A detailed analysis of 16S rRNA gene sequencing and conventional PCR-based testing for the diagnosis of bacterial pathogens and discovery of novel bacteria

22 This study represents the first analysis of the bacterial community in chickens 23 affected by swollen head syndrome, utilizing 16S rRNA gene sequencing. 24 Samples were obtained from clinical laying chickens and were examined for 25 the presence of Avibacterium paragallinarum (APG) and Ornithobacterium 26 rhinotracheale (ORT) using conventional polymerase chain reaction (PCR). 27 From the samples, five APG -positive (APG) and APG -negative (N -APG) 28 samples were chosen, along with five specific pathogen-free chickens, for 16S 29 rRNA gene sequencing. Results showed that APG and ORT were widely 30 detected in the chicken samples with swollen head syndrome ( SHS, 9/10), 31 while APG was detected in all five specific pathogen-free (SPF) samples. In 32 contrast, conventional PCR sensitivity was found to be inadequate for 33 diagnosis, with only 35.7% (5/14) and 11.1% (1/9) sensitivity for APG and ORT, 34 respectively, based on 16S rRNA gene sequencing data. Furthermore, 16S 35 rRNA gene sequencing was able to quantify the bacteria in the samples, 36 revealing that the relative abundance of APG in the APG group ranged from 37 2.7% to 81.3%, while the relative abundance of APG in the N -APG group 38 ranged from 0.1% to 21.0%. Notably, a low level of APG was also detected in 39 all 5 SPF samples. The study also identified a significant number of animal and 40 human common bacterial pathogens, including but not limited to 41 Gallibacterium anatis , Riemerella columbina , Enterococcus cecorum , 42 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Mycoplasma synoviae, Helicobacter hepaticus, and Staphylococcus lentus. In 43 conclusion, 16S rRNA gene sequencing is a valuable tool for bacterial 44 pathogen diagnosis and the discovery of novel bacterial pathogens, while 45 conventional PCR is not reliable for diagnosis. 46

47 16S rRNA gene sequencing, conventional p olymerase chain reaction, 48 diagnosis, bacterial community, Avibacterium paragallinarum , swollen head 49 syndrome 50 51 1 Introduction 52 Polymerase chain reaction (PCR) is a widely used method for diagnosing 53 infectious diseases in humans and animals. However, conventional PCR may 54 not be able to detect samples with a low number of copies (He et al., 1994). In 55 addition to real -time and digital PCR, 16S rRNA sequencing is also a useful 56 tool for bacterial identification and disease diagnosis (Fida et al., 2021; 57 Rampini et al., 2011). 58 Swollen head syndrome (SHS), a condition that affects broilers, broiler 59 breeders, and layers, is a widespread problem in many countries around the 60 world. First reported in 1984 in South Africa, SHS has since been identified in 61 numerous other countries, including Spain, France, the United Kingdom, the 62 Netherlands, Canada, Israel, Japan, Iraq, and China (Al-Hasan et al., 2021; 63 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Hafez and Löhren, 1990; Nakamura et al., 1997) . Several causative agents, 64 including Avibacterium paragallinarum (APG), Rhinotracheitis virus, 65 Escherichia coli , Ornithobacterium rhinotracheale (ORT), and 66 Metapneumovirus, have been identified thus far (Abdelmoez et al., 2019; 67 Al-Hasan et al., 2022; Al -Hasan et al. , 2021; Droual and Woolcock, 1994) . 68 Identifying these agents is crucial in preventing the spread of SHS. 69 APG is a significant causative agent to chicken disease, as it causes 70 infectious coryza (IC), an acute upper respiratory disease (Gallardo et al., 71 2020; Paudel et al., 2017) . IC is characterized by mucous nasal discharge, 72 facial swelling, and conjunctivitis (Balouria et al., 2019; Xu et al., 2019) . 73 Moreover, IC is responsible for decreased egg production (10% -40%) and 74 increased unthrifty chickens (Heuvelink et al., 2018). Additionally, IC can have 75 negative consequences for broiler chickens, as the reported IC cases in 76 California resulted in increased mortality (8% -15%), leading to significant 77 financial losses for the chicken industry (Crispo et al., 2019). Another bacterial 78 pathogen identified in SHS is ORT, which is reported globally (Al-Hasan et al., 79 2021; Barbosa et al., 2019) . ORT infection causes respiratory symptoms, 80 growth retardation, reduced egg production, and mortality, resulting in 81 economic losses for the poultry industry. Specifically, the respiratory symptoms 82 of ORT infection consist of tracheitis, pericarditis, sinusitis, and exudative 83 pneumonia, with fibrin purulent lesions and often unilateral pneumonia 84 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint (Barbosa et al., 2019). 