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324 Bioinformatics of Autoimmune Diseases
for assembly, MetaBAT2 for binning, Prodigal for gene prediction, DIAMOND for functional
annotation, and MinPath for pathway inference. The pipeline maintains modularity, processes each
sample independently, and compiles outputs into comparative matrices used in statistical and visual
analysis. It builds on the same dataset and metadata format used in the earlier classication-based
approach, maintaining consistency across analytical strategies.
def ensure_dir(path):
os.makedirs(path, exist_ok=True)
The ensure _ dir function is a utility that checks whether a given directory exists, and if
not, creates it. This is used repeatedly throughout the pipeline to ensure that intermediate and nal
results are stored in organized directories without raising errors due to missing paths. It has no output other than silently ensuring the le system is ready for the pipeline steps.
def load_metadata(meta_file):
df = pd.read_csv(meta_file)
df.columns = df.columns.str.strip()
df['runID'] = df['runID'].astype(str)
return df
The load _ metadata function reads the metadata CSV le into a pandas DataFrame. It
ensures that column names are stripped of excess whitespace and converts the runID column
to a string type to ensure consistent matching between FASTQ le names and metadata entries.
The output is a DataFrame containing information about each sample, including its experimental
condition and gender, which is later used for grouping, stratied analysis, and annotation of visual
plots.
def get_sample_pairs(raw_dir):
files = glob(os.path.join(raw_dir, "*.fastq.gz"))
samples = {}
for f in files:
base = os.path.basename(f)
sid = base.split("_")[0].split(".")[0]
if sid not in samples:
samples[sid] = [None, None]
if "_1" in base or "_R1" in base:
samples[sid][0] = f
elif "_2" in base or "_R2" in base:
samples[sid][1] = f
return samples
The get _ sam ple _ pairs function scans the raw FASTQ directory and identies matching
pairs of forward and reverse read les based on consistent naming conventions. It returns a dictionary where each key is a sample ID, and the value is a tuple containing paths to the forward and
reverse read les. This function enables the pipeline to iterate sample-wise through the data without
hardcoding le names, and its output is used to automate processing of all samples in a loop.
def run_fastqc(read1, read2, outdir):
cmd = f"fastqc -t {THREADS} -o {outdir} {read1} {read2}"
subprocess.run(cmd, shell=True, check=True)
The run _ fastqc function runs the FastQC tool on the input forward and reverse reads of a
sample. FastQC assesses the quality of sequencing reads by reporting metrics like per-base quality
scores, GC content, and sequence duplication levels. The output consists of an HTML report and a
325 Roles of Bacteria in Autoimmune Diseases
zipped summary le for each read le, stored in the quality control output directory. These results
are intended for quality assessment and troubleshooting before assembly begins.
def run_megahit(read1, read2, sample_id, outdir):
sample_out = os.path.join(outdir, sample_id)
cmd = f"megahit -1 {read1} -2 {read2} -o {sample_out} -t {THREADS}"
subprocess.run(cmd, shell=True, check=True)
return os.path.join(sample_out, "final.contigs.fa")
The ru n _ megahit function runs MEGAHIT to perform metagenomic assembly on the
paired-end reads of a sample. MEGAHIT is an assembler designed for short-read data from complex microbial communities. The function generates a directory containing all intermediate les
and returns the path to the assembled contigs le, usually named final.contigs.fa. This le
contains contiguous sequences reconstructed from overlapping reads and represents fragments
of microbial genomes found in the sample. These contigs are used in all subsequent steps of the
pipeline.
def index_contigs(contigs):
cmd = f"samtools faidx {contigs}"
subprocess.run(cmd, shell=True, check=True)
The index _ contigs function uses samtools faidx to index the contig le. This index
enables efcient access to specic contigs when mapping reads back to the assembly. There is no
direct output from this function, but the presence of an index le (.fa i) is necessary for alignment
tools to work correctly in downstream steps.
