Microbial Diversity - Science topic

Also, I suggest you consider the inverse where the microbial metabolic processes, specifically the electron transfer to electron acceptors modifies the surface chemistry of clay minerals. I have experienced this at in-situ bioremediation sites where the cohesiveness vs the uncontaminated and untreated clays was substantially decreased. Of course we did not have the option of evaluating the clays since we were working for other purposes.

I think you can follow the following steps to get good results

Number of Samples: Start with 10-20 samples per water body. Adjust based on preliminary results and diversity estimates.

Agar: Use a combination of R2A Agar and Marine Agar to maximize the diversity of cultured microorganisms.

DNA Sequencing Technique: 16S rRNA gene sequencing using Illumina MiSeq is recommended for a balance of cost and information. For more comprehensive data, consider metagenomics using Illumina HiSeq or NovaSeq.

SilvaNGS - simple and win-based. https://ngs.arb-silva.de/silvangs/ It is probably less flexible than all nice approaches mentioned above but it is quite useful for first look or for someone who needs an established reliable pipeline

For Sanger, you need a purified PCR product. I am not sure what you mean by as much data as possible - you will get a single consensus sequence for each direction up to ~800 base. I would recommend talking to a sequencing lab or whoever has a sequencing machine at your university to discuss your options.

Primary media which can be used to access the microbial diversity present in the sample are Nutrient Agar Media or Luria Bertani Agar Media, Peptone water, Nutrient Broth and Luria Bertani broth.

Every journal format followed according to own system in following rsearch item history ,writing researcher name and literature review

I have been using them many times when working with grapevine-associated microbiome. As a rule of thumb I would check before-hand if they match the specific target you want to prevent and then they should work quite well. Only recently I had problems with wheat mitochondrial DNA and so far I have not had any luck using mPNA

These two recent publications published in 2021 hopefully fits the discussed issue; describing the potential relationships between microbial biomass and diversity and the impacts in the case of soil agronomic management practices.

One school of though considers not to introduce any new microbe in already existing local soil microbial diversity , it could disturb the existing microbial synergisms...and in the process, introduction of new microbe could disturb the functional microbial diversity of a soil..

Marco E. Mng’ong’o

The points/methods suggested by are only applicable if one is working with any known candidate as growing the whole microbial community together or in a predefined medium is not practical. Microscopic methods or biochemical methods are primarily used for particular isolates and never used for a soil communities. I agree with Phil Geis that you most certainly can not determine the microbial diversity without molecular methods. Just to let you know, even molecular methods are not capable to fully determine the complete diversity in any natural environments. So, you can either ask for the assistance from your colleagues in same institute (as they have good lab for molecular analysis), hire commercial service for simpler methods like T-RFLP etc. or just drop the idea to study of microbial diversity. You can instead focus on any particular organism.

Dear colleague,

Please see this study:

We discuss aspects of how the loss of microbial diversity impacts ecosystem functions, with a focus on the degradation of organic pollutants.

To date, I only know the work Chen et al. (2019) on this theme. Being that for 7 years I am inserted in the field of study microbial diversity and degradation of organic pollutants in the soil.

Alpha diversity indices are most influenced by the abundance of local communities. When looking for beta diversities, the index you are using is crucial to understand if these abundances are taken into account or not. Bray-Curtis and Sorensen, for example, are based on the same equation. The only difference is the assumption of abundance-based (Bray-Curtis) or presence/absence (Sorensen). You can normalize alpha diversity matrices before running beta diversity tests by using Raupp-Crick correction, for example. It decreases the bias that different abundances would cause in beta diversity inferences. I would rather use beta Bray balanced.

I following the best answer.

Hello, if you need find out total biodiversity of river water the better way is DNA Pyrosequencing. By culture cultivation on different nutrient media will show only cultivable forms of microorganisms.

Wakita et al. showed that family-, genus-, and species-level analyses are appropriate to correlate the fecal microbiome and the fecal metabolome ( ). Even though the amplification of short 16S rDNA variable regions can result in the inability to determine the species-level with a high level of confidence, genus- and family-levels are still reachable and valid.

Meanwhile, due to the easiness of classifying sequences to phylum-level, the Firmicutes/Bacteroidetes ratio is frequently used in the scientific literature. However, the biological meaning of this is limited, especially if you are interested in the metabolome changes induced by diet. The popular index, Firmicutes/Bacteroidetes ratio, is based on the phylum level and was initially associated with obesity and certain health states. Interestingly, new data refute its validity (reviewed by Magne et al., ).