85 Co-infection with other bacteria or viruses can often affect the 86 pathogenicity and persistence of APG in the host, and the environmental 87 conditions also play a crucial role . Various studies have highlighted the 88 importance of early diagnosis (Ali et al., 2022a; Ali et al., 2024; Ali et al., 2022b; 89 Zhang et al., 2021) and vaccination (Quan et al., 2018; Zhang et al., 2015; 90 Zhang et al., 2018a; Zhang et al., 2018b; Zhang et al., 2022; Zhang et al., 2023; 91 Zhu et al., 2017) as effective tools in combating IC and preventing the infection 92 of other pathogens. 93 The human and animal bodies are inhabited by a diverse community of 94 microorganisms, such as archaea, bacteria, fungi, and viruses (Libertucci and 95 Young, 2019) . The state of health and disease depends on the interplay 96 between the host's immune responses, the native microbiota, and possible 97 pathogens. Maintaining a balanced bacterial community can help prevent 98 infections by providing colonization resistance (Libertucci and Young, 2019). Analyzing 99 bacterial communities has the potential to pinpoint the pathogen responsible 100 for an infection. 101 Recent studies have documented the bacterial community in healthy and 102 infected human hosts. However, the microbiota of chickens affected by 103 infectious coryza remains unexplored. To address this gap, we conducted a 104 study in which we analyzed samples from chickens with swollen head 105 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint syndrome for APG and ORT using PCR. Subsequently, we examined the 106 bacterial community of the chicken infraorbital sinus through 16S rRNA 107 sequencing and compared it to that of SPF chickens. 108 109 2 Materials and Methods 110 2.1 Study design and sample collection 111 This study compared the effectiveness of conventional PCR and 16S 112 rRNA sequencing in detecting pathogens. Aseptic clinical samples (swabs) 113 were collected from chicken infraorbital sinuses with SHS and categorized into 114 APG positive (APG, 5 samples) and APG negative (N-APG, 5 samples) (Table 115 1). To identify and type APG, a multiplex PCR was carried out as described 116 previously (Table 2) (Sakamoto et al., 2012) . SPF chicken samples (Mock 117 group) were also tested for APG, and all samples were screened for ORT 118 using PCR. Additionally, bacteria isolation and 16S rRNA sequencing were 119 performed on all collected samples. The chickens with SHS were sourced from 120 various locations in China, while Wohua Biotech Animal Center provided the 121 SPF chickens . All experiments were conducted with the approval of the 122 Committee on the Ethics of Animal Experiments of Wohua Biotech company. 123 124 2.2 Isolation and identification of Avibacterium paragallinarum 125 Chicken infraorbital sinus swabs were collected using the previously 126 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint described method (Blackall and Soriano-Vargas, 2020). Briefly, the area under 127 the eyes was cauterized with a hot iron spatula, followed by an incision in the 128 sinus cavity using sterile scissors. A sterile cotton swab was then inserted 129 deep into the sinus cavity for sampling. Swab samples were collected for PCR 130 testing, 16S rRNA sequencing, and bacteria isolation. Samples were boiled in 131 ultrapure water to serve as a PCR template for identifying APG and ORT. 132 Swab samples were inoculated onto Tryptic Soy Agar (TSA) supplemented 133 with 5% chicken serum and 50 μg/mL nicotinamide adenine dinucleotide 134 (NAD). The bacteria were cultured overnight at 37 °C with 5% CO 2, and their 135 colonies were identified by PCR using specific primers of APG and ORT (Table 136 2). The samples were then tested with multiple PCR to amplify the HMTp210 137 gene of serotypes A, B, and C and the 16S rRNA gene of ORT (Table 2). DNA 138 from serotypes A, B, and C of APG strains were used as templates for PCR. 139 140 2.3 DNA extraction, PCR amplification, and 16S rRNA sequencing 141 We collected ten samples (infraorbital sinus swabs) from chickens with SHS 142 and five samples from SPF chickens (Mock) for 16S rRNA