Experimental tests strongly challenge the evidence of a healthy human blood microbiome

Experimental tests strongly challenge the evidence of a healthy human blood microbiome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Experimental tests strongly challenge the evidence of a healthy human blood microbiome Leandro Di Gloria, Simone Baldi, Lavinia Curini, Sara Bertorello, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3921147/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The advent of next-generation sequencing (NGS) technologies has made it doable to investigate the microbial communities in various environments, including all the different human body districts. Hence, the previously established belief of several body sites’ sterilities, including human blood, has now been challenged. However, the metagenomics investigation of habitats with an anticipated low microbial biomass may be susceptible to many and dangerous misinterpretations. Here, we critically evaluate the results of 16S targeted amplicon sequencing performed on the total DNA collected from healthy donors’ blood samples in the light of specific negative controls aimed at addressing potential bias in order to supply the research in this area with trustworthy data. Our negative controls, were prepared by inflating the initial DNA quantity through sequences that can be recognised and subsequently discarded. We found that only three organisms resulted sporadically present among the samples, mostly attributable to bacteria ubiquitous in laboratory reagents. Despite we are not fully able to confirm or deny the existence of healthy blood microbiota, our results strongly suggest that living bacteria, or at least their residual DNA sequences, are not a common and peculiar feature of human blood in healthy people. Finally, our study poses relevant questions on the design of controls in this peculiar research topic in order to avoid spurious results that appear to contaminate current high throughput research. Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The high diversity within the prokaryotic domain allows bacteria and archaea to thrive in almost every environment of the world, including other organisms. Notably, bacteria inhabit many human tissues forming symbiotic relationship with the host, constituting the so-called microbiome, a crucial entity considered on par with human organs [ 1 ]. Under physiological conditions, our immune system and physiological barriers such as the gut-vascular and the blood-brain barriers, prevent the microbial colonization of districts that should remain sterile in healthy subjects. This understanding has prevailed until recent years when advancement in next-generation sequencing (NGS) has enabled a cost-effective molecular survey of microbes in body habitats, challenging established beliefs. Among these faiths there is the view that the blood of healthy subjects is a sterile environment [ 2 ]. Recent NGS technologies, have revealed the presence of bacterial genera such as Pseudomonas , Propionibacterium , Halomonas , Streptomyces , Sphingomonas, Staphylococcus , Leifsonia , Bacillus , Flavobacteria , Serratia etc. in healthy blood samples [ 2 – 5 ]. However, authors highlighted potential source of contamination and procedural errors in these studies. As early as 2001, Nikkari and colleagues documented the presence of 16S DNA in healthy blood samples, and empathized that bacteria detected may derive from used reagents or incorrect sampling [ 6 ]. In detail, microbes from skin flora including Cutibacter , Streptococcus, Sphingomonas and Bacillus genera are well recognized to contaminate blood cultures, with the venepuncture process introducing skin microbes into the samples [ 7 – 9 ]. Moreover, due to the high sensitivity of NGS sequencing to noises and contaminants [ 5 , 6 , 10 ], it has been established that the presence of contaminants, referred to as “kitome”, is unavoidable in both DNA extraction and PCR kits, especially when targeting low microbial biomass environments such as blood [ 10 – 14 ]. The sequencing procedure can itself contribute to ecological diversity inflation in samples, introducing potential issue like “index hopping”, polymerase errors and contamination from residual sequences deriving from prior sequencing runs [ 13 , 15 ]. Furthermore, PCR-based analysis of low bacterial biomass tissues involves off-target amplification of the host DNA due to the overwhelming prevalence of human cells [ 16 ]. Despite these challenges, many authors have hypothesized that DNA reads obtained from sequencing healthy blood samples may belong to bacteria characteristic of this human district, proposing the existence of a “human healthy blood microbiota (HBM)”. Remarkably, in 2022, Khan and colleagues defined the existence of a blood microbiota as "already established", speculating that bacteria from organs, especially the gut, may translocate into the circulatory system without epithelial impairment using a yet unknown mechanism [ 17 ]. According to current literature [ 2 ], the potential HBM is mainly composed by Proteobacteria. The out-of-gut origin of blood microbes contrasts with the well-established prevalence of Firmicutes, Bacteroidetes and Actinomycetes in the gut [ 17 , 18 ]. Other authors suggest that a more likely source of blood bacteria may be the skin or the oral mucosa [ 2 , 17 ]. Conversely, in 2016 Santiago and colleagues explored the serum microbiota composition in cirrhotic patients and reported that 69% of the identified bacterial sequences in their negative controls belonged to the Proteobacteria phylum [ 5 ], mimicking the profile often attributed to the healthy blood microbiota. In 2023, Tan and collaborators raised a strong counterpoint, reporting no common species among 9.770 healthy human blood sequences collected from databases and concluding that hypothetical blood microbiota members may be sporadic microbes that transiently migrate in the bloodstream [ 19 ]. Regardless, it is crucial to consider that the detected DNA sequences may originate from destroyed bacteria rather than viable ones, with relevance for the concept of microbiota as a community of living organism that interact among themselves and with the host in defining the so-called holobiont [ 20 ]. In addition, in this intricate scenario adopting different DNA extraction protocols could further complicate this research, impacting the quality, quantity and purity of extracted DNA or its preservation, subsequently affecting the detectable bacterial sequences in the blood [ 17 , 21 – 23 ]. Given the complexities mentioned above, delving into this challenging t opic requires many precautions, notably the inclusion of negative controls in the study design. However, this undertaking is not always straightforward, particularly with negative controls composed of pure sterile water, which are often challenging to sequence. This difficulty hampers our ability to finely discern which reads may be influenced by the several biases described. So, the primary aim of this study was to investigate the microbial composition of healthy human blood, employing controls that may permit the discrimination between contaminants and potential blood commensals. The ultimate goal was to provide insights into the potential existence and characteristics of HBM. In fact, c onfirming the presence of commensal bacteria or their DNA fragments in healthy blood would valid the hypothesis regarding their interaction with the immune system, potentially unveiling new biomarkers. METHODS Sample collection and DNA extraction In this study we collected EDTA-anticoagulated whole blood from six healthy Italian volunteers (three male and three female), ranged in age from 25 to 35 years. For each subject, the first mL of blood has been discarded to reduce the risk of contamination from skin microbes. Once collected, the samples have been immediately processed as described below. In addition, two different types of controls, prepared by inflating the initial DNA quantity through sequences that can be recognised and subsequently discarded, have been included. Specifically, our controls included 1) the off-target DNA control (OTC) from C2C12 cells (immortalized mouse myoblast cell line) cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with fetal bovine serum (FBS) 10%, penicillin and streptomycin and washed three times in p hosphate buffered saline (PBS) solution before being collected; 2) the microbial DNA from a E.coli negative control (ENC) extracted from an overnight, kanamicin selected LB culture of kanamycin resistant Escherichia coli strain K12 at 1:1000 inoculum. For both samples and controls, we used two different DNA extraction kits, namely the DNeasy® Blood & Tissue Kit (Qiagen, Hilden, Germany) (specifically designed for the purification of total DNA from the blood environment) and the QIAamp® DNA Microbiome Kit (Qiagen, Hilden, Germany) (designed for enhancing the purification of DNA from intact bacterial cells though lysis of host cells and enzymatic digestion of free DNA prior to the lysis of bacteria), following manufacturer instructions. Hence, a total of 16 DNA samples has been collected from 6 subjects and 2 controls. 