Food Microbiology, International Journal of Food Microbiology

Archisman Mahapatra

Making a phylogentic tree is task 1 and annotating it is task 2.

It is not automatic and you have to do manual preparation for annotating the tree.

qiime and mothur are replaced by improved pipeline. Do not miss the chance to get look at the alternatives.

please read about DADA2, QIIME2, Mothur, MG-RAST, Galaxy server, Unipro Ugene, BioEdit and choose according your need/expertise

1. You can use either CTAB method or phenol-chloroform method for isolation of DNA from individual bacterial or archaeal strains and different kits, such as FastPrep DNA, DNeasy Power etc. are available commercially for extraction of metagenomic DNA from environmental samples.

2. To study microbial diversity, 16S rRNA based Illumina sequencing 300 PE is better option as compared to llumina sequencing 2500 PE

Very important question

Salma Mukhtar your answer is great

Greetings!

You have a wide scope of opportunities around the globe. I had come across some labs in Germany,

*DLR-Germany (You can apply for DAAD-overseas fellowship)

*European Molecular Biology Laboratory (EMBL) research centers (You can opt funding option from EMBL schemes)

I would suggest you to consider USA and France for short term internship programs since they have more funding options and has active research in the topics you are interested in. I shall be glad to give you further details.

Cheers!

Manobala

Thanks a million Simon and Bhaskar. That's an excellent breakdown of number of samples vs reads. I want to start with about 40 samples and not too sure about the diversity. Its seems like 5-6 million read is a good place to start with.

Chloroflexi are rarely isolated and usually hard to work with. You can directly plate the soil on R2A agar plates or a dilute Mineral Medium supplemented with CMcellulose or make an enrichment broth (MM+CMC) and plate it on the same medium and R2A agar. Then look for small white rhizoid colonies. You might want to include some antifungals and anti-bacillus agents (those tend to spread a lot). This is a long shot, not much is known about this genus (or the whole phylum for that matter) so designing a medium specific for a particular genus is difficult.

Dear Hui,

the publications of György Kröel-Dulay (https://www.researchgate.net/profile/Gyorgy_Kroel-Dulay) and his research group may be of interests.

HTH,

Ákos

They made a first Actinobacteria-specific PCR with a1.2 kb amplicon length, and then another PCR with universal primers to amplify the V3-V4 region of the 16SrRNA.

Hi! Also, for the a-diversity you can do t-test and check the p value for evaluating the significanc.

I think it should not confuse as they are pretty different in meaning. Fungal community is the presence of many types of fungi as a whole in a community/society. Fungal diversity is the extent of different species or subspecies present in that community. If the diversity is low, there are only few taxonomic categories present or if it is high diversity there are more number of taxonomic classes present. The broadness or narrowness of diversity means how closely or distantly the fungal taxa are related. If you think same concept in human population (just for example) a community is the people together, and diversity (lets say gender) on a category level is low if we consider sex of people as there will be only three functional categories. And if we consider (age groups of 5 years) age group as a functional unit, there will be 20 classes (if age is 100 years), in this case this is highly diverse community.

In case of microbiology, the genus level is commonly taken as functional class to evaluate the diversity in fungal or bacterial community.

Additionally, if you are using phyloseq for the sequences data visualisation, you can have the physical distance information in the metadata file. While you plot PCoA or NMDS plots, you can have the physical distance factor in the plot command and it will consider the physical distance too. For better understanding, you can also add the arrows in the graphs which will elaborate the effect of physical distance on the samples and overall community.

Here is a good paper comparing these two methods

Rs has been recently reported in glasshouses on ornamentals and vegetables (hydroponics) in European countries. Can it be a source for field infection? Drainage water from greenhouses often runs strait to local rivers...

Hi Vijay,

Rarefaction curves can give you an ideia of microbial diversity looking to the form of the curve. Traditionally they have been used to know if the throughput of your samples was high enough to sample all the richness(=microorganisms) present in the your samples. Therefore, as it was said before, they are more suitable to richness estimation. Although there are more proper metrics, such as Chao1 and Faith's PD (alpha diversity), unweighted UniFrac (beta diversity). Nevertheless, image that you have two samples with the same number of OTUs sampled at some depth (let's say 40,000 reads). You can look to the form that the two curves take in order to get some insights about the diversity (weighted richness). For instance image this scenario, if curve A has the same number of OTUs (=richness) as curve B at 40,000 reads (subsampling depth), sample A has a community more evenly distributed than B. Why? Because it requires to subsample fewer number of reads to get higher number of OTUs/taxa/microorganisms meaning that these have, probably, equal or similar frequencies.

