterologous expression strategies, depending on the fungal species. Part II, associating SM with BGC, is divided into three main approaches: (1) homology search (2) retro-biosynthesis and (3) comparative genomics.

Resistance Gene-Directed Genome Mining of 50 Aspergillus Species.

Fungal secondary metabolites are a rich source of valuable natural products, and genome sequencing has revealed a proliferation of predicted biosynthetic gene clusters in the genomes. However, it is currently an unfeasible task to characterize all biosynthetic gene clusters and to identify possible uses of the compounds. Therefore, a rational approach is needed to identify a short list of gene clusters responsible for producing valuable compounds. To this end, several bioactive clusters include a resistance gene, which is a paralog of the target gene inhibited by the compound. This mechanism can be used to identify these clusters. We have developed the FRIGG (fungal resistance gene-directed genome mining) pipeline for identifying this type of biosynthetic gene cluster based on homology patterns of the cluster genes. In this work, the FRIGG pipeline was run using 51 Aspergillus and Penicillium genomes, identifying 72 unique families of putative resistance genes. The pipeline also identified the previously characterized resistance gene inpE from the fellutamide B cluster, thereby validating the approach. We have successfully developed an approach to identify putative valuable bioactive clusters based on a specific resistance mechanism. This approach will be highly useful as an ever-increasing amount of genomic data becomes available; the art of identifying and selecting the right clusters producing novel valuable compounds will only become more crucial. IMPORTANCE Species belonging to the Aspergillus genus are known to produce a large number of secondary metabolites; some of these compounds are used as pharmaceuticals, such as penicillin, cyclosporine, and statin. With whole-genome sequencing, it became apparent that the genetic potential for secondary metabolite production is much larger than expected. As an increasing number of species are whole-genome sequenced, thousands of secondary metabolite genes are predicted, and the question of how to selectively identify novel bioactive compounds from this information arises. To address this question, we have created a pipeline to predict genes involved in the production of bioactive compounds based on a resistance gene hypothesis approach.

Uncovering secondary metabolite evolution and biosynthesis using gene cluster networks and genetic dereplication.

The increased interest in secondary metabolites (SMs) has driven a number of genome sequencing projects to elucidate their biosynthetic pathways. As a result, studies revealed that the number of secondary metabolite gene clusters (SMGCs) greatly outnumbers detected compounds, challenging current methods to dereplicate and categorize this amount of gene clusters on a larger scale. Here, we present an automated workflow for the genetic dereplication and analysis of secondary metabolism genes in fungi. Focusing on the secondary metabolite rich genus Aspergillus, we categorize SMGCs across genomes into SMGC families using network analysis. Our method elucidates the diversity and dynamics of secondary metabolism in section Nigri, showing that SMGC diversity within the section has the same magnitude as within the genus. Using our genome analysis we were able to predict the gene cluster responsible for biosynthesis of malformin, a potentiator of anti-cancer drugs, in 18 strains. To proof the general validity of our predictions, we developed genetic engineering tools in Aspergillus brasiliensis and subsequently verified the genes for biosynthesis of malformin.

Investigation of inter- and intraspecies variation through genome sequencing of Aspergillus section Nigri.

Aspergillus section Nigri comprises filamentous fungi relevant to biomedicine, bioenergy, health, and biotechnology. To learn more about what genetically sets these species apart, as well as about potential applications in biotechnology and biomedicine, we sequenced 23 genomes de novo, forming a full genome compendium for the section (26 species), as well as 6 Aspergillus niger isolates. This allowed us to quantify both inter- and intraspecies genomic variation. We further predicted 17,903 carbohydrate-active enzymes and 2,717 secondary metabolite gene clusters, which we condensed into 455 distinct families corresponding to compound classes, 49% of which are only found in single species. We performed metabolomics and genetic engineering to correlate genotypes to phenotypes, as demonstrated for the metabolite aurasperone, and by heterologous transfer of citrate production to Aspergillus nidulans. Experimental and computational analyses showed that both secondary metabolism and regulation are key factors that are significant in the delineation of Aspergillus species.

Novofumigatonin biosynthesis involves a non-heme iron-dependent endoperoxide isomerase for orthoester formation.

