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29 results for “Phototroph”
Data collection for Tsuji et al., 2023, Anoxygenic phototrophic Chloroflexota member uses a Type I reaction center
<p>Supplementary data files associated with Tsuji et al., 2023, "Anoxygenic phototrophic <i>Chloroflexota</i> member uses a Type I reaction center". These files are used by code in a corresponding GitHub repository (<a href="https://github.com/jmtsuji/Ca-Chlorohelix-allophototropha-RCI">https://github.com/jmtsuji/Ca-Chlorohelix-allophototropha-RCI</a>) that shows how various analyses that are presented in the paper were conducted.</p><p>Files included:</p><ul><li>HPLC-based spectroscopy data ("...-HPLC-run1.tsv.gz" or "...-HPLC-run2.tsv.gz") -- hyper-spectral data files, generated by a diode array detector, that are associated with pigment analyses in the paper. See the corresponding Github repo for how these files are analyzed.</li><li>Supplementary data about "<i>Ca. </i>Chloroheliales"-associated RCI:<ul><li>I_TASSER_homology_models_full_output.tar.gz -- Gzipped tarball containing the full output from I-TASSER for homology models of key phototrophy-related genes encoded by '<i>Candidatus </i>Chlorohelix allophototropha' and '<i>Candidatus </i>Chloroheliales bin L227-5C'. After unpacking the tarball, view a summary of the I-TASSER output for each gene by clicking on the 'index.html' file in that gene's folder.</li></ul></li><li>Boreal Shield lake survey data:<ul><li>lake_survey_MAGs.tar.gz -- Gzipped tarball containing the full collection of 756 metagenome-assembled genomes (MAGs) recovered from the Boreal Shield lake survey, corresponding to those mentioned in Supplementary Data 3. The FastA nucleotide genome sequences, FastA nucleotide predicted protein-coding gene sequences, FastA amino acid predicted protein sequences, and Genome Flat Files (GFFs) for all genomes are provided in the fna, ffn, faa, and gff subdirectories, respectively.</li><li>lake_survey_MAGs_eggnog_annotations.tar.gz -- Gzipped tarball containing annotations (produced via EggNOG) for all predicted proteins among the 756 MAGs recovered from lake metagenome data. Because proteins were pre-clustered prior to annotation, a "orf2gene" file inside the tarball maps the gene clusters to the ORF IDs used for each genome.</li><li>lake_survey_MAGs_featureCounts.tsv.gz -- GZipped tab-separated table containing the mapping statistics of metatranscriptome reads on all protein-coding genes from the 756 MAGs recovered from lake metagenome data.</li><li>lake_survey_Ca_Chloroheliales_MAGs_info.tar.gz -- A subset of information from the previous three files specific to genome bins ELA319 and ELA729, which represent RCI-encoding "<i>Ca</i>. Chloroheliales" members.</li></ul></li><li>Intermediate files involved in some of the genome assembly work in this paper:<ul><li>Capt_S15_sequencer_data_raw.tar.gz -- Gzipped tarball containing the raw Illumina MiSeq output data for the '<i>Candidatus </i>Chlorohelix allophototropha' subculture 15 sequencing run. The run represents a read cloud sequencing run relying on TELL-Seq technology. Indices can be parsed directly from raw output data using the Tell-Read pipeline.</li><li>scaffold.full.fasta.gz -- the assembled scaffolds generated using Tell-Read and Tell-Link on the above raw MiSeq output data.</li><li>Ca_Chloroheliaceae_bin_L227_5C_prokka_ORFs.faa.gz -- predicted open reading frames (ORFs) from the curated genome of '<i>Candidatus </i>Chloroheliales bin L227-5C'. These ORFs were predicted using prokka and were used for some of the analyses presented in the paper. Most analyses used the annotations available on NCBI (generated by PGAP) for this strain.</li></ul></li></ul>
Figure 12 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 12. GPP per unit weight of dry phytomass of consortiums of the photic zones in Montenegro caves.
Figure 4 in Phototrophic communities of Ahshtyrskaya Cave in the condition of artificial light
Figure 4. Scatter plots of 18 sites based on: a) total species composition, b) higher plants species composition, c) algae and cyanobacteria species composition; d) cluster analysis for 18 sites based on total species composition of communities in different cave zones.
Figure 3 in Phototrophic communities of Ahshtyrskaya Cave in the condition of artificial light
Figure 3. Scheme of Akhshtyrskaya Cave with the location of the lamps, scheme is given by Isaev & Starikh (2002).
Figure 1 in Biodiversity of phototrophs in illuminated entrance zones of seven caves in Montenegro
Figure 1. Map of the studied caves in Montenegro and photographs of their entrance zones. Cave numbers correspond to numbers in Map. 1- Obodska Pećina, 2- Golubinja Pećina, 3- Njegoš Pećina, 4- Veluštica Pećina, 5- untitled cave, 6- Vrbačka jama, 7- Jama ER-1.
