Biomedical Microbial Products

Question 1

The Final Report - 70% of Grade

Answer 1

TITLE: Screening and evaluation of bioactive molecules production by soil-dwelling microbial species.

AIM: The aim of this experiment is to detect isolates that can produce biologically active compounds!

Reading:
Basic Biotechnology (3rdEd), Edited by C. Ratledgeand B. Kristiansen. Cambridge University Press. 2006.

*Principles of Modern Microbiology, M. Whelis. Jones and Bartlett Publishers. 2008.

*Metabolic Engineering: Principles and Methodologies. Stephanopoulos, Aristidouand Nielsen. Academic Press. 1999.

*Other books: Basic Biotechnology (Präve, Ed); Biotechnology: An Introduction (Barnum); Bioprocess Engineering Principles (Doran)

*Review papers / scientific articles

The Practical:
*Analysis of composition of soil microbiota by plating and by genome sequencing

*Isolation of potential Actinomycetes

*Screening of bioactivities produced by isolates in solid media

*Isolation of best producers

*Screening of bioactivities by isolates in liquid media

The Report is the main piece of assessment:
The Practical Report accounts for 70% of the final mark It should contain:

*A detailed account of the experiment

*The different techniques and analyses performed

*The statistical treatment performed (if any)

*A comprehensive discussion of the results obtained

*What are the weak / less relevant / most important issues

*Identification of parts of the experiment that could be modified to improve the quality of the experimental data.

IMPORTANT: There are no right or wrong results –however, the conclusions must be consistent and coherent, well explained, and supported by the data analysis.

AMPLICON SEQUENCING TUTORAL
Why do we analyse the environmental / soil samples?

We are interested to know what kind of fungi or bacteria are in a sample, as opposed to counting under a microscope or plating techniques like we did in the lab. DNA sequencing is a much more robust and accurate representation of the microbial populations in these soils.
Over 90% of these organisms found are uncultured in the lab / plated out as we do not know what type of conditions under which they can be grown > consider for write ups!
Culturing is labour intensive.
Only an estimated 1-2% of environmental bacteria can be cultured in the laboratory
Identification of isolates is tricky Via biochemical methods (gram stain, catalase, oxidase, motility, etc.)
Not fully conclusive

We are sequencing forest and campus soils to understand microbial composition of these environments, providing an alternative to plate count methods. We are looking for taxa that are KNOWN producers of antibiotics / bioactive compounds > search the literature based on the results you get!

Why sequence environmental samples?
To unlock the “hidden” biodiversity and understand ecosystem dynamics in environments such as:
gut
soil
plant roots (crops)
oral cavity
deep sea hydrothermal vents
Why sequence environmental samples? To understand how microbial communities assemble and how changes in the environment affect them.

BMS3060:
Objectives:
To obtain isolates with ability to synthesise natural products
To compare traditional methods versus modern methods used in microbial ecology

For the forest and campus soil we will carry out basic spread plating to isolate single species and then will assay for antibiotic or anti-fungal activity.
With the plates we will also carry out Colony counts > Colony counts& visual inspection > should tell us % Fungi% Actinomycetes% Other bacteria.

We will also carry out DNA extraction for both soil types > DNA sequencing results > Relative abundance & taxonomic identification > % relative abundance at any taxonomic level of interest.
Therefore we will compare these 2 methods: spread plates and colony counts vs DNA extraction > discuss in method write up.
- DNA extraction: based on column (HPLC) , DNA binds to column and only DNA left bound to column after many washing steps so you can elute it.

How to handle data received from DNA extraction:
- DNA will be sent off for sequencing > will find genes for 16S rRNA and ITS2
- Results of sequencing will be sent back to us and we will analyse it.
- Can use this to establish the microbial community and thus, compounds with biological activity.
- Practical based around basic spread plate techniques.
- Sequencing DNA in the soil will give us a better idea of the taxa found in the soil. Used to determine microbial community.
- Aim: To isolate antibiotic producing compounds, comparing Forest vs Campus soil.
- Compare these 2 methods in the writeup > basic plating vs DNA extraction.

There are 2 types of sequencing strategies :
1. Shotgun (who is there and what can they do) - genomes are broken into small fragments, everything is sequenced and allows the construction of genomes and functional analysis.
DNA fragments are assembled in silico
Results in metagenomes.

We will be focusing on Amplicon sequencing.
2. Amplicon Sequencing (“who is there?) > A variable region of interest (barcode) is amplified via PCR

• Amplicon sequencing only sequences a variable region of interest. Amplicon sequence variant = the species. Get a relative abundance of species in the community.

When we are interested in a variable region of interest, variable between different species, essentially a DNA marker. So we select the DNA marker that is a specific gene that is presented in all the genomes of bacteria that we’re interested in e.g. in our case the gene coding for 16S ribosomal subunit for bacteria. Then we amplify this gene using PCR, so the DNA in our sample is now the amplified copies of this DNA marker and then we sequence that. It gives us taxonomic (who is there) and abundance data (how many of them are there?).
Only the amplicon is sequenced = targeted method!
Results in counts of amplicon sequence variants.

Amplicon sequencing: choice of genetic marker: Bacteria vs Fungi:

V3-V4 > 16S Ribosomal subunit > Bacteria
ITS2 > Internal transcribed spacer > Fungi

Bacterial DNA Marker - 16S rRNA gene:
Some regions are constant / conserved between bacteria but some are variable regions across different taxa, means we can use these to sequence and see how distantly related each species in our samples are. We have chosen V3-V4 regions of the 16S rRNA specifically as our DNA marker / amplicon.

16S rRNA has constant regions and hypervariable regions which are specific for certain species > we are interested in these parts > V3 and V4.
- Our choice for primers are 341F forward primer and 806R reverse primer. They bind to the constant regions and we read the variable regions.
Illumina NovaSeq 6000 sequencer (mention that we used this in report > third party called Next-Gen (Illumina) sequencing.

Hypervariable regions:
Phylogenetic marker
Allows determination of phylogenic distance between two (or more) sequences

Requirements for a genetic marker:
Good primers (universal amplification, lack of non-target amplification
Amplicon length to match sequencing platform capability
Must be chosen with sequencing method and downstream analysis capabilities in mind

Our choices for soil DNA sequencing:
V3-V4 primers: 341F forward primer & 806R reverse primer
Fragment length:~470 nt
Paired end sequencing
Reads partially overlap
Illumina NovaSeq 6000 (sequencer)
Read length: 2x250nt, paired-end

AMPLICON SEQUENCING WORK FLOW:
Collect an environmental sample > DNA extraction from environmental sample > Amplify DNA markers > High throughput sequencing > Bioinformatic processing > Species identification > Ecological analysis.

The amplicon is sequenced from 2 ends (paired end sequencing).
Primers are non biological sequences and MUST BE removed in silico.
Forward and reverse reads are reversed in silico based on the alignment of the overlapping region.

Illumina Sequencing:
Amplicon DNA is stuck to platform, different labelled nucleotides come in and bind to the correct nucleotide, they release a fluorescence signal which the machine detects as a colour change and assigns a nucleotide at that point. Will give you a readout so that you have a sequence. ERROR PRONE!
However, newer companies have been able to reduce errors.

Quality score of each nucleotide. Over 30 is good and under 30 is bad quality. Important because we don’t want bad quality reads, will interfere with downstream analysis. Plot the quality scores, errors more likely towards the end.
- The new illumina sequencing platforms we will be using however have really low error rates, as low as 0.2%!

The output of the sequencing are FASTQ files for forward and reverse reads. These contain: read label, sequence and quality score for each nucleotide between 1-40, 40 being the highest.
Quality scores1-40
>30 = good<30 = bad
Plot the quality scores of all the reads in a sample.
Errors are more likely to occur towards the end of the read, may need to trim these off.

Sequencing campus and forest soil samples:
Sequencing company: Novogene
Sequencing platform: Illumina NovaSeq 6000 (2x250nt)
Error rates as low as 0.2%
So our forest and campus soil samples will be sent to the Illumina sequencing company and they will send our results back to us so we can analyse it.

