Advantages of Next-Generation Technologies in Comparison to Standard and Commonly Used Methods in the Identification of Colonized Bacteria in the Herniated Lumbar Disc
The prevalence of low back pain (LBP) due to lumbar disc herniation (LDH) was recorded as 31.9% in the year 2022. Studies carried out around the world have failed to confirm the primary cause of disc herniation. Among the multiple hypothesized contributing factors, a low-grade bacterial infection has been identified as one of the major causes of LDH. Researchers have reported that Propionibacterium acnes (P. acnes) is the predominant bacterial species isolated using culture-derived methods. However, biofilm formation leads to a low bacterial yield in culture methods. Although culture methods remain the gold standard for the identification of bacterial species, there is a growing need for the usage of advanced techniques that are more sensitive, reliable, less time-consuming, and precise. Advancement of high-throughput sequencing tools allows thorough mining of complete bacterial profiles, even for bacteria that are challenging to cultivate in conventional laboratory settings. Currently, both high-throughput sequencing and omics have opened a new avenue, providing clear evidence for addressing queries related to bacterial contamination that have been frequently addressed in culture isolates of herniated discs over the past few decades. This review evaluates how advanced techniques in microbial identification have revolutionized our understanding of bacteria in disc health. Traditional methods confirmed the existence of known bacteria, but advanced techniques revealed a vast, previously unseen diversity, challenging the output of culture-based methods. This new information has even overturned the understanding of the role of P. acnes in evaluating disc health. Advanced techniques have opened a window to the hidden world of microbes and have been attributed to altered views on bacterial communities in healthy and herniated discs.
1. Introduction
Lumbar disc herniation (LDH) is a localized displacement of the lumbar disc that bulges out from the demarcated regions of the space contour of the intervertebral disc (IVD) [1]. LDH is one of the major contributory factors for low back pain (LBP) [2] that affects subjects of all age categories [3]. Persistent pain associated with LDH has been found to induce prolonged depressive behaviors [2]. The incidence of LDH in people with LBP was found to be 31.9% [4] and the lifetime prevalence of LBP was recorded as 83% in Japan [5]. LBP continues to hold the highest number of global burden of disease on a worldwide scale [6]. By 2050, it is suggested that 843 million people will report LBP which is a 36.4% rise compared to 2020 [6].
LDH is the rupture of the annulus fibrosus (AF) because of lumbar disc degeneration or prolonged strain on the back [7]. Compression of dorsal and/or ventral nerve roots due to bulging discs results in symptoms such as low back discomfort, leg pain (sciatica), muscular spasm, and limited trunk movement [7]. A perspective proposed by Rajasekaran et al. [8]. Suggested that disc herniation frequently occurs due to endplate junction failure rather than rupture of AF.
Although the pathophysiology under LBP is often considered multifactorial [9], etiology of LDH is not clearly understood. Invasion of microbes [9–17], physical loading [18–20], obesity [21, 22], smoking [23], local inflammatory agents [24–26], and spinopelvic parameters [27, 28] are considered as few predisposing factors. This review aims to identify the bacteria in the lumbar disc of patients with LDH using different types of updated diagnostic tools in order to select a most suitable approach.
2. Microbes in the Development of LDH
Recent studies have documented the presence of various bacteria not only in degenerated IVD [29] but also in discs that exhibit modic changes [30, 31]. The precise role of these bacterial infections still remains uncertain. Studies have shown particularly discs infected with anaerobic bacteria exhibited a higher probability of developing modic changes in adjacent vertebrae [9, 11, 32, 33].
Studies confirmed the correlation of Propionibacterium acnes (P. acnes) in the development of modic changes and disc degeneration [11, 29, 33–35]. Based on the study findings, the presence of P. acnes was revealed in more than 35% of the cultured disc samples [13]. Histological findings emphasize the involvement of P. acnes in disc degeneration, after inoculating the wild-type strain of P. acnes in the IVDs of rabbits [35]. Furthermore, a similar study carried out for bacterial culture on a herniated lumbar disc concluded the presence of various bacterial isolates such as P. acnes, Gemella morbillorum, and coagulase-negative Staphylococcus species with positive isolations for anaerobic species (6%) and for aerobic species (12%) [10]. It is strongly suspected that the presence of P. acnes could play a pivotal role in the development of LDH [13, 33–38]. Presence of P. acnes is significantly higher in herniated discs with annular tears compared to those without such a tear [39]. Several studies have proposed rationalizing the role of P. acnes in the activation of the inflammatory microenvironment and in the pathogenesis associated with lumbar disc degeneration [34, 36, 37, 40–45]. The molecular mechanism for apoptosis of NP cells through the Toll-like receptor 2 (TLR2)/Jun N-terminal kinase pathway induced by P. acnes [37] shows the expression of specific short interfering RNA and competition for the binding of TLR2 to the TLR2 antagonist (CU-CPT22). This substantially decreases the increase in Bax and cleaves the caspase-3 enzyme induced by P. acnes. This mechanism suggests that TLR2 plays an important role in the induction of apoptosis by P. acnes [37]. This pathway is called NP apoptosis mediated P. acnes through the N-terminal kinase pathway TLR2/c-Jun and mitochondrial-mediated cell death [37]. Electron microscopy observations confirmed that P. acnes triggered autophagy in a specific form known as xenophagy [37]. Autophagy programing was highlighted as vital in maintaining NP cell survival and to trigger apoptosis. Variation in autophagic flux is observed in accordance with the different stages of disc degeneration [46]. Although previous studies have used the term apoptosis, recent studies have confirmed that pyroptosis is the main mechanism, driven by P. acnes through the pyrin domain of the reactive oxygen species NLR family that contains the 3 signaling pathway (ROS-NLRP3) [42]. The NLRP3 is a well-studied multiprotein complex secreted by the immune system in response to infection and their toxins [42, 43]. In the event of pyroptosis, substantial releases of IL-1β and IL-18 occur, exerting detrimental effects on adjacent healthy NP cells and exacerbating the condition of degeneration of the lumbar disc degeneration [42, 43]. Studies further confirmed the exacerbating effects of P. acnes infection in both in vitro and in vivo (rat models) [41, 45]. The effects of pyroptosis severely affect healthy NP cells to encourage IVD for degeneration and increase the expression of TNF-alpha, IL-1β, IL-6, matrix metalloproteinase-13, a disintegrin, and metalloproteinase with thrombospondin motifs 4, 5 and negatively regulate aggrecan and collagen II [42].