16S Sequencing and Bioinformatics Analysis The extracted DNA was sent to IGA Technology Services (Udine, Italy) for 16S amplicon paired-end sequencing (2 x 300 cycles, 50.000 reads) on the MiSeq Illumina platform, according to the Illumina 16S Metagenomic Sequencing Library Preparation protocol. In particular, the V3–V4 hypervariable region has been amplified using the primer pair 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACNVGGGTWTCTAATCC). The demultiplexed sequence reads were processed in the QIIME2 2022.8 environment. Briefly, the sequencing primers and the reads without primers were removed using the Cutadapt tool. DADA2 was used to perform paired-end reads filtering, merging and chimeras removal steps after trimming low quality nucleotides from both forward and reverse reads. Hence, ASVs (amplicon sequence variants) were generated and the taxonomic assignments were performed through Vsearch using the SILVA SSU database (release 138). Every sequence unassigned at the domain taxonomic level or associated to chloroplasts or mitochondria according to SILVA has been discarded. Moreover, every cross-amplified host DNA has been identified and removed by aligning the ASVs to GRCm39 (murine reference genome) and GRCh38 (human reference genome) through Bowtie2 2.2.5. Finally, every genus with a mean relative abundance less than 0.01% (computed considering also the host DNA in the total library size) has been removed to further avoid probable contaminants [ 11 , 24 ]. In addition, every genus found in less than three out of twelve blood samples has been considered as a potential contaminant or at least as far from being a common healthy blood bacteria and accordingly discarded. The analysis of bacterial communities was performed in R 4.3.0. The packages phyloseq 1.44.0, vegan 2.6-4, ggplot2 3.4.2 and ggvenn 0.1.10 were used to plot data and results. A rarefaction analysis on genera was performed on every sample using the function rarecurve (step 100 reads), further processed to highlight saturated samples (arbitrarily defined as samples with a final slope in the rarefaction curve with an increment in genus number per reads < 1e-4). The most abundant bacteria have been defined according to average percentage abundance among the samples. Principal Coordinate Analyses (PCoAs) were performed using the Hellinger distance on Hellinger transformed genera abundances to address the compositionality of the data [ 25 ]. Finally, a Venn Diagram has been used to display the genus shared between ENCs and blood samples. Further details about the processing of reads and the subsequent bacterial community analysis are reported as a publicly available scripts (see Data Availability section). RESULTS Percentage of reads retained after quality and abundance filtering The sequencing of all samples was successful, except the failure of the DNA sample extracted with the QIAamp® DNA Microbiome Kit. A total of 690228 reads has been obtained of which 451628 (65.4%) originated from samples extracted with the QIAamp® DNA Microbiome Kit and 238600 (34.6%) from samples extracted with the DNeasy® Blood & Tissue Kit. From a further check, the obtained data showed different sequencing depths among the samples although they have been sequenced in the same lane and run (Fig. 1 ). Indeed, ENC controls extracted with the two kits feature the largest number of reads, amounting to 56.3% of the total obtained reads. Excluding ENC samples, the majority of acquired reads were discarded during the quality filtering steps, leaving an average of 7.5% of sequences for subsequent analyses. Specifically, samples processed with QIAamp® DNA Microbiome Kit exhibited a lower loss of reads after filtering compared to samples processed with DNeasy® Blood & Tissue Kit (9.3% vs 4.0%). The abundance of human off-targets DNA was significantly lower in samples extracted though QIAamp® DNA Microbiome Kit (3.8% of total sequences) compared to the samples extracted with the DNeasy® Blood & Tissue Kit (40.8% of total sequences). The additional filters based on relative abundances and prevalence had a limited impact on decreasing the reads’ number, excluding approximately the 0.2% of the original reads (Fig. 1 ). Although the majority of reads have been discarded through the aforementioned filters, each sample appears to be saturated ( Figure S1 ). Therefore, it is conceivable that increasing the sequencing depth would not lead to the identification of further bacteria. Taxa distribution in the whole dataset After filtering, the dataset retained a total of twenty-five genera (Table 1 ), among which twenty-three successfully classified at genus rank and two classified only at family rank. All the genera were members of 6 phyla, namely Proteobacteria, Actinobacteriota, Bacteroidota, Firmicutes, Verrucomicrobiota and Campilobacterota phyla. Anyway, the complete list of genera and samples in which they were found is provided in Table 1 . Of note, each genus has been identified in at least one sample, using both DNA extraction kits, with the exception of Flavobacterium and Lachnospiraceae_ND3007 which were detected only in samples extracted with DNeasy® Blood & Tissue Kit. However, nearly every genus was also detected in the negative controls, as discussed in more detail below. Table 1 Table of all identified bacterial taxa. The presence is confirmed when a particular genus is found in at least one sample within the groups, denoted by an asterisk (*) in the corresponding column. The four groups are: C-DBT (controls processed with DNeasy® Blood & Tissue Kit), C-MIC (controls processed with QIAamp® Microbiome kit), B-DBT (blood samples processed with DNeasy® Blood & Tissue Kit) blood samples and B-MIC (blood samples processed with QIAamp® Microbiome kit). Phylum Genera C-MIC C-DBT B-MIC B-DBT Proteobacteria Escherichia-Shigella * * * * Proteobacteria Burkholderia-Caballeronia * * * * Actinobacteriota Leifsonia * * * * Proteobacteria Sphingomonas * * * * Actinobacteriota Cutibacterium * * * * Bacteroidota Bacteroides * * * * Firmicutes Clostridia_UCG-014 * * * Proteobacteria Methylobacterium-Methylorubrum * * * * Verrucomicrobiota Akkermansia * * * Firmicutes Ruminococcus * * * Proteobacteria Family Xanthobacteraceae * * * * Campilobacterota Helicobacter * * * Proteobacteria Pseudomonas * * * Actinobacteriota Bifidobacterium * * * * Bacteroidota Flavobacterium * * Firmicutes Lactobacillus * * Firmicutes Phascolarctobacterium * * Firmicutes Roseburia * * * Firmicutes Lachnospiraceae_ND3007 * * Bacteroidota Alistipes * * * Bacteroidota Prevotella * * * Firmicutes Christensenellaceae_R-7 * * * Firmicutes Family Lachnospiraceae * * * Firmicutes Subdoligranulum * * * * Firmicutes Coprococcus * * Comparison of Control and Blood Sample Microbial Profiles The most abundant phyla in blood samples were Proteobacteria (60,67%), Actinobacteriota (16,37%), Firmicutes (10,42%), Actinobacteriota (16,37%) and Verrucomicrobiota (2,01%) (Fig. 2 A) while the five most represented genera were Burkholderia-Caballeronia-Paraburkholderia (51,94%), Leifsonia (13,87%), Sphingomonas (5,53%), Bacteroides (5,56%) and Clostridia UCG-014 (3,18%) (Fig. 2 B). Apart from their abundances, each of these taxa's traces were found in control samples as well. Specifically, the abundances in OTC sample extracted with DNeasy® Blood & Tissue Kit, closely resemble those observed in blood samples. Furthermore, upon removing E. coli reads from the control samples (used as spike signal) the relative abundances of the contaminant bacteria become more apparent (Fig. 2 C, 2 D). Consequently, the abundances in negative controls closely resembled those observed in blood samples (Fig. 2 C, 2 D ). We further compared samples’ profiles through a PCoA which revealed dissimilarity between the controls and the blood samples. Only the OTC sample extracted with DNeasy® Blood & Tissue Kit clustered with blood samples ( Fig. 3 A ) . The same analysis after excluding E. coli from those controls showed that three out of four controls closely resemble blood samples, strongly suggesting a similar abundance profile. The only exception was the OTC sample extracted with the DNA Microbiome kit, which exhibited a distinct profile, positioning in the region that was originally occupied by E. coli controls ( Fig. 3 B ) . Moreover, with the exclusion of OTC samples, the two extraction kits did not lead to notably different profiles for each sample pair. When considering samples derived from the same subject, some pairs exhibit greater dissimilarity, while others appear more similar, seemingly without a discernible pattern. (Fig. 3 A and B ). Investigating the unexpected behaviour of the mis-positioned OTC mice control that clustered in the E. coli region, we observed that the large majority of the reads in this control were actually dominated by E. coli reads. The removal of such reads, reasonably deriving from cross-contamination by E. coli samples, led in fact to a greater decrease of the two main coordinates (indicating less variation among samples) and the positioning of the contaminated OTC much closer to all other samples that eventually appear as ensemble of unresolved individuals in the community. Three genera, Coprococcus , Lactobacillus and Phascolarctobacterium , were exclusively identified in blood samples and absent in negative controls (Fig. 4 A). More specifically, each of these three genera was found in lower abundance in no more than three samples from different donors and from samples processed with different extraction kits (Fig. 4 B). DISCUSSION The ongoing discussion on the sterility of healthy blood has significant implications for the upcoming research, especially in the physiology field. Nevertheless, our study underscores that exploring this topic is a complex journey fraught with challenges stemming from both technical and environmental sources, introducing noise and contaminants. We explored the presence of a potential blood microbiome in samples from six healthy subjects, utilizing two distinct DNA extraction kits: one specifically designed for extracting microbial DNA from blood and the other intended to eliminate human DNA while isolating microbial DNA. In addition, we introduced two control samples rich of known microbial DNA for an easier identification and exclusion: one comprising mice DNA (off target control), and another enriched with E. coli DNA. In our experience, negative controls consisting of pure sterile water are often challenging to sequence due to the insufficient total amount of DNA detected after PCR, especially given the cycle number characteristic of the conventional Illumina protocol. While this outcome validates the overall sterility of the workflow, it does not allow us to discern which reads may stem from the various biases described. To overcome this limitation, we hypothesize the possibility of "enhancing" these negative controls by increasing the initial DNA quantity through sequences that can be recognized and subsequently discarded. Firstly, we noted an unsuccessful sequencing attempt for one healthy blood sample processed with the QIAamp® DNA Microbiome Kit. However, all other samples, including the corresponding sample from the same subject processed with the DNeasy® Blood & Tissue Kit, and even the negative controls, were successfully sequenced. Nevertheless, we view this issue as a valuable clue regarding the estimable microbiota richness, at least for this particular subject. Overall, the samples obtained using the DNeasy® Blood & Tissue Kit feature a high number of off-target sequences despite the specificity of the used primers, implying an extremely low bacterial biomass, if any. Conversely, the samples obtained through the QIAamp® DNA Microbiome Kit, designed to remove host DNA, were nearly devoid of host sequences. However, irrespective of the DNA extraction kit, only a limited