Host-Pathogen Interactions in K. pneumoniae Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population

Abstract

Background: Klebsiella pneumoniae (K. pneumoniae) urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of K. pneumoniae. Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with K. pneumoniae urinary tract infections through genome-wide association studies (GWAS). Methods: Genotyping data are obtained from participants with a history of urinary tract infections enrolled at the Tri-Service General Hospital as part of the Taiwan Precision Medicine Initiative (TPMI). A case-control study employing GWAS is designed to detect potential susceptibility single-nucleotide polymorphisms (SNPs) in patients with K. pneumoniae-related urinary tract infections. The associated genes are determined using a genome browser, and their expression profiles are validated via the GTEx database. The GO, Reactome, DisGeNET, and MalaCards databases are also consulted to determine further connections between biological functions, molecular pathways, and associated diseases between these genes. Results: The results identified 11 genetic variants with higher odds ratios compared to controls. These variants are implicated in processes such as adhesion, protein depolymerization, Ca²⁺-activated potassium channels, SUMOylation, and protein ubiquitination, which could potentially influence the host immune response. Conclusions: This study implies that certain risk variants may be linked to K. pneumoniae infections by affecting diverse molecular functions that can potentially impact host immunity. Additional research and follow-up studies are necessary to elucidate the influence of these risk variants on infectious diseases and develop targeted interventions for mitigating the spread of K. pneumoniae urinary tract infections.

1. Introduction

Urinary tract infection (UTI) is the most common bacterial infection worldwide. It is also an important issue in hospitalized patients because of increased antibiotic resistance and increased morbidity and mortality in immunocompromised patients or those with cancer [1]. In both ambulatory and hospitalized patients, K. pneumonia is the second most common bacterial pathogen associated with UTIs around the world, and the frequency of K. pneumoniae infection has increased in hospitals or nursing homes [2]. Although K. pneumonia is considered an opportunistic pathogen, several specific capsular serotypes of K. pneumonia with increased production of capsule polysaccharide are considered to be hypervirulent K. pneumonia. In addition to the increased production of capsule polysaccharide, which helps the bacteria resist phagocytosis, it is also believed that these hypervirulent K. pneumonia may have an impact on host immune reactions and cause invasive infections [3]. They are also capable of hydrolyzing several kinds of antibiotics, especially carbapenems, which are so-called Klebsiella pneumoniae carbapenemase-producers, making them a threat to vulnerable patients due to their resistance to multiple antibiotics [4] and causing considerable healthcare costs [2].

To further understand the possible risk factors between K. pneumonia and the hosts, we use genome-wide association studies (GWAS) in order to determine possible common factors in the genomes of patients with UTIs caused by K. pneumonia.

2. Materials and Methods

2.1. Study Participants and Ethical Approval

All participants in this study were recruited from the Tri-Service General Hospital (Taipei, Taiwan; TSGH) to join the Taiwan Precision Medicine Initiative (TPMI) [5]. The TPMI is held by Academia Sinica (Taipei, Taiwan) in partnership with 16 top medical centers in Taiwan and aims to establish a database consisting of comprehensive clinical data and the genetic profiles of one million Taiwanese Han population participants. Participants were recruited from medical centers and genotyped using Academia Sinica. The protocol of this study was reviewed and approved by the Institutional Review Board of the Tri-Service General Hospital (NO.: 2-108-05-038).

The profiles of all patients included in our study are detailed in

Table 1. Sample group information for GWAS analysis.

