Exploratory pilot study on transcriptome analysis in blood and urine for identifying potential miRNA biomarker candidates to dustinguish of bacterial vs. viral infections in children with fever

Exploratory pilot study on transcriptome analysis in blood and urine for identifying potential miRNA biomarker candidates to dustinguish of bacterial vs. viral infections in children with fever | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Exploratory pilot study on transcriptome analysis in blood and urine for identifying potential miRNA biomarker candidates to dustinguish of bacterial vs. viral infections in children with fever View ORCID Profile Tatjana Kiseļova , View ORCID Profile Baiba Vilne , View ORCID Profile Gunda Zvīgule-Neidere , View ORCID Profile Dace Gardovska doi: https://doi.org/10.1101/2025.03.28.25324176 Tatjana Kiseļova 1 Riga Stradiņš University , Riga, Latvia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tatjana Kiseļova For correspondence: tatjana.kiselova{at}rsu.lv Baiba Vilne 1 Riga Stradiņš University , Riga, Latvia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Baiba Vilne Gunda Zvīgule-Neidere 1 Riga Stradiņš University , Riga, Latvia 2 Children’s Clinical University Hospital , Riga, Latvia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gunda Zvīgule-Neidere Dace Gardovska 1 Riga Stradiņš University , Riga, Latvia 2 Children’s Clinical University Hospital , Riga, Latvia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dace Gardovska Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background There is a lack of fast, reliable and non-invasive methods for distinguishing between bacterial and viral infections in modern clinical practice. It negatively impacts the effectiveness of treatment and increases the risk of unnecessary antibiotic administration and thus antibiotic resistance spread. Using microRNA biomarkers in urine may become the solution to the problems listed. miRNAs in urine are relatively stable and can provide an indirect yet specific insight into systemic infections, thus offering a promising diagnostic tool. Combining urine microRNA analysis with blood transcriptomics allows to explore systemic changes that are not limited to a single biological fluid, improving reliability by cross-verifying findings. Methods In this pilot study, the analysis of urine microRNA and blood full transcriptome sequencing data was carried out in children with bacterial (n = 7), viral infections (n = 7) and controls (n = 8) with the goal of determining microRNA diagnostic signatures that distinguish between the said infections, and connecting differentially expressed urine microRNA with differentially expressed transcripts in blood. Using LASSO regularised regression and ANOVA statistical analysis, microRNA biomarker candidates were prioritised and miRNA:mRNA high-likelihood interactions were analysed using correlation analysis and target prediction to further explore systemic changes in response to infection. The differential expression analysis revealed unique features and common patterns in microRNA expression in infections with bacterial or viral etiology. Results The resultant five microRNAs chosen by both feature selection methods have shown the ability to cluster bacterial infections patients, viral infections and controls. Subsequent correlation analysis and target prediction revealed the role of microRNA regulation in various immune processes such as the interaction of cytokine and cytokine receptors, as well as the regulation of T-cell activation. Conclusions Our study suggests that prioritised urine microRNA biomarkers have a strong potential for implementation in clinical diagnostics. This could enable a targeted and timely treatment, potentially reducing the risk of spread of antibiotic resistance. Background Fever in children is one of the most common reasons for doctor visits and emergency medical assistance. While it is often caused by self-limiting viral infections that resolve with symptomatic treatment, it can be the first sign of serious bacterial infections [ 1 ]. An accurate and timely diagnosis of infection etiology is critical for effective treatment, as empirical antibiotic administration in suspected bacterial cases is common due to the lack of reliable diagnostic tests. This approach, however, poses risks, including unwanted side effects and spread of antibiotic resistance, which remains a significant challenge, especially in hospital settings [ 1 , 2 ]. Rapid diagnostics of infection etiology are of vital importance when it comes to improving the efficacy of treatment [ 3 ]. For instance, in the case of bacterial sepsis, every hour without antibiotics increases morbidity by 8% [ 4 ]. Differentiating between bacterial and viral infections based solely on symptoms is challenging due to overlapping clinical presentations [ 5 , 6 ]. However, on the molecular level, bacterial and viral infections induce distinct signal pathways. The immune response against viral infections is more connected with activation of interferon synthesis. Bacterial infections, in turn, activate integrin signal pathways [ 7 ]. This also implies that host gene expression varies depending on etiology of the infection, which has been covered by multiple studies. It has been proven that the etiology of infections in children caused by viruses, gram-negative bacteria ( Escherichia coli ) or gram-positive bacteria ( Staphylococcus aureus or Streptococcus pneumoniae ) can be determined solely based on gene expression profile in blood [ 8 ]. Another study confirms that during influenza A H1N1/09 infection increased expression of inflammatory pathway genes has been observed as well as reduced expression of adaptive immune pathway genes [ 9 ]. Two-transcript signature in blood has been shown to be sufficient to distinguish between bacterial and viral infections in febrile children with high sensitivity and specificity (Herberg et al., 2016). A recent review covering studies conducted over the past 15 years highlights the need for further research to realise the full diagnostic potential of transcriptomics [ 11 ]. Signatures discovered in adult patients have been shown to have a decrease in performance in paediatric cohorts which is especially prominent in patients 12 years of age and younger [ 12 ]. Advancement of diagnostic methods through novel technologies is vital to improve treatment precision. Micro (mi)-RNAs are attractive targets for the development of diagnostic methods due to their involvement in gene regulation and high stability in bodily fluids [ 13 ]. miRNAs are small (19 to 25 nucleotides) single-stranded non-coding RNA molecules that regulate post-transcriptional gene silencing. More than 60% of human protein-coding genes contain at least one conserved miRNA binding site [ 14 ]. The role of miRNAs in immune system regulation is being actively studied [ 15 ]: miR-146, for example, reduces excessive inflammation in bacterial infections by downregulating cellular lipopolysaccharide sensitivity [ 16 ], while miR-155 influences adaptive immune responses to viral infections [ 17 ]. Studies on miRNA in blood have shown their potential to distinguish between acute respiratory bacterial and viral infections in adults [ 18 ]. The use of urinary transcriptome for diagnostic purposes has been extensively evaluated for diseases related to the urinary tract: various renal disorders [ 19 , 20 ], prostate and bladder cancer [ 21 , 22 ], as well as urinary tract infections, including in paediatric cohorts [ 23 ]. Beyond these applications, urine miRNA has shown potential to identify systemic conditions, such as chronic inflammatory diseases in children like atopic dermatitis [ 24 ]. The use of urine in paediatric care and diagnostics is particularly promising because it can be collected non-invasively, unlike blood, which can be difficult to collect in sufficient quantities for diagnostic procedures. This exploratory pilot study investigates the diagnostic potential of urine miRNA signatures to distinguish bacterial and viral infections in paediatric patients. Using small RNA sequencing data, we identified a miRNA profile capable of differentiating these infections with feature selection methods, including penalised logistic regression and ANOVA. Additionally, we integrated these findings with differential expression (DE) mRNA analysis to connect blood and urine transcriptomes, offering novel insights into disease mechanisms. These results contribute to the emerging field of non-invasive diagnostic tools and highlight the utility of urine miRNAs in paediatric infection diagnostics. Materials and Methods RNA extraction and sequencing Blood samples were collected and preserved in PAXgene Blood RNA Tubes (PreAnalytiX), while urine samples were collected in Urine Collection and Preservation Tubes (Norgen Biotek), according to the manufacturer’s specifications. RNA extraction was performed using the PAXgene 96 Blood RNA Kit for blood samples and the Urine Total RNA Purification Maxi Kit for urine samples, adhering to the manufacturer’s protocol. For blood samples, whole transcriptome paired-end sequencing was conducted with 100 million reads per sample and a fragment length of 150 bp. Library preparation used the MGIEasy RNA Directional Library Prep Set, and sequencing was performed on the MGISEQ-2000 platform with the MGISEQ-2000RS High-Throughput Sequencing Set, following the manufacturer’s recommendations. Urine samples were subjected to single end small RNA sequencing with up to 50 million reads per sample and a fragment length of 50 bp. Library preparation was performed using the MGIEasy Small RNA Library Prep Kit, and sequencing was carried out on the DNBSEQ-G400 platform using the DNBSEQ-G400RS High-Throughput Sequencing Set (Small RNA). Before sequencing, ribosomal RNA was removed from all samples using the MGIEasy rRNA Depletion Kit, according to the manufacturer’s protocol. RNA data pre-processing Quality control of raw sequencing reads was performed using FastQC v0.11.9 [ 25 ] with MultiQC v1.14 [ 26 ] for visualisation. Sequence alignment was performed to human genome build GRCh38.p13 [ 27 , 28 ]. Mature and hairpin reference miRNA sequences were downloaded from miRBase (mirbase.org , release 22.1; Kozomara et al., 2019). Adapter trimming, removing short fragments (>18 nt according to research data on miRNA size [ 30 ]), mapping, and miRNA quantification were carried out using miRDeep2 v0.1.2 [ 31 ]. Since the miRDeep2 algorithm counts both mature miRNAs and its precursors, to avoid inconsistencies those were merged, and the median counts were calculated. STAR v2.7.8a [ 32 ] with default parameters for paired-end sequencing data was used for full transcriptome sequences mapping and adapter clipping. Quality trimming was omitted since it can potentially worsen mapping results [ 33 ]. Mapping quality control was performed using Qualimap v2.3 [ 34 ]. Samples with a very small proportion of mapped sequences (less than 1%) were excluded from the downstream analysis. The mapped .bam files from the multiple sequencing lanes of one sample were merged with Samtools v1.10 [ 35 ]. Gene expression counts matrix was produced using R statistical software v4.3.2 [ 36 ] package featureCounts [ 37 ]. Multivariate statistical analysis After removing miRNAs with consistently low counts (median <10) count matrix was normalised using the TMM method as implemented in the edgeR package [ 38 ]. Differential expression analysis for miRNA data was performed by fitting a generalised linear model with Bayesian empirical statistics to smooth the standard errors as implemented in R package limma [ 39 ]. DE analysis was conducted to compare three conditions: bacterial infections versus controls, viral infections versus controls, and bacterial versus viral infections. Statistical significance was determined using a nominal p-value threshold of < 0.05, while biologically significant expression changes were identified based on log2-fold change (log2FC) thresholds of ≥ 1 or ≤ -1. For blood transcriptome data, DEGs were determined by combining the method described above with fitting a negative binomial generalised linear model to count data and normalising for sequencing depth as implemented in the DESeq2 standard protocol [ 40 ]. The results of two methods were combined by calculating the average log2FC value and the p-value according to the Fisher method [ 41 ] as implemented in the R package metap [ 42 ]. Statistical significance was determined using a Bonferroni-Hochberg (BH) multiple testing corrected p-value threshold of < 0.05, while