Priscila Thiago Dobbler

Summary: Ericoid mycorrhizal (ErM) fungi play a crucial role across terrestrial ecosystems, forming mutualistic symbiosis with Ericaceae and contributing to soil organic matter dynamics. However, compared to other fungal groups, their biogeography remains unknown. Here, we combined several analytical approaches to analyze a newly compiled, large‐scale dataset comprising 39 163 soil samples and more than 13 million ITS rRNA sequences assigned to ErM fungi. Specifically, we asked: What are the global patterns of ErM fungal species richness and relative abundance (out of all fungi) and their predictors, and how is the distribution of ErM fungi associated with soil carbon content at the global scale? We show that ErM fungi reach their highest species richness in very high latitudes. Soil chemistry is a stronger predictor of ErM fungal species richness than climate or ericoid vegetation cover. The relative abundance of ErM fungi is highest in soils with high surface carbon content, supporting their proposed role in soil carbon storage. Furthermore, we predict that climate change will reduce ErM fungal abundance across 38% of the land cover of their current global distribution. Our study shows distinct biogeographic patterns of ErM fungi compared with arbuscular and ectomycorrhizal fungi and indicates the vulnerability of ErM fungi to climate change.

Background: Understanding the temporal variability of the microbiome is critical for translating associations of the microbiome with health and disease into clinical practice. The aim of this study is to assess the extent of temporal variability of the human urinary microbiota. A pair of urine samples were collected from study participants at 3–40-month interval. DNA was extracted and the bacterial V4 hypervariable region of the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The alpha diversity of paired samples was analyzed using Chao1 and Shannon indices and PERMANOVA was used to test the factors influencing beta diversity. Results: A total of 63 participants (43 men and 20 women with a mean age of 63.0 and 57.1 years, respectively) were included in the final analysis. An average of 152 ± 128 bacterial operational taxonomic units (OTUs) were identified in each urine sample from the entire cohort. There was an average of 41 ± 32 overlapping OTUs in each sample pair, accounting for 66.3 ± 29.4% of the relative abundance. There was a clear correlation between the number of overlapping OTUs and the relative abundance covered. The difference in Chao1 index between paired samples was statistically significant; the difference in Shannon index was not. Beta diversity did not differ significantly within the paired samples. Neither age nor sex of the participants influenced the variation in community composition. With a longer interval between the collections, the relative abundance covered by the overlapping OTUs changed significantly but not the number of OTUs. Conclusion: Our findings demonstrated that, while the relative abundance of dominant bacteria varied, repeated collections generally shared more than 60% of the bacterial community. Furthermore, we observed little variation in the alpha and beta diversity of the microbial community in human urine. These results help to understand the dynamics of human urinary microbiota and enable interpretation of future studies.

Understanding the temporal variability of the microbiome is critical for translating associations of the microbiome with health and disease into clinical practice. The aim of this study is to assess the extent of temporal variability of the human urinary microbiota. A pair of catheterized or mid-stream urine samples were collected from study participants at 3–40-month interval. DNA was extracted and the bacterial V4 hypervariable region of the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The alpha and beta diversity of paired samples was analyzed using Chao1 and Shannon indices and PERMANOVA. A total of 63 participants (43 men and 20 women with a mean age of 63.0 and 57.1 years, respectively) were included in the final analysis. An average of 152 ± 128 bacterial operational taxonomic units (OTUs) were identified in each urine sample from the entire cohort. There was an average of 41 ± 32 overlapping OTUs in each sample pair, accounting for 66.3 ± 29.4% of the relative abundance. There was a clear correlation between the number of overlapping OTUs and the relative abundance covered. The difference in Chao1 index between paired samples was statistically significant; the difference in Shannon index was not. Beta diversity did not differ significantly within the paired samples. Neither age nor sex of the participants influenced the variation in community composition. With a longer interval between the collections, the relative abundance covered by the overlapping OTUs changed significantly but not the number of OTUs. The abundance of bacterial taxa present in both collections fluctuated, but the proportion of these taxa in the community was about two-thirds of the total bacterial community. No significant intraindividual differences in beta diversity were observed between the two urine samples. Data Summary: The raw DNA sequences together with anonymized sample metadata have been deposited at the NCBI SRA under the accession number PRJNA1093489. Processing scripts are deposited at a public repository https://doi.org/10.5281/zenodo.12556460 . Processed sequencing files and tables including full taxonomic assignment are deposited at https://doi.org/10.6084/m9.figshare.26046355 . Importance: This study represents a comprehensive investigation dedicated specifically to the subject of urinary microbiome (UM) temporal stability. Our dataset (n = 63) consists of a relatively large group of patients of one ethnic origin but varying ages and both sexes. Additionally, samples from individual participants are separated by different lengths of time. This approach allows us to assess the effects of three variables on the stability of human UM. Our findings demonstrate that, while the relative abundance of dominant bacteria varies, repeated collections generally share more than 60% of the bacterial community. Furthermore, we observe little variation in the alpha and beta diversity of the microbial community in human urine. These results help to understand the dynamics of human UM and enable interpretation of future studies.

