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Natural competence allows bacteria to respond to environmental and nutritional cues by taking up free DNA from their surroundings, thus gaining both nutrients and genetic information. In the Gram-negative bacterium Haemophilus influenzae, the genes needed for DNA uptake are induced by the CRP and Sxy transcription factors in response to lack of preferred carbon sources and nucleotide precursors. Here we show that one of these genes, HI0659, encodes the antitoxin of a competence-regulated toxin-antitoxin operon ('toxTA'), likely acquired by horizontal gene transfer from a Streptococcus species. Deletion of the putative toxin (HI0660) restores uptake to the antitoxin mutant. The full toxTA operon was present in only 17 of the 181 strains we examined; complete deletion was seen in 22 strains and deletions removing parts of the toxin gene in 142 others. In addition to the expected Sxy- and CRP-dependent-competence promoter, HI0659/660 transcript analysis using RNA-seq identified an internal antitoxin-repressed promoter whose transcription starts within toxT and will yield nonfunctional protein. We propose that the most likely effect of unopposed toxin expression is non-specific cleavage of mRNAs and arrest or death of competent cells in the culture. Although the high frequency of toxT and toxTA deletions suggests that this competence-regulated toxin-antitoxin system may be mildly deleterious, it could also facilitate downregulation of protein synthesis and recycling of nucleotides under starvation conditions. Although our analyses were focused on the effects of toxTA, the RNA-seq dataset will be a useful resource for further investigations into competence regulation.

Maf1 is the master repressor of RNA polymerase III responsible for transcription of tRNAs and 5S rRNAs. Maf1 is negatively regulated via phosphorylation by the mTOR pathway, which governs protein synthesis, growth control, and lifespan regulation in response to nutrient availability. Inhibiting the mTOR pathway extends lifespan in various organisms. However, the downstream effectors for the regulation of cell homeostasis that are critical to lifespan extension remain elusive. Here we show that fission yeast Maf1 is required for lifespan extension. Maf1’s function in tRNA repression is inhibited by mTOR‐dependent phosphorylation, whereas Maf1 is activated via dephosphorylation by protein phosphatase complexes, PP4 and PP2A. Mutational analysis reveals that Maf1 phosphorylation status influences lifespan, which is correlated with elevated tRNA and protein synthesis levels in maf1∆ cells. However, mTOR downregulation, which negates protein synthesis, fails to rescue the short lifespan of maf1∆ cells, suggesting that elevated protein synthesis is not a cause of lifespan shortening in maf1∆ cells. Interestingly, maf1∆ cells accumulate DNA damage represented by formation of Rad52 DNA damage foci and Rad52 recruitment at tRNA genes. Loss of the Rad52 DNA repair protein further exacerbates the shortened lifespan of maf1∆ cells. Strikingly, PP4 deletion alleviates DNA damage and rescues the short lifespan of maf1∆ cells even though tRNA synthesis is increased in this condition, suggesting that elevated DNA damage is the major cause of lifespan shortening in maf1∆ cells. We propose that Maf1‐dependent inhibition of tRNA synthesis controls fission yeast lifespan by preventing genomic instability that arises at tRNA genes. In response to nutrient availability, mTOR inactivates Maf1 via phosphorylation. This results in hyperactivation of Pol III‐mediated transcription, leading to DNA damage due to collisions between the replication and transcription machineries. Such DNA damage may shorten lifespan. mTOR‐mediated activation of cellular processes and increased energy expenditure also negatively affect lifespan.

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno , achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Background Pan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences’ (PacBio’s) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data. Results Analysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes ) was the predominant species throughout, but was found at distinct relative abundances by site. Conclusions Our microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt ) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.

Respiratory viral infections contribute substantially to global infant losses and disproportionately affect preterm neonates. Using our previously established neonatal murine model of influenza infection, we demonstrate that three-day old mice are exceptionally sensitive to influenza virus infection and exhibit high mortality and viral load. Intranasal pre- and post-treatment of neonatal mice with Lactobacillus rhamnosus GG (LGG), an immune modulator in respiratory viral infection of adult mice and human preterm neonates, considerably improves neonatal mice survival after influenza virus infection. We determine that both live and heat-killed intranasal LGG are equally efficacious in protection of neonates. Early in influenza infection, neonatal transcriptional responses in the lung are delayed compared to adults. These responses increase by 24 hours post-infection, demonstrating a delay in the kinetics of the neonatal anti-viral response. LGG pretreatment improves immune gene transcriptional responses during early infection and specifically upregulates type I IFN pathways. This is critical for protection, as neonatal mice intranasally pre-treated with IFNβ before influenza virus infection are also protected. Using transgenic mice, we demonstrate that the protective effect of LGG is mediated through a MyD88-dependent mechanism, specifically via TLR4. LGG can improve both early control of virus and transcriptional responsiveness and could serve as a simple and safe intervention to protect neonates.