Novofumigatonin (1), isolated from the fungus Aspergillus novofumigatus, is a heavily oxygenated meroterpenoid containing a unique orthoester moiety. Despite the wide distribution of orthoesters in nature and their biological importance, little is known about the biogenesis of orthoesters. Here we show the elucidation of the biosynthetic pathway of 1 and the identification of key enzymes for the orthoester formation by a series of CRISPR-Cas9-based gene-deletion experiments and in vivo and in vitro reconstitutions of the biosynthesis. The novofumigatonin pathway involves endoperoxy compounds as key precursors for the orthoester synthesis, in which the Fe(II)/α-ketoglutarate-dependent enzyme NvfI performs the endoperoxidation. NvfE, the enzyme catalyzing the orthoester synthesis, is an Fe(II)-dependent, but cosubstrate-free, endoperoxide isomerase, despite the fact that NvfE shares sequence homology with the known Fe(II)/α-ketoglutarate-dependent dioxygenases. NvfE thus belongs to a class of enzymes that gained an isomerase activity by losing the α-ketoglutarate-binding ability.

Linking secondary metabolites to gene clusters through genome sequencing of six diverse Aspergillus species.

The fungal genus of Aspergillus is highly interesting, containing everything from industrial cell factories, model organisms, and human pathogens. In particular, this group has a prolific production of bioactive secondary metabolites (SMs). In this work, four diverse Aspergillus species (A. campestris, A. novofumigatus, A. ochraceoroseus, and A. steynii) have been whole-genome PacBio sequenced to provide genetic references in three Aspergillus sections. A. taichungensis and A. candidus also were sequenced for SM elucidation. Thirteen Aspergillus genomes were analyzed with comparative genomics to determine phylogeny and genetic diversity, showing that each presented genome contains 15-27% genes not found in other sequenced Aspergilli. In particular, A. novofumigatus was compared with the pathogenic species A. fumigatus This suggests that A. novofumigatus can produce most of the same allergens, virulence, and pathogenicity factors as A. fumigatus, suggesting that A. novofumigatus could be as pathogenic as A. fumigatus Furthermore, SMs were linked to gene clusters based on biological and chemical knowledge and analysis, genome sequences, and predictive algorithms. We thus identify putative SM clusters for aflatoxin, chlorflavonin, and ochrindol in A. ochraceoroseus, A. campestris, and A. steynii, respectively, and novofumigatonin, ent-cycloechinulin, and epi-aszonalenins in A. novofumigatus Our study delivers six fungal genomes, showing the large diversity found in the Aspergillus genus; highlights the potential for discovery of beneficial or harmful SMs; and supports reports of A. novofumigatus pathogenicity. It also shows how biological, biochemical, and genomic information can be combined to identify genes involved in the biosynthesis of specific SMs.

FunGeneClusterS: Predicting fungal gene clusters from genome and transcriptome data.

INTRODUCTION: Secondary metabolites of fungi are receiving an increasing amount of interest due to their prolific bioactivities and the fact that fungal biosynthesis of secondary metabolites often occurs from co-regulated and co-located gene clusters. This makes the gene clusters attractive for synthetic biology and industrial biotechnology applications. We have previously published a method for accurate prediction of clusters from genome and transcriptome data, which could also suggest cross-chemistry, however, this method was limited both in the number of parameters which could be adjusted as well as in user-friendliness. Furthermore, sensitivity to the transcriptome data required manual curation of the predictions. In the present work, we have aimed at improving these features. RESULTS: FunGeneClusterS is an improved implementation of our previous method with a graphical user interface for off- and on-line use. The new method adds options to adjust the size of the gene cluster(s) being sought as well as an option for the algorithm to be flexible with genes in the cluster which may not seem to be co-regulated with the remainder of the cluster. We have benchmarked the method using data from the well-studied Aspergillus nidulans and found that the method is an improvement over the previous one. In particular, it makes it possible to predict clusters with more than 10 genes more accurately, and allows identification of co-regulated gene clusters irrespective of the function of the genes. It also greatly reduces the need for manual curation of the prediction results. We furthermore applied the method to transcriptome data from A. niger. Using the identified best set of parameters, we were able to identify clusters for 31 out of 76 previously predicted secondary metabolite synthases/synthetases. Furthermore, we identified additional putative secondary metabolite gene clusters. In total, we predicted 432 co-transcribed gene clusters in A. niger (spanning 1.323 genes, 12% of the genome). Some of these had functions related to primary metabolism, e.g. we have identified a cluster for biosynthesis of biotin, as well as several for degradation of aromatic compounds. The data identifies that suggests that larger parts of the fungal genome than previously anticipated operates as gene clusters. This includes both primary and secondary metabolism as well as other cellular maintenance functions. CONCLUSION: We have developed FunGeneClusterS in a graphical implementation and made the method capable of adjustments to different datasets and target clusters. The method is versatile in that it can predict co-regulated clusters not limited to secondary metabolism. Our analysis of data has shown not only the validity of the method, but also strongly suggests that large parts of fungal primary metabolism and cellular functions are both co-regulated and co-located.

