Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity

C S McConnel; G S Slanzon; L M Parrish; S C Trombetta; L F Shaw; D A Moore; W M Sischo

doi:10.1371/journal.pone.0278664

. 2022 Dec 1;17(12):e0278664. doi: 10.1371/journal.pone.0278664

Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity

C S McConnel ^1,^*,^#, G S Slanzon ^1,^¤,^#, L M Parrish ^1,^‡, S C Trombetta ^1,^‡, L F Shaw ^1,^‡, D A Moore ¹, W M Sischo ¹

Editor: Angel Abuelo²

PMCID: PMC9714867 PMID: 36454999

Abstract

Gastrointestinal (GI) disease is a major health concern in preweaned dairy calves. The objective of this fixed cohort study was to use RNA isolated from preweaned Holstein and Jersey heifer calf feces to study the molecular adaptations to variable clinical GI disease. The study was conducted on a commercial calf ranch in the western U.S. Enrolled calves were assessed twice daily for variations in demeanor, milk intake, and hydration. Fecal consistency scores were recorded at enrollment (day 1), and on the day (day 10) that a fecal sample was collected for differential gene expression (DGE). Calves with diarrhea on either day were classified as having either uncomplicated, localized GI disease (scours), or systemic GI disease (systemic enteritis). Eighty-four calves’ fecal RNA was evaluated for DGE, of which 33 calves (n = 20 Holstein; n = 13 Jersey) were consistently healthy. The remaining 51 calves (n = 23 Holstein; n = 28 Jersey) experienced varying severity of GI disease during the sampling window. Genes of interest were related to the inflammatory response (i.e., IFNG, NFKB1, NOD2, TLR2, and TLR4) and cell membrane or cytoplasmic transport (i.e., AQP3, FABP2, KRT8 and SLC5A1). Breed-specific findings indicated that AQP3, IFNG, and TLR4 were upregulated in Holsteins with systemic enteritis, whereas KRT8 was downregulated in systemically affected Jerseys. Holsteins did not appear affected by scours aside from a tendency for DGE of toll-like receptors (TLRs) on the day of diarrhea. However, Jersey calves consistently demonstrated a tendency to upregulate IFNG, NFKB1, and TLR4 when affected with either scours or systemic enteritis. These findings were more pronounced in systemically affected Jersey calves and were observed as a delayed response to both scours and systemic enteritis. These findings support previous observations suggesting that Holstein calves may be better equipped than Jersey calves to rapidly fight pathogen invasion.

Introduction

Research into dairy heifer health problems tends to simplify disease into preweaned or postweaned gastrointestinal (GI) or respiratory disease, and diagnostic accuracy is often hindered by a lack of detail [1–3]. The diagnosis of GI disease in particular has relied on the observation of diarrhea, but we historically have lacked the knowledge and tools to discriminate between GI disease phenotypes [4, 5]. Although blood biomarkers of inflammation, oxidative stress, metabolism and hydration provide additional insight into overall animal health, these analyses do not describe the actual biological adaptations of the GI tract (GIT) during the onset and progression of disease [6]. Furthermore, standardized methods of classification have been lacking such that what little information is available is often unproductive. As a result, inconsistencies in disease data presentation hinder descriptions, comparisons, and investigations into the overall consequences of disease on heifer health and wellbeing [7]. These misspecifications of disease processes can result in inappropriate therapeutic and preventive actions that impact lifetime wellbeing and put the dairy industry’s reputation at risk for failing to adequately address preventable losses [8].

Efforts to discriminate dairy calf GI disease phenotypes more fully have included transcriptomic assessments of fecal RNA [6]. GI tract epithelial cell exfoliation is associated with the natural turnover process to preserve tissue structure [9], and the daily shedding of these cells into feces provides a source of RNA to evaluate gene expression [10]. This provides a noninvasive sampling method in place of tissue biopsies or postmortem tissue analysis [11]. Importantly, fecal RNA can be differentiated based on the presence of a polyA tail in mammalian RNA that is absent in bacterial RNA. Consequently, Rosa et al. [6] demonstrated differential expression of genes related to cell membrane transport and inflammation in fecal RNA from neonatal Jersey male calves following mild diarrhea. This suggests that RNA isolated from fecal samples provides a potential tool for the evaluation of physiological changes due to GI disease. However, the inflammatory response observed by Rosa et al. in fecal RNA was delayed compared to the pattern of fecal scores and proinflammatory blood biomarkers. Accordingly, further research of transcriptional alterations is warranted to study the intestinal response to inflammation and its variation over time in neonatal dairy calves of different breeds experiencing diverse manifestations of GI disease [12, 13].

Therefore, the current study evaluated transcriptional changes detected in fecal RNA from neonatal dairy calves experiencing varying severity of GI disease with and without therapeutic interventions. We hypothesized that inflammatory and cellular transport signals would be differentially expressed in response to adaptations related to breeds and disease severity and timing. Thus, the objective of this study was to use RNA isolated from fecal samples to study the molecular adaptations to variable clinical GI disease via the expression of select genes involved in inflammation and cellular transport.

Materials and methods

Ethics statement

The research protocol was reviewed and approved by the Institutional Animal Care and Use Committee of Washington State University (ASAF#6414).

Study design and calf enrollment

This fixed cohort study was conducted on a commercial calf ranch in the western U.S. between July 13 and August 6, 2019. The ranch housed approximately 25,000 Holstein, Jersey, and crossbred calves through 200 days of age sourced from multiple dairies. Breed differences were variable within the source dairies ranging from Holstein and Jersey, Holstein or Jersey only, or crossbred from a single source. Calves were fed colostrum at the dairy of birth, transited <2 hours to the ranch, and arrived at the ranch at ≤1 day of age. Transfer of passive immunity was assessed via blood samples obtained from 1-day old calves via jugular venipuncture to measure total serum protein (TSP) using a calibrated refractometer [14].

Animal husbandry

On-farm personnel were responsible for all primary care of the calves including feeding, cleaning, watering, and bedding maintenance. The calves were housed in adjacent individual hutches with no direct contact between calves. Upon entry to the ranch all calves were dehorned using a dehorning paste (Dr. Naylor Dehorning Paste, H.W. Naylor Co. Inc., Morris NY, USA), sprayed with a fly control spray (Ultra-Boss Pour-on Insecticide, Intervet Inc., Merck Animal Health, Omaha NE, USA), and ear notched and tested for bovine viral diarrhea virus (BVDV) using PCR. An enteric clostridial vaccine (Ultrabac CD, Zoetis Inc., Kalamazoo MI, USA) and a vitamin complex with selenium and Vitamin E (MU-SE, Intervet Inc., Merck Animal Health, Omaha NE, USA) also were administered on arrival. An intranasal viral respiratory vaccine (Vista Once SQ, Intervet Inc., Merck Animal Health, Omaha NE, USA) was administered at 2 days of age and a booster was administered at 20 days of age. A 7-way clostridial vaccine (Ultrabac 7, Zoetis Inc., Kalamazoo MI, USA) was administered at 45 days of age. Calves were kept in hutches through approximately 90 days of age.

Calves were fed 2 liters of a custom milk blend in a bottle twice daily from 1 to 15 days of age, and 3 liters of the milk blend thereafter until 52 days of age. From 53 to 60 days of age, calves were fed 2 L once per day and weaned thereafter. The milk blend consisted of pasteurized waste milk together with milk replacer and was targeted for an optimal composition of 13% solids, 22–24% fat, and 28% protein. Under the oversight of a licensed veterinarian following veterinary feed directive guidelines, all calves enrolled in the study and between 5 to 12 days of age received milk medicated with neomycin oxytetracycline per the labeled dose (10 mg/lb body weight/day; Neo-Oxy 100/100 MR, PharmGate, Omaha NE, USA), to combat ongoing GI disease problems. Two liters of customized oral electrolyte solution also were offered after the afternoon feeding during this same risk period from 5 to 12 days of age. Water was available between milk feedings, and a handful of grain mix consisting of pellets, molasses, and whole corn was offered from 3 days of age and steadily increased to approximately 2.25 kg by day 30 with free choice thereafter.

