Characterizing the blood microbiota in healthy and febrile domestic cats via 16s rRNA sequencing

This study aimed to evaluate the blood bacterial microbiota in healthy and febrile cats. High-quality sequencing reads from the 16S rRNA gene variable region V3-V4 were obtained from genomic blood DNA belonging to 145 healthy cats, and 140 febrile cats. Comparisons between the blood microbiota of healthy and febrile cats revealed dominant presence of Actinobacteria, followed by Firmicutes and Proteobacteria, and a lower relative abundance of Bacteroidetes. Upon lower taxonomic levels, the bacterial composition was significantly different between healthy and febrile cats. The families Faecalibacterium and Kineothrix (Firmicutes), and Phyllobacterium (Proteobacteria) experienced increased abundance in febrile samples. Whereas Thioprofundum (Proteobacteria) demonstrated a significant decrease in abundance in febrile. The bacterial composition and beta diversity within febrile cats was different according to the affected body system (Oral/GI, systemic, skin, and respiratory) at both family and genus levels. Sex and age were not significant factors affecting the blood microbiota of febrile cats nor healthy ones. Age was different between young adult and mature adult healthy cats. Alpha diversity was unaffected by any factors. Overall, the findings suggest that age, health status and nature of disease are significant factors affecting blood microbiota diversity and composition in cats, but sex is not.

Overall, phylogenetic diversity did not significantly differ between sex or age groups in healthy cats, according to the Bray-Curtis beta diversity analysis, as distinct clusters could not be observed (p > 0.05; Fig. S1).
According to the Kruskal-Wallis rank sum test used to compare healthy cats by sex no significant difference in median relative abundance between sexes was observed at the level of phylum or family levels (Fig. S2).
There were also no notable compositional differences at the phylum level between age subgroups.However, significant differences of taxonomic distribution were observed, at family level among the four age subgroups within the healthy cat cohort (Kruskal-Wallis, p = 0.0055), particularly between the young adult and mature adult groups (pairwise Wilcoxon, p = 0.0098).Young adults and mature adults were significantly different (Wilcoxon, p < 0.05).The largest differences between the young adult and mature adult cohort were at family taxonomic
Assessments of composition and richness by sex did not differ significantly at phylum nor family level.Furthermore, the febrile group demonstrated non-significant differences at the family level by age group (Kruskal-Wallis, p = 0.053; Pairwise Wilcoxon, p > 0.05) (Fig. 4).The results of the PERMANOVA analysis performed on the Bray-Curtis distances using the adonis2 function revealed no significant differences in beta diversity between the four age groups (p = 0.08) (Fig. 5).
Although the pairwise Wilcoxon test revealed no significant differences at the phylum level for affected body systems in febrile cats, family-level comparisons yielded significant results via the Kruskal-Wallis rank sum test (p = 2.2E−16).Further, a pairwise Wilcoxon test of febrile cats by affected body system revealed no significant differences at the phylum level.It did, however, point out significant differences in family and genus composition, between almost all affected body system groups (Oral/Gastrointestinal(GI) vs Respiratory, p = 0.0027; Oral/ GI vs Skin, p = 6.2e−5;Respiratory vs Skin, p = 2.4e−16; Respiratory vs Systemic, p = 6.1e−9;Skin vs Systemic, p = 0.00089), except for the family composition between systemic and Oral/ GI groups, which did not demonstrate significant differences (p = 0.122) (Fig. 6).

Comparing the healthy and febrile groups
While the Kruskal-Wallis rank sum test showed no differences in phyla composition, it did illustrate significant differences in family composition, between healthy and febrile cats (p = 0.0104; Figs.7 and 8).For this analysis, families were filtered based on a median relative abundance > 1% of all the samples.
Testing for differential relative abundance between healthy and febrile cats revealed eleven phylotypes significantly increased in the febrile group (n = 140), and two phylotypes significantly increased in the healthy group (n = 145) (Fig. 9).
The most extreme of the log2fold changes belonged to the Firmicutes and Proteobacteria phyla.Specifically, the genera Faecalibacterium and Kineothrix (Firmicutes), and Phyllobacterium (Proteobacteria) experienced increased    Several diversity indices (Chao, Observed, Shannon, inverse Simpson) showed that alpha diversity did not differ significantly between healthy or febrile cats (Table 1).
The exception being between Shannon diversity indices (p = 0.039) and InvSimpson indices (p = 0.009) of all four age groups, across healthy and febrile cats (according to Kruskal-Wallis test).The mean Shannon diversity index for all cats was 2.33 ± 1.30.However, a PERMANOVA test based on Bray-Curtis distances showed that the beta diversity of the feline blood microbiota was influenced by the health status (febrile vs healthy) of the animal (PERMANOVA R-squared = 0.03042, F = 9.86, p = 0.000999) (Fig. 10).
Bacterial community structure and phylogenetic diversity significantly differed when comparing febrile cats by affected body system with healthy cats, according to the Bray-Curtis beta diversity analysis, as distinct clusters could be observed (Adonis2 P = 0.001, F = 3.37; Fig. 11).

