Taste-specific cell assemblies in a biologically informed model of the nucleus of the solitary tract.

Although the cellular organization of many primary sensory nuclei has been well characterized, questions remain about the functional architecture of the first central relay for gustation, the rostral nucleus of the solitary tract (NTS). Here we used electrophysiological data recorded from single cells in the NTS to inform a network model of taste processing. Previous studies showed that electrical stimulation of the chorda tympani (CT) nerve initiates two types of inhibitory influences with different time courses in separate groups of NTS cells. Each type of inhibition targeted cells with distinct taste response properties. Further analyses of these data identified three NTS cell types differentiated by their latency of evoked response, time course of CT evoked inhibition, and degree of selectivity across taste qualities. Based on these results, we designed a model of the NTS consisting of discrete, reciprocally connected, stimulus-specific "cell" assemblies. Input to the network of integrate-and-fire model neurons was based on electrophysiological recordings from the CT nerve. Following successful simulation of paired-pulse CT stimulation, the network was tested for its ability to discriminate between two "taste" stimuli. Network dynamics of the model produced biologically plausible responses from each unit type and enhanced discrimination between taste qualities. We propose that an interactive network of taste quality specific cell assemblies, similar to our model, may account for the coherence in across-neuron patterns of NTS responses between similar tastants.

Single-molecule sequencing detection of N6-methyladenine in microbial reference materials.

The DNA base modification N6-methyladenine (m6A) is involved in many pathways related to the survival of bacteria and their interactions with hosts. Nanopore sequencing offers a new, portable method to detect base modifications. Here, we show that a neural network can improve m6A detection at trained sequence contexts compared to previously published methods using deviations between measured and expected current values as each adenine travels through a pore. The model, implemented as the mCaller software package, can be extended to detect known or confirm suspected methyltransferase target motifs based on predictions of methylation at untrained contexts. We use PacBio, Oxford Nanopore, methylated DNA immunoprecipitation sequencing (MeDIP-seq), and whole-genome bisulfite sequencing data to generate and orthogonally validate methylomes for eight microbial reference species. These well-characterized microbial references can serve as controls in the development and evaluation of future methods for the identification of base modifications from single-molecule sequencing data.

FDA-ARGOS is a database with public quality-controlled reference genomes for diagnostic use and regulatory science.

FDA proactively invests in tools to support innovation of emerging technologies, such as infectious disease next generation sequencing (ID-NGS). Here, we introduce FDA-ARGOS quality-controlled reference genomes as a public database for diagnostic purposes and demonstrate its utility on the example of two use cases. We provide quality control metrics for the FDA-ARGOS genomic database resource and outline the need for genome quality gap filling in the public domain. In the first use case, we show more accurate microbial identification of Enterococcus avium from metagenomic samples with FDA-ARGOS reference genomes compared to non-curated GenBank genomes. In the second use case, we demonstrate the utility of FDA-ARGOS reference genomes for Ebola virus target sequence comparison as part of a composite validation strategy for ID-NGS diagnostic tests. The use of FDA-ARGOS as an in silico target sequence comparator tool combined with representative clinical testing could reduce the burden for completing ID-NGS clinical trials.

Extra-Chromosomal DNA Sequencing Reveals Episomal Prophages Capable of Impacting Virulence Factor Expression in Staphylococcus aureus.

Staphylococcus aureus is a major human pathogen with well-characterized bacteriophage contributions to its virulence potential. Recently, we identified plasmidial and episomal prophages in S. aureus strains using an extra-chromosomal DNA (exDNA) isolation and sequencing approach, uncovering the plasmidial phage ϕBU01, which was found to encode important virulence determinants. Here, we expanded our extra-chromosomal sequencing of S. aureus, selecting 15 diverse clinical isolates with known chromosomal sequences for exDNA isolation and next-generation sequencing. We uncovered the presence of additional episomal prophages in 5 of 15 samples, but did not identify any plasmidial prophages. exDNA isolation was found to enrich for circular prophage elements, and qPCR characterization of the strains revealed that such prophage enrichment is detectable only in exDNA samples and would likely be missed in whole-genome DNA preparations (e.g., detection of episomal prophages did not correlate with higher prophage excision rates nor higher excised prophage copy numbers in qPCR experiments using whole-genome DNA). In S. aureus MSSA476, we found that enrichment and excision of the prophage ϕSa4ms into the cytoplasm was temporal and that episomal prophage localization did not appear to be a precursor to lytic cycle replication, suggesting ϕSa4ms excision into the cytoplasm may be part of a novel lysogenic switch. For example, we show that ϕSa4ms excision alters the promoter and transcription of htrA2 , encoding a stress-response serine protease, and that alternative promotion of htrA2 confers increased heat-stress survival in S. aureus COL. Overall, exDNA isolation and focused sequencing may offer a more complete genomic picture for bacterial pathogens, offering insights into important chromosomal dynamics likely missed with whole-genome DNA-based approaches.

Making the Leap from Research Laboratory to Clinic: Challenges and Opportunities for Next-Generation Sequencing in Infectious Disease Diagnostics.

Next-generation DNA sequencing (NGS) has progressed enormously over the past decade, transforming genomic analysis and opening up many new opportunities for applications in clinical microbiology laboratories. The impact of NGS on microbiology has been revolutionary, with new microbial genomic sequences being generated daily, leading to the development of large databases of genomes and gene sequences. The ability to analyze microbial communities without culturing organisms has created the ever-growing field of metagenomics and microbiome analysis and has generated significant new insights into the relation between host and microbe. The medical literature contains many examples of how this new technology can be used for infectious disease diagnostics and pathogen analysis. The implementation of NGS in medical practice has been a slow process due to various challenges such as clinical trials, lack of applicable regulatory guidelines, and the adaptation of the technology to the clinical environment. In April 2015, the American Academy of Microbiology (AAM) convened a colloquium to begin to define these issues, and in this document, we present some of the concepts that were generated from these discussions.

A knowledge-based method for association studies on complex diseases.

Complex disorders are a class of diseases whose phenotypic variance is caused by the interplay of multiple genetic and environmental factors. Analyzing the complexity underlying the genetic architecture of such traits may help develop more efficient diagnostic tests and therapeutic protocols. Despite the continuous advances in revealing the genetic basis of many of complex diseases using genome-wide association studies (GWAS), a major proportion of their genetic variance has remained unexplained, in part because GWAS are unable to reliably detect small individual risk contributions and to capture the underlying genetic heterogeneity. In this paper we describe a hypothesis-based method to analyze the association between multiple genetic factors and a complex phenotype. Starting from sets of markers selected based on preexisting biomedical knowledge, our method generates multi-marker models relevant to the biological process underlying a complex trait for which genotype data is available. We tested the applicability of our method using the WTCCC case-control dataset. Analyzing a number of biological pathways, the method was able to identify several immune system related multi-SNP models significantly associated with Rheumatoid Arthritis (RA) and Crohn's disease (CD). RA-associated multi-SNP models were also replicated in an independent case-control dataset. The method we present provides a framework for capturing joint contributions of genetic factors to complex traits. In contrast to hypothesis-free approaches, its results can be given a direct biological interpretation. The replicated multi-SNP models generated by our analysis may serve as a predictor to estimate the risk of RA development in individuals of Caucasian ancestry.