def run_bwa_mapping(contigs, read1, read2, bam_out):
index_cmd = f"bwa index {contigs}"
aln_cmd = (
f"bwa mem -t {THREADS} {contigs} {read1} {read2} | "
"samtools view -bS - | "
f"samtools sort -o {bam_out}"
)
index_bam = f"samtools index {bam_out}"
subprocess.run(index_cmd, shell=True, check=True)
subprocess.run(aln_cmd, shell=True, check=True)
subprocess.run(index_bam, shell=True, check=True)
The run_bwa _ mapping function runs BWA to align the original paired-end reads to the
assembled contigs. It creates a BWA index of the contigs, performs paired-end alignment, and sorts
and indexes the resulting BAM le. This BAM le reects how many reads map back to each contig
and is critical input for MetaBAT2 binning. The output of this function includes a .bam le and its
.bai index, which are saved in the binning directory.
def run_metabat(contigs, bam_file, bin_dir):
cmd = (
f"metabat2 "
f"-i {contigs} "
f"-a <(jgi_summarize_bam_contig_depths "
f"--outputDepth depth.txt "
f"{bam_file}) "
f"-o {os.path.join(bin_dir, 'bin')}"
)
subprocess.run(cmd, shell=True, executable='/bin/bash', check=True)
326 Bioinformatics of Autoimmune Diseases
The run _ metabat function uses MetaBAT2 to perform contig binning based on coverage
and sequence composition. MetaBAT2 groups contigs into bins, which ideally represent individual
microbial genomes or genome fragments. The function relies on a depth le calculated from the
BAM le to estimate coverage. The output consists of multiple bin les (bin.1.fa, bin.2.fa, etc.), each
containing a set of contigs likely to originate from the same organism. These bins are valuable for
MAG analysis and can be used to investigate strain-level diversity.
def run_minpath(annotated_file, sample_id, outdir):
input_file = os.path.join(outdir, f"{sample_id}_ko.list")
with open(annotated_file) as f_in, open(input_file, "w") as f_out:
for line in f_in:
parts = line.strip().split("\t")
if len(parts) >= 2:
f_out.write(f"{parts[1]}\n")
minpath_out = os.path.join(outdir, f"{sample_id}_minpath.out")
cmd = f"MinPath.py -any {input_file} -map KO -report {minpath_out}"
subprocess.run(cmd, shell=True, check=True)
return minpath_out
The r u n _ prodigal function runs Prodigal to predict protein-coding genes from the con-
tigs. Prodigal outputs a FASTA le of predicted amino acid sequences for ORFs, and this .faa
le is used for functional annotation. The quality and quantity of predicted proteins reect the
coding potential of the metagenome and are fundamental to downstream inference of microbial
function.
def run_diamond(faa, sample_id, outdir):
diamond_out = os.path.join(outdir, f"{sample_id}_diamond.tsv")
cmd = (
f"diamond blastp "
f"-d {DATABASE} "
f"-q {faa} "
f"-o {diamond_out} "
f"-f 6 "
f"-k 1 "
f"-p {THREADS}"
)
subprocess.run(cmd, shell=True, check=True)
return diamond_out
The run _ diamond function executes DIAMOND, a fast sequence aligner that maps the pre-
dicted protein sequences against a reference protein database such as UniProt or KEGG. It produces
a tab-delimited le with matches for each query protein to known functions. Each line includes
elds such as query ID, target function ID, alignment length, percent identity, and e-value. This le
is a key input for quantifying gene functions and reconstructing microbial metabolism.
def count_function_hits(tsv_file):
df = pd.read_csv(tsv_file, sep="\t", header=None)
df.columns = [
"query", "target", "pident", "length", "mismatch", "gapopen",
"qstart", "qend", "sstart", "send", "evalue", "bitscore"
]
return df["target"].value_counts().to_dict()
The count _ function _ hits function reads the DIAMOND output and counts the fre-
quency of each functional annotation (typically by KO or UniProt ID). It returns a dictionary where
327 Roles of Bacteria in Autoimmune Diseases
keys are function IDs and values are the number of genes in the sample that match each function.