Clinical health assessments and therapeutic interventions

Two on-farm veterinarians oversaw general calf health management and treatment protocols for the ranch. Diagnoses and treatments were based on input from calf health managers’ clinical assessments. Morbidity and mortality records included broad disease diagnoses and associated treatments and were managed on-farm using DairyComp 305 (Valley Agricultural Software, Tulare, CA) with record oversight and health data compilation provided through The HEALTHSUM Syndicate LLC (Sunnyside, WA, USA).

For the purposes of this study, a PhD candidate and WSU veterinarian conducted twice daily clinical evaluations based on a standardized calf health-scoring system [4], prior to and at the time of each a.m. and p.m. milk feeding. Assessments began on the day of arrival through the completion of the follow-up period to 21 days of age. Primary points of consideration included demeanor (bright/alert/responsive vs depressed/dull), milk intake (good appetite; did not finish the milk or did not consume any of what was offered; orogastric intubation), hydration status (mm of ocular recession), and fecal consistency scores (1 = well-formed fecal samples; 2 = semi-formed fecal samples; 3 = loose fecal samples; 4 = watery fecal samples). Due to inconsistencies in fecal appearance within the calf hutches and an inability to observe each calf defecate at the time of all clinical evaluations, fecal consistency scores only were recorded at the time of fecal sampling. Rectal temperature also was recorded for all calves at the time of sampling utilizing a digital thermometer cleaned with alcohol swabs between calves.

Calves with fecal scores of 3 or 4 were diagnosed with diarrhea and classified as having either uncomplicated, localized GI disease (scours), or systemic GI disease (systemic enteritis). Calves with scours had no signs of clinical disease aside from diarrhea. Calves with systemic enteritis had diarrhea along with a behavioral score ≥1 on a matrix incorporating demeanor, mobility, ear positioning, sucking reflex, hydration status based on ocular recession, appetite, and interest in interaction (Table 1). Calves with systemic enteritis were treated medically by farm personnel based on farm protocols incorporating the following alone or in combination: IV fluid therapy (lactated Ringer’s solution), anti-inflammatories (flunixin meglumine or dexamethasone), a commercial yeast product (Celmanax SCP, Arm & Hammer, Church & Dwight Co., Inc., Ewing Township, NJ, USA), a proprietary customized probiotic, kaolin-pectin, or variable IM or SQ antibiotics (ceftiofur crystalline free acid, florfenicol, or oxytetracycline). Calves with fecal scores <3 at the time of sampling were classified as consistently healthy if they did not demonstrate abnormal clinical parameters or behavioral scores during a period of 21 days after arrival or receive any treatment for GI disease, respiratory disease, or other health problems during the sampling period.

Table 1. Behavioral assessment scoring matrix for preweaned Holstein and Jersey calves.

Score	Demeanor	Mobility	Ears	Sucking Reflex	Ocular recession	Appetite	Interaction
0	Bright, alert, responsive	Actively mobile	Erect	Strong	None noted	Robust; consumes all milk on offer	Interactive
1	Mildly depressed, dull, less responsive	Capable of standing with minimal encouragement	Slightly drooped	Slightly diminished	Slightly sunken; <3 mm	Slow to drink; ≤25% of milk remains	Sluggish
2	Moderately depressed, dull, less responsive	Capable of standing with moderate encouragement	Drooped	Markedly diminished	Moderately sunken; 3–4 mm	Slow to drink; ≤50% of milk remains	Minimal interest; weak
3	Markedly depressed, markedly unresponsive	Capable of standing with assistance	Drooped; limp	No suckle; lethargic	Markedly sunken; 5–6 mm	Slow to drink; ≤75% of milk remains	Uninterested; lethargic
4	Unresponsive	Incapable of standing; sternal or lateral recumbency	Drooped; inert	No suckle; inert	Markedly sunken; >6 mm	Inappetant	Incapable of interaction

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Fecal samples for differential gene expression (DGE)

Fecal samples were collected per rectum between July 17 and August 6, 2019 (Fig 1). Heifer calves were randomly enrolled into the sampling scheme and allocated to an initial sampling day (day 1 enrollment sample) between 4 to 12 days of age to have fecal samples (~25–250 g) collected after the morning feeding for microbiome analysis used in a concurrent study [15]. This provided the opportunity to sample healthy calves and calves with previously undiagnosed scours but no other clinical abnormalities. Calves that demonstrated systemic enteritis (i.e., diarrhea plus a behavioral score ≥1) during this same period but prior to a random sample were sampled only once at the time of initial clinical diagnosis irrespective of the randomized sampling scheme.

Nine days after the initial fecal collection each calf was sampled again (day 10 DGE sample; 13–21 days of age) in an effort to align the fecal RNA transcriptomic analysis with previously documented delays in gene expression following evidence of diarrhea [6]. Calves that were initially sampled when healthy or with scours but progressed to systemic enteritis by 12 days of age, were sampled again on the day that systemic enteritis was diagnosed. These calves then were sampled twice more 9 days following both the first (healthy or scours) and second (systemic enteritis) samples to provide 2 day 10 samples. No calf was sampled more than 4 times in total and only one sample ultimately was analyzed for DGE, with preference given to the sample obtained during or following systemic enteritis (e.g., calves with systemic enteritis only were evaluated for disease progression in terms of healthy to systemic, systemic to healthy, or systemic to scours). Fecal samples were collected in sterile Whirl-Pak sampling bags (Cat #14-955-186, Thermo Fisher Scientific, Waltham, MA, USA) and immediately placed in a cooler with ice packs.

RNA extraction

Fecal samples remained on ice until they were processed in an on-farm laboratory within 2–4 hours of collection. Approximately 0.3 g of feces was weighed into a 15 mL conical tube containing 3 mL of RNAlater (Cat #AM7021, Thermo Fisher Scientific, Waltham, MA, USA) and then thoroughly mixed by vortex for 30 sec. Fecal bags and RNAlater tubes containing feces were then frozen onsite at -20°C. Samples were transported on dry ice to the WSU Field Disease Investigation Unit laboratory and stored at -80°C for approximately 3 months prior to processing. Fecal dry matter was performed by weighing out 1 g of feces and drying the samples in an incubator at 37°C for 72 hours. Percent dry matter was calculated by dividing the dry weight by the wet weigh multiplied by 100. Fecal samples in RNAlater were defrosted on ice and vortexed thoroughly for 30 sec at maximum speed. To aid in the removal of RNAlater [16], 3 mL of chilled PBS (Cat #AM1840, MagMAX Total Nucleic Acid Isolation Kit, Life Technologies Corp., Austin, TX, USA) was added and each sample was thoroughly mixed by vortex for 30 sec at maximum speed. To remove solids, samples were then centrifuged at 700 g for 1 min at 4°C. To pellet the cells the supernatant was transferred to a new 15 mL conical tube and centrifuged at maximum speed (4500 g) for 5 min at 4°C. The supernatant was discarded, and the remaining pellet was resuspended in 235 uL of Lysis Buffer provided by the MagMAX kit. The resuspension then was transferred to a bead tube supplied by the MagMax kit. RNA extraction was carried out beginning at the bead beating step in the MagMAX protocol. 90 uL of RNA Storage Solution (Cat #AM7001, Invitrogen, Carlsbad, CA, USA) was used for the final elution step in place of the MagMAX Elution buffer to improve preservation of RNA. RNA was quantified using a Qubit 4 Fluorometer (Cat #33226, Invitrogen, Carlsbad, CA, USA). RNA integrity was determined through fragment analysis using a 5200 Fragment Analyzer System (Agilent, Santa Clara, CA, USA), and 60ul of eluted RNA was stored at -80°C until submission for DGE analysis.