number of sequences for each sample passed the quality filters during the FASTQ processing, except for the E.coli controls which underwent processing with no substantial loss of reads. This outcome confirms both the unusual behaviour of the blood sample sequences and the processing effectiveness. Similar challenges were also encountered during the processing of the OTC samples. Indeed, Glassing and colleagues reported that when sequencing DNA extracted from blood samples with the MoBio PowerMax® Soil DNA Isolation Kit, they obtained around 2000 sequences. However, despite the significantly greater depth of sequencing, only 25% of these sequences were identified as prokaryotes [ 11 ]. Additionally, the authors noted that only minor traces of the genera Anaerostipes , Mogibacterium , Subdoligranulum , Halocella and Sphingobium were exclusively present in their blood samples and not in blank controls, as per their abundance filters. Among these genera only reads from Subdoligranulum have been identified in our blood samples as well as in our controls. Specifically, in our dataset, after applying the abundance filters, only twenty-five genera were detected, despite the permissive thresholds used. In agreement with previous reports, the most abundant phylum that we have observed in blood samples is Proteobacteria. Given the speculations about the potential sources of bacteria in healthy blood, it’s noteworthy that this phylum has been reported as the most abundant in the healthy lungs [ 26 ]. However, it is also frequently the most abundant in blank controls [ 5 , 14 ], a pattern consistent with our control samples. Notably, the most abundant genera identified in blood samples were found also in each control, exhibiting the same abundances profile, particularly in the mice DNA control processed with DNeasy® Blood & Tissue Kit and in the E.coli controls after discarding the spike signal . Probably explanations of these results include the possibility that these bacteria originate from an environmental contamination. These remnants could than been sequenced alongside the off-target DNA. However, we observed a distinct microbial profile in the mice DNA control processed with the QIAamp® DNA Microbiome Kit. We assume that the variability in controls processed with different kits could be attributed to 1) differences in the “kitome” of the two extraction kits; 2) stochastic amplification of extremely low abundant DNA templates [ 27 , 28 ]; 3) different contaminants present in various samples within a single sequencing lane [ 29 ]. Finally, to further explore the distinctive features distinguishing healthy blood samples from the controls, we checked which bacterial DNA have been exclusively sequenced in the blood samples. This analysis revealed that the genera Coprococcus , Lactobacillus and Phascolarctobacterium have been identified only in blood samples, regardless of the extraction kit used, but only in a limited number of specimens. This outcome can be attributed to their actual presence in the healthy blood of only some individual or, alternatively, to a shared portion of the kitome present in both extraction kits or introduced by the PCR reagents. Notably, these sequences were produced in some samples in our dataset, with the exception of the controls. In detail, both Lactobacillus and Coprococcus are recognized as common bacteria of intestinal flora and as possible contaminants from the DNA extraction kit [ 11 , 30 ]. Meanwhile Phascolarctobacterium is also an intestinal commensal genus, not reported as known kit contaminants, but identified in traces in only two out six subjects. It’s relevant to know that many contaminants are often associated to gastrointestinal tract or skin [ 11 ] whereas the community profile of our samples doesn’t align with either of those environments. Moreover, as far as we know, Phascolarctobacterium has not been identified in any other research on healthy blood microbiota. Conversely, Coprococcus was reported as a potential blood bacterium by Jagare and colleagues. However, its abundance overcame the threshold chosen by the authors only in the blood of patients with gut disease, not in healthy subjects and negative controls, where it was also detected [ 31 ]. For these reasons, we cannot conclusively assert that these bacterial traces are common features of human blood. On the other hand, the most abundant genera in our dataset are constantly present in every blood sample, even though each of these taxa is also detected in every control and reported as common contaminants originating from reagents [ 10 , 11 , 30 ]. It is remarkable that the reported most abundant genera alone constitute the majority of our dataset. Even considering the high sensitivity of PCR amplification, the prevalence of well-known contaminants as the most abundant genera implies an abundance of the hypothetical true healthy blood bacteria close or equal to zero. Although it is conceivable that some bacteria or related DNA sequence in laboratory kits may overlap with those found in the healthy blood, it seems improbable that this holds true for each of them. Nevertheless, we do not rule out the possibility that some living bacteria, or at least traces of them, may sporadically and transiently be present in bloodstream of healthy subjects. However, such occurrence does not match with an established ecological community such as the microbiota. CONCLUSIONS Our analyses and methodologies strongly support the relevance of considering issues and risks when exploring the healthy blood environment or similar ones through the 16S rRNA NGS. Based on our data, we cannot definitively affirm or deny the existence of healthy blood bacteriota. However, the described results cast relevant doubts on the notion that certain bacteria, or their residual DNA sequences, are a common and distinctive feature of the healthy human blood. Consequently, we firmly emphasize that, at current state of knowledge, further, extremely careful research is needed before asserting the existence of a healthy blood microbiota. STRENGTH AND LIMITATIONS OF THIS STUDY Although a modest number of subjects have been recruited for this study, we assert the reliability of our conclusions as they were derived by identifying consistent patterns and presences that are expected to be characteristic of a particular environment, rather than relying solely on statistical comparisons with a larger sample size. To strengthen our observations, the present data are in agreement with findings frequently reported in other larger-scale studies. Of course, expanding the number of samples and controls would strengthen our conclusion. Nevertheless, we have highlighted a concurrence in the profiles of our controls and the healthy blood samples, a finding that we consider highly informative. Furthermore, our study paves the way to designing a more targeted procedural approach in this research area or, at least, aims to underscore the need for precautions that must be adopted when exploring such topics through NGS. Implementing relative abundance filters is advisable to avoid contaminants and sequencing errors in low bacterial biomass environments but, inevitably, arbitrary threshold values are applied. In this regard, we settled our thresholds after an accurate investigation of the literature attempting to achieve permissive yet efficient filtering strategies. Finally, it is remarkable that we purposely did not perform in silico decontaminations based on the comparison with the controls. This choice was made to focus on evaluating results achievable by sequencing the blood samples and the kitome itself, without incorporating statistics whenever possible. However, alternative approaches, such as the use of decontamination algorithms, may change the estimated taxonomic profile of the blood samples. In all cases, the raw reads in FASTQ format released as publicly available to allow any re-analysis with different settings. Declarations Author Contribution AA, MR, EN, and LDG made substantial contributions to the conception and design of the work. MR, AA, and LDG contributed to the design of the work. SBa, SBe, GN, LC, LDG, and FC were involved in the acquisition and analysis of data. LDG, EN, MR, SBa, and AA contributed to the interpretation of data. EN, LDG, and SBa drafted the work, and AA and MR revised it. All authors have approved the submitted version (and any substantially modified version that involves the author's contribution to the study) and have agreed to be personally accountable for their own contributions. They also agree to ensure that questions related to the accuracy or integrity of any part of the work, even those in which they were not personally involved, are appropriately investigated, resolved, and documented in the literature ACKNOWLEDGEMENTS This work was supported by European Union NextGenerationEU and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP) with the projects MNESYS and THE - Tuscany Health Ecosystem - Mission 4 Component 2 - Investment 1.5; This work was further supported by the FONZIE project. DATA AVAILABILITY The sequencing reads for samples used in this study have been deposited in the NCBI GEO (Gene Expression Omnibus) database under the accession code GSE254843. Analysis scripts for the bioinformatics processing of reads and for the microbial community analyses are freely available at https://github.com/LeandroD94/Papers/tree/main/2024_seeking_blood_microbiota. References Najmanova L, Videnska P, Cahova M. Healthy microbiome - a mere idea or a sound concept? Physiol Res. 2022;71:719–38. Castillo DJ, Rifkin RF, Cowan DA, Potgieter M. The Healthy Human Blood Microbiome: Fact or Fiction? Front Cell Infect Microbiol. 2019;9:148. Damgaard C, Magnussen K, Enevold C, Nilsson M, Tolker-Nielsen T, Holmstrup P, et al. Viable bacteria associated with red blood cells and plasma in freshly drawn blood donations. PLoS One. 2015;10:e0120826. Dinakaran V, Rathinavel A, Pushpanathan M, Sivakumar R, Gunasekaran P, Rajendhran J. Elevated levels of circulating DNA in cardiovascular disease patients: metagenomic profiling of microbiome in the circulation. PLoS One. 2014;9:e105221. Santiago A, Pozuelo M, Poca M, Gely C, Nieto JC, Torras X, et al. Alteration of the serum microbiome composition in cirrhotic patients with ascites. Sci Rep. 2016;6:25001. Nikkari S, McLaughlin IJ, Bi W, Dodge DE, Relman DA. Does blood of healthy subjects contain bacterial ribosomal DNA? J Clin Microbiol. 2001;39:1956–9. Moriyama K, Ando C, Tashiro K, Kuhara S, Okamura S, Nakano S, et al. Polymerase chain reaction detection of bacterial 16S rRNA gene in human blood. Microbiol Immunol. 2008;52:375–82. Ramirez Galleymore P, Gordon Sahuquillo M. Antisepsis for blood culture extraction. Blood culture contamination rate. Med Intensiva (Engl Ed). 2019;43 Suppl 1:31–4. Opota O, Croxatto A, Prod'hom G, Greub G. Blood culture-based diagnosis of bacteraemia: state of the art. Clin Microbiol Infect. 2015;21:313–22. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014;12:87. Glassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 2016;8:24. Stinson LF, Keelan JA, Payne MS. Identification and removal of contaminating microbial DNA from PCR reagents: impact on low-biomass microbiome analyses. Lett Appl Microbiol. 2019;68:2–8. Nearing JT, Comeau AM, Langille MGI. Identifying biases and their potential solutions in human microbiome studies. Microbiome. 2021;9:113. Olomu IN, Pena-Cortes LC, Long RA, Vyas A, Krichevskiy O, Luellwitz R, et al. Elimination of "kitome" and "splashome" contamination results in lack of detection of a unique placental microbiome. BMC Microbiol. 2020;20:157. Degnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183–94. Walker SP, Barrett M, Hogan G, Flores Bueso Y, Claesson MJ, Tangney M. Non-specific amplification of human DNA is a major challenge for 16S rRNA gene sequence analysis. Sci Rep. 2020;10:16356. Khan I, Khan I, Jianye Z, Xiaohua Z, Khan M, Hilal MG, et al. Exploring blood microbial communities and their influence on human cardiovascular disease. J Clin Lab Anal. 2022;36:e24354. Rinninella E, Raoul P, Cintoni M, Franceschi F, Miggiano GAD, Gasbarrini A, et al. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms. 2019;7. Tan CCS, Ko KKK, Chen H, Liu J, Loh M, Consortium SGKH, et al. No evidence for a common blood microbiome based on a population study of 9,770 healthy humans. Nat Microbiol. 2023;8:973–85. Bordenstein SR, Theis KR. Host Biology in Light of the Microbiome: Ten Principles of Holobionts and Hologenomes. PLoS Biol. 2015;13:e1002226. Lam NY, Rainer TH, Chiu RW, Lo YM. EDTA is a better anticoagulant than heparin or citrate for delayed blood processing for plasma DNA analysis. Clin Chem. 2004;50:256–7. Foley C, O'Farrelly C, Meade KG. Technical note: Comparative analyses of the quality and yield of genomic DNA from invasive and noninvasive, automated and manual extraction methods. J Dairy Sci. 2011;94:3159–65. Kotikalapudi R, Patel RK. Comparative study of the influence of EDTA and sodium heparin on long term storage of cattle DNA. Cell J. 2015;17:181–6. Karstens L, Asquith M, Davin S, Fair D, Gregory WT, Wolfe AJ, et al. Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments. mSystems. 2019;4. Legendre P, Legendre L. Chapter 7 - Ecological resemblance. In: Legendre P, Legendre L, editors. Developments in Environmental Modelling: Elsevier; 2012. p. 265–335. Bingula R, Filaire E, Molnar I, Delmas E, Berthon JY, Vasson MP, et al. Characterisation of microbiota in saliva, bronchoalveolar lavage fluid, non-malignant, peritumoural and tumour tissue in non-small cell lung cancer patients: a cross-sectional clinical trial. Respir Res. 2020;21:129. van Oorschot RA, Ballantyne KN, Mitchell RJ. Forensic trace DNA: a review. Investig Genet. 2010;1:14. Buckleton J. Validation issues around DNA typing of low level DNA. Forensic Sci Int Genet. 2009;3:255–60. Lusk RW. Diverse and widespread contamination evident in the unmapped depths of high throughput sequencing data. PLoS One. 2014;9:e110808. Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations. Trends Microbiol. 2019;27:105–17. Jagare L, Rozenberga M, Silamikelis I, Ansone L, Elbere I, Briviba M, et al. Metatranscriptome analysis of blood in healthy individuals and irritable bowel syndrome patients. J Med Microbiol. 2023;72. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3921147","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272579889,"identity":"00f4bed8-cc4b-4693-a40b-969dfff00102","order_by":0,"name":"Leandro Di Gloria","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Leandro","middleName":"Di","lastName":"Gloria","suffix":""},{"id":272579890,"identity":"53086b2d-fe93-4c46-997e-81d975820517","order_by":1,"name":"Simone Baldi","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Baldi","suffix":""},{"id":272579891,"identity":"434f8596-bc03-4417-9e55-b462f016a463","order_by":2,"name":"Lavinia Curini","email":"","orcid":"","institution":"Centro Cardiologico Monzino, IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Lavinia","middleName":"","lastName":"Curini","suffix":""},{"id":272579892,"identity":"17c962a9-452e-41cf-a579-fa7468b6c8be","order_by":3,"name":"Sara Bertorello","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Bertorello","suffix":""},{"id":272579893,"identity":"b330dea8-221c-4edb-a1d1-47aae57b7d65","order_by":4,"name":"Giulia Nannini","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Nannini","suffix":""},{"id":272579894,"identity":"e6c30527-dbd9-47a3-a071-f32fae7af99f","order_by":5,"name":"Francesco Cei","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Cei","suffix":""},{"id":272579895,"identity":"48e448c1-dcfe-4fa1-b2a2-4031499ca024","order_by":6,"name":"Elena Niccolai","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Niccolai","suffix":""},{"id":272579896,"identity":"55e4cb3a-0770-43e0-a95b-1fdf202d7065","order_by":7,"name":"Matteo Ramazzotti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYEgANvYGBoYEGwkGNpxKmBkbkHgGEmw8B4Ba0oBacOpB18IgkQCk00D2Ydeg237++IMPFQz5/NMOP/74o+JPHZ/kG7MHDxIsGPjkG7BqMTuTzNg44wyD5YzbaQYGEmeADpPOMTdISMDtMLMDyYzNvG0MBgy3EwwSDNvAWswkEn/g0XL+MWPzX6AW+dvpHw4kgrRInjGTwGvLDaAtjEAtBrdzDBsOgrRI8BDS8thwZs8ZCQPD2znFjA1njCXbeNLKQFp42NgScDgs8cGHHxU2BnK30zcDQ0yOX7798DbJHwl1cvLNB7BbAwESmEI8+NSPglEwCkbBKMAPAE/iUc3rDwKRAAAAAElFTkSuQmCC","orcid":"","institution":"University of Florence","correspondingAuthor":true,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Ramazzotti","suffix":""},{"id":272579897,"identity":"084707b7-1fd9-4d86-9e51-40ee71e43eb0","order_by":8,"name":"Amedeo Amedei","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Amedeo","middleName":"","lastName":"Amedei","suffix":""}],"badges":[],"createdAt":"2024-02-02 14:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921147/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51120342,"identity":"91fd2be3-27ca-475e-a55f-38e4aec4272c","added_by":"auto","created_at":"2024-02-14 13:16:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":381243,"visible":true,"origin":"","legend":"\u003cp\u003eBar plots displaying the abundances of reads in samples as a whole (green), after quality filters (orange) and after contaminant removal (red). The percentage of removed off-target reads is reported at the top of each bar. The Y-axis is scaled to enhance the visibility of lower abundances.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3921147/v1/c3c700d4a65ee1d2ccbff255.png"},{"id":51120343,"identity":"80d09d23-d1f9-4db6-a4ea-4b001798b3cd","added_by":"auto","created_at":"2024-02-14 13:16:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":533523,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of percent abundance of the five most abundant phyla (A, C) and genera (B, D) in negative controls and samples before (A, B) and after (C, D) removing \u003cem\u003eE. coli\u003c/em\u003e from the controls where it was used as spike. “Others” includes every taxon below rank 5.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3921147/v1/f0e7e9f165f5fd05bb139c7a.png"},{"id":51120344,"identity":"9b2e44fb-7538-4ad4-9d28-badd00e84c4e","added_by":"auto","created_at":"2024-02-14 13:16:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":317455,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA-based community analysis on Hellinger distances. The label on each point shows the type of control samples or the identity of the 6 healthy donors. A) Non-processed samples B) Samples after removing \u003cem\u003eE. coli\u003c/em\u003e specific reads from the ENC. C) Samples after removing \u003cem\u003eE. coli\u003c/em\u003e specific reads from all the controls. OTC (off-target control): genomic DNA form C2C12 cultured mouse cells. ENC (\u003cem\u003eE. coli\u003c/em\u003e negative control): genomic DNA from Escherichia coli.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3921147/v1/7cca5e4a25424cf2e44b208e.png"},{"id":51120346,"identity":"b5813bf4-55ee-4990-b028-83c7e15a0025","added_by":"auto","created_at":"2024-02-14 13:16:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":314249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxa \u003c/strong\u003edistribution. A) Venn diagram illustrating the number of genera shared among controls (grey) and blood samples obtained using extraction kits (coral, blue). B) Stacked bar plot representing the cumulate percent abundance of the three genera exclusive of the healthy blood group. Abundance is calculated by summing up the percent abundance across each sample (12 samples, totalling 1200%). The Y-axis breaks between 50 and 1200 have been hidden to improve the visibility of lower values.