16S rRNA gene sequencing of mock microbial populations- impact of DNA extraction method, primer choice and sequencing platform

peer-reviewedBackground Next-generation sequencing platforms have revolutionised our ability to investigate the microbiota composition of complex environments, frequently through 16S rRNA gene sequencing of the bacterial component of the community. Numerous factors, including DNA extraction method, primer sequences and sequencing platform employed, can affect the accuracy of the results achieved. The aim of this study was to determine the impact of these three factors on 16S rRNA gene sequencing results, using mock communities and mock community DNA. Results The use of different primer sequences (V4-V5, V1-V2 and V1-V2 degenerate primers) resulted in differences in the genera and species detected. The V4-V5 primers gave the most comparable results across platforms. The three Ion PGM primer sets detected more of the 20 mock community species than the equivalent MiSeq primer sets. Data generated from DNA extracted using the 2 extraction methods were very similar. Conclusions Microbiota compositional data differed depending on the primers and sequencing platform that were used. The results demonstrate the risks in comparing data generated using different sequencing approaches and highlight the merits of choosing a standardised approach for sequencing in situations where a comparison across multiple sequencing runs is required.This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Numbers SFI/12/RC/2273 and 11/PI/1137 and by FP7 funded CFMATTERS (Cystic Fibrosis Microbiome-determined Antibiotic Therapy Trial in Exacerbations: Results Stratified, Grant Agreement no. 603038)

Stack sorting with restricted stacks

The (classical) problem of characterizing and enumerating permutations that can be sorted using two stacks connected in series is still largely open. In the present paper we address a related problem, in which we impose restrictions both on the procedure and on the stacks. More precisely, we consider a greedy algorithm where we perform the rightmost legal operation (here "rightmost" refers to the usual representation of stack sorting problems). Moreover, the first stack is required to be $\sigma$-avoiding, for some permutation $\sigma$, meaning that, at each step, the elements maintained in the stack avoid the pattern $\sigma$ when read from top to bottom. Since the set of permutations which can be sorted by such a device (which we call $\sigma$-machine) is not always a class, it would be interesting to understand when it happens. We will prove that the set of $\sigma$-machines whose associated sortable permutations are not a class is counted by Catalan numbers. Moreover, we will analyze two specific $\sigma$-machines in full details (namely when $\sigma=321$ and $\sigma=123$), providing for each of them a complete characterization and enumeration of sortable permutations

Analysis of VEGF-A Regulated Gene Expression in Endothelial Cells to Identify Genes Linked to Angiogenesis

Angiogenesis is important for many physiological processes, diseases, and also regenerative medicine. Therapies that inhibit the vascular endothelial growth factor (VEGF) pathway have been used in the clinic for cancer and macular degeneration. In cancer applications, these treatments suffer from a “tumor escape phenomenon” where alternative pathways are upregulated and angiogenesis continues. The redundancy of angiogenesis regulation indicates the need for additional studies and new drug targets. We aimed to (i) identify novel and missing angiogenesis annotations and (ii) verify their significance to angiogenesis. To achieve these goals, we integrated the human interactome with known angiogenesis-annotated proteins to identify a set of 202 angiogenesis-associated proteins. Across endothelial cell lines, we found that a significant fraction of these proteins had highly perturbed gene expression during angiogenesis. After treatment with VEGF-A, we found increasing expression of HIF-1α, APP, HIV-1 tat interactive protein 2, and MEF2C, while endoglin, liprin β1 and HIF-2α had decreasing expression across three endothelial cell lines. The analysis showed differential regulation of HIF-1α and HIF-2α. The data also provided additional evidence for the role of endothelial cells in Alzheimer's disease

Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis

peer-reviewedRapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number ofmethodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance inmicrobiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced withMiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2273 and 11/PI/1137 and by FP7 funded CFMATTERS (Cystic Fibrosis Microbiome-determined Antibiotic Therapy Trial in Exacerbations: Results Stratified, Grant Agreement no. 603038)

Microbial Succession and Flavor Production in the Fermented Dairy Beverage Kefir

peer-reviewedKefir is a putatively health-promoting dairy beverage that is produced when a kefir grain, consisting of a consortium of microorganisms, is added to milk to initiate a natural fermentation. Here, a detailed analysis was carried out to determine how the microbial population, gene content, and flavor of three kefirs from distinct geographic locations change over the course of 24-h fermentations. Metagenomic sequencing revealed that Lactobacillus kefiranofaciens was the dominant bacterial species in kefir during early stages of fermentations but that Leuconostoc mesenteroides became more prevalent in later stages. This pattern is consistent with an observation that genes involved in aromatic amino acid biosynthesis were absent from L. kefiranofaciens but were present in L. mesenteroides. Additionally, these shifts in the microbial community structure, and associated pathways, corresponded to changes in the levels of volatile compounds. Specifically, Acetobacter spp. correlated with acetic acid; Lactobacillus spp. correlated with carboxylic acids, esters and ketones; Leuconostoc spp. correlated with acetic acid and 2,3-butanedione; and Saccharomyces spp. correlated with esters. The correlation data suggest a causal relationship between microbial taxa and flavor that is supported by observations that addition of L. kefiranofaciens NCFB 2797 increased the levels of esters and ketones whereas addition of L. mesenteroides 213M0 increased the levels of acetic acid and 2,3-butanedione. Finally, we detected genes associated with probiotic functionalities in the kefir microbiome. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein and can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods. IMPORTANCE Traditional fermented foods represent relatively low-complexity microbial environments that can be used as model microbial communities to understand how microbes interact in natural environments. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein. In the process, the link between individual species, and associated pathways, with flavor compounds is revealed and several genes that could be responsible for the purported gut health-associated benefits of consuming kefir are identified. Ultimately, in addition to providing an important fundamental insight into microbial interactions, this information can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods