My assigned microbe: Faecalibacterium prausnitziiManuscript Analysis
1) The assigned microbe, Faecalibacterium prausnitzii is a bacterial species that benefits
both humans and animals. It is found in the avian and the mammalian gut and
occasionally in cow milk (dairy). Its interactions occur in the human colon and have
metabolic functions related to inflammatory characteristics that lead to obesity in humans
(Savin et al., 2019). milk being a human diet, is a potential source of F. prausnitzii, and
can be used in the research work on the diversity of phylotypes of the microbe.
2) One of the main problems that the researchers addressed in the manuscript is
investigating phylotypes’ diversity of Faecalibacterium prausnitzii in dairy cow milk.
Also, the researchers analyzed and addressed concerns about how the microbiome of
milk samples from twenty cows was used to examine Faecalibacterium 16s V4
sequences’ diversity (Savin et al., 2019). Besides, the research focused on identifying the
Faecalibacterium genus through the 16s rRNA method (amplicon sequencing) to
distinguish it from other microbes in the Ruminococcaceae family.
3) The two effective methods that the researchers used that I did not know about are milk
microbes, cows, and DNA sequencing. The other technique used is the amplicon DNA
sequences’ processing and analysis.
•
The milk microbes, cows, and DNA sequencing involved various steps. DNA
sequencing works according to the Australian Code of Practice for Animal Use in
Scientific Procedures. The method involves using cows’ milk to develop the
analysis of DNA sequences and presence. The research followed specific steps
and procedures for experimentation. First, they husbanded cows and collected
milk samples. Previously, the cows fed on grains and pasture and were taken into
the milking parlor (Savin et al., 2019). The milking procedure took place twice a
day, and the team did not allow carry-overs for different milk samples. Afterward,
the milk samples were transported and stored, ready for DNA extraction within
about 24 hours.
Consequently, the researchers prepared an enriched amount of microbial BNA
and Trition detergent added to the milk samples. The detergent dissolves fat
globules and enables their settlement or suspension. The addition of EDTA
removes the pelleted material (Casein micelles) that solubilizes. The development
procedure allows analyzing the DNA present in the pellet (Savin et al., 2019). 16s
v4 primers enabled the researchers to test the bacterial DNA sequences and their
presence. Other control samples used had DNA from milk but with the same
buffer, detergent, and the DNA polymerase enzyme. All the samples were
subjected to parallel DNA sequencing and PCR reactions.
•
Researchers’ other methods in their experimental analysis involved processing and
analyzing amplicon DNA sequences. The process works through filtering the
fastq files of DNA sequences using the Trimmomatic software (Savin et al.,
2019). FastQC then generates the quality-filtered data and raw data sequence. The
basic Unix utilities enabled the researchers to assemble paired-end sequences
using the PANDAseq program. After that, the researchers used Unix utilities like
gawk and grep to count and replicate sequences that are 100 percent identical
(Savin et al., 2019). In addition, the researchers used MegaBLAST to compare
and analyze the sequences that were de-replicated and those in the gene sequence
database (Greengenes Ribosomal RNA gene). Last, the researchers assigned taxa
for clustered sequences using the prokaryote genome and 16s rRNA databases.
4) Figure 1.
Figure 1.0 provides the stacked barplot for 21 dairy cow milk samples (Holstein) with an
expression in CPM (counts per million) that relates to the taxa present in the sequence
database (Greengenes rRNA). The researchers performed this experiment (rarefaction
analysis of sequence data) to test whether good sequence reads were developed for taxa
presence and detection (Savin et al., 2019). The figure illustrates the counts of clusters,
read pairs, and taxa, which are plotted. The researchers’ hypothesis was to identify all
taxa with 0.1% presence abundance and other taxa (the rare ones). Figure 1.0
demonstrates that beyond 100,000 different 16s sequences, the discovered number of taxa
have no value (Savin et al., 2019). Also, there is a significant reduction in sequence
clusters and taxa numbers approaching a ceiling.
