Bacterial community succession and degradation patterns of hydrocarbons in seawater at low temperature

The risk of oil spills in cold marine environments is expected to increase in response to trans-Arctic shipping and as Arctic oil reserves get exploited. Marine hydrocarbon-degrading microbes can reduce the impact of spilled hydrocarbons, but their degradation capabilities at low temperature are yet to be uncovered. We combined DNA amplicon sequencing and chemometrics to investigate the effect of decreasing temperature (0-15 °C) on the succession and function of hydrocarbon-degrading bacteria in seawater. The bacterial community and degradation patterns were investigated at time points when a similar amount of hydrocarbons was mineralised at the different temperatures. This allowed decomposing the effect of temperature into a main component related to the reduced microbial activity at low temperature and a secondary effect. The reduced microbial activity at low temperature delayed the microbial community succession and degradation rates. The secondary effect of temperature was most pronounced at 0 °C, where (1) degradation of the least water-soluble n-alkanes (>C12) was suppressed in contrast to a relative stronger degradation of the most water-soluble n-alkanes (<C12) and polycyclic aromatic hydrocarbons; and (2) bacterial taxa which we identified as psychrosensitive were inhibited, whereas taxa identified as psychrophilic flourished.

Balances: a New Perspective for Microbiome Analysis

High-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. In this work we focus on the identification of microbial signatures, groups of microbial taxa that are predictive of a phenotype of interest. We do this by acknowledging the compositional nature of the microbiome and the fact that it carries relative information. Thus, instead of defining a microbial signature as a linear combination in real space corresponding to the abundances of a group of taxa, we consider microbial signatures given by the geometric means of data from two groups of taxa whose relative abundances, or balance, are associated with the response variable of interest. In this work we present selbal, a greedy stepwise algorithm for selection of balances or microbial signatures that preserves the principles of compositional data analysis. We illustrate the algorithm with 16S rRNA abundance data from a Crohn's microbiome study and an HIV microbiome study. IMPORTANCE We propose a new algorithm for the identification of microbial signatures. These microbial signatures can be used for diagnosis, prognosis, or prediction of therapeutic response based on an individual's specific microbiota.

A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America

HIV infection has a tremendous impact on the immune system's proper functioning. The mucosa-associated lymphoid tissue (MALT) is significantly disarrayed during HIV infection. Compositional changes in the gut microbiota might contribute to the mucosal barrier disruption, and consequently to microbial translocation. We performed an observational, cross-sectional study aimed at evaluating changes in the fecal microbiota of HIV-infected individuals from Colombia. We analyzed the fecal microbiota of 37 individuals via 16S rRNA gene sequencing; 25 HIV-infected patients and 12 control (non-infected) individuals, which were similar in body mass index, age, gender balance and socioeconomic status. To the best of our knowledge, no such studies have been conducted in Latin American countries. Given its compositional nature, microbiota data were normalized and transformed using Aitchison's Centered Log-Ratio. Overall, a change in the network structure in HIV-infected patients was revealed by using the SPIEC-EASI MB tool. Genera such as Blautia, Dorea, Yersinia, Escherichia-Shigella complex, Staphylococcus, and Bacteroides were highly relevant in HIV-infected individuals. Differential abundance analysis by both sparse Partial Least Square-Discriminant Analysis and Random Forest identified a greater abundance of Lachnospiraceae-OTU69, Blautia, Dorea, Roseburia, and Erysipelotrichaceae in HIV-infected individuals. We show here, for the first time, a predominantly Lachnospiraceae-based signature in HIV-infected individuals.

When relative and absolute information matter: Compositional predictor with a total in generalized linear models

The analysis of compositional data (CoDa) consists in the study of the relative importance of parts of a whole rather than the size of the whole because absolute information is either unavailable or not of interest. On the other hand, when absolute and relative information are both relevant, research hypotheses concern both. This article introduces a model including both the logratios used in CoDa and a total variable carrying absolute information as predictors in an otherwise standard statistical model. It shows how logratios can be tailored to the researchers’ hypotheses and alternative ways of computing the total. The interpretational advantages with respect to traditional approaches are presented and the equivalence and invariance properties are proven. A sequence of nested models is presented to test the relevance of relative and absolute information. The approach can be applied to dependent metric, binary, ordinal or count variables. Two illustrations are provided, the first on tourist expenditure and satisfaction and the second on solid waste management and floating population.

Normalization and microbial differential abundance strategies depend upon data characteristics

BACKGROUND

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.

RESULTS

Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.

CONCLUSIONS

These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

A phylogenetic transform enhances analysis of compositional microbiota data

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines—compositional data analysis (CODA) and log ratio transformations—KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension.