Microbial Communities - Science topic

Bruno Peeters That's great.

Thank you very much for your help!

It is a very common practice and can either be done with MAGs or simply by 16S*.

* = depending on several things.

Kind of a broad question but here are some suggestions

* https://www.nature.com/articles/s41592-019-0634-1

* https://www.mdpi.com/2076-2607/12/1/39

* (and references within)

AI in this context is just automation of the things we already know or understand. There is nothing new which can be just. The field require more deeper fundamental understanding which can further be exploited by AI implementation.

There are already a lot of models and methods and new ones are popping every other day, but they have similar concept at the center and which does not work good need not talk perfectly.

The above answer also appears to be generated by AI.

In marker gene metagenome sequencing, such as 16S rRNA sequencing used to profile microbial communities, several factors can contribute to classification bias. While primer mismatches can cause variability, they are not the primary source of bias. Here are some other significant causes:

As per the LI-COR instruction manual for Li-8100A, negative flux values are valid measurements. You should not discard it from your analysis.

These are very interesting topics for PhD level research.

It has been six days since I last reached out to Dr. PBritoParada, with Dr. AnnaKaksonen CC’d in the correspondence. As I have not received a response, I have sent a gentle reminder #Elsevier #MineralsEngineering #PeerReview

Microbial communities within complex ecosystems exhibit genetic interactions and co-evolution through mechanisms such as horizontal gene transfer (HGT), metabolic cooperation, competitive interactions, and symbiotic relationships. HGT allows microbes to exchange genetic material horizontally, leading to the acquisition of new traits that can shape the composition and function of the community. Metabolic cooperation involves the exchange of metabolites or complementary metabolic functions among different species, influencing the fitness and evolutionary trajectory of each other. Competitive interactions drive the evolution of traits that enhance the competitive ability of individual species or strains within the community, leading to an arms race for resources and niche partitioning. Symbiotic relationships, whether mutualistic, commensal, or parasitic, drive co-evolutionary dynamics as the fitness of one organism is linked to its symbiotic partner(s), resulting in the evolution of specialized traits optimized for mutual benefit. Overall, understanding these complex interactions is crucial for deciphering microbial ecosystem dynamics and their potential applications in various fields.

Some variable regions are simply not suitable for accurate identification, see eg

Abhijeet Singh both kits offer the same multiplexing capacity, if I understand the question you're asking - both 16S kit and the native kit that we have are "24 barcoding", native / 16S.

I am rather curious about the necessity of 16S in terms of sequencing success - I can see low complexity microbial samples getting sequenced just as succcessfully with a native kit as with 16S, but without the PCR amplification bias, which in fact affects relative quantification negatively, rather than being prerequisite for it as you seem to state (becasue amplification efficiency drops steeply after 60%+ GC content of the amplicon). PCR amplification probably makes a positive difference when trying to detect low-abundance species, but I am not interested in those in this project.

Detergent waste is a serious threat to water,The detergents reducing the natural water quality, pH changes in soil and water, eutrophication, reducing light transmission, and increasing salinity in water sources. Many laundry detergents contain approximately 35 to 75% phosphate salts. Phosphates can cause a variety of water pollution problems. In wastewater treatment plants, detergents from residential wastewater are removed through a combination of physical, chemical, and biological processes. Liquid laundry detergents can be made biodegradable and eco-friendly by including alkyl polyglucosides, polyoxyethylene lauryl ether, and a thickener. Choose phosphate-free detergents, soaps, and household cleaners.

Mohammad Eshaq Faiq Thanks. But i want specific package or statistical tools to measure stability of the community. This is very general answer. Hope you understand.

No problem at all.

Good luck with the experiments.

Microbial communities evolve and adapt to rapid environmental changes through a variety of mechanisms, including:

Microbial communities play a vital role in ecosystem stability and function. They are responsible for a wide range of processes, including nutrient cycling, decomposition, and disease control. When microbial communities are disrupted by rapid environmental changes, it can have a number of negative consequences for ecosystem stability and function. For example, if the microbes responsible for nutrient cycling are disrupted, it can lead to nutrient imbalances in the ecosystem, which can harm other organisms. Additionally, if the microbes responsible for disease control are disrupted, it can lead to outbreaks of disease.

Here are some specific examples of how rapid environmental changes can disrupt microbial communities:

It is important to understand how microbial communities evolve and adapt to rapid environmental changes in order to protect ecosystem stability and function. By reducing our impact on the environment and helping microbial communities to adapt to change, we can help to ensure that healthy ecosystems continue to provide us with the vital services that we rely on.

might have a look at rust+memgraph or rust plus plugin. might be enough to fill your needs. fwiw, mojo is being touted as an robust alt for rust- im just getting started with it.

Внесение биокомпоста улучшает биота почвы в результате повышает урожайность пшеницы и рисча

The classic method is four fractions: ＞2mm、2-0.25mm、0.25-0.053mm and ＜0，，053mm

Thank you so much for your reply.

I found this: https://cran.r-project.org/web/packages/indicspecies/vignettes/IndicatorSpeciesAnalysis.html

However, it seems you will have to perform the analysis for each year separately.

That being said, why would you want to do it because ISA is used to identify important species in a community (typically abundant and widespread) for a specific study site across as many years as possible. Then, the species identified as important (using a certain threshold value) can be monitored consecutive years.

To investigate different indicators for different years seem counterintuitive. Or is it more of a theoretical thing where you want to investigate how the "important" species change over time which will point to changing environmental drivers?

It sounds like you have a lot of interesting questions to investigate! Here are some suggestions on how to approach your analysis:

For all these analyses, it would be useful to calculate alpha and beta diversity metrics such as Shannon diversity index and Bray-Curtis dissimilarity, and to visualize the data using ordination plots. Additionally, you may want to use differential abundance analysis to identify specific microbial taxa that are differentially enriched or depleted between treatments, time points or genotypes. There are several R packages available for performing these analyses, including vegan, nlme, and DESeq2.

Good luck with your analysis!

Dear Laliteshwari Bhardwaj,

To do the PCA or NMDS analysis you can use the PAST software (https://www.nhm.uio.no/english/research/resources/past/)

To interpret the results you can consult the GUSTA ME (https://sites.google.com/site/mb3gustame/home?overridemobile=true)

Dear @Ajoy Kumar Saha You can access the links given below; hope, these should be useful to you:

Its consistency is basically like a rock, yes. Crushing it with mortar and pestle is impossible because it's too hard. Plus, it should not defrost.

This depends on the question that you are trying to answer. There are two main tools that you can use i.e. spectroscopy and sequencing. Spectroscopic techniques can help you study proteins and microbial interactions whereas genomic or sequencing techniques can help you identify which microbes are present in the ecosystem. Another approach is to study RNA and see which genes are being transcribed and see if a group of microorganisms are active or non-active. Again, first you should know what you want to study because that will help you choose the right tool.

Thanks for your reply and attention. Our team has sequenced 200 single micron-scale clusters of anammox consortia. We did discover several interesting results that differed from the bulk-scale researches. Coinciding with your suggestion, we are now looking for some metabarcoding methods to enlarge the sampling throughput.

A CCA should be applied for analysis of species composition and its relation to environmental variables from place to place along environmental gradients. If you want to analyse species distribution and its relation to environmental variables on one site with a set of subplots you should use an RDA (redundancy analysis). The CCA gives a unimodal model, the RDA a linear model, both are direct gradient analysis. There is no minimum of data samples, but a good CCA and RDA (without distortive arch-effect) requires much less environmental variables than samples or inversely much more samples than env. variables. The selection of the variables to use can by done by a PCA. Keep in mind that the samples have to be independent viz. from different places and best within one comparable season. If you want to analyse a time series of true replicates as for an experiment you should use time series analysis, e.g., a DFA (dynamic factor analysis) - or modelling (GLM, GAM), which could also be a good alternative for your issue.

I recommend reading this article

I don't find it suspicious that you can't find any literature on using a suction cup to obtain a sample of the microbial population in the soil. Normally soil samples for microbial population determinations are shaken into solutions (e.g. buffers, saline, etc.) and the suspension or filtrate is used for further determinations. As a practical matter, it is not obvious why you are considering using a suction cup. Using a suction cup is much more labour intensive and would take much more time to prepare the sample. This is probably why nobody uses it and why you can't find any mention of it in the literature.

Best regards

Vit

How do you see the microbial sequencing through shortgun and amplicon , any major difference you see, lets debate thank you

There are a several issues here. The first is unbalanced sampling. If each of the triplicate samples is the same size as the individual samples then just taking one of them for the combined analysis is a sensible thing to do. If you pool them then the combined triplicates will have more taxa in them and appear different, which is not what you want. If the combined triplicate sample is the same size as the individual samples then you can pool them.

The second issue is what the nature of the data is. What are your counts - read number? Presence/absence? Whatever the answer you need to select an appropriate resemblance measure. I would normally choose Bray-Curtis and depending on the range of numbers in each sample I would generally recommend a transformation. If you don't transform the pattern you get just reflects variation in the most abundant taxa. A strong (log, fourth root) transform brings more taxa into play. I would use nMDS as my ordination method, but that is my choice.

The third is how to answer your question, which is about differences (not clustering, per se) between spring types. For this you should use some appropriate testing framework working on the resemblances, such as ANOSIM or PERMANOVA.

Dear Dr Hira

Please kindly see the following attached files and sites too,

good Luck

Thanks to the metagenomic analysis of soil using i.a. Illumina or other methods that you can estimate always from domain to genuses, but never to species.

Both water solubility and adsorption affinity are responsible and affect the microbial population up to a great extent. And, to study the soil in order to evaluate soil microbial toxicity and their soil bioavailability for the purpose of managing pesticide residue with potential bioremediation of contaminated soil, assessment of adsorption along with the dissipation characteristics will be needed.

Dear Valeria Agamennone

These articles might be helpful, have a look:

1. https://res.mdpi.com/d_attachment/applsci/applsci-09-04035/article_deploy/applsci-09-04035.pdf

2. https://res.mdpi.com/d_attachment/nanomaterials/nanomaterials-11-01777/article_deploy/nanomaterials-11-01777.pdf

3. https://www.preprints.org/manuscript/201911.0130/v1/download

Kind Regards

Qamar Ul Islam

As explained by Abdulmuhsin S. Shihab . however, for spearman’s correlation, you don’t use chi-squared.

It all depends on the research questions and then on the money/time available.

Is the research question about looking at the bacterial community structure (e.g. looking at differences in community composition between treatments), looking change in structure and diversity (i.e. looking at changes in structure but also identify the bacteria), looking activity/active microorganisms, looking at changes in relative abundance (e.g. changes over time of bacteria in activated sludge)...

Really this is the first question you need to answer before looking for methods, what is the question. There is no perfect method, it all depends what is the question you ask.

You can use fingerprinting methods (e.g. DGGE, T-RFLP, ARISA) if you are just interested in changes/differences in community structure between samples without interest into who is there. This is quick and relatively cheap but limited in term of outcome.

Now if you want to have a look on changes/differences in community structure and as well diversity (identification of OTU), you should use metataxonomic (also called metabarcoding, amplicon sequencing). You gain much better resolution than with fingerprinting but more expensive and need more skills to analyse the data. You gain data only on a specific genes, the obvious one being 16S.

Quantitative PCR was mentioned (also called real-time PCR), but this method is to asses the abundance of a specific gene. Even if it is the best method to assess microbial abundance you do not access to the community structure. However, you can look at any genes to determine their abundance. It is quick and fairly easy to run.

Then there are also metatranscriptomic looking at RNA rather than DN, so you are focusing on structure of the "active community".

Metagenomic could also be used to look all the genes/genomes present in your samples. The highest resolution you can get, but also one of the most expensive and difficult methods to analyse.

Again, the research questions should determine the methods than should be selected. It is important to be clear about what you want to answer.

I am in agreement with Anoop. Nowadays, molecular techniques specially combined with stable isotope tracing analysis may be beneficial for soil microbial community monitoring in soil. Some interesting recent references linked below:

hello Stephanie Greige

1. For whole microbial community analysis you can use the methods: DNA-DNA reassociation, G+C fractionation, whole genome sequencing, metagenomics, metaproteomic, proteogenomic, and metatranscriptomic.

2. For partial community analysis you can use methods as you mentioned:

a. Genetic fingerprinting techniques such as ARDRA, SSCP, T-RFLP, DGGE, RISA, LH-PCR, RAPD.

b. Clone library method

c. Q- PCR (real-time PCR)

d. FISH, dot-blot hybridization

e. Microbial lipid analysis

f. DNA microarrays

g. Microautoradiography and isotope array

h. Microautoradiography and isotope array

i. CARD-FISH, Raman-FISH, NanoSIMS

Check them out and find the suitable methods for your samples.

Hope it would help

Goodluck :)

Kindly check the following links:

Dear Dr. Bulbul Ahmed,

Hi,

Due to a considerable amount of data in biodiversity and the existence of complexity, the analysis of existing relationships will not be possible without the use of plots.

Best regards,

Saeed

To conclude your study you can identify the sources of specific microbiota on a specific population with their feeding habit. The ranges of microbiota vary with feeding zone and water quality.

Dear Corinne Bardot

These articles may be helpful.

Good Luck

Chinaza Godswill Awuchi Thank you for replying however I'm actually looking for an explanation in the opposite direction starting from the mother plant. Stock plants can generate multiple rounds of cuttings and the position, harvest time, size of cuttings etc are of importance how the quality of material becomes. One may optimize the growth of cuttings itself but I am wondering what actually causes stockplants to produce derived cuttings of lower quality.

see this article it is very interesting

Dear Dr Jamal,

Here is the link to "Response of the arbuscular mycorrhizal fungi diversity and community in maize and soybean rhizosphere soil and roots to intercropping systems with different nitrogen application rates": https://www.sciencedirect.com/science/article/pii/S0048969720333301

The r-square value generally tells you the percent of the variation 'explained' by the axis. So this score tells you that Axis 1 'explains' approximately 95% of the variation in separation of the bacterial communities, whereas Axis 2 explains very little of the variation. Therefore, any environmental variable that aligns strongly with Axis 1 is likely to be a strong environmental influence on the bacterial communities. Conversely, an environmental variable that is strongly aligned with Axis 2 is much less likely to be influencing the communities, although they may be important in the absence of environmental variables that strongly align with Axis 1. For example, if Axis 1 is very strongly aligned with the salinity gradient, then it suggests that salinity is overriding most other environmental variables (and if you did a separate study with no salinity gradient, then other variables may become more important in separating the communities).

Hello Mr Godwin I think the simplest software to perform this type of analysis is PAST (https://folk.uio.no/ohammer/past/ ), because its interface is very friendly. As you want to add some physicochemical parameters in the analysis, I recommend that you do a CCA. Any questions can contact me. Good luck!

Dear Abhijeet Singh ,

Bunch of thanks for your answer. I hope it would be helpful Abhijeet Singh

Here is the message in response from GTDB in case you are interested...

We collapsed Comamonadaceae, Oxalobacteraceae, Alcaligenaceae and Sutterellaceae with the paraphyletic family Burkholderiaceae because they form a robust monophyletic group together and were comparatively shallow families according to their relative evolutionary divergence (see https://www.nature.com/articles/nbt.4229/).

So apparently this was an intentional move by GTDB, but the updated version of SILVA is not adopting it.

Please Cite this Article :

260/280 ratio between 1.8-2.0 is recommended by nanodrop, Qubit measurement would be absolute. Also, there should be no or minimal DNA smear in the agarose gel run. Commercial soil DNA isolation kit (like MoBio) would be useful to improve the purity and concentration. You can easily get the desired concentration and purity by this.

This may sound like heresy, but getting to far into the details will get you lost in the weeds of technology. We work with over 3,000 species of microbes (bacteria, yeast, fungi and protozoa) silmultaneously. We focus on the whole system; not on the details. See if there is enough information to start to develop control equations and what parameters are relevant. This approach has allowed us to begin the industrialization of the microbiome to produce protein and lipids using waste organic biomass as a feedstock.

Hi All,

If you still considering using ONT to run full 16S rRNA gene sequencing, I have developed a workflow to analyse these sequences using QIIME2 pipeline.

https://github.com/DeniRibicic/q2ONT

As Abijeet mentioned, use of 16S information is rather suited for taxonomical information. Nevertheless, there are available predictive algorithms that claim to infer functionality based on 16S rRNA gene. Such one is PICRUSt, also integrated in qiime2 pipeline as a plugin. However, you have to be extra careful interpreting these results as these algorithms are trained on specific 16S profiles coming from specific environments. Also, one has to remember that two organisms with identical 16S rRNA gene sequence can own completely different genome, therefore function.

Following the answer of this question

I am following to answer of your question

Thanks all for your comments. Haitao Wang I agree with your suggestion but I guess the 515F/806R are shown to be biased against both the Crenarchaeota/Thaumarchaeota (https://msystems.asm.org/content/1/1/e00009-15) so after going through some articles I found that the V4-V5 primers 515F/926R performs well and can reduce bias and can detect more environmentally important taxa including archaea. I just posted here as I thought it might be helpful.

Best,

Sandipan

Dear Dr Weiyan Wang 王威雁

Emission of nitrous oxide (N₂O) or the whole NOx package is inevitable consequence of the soil nitrogen cycle. Excess use of nitrogenous fertilizers do create more of oxides through biological and chemical processes. Easiest option would be be to use fertilizers sparingly so that the plants are able to utilize them completely or mostly with minimum surplus. Crop rotation with legumes and pastures can also enrich soil nitrogen which is more bioavailable due to being organic in nature, it is the most eco-friendly option.

Many efforts focus on using nitrification inhibitors but hindering the nitrogen cycle is never an option to curb NOx emission since excess of reduced fraction always givers rise to excess of oxidized fractions sooner or later under aerobic conditions. Maintenance of partial anaerobicity ensures slower decomposition rates of the organics but too much ammonia is not conducive for the environment if its is being used for fisheries purpose in tandem with agriculture. Ultimately is depends on the soil stoichiometry, mind set of the person involved and the quality of the fertilizer used. Specific application of nitrous oxide reducing bacteria at the rhizosphere of the crop can effectively reduce the problem but one needs to study the efficacy of particular strains in this regard.

I hope this points you to the right direction.

With regards,

Dr. Abhishek Mukherjee

Hello, for more reliable answer more details needed.

1. how long you want to keep microorganism?

2. type of microorganism spore-forming or not?

If it spore-forming microorganisms just grow it in tube with media and wait till sporulation after you can keep it in any conditions for years.

If it not spore forming you can use reseeding of microorganisms once a

Two weeks - 1 month, made glycerol stocks as already said above or you can perform lyophilisation and keep again for years.

Good luck!

The use of transformation before ordination methods such as nMDS, is partly to reduce the weight of outlier samples. In other case would be to allow comparison between variables with different units.

One way to assess if you need to perform any transformation is to do a Draftsman plot in PRIMER (Analyse, Draftsman Plot). It will plot the variables against each other and you can assess if the data are squeezed against one axis or are evenly distributed. If the data are against one axis they probably need transformation. In general, if you have large differences between samples, transforming your data will not "hurt". You can always run the nMDS without transformation and assess if the 2D stress of the MDS is good (i.e. < 0.2 at least), or not, and if clearly you have outliers or not.

For the choice of transformation, basically each method have a different strength, from "weak" to "strong" transformation as follow: square root < square root < fourth root < log.

Note that if your data include zero, for log you need to do log (x+1).

