SantiagoSanchezF/mgnify_study_descriptions huggingface dataset:
EMG produced TPA metagenomics assembly of PRJEB10293 data set (Tongue metagenome from 9 healthy individuals.).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJEB10293, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system:Oral.

Biome: root:Host-associated:Human:Digestive system:Oral
EMG produced TPA metagenomics assembly of PRJNA650272 data set (Microbiomes in supragingival biofilms and saliva of adolescent patients with provoked and naturally occurring gingivitis relative to gingival health).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA650272, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system:Oral:Saliva.

Biome: root:Host-associated:Human:Digestive system:Oral:Saliva
EMG produced TPA metagenomics assembly of PRJNA528558 data set (oral metagenome Metagenome).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA528558, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system:Oral.

Biome: root:Host-associated:Human:Digestive system:Oral
EMG produced TPA metagenomics assembly of PRJNA656314 data set (Environmental concentrations of antibiotics alter the zebrafish gut microbiome structure and potential functions).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA656314, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Fish:Digestive system.

Biome: root:Host-associated:Fish:Digestive system
EMG produced TPA metagenomics assembly of PRJNA544171 data set (Zebrafish Gut Microbiome).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA544171, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Fish:Digestive system.

Biome: root:Host-associated:Fish:Digestive system
EMG produced TPA metagenomics assembly of PRJNA419274 data set (Alteration in the Oral microbiota of Indian Antartic expedition members during ship journey and stay at Antartica).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA419274, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system:Oral:Saliva.

Biome: root:Host-associated:Human:Digestive system:Oral:Saliva
EMG produced TPA metagenomics assembly of PRJNA576566 data set (metagenomic studies of vaginal microbiome in HPV16 infection).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA576566, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Reproductive system:Vagina.

Biome: root:Host-associated:Human:Reproductive system:Vagina
16S rRNA gene sequences of human airway microbiota

We characterized the airway microbiota of healthy Korean adults. The study population consisted of monozygotic twins, dizygotic twins, and family members of twin pairs. All subjects had not taken antibiotics within three months prior to sampling and had no current symptoms of chronic lung diseases, such as asthma and COPD. A total of 304 sputum samples collected from each subject were analyzed for airway microbiota composition using next generation sequencing of partial 16S rRNA gene sequence.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Sputum
HoloFood Chicken Caecum+Ileum Metagenome

Metagenomic raw reads, assemblies, and bins derived from HoloFood chicken caecum and ileum samples. Samples selected for this batch were randomised among trials (feed), age, and breed to overcome batch effect. The caecum samples in this project contributed to the chicken caecum MAG catalogue (project: PRJEB55374 [ERP140263]), and the ileum samples contributed to the chicken ileum MAG catalogue (project: PRJEB55375 [ERP140264]).

Biome: root:Host-associated:Birds:Digestive system
EMG produced TPA metagenomics assembly of PRJNA728379 data set (Metagenome of pig fecal bacteria).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA728379, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Mammals:Digestive system:Fecal.

Biome: root:Host-associated:Mammals:Digestive system:Fecal
EMG produced TPA metagenomics assembly of PRJNA471937 data set (Pig gut metagenome Metagenomic assembly).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA471937, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Mammals:Digestive system:Fecal.

Biome: root:Host-associated:Mammals:Digestive system:Fecal
EMG produced TPA metagenomics assembly of PRJNA312048 data set (gut microbe Metagenome).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA312048, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Mammals:Digestive system.

Biome: root:Host-associated:Mammals:Digestive system
EMG produced TPA metagenomics assembly of PRJNA677897 data set (Whole metegenome sequencing of the Sicilian Black pig fecal microbiome).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA677897, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Mammals:Digestive system:Fecal.

Biome: root:Host-associated:Mammals:Digestive system:Fecal
EMG produced TPA metagenomics assembly of PRJNA400119 data set (pig gut metagenome Metagenome).

The Third Party Annotation (TPA) assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA400119, and was assembled with metaSPAdes v3.15.3. This project includes samples from the following biomes: root:Host-associated:Mammals:Digestive system.