This function is called for each sample, and its output is stored for matrix building.
def build_abundance_matrix(hit_dicts):
all_functions = set()
for hits in hit_dicts.values():
all_functions.update(hits.keys())
all_functions = sorted(all_functions)
rows = []
for sample, hits in hit_dicts.items():
row = [hits.get(func, 0) for func in all_functions]
rows.append(row)
df = pd.DataFrame(rows, index=hit_dicts.keys(), columns=all_functions).T
return df
The build _ abundance _ matrix function aggregates the function hit dictionaries across
all samples to create a single function-by-sample abundance matrix. This matrix contains the number of genes assigned to each function per sample and forms the basis for statistical testing and
multivariate analysis. It is output as a pandas DataFrame and also saved to a .ts v le.
def run_minpath(annotated_file, sample_id, outdir):
input_file = os.path.join(outdir, f"{sample_id}_ko.list")
with open(annotated_file) as f_in, open(input_file, "w") as f_out:
for line in f_in:
parts = line.strip().split("\t")
if len(parts) >= 2:
f_out.write(f"{parts[1]}\n")
minpath_out = os.path.join(outdir, f"{sample_id}_minpath.out")
cmd = f"MinPath.py -any {input_file} -map KO -report {minpath_out}"
subprocess.run(cmd, shell=True, check=True)
return minpath_out
The run _ minpath function reconstructs metabolic pathways from the list of predicted func-
tional annotations. It parses the DIAMOND output to extract unique function IDs (e.g., KOs), writes
them to a le, and runs MinPath to infer the minimal set of pathways consistent with the functional
annotations. The output is a report listing inferred pathways for each sample. This provides insight
into the biological processes active in the microbial community.
def plot_function_heatmap(df, metadata, outdir):
top = df.sum(axis=1).nlargest(25).index
heat = (
df.loc[top]
.T
.reset_index()
.merge(metadata, left_on="index", right_on="runID")
.set_index("runID")
)
sns.heatmap(heat[top], cmap="mako")
plt.title("Top 25 Functions")
plt.tight_layout()
plt.savefig(os.path.join(outdir, "function_heatmap.png"))
The plot _ function _ heatmap function visualizes the top 25 most abundant func-
tions using a clustered heatmap. It selects the most frequently observed functions across samples,
328 Bioinformatics of Autoimmune Diseases
transposes the matrix, merges it with metadata, and generates a heatmap using seaborn. The output
image helps identify functional signatures and groupings across samples.
def plot_pca(df, metadata, outdir):
comp = PCA(n_components=2).fit_transform(df.T)
pc_df = pd.DataFrame(comp, columns=["PC1", "PC2"])
pc_df["runID"] = df.columns
pc_df = pc_df.merge(metadata, on="runID")
sns.scatterplot(data=pc_df, x="PC1", y="PC2",
hue="condition", style="gender")
plt.title("PCA of Functional Profiles")
plt.tight_layout()
plt.savefig(os.path.join(outdir, "pca_functions.png"))
The plot _ pca function performs PCA on the function abundance matrix. It reduces the
matrix into two dimensions, merges the PCA scores with the metadata, and generates a scatter plot
where samples are colored by condition and shaped by gender. This allows users to assess functional
differences between sample groups and observe patterns of similarity or separation in the data.
def analyze_differential(df, metadata, outdir):
condition_map = metadata.set_index("runID")["condition"].to_dict()
ctrl = [c for c in df.columns if condition_map.get(c, "").lower() ==
"control"]
case = [c for c in df.columns if condition_map.get(c, "").lower() == "ms"]
results = []
for func in df.index:
stat, pval = ttest_ind(
df.loc[func, ctrl],
df.loc[func, case],
equal_var=False
)
results.append((func, pval))
df = pd.DataFrame(results, columns=["function", "p_value"])
df = df.sort_values("p_value")
output_path = os.path.join(outdir, "differential_functions.csv")
df.to_csv(output_path, index=False)
The analyze _ differential function performs Welch’s t-tests on the abundance values
of each function between two groups dened in the metadata (e.g., Control versus MS). It outputs
a CSV le listing each function and its p-value, ranked by statistical signicance. This analysis
identies candidate microbial functions that are enriched or depleted in one group relative to the
other.
