Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women.

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Association between pre-exercise food ingestion timing and reactive hypoglycemia: Insights from a large database of continuous glucose monitoring data.

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.

Large datasets of self-reported continuous glucose monitoring and food events are used here for the first time to get insights into reactive hypoglycemia, a condition often regarded as negative for endurance performance eventsUsing a binomial non-linear logistic regression model, the association between pre-exercise food ingestion timing and reactive hypoglycemia revealed the presence of an unfavourable window, when reactive hypoglycemia is more likely to occur.Results confirm an individual predisposition to reactive hypoglycemia and, for 8 in 100 individuals, the pre-exercise food ingestion timing can meaningfully impact the likelihood of experiencing reactive hypoglycemia.

Development of a targeted amplicon sequencing method for genotyping Cyclospora cayetanensis from fresh produce and clinical samples with enhanced genomic resolution and sensitivity.

Outbreaks of cyclosporiasis, an enteric illness caused by the parasite Cyclospora cayetanensis, have been associated with consumption of various types of fresh produce. Although a method is in use for genotyping C. cayetanensis from clinical specimens, the very low abundance of C. cayetanensis in food and environmental samples presents a greater challenge. To complement epidemiological investigations, a molecular surveillance tool is needed for use in genetic linkage of food vehicles to cyclosporiasis illnesses, estimation of the scope of outbreaks or clusters of illness, and determination of geographical areas involved. We developed a targeted amplicon sequencing (TAS) assay that incorporates a further enrichment step to gain the requisite sensitivity for genotyping C. cayetanensis contaminating fresh produce samples. The TAS assay targets 52 loci, 49 of which are located in the nuclear genome, and encompasses 396 currently known SNP sites. The performance of the TAS assay was evaluated using lettuce, basil, cilantro, salad mix, and blackberries inoculated with C. cayetanensis oocysts. A minimum of 24 markers were haplotyped even at low contamination levels of 10 oocysts in 25 g leafy greens. The artificially contaminated fresh produce samples were included in a genetic distance analysis based on haplotype presence/absence with publicly available C. cayetanensis whole genome sequence assemblies. Oocysts from two different sources were used for inoculation, and samples receiving the same oocyst preparation clustered together, but separately from the other group, demonstrating the utility of the assay for genetically linking samples. Clinical fecal samples with low parasite loads were also successfully genotyped. This work represents a significant advance in the ability to genotype C. cayetanensis contaminating fresh produce along with greatly expanding the genomic diversity included for genetic clustering of clinical specimens.

Development and Single Laboratory Evaluation of a Refined and specific Real-time PCR Detection Method, Using Mitochondrial Primers (Mit1C), for the Detection of Cyclospora cayetanensis in Produce.

Regulatory methods for detection of the foodborne protozoan parasite Cyclospora cayetanensis must be specific and sensitive. To that end, we designed and evaluated (in a single laboratory validation) a novel and improved primer/probe combination (Mit1C) for real-time PCR detection of C. cayetanensis in produce. The newly developed primer/probe combination targets a conserved region of the mitochondrial genome of C. cayetanensis that varies in other closely related organisms. The primer/probe combination was evaluated both in silico and using several real-time PCR kits and polymerases against an inclusivity/exclusivity panel comprised of a variety of C. cayetanensis oocysts, as well as DNA from other related Cyclospora spp. and closely related parasites. The new primer/probe combination amplified only C. cayetanensis, thus demonstrating specificity. Sensitivity was evaluated by artificially contaminating cilantro, raspberries, and romaine lettuce with variable numbers (200 and 5) of C. cayetanensis oocysts. As few as 5 oocysts were detected in 75%, 67.7%, and 50% of the spiked produce samples (cilantro, raspberries, and romaine lettuce), respectively, all uninoculated samples and no-template real-time PCR controls were negative. The improved primer/probe combination should prove an effective analytical tool for the specific detection of C. cayetanensis in produce.

Restriction Endonuclease-Based Modification-Dependent Enrichment (REMoDE) of DNA for Metagenomic Sequencing.