Amplicon sequencing workflow: Bioinformatics:
We have two reads (forward, reverse) of varying quality bases. We also have non-biological sequences (PCR primers).

What do we do now?
Trim them! (Quality control)
Align them based on the overlapping region.
Then merge the reads so that we obtain the entire V3-V4 region.
Errors would have a big impact during alignment and assembly.
When trimming, ensure overlap remains.
Need to trim primers as they are non biological sequences, could impact analysis. Quality control measure to remove unreliable data.
- Now you have two good quality reads you need to align them. Must overlap to merge the reads to obtain the entire V3-V4 region. When trimming, ensure overlap remains.
- Final step with the single merged read of V3-V4 region is to align it to a taxonomy database to find out what species it is!
- Results = amplicon sequence variants, relative diversity of ASV % and taxonomic classification for each ASV%. Variable only to the genus level so can’t actually identify species

We now have a single merged read of the V3-V4 region. What next?
Align it to a taxonomy database (Silva) to find out what species it is from = species identification.

Results at the end of the amplicon sequencing pipeline:
Amplicon sequence variants > nuclelotide sequecnes corresponding to V3-V4 region of each species in the sample!
Relative diversity of each ASV%
Taxonomic classification for each ASV.

-. Take output (composition of soil in terms of microbial community) to Excel or R studio.
- Use the Dada2 package in R studio to analyse the output. Plot quality profile, trim, core algorithm, merge reads and then can visualise results in R. Bar plots for relative abundance! Can analyse diversity and species richness > beta diversity plots, compares diversity between ecosystems (ordinance) and alpha diversity metric (Shannon index) > within sample diversity, measures richness in a single habitat.

Amplicon Sequecning Pipeline:

Soil samples > DNA extraction > Sequencing by third party Illumina > FASTQ - 2x forward and reverse outputs , align them at overlap and merge them to get a biologically representative sequence and also need to remove non biological reads > construction of amplicon sequence variants using dada2 > dada2 will align reads against database, for us is the Silva ribosomal RNA database to provide us with the taxonomic information to allow us to make plots to relate different taxa in different samples.

Amplicon sequence analysis in R:
R is a programming language
Rstudio is the “editor” software
Amplicon sequence analysis is carried out in Rstudio using the dada2 package
Dada2 is a package (collection of functions / code) that was written specifically for analysis of amplicon sequencing data

We will be using the R programming language to analyse the output of the sequencing and to align the sequencing reads to a database of known sequences.

We use ‘dada2’ software package > it will quality filter, correct read errors, forward-reverse read pair merging, chimera removal, inference of biologically meaningful sequences and aligns sequences with a taxonomic database, so for each read it can tell you kingdom, phylum, class, order, family, genus and species each amplicon sequence variant corresponds to.

How does ‘dada2’ work?
Works by correcting errors in specific sequencing reads to obtain biological sequences called Amplicon Sequence Variants (ASVs). ASV%.
Filter and trim reads > remove low quality reads and the primer sequences ( the non biological sequences left over from PCR amplification).
We can filter and trim based on FASTQ quality score for each nucleotide > if the quality score is below 30, if its above 30, the quality is fine. But after trimming, must ensure they is still an overlap so that we can still merge the sequences together!
Dada2 also counts the occurrence of each read within a sample (count data).
STEPS:
Quality control > Trim low quality endsTrim primersOther QC options / parameters
Dada2 core algorithm > Error correctingMachine learning
Merge reads
Generate taxonomy data in Silva

VISUALISING RESULTS:
Can plot the data as bar plots (abundance), alpha diversity metric to tell you how diverse each sample is (intra sample diversity), and a beta diversity plot which tells you how different or similar each sample is > inter sample diversity.

Alpha diversity:
Measures richness in a single habitat
Within-sample diversity
Common indices: Shannon index, Simpson’s index, Species richness.
Example: The number of species in a forest patch.

Beta Diversity:
Estimates of similarity or dissimilarity between populations (between samples)
Helps to compare diversity between ecosystems.
Example: How species differ between a forest and a grassland.

