#Microbial taxonomic
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covid-safer-hotties · 1 month ago
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By Vijay Kumar Malesu
In a recent pre-print study posted to bioRxiv*, a team of researchers investigated the predictive role of gut microbiome composition during acute Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in the development of Long Coronavirus Disease (Long COVID) (LC) and its association with clinical variables and symptom clusters.
Background LC affects 10–30% of non-hospitalized individuals infected with SARS-CoV-2, leading to significant morbidity, workforce loss, and an economic impact of $3.7 trillion in the United States (U.S.).
Symptoms span cardiovascular, gastrointestinal, cognitive, and neurological issues, resembling myalgic encephalomyelitis and other post-infectious syndromes. Proposed mechanisms include immune dysregulation, neuroinflammation, viral persistence, and coagulation abnormalities, with emerging evidence implicating the gut microbiome in LC pathogenesis.
Current studies focus on hospitalized patients, limiting generalizability to milder cases. Further research is needed to explore microbiome-driven predictors in outpatient populations, enabling targeted diagnostics and therapies for LC’s heterogeneous and complex presentation.
About the study The study was approved by the Mayo Clinic Institutional Review Board and recruited adults aged 18 years or older who underwent SARS-CoV-2 testing at Mayo Clinic locations in Minnesota, Florida, and Arizona from October 2020 to September 2021. Participants were identified through electronic health record (EHR) reviews filtered by SARS-CoV-2 testing schedules.
Eligible individuals were contacted via email, and informed consent was obtained. Of the 1,061 participants initially recruited, 242 were excluded due to incomplete data, failed sequencing, or other issues. The final cohort included 799 participants (380 SARS-CoV-2-positive and 419 SARS-CoV-2-negative), providing 947 stool samples.
Stool samples were collected at two-time points: weeks 0–2 and weeks 3–5 after testing. Samples were shipped in frozen gel packs via overnight courier and stored at −80°C for downstream analyses. Microbial deoxyribonucleic acid (DNA) was extracted using Qiagen kits, and metagenomic sequencing was performed targeting 8 million reads per sample.
Taxonomic profiling was conducted using Kraken2, and functional profiling was performed using the Human Microbiome Project Unified Metabolic Analysis Network (HUMAnN3).
Stool calprotectin levels were measured using enzyme-linked immunosorbent assay (ELISA), and SARS-CoV-2 ribonucleic acid (RNA) was detected using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
Clinical data, including demographics, comorbidities, medications, and symptom persistence, were extracted from EHRs.
Machine learning models incorporating microbiome and clinical data were utilized to predict LC and to identify symptom clusters, providing valuable insights into the heterogeneity of the condition.
Study results The study analyzed 947 stool samples collected from 799 participants, including 380 SARS-CoV-2-positive individuals and 419 negative controls. Of the SARS-CoV-2-positive group, 80 patients developed LC during a one-year follow-up period.
Participants were categorized into three groups for analysis: LC, non-LC (SARS-CoV-2-positive without LC), and SARS-CoV-2-negative. Baseline characteristics revealed significant differences between these groups. LC participants were predominantly female and had more baseline comorbidities compared to non-LC participants.
The SARS-CoV-2-negative group was older, with higher antibiotic use and vaccination rates. These variables were adjusted for in subsequent analyses.
During acute infection, gut microbiome diversity differed significantly between groups. Alpha diversity was lower in SARS-CoV-2-positive participants (LC and non-LC) than in SARS-CoV-2-negative participants.
Beta diversity analyses revealed distinct microbial compositions among the groups, with LC patients exhibiting unique microbiome profiles during acute infection.
Specific bacterial taxa, including Faecalimonas and Blautia, were enriched in LC patients, while other taxa were predominant in non-LC and negative participants. These findings indicate that gut microbiome composition during acute infection is a potential predictor for LC.
Temporal analysis of gut microbiome changes between the acute and post-acute phases revealed significant individual variability but no cohort-level differences, suggesting that temporal changes do not contribute to LC development.