Moreover, the figure also elaborates on microbiome content, where the researchers found
the dominant phyla in the milk samples. Bacteroidetes, Proteobacteria, and
Actinobacteria were the most dominant bacterial families (Savin et al., 2019). However,
the main focus was on the Faecalibacterium in milk (dairy cow) and 16s rRNA diversion
sequence for the same bacterial species. Also, the visual assessment in the figure shows
that milk microbiomes vary across the 21 cows at the family level (Savin et al., 2019).
Last, the figure shows that in two cows, Pseudomonadaceae was dominant for the
majority of the bacteria. Hence, other researchers mostly compare Faecalibacterium and
isolate its 16s rRNA amplicon sequences related to some species.
The experimental results provide key points in understanding Faecalibacterium
prausnitzii in dairy cow milk. The results show that plotting abundance data on bar charts
helps visualize the sequence clusters through various algorithms (Savin et al., 2019).
Besides, I have learned that Faecalibacterium related sequences and cluster muscle align
to 16s V4 sequences in about thirty-five Ruminococcaceae species. This implies that the
final diversity indices should fall within the expected microbiome ranges provided in the
milk analysis graph.
5) The manuscript provides major findings that illustrate how Faecalibacterium prausnitzii
occurs in dairy cow milk. The following are my interpretations of the conclusions of the
manuscript:
•
Faecalibacterium prausnitzii, a bacterial genus rare in milk but beneficial to
humans, varies in abundance for the 21 cows used (Savin et al., 2019). Therefore,
it is true to recognize that each cow possessed a unique pattern of 16s rRNA
amplicon sequence.
•
Each cow has its own characteristic Faecalibacterium prausnitzii population. This
is evident from the network of Faecalibacterium prausnitzii sequence clusters and
the Greengenes pattern of OTU distribution. Besides, the data provides elaborate
evidence that multiple strains are present in milk (Savin et al., 2019). This means
that most undefined Faecalibacterium species come from the rumen, soil, faeces,
water, animal skin, and milking equipment in the dairy farm areas.
•
The research confirms that about eight abundant phylotypes have aligned
sequence from the phylogenetic analysis (Savin et al., 2019). This implies that the
abundant phylotypes closely relate to some Faecalibacterium prausnitzii strains
that do not occur in bovine and human faeces. I can conclude that
Faecalibacterium prausnitzii related 16s rRNA sequences from a cow contribute
to a minimum of three clusters. The network diagram also provided that multiple
cows exhibited the same bacteria source (Savin et al., 2019). Therefore, the
research hypothesis that the researchers confirmed can invoke an acquisition of
minimum bacterial cells at random from unknown origins.
•
Similarly, the research findings provide that DNA sequence data from milk,
water, faeces, and other dairy farm microbiomes can assist in identifying milk
bacteria and their sources. This happens especially when the closed-loop
sequencing of large reads is used (Savin et al., 2019). Also, multiple cows in a
dairy herd can have different types of Faecalibacterium. Also, functional analysis
plays a critical role in isolating strains from milk. The research work gave
adequate evidence since we can conclude that milk offers an opportunity to
determine and discover Faecalibacterium prausnitzii bacteria for maximum
probiotic applications (Savin et al., 2019).
Model Summary
6)
7) The diagram above illustrates how Faecalibacterium prausnitzii in dairy cow milk enters
the human body. It is butyrate-producing bacterial species found in the gut and the colon
(intestines). The bacteria lead to intestinal disorders in humans (Bag et al., 2017).
Faecalibacterium prausnitzii distributes as shown in the human body and leads to
obesity.
References
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Savin, K., Zawadzki, J., Auldist, M., Wang, J., Ram, D., Rochfort, S., & Cocks, B. (2019).
Faecalibacterium diversity in dairy cow milk. PLOS ONE, 14(8), e0221055.
https://doi.org/10.1371/journal.pone.0221055
Bag, S., Ghosh, T., & Das, B. (2017). Complete Genome Sequence of Faecalibacterium
prausnitzii Isolated from the Gut of a Healthy Indian Adult. Genome Announcements, 5(46),
7-11. https://doi.org/10.1128/genomea.01286-17
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