Search for: All records

The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges—that is the bacterial strains or species that a bacteriophage can successfully infect and kill—is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision—however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application.

 

Meiotic recombination landscapes differ greatly between distantly and closely related taxa, populations, individuals, sexes, and even within genomes; however, the factors driving this variation are yet to be well elucidated. Here, we directly estimate contemporary crossover rates and, for the first time, noncrossover rates in rhesus macaques (Macaca mulatta) from four three-generation pedigrees comprising 32 individuals. We further compare these results with historical, demography-aware, linkage disequilibrium–based recombination rate estimates. From paternal meioses in the pedigrees, 165 crossover events with a median resolution of 22.3 kb were observed, corresponding to a male autosomal map length of 2,357 cM—approximately 15% longer than an existing linkage map based on human microsatellite loci. In addition, 85 noncrossover events with a mean tract length of 155 bp were identified—similar to the tract lengths observed in the only other two primates in which noncrossovers have been studied to date, humans and baboons. Consistent with observations in other placental mammals with PRDM9-directed recombination, crossover (and to a lesser extent noncrossover) events in rhesus macaques clustered in intergenic regions and toward the chromosomal ends in males—a pattern in broad agreement with the historical, sex-averaged recombination rate estimates—and evidence of GC-biased gene conversion was observed at noncrossover sites.

 

Recent studies have highlighted variation in the mutational spectra among human populations as well as closely related hominoids—yet little remains known about the genetic and nongenetic factors driving these rate changes across the genome. Pinpointing the root causes of these differences is an important endeavor that requires careful comparative analyses of population-specific mutational landscapes at both broad and fine genomic scales. However, several factors can confound such analyses. Although previous studies have shown that technical artifacts, such as sequencing errors and batch effects, can contribute to observed mutational shifts, other potentially confounding parameters have received less attention thus far. Using population genetic simulations of human and chimpanzee populations as an illustrative example, we here show that the sample size required for robust inference of mutational spectra depends on the population-specific demographic history. As a consequence, the power to detect rate changes is high in certain hominoid populations while, for others, currently available sample sizes preclude analyses at fine genomic scales.

 

Bacteriophages are being widely harnessed as an alternative to antibiotics due to the global emergence of drug-resistant pathogens. To guide the usage of these bactericidal agents, characterization of their host specificity is vital—however, host range information remains limited for many bacteriophages. This is particularly the case for bacteriophages infecting the Microbacterium genus, despite their importance in agriculture, biomedicine, and biotechnology. Here, we elucidate the phylogenomic relationships between 125 Microbacterium cluster EA bacteriophages—including members from 11 sub-clusters (EA1 to EA11)—and infer their putative host ranges using insights from codon usage bias patterns as well as predictions from both exploratory and confirmatory computational methods. Our computational analyses suggest that cluster EA bacteriophages have a shared infection history across the Microbacterium clade. Interestingly, bacteriophages of all sub-clusters exhibit codon usage preference patterns that resemble those of bacterial strains different from ones used for isolation, suggesting that they might be able to infect additional hosts. Furthermore, host range predictions indicate that certain sub-clusters may be better suited in prospective biotechnological and medical applications such as phage therapy. 

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments. 

Abstract Bacteriophages, infecting bacterial hosts in every environment on our planet, are a driver of adaptive evolution in bacterial communities. At the same time, the host range of many bacteriophages—and thus one of the selective pressures acting on complex microbial systems in nature—remains poorly characterized. Here, we computationally inferred the putative host ranges of 40 cluster P mycobacteriophages, including members from 6 subclusters (P1–P6). A series of comparative genomic analyses revealed that mycobacteriophages of subcluster P1 are restricted to the Mycobacterium genus, whereas mycobacteriophages of subclusters P2–P6 are likely also able to infect other genera, several of which are commonly associated with human disease. Further genomic analysis highlighted that the majority of cluster P mycobacteriophages harbor a conserved integration-dependent immunity system, hypothesized to be the ancestral state of a genetic switch that controls the shift between lytic and lysogenic life cycles—a temperate characteristic that impedes their usage in antibacterial applications. 

Bacteriophages infecting bacteria of the genus Gordonia have increasingly gained interest in the scientific community for their diverse applications in agriculture, biotechnology, and medicine, ranging from biocontrol agents in wastewater management to the treatment of opportunistic pathogens in pulmonary disease patients. However, due to the time and costs associated with experimental isolation and cultivation, host ranges for many bacteriophages remain poorly characterized, hindering a more efficient usage of bacteriophages in these areas. Here, we perform a series of computational genomic inferences to predict the putative host ranges of all Gordonia cluster DR bacteriophages known to date. Our analyses suggest that BiggityBass (as well as several of its close relatives) is likely able to infect host bacteria from a wide range of genera—from Gordonia to Nocardia to Rhodococcus, making it a suitable candidate for future phage therapy and wastewater treatment strategies. 

ABSTRACT Here, we characterized the complete genome of the Siphoviridae BiggityBass, a lytic subcluster DR bacteriophage infecting Gordonia terrae CAG3. Its 63.2-kb genome contains 84 protein-coding genes, of which 40 could be assigned a putative function. BiggityBass is related most closely to AnClar and Yago84 with 90.61% and 90.52% nucleotide identity, respectively. 

ABSTRACT We characterized the complete genome of the cluster P mycobacteriophage Phegasus. Its 47.5-kb genome contains 81 protein-coding genes, 36 of which could be assigned a putative function. Phegasus is most closely related to two subcluster P1 bacteriophages, Mangethe and Majeke, with an average nucleotide identity of 99.63% each. 

Abstract Bacteriophages infecting pathogenic hosts play an important role in medical research, not only as potential treatments for antibiotic-resistant infections but also offering novel insights into pathogen genetics and evolution. A prominent example is cluster K mycobacteriophages infecting Mycobacterium tuberculosis, a causative agent of tuberculosis in humans. However, as handling M. tuberculosis as well as other pathogens in a laboratory remains challenging, alternative nonpathogenic relatives, such as Mycobacterium smegmatis, are frequently used as surrogates to discover therapeutically relevant bacteriophages in a safer environment. Consequently, the individual host ranges of the majority of cluster K mycobacteriophages identified to date remain poorly understood. Here, we characterized the complete genome of Stinson, a temperate subcluster K1 mycobacteriophage with a siphoviral morphology. A series of comparative genomic analyses revealed strong similarities with other cluster K mycobacteriophages, including the conservation of an immunity repressor gene and a toxin/antitoxin gene pair. Patterns of codon usage bias across the cluster offered important insights into putative host ranges in nature, highlighting that although all cluster K mycobacteriophages are able to infect M. tuberculosis, they are less likely to have shared an evolutionary infection history with Mycobacterium leprae (underlying leprosy) compared to the rest of the genus’ host species. Moreover, subcluster K1 mycobacteriophages are able to integrate into the genomes of Mycobacterium abscessus and Mycobacterium marinum—two bacteria causing pulmonary and cutaneous infections which are often difficult to treat due to their drug resistance.