Amino acid usage is asymmetrically biased in AT- and GC-rich microbial genomes.

INTRODUCTION: Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates. RESULTS: We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB. CONCLUSION: Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.

CMG-biotools, a free workbench for basic comparative microbial genomics.

BACKGROUND: Today, there are more than a hundred times as many sequenced prokaryotic genomes than were present in the year 2000. The economical sequencing of genomic DNA has facilitated a whole new approach to microbial genomics. The real power of genomics is manifested through comparative genomics that can reveal strain specific characteristics, diversity within species and many other aspects. However, comparative genomics is a field not easily entered into by scientists with few computational skills. The CMG-biotools package is designed for microbiologists with limited knowledge of computational analysis and can be used to perform a number of analyses and comparisons of genomic data. RESULTS: The CMG-biotools system presents a stand-alone interface for comparative microbial genomics. The package is a customized operating system, based on Xubuntu 10.10, available through the open source Ubuntu project. The system can be installed on a virtual computer, allowing the user to run the system alongside any other operating system. Source codes for all programs are provided under GNU license, which makes it possible to transfer the programs to other systems if so desired. We here demonstrate the package by comparing and analyzing the diversity within the class Negativicutes, represented by 31 genomes including 10 genera. The analyses include 16S rRNA phylogeny, basic DNA and codon statistics, proteome comparisons using BLAST and graphical analyses of DNA structures. CONCLUSION: This paper shows the strength and diverse use of the CMG-biotools system. The system can be installed on a vide range of host operating systems and utilizes as much of the host computer as desired. It allows the user to compare multiple genomes, from various sources using standardized data formats and intuitive visualizations of results. The examples presented here clearly shows that users with limited computational experience can perform complicated analysis without much training.

Veillonella, Firmicutes: Microbes disguised as Gram negatives.

The Firmicutes represent a major component of the intestinal microflora. The intestinal Firmicutes are a large, diverse group of organisms, many of which are poorly characterized due to their anaerobic growth requirements. Although most Firmicutes are Gram positive, members of the class Negativicutes, including the genus Veillonella, stain Gram negative. Veillonella are among the most abundant organisms of the oral and intestinal microflora of animals and humans, in spite of being strict anaerobes. In this work, the genomes of 24 Negativicutes, including eight Veillonella spp., are compared to 20 other Firmicutes genomes; a further 101 prokaryotic genomes were included, covering 26 phyla. Thus a total of 145 prokaryotic genomes were analyzed by various methods to investigate the apparent conflict of the Veillonella Gram stain and their taxonomic position within the Firmicutes. Comparison of the genome sequences confirms that the Negativicutes are distantly related to Clostridium spp., based on 16S rRNA, complete genomic DNA sequences, and a consensus tree based on conserved proteins. The genus Veillonella is relatively homogeneous: inter-genus pair-wise comparison identifies at least 1,350 shared proteins, although less than half of these are found in any given Clostridium genome. Only 27 proteins are found conserved in all analyzed prokaryote genomes. Veillonella has distinct metabolic properties, and significant similarities to genomes of Proteobacteria are not detected, with the exception of a shared LPS biosynthesis pathway. The clade within the class Negativicutes to which the genus Veillonella belongs exhibits unique properties, most of which are in common with Gram-positives and some with Gram negatives. They are only distantly related to Clostridia, but are even less closely related to Gram-negative species. Though the Negativicutes stain Gram-negative and possess two membranes, the genome and proteome analysis presented here confirm their place within the (mainly) Gram positive phylum of the Firmicutes. Further studies are required to unveil the evolutionary history of the Veillonella and other Negativicutes.