Similar discussion:

https://www.researchgate.net/post/Does_anyone_know_where_to_download_or_purchase_the_fortran_program_DISTLM_for_PERMANOVA_Anderson_2005

Hi

I my modest opinion the concept live cannot be used for viruses. Probably is better to use infective and it's directly related

For fungi there are primers are known as ITS1 and ITS4, that amplify the specific region of ITS that contains 5.8s gene, those primers amplify just DNA from fungi because they are specific for fungi, even in basidiomycetes the combination of primers ITS1 and ITS4-B amplify just DNA from this type of fungi. You can use thecniques like RAPD´s or RFLP´s to diferenciate the species involved in microbiomes.

The following articles may clear better yuor doubts:

-ITS primers with enhanced specificity for

basidiomycetes - application to the identification

of mycorrhizae and rusts

M. GARDES and T. D. BRUNS (1993)

-Nuclear ribosomal internal transcribed spacer (ITS)

region as a universal DNA barcode marker for Fungi.

Schoch et. al (2012)

-AMPLIFICATION AND DIRECT

SEQUENCING OF FUNGAL

RIBOSOMAL RNA GENES

FOR PHYLOGENETICS.

White et. al (1990)

Have you made any other determination to know the amount of microorganisms? Some authors criticize BIOLOG because the dye itself can be harmful to microorganisms. Is the inoculum density the same between the two soils?

Structural ,functional and metabolic diversity of microbial communities of a crops microbiome is supposed to be much higher under rhizosphere of crops with intercropping than crops without intercrps,that will indirectly aid in sustaining the production...

I would reccommed microbial 16s amplicon libraries (for bacteria) and/or 18s for eukaryotes (ngs) these techniques are much cheaper than before and give you some idea of what might be causing the change (i.e. loads of e.coli probably = anthropgenic contam) for functional shifts shotgun metagenomics may be your best bet but it is pricy, complicated and requires huge amounts of computing power to assemble contigs (without assembly it is difficult to accurately determine function).

DGGE works for determining if there are shifts (i.e yes or no) but tells you little else and is a pretty outdated method if you plan yo publish.

There are numerous software available for Metagenomics ,each having a fair share of advantages and disadvantages. It is much better to use combination of tools rather than emphasizing upon a single tool. QIIME and Mothur are quite good for metagenomics and metatranscriptomics. For beginners its much better to start from SilvaNGS or MG-RAST. For extensive statistical analysis one can use METAGENAssist. Galaxy also provide many incorporated tools , and one can easily design his/her workflow.

Hope this helps

Regards

Pankaj

Hi Michael. How many factors do you have in total? I think partial RDA might solve your problem. Using pRDA (VPA), you can get the explanation rate of each category of your factors. See here:

Otherwise, another way to get the explanation rate of each factor is PERMANOVA or MRM (multiple regressions on matrices).

It appears there is other microbial community. The metagenome of floating fern Azolla reveals endophytes that do not fix N2 but may denitrfy. The colonization of plants cyanobacteria has been intensively studied in the Gunnera symbiosis. The plant organs colonized in this host are stem glands. These glands secrete viscous mucilage of low pH that attracts Nostoc and induces differentiation on motile hormogonia etc.. For more details consult sciencedirect. com

Hi Malcolm,

Thanks for your answer :) I am still a bit confused about the results I get with the permanova. So the % of variance explained by depth and comp are both similar (around 30%), but one is significant, while the other is not... How am I suppose to understand that? I was wondering if it's because the number of samples within comp group is too low for the test to be robust (n=6), whereas is not the case for depth (n=9). Would it be the reason why I have a similar percentage of variance explained but one is significant and not the other? Thanks.

Thats right. But i posted this question to see if someone really has seen something like this.. otherwise its fine...

Thanks anyway!

Arslan

Hello Muhammad,

You can use shannon diversity as a measure.

As observed in many studies, healthy microbial community is stable and composition is evenly distributed, leading to higher shannon diversity. In case of dysbiosis, where only few microbes dominate the microbial community, the shannon is expected to go down.

So, personally I feel shannon diversity index can be a good quantitative measure for dysbiosis.

I hope this helps.

Anubhav

Good topic.