	Case Group (n = 62)	Control Group (n = 1763)	p Value
K. pneumonia	62	0	-
Other pathogens	0	1763	-
Sex
Male, no, (%)	18 (29.0%)	394 (22.3%)	0.216
Female, no, (%)	44 (71.0%)	1369 (77.7%)
Age, median (SD), years	62.5 ± 16.52	61 ± 18.45
<20, no (%)	-	15 (0.9%)	0.466
20–30, no, (%)	3 (4.8%)	161 (9.1%)	0.245
31–50, no, (%)	9 (14.5%)	387 (22.0%)	0.163
51–70, no, (%)	29 (46.8%)	710 (40.3%)	0.305
>70, no, (%)	21 (33.9%)	490 (27.8%)	0.295
Medical Hx
w/o * any Hx, no, (%)	16 (25.8%)	444 (25.2%)	0.912
Diabetes mellitus, no, (%)	24 (38.7%)	659 (37.4%)	0.832
Malignancy, no, (%)	7 (11.3%)	195 (11.1%)	0.955
Chronic kidney disease, n, (%)	8 (12.9%)	208 (11.8%)	0.791
Autoimmune disease, no, (%)	6 (9.7%)	165 (9.4%)	0.933
Urinary tract stone, no, (%)	5 (8.1%)	139 (7.9%)	0.959
Chronic virus infection, no, (%)	5 (8.1%)	137 (7.8%)	0.932

* w/o: without.

. Additionally, we have listed several underlying diseases that are considered to pose a vulnerability to UTI, including diabetes mellitus, malignancy, chronic kidney disease, and a history of urinary tract stones. These medical histories were documented at the time of enrollment in the TPMI project for each patient.

2.2. Pathogen Identification in a Patient with UTI

Urine samples for bacterial culture were collected at the same time that the patients were transferred to our hospital because of UTI-related symptoms. All urine culture results were confirmed by MALDI-TOF with VITEK^® MS (bioMérieux, Marcy-l’Étoile, France).

2.3. Genotyping

Genotyping of TPMI participants was performed as follows: First, approximately 3 mL of peripheral blood per participant was collected into EDTA vacutainers. Genomic DNA was extracted from blood cells using the QIAsymphonyTM SP Stander protocol (Qiagen, Hilden, Germany). Next, the genomic DNA was characterized by its variants under a customized SNP array called the Axiom Genome-Wide TPM plate, which was developed by Academia Sinica (Taipei, Taiwan) and Thermo Fisher Scientific Inc. (Waltham, MA, USA).

2.4. GWAS Analysis

The steps of the GWAS are presented in Figure 1. A total of 1860 patients with UTI were selected from among the TSGH TPMI participants. After removing some unqualified samples, 1825 patients, including 62 UTIs with K. pneumoniae, were defined as case groups. The remaining 1763 patients with UTIs infected with other pathogens, such as E. coli, were included in the control group (Table 1). Genotyping data of these UTI patients from TPM array results were first filtered out by low typing call rate SNP (<80%) and then applied to association analysis by chi-squared test (case group vs. control) using PLINK 1.9 (https://zzz.bwh.harvard.edu/plink/ (accessed on 16 January 2023)) software [6]. Variants with low quality (minor allele frequency less than 0.05 and Hardy-Weinberg equilibrium less than 1 × 10⁻⁶) were removed using PLINK, and highly significant p-values (less than 1 × 10⁻⁵) were selected for further analysis. For linkage disequilibrium (LD) analysis, the GWAS results were loaded onto LocusZoom [7] to observe the LD relationship of each variant.

2.5. Variant Annotations

For variant annotations, genes were identified using the RefSeq Database (https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/refseq/ (accessed on 16 January 2023)) based on wANNOVAR (https://wannovar.wglab.org/ (accessed on 16 January 2023)) [8], and to compare allele frequency in other race populations, the public domain databases 1000 genome [9], Genome Aggregation Database (gnomAD) [10], and Taiwan BioBank (https://taiwanview.twbiobank.org.tw/index (accessed on 17 January 2023)) were applied, respectively. Gene functional characterization was performed using Gene Ontology [11], and related pathways were searched in the Reactome pathway database [12] using Enrichr (https://maayanlab.cloud/Enrichr/ (accessed on 17 January 2023)) [13]. To investigate the variant expression profiles, the Protein Atlas (https://www.proteinatlas.org/ (accessed on 17 January 2023)) and GTEx (https://gtexportal.org/home/ (accessed on 17 January 2023)) were used. In addition, DisGeNET (https://www.disgenet.org/ (accessed on 18 January 2023)) and MalaCards (https://www.malacards.org/ (accessed on 18 January 2023)) [14] were used to determine relationships between genes and diseases.