Discussion
To the best of the authors' knowledge, this is the first investigation exploring and comparing the blood bacterial microbiota between healthy cats and cats with fever.Furthermore, this is the first study of this magnitude (n = 145) to explore the blood microbiome of healthy cats.Whereas a previous investigation only looked at the blood microbiome of six healthy kittens 15 , the present work included 285 client-owned domestic cats (145 healthy, 140 febrile).
Fifteen samples from healthy cats failed to amplify any genetic material, this was expected since the blood microbiome is considered a lower-yield environment when compared to other well-studied sites 25,26 .This lowyield environment is largely caused by the inherent low bacterial biomass of the blood.Even so, next-generation sequencing allows this level of biomass to be characterized 27 .
In accordance with research in dogs 28 , the healthy blood microbiota of cats in this study was dominated at the phylum level by Actinobacteria (median relative abundance 39.33%) and Firmicutes (32.02%), as opposed to previous research in cats where it was dominated by Bacteroidetes (67.60%) and Proteobacteria (22.26%).In humans, the most abundant phyla were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes 7,29   of the many possible explanations for this discrepancy is that previous research analyzed samples from just six healthy, lab raised kittens (< 8 months old) originating from just two litters on the same diet 15 .As studies of the gut microbiome show, factors such as age, sex, environment, litter, and diet can all influence the gut microbiome 28,30 .Previous research comparing the gut and blood microbiota suggests that factors influencing the gut likely affect the blood as well 14 .Different methodologies or analyses could contribute to the differences between our two studies, such as differences in region of amplification in the 16S gene or in bio-informatics flow.Phyla such as Actinobacteria great in number and diverse in niches occupied.These habitats include soil, rhizosphere, and marine and freshwater systems 31 .Therefore, relevance of taxa present is best inferred at the family level.The most abundant of which being: Pseudonocardiaceae (15.85%) which are aerobic, Gram-positive, and non-motile bacteria with low clinical significance.However, the genus Pseudonocardia had an important role in biotechnology due to its anti-bacterial, anti-fungal, and anti-tumor metabolites 32 ; Nocardiopsaceae (15.26%), largely represented by the genus Nocardia which are nonmotile, non-spore-forming, pleomorphic gram-positive, facultative intracellular organisms.Feline nocardiosis is an opportunistic pathogen strongly associated with immunosuppressive disorders caused by Feline Leukemia Virus (FeLV) and Feline immunodeficiency virus (FIV) 33 .With a tendency to lie latent, it is not unbelievable to discover circulating evidence of this pathogen in healthy cats; SporolactoBacillaceae (14.73%) which are heat resistant and endospore forming rods.Sporolacto-Bacillaceae are not clinically relevant, however their spores can be found in raw cows' milk, as well as commercial probiotics 34 .
There were no significant differences in the alpha or beta diversity of the blood microbiota by age and sex within the healthy group.Therefore, these relative abundances could represent a degree of normality, or "eubiosis" in domestic cats 35 .This is in line with recent studies which found that individual variation had minimal effect when compared to stronger factors like disease and antibiotic use 36 .This "Anna Karenina principle" has been applied to animal microbiomes before, in which diseased or dysbiotic individuals vary more than healthy ones 37 .However, significant differences in the family composition between young adult and mature adult cats in healthy cats point to more distinct taxonomic makeups.Similar age-related associations have been noted in the canine gut microbiota and human blood microbiome 28,38 , but further research is required to elucidate that relationship.
It is evident that in the febrile group, the type of disease is a stronger driver than both sex and age, which did not express notable differences.Cats from each of the four groups (Systemic; Respiratory; Skin; Oral/Gastrointestinal) had different microbiota compositions when compared to the members of other groups.The development of these distinct, disease-specific taxonomic compositions is supported by research in humans 18,39,40 .Moreover, these specific states of dysbiosis can be of clinical relevance by helping to identify and perhaps mitigate effects of their respective diseases since the identification of potential biomarkers is considered a challenging task for many diseases.The blood microbiome analysis is considered a noninvasive approach to identifying disease biomarkers, and in the future may increase the accuracy of disease classification in cats and the efficacy of therapies.Nevertheless, many unresolved questions remain.The distinction between febrile and healthy cats at the lower taxonomic rank of families warrants further investigation of potential bacterial biomarkers in blood samples that are predictive of clinical syndromes.To this end, the data found in this study serve as a baseline reference for future studies on diagnostic testing in clinical settings.