I hope this helps.

Cheers,

António

This is some good background information:

Hi Saurav, That seems pretty high at the phylum level. Although they are 30% of the reads, how many different taxa do they represent? I am suspicious that mitochondrial or plastid DNA from a eukaryote might be being amplified and seen as bacterial, but not classified further. If its relatively few taxa I would try blasting the sequence in genbank(s) to see if it provides any insight. Good Luck!

Hi Rajeev,

This depends a bit on what you need from the study. The V4 region has (probably) the closest to universal primer set and it has the advantage that you can completely sequence through in both directions (on a MiSeq) the amplified region of 254 base and perform sequencing error correction (I do this in the Mother pipeline). I haven't used it, but there seems to be a lot of positive talk about the use of DADA4 in the Qiime package for helping go deal with the issue of sequence error. You might want to check out the recommended protocol from the earth microbiome project, http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/16s/

I use this for a variety of sample types. Again I have to stess that there is no perfect primer set/target region, but V4 has worked well for many, including me.

Good luck,

Frank

The colors suggest a positive or negative relation between the environmental parameters and the abundance of the different classes/genera. 

The top dendrogram shows which environmental parameters have the most similar responses. The side dendrogram shows which classes/genera are behaving most similar.

cf.: https://www.researchgate.net/post/Elastase_assay_using_Elastin-Congo_Red_assay_principle_i_needed  

Reply by 

Adam B Shapiro · 690.36   ResearchGate SCORE · Entasis therapeutics

" The premise of the assay is that digestion of elastin by elastase allows the Congo Red dye to be released from the dye-elastin complex. The original reference for this assay is Biochemical Journal volume 78 page 156 (1961) by none other than Fred Sanger. Another useful reference for elastase methods is Methods in Enzymology volume 19 pages 113-140 (1970).

Any absorbance value that is greater than the background value by a statistically significant amount should be considered as positive. The initial rate of absorbance increase should be directly proportional to the amount of elastase present.

Jun 14, 2016          " (End of text citation for convenience)

Dear Catalina,

If you have densiotometric data that comes along with your fingerprint data. You can quantify your bands from a molecular marker with known concentrations. Your software will do that part. The below mentioned papers will tell you how to extract fingerprint data to a matrix format with frequency/quantity. Then you will have to go to vegan package in R to calculate distance matrix and further stuff.

Dr. Ewald Schnughas good and justified concerns about urbanization agri lands. But to de-urbanize may be not a cost-effective and time saving option. Rather take care in future may be more practical option to save agri lands for sustainable food production.

temperature and pressure will be gradually depleted in line with increase of altitude.  it will directly impact to the metabolism of microbes.  microbes are sensitive to environment changing.  indirectly, low temperature will inhibit decomposition of organic compound then will deplete population of heteretrophic microbes as well.  

possibly the colour is caused by humins  which often purify with dna. You could try ethanol precipitation ( with glycogen added if you do not have very much dna as a co precipitant then do an OD260/280 to ascertain dna purity Dusan makes a good point that when you do have interfering substances sometimes using less samples allows a PCR to work rather than using more and having more interference. Having said that any substance that absorbs at 260 will make you think that you have more dna than you actuallu do have

Hi Wei

Your sampling should reflect the research question you want to ask,  As you know, soil is full of microsites, each with is own microbial diversity. So depending on what you wish to know you have to sample accordingly. It is alo necessary to validate your experimental methods to ensure that they are giving you a true representation.

Best wishes

Chris

 Hi Muhammad,

I think the authors chose visually estimation maybe because they had no better methods at that time. The software, such as Quantity One, should be not such popular like today.  In my private opinion, I think the software-based analysis would be much more sensitive and  accurate than our eyes. So I recommend that you should better use software (e.g. Quantity One) to analyze your DGGE bands.

regards,

Guangshan

Stefano, 

As you have found the literature has contrasting reports. I think it will depend on what you are trying to do, if you have a large virus that is clonal then it probably possible to optimize conditions for accurate counting. For a population of viruses of different sizes and genome sizes such as in natural seawater samples , I am skeptical that you can accurately count all viruses within a sample.  

