Colorectal cancer

1. Biodiversity and richness shifts
of mucosa-associated gut
microbiota
with progression of colorectal
cancer

2. Colorectal cancer (CRC), a digestive tract tumor, is one of the most common forms of cancer and a major cause of cancer morbidity
and mortality globally [1]. The etiological factors and pathogenic mechanisms involved in CRC progression are compli- cated and
heterogeneous [2]. Lifestyle factors, heredity factors, and dietary patterns are the most significant contributory agents in the
development of CRC [3]. The host-associated gut microbiota has also been suspected frequently as a critical factor in the occurrence
and progression of CRC [4e6].
Human intestinal microorganisms, also known as the gut microbiota, include more than 1000 heterogeneous species of mi-
croorganisms that constitute approximately 90% of all cells in the human body [7,8]. The gut microbiota plays important roles in
metabolism, pathogen resistance, immune system education, and modulation of gastrointestinal development in the host and has been
considered an essential “organ” of the human body [9e13]. Changes in the composition of the gut microbiota are associated with a
variety of diseases, including obesity, malnutrition, and in- flammatory bowel disease [8,14]. Owing to the fact that a large number of
vital functions of intestinal microbiota are known, extensive efforts have been made to gain a better understanding of the relationship
between the complex colon microbiota and colon cancer. In 1969, the correlation between the gut bacteria and the incidence rates of
CRC was revealed by comparing the fecal flora of subjects from different geographical areas [15]. Moore and Moore have reported
positive correlations between the Bacteroides and Bifidobacterium species with a high risk of CRC, and Lactobacillus species and
Eubacterium aerofaciens with a low risk of CRC [16]. The relationship of 7-a dehydroxylating bacteria with high CRC risk and
Lactobacillus plantarum with low CRC risk has been reported by O’Keefe et al. [17]. Therefore, these studies have mainly focused on
revealing the relationship between the specific gut bacterial taxa and the occurrence of colon cancer. Bacterial dysbiosis i.e., an altered
diversity and composition segregation of the gut microbiota has been identified in fecal samples of CRC patients or high-risk population
and healthy population by high-throughput sequencing studies [6,18e22]. Several independent research groups have reported changes
in bacterial abundances for samples collected from the tumor sites and surrounding non-tumor sites by following similar sequencing
methods [8,23e27]. Furthermore, several opportunistic pathogenic bacterial species including Fuso- bacterium nucleatum,
Streptococcus gallolyticus, enterotoxigenic Bacteroides fragilis, Enterococcus faecalis, and Escherichia coli have been identified to be
associated with the development of CRC [4,23,28e32]. A bacterial “driver-passenger” model and an “alpha- bugs” model has been
proposed based upon the sequencing data and the results of animal model experiments [33,34]. The models have suggested a role of
“cross-talk” between the gut microbiota and the host during CRC progression, and thus provide insights for elucidating the pathogenic
mechanisms of gut bacteria in CRC progression.
Despite increasing scientific efforts, the correlations between
the complex gut microbiota and CRC progression remain unclear. Most of the studies have been carried out using the fecal samples for
understanding the development of the gut microbiota during the progression of colorectal adenoma-carcinoma [35e39]. How- ever,
limited studies have been carried out for evaluating the mucosa-associated gut microbial richness and biodiversity shifts among
different stages of CRC [40]. In the present study, 23 pairs of samples were collected from the colorectal tumor sites and the
surrounding tissues from stage I to IV CRC patients. The microbial composition of the samples was then analyzed by sequencing the V4
region of the 16S ribosomal RNA gene using the Illumina MiSeq next generation sequencer. Therefore, in this study, we evaluated gut
bacterial alterations at the tumor sites and surrounding healthy sites in different stages of CRC.

3. • In this study, 23 patients (11 males and 12 females) ranging in age from 49 to 70
years (Table S1) diagnosed with CRC were selected from the department of
general surgery, Qilu Hospital of Shandong University. All the patients enrolled in
our study were from Shandong province of China, and had similar dietary habits.
The enrolled patients were suffering from adenocarcinoma. Sam- ples were
collected in pairs from tumor tissue (on-tumor sites) and the surrounding non-
tumor tissue (off-tumor sites, at least 5 cm away from the margin of the tumor)
sequentially from the CRC patients [41]. The collected tissue samples were
immediately DNA extraction. Postoperative pathological staging was determined
for each individual patient according to the 7th edition of the UICC/ AJCC TNM
staging system for CRC [41]. None of the patients received antibiotics before
sample collection. Written informed consent was obtained from all participants
after an explanation of the study was provided to them. The Ethics Committee of
Qilu Hospital, Shandong University approved this study, which was conducted in
accordance with the approved guidelines. The DNA isolation, sequencing and
data analysis were carried out in Novo- gene Co., Ltd. (Beijing, China).