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3921147/v1/fef10ecb0847435d33f15537.png"},{"id":55799584,"identity":"bc00d736-509e-47da-b574-c87ccf175169","added_by":"auto","created_at":"2024-05-03 12:13:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921147/v1/b948ff58-af2e-46e1-a0b1-e3459212545a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experimental tests strongly challenge the evidence of a healthy human blood microbiome","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe high diversity within the prokaryotic domain allows bacteria and archaea to thrive in almost every environment of the world, including other organisms. Notably, bacteria inhabit many human tissues forming symbiotic relationship with the host, constituting the so-called microbiome, a crucial entity considered on par with human organs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Under physiological conditions, our immune system and physiological barriers such as the gut-vascular and the blood-brain barriers, prevent the microbial colonization of districts that should remain sterile in healthy subjects. This understanding has prevailed until recent years when advancement in next-generation sequencing (NGS) has enabled a cost-effective molecular survey of microbes in body habitats, challenging established beliefs. Among these faiths there is the view that the blood of healthy subjects is a sterile environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent NGS technologies, have revealed the presence of bacterial genera such as \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003ePropionibacterium\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eSphingomonas, Staphylococcus\u003c/em\u003e, \u003cem\u003eLeifsonia\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eFlavobacteria\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e etc. in healthy blood samples [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, authors highlighted potential source of contamination and procedural errors in these studies. As early as 2001, Nikkari and colleagues documented the presence of 16S DNA in healthy blood samples, and empathized that bacteria detected may derive from used reagents or incorrect sampling [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In detail, microbes from skin flora including \u003cem\u003eCutibacter\u003c/em\u003e, \u003cem\u003eStreptococcus, Sphingomonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e genera are well recognized to contaminate blood cultures, with the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003evenepuncture process\u003c/span\u003e introducing skin microbes into the samples [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, due to the high sensitivity of NGS sequencing to noises and contaminants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it has been established that the presence of contaminants, referred to as \u0026ldquo;kitome\u0026rdquo;, is unavoidable in both DNA extraction and PCR kits, especially when targeting low microbial biomass environments such as blood [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sequencing procedure can itself contribute to ecological diversity inflation in samples, introducing potential issue like \u0026ldquo;index hopping\u0026rdquo;, polymerase errors and contamination from residual sequences deriving from prior sequencing runs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, PCR-based analysis of low bacterial biomass tissues involves off-target amplification of the host DNA due to the overwhelming prevalence of human cells [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these challenges, many authors have hypothesized that DNA reads obtained from sequencing healthy blood samples may belong to bacteria characteristic of this human district, proposing the existence of a \u0026ldquo;human healthy blood microbiota (HBM)\u0026rdquo;. Remarkably, in 2022, Khan and colleagues defined the existence of a blood microbiota as \"already established\", speculating that bacteria from organs, especially the gut, may translocate into the circulatory system without epithelial impairment using a yet unknown mechanism [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to current literature [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the potential HBM is mainly composed by Proteobacteria. The out-of-gut origin of blood microbes contrasts with the well-established prevalence of Firmicutes, Bacteroidetes and Actinomycetes in the gut [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Other authors suggest that a more likely source of blood bacteria may be the skin or the oral mucosa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, in 2016 Santiago and colleagues explored the serum microbiota composition in cirrhotic patients and reported that 69% of the identified bacterial sequences in their negative controls belonged to the Proteobacteria phylum [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], mimicking the profile often attributed to the healthy blood microbiota. In 2023, Tan and collaborators raised a strong counterpoint, reporting no common species among 9.770 healthy human blood sequences collected from databases and concluding that hypothetical blood microbiota members may be sporadic microbes that transiently migrate in the bloodstream [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless, it is crucial to consider that the detected DNA sequences may originate from destroyed bacteria rather than viable ones, with relevance for the concept of microbiota as a community of living organism that interact among themselves and with the host in defining the so-called holobiont [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, in this intricate scenario adopting different DNA extraction protocols could further complicate this research, impacting the quality, quantity and purity of extracted DNA or its preservation, subsequently affecting the detectable bacterial sequences in the blood [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the complexities mentioned above, delving into \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ethis\u003c/span\u003e challenging \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003eopic requires many precautions, notably the inclusion of negative controls in the study design. However, this undertaking is not always straightforward, particularly with negative controls composed of pure sterile water, which are often challenging to sequence. This difficulty hampers our ability to finely discern which reads may be influenced by the several biases described.\u003c/p\u003e \u003cp\u003eSo, the primary aim of this study was to investigate the microbial composition of healthy human blood, employing controls that may permit the discrimination between contaminants and potential blood commensals. The ultimate goal was to provide insights into the potential existence and characteristics of HBM. In fact, \u003cem\u003ec\u003c/em\u003eonfirming the presence of commensal bacteria or their DNA fragments in healthy blood would valid the hypothesis regarding their interaction with the immune system, potentially unveiling new biomarkers.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and DNA extraction\u003c/h2\u003e \u003cp\u003eIn this study we collected EDTA-anticoagulated whole blood from six healthy Italian volunteers (three male and three female), ranged in age from 25 to 35 years. For each subject, the first mL of blood has been discarded to reduce the risk of contamination from skin microbes. Once collected, the samples have been immediately processed as described below.\u003c/p\u003e \u003cp\u003eIn addition, two different types of controls, prepared by inflating the initial DNA quantity through sequences that can be recognised and subsequently discarded, have been included. Specifically, our controls included 1) the off-target DNA control (OTC) from C2C12 cells (immortalized mouse myoblast cell line) cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with fetal bovine serum (FBS) 10%, penicillin and streptomycin and washed three times in \u003cem\u003ep\u003c/em\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehosphate buffered saline\u003c/span\u003e (PBS) solution before being collected; 2) the microbial DNA from a \u003cem\u003eE.coli\u003c/em\u003e negative control (ENC) extracted from an overnight, kanamicin selected LB culture of kanamycin resistant \u003cem\u003eEscherichia coli\u003c/em\u003e strain K12 at 1:1000 inoculum.\u003c/p\u003e \u003cp\u003eFor both samples and controls, we used two different DNA extraction kits, namely the DNeasy\u0026reg; Blood \u0026amp; Tissue Kit (Qiagen, Hilden, Germany) (specifically designed for the purification of total DNA from the blood environment) and the QIAamp\u0026reg; DNA Microbiome Kit (Qiagen, Hilden, Germany) (designed for enhancing the purification of DNA from intact bacterial cells though lysis of host cells and enzymatic digestion of free DNA prior to the lysis of bacteria), following manufacturer instructions.\u003c/p\u003e \u003cp\u003eHence, a total of 16 DNA samples has been collected from 6 subjects and 2 controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e16S Sequencing and Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eThe extracted DNA was sent to IGA Technology Services (Udine, Italy) for 16S amplicon paired-end sequencing (2 x 300 cycles, 50.000 reads) on the MiSeq Illumina platform, according to the Illumina 16S Metagenomic Sequencing Library Preparation protocol. In particular, the V3\u0026ndash;V4 hypervariable region has been amplified using the primer pair 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACNVGGGTWTCTAATCC).\u003c/p\u003e \u003cp\u003eThe demultiplexed sequence reads were processed in the QIIME2 2022.8 environment. Briefly, the sequencing primers and the reads without primers were removed using the Cutadapt tool. DADA2 was used to perform paired-end reads filtering, merging and chimeras removal steps after trimming low quality nucleotides from both forward and reverse reads. Hence, ASVs (amplicon sequence variants) were generated and the taxonomic assignments were performed through Vsearch using the SILVA SSU database (release 138). Every sequence unassigned at the domain taxonomic level or associated to chloroplasts or mitochondria according to SILVA has been discarded. Moreover, every cross-amplified host DNA has been identified and removed by aligning the ASVs to GRCm39 (murine reference genome) and GRCh38 (human reference genome) through Bowtie2 2.2.5. Finally, every genus with a mean relative abundance less than 0.01% (computed considering also the host DNA in the total library size) has been removed to further avoid probable contaminants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, every genus found in less than three out of twelve blood samples has been considered as a potential contaminant or at least as far from being a common healthy blood bacteria and accordingly discarded. The analysis of bacterial communities was performed in R 4.3.0. The packages phyloseq 1.44.0, vegan 2.6-4, ggplot2 3.4.2 and ggvenn 0.1.10 were used to plot data and results. A rarefaction analysis on genera was performed on every sample using the function rarecurve (step 100 reads), further processed to highlight saturated samples (arbitrarily defined as samples with a final slope in the rarefaction curve with an increment in genus number per reads\u0026thinsp;\u0026lt;\u0026thinsp;1e-4). The most abundant bacteria have been defined according to average percentage abundance among the samples. Principal Coordinate Analyses (PCoAs) were performed using the Hellinger distance on Hellinger transformed genera abundances to address the compositionality of the data [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, a Venn Diagram has been used to display the genus shared between ENCs and blood samples. Further details about the processing of reads and the subsequent bacterial community analysis are reported as a publicly available scripts (see \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003eData Availability\u003c/span\u003e section).