3. Results

3.1. Variants from GWAS Analysis

After GWAS analysis, the filtered case and control groups (case group: 62 patients; and control group: 1763 patients), together with 241,217 SNP, passed through the SNP calling rate, minor allele frequency, and Hardy-Weinberg equilibrium. There were 13 significant variants obtained from the case group, and the Manhattan plot of the GWAS results is presented in Figure 2. It was found that variants from the case group had a higher odds ratio than the control groups (

Table 2. Variants obtained from the GWAS result with high significance in the case group.

CHR	Position	SNP ID	Ref a	Alt b	p-Value c	Odd Ratio	Region	Relative Gene
1	154728516	rs6678353	G	T	2.69 × 10−6	2.44	intronic	KCNN3
3	13236137	rs530922	G	A	5.21 × 10−6	2.41	intergenic	IQSEC1; NUP210
3	33156385	rs76541491	C	T	1.53 × 10−6	2.76	intronic	SUSD5
11	12141089	rs61875193	T	C	5.84 × 10−6	2.75	intronic	MICAL2
12	105521299	rs73387413	G	A	4.98 × 10−6	2.52	intergenic	C12orf75; CASC18
12	105524194	rs12313615	G	A	9.68 × 10−6	2.38	intergenic	C12orf75; CASC18
17	58637506	rs117166327	G	A	5.64 × 10−6	2.89	intronic	TEX14
19	55261525	rs62126347	G	A	4.39 × 10−6	2.87	downstream	HSPBP1
19	55263581	rs62126348	T	C	4.06 × 10−6	2.88	intronic	HSPBP1
19	55267754	rs11672710	C	T	3.70 × 10−6	2.90	intronic	HSPBP1
19	55272184	rs10411896	A	C	4.57 × 10−6	2.87	intronic	HSPBP1
X	22810351	rs1034726	G	C	3.51 × 10−6	3.12	ncRNA_intronic	PTCHD1-AS
X	142786494	rs12687449	C	T	6.42 × 10−6	2.83	intergenic	MAGEC2; SPANXN4

^a Allele from the control group. ^b Allele from the case group. ^c Filtered by p < 0.05.

), and most SNP allele frequencies (rs10411896, rs11672710, rs12313615, rs61875193, rs62126347, rs62126348, rs76541491, and rs117166327) were similar to the East Asian and Taiwan Han (TPMI and Taiwan Biobank) populations, which were different from other human races (

Table 3. Variant allele frequency from other genetic projects.

This Study				TPMI a	Twbank b	1000 g c				gnomAD d
SNP ID	Alt	Case	Control			AFR e	AMR	EAS	EUR	AFR	AMR	EAS	FIN	NFE
rs6678353	T	0.331	0.168	0.188	0.179	-	-	-	-	-	-	-	-	-
rs530922	A	0.702	0.494	0.493	0.489	-	-	-	-	-	-	-	-	-
rs76541491	T	0.234	0.100	0.101	0.110	0.001	0.016	0.120	0.015	0.005	0.004	0.103	0.022	0.021
rs61875193	C	0.202	0.084	0.088	0.087	0.110	0.010	0.085	0.011	0.108	0.016	0.083	0.002	0.010
rs73387413	A	0.266	0.126	0.135	0.124	0.062	0.170	0.120	0.074	0.073	0.199	0.127	0.078	0.086
rs12313615	A	0.298	0.152	0.165	0.158	0.400	0.260	0.150	0.180	0.380	0.268	0.150	0.169	0.191
rs117166327	A	0.177	0.069	0.071	0.085	-	-	0.060	.	0.000	0.000	0.066	0.000	0.000
rs62126348	C	0.186	0.073	0.077	0.071	0.043	0.020	0.100	0.075	0.046	0.037	0.077	0.093	0.075
rs11672710	T	0.186	0.073	0.076	0.071	0.036	0.019	0.100	0.075	0.042	0.035	0.077	0.093	0.075
rs62126347	A	0.186	0.073	0.077	0.069	0.023	0.019	0.100	0.075	0.027	0.034	0.077	0.093	0.075
rs10411896	C	0.186	0.074	0.078	0.072	0.460	0.230	0.100	0.320	0.445	0.220	0.081	0.291	0.326
rs1034726	C	0.189	0.069	0.069	0.065	-	-	-	-	-	-	-	-	-
rs12687449	T	0.226	0.094	0.095	0.093	-	-	0.058	-	0.000	0.000	0.088	0.000	0.000