4. • The genomic DNA was extracted from each tissue sample using the method of CTAB-PVP
(cetyltrimethylammonium bromide- polyvinylpyrrolidone) as already described [42,43].
Briefly, 1 ml of CTAB extraction buffer (100 mM TriseHCl, 1.4 M NaCl, 20 mM EDTA, 2%
CTAB, 1% polyvinylpyrrlidone, and 0.4% b-mercaptoe- thanol) buffer and the lysozyme
enzyme were added to each sample, and these samples were lysed by placing them in a
water bath at 65 ○C with shaking for 2e3 h. Samples were then centri- fuged at 12,000
rpm for 1 min and 950 ml of phenol/chloroform/ isoamyl alcohol (25:24:1) was added.
Samples were mixed by vortexing for 10 s and then centrifuged for 10 min at 12,000
rpm. The upper aqueous phase was transferred to a new Eppendorf tube, and then, the
sample volume of chloroform/isoamyl alcohol (24:1) was added. Samples were again
vortexed for 10 s and centrifuged for 10 min at 12,000 rpm. The upper aqueous phase
was transferred to a new tube and 0.75 ml of isopropanol was added and mixed. Samples
were left at —20 ○C overnight and then centrifuged for 20 min at 12,000 rpm. The
supernatant was removed, the pellet was washed twice with 1 ml of 70% AR grade
ethanol and centrifuged for 1 min at 12,000 rpm. Ethanol was removed, and DNA was
re-suspended in 30 ml of sterile TE. DNA concentration and purity were determined by
performing 1% agarose gel electrophoresis.

5. • The extracted DNA was subjected to Illumina MiSeq sequencing in Novogene Co., Ltd. (Beijing, China). The composition and di-
versity of the bacterial communities in each sample were deter- mined according to the protocol described by Caporaso et al.
[44]. Briefly, the V4 region of the 16S rRNA gene was amplified using the 515F (50 GTGCCAGCMGCCGCGGTAA 30) and
806R (50 GGAC-
• TACVSGGGTATCTAAT 30) set of primers with a barcode. The PCR reactions were set up in a total volume of 30 ml consisting of 15
ml of Phusion ® High-Fidelity PCR Master Mix (New England Biolabs),
• 0.2 mM forward and reverse primers, and 5e100 ng of DNA tem- plate. The reaction conditions were as follows: 94 ○C for 3
min,
• followed by 30 cycles of 94 ○C for 45 s, 50 ○C for 60 s, and 72 ○C for 90 s, with a final extension of 72 ○C for 10 min. Then,
the PCR
• products were run on agarose gel electrophoresis and then purified using the GeneJET Gel Extraction Kit (Thermo
Scientific).
• Sequencing libraries were generated using an NEB Next® UltraTM DNA Library Prep Kit for Illumina (NEB, USA) following the
manu-
• facturer’s instructions and index codes were assigned. The quality of the library was assessed using the Qubit@ 2.0
Fluorometer
• (Thermo Scientific) and Agilent Bioanalyzer 2100 system. During
• PCR, ddH2O was used as template for negative control to make sure that there was no DNA contamination of the PCR
amplification system. Finally, the library was sequenced using the Illumina MiSeq PE300 platform (Novogene Co., Ltd Beijing,
China) and 250e300 bp paired-end reads were generated.

6. • Paired-end reads from the original DNA fragments were merged using a very fast and accurate software tool namely, FLASH (Fast
Length Adjustment of SHort reads) [45], which is used to find the correct overlap between the paired-end reads. Paired-end reads
were assigned to each sample according to the unique barcode of each sample. The QIIME [46] software package was employed
to filter the sequences and then the pick_de_novo_otus.py function was used to select operational taxonomic units (OTUs) by
con- structing an OTU table. Sequences with a threshold of 97% pairwise identity were assigned to the same OTUs [46e50].
UPARSE64 was used for the OTU redundancy reduction, OTU cluster and OTU abundance analysis. Mothur (V1.25.0) and SSUrRNA
database of SILVA were used to assign the taxonomy. The QIIME [46] software package was employed to analyze the alpha (within
sample) and beta (between sample) diversity. LEfSe software was used for sig- nificant difference analysis.
• 2.5. Accession numbers for the sequencing data
• The sequence information has been deposited in the NCBI Short Read Archive database under accession numbers SRR2082612 to
SRR2082657.
• Results
• Richness and diversity analysis of the sequencing data