sequencing. We 143 extracted the microbial DNA from each sample using the HiPure DNA Kits 144 (Magen, China) following the product introduction. We quantified the DNA 145 extractions using ultraviolet spectroscopy and amplified the V3 –V4 domain of 146 bacterial 16S rRNA genes by PCR (95 °C for 5 min, followed by 30 cycles at 147 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint 95 °C for 1 min, 60 °C for 1 min, and 72 °C for 1 min, and a final extension at 148 72 °C for 7 min) using universal primers (Table 2). A 50 μL PCR mixture 149 contained 10 μL of 5X Reaction Buffer, 10 μL of 5X High GC Enhancer, 1.5 μL 150 of 2.5 mM dNTPs, 1.5 μL of each primer (10 μM), 0.2 μL of High-Fidelity DNA 151 Polymerase, and 50 ng of template DNA (sequencing library preparation). We 152 analyzed the amplicons using 2% agarose gels and purified them using the 153 AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA) following the 154 manufacturer's instructions. We normalized, pooled, and sequenced the 155 purified amplicons on the Illumina NovaSeq 6000 Sequencing System 156 according to standard protocols. 157 158 2.4 Bioinformatic analysis 159 We conducted 16S rRNA sequencing analysis as follows. Quality control, 160 clustering, and high-throughput search were performed using Usearch, a 161 robust sequence analysis software (http://www.drive5.com/usearch/). To 162 ensure high-quality clean reads, raw reads were further filtered and assembled 163 using FASTP (Chen et al., 2018) . Clean tags were then clustered into 164 operational taxonomic units (OTUs) of ≥ 97% similarity using UPARSE (Edgar, 165 2013). To classify representative OTU sequences into organisms, we used a 166 naïve Bayesian model with an RDP classifier based on the SILVA rRNA 167 database (https://www.arb-silva.de/) (Wang et al., 2007). Our analysis included 168 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint species composition, indicator species, alpha diversity, beta diversity, and 169 function analysis, which were all conducted using Omicsmart 170 (http://www.omicsmart.com). The abundance statistics of domain, Phylum, 171 Class, Order, family, genus, and species taxonomy were visualized in a 172 stacked bar plot using Omicsmart. We also generated a network of correlation 173 coefficients using Omicsmart. The raw sequencing reads were deposited into 174 the Sequence Read Archive (SRA) database of NCBI with an accession 175 number PRJNA1080319. 176 177 2.5 Statistical analysis 178 All statistical analyses were conducted using GraphPad Prism software. 179 One-way ANOVA with Turkey's multiple comparison test was employed to 180 determine significant differences among groups. A P value of less than 0.05 181 was considered statistically significant. The results are presented as means 182 with standard error of the mean (SEM) for each group. 183 184 3 Results 185 3.1 Bacterial identification by 16S rRNA sequencing and conventional 186 PCR 187 PCR was used to examine Infraorbital sinus samples of SPF chicken and 188 chicken with SHS for APG and ORT. Positive controls for APG serotypes A, B, 189 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint and C were set in the PCR. A total of 5 APG positive samples and 5 APG 190 negative samples of chicken with SHS were identified and collected for 16S 191 rRNA sequencing analysis (Figure 1). Three serotype B (APG -1, APG-2, and 192 APG-5) and 2 serotype A (APG -3 and APG -4) APGs were identified and 193 isolated. One of the five APG -positive samples was also identified as 194 ORT-positive (Figure 1A). In contrast, samples from SPF chicken were 195 determined to be negative for both APG and ORT (Figure 1G). This study 196 compared conventional PCR with 16S rRNA sequencing for identifying 197 bacteria. Positive bands on the gel were considered as positive. Results 198 showed that 14 out of 15 samples were identified as APG -positive by 16S 199 rRNA sequencing, while only 5 out of 15 were identified by conventional PCR. 200 Compared to 16S rRNA sequencing, the sensitivity of conventional PCR for 201 APG detection is 35.7% (5/14). For ORT, only 1 out of 15 samples was 202 identified as positive by conventional PCR, while nine out of fifteen were 203 identified as positive by 16S rRNA sequencing (Figure 1, Supplemental Figure 204 1). Compared to 16S rRNA sequencing, the sensitivity of conventional PCR for 205 ORT detection is 11.1% (1/9). The study concludes that 16S rRNA sequencing 206 has a much higher sensitivity compared to conventional PCR. 207 208 3.2 Species composition analysis 209 Figure 2G lists the top 10 species, with APG being the most abundant in 210 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint the APG -positive group. APG was found to be the predominant bacteria in 211 most of the samples from the APG group , with abundance percentages of 212 2.69%, 81.26%, 65.50%, 76.58%, and 43.76% (APG-1 to APG-5), respectively. 