^a Taiwan Precision Medicine Initiative. ^b Taiwan Biobank. ^c 1000 Genomes global minor allele frequency. ^d genom AD (genomes) allele frequencies. ^e AFR: African. AMR: American. EAS: East Asian. EUR: European. FIN: Finnish. NFE: Non-Finnish European.

).

According to the RefSeq results, these risk alleles belonged to the genes C12orf75, CASC18, HSPBP1, IQSEC1, KCNN3, MAGEC2, MICAL2, NUP210, PTCHD1-AS, SPANXN4, SUSD5, and TEX14 in the case group (Table 2). Interestingly, MIR4435-2HG and PTCHD1-AS are non-coding RNAs that regulate target genes without coding proteins.

Further analysis using linkage disequilibrium (LD) showed that in the case group, the variant rs61875193 had a high correlation with rs58910113 (Figure 3A), and rs73387413, which is highly associated with rs12313615, also showed the same result (Figure 3B). Surprisingly, rs11672710 not only had a high association with rs62126347, rs62126348, and rs10411896 in HSPBP1 but also with rs4806651 and rs4806653 in PPP6R1 and rs4337407 in TMEM86B (Figure 3B).

3.2. Functional Annotations of Risk Genes

Table 4. GO annotation of variant genes ^a.

GO_ID	Term	p-Value	Gene
GO:0000291	nuclear-transcribed mRNA catabolic process, exonucleolytic	0.036	LSM3
GO:0001947	heart looping	0.044	MICAL2
GO:0003143	embryonic heart tube morphogenesis	0.048	MICAL2
GO:0006409	tRNA exports from the nucleus	0.035	NUP210
GO:0006662	glycerol-ether metabolic process	0.005	TMEM86B
GO:0007094	mitotic spindle assembly checkpoint signaling	0.022	TEX14
GO:0010921	regulation of phosphatase activity	0.029	PPP6R1
GO:0030042	actin filament depolymerization	0.011	MICAL2
GO:0031398	positive regulation of protein ubiquitination	0.003	MAGEC2; HSPBP1
GO:0033962	P-body assembly	0.013	LSM3
GO:0035304	regulation of protein dephosphorylation	0.042	PPP6R1
GO:0036151	phosphatidylcholine acyl-chain remodeling	0.031	TMEM86B
GO:0043268	positive regulation of potassium ion transport	0.039	KCNN3
GO:0043470	regulation of the carbohydrate catabolic process	0.044	NUP210
GO:0043666	regulation of phosphoprotein phosphatase activity	0.039	PPP6R1
GO:0043928	exonucleolytic catabolism of deadenylated mRNA	0.034	LSM3
GO:0045841	negative regulation of the mitotic metaphase/anaphase transition	0.023	TEX14
GO:0046485	ether-lipid metabolic process	0.015	TMEM86B
GO:0051031	tRNA transport	0.038	NUP210
GO:0051261	protein depolymerization	0.029	MICAL2
GO:0051438	regulation of ubiquitin-protein transferase activity	0.030	MAGEC2
GO:0051443	positive regulation of ubiquitin-protein transferase activity	0.032	MAGEC2
GO:0051547	regulation of keratinocyte migration	0.013	IQSEC1
GO:0051549	positive regulation of keratinocyte migration	0.010	IQSEC1
GO:0061371	determination of heart left/right asymmetry	0.048	MICAL2
GO:0071173	spindle assembly checkpoint signaling	0.022	TEX14
GO:0071174	mitotic spindle checkpoint signaling	0.022	TEX14
GO:0071431	tRNA-containing ribonucleoprotein complex exports from the nucleus	0.035	NUP210
GO:0097064	ncRNA exports from the nucleus	0.039	NUP210
GO:0120182	regulation of focal adhesion disassembly	0.006	IQSEC1
GO:0120183	positive regulation of focal adhesion disassembly	0.006	IQSEC1
GO:0150117	positive regulation of cell-substrate junction organization	0.025	IQSEC1
GO:1900542	regulation of the purine nucleotide metabolic process	0.044	NUP210
GO:1901381	positive regulation of potassium ion transmembrane transport	0.045	KCNN3
GO:1904064	positive regulation of cation transmembrane transport	0.042	KCNN3