The main function coordinates all of these processes in sequence. It loops over each sample to
apply quality control, assemble contigs, map reads, perform binning, predict genes, annotate proteins, and reconstruct pathways. It then compiles functional proles across all samples and produces
a suite of visualizations and tables that summarize the functional landscape of the metagenomic
dataset. By the end of its execution, the main() function has taken raw sequence data through a
complete genome-resolved functional analysis, producing interpretable and biologically meaningful outputs that can be used in downstream research.
9.4 SUMMARY
This chapter delves deeply into the multifaceted roles that bacteria play in the onset and progression of autoimmune diseases, combining biological theory, immunological mechanisms, and
329 Roles of Bacteria in Autoimmune Diseases
computational metagenomics. It starts by emphasizing the dual nature of bacteria: while some species promote health by aiding digestion, producing vitamins, and supporting immune homeostasis,
others contribute to disease through virulence factors that damage tissues and modulate immune
signaling. The immune system’s intricate balance is illustrated, where innate responses mediated by
TLRs and subsequent adaptive responses are meant to protect the host but can become pathological when misdirected. Mechanisms such as molecular mimicry, bystander activation, disruption of
epithelial barriers, altered immune cell differentiation, chronic immune stimulation, and epigenetic
modications are each explored in detail, offering a rich account of how microbial agents can provoke and sustain autoimmunity.
Molecular mimicry is shown as a key initiating mechanism, where structural similarities between
microbial and host proteins lead to cross-reactive immune responses. This is supported by genomic
and proteomic evidence linking microbial peptides to autoantigens in diseases such as MS and
T1D. Bystander activation describes a scenario in which non-specic inammatory environments,
particularly those created during bacterial infections or gut dysbiosis, awaken dormant autoreactive T cells. This is complemented by the concept of “leaky gut”, where barrier dysfunction allows
bacterial components like LPS to enter the bloodstream and act as chronic immune triggers. Such
disruptions not only initiate but also perpetuate immune activation, establishing a vicious cycle that
sustains autoimmunity.
Furthermore, the chapter highlights how microbiota-derived metabolites, especially SCFAs like
butyrate and propionate, inuence immune cell fate. These metabolites drive the balance between
regulatory T cells and pro-inammatory Th17 cells, a central axis in autoimmune pathology. A lack
of SCFA-producing bacteria shifts this balance toward inammation, providing a clear mechanistic
link between gut dysbiosis and diseases such as RA and inammatory bowel disease. The role of
epigenetic modulation is also introduced, where bacterial products can reprogram immune cells
through DNA methylation and histone modications, leading to long-term changes in gene expression and immune responsiveness.
The chapter then transitions into metagenomics, showcasing how these complex host–microbe
interactions can be studied using high-throughput sequencing technologies. Amplicon-based
metagenomics, particularly 16S rRNA sequencing, is presented as a cost-effective and widely
adopted method to study microbial diversity. The chapter includes a practical example using data
from MS patients and healthy individuals, processed through QIIME 2. The pipeline—from raw
data import and quality control to denoising, taxonomy assignment, and diversity analysis—is
meticulously described, highlighting each step’s purpose and output. Visualizations and statistical
assessments derived from these steps offer insight into microbial community structure and its correlation with disease status.
Shotgun metagenomics is introduced as a more comprehensive approach, capable of capturing both taxonomic and functional proles of microbial communities. The chapter presents two
Python-based pipelines: a classication-based approach using Kaiju and HUMAnN3, and an
assembly-based approach involving MEGAHIT, MetaBAT2, and DIAMOND. These workows
provide genome-level resolution and allow for metabolic pathway reconstruction through tools like
MinPath. Sample metadata and analysis outputs, such as heatmaps, PCA plots, and differential
abundance proles, further contextualize the biological relevance of the ndings. The chapter also
includes specic technical details about software installation, data structures, and QIIME 2 artifacts and visualizations, ensuring reproducibility and clarity.
Ultimately, the chapter presents a comprehensive synthesis of microbiology, immunology, and
bioinformatics. It underscores the importance of microbial ecology in human health and disease,
and how modern computational tools can unravel the complex, often hidden, relationships between
our immune system and the microbiome. It also positions metagenomics as a powerful framework not just for academic inquiry, but for clinical diagnostics and personalized medicine, where
microbiome-targeted interventions could one day modulate immune responses and treat autoimmune diseases.