The plausible biological involvement of P. acnes in degenerative disc disease is highlighted, with IL-1β identified as the primary inflammatory mechanism that instrumented the host’s response to P. acnes infection [44]. This was further supported by a few other studies [42, 43]. P. acnes stimulate the secretion of IL-1β via an interaction between P. acnes-specific pathogen-associated molecular patterns and TLR2 found on the surface of NP and AF cells [36]. This interaction leads to the activation of nuclear factor kappa B (NF-κB) signaling pathway [41, 45] and results in the production of pro-IL-1β [44]. A similar pathway results from the hyaluronidase enzyme released by P. acnes [44]. Furthermore, released hyaluronidase digests the lubricating hyaluronic acid into short fragments within the disc matrix by activating NF-κB [43]. This leads to the production of pro-IL-1β which later matures to form IL-1β. IL-1-synthesized IL-1β can activate additional signaling pathways, triggering the synthesis of matrix metalloproteinase and a disintegrin and metalloproteinase with thrombospondin motif enzymes (ADAMTSs), which is responsible for the breakdown of the extracellular matrix [43]. Consequently, damage is generated associated with the molecular patterns of P. acnes [43]. The interaction further stimulates NF-κB, giving rise to a secondary positive feedback loop [43]. As a result, intensity of inflammatory response is aggravated by the effects of the degenerative process caused by P. acnes [44].
As shown in Figure 1, during degenerative disc disease, NP and AF cells secrete IL-1β; this in turn activates proteolytic enzymes including MMPs and ADAMTS 4/5 to invade the ECM. P. acnes produces pathogen-associated molecular pattern molecules (PAMPs) and ECM degradation products damage-associated molecular pattern molecules (DAMPs) that bind to TLRs and activate the NF-κB pathway [44]. This results in continuous production of IL-1β. PAMPs promote degenerative changes by synthesizing and increasing the IL-1 concentration of IL-1β by different pathways. It binds to TLRs and activates the NF-κB pathway and results in the synthesis of pro-IL-1β. Furthermore, P. acnes PAMP also activates NLRP3 and caspase I inflammasomes to induce the activation and secretion of IL-1β. IL-1β induces the production of chemokines that lead to the entrance of proteolytic matrix MMPs into the ECM. This, in turn, further promotes the degradation process. IL-1β itself binds to IL-1R and stimulates NF-κB pathway and further increases the secretion of IL-1β. The angular fissure is a characteristic feature of a degenerated disc commonly associated with nerve growth, and IL-1β stimulates the transcription of genes corresponding to the secretion of NGF and BDNF [44].
The diagrammatic pathway of P. acnes in the activation of IL-1 transcription of IL-1β and its enrichment by actively participating in various interconnected pathways [44]. ADAMTS-4/5, a disintegrin and metalloproteinase with thrombospondin motifs 4/5; AF, annulus fibrosus; BDNF, brain-derived neurotrophic factor; DAMP, damage-associated molecular patterns; PAMP, pathogen-associated molecular patterns; NLRP3, the pyrin domain of the NLR family that contains 3; ECM, extracellular matrix; IL-1R, interleukin-1 receptor; MMP, matrix metalloproteinase; NF-κB, nuclear factor kappa B; NGF, nerve growth factor; NP, nucleus pulposus; TLR, Toll-like receptors.
