Introduction 63
Despite decades of study and attempts at control and prevention, bovine respiratory disease 64 (BRD) remains a major cause of morbidity, mortality, and economic losses in the cattle industry 65 worldwide. BRD is a polymicrobial infection and it accounts for approximately 70-80% of the cattle 66 feedlot morbidity in the USA (1). Losses are estimated to be $23.60 per calf and they add up to an 67 annual loss of over one billion dollars to the US cattle industry alone (2). BRD also results in the use 68 of widespread therapeutics and antibiotics in feedlots, which increasingly raises public health 69 concerns of promoting antibiotic resistance (3, 4) . 70
The pathophysiology of BRD involves complex interactions between host, pathogen, 71 environment, genetic and management factors. In feedlot cattle, BRD is initiated by viral infection 72 exacerbated by stress due to travel which is typically followed by secondary infection by commensal 73 bacteria (5). Viral infection can cause increased susceptibility to secondary bacterial infections by 74 immunosuppression or by inflammation causing damage to the epithelium of upper airways and 75 injuring lung parenchyma. Such damage facilitates the migration of bacterial pathogens and 76 colonization of the lower respiratory tract. If unresolved, continued BRD advances to lower airway 77 regions, eventually causing bronchopneumonia. Many viral pathogens have been implicated in BRD, 78 including bovine viral diarrhea virus (BVDV), bovine herpesvirus 1 (BoHV-1), bovine respiratory 79 syncytial virus (BRSV), bovine parainfluenza 3 (PI-3), and more recently bovine coronavirus 80 (BCoV). In sum, more than a dozen pathogenic bacterial and viral agents have been implicated in 81 BRD establishment and progression, prompting the development of vaccines to aid in protecting 82 against infection with several of these organisms. However, vaccination and other prevention 83 strategies have failed to stop the disease and alleviate these losses in production. Both bacterial and 84 viral coinfections are common in BRD, so it can be difficult to directly implicate any one pathogen. 85
The latest additions to the list of BRD-associated viruses include a class of virus previously 86 unknown to infect cattle. Orthomyxoviruses enveloped viruses with segmented negative-sense 87 single-stranded RNA genomes, including seven genera, four of which are influenza viruses. Influenza 88 D virus (IDV) is the most recently discovered member of the family and it is well established that 89 cattle are the definitive host for IDV (6). IDV RNA has been found in nasal secretions from cattle 90 with respiratory disease throughout North America (2, 6-10), Europe (11) (12) (13) (14) , and East Asia (15, 16) . 91
Although infection with IDV causes only mild respiratory disease in experimentally infected calves 92 (17-20), a retrospective metagenomic BRD case-control study analysis of a set of 100 calves 93 identified a positive correlation between the presence of IDV genomic RNA in nasal swabs and BRD 94 symptoms in cattle (9). Additional retrospective case-control studies of feedlot cattle in Canada (10), 95 the USA, and Mexico (2) have also found a higher prevalence of IDV in cattle with symptomatic 96 BRD compared with healthy controls from similar sample sizes (n=93 and n=116). However, it 97 remains unclear how the mild pathogenic effects of IDV infection would cause BRDC. 98
In 2016, a second orthomyxovirus, influenza C virus (ICV), was detected in cattle with BRD 99 in the midwestern USA (21, 22) . ICV was previously unknown to infect cattle, and it is uncertain 100 how long ICV has existed in the cattle population. ICV has been identified in Alberta, Canada, and 101 Texas, Oklahoma, Missouri, Colorado, Montana, Nebraska, Minnesota and Kansas, USA (10, 22) . 102
Understanding the potential role of ICV in disease in cattle is in its infancy. ICV was first identified 103 in animals with BRD (21), suggesting that ICV may have an associative role in BRD. However, a 104 recent retrospect case-control metagenomics study of feedlot cattle respiratory tract samples found no 105 association between cattle with BRD and corresponding samples with reads mapping to ICV (10). 106
Additional exploration of the role of ICV in cattle health is needed to understand the significance of 107 this orthomyxovirus. 108
