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IntroductionPulmonary alveolar proteinosis related to mutations in the methionine tRNA synthetase (MARS1) gene is a severe, early-onset disease that results in death before the age of 2 years in one-third of patients. It is associated with a liver disease, growth failure and systemic inflammation. As methionine supplementation in yeast models restored normal enzymatic activity of the synthetase, we studied the tolerance, safety and efficacy of daily oral methionine supplementation in patients with severe and early disease.MethodsFour patients received methionine supplementation and were followed for respiratory, hepatic, growth and inflammation-related outcomes. Their course was compared to those of historical controls. Reactive oxygen species production by patient monocytes before and after methionine supplementation was also studied.ResultsMethionine supplementation was associated with respiratory improvement, clearance of the extracellular lipoproteinaceous material and discontinuation of whole-lung lavage in all patients. The three patients who required oxygen or noninvasive ventilation could be weaned off within 60 days. In addition, liver dysfunction, inflammation and growth delay improved or resolved. At a cellular level, methionine supplementation normalised the production of reactive oxygen species by peripheral monocytes.ConclusionMethionine supplementation was associated with important improvements in children with pulmonary alveolar proteinosis related to mutations in the MARS1 gene. This study paves the way for similar strategies for other tRNA synthetase deficiencies.

Additional file 1: Supplementary Figures. Supplementary Figure 1. Microbiome profiles and differences in abundances of specific taxa in HEs compared to LEs based on the “universal” approach (16S rRNA gene sequencing). A. Whole study cohort (n=100). B. Matched study subset (n=30). AI/BI. PCoA plots (RSV based); AII/BII. ANOVA analysis at phylum level and AIII/BIII at genus level on the 100 most abundant taxa. AIV-VII/BIV-VII. Relative abundances of individual genera. Supplementary Figure 2. Significant positive and negative correlation of specific taxa with emitted methane concentrations based on “universal” approach 16S rRNA gene sequencing, Spearman-based regression analysis. A. Whole study cohort (n=100). B. matched study subset only (n=30). I-V. Significant positive correlation with emitted methane. VI-X. Significant negative correlation with emitted methane. (100 most abundant genera; Spearman); r=Spearman’s rho correlation coefficient (rs). Supplementary Figure 3. Co-correlation network of taxa associated with HE and LE based on “universal” approach 16S rRNA gene sequencing and Spearman’s rho. Networks showing connections of the 100 most abundant genera of A. the whole study cohort (n=100), B. our matched study subset (n=30), C. HE only (n=15) and D. LE only (n=15). Taxa highlighted in red and blue were shown to be most significantly different in LEfSe and ANOVA analysis. Supplementary Figure 4. High methane emitters showed higher absolute abundances of methanogens compared to low emitters (n=100). Bacterial absolute abundances was overall similar. For three samples, data of bacteria was not obtained. Supplementary Figure 5. Differences in alpha and beta diversity based on the “universal” approach of 16S rRNA gene sequencing between high (HE) and low methane emitters (LE). Profile of the study subset (n=30). A.I. The Shannon diversity index of revealed significant differences in alpha diversity between both groups (RSV based; ANOVA); A.II. Microbial community clustered significantly different in the RDA plot (RSV based). B.I. LeFSe analysis of the 100 most abundant phyla and B.II.-III. Relative abundance of selected phyla shown in ANOVA plots. C.I. LeFSe analysis of the 100 most abundant genera and C.II.-III. Relative abundance of selected phyla shown in ANOVA plots. Supplementary Figure 6. Bubble plot overview on subsystems at the highest (I.) and at functional level (II.) based on shotgun metagenome analysis. In II., the 50 most abundant features are shown.; n=30. Supplementary Figure 7. Relative abundance of the most significantly different subsystems of HEs compared to LEs shown in ANOVA plots based on shotgun metagenome analysis (Subsystems). I. At highest subsystem level (level 1) and II. level3. (100 most features; n=30). Supplementary Figure 8. Bubble plots of gut microbiome of HEs and LEs based on shotgun metagenome (RefSeq). I. Supplementary Figure 9. Significant differences were also observed at species level based on shotgun metagenome analysis (RefSeq). I. Bubble plot of the 50 most abundant taxa. II. LefSe analysis and III. ANOVA plot of 100 most abundant taxa. (n=30). Supplementary Figure 10. Microbial community differs significantly with respect to methane production based on shotgun metagenome analysis (RefSeq). I. LEfSe analysis and II. ANOVA plot at superkingdom level. III. PCoA plot at RSV level. IV. ANOVA plot showing significant differences at phylum (100 most abundant) and V. genus level (50 most abundant taxa). (n=30). Supplementary Figure 11. Diversity and composition of the archaeal community as detected in HE and LE samples based on shotgun metagenomic analyses (RefSeq). I Alpha diversity based on Shannon index, II. RDA plot, III. PCoA plot, IV: LEfSe analysis on genus level. Supplementary Figure 12. Archaeal network in LE and HE (blue) based on shotgun metagenomics information (RefSeq). Supplementary Figure 13. Correlations with dietary intake. BIOENV analysis showing explanatory variables triggering the microbial communities of HEs (blue) and LEs (red) based on Euclidean distances that were superimposed on a Non-metric multidimensional scaling (NMDS) plot derived from Bray-Curtis dissimilarities of HE and LE samples (stress:0.1939). Methanobrevibacter read counts were included as a variable for better orientation.