The only body system groups that were not significantly different were the Systemic and Oral/Gastrointestinal groups.These results align with previous studies demonstrating a significant increase in Proteobacteria abundance in the blood of dogs afflicted with gastrointestinal disease 21,41 .It is noteworthy that Proteobacteria, as Gram-negative bacteria, are known producers of endotoxins, which could explain their heightened abundance in systemic conditions such as fever.Moreover, the association between similar microbiota compositions in systemic and gastrointestinal diseased cats, characterized by elevated Proteobacteria levels, provides a potential link to the "leaky gut" phenomenon, which is commonly observed in animals, specifically dogs and cats [41][42][43] .According to this theory, disruptions in gut barrier integrity can lead to increased permeability, allowing the translocation of bacteria and their byproducts into systemic circulation.Proteobacteria, being a common player in human disease and a notable endotoxin producer 44 , could contribute to the development of a "leaky gut" and subsequently facilitate the entry of their bacterial components, and a rise in relative abundance in the bloodstream.
The same three phyla dominated both the febrile and the healthy groups (Actinobacteria, followed by Firmicutes and Proteobacteria), with the febrile group demonstrating a slight (non-significant) increase in Proteobacteria (29.23% in febrile as compared to 24.01% in healthy).Also, within the febrile group, a 25 log2fold increase of the genus Phyllobacterium was appreciated.This change can be attributed to the negligible concentration of the genus in the healthy group (< 0.0001%), and then a still negligible, but slight increase in the febrile group (< 1%).Phyllobacterium is a group occurring in the roots of plants and has recently been associated as a gut microbial marker of disease in mice 45 .Also in the febrile group, the genera Kineothrix and Faecalibacterium experienced a dramatic increase in relative abundance (> 25-fold).Both Kineothrix and Faecalibacterium are well known beneficial gut bacteria, responsible for producing short chain fatty acids, such as butyrate which supports digestion and immune function.Furthermore, both genera of Firmicutes can be included in commercial probiotic solutions 46,47 .The presence of these beneficial gut bacteria in the blood of febrile cats further supports the translocation of bacteria from the gut to blood in states of disease.
Families which had > 1% relative median abundance accounted for 71.46% of the total reads of healthy cats, as opposed to febrile cats where they only accounted for 54.89%.This lower richness in healthy cats further supports a state of eubiosis where a few "healthy" bacteria dominate the blood, whereas in disease, dysbiosis occurs.These changes are in line with differences described in many of the studies comparing blood microbiomes in health and disease [10][11][12]16,18,21,22 .
One of the limitations of this study is the sampling design, that was cross-sectional.While a recent study shows that fecal microbiota compositions are relatively stable when examined longitudinally, further research would be needed to make the same claim in the blood 36 .Another limitation of this study is the relatively low number of animals in certain affected body systems.It is crucial for future research to investigate the effects of these specific disease states on the blood microbiota to gain a more comprehensive understanding of how various health conditions influence microbial communities.Furthermore, yet another important limitation of this study is the lack of control over the diet of each individual cat.As these were client-owned pets and not laboratory cats, they were being fed a variety of food types and quantities.Perhaps the most important example of this limitation, which requires attention in future studies, is the potential for difference in blood microbiota between kittens pre and post weaning.As mentioned previously, diet is a major driver in the composition of blood microbiota and the switch from maternally produced milk to externally created food products should reflect that 14 .Finally, one should be aware that 16s rRNA sequencing generates compositional datasets for which differential abundance analysis implies several assumptions that can be overly reductionist 48 .Additionally, in a clinical setting, 16S sequencing in conjunction with culture could be useful for identifying viable bacteria.does not immediately imply microbial viability, but simply bacterial DNA presence.Also, studies have concluded that 16s rRNA sequencing methods are far more effective at qualitative assessments of complex microbial communities and are only semi-quantitative in the absence of adjunct techniques such as culture or propidium monoazide (PMA) treatment during extraction 49 .In future studies, the aforementioned techniques will have to be employed to assess the viability of said microbes.