Due to the instability of fecal RNA and Nanostring’s allowance for DV values as a quality measurement, RNA integrity values were not used for quality assessments. Instead, samples with DV200 (percentage of RNA fragments >200 nucleotides) values >50% and concentrations >20 ng/uL were submitted for diagnostic processing at the Primate Diagnostic Services Laboratory (University of Washington, Seattle, WA, USA). Samples with DV200 values <50% and RNA concentrations <35 ng/uL were re-extracted using frozen feces with a Trizol (Cat #15596018, Invitrogen, Carlsbad, CA, USA) extraction method. An RNA Clean & Concentrator Kit (Cat #R1013, Zymo Research, Irvine, CA, USA) was used on all re-extracted samples, and those re-extracted samples with concentrations >60ng/uL were submitted for diagnostic processing as well.

Gene selection and gene expression analysis

Nine candidate genes were selected for this study (Table 2). These genes were related to the inflammatory response (i.e., IFNG, NFKB1, NOD2, TLR2, and TLR4) and cell membrane or cytoplasmic transport (i.e., AQP3, FABP2, KRT8 and SLC5A1). They were selected based on previous investigations into inflammatory pathologies and intestinal mucosa homeostasis [17–19], and gene expression analysis in fecal RNA from nondiarrheic dairy calves [12] and dairy calves undergoing mild diarrhea [6]. The genes glyceraldehyde 3-phosphate dehydrogenase (GAPDH), ribosomal protein S15a (RPS15A) and ribosomal protein 9 (RPS9) were used as internal control genes based on their previous identification as suitable controls [6].

Table 2. Genes examined for differential expression from fecal mRNA obtained from Holstein and Jersey calves.

Symbol	Name	Biological function	Biological function and process
AQP3	Aquaporin 3	Cell membrane	Cell membrane with a water channel function—epithelial cell marker
FABP2	Fatty acid-binding protein 2	Cytoplasm	Fatty acid-binding protein—epithelial cell marker
IFNG	Interferon, gamma	Inflammatory response	Important antiviral activity and immunoregulatory functions
KRT8	Keratin 8	Cytoplasm	Maintain gut microbiota homeostasis—epithelial cell marker
NFKB1	Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	Inflammatory response	Involved in many biological processes: inflammation, immunity, differentiation, cell growth, tumorigenesis, and apoptosis
NOD2	Nucleotide-binding oligmerization domain 2	Inflammatory response	Recognizes bacteria molecules and stimulates an immune reaction
SLC5A1	Solute carrier family 5 member A1	Cell membrane	Cell membrane with a sodium-glucose transporter function
TLR2	Toll-like receptor 2	Inflammatory response	Participates in the innate immune response to gram positive microbial agents
TLR4	Toll-like receptor 4	Inflammatory response	Participates in the innate immune response to gram negative microbial agents

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Gene expression analysis of the 9 target genes and 3 housekeeping genes was performed using the Nanostring System (Nanostring Technologies, Seattle, Washington, USA), as previously described [20]. Gene expression was measured using a custom TagSet for the selected genes (S1 Table) on the Nanostring nCounter Analysis System (Nanostring Technologies, Seattle, Washington, USA). NanoString technology uses a digital color‐coded barcode tag with single‐molecule imaging that can detect and count hundreds of unique transcripts per reaction. The geometric mean of the internal control genes was used to normalize the DGE and data retention was accomplished in consultation with NanoString given the novelty of this project’s use of fecal mRNA. Briefly, data selection required average raw counts ≥ 4, > 5 genes with expression above background, and > 20 for the sum of raw counts for all genes within on sample divided by the scaling factor. The analysis of data was performed using nSolver Analysis Software 4.0, including nCounter Advanced Analysis (version 2.0, Nanostring Technologies, Seattle, Washington, USA). More specifically, fold changes and p‐values were calculated using nCounter default settings and a Benjamini-Yekutieli (B-Y) correction for multiple comparisons. The B-Y correction makes the assumption that there may be some biological connection between genes and returns moderately conservative estimates of false discovery rate (FDR). The FDR is the proportion of genes with equal or greater evidence for differential expression (i.e., equal or lower raw p-value) than are expected to be “false discoveries” due to chance.

Sample size

Previous DGE studies have shown that a minimum of 8 animals should be targeted per group (diseased, healthy), with the sample size based on expectations for a multivariate analysis estimating the log fold-change in a target gene’s DGE following a negative binomial distribution [21]. For the purposes of this study the enrollment period was based on the historical prevalence of GI disease of variable severity, mortality rates, average number of calf arrivals per day, and anticipated challenges associated with fecal RNA isolation. Expectations were for quality RNA samples from a minimum of 8 Holstein and 8 Jersey calves within each health status category (consistently healthy, scours, systemic enteritis).

Results

Calves and clinical outcomes

A total of 183 heifer calves were enrolled with RNA extracted from fecal samples and submitted for diagnostic processing at the Primate Diagnostic Services Laboratory using the Nanostring System. Average fecal dry weight percentages for consistently healthy, scouring, or systemically affected calves were 26.4 ± 6.2%, 12.5 ± 5.7%, and 13.9 ± 5.9%, respectively. A total of 99 samples (n = 38 consistently healthy; n = 47 scours; n = 14 systemic enteritis) were excluded from analysis primarily due to insufficient fecal mRNA raw counts or expression above background. Details related to the 84 Holstein and Jersey heifer calves remaining in the analysis are presented in Table 3. Those calves were sourced from 22 separate dairies and all calves were BVDV negative, the average TSP was 6.5 ±0.8 g/dL, and the average ages at enrollment and DGE samples were 7.7 ±2.5 days and 16.7 ±2.5 days, respectively.

Table 3. Details related to enrolled Holstein and Jersey calves.

Systemic behavioral scores (Table 1) and treatments (flunixin and antibiotics) were assigned or administered during the 10-day period from enrollment to DGE sampling.