However, machine learning models demonstrated that microbiome data during acute infection, when combined with clinical variables, predicted LC with high accuracy. Microbial predictors, including species from the Lachnospiraceae family, significantly influenced model performance.
Symptom analysis revealed that LC encompasses heterogeneous clinical presentations. Fatigue was the most prevalent symptom, followed by dyspnea and cough.
Cluster analysis identified four LC subphenotypes based on symptom co-occurrence: gastrointestinal and sensory, musculoskeletal and neuropsychiatric, cardiopulmonary, and fatigue-only.
Each cluster exhibited unique microbial associations, with the gastrointestinal and sensory clusters showing the most pronounced microbial alterations. Notably, taxa such as those from Lachnospiraceae and Erysipelotrichaceae families were significantly enriched in this cluster.
Conclusions To summarize, this study demonstrated that SARS-CoV-2-positive individuals who later developed LC exhibited distinct gut microbiome profiles during acute infection. While prior research has linked the gut microbiome to COVID-19 outcomes, few studies have explored its predictive potential for LC, particularly in outpatient cohorts.
Using machine learning models, including artificial neural networks and logistic regression, this study found that microbiome data alone predicted LC more accurately than clinical variables, such as disease severity, sex, and vaccination status.
Key microbial contributors included species from the Lachnospiraceae family, such as Eubacterium and Agathobacter, and Prevotella spp. These findings highlight the gut microbiome’s potential as a diagnostic tool for identifying LC risk, enabling personalized interventions.
*Important notice: bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
Journal reference: Preliminary scientific report. Isin Y. Comba, Ruben A. T. Mars, Lu Yang, et al. (2024) Gut Microbiome Signatures During Acute Infection Predict Long COVID, bioRxiv. doi:https://doi.org/10.1101/2024.12.10.626852. www.biorxiv.org/content/10.1101/2024.12.10.626852v1.full
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magz · 8 months ago
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New antibiotic kills pathogenic bacteria, spares healthy gut microbes
Article Date: May 29, 2024
Article Blurb: 
Researchers have developed a new antibiotic that reduced or eliminated drug-resistant bacterial infections in mouse models of acute pneumonia and sepsis while sparing healthy microbes in the mouse gut. The drug, called lolamicin, also warded off secondary infections with Clostridioides difficile, a common and dangerous hospital-associated bacterial infection, and was effective against more than 130 multidrug-resistant bacterial strains in cell culture.
[...]
Numerous studies have found that antibiotic-related disturbances to the gut microbiome increase vulnerability to further infections and are associated with gastrointestinal, kidney, liver and other problems.
[...] In a series of experiments, Muñoz designed structural variations of the Lol inhibitors and evaluated their potential to fight gram-negative and gram-positive bacteria in cell culture. One of the new compounds, lolamicin, selectively targeted some “laboratory strains of gram-negative pathogens including Escherichia coli, Klebsiella pneumoniae and Enterobacter cloacae,” the researchers found. Lolamicin had no detectable effect on gram-positive bacteria in cell culture. At higher doses, lolamicin killed up to 90% of multidrug-resistant E. coli, K. pneumoniae and E. cloacae clinical isolates. 
When given orally to mice with drug-resistant septicemia or pneumonia, lolamicin rescued 100% of the mice with septicemia and 70% of the mice with pneumonia, the team reported.  
Extensive work was done to determine the effect of lolamicin on the gut microbiome. 
“The mouse microbiome is a good tool for modeling human infections because human and mouse gut microbiomes are very similar,” Muñoz said. “Studies have shown that antibiotics that cause gut dysbiosis in mice have a similar effect in humans.”
Treatment with standard antibiotics amoxicillin and clindamycin caused dramatic shifts in the overall structure of bacterial populations in the mouse gut, diminishing the abundance several beneficial microbial groups, the team found.
“In contrast, lolamicin did not cause any drastic changes in taxonomic composition over the course of the three-day treatment or the following 28-day recovery,” the researchers wrote. 