The potential association of these orthomyxoviruses with BRD could illuminate more about 109 BRD pathogenesis. Previous studies of IDV/ICV in case-control cattle have included limited sample 110 sizes and had mixed numbers of subjects from different premises. We hypothesized that exploring a 111 larger sample-size from individual premises would provide better clarity about IDV/ICV and 112 relationship to BRD by reducing the confounding variation contributed by differing management 113 practices and conditions at separate premises. Since cattle serve as a natural reservoir of IDV, the 114 mere presence of the virus may not correlate with disease. Hence, we believed that quantitative 115 evaluation of both health status and virus copy numbers could provide a robust statistical evaluation 116 of potential correlation of IDV or ICV with BRD. Here, probe-based real-time RT-PCRs were 117 utilized to screen for these viruses in deep nasal swabs from cattle. This technique provides the 118 ability to identify viral genomic RNA in a sensitive, specific and quantitative manner. Three separate 119 case-control cohorts of approximately 200 animals each were screened to retroactively determine the 120 prevalence of IDV and ICV in US cattle from 2011 to 2014, making this the largest case-control 121 investigation examining the relationship between these new orthomyxoviruses and BRD. 122
All animal care and sample collections were approved and performed in accordance with the 125 Institutional Animal Care and Use Committee at Washington State University (#04110). Cattle from 126 three locations were selected for the study. One study cohort (n=200) originated from calf-raising 127 operations in New Mexico state (23) consisting of female Holstein calves aged 23 to 76 days, 128 sampled from August to October 2011. The other two cohorts were from beef cattle feedlots (24). 129
One feedlot cohort (n=200) located in the state of Colorado consisted of male cattle, 98.5% Angus 130 and 1.5% Red Angus; animals were sampled between October to November 2012. The third cohort 131 (n=199) was from a beef cattle feedlot in Washington state with female cattle, 60.8% Angus, 14.1% 132 Charolais, 18.1% crossbred, 4.5% Red Angus, and 2.5% Hereford; 97% of animals were sampled 133 from January to July 2014, and 3% were sampled in October 2013. Each animal was individually 134 observed and scored for respiratory health symptoms using the McGuirk scoring system (McGuirk, 135 2008) , with health scores ranging from 0 to 12. Animals with McGuirk health scores ≥5 were 136 classified as clinically affected BRD cases and those with scores ≤4 were classified as healthy 137 controls. One deep pharyngeal and one mid-nasal swab were collected from each animal and pooled 138 together in the viral transport medium. 139
One aliquot of each pooled swab sample was tested for BRSV, BoHV-1, BCoV, and BVDV 141 by the California Animal Health and Food Safety Lab System (Davis, CA) using real-time PCR or 142 RT-PCR. Viral nucleic acids were extracted from a separate aliquot of each nasal swab using the 143 MagMAX-96 Pathogen RNA/DNA kit following the manufacturer's instructions. Nucleic acids were 144 tested for PI3, IDV, and ICV using real-time RT-PCR on a 7500 Fast Real-Time PCR System 145 (Applied Biosystems, Foster City, CA) using previously reported primer and probe sets 146 (Supplemental Table A For ICV, 3 µL of template was used in a 20 µL reaction using the Path-ID Multiplex One-156
Step RT-PCR Kit (Applied Biosystems) with 0.4 µM of each primer and 0.2 µM of each probe; 157 cycling conditions were 48 °C for 10 min, 95 °C for 10 min, and 45 cycles of 95 °C for 15 s and 60 158 °C for 45s. 159
For IDV and ICV, standard curves of transcribed RNA template corresponding to each target 161 (IDV PB1 gene bases 1200 to 1600 and ICV M gene bases 545 to 1105) were generated. RNA was 162 transcribed from PCR products encoding the desired bases downstream of a T7 promoter region 163 using MEGAScript T7 Transcription Kit (Invitrogen, Carlsbad, CA) followed by purification using 164 the MEGAClear Transcription Clean-Up Kit (Invitrogen) and quantification using a NanoDrop lite 165 instrument (Thermo Scientific, Waltham, MA). To generate standard curves, serial 10-fold dilutions 166 of transcription product ranging from 10 -1 to 10 8 copies were included in triplicate in each real-time 167
RT-PCR assay. The limit of detection for the IDV assay was 1 copy (Ct value 38.5 to 40) and for 168 ICV it was 100 copies (Ct value 39 to 40). Three technical replicates of standards were used in all 169 real-time RT-PCR assays, as was a negative control of nuclease-free water. Unknown samples were 170 tested in duplicate. The coefficient of variance for all specimens (samples and standards) was Ct < 171 0.03. 172
Statistical analyses were performed using Prism version 8 (GraphPad, San Diego, CA). 174
Mann-Whitney U tests were performed to compare clinical health scores between state cohorts, with 175 two-tailed p values calculated. Viral copy numbers were determined by fitting Ct values to the 176 standard curves generated using transcribed RNA representing the target sequence of each RT-PCR. 177
