Biology subjects

Calle, M. L.

Publications and source records attributed to Calle, M. L..

7 recordsLinked to original sources

bioRxiv2026

Understanding the bias of compositional microbiome differential abundance estimation

One of the most relevant objectives in microbiome studies is the identification of microbial species that are differentially abundant across conditions. However, the compositional nature of microbiome data complicates this task. Interdependence among components leads to spurious associations when the abundances of each component are analyzed separately. Due to the growing awareness of the challenges of compositional data analysis (CoDA), log-ratio transformations, such as the additive log-ratio (alr) or the centered log-ratio (clr) transformations, have become increasingly popular in microbiome studies. Several studies have compared the performance of compositional and non-compositional methods through simulations. However, the debate between these two frameworks remains unresolved, creating confusion among researchers. Rather than relying on simulation-based results, this work provides theoretical results that enable a more rigorous and conclusive analysis of the problem, contributing to a better understanding of differential abundance estimation. We provide theoretical expressions of the bias of differential abundance estimation related to the use of proportions (total sum scaling) and log-ratio transformations (alr and clr) when estimates are interpreted as absolute rather than relative to a reference. The factors that most strongly influence the bias are the magnitude and direction of the effects, the dimension of the composition, the proportion of differentially abundant variables, and the distribution of relative abundances. The findings of this work strongly support the use of CoDA transformations; however, they also highlight that even when log-ratio transformations are applied, interpreting the results outside of a CoDA framework can still lead to biased conclusions. Among CoDA transformations, alr has several advantages over clr: its reference is more explicit, which reduces the risk of interpreting estimates as absolute rather than relative, and it facilitates the replication of results in independent studies, as it only requires assessing changes relative to the same reference rather than reconstructing the full composition. In this work, we propose a heuristic method for selecting a suitable alr reference component, which will enable a more widespread use of this transformation.

bioinformatics↗

bioRxiv2025

Impact of gestational antibiotics on maternal and offspring gut microbiota and growth in pigs

Maternal microbiota modulates the development of the microbiota in the offspring. Effects of gestational antibiotics are not well understood, as most studies have focused on the perinatal period. We treated sows with penicillin, tetracycline or saline on days 78-80 of the 114-119-day gestation, a critical period in the fetal immune system development. Microbiotas were analyzed by 16S rRNA gene amplicon sequencing in sow feces and vagina at days 77 and 113, in colostrum, and in piglet feces at three days, three weeks and ten weeks of age. Sow fecal microbiota changed during pregnancy, but less in antibiotic groups. No significant effects on sow microbiota remained on day 113. The piglets of the antibiotic-treated sows had lower Firmicutes+Actinobacteriota to Bacteroidota+Proteobacteria ratio, lower alpha diversity and higher relative abundance of Escherichia at three days. At ten weeks, they exhibited higher alpha diversity, had higher of Prevotella and Clostridium sp. CAG-127, and smaller increase of Limosilactobacillus than the control. Increased alpha diversity at 10 weeks was associated with lower weight gain during nursing. Oliverpabstia and Mitsuokella were positively associated with growth, while CAG-127 and Campylobacter B were negatively associated. Sow antibiotic treatment decreased the positive effect of Oliverpabstia and Mitsuokella and increased the negative effects of CAG-127 and Campylobacter B. Gestational antibiotics may have had adverse effects on microbiota and growth of the offspring, even if their effects on maternal microbiota were undetectable by parturition.

microbiology↗

bioRxiv2022

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of NGS data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic datasets. Using more than 6.76{middle dot}108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials. Using these profiles, we found evidence of several ARGs conferring resistance for critically important antimicrobials (CIA) being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs.

bioinformatics↗

bioRxiv2022

coda4microbiome: compositional data analysis for microbiome studies

MotivationOne of the main challenges of microbiome analysis is its compositional nature that if ig-nored can lead to spurious results. This is especially critical when dealing with microbiome variable selection since classical differential abundance tests are known to provide large false positive rates. ResultsWe developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The core functions of the library are aimed at the identification of microbial signatures and involve variable selection in generalized linear models with compositional covariates. All algorithms are accompanied by meaningful graphical representations that allow a better interpretation of the results. Availabilitycoda4microbiome is implemented as an R package and is available at CRAN https://cran.r-project.org/web/packages/coda4microbiome/index.html. Contactmalu.calle@uvic.cat Supplementary informationcoda4microbiome project website: https://malucalle.github.io/coda4mi-crobiome/.

microbiology↗

bioRxiv2022

IDENTIFICATION OF DYNAMIC MICROBIAL SIGNATURES IN LONGITUDINAL STUDIES

The study of microbiome dynamics is key for unveiling the role of the microbiome in human health. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where compositions measured at different times can yield to different subcompositions. We propose a new compositional data analysis (CoDA) algorithm for inferring dynamic microbial signatures. The algorithm performs penalized regression over the summary of the log-ratio trajectories (the area under these trajectories) and the inferred microbial signature is expressed as a log-contrast model. Graphical representations of the results are provided to facilitate the interpretation of the analysis: plot of the log-ratio trajectories, plot of the signature and plot of the prediction accuracy of the model. The new proposal is illustrated with data on the developing microbiome of infants. The algorithm is implemented in the R package "code4microbiome" (https://cran.r-project.org/web/packages/coda4microbiome/) that is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/

bioinformatics↗

bioRxiv2018

Host genetic associations with the gut microbiota in HIV-1-infected subjects: a pilot exploratory study

The impact of host genetics on gut microbial dynamics is debated. No study to date has investigated the possible role of host genetics in shaping the gut microbiota in HIV-1 infected subjects. With the aim of generating preliminary data to inform future host genetic studies, we performed an exploratory host exome analysis of 147 subjects either infected or at risk of becoming infected with HIV-1 from the MetaHIV cohort in Barcelona. Using a DNA microarray chip, we sought to identify host genetic variants associated to three specific microbial features with a potentially inheritable component, and which were previously found to be associated with gut dysbiosis in HIV infection, i.e.: gut enterotype, presence of methanogenic archaea and microbial gene richness. After correction for multiple comparisons, we did not observe any statistically significant association between the hosts genetic landscape and the explored gut microbiome traits. These findings will help design future, adequately-powered studies to assess the influence of host genetics in the microbiome of HIV-1-infected subjects.

genomics↗

bioRxiv2017

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. One of the main goals in microbiome analysis is the identification of microbial species that are differentially abundant among groups of samples, or whose abundance is associated with a variable of interest. Most available methods for microbiome abundance testing perform univariate tests for each microbial species or taxa separately, ignoring the compositional nature of microbiome data.\n\nWe propose an alternative approach for microbiome abundance testing that consists on the identification of two groups of taxa whose relative abundance, or balance, is associated with the response variable of interest. This approach is appealing, since it has direct translation to the biological concept of ecological balance between species in an ecosystem. In this work, we present selbal, a greedy stepwise algorithm for balance selection. We illustrate the algorithm with 16s abundance data from an HIV-microbiome study and a Crohn-microbiome study.\n\nImportanceA more meaningful approach for microbiome abundance testing is presented. Instead of testing each taxon separately we propose to explore abundance balances among groups of taxa. This approach acknowledges the compositional nature of microbiome data.

bioinformatics↗