213 Although APG was not detected in the APG -negative group by PCR, 214 sequencing data showed a relatively high abundance in those samples. 215 Specifically, N-APG-1, N-APG-2, N-APG-3, and N-APG-4 showed abundance 216 percentages of 20.96%, 1.18%, 1.79%, and 0.062%, respectively. It is worth 217 noting that APG was found not only in infected chickens but also in SPF 218 chickens (Table S1). The abundance of APG in the APG group was 219 significantly higher than that of the Mock (P=0.0036, Figure 3) and N -APG 220 (P=0.0113, Figure 3) groups. PCR detected ORT in one of the selected 221 samples (Figure 1), with a higher abundance of ORT in that sample found by 222 16S rRNA sequencing (0.65%, Table S1). ORT was also widely detected in 223 samples from chickens with SHS by 16S rRNA sequencing, although at too 224 low an abundance to be detected by PCR (Figure 1, Table S1). 225 226 3.3 Indicator species analysis 227 To compare the species in the Mock, APG, and N -APG groups, a Venn 228 analysis was conducted using the R project Venn Diagram package (version 229 1.6.16) to identify unique and common species (Figure 4). The results showed 230 that 1179, 1243, and 1885 OTUs were identified in Mock, APG, and N -APG, 231 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint respectively (Figure 4). As illustrated in Figure 4A, 409 OTUs were shared by 232 all groups. In terms of biological classification, from phylum to species analysis 233 (Figure 4B-G), APG and N -APG exhibited a larger number of unique bacteria 234 compared to the Mock group. To calculate the comparison of species between 235 the different groups, Welch's t -test in the R project Vegan package (version 236 2.5.3, Supplemental Figure 2) was used. The results showed that the 237 abundance of APG in the APG group is significantly higher than that in the 238 Mock group (P=0.02, Supplemental F igure 2). Furthermore, significant 239 differences in the abundance of Eubacterium sp Marseille -P3202, bacterium 240 ic1379, Akkermansia muciniphila, Alistipes Sp CHKCI003 , bacterium ic1296, 241 Ralstonia pickettii , Coriobacteriaceae bacterium CHKCI002 , Shewanella SP 242 FDAARGOS 354 between Mock and APG group were also detected 243 (Supplemental F igure 2). The Mock group exhibited a significantly higher 244 abundance of Eubacterium sp Marseille -P3202, Alistipes Sp CHKCI003 , 245 bacterium ic1296, and Coriobacteriaceae bacterium CHKCI002 than the 246 N-APG group. Meanwhile, the APG group showed a significantly higher 247 abundance of Avibacterium paragallinarum, Alistipes inops, and Bacteroides 248 sp Smarlab 3302398 than the N-APG group (Supplemental Figure 2). 249 250 3.4 Alpha diversity analysis 251 The study examined alpha diversity through the analysis of the Chao1 and 252 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Shannon indexes . The calculations for Chao1 and Shannon index were 253 conducted using QIIME version 1.9.1, while the OTUs rarefaction curve was 254 generated using the R project ggplot2 package (version 2.2.1). Statistical 255 analysis was carried out using Welch's t method. The average Chao1 index for 256 the Mock, APG, and N -APG samples were 737.43, 957.46, and 884.82, 257 respectively (Figure 5A&B). Notably, no significant difference in the Chao1 258 index was observed within these groups (P>0.05, Figure 5C-E). On the other 259 hand, the Shannon index of the Mock group was found to be higher compared 260 to the APG and N -APG groups (Figure 5F-J). Specifically, the Shannon index 261 for the Mock group ranged from 6.66 to 7.23, and its average was significantly 262 higher than that of the APG group (Figure 5H). 263 264 3.5 Beta diversity analysis 265 To analyze the bacterial communities among different groups, Bray-Curtis 266 similarity was calculated, and Principal Coordinate Analysis (PCoA) was 267 performed based on Bray-Curtis distances. This helped visualize the similarity 268 of bacterial community structures among these groups (Figure 6A). The results 269 showed that PCo1 and PCo2 accounted for 30.73% and 24.85% of the total 270 variation, respectively. The samples displayed a distinct clustering in each 271 group, and there was a partial crossover between the APG and N-APG groups. 