EXTRA POINT:
Not only have we sequenced the bacteria marker 16S rRNA, we also sequenced a fungal DNA marker called ITS2 which is also in a ribosomal gene. So in fungi, the target for our amplification and sequencing will be the ITS2 variable regions.
Difficult to do on uni computers however, so this analysis will be done for us! But we can do the plots ourself once we receive the analysis.

How to do the actual sequencing analysis to interpret your data (daniel farkas):
What you are doing will come up in the ‘environment’ tab
Entire script will be available, all you have to do is run it.
Install packages first > dada2 and phyloseq
Press control, enter to run the code, will come up in environment.
Sample ID
Give software folder to save it.
Forward reads and reverse reads > Will generate plots > if quality if above 30 =good and do not need to trim.
Control and enter the bits in white only.
Trimleft the number of nucleotides we are told.
Little arrow means it can take a new command.
Dots should roughly follow the black line on plots.
Merge reads
Minover lap = 12, 20 or 30. Try all of them, start with 30.
You want a loss of maximum 10-20% of the reads!
Assign taxonomy to determine species.
Plotting > relative abundance. Only show above @2%, anything not shown will be discarded as the % of abundance is not high enough. Diversity of forest samples was less than that for the campus last year. Can also plot genus of interest > .g. Streptomyces as they are a producer of secondary metabolites > could then explain why you see a difference in results between the two methods.
Beta diversity plot too.
Do extra reading on alpha and beta diversity plots.

Also do ITS2 data.

Question 2

The Multi-selection test

Answer 2

The extended MCQ accounts for 30% of the final mark

Structure and Objectives:

*You should critically read and analyse 2 articles related to the subjects under study.

The articles will be available in SurreyLearn.

*The test consists of 5 Questions with 5 answers each from which you need to choose the right ones (generally >1)

*You will have access to a model X-MCQ with papers so you can practice

*We will discuss the contents of the papers in tutorials to be held before the test.

Question 3

Lecture Content

Answer 3

Primary vs Secondary Metabolites:
Curve:

Primary >
We are plotting the number of cells. So the concentration of cells are we can see that the curve in blue, the top go here starts with a lag phase.
So there’s no increase in the number of cells or a very slight increase in the number of cells.

And then the cells start growing very fast, very, very fast until there is a deceleration.
The fast growing rhythmic or financial growth and the phase where growth slowdown is a deceleration stage and then the culture doesn’t show any any net increase in concentration of cells.

This is called the stationary phase.

Now, if we measure the concentration of metabolites, we will see that there is a group of metabolites that follows almost perfectly the growth curve.
This is shown here in red, dark pink colour.

There is a log phase, full production of the metabolite and then a fourth increase, negative generation and then a stationary phase.

This is these are called primary metabolites - are compounds that are synthesised during growth to supply precursors for cellular material for biosynthesis and for energy metabolism.

So primary metabolites are associated to primary metabolism, which is the metabolism associated with growth and energy production.

Secondary >
There are other metabolites that we are measuring during cell growth and we see there is no significant increase until the culture enters the deceleration phase.
And in that particular case is where these metabolites start increasing at very high rates.

They’re all shown here with the continuous plot and these are secondary metabolite and these secondary metabolites are compounds produced which are not directly involved in normal growth or development or reproduction of the organism.

So these secondary metabolites are produced and their production is not associated with growth or energy production.

These are what they’re called secondary metabolites or secondary metabolism.

These are present mostly in bacteria and fungi and plants, especially in plants, they are very good produces of secondary metabolite.

That means that the the precursors generated in primary metabolism will be used in the pathways for secondary metabolism.

Primary and secondary metabolism are interlinked > So primary metabolism generates energy under the form of ATP, generates precursor for biomass, generates precursor for secondary metabolism.

Secondary metabolites are many different products such as antibiotics,
flavour compounds, pigments and other biotin molecules.

Something to consider for the experiment:
The important thing here, and this is something we are going to have to consider when we do our experimental model is that some specific secondary metabolites are often rejected and are often limited to a small set of species belonging to a phylogenetic group, for instance, a component such a polyketide.