3. Culture Methods and Interference due to Biofilm
The microbiologic culture method is considered as the gold standard for its high sensitivity [47]. It is approximated that only a minimal percentage, ranging from 1% to 2% of microbial species have been successfully cultured [48]. Until recently, the study of microorganisms of interest was limited to a few isolates of source materials. This restriction arose from limitations in the composition of culture medium, which did not accurately reflect and mimic the dynamic nutrient supply present in the source environment [49, 50]. Hence, many microbes that are difficult to isolate using conventional laboratory techniques are ignored to be updated using the above method [51]. Enrichment culture is commonly believed to increase the chances of isolating previously uncultured microorganisms by increasing their abundance in the culture [52]. Enrichment culture methods can be formulated and applied to establish a model to dissect mixed cultures and investigate microbial dark matter [52].
In studies focused on bacterial cultures within herniated lumbar discs, P. acnes is well documented and has received attention in the literature [9, 11, 13, 29, 32, 33, 35–37] and has been identified as an opportunistic pathogen that induces the formation of biofilms around the herniated IVD [53, 54]. Furthermore, biofilms facilitate the processing of nutrients, cross-feeding, elimination of potentially harmful metabolic by-products, and establishment of a suitable physicochemical environment, such as maintaining an optimal oxidation-reduction potential [55]. Bacterial microcommunities are protected within a biofilm, formed by their own processes, or facilitated by the naturally favorable environment within the IVD [56]. In situ demonstration of the 3D biofilm structure of P. acnes in herniated lumbar disc tissue has been confirmed by DNA stain SYTO9 in disc samples [54]. Bacterial biofilms, which arise from both the organism and the disc environment, have the potential to play a role in disc herniation and discogenic pain [56]. This is attributed to the low isolation rate in culture techniques and the decrease in sensitivity due to false negative results [53, 54]. Therefore, sonication or homogenization is highly recommended for the mechanical breakdown of the biofilm to improve the isolation rate of the culture and is essential to prevent false negatives and obtain an accurate depiction of the microbial burden [53, 54].
Table 1 summarizes the traditional culture, PCR, and sanger sequencing methods for identification of bacteria. The respective table also emphasizes how the results are aligned to narrow down toward a particular bacterial species.
Table 1.
Traditional methods used in microbial analysis.
The study recruited approximately 67 subjects. Nuclear material evacuation during surgery was not feasible in three patients and magnetic resonance imaging was not obtained in the other three patients. Among the 61 study subjects, 28 cultures were positive. Positive anaerobic culture was observed in 26 subjects and 7% of the subjects had two microbes for both aerobic and anaerobic cultures. In 3% of the subjects, only aerobic bacteria were isolated. 16S rDNA PCR was applied using universal primers in all tissues. Anaerobic disc cultures that generated positive results for P. acnes gave a single-band appearance in 16S rDNA PCR and negative cultures resulted in negative PCR
Colonies isolated from culture plates were confirmed using 16S rDNA PCR. P. acnes was detected in 21 samples (n = 80). In aerobic culture, 5/80 were CoNS species.
Anaerobic culture isolates were identified using 16S rDNA PCR. In IVD, 16 out of 76 were positive for P. acnes. Histological stain revealed the visible morphological identity of P. acnes in 7 of 15 culture positives
2. Sanger sequencing for the positive 16S rDNA samples
3. FISH and confocal microscopy
With 16S rDNA PCR, bacteria were identified in both LDH subjects (16/51) and control subjects (6/7). Sanger sequencing was performed on PCR-positive samples, where 9/16 LDH subjects and 6/7 control showed positive results for the presence of bacteria. Only two LDH samples showed alignment with P. acnes
Tissue embedded bacterial aggregates accompanied by inflammatory cells were observed in 7/51 LDH subjects. P. acnes aggregates were found in two LDH subjects
P. acnes genome count and anaerobic culture were evaluated on all discs. Culture was positive in 130 (n = 290) patients, of whom 115 patients had P. acnes. CoNS was isolated in 31 cases and 3% were identified with α-hemolytic Streptococci. Mixed growth was observed in 24 patients. For quantification, the threshold limit was defined as 1 × 103 CFU/mL. The value above (1 × 103 CFU/mL) was declared as discs with abundant P. acnes and vice versa. Cases (n = 39) of culture-positive discs were found to be abundant with P. acnes. The count of P. acnes genomes was in the range of 2 to 58,331 in P. acnes-positive discs (n = 259) where the median was 260 genomes per 500 ng of total DNA. Significant difference (p < 0.0001) in P. acnes genome count was observed in discs with abundant P. acnes compared to discs with nonabundant P. acnes
For herniated disc tissue, 6/101 reported positive for anaerobic culture and 12/101 positive for aerobic culture. Aerobic culture isolates were identified as CoNS spp. The results of the anaerobic culture revealed the presence of P. acnes and Gemella morbillorum
Anaerobic culture was performed using tryptone soy broth in 46 patients. 16S rRNA PCR was applied using specific primers for P. acnes in all culture broths. P. acnes was confirmed in 9 of 43 discs.