Many more years of research are needed to extend the findings, Hergenrother said. 
[More in Article]
Note: The main scientific journal itself is paywalled (and not yet available in unpaywall nor sci-hub), Nature Journal Link
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eddieintheocean · 1 year ago
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lil guys of the Ediacaran
the Ediacaran period was around ~635 million years ago, happening before the Cambrian. This time saw the rise of the first group of animals and the oldest multicellular life. The fossil record is sparse due to the fact that the creatures did not have hard shells, which are the thing most easily fossilised.
the Ediacaran biota appeared ~570 million years ago, and are very taxonomically ambiguous, they may relate to some groups, like cnidaria and protozoans, but some have suggested completely new phylums that we do not have today.
the majority of Ediacaran biota were sessile (lacking the ability to move by themselves) and were soft-bodied. many also lacked mouths and a gut.
here are some lil guys!
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These frondose fossils are very controversial, considering they appear to be leafy plants, but may very well be anything from animal or protist, to stem fungi (https://www.science.org/doi/10.1126/science.1099727)
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this is an Erniettamorph and we know basically nothing about these! The fossils were found in places where photosynthesis would not have been possible, and one type of these fossils has been found with the tubes completely covered and filled with sand, which makes the possibility of osmotic feeding from the surrounding water difficult as it would reduce the metabolically active volume. (https://www.pnas.org/doi/full/10.1073/pnas.0904836106)
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Dickinsonia is a more studied animal. and it probably was an animal, due to cholesterol molecules found in fossils. Fossils range from millimetres to ~1.4 metres, and have near bilateral symmetry and rib-like segments. They were a mobile creature and ate microbial mats (trace fossils have been found which are most likely impressions from feeding) (https://doi.org/10.1017/S175569102300004X) A study from 2022 has suggested that they temporarily stuck themselves to substrate with mucus, which suggests a life in shallow waters (https://doi.org/10.1017/S0016756821000194)
support me on ko-fi?
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leedsomics · 4 months ago
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Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome
Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation. http://dlvr.it/TDYzpz
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evoldir · 4 months ago
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Fwd: Course: Online.Metabarcoding.2Places.Oct7-10
Begin forwarded message: > From: [email protected] > Subject: Course: Online.Metabarcoding.2Places.Oct7-10 > Date: 23 September 2024 at 05:44:48 BST > To: [email protected] > > > > > Can you please repost letting people know we onl;y have 2early bird places left for our new course on Metabarcoding and Metagenomics, if you would be > kind enough to post the followingto thecourses and workshop page of Evoldir please, Thank you, Oliver > > ONLINE COURSE – Introduction to Metabarcoding and Metagenomics Analysis > (IMAM01) > > ONLY 2 X EARLY BIRD PLACES LEFT > PR stats have added 10 early birdplaceswith 10% off reducing fees to > £432.00.  The first 10 tickets are first come first serve basis when > you book via our website* > > https://ift.tt/g8bBok7 > > Instructor- Edinburgh Genomics > > 7th - 10th October 2024 > > Please feel free to share! > > COURSE OVERVIEW-Metabarcoding and metagenomics study genetic > material recovered from environmental samples. Both methods provide > a comprehensive view of microbial communities which are present in > various ecosystems. The ability to identify organisms from traces of > genetic material in environmental samples has reshaped the way we see > life on earth. Especially for microorganisms, metagenomic techniques > have granted us unprecedented insight into the microbiome of animals > and the environment more broadly > > Metabarcoding and metagenomics are both methods to study the composition > of these complex communities. Where metabarcoding focusses on looking > at a single or a combination of marker genes, metagenomics looks into > everything within a community. > > During this course we will look at the differences and similarities > between these two methods. We explain how to process the data using both > short and long reads data, we take a look at the pros and cons and some > of the pitfalls. We will guide you through the different approaches to > take when processing the data and walk you through using some of the > tools which are considered to be golden standard in the field. You will > have hands on experience processing real data. > > By the end of the course, participants should: > > Understand the basic concepts behind metabarcoding and metagenomics Work > with both short and long read data for both metabarcoding and metagenomics > Be able to use Qiime2 and NanoClust for analysis of metabarcoding Know > different methods (metaphlan, humann) for marker based taxonomic and > functional annotation of metagenomics data Create and annotated metagenome > assembled genomes (using megahit, checkm, gtdb-tk) Be able to annotated > antibiotic resistance genes in metagenomics data > > Please [email protected] any questions. > > A full list of our live courses can be found here > > > > Best wishes, > > Oliver > > Oliver Hooker PhD. > PR stats > > Oliver Hooker