^a Selected from p-value < 0.05.

shows that 35 significant GO terms are identified, and further categories in Figure 4A present 14 classes, including carbohydrate catabolism, cation-potassium transport, cell-cell interaction, heart development, keratinocyte migration, lipid metabolism, mitotic phase, ncRNA transport, protein depolymerization, protein phosphoprotein, protein ubiquitination, purine metabolism, RNA catabolism, and tRNA catabolism. These biological functions belong to cell growth, metabolism, migration, and interactions. Notably, there are several genes involved in Ca²⁺/K⁺ regulation (KCNN3), protein ubiquitination (HSPBP1 and MAGEC2), and the breaking down of protein polymers (MICAL2), which might have connections to the K. pneumonia infections. TMEM86B and PPP6R1, which were identified from LD analysis, played a role in lipid metabolism and phosphatase activity, respectively, which may affect immune cell activity and cause K. pneumoniae infection.

3.3. Pathway Analysis

After searching the Reactome database, 26 significant pathways were successfully identified (

Table 5. Reactome annotation of variant genes ^a.

Reactome ID	Term	p-Value	Gene
R-HSA-72203	processing of capped intron-containing pre-mRNA	0.026	NUP210; LSM3
R-HSA-159227	transport of SLBP-independent mature mRNA	0.036	NUP210
R-HSA-159230	transport of SLBP-dependent mature mRNA	0.037	NUP210
R-HSA-159231	transport of mature mRNA derived from an intronless transcript	0.043	NUP210
R-HSA-159234	transport of mature mRNAs derived from intronless transcripts	0.044	NUP210
R-HSA-165054	Rev-mediated nuclear export of HIV RNA	0.036	NUP210
R-HSA-168271	transport of ribonucleoproteins into the host nucleus	0.036	NUP210
R-HSA-168274	export of viral ribonucleoproteins from the nucleus	0.037	NUP210
R-HSA-168276	NS1-mediated effects on host pathways	0.043	NUP210
R-HSA-168325	viral messenger RNA synthesis	0.048	NUP210
R-HSA-168333	NEP/NS2 interacts with cellular export machinery	0.036	NUP210
R-HSA-176033	interactions of Vpr with host cellular proteins	0.039	NUP210
R-HSA-177243	interactions of Rev with host cellular proteins	0.038	NUP210
R-HSA-180746	nuclear import of Rev protein	0.035	NUP210
R-HSA-180910	Vpr-mediated nuclear import of PICs	0.036	NUP210
R-HSA-430039	mRNA decays by 5 to 3 exoribonucleases	0.016	LSM3
R-HSA-1296052	Ca2+-activated K+ channels	0.008	KCNN3
R-HSA-1482788	acyl chain remodeling of PC	0.028	TMEM86B
R-HSA-3232142	SUMOylation of ubiquitinoylation proteins	0.040	NUP210
R-HSA-3301854	nuclear pore complex (NPC) disassembly	0.037	NUP210
R-HSA-4085377	SUMOylation of SUMOylation proteins	0.036	NUP210
R-HSA-4570464	SUMOylation of RNA-binding proteins	0.048	NUP210
R-HSA-4615885	SUMOylation of DNA replication proteins	0.046	NUP210
R-HSA-5619107	defective TPR may confer susceptibility to thyroid papillary carcinoma	0.033	NUP210
R-HSA-8875513	MET interacts with TNS proteins	0.005	TNS3
R-HSA-8875878	MET promotes cell motility	0.029	TNS3

^a Selected from p-value < 0.05.