LDH patients (n = 120) were included, and disc samples were subjected to aerobic and anaerobic culture. 50% of the discs showed bacterial growth. Anaerobic culture isolates were identified using the Rapid ID 32A kit. Identified P. acnes when analyzed with 16S rRNA PCR for specific P. acnes primers 46/120 gave positive results
Disc tissues (n = 37) were incubated in thioglycolate broth for 14 days and the presence of P. acnes was analyzed using specific 16S rRNA PCR primers, where 62.2% gave positive results
Anaerobic and 16S rRNA specific primers for P. acnes
Disc tissues (n = 108) were inoculated in tryptic soy broth and identified using 16S rRNA PCR specific primers; 23/108 showed positive results for P. acnes
4. Applications of High-Throughput Technologies for P. acnes Identification Across Diverse Body Sites Under Disease Context
P. acnes has been identified beyond its usual residence of skin flora. The presence of P. acnes was detected in the following infections: sarcoidosis, juvenile myelomonocytic leukemia (JMML), gastric cancer, dental infections, cervical disc disease, prostate cancer, and various soft tissue infections. A substantial number of studies in the context of the identification of P. acnes in different diseases have utilized traditional methodologies, instead of high-throughput techniques [60–66].
4.1. Sarcoidosis Infection
In a study conducted by Zhao et al. (2017) in assessing the bacterial profile on the lymph node biopsy from patients diagnosed with sarcoidosis, compared with the control group and tuberculosis group using 16S high-throughput sequencing, P. acnes has been exclusively identified in sarcoidosis group, where the control group and tuberculosis group did not show the presence of P. acnes. The following study has suggested a significant involvement of P. acnes with the disease status [67].
4.2. Gastric Cancer
High-throughput 16S rRNA sequencing was employed to investigate the bacterial dysbiosis of gastric microbiota and to identify the possible bacterial involvement in the incidence of gastric cancer using the Illumina method in gastric cancer tissues and adjacent normal tissues. Sequencing data detected greater abundance of P. acnes in gastric cancer tissues when compared to control tissues. This indicated the possible bacterial dysbiosis of mucosal microbiota occurred in this disease context [68].
A similar study aimed to examine the contribution of gastric microbiota in the risk incidence of gastric cancer. 16S rRNA sequencing revealed the highest detection rate of P. acnes from the specimens, and the study concluded that individuals with increased abundance of P. acnes are at an increased risk of developing gastric cancer [69].
4.3. JMML
A case report of a two-year-old baby, diagnosed with JMML. Traditional methods fail to isolate the pathogen, and most of the antibiotics become ineffective. NGS analysis revealed the presence of overabundance of P. acnes compared to controls [70].
4.4. Endocarditis
A study has been carried out to describe the management of P. acnes with methods such as blood culture, valve culture, valve sequencing via 16S, and histopathological demonstration. Valve sequencing via 16S was declared as the most reliable diagnostic method as it detected 95% of specimens positive for P. acnes including the patients who had taken antimicrobial therapy 26 days before surgery. Furthermore, histopathological examination of all patients had been validated for active infective endocarditis, to rule out the contamination query of P. acnes [71].
4.5. Prostate Cancer
Advanced molecular diagnostic methods such as 16S rRNA and total RNA sequencing tests have been carried out to assess the technology in detecting bacterial and viral pathogens in high-grade prostate cancer tissues. 16S sequencing results confirmed the presence of P. acnes in 95% of samples examined [72]. A similar study has been conducted on tissues such as tumor, peritumor, and nontumor for the detailed analysis of microbiome which could possibly address the pathologic etiology of prostate disease background using massive ultradeep pyrosequencing method. P. acnes has been found to be the predominant genera [73].
4.6. Chronic Meningitis
A high-throughput metagenomic shotgun sequencing was carried out to detect microbes in CSF samples in conditions like before and during the times of treatments. P. acnes sequences were identified, and reduction of the sequences was observed during the treatment phase and it was concluded that P. acnes as the causative pathogen for chronic meningitis [74].
4.7. Lung Abscess in Heart Transplantation
Pulmonary lesion was assessed for matrix-assisted laser desorption ionization mass spectrometry-time of flight and 16S rRNA sequencing tests on a 29-year-old patient who has undergone cardiac transplant 10 years ago. P. acnes was identified, and it was declared that the particular patient had long-term clinical history for seborrheic dermatitis, as such it was highlighted as a possible route entry for P. acnes as an opportunistic pathogen [75].
5. Advanced Techniques Used to Determine Microbes
5.1. Sequencing of Microbes
Genomic sequencing represents an initial illustration of “big data” technology due to the vast volume of data it generates [76]. This process produces a large amount of genetic information, characterized by its complexity and diversity [77]. Sequencing platforms are classified into generation gaps such as first generation, second generation, and third generation. Next-generation sequencing (NGS) has gained widespread recognition as a sequencing platform, primarily due to its exceptional high-throughput and massive parallel sequencing of millions of short fragment DNA with extensive applications and dramatic cost reduction [78, 79]. The 16S rRNA amplicon consists of nine hypervariable regions that account for the sequence diversity observed between different bacterial species [80]. Direct sequencing of 16S rRNA genes is a widely adopted approach to evaluate the taxonomy, abundance, and functional attributes of bacterial populations within complex microbial communities [81]. This method provides powerful information on the diverse bacterial population present in various environments. Long-read amplicon sequencing is applied to focus the entire ribosomal RNA operon targeting the combined 16S-ITS-23S regions [82]. This enables the retrieval of 16S and 23S gene sequences from a single read, enhancing the resolution of microbial communities up to the strain level. This approach also improves the precision of diversity, divergence measurements, and phylogenetic estimations [83, 84].