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rbesjournal1 · 6 months ago
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Microbes of sponges have diverse associations, including true symbiosis. Sponges, being evolutionarily ancient sessile filter feeders, host diverse and abundant microbial species that play crucial roles in host metabolism. Although the microbial symbionts of sponges are widely distributed within the organism (up to 40% of their volume), the ecological relationships and interactions between bacteria and their sponge host remain largely unexplored for many species. The present study was one of the first attempts to isolate symbiotic bacteria from the sponge Raspaciona aculeata.
Materials and Methods: After isolation on marine agar medium, the isolates were characterized for different colony morphology. The 16S rDNA taxonomic analysis was carried out on bacteria isolates.
Results: Following an incubation period of two weeks at 25°C, only 13 bacterial strains were isolated with a very low rate of genetic biodiversity. All strains belonged to the Gammaproteobacteria class (Pseudomonadaceae family), except one (isolate AL-18ra) belonging to the Bacilli class (Bacillaceae family).
Conclusion: The obtained results are of great importance for advancing the understanding of symbiosis phenomena within the sponge species Raspaciona aculeata to study its bioapplication potential.
#translation #appl #research #biotechnology #biodegradation
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jhavelikes · 1 year ago
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Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.
Unraveling the functional dark matter through global metagenomics | Nature
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healthcaremarketfmi · 2 years ago
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Metagenomics Market Size 2022 Growth Share, Industry Dynamics, Top Trends and Regional Analysis Forecast to 2030
According to a recently released report by Future Market Insights, widespread adoption of metagenomics tools in environmental monitoring and pollution reduction projects is estimated to boost the metagenomics market growth. Recent advancements in technology are aiding in the substantial reduction of costs incurred for sequencing paving way for the development of genetic sequencing on a large scale. This has opened up new opportunities for leveraging metagenomic tools on a large scale for monitoring of environmental microbe communities. Moreover, the efficacy of metagenomic tools in tackling environmental toxicology is further contributing to its growing popularity. For instance, a recent development saw the use of metagenomics in predicting the extent and presence of contamination in the environment while another research outlined how metagenomics can be leveraged to attenuate pollutants in the environment using unculturable microbes present in the natural environment. The use of metagenomics to understand the influence biostimulation can have on microbial communities is also under progress.
Growing concerns about environmental conditions are prompting authorities around the world to invest in researches to improve the efficiency and scale of bioremediation. In addition to this, bioremediation is gradually gaining traction owing to its cost-effective and eco-friendly chemical treatment. As metagenomics is vital to the success of bioremediation, the market is estimated to grow as the alternative chemical treatment process gains popularity.
Clinical Diagnostics to Emerge as a Vital Revenue Pocket
Clinical diagnostics and bioremediation are estimated to emerge as the highest revenue generating segments in the foreseeable future. The term metagenomic diagnostics is slowly gaining in prominence as studies experiment to widen the scope of application of metagenomics in the healthcare industry. For instance, multiple studies have alluded to the development of a new diagnostic procedure termed as “shotgun metagenomics” which uses DNA sequencing to identify different pathogens and aid in the development of effective targeted treatment by predicting their resistance against different medications. Metagenomic diagnostics presents a promising prospect that could help predict and manage the treatment of a plethora of diseases. Further, metagenomics can also potentially discover hidden genetic features that could prove vital in the progression of an assortment of biotechnological applications such as the discovery of novel enzymes, bioactive molecules, and genes with significantly better or altogether new biochemical functionalities.