biologically meaningful expression changes were identified based on log2-fold change (log2FC) thresholds of ≥ 1 or ≤ -1. Significantly overrepresented pathways were identified via gene ontologies in DAVID [ 43 ]. Prioritisation of potential diagnostic feature miRNAs DE miRNAs in bacterial vs. viral infections were used to identify potential diagnostic features. Penalised sparced logistic regression as implemented in the R package glmnet [ 44 ] was used to determine diagnostic signatures for bacterial infections, viral infections, and controls. A one-vs-all approach was used to build the model: the bacterial infection group served as the target group compared to the viral infection group and controls. The least absolute shrinkage and selection operator (LASSO) or L1 regularisation was used to determine the smallest number of features while maintaining diagnostic accuracy. For optimal lambda coefficient calculation, leave-one-out cross-validation (LOOCV) was implemented. Accuracy, positive predictive value, the F1 score, and the Matthew correlation coefficient were calculated based on the confusion matrix to evaluate the performance of the model (Chicco and Jurman, 2020). ROC curve was generated via the R package pROC [ 46 ], along with the AUC score. As an additional feature selection method, ANOVA (ANalysis Of VAriance) was implemented as in Python library scikit-learn feature selection function SelectKBest() [ 47 ]. Features with significant expression level differences between bacterial and viral infections (adjusted p-value < 0.05) were selected and the F-values were calculated. Only features deemed important by both implemented methods were selected for further investigation. In addition to the one-vs-all approach, binary classification models were also constructed for pairwise comparisons between each infection group (bacterial vs. viral, bacterial vs. control, viral vs. control). A similar approach for feature selection as described in this chapter was applied to mRNA data. miRNA:mRNA interaction network To evaluate the potential interactions between miRNA biomarkers and differentially expressed genes in blood transcriptome, a miRNA target prediction analysis was performed. Three databases with distinct interaction detection methods were selected: one based on functional study results ( miRTarBase ; Huang et al., 2022), one using computational predictions ( DIANA-microT ; Tastsoglou et al., 2023), and one based on sequence similarity ( TargetScan ; McGeary et al., 2019). mRNAs and miRNAs groups were considered to be linked if the interaction was identified in at least one of the three databases. According to the widely accepted understanding, binding of miRNA to the corresponding mRNA induces translational inhibition and/or mRNA degradation [ 51 , 52 ]. Therefore, miRNA interactions were only examined in down-regulated genes when comparing bacterial with viral infections. In addition to database analysis, correlation analysis was performed between the potential miRNA diagnostic feature and the genes predicted to interact with them. Pearson’s correlation coefficient was calculated using the R package Hmisc [ 53 ]. The correlation was considered statistically significant if the coefficient was negative and with BH-adjusted p-value < 0.05. For visualizing the miRNA-gene interaction network, Cytoscape v3.10.2 [ 54 ] was used, while functional enrichment analysis was performed using the ToppFun v50 database (version from 30.11.2023; Chen et al., 2009). Results with a BH-adjusted p-value < 0.05 were considered statistically significant. Results Pilot study design The cohort was recruited from paediatric patients from the Children’s Clinical University Hospital (Riga, Latvia) aged one month to 18 years with fever and confirmed bacterial or viral infections. The control group is made up of children without fever or other signs of infection. Basic clinical information about the pilot study cohort is available in Table 1 , while list of diagnosed infections in bacterial and viral groups is available in Additional File 1: Supplementary Table 1. The outline of the main study workflow is described in Fig 1 . Download figure Open in new tab Fig 1. Outline of the main study workflow. 22 paediatric patients aged one month to 18 years were included in the study, comprising seven with proven bacterial infections, seven with proven viral infections, and eight controls without infection. RNA-seq was performed for both blood and urine samples, generating full transcriptome RNA-seq data and small RNA-seq data consecutively. View this table: View inline View popup Download powerpoint Table 1. Basic information on study cohort. miRNA differential expression profiles in bacterial, viral infections and controls Differential expression analysis between the bacterial infection group (n=7) and controls (n=8) identified 64 nominally significant miRNAs (log2FC > 1 or < -1, p < 0.05), with 44 (69%) up-regulated (median log2FC = 1.81, IQR = 1.42) and 20 (31%) down-regulated (median log2FC = -1.60, IQR = 0.85) in the bacterial group ( Fig 2A ). Similarly, between the viral infection group (n=7) and controls ( Fig 2B ), 50 miRNAs were differentially expressed, 35 (70%) up-regulated (median log2FC = 1.64, IQR = 0.45) and 15 (30%) down-regulated (median log2FC = -1.58, IQR = 0.55). In the comparison of bacterial versus viral infections ( Fig 2C ), 26 miRNAs were differentially expressed, evenly divided between up-regulated (13, 50%, median log2FC = 2.13, IQR = 0.87) and down-regulated (13, 50%, median log2FC = -1.33, IQR = 0.76). These miRNAs were used for diagnostic feature selection to distinguish between bacterial and viral infections (see Additional File 1: Supplementary Table 2). Download figure Open in new tab Fig 2. Exploring DE miRNAs and selecting urine miRNA signature for distinguishing infection etiology in febrile children. Volcano plots demonstrating identified differentially expressed (DE) miRNAs, comparing (A) bacterial infection group (n = 7) with controls (n = 8); (B) viral infection group (n = 7) with controls; (C) bacterial infection group against viral infection group. Up-regulated miRNA marked in red, down-regulated, in turquoise, miRNA not corresponding to the definition of DE miRNA in this study, in grey. (D) Heatmap along with agglomerative hierarchical cluster analysis using Z-score for selected miRNA biomarker candidates differentiating between bacterial and viral infections. Negative Z-score marked in blue, positive Z-score marked in red, Z-score reaching zero marked in white. In cluster analysis patients with bacterial infections marked in dark red, with viral infections – in light blue, controls – in green. (E) Principal component analysis with miRNA diagnostic signature selected based on LASSO penalized logistic regression and ANOVA analysis results. Samples from patients with bacterial infections (B) marked in red, from patients with viral infections (V) - blue, and from controls (C) - green. (F) Contribution of selected miRNAs to the principal components. The colour scale represents the contribution to the first principal component in percents. Selection of miRNA signature to differentiate infection etiology Two feature selection approaches were combined to select miRNA signatures that are distinct between groups: logistic regression model with LASSO regularisation (Kammer et al., 2022) for bacterial versus viral infections and controls and variance analysis (ANOVA) [ 57 ] between bacterial and viral infections. As an exploratory analysis for miRNA diagnostic signature prioritisation binary models were fitted to evaluate if miRNA urine signatures are suitable for distinguishing between infection aetiologies and controls. The result of this analysis is available in Additional File 1: Supplementary Fig 1 and Additional File 1: Supplementary Table 3. As a result of feature selection in the logistic regression model using the one-vs-all approach, 10 miRNAs with non-zero coefficients were selected, which are summarized in Additional File 1: Supplementary Table 4. Of the selected miRNAs, seven were up-regulated in the bacterial infection group compared to the viral infection group, and three were down-regulated. Table 2 presents the results of the evaluation of the model’s classification performance. All the parameters listed combined with the ROC curve (Additional File 1: Supplementary Fig 2) and AUC score value of 0.981 indicate good performance across all classification thresholds. View this table: View inline View popup Download powerpoint Table 2. Confusion matrix and associated performance metrics for miRNA signature selected via LASSO regularized regression. As a result of the additional application of ANOVA for diagnostic feature evaluation, 11 miRNAs were identified as significant (BH-adjusted p-value < 0.05), as shown in the Additional File 1: Supplementary Table 4. Nine of these miRNAs are up-regulated, while two are down-regulated. Combining both methods described above, six miRNAs identified as important diagnostic features were selected: hsa-miR-136-5p , hsa-miR-513c-3p , hsa-miR-514a-5p , hsa-miR-514a-3p , hsa-miR-1-3p , hsa-miR-507 . hsa-miR-1-3p was excluded from downstream analysis due to its low expression level compared to other selected miRNAs (10,3 ± 5,5 mean relative expression). The overlook on the selected miRNA signature is available in Table 3 . View this table: View inline View popup Download powerpoint Table 3. Overview of selected miRNA signature for distinguishing between bacterial and viral infections. Overview on miRNA signature selected via LASSO regularization (based on LASSO coefficient) and ANOVA (based on p-value), along with summary on expression levels and up-or down-regulation. The results of the hierarchical cluster analysis (see Fig 2D ) demonstrate clear differentiation of the patient groups using the selected miRNA diagnostic signature. Along with the heatmap, this indicates significant expression differences between the groups and the strong ability of the signature to distinguish the bacterial infection group from the viral infection group and controls. The results of the hierarchical agglomerative clustering (top of Fig 2D ) show greater similarity between the viral infection group and the control group than between the bacterial infection group and the other comparison groups. However, a sample from the bacterial infection group (Sample 2) is clustered with the patients with viral infection, which is also observed in Fig 2E in the principal components analysis. Figs 2E and 2F show the results of the principal component analysis (PCA), using only the selected miRNA diagnostic signature. The first principal component explains 62.7% of the data variability and the second component explains 18.1%. First two principal components are sufficient to explain at least 80% of the data variability, while using three components allows explaining 91.4% of the variability. Fig 2F shows the contribution of each individual miRNA to the principal components. The largest contributions to the first principal component come from hsa-miR-514a-5p (31.2%), hsa-miR-513c-3p (27.9%), and hsa-miR-507 (25.0%), while for the second component, hsa-miR-514a-5p represents 58.8%. It can be concluded that hsa-miR-514a-5p provides the largest contribution to explaining the variability of the data. The performance of the derived signature was compared to the levels of C-reactive protein (CRP) that are commonly used to assess the etiology of infection. Although there is no widely accepted clinical range, levels greater than 50 mg/dL have been reported to indicate bacterial infection, however elevated CRP levels indicate ongoing inflammation which may not be connected to infection [ 58 ]. CRP levels for this study were utilised retrospectively and thus are only available for the bacterial and viral infections groups (for five out of seven patients). Out of seven patients with bacterial infection, 71% have elevated levels of CRP and none of the patients in the viral infection group have elevated CRP levels. It shows that our derived diagnostic signature is more accurate in predicting bacterial infections. Full transcriptome differential expression profiles and functional analysis in bacterial, viral infections and controls Differential expression (DE) analysis between the bacterial patient group and controls ( Fig 3A ) identified 5,860 differentially expressed genes (DEGs), with 2,811 (47.97%) up-regulated and 3049 (52.03%) down-regulated. The up-regulated genes had a median log2FC of 1.50 (IQR = 0.81), while the down-regulated genes had