Furthermore, metagenomic sequencing is expected to play an important role alongside traditional diagnostic methods in the detection of pathogens in a more comprehensive way [85]. This method provides crucial data for various applications, including the detection of various microbial profiles with their comprehensive characterization, such as phylogenetic analysis, precise strain-level typing, characterization of antigenic epitopes, and antibiotic resistome of emerging pathogenic genomes [17, 86] and the discovery of novel pathogens [86].
Additionally, a comprehensive analysis of raw reads is performed using selected software packages, such as quantitative insights into microbial ecology and data analysis decision and action for taxonomic classification and diversity analysis [17]. Quantitative insights into the microbial ecology package executes a series of steps, such as quality control analysis, generating a mapping file, sequence demultiplexing, sequence clustering for the establishment of taxonomic unit setting, chimera removal, and taxonomy assignment [87]. Divisive amplicon denoising algorithm package 2 introduces an innovative quality-aware model for handling Illumina amplicon errors and ensures a comprehensive amplicon workflow [88]. Predictions of taxonomic and functional profiles within metagenomic data were made using software packages, such as phylogenetic investigations of communities by reconstruction of unobserved states and statistical analysis of taxonomic and functional profiles [17].
In the realm of molecular phylogeny, the exploration of organisms or genetic relationships involves the analysis of homologous DNA or protein sequences [89]. A phylogenetic tree serves as a graphical representation that illustrates the evolutionary relationships among different bacterial species, depicting their shared ancestry and divergence [90]. DecontaMiner is a new fully automated software tool for identifying potentially contaminated organisms in NGS sequenced samples. These contaminants can come from laboratory processes or are inherent to the biological source itself [91].
A metagenomic study on cartilage end plates and NP revealed that the normal IVD microbiome exhibits comparable bacterial diversity in both the end plate and the NP regions. These findings strongly suggest the presence of an endplate–NP axis within the IVD microbiome under normal conditions [92]. This axis shares microbial species with different population sizes and may play a significant role in maintaining the equilibrium of the microbiome within the IVD [92]. Another similar study on the abundance of bacteria and their biodiversity in the normal disc, the degenerate disc, and the herniated disc using 16S rRNA NGS observed that P. acnes was present in moderate percentages in all three groups, ranking 9th in the normal disc group with 3.07%, 8th in the disc degeneration group with 3.88% and 12th in the disc herniation group, comprising 1.56% of microbial population [17]. Furthermore, analysis of bacterial abundance revealed the presence of 355 different species in normal discs, while herniated discs exhibited 322 microbial species. These findings question the “sterile” nature of healthy lumbar discs and express a broad spectrum of bacterial distribution [17].
5.2. Improved Packages to Eliminate Contaminant Taxa From Sequence Data Obtained Through High-Throughput Technologies
Making contaminant-free DNA is a difficult task. Further, when amplification techniques like real-time PCR technique is considered a minute level of contamination, it can provide a significant impact in results due to high sensitivity [93, 94]. Contaminant sequences heavily impact the result interpretation and controversial queries in low-bacterial mass environments [94–96]. When it comes to the contaminant taxa analysis for a low microbial environment, assessment for contamination in controls, particularly negative control, is vital for the possible check to discriminate true microbiota from noise data [95]. Advanced computational bioinformatics and statistical algorithms have been developed in replying to contaminant sequences that may occur from high-throughput sequencing techniques such as 16S rRNA sequencing and shotgun metagenomics. These packages increased the accuracy for the sequence retrieved for microbial identification and their abundance in samples. As such, each software works under different kinds of principles to eliminate possible contamination [97] as shown in Table 2.
Table 2.
Details of technologies and the details regarding the methodology and working mechanism in minimizing the contaminant taxon interference in final sequence data.
The selection of the above mechanism depends on the presence of actual bacterial load in the clinical sample assessed [97]. Prevalence-based contamination method is designed for samples which originally have extremely low mass for microbes where frequency-based contamination is designed for samples which have high microbial load. Decontam eliminates the external contaminant taxa and not the cross contamination [97]
Synthesizing taxonomic tree and contamination removal
Statistical, computational and mathematical-related packages and working on classification score-based comparative approach
Recentrifuge is a powerful tool working on selective-based contamination removal including crossovers. It provides a score-based approach for comparative analysis of multiple samples, mainly on low numbers of microbe’s samples, where removal of contamination is essentially addressed. It focuses on novel approaches, which include statistical, computational and mathematical methods. It inspects the classification score at every step of the process. Recentrifuge initially synthesizes taxonomic trees and detects contaminant taxa through assessing control samples, as it has fewer reads than actual. Contaminants are noted in detail with their frequency of occurrence for each taxon appearing in controls. If the same taxa are observed in other samples, then, the algorithm grades the contamination status in each sample and removes contaminants [95].