Metagenomics Market Growth Underpinned by its Potential Application in Antibiotics Production
Metagenomics provides a detailed analysis of the taxonomic composition of microbe communities. It provides accurate information with regards to the gene characteristics specific to a particular community. The information provided by metagenomic tools can be effectively utilized for the production of an assortment of antibiotics. Recent research in the direction was conducted by scientists at UC Berkley who concluded leveraging metagenomics for bioprospecting soil can aid in the development of a plethora of antibiotics. The study used metagenomics to identify the genes that produce antibiotics and antifungals to combat diseases in microorganisms and stated that the same chemicals can potentially aid in combating bacterial and fungal infections in humans as well.
For more information: https://www.futuremarketinsights.com/reports/metagenomics-market
Library Preparation Kits find Growing Consumer Base as Gene Sequencing Gains Centerstage
DNA and RNA sequencing practices are gradually gaining traction owing to their ability to provide a massive set of valuable data about gene sequences in a small duration of time. The data generated through gene sequencing is then analysed by clinicians, scientists, and researchers who then used the derived knowledge in various verticals such as agriculture, forensics, diagnostics, pharmaceuticals, and other applications. Library preparation kits provide the means for efficient and quick extraction of gene segments which are then used for analysis. Moreover, advancements in technology are aiding library kit manufacturers in offering improved products that reduce error and stress while improving accuracy. The FMI report opines the factors will prove vital in the growth of the segment which is estimated to contribute significantly to the metagenomics market growth.
Metagenomics Market Continues to Propel in North America, Upheld by Government Funding for Epigenetics R&D
The FMI study opines metagenomics market in North America is estimated to continue to proliferate and remain at the helm of the global metagenomics market growth. Presence of advanced infrastructure coupled with continuous government funding for research and development in the field of epigenetics are the vital factors that are estimated to drive the growth of the market in the region. Further, the presence of favorable regulations promoting research and development for improving the quality of healthcare in the countries in the region are expected to fuel the market growth.
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myfeeds · 2 years ago
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Archaea in a warming climate become less diverse more predictable
Science News from research organizations 1 2 Date: May 5, 2023 Source: University of Oklahoma Summary: Using a long-term multifactor experimental field site researchers showed that experimental warming of a tallgrass prairie ecosystem significantly altered the community structure of soil archaea and reduced their taxonomic and phylogenetic diversity. Share: advertisement FULL STORY Led by Jizhong Zhou, Ph.D., the director of the Institute for Environmental Genomics at the University of Oklahoma, an international research team conducted a long term experiment that found that climate warming reduced the diversity of and significantly altered the community structure of soil archaea. Their findings are published in the journal Nature Climate Change. At the microbiological level, life can be described as belonging to one of three kingdoms — how species are described in relation to one another. Eukarya contains complex organisms like animals and plants and microorganisms such as fungi. The other two categories, bacteria and archaea, are comprised only of microorganisms. Archaea are prevalent in a range of environments, from some of the most hostile like volcanoes and permafrost. However, archaea are also common in the human microbiome and as an important part of soil ecology. “As temperature is a major driver of biological processes, climate warming will impact various ecological communities,” Zhou said. “Based on long-term time-series data, our previous studies revealed that experimental warming leads to the divergent succession of soil bacterial and fungal communities, accelerates microbial temporal scaling, reduces the biodiversity of soil bacteria, fungi and protists, but increases bacterial network complexity and stability. However, how climate warming affects the temporal succession of the archaeal community remains elusive. Archaea are ubiquitously present in soil and are vital to soil functions, e.g., nitrification and methanogenesis.” Using a long-term multifactor