Calf ID	Breed¹	TSP (g/dL)²	Enrollment age (days)³	Enrollment sample⁴^-⁶	DGE age (days)⁷	DGE sample⁴^-⁶	Flunixin (Y/N)	Antibiotic (drug/N)
1377	H	6.1	10	Healthy	19	Healthy	N	N
1382	H	4.7	10	Healthy	19	Healthy	N	N
2154	H	8.5	4	Healthy	13	Healthy	N	N
2258	H	5.7	7	Healthy	16	Healthy	N	N
2312	H	7.0	11	Healthy	20	Healthy	N	N
2788	H	5.0	4	Healthy	13	Healthy	N	N
2812	H	6.4	5	Healthy	14	Healthy	N	N
3044	H	5.3	10	Healthy	19	Healthy	N	N
3647	H	6.2	11	Healthy	20	Healthy	N	N
6980	H	6.2	8	Healthy	17	Healthy	N	N
6985	H	6.8	6	Healthy	15	Healthy	N	N
7738	H	6.6	8	Healthy	17	Healthy	N	N
9542	H	6.0	7	Healthy	16	Healthy	N	N
10981	H	5.6	4	Healthy	13	Healthy	N	N
15507	H	5.8	8	Healthy	17	Healthy	N	N
29043	H	5.3	9	Healthy	18	Healthy	N	N
29055	H	6.1	4	Healthy	13	Healthy	N	N
44807	H	6.0	6	Healthy	15	Healthy	N	N
44808	H	6.0	5	Healthy	14	Healthy	N	N
90027	H	5.6	7	Healthy	16	Healthy	N	N
5311	J	7.9	6	Healthy	15	Healthy	N	N
92977	J	7.4	9	Healthy	18	Healthy	N	N
93003	J	7.5	6	Healthy	15	Healthy	N	N
93022	J	8.0	11	Healthy	20	Healthy	N	N
93024	J	7.3	4	Healthy	13	Healthy	N	N
93042	J	6.6	9	Healthy	18	Healthy	N	N
93060	J	6.1	7	Healthy	16	Healthy	N	N
93077	J	6.6	4	Healthy	13	Healthy	N	N
93078	J	6.9	5	Healthy	14	Healthy	N	N
93098	J	6.8	10	Healthy	19	Healthy	N	N
93120	J	7.1	8	Healthy	17	Healthy	N	N
93135	J	6.4	10	Healthy	19	Healthy	N	N
122663	J	7.6	6	Healthy	15	Healthy	N	N
6981	H	5.0	7	Scours	16	Healthy	N	N
16663	H	6.0	10	Scours	19	Healthy	N	N
23328	H	5.7	5	Scours	14	Healthy	N	N
44805	H	6.5	6	Scours	15	Healthy	N	N
44821	H	5.6	11	Scours	20	Healthy	N	N
90032	H	7.3	7	Scours	16	Healthy	N	N
93031	H	6.5	5	Scours	14	Healthy	N	N
94001	H	6.8	12	Scours	21	Healthy	N	N
92965	J	6.5	8	Scours	17	Healthy	N	N
92972	J	6.9	7	Scours	16	Healthy	N	N
92988	J	7.9	9	Scours	18	Healthy	N	N
93025	J	7.1	9	Scours	18	Healthy	N	N
93051	J	6.6	10	Scours	19	Healthy	N	N
93066	J	7.2	9	Scours	18	Healthy	N	N
93069	J	6.4	4	Scours	13	Healthy	N	N
93080	J	6.8	8	Scours	17	Healthy	N	N
93081	J	7.1	9	Scours	18	Healthy	N	N
93082	J	7.2	9	Scours	18	Healthy	N	N
93100	J	7.1	8	Scours	17	Healthy	N	N
93103	J	6.8	9	Scours	18	Healthy	N	N
93108	J	6.0	6	Scours	15	Healthy	N	N
93122	J	7.5	7	Scours	16	Healthy	N	N
122699	J	6.6	8	Scours	17	Healthy	N	N
93096	J	6.8	6	Scours	15	Scours	N	N
93131	J	7.7	9	Scours	18	Scours	N	N
2806	H	5.7	12	Healthy	21	Scours	N	N
3648	H	5.6	4	Healthy	13	Scours	N	N
5171	H	6.5	6	Healthy	15	Scours	N	N
7185	H	5.5	5	Healthy	14	Scours	N	N
23324	H	6.1	10	Healthy	19	Scours	N	N
23334	H	6.3	5	Healthy	14	Scours	N	N
29052	H	5.5	10	Healthy	19	Scours	N	N
29070	H	6.0	8	Healthy	17	Scours	N	N
93138	H	6.0	4	Healthy	13	Scours	N	N
92998	J	7.0	4	Healthy	13	Scours	N	N
93010	J	5.7	4	Healthy	13	Scours	N	N
93014	J	6.4	8	Healthy	17	Scours	N	N
93035	J	7.0	6	Healthy	15	Scours	N	N
25217	H	5.3	10	Systemic	19	Healthy	Y	N
90029	H	6.3	10	Systemic	19	Healthy	Y	ceftiofur
93132	H	6.4	12	Systemic	21	Healthy	Y	ceftiofur
93020	J	7.9	12	Systemic	21	Healthy	Y	ceftiofur
93054	J	7.8	10	Systemic	19	Healthy	Y	N
93056	J	7.2	8	Systemic	17	Healthy	Y	N
93105	J	7.1	12	Systemic	21	Healthy	Y	ceftiofur
2799	H	5.3	9	Systemic	18	Scours	Y	florfenicol
5312	J	7.4	12	Systemic	21	Scours	Y	ceftiofur
23332	H	5.3	4	Healthy	13	Systemic	Y	ceftiofur
23338	H	5.4	4	Healthy	13	Systemic	Y	N
2063	J	4.7	11	Healthy	20	Systemic	Y	N
93087	J	6.0	11	Healthy	20	Systemic	Y	ceftiofur

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¹Breed: H = Holstein; J = Jersey

²TSP: Total serum protein

³Enrollment age: Age at which calves were enrolled into this study and fecal samples collected for microbiome analysis [15].

⁴Healthy: calves with fecal scores <3 at the time of sampling without abnormal clinical parameters or behavioral scores indicative of GI disease, respiratory disease, or other health problems.

⁵Scours: calves with fecal scores of ≥3 and classified as having uncomplicated, localized GI disease based on a behavioral score of 0.

⁶Systemic: calves with fecal scores ≥3 and systemic GI disease (systemic enteritis) as evidenced by a behavioral score ≥1.

⁷DGE age: Age at which fecal samples were collected for RNA extraction.

A total of 33 calves (n = 20 Holstein; n = 13 Jersey) were consistently healthy. The remaining 51 calves (n = 23 Holstein; n = 28 Jersey) experienced varying severity of GI disease at some point during the sampling window. Average TSP was the same for healthy (6.5 ±0.9 g/dL) and diseased calves (6.5 ±0.8 g/dL); however, the average TSP for Jerseys (6.9 ±0.7 g/dL) was greater than for Holsteins (6.0 ±0.7 g/dL; P < 0.0001). Twenty-three of the diseased calves (n = 8 Holstein; n = 15 Jersey) were scouring at the time of their enrollment sample but healthy 9 days later when sampled for DGE. Two Jersey calves were scouring at both sampling times. Thirteen calves (n = 9 Holstein; n = 4 Jersey) were healthy when enrolled but scouring by the time of their DGE sample.

Thirteen of the 51 calves with GI disease demonstrated systemic enteritis during the sampling period (Table 4). All 13 were treated with flunixin and 8 were treated with antibiotics during the sampling period (Table 3). Seven of the systemic enteritis calves (n = 9 Holstein; n = 4 Jersey) were systemically ill when enrolled (average behavioral score = 8; range 2–17) but progressed to healthy status at least 7 days prior to their DGE sample (systemic to healthy). Two calves (n = 1 Holstein; n = 1 Jersey) were systemically ill when enrolled (behavioral scores 14 and 8, respectively) and scouring 9 days later when sampled for DGE (systemic to scours). Four calves (n = 2 Holstein; n = 2 Jersey) were healthy when enrolled but progressed to systemic enteritis (average behavioral score = 12; range 7–18) when sampled for DGE 9 days later (healthy to systemic).

Table 4. Timing and duration of clinical signs for calves with systemic GI disease.

Behavioral scores are based on the behavioral assessment scoring matrix presented in Table 1.