These are antibiotics that are limited to a small set of species from the actinobacteria.

So you want to find polyketide. We need to go towards some actinobacteria in order to produce those or to screen for them.

Microbial Bioproducts > Secondary Metabolites:
Secondary metabolites are compounds that are synthesised when growth slows down, so not associated to the growth.

We have the classic single known metabolites are the antibiotics and the radicals are produced by filamentous prokaryotes and eukaryotes, fungi.

Currently, antibiotics are becoming a problem because of antibiotic resistance and there are no new antibiotics being discovered > an important line of study for the future!

When discussing antibiotics talk about these aspects:
The product > producer microorganism, production process > application.

Question 4

MCQ - Article 1:

A Phenotypic and Genotypic Analysis of the Antimicrobial Potential of Cultivable Streptomyces Isolated from Cave Moonmilk Deposits

https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2016.01455/full

Answer 4

Summary and Main Objectives:

Background: Moonmilk, a speleothem found in limestone caves, hosts a rich microbiome, including Actinobacteria, which are known for their antimicrobial properties.

Objective: The study aimed to isolate and analyze the antimicrobial potential of Streptomyces strains from moonmilk.

Methods: Researchers isolated 78 Streptomyces strains and conducted phylogenetic analysis, antibacterial and antifungal activity tests, and genome mining for biosynthetic genes.

Findings: The Streptomyces strains displayed strong inhibitory activities against a wide range of bacteria and fungi. Notably, 90% of the strains showed strong growth suppression against the multi-drug resistant fungus Rasamsonia argillacea.

Conclusion: The study supports the idea that moonmilk could be a promising source of novel antimicrobial compounds due to the diverse and prolific Streptomyces population1.

Key Points:

Moonmilk Microbiome: Moonmilk, a speleothem found in limestone caves, hosts a rich microbiome, including Actinobacteria, which are known for their antimicrobial properties.

Isolation of Streptomyces: Researchers isolated 78 Streptomyces strains from moonmilk deposits.

Antimicrobial Activity: The isolated strains displayed strong inhibitory activities against a wide range of bacteria and fungi, including multi-drug resistant strains.

Genomic Analysis: Genome mining revealed biosynthetic genes responsible for antimicrobial compound production.

Significant Findings: 90% of the strains showed strong growth suppression against the multi-drug resistant fungus Rasamsonia argillacea.

Objectives:
Isolation and Identification: To isolate and identify Streptomyces strains from moonmilk deposits.

Antimicrobial Testing: To evaluate the antibacterial and antifungal activities of the isolated strains.

Genomic Exploration: To conduct phylogenetic analysis and genome mining for biosynthetic genes related to antimicrobial production.

Introduction:
The introduction of the article “A Phenotypic and Genotypic Analysis of the Antimicrobial Potential of Cultivable Streptomyces Isolated from Cave Moonmilk Deposits” discusses the rich microbiome found in moonmilk speleothems of limestone caves, particularly focusing on Actinobacteria.
Historical texts suggest that moonmilk had therapeutic properties, hinting that its actinobacterial population might be a source of natural products beneficial for human health.
The study aims to isolate and analyze cultivable Actinobacteria from moonmilk in the Grotte des Collemboles, Belgium, to evaluate their taxonomic profile and potential for biosynthesizing antimicrobials1.

Materials and Methods:
Sample Collection: Moonmilk samples were collected from the Grotte des Collemboles cave in Belgium.

Isolation of Actinobacteria: The samples were processed to isolate Actinobacteria using selective media.
Phenotypic Characterization: The isolated strains were characterized based on their morphological and biochemical properties.

Genotypic Analysis: DNA was extracted from the isolates, and 16S rRNA gene sequencing was performed to identify the bacterial strains.

Antimicrobial Activity Testing: The antimicrobial potential of the isolates was tested against various pathogenic microorganisms using agar diffusion methods.

This section provides a detailed methodology for isolating and analyzing Actinobacteria from moonmilk, aiming to explore their potential for producing antimicrobial compounds1.

Results and Discussion:
Diversity of Isolates: The study successfully isolated 78 Streptomyces strains from moonmilk samples. These isolates exhibited a wide range of morphological and biochemical characteristics.