The arithmetic mean values were calculated. To compare these lognormal-distributed viral copy data, copy numbers were log transformed and a parametric t-test using Welch's correction was performed. 179
In this analysis, one-tailed p values were calculated. Spearman correlations with two-tailed p-values 180 were performed to correlate clinical scores with viral copy number; percentages were compared via a 181
Fisher's exact test. 182
IDV and ICV were detected in feedlot cattle from Colorado and Washington. However, no 185 IDV or ICV was detected in the cohort from Holstein calves from New Mexico. The prevalence of 186 IDV was similar in both feedlot cohorts (Colorado: 5.0%, Washington: 5.5%) ( Table 1) . The 187
Colorado cohort demonstrated a lower prevalence of IDV in BRD cases compared with controls 188 (cases: 3.0%, controls: 7.1%). The Washington cohort demonstrated a similar prevalence in control 189 and case animals (cases: 6.0%, controls: 5.1%). 190
Notably, ICV was identified in more animals than was IDV ( corresponding log viral copy values were different between cases and controls. Viral copy numbers 199 were 0.75 logs higher in BRD case animals ( Figure 1A ). The trend toward mean viral copy numbers 200 being higher in BRD cases compared with controls approached statistical significance (p = 0.097) 201 and this trend was the same in the overall result, as well as in both of the herds independently 202 (Colorado: p=0.20; Washington: p=0.26 ). In addition, a significant positive correlation was found 203 between viral copy numbers and BRD score in the combined results (p=0.020) and in the Colorado 204 cohort (p=0.024) ( Table 2) . 205 BRD case and control animals positive for ICV RNA both demonstrated a range of detectable 206
Ct values (range: control 24.12 to 39.28, case 25.14 to 35.69) and copy numbers ( Figure 1B) . Similar 207 to IDV, despite a higher prevalence of ICV in control cattle, the BRD case cattle mean viral copy 208 levels were about 1 log greater, which was statistically significant in the combined cohort analysis 209 (p=0.0004). As shown in Table 3 , a positive correlation was found between ICV viral copy number 210 and BRD score, which was statistically significant in the combined cohort data (Spearman r value = 211 0.44, p=0.0009) and in each cohort separately (Colorado p=0.022, Washington p=0.044), indicating 212 that animals showing more severe clinical symptoms had more viral RNA extracted from nasal 213 swabs. 214
In addition to IDV and ICV, nasal swabs were screened for the five viruses most frequently 216 associated with BRD namely PI3, BoHV-1, BCoV, BVDV, and BRSV. To determine if there was an 217 association of viral presence and BRD symptoms, a Fisher's exact test was used to evaluate the 218 significance of odds ratio values calculated between the presence of each virus and it is a BRD case 219 animal. The analysis showed that PI3, BoHV-1, BCoV, BRSV, and BVDV displayed odds ratios >1 220 (Table 4) , suggesting an association of the presence of each of these viruses with BRD clinical 221 symptoms. Of these viruses, the odds ratios of BCoV and BRSV were statistically significant by 222
Fisher's exact test (p=0.0007 and p=0.0008, respectively), and that of BoHV-1 approached 223 significance (p=0.061). In contrast, the odds ratio for IDV was 0.74, indicating no association with 224 BRD health status. For ICV, the odds ratio of the combined herds was 0.53, which approached 225 statistical significance (p=0.062), indicating a negative correlation between the presence of ICV and the demonstration of BRD symptoms. Odds ratios of individual herds is provided in supplementary 227 material (supplementary tables 1&2). 228
Each cohort investigated presented particular characteristics regarding sex, age, breed, and and a similar trend was observed in IDV-positive animals. In BRD case cattle, the geometric mean of 247 detectable IDV viral RNA levels was nearly 2 logs higher in co-infected animals (1.30x10 4 ) than 248 those only infected with IDV (2.19x10 2 ). However, ICV levels between these groups were similar to 249 one another. there was no significant evidence for higher IDV prevalence in BRD symptomatic cattle when 313 compared with BRD asymptomatic cattle. These different correlation results amongst studies may be 314 related to the vastly different scoring systems used to classify animals as case or control. The location 315 (country), management practices, sampling year, age, season, and sample sizes used may all 316 contribute to the between study correlational differences observed. 317