Additional file 1: Supplementary Figure 1: Analogue astronauts in the field during the AMADEE-15 Mars-landing simulation mission. Supplementary Figure 2: CLSM micrographs of FISH from sulfidic spring samples. Supplementary Figure 3: Archaeal signatures obtained using the Archaea-targeting primer set. Supplementary Figure 4: Archaeal signatures obtained with the universal primer set. Supplementary Figure 5: Bacterial signatures obtained with the “universal” primer set, PMA untreated samples. Supplementary Figure 6: The core taxa of the hypersaline environment and the acidic environment*. Supplementary Figure 7: Parameters shaping the community. Supplementary Figure 8: The MASE core microbial taxa. Supplementary Figure 9: The core microbiome of PMA untreated samples depicted in a Venn diagram.Supplementary Figure 10: Phylum-level, taxonomic information derived from the metagenomic dataset. Supplementary Figure S11: Predicted KEGG3 pathways (Tax4fun) and network of functional genes of the MASE site microbiomes. Supplementary Figure 12: Comparison of metabolic pathways for the core microbiome and the cultivated microorganisms. Supplementary Figure 13: Total organic carbon in MASE samples, in %.

Additional file 3: Supplementary Items 6-13. Supplementary Item 6. Heatmap of amino acid flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 7. Heatmap of C1-C4 flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 8. Heatmap of complex compound flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 9. Heatmap of fat flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 10. Heatmap of nucleotide flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 11. Heatmap of other metabolite flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 12. Heatmap of sugar flux predictions according to MICOM (universal primer: 515F-806R; n=30). Supplementary Item 13. Heatmap of vitamine flux predictions according to MICOM (universal primer: 515F-806R; n=30).

Additional file of Taxonomic and functional analyses of intact microbial communities thriving in extreme, astrobiology-relevant, anoxic sites

Additional file 10.

Leaf-inhabiting fungi are an important, but often overlooked component of molecular biodiversity studies. To understand their diversity and function in relation to plant species and climate, the phyllospheres of 14 phylogenetically diverse ornamental plant species were analyzed under different controlled greenhouse conditions. We found unexpectedly high fungal diversity (H′ = 2.8–6.5), OTU numbers (449–1050) and abundances (103–106 CFU cm-2 leaf surface) associated with all plants studied indoors. Despite experimental limitations, the composition of fungal communities were inclined toward a plant species-dependent pattern compared to the ambient climatic variables. Most detected fungi were patho- and saprotrophs showing a yeast-like growth morphology and were associated to the groups of endophytes and potential plant pathogens in a plant species-specific manner. A representative strain collection showed that 1/3 of the tested fungi (mainly Penicillium, Cladosporium, and Cryptococcus spp.) were able to inhibit mycelial growth and 2/3 inhibit sporulation of the plant pathogen Botrytis cinerea by the production of antifungal volatile organic compounds (VOCs) completely. This study indicates that plant leaves harbor a stable phyllosphere fungal diversity in diverse microclimates and enrich distinctive functional guilds.

Sequence data analysis pipelines. (DOCX 49Â kb)