Graph-based Analysis of Genetic Features Associated with Mobile

Elements in Crohn’s Disease and Healthy Gut Microbiomes

Julia Warnke-Sommer

and Hesham Ali

Pathology and Microbiology, University of Nebraska Medical Center, Omaha, 68198 U.S.A.

College of Information Science and Technology, University of Nebraska Omaha, 68106, U.S.A.

Keywords: Next Generation Sequencing, Graph Theory, Metagenomics, Mobile Elements, Crohn’s Disease.

Abstract: Horizontal gene transfer is a major driver of bacterial evolution and adaptation to niche environments. This

holds true for the complex microbiome of the human gut. Crohn’s disease is a debilitating condition

characterized by inflammation and gut bacteria dysbiosis. In previous research, we analyzed transposase

associated antibiotic resistance genes in Crohn’s disease and healthy gut microbiome metagenomics data

sets using a graph mining approach. Results demonstrated that there were significant differences in the type

and bacterial distribution of transposase-associated antibiotic resistance genes in the Crohn’s and healthy

data sets. In this paper, we extend the previous research by considering all gene features associated with

transposase sequences in the Crohn’s disease and healthy data sets. Results demonstrate that some

transposase-associated features are more prevalent in Crohn’s disease data sets than healthy data sets. This

study may provide insights into the adaptation of bacteria to gut conditions such as Crohn’s disease.

1 INTRODUCTION

The human gut microbiome represents a wealth of

biological organisms and their related functions. The

composition and abundances of these microorga-

nisms have been found to be associated with

numerous important health characteristics. For

example the gut microbiome has been found to help

regulate immune function and development

(Sommer, Bäckhed, 2013) influence dietary

metabolism (Turnbaugh et al, 2006) and even

modulate mood and behaviour (Foster, McVey

Neufeld, 2013). The community of the gut

microbiome is highly complex with a wide array of

diverse microbial members that interact with each

other as well as with the host. In a healthy state, this

consortium of microorganisms is well adapted to

perform multiple functions that are beneficial to the

host as well as for maintaining the homeostasis of

the gut environment (LeBlanc et al, 2013) (Kamada

et al, 2013). These community members are

specifically adapted to flourish in the gut

environment and are flexible to adapt to changes to

the environment such as diet alterations (David et al,

2014) (O’Sullivan et al, 2009).

One such mechanism that bacteria utilize to

adapt to their environments is the transfer of small

amounts genetic material called mobile genetic

elements. Mobile genetic elements include plasmids,

transposons, and bacteriophage related sequences. In

particular, transposons are segments of DNA that

can remove and insert themselves in different

regions of the same or different genomic sequence.

Also known as “jumping genes” these transposons

many times utilize specialized genes called

transposases to catalyse the excision and movement

of the transposon sequence. In bacteria, transposons

often carry accessory genes such as antibiotic

resistance genes that can confer specialized

functions to the organism in which it is integrated.

The horizontal gene transfer of genes between

bacteria is a major driver of bacterial evolution and

allows for adaptation of a given bacterial species to

its environmental niche (Ochman, Lawrence,

Groisman, 2000).

Dysbiosis of the gut microbiome occurs in many

disease states including inflammatory bowel disease

(IBD) such as Crohn’s disease and ulcerative colitis

(Manichanh et al, 2012). According to research, this

dysbiosis may facilitate increased horizontal gene

transfer between bacterial members of the gut

microbiome, promoting the spread of virulence

104

Warnke-Sommer J. and Ali H.

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes.

DOI: 10.5220/0006254201040114

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 104-114

ISBN: 978-989-758-214-1

factors and antibiotic resistance genes (Stecher,

Maier, Hardt, 2013).

Next generation sequencing technologies can be

applied to investigate the composition of the gut

microbiome. These technologies are capable of

producing millions of short DNA fragments called

reads from a given input DNA sample. The reads are

produced at such a high coverage of the original

sample sequence that many overlap. Graph based

tools called assemblers are used to assemble the

reads into longer stretches of sequence called contigs

that are used for downstream analysis (Nagarajan,

Pop, 2013). We have developed an assembler tool

called Focus (Warnke, Ali, 2014) that relies on a

novel assembly graph called the hybrid graph. The

hybrid graph models the overlap relationships

between reads in a given data set at multiple levels

of granularity.