Hi Upendra,

In my opinion, other than these three, you can also construct clone library to study the microbial diversity.

Among these four techniques, TRFLP requests least efforts in terms of both experimental part and data analysis part. It gives quantitative data on the community structure but it is hard to tell who are the key players in the community.

DGGE is a bit complicate when doing the experiment, while the data analysis is easy. The data is not very quantitative as you are comparing the brightness of the bands. You can then cut the bands out for the sequencing which shows you the key players in the community.

Construct clone library is easy in terms of doing experiment. The data analysis part is straight forward. The data will not be quantitative but you will know the species in the community. The whole process may be a bit costly too depends on how many colonies you plan to sequence.

Illumina is easy in terms of experimental part then you need more efforts to analyze the data. Illumina would give the most information. The data will be quantitative and tell you the taxa information. 

This is difficult to address in such general terms. The answer really depends on the exact structure of your data, the experiment, the specific research question and probably more. Get your data into R, familiarize yourself a bit with ecological statistics, and packages like phyloseq & vegan that offer many useful tools. Assuming you have a relatively large, Next-Gen Sequencing dataset, a useful first stop could be to work on an ordination (e.g. Non Metric Dimensional Scaling, NMDS). (There will be data cleaning and normalization steps to go through before you can do this though). This will give you an overview over the overall change of the community with time and you can create biplots including the correlation of the OTUs with this overall trend. This will allow you to determine a set of candidate OTUs that are associated with the community change. You can then extract the timeseries of abundance data for these OTUs and study them in greater detail. If this is still a large number of OTUs you can subject this subset to e.g. cluster analysis, to see if there are different types of "typical" time dependet patterns (e.g. increase, decrease, spike at a certain time, etc.). You can also plot OTUs individually against time. You can then test for linear trends (regression), or significant changes between defined periods (e.g. all early against all late timepoints, using a T-test) or any of the more elaborate techniques of time series analysis. You can also sort the list for the most abundant types, or the largest differences over time, to find particularly interesting OTUs. Take some time to explore but always keep your research question in mind.

Dear Krishnamoorthy Ramasamy

Pls. find the attached files, will help in your research, good luck

Regards

Prof. Houda Kawas

I have streaked my stocks and there is only mucoid colonies.

It seems like a microalgae.

Hi Peng

Which rust are you working with, and at which stageof its life cycle? If you work with teliospores, these should be all different as they derive from sexual reporoduction. Other stages ( like uredia) are derived from asexual multiplication, so you can assume greater genetic homogeneity.

Best regards

You can go for 16S - however you will only end up at genus level with Bacilli of the cereus group - their 16S is virtually completely identical and therefor a species level identification will not be achieved by most molecular standard techniques

The best place to look for such information is "The List of Prokaryotic Names with Standing in Nomenclature" (also known  as Euzeby's list). For Vibrio see http://www.bacterio.net/vibrio.html.

Hello Maria. My main reference is Komarek's Süßwasserflora von Mitteleuropa, book 3. I have been working for many years in the US but was trained on algae taxonomy in France. I do use new genus names such as Dolichospermum and Cuspidothrix.

Thank you Robert. Those are good ideas !

Soil rich in organic matter is most suitable habitat for the isolation of Actinomycetes

Dear Tariq Ali,

For study of molecular diversity of a gene in different E. coli isolates, you can go through PFGE or MLST (Multi locus sequence typing). We already studied on diversity of blaCCTX-M-15 in E. coli.  the article was published in Microbial Drug Resistance (Impact Factor: 2.49) and I have attached herewith. Hope it will be use full to use.

Good Luck.

Regards,

Dr Anand

The number of samples depends on your experimental design and the heterogenity of your area. Usually the number of samples required is  if the carbon content is high and there is not a really good answer. 

We have been usually taken 10 samples per plot. This is too much if you have a well replicate experimental design (many blocks and plots) and might not be enough if you have a high carbon soil (not very probable in Iran I think). 

In sampling it might be good to go for a standard depth below the humus layer and take the humus and litter layer separately. The reason is that humus accumulates on the mineral soil and will falsify your samples.  Random sampling within a plot might be avisable. But you might also go for different distances from the trees.  