Genotypic Identification: 16S rRNA gene sequencing identified the isolates as belonging to various Streptomyces species, with some potentially representing new species.

Antimicrobial Activity: Many of the isolates demonstrated significant antimicrobial activity against a range of pathogenic bacteria and fungi. This suggests that moonmilk-derived Streptomyces could be a valuable source of new antimicrobial compounds.

Biosynthetic Gene Clusters: The presence of biosynthetic gene clusters related to antimicrobial production was confirmed in several isolates, supporting their potential for producing novel antibiotics.

The discussion highlights the importance of exploring unique and extreme environments like caves for discovering new microorganisms with potential biotechnological applications. The findings underscore the therapeutic potential of cave-derived Streptomyces and the need for further research to fully exploit their antimicrobial properties1

Conclusion:
The Conclusion section of the article “A Phenotypic and Genotypic Analysis of the Antimicrobial Potential of Cultivable Streptomyces Isolated from Cave Moonmilk Deposits” emphasizes the promising potential of Streptomyces strains isolated from moonmilk as sources of new antimicrobial compounds.
The study highlights the diversity and significant antimicrobial activity of these isolates, suggesting that cave environments are valuable reservoirs for discovering novel bioactive substances.
The authors recommend further exploration and characterization of these strains to fully harness their biotechnological potential1.

Question 5

MCQ - Article 2:

Isolation, Characterization, and Antibacterial Activityof Hard-to-Culture Actinobacteria from CaveMoonmilk Deposits

https://www.mdpi.com/2079-6382/7/2/28

Answer 5

Summary:

Background: Moonmilk deposits in caves are rich in diverse Actinobacteria, which are known for their ability to produce antibiotics.

Objective: The study aimed to isolate and characterize hard-to-culture Actinobacteria from moonmilk and evaluate their antibacterial activity.

Methods: Researchers used a rehydration-centrifugation method and other strategies to isolate Actinobacteria. They conducted taxonomic analyses and bioactivity screenings to identify and assess the antibacterial potential of the isolates.

Findings: The study successfully isolated 40 strains of Actinobacteria, including new representatives of several genera such as Agromyces, Amycolatopsis, and Micromonospora. Some of these isolates displayed high antibacterial activities.

Conclusion: The methodologies applied allowed the isolation of rare and hard-to-culture Actinobacteria with significant antibacterial potential, highlighting the importance of exploring extreme environments for new antibiotic sources1.

Key Points and Conclusions:

Rich Microbiome: Moonmilk deposits in caves are rich in diverse Actinobacteria, known for their antibiotic production capabilities.

Isolation Techniques: Researchers used innovative methods like rehydration-centrifugation to isolate hard-to-culture Actinobacteria.

Diverse Strains: The study isolated 40 strains, including new representatives of genera such as Agromyces, Amycolatopsis, and Micromonospora.

Antibacterial Activity: Several isolates displayed significant antibacterial activities against various pathogens.

Objectives:
Isolation: To isolate hard-to-culture Actinobacteria from moonmilk deposits.

Characterization: To conduct taxonomic analyses of the isolated strains.

Antibacterial Evaluation: To screen the isolates for antibacterial activity and identify potential new antibiotics

Conclusions:
Successful Isolation: The methodologies applied were effective in isolating rare and hard-to-culture Actinobacteria.

Antibacterial Potential: The isolated strains showed significant antibacterial potential, underscoring the importance of exploring extreme environments for new antibiotic sources.

Introduction:
Bioprospecting for Natural Compounds: There is a renewed interest in exploring microorganisms from poorly explored and extreme environments for new natural compounds, driven by the need to combat resistance to current antimicrobials, herbicides, antivirals, and anticancer agents.

Cave Environments: Caves, despite being nutrient-poor (oligotrophic), support a rich and diverse microbial life. This phenomenon, known as “the Paradox of the Plankton,” suggests that limited resources can still support a wide range of species.

Actinobacteria in Caves: Actinobacteria, prolific producers of secondary metabolites, are abundant in limestone caves. These environments are expected to host Actinobacteria with unique metabolomes, potentially important for novel drug discovery.