), and 11 classes of pathways are shown in Figure 4B: cation-potassium transport, MET, mRNA metabolism, mRNA transport, NS1/NS2 protein, nuclear pore complex, phosphatidylcholine acyl chain, Rev/Vpr protein, ribonucleoproteins, SUMOylation, and TPR in papillary thyroid carcinoma. Many pathways are involved in mRNA regulation and SUMoylation by the gene NUP210, which may alter the immune system relative to the UTI caused by K. pneumoniae. Pathways identified from KCNN3 (Ca²⁺-activated K⁺ channels) and TMEM86B (acyl chain remodeling of phosphatidylcholine) fit the GO results, indicating that patients with abnormal cation-potassium transport or abnormal lipid metabolism are at a high risk of K. pneumoniae infection.

3.4. Investigation of Risk Gene Expressions

According to the Protein Atlas results in

Table 6. Disease association analysis from DisGeNET ^a.

Gene	Immune Cell Type Expression Cluster a	Immune Cell Type Specificity b
C12orf75	Plasmacytoid DCs—Unknown function (mainly)	Immune cell enhanced (plasmacytoid DC)
FAM209B	Eosinophils—Unknown function (mainly)	Immune cell-enhanced (eosinophil)
HSPBP1	Non-specific—Mitochondria (mainly)	Low immune cell specificity
IQSEC1	Non-specific—Transcription (mainly)	Low immune cell specificity
KCNN3	Basophils—Unknown function (mainly)	Immune cell-enhanced (basophil and memory B-cell)
LSM3	Non-specific—Inflammatory response (mainly)	Low immune cell specificity
MICAL2	Neutrophils—Inflammatory response (mainly)	Immune cell-enhanced (neutrophil)
NUP210	Non-specific—mRNA processing (mainly)	Low immune cell specificity
PPP6R1	Plasmacytoid DCs—Unknown function (mainly)	Immune cell enhanced (plasmacytoid DC)
SOCAR	Basophils—Unknown function (mainly)	Low immune cell specificity
SUSD5	B-cells—Adaptive immune response (mainly)	Not detected in immune cells
TMEM86B	T-cells—Unknown function (mainly)	Not detected in immune cells
TNS3	Monocytes—Unknown function (mainly)	Group-enriched (classical monocyte, plasmacytoid DC, non-classical monocyte, intermediate monocyte, myeloid DC, memory B-cell, and naive B-cell)

^a RNA expression data has been used to classify protein-coding genes, and similar gene expression patterns were clustered together into immune cells. ^b Consensus transcriptomics data was used to classify all genes according to their immune cell-specific expression into two different schemas: the specificity category and the distribution category, based on the evidence data from the Human Protein Atlas.

, the RNA expression of the genes C12orf75, FAM209B, HSPBP1, IQSEC1, KCNN3, LSM3, MICAL2, NUP210, PPP6R1, SOCAR, SUSD5, TMEM86B, and TNS3 was detected in immune cells. In particular, some of these proteins, such as C12orf75, FAM209B, KCNN3, MICAL2, PPP6R1, and TNS3, showed high immune cell specificity. Combined with the GO and Rectome analysis results, the changes in the variants further regulated the activity of immune cells. Unfortunately, few expression patterns were recorded in the GTEx database for these variants; only rs140411896 in HSPBP1 and rs4337407 in TMEM86B presented changes in gene expression in whole blood compared to the non-mutated alleles (Figure 5A: HSPBP1 down expression occurred with the rs10411896 mutation and Figure 5B: TMEM86B up expression with the rs4337407 mutation). These findings are worthy of further study to determine the relationship between the Taiwan Han population’s special genetic allele affections in immune cells and K. pneumoniae infection in the urinary tract.