7. • PCR amplification was done for the V4 regions of the 16S rRNA genes corresponding to
23 pairs of samples collected from tumor tissues and surrounding non-tumor healthy
tissues. The amplified products were then subjected to the Illumina MiSeq sequencing
to assess the relative abundance of DNA in the gut bacterial species. The average
number of raw reads per sample and clean reads per sample were 66,511 and 54,591,
respectively. The sequencing data yielded a total of 2,510,896 filtered reads with an
average length of 225 bp. The reads were then grouped into 9081 OTUs for non-CRC
tissues and 8554 OTUs for CRC tissues when compared to the reference databases using
a threshold of 97% sequence identity (Fig. S1). Good’s coverage was used to measure
the sequence saturation of the samples in each group (Fig. S2). The results indi- cated
that most of the microbial diversity was captured in the data set. However, additional
iterative sequencing efforts might be able to detect rare new phylotypes. The richness
and diversity of OTUs were observed to be slightly higher in non-CRC healthy tissues as
compared to the CRC tissues (Fig. 1A). However, significant statis- tical differences were
observed between the CRC tissues and non- CRC healthy tissues in CRC stage III only
(Fig. 1A). Interestingly, the gut genera showed significant statistical differences at early
and later stages of CRC (Fig. 1B).

8. • The overall community composition for each sample was analyzed as described in the methods
section. The analysis of each individual sample revealed changes in microbial composition in CRC
tissue as compared to the surrounding non-cancerous colon tissue collected from an individual
patient (Fig. S3). The overall compositional differences in the gut bacterial taxa were also
analyzed at different stages for CRC tissues and the surrounding normal tissues (Fig. 2 and Table
1). Proteobacteria was identified as the most predominant phylum at the tumor sites by
sequencing, accounting for 21.92% of all OTUs followed by Bacteroidetes, Fir- micutes,
Fusobacteria, and Verrucomicrobia, which accounted for 14.67%, 12.75%, 5.67%, and 0.72% of all
OTUs, respectively. Proteo- bacteria accounted for 31.25% of all OTUs at the surrounding off-
tumor sites, followed by Bacteroidetes, Firmicutes, Fusobacteria, and Verrucomicrobia, which
accounted for 25.97%, 17.72%, 3.58%, and 1.51% of all OTUs, respectively (Table 1). In
conclusion, we observed only a slight decrease in the relative overall microbial composition at
the tumor sites as compared to the surrounding off- tumor sites. Additionally, no significant
statistical difference was observed in the overall composition of phyla between the CRC and the
surrounding non-CRC tissues (Table 1). These findings were observed to be consistent with that
of the microbial richness and diversity (Fig. 1).
• The differences in the overall abundances of the gut bacterial taxa, in CRC patients of different
stages, were further analyzed in

9. • detail at the phylum and family levels. In tissues collected from early stage CRC patients,
Proteobacteria was observed to be the most predominant phylum by sequencing,
accounting for 33.6% of all OTUs followed by Bacteroidetes, Firmicutes, Fusobacteria,
and Verrucomicrobia, which accounted for 14.9%, 9.79%, 2.36%, and 0.79% of all OTUs,
respectively. In late-stage CRC patients, Bacter- oidetes accounted for 25.75% of all OTUs
followed by Firmicutes, Proteobacteria, Fusobacteria, and Verrucomicrobia, which
accounted for 20.68%, 19.56%, 6.9%, and 1.44% of all OTUs, respec- tively (Table 1). The
analysis results revealed an increasing compositional trend of Bacteroidetes, Firmicutes,
Fusobacteria, and Verrucomicrobia and a decreasing compositional trend of
Proteobacteria during the progression of CRC. The changes in abundances of top twelve
genera in different phyla were also analyzed (Table 2). In the phylum of Proteobacteria,
Escherichia, Halomonas, and Shewanella showed relatively high abundance at the early
stages of CRC. In the phylum of Bacteroidetes, Bacteroides and Prevotella exhibited
relatively high abundance at the late stages of CRC. In the phylum of Firmicutes,
Peptostreptococcus, Streptococcus, and Ruminococcus showed relatively high abun-
dance at the late stages of CRC, while Granulicatella and Lactoba- cillus demonstrated
relatively high abundance at the early stages of CRC. This detailed analysis suggested the
composition segregation of gut microbiota species during the progression of CRC.

10. • Furthermore, the relatively highly abundant gut genera were
observed to be different at early and at late stages of CRC as shown
in Fig. 3. These results further verified the differences in bacterial
compositions at different stages of CRC.