Calf ID	Breed¹	Disease progression²^-⁴	Initial age with systemic signs (days)	Rectal temp (°C) on day of initial systemic signs	Behavioral score on day of initial systemic signs	Final age with systemic signs (days)	DGE age (days)⁵	Time between systemic signs and DGE sample (days)
25217	H	systemic to healthy	10	38.4	5	11	19	8
90029	H	systemic to healthy	10	37.5	15	12	19	7
93132	H	systemic to healthy	12	39.2	3	13	21	8
93020	J	systemic to healthy	12	38.8	17	12	21	9
93054	J	systemic to healthy	10	37.2	12	11	19	8
93056	J	systemic to healthy	8	39.2	3	8	17	9
93105	J	systemic to healthy	12	38.9	2	12	21	9
2799	H	systemic to scours	9	39.5	14	9	18	9
5312	J	systemic to scours	12	37.9	8	12	21	9
23332	H	healthy to systemic	12	38.4	10	18	13	0
23338	H	healthy to systemic	12	38.1	7	15	13	0
2063	J	healthy to systemic	20	40.1	12	20	20	0
93087	J	healthy to systemic	20	36.0	18	20	20	0

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¹Breed: H = Holstein; J = Jersey

²Healthy: calves with fecal scores <3 at the time of sampling without abnormal clinical parameters or behavioral scores indicative of GI disease, respiratory disease, or other health problems.

³Scours: calves with fecal scores of ≥3 and classified as having uncomplicated, localized GI disease based on a behavioral score of 0.

⁴Systemic: calves with fecal scores ≥3 and systemic GI disease (systemic enteritis) as evidenced by a behavioral score ≥1.

⁵DGE age: Age at which fecal samples were collected for RNA extraction.

Differential gene expression

In agreement with a study by Rosa and Osorio [12], raw counts reflecting mRNA abundance in fecal RNA were 8-fold greater on average for KRT8 than the other endogenous genes of interest (S2 Table). NOD2 was not expressed in numerous samples and was removed from further analysis. Log₂ fold-changes (ratios) in gene expression were evaluated for diseased calves versus those that were consistently healthy. DGE was evaluated overall and by breed for calves with any GI disease (Table 5), scours or systemic enteritis specifically (Table 6), and scours or systemic enteritis based on the day of diagnosis (Tables 7 and 8 and Figs 2 and 3). These sequential analyses were performed in an effort to assess and highlight the importance of discriminating between breed-specific GI disease phenotypes when evaluating the consequences of disease on calf health and wellbeing. Estimates of false discovery rate using the B-Y correction indicated that TLR4 (B-Y P = 0.01), AQP3 (B-Y P = 0.03), and IFNG (B-Y P = 0.06) were expressed differentially in calves with GI disease at any point during the sampling period (Table 5). It is worth acknowledging that less conservative uncorrected estimates indicated potential differential expression of additional genes including KRT8 (uncorrected P = 0.03), FABP2 (uncorrected P = 0.05), and NFKB1 (uncorrected P = 0.07). When analyzed by breed for calves with GI disease at any point only TLR4 appeared upregulated in Jerseys (B-Y P = 0.07). However, uncorrected estimates suggested the potential for differential expression of AQP3, TLR2, and TLR4 in Holsteins, and FABP2, IFNG, KRT8, and NFKB1 in Jerseys (uncorrected P < 0.1; Table 5).

Table 5. Log₂ fold-changes for fecal mRNA gene expression in calves with clinical symptoms of GI disease at any point from enrollment until the day 10 differential gene expression (DGE) sample (n = 23 Holstein, n = 28 Jersey) as compared to consistently healthy calves of the same breed(s) (n = 20 Holstein, n = 13 Jersey).

Breed	Gene Name	Log₂ fold change	Std error (log₂)	Lower CL (log₂)	Upper CL (log₂)	P-value	B-Y p-value¹
Combined (Holstein, Jersey)	AQP3	0.89	0.27	0.36	1.42	0.0013	0.0300
	FABP2	-0.81	0.40	-1.60	-0.01	0.0464	0.2900
	IFNG	0.85	0.30	0.26	1.44	0.0052	0.0600
	KRT8	-0.45	0.21	-0.86	-0.04	0.0317	0.2400
	NFKB1	0.41	0.23	-0.04	0.87	0.0729	0.3900
	SLC5A1	0.44	0.29	-0.12	1.01	0.1210	0.5000
	TLR2	-0.24	0.26	-0.74	0.26	0.3399	1.0000
	TLR4	1.03	0.28	0.49	1.58	0.0003	0.0100
Holstein	AQP3	0.98	0.35	0.28	1.67	0.0073	0.2700
	FABP2	-0.56	0.56	-1.64	0.54	0.3098	1.0000
	IFNG	0.52	0.40	-0.27	1.30	0.1890	1.0000
	KRT8	-0.38	0.29	-0.94	0.19	0.1867	1.0000
	NFKB1	-0.01	0.32	-0.64	0.61	0.9765	1.0000
	SLC5A1	0.31	0.34	-0.36	0.98	0.3557	1.0000
	TLR2	-0.81	0.36	-1.51	-0.10	0.0258	0.3500
	TLR4	0.90	0.41	0.10	1.71	0.0285	0.3500
Jersey	AQP3	0.73	0.47	-0.18	1.65	0.1155	0.5400
	FABP2	-1.09	0.59	-2.25	0.06	0.0631	0.3400
	IFNG	1.20	0.48	0.26	2.13	0.0131	0.1200
	KRT8	-0.70	0.28	-1.25	-0.14	0.0158	0.1200
	NFKB1	0.87	0.39	0.10	1.64	0.0281	0.1700
	SLC5A1	0.57	0.50	-0.40	1.54	0.2382	0.8900
	TLR2	0.44	0.36	-0.27	1.15	0.2102	0.8700
	TLR4	1.16	0.36	0.45	1.87	0.0019	0.0700

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¹B-Y p-value: Benjamini-Yekutieli correction for multiple comparisons.

Table 6. Log₂ fold-changes for fecal mRNA gene expression in calves with scours (n = 17 Holstein, n = 21 Jersey) or systemic enteritis (n = 6 Holstein, n = 7 Jersey) at any point from enrollment until the day 10 differential gene expression (DGE) sample as compared to consistently healthy calves of the same breed(s) (n = 20 Holstein, n = 13 Jersey).