In previous research (Warnke-Sommer, Ali,

2016), we demonstrated that transposase sequences

are associated with graph structure due to their

repetitive nature within a single genome or across

multiple different genomes. The distribution of

transposase sequences was studied in gut

microbiomes of individuals with Crohn’s disease

and healthy individuals. Graph mining was used to

explore genomic regions in proximity to the

transposase sequences for antibiotic resistance

genes. Regions around transposase sequences were

enriched for different types of antibiotic resistances

genes in the Crohn’s disease versus the healthy gut

microbiome. The distribution of these antibiotic

resistance gene groups across genera is also different

between Crohn’s disease gut microbiomes and

healthy gut microbiomes with most antibiotic

resistance genes in the healthy gut samples coming

from Bacteroides. The distribution of resistance

genes in the Crohn’s disease samples was more

diverse across bacterial genera. This study was

conducted to first demonstrate the capability of the

hybrid graph to capture biological features in its

graph structures. Finally this study also provided

insights into the distribution of antibiotic resistance

in the gut microbiome of Crohn’s disease, a disease

whose resulting complications are often treated with

antibiotics regimens (Roy, Lichitiger, 2016).

This paper extends the analysis conducted in

(Warnke-Sommer, Ali, 2016) to examine all gene

features associated with transposase sequences in the

Crohn’s disease and healthy metagenomics data sets.

Antibiotic resistance genes are commonly known to

be in association with transposase sequences (Ghosh

et al, 2013). However, it would be beneficial to

examine additional genetic features associated with

Figure 1: The overlap graph. Each read is mapped to a

node in the overlap graph. Edges represent overlap

relationships. Each edge is weighted according to the

length of the overlap region shared between a pair of

reads.

transposase sequences in Crohn’s disease and

healthy gut microbiome samples. The ability to

determine transposase associated gene differences in

Crohn’s and healthy gut microbiomes provides

valuable insights into potential niche adaptations of

bacterial species in gut environments afflicted with

Crohn’s disease. Results demonstrate that

transposase associated gene features are significantly

different in Crohn’s disease versus healthy gut

microbiomes. Crohn’s disease samples had

significantly greater hits to several functional

categories, including beta-glucoside metabolism,

maltose and maltodextrin utilization, and heme,

hemin uptake and utilization systems in gram

positives.

2 METHODS

In this section, the pipeline for analysing the

transposase-associated gene features in the Crohn’s

disease and healthy gut microbiomes is discussed.

First, the graph model applied for modelling the

metagenomics reads and their overlap relationships

is described. This graph model is part of the Focus

assembler pipeline described in detail in (Warnke,

Ali, 2014). Second, the database that is used to

functionally annotate gene features is briefly

discussed. Finally, the graph mining approach for

extracting transposase related gene features from the

hybrid graph is presented.

2.1 Graph Model

The overlap graph approach is commonly used to

model reads and their corresponding read overlap

relationships (Miller, Koren, Sutton, 2010). In this

approach, each read is mapped to a node in the

overlap graph and edges represent the overlap

relationships between reads. The edges can be

ATCAGC

TAGAGC

CGTGGA

AGCCCT

CTTCGT

CTTCGA

GATCGA

GATCCA

CCAAGC

Overlap

Graph

)

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes

105

Figure 2: The multilevel graph set and hybrid graph. The initial overlap graph G0 is shown above. The DNA sequences

beside G0 represent contigs that can be inferred from the overlap graph. Notice that the overlap graph captures the

sequence variation between the two contigs. The multilevel graph set is created by reclusively matching and merging nodes

to produces a succession of graphs G0 ... G3. The graph G3 is over reduced and does not capture the bubble structure in the

original overlap graph. The hybrid graph integrates the three graph levels to produce a representation that is concise yet

captures all sequence structure in the read set.

weighted to reflect overlap characteristics such as

overlap length or identity. See Figure 1 for an

example of the overlap graph.

The Focus assembly algorithm first performs

seed-and-extend pairwise alignment to discover

overlap relationships between reads (Warnke, Ali,

2014). Once the pairwise alignment module is

completed, the overlap relationships and reads are

loaded into an overlap graph representation, denoted

as G

. In this graph, edges are weighted by the

lengths of the reads’ shared overlaps.

The overlap graph G

is highly complex and can

contain millions of node, making it difficult to

recover any useful information from its structures.

To address this issue, the overlap graph G

recursively reduced with heavy edge matching and

node merging to produce a series of reduced graphs

, G

… G

, where |G

| ≥ | G

| ≥ … | G

|. Heavy

edge matching is a maximal matching that is

generated with preference for heavier edge weights

(Karypis, Kumar, 1998). Focus attempts to find a

maximal heavy edge matching that satisfies minimal

edge weight thresholds set by the user. Once this

matching is found, the endpoints of the edges in the

matching are merged to form the new nodes in G

An edge between two merged nodes in G

assigned the summed weight of the edges between

the merged nodes’ child nodes in G

Figure 2 demonstrates graph reduction and

multilevel graph set. Notice that G

does not capture

a bubble in the overlap graph and is over reduced.