To date, only one other case-control study investigating both IDV and ICV in cattle has been 318 reported (10). This metagenomics study, described above, found no ICV in BRD case cattle but 319 identified several control animals with ICV. Their results agree with our finding of ICV in healthy 320 cattle, and a higher prevalence in control animals than in cases. 321
Although more prevalent in healthy cattle, in the current study ICV RNA levels were 322 significantly lower in BRD asymptomatic cattle, and IDV RNA levels showed a trend in this direction, as well. This result is in agreement with previous observations of IDV RNA levels in sick 324 and healthy cattle (2, 32). The relative abundance of virus in each health status was not reported for 325 ICV in the only other cattle ICV case-control report (10). Taken together, these results suggest that 326 the level of orthomyxovirus infection may be of more relevance to BRD clinical symptoms rather 327 than the simple presence or absence of viral RNA. It is also possible that IDV-and ICV-positive 328 BRD control animals represent cattle with subclinical BRD. This prospect is consistent with the 329 identification of IDV in a pig with subclinical infection in the US (33) Overall, a similar prevalence of RT-PCR-identified IDV and ICV was found in the BRD case 343 and control cattle in both orthomyxovirus-positive herds tested in this study. It has been suggested (9, 344 14, 17) that the main role of IDV in respiratory disease is contributing to effects initiated by infection 345 with other pathogens associated with respiratory disease in cattle. The results of this study provide 346 further evidence to support this hypothesis. The results here suggest that while neither IDV nor ICV 347 causes BRD directly, these orthomyxoviruses are better able to replicate in cattle with another viral 348 respiratory infection and as such may contribute to illness in BRD. Increased replication of IDV or 349 ICV may contribute to BRD through tissue damage. Following experimental infection of calves, IDV 350 can be detected in samples of nasal swabs, tracheal swabs, and bronchoalveolar lavage fluid, as well 351 as in tissues including nasal turbinate, trachea, bronchus, and lung (17-20) . Inflammation in the 352 trachea, as evidenced by neutrophil infiltration and mild epithelial attenuation, has been observed in 353 multiple studies of experimental IDV infection in cattle (17, 19, 20) . To a lesser degree, lesions have 354 also been observed in lung tissue in some studies (18) (19) (20) . An interesting alternative possible mechanism for IDV or ICV orthomyxoviruses to affect 362 BRD pathogenesis is through disruption of the bovine immune system. Viruses employ diverse 363 tactics to subvert the host immune responses. For example, influenza A viruses (IAVs) can 364 dysregulate innate immune antiviral responses in certain target cells, which promotes persistent IAV 365 circulation in asymptomatic reservoir hosts (36-38). Particularly because IDV can be found in 366 asymptomatic cattle, it is intriguing to consider if similar action is performed by this orthomyxovirus. 367
A recent study showed that IDV infection could lead to suppression of cytokine production in cattle. 368
In bronchioalveolar fluid collected two days after IDV infection, calves demonstrated upregulation of 369 two negative regulators of cytokine production (SOCS1 and SOCS3), and the proinflammatory type 370 one interferon pathway was decidedly unaffected (18). By suppressing immunity in the bovine respiratory tract, IDV may promote an environment permissive to other viral infections, thus 372 promoting their establishment and facilitating the development of the disease. Delineation of the 373 mechanism of orthomyxovirus infection of cattle leading to BRD should focus on characterizing the 374 effects of IDV or ICV infection on bovine innate immunity. 375
Too often infections are studied in isolation when in reality there are most often multiple 376 infections that all interact with the host immune system and this amalgamation results in the 377 manifestation of the disease. As a consequence of this, the scientific community needs to embrace 378 this added complexity as it will be necessary to clearly understand not only the infection process but 379 also the manifestation of the disease. This approach should also inform the development of novel 380 intervention methods. Studying processes like virus community assembly and identifying rules of 381 the assembly could aid in the development of optimal and sustainable strategies for long term 382 management of both animal health and productivity. This may also lead to more efficient and 
The authors declare no conflicts of interest. 