Breed	GI disease severity¹^,²	Gene Name	Log₂ fold change	Std error (log₂)	Lower CL (log₂)	Upper CL (log₂)	P-value	B-Y p-value³
Combined (Holstein, Jersey)	Scours	AQP3	0.61	0.29	0.04	1.18	0.0341	0.6400
		FABP2	-0.55	0.43	-1.40	0.30	0.2030	0.8400
		IFNG	0.50	0.31	-0.12	1.11	0.1174	0.8400
		KRT8	-0.31	0.22	-0.76	0.12	0.1611	0.8400
		NFKB1	0.33	0.25	-0.15	0.82	0.1703	0.8400
		SLC5A1	0.57	0.31	-0.06	1.18	0.0751	0.8400
		TLR2	-0.37	0.28	-0.92	0.19	0.1959	0.8400
		TLR4	0.78	0.30	0.19	1.38	0.0106	0.3900
Holstein	Scours	AQP3	0.62	0.40	-0.15	1.41	0.1110	1.0000
		FABP2	-0.21	0.58	-1.36	0.92	0.7021	1.0000
		IFNG	0.00	0.39	-0.76	0.76	0.9928	1.0000
		KRT8	-0.12	0.27	-0.67	0.40	0.6253	1.0000
		NFKB1	-0.10	0.36	-0.81	0.61	0.7704	1.0000
		SLC5A1	0.26	0.37	-0.47	0.99	0.4755	1.0000
		TLR2	-0.97	0.40	-1.79	-0.19	0.0175	0.6500
		TLR4	0.67	0.46	-0.23	1.58	0.1407	1.0000
Jersey	Scours	AQP3	0.52	0.49	-0.43	1.47	0.2738	1.0000
		FABP2	-0.89	0.64	-2.12	0.37	0.1591	0.7400
		IFNG	0.97	0.51	-0.03	1.96	0.0563	0.4200
		KRT8	-0.65	0.34	-1.32	0.01	0.0538	0.4200
		NFKB1	0.80	0.40	0.01	1.58	0.0450	0.4200
		SLC5A1	0.82	0.53	-0.22	1.85	0.1182	0.6300
		TLR2	0.36	0.40	-0.43	1.14	0.3665	1.0000
		TLR4	0.91	0.39	0.14	1.68	0.0224	0.4200
Combined (Holstein, Jersey)	Systemic enteritis	AQP3	1.69	0.42	0.87	2.50	0.0003	0.0010
		FABP2	-1.57	0.77	-3.06	-0.07	0.0413	0.1900
		IFNG	1.89	0.42	1.08	2.70	0.0001	0.0005
		KRT8	-0.86	0.34	-1.51	-0.19	0.0144	0.0800
		NFKB1	0.64	0.37	-0.09	1.37	0.0804	0.3300
		SLC5A1	0.11	0.39	-0.67	0.88	0.7755	1.0000
		TLR2	0.11	0.42	-0.69	0.93	0.7726	1.0000
		TLR4	1.75	0.42	0.93	2.58	0.0003	0.0011
Holstein	Systemic enteritis	AQP3	1.97	0.46	1.07	2.87	0.0007	0.0200
		FABP2	-1.52	1.21	-3.84	0.86	0.1629	1.0000
		IFNG	1.98	0.51	0.98	2.98	0.0011	0.0200
		KRT8	-1.08	0.72	-2.47	0.32	0.1058	0.7900
		NFKB1	0.25	0.62	-0.97	1.47	0.6236	1.0000
		SLC5A1	0.44	0.37	-0.29	1.16	0.2165	1.0000
		TLR2	-0.34	0.76	-1.84	1.15	0.5902	1.0000
		TLR4	1.57	0.54	0.53	2.62	0.0070	0.0900
Jersey	Systemic enteritis	AQP3	1.37	0.78	-0.17	2.90	0.0746	0.3500
		FABP2	-1.69	1.30	-4.32	0.85	0.1576	0.6500
		IFNG	1.89	0.74	0.44	3.33	0.0144	0.1100
		KRT8	-0.83	0.25	-1.32	-0.33	0.0026	0.0300
		NFKB1	1.08	0.59	-0.07	2.23	0.0633	0.3400
		SLC5A1	-0.16	0.72	-1.60	1.24	0.7978	1.0000
		TLR2	0.71	0.59	-0.43	1.87	0.1938	0.7200
		TLR4	1.93	0.74	0.48	3.39	0.0154	0.1100

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¹Scours: calves with fecal scores of ≥3 and classified as having uncomplicated, localized GI disease based on a behavioral score of 0.

²Systemic: calves with fecal scores ≥3 and systemic GI disease (systemic enteritis) as evidenced by a behavioral score ≥1.

³B-Y p-value: Benjamini-Yekutieli correction for multiple comparisons.

Table 7. Log₂ fold-changes for fecal mRNA gene expression in calves with scours diagnosed on day 1 at enrollment (scours to healthy; n = 8 Holstein, n = 15 Jersey), or at the time of the day 10 differential gene expression (DGE) sample (healthy to scours; n = 9 Holstein, n = 4 Jersey), as compared to consistently healthy calves of the same breed(s) (n = 20 Holstein, n = 13 Jersey).

Breed	Timing of scours¹	Gene Name	Log₂ fold change	Std error (log₂)	Lower CL (log₂)	Upper CL (log₂)	P-value	B-Y p-value²
Combined (Holstein, Jersey)	Day 1	AQP3	0.59	0.27	0.07	1.12	0.0287	0.5300
		FABP2	-0.36	0.49	-1.32	0.60	0.4610	1.0000
		IFNG	0.53	0.33	-0.12	1.18	0.1040	0.7700
		KRT8	-0.20	0.26	-0.71	0.30	0.4173	1.0000
		NFKB1	0.66	0.28	0.10	1.21	0.0214	0.5300
		SLC5A1	0.75	0.42	-0.07	1.57	0.0720	0.6700
		TLR2	0.08	0.33	-0.56	0.73	0.7811	1.0000
		TLR4	0.62	0.35	-0.06	1.30	0.0704	0.6700
Holstein	Day 1	AQP3	0.34	0.38	-0.42	1.09	0.3452	1.0000
		FABP2	0.01	0.68	-1.32	1.35	0.9780	1.0000
		IFNG	-0.14	0.41	-0.94	0.66	0.7179	1.0000
		KRT8	-0.08	0.39	-0.86	0.69	0.8119	1.0000
		NFKB1	0.43	0.58	-0.69	1.57	0.4039	1.0000
		SLC5A1	0.53	0.52	-0.47	1.54	0.2774	1.0000
		TLR2	-0.34	0.66	-1.64	0.96	0.5657	1.0000
		TLR4	0.26	0.67	-1.03	1.57	0.6590	1.0000
Jersey	Day 1	AQP3	0.61	0.45	-0.29	1.50	0.1677	0.6900
		FABP2	-0.65	0.69	-2.00	0.70	0.3250	1.0000
		IFNG	1.00	0.49	0.03	1.96	0.0426	0.3800
		KRT8	-0.53	0.35	-1.18	0.15	0.1222	0.6800
		NFKB1	0.93	0.42	0.10	1.75	0.0292	0.3800
		SLC5A1	0.88	0.64	-0.38	2.14	0.1617	0.6900
		TLR2	0.62	0.42	-0.18	1.44	0.1275	0.6800
		TLR4	0.86	0.41	0.06	1.66	0.0357	0.3800
Combined (Holstein, Jersey)	Day 10	AQP3	0.57	0.62	-0.64	1.78	0.3365	1.0000
		FABP2	-1.08	0.76	-2.56	0.40	0.1391	1.0000
		IFNG	0.21	0.57	-0.89	1.32	0.6958	1.0000
		KRT8	-0.37	0.29	-0.94	0.20	0.1900	1.0000
		NFKB1	-0.20	0.30	-0.81	0.39	0.4860	1.0000
		SLC5A1	0.26	0.33	-0.38	0.91	0.4114	1.0000
		TLR2	-1.14	0.32	-1.79	-0.53	0.0008	0.0300
		TLR4	1.08	0.53	0.04	2.12	0.0420	0.5200
Holstein	Day 10	AQP3	0.88	0.68	-0.45	2.21	0.1693	1.0000
		FABP2	-0.42	0.85	-2.06	1.24	0.5917	1.0000
		IFNG	0.12	0.56	-0.97	1.22	0.8004	1.0000
		KRT8	-0.16	0.30	-0.76	0.42	0.5677	1.0000
		NFKB1	-0.58	0.39	-1.32	0.18	0.1219	1.0000
		SLC5A1	0.01	0.42	-0.79	0.83	0.9587	1.0000
		TLR2	-1.54	0.40	-2.32	-0.75	0.0008	0.0300
		TLR4	1.03	0.58	-0.10	2.16	0.0702	0.8700
Jersey	Day 10	AQP3	-0.07	2.23	-4.32	4.30	0.9624	1.0000
		FABP2	-2.51	2.36	-6.64	2.12	0.1734	1.0000
		IFNG	0.31	2.29	-4.32	4.81	0.8417	1.0000
		KRT8	-0.65	1.10	-2.84	1.51	0.3996	1.0000
		NFKB1	0.54	0.45	-0.34	1.41	0.2085	1.0000
		SLC5A1	0.81	0.56	-0.30	1.90	0.1330	1.0000
		TLR2	-0.48	0.73	-1.89	0.95	0.3990	1.0000
		TLR4	1.18	2.01	-2.74	5.11	0.4096	1.0000

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¹Scours: calves with fecal scores of ≥3 and classified as having uncomplicated, localized GI disease based on a behavioral score of 0.