Not all levels of the multilevel graph set will be

appropriate for representing all regions of genomic

sequence such as complex repeats or other sequence

variation. To address this issue, nodes are selected

from various levels of the multilevel graph set and

integrated to create a novel graph model called the

hybrid graph. The hybrid graph represents the input

data set as concise as possible while still capturing

input data set features. The nodes are selected from

the multilevel graph set as follows. First the nodes of

the coarsest graph G

are evaluated. If a node is

found to be a representative of a single contiguous

sequence, then it will be added to the hybrid graph.

If the reads that this node represents do not assemble

into a contiguous sequence, then the algorithm does

not add the node to the hybrid graph. Instead its

child nodes in G

n-1

are evaluated to examine whether

their corresponding reads form a contiguous

sequence. If they do, then they are added to the

hybrid graph. If not, then their child nodes in G

n-2

are

evaluated. This process continues until G

reached. The hybrid graph will contain all of the

nodes in G

, G

, ... G

that have been evaluated and

found to form a contiguous region of sequence. Thus

the hybrid graph represents the input data set as

concisely as possible while capturing input sequence

variation.

CGTTAGCCTAG

ATCGATAGATC

TTCGATCGA

TCGTGGAT

CCGAT

AGCTAAGCTCCCT

CGTTAGCCTAG

ATCGATAGATC

TCGTGGAT

TTCGATCGA

CCGAT

AGCTAAGCTCCCT

Graph

Spectrum

Hybrid

Graph

’

Integra on

mul ple

graph

levels

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

106

Figure 3: Graph mining for discovering biologically

significant features. In (a) there are three genomes that

share a common transposase sequence. In two of these

genomes, a gene feature is associated with the transposase.

In (b) the hybrid graph representing these genomes is

shown. The common transposase region is reduced to a

single node that has many branching paths representing

the three unique genomes. Observe that the gene features

are represented by nodes that are within a short path length

from the node representing the shared transposase

sequence. Any genetic features associated with this

transposase can be determined by exploring the node

neighbourhood centered on the node representing the

repeated transposase sequence.

Please see (Warnke, Ali, 2014) for a formal

description of the multilevel graph set and hybrid

graph construction.

2.2 Seed Subsystems and ACLAME

Database

The Seed Subsystems (Overbeek et al, 2014) is an

organizational database for gene annotation. It is

organized into several hierarchal levels of functional

subsystems. This database has annotated protein

sequences that are organized into families called

Figfams based on sequence similarity and function

(Meyer, Overbeek, Rodriguez, 2009). These Figfams

are then annotated with the subsystem functional

categories.

In this paper, the gene prediction software

FragGeneScan (Rho, Tang, Ye, 2010) was used to

predict genes within the metagenomics contigs

produced by Focus. These predicted genes were then

aligned against the seed subsystem using the

DIAMOND software tool (Buchfink, Xie, Huson,

2015). The predicted genes were assigned

annotations at subsystem level 2 according to the

Figfam hit that they were most similar to. The

minimum score for each protein hit was set to 40%

minimum sequence identity with a minimum

alignment length of 50.

The ACLAME database contains a comprehensi-

ve collection of prokaryotic mobile elements (Leplae

et al, 2004). As before, the DIAMOND software

was used to align the predicted genes against the

ACLAME database with a minimum sequence

identity score of 40% and minimum alignment

length of 50. Any contigs with hits were annotated

accordingly.

Finally, the nodes in the hybrid graph were

annotated according to their corresponding contigs’

annotations as determined by the SEED or

ACLAME databases.

2.3 Graph Mining for Biological

Features Associated with Mobile

Elements

In (Warnke-Sommer, Ali, 2016) it was demonstrated

that graph structure was associated with biologically

significant features. In particular, sequence regions

that are repetitive within the same genome or are

present in multiple different genomes were found to

be represented by nodes that have a large degree. As

shown by Fig. 3, a region that is repeated within the

same genome or is present in multiple different

genomes will be reduced to a single node in the

hybrid graph. In the hybrid graph, the number of

paths containing a node representing a conserved or

repeated sequence may indicate the number of

distinct genomic regions that contain that repeated

sequence. In (Warnke-Sommer, Ali 2016) the

Shannon's diversity score was used to capture the

number of paths entering and exiting from a node.

The formula (Shannon, 2001) used is given by:











ln









,





where n is the number of incident edges, w

is the

weight of the ith edge, and w

total

is the total weight of

all of the incident edges. For a given node, this

formula captures both the number of incident edges

as well as the evenness of edges’ weights. Recall

that the weight of a given edge represents the

Genome

Transposase

Gene

feature

Hybrid

graph

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes

107

summed length of the overlaps between the reads

represented by the endpoints of that edge. Thus the

Shannon's diversity score takes the read coverage of

paths entering and exiting a node into consideration.

The greater the number of edges incident to a given

node and the more even the edges’ weights are, the

higher that the Shannon's score of that node will be.