Conclusions
This study represents the first investigation exploring and comparing the blood-bacterial microbiota between healthy cats and cats with fever.By examining a larger sample size than previous studies, this investigation provides novel insights into the composition and diversity of the feline blood microbiota.The results indicated that in both healthy and febrile groups, sex was not a significant factor of microbial composition or diversity.Age exhibited some influence on the blood bacterial microbiota only in healthy cats, at family level, particularly between young adult and mature adult cats.While no differences at phylum level were observed between healthy and febrile cats, significant differences were illustrated at the lower taxonomic rank of families.Moreover, febrile cats displayed distinct familial compositions and beta diversities based on the underlying diseased organ system.Overall, the findings suggest that health status, nature of disease and age are significant drivers of blood microbial diversity and composition in cats, but sex is not.Understanding the factors that contribute to these differences can provide insights into the dynamics of the feline blood microbiome throughout different life stages and can have implications for managing feline health and disease prevention.Furthermore, the data found in this study can serve as a baseline reference in the future, as next-generation sequencing techniques pave the way for the future of diagnostic testing.

Samples origin
This study used stored genomic DNA samples purified from blood of client-owned domestic cats obtained from an extramural grant (FONDECYT REGULAR 1191462, PI: Dr. Ananda Muller).All experimental protocols from the former study were approved by the IACUC (Approval number: 353/2019), and consent was obtained from cat owner's as well as respective authority of Veterinary Hospital.The use of DNA samples for the current study was also approved by the Ross University School of Veterinary Medicine IACUC (TSU12.9.21).All methods were carried out in accordance with relevant guidelines and regulations and reported in accordance with ARRIVE guidelines.A cross-sectional consecutive sampling of 300 domestic client-owned cats (healthy [n = 160] and febrile [n = 140]) that had presented to the Veterinary Hospital (September 2019 to January 2021) was performed.To avoid sample contamination, blood samples were collected from all 300 cats in a safe and sterile fashion.The fur was shaved, and sterile gloves were used.Aseptic preparation of the venipuncture site was performed, using povidone-iodine with 70% ethyl alcohol.Blood was drained from a cephalic vein using a sterile single-use pediatric blood collection set (multiple sample adapter), disposable syringe and pediatric tubes (Nipro).Samples were then stored in anticoagulant-EDTA tubes and frozen in -80 °C until DNA extraction.

Cats and inclusion criteria
Healthy cats (n = 160) Cats which presented to the Veterinary Hospital for vaccination, spaying, or as blood donors were considered healthy based on physical examination (inspection, palpation, percussion, auscultation, temperature measuring).
Any cat with abnormalities on physical examination was excluded.Since host-associated microbiomes can be rapidly altered by exposure to antibiotics 50 , cats receiving antibiotic therapy within 30 days prior to consultation, were also excluded.Additionally, FIV and FeLV status (qualitative chromatography immunoassay for FIV Ab/ FeLV Ag in serum) were obtained for all cats (healthy and febrile), as well as testing for Hemotropic Mycoplasma spp., Cytauxzoon felis, was performed via conventional (c) PCR.For the complete blood count, the following parameters were analyzed: red blood cell (RBC), white blood cell (WBC) and platelet counts; hemoglobin concentration; packed red cell volume; mean corpuscular volume (MCV); and mean corpuscular hemoglobin concentration (MCHC).An automated hematology analyzer, KX-21N (Sysmex©, Japan), was used.The blood smears were stained with rapid staining (Hemacolor ® , Merck) for a differential WBC count.Only cats which were negative for the aforementioned pathogens and had a normal CBC were included in the healthy group.Age and sex were recorded at the time of blood sampling.The healthy group was composed of 160 healthy cats ranging from 2 months to 15 years (median age: 2 years 6 months) old, both males and females.The sex subgroups were composed by 50.6% (81/160) females and 49.4% (79/160) males, of all ages.Age subgroups were formed accordingly to the 2021 AAHA/AAFP Feline Life Stage Guidelines 51 , as follows: kitten (birth up to 1 year); young adult (1-6 years); mature adult (7-10 years); senior (> 10 years).The total number of cats per age category was 60 kittens (subdivided in: 2 months-6 months [n = 30] and 7 months-1 year [n = 30]), 60 young adults (subdivided in: > 1 year-3 years [n = 30] and 4-6 years [n = 30]), 30 mature adults and 10 seniors.

Febrile cats (n = 140)
Cats were included if they had a body temperature > 102.5 °F (39.2 °C) for at least 24 h and during physical examination independent of a specific diagnosis.Any cat treated with antibiotics within 30 days prior to the beginning of the study was excluded.Cats with elevated body temperature likely due to hyperthermia from excitement, seizures, or environmental factors 52 were also excluded.Age and sex were documented.Initial or definitive diagnoses were recorded at the time of blood sampling and used for categorizing abnormalities by affected body system.