Shotgun metagenomic sequencing in human plasma [100]
Statistical modeling analysis and metadata filtering
Microbial abundance and frequency
Squeegee: This is another de novo contamination detecting package, Squeegee prediction is compared with negative control data in low-bacterial load samples and calculates the exact removal of contaminants.
Since squeegee works without any prior idea about input dataset, stable microbial distribution data documentation in different sites in the body is performed to have an accurate check whether those microbes are ubiquitous to the particular site. Further it is helpful to detect the bacterial contamination that may arise while processing due to the external environment.
Squeegee separates taxa into contaminants and original data according to sample-specific microbes relying on the microbial abundance across different datasets. Since squeegee contaminant detection tool does not rely on negative control taxa details, it detects batch specific contaminants or cross contamination. According to the above package, diverse evidence such as the species prevalence rate, metagenomic distance of the samples relevant to certain species and the coverage of genomes of the respective species is needed to predict contamination species [96].
Under this method, combined ideas of Bayes algorithm and Gibbs sampling are used to assess the data from large bacterial 16S rRNA marker gene next-generation sequencing libraries
This program employs Bayesian algorithms to figure out and quantify agents that contributed to contamination in samples by modeling how microbes from a variety of environments mix into the environment [102].
Source tracker utilized the data or information from several species, and allows parallel estimation of proportion of different source environments contributing to a particular sink environment, including the rough estimate from unknown sources [103].
K-mer frequencies: were employed to make scaffolds
Single-copy genes
A software package designed to detect contaminant metagenome assembled genomes. Several methods were established to detect and address contamination in a eukaryotic genome assembly project:
K-mer frequencies were employed to make scaffolds according to the patterns from their DNA sequences. Then, bacterial single-copy genes analysis is performed to investigate the extent or to estimate the contamination from bacterial sources available in the assembly. Advanced visualization tools can be used to visualize the contaminants and to remove it effectively [105].
A newly discovered tool, designed to assess the comprehensiveness and to assess contamination status of a genome in a variety of sources such as isolated organisms, single cells, or metagenomic samples using lineage-specific marker genes. Genome contamination assessment can be estimated using number of multicopy marker genes detected in each marker set. CheckM works under specific statistical equations to estimate the contamination. Outcomes from different strains or species that are closely related are binned into a single putative genome and identified by assessing the average amino acid identity between multicopy marker genes [107].
Genomic quality control in microbial isolates [107].
5.3. Proteomics and Metabolomics for the Identification of Bacteria in LDH
Proteomics and metabolomics have been proven to be potent platforms to unravel biochemical changes within biological systems under specific environmental conditions [108]. Although advancements have been made to enhance sensitivity and specificity in detecting proteins and small metabolites, there are still challenges in interpreting data, especially when it comes to understanding the clinical implications of pathogenic bacteria [108].
To reveal the host–microbe interaction in a clear way, differentially expressed metabolites or metabolites unique to the microbe play crucial roles in the growth, survival, and intercellular communication pathways [12]. They can serve as valuable connectors connecting various microbial genera [12]. Fluctuations in the abundance of microbial populations dictate many alterations in the composition of the body’s metabolome composition [109]. Recent advances in omics approaches, which include phylogenetic marker-based microbiome profiling, shotgun metagenomics, metatranscriptomics, metaproteomics, and metabolomics, have facilitated effective characterization of microbial communities [110]. The protein–RNA–interacting network revealed novel regulatory mechanisms and identified key genes that could play crucial roles in the pathogenesis of IVD degeneration [111]. Data extracted from transcriptomics and proteomics offer a broader and more accurate understanding of gene expression and protein synthesis within the cell [112]. When proteins related to the complement cascade were considered, matrix reorganization, apoptosis, and angiogenesis were presented exclusively in these disc tissues [113]. Proteomics is capable of quantifying protein expression at femtomole levels, as well as the details regarding bacterial viability, proliferation, and dynamic host defense responses [113]. Though extended cultures and DNA amplification can reveal the presence of bacteria, only the identification of translated protein products can confirm active infection or host defense responses in disc tissues [113]. The detection of a larger number of proteins can be attributed to amplified immune and inflammatory responses in degeneration or infection, Furthermore, this study identified a close interaction between the biological process of defense response to the bacterium and numerous degradative proteins [113].
A recent investigation of lumbar discs microbial proteomics characterization has confirmed the existence of conserved bacterial ribosomal proteins, metabolic proteins, and functional proteins [69]. These findings signify the presence of bacteria, their associated biochemical pathways, and metagenomic pathways [69]. Furthermore, proteomics impartially eliminates the possibility of in vivo contamination by introducing comprehensive biochemical pathways that elucidate the interaction between microbes and the host and represent active ongoing infection [40, 113].