Any incident edges that are spurious such as a single

read with poor end qualities will have minimal

impact on the Shannon index score as a edge

representing an overlap with this read will likely be

low coverage in comparison to other paths entering

and exiting the node. Thus the Shannon's index score

is robust against false positive edges in the hybrid

graph due to sequencing error and artefacts.

Observe that in Fig. 3, the nodes within a short

path distance of the node annotated with the

transposase sequence are annotated with additional

genetic features. Analysing the graph neighbourhood

centered on a given node annotated with a

transposase sequence can discover any gene features

related to that transposase sequence. Given a node

that is annotated with a transposase sequence and

meets requirements described in the next paragraph,

all its neighbouring nodes within a path length of

three are examined to determine if they are

annotated with any additional gene features. Any

gene features that are found are considered to be

transposase associated.

In this paper, nodes annotated with transposase

sequences and had a Shannon's score greater than 1

were chosen for neighbourhood analysis. The

minimum Shannon's score of one was chosen

because nodes that have a Shannon's degree greater

than one are part of multiple paths, indicating that

they may be part of multiple genomic sequences.

Greater occurrence in multiple sequence regions

may indicate that a particular transposase sequence

has spread between multiple genomic regions and

may have been or is currently a contributor of

bacterial evolution and adaptation. The ability to

analyse genetic features associated with transposase

sequences that occur across multiple genomic

regions, whether in the same genome or across

different genomes, may provide insight into

functions that have allowed bacterial species to

adapt to their niche environmental conditions.

3 RESULTS

This section presents the results of examining the

gene features associated with transposase sequences

in the Crohn's disease and healthy gut microbiome

data sets. First, gene features associated with

transposase sequences are extracted as described in

the methods. Each gene feature is assigned a level 2

subsystem classification. The counts for each

subsystem are normalized per data set by dividing

the subsystem count by the total number of nodes

explored by neighbourhood analysis and then

multiplying by a thousand to get number of

subsystem hits per thousand nodes. Next the Mann-

Whitney U test was used to determine whether there

is a significant difference in subsystem category hits

for the Crohn's disease versus the healthy data sets.

For subsystems that have significantly different

number of hits between the Crohn's disease and

healthy data sets, the distribution of bacterial genera

in which the subsystems occurs was determined.

Finally, three of the significant features: Mannose

binding, Beta galactoside, and Heme binding in

gram positives are explored in more detail in the

context of their possible roles in the gut microbiome

of Crohn's disease individuals and previous

literature.

3.1 Subsystem Analysis

This work extends the research on antibiotic

resistance genes applied to the thirteen data sets in

(Warnke-Sommer, Ali, 2016). The same data sets

are used in this analysis. Please see (Warnke-

Sommer, Ali, 2016) for a description of the data sets'

characteristics and distribution of bacterial genera.

To begin, all transposase associated gene

features were assigned a level 2 subsystem classify-

cation. If a given subsystem category did not have at

least five hits for at least two of the data sets it was

not included in subsequent analysis. Fig. 4 displays

the median subsystem category hits per thousand

nodes for the significant subsystems. Many of these

subsystems are related to metabolic functions. For

example, lactate fermentation had many more hits in

the the Crohn’s disease data sets in comparison to

the healthy data sets. In the next section, we see that

this function is found in Lactobacillus. According to

(Beiko, Harlow, Ragan, 2005) metabolic genes may

be preferentially transferred horizontally between

bacterial types, allowing for adaptation to novel

energy sources.

Here we will give a brief description of each of

the subsystems that were significantly different in

the Crohn’s and healthy gut microbiome data sets.

 Alginate metabolism: Alginate is a

polysaccharide found in the cell walls of brown

algae and capsules of soil bacteria (Draget,

Smidsrød, Skjåk‐Bræk, 2000). Alginates are

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

108

Figure 4: Transposase associated subsystem differences between Crohn’s disease and healthy gut microbiome samples.

Median gene hits to each significant subsystem are shown for Crohn’s (red) and healthy (blue) samples.

commonly added to food as a thickener or

stabilizer (Aliste, Vieira, Del Mastro, 2000).

 Beta-Glucoside metabolism: A glucoside is a

glucose sugar molecule(s) that is attached to

another non-sugar functional group. They are

commonly found in plant material (Hollman et

al, 1999).

 Cellulosome: Cellulosomes are large enzymes

capable of digesting cellulose, a polysaccharide

that is the major component of cell walls (Doi et

al, 2003).

 Chorismate synthesis: Chorismate is a precursor

in the synthesis of aromatic amino acids in most

prokaryotes (Hopper, Rao, 2013).

 Conjugative transposon, Bacteroides: This is a

DNA element that excises itself, forms a

circular intermediate, and then reintegrates itself

into the same genome or transfers between cells

to a different genome (Salyers et al, 1995).