a median log2FC of -1.38 (IQR = 0.54). Similarly, DE analysis between the viral patient group and controls ( Fig 3B ) revealed 2,218 DEGs, with 1,050 (47.34%) up-regulated and 1,168 (52.66%) down-regulated. The median log2FC for the up-regulated genes was 1.42 (IQR = 0.64), and for the down-regulated genes, it was -1.48 (IQR = 0.63). In the comparison between the groups of bacterial and viral patients ( Fig 3C ), 3,675 DEGs were identified, with 2,213 (60.22%) up-regulated and 1,462 (39.78%) down-regulated. The median log2FC for the up-regulated genes was 1.42 (IQR = 0.65), while for the down-regulated genes it was -1.37 (IQR = 0.57). Download figure Open in new tab Fig 3. Volcano plots demonstrating identified differentially expressed (DE) transcripts in blood. (A) Comparing the bacterial infection group (n=7) against controls (n=8); (B) viral infection group (n=7) against controls (B); (C) bacterial infection group against the viral infection group. Up-regulated transcripts marked in red, down-regulated – in turquoise, mRNA not corresponding to the DE mRNA definition of this study – in grey. The top five up-regulated and down-regulated mRNA names are marked next to the corresponding plot points. Using the identified DEGs, functional analysis was performed using gene ontologies (GO) in DAVID [ 43 ]. All the observed pathways (overview provided in Table 4 ) are not only concordant with the known biology of infection, but also highly significant in the used dataset, such as adaptive immune response, inflammatory response, and defense response to virus in case of viral infections. View this table: View inline View popup Table 4. Top-5 GO terms associated with DEGs in three comparison groups. To ensure that the findings of this study align with previous research, differentially expressed genes (DEGs) for the bacterial vs. control and viral vs. control comparison groups were evaluated against published mRNA diagnostic signatures for bacterial and viral infections [ 9 , 59 – 65 ]. This analysis identified 121 matched signatures of the 245 reported for bacterial infections and 63 matched signatures of the 276 reported for viral infections. In addition to exploring the urine miRNA diagnostic signature for bacterial infections vs. viral infections and controls, a similar approach for diagnostic feature selection described in Methods section 2.5 was applied to blood mRNA data. The results of the analysis mentioned are listed in Additional File 1: Supplementary Fig 3 and Additional File 1: Supplementary Table 5. The signature for bacterial vs. viral infections did not demonstrate sufficient ability to differentiate infection groups (results not shown). For bacterial infections vs controls, a four-gene signature was derived: FCRL3 , WNT10B , PODN and MMP23B ; for viral infections versus controls group, the ADAMTS5 gene was shown to be able to differentiate between patients and the control group. miRNA:mRNA regulatory network of signature miRNA and differentially expressed genes To evaluate the systemic changes that occur during the infection process, the interaction between host-response miRNA diagnostic signature found in urine and DEGs in the blood of the same patients was evaluated using a target prediction analysis. Since miRNA conducts gene regulation by mediating translation inhibition [ 13 ] through the promotion of translational inhibition and mRNA degradation, for correlation analysis only down-regulated genes were included. 34 pairs of miRNAs and DEG signature with statistically significant negative correlation were discovered using Pearson’s correlation. Three out of five miRNAs showed negative correlations, potentially indicating interactions with the DEG list: hsa-miR-507 (25 pairs), hsa-miR-514a-3p (3 pairs) and hsa-miR-513c-3p (6 pairs). The full list of putative interactions is available in Additional File 1: Supplementary Table 6. miRNA:mRNA interaction network was visualised using discovered pairs and data from the ToppFun database about potential biological functions of genes (see Fig 4 ). Gene functions that focus on innate and adaptive immune response formation were highlighted to be included in the network, specifically lymphocyte differentiation, cytokine and cytokine receptor interaction, as well as regulation of T-cell activation. Download figure Open in new tab Fig 4. Integrated analysis and interaction network of signature miRNAs in urine and respective blood mRNA. miRNAs shown in orange, genes in blue, gene functions in green. The network was visualised using Cytoscape v3.10.2 [ 54 ]. Furthermore, functional enrichment analysis was performed for 21 miRNAs that were differentially expressed in bacterial or viral infection patient groups compared to controls; however, they were not DE when comparing patient groups (the full list is available in Additional File 1: Supplementary Table 7). The main goal of this analysis was to accompany the information gained after creating the interaction network in Fig 4 on the role of miRNA in the regulation of the immune response and to discover which miRNA-regulated pathways may not be influenced by the etiology of the infection. Four of the miRNA mentioned ( hsa-miR-204-3p , hsa-miR-27b-5p , hsa-miR-508-3p , and hsa-miR-508-5p ) are part of non-canonical NF-κB signal pathway, as well as the regulation of the synthesis of IL-1 along with hsa-miR-708-3p . hsa-miR-204-3p , hsa-miR-27b-5p , hsa-miR-708-3p , hsa-let-7f-1-3p and hsa-miR-200c-3p (the only miRNA down-regulated out of all mentioned above) are a part of cytokine synthesis regulation. All the mentioned pathways are part of the innate immune response and initiate and sustain the inflammatory process regardless of the etiology of the infection [ 66 – 68 ]. Discussion The potential applications and importance of urine biomarkers in paediatric care is a highly promising area of research. Urine can be collected easily and non-invasively [ 69 ]. Considering the lack of reliable and rapid infection etiology diagnostic methods, the search for new biomarkers is an important step to improve the quality of healthcare [ 70 ]. The use of transcriptome and specifically miRNAs in diagnostics has considerable potential as a tool to leverage in personalised medicine approaches. High stability of miRNA in biological fluids suggests