Previous Research: Earlier attempts to isolate antibiotic-producing Actinobacteria from moonmilk deposits in the “Grotte des Collemboles” cave primarily recovered Streptomyces species. However, high-throughput sequencing revealed a more complex actinobacterial community, indicating the presence of many hard-to-culture genera.

Challenges in Culturing: The difficulty in isolating non-Streptomyces Actinobacteria is attributed to their slower growth and specific nutrient requirements, making them rare and challenging to culture.

The introduction sets the stage for the study by emphasizing the potential of cave-dwelling Actinobacteria as a source of new antibiotics and the challenges associated with isolating these hard-to-culture microorganisms1.

Results and Discussion:

Results:
Isolation Success: The study successfully isolated 40 strains of Actinobacteria from moonmilk deposits using the rehydration-centrifugation method and other strategies.

Diverse Genera: The isolates included new representatives of several genera such as Agromyces, Amycolatopsis, Kocuria, Micrococcus, Micromonospora, Nocardia, and Rhodococcus, as well as additional new streptomycetes.

Antibacterial Activity: Bioactivity screenings revealed that some isolates displayed high antibacterial activities against various pathogens.

Genomic Potential: Genome mining uncovered a strong potential for the production of natural compounds, indicating the presence of biosynthetic gene clusters responsible for antimicrobial production.

Discussion:
Methodological Effectiveness: The applied methodologies were effective in isolating rare and hard-to-culture Actinobacteria, which are often overlooked in standard culturing techniques.

Antibacterial Potential: The significant antibacterial activities observed in some isolates highlight the potential of moonmilk-derived Actinobacteria as sources of new antibiotics.

Environmental Adaptations: The unique environmental conditions of the cave likely contribute to the distinct metabolomes of the isolated Actinobacteria, making them valuable for bioprospecting.

Future Directions: The study suggests further exploration of cave environments and the development of more refined isolation techniques to uncover additional rare Actinobacteria with potential biomedical applications1

Materials and Methods:
Sample Collection: Moonmilk samples were collected from the “Grotte des Collemboles” cave.
Isolation Techniques: The researchers used a rehydration-centrifugation method to reduce filamentous species and tested various strategies to isolate rare Actinobacteria.

Culture Media: Components of the International Streptomyces Project (ISP) medium number 5 were autoclaved separately, and the moonmilk suspension was diluted to improve colony formation.

Incubation: Prolonged incubation times were employed to enhance the growth of hard-to-culture species.

Taxonomic Analysis: The isolates were identified through taxonomic analyses, revealing new representatives of several genera, including Agromyces, Amycolatopsis, and Rhodococcus.

Bioactivity Screening: The antibacterial activities of the isolates were assessed, and genome mining was conducted to explore their potential for producing natural compounds1.

Conclusions:
Diverse Actinobacteria: The study successfully isolated a diverse range of hard-to-culture Actinobacteria from cave moonmilk deposits.

Antibacterial Potential: Many of these isolates exhibited significant antibacterial activity, indicating their potential as sources of new antibiotics.

Methodological Success: The techniques used, such as rehydration-centrifugation and prolonged incubation, were effective in isolating rare Actinobacteria.

Future Research: The findings suggest that further exploration of cave environments could yield additional novel microorganisms with valuable bioactive properties1.

Challenges:
Isolation Difficulty: Isolating hard-to-culture Actinobacteria from moonmilk deposits required innovative techniques, such as rehydration-centrifugation and prolonged incubation periods.

Contamination Risk: Ensuring samples were free from contamination was crucial, given the unique and sensitive nature of the cave environment.

Growth Conditions: Finding the optimal growth conditions for these rare bacteria was challenging, as they often require specific and sometimes unknown conditions to thrive.

Taxonomic Identification: Accurately identifying the isolated bacteria involved complex taxonomic analyses, which can be time-consuming and require specialized knowledge.

Bioactivity Testing: Assessing the antibacterial activity of the isolates involved extensive screening and genome mining to identify potential bioactive compounds1.