Metagenomic sequencing is a swift and powerful tool to ascertain the presence of an organism of interest in a sample. However, sequencing coverage of the organism of interest can be insufficient due to an inundation of reads from irrelevant organisms in the sample. Here, we report a nuclease-based approach to rapidly enrich for DNA from certain organisms, including enterobacteria, based on their differential endogenous modification patterns. We exploit the ability of taxon-specific methylated motifs to resist the action of cognate methylation-sensitive restriction endonucleases that thereby digest unwanted, unmethylated DNA. Subsequently, we use a distributive exonuclease or electrophoretic separation to deplete or exclude the digested fragments, thus enriching for undigested DNA from the organism of interest. As a proof of concept, we apply this method to enrich for the enterobacteria Escherichia coli and Salmonella enterica by 11- to 142-fold from mock metagenomic samples and validate this approach as a versatile means to enrich for genomes of interest in metagenomic samples. IMPORTANCE Pathogens that contaminate the food supply or spread through other means can cause outbreaks that bring devastating repercussions to the health of a populace. Investigations to trace the source of these outbreaks are initiated rapidly but can be drawn out due to the labored methods of pathogen isolation. Metagenomic sequencing can alleviate this hurdle but is often insufficiently sensitive. The approach and implementations detailed here provide a rapid means to enrich for many pathogens involved in foodborne outbreaks, thereby improving the utility of metagenomic sequencing as a tool in outbreak investigations. Additionally, this approach provides a means to broadly enrich for otherwise minute levels of modified DNA, which may escape unnoticed in metagenomic samples.

SKESA: strategic k-mer extension for scrupulous assemblies.

SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity, handles low-level contamination in reads, is fast, and produces an identical assembly for the same input when assembled multiple times with the same or different compute resources. SKESA has been used for assembling over 272,000 read sets in the Sequence Read Archive at NCBI and for real-time pathogen detection. Source code for SKESA is freely available at https://github.com/ncbi/SKESA/releases .

How user intelligence is improving PubMed.

PubMed is a widely used search engine for biomedical literature. It is developed and maintained by the US National Library of Medicine/National Center for Biotechnology Information and is visited daily by millions of users around the world. For decades, PubMed has used advanced artificial intelligence technologies that extract patterns of collective user activity, such as machine learning and natural language processing, to inform the algorithmic changes that ultimately improve a user's search experience. Although these efforts have led to objective improvements in search quality, the technical underpinnings remain largely invisible and go largely unnoticed by most users. Here we describe how these 'under-the-hood' techniques work within PubMed and report how their effectiveness and usage is assessed in real-world scenarios. In doing so, we hope to increase the transparency of the PubMed system and enable users to make more effective use of the search engine. We also identify open challenges and new opportunities for computational researchers to explore the potential of future improvements.

Towards PubMed 2.0.

Staff from the National Center for Biotechnology Information in the US describe recent improvements to the PubMed search engine and outline plans for the future, including a new experimental site called PubMed Labs.

GenBank.

GenBank® (www.ncbi.nlm.nih.gov/genbank/) is a comprehensive database that contains publicly available nucleotide sequences for 370 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or the NCBI Submission Portal. GenBank staff assign accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Nucleotide database, which links to related information such as taxonomy, genomes, protein sequences and structures, and biomedical journal literature in PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Recent updates include changes to policies regarding sequence identifiers, an improved 16S submission wizard, targeted loci studies, the ability to submit methylation and BioNano mapping files, and a database of anti-microbial resistance genes.

GenBank.

GenBank(®) (www.ncbi.nlm.nih.gov/genbank/) is a comprehensive database that contains publicly available nucleotide sequences for over 340 000 formally described species. Recent developments include a new starting page for submitters, a shift toward using accession.version identifiers rather than GI numbers, a wizard for submitting 16S rRNA sequences, and an Identical Protein Report to address growing issues of data redundancy. GenBank organizes the sequence data received from individual laboratories and large-scale sequencing projects into 18 divisions, and GenBank staff assign unique accession.version identifiers upon data receipt. Most submitters use the web-based BankIt or standalone Sequin programs. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the nuccore, nucest, and nucgss databases of the Entrez retrieval system, which integrates these records with a variety of other data including taxonomy nodes, genomes, protein structures, and biomedical journal literature in PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP.

GenBank.

GenBank(®) (http://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive database that contains publicly available nucleotide sequences for over 300 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and GenBank staff assign accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP.

GenBank.

GenBank is a comprehensive database that contains publicly available nucleotide sequences for over 280,000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and GenBank staff assign accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the National Center for Biotechnology Information (NCBI) Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI home page: www.ncbi.nlm.nih.gov.

Why does a protein's evolutionary rate vary over time?

The sequences of different proteins evolve at different rates. The relative evolutionary rate (ER) of a single protein also changes over evolutionary time. The cause of this ER fluctuation remains uncertain, and study of this phenomenon may shed light on protein evolution more broadly. We have characterized ER fluctuation in mammals and Drosophila. We found little correlation between the amount of rate variation observed for a protein and such factors as its expression level or phylogenetic distribution. Perhaps more surprisingly, we found little correlation between our measure of rate variation and ER itself. We also investigated the extent to which the ERs of different domains of a protein vary independently. We found that rates of different domains do tend to vary together. In fact, rates at positions in different domains are coupled just as strongly as rates at equally distant positions in the same domain. These findings provide clues to the protein evolutionary process.