In this report, we present OLAF-Seq, a novel strategy to construct a long-read sequencing library such that adjacent fragments are linked with end-terminal duplications. We use the CRISPR-Cas9 nickase enzyme and a pool of multiple sgRNAs to perform non-random fragmentation of targeted long DNA molecules (> 300kb) into smaller library-sized fragments (about 20 kbp) in a manner so as to retain physical linkage information (up to 1000 bp) between adjacent fragments. DNA molecules targeted for fragmentation are preferentially ligated with adaptors for sequencing, so this method can enrich targeted regions while taking advantage of the long-read sequencing platforms. This enables the sequencing of target regions with significantly lower total coverage, and the genome sequence within linker regions provides information for assembly and phasing. We demonstrated the validity and efficacy of the method first using phage and then by sequencing a panel of 100 full-length cancer-related genes (including both exons and introns) in the human genome. When the designed linkers contained heterozygous genetic variants, long haplotypes could be established. This sequencing strategy can be readily applied in both PacBio and Oxford Nanopore platforms for both long and short genes with an easy protocol. This economically viable approach is useful for targeted enrichment of hundreds of target genomic regions and where long no-gap contigs need deep sequencing.

Clustered regularly interspaced short palindromic repeats (CRISPR) CRISPR-associated protein 9 (Cas9), including specific guide RNAs (gRNAs), can excise integrated human immunodeficiency virus type 1 (HIV-1) provirus from host chromosomes. To date, anti-HIV-1 gRNAs have been designed to account for off-target activity, however, they seldom account for genetic variation in the HIV-1 genome within and between patients, which will be crucial for therapeutic application of this technology. This analysis tests the ability of published anti-HIV-1 gRNAs to cleave publicly available patient-derived HIV-1 sequences to inform gRNA design and provides basic computational tools to researchers in the field.

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using \"topics\" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.

The CRISPR/Cas9 system has been proposed as a cure strategy for HIV. However, few published guide RNAs (gRNAs) are predicted to cleave the majority of HIV-1 viral quasispecies (vQS) observed within and among patients. We report the design of a novel pipeline to identify gRNAs that target HIV across a large number of infected individuals. Next generation sequencing (NGS) of LTRs from 269 HIV-1-infected samples in the Drexel CARES Cohort was used to select gRNAs with predicted broad-spectrum activity. In silico , D-LTR-P4-227913 (package of the top 4 gRNAs) accounted for all detectable genetic variation within the vQS of the 269 samples and the Los Alamos National Laboratory HIV database. In silico secondary structure analyses from NGS indicated extensive TAR stem-loop malformations predicted to inactivate proviral transcription, which was confirmed by reduced viral gene expression in TZM-bl or P4R5 cells. Similarly, a high sensitivity in vitro CRISPR/Cas9 cleavage assay showed that the top-ranked gRNA was the most effective at cleaving patient-derived HIV-1 LTRs from five patients. Furthermore, the D-LTR-P4-227913 was predicted to cleave a median of 96.1% of patient-derived sequences from other HIV subtypes. These results demonstrate that the gRNAs possess broad-spectrum cutting activity and could contribute to an HIV cure.

Background Bacterial vaginosis (BV) affects 20–50% of reproductive-age female patients annually, arising when opportunistic pathogens outcompete healthy vaginal flora. Many patients fail to resolve symptoms with a course of metronidazole, the current first-line treatment for BV. Our study was designed to identify genomic variation associated with metronidazole resistance among strains of Gardnerella vaginalis spp. (GV), a genus of biogenic-amine-producing bacteria closely associated with BV pathogenesis, for the development of a companion molecular diagnostic. Methods Whole-genome sequencing and comparative genomic metrics, including average nucleotide identity and GC content, were performed on a diverse set of 129 GV genomes to generate data for detailed taxonomic analyses. Pangenomic analyses were employed to construct a phylogenetic tree and cluster highly related strains within genospecies. G. vaginalis spp. clinical isolates within our collection were subjected to plate-based minimum inhibitory concentration (MIC) testing of metronidazole ( n  = 60) and clindamycin ( n  = 63). DECIPHER and MAFFT were used to identify genospecies-specific primers associated with antibiotic-resistance phenotypes. PCR-based analyses with these primers were used to confirm their specificity for the relevant genospecies. Results Eleven distinct genospecies based on standard ANI criteria were identified among the GV strains in our collection. Metronidazole MIC testing revealed six genospecies within a closely related phylogenetic clade contained only highly metronidazole-resistant strains (MIC ≥ 32 µg/mL) and suggested at least two mechanisms of metronidazole resistance within the eleven GV genospecies. All strains within the six highly metronidazole-resistant genospecies displayed susceptibility to clinically relevant clindamycin concentrations (MIC ≤ 2 µg/mL). A PCR-based molecular diagnostic assay was developed to distinguish between members of the metronidazole-resistant and mixed-response genospecies, which should be useful for determining the clade membership of various GV strains and could assist in the selection of appropriate antibiotic therapies for BV cases. Conclusions This study provides comparative genomic and phylogenetic evidence for eleven distinct genospecies within the genus Gardnerella vaginalis spp., and identifies genospecies-specific responses to metronidazole, the first-line treatment for BV. A companion molecular diagnostic assay was developed that is capable of identifying essentially all highly metronidazole-resistant strains that phylogenetically cluster together within the GV genospecies, which is informative for antibiotic treatment options.