²B-Y p-value: Benjamini-Yekutieli correction for multiple comparisons.

Table 8. Log₂ fold-changes for fecal mRNA gene expression in calves with systemic enteritis ≥7 days prior to the day 10 differential gene expression (DGE) fecal sample (i.e., systemic to healthy; n = 3 Holstein, n = 4 Jersey), or on the day of the DGE fecal sample (i.e., healthy to systemic; n = 2 Holstein, n = 2 Jersey), as compared to consistently healthy calves of the same breeds (n = 20 Holstein, n = 13 Jersey).

Breed	Timing of systemic enteritis¹	Gene Name	Log₂ fold change	Std error (log₂)	Lower CL (log₂)	Upper CL (log₂)	P-value	B-Y p-value²
Combined (Holstein, Jersey)	Prior	AQP3	1.87	0.55	0.79	2.94	0.0036	0.0300
		FABP2	-2.09	1.01	-4.06	-0.11	0.0399	0.2600
		IFNG	2.33	0.47	1.41	3.25	0.0002	0.0100
	(7–10 days prior to fecal sample collection)	KRT8	-0.42	0.31	-1.03	0.19	0.1562	0.5800
		NFKB1	0.90	0.57	-0.22	2.01	0.0919	0.3800
		SLC5A1	0.61	0.33	-0.03	1.25	0.0620	0.3300
		TLR2	-0.04	0.52	-1.06	0.99	0.9351	1.0000
		TLR4	2.18	0.53	1.14	3.21	0.0010	0.0200
Combined (Holstein, Jersey)	Current	AQP3	0.94	1.24	-1.51	3.38	0.2977	1.0000
		FABP2	-0.23	2.44	-5.06	4.55	0.8878	1.0000
		IFNG	1.32	1.43	-1.47	4.13	0.2217	1.0000
	(day of fecal sample collection)	KRT8	-1.65	1.18	-4.06	0.66	0.1004	1.0000
		NFKB1	0.82	0.81	-0.76	2.40	0.1772	1.0000
		SLC5A1	-0.48	1.36	-3.18	2.20	0.6063	1.0000
		TLR2	1.04	0.95	-0.81	2.91	0.1582	1.0000
		TLR4	0.63	0.96	-1.25	2.52	0.3532	1.0000

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¹Systemic enteritis: calves with fecal scores ≥3 and systemic GI disease as evidenced by a behavioral score ≥1.

²B-Y p-value: Benjamini-Yekutieli correction for multiple comparisons.

A more refined analysis based on disease severity indicated that systemic enteritis impacted fecal mRNA DGE more substantially than uncomplicated scours (Table 6). AQP3, IFNG, and TLR4 were upregulated in calves with systemic enteritis (B-Y P ≤ 0.001), and KRT8 appeared downregulated (B-Y P = 0.08). Breed-specific log₂ fold-changes indicated that systemically affected Holsteins (n = 6) upregulated AQP3 (1.97 ± 0.90; B-Y P = 0.02), IFNG (1.98 ± 1.00; B-Y P = 0.02), and TLR4 (1.57 ± 1.05; B-Y P = 0.09), whereas Jerseys with systemic enteritis (n = 7) downregulated KRT8 (-0.83 ± -0.50; B-Y P = 0.03). However, uncorrected estimates suggested that systemically affected Jerseys also upregulated AQP3, IFNG, TLR4 along with NFKB1 (uncorrected P < 0.1). Although there were no differences noted in calves with scours using the B-Y correction, uncorrected estimates suggested a similar pattern of DGE in Jerseys with scours (n = 21) as compared to those with systemic enteritis. Scouring Jerseys appeared to upregulate IFNG, TLR4 and NFKB1 and downregulate KRT8 (uncorrected P < 0.1). On the other hand, Holsteins with scours (n = 17) did not appear to be similarly affected and only demonstrated evidence of TLR2 downregulation (uncorrected P = 0.02).

When calves with scours were analyzed based on the day of diagnosis (Table 7 and Figs 2 and 3), only TLR2 was differentially expressed in Holsteins that were healthy at enrollment but diagnosed with scours on day 10 at the time of the DGE sample (n = 9; -1.54 ± -0.79; B-Y P = 0.03). Uncorrected estimates also suggested an upregulation of TLR4 in Holsteins diagnosed with scours on day 10 (uncorrected P = 0.07). Conversely, Jerseys that were scouring at enrollment but healthy on day 10 at the time of the DGE sample only appeared to upregulate TLR4 (n = 15; uncorrected P = 0.04). Similarly, upregulation of IFNG and NFKB1 also was indicated in Jerseys diagnosed with scours on day 1 but healthy on day 10 (uncorrected P < 0.05).

When calves with systemic enteritis were analyzed based on the day of diagnosis (Table 8 and Figs 2 and 3), those that were affected at the time of enrollment but returned to a healthy status ≥7 days prior to the DGE sample (n = 3 Holstein; n = 4 Jersey) upregulated IFNG (B-Y P = 0.01), TLR4 (B-Y P = 0.02), and AQP3 (B-Y P = 0.03) with particularly notable log₂ fold-changes (2.33 ± 0.92, 2.18 ± 1.04, and 1.87 ± 1.08, respectively). In addition, uncorrected estimates also suggested upregulation of NFKBI and SLC5AI along with downregulation of FABP2 in those same calves (uncorrected P < 0.1). Although evidence was lacking for overall DGE (B-Y P ≤ 1.0) in the 4 calves that were healthy at the time of enrollment but experienced systemic enteritis on day 10 when sampled for DGE (n = 2 Holstein; n = 2 Jersey), breed-specific heat maps suggested that the 2 Holsteins (Fig 2) might have been more severely affected at that time than the 2 Jerseys (Fig 3).

The various breed-specific differences stimulated further investigation into whether healthy Holstein and Jersey calves might differentially express the genes of interest. Although no genes were differentially expressed based on the B-Y correction, a less conservative estimate (uncorrected p ≤ 0.05) did indicate upregulation of TLR2 and downregulation of KRT8 in healthy Holsteins as compared to healthy Jerseys (Table 9).

Table 9. Log₂ fold-changes for fecal mRNA gene expression in healthy Holstein (n = 20) vs healthy Jersey (n = 13) calves.

Breed	Gene Name	Log₂ fold change	Std error (log₂)	Lower CL (log₂)	Upper CL (log₂)	P-value	B-Y p-value¹
Healthy Holsteins compared to healthy Jerseys	AQP3	-0.29	0.43	-1.15	0.57	0.4896	1.0000
	FABP2	-0.26	0.52	-1.29	0.75	0.5976	1.0000
	IFNG	0.28	0.46	-0.62	1.18	0.5286	1.0000
	KRT8	-0.65	0.28	-1.18	-0.10	0.0221	0.2700
	NFKB1	0.38	0.40	-0.40	1.16	0.3240	1.0000
	SLC5A1	0.04	0.48	-0.89	0.99	0.9285	1.0000
	TLR2	0.77	0.34	0.12	1.42	0.0219	0.2700
	TLR4	0.11	0.36	-0.58	0.82	0.7381	1.0000

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¹B-Y p-value: Benjamini-Yekutieli correction for multiple comparisons.