Sequential seasonal H1N1 influenza virus infections protect ferrets against novel 2009 H1N1 influenza virus.

Individuals <60 years of age had the lowest incidence of infection, with ~25% of these people having preexisting, cross-reactive antibodies to novel 2009 H1N1 influenza. Many people >60 years old also had preexisting antibodies to novel H1N1. These observations are puzzling because the seasonal H1N1 viruses circulating during the last 60 years were not antigenically similar to novel H1N1. We therefore hypothesized that a sequence of exposures to antigenically different seasonal H1N1 viruses can elicit an antibody response that protects against novel 2009 H1N1. Ferrets were preinfected with seasonal H1N1 viruses and assessed for cross-reactive antibodies to novel H1N1. Serum from infected ferrets was assayed for cross-reactivity to both seasonal and novel 2009 H1N1 strains. These results were compared to those of ferrets that were sequentially infected with H1N1 viruses isolated prior to 1957 or more-recently isolated viruses. Following seroconversion, ferrets were challenged with novel H1N1 influenza virus and assessed for viral titers in the nasal wash, morbidity, and mortality. There was no hemagglutination inhibition (HAI) cross-reactivity in ferrets infected with any single seasonal H1N1 influenza viruses, with limited protection to challenge. However, sequential H1N1 influenza infections reduced the incidence of disease and elicited cross-reactive antibodies to novel H1N1 isolates. The amount and duration of virus shedding and the frequency of transmission following novel H1N1 challenge were reduced. Exposure to multiple seasonal H1N1 influenza viruses, and not to any single H1N1 influenza virus, elicits a breadth of antibodies that neutralize novel H1N1 even though the host was never exposed to the novel H1N1 influenza viruses.

GenBank.

GenBank® (http://www.ncbi.nlm.nih.gov) is a comprehensive database that contains publicly available nucleotide sequences for almost 260 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and GenBank staff assigns accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI home page: www.ncbi.nlm.nih.gov.

Complex patterns of human antisera reactivity to novel 2009 H1N1 and historical H1N1 influenza strains.

BACKGROUND: During the 2009 influenza pandemic, individuals over the age of 60 had the lowest incidence of infection with approximately 25% of these people having pre-existing, cross-reactive antibodies to novel 2009 H1N1 influenza isolates. It was proposed that older people had pre-existing antibodies induced by previous 1918-like virus infection(s) that cross-reacted to novel H1N1 strains. METHODOLOGY/PRINCIPAL FINDINGS: Using antisera collected from a cohort of individuals collected before the second wave of novel H1N1 infections, only a minority of individuals with 1918 influenza specific antibodies also demonstrated hemagglutination-inhibition activity against the novel H1N1 influenza. In this study, we examined human antisera collected from individuals that ranged between the ages of 1 month and 90 years to determine the profile of seropositive influenza immunity to viruses representing H1N1 antigenic eras over the past 100 years. Even though HAI titers to novel 2009 H1N1 and the 1918 H1N1 influenza viruses were positively associated, the association was far from perfect, particularly for the older and younger age groups. CONCLUSIONS/SIGNIFICANCE: Therefore, there may be a complex set of immune responses that are retained in people infected with seasonal H1N1 that can contribute to the reduced rates of H1N1 influenza infection in older populations.

Domain enhanced lookup time accelerated BLAST.

BACKGROUND: BLAST is a commonly-used software package for comparing a query sequence to a database of known sequences; in this study, we focus on protein sequences. Position-specific-iterated BLAST (PSI-BLAST) iteratively searches a protein sequence database, using the matches in round i to construct a position-specific score matrix (PSSM) for searching the database in round i + 1. Biegert and Söding developed Context-sensitive BLAST (CS-BLAST), which combines information from searching the sequence database with information derived from a library of short protein profiles to achieve better homology detection than PSI-BLAST, which builds its PSSMs from scratch. RESULTS: We describe a new method, called domain enhanced lookup time accelerated BLAST (DELTA-BLAST), which searches a database of pre-constructed PSSMs before searching a protein-sequence database, to yield better homology detection. For its PSSMs, DELTA-BLAST employs a subset of NCBI's Conserved Domain Database (CDD). On a test set derived from ASTRAL, with one round of searching, DELTA-BLAST achieves a ROC5000 of 0.270 vs. 0.116 for CS-BLAST. The performance advantage diminishes in iterated searches, but DELTA-BLAST continues to achieve better ROC scores than CS-BLAST. CONCLUSIONS: DELTA-BLAST is a useful program for the detection of remote protein homologs. It is available under the "Protein BLAST" link at http://blast.ncbi.nlm.nih.gov.

Database resources of the National Center for Biotechnology Information.

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Website. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Probe, Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.