11. • Multivariate statistical analysis was performed to further analyze the structure of
community changes in the gut microbiota during the progression of CRC. Stages
I-II were considered as early stages of CRC, whereas stages III-IV were considered
as late stages. PCA was applied to analyze the relationships between the com-
munity compositions of the gut microbiota and CRC developmental stages. In the
PCA plot, each symbol represents the gut microbiota of the CRC tissue and the
surrounding non-CRC tissue of a given CRC patient with a different status of CRC.
Interestingly, PCA analysis revealed imperfect but statistically significant
separations of the gut microbiota based on CRC status (Fig. 4). In contrast, no
signifi- cant differences were observed in community compositions of the CRC
tissues and the surrounding non-CRC tissues (Fig. 4). The re- sults of the
multivariate statistical analysis revealed an overall structural segregation of the
gut microbiota during the progression of CRC. The findings were consistent with
that of the composition (Fig. 3) and diversity analysis (Fig. 1B).

12. • A specific OTU that exhibited significant difference was identi- fied
using the LEfSe software (Fig. 5). The abundance of Akker- mansia,
Fusobacterium, Peptostreptococcus, Streptococcus, and
Ruminococcus was observed to be significantly higher at the genus
level in late stage CRC patients. Akkermansia and Ruminococcus were
enriched in non-CRC tissues, while Fusobacterium, Peptos-
treptococcus, Streptococcus were enriched in late stage CRC tissues.
The abundances of Granulicatella and Lactobacillus were significantly
decreased in the late stage CRC patients, and were high in the non-
tumor samples as compared to the tumor samples from CRC
patients. Moreover, B. fragilis were specifically enriched in the non-
CRC tissues at the late stages of CRC.

13. • Alterations of gut microbiota occur in various diseases [8], including Crohn’s disease, ulcerative colitis, celiac
disease, and allergic inflammation. A correlation between bacterial dysbiosis and colorectal cancer has been
suggested by recent studies [6,34,51,52]. Moreover, evidence suggests that these microbes may also affect
the response of CRC patients to therapeutics [52]. However, most data from the human gut microbiota have
been derived from fecal samples or late-stage CRC tissues [35e37,53,54]. The mucosa-associated gut
microbial richness and biodiversity shifts associated with the progression of CRC have remained largely
unexamined [53]. Moreover, Russo et al. reported that the compositions of bacterial communities in tissues
from CRC patients are different from those in stool samples [27]. Therefore, in the present study, we aimed
to investigate the composition and structural segregation of the mucosa-associated gut microbiota in CRC
patients at different developmental stages. We propose to further elucidate the dy- namics of the CRC-
associated microbiome during the progres- sion of CRC.
• No significant differences were observed in the overall microbial
• richness and composition between the CRC tissue and the sur- rounding non-CRC tissue samples (Table 1,
Figs. 1 and 3). These observations are further supported by several previous studies that have suggested
that a patient’s intestinal ecosystem might be more significant in shaping the microbiota than the generic
microenvi- ronment of a colon tumor or normal colonic tissue [23]. Because no significant differences in
overall microbial richness and biodiversity were observed between the CRC tissue and surrounding non-CRC
tissue samples, we might expect changes in biodiversity and rich- ness of the gut microbiota with the
progression of CRC. This spec- ulation was further supported by our findings, which revealed an overall
composition and structural segregation of gut microbiota at

14. • different developmental stages of CRC (Figs. 1B, 3 and 4). Our results suggest that the CRC-associated
microbiome is dynamic, with changes during CRC progression.
• A detailed analysis of the altered bacterial taxa can provide clues regarding their potential roles in the
progression of CRC. Bacteroides species are closely associated with an increased risk of CRC because of
their ability to convert bile to fecapentaenes, which are considered carcinogenic or mutagenic metabolites
[16]. Statistical analysis revealed an increased relative abundance of Bacteroidetes in both tumor tissues
and surrounding non-tumor tissues during the CRC progression (Table 2). Our data suggest the possible
involvement of Bacteroidetes in the progression of CRC. Fusobacterium species has been recently
speculated to contribute to colon cancer, and several studies have observed an increased relative
abundance of this bacterium in CRC tissue [23,24,55]. Fusobacterium, Peptostreptococcus, Streptococcus,
and Ruminococcus were enriched with the progression of the disease, suggesting the participation of this
bacterium in CRC progression. Our results were observed to be consistent with several other previously
reported studies [53,56]. The detection of the well- known pathogen, B. fragilis, further verified its
involvement in tumorigenesis. Several studies have determined beneficial roles of probiotic bacteria in the
human gut. The significantly reduced relative abundances of Granulicatella and Lactobacillus associated
with the progression of CRC suggest a potential beneficial role of these species in suppressing tumor
progression. To conclude, our initial findings provide a comprehensive view of the human mucosa-
associated gut microbiota associated with different stages of CRC.