Search for Cry proteins expressed by Bacillus spp. genomes, using hidden Markov model profiles.
Ontology highlight
ABSTRACT:
SUBMITTER: Castillo-Esparza JF
PROVIDER: S-EPMC6314945 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure.ResultsWe developed enhancer HMM (eHMM), a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. Both consist of a central stretch of accessible DNA flanked by nucleosomes with distinct histone modification patterns. We evaluated the performance of eHMM within and across cell types and developmental stages and found that eHMM successfully predicts enhancers with high precision and recall comparable to state-of-the-art methods, and consistently outperforms those in terms of accuracy and resolution.ConclusionseHMM predicts active enhancers based on data from chromatin accessibility assays and a minimal set of histone modification ChIP-seq experiments. In comparison to other 'black box' methods its parameters are easy to interpret. eHMM can be used as a stand-alone tool for enhancer prediction without the need for additional training or a tuning of parameters. The high spatial precision of enhancer predictions gives valuable targets for potential knockout experiments or downstream analyses such as motif search.
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Project description:Bacteria exploit an arsenal of antimicrobial peptides and proteins to compete with each other. Three main competition systems have been described: type six secretion systems (T6SS); contact dependent inhibition (CDI); and bacteriocins. Unlike T6SS and CDI systems, bacteriocins do not require contact between bacteria but are diffusible toxins released into the environment. Identified almost a century ago, our understanding of bacteriocin distribution and prevalence in bacterial populations remains poor. In the case of protein bacteriocins, this is because of high levels of sequence diversity and difficulties in distinguishing their killing domains from those of other competition systems. Here, we develop a robust bioinformatics pipeline exploiting Hidden Markov Models for the identification of nuclease bacteriocins (NBs) in bacteria of which, to-date, only a handful are known. NBs are large (>60 kDa) toxins that target nucleic acids (DNA, tRNA or rRNA) in the cytoplasm of susceptible bacteria, usually closely related to the producing organism. We identified >3000 NB genes located on plasmids or on the chromosome from 53 bacterial species distributed across different ecological niches, including human, animals, plants, and the environment. A newly identified NB predicted to be specific for Pseudomonas aeruginosa (pyocin Sn) was produced and shown to kill P. aeruginosa thereby validating our pipeline. Intriguingly, while the genes encoding the machinery needed for NB translocation across the cell envelope are widespread in Gram-negative bacteria, NBs are found exclusively in γ-proteobacteria. Similarity network analysis demonstrated that NBs fall into eight groups each with a distinct arrangement of protein domains involved in import. The only structural feature conserved across all groups was a sequence motif critical for cell-killing that is generally not found in bacteriocins targeting the periplasm, implying a specific role in translocating the nuclease to the cytoplasm. Finally, we demonstrate a significant association between nuclease colicins, NBs specific for Escherichia coli, and virulence factors, suggesting NBs play a role in infection processes, most likely by enabling pathogens to outcompete commensal bacteria.
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Project description:Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes' genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons.
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Project description:Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user's manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.
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Project description:Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events.We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences.Our results demonstrate that jumps in our jumping profile HMM often correspond to recombination breakpoints; our approach can therefore be used to detect recombinations in genomic sequences. The recombination breakpoints identified by jpHMM were found to be significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.
| S-EPMC1525204 | biostudies-literature
Project description:Cry1Aa, an insecticidal protein produced by Bacillus thuringiensis, has been shown to bind to cadherin-like protein, BtR175, in Bombyx mori (silkworm) midgut. We previously reported three variant alleles of BtR175 (BtR175a, b and c). When transiently expressed in COS7 cells, all the three BtR175 variants bound to Cry1Aa. We stably expressed BtR175b in HEK293 cells. These BtR175b-expressing cells swelled and died in the presence of activated Cry1Aa in a dose- and time-dependent manner, showing that BtR175b itself can impart Cry1Aa-susceptibility to mammalian cells. These cells were more susceptible to Cry1Aa than to Cry1Ab and Cry1Ac. Since dispersed B. mori midgut cells were reported to be highly susceptible to Cry1Ac, this result suggested that other Cry1Ac-specific receptor(s) were simultaneously working with BtR175 in the midgut cells. Advantages are also discussed of applying these transfected mammalian cells to toxicity assays of mutant Cry proteins.
| S-EPMC1223121 | biostudies-literature
Project description:BackgroundTransgenic corn engineered with genes expressing insecticidal toxins from the bacterium Bacillus thuringiensis (Berliner) (Bt) are now a major tool in insect pest management. With its widespread use, insect resistance is a major threat to the sustainability of the Bt transgenic technology. For all Bt corn expressing Cry toxins, the high dose requirement for resistance management is not achieved for corn earworm, Helicoverpa zea (Boddie), which is more tolerant to the Bt toxins.Methodology/major findingsWe present field monitoring data using Cry1Ab (1996-2016) and Cry1A.105+Cry2Ab2 (2010-2016) expressing sweet corn hybrids as in-field screens to measure changes in field efficacy and Cry toxin susceptibility to H. zea. Larvae successfully damaged an increasing proportion of ears, consumed more kernel area, and reached later developmental stages (4th - 6th instars) in both types of Bt hybrids (Cry1Ab-event Bt11, and Cry1A.105+Cry2Ab2-event MON89034) since their commercial introduction. Yearly patterns of H. zea population abundance were unrelated to reductions in control efficacy. There was no evidence of field efficacy or tissue toxicity differences among different Cry1Ab hybrids that could contribute to the decline in control efficacy. Supportive data from laboratory bioassays demonstrate significant differences in weight gain and fitness characteristics between the Maryland H. zea strain and a susceptible strain. In bioassays with Cry1Ab expressing green leaf tissue, Maryland H. zea strain gained more weight than the susceptible strain at all concentrations tested. Fitness of the Maryland H. zea strain was significantly lower than that of the susceptible strain as indicated by lower hatch rate, longer time to adult eclosion, lower pupal weight, and reduced survival to adulthood.Conclusions/significanceAfter ruling out possible contributing factors, the rapid change in field efficacy in recent years and decreased susceptibility of H. zea to Bt sweet corn provide strong evidence of field-evolved resistance in H. zea populations to multiple Cry toxins. The high adoption rate of Bt field corn and cotton, along with the moderate dose expression of Cry1Ab and related Cry toxins in these crops, and decreasing refuge compliance probably contributed to the evolution of resistance. Our results have important implications for resistance monitoring, refuge requirements and other regulatory policies, cross-resistance issues, and the sustainability of the pyramided Bt technology.
| S-EPMC5201267 | biostudies-literature