272 These findings suggested significant differences in the bacterial composition 273 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint among the three groups. To further test this, the grouping information test was 274 conducted, and unweighted -unifrac was selected for distance analysis. The 275 Adonis (Permanova) test was calculated in the R project Vegan package 276 (version 2.5.3). The Adonis test demonstrated that the beta diversity of the 277 APG and N -APG group was significantly higher than that of the Mock group 278 (Figure 6B and 6C, P=0.03 for APG v Mock, P=0.026 for N -APG v Mock). 279 However, there was no significant difference between the beta diversity of 280 APG and N-APG (P=0.28). Sample distance analysis showed that four of the 281 mock samples were in one branch (Figure 6E), while among the APG or 282 N-APG group, the samples showed different sample distances from each other 283 (Figure 6E). 284 285 3.6 Function analysis 286 We utilized Tax4Fun (version 1.0) to conduct KEGG pathway analysis of 287 the OTUs (Figure 7A). Our findings indicated that carbohydrate metabolism, 288 membrane transport, translation, folding, sorting and degradation, nucleotide 289 metabolism, replication, and repair, glycan biosynthesis, and metabolism 290 pathway were downregulated in all samples of the APG group and 3/5 291 samples in the N -APG group, in comparison to the Mock group. Conversely, 292 an upregulated pathway of amino acid metabolism, xenobiotics biodegradation 293 and metabolism, signal transduction, cell motility, cell growth and death, lipid 294 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint metabolism, metabolism of other amino acids, endocrine system, metabolism 295 of terpenoids and polyketides, and biosynthesis of other secondary 296 metabolites was detected in samples of the APG group and 3/5 samples in the 297 N-APG group (Figure 7A). We also performed microbiome phenotype analysis 298 using BugBase. Our findings indicated that the APG group demonstrated an 299 upregulated level of contains mobile elements, stress -tolerant, potentially 300 pathogenic, facultatively anaerobic, forms biofilms, and gram -negative (Figure 301 7B), while the APG and N -APG groups showed a decreased anerobic and 302 gram-positive level (Figure 7B). Additionally, we utilized the FAPROTAX 303 database (Functional Annotation of Prokaryotic Taxa) and associated software 304 (version 1.0) to generate the ecological functional profiles of bacteria. Our 305 analysis demonstrated that chicken samples with SHS had a higher 306 abundance of animal parasites or symbionts (Figure 7C). In contrast, the 307 bacterial community in the Mock group was associated with 308 chemoheterotrophy, fermentation, animal parasites or symbionts, human gut, 309 and mammal gut, etc (Figure 7C). Compared to the Mock group, most samples 310 from the APG and N -APG groups showed an imbalanced bacteria community 311 (Figure 7C). 312 313 4 Discussion 314 Opportunistic pathogens are commonly present in healthy individuals but 315 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint in low quantities. When the balance of the bacterial community is disrupted, 316 these pathogens can proliferate and lead to local or systemic changes. In this 317 study, 16S rRNA gene sequencing revealed the presence of APG in both APG 318 PCR-positive and APG PCR-negative chickens, as well as SPF chickens. 319 Opportunistic pathogens can cause severe infections in individuals with 320 compromised immune systems or microbiota. The symbiotic relationship 321 between various microorganisms can hinder the colonization of opportunistic 322 pathogens, preventing their expansion. However, these pathogens can initiate 323 infection and expansion by inhibiting commensal bacteria, leading to changes 324 in the microbiota's structure and proportion. This disruption allows pathogenic 325 bacteria to colonize and gain a growth advantage quickly . Recent studies 326 suggest that Staphylococcus chromogenes plays a promoting role in infectious 327 rhinitis caused by APG infection. It provides the necessary nutritional factor, 328 nicotinamide adenine dinucleotide (NAD), for the growth of APG and 329 accelerates its biosynthesis and release from host cells, promoting its survival 330 and growth. Animal models have shown that antibiotics directed against 331 Staphylococcus chromogenes can prevent APG infection (Wu et al., 2021). 