Metagenomics provides a tool to assess the functional potential of environmental and host‐associated microbiomes based on the analysis of environmental DNA: assembly, gene prediction and annotation. While gene prediction is straightforward for most bacterial and archaeal taxa, it has limited applicability in the majority of eukaryotic organisms, including fungi that contain introns in gene coding sequences. As a consequence, eukaryotic genes are underrepresented in metagenomics datasets and our understanding of the contribution of fungi and other eukaryotes to microbiome functioning is limited. Here, we developed a machine intelligence‐based algorithm that predicts fungal introns in environmental DNA with reasonable precision and used it to improve the annotation of environmental metagenomes. Intron removal increased the number of predicted genes by up to 9.1% and improved the annotation of several others. The proportion of newly predicted genes increased with the share of eukaryotic genes in the metagenome and—within fungal taxa—increased with the number of introns per gene. Our approach provides a tool named SVMmycointron for improved metagenome annotation, especially of microbiomes with a high proportion of eukaryotes. The scripts described in the paper are made publicly available and can be readily utilized by microbiome researchers analysing metagenomics data.

The purpose was identify an association between meconium microbiome, extra-uterine growth restriction, and head circumference catch-up. Materials and methods: Prospective study with preterm infants born Polynucleobacter (p = 0.0163), Gp1 (p = 0.018), and Prevotella (p = 0.038) appeared in greater abundance in meconium of preterm infants with adequate birth weight for GA. Thirty (47.6%) children reached head circumference catch-up before 6 months CA and 33 (52.4%) after 6 months CA. Salmonella (pFlavobacterium (p = 0.026), and Burkholderia (p = 0.026) were found to be more abundant in meconium in the group of newborns who achieved catch-up prior to 6th month CA. Conclusion: Meconium microbiome abundance was related to adequacy of weight for GA. Meconium microbiome differs between children who achieve head circumference catch-up by the 6th month of corrected age or after this period.

Background: Early-onset neonatal sepsis (EONS) remains one of the leading causes of morbidity and mortality related to premature birth, and its diagnosis remains difficult. Our goal was to evaluate the intestinal microbiota of the first meconium of preterm newborns and ascertain whether it is associated with clinical EONS. Methods: In a controlled, prospective cohort study, samples of the first meconium of premature infants with a gestational age (GA) ≤32 weeks was obtained at Hospital de Clínicas de Porto Alegre and DNA was isolated from the samples. 16S rDNA based microbiota composition of preterm infants with a clinical diagnosis of EONS was compared to that of a control group. Results: 40 (48%) premature infants with clinical diagnosis of EONS and 44 (52%) without EONS were included in the analysis. The most abundant phylum detected in both groups, Proteobacteria, was more prevalent in the sepsis group (p = .034). 14% of variance among bacterial communities (p = .001) correlated with EONS. The genera most strongly associated with EONS were Paenibacillus, Caulobacter, Dialister, Akkermansia, Phenylobacterium, Propionibacterium, Ruminococcus, Bradyrhizobium, and Alloprevotella. A single genus, Flavobacterium, was most strongly associated with the control group. Conclusion: These findings suggest that the first-meconium microbiota is different in preterm neonates with and without clinical EONS.

Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples obtained from 40 subjects were characterized using high throughput microbiome sequence data processed via DADA2 error correction combined with PIME machine-learning de-noising and taxa binning/parsing of prevalent ASVs at the single nucleotide level of resolution. Application of ALDEx2 differential abundance analysis with assessed effect sizes and stringent PICRUSt2 predicted metabolic pathways. This multivariate machine-learning approach significantly differentiated DEPR (n = 20) vs. NODEP (n = 20) (PERMANOVA P < 0.001) based on microbiome taxa clustering and neurocircuit-relevant metabolic pathway network analysis for GABA, butyrate, glutamate, monoamines, monosaturated fatty acids, and inflammasome components. Gut microbiome dysbiosis using ASV prevalence data may offer the diagnostic potential of using human metaorganism biomarkers to identify individuals with a depression phenotype.

The data used for profiling microbial communities is usually sparse with some microbes having high abundance in a few samples and being nearly absent in others. However, current bioinformatics tools able to deal with this sparsity are lacking. pime (Prevalence Interval for Microbiome Evaluation) was designed to remove those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of pime were compared against existing methods and tested using 16S rRNA independent data sets. pime filters microbial taxa not shared in a per treatment prevalence interval started at 5% prevalence with increasing increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees were calculated to predict the likelihood of detecting differences in treatments. The best prevalence‐filtered data set was user‐selected by choosing the prevalence interval that kept a large portion of the 16S rRNA sequences in the data set while also showing the lowest error rate. To obtain the likelihood of introducing type I error while building prevalence‐filtered data sets, an error detection step based was also included. A pime reanalysis of published data sets uncovered other expected microbial associations than previously reported, which may be masked when only relative abundance was considered.