Biome: root:Host-associated:Mammals:Digestive system
gut microbiota and ventilator-associated pneumonia

The aim of this study is to assess the link between gut microbiota composition and subsequent occurrence of ventilator-associated pneumonia in critically ill influenza patients. Every patient admitted to intensive care unit from October 2019 to March 2020 who received at least 2 days of invasive mechanical ventilation were included (ancillary study of Microbe study, NCT04131569). Rectal swabs were sampled and DNA extraction was performed by QIAamp® PowerFecal® Pro DNA kit (QIAgen®). V3-V4 regions of 16SRNA coding gene was amplified to analyse the bacterial kingdom of lung microbiota (bacteriobiota) and ITS2 to analyse the fungal kingdom (mycobiota). Sequencing (2x250bp paired-end reads) was performed on MiSeq sequencer (Illumina®) and DADA2 pipeline on R software was used for bioinformatics analyses.

Biome: root:Host-associated:Human:Digestive system:Hindgut:Rectum
Effect of storage on the microbial community present in Cystic fibrosis sputum

This study aims to invesitigate if storage at room temperature affect the microbial community present in Cystic fibrosis sputum.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Sputum
Microbial community analysis of sputum samples from adults and pediatric patients

The goal of the study is to understand the similarities and differences in the microbial communities in adults and pediatric patients

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Sputum
Gut microbiome predicts efficacy and immune-related toxicities of advanced non-small cell lung cancer patients treated with anti-PD-1/PD-L1 Ab-based immunotherapy

The gut microbiome (GM) plays an important role in shaping systemic immune responses. Preclinical and clinical data suggest that GM influences anti-PD-1/PD-L1 or -CTLA-4 Ab-mediated anti-cancer responses. Furthermore, there is strong evidence that antibiotics (ATB) worsen clinical outcomes based on multiple retrospective and one prospective studies using immune checkpoint inhibition (ICI). However, whether GM profiling, at baseline or post-ATB, could represent a biomarker of response in advanced non-small cell lung cancer (NSCLC) during ICI therapy remains unknown. Moreover, the relationship between specific gut microbiota and incidence of immune-related adverse events (irAEs) remain to be precisely elucidated. The objective of the present study was to characterize the impact of ATB on specific GM signatures using metagenomics sequencing in a cohort of 70 patients with advanced NSCLC treated with anti-PD-1/anti-PD-L-1. Primary endpoints were to define specific gut microbiota associated with response and non-response, progression-free survival (PFS), overall survival (OS), and irAEs in both patients who received ATB and those who did not receive ATB.

Biome: root:Host-associated:Human:Digestive system:Large intestine:Fecal
The murine lung microbiome in relation to the intestinal and vaginal bacterial communities

The first description of the bacterial population of the lung microbiota in mice. Bacterial communities from broncho-alveolar lavage fluids and lung tissue were compared to samples taken from fecal matter (caecum) and vaginal lavage fluid from female BALB/cJ mice.

Biome: root:Host-associated:Mammals:Respiratory system
Cross sectional analysis of 16S rRNA sequences obtained from bronchiectasis patient airway samples Targeted Locus (Loci)

Non-CF bronchiectasis airway microbiota.  Cross sectional analysis of 16S rRNA sequences obtained from bronchiectasis patient airway samples.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Lung
EMG produced TPA metagenomics assembly of PRJNA728374 data set (Gut microbiome responses to dietary intervention with hypocholesterolemic vegetable oils.).

The Third Party Annotation (TPA)  assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA728374, and was assembled with megahit v1.2.9. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system.

Biome: root:Host-associated:Human:Digestive system
EMG produced TPA metagenomics assembly of PRJNA819247 data set (Infant gut microbiome Raw sequence reads).

The Third Party Annotation (TPA)  assembly was derived from the primary whole genome shotgun (WGS) data set PRJNA819247, and was assembled with metaspades v3.15.3. This project includes samples from the following biomes: root:Host-associated:Human:Digestive system.