its high biomarker potential [ 71 ]. Furthermore, the role of miRNAs as upstream regulators indicates that a single miRNA biomarker may be sufficient to detect systemic changes such as activation and deactivation of immune pathways, making miRNAs even more attractive candidates as disease diagnostics tools [ 15 ]. The studies of infection biomarkers up to date have primarily focused on blood mRNA transcripts, but growing recognition of miRNAs as prominent regulators of various biological pathways and diseases made us turn our attention to miRNAs. The DE analysis performed highlighted significant differences between the expression profiles of patient groups and controls. It suggests not only the importance of miRNAs in the regulation of the immune response and its involvement in the infection process [ 13 , 72 , 73 ], but also the fact that these complex changes can be detected in urine that has not previously been reported. In comparison between DE miRNA in the viral infection group against controls and the bacterial infection group against controls, it was found that multiple up-regulated and down-regulated miRNAs overlap between these comparison groups. These findings may suggest the role of these miRNAs in the regulation of nonspecific immune response that is not related to the etiology of infection. The latter is partially confirmed by comparison of miRNAs that are DE between patient groups and controls, however, are not DE when comparing between patient groups: all the relevant pathways discovered are related to innate immune response and inflammation initiation regardless of infection etiology. It has been shown that fever in rat models activates additional inflammation inducing pathways that may be miRNA-regulated [ 74 ] which may suggest that a similar process is happening in a human host organism. During the feature selection process for diagnostic signature that could distinguish between bacterial and viral infections, five up-regulated miRNA in bacterial versus viral infections patient groups (specifically hsa-miR-136-5p , hsa-miR-513c-3p , hsa-miR-514a-5p , hsa-miR-514a-3p , and hsa-miR-507 ) were selected as a potential diagnostic signature to differentiate the etiology of infection. Two feature selection methods were combined: logistic regression with LASSO regularisation [ 56 ] and ANOVA [ 75 ]. The rationale behind this combination was the widespread use of LASSO regularised logistic regression in multi-omics data analysis and the comparative simplicity of ANOVA and its ability to reduce the occurrence of Type I and Type II errors in feature selection, particularly in small datasets with a large number of features [ 57 ]. The implemented logistic regression was using the one-vs-all principle, which is widely used in the selection of clinically significant feature: even when multiple groups are present, the samples are divided into the “relevant” class (which detection is the most significant for clinical interpretation) and the “other” class [ 45 ]. The miRNA diagnostic signature discovered has been shown to be a good classifier between bacterial infection and other patients. Furthermore, it has shown a higher sensitivity (90,9% versus 71%) than the currently widely used CRP levels, indicating the potential to provide more accurate diagnostics and avoid antibiotic administration when not necessary. Although the focus of this study is the exploratory discovery of the diagnostic signature of miRNA for bacterial and viral infections, blood derived mRNAs from the same patients were used to compare the discovered DEG profile with DEGs reported in various studies, and report on significantly overrepresented pathways. Most pathways discovered via functional annotation are connected to immune response (adaptive immune response, inflammatory response, defence response to virus, etc.) which corresponds with previous findings of the above-mentioned studies [ 9 , 59 – 65 ]. However, when comparing the DEG profile discovered in this study with other studies mentioned, it has been observed that only 49% and 23% of genes overlap between bacterial infections vs. controls and viral infections vs. controls accordingly. This highlights the fact previously described in the systematic comparison of published gene signatures for bacterial and viral infections: the gene signatures will vastly vary depending on the validation population and will perform best in the studied demographic specifically [ 12 ]. The genes highlighted in this study have reports on playing a role in immune system functions, namely WNT10B and FCRL3 [ 76 , 77 ]. ADAMTS5 has been mentioned as a virus-specific immunity regulator [ 78 ], therefore, may be of particular interest when studying infection biomarkers. Interactions of miRNAs from a selected diagnostic signature with DE genes of the same patients were identified between data from two different biological materials: urine and blood. Most of the literature on infection transcriptomics focuses on the whole blood transcriptome, and the objective of this exploratory pilot study was to investigate the whole blood transcriptome, while also exploring potential miRNA biomarker candidates in urine, given their prognostic value highlighted in other publications [ 69 , 71 , 79 ]. As there have been studies that have found the connection between the blood and urine transcriptome and miRNA content, which can be both cell-free and part of exosome cargo, our aim was to prove that complex immune changes during the infection would also be detectable in urine, as RNA in urine could be derived from RNA in bloodstream that has been filtered by the glomerulus [ 80 ]. Although there has been no direct proof of this statement to date, it has been observed that urine may contain tumor-derived nucleic acids from the non-urinary tract [ 81 ] and fetal DNA [ 82 ]. The results of this study further develop the possible connection between the blood and urine transcriptome and miRNA content in particular. The search of the literature to compare findings related to miRNA DE profiles in bacterial and viral infections yielded limited results, mainly due to the scarcity of relevant studies. Poore et al. (2018) study on miRNA diagnostic signatures for bacterial and viral infections in adult blood has not discovered a single statistically