 Fermentations: Lactate: In bacteria, lactate is

produced by the fermentation of carbohydrates

Garvie, 1980).

 Fructooligosaccharides(FOS) and Raffinose

Utilization: FOS and Raffinose are

nondigestible oligosaccharides that can be

found in plants (Barrangou et al, 2006).

 Heme hemin uptake and utilization systems in

Gram Positives: Iron is often an enzyme

cofactor in many prokaryotic biological

processes. Many pathogens obtain iron through

the uptake of heme (Anzaldi, Skaar, 2010).

 Iron acquisition in Vibrio: This includes Ton-B

dependent transport of heme in Gram Negatives

(

Stojiljkovic, Hantke, 1992).

 Maltose and Maltodextrin Utilization: Maltose

is a disaccharide that can be formed from the

digestion of starch (Nichols et al, 2003).

Maltodextrin is a modified starch that is

commonly used as a food additive (Nickerson,

McDonald, 2012).

 NAD and NADH cofactor biosynthesis global:

The pyridine nucleotide redox pair

NAD/NADH is an essential cofactor for all

living organisms (Kurnasov et al, 2003).

 Phage integration and excision: Bacteriophages

are capable of integrating their DNA into a host

genome (Paatero et al, 2008).

 Proteasome bacterial: These perform protein

degradation in bacteria to maintain homeostasis

(Butler et al, 2006.

 tRNA aminoacylation: Aminoacyl-tRNA

synthetases catalyse the addition of amino acid

to a transfer RNA (Park, Schimmel, Kim,

2008).

0.0001

0.001

0.01

0.1

100

‐

(

)

Median

um ber

Hits

per

Thousand

odes

Transposase Associated Subsystem Differences

between Crohn's Disease and Healthy Gut

Microbiome Samples

Healthy

Crohns

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes

109

Figure 5: Distribution of Genera for Significant Subsystems. (A) shows the genera distribution for subsystems with a

greater number of hits in Crohn’s samples and (B) shows the genera distribution for subsystems with a greater number of

hits in the healthy samples.

Fig. 5 displays the genera distribution for each of the

significant subsystems. In the healthy samples, most

of the transposase-associated subsystems were found

in Bacteroides. The transposase-associated

subsystems that were found in the Crohn’s disease

samples were more distributed across bacterial

genera. For an example, the subsystem Fermenta-

tion:Lactate was found mostly in Lactobacillus. The

subsystem Fructooligosaccharides (FOS) and

Raffinose utilization was found commonly in

Bifidobacterium. Species of Bifidobacterium are

known to be capable of fermenting

Fructooligosaccharides (Rossi et al, 2005).

Finally, Table 1 displays the subsystems that

were not found to be significant between the

Crohn’s disease and healthy gut microbiome data

sets. A few of these subsystems, such as At1g69340,

appear to come from Arabidopsis thaliana. We

would like to note that the SEED subsystem contains

prokaryote orthologs of Arabidopsis thaliana

(Gerdes et al, 2011).

3.2 in-Depth Analysis of Pathologically

Relevant Subsystems

In this section, an in-depth summary of the Heme,

Hemin Uptake and Utilization, Maltose and

Maltodextrin Utilization, and Beta-Glucoside

Metabolism subsystems is presented.

The subsystem Heme, Hemin, Uptake and

Utilization are found most commonly in

Streptococcus and Lactobacillus as well as in

unknown genera. Iron is a cofactor of many enzymes

in living systems. Its uptake is essential for

pathogenic infection (Anzaldi, Skaar, 2010). In

mammalian systems, the most abundant form of iron

is bound in heme. Thus many bacterial systems have

developed for the uptake of heme. The availability

of host heme to bacterial pathogens can greatly

increase the difficulty of clearing an infection

(Contreras et al, 2014).

The subsystem Maltose and Maltodextrin is

found in Bifidobacterium, Lactobacillus, Collinsella,

and Enterococcus. Some studies have found that the

food additive Maltodextrin can alter the bacterial

homeostasis of the intestine (Nickerson, Chanin,

McDonald, 2015). Maltodextrin has also been found

to increase adhesion of Crohn’s disease associated

ecoli (Nickerson, McDonald, 2012) and promote

Salmonella mucosal colonization and survival

(Nickerson et al, 2014).

According to Fig. 5 beta-glucoside subsystem

functions are found most commonly in

Ruminococcus, Clostridium, and Lactobacillus

genera. A large portion of the beta-glucoside

functions occurs in unknown genera. Glucosidases

are capable of producing metabolites that are

implicated in colon cancer (Sobhani et al, 2013).

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

110

Table 1: Subsystems that were not significantly different between Crohn’s disease and healthy gut microbiome samples.

For each subsystem the median number of hits per thousand nodes is shown for the Crohns disease (C) and healthy (H) gut

microbiome samples.