Biome: root:Host-associated:Human:Digestive system
ARGs study in bioelectrochemical remediation

Elimination of several antibiotics in water by bioelectrochemical cells. The main objective is study how the concentration of antibiotic resistant genes (ARG) changed depending on the voltage application.

Biome: root:Environmental:Aquatic:Freshwater:Drinking water
Potential microbial consortium involved in biodegradation of diesel in mangrove sediment explored by metagenomics analysis

This study aimed to explore microbial communities potentially involved in diesel-, hexadecane- and phenanthrene- degradation in microcosm studies, constructed using mangrove sediment from Bangkhuntein, Bangkok, Thailand

Biome: root:Environmental:Aquatic:Marine:Intertidal zone:Mangrove swamp
This a study of how variability of the soil environment affects the soil microbial function and diversity

This study has a total of 183 samples from 4 time points and 3 paired pasture and woodland sites. We have obtained continuous measurements of soil moisture and temperature for the study period and we sequenced the soil metagenome by Illumina Hiseq.

Biome: root:Environmental:Terrestrial:Soil
Freshwater stream mat viral communities from Ille-et-Vilaine, France

The viral community from an iron rich microbial mat is analyzed

Biome: root:Environmental:Aquatic:Freshwater:Lotic:Mid stream
Analysis of the Gut Microbiota: An Emerging Source of Biomarkers for Immune Checkpoint Blockade Therapy in Non-Small Cell Lung Cancer

Background: The human gut harbors around 1013-1014 microorganisms, collectively referred to as gut microbiota. Recent studies have found that the gut microbiota may have an impact on the interaction between immune regulation and anti-cancer immunotherapies.
Methods: In order to characterize the diversity and composition of commensal microbiota and its relationship with response to immune checkpoint blockade (ICB), 16S ribosomal DNA (rDNA) sequencing was performed on 69 stool samples from advanced non-small cell lung cancer (NSCLC) patients prior to treatment with ICB.
Results: The use of antibiotics and ICB-related skin toxicity were significantly associated with reduced gut microbiota diversity. However, antibiotics (ATB) usage was not related to low ICB efficacy. Phascolarctobacterium was enriched in patients with clinical benefit and correlated with prolonged progression-free survival, whereas Dialister was more represented in patients with progressive disease, and its higher relative abundance was associated with reduced progression-free survival and overall survival, with independent prognostic value in multivariate analysis.
Conclusions: Our results corroborate the relation between the baseline gut microbiota composition and ICB clinical outcomes in advanced NSCLC patients, and provide novel potential predictive and prognostic biomarkers for immunotherapy in NSCLC.

Biome: root:Host-associated:Mammals:Digestive system:Fecal
A multiomics study on the effects of TRIKAFTA on CF patients

Novel small molecule therapies for cystic fibrosis (CF) are showing promising efficacy and becoming more widely available since recent FDA approval. The newest of these, Trikafta (TKT), is a triple therapy utilizing Ivacaftor, a CF transmembrane conductance regulator (CFTR) potentiator, combined with Tezacaftor and Elexacaftor, CFTR correctors. However, it is not yet known how these drugs will affect polymicrobial lung infections, which are the leading cause of morbidity and mortality among people with CF (pwCF). Here, we analyzed the sputum microbiome and metabolome from pwCF (n=24) before and after TKT therapy using 16S rRNA gene amplicon sequencing and untargeted metabolomics. The lung microbiome diversity, particularly its evenness, was increased (p = 0.044) and the microbiome profiles were different between individuals before and after therapy (PERMANOVA F=1.92, p=0.044). Despite these changes, the microbiomes were more similar within an individual than across the sampled population. There were no specific microbial taxa that were different in abundance before and after therapy, but collectively, the log-ratio of anaerobes to pathogens significantly decreased. The sputum metabolome also showed changes due to TKT. Beta-diversity increased after therapy (PERMANOVA F=4.22, p=0.022) and was characterized by greater variation across subjects while on treatment. This significant difference in the metabolome was driven by a decrease in peptides, amino acids, and metabolites from the kynurenine pathway. Metabolism of the three small molecules that make up TKT was extensive, including previously uncharacterized structural modifications. This study shows that TKT therapy affects both the microbiome and metabolome of airway mucus. This effect was stronger on sputum biochemistry, which may reflect changing niche spaces for microbial residency in lung mucus as the drug’s effects take hold, which then leads to changing microbiology.Why: exploratory study, not much is known about TRIKAFTA’s impact on microbiome/metabolome of CF lung Method: acquired samples pre- and post-TRIKAFTA intervention from 2 clinics for a total of 20 (subject to change) patients; 16S metagenomics (DNA extraction, PCR amplification, sequencing, analysis); metabolomics (methanol prep, LC-MS, analysis)