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePercentage of reads retained after quality and abundance filtering\u003c/h2\u003e \u003cp\u003eThe sequencing of all samples was successful, except the failure of the DNA sample extracted with the QIAamp\u0026reg; DNA Microbiome Kit. A total of 690228 reads has been obtained of which 451628 (65.4%) originated from samples extracted with the QIAamp\u0026reg; DNA Microbiome Kit and 238600 (34.6%) from samples extracted with the DNeasy\u0026reg; Blood \u0026amp; Tissue Kit.\u003c/p\u003e \u003cp\u003eFrom a further check, the obtained data showed different sequencing depths among the samples although they have been sequenced in the same lane and run (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Indeed, ENC controls extracted with the two kits feature the largest number of reads, amounting to 56.3% of the total obtained reads. Excluding ENC samples, the majority of acquired reads were discarded during the quality filtering steps, leaving an average of 7.5% of sequences for subsequent analyses. Specifically, samples processed with QIAamp\u0026reg; DNA Microbiome Kit exhibited a lower loss of reads after filtering compared to samples processed with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit (9.3% vs 4.0%). The abundance of human off-targets DNA was significantly lower in samples extracted though QIAamp\u0026reg; DNA Microbiome Kit (3.8% of total sequences) compared to the samples extracted with the DNeasy\u0026reg; Blood \u0026amp; Tissue Kit (40.8% of total sequences).\u003c/p\u003e \u003cp\u003eThe additional filters based on relative abundances and prevalence had a limited impact on decreasing the reads\u0026rsquo; number, excluding approximately the 0.2% of the original reads (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the majority of reads have been discarded through the aforementioned filters, each sample appears to be saturated (\u003cb\u003eFigure S1\u003c/b\u003e). Therefore, it is conceivable that increasing the sequencing depth would not lead to the identification of further bacteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTaxa distribution in the whole dataset\u003c/h2\u003e \u003cp\u003eAfter filtering, the dataset retained a total of twenty-five genera (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), among which twenty-three successfully classified at genus rank and two classified only at family rank. All the genera were members of 6 phyla, namely Proteobacteria, Actinobacteriota, Bacteroidota, Firmicutes, Verrucomicrobiota and Campilobacterota phyla. Anyway, the complete list of genera and samples in which they were found is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of note, each genus has been identified in at least one sample, using both DNA extraction kits, with the exception of \u003cem\u003eFlavobacterium\u003c/em\u003e and \u003cem\u003eLachnospiraceae_ND3007\u003c/em\u003e which were detected only in samples extracted with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit. However, nearly every genus was also detected in the negative controls, as discussed in more detail below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of all identified bacterial taxa. The presence is confirmed when a particular genus is found in at least one sample within the groups, denoted by an asterisk (*) in the corresponding column. The four groups are: C-DBT (controls processed with \u003cem\u003eDNeasy\u0026reg;\u003c/em\u003e Blood \u0026amp; Tissue Kit), C-MIC (controls processed with QIAamp\u0026reg; Microbiome kit), B-DBT (blood samples processed with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit) blood samples and B-MIC (blood samples processed with QIAamp\u0026reg; Microbiome kit).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenera\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-MIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC-DBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB-MIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB-DBT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia-Shigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia-Caballeronia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLeifsonia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSphingomonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCutibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClostridia_UCG-014\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerrucomicrobiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAkkermansia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFamily Xanthobacteraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCampilobacterota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHelicobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFlavobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhascolarctobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRoseburia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLachnospiraceae_ND3007\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlistipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChristensenellaceae_R-7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFamily Lachnospiraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSubdoligranulum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoprococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Control and Blood Sample Microbial Profiles\u003c/h2\u003e \u003cp\u003eThe most abundant phyla in blood samples were Proteobacteria (60,67%), Actinobacteriota (16,37%), Firmicutes (10,42%), Actinobacteriota (16,37%) and Verrucomicrobiota (2,01%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) while the five most represented genera were \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e (51,94%), \u003cem\u003eLeifsonia\u003c/em\u003e (13,87%), \u003cem\u003eSphingomonas\u003c/em\u003e (5,53%), \u003cem\u003eBacteroides\u003c/em\u003e (5,56%) and \u003cem\u003eClostridia UCG-014\u003c/em\u003e (3,18%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eApart from their abundances, each of these taxa's traces were found in control samples as well. Specifically, the abundances in OTC sample extracted with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit, closely resemble those observed in blood samples. Furthermore, upon removing \u003cem\u003eE. coli\u003c/em\u003e reads from the control samples (used as spike signal) the relative abundances of the contaminant bacteria become more apparent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Consequently, the abundances in negative controls closely resembled those observed in blood samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further compared samples\u0026rsquo; profiles through a PCoA which revealed dissimilarity between the controls and the blood samples. Only the OTC sample extracted with DNeasy\u0026reg; Blood \u0026amp; Tissue Kit clustered with blood samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The same analysis after excluding \u003cem\u003eE. coli\u003c/em\u003e from those controls showed that three out of four controls closely resemble blood samples, strongly suggesting a similar abundance profile. The only exception was the OTC sample extracted with the DNA Microbiome kit, which exhibited a distinct profile, positioning in the region that was originally occupied by \u003cem\u003eE. coli\u003c/em\u003e controls \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Moreover, with the exclusion of OTC samples, the two extraction kits did not lead to notably different profiles for each sample pair. When considering samples derived from the same subject, some pairs exhibit greater dissimilarity, while others appear more similar, seemingly without a discernible pattern. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInvestigating the unexpected behaviour of the mis-positioned OTC mice control that clustered in the \u003cem\u003eE. coli\u003c/em\u003e region, we observed that the large majority of the reads in this control were actually dominated by \u003cem\u003eE. coli\u003c/em\u003e reads. The removal of such reads, reasonably deriving from cross-contamination by \u003cem\u003eE. coli\u003c/em\u003e samples, led in fact to a greater decrease of the two main coordinates (indicating less variation among samples) and the positioning of the contaminated OTC much closer to all other samples that eventually appear as ensemble of unresolved individuals in the community.\u003c/p\u003e \u003cp\u003eThree genera, \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003ePhascolarctobacterium\u003c/em\u003e, were exclusively identified in blood samples and absent in negative controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). More specifically, each of these three genera was found in lower abundance in no more than three samples from different donors and from samples processed with different extraction kits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe ongoing discussion on the sterility of healthy blood has significant implications for the upcoming research, especially in the physiology field. Nevertheless, our study underscores that exploring this topic is a complex journey fraught with challenges stemming from both technical and environmental sources, introducing noise and contaminants.\u003c/p\u003e \u003cp\u003eWe explored the presence of a potential blood microbiome in samples from six healthy subjects, utilizing two distinct DNA extraction kits: one specifically designed for extracting microbial DNA from blood and the other intended to eliminate human DNA while isolating microbial DNA. In addition, we introduced two control samples rich of known microbial DNA for an easier identification and exclusion: one comprising mice DNA (off target control), and another enriched with \u003cem\u003eE. coli\u003c/em\u003e DNA. In our experience, negative controls consisting of pure sterile water are often challenging to sequence due to the insufficient total amount of DNA detected after PCR, especially given the cycle number characteristic of the conventional Illumina protocol. While this outcome validates the overall sterility of the workflow, it does not allow us to discern which reads may stem from the various biases described. To overcome this limitation, we hypothesize the possibility of \"enhancing\" these negative controls by increasing the initial DNA quantity through sequences that can be recognized and subsequently discarded.