Subsystem C. H. Subsystem C. H.

16S rRNA modification within P site of

ribosome

1 1 Entner-Doudoroff Pathway 3 2

5-FCL-like protein 9 7 Folate Biosynthesis 5 2.5

ABC transporter oligopeptide (TC

3.A.1.5.1)

1 0

Galactosylceramide and Sulfatide

metabolism

9 9

Alanine biosynthesis 7 0 Glycogen metabolism 6 2.5

Alkanesulfonate assimilation 0 4 Group II intron-associated genes 6 3.5

Ammonia assimilation 7 3.5 Heat shock dnaK gene cluster extended 3 6.5

Aromatic conversions and predicted Co2

transporter cluster

5 1

High affinity phosphate transporter and

control of PHO regulon

10 1

At1g69340 At2g40600 2 3 Histidine Biosynthesis 4 0.5

At3g21300 8 0.5 L-fucose utilization 0 2.5

At5g63420 7 1 L-rhamnose utilization 2 3

ATP-dependent Nuclease 5 0

Lactose and Galactose Uptake and

Utilization

9 2

Bacterial Cell Division 9 3.5 Mannose Metabolism 3 1.5

Bacterial Cytoskeleton 9 1 Methionine Biosynthesis 8 5

Beta-lactamase 1 3 Multidrug Resistance Efflux Pumps 10 5

Beta-lactamase cluster in Streptococcus 2 2 Oxidative stress 2 0.5

C jejuni colonization of chick caeca Potassium homeostasis 8 3.5

Calvin-Benson cycle 6 2 Purine conversions 15 2.5

Cell Division Subsystem including

YidCD

5 1 Restriction-Modification System 15 17.5

Cell division-ribosomal stress proteins

cluster

3 1 Ribonucleotide reduction 4 0.5

Chitin and N-acetylglucosamine

utilization

5 5.5 Ribosome LSU bacterial 10 1.5

D-Galacturonate and D-Glucuronate

Utilization

4 7.5 Sialic Acid Metabolism 4 0

De Novo Pyrimidine Synthesis 4 1 Streptococcal group antigen operons 5 0

DMT transporter 2 1

Tetracycline resistance, ribosome

protection type

3 0

DNA Repair Base Excision 7 2.5 Transcription factors bacterial 2 2.5

DNA repair, bacterial 13 4 tRNA modification Archaea 2 2

DNA repair, bacterial RecFOR pathway 0 4.5 Universal GTPases 5 2

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes

111

Fig. 6 provides a more granular view of the level

3 subsystems classification counts for Heme, Hemin

Uptake and Utilization, Maltose and Maltodextrin

Utilization, and Beta-Glucoside Metabolism.

Figure 6: Level 3 subsystem counts for Heme, Hemin

Uptake and Utilization Systems, Maltose and Maltodextrin

Utilization., and Beta-Glucoside Metabolism.

4 CONCLUSIONS

In conclusion, this paper presents an extension of

previous work using graph mining to explore

transposase associated genetic elements in Crohn’s

disease and health individuals. Graph mining is

shown to be a powerful tool that can be applied to

extract biologically relevant features of the input

dataset.

Flat fasta files of contigs loose information, as

relationships between the contigs are lost in flat

files. The hybrid graph maintains all of the global

and local relationships within its structures. This is

especially important for difficult to assemble regions

such as repetitive regions, including transposes.

Many assemblers produce fragmented results in

these areas, as it is difficult to place repetitive

regions. In contrast, the hybrid graph maintains all

possible relationships allowing for extraction of

information even from difficult to assemble areas.

Results demonstrate several important

transposase associated genetic features that are more

prevalent in Crohn’s disease gut microbiome

samples than healthy samples. Several of these

functions have been implicated in previous research

as biologically relevant to Crohn’s disease and

associated conditions. The results in this paper

provide insights into gene features that may allow

gut bacteria to adapt to their ecological niches.

ACKNOWLEDGEMENTS

Thank you to the UNO Bioinformatics lab for their

valuable input and discussion on this paper.

REFERENCES

Aliste, A. J., Vieira, F. F., Del Mastro. N. L. Radiation

effects on agar, alginates and carrageenan to be used

as food additives. Radiation Physics and Chemistry

57.3 (2000): 305-308.

Anzaldi, L. L., Skaar, E. P. Overcoming the heme

paradox: heme toxicity and tolerance in bacterial

pathogens. Infection and immunity 78.12 (2010):

4977-4989.

Barrangou, R., et al. Global analysis of carbohydrate

utilization by Lactobacillus acidophilus using cDNA

microarrays. Proceedings of the National Academy of

Sciences of the United States of America 103.10

(2006): 3816-3821.