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Sputum
Lung microbiota but not mycobiota predicts day 28 mortality in critically ill influenza patients: a feasibility study

The aim of this study is to assess the prognostic value of lung microbiota in critically ill influenza patients. Every PCR-proven influenza patient admitted to intensive care unit of two hospitals for respiratory failure during 2019-2020 pandemics were included (NCT04131296). Sputum or tracheal aspirates were sampled and DNA extraction was performed by QIAamp® PowerFecal® Pro DNA kit (QIAgen®). V3-V4 regions of 16SRNA coding gene was amplified to analyse the bacterial kingdom of lung microbiota (bacteriobiota) and ITS2 to analyse the fungal kingdom (mycobiota). Sequencing (2x250bp paired-end reads) was performed on MiSeq sequencer (Illumina®) and DADA2 pipeline on R software was used for bioinformatics analyses.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Lung
Oral microbiome data of never-smoking subjects with lung adenocarcinoma

Oral microbiome data of never-smoking subjects with lung adenocarcinoma

Biome: root:Host-associated:Human:Digestive system:Oral
Large multicentred study of cystic fibrosis sputum from patients across 11 sites in Europe and the USA.

Large multicentred study of cystic fibrosis sputum from patients across 11 sites in Europe and the USA.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Sputum
Low IgA associated with oropharyngeal microbiota changes and lung disease in primary antibody deficiency

Common Variable Immunodeficiency (CVID) and X-linked agammaglobulinemia (XLA) are primary antibody deficiencies characterised by hypogammaglobulinemia and recurrent infections, which can lead to structural airway disease (AD) and interstitial lung disease (ILD). We investigated associations between serum IgA, oropharyngeal microbiota composition and severity of lung disease in these patients.
In this cross-sectional multicentre study we analysed oropharyngeal microbiota composition of 86 CVID patients, 12 XLA patients and 49 healthy controls (HC) using next-generation sequencing of the 16S rRNA gene. qPCR was used to estimate bacterial load. IgA was measured in serum. High resolution CT scans were scored for severity of AD and ILD.
Oropharyngeal bacterial load was increased in CVID patients with low IgA (p=0.013) and XLA (p=0.029) compared to HC. IgA status was associated with distinct beta (between-sample) diversity (p=0.039), enrichment of (Allo)prevotella, and more severe radiographic lung disease (p=0.003), independently of recent antibiotic use. AD scores were positively associated with Prevotella, Alloprevotella, and Selenomonas, and ILD scores with Streptococcus and negatively with Rothia.
In clinically stable patients with CVID and XLA, radiographic lung disease was associated with IgA deficiency and expansion of distinct oropharyngeal bacterial taxa. Our findings highlight IgA as a potential driver of upper respiratory tract microbiota homeostasis.