An initial investigation of the profile of bacterial metabolites was carried out in degenerate discs and normal disc cells to identify the presence of bacteria within disc tissues, focusing on microbe survival, colonization, and replication of microbes [12]. The findings suggested that both the control and the degenerated discs expressed microbial metabolites. However, significant variations in parameters such as concentration, peak levels, and spectral values were observed [13]. In particular, the investigation documented 64 microbe-derived metabolites, of which 39 were exclusively associated with the presence of bacteria. Furthermore, of the 39 metabolites listed above, nine were classified as primary metabolites that were associated with bacterial growth [13]. These metabolites were found to participate in three key pathways: autoinducer-2 biosynthesis, peptidoglycan biosynthesis, and chorismate metabolism [12]. The data obtained from metabolomics were used to construct detailed metabolic pathways. Bacterial identification was facilitated by pinpointing the specific metabolite expressed within the relevant pathways. Furthermore, the study confirmed the presence of metabolites that are uniquely associated with P. acnes, intestinal flora, and Mycobacterium tuberculosis [12]. The benefits and significance of high-throughput techniques in the context of diseases are summarized in Table 3.
Table 3.
High-throughput techniques and their benefits associated with disease contexts.
High-throughput techniques
Benefits and significance
16S rRNA sequencing
16S rRNA is found to be the frequently applied region for bacterial identification [114]. It becomes a valuable tool in detecting both known and novel bacteria [115, 116]. It provides details regarding species description like searching for species taxonomy, phylogenetic studies [114], and genetic evolutionary documentation in clinical and environmental samples. It is beneficial in detecting fastidious bacteria or truly unculturable bacteria [117] and also applicable in assessing taxonomic diversity (alpha-diversity or the beta-diversity indices) of bacterial species [115, 118] to account the bacterial abundance impact on certain disease conditions, as such 16S rRNA sequencing became a successful tool in significantly differentiating healthy vs, diseased status. Bacterial abundance resulting from 16S rRNA data showed that pathogenic bacteria exist in both the control and diseased subjects in various distributions, as 16S sequence data effectively categorize bacterial diversity which is getting altered with the clinical or health status of an individual [115]. Hence, 16S data is beneficial in the health sector to discriminate healthy vs. diseased subjects in relation to relative abundance of bacteria [68, 118–122].
It is considered to be more sensitive in bacterial detection and diagnosing infections [123–125] compared to conventional PCR tests. The 16S sequencing technology contributes towards the examination of bacterial etiology towards disease context. It is also considered cost-effective and rapid. As such, reduced turnaround time compared to the standard culture method up to antibiotic-sensitive assay. Individual assessment of bacterial distribution patterns will shift the treatment or antibiotic therapeutic strategies towards personalized medicine [126].
The 16S rRNA sequencing test is also beneficial to apply as a gut screening test, which could assist in the clinical diagnosis and to investigate microbial composition. In addition, 16S data has been also applied in determining the reference range for bacterial distribution in the gut microbiota by calculating bacterial abundance, and these data were employed to establish a central 99% reference range along with confidence intervals for every target [127]. Further, 16S data can be merged with sophisticated software such as PICRUSt. PICRUSt employs a computational way for predicting functional investigation of metagenomes, and this enables the possible metabolic pathways [128].
Shotgun metagenomics
SM sequencing allows the sequencing of all nucleotides in a sample, where 16S targets the presence of bacteria sequences can be used to identify broad-based pathogens that are difficult to isolate under culture techniques such as bacteria, fungi, virus, and parasites present in a sample [129–131]. It is also considered as a beneficial approach in the context of polymicrobial infection, due to its large coverage of organisms [129, 130]. In addition, MG data have shown a significant improvement in identifying bacteria to their genus as well as species and strain level [131] when compared to 16S sanger sequencing [132].
SM has also shown significantly better sensitivity on detection of the anaerobes [133] compared to culture techniques [131]. Traditional culture methods showed poor performance, with the broad panel of pathogen involvement in particular disease context, and antibiotic consumption impacts on the result reproducibility. Only a significant number of bacterial infections caused by Pasteurella multocida and Clostridium perfringens can only be identified by SM [131]. SM is highly sensitive, as it can detect presence of bacteria even in the presence of low bacterial DNA levels in macroscopically ‘healthy tissue’ demonstrating a potential subclinical infection spread [131]. MG data can be used to calculate relative abundance of clinically important bacterial species and antibiotic resistance genes of multidrug-resistant bacteria. In addition to that, it also provides functional information of bacterial metabolic pathways and molecular functions using sophisticated metabolic analysis networks [129, 130, 134]. MG-RAST metagenomic analysis detected the virulence factors such as motility or chemotaxis and uptaking of iron components. MG data are beneficial in comprehensive characterization of bacterial strain’s resistome and virulome [133, 135]. Big data generation including taxonomic as well as functional information such as antimicrobial resistance, virulence data, and metabolic network provide advanced details in the microbiological sector. These results will enhance the explanation behind infectious pathophysiology in the near future for a betterment in medical care [131, 135]. MG studies are used to establish the prevalence of gut protozoa across different culturally diverse populations. MG has the capability to identify an unlimited number of eukaryotic symbionts in parallel by adhering to a single sampling and analysis [136]. This technique also estimates the relative bacterial abundance from gut microbiota. In addition, it also can be used to construct an in silico metabolic model called a genome scale metabolic network model for bacterial pathway prediction [137].