Figure 2 in Biodiversity of phototrophs in illuminated entrance zones of seven caves in Montenegro
Figure 2. The dendrogram distribution diagram based on a comparison of all flora (A) and algal flora (B) of caves using the Jacquard floristic similarity coefficient. The dendrogram distribution diagram based on the comparison of all flora (C) and algal flora (D) of caves using the Shorygin floristic similarity coefficient. U - Untitled cave; N - Njegoš Pećina; Ve - Veluštica Pećina; G - Golubinja Pećina; Vr - Vrbačka jama; O - Obodska Pećina; J - Jama ER-1.
Binary (presence/absence) maps of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020
<p>Maps of presence/absence of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020</p> <p>Presence/Absence is binary, where 0 is "not biofilm" and 1 is "biofilm". <strong>Binary maps generated from probability maps using date-by-date logistic models of Roncoroni et al. (2022)</strong>.</p> <p>For further information see Roncoroni et al.(2022), <a href="http://dx.doi.org/10.1080/01431161.2022.2079963">http://dx.doi.org/10.1080/01431161.2022.2079963</a></p> <p>Details:</p> <ul> <li>Format: .ascii </li> <li>Name format: mmdd_Binary (where mm is the month, and dd the day)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul>
Figure 7 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 7. Bacterial biomass in the consortiums of the photic zones in Montenegro caves.
Figure 4 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 4. Micromycetes biomass in the consortiums of the photic zones in Montenegro caves.
Figure 9 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 9. Photorophic respiration in the consortiums of the photic zones in Montenegro caves.
Figure 2 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 2. Phototrophic biomass in the consortiums of the photic zones in Montenegro caves.
Figure 1 in Сarbon fluxes intensity from substrates and phototrophic consortiums of the photic zones in Montenegro caves
Figure 1. The consortiums of Montenegro caves.
Figure 1 in Phototrophic communities of Ahshtyrskaya Cave in the condition of artificial light
Figure 1. Lampenflora.
Figure 2 in Phototrophic communities of Ahshtyrskaya Cave in the condition of artificial light
Figure 2. One of the illuminated distant halls.
Data for: Linking cell size, Vmax, and Km in phototrophs and chemotrophs: Insights from Bayesian inference
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Data collection for Tsuji et al., 2020, Microbial ecology of phototrophs in Boreal Shield lakes, Chapter 3: Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes (PhD thesis)
<p>This data collection includes supplementary or raw data files related to Chapter 3 of the PhD thesis of Jackson M. Tsuji, "Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes" (in "Microbial ecology of phototrophs in Boreal Shield lakes"). Specifically, the following files are included:</p> <ul> <li>ASV_table_non_rarefied_counts.tsv.gz -- non-rarefied ASV table containing 16S rRNA gene amplicon data presented in this study as raw counts. Beyond the index column and sample columns, two additional columns, "Consensus.Lineage" and "Sequence" are included in the table. These columns include the taxonomic classification of the ASV (according to Silva) and the ASV sequence, respectively.</li> <li>ASV_table_non_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>ASV_table_rarefied_counts.tsv.gz -- same as "ASV_table_non_rarefied_counts.tsv.gz", except that data is rarefied to 12,000 sequences per sample. Five samples were dropped due to having <12,000 sequences.</li> <li>ASV_table_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>MAG_abundances_to_unassembled_reads.tsv.gz -- table like an ASV table showing the relative abundances (expressed as percentages) of metagenome-assembled genomes within metagenomes. Aside from the index column and sample columns, additional columns are included to provide the taxonomic classification of the MAGs (based on the Genome Taxonomy Database) and the CheckM statistics of the MAGs. Relative abundances of MAGs in a metagenome are calculated as the number of mapped reads to the MAGs from the given metagenome divided by the total number of unassembled metagenome reads for that metagenome (times 100%).</li> <li>MAG_abundances_to_assembled_reads.tsv.gz -- same as above, except that relative abundances are divided by the total number of unassembled metagenome reads for that metagenome that mapped to that metagenome's assembled contigs.</li> <li>core_sample_metadata.tsv -- table of core physico-chemical and geographic metadata for the samples in this study (used to build biplots presented in the chapter). Note that "nd" means "no data available", and any measurements below detection limits have been set to 0. A limited number of values were inferred from other sampling time points -- these are noted in the table for TDFe measurements, and in addition, the light attenuation coefficient for Lake 373 in Sept. 2017 was inferred from the Sept. 2016 coefficient due to no light data being available for Sept. 2017 samples.</li> <li>metadata_descriptions.tsv -- descriptions of all metadata columns in the above file.</li> </ul> <p> </p>