Biome: root:Host-associated:Human:Respiratory system:Nasopharyngeal:Pharynx
Respiratory airway microbiomes in stage I non-small cell lung cancer

In this study, 16S sequencing was performed to assess the bacterial microbiomes in DNA isolated from pre-operative bronchial lavage fluid samples of patients with pathologic stage I non-small cell lung cancer. Thirty-five of this study's bronchial lavage fluid samples were independently processed for DNA isolation and 16S sequencing using the same methods as this study's in a past study whose data are available in the EBI ENA repository with accession number PRJEB29934 (patient identifiers used in both studies are the same). Samples were obtained from the Lung Cancer Biospecimen Resource Network, University of Virginia, VA, USA. For the samples procured by the repository, normal saline was used for collection of 20-40 ml of bronchial lavage fluids, which were centrifuged at 1,300 g for 25 minutes, following which supernatants were collected and stored at -80 oC or in liquid nitrogen.QIAamp UCP Pathogen Mini kit and Pathogen Lysis L tubes (Qiagen) were used to extract DNA from bronchial lavage fluid (0.4 ml). The protocol "Pretreatment of Pathogen DNA from 400 μl Whole Blood (Mechanical Pre-lysis Protocol)" provided with the kit was used. The kit's ATL buffer with reagent Dx was added to samples to make their volume to 0.4 ml before mechanical lysis. The study included 3 negative controls for DNA extraction, an empty, sterile, nuclease-free 1.5-ml microcentrifuge tube. DNA was also isolated in duplicate from a positive control, ZymoBIOMICS Microbial Community Standard (product D6300, Zymo, Irvine, CA). Bronchial lavage fluid samples of two patients were mixed 1:1 volume for DNA extraction in duplicate from final 0.4 ml. This was done for two sets of two-patients samples. 16S sequencing of isolated DNA, involving 16S PCR, sequencing library generation, and library sequencing, was performed in one batch. The 16S sequencing method suggested in the 16S Metagenomic Library preparation guide of Illumina (San Diego, CA) was followed. An ~464 bp amplicon of bacterial 16S rRNA V3-V4 region was amplified with forward and reverse primers of sequences TCGTCGGCAGCGTC-ad-CCTACGGGNGGCWGCAG and GTCTCGTGGGCTCGG-ad-GACTACHVGGGTATCTAATCC, respectively (ad = AGATGTGTATAAGAGACAG). DNA (25 ng) was subjected to 35 cycles of PCR with annealing temperature of 55 oC and KAPA HiFi HotStart DNA polymerase. For samples for which 25 ng DNA was unavailable, the maximum volume of a sample's DNA preparation was used. Amplified DNA was purified with AMPure XP beads and was indexed with Nextera XT index kit in an 8-cycle PCR to generate sequencing libraries. Libraries were purified with AMPure XP beads and 12 pmoles of each library was sequenced on a MiSeq instrument (Illumina) with v3 sequencing reagents to obtained paired 300 bp reads. A PhiX library was spiked in at 10% molarity. Multiplexed libraries were sequenced twice using two MiSeq sequencing runs. Sequencing data uploaded to ENA has merged raw (CASAVA de-multiplexed) fastq.gz files of the two runs.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Lung
Comparison of five DNA extraction and purification methods for microbiome characterization from the lung tissue.

A total of 7 individuals (four children and three adults) selected on the basis of unexpected death were included in this study.For each autopsied subject, the right upper lobe was removed, small samples were obtained from deep lung tissue, cut into small pieces, divided in five aliquots (0.4 g) and frozen at -80oC for DNA extraction. Five DNA extraction protocols were applied on each piece o sample: protocol 1, using the QIAamp DNA Mini kit (Qiagen); adding a bead-beating step (protocol 2); a phenol-chloroform step (protocol 3); both bead-beating and phenol-chloroform steps (protocol 4); and a pre-treatment step followed by the bead-beating and phenol-chloroform steps (protocol 5).For all samples, the 16S rRNA gene analysis for the bacteria and the internal transcribed spacer (ITS) region for fungal community were carried out, respectively. PCR amplification of 469  bp of the V3-V4 regions in the case of bacteria, and  spanning about 289 bp of the small subunit and the 5.8S region of the rRNA operon in the case of fungi were performed and sequencing of the amplicons using the Illumina MiSeq with 300 bp x2 chemistry.

Biome: root:Host-associated:Human:Respiratory system:Pulmonary system:Lung