DNA extraction/purification
Genomic DNA was obtained using a commercial DNA extraction kit (DNeasy Blood and Tissue Kit Protocol for Animal Tissues-QIAGEN), according to the manufacturer instructions.The integrity of each blood-extracted DNA was confirmed by an endogenous control Conventional PCR targeting the feline 28S rDNA (Peters et al.  2008)  53 , being positive for all 300 samples.Negative controls in the form of nuclease-free water (NFW) were utilized to ensure sterile collection methods.For every 30 samples, one NFW was used as template DNA, totaling 10 negative controls.None of said negative controls demonstrated any amplification.Purified DNA was stored at − 80 °C, until 16S rRNA gene amplicon sequencing.

Metagenomic analyses: V3-V4 16S rRNA gene sequencing
All 300 cats' genomic DNA samples were sent to Macrogen (Geumcheon-gu, Seoul, South Korea) for 16S rRNA gene sequencing using the Illumina MiSeq platform.At Macrogen, total DNA was assessed for its quantity (picogreen method using Victor 3 fluorometry) and quality (gel electrophoresis method).The variable V3-V4 region of the 16S rRNA gene was amplified generating PCR products with a length of ~ 460 bp.For library construction, the Illumina official guide for 16S rRNA gene sequencing library preparation was used as a reference.As such, metagenome amplicon sizes were verified by running on an Agilent Technologies 2100 Bioanalyzer using a DNA 1000 chip.Library quantity of DNA templates (ng/µl) was performed using qPCR according to the

Figure 1 .
Figure 1.Relative abundance of bacterial phyla over 1% (A) and top 10 most abundant families (B) in 145 blood samples of healthy domestic cats, determined by 16S rRNA gene sequencing."Other" groups those phyla with a median relative abundance ≤ 1% or not in the top 10 most abundant families.

Figure 2 .
Figure 2. Taxonomic distribution of the healthy feline blood microbiota (n = 145) by age group, at the family level.Only families with a median relative abundance > 1% are included in the graph.

Figure 3 .
Figure 3. Relative abundance of bacterial phyla over 1% abundance (A) and top 10 most abundant families (B) in 140 blood samples of febrile domestic cats, determined by 16S rRNA gene sequencing."Other" groups those phyla with a median relative abundance ≤ 1% or not in the top ten at family level.

Figure 4 .
Figure 4. Taxonomic distribution of the febrile feline blood microbiota (n = 140) by age group, at family level.Only families with a median relative abundance > 1% are included in the graph.

Figure 6 .
Figure 6.Taxonomic distribution of the febrile feline blood microbiota (n = 140) at family level by affected body system.Only families with a median relative abundance > 1% are included.

Figure 7 .
Figure 7. Significantly different (p = 0.0104) compositions of taxa between healthy and febrile cats, across all samples (n = 285) at the family level, which had a median relative abundance > 1%, in healthy (n = 145) and febrile (n = 140) cat samples.

Figure 8 .
Figure 8. Pie charts representing the composition of healthy cats by phyla (A, greater than 1% median relative abundance) and family (B, top ten by median relative abundance), and febrile cats by phyla (C, greater than 1% median relative abundance) and family (D, top ten by median relative abundance)."Other" groups those phyla with a median relative abundance ≤ 1% or not in the top ten at family level.

Figure 9 .
Figure 9. Differential abundance analysis (DESeq) assessing OTUs significantly changed (Padj < 0.05) in the blood microbiota of febrile cats (n = 140) compared with healthy cats (n = 145).Each circle represents significant OTUs at the genus level (x-axis), colored by Phylum.The positive values of log2 Fold Change (y-axis) indicate higher relative abundance in febrile cats and negative values show for higher relative abundance in healthy cats.

Figure 10 .
Figure 10.Principal Component Analysis (PCoA) of the blood microbiota of Healthy (n = 145) and Febrile cats (n = 140) using Bray-Curtis distances (R2 = 0.03, p < 0.01).Each data point represents an individual sample and ellipses represent a 95% confidence interval.The percentages in parenthesis are the proportion of variation explained by the PCoA axis.

Table 2 .
Number of febrile cats initially categorized by affected body system.Only the highlighted groups were used for intrafebrile group comparisons (by body system or age).The remainder were used for descriptive analysis of the febrile group and comparison with the healthy group.