Beiko, R. G., Harlow T. J., Ragan, M. A. Highways of

gene sharing in prokaryotes. Proceedings of the

National Academy of Sciences of the United States of

Sortase

LPXTG

specific

Two‐component

response

regulator

colocalized

with

HrtAB

transporter,

Two‐component

sensor

kinase

SA14‐24,

Zn‐dependent

hydrolase

(beta‐

lactamase

superfamily),

Heme,

Hemin

ake

and

U. liza. on

ems

ta‐Glucosid

tabolism

6‐phospho‐beta‐

glucosidase,

Beta‐glucosidase

Beta‐glucoside

bgl

operon

an: terminator,

BglG

family,

Outer

surface

protein

unknown

func: on,

cellobiose

operon,

PTS

system,

beta‐

glucoside‐specific

IIB

component

PTS

system,

cellobiose‐

specific

IIA

component,

PTS

system,

cellobiose‐

specific

IIB

component,

PTS

system,

cellobiose‐specific

IIC

component,

Tran

crip: onal

an: terminator

lichenan

operon,

BglG

family,

Maltose

and

Maltodextrin

U/ liza/ on

Alpha‐amylase,

Beta‐

phosphoglucomutas e,

Maltodextrin

glucosidase

Maltose‐6'‐

phosphate

glucosidase

Maltose/maltodextrin

ABC

transporter,

permease

protein

MalF,

Maltose/maltodextrin

ABC

transporter,

permease

protein

MalG,

Maltose/maltodextrin

transport

ATP‐binding

protein

MalK,

Maltose

operon

transcripHonal

repressor

MalR,

LacI

family,

Maltose

phosphorylase

Neopullulanase

Oligo‐1,6‐glucosidase

PTS

system,

maltose

and

glucose‐specific

IIC

component

(c),

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

112

America 102.40 (2005): 14332-14337.

Buchfink, B., Xie, C., Huson. D. H., Fast and sensitive

protein alignment using DIAMOND. Nature methods

12.1 (2015): 59-60.

Butler, S. M., et al. Selfcompartmentalized bacterial

proteases and pathogenesis. Molecular microbiology

60.3 (2006): 553-562.

Contreras, H., et al. Heme uptake in bacterial pathogens.

Current opinion in chemical biology 19 (2014): 34-41.

David, L. A., et al. Diet rapidly and reproducibly alters the

human gut microbiome. Nature 505.7484 (2014): 559-

563.

Doi, R. H., et al. Cellulosomes from mesophilic bacteria.

Journal of bacteriology 185.20 (2003): 5907-5914.

Draget, Ingar K., Smidsrød, O., SkjåkBræk, G.

Alginates from algae. Biopolymers Online (2005).

Foster, A., McVey Neufeld. K. A., Gut–brain axis: how

the microbiome influences anxiety and depression.

Trends in neurosciences 36.5 (2013): 305-312.

Garvie, E. I. Bacterial lactate dehydrogenases.

Microbiological reviews 44.1 (1980): 106.

Gerdes, S., et al. Synergistic use of plant-prokaryote

comparative genomics for functional annotations.

BMC Genomics 12.S2. (2011): doi: 10.1186/1471-

2164-12-S1-S2

Ghosh, T. S., et al. In silico analysis of antibiotic

resistance genes in the gut microflora of individuals

from diverse geographies and age-groups. PLoS One

8.12 (2013): e83823.

Hollman, P.C. H, et al. The sugar moiety is a major

determinant of the absorption of dietary flavonoid

glycosides in man. Free radical research 31.6 (1999):

569-573.

Hopper, P. P. W., Rao. R., Comprehensive database of

Chorismate synthase enzyme from shikimate pathway

in pathogenic bacteria. BMC Pharmacology and

Toxicology 14.1 (2013): 1.

Kamada, N., et al. Control of pathogens and pathobionts

by the gut microbiota. Nature Immunology 14.7

(2013): 685-690.

Karypis, G., Kumar, V., A fast and high quality multilevel

scheme for partitioning irregular graphs. SIAM

Journal on scientific Computing 20.1 (1998): 359-392.

Kurnasov, O., et al. NAD biosynthesis: identification of

the tryptophan to quinolinate pathway in bacteria.

Chemistry & biology 10.12 (2003): 1195-1204.

LeBlanc, Jean Guy, et al. "Bacteria as vitamin suppliers to

their host: a gut microbiota perspective." Current

opinion in biotechnology 24.2 (2013): 160-168.

Leplae, R., et al. ACLAME: a CLAssification of Mobile

genetic Elements. Nucleic acids research 32.suppl 1

(2004): D45-D49.

Manichanh, C., et al. The gut microbiota in IBD. Nature

Reviews Gastroenterology and Hepatology 9.10

(2012): 599-608.

Meyer, F., Overbeek, R., Rodriguez., A., FIGfams: yet

another set of protein families. Nucleic acids research

37.20 (2009): 6643-6654.

