Who's minding the shop? The role of Canadian research ethics boards in the creation and uses of registries and biobanks

<p>Abstract</p> <p>Background</p> <p>The amount of research utilizing health information has increased dramatically over the last ten years. Many institutions have extensive biobank holdings collected over a number of years for clinical and teaching purposes, but are uncertain as to the proper circumstances in which to permit research uses of these samples. Research Ethics Boards (REBs) in Canada and elsewhere in the world are grappling with these issues, but lack clear guidance regarding their role in the creation of and access to registries and biobanks.</p> <p>Methods</p> <p>Chairs of 34 REBS and/or REB Administrators affiliated with Faculties of Medicine in Canadian universities were interviewed. Interviews consisted of structured questions dealing with diabetes-related scenarios, with open-ended responses and probing for rationales. The two scenarios involved the development of a diabetes registry using clinical encounter data across several physicians' practices, and the addition of biological samples to the registry to create a biobank.</p> <p>Results</p> <p>There was a wide range of responses given for the questions raised in the scenarios, indicating a lack of clarity about the role of REBs in registries and biobanks. With respect to the creation of a registry, a minority of sites felt that consent was not required for the information to be entered into the registry. Whether patient consent was required for information to be entered into the registry and the duration for which the consent would be operative differed across sites. With respect to the creation of a biobank linked to the registry, a majority of sites viewed biobank information as qualitatively different from other types of personal health information. All respondents agreed that patient consent was needed for blood samples to be placed in the biobank but the duration of consent again varied.</p> <p>Conclusion</p> <p>Participants were more attuned to issues surrounding biobanks as compared to registries and demonstrated a higher level of concern regarding biobanks. As registries and biobanks expand, there is a need for critical analysis of suitable roles for REBs and subsequent guidance on these topics. The authors conclude by recommending REB participation in the creation of registries and biobanks and the eventual drafting of comprehensive legislation.</p

A Multi-Omic Systems-Based Approach Reveals Metabolic Markers of Bacterial Vaginosis and Insight into the Disease

<div><h3>Background</h3><p>Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms.</p> <h3>Principal Findings</h3><p>16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to <em>Dialister</em> spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and <em>Mobiluncus</em> spp., and with pain, diethylene glycol and <em>Gardnerella</em> spp.</p> <h3>Conclusions</h3><p>The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV.</p> </div

Richness and Diversity of Each Sample.

<p>Rarefaction curves showing the richness of microbiomes for all samples colored by Nugent score (A; green = 0–3, orange = 4–6, red = 7–10) or Amsel classification (B; red = positive, green = negative) are presented along with Shannon diversity indexes (C), with samples grouped by Nugent score or Amsel criteria and colored as in rarefaction curves.</p

Network path lengths to BV.

<p>Measured as ¹Days since ²Times weekly ³Times monthly ⁴In past 6 months.</p

Quantitative PCR Enumeration of Lactobacillus inners by Sample.

1<p>Only samples found to contain <i>L. iners</i> are shown</p

Sample Descriptions.

<p>BV Symptoms: (D)ischarge, (O)dor, (P)ain, (I)tching, p(H), (C)lue cells; Ethnicity: (A)frican American, A(S)ian American, (C)aucasian; Marital status: (M)arried, (S)ingle/Divorced; Tampon use/New partner (in past 6 months): (Y)es, (N)o; Oral and Anal sex frequency is denoted as times monthly, Vaginal sex and Bathing frequency is weekly; Condom use: (N)o, (L)ess than half of the time, (M)ore than half of the time, (A)lways.</p>nr<p>Not reported; ¹Biotype as per Ravel <i>et al.</i>, 2011; ²Near equal numbers of <i>L. gasserii</i> were detected.</p

Heatmap of Taxonomic Enrichment by Sample.

<p>Shown is a heatmap of the relative enrichment of the most abundant thirty microbial genera across the entire sample set and the relationships among samples. Highly abundant genera tend toward bright green, while less abundant genera tend toward blue, as shown in the key. Dendrograms show the relationship among samples. Red bars in the dendrogram show the relationship of samples with symptomatic BV. Nugent scores are presented beneath the dendrogram.</p

SBVII Sub-Network View of Linear Relationships among Variables.

<p>Pearson's (between parametric data) and Spearman's (between non-parametric and either parametric or non-parametric data) correlations >0.6 (green) or <−0.4 (red) are shown as edges connecting patient metadata relating to demographics, hygiene and sexual behaviors and sexual practices, OTUs, microbial genera, metabolites and patient symptoms. Figures presented represent sub-networks of the complete network (Fig S2). Node identities are listed or described in the key. The identities of numbered metabolites are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056111#pone.0056111.s007" target="_blank">Table S2</a>.</p

Venn Diagram of Discriminative Metabolites.

<p>Shown is a Venn diagram of the numbers of unique and shared metabolites that delineate high Nugent scoring (≥7), Type I symptomatic BV or Type II symptomatic BV from other samples.</p