Have a look on the article from Ramette 2007 which should help you to understand more about db-RDA and CAP which both correspond overall to a RDA and CA respectively. Below are citations from this article about db-RDA:

db-RDA: “When the data set consists of a matrix of distances between objects, distance-based RDA (db-RDA; Legendre & Anderson, 1999) can be applied to determine how well additional environmental parameters can explain the variation among objects in the matrix. The technique first applies a PCoA on the distance matrix to convert it back to a rectangular table containing rows of objects by columns of PCoA coordinates. Those new, uncorrelated coordinates thus correspond to synthetic ‘species’ variables that are then related to additional environmental parameters using a

classical RDA. For instance, db-RDA was successfully used to determine how the variation in matrices of genomic distances among environmental strains could be explained by factors such as soil parameters, host plant species, and spatial scale, each factor being taken alone or in combination”

CAP: is a canonical analysis on the principal coordinates for any resemblance matrix, including a permutation test. CAP takes into consideration the structure of the data. So, it is more likely to separate your different levels if there is no strong difference and is good to show the interaction between factors.

db-RDA “can be applied to determine how well additional environmental parameters can explain the variation among objects in the matrix”. In contrast, CAP do not try to increase the explanation of the variance with other parameters, but only perform correlations. I think this is the major difference. Overall, the choice of the “best” method depends on what you want to do and show, and there is not necessarily a unique answer but better to try different options. See this RG question

Reference:

A. Ramette (2007) Multivariate analyses in microbial ecology, FEMS Microbiology Ecology, 62, 142-160

Hello coleague

Well, I always have transfered Excel sheets into PAST without problems merely using Ctrl+C/Ctrl+V. I particularly don't know how MG-RAST tsv files looks like, because I don't work with this kind of analysis, so that is my question: do you put the information you need in an ordinary Excel sheet, I mean, rows and columnsl or the arrangement of data is different from that?

Aimeric , very intelligently framed question . Having given a soil without any micronutrient constraint, application of  K  will induce an improvement in uptake efficiency of both N and P , and this  will  impose cascading effect on proliferation  of soil microbes by and large . however , certain K-solubilizers or silicate solubuilizers could further expand their number by metabolizing the additional K released from microbial action . Needless to say ,  effective microbes are biggest sink for nutrients N, P, and to a lesser extent  K  . Such solubilizers  will display much better resilience either under salinity or under any other abiotic stress,  as rightly opined by Alii.

Dear Krishna,

Have a look at the following papers.

1.On the molecular mechanism of GC content variation among eubacterial genomes

Hao Wu, Zhang Zhang, Songnian Hu and Jun Yu,TRENDS IN ECOLOGY & EVOLUTION · MAY 2005

2.Taxonomy of Lactobacilli and Bifidobacteria by Giovanna E. Felis and Franco Dellaglio,http://www.biology-direct.com/content/7/1/2

Good luck!

Dr Singh , few days back , in some of  RG questions , I responded by saying do we have some indicator microbes specific to specific soil conditions in order to judge the general health of the soil microbiologically . Likewise we have probably yet not attempted to analyse the AMs diversity of different crop rhizopshere , annual versus perennial  fro example. Rhizosphere of one crop to another crop . Can we sythesize a consortium of rhizocompetent microbes including AMs using a process called rhizosphere  hybridization .

There will be surely two microbial niche , one for contaminated soil and another for normal soil depending upon the type of heavy metals. But , from such contaminated soils , there is a possibility to isolate some microbes well adjusted to such adverse conditions as a part of microbial remediation of contaminated soils.

Please read the textbook, "Practical Biotechnology" by Sadashivan

You can calculate the Probability of Interspecific Encounter (PIE), which is an alternative measure to compositional dissimilarity (Hurlbert 1971):

Other useful references:

PIE is a simple, probabilistic measure, of the –potential– interaction between species. It is not spatially explicit, meaning that PIE is calculated WITHIN a given sample or inventory. However, you can calculate co-dominance (F), which is the probability that two individuals from two samples belong to the same species (Chave and Leigh 2002): http://chave.ups-tlse.fr/chave/chave-tpb02.PDF

Note that F = 1 – PIE at the inter-sample level, i.e., in a triangular association matrix between each pair of plots.

Chave and Leigh 2002 include a neutral model with 'null' expectations. You can develop your own null models under different scenarios of species aggregation and diversity.

I do not think that beta-diversity measures that take into account the species' abundances are the same thing as a measure of the (potential) interaction between species, or the most direct approach to species' density.