332 PCR is a rapid technique for identifying pathogens based on their DNA. 333 While conventional PCR can detect a range of DNA copies, real -time PCR is 334 more sensitive and can detect lower levels of DNA. The intensity of the PCR 335 signal is influenced by factors such as the copy number of the target gene, 336 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint PCR reaction parameters, and cycling conditions. However, microbiota 337 analysis has shown that PCR may not be sufficiently sensitive for pathogen 338 identification. For instance, APG is an opportunistic pathogen of chickens that 339 is commonly found in their infraorbital sinuses. Furthermore, microbiota 340 sequencing has identified a significant number of animal pathogenic bacteria. 341 It is worth noting that chickens harbor not only non -pathogenic bacteria, but 342 also pathogenic bacteria that are capable of infecting humans. 343 Vaccines are highly effective tools in preventing the spread of infectious 344 diseases (Zhang et al. , 2015; Zhang et al. , 2018a; Zhang et al. , 2018b) . 345 Inactivated whole-cell and recombinant subunit protein vaccines are 346 commonly used to protect against specific pathogens (Zhang et al., 2016; 347 Zhang et al., 2015). It’s reported that double vaccination with an inactivated IC 348 vaccine is more effective in preventing infectious coryza caused by APG 349 serovar A, B, and C, compared to a single vaccination (Guo et al., 2022) . 350 Characterizing the pathogenicity of APG isolates is essential for developing an 351 effective inactivated IC vaccine (Caballero-Garcia et al., 2022). 352 It is essential to acknowledge the limitations of this study. While 353 conventional PCR can identify pathogens, it cannot quantify them. Future 354 studies may consider using real -time PCR in conjunction with 16S rRNA 355 sequencing to address this. It should also be noted that there are limitations to 356 16S rRNA sequencing, including the high cost of equipment compared to PCR 357 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint testing, and its restriction to bacterial pathogens. Additionally, 16S rRNA 358 sequencing may not effectively discriminate between certain species due to 359 high sequence similarities. However, this can be resolved by altering the 360 specific region of the 16S rRNA gene sequence (Fida et al., 2021). It is worth 361 noting that 16S rRNA gene sequencing is based on PCR amplification, which 362 can lead to the amplification of genes with high abundance and the omission of 363 those with low abundance. Additionally, base pair mismatches may be 364 introduced during the sequencing process. 365 To summarize, utilizing the sequencing of the 16S rRNA gene is a potent 366 means of identifying bacterial pathogens. This technique applies to analyzing 367 bacterial communities in samples and identifying new bacteria. In clinical 368 laboratories, PCR is more commonly used than 16S rRNA sequencing. While it 369 is the most widespread method, its sensitivity and accuracy do not match that 370 of 16S rRNA sequencing. The signal intensity from conventional PCR is 371 contingent upon the bacterial abundance. As sequencing identification 372 technology becomes more accessible and cost -effective, it is likely more 373 clinical microbiologists will adopt this method in their lab's workflow. 374 375 AUTHOR CONTRIBUTIONS 376 Conceptualization, planning, and management: GF Zhang. Laboratory 377 experimental design: GF Zhang. Laboratory execution of experiments, and 378 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint data acquisition, analysis, and interpretation: MN Li, N Wang , T Wang, S 379 Zhang, Q Han, CC Zhang, YQ Shi, PZ Qiao, CL Man, T Feng, YY Li, XM You, 380 Z Zhu, KJ Quan, TL Xu, GF Zhang. Drafting and revision of the manuscript: GF 381 Zhang. Manuscript editing and approval: GF Zhang, TL Xu, KJ Quan, Z Zhu. 382 383 ACKNOWLEDGMENTS 384 This study was supported by the Taishan Industrial Expert Programme 385 (tscx202306107). 386 387 CONFLICT OF INTEREST 388 The authors declare no conflict of interest. 389 390 DATA AVAILABILITY STATEMENT 391 Data that support the findings of this study are available from the 392 corresponding author upon reasonable request. 393 394