330 Bioinformatics of Autoimmune Diseases
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Gene Therapy and
10
Autoimmune Diseases
10.1 GENE THERAPY AS A NEW APPROACH TO TREATING DISEASE
Gene therapy represents a transformative approach in modern medicine, offering the potential to
correct underlying genetic causes of disease rather than merely treating symptoms. Unlike traditional pharmacologic treatments that often require lifelong administration and may carry systemic
side effects, gene therapy aims for lasting effects by directly modifying the genetic material within
a patient’s cells. This strategy is particularly compelling for autoimmune diseases, where dysregulated gene expression or immune cell dysfunction drives chronic, self-directed inammation. By
delivering functional genes, silencing harmful ones, or editing gene sequences altogether, gene
therapy holds the promise of reprogramming the immune system, restoring immune tolerance, and
halting disease progression at its molecular roots.
In the laboratory, the process begins with identifying a suitable therapeutic gene or genetic target. This selection is guided by insights from whole-genome sequencing (WGS), transcriptomic
proling, or epigenetic mapping, which reveal aberrant pathways and candidate genes contributing
to autoimmunity. For instance, in systemic lupus erythematosus (SLE), gene therapy efforts may
target the overactivation of interferon-stimulated genes, while in type 1 diabetes (T1D), correcting
deciencies in forkhead box P3 (FOXP3) expression in regulatory T cells is a promising strategy.
Depending on the therapeutic goal, one might choose among several gene therapy modalities: gene
augmentation, gene silencing, or gene editing.
10.1.1 GENE AUGMENTATION IN GENE THERAPY
Gene augmentation therapy involves introducing a functional copy of a gene into a patient’s cells
to supplement or replace a defective or missing gene responsible for disease. The process begins
with identifying the genetic defect underlying the autoimmune condition through techniques such
as WGS or transcriptome proling. This information helps in selecting a candidate therapeutic gene
that can restore proper cellular function. For example, in autoimmune polyendocrine syndrome type
1 (APS-1), caused by mutations in the AIRE gene, restoring the expression of a functional AIRE
gene can help reestablish central immune tolerance.
Once the therapeutic gene is selected, it is cloned into an appropriate expression vector, typically
a viral backbone engineered to carry human genes safely. Lentiviral and adeno-associated viral
(AAV) vectors are commonly used because of their efciency and relatively low immunogenicity.
The therapeutic gene is placed under the control of a promoter sequence that regulates when and
where the gene is expressed. In autoimmune diseases, the choice of promoter may be critical; for
instance, tissue-specic promoters can restrict gene expression to immune cells, minimizing offtarget effects and improving therapeutic precision.
The next step involves the production and purication of the viral vectors carrying the therapeutic gene. This is achieved by co-transfecting helper plasmids into packaging cell lines, allowing the
production of viral particles that contain the therapeutic construct but lack the ability to replicate.
These vectors are collected, puried, and tested for concentration, purity, and transduction efciency. Simultaneously, the target cells are harvested from the patient, which may include hematopoietic stem cells, T lymphocytes, or other immune-relevant cells, depending on the disease. These
cells are isolated through leukapheresis and further enriched using magnetic or ow-based sorting.
331 D OI : 10.120 1/ 97810 0368 5432-10
332 Bioinformatics of Autoimmune Diseases
FIGURE 10.1 AAV-mediated gene augmentation.
In an ex vivo approach, the patient’s cells are transduced with the viral vector in a sterile, controlled laboratory environment. The cells are activated with specic cytokines to enhance transduction efciency, and the viral particles are introduced. The virus delivers the gene into the
nucleus, where it integrates into the genome or remains as an episome, depending on the vector type
(Figure 10.1). Following transduction, the cells are cultured, expanded, and tested for transgene
expression, viability, and absence of contamination. Quality control tests, including ow cytometry
and PCR, ensure the therapeutic gene is properly expressed without harmful effects.
After these validation steps, the modied cells are infused back into the patient through intravenous injection. The goal is for these cells to engraft, survive, and express the therapeutic gene in
vivo, thus restoring the defective immune function. In some autoimmune diseases, this can lead to
the long-term restoration of immune regulation, reduction in autoantibody production, or suppression of autoreactive lymphocytes. Continuous monitoring is essential post-therapy to evaluate gene
expression, immune response, and clinical outcomes, ensuring that the augmentation is both safe
and effective.