Proteomics
Proteomics can be applied for the detection and quantification of complete protein sets [138]. It offers a greater species-level resolution in the identification of bacteria [139] than 16S technique [140]. Proteomic techniques can be applied to compare and evaluate genetic content and protein abundance found in surface of bacterial strains [138].
This technique has been also considered as a promising tool to detect disease status, for example, prostate cancer [141]. It also aids to discover novel insights of specific protein expression during the times of acute bacterial and viral infections; as such, this approach lays the foundation for biological discoveries of innovative early therapeutic targets and identification of diagnostic as well as prognostic biomarkers associated with disease context [141–144]. Quantitative proteomics is applicable to explain the pathogen entry and underscore the understanding disease-related biomarkers behind host–microbe interaction and host protein–protein interaction during infection [145–147] and it also contributes to cellular proteome profiling of S. aureus on assessing immune response to daptomycin antibiotic treatment and to discover comprehensive analysis of infectious bacterial proteins and antibacterial molecular mechanism of daptomycin [148]. Analysis of results on metaproteomics emphasizes the variation or fluctuation in microbiome based on disease location, with the dietary habits demonstrating greatest advantage for Crohn’s disease patients with colonic engagement compared to those with ileal-only disease [149]. Proteomics has laid the foundation in immune modulation mechanisms on host–cell responses during infection where disease pathogenesis varies with steps such as engulfment, endocytosis, and other signaling pathways. These variations impact the expression patterns of adhesion and invasion proteins. The above outcomes of the study suggest that both host and pathogen factors determine commensal or infectious switching nature of certain bacteria in the human body [150].
Metabolomics
Although proteomics offers valuable information, the bottleneck part of this concept is lacking performance in identifying varieties of posttranslational modifications of a single protein that causes crosstalk among signal pathways. Metabolomics offer a comprehensive metabolic signature specific for a particular disease condition by highlighting the variation in specific metabolites. It also investigates the interaction between microbiota and disease-oriented metabolites [151–153]; as such, it provides new insights for microbial interaction which impacts disease pathologic status. The above understanding paves the way for targeted therapeutic interventions [154–157] such as drug discovery includes novel microbial metabolites with antimicrobial, anticancer, or immunomodulatory mechanisms [158]. A distinct pattern of metabolite variation associated with a particular disease context may lead to the selection of accurate biomarkers for early detection and disease monitoring precision medicine [159]. Furthermore, interconnecting metabolomics and microbiome data marks the foundation for host–microbial intervention from disease pathogenesis to management [156, 159]. Metabolomics incorporation with the machine learning approach is beneficial for early detection of diseases and to predict the outcomes of particular disease using the biomarkers or metabolites which provide significant contribution for the disease pathophysiology. Improved algorithm applications lead to disease characterization and transition of treatment strategy towards precision medicine in the future. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.
Abbreviations: PCR, polymerase chain reaction; PICRUSt, phylogenetic investigation of communities by reconstruction of unobserved states; MG-RAST, metagenomic rapid annotations using subsystems technology; MG, metagenomic.
6. Conclusions
Until recent years, the potential impact of microbial contamination in the disc was hypothesized not only during sampling but also during disc-based experimental processes. Advanced techniques have overcome this ambiguity by employing bioinformatic pipelines, software packages, metagenomic analysis, and biochemical pathways that produce specific functional proteins and microbial metabolic proteins. Sequencing alone may not differentiate native bacteria and contaminant taxa, as it sequences the presence of all bacterial DNA. Hence, we suggest employing appropriate contaminant removing algorithms combined with sophisticated bioinformatics packages recommended for sequencing would enhance the outcomes.
The omics laboratory, with its interactive knowledge, has not only resolved the limitations of traditional practices but has expanded the scope and outcomes. In addition, omics with different compartments such as metaproteomics, metabolomics, and transcriptomics have successfully enhanced the results from the interference of contaminant taxa by distinguishing functional bacterial components such as proteins/metabolites/transcriptomes in host biological pathways rather than merely identifying all the DNA present. Hence, these functional roles support the differentiation of actual bacterial presence from contaminant taxa. The innovation of advanced omics and sequencing techniques have revolutionized the understanding of the microbial world, revealed a world of unseen diversity, and competed with the gold standard long-term culture-based methods. The uncertainties in the culture method are rectified by the precision and comprehensiveness of these advanced high-throughput approaches, providing a much deeper understanding of microbial function and community dynamics. Metagenomics examines the combined genomic potential of a community, providing unparalleled roles into functional diversity and interspecies interactions. By dominating the unreliability of culture and enabling access to the new microbial world, these techniques fundamentally remodify our understanding of microbial communities and their roles in human health and beyond it. As such, the present review evidenced that herniated lumbar discs have a bacterial community, through the intestinal/skin/spinal axis and cutting-edge technologies is the recommended tool not only to identify microbes but also to evaluate their role in disc health.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding
This study was financially supported by the University of Sri Jayewardenepura, Grant No.: ASP/01/RE/AHS/2021/87.
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