experimental field site at OU’s Kessler Atmospheric and Ecological Field Station, the researchers showed that experimental warming of a tallgrass prairie ecosystem significantly altered the community structure of soil archaea and reduced their taxonomic and phylogenetic diversity. In contrast to the researchers’ previous observations in bacteria and fungi, their finds show that climate warming leads to convergent succession of the soil archaeal community, suggesting archaeal community structures would become more predictable in a warmer world. advertisement Story Source: Materials provided by University of Oklahoma. Original written by Chelsea Julian. Note: Content may be edited for style and length. Journal Reference: Ya Zhang, Daliang Ning, Linwei Wu, Mengting Maggie Yuan, Xishu Zhou, Xue Guo, Yuanliang Hu, Siyang Jian, Zhifeng Yang, Shun Han, Jiajie Feng, Jialiang Kuang, Carolyn R. Cornell, Colin T. Bates, Yupeng Fan, Jonathan P. Michael, Yang Ouyang, Jiajing Guo, Zhipeng Gao, Zheng Shi, Naijia Xiao, Ying Fu, Aifen Zhou, Liyou Wu, Xueduan Liu, Yunfeng Yang, James M. Tiedje, Jizhong Zhou. Experimental warming leads to convergent succession of grassland archaeal community. Nature Climate Change, 2023; DOI: 10.1038/s41558-023-01664-x Cite This Page: University of Oklahoma. “Archaea in a warming climate become less diverse, more predictable.” ScienceDaily. ScienceDaily, 5 May 2023. . University of Oklahoma. (2023, May 5). Archaea in a warming climate become less diverse, more predictable. ScienceDaily. Retrieved May 5, 2023 from https://ift.tt/v9ZtBFp University of Oklahoma. “Archaea in a warming climate become less diverse, more predictable.” ScienceDaily. https://ift.tt/v9ZtBFp (accessed May 5, 2023).
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What does the Name Change Entail? Differentiation of Strains for Bacterial Names
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The frequent change of bacterial names and the consequences have been discussed in brief, with recent examples of Bacillus spp, and Lactococcus spp.
Keywords: Bacterial classification, B. paralicheniformis, B. licheniformis, L. plantarum and L. pentosus
Opinion
The bacterial classification change of names “Bacillus licheniformis into B. paralicheniformis, Lactobacillus plantaruam, L. pentosus and L. paraplantarum” and implications. The change of a strain name creates many hurdles in representing, patenting, and in academic events. Even more important, patents filed under the previous name may disappear as the new name appears. The incorporation of a new name locally is easy, but this is a major concern globally. In 2019, we filed a patent on the Bioprocess developed for the purification of (B. licheniformis), bacteriocin as a result, the patent granted. During the submission of the whole genome sequence (WGS) in the NCBI portal, we were informed that this is B. paralicheniformis, but based on 16S rRNA sequence it was B. licheniformis. Identical incident observed involving L. plantarum, after submitting the genome sequence, it was informed as L. pentosus. Current microbial classifications based on 16S rRNA gene sequences, and also a few housekeeping genes [1-4] have several limitations. They begin with low phylogenetic resolution at various taxonomic ranks [5], followed by missing diversity because of primer mismatches [6], finally formation of corrupt tree topologies by drawing together various disparate groups [7]. Subsequently, we discovered the incidents that led to a change in the names of a few bacterial strains. It was a certain type of nucleotide changes to non-standard housekeeping genes such as rec A. Surprisingly, if the 99.9 % similarities found in the 16S rRNA right away it may be sequence blasted at nucleotide level and given a specific name. If not, above 99.0 % sequence similarity may be considered as another but, without considering the 16S rRNA. The dependence more on other than 16S rRNA sequence may be followed/considered/ ignored. These kinds of sudden changes in the name, causes lot of complications. The change in the name sometimes disqualifies the strain for human applications, as it has not listed in the local food safety guidelines. The name changes also created irreparable damage in commercializing the products. At the end, we should understand and accept that nothing changes the nature of the objects under classification the level of variations needs accounted for in any proposal. The classification scheme could be measured based on the number of people subscribe to it. Therefore, we may conclude that classification schemes of changing the names are rarely “right” or “wrong” but considered as simple and formal procedures to relax the complexity. This subsequently, provides a common acceptable nomenclature.