As of now, there are no Food and Drug Administration (FDA) approved gene augmentation
therapies specically for the treatment of autoimmune diseases. While gene therapy has made signicant strides in the treatment of monogenic disorders such as spinal muscular atrophy and certain
types of inherited blindness, its application in autoimmune diseases remains largely in the preclinical or early clinical trial phases. The complexity of autoimmune conditions, which often involve
multifactorial genetic and environmental interactions, presents additional challenges for gene augmentation approaches. However, ongoing research and early-phase trials are exploring the feasibility of using gene therapy to restore immune tolerance, modulate cytokine expression, or enhance
regulatory T-cell function in conditions such as T1D, multiple sclerosis (MS), and SLE, signaling
promising directions for future FDA approval.
10.1.2 GENE SILENCING IN GENE THERAPY
Gene silencing in gene therapy is a technique designed to reduce or completely shut down the
expression of specic genes that are either mutated or overactive and contribute to disease. In the
context of autoimmune diseases, certain genes that regulate inammatory pathways or immune
333 Gene Therapy and Autoimmune Diseases
activation can become dysregulated, leading to chronic immune responses against the body’s own
tissues. Gene silencing offers a way to therapeutically dampen these responses by targeting key
genes involved in cytokine production, antigen presentation, or immune cell activation. For instance,
silencing the expression of pro-inammatory cytokines such as tumor necrosis factor-alpha (TNFα) or IL-17 has been explored in diseases like rheumatoid arthritis (RA) and psoriasis, where these
molecules play central roles in disease progression.
The process of gene silencing typically begins with the identication of a gene whose overexpression contributes to the autoimmune pathology. Bioinformatic analyses and transcriptomic
proling help pinpoint such candidate genes. Once a target gene is selected, a silencing strategy is
developed using molecular tools such as small interfering Ribonucleic acid (RNA) or siRNA, short
hairpin RNA (shRNA), antisense oligonucleotides (ASOs), or CRISPR interference (CRISPRi).
siRNAs and shRNAs work by leveraging the RNA-induced silencing complex (RISC) within the
cell, which binds to the target messenger RNA (mRNA) and promotes its degradation, preventing
the production of the corresponding protein. ASOs, on the other hand, are short synthetic strands of
nucleotides that bind to the target mRNA and block its translation or promote degradation through
RNase H activity (Figure 10.2).
Delivery of the gene silencing agents is a crucial step that determines the success of the therapy.
These molecules can be introduced into cells either ex vivo or in vivo, depending on the disease
model and the target tissue. Viral vectors such as lentiviruses or adeno-associated viruses (AAVs)
are commonly used to deliver shRNAs or CRISPRi constructs, ensuring stable and long-term gene
repression. Non-viral methods such as lipid nanoparticles and electroporation are often preferred
for siRNA or ASO delivery due to their lower immunogenicity and ease of use. For example, lipidbased delivery systems have been used to introduce siRNA targeting STAT3 in T cells, a transcription factor involved in multiple autoimmune responses.
After delivery, the gene silencing constructs enter the target cells and begin interacting with
their mRNA targets. siRNAs and shRNAs guide the RISC complex to degrade the target mRNA,
effectively silencing gene expression. In the case of CRISPRi, a catalytically inactive Cas9 protein
(dCas9) is fused to a repressor domain and guided to the promoter region of the target gene by a
specic gRNA, where it blocks transcription initiation without cutting the DNA. The effectiveness
of silencing is then monitored using molecular techniques such as quantitative RT-polymerase chain
reaction (PCR) and Western blotting to assess reductions in mRNA and protein levels. Successful
gene silencing results in diminished expression of the pathogenic gene, potentially reducing inammation and autoimmunity without permanently altering the genome.
Gene silencing offers a reversible, tunable, and highly specic method for controlling gene
expression, which is especially advantageous in autoimmune diseases characterized by uctuating
FIGURE 10.2 Gene silencing.
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