4. Discussion

4.1. Disease Association Analysis

K. pneumonia is a Gram-negative bacterium commonly found in the human gut and environment, and it is also known to be the major cause of healthcare-associated infections, especially pneumonia, bloodstream infections, and urinary tract infections. Previous studies have reported that K. pneumoniae infection is a rare cause of community-acquired pneumonia (CAP) in North America, Europe, and Australia [15]. However, investigations in eight Asian countries, including Taiwan, have reported that K. pneumoniae is highly prevalent [16]. Several drug-resistant K. pneumoniae variants have also posed a significant public health threat due to their difficulty in treatment and higher mortality rates, such as carbapenem-resistant K. pneumonia (CRKP), extended-spectrum beta-lactamase (ESBL)-producing K. pneumonia, and hypervirulent K. pneumoniae (hvKp). Due to the widespread resistance to antibiotics and the possibility of causing more severe infections, it is crucial to understand the specific resistance patterns and infectious pathogenesis, which can contribute to improving disease prevention, infection control, and further antibiotic development.

Patients with chronic diseases such as diabetes, cancer, and chronic kidney disease are believed to have a higher risk of developing K. pneumoniae UTIs [17]. Although our research revealed no significant differences in the ratio of underlying conditions and diseases between the case group and the control group, which included diabetes mellitus, malignancy, chronic kidney disease, and other chronic viral infections, we did observe significant differences in certain genetic variants. Consequently, it suggests that the variation leading to the infection pathogen of urinary tract infections (UTI) in these patients might be more closely related to their genes than their underlying conditions.

Nevertheless, all the chronic diseases mentioned above have a prolonged and enduring course. Predicting whether patients in both groups will develop these diseases later on remains challenging. Nonetheless, this information could still provide valuable insights into the potential pathogenesis of UTI with K. pneumoniae infection.

4.2. Genotyping

By October 2022, the TPMI team had 382,259 genotyped samples. This genomic information is also combined with other clinical records, such as ICD10 disease codes, laboratory tests, medications (drug usage), vital signs, image descriptions, and operation notes, which makes the TPMI the largest genetic health association study compared to other similar projects held in the United States or Japan. The TPMI SNP array was modified from the Axiom Genome-Wide SNP Array Plate and can test approximately 130 thousand known risk variants, 580 thousand mapping SNPs, and 20 thousand copy number variant markers based on Taiwanese reference genomes and Taiwan Biobank whole genome sequencing data.

The risk variants from the GWAS in this study showed that most allele frequencies in the East Asian population, which were also close to the Taiwanese population, were the highest compared to other species. This phenomenon was also observed in the variants rs4337407, rs4806651, rs4806653, and rs58910113 from the LD analysis, not only showing a higher LD association with lead variants than with species, but also that their minor allele frequencies were higher in African or Asian population groups. Some variants of rs530922, rs1034726, rs6678353, rs12687449, and rs117166327 show non-or rare frequency records in the published database, which may be Taiwan-specific variants for K. pneumoniae infection risk factors.

K. pneumoniae is an opportunistic pathogen with compromised immune systems due to phagocytosis by epithelial cells, macrophages, neutrophils, and DCs, or weakened by other infections [4]. As shown in Table 6 and Figure 5, the genes C12orf75, FAM209B, HSPBP1, IQSEC1, KCNN3, LSM3, MAGEC2, MICAL2, NUP210, PPP6R1, SOCAR, SPANXN4, SUSD5, TMEM86B, and TNS3 were involved in immune cells, and GTEx analysis showed that HSPBP1 and TMEM86B expression could be altered by specific genetic variants. Thus, our findings can also contribute to further clarifying the role of these risk genes in the relationship between K. pneumoniae infection and immune system defects.

Galeas-Pena reported that K. pneumoniae infection activates the signaling of nuclear factor kappa B (NF-κβ), promoting the recruitment of immune cells [18]. The gene PPP6R1, which plays an important role in protein phosphorylation, has been shown to limit the activation of the NF-κβ pathway by reducing IκBε phosphorylation [19]. This mechanism is probably caused by Slfn2 interacting with PPP6R1, leading to reduced type I IFN-induced activation of NF-κB signaling [20]. PPP6R1 is also known as a regulatory subunit of PP6, which can negatively regulate NF-κβ signaling [21], highlighting the importance of phosphatase activity in the immune activation process during K. pneumoniae infection.