Bayesian prediction of bacterial growth temperature range based on genome sequences.

BACKGROUND: The preferred habitat of a given bacterium can provide a hint of which types of enzymes of potential industrial interest it might produce. These might include enzymes that are stable and active at very high or very low temperatures. Being able to accurately predict this based on a genomic sequence, would thus allow for an efficient and targeted search for production organisms, reducing the need for culturing experiments. RESULTS: This study found a total of 40 protein families useful for distinction between three thermophilicity classes (thermophiles, mesophiles and psychrophiles). The predictive performance of these protein families were compared to those of 87 basic sequence features (relative use of amino acids and codons, genomic and 16S rDNA AT content and genome size). When using naïve Bayesian inference, it was possible to correctly predict the optimal temperature range with a Matthews correlation coefficient of up to 0.68. The best predictive performance was always achieved by including protein families as well as structural features, compared to either of these alone. A dedicated computer program was created to perform these predictions. CONCLUSIONS: This study shows that protein families associated with specific thermophilicity classes can provide effective input data for thermophilicity prediction, and that the naïve Bayesian approach is effective for such a task. The program created for this study is able to efficiently distinguish between thermophilic, mesophilic and psychrophilic adapted bacterial genomes.

On the origins of a Vibrio species.

Thirty-two genome sequences of various Vibrionaceae members are compared, with emphasis on what makes V. cholerae unique. As few as 1,000 gene families are conserved across all the Vibrionaceae genomes analysed; this fraction roughly doubles for gene families conserved within the species V. cholerae. Of these, approximately 200 gene families that cluster on various locations of the genome are not found in other sequenced Vibrionaceae; these are possibly unique to the V. cholerae species. By comparing gene family content of the analysed genomes, the relatedness to a particular species is identified for two unspeciated genomes. Conversely, two genomes presumably belonging to the same species have suspiciously dissimilar gene family content. We are able to identify a number of genes that are conserved in, and unique to, V. cholerae. Some of these genes may be crucial to the niche adaptation of this species.

Genomic taxonomy of Vibrios.

BACKGROUND: Vibrio taxonomy has been based on a polyphasic approach. In this study, we retrieve useful taxonomic information (i.e. data that can be used to distinguish different taxonomic levels, such as species and genera) from 32 genome sequences of different vibrio species. We use a variety of tools to explore the taxonomic relationship between the sequenced genomes, including Multilocus Sequence Analysis (MLSA), supertrees, Average Amino Acid Identity (AAI), genomic signatures, and Genome BLAST atlases. Our aim is to analyse the usefulness of these tools for species identification in vibrios. RESULTS: We have generated four new genome sequences of three Vibrio species, i.e., V. alginolyticus 40B, V. harveyi-like 1DA3, and V. mimicus strains VM573 and VM603, and present a broad analyses of these genomes along with other sequenced Vibrio species. The genome atlas and pangenome plots provide a tantalizing image of the genomic differences that occur between closely related sister species, e.g. V. cholerae and V. mimicus. The vibrio pangenome contains around 26504 genes. The V. cholerae core genome and pangenome consist of 1520 and 6923 genes, respectively. Pangenomes might allow different strains of V. cholerae to occupy different niches. MLSA and supertree analyses resulted in a similar phylogenetic picture, with a clear distinction of four groups (Vibrio core group, V. cholerae-V. mimicus, Aliivibrio spp., and Photobacterium spp.). A Vibrio species is defined as a group of strains that share > 95% DNA identity in MLSA and supertree analysis, > 96% AAI, < or = 10 genome signature dissimilarity, and > 61% proteome identity. Strains of the same species and species of the same genus will form monophyletic groups on the basis of MLSA and supertree. CONCLUSION: The combination of different analytical and bioinformatics tools will enable the most accurate species identification through genomic computational analysis. This endeavour will culminate in the birth of the online genomic taxonomy whereby researchers and end-users of taxonomy will be able to identify their isolates through a web-based server. This novel approach to microbial systematics will result in a tremendous advance concerning biodiversity discovery, description, and understanding.