Despite the advances in NGS, we remain a long way off to know the bacterial diversity in soil, and surprisingly there is not many studies that try to tackle this difficult (still impossible at the moment?) question. Roesch et al., (2007) estimated between 2000 to 10,000 OTU per gram of soil, similar values than Torsvik et al (1990), but lower than Gans et al (2005) 10^7. Schloss and Handelsman, (2006) give the number of 2000-5000 of OTU.

 The recent Opinion article from Prosser (2015) give the following values: “Molecular studies (those that describe microbial taxonomy on the basis of the 16S rRNA gene) indicate that up to 1 million different bacterial and archaeal species are present in 10 g of soil, in the context of approximately 1 billion microbial cells”, citing most previous studies. It should be kept in mind that despite the advances of NGS, nobody has yet sequenced all the bacteria in a gram of soil. Again, Prosser (2015) remind us this fact: “so complete coverage of a soil metagenome is not usually obtained; for example, in one study, it was shown that deep coverage of the majority of a soil community was not achieved, even with 300 Gbp of sequence data”, citing the study from Howe et al. (2014).

 If you are interested in reading more about this debate of bacterial diversity in soil you can read Delmont et al. (2011) and several articles from Curtis (Curtis et al., 2002; Curtis and Sloan, 2004, 2005)…

Keep in mind 1) the diversity of soils, 2) the heterogeneity of soil at the microscale, 3) the changes in soil properties with soil depths 4) and over time. All these characteristics will influence the richness of bacterial community (and other microorganisms) in soil and their estimation.

Sorry I do not have numbers for sea or freshwater, although Zhou et al. (2015) mentioned some studies for diverse ecosystems.

Looking forward any point of view and articles on this subject!

Curtis, T.P., Sloan, W.T., 2005. MICROBIOLOGY: Exploring microbial diversity--a vast below. Science 309, 1331–1333. doi:10.1126/science.1118176

Curtis, T.P., Sloan, W.T., 2004. Prokaryotic diversity and its limits: microbial community structure in nature and implications for microbial ecology. Current Opinion in Microbiology 7, 221–226. doi:10.1016/j.mib.2004.04.010

Curtis, T.P., Sloan, W.T., Scannell, J.W., 2002. Estimating prokaryotic diversity and its limits. Proceedings of the National Academy of Sciences of the United States of America 99, 10494 –10499. doi:10.1073/pnas.142680199

Delmont, T.O., Robe, P., Cecillon, S., Clark, I.M., Constancias, F., Simonet, P., Hirsch, P.R., Vogel, T.M., 2011. Accessing the soil metagenome for studies of microbial diversity. Applied and Environmental Microbiology 77, 1315–1324. doi:10.1128/AEM.01526-10

Howe, A.C., Jansson, J.K., Malfatti, S.A., Tringe, S.G., Tiedje, J.M., Brown, C.T., 2014. Tackling soil diversity with the assembly of large, complex metagenomes. Proceedings of the National Academy of Sciences 111, 4904–4909. doi:10.1073/pnas.1402564111

Prosser, J.I., 2015. Dispersing misconceptions and identifying opportunities for the use of “omics” in soil microbial ecology. Nature Reviews Microbiology advance online publication. doi:10.1038/nrmicro3468

Roesch, L.F.W., Fulthorpe, R.R., Riva, A., Casella, G., Hadwin, A.K.M., Kent, A.D., Daroub, S.H., Camargo, F.A.O., Farmerie, W.G., Triplett, E.W., 2007. Pyrosequencing enumerates and contrasts soil microbial diversity. The ISME Journal 1, 283–290.

Schloss, P.D., Handelsman, J., 2006. Toward a Census of Bacteria in Soil. PLoS Comput Biol 2, e92. doi:10.1371/journal.pcbi.0020092

Zhou, J., He, Z., Yang, Y., Deng, Y., Tringe, S.G., Alvarez-Cohen, L., 2015. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. mBio 6, e02288–14. doi:10.1128/mBio.02288-14

You pose a good question - because co-occurrence networks are just that - and the assumption that co-occurrence translates to a a real ecological relationship is not necessarily strong. Clues (but not proof) can come from the phylogenetic assignments (e.g., a positive co-occurrence with a dominant phylotype with a known physiology such as N-fixation could be inferred as a positive trophic interaction), but real proof would only come with experimental studies (good, old-fashioned co-culturing experiments, for example).