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jbenevento · 2 years ago
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Abstract
Abundance and Diversity of Oyster Microbiomes from Seven Sites in the Hudson River Estuary
The eastern oyster (Crassostrea virginicia) is an important keystone and indicator species that inhabits marine ecosystems. However, the past few decades have raised concern regarding the anthropogenic impact on oyster populations. This study investigates the pathogens present within oyster microbiomes at different restoration stations in New York and New Jersey that possess varying environmental conditions such as sediment type, water temperature, and ecosystem type. Oysters were collected at the monitoring stations and stored in a freezer at -80° C until DNA extraction was conducted. Oysters were shucked to collect their tissues and either the phenol-chloroform protocol or the E.Z.N.A. Mollusc DNA Kit (Omega Bio-Tek) was utilized. The full length bacterial ribosomal RNA gene was amplified by PCR and confirmed by gel electrophoresis. Amplicons were sequenced on a Nanopore MinION according to the manufacturer’s instructions. Sequences were quality filtered and taxonomic identification was made using MIrROR (Microbial Identification using rRNA Operon Region; Seol et al. 2022). Oysters found in sandy sediment located near bays during October of 2021 had a low total reads as opposed to other areas. Using Shannon’s Diversity Index, oyster microbiomes from lagoons with muddy sediment exhibited a low species diversity despite a high read count. This may have resulted from little circulation in the water leading to minimal diversity. These findings suggest that certain environmental conditions may influence oyster microbiome health. Future research could investigate other environmental parameters such as pH, turbidity, and oxygen concentration of water.
Keywords: eastern oyster, environmental conditions, anthropogenic impact, microbiome, Shannon’s Diversity Index
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leedsomics · 6 months ago
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Characterization of a marine bacteria through a novel metabologenomics approach
Exploiting microbial natural products is a key pursuit of the bioactive compound discovery field. Recent advances in modern analytical techniques have increased the volume of microbial genomes and their encoded biosynthetic products measured by mass spectrometry-based metabolomics. However, connecting multi-omics data to uncover metabolic processes of interest is still challenging. This results in a large portion of genes and metabolites remaining unannotated. Further exacerbating the annotation challenge, databases and tools for annotation and omics integration are scattered, requiring complex computations to annotate and integrate omics datasets. Here we performed a two-way integrative analysis combining genomics and metabolomics data to describe a new approach to characterize the marine bacterial isolate BRA006 and to explore its biosynthetic gene cluster (BGC) content as well as the bioactive compounds detected by metabolomics. We described BRA006 genomic content and structure by comparing Illumina and Oxford Nanopore MinION sequencing approaches. Digital DNA:DNA hybridization (dDDH) taxonomically assigned BRA006 as a potential new species of the Micromonospora genus. Starting from LC-ESI(+)-HRMS/MS data, and mapping the annotated enzymes and metabolites belonging to the same pathways, our integrative analysis allowed us to correlate the compound Brevianamide F to a new BGC, previously assigned to other function. http://dlvr.it/TBpG2S
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evoldir · 5 months ago
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Fwd: Course: Online.ShotgunMetagenomics.Dec10-13
Begin forwarded message: > From: [email protected] > Subject: Course: Online.ShotgunMetagenomics.Dec10-13 > Date: 29 August 2024 at 05:44:19 BST > To: [email protected] > > > > Dear all, > > We are excited to announce our upcoming online course, "Shotgun > Metagenomics Processing and Analysis," taking place from December > 10-13. This course is designed to provide a comprehensive overview of > advanced methodologies for studying microbial communities using shotgun > metagenomics data. > > Course website: ( > https://ift.tt/S3b8LQV ) > > Course Overview: Participants will engage in both lectures and hands-on > lab sessions to tackle key challenges in metagenomics, including: Data > preprocessing Taxonomic profiling Strain-level characterization Microbial > genome