Data from: A metabarcoding framework for facilitated survey of endolithic phototrophs with tufA
Background: In spite of their ecological importance as primary producers and microbioeroders of marine calcium carbonate (CaCO3) substrata, endolithic phototrophs spanning both prokaryotic (the cyanobacteria) and eukaryotic algae lack established molecular resources for their facilitated survey with high throughput sequencing. Here, the development of a metabarcoding framework for the elongation factor EF-Ttu (tufA) was tested on four Illumina-sequenced marine CaCO3 microfloras for the characterization of their endolithic phototrophs, especially the abundant bioeroding Ostreobium spp. (Ulvophyceae). The framework consists of novel tufA degenerate primers and a comprehensive database enabling Operational Taxonomic Unit (OTU) identification at multiple taxonomic ranks with percent identity thresholds determined herein. Results: The newly established tufA database comprises 4057 non-redundant sequences (from 1339 eukaryotic and prokaryotic phototrophs, and 2718 prokaryotic heterotrophs) including 27 classes in 10 phyla of phototrophic diversity summarized from data mining on GenBank®, our barcoding of >150 clones produced from coral reef microfloras, and >300 eukaryotic phototrophs (>230 Ulvophyceae including >100 'Ostreobium' spp., and >70 Florideophyceae, Phaeophyceae and miscellaneous taxa). Illumina metabarcoding with the newly designed primers resulted in 802 robust OTUs including 618 phototrophs and 184 heterotrophs (77 and 23 % of OTUs, respectively). Phototrophic OTUs belonged to 14 classes of phototrophs found in seven phyla, and represented ~98 % of all reads. The phylogenetic profiles of coral reef microfloras showed few OTUs in large abundance (proportion of reads) for the Chlorophyta (Ulvophyceae, i.e. Ostreobium and Phaeophila), the Rhodophyta (Florideophyceae) and Haptophyta (Coccolithophyceae), and a large diversity (richness) of OTUs in lower abundance for the Cyanophyta (Cyanophyceae) and the Ochrophyta (the diatoms, 'Bacillariophyta'). The bioerosive 'Ostreobium' spp. represented four families in a large clade of subordinal divergence, i.e. the Ostreobidineae, and a fifth, phylogenetically remote family in the suborder Halimedineae (provisionally assigned as the 'Pseudostreobiaceae'). Together they harbor 85–95 delimited cryptic species of endolithic microsiphons. Conclusions: The novel degenerate primers allowed for amplification of endolithic phototrophs across a wide phylogenetic breadth as well as their recovery in very large proportions of reads (overall 98 %) and diversity (overall 77 % of OTUs). The established companion tufA database and determined identity thresholds allow for OTU identification at multiple taxonomic ranks to facilitate the monitoring of phototrophic assemblages via metabarcoding, especially endolithic communities rich in bioeroding Ulvophyceae, such as those harboring 'Ostreobium' spp., Phaeophila spp. and associated algal diversity.
Data from: Light availability impacts structure and function of phototrophic stream biofilms across domains and trophic levels
Phototrophic biofilms are ubiquitous in freshwater and marine environments where they are critical for biogeochemical cycling, food webs and in industrial applications. In streams, phototrophic biofilms dominate benthic microbial life and harbor an immense prokaryotic and eukaryotic microbial biodiversity with biotic interactions across domains and trophic levels. Here, we examine how community structure and function of these biofilms respond to varying light availability, as the crucial energy source for phototrophic biofilms. Using metatranscriptomics, we found that under light limitation dominant phototrophs, including diatoms and cyanobacteria, displayed a remarkable plasticity in their photosynthetic machinery manifested as higher abundance of messenger RNAs (mRNAs) involved in photosynthesis and chloroplast ribosomal RNA. Under higher light availability, bacterial mRNAs involved in phosphorus metabolism, mainly from Betaproteobacteria and Cyanobacteria, increased, likely compensating for nutrient depletion in thick biofilms with high biomass. Consumers, including diverse ciliates, displayed community shifts indicating preferential grazing on algae instead of bacteria under higher light. For the first time, we show that the functional integrity of stream biofilms under variable light availability is maintained by structure-function adaptations on several trophic levels. Our findings shed new light on complex biofilms, or "microbial jungles", where in analogy to forests, diverse and multi-trophic level communities lend stability to ecosystem functioning. This multi-trophic level perspective, coupling metatranscriptomics to process measurements, could advance understanding of microbial-driven ecosystems beyond biofilms, including planktonic and soil environments.
Data from: A metabarcoding framework for facilitated survey of endolithic phototrophs with tufA
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Data from: Light availability impacts structure and function of phototrophic stream biofilms across domains and trophic levels
Open the record for dataset details and reuse information.
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