Bacterial cell-cell surface interactions are the core source of pathogen infection. Previous reports have demonstrated that alterations in focal adhesion and the actin cytoskeleton play important roles in the bacterial invasion of host cells [22]. In addition, Hsu et al. indicated that Rho is involved in the activation of focal adhesion through the phosphorylation of focal adhesion kinase, which could affect the induction of cell cytoskeleton rearrangements. The same research also indicated that Cdc42 and the PI3K/Akt pathway are activated to induce cell cytoskeleton rearrangement via K. pneumoniae adhesion [23]. Functional annotation of IQSEC1 and MICAL2, including focal adhesion and actin filament depolymerization, showed that mutations in both genes could pose potential risks to K. pneumoniae invasion.

In addition to the innate immune perturbations associated with K. pneumoniae, dysregulation of electrolyte homeostasis [24] and protein ubiquitination may also affect host immune immunity. Immune responses activated by ion channel transporters such as calcium, magnesium, sodium, potassium, and zinc have been reported, and pathogen infection also requires ion equilibrium changes in the living cell environment [25]. According to the results of studying Galleria mellonella, which is used as a K. pneumoniae infection model organism, the amounts of calcium, potassium, magnesium, and phosphorus are altered during K. pneumoniae infection [26]. Zhang also indicated that the calcium signaling pathway increased macrophage activity resulting from K. pneumoniae infection [27], and further research presented that TRPC1 (Transient receptor potential channel 1) could mediate Ca²⁺ entry and activate NF-κβ/Jun, leading to the proinflammatory response [28]. Thus, the mutation of KCNN3, which belongs to the Ca²⁺-activated potassium channels, may increase Ca²⁺ accumulation in living cells through TRPC1 and promote Ca²⁺ levels during K. pneumoniae infection.

Research has shown that K. pneumonia can interfere with or suppress host immunity and inflammation reactions by affecting the ubiquitination of various cellular signals [29,30]. What is more, a small ubiquitin-related modifier (SUMO) can be involved in various biological processes, including immunopathology and inflammation [31], which can also be affected by K. pneumonia and cause extensive inflammation damage [32]. In conclusion, according to these results, protein ubiquitination plays a role in the activation of the innate immune response and subsequent release of cytokines when facing bacterial invasion. Furthermore, our finding of risk genes for ubiquitination and SUMOylation may play a great part in the cellular immune responses against K. pneumoniae invasion. Thus, patients with risk genes affecting the regulation of the innate immune response may be more susceptible to K. pneumonia infection, although further research is needed to identify the relationships between these factors and K. pneumoniae infection, especially UTIs.

4.3. Limitations

Despite all the findings mentioned above, there are still some limitations to our research. Although the results had reached statistical significance, there still might be some pitfalls for our results to be extrapolated to the normal population due to the low number of case-group specimens. Our research excluded several samples that had co-infections with other pathogens in order to demonstrate our findings more clearly. However, it might have further information that can contribute to the pathogenesis.

Moreover, UTI has been known to have a high recurrence rate [2], but it is difficult to compare the recurrent rate and the prognosis between the case group and control group because it is difficult to define the first episode of UTI and track the following medical history of recurrent UTI events. We still need further delicate studies to reveal the difference between these patients. However, we believe our findings can pave the way for the following research.

5. Conclusions

The detection of a GWAS based on the Taiwanese population in this study supports the hypothesis that some risk variants might correspond to K. pneumoniae UTI. In addition, these variants probably affect diverse bio-molecular functions, such as Ca²⁺-activated potassium channels, focal adhesion, NF-κβ signaling, SUMOylation, and protein ubiquitination. These functional genes may affect the host immune system, thereby increasing the ability of K. pneumoniae to invade. Thus, our results might also explain why, although K. pneumoniae UTI is believed to be related to healthcare, it is still the second most common pathogen in uncomplicated UTI. Overall, we still need further studies of the detailed molecular mechanisms between the host immune system and K. pneumonia UTI to fully understand the pathogenesis of the K. pneumoniae infection.