significant (FDR ≤ 0.01) DE miRNA between patients with viral infections and controls. When applying the same threshold to the results of this study, a miRNA was selected, hsa-miR-508-3p , which is upregulated in this comparison group. According to the literature, it is involved in apoptosis regulation which can be a part of non-specific immune response [ 83 ]; however, there is no further information on its connection to infectious diseases. When comparing DE miRNA lists of bacterial infections vs. controls and bacterial vs. viral infections, nine and two of DE miRNAs from this study match with the results of Poore et al. Until now, there have been limited reports on the regulatory role of the aforementioned diagnostic feature miRNA. Expression of hsa-miR-136-5p mediated the synthesis of molecules involved in inflammation formation - IL-1β, IL-6, TNF-α, IFN-α, IKKβ and NF-κB - in rat models [ 84 ]. All the mentioned compounds are involved in the formation of an innate immune response. hsa-miR-507 has been associated with chronic kidney disease, which may indicate its role in the inflammation response in the urinary tract [ 20 ]. hsa-miR-513c-3p is mentioned as a candidate biomarker for the distinction between HIV-1 and HIV-2 viral infections [ 85 ]. hsa-miR-514a-3p is involved in autophagy, which is an important part of the anti-inflammatory response [ 86 ]. As mentioned previously, the literature on miRNA infection markers is limited, restricting the possibilities of comparing results with other papers or validating the mentioned finds in independent cohorts. The study by Poore et al. (2018) mentioned above highlights five miRNAs to distinguish bacterial and viral respiratory infections (pneumonia caused by Streptococcus pneumoniae infection and H3N2 flu) - hsa-miR-942-5p , hsa-miR-342-5p , hsa- miR-503-5p , hsa-miR-199-5p and hsa-miR-30a-5p . Of five, none of the described miRNAs was selected as an important diagnostic signature. Logistic regression highlighted hsa-miR-30a-3p , which comes from the 3p arm of the same pre-miRNA as hsa-miR-30a-5p and may possess similar functions [ 87 ]. The observed differences between the study results could be explained by the fact that the overall quantity of miRNA in urine is much lower than that in blood [ 88 ], which may explain the different profile of discovered miRNAs. In addition, it is not known how the miRNA expression in blood and urine correlates, so it is possible that diagnostic signatures discovered by Poore and colleagues do not translate into expression in urine in the same way. While the linear regression method used during this study showed good performance in distinguishing bacterial infections from viral infections and controls, the same approach did not achieve the same results for determining viral infections from bacterial infections and controls. This corresponds to results from previously cited findings by Poore et al. (2018). The study by Hu et al. (2013), in addition, state that they have not found significant differences between gene expression profiles in afebrile virus-infected and afebrile non-infected children, which may prove that it is much harder to detect gene expression changes in virus-infected paediatric cohorts than in bacteria-infected cohorts. We acknowledge that our study, while showing promising results, has some limitations. This pilot study was limited by the small cohort size which may lead to the overfitting of the model. LOOCV was used to mitigate this risk and ensure the model captures general biological variability rather than being overly influenced by specific samples. Samples were collected at a single time point, which varied depending on the patient’s hospital admission day. A more standardised collection protocol, involving multiple time points during the infection, could reflect greater variability and enhance the model’s scaling to other cohorts. While patients with different bacterial infections were included to improve generalisation, this diversity may have limited the identification of specific miRNAs. On the contrary, the viral infection cohort included only patients with influenza, which could have the opposite effect. A larger and more diverse validation cohort would ensure the robustness of the identified diagnostic features and their suitability for clinical application. Additionally, the relationship between urine and blood transcriptomes remains unexplored, making it challenging to determine the scalability of the connections observed in this study. Conclusions To the best of our knowledge, this is the first study to use urine miRNA for the prioritisation of the diagnostic signature for infections that are not related to the urinary tract. The role of miRNA in the regulation of immunologic response is not well studied (Zhou et al., 2018), therefore, it requires further investigation. The insights obtained during this study can lead to the development of new concepts in this area of research. The urine miRNA diagnostic signatures identified in this study offer a rapid and precise tool for future molecular diagnostics, which could be used both in clinical practice and for broader research aimed at deepening the understanding of infection mechanisms. In emergency care, a quick and accurate differentiation between bacterial and viral infections is crucial for making decisions about the appropriate treatment protocols. The findings on miRNA expression in urine suggest the possibility of using urine to characterise systemic processes not only in the context of metabolomics [ 90 ], but also in transcriptomics. The comparison carried out in this study shows that the logistic regression model obtained distinguishes bacterial infections from other samples with higher precision than the other methods used. To further develop the insights gained from this pilot exploratory study, more data should be collected from a larger cohort of patients for a robust validation, and a more detailed comparison of this method with other currently available diagnostic techniques should be performed. Data Availability The gene expression datasets used and analysed during the current study is available in Gene Expression Omnibus under accession numbers GSE290693 and GSE290432 as well as in Riga Stradins University Dataverse from