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Antonie 534 Van Leeuwenhoek 110, 1647-1657. 10.1007/s10482-017-0914-7. 535 536 Figures and Tables Legend 537 Figure 1 The PCR method was employed to identify APG and ORT in samples 538 from commercial layer and SPF chickens (A-G). Lane M denotes the molecular 539 weight standard, while samples A, B, and C serve as positive controls for APG 540 serotypes A, B, and C. In total, ten clinical samples (2451, 2469, 2465, 2344, 541 HY01, 2369, 2398, 2357, 2370, and 2464) and five SPF chicken samples 542 underwent conventional PCR testing for APG and ORT. 543 Figure 2 Analysis of the composition of bacterial communities. Through 16S 544 rRNA amplicon sequencing, we identified the microbiota inhabiting the 545 infraorbital sinus of 15 chickens. We analyzed the relative abundances of 546 domain, phylum, class, order, family, genus, and species, and displayed the 547 top ten (A-G). 548 Figure 3 Analysis of the relative abundance of APG in APG, N-APG, and Mock 549 groups. Compare the relative abundance of APG across different groups, and 550 note that it is significantly higher than that of Mock and N -APG groups (A, B, 551 and C). 552 Figure 4 Indicator species analysis. Venn diagrams are used to illustrate the 553 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint indicator at OTU, Phylum, class, order, family, genus, and species level. 554 Figure 5 Alpha diversity analysis was conducted, resulting in the calculation of 555 the Chao1 and Shannon indexes. (A) The Chao1 index was determined for 556 each sample. (B) The average Chao1 index was calculated for three groups. 557 (C-E) Statistical analysis was performed to compare the different groups. (F) 558 The Shannon index was determined for each sample. (G) The average 559 Shannon index was calculated for each group. (H -J) Statistical analysis was 560 conducted to compare the different groups. 561 Figure 6 This study conducted beta diversity analysis using PCoA analysis for 562 three groups and analyzed beta diversity between each group. We also 563 created a species distance heatmap. 564 Figure 7 We used Tax4Fun (version 1.0) to infer KEGG pathway analysis of 565 OTUs and conducted heat map analysis. Additionally, we classified 566 microbiome phenotypes of bacteria using BugBase and generated ecological 567 functional profiles of bacteria using FAPROTAX database (Functional 568 Annotation of Prokaryotic Taxa) and associated software (version 1.0). 569 570 571 572 573 574 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 1 575 576 577 578 579 580 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 2 581 582 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 3 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 4 Indicator species analysis 600 601 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 5 Alpha diversity analysis 602 603 604 605 606 607 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 6 Beta diversity analysis 608 609 610 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Figure 7 Function analysis 611 612 613 614 615 616 617 618 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Table 1 Samples in this study. 619 No ID Samples Origin PCR identification Serovar 1 2451 APG-1 Hebei APG B 2 2469 APG-2 Jiangsu APG B 3 2465 APG-3 Guangdong APG/ORT A/ORT 4 2344 APG-4 Guizhou APG A 5 HY01 APG-5 Hebei APG B 6 2369 N-APG-1 Henan - - 7 2398 N-APG-2 Jiangsu - - 8 2357 N-APG-3 Shandong - - 9 2370 N-APG-4 Sichuan - - 10 2464 N-APG-5 Shandong - - 620 Table 2 Primers used in this study. 621 Primer Sequence (5' to 3') target ORT-F GAGAATTAATTTACGGATTAAG ORT 16S rRNA ORT-R TTCGCTTGGTCTCCGAAGAT ORT 16S rRNA ABC- F GGCTCACAGCTTTATGCAACGAA HMTp210 A-R CGCGGGATTGTTGATTTTGTT HMTp210 B-R GGTGAATTTCACCACACCAC HMTp210 C-R TAATTTTCTTATTCCCAGCATCAATACCAT HMTp210 341F CCTACGGGNGGCWGCAG V3–V4 806R GGACTACHVGGGTATCTAAT 622 623 624 625 626 627 628 629 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Supplemental Figure 1 The heatmap of species identified in different 630 samples. 631 632 633 634 635 636 637 638 639 640 641 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint Supplemental Figure 2 Indicator species analysis 642 643 644 645 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted June 27, 2024. ; https://doi.org/10.1101/2024.06.26.600821doi: bioRxiv preprint