\u003c/p\u003e \u003cp\u003eFirstly, we noted an unsuccessful sequencing attempt for one healthy blood sample processed with the QIAamp\u0026reg; DNA Microbiome Kit. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, all other samples, including the corresponding sample from the same subject processed with the\u003c/span\u003e DNeasy\u0026reg; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBlood \u0026amp; Tissue Kit, and even the negative controls, were successfully sequenced. Nevertheless, we view this issue as a\u003c/span\u003e valuable clue regarding the estimable microbiota richness, at least for this particular subject.\u003c/p\u003e \u003cp\u003eOverall, the samples obtained using the DNeasy\u0026reg; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBlood \u0026amp; Tissue Kit feature a high number of off-target sequences despite the specificity of the used primers, implying an extremely low bacterial biomass, if any. Conversely, the samples obtained through the\u003c/span\u003e QIAamp\u0026reg; DNA Microbiome Kit, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edesigned to remove host DNA, were nearly devoid of host sequences. However, irrespective of the DNA extraction kit, only a limited number of sequences for each sample passed the quality filters during the FASTQ processing, except for the\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eE.coli\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003econtrols which underwent processing with no substantial loss of reads. This outcome confirms both the unusual behaviour of the blood sample sequences and the processing effectiveness.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSimilar challenges were also encountered during the processing of the OTC samples. Indeed, Glassing and colleagues reported that when sequencing DNA extracted from blood samples with the MoBio PowerMax\u0026reg; Soil DNA Isolation Kit, they obtained around 2000 sequences. However, despite the significantly greater depth of sequencing, only 25% of these sequences were identified as prokaryotes\u003c/span\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdditionally, the authors noted that only minor traces of the genera\u003c/span\u003e \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eMogibacterium\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eHalocella\u003c/em\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cem\u003eSphingobium\u003c/em\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ewere exclusively present in their blood samples and not in blank controls, as per their abundance filters.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAmong these genera only reads from\u003c/span\u003e \u003cem\u003eSubdoligranulum\u003c/em\u003e have been identified in our blood samples as well as in our controls. Specifically, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ein our dataset, after applying the abundance filters, only twenty-five genera were detected, despite the permissive thresholds used.\u003c/span\u003e In agreement with previous reports, the most abundant phylum that we have observed in blood samples is Proteobacteria. Given the speculations about the potential sources of bacteria in healthy blood, it\u0026rsquo;s noteworthy that this phylum has been reported as the most abundant in the healthy lungs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, it is also frequently the most abundant in blank controls [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a pattern consistent with our control samples.\u003c/p\u003e \u003cp\u003eNotably, the most abundant genera identified in blood samples were found also in each control, exhibiting the same abundances profile, particularly in the mice DNA control processed with DNeasy\u0026reg; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBlood \u0026amp; Tissue Kit and in the\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eE.coli\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003econtrols after discarding the spike signal\u003c/span\u003e. Probably explanations of these results include the possibility that these bacteria originate from an environmental contamination. These remnants could than been sequenced alongside the off-target DNA. However, we observed a distinct microbial profile in the mice DNA control processed with the QIAamp\u0026reg; DNA Microbiome Kit. We assume that the variability in controls processed with different kits could be attributed to 1) differences in the \u0026ldquo;kitome\u0026rdquo; of the two extraction kits; 2) stochastic amplification of extremely low abundant DNA templates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; 3) different contaminants present in various samples within a single sequencing lane [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, to further explore the distinctive features distinguishing healthy blood samples from the controls, we checked which bacterial DNA have been exclusively sequenced in the blood samples.\u003c/p\u003e \u003cp\u003eThis analysis revealed that the genera \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003ePhascolarctobacterium\u003c/em\u003e have been identified only in blood samples, regardless of the extraction kit used, but only in a limited number of specimens. This outcome can be attributed to their actual presence in the healthy blood of only some individual or, alternatively, to a shared portion of the kitome present in both extraction kits or introduced by the PCR reagents. Notably, these sequences were produced in some samples in our dataset, with the exception of the controls.\u003c/p\u003e \u003cp\u003eIn detail, both \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eCoprococcus\u003c/em\u003e are recognized as common bacteria of intestinal flora and as possible contaminants from the DNA extraction kit [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Meanwhile \u003cem\u003ePhascolarctobacterium\u003c/em\u003e is also an intestinal commensal genus, not reported as known kit contaminants, but identified in traces in only two out six subjects. It\u0026rsquo;s relevant to know that many contaminants are often associated to gastrointestinal tract or skin [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] whereas the community profile of our samples doesn\u0026rsquo;t align with either of those environments. Moreover, as far as we know, \u003cem\u003ePhascolarctobacterium\u003c/em\u003e has not been identified in any other research on healthy blood microbiota. Conversely, \u003cem\u003eCoprococcus\u003c/em\u003e was reported as a potential blood bacterium by Jagare and colleagues. However, its abundance overcame the threshold chosen by the authors only in the blood of patients with gut disease, not in healthy subjects and negative controls, where it was also detected [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor these reasons, we cannot conclusively assert that these bacterial traces are common features of human blood. On the other hand, the most abundant genera in our dataset are constantly present in every blood sample, even though each of these taxa is also detected in every control and reported as common contaminants originating from reagents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is remarkable that the reported most abundant genera alone constitute the majority of our dataset. Even considering the high sensitivity of PCR amplification, the prevalence of well-known contaminants as the most abundant genera implies an abundance of the hypothetical true healthy blood bacteria close or equal to zero. Although it is conceivable that some bacteria or related DNA sequence in laboratory kits may overlap with those found in the healthy blood, it seems improbable that this holds true for each of them. Nevertheless, we do not rule out the possibility that some living bacteria, or at least traces of them, may sporadically and transiently be present in bloodstream of healthy subjects. However, such occurrence does not match with an established ecological community such as the microbiota.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur analyses and methodologies strongly support the relevance of considering issues and risks when exploring the healthy blood environment or similar ones through the 16S rRNA NGS. Based on our data, we cannot definitively affirm or deny the existence of healthy blood bacteriota. However, the described results cast relevant doubts on the notion that certain bacteria, or their residual DNA sequences, are a common and distinctive feature of the healthy human blood. Consequently, we firmly emphasize that, at current state of knowledge, further, extremely careful research is needed before asserting the existence of a healthy blood microbiota.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSTRENGTH AND LIMITATIONS OF THIS STUDY\u003c/h2\u003e \u003cp\u003eAlthough a modest number of subjects have been recruited for this study, we assert the reliability of our conclusions as they were derived by identifying consistent patterns and presences that are expected to be characteristic of a particular environment, rather than relying solely on statistical comparisons with a larger sample size. To strengthen our observations, the present data are in agreement with findings frequently reported in other larger-scale studies. Of course, expanding the number of samples and controls would strengthen our conclusion. Nevertheless, we have highlighted a concurrence in the profiles of our controls and the healthy blood samples, a finding that we consider highly informative.