If you need to identify the prokaryotic microbial diversity in a sample for sure you will get much more information using NGS techniques, after extracting DNA and amplyfying 16S rRNA genes using different primers that are suitable for bacteria and archaea. Illumina is nowadays the most used technology, although the choosen sequecing technology would depend on the type of results you need and the budget you have (now NGS sequencing is getting cheaper and cheaper). Most sequencing companies also offer the bioinformatics work to a certain extent, but further you will profit more the data if later you do your own bioinformatics work for refinement and search of specific issues.

Not much to go on but I would suggest having a go at visualisation in QIIME or phyloseq to see if you get a similar result. If you still have the problem you can have a look at the Mothur forum, particularly if it is a problem with how you generated the Biom table.

You can use Robertson's cooked meet medium or Thioglycolate broth for anaerobic organisms growth. Cooked meet medium contains heart or liver chopped pieces which inhibits the growth of aerobes and make condition anaerobic like clostridiums. Thioglycolate do the same thing, makes the condition anaerobic and enhances anaerobes growth conditions.

Hi Jamil,

Just before you continue, ensure that your sequencing data is clean and there is no errors in your sequencing data. Looking at the image you have uploaded, your sequence is ~99% similar to the nearest phylogenetic neighbour according to blast. Chances are that your isolate is most likely the organism blast is suggesting. Note however that anyone can upload sequences to blast, including partial sequences, new strains, etc. and as such blast is not always the best website to use. Might I suggest that if you want to search type strains, obtain sequences, etc. for phylogeny you could try the Ribosomal Database Project: http://rdp.cme.msu.edu/seqmatch/seqmatch_intro.jsp

Simply paste your sequence, and change the options to the following..>

Strain: Type

Source: Isolate

Leave the rest as default. 

This should load a screen with the nearest closest type relative, and from there click "View selectable matches"

This will bring up the type strains, which will provide you with less cumbersome information. You can also take the sequences and generate trees with the data. Feel free to message me if you require additional help.

Hope this helps :) 

Dear Francesco,

please find attached some publications I personally find good. And I also would like to suggest you the publication of my colleagues, who worked with QIIME and Cytoscape to produce their networks:

Hope you find it useful.

Anastasia

As previous answers, you MUST HAVE replicates, and the article from Prosser (2010) clearly answer your question: "replicate or lie" regardless the techniques you use. It is not preferable, it is a duty to have replicates and unfortunately some articles still published without proper replication when using metagenomic. As a reviewer, I reject articles without replication if metagenomics are the main results, or ask to remove those data or mentioned the results briefly as no conclusions can be drawn from non-replicated analysis...

 The article from Prosser should be read by every researcher...

 To answer the second part of your question, yes metagenomic can give you information about change over time. You might find the article from Fierer and Ladau (2012) useful: "Predicting microbial distribution in space and time".

Keep in mind that everything is a source of potential contamination. Not just the PCR reagents but extraction kits, tubes, etc. And the contamination profiles can change from lot to lot.

So the explanation could be as simple as using a different bag of tubes to dilute the control. However, I think your comment about cross talk between your lung samples and the mock may be closer to the mark. We recently looked at indexing as a source of potential contamination - this was not a rigorously controlled study ;-) The run was a MiSeq V3 with about 25 bacterial genomes. We filled up the sample sheet to a full 96 with imaginary samples (dual indexed). Every one of those imaginary samples received around 200-300 filter pass paired reads. These were confirmed to have originated from the real samples on the run. Obviously this raises more issues with interpreting 16S data particularly for low level taxa.

sonication and crushing of roots helps in isolating endophyte loosely associated with roots

Lukasz, none of your photographs is definitively identifiable as any parasite of humans or domestic animals that I am aware of.  However, these are quite diverse, so it would be best if you specified a host species if you are looking at soil samples for potential contamination.  As it is, there are also many free-living organisms in the soil that have eggs and cysts similar to those of parasitic helminths and protists.  Also, other materials such as pollen grains will be found in any soil samples and will resemble parasite stages.  In the past, I have done soil surveys for parasites, but I always know which specific parasite and host species I am looking for, and compare my findings to known parasite stages from fecal samples.

You should amplify first. Otherwise the target is a very small percentage of the total DNA in the reaction and the signal could be poor. If you then sequence with either or both of those same primers, you should have enough information to identify the strains to something approximating the genus level. If you need better resolution among close relatives, consider the ITS region.

make sure the surface of your agar plate is dry before culture streaking