https://doi.org/10.48510/FK2/GZBT9C . The code used to generate and analyze the data is available on GitHub, DOI: 10.5281/zenodo.14845445 https://github.com/tkiselova/miRNA-biomarkers https://doi.org/10.48510/FK2/GZBT9C https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290432 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290693 Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Riga Stradins University (Nr.6-2/4/ 2, dated 25.04.2019) and was carried out in accordance with the Declaration of Helsinki. All legal guardians of the recruited subjects gave their written informed consent to participate. Consent for publication Not applicable. The manuscript does not contain any individual personal data. Availability of data and materials The gene expression datasets used and analysed during the current study is available in Gene Expression Omnibus under accession numbers GSE290693 and GSE290432 as well as in Riga Stradins University Dataverse from https://doi.org/10.48510/FK2/GZBT9C . The code used to generate and analyze the data is available on GitHub, DOI: 10.5281/zenodo.14845445 Competing interests The authors declare that they have no competing interests. Funding This study was supported by funding from Riga Stradins University (project name “Identification of bacterial vs viral infection biomarkers in children with fever by transcriptome analysis in urine”). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions DC and GZN contributed to the conception, design and securing the funding; GZN contributed to the collection of study materials and clinical data. TK and BV contributed to data bioinformatical pre-processing, analysis, interpretation and visualization. TK drafted the first version of the manuscript, all authors contributed to manuscript editing. All the authors have read and approved the final version of the manuscript. Additional files Additional file 1: .zip file containing all the Supplementary tables and figures. - S1fig_binary_panel.pdf: Supplementary Figure 1. Selection of miRNA binary signatures from urine to distinguish infection etiology in febrile children. (A) Heatmap along with agglomerative hierarchical cluster analysis for selected miRNA signature differentiating between bacterial infections and controls. (B) The ROC curve for bacterial infections vs. controls. (C) Heatmap along with agglomerative hierarchical cluster analysis for selected miRNA signature differentiating between viral infections and controls. (D) The ROC curve for viral infections vs. controls. (E) Heatmap along with agglomerative hierarchical cluster analysis for selected miRNA signature differentiating between bacterial infections and viral infections. (F) The ROC curve for bacterial infections vs. viral infections. - S1table_diagnosis_list.xlsx: Supplementary Table 1. List of confirmed diagnoses in bacterial infection patient group and viral infection patient group. - S2fig_ROC_curve.pdf: Supplementary Figure 2. ROC curve for model used for miRNA selection. - S2table_diff_expr.xlsx: Supplementary Table 2. A list of differentially expressed miRNAs comparing bacterial to viral infections. miRNAs were considered differentially expressed if they had a log2FC > 1 or < -1 and a nominal p-value < 0.05. - S3fig_mRNA_panel.pdf: Supplementary Figure 3. Selection of mRNA blood-based binary signatures to distinguish infection etiology in febrile children. (A) Heatmap with agglomerative hierarchical cluster analysis differentiating between bacterial infections and controls. (B) The ROC curve for bacterial infections vs. controls. (C) Heatmap with agglomerative hierarchical cluster analysis differentiating between viral infections and controls. - S3table_conf_metrics_binary.xlsx: Supplementary Table 3. Confusion matrix and associated performance metrics for models for the binary classification of urine miRNA between comparison groups. - S4table_miRNA_list.xlsx: Supplementary Table 4. Overview of selected miRNA diagnostic signatures. - S5table_mRNA_metrics.xlsx: Supplementary Table 5. Confusion matrix and associated performance metrics of LASSO regularized regression for the binary classification of blood mRNA between the bacterial infections group and controls. - S6table_Pearsons.xlsx: Supplementary Table 6. The results of the Pearson’s correlation analysis for miRNA and their respective regulated genes. The analysis was performed between the diagnostic signature of miRNA that distinguishes bacterial and viral infections in febrile children and differentially expressed transcripts among the groups of bacterial and viral infection patients in blood. A statistically significant correlation was defined as having an adjusted p-value < 0.05 and a correlation coefficient < 0. - S7table_diff_expr_BC_VC.xlsx: Supplementary Table 7. A list of miRNAs that are differentially expressed in the bacterial or viral patient group compared to the controls, but not differentially expressed when comparing the patient groups to each other. Acknowledgements We would like to thank the nurses, patients and their legal guardians for participating in this study. We would also personally like to thank our colleague Līvija Bārdiņa for technical support in preparation for the data analysis. List of abbreviations ANOVA Analysis of variance BH Benjamini-Hochberg multiple testing correction bp Base pair CRP C-reactive protein DAVID Database for Annotation, Visualization, and Integrated Discovery DE Differential expression DEG Differentially expressed gene FC Fold change FDR False discovery rate FN False negative FP False positive GO Gene ontology HIV Human immunodeficiency virus IFN Interferon IKKβ Inhibitor of nuclear factor kappa-B kinase subunit beta IL Interleukin IQR Interquartile range LASSO Least absolute shrinkage and selection operator LOOCV Leave-one-out cross-validation AUC Area under curve miRNA microRNA mRNA Matrix RNA NF-κB Nuclear factor kappa-B Nt Nucleotide PCA Principal component analysis PPV Positive predictive value RNA-seq RNA sequencing TMM Trimmed mean of the log expression ratios (M values) TN True negative TNF-α Tumor necrosis factor α TP True positive ROC Receiver operating characteristic curve References 1. ↵ Whitburn S , Costelloe C , Montgomery AA , Redmond NM , Fletcher M , Peters TJ , et al. 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