\u003c/p\u003e \u003cp\u003eFurthermore, our study paves the way to designing a more targeted procedural approach in this research area or, at least, aims to underscore the need for precautions that must be adopted when exploring such topics through NGS. Implementing relative abundance filters is advisable to avoid contaminants and sequencing errors in low bacterial biomass environments but, inevitably, arbitrary threshold values are applied. In this regard, we settled our thresholds after an accurate investigation of the literature attempting to achieve permissive yet efficient filtering strategies. Finally, it is remarkable that we purposely did not perform in silico decontaminations based on the comparison with the controls. This choice was made to focus on evaluating results achievable by sequencing the blood samples and the kitome itself, without incorporating statistics whenever possible. However, alternative approaches, such as the use of decontamination algorithms, may change the estimated taxonomic profile of the blood samples. In all cases, the raw reads in FASTQ format released as publicly available to allow any re-analysis with different settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAA, MR, EN, and LDG made substantial contributions to the conception and design of the work. MR, AA, and LDG contributed to the design of the work. SBa, SBe, GN, LC, LDG, and FC were involved in the acquisition and analysis of data. LDG, EN, MR, SBa, and AA contributed to the interpretation of data. EN, LDG, and SBa drafted the work, and AA and MR revised it. All authors have approved the submitted version (and any substantially modified version that involves the author's contribution to the study) and have agreed to be personally accountable for their own contributions. They also agree to ensure that questions related to the accuracy or integrity of any part of the work, even those in which they were not personally involved, are appropriately investigated, resolved, and documented in the literature\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThis work was supported by European Union NextGenerationEU and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP) with the projects MNESYS and THE - Tuscany Health Ecosystem - Mission 4 Component 2 - Investment 1.5; This work was further supported by the FONZIE project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing reads for samples used in this study have been deposited in the NCBI GEO (Gene Expression Omnibus) database under the accession code GSE254843. Analysis scripts for the bioinformatics processing of reads and for the microbial community analyses are freely available at https://github.com/LeandroD94/Papers/tree/main/2024_seeking_blood_microbiota.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNajmanova L, Videnska P, Cahova M. Healthy microbiome - a mere idea or a sound concept? Physiol Res. 2022;71:719\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastillo DJ, Rifkin RF, Cowan DA, Potgieter M. The Healthy Human Blood Microbiome: Fact or Fiction? Front Cell Infect Microbiol. 2019;9:148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamgaard C, Magnussen K, Enevold C, Nilsson M, Tolker-Nielsen T, Holmstrup P, et al. Viable bacteria associated with red blood cells and plasma in freshly drawn blood donations. PLoS One. 2015;10:e0120826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinakaran V, Rathinavel A, Pushpanathan M, Sivakumar R, Gunasekaran P, Rajendhran J. Elevated levels of circulating DNA in cardiovascular disease patients: metagenomic profiling of microbiome in the circulation. PLoS One. 2014;9:e105221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantiago A, Pozuelo M, Poca M, Gely C, Nieto JC, Torras X, et al. Alteration of the serum microbiome composition in cirrhotic patients with ascites. Sci Rep. 2016;6:25001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikkari S, McLaughlin IJ, Bi W, Dodge DE, Relman DA. Does blood of healthy subjects contain bacterial ribosomal DNA? J Clin Microbiol. 2001;39:1956\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoriyama K, Ando C, Tashiro K, Kuhara S, Okamura S, Nakano S, et al. Polymerase chain reaction detection of bacterial 16S rRNA gene in human blood. Microbiol Immunol. 2008;52:375\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamirez Galleymore P, Gordon Sahuquillo M. Antisepsis for blood culture extraction. Blood culture contamination rate. Med Intensiva (Engl Ed). 2019;43 Suppl 1:31\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpota O, Croxatto A, Prod'hom G, Greub G. Blood culture-based diagnosis of bacteraemia: state of the art. Clin Microbiol Infect. 2015;21:313\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014;12:87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 2016;8:24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStinson LF, Keelan JA, Payne MS. Identification and removal of contaminating microbial DNA from PCR reagents: impact on low-biomass microbiome analyses. Lett Appl Microbiol. 2019;68:2\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNearing JT, Comeau AM, Langille MGI. Identifying biases and their potential solutions in human microbiome studies. Microbiome. 2021;9:113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlomu IN, Pena-Cortes LC, Long RA, Vyas A, Krichevskiy O, Luellwitz R, et al. Elimination of \"kitome\" and \"splashome\" contamination results in lack of detection of a unique placental microbiome. BMC Microbiol. 2020;20:157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker SP, Barrett M, Hogan G, Flores Bueso Y, Claesson MJ, Tangney M. Non-specific amplification of human DNA is a major challenge for 16S rRNA gene sequence analysis. Sci Rep. 2020;10:16356.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan I, Khan I, Jianye Z, Xiaohua Z, Khan M, Hilal MG, et al. Exploring blood microbial communities and their influence on human cardiovascular disease. J Clin Lab Anal. 2022;36:e24354.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinninella E, Raoul P, Cintoni M, Franceschi F, Miggiano GAD, Gasbarrini A, et al. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms. 2019;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan CCS, Ko KKK, Chen H, Liu J, Loh M, Consortium SGKH, et al. No evidence for a common blood microbiome based on a population study of 9,770 healthy humans. Nat Microbiol. 2023;8:973\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordenstein SR, Theis KR. Host Biology in Light of the Microbiome: Ten Principles of Holobionts and Hologenomes. PLoS Biol. 2015;13:e1002226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam NY, Rainer TH, Chiu RW, Lo YM. EDTA is a better anticoagulant than heparin or citrate for delayed blood processing for plasma DNA analysis. Clin Chem. 2004;50:256\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoley C, O'Farrelly C, Meade KG. Technical note: Comparative analyses of the quality and yield of genomic DNA from invasive and noninvasive, automated and manual extraction methods. J Dairy Sci. 2011;94:3159\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotikalapudi R, Patel RK. Comparative study of the influence of EDTA and sodium heparin on long term storage of cattle DNA. Cell J. 2015;17:181\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarstens L, Asquith M, Davin S, Fair D, Gregory WT, Wolfe AJ, et al. Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments. mSystems. 2019;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegendre P, Legendre L. Chapter 7 - Ecological resemblance. In: Legendre P, Legendre L, editors. Developments in Environmental Modelling: Elsevier; 2012. p. 265\u0026ndash;335.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBingula R, Filaire E, Molnar I, Delmas E, Berthon JY, Vasson MP, et al. Characterisation of microbiota in saliva, bronchoalveolar lavage fluid, non-malignant, peritumoural and tumour tissue in non-small cell lung cancer patients: a cross-sectional clinical trial. Respir Res. 2020;21:129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Oorschot RA, Ballantyne KN, Mitchell RJ. Forensic trace DNA: a review. Investig Genet. 2010;1:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckleton J. Validation issues around DNA typing of low level DNA. Forensic Sci Int Genet. 2009;3:255\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLusk RW. Diverse and widespread contamination evident in the unmapped depths of high throughput sequencing data. PLoS One. 2014;9:e110808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations. Trends Microbiol. 2019;27:105\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJagare L, Rozenberga M, Silamikelis I, Ansone L, Elbere I, Briviba M, et al. Metatranscriptome analysis of blood in healthy individuals and irritable bowel syndrome patients. J Med Microbiol. 2023;72.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3921147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3921147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe advent of next-generation sequencing (NGS) technologies has made it doable to investigate the microbial communities in various environments, including all the different human body districts. Hence, the previously established belief of several body sites\u0026rsquo; sterilities, including human blood, has now been challenged. However, the metagenomics investigation of habitats with an anticipated low microbial biomass may be susceptible to many and dangerous misinterpretations. Here, we critically evaluate the results of 16S targeted amplicon sequencing performed on the total DNA collected from healthy donors\u0026rsquo; blood samples in the light of specific negative controls aimed at addressing potential bias in order to supply the research in this area with trustworthy data.\u003c/p\u003e \u003cp\u003eOur negative controls, were prepared by inflating the initial DNA quantity through sequences that can be recognised and subsequently discarded. We found that only three organisms resulted sporadically present among the samples, mostly attributable to bacteria ubiquitous in laboratory reagents.\u003c/p\u003e \u003cp\u003eDespite we are not fully able to confirm or deny the existence of healthy blood microbiota, our results strongly suggest that living bacteria, or at least their residual DNA sequences, are not a common and peculiar feature of human blood in healthy people. Finally, our study poses relevant questions on the design of controls in this peculiar research topic in order to avoid spurious results that appear to contaminate current high throughput research.\u003c/p\u003e","manuscriptTitle":"Experimental tests strongly challenge the evidence of a healthy human blood microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-14 13:16:48","doi":"10.21203/rs.3.rs-3921147/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f7a7f9f6-24f2-4514-ba2f-3833a70965ae","owner":[],"postedDate":"February 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-03T12:05:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-14 13:16:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3921147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921147","identity":"rs-3921147","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}