reconstruction Functional potential characterization Integrative > statistical data analysis By the end of the course, attendees will gain > familiarity with cutting-edge bioinformatics tools and visualization > techniques essential for metagenomics research. > > Target Audience: This course is ideal for researchers and students > looking to enhance their skills in analysing high-throughput microbiome > data. A basic knowledge of the command line is recommended; we suggest > completing an online tutorial if you're new to this. > > Learning Outcomes: Understanding the goals and approaches in studying > microbial communities Proficiency in taxonomic, gene-related, and > strain-level characterization using reproducible workflows Expertise in > statistical analyses and visualization tools for microbiome studies > > For the full list of our courses and workshops, please visit: ( > https://ift.tt/S3b8LQV ) > > Best regards, > > Carlo > > > Carlo Pecoraro, Ph.D > Physalia-courses DIRECTOR > [email protected] > mobile: +49 17645230846 > > > "[email protected]"
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rnomics · 8 months ago
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#cancers, Vol. 16, Pages 1923: Exploring Gut Microbiome Composition and Circulating Microbial DNA Fragments in Patients with Stage II/III Colorectal #cancer: A Comprehensive Analysis
Background: Colorectal #cancer (CRC) significantly contributes to #cancer-related mortality, necessitating the exploration of prognostic factors beyond TNM staging. This study investigates the composition of the gut microbiome and microbial DNA fragments in stage II/III CRC. Methods: A cohort of 142 patients with stage II/III CRC and 91 healthy controls underwent comprehensive microbiome analysis. Fecal samples were collected for 16S #rRNA sequencing, and blood samples were tested for the presence of microbial DNA fragments. De novo clustering analysis categorized individuals based on their microbial profiles. Alpha and beta diversity metrics were calculated, and taxonomic profiling was conducted. Results: Patients with CRC exhibited distinct microbial composition compared to controls. Beta diversity analysis confirmed CRC-specific microbial profiles. Taxonomic profiling revealed unique taxonomies in the patient cohort. De novo clustering separated individuals into distinct groups, with specific microbial DNA fragment detection associated with certain patient clusters. Conclusions: The gut microbiota can differentiate patients with CRC from healthy individuals. Detecting microbial DNA fragments in the bloodstream may be linked to CRC prognosis. These findings suggest that the gut microbiome could serve as a prognostic factor in stage II/III CRC. Identifying specific microbial markers associated with CRC prognosis has potential clinical implications, including personalized treatment strategies and reduced healthcare costs. Further research is needed to validate these findings and uncover underlying mechanisms. https://www.mdpi.com/2072-6694/16/10/1923?utm_source=dlvr.it&utm_medium=tumblr
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royalvoxpost · 2 years ago
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#Metagenomics #Bioinformatics: new computational method can recover complete viral genomes and detect virus-host pairs from metagenomic Hi-C data. The method could also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities https://t.co/hD9juMcjX9 https://t.co/CTgXvVOsQp
— The Royal Vox Post (@RoyalVoxPost) Jan 31, 2023
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cdifffoundation · 7 years ago
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Researchers Examine Changes to the Microbiota Composition and Metabolic Profiles of Patients with Recurrent Clostridium difficile Infection (rCDI) Following Treatment with Faecal Microbiota Transplant (FMT)
Researchers Examine Changes to the Microbiota Composition and Metabolic Profiles of Patients with Recurrent Clostridium difficile Infection (rCDI) Following Treatment with Faecal Microbiota Transplant (FMT)
Microbial taxonomic and metabolic alterations during faecal microbiota transplantation to treat Clostridium difficile infection
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Gwénaëlle Le Gall1,
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Marianne Defernez
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Ian L.P. Beales
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Ngozi Franslem-Elumogo
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Arjan Narbad
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