Miller, J. R., Koren, S., Sutton. G., Assembly algorithms

for next-generation sequencing data. Genomics 95.6

(2010): 315-327.

Nagarajan, Niranjan, and Mihai Pop. "Sequence assembly

demystified." Nature Reviews Genetics 14.3 (2013):

157-167.

Nichols, B. L., et al. The maltase-glucoamylase gene:

common ancestry to sucrase-isomaltase with comple-

mentary starch digestion activities. Proceedings of the

National Academy of Sciences 100.3 (2003): 1432-

1437.

Nickerson, K. P., McDonald. C. Crohn's disease-

associated adherent-invasive Escherichia coli adhesion

is enhanced by exposure to the ubiquitous dietary

polysaccharide maltodextrin. PLoS One7.12 (2012):

e52132.

Nickerson, K. P., et al. The dietary polysaccharide

maltodextrin promotes Salmonella survival and

mucosal colonization in mice. PloS one9.7 (2014):

e101789.

Nickerson, K.P., Chanin, R. McDonald., C., Deregulation

of intestinal anti-microbial defense by the dietary

additive, maltodextrin. Gut microbe 6.1 (2015): 78-83.

O'sullivan, O., et al. Comparative genomics of lactic acid

bacteria reveals a niche-specific gene set. BMC

microbiology 9.1 (2009): 1.

Ochman, H., Lawrence, J.G., and Groisman, E.A. Lateral

gene transfer and the nature of bacterial innovation.

Nature 405.6784 (2000): 299-304.

Overbeek, R., et al. The SEED and the Rapid Annotation

of microbial genomes using Subsystems Technology

(RAST). Nucleic acids research 42.D1 (2014): D206-

D214.

Paatero, A. O., et al. Bacteriophage Mu integration in

yeast and mammalian genomes. Nucleic acids

research 36.22 (2008): e148-e148.

Park, S.G., Schimmel, P., Kim., S. Aminoacyl tRNA

synthetases and their connections to disease.

Proceedings of the National Academy of Sciences

105.32 (2008): 11043-11049.

Rho, M., Tang, H., Ye, Y., FragGeneScan: predicting

genes in short and error-prone reads. Nucleic acids

research 38.20 (2010): e191-e191.

Rhodes, J. M., and B. J. Campbell. Inflammation and

colorectal cancer: IBD-associated and sporadic cancer

compared. Trends in molecular medicine 8.1 (2002):

10-16.

Rossi, M., et al. Fermentation of fructooligosaccharides

and inulin by bifidobacteria: a comparative study of

pure and fecal cultures. Applied and environmental

microbiology 71.10 (2005): 6150-6158.

Roy, A., Lichtiger, S. Clostridium difficile Infection: A

Rarity in Patients Receiving Chronic Antibiotic

Treatment for Crohn’s Disease. Inflammatory bowel

diseases 22.3 (2016): 648.

Salyers, A. A., et al. Conjugative transposons: an unusual

and diverse set of integrated gene transfer elements.

Microbiological reviews 59.4 (1995): 579-590.

Shannon, C. E., A mathematical theory of communication.

ACM SIGMOBILE Mobile Computing and

Communications Review 5.1 (2001): 3-55.

Sobhani, I., et al. Microbial dysbiosis and colon

Graph-based Analysis of Genetic Features Associated with Mobile Elements in Crohnâ

Zs Disease and Healthy Gut Microbiomes

113

carcinogenesis: could colon cancer be considered a

bacteria-related disease?. Therapeutic advances in

gastroenterology 6.3 (2013): 215-229.

Sommer, F., Bäckhed, F. The gut microbiota—masters of

host development and physiology. Nature Reviews

Microbiology 11.4 (2013): 227-238.

Stecher, B., Maier, L., Hardt. WD,. ‘Blooming' in the gut:

how dysbiosis might contribute to pathogen evolution.

Nature Reviews Microbiology 11.4 (2013): 277-284.

Stojiljkovic, I., Hantke. K. Hemin uptake system of

Yersinia enterocolitica: similarities with other TonB-

dependent systems in gram-negative bacteria. The

EMBO journal 11.12 (1992): 4359.

Turnbaugh, P. J., et al. An obesity-associated gut

microbiome with increased capacity for energy

harvest. Nature 444.7122 (2006): 1027-131.

Warnke-Sommer, J., and Ali, H. "Graph mining for next

generation sequencing: leveraging the assembly graph

for biological insights." BMC genomics 17.1 (2016): 1.

Warnke, J., Ali H,. Focus: a new multilayer graph model

for short read analysis and extraction of biologically

relevant features. Proceedings of the 5th ACM

Conference on Bioinformatics, Computational

Biology, and Health Informatics. ACM, 2014.

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

114