Discussion

The findings from this study confirmed our hypothesis that inflammatory and cellular transport signals would be differentially expressed in fecal RNA in response to adaptations related to breeds and GI disease severity and timing. Our results suggested that preweaned dairy calves upregulate the genes AQP3, IFNG, and TLR4 when experiencing undifferentiated GI disease (scours or systemic enteritis; Table 5). A more refined look at GI disease severity and timing indicated that the DGE in fecal RNA was due primarily to systemic enteritis (Table 6), with the inflammatory response and cellular transport signals potentially delayed compared to clinical signs (Table 8) even though all systemically affected calves were treated with flunixin and most (9/13) were treated with antibiotics (Table 3). Additionally, breed-specific findings indicated that upregulation of AQP3, IFNG, and TLR4 in fecal RNA was driven by systemically affected Holsteins, whereas KRT8 was downregulated in Jersey calves with systemic enteritis (Table 6). Furthermore, TLR2 was downregulated in Holstein calves with scours on the day of the DGE fecal sample (Table 7). These findings highlight the importance of discriminating between breed-specific GI disease phenotypes when describing, comparing, and investigating the overall consequences of disease on calf health and wellbeing.

None of the other genes evaluated in this study demonstrated DGE associated with GI disease when using the B-Y correction. This included NOD2 which frequently was left undetected. Unfortunately, the case numbers were small for select DGE assessments of breed-specific impacts of disease severity, timing of disease, treatment effects, and disease resolution or progression. These factors and the inability to track scours duration limited our ability to differentiate expression patterns more robustly. Nonetheless, less conservative estimates of DGE suggested informative breed-specific patterns. Unlike their response to systemic enteritis, Holstein calves did not appear affected by scours aside from evidence of DGE of toll-like receptors (TLRs) on the day of diarrhea (Table 7 and Fig 2). On the other hand, Jersey calves consistently demonstrated a tendency to upregulate IFNG, NFKB1, and TLR4 when affected with either scours or systemic enteritis (Tables 5–7). These findings were more pronounced in systemically affected Jersey calves (Table 6) and were observed as a delayed response to both scours and systemic enteritis (Tables 7 and 8 and Fig 3).

These findings substantiate a previous study suggesting that fecal RNA might be used as a noninvasive tool to investigate intestinal transcriptomic profiling of dairy calves [6]. In that study of 8 neonatal Jersey male calves, upregulation of TLR4 was delayed relative to an increase in fecal scores indicative of mild diarrhea. A similar delay in TLR4 upregulation appeared to be present in the scouring Jersey calves in our study. On the other hand, the apparent upregulation of TLR4 in scouring Holstein calves in our study overlapped with both the occurrence of scours and downregulation of TLR2. Although this TLR2 downregulation may have been influenced by the tendency for higher levels of TLR2 expression in healthy Holstein as compared to Jersey calves (Table 9), a similar downregulation of TLR2 was demonstrated by Rosa et al. [6] suggesting that much of inflammatory response observed in fecal RNA within that study was due to the upregulation of TLR4.

TLRs can detect conserved molecular products of microorganisms and help regulate host adaptive immune responses [22]. Specifically, TLR2 and TLR4 help direct the early innate immune response to microbial challenge [23]. Gram positive lipoteichoic acids (LTA) are recognized by TLR2 whereas TLR4 is responsible for gram negative lipopolysaccharide (LPS) recognition and cell-signaling [24]. Each of these toll-like receptors leads to the expression of distinct inflammatory genes and the induction of various cytokines [23]. For example, an invading pathogen can activate the TLR4 signaling pathway leading to the activation of NFKB1 which promotes the transcription of genes encoding proinflammatory proteins such as IL1B, TNFA, IL6 and IL8 [25]. This may explain the tendency in our study for concomitant upregulation of both TLR4 and NFKB1 in Jersey calves affected with either scours or systemic enteritis. Furthermore, this finding was in agreement with the demonstration by Rosa et al. [6] of NFKB1 upregulation following a similar delay to that observed for TLR4.

In the present study, IFNG was upregulated in response to systemic enteritis across breeds but with greater effect in systemic Holsteins than Jerseys. On the other hand, IFNG only appeared to be upregulated as a delayed response to scours in Jerseys. Although Rosa et al. [6] demonstrated a time-dependent IFNG downregulation in 8 Jersey male calves following mild diarrhea; overall, they showed a pattern of fecal score and pro-inflammatory blood biomarkers (peaking both between 2 and 3 weeks of age) that indicated a pro-inflammatory response during diarrhea in neonatal Jersey calves. It is worth mentioning that a previous study demonstrated that expression of IL23R which stimulates interferon gamma (IFNγ) production by T cells was reduced prior to gradual weaning in Jersey relative to Holstein calves [26]. Regardless of potential breed-specific differences, interferons were originally discovered as agents that interfere with viral replications [27], and IFNγ specifically has well-documented antiviral and immunoregulatory properties [28]. Furthermore, IFNγ has been shown to be both an inducer as well as a regulator of inflammation [29], a duality that confers both resistance and tolerance mechanisms. Although resistance mechanisms are viewed as the primary function of immunity, disease tolerance also is essential in terms of tissue damage control mechanisms that limit the health and fitness costs of infection [30].

The upregulation of AQP3 in our study was primarily a function of systemic disease in Holsteins although there was evidence of an effect in systemically affected Jerseys and across both breeds with scours. Aquaporin-3 is a cell membrane transporter that transports water, glycerol, urea, and other small uncharged solutes across the plasma membrane [31, 32]. It has been shown to be down-regulated during inflammatory bowel disease in humans [33] and rats [34], and mislocalization of Aquaporin-3 within enterocytes was shown to alter the flux of water through enterocytes in mice with bacterial-induced diarrhea [35]. Furthermore, Rosa et al. [6] demonstrated reduced AQP3 expression in the fecal RNA from Jersey male calves experiencing mild diarrhea.

As importantly, Rosa and Osorio [12] demonstrated that mRNA expression of AQP3 tended to be greater in polymorphonuclear leukocytes (PMN) than fecal RNA. Therefore, it is plausible that the fecal RNA samples in our study contained PMN mRNA transcripts and the upregulation of AQP3 and other genes of interest may have been influenced by PMN infiltration into the intestinal lumen. The transmigration of PMNs into the lumen depends on a variety of activities and signaling events with the goal to maintain homeostasis within the intestine. The various signals that direct such migration include the coordinated efforts of cytokines, adhesion molecules, and highly specific chemokines [36].

The infiltration of immune cells may occur due to altered junction connections and impaired intestinal barrier function [37, 38]. Localized and systemic inflammatory conditions are associated with a leaky epithelial barrier and increased intestinal permeability [39]. A leaky intestinal epithelial barrier allows luminal commensal organism components such as LTA and LPS to enter the submucosa and stimulate the immune system leading to the secretion of regulatory cytokines. The interplay of immune cells, LTA, LPS, and cytokines ultimately augments the inflammatory state of the intestine [40]. In fact, it appears that both LTA and LPS can disrupt barrier function by stimulating increased expression of inducible nitric oxide synthase which leads to increased concentrations of nitric oxide [41]. In addition to disrupting tight junction proteins, nitric oxide is implicated in the induction of enterocyte apoptosis and an accelerated rate of intestinal epithelial cell (IEC) shedding potentially exceeding the rate of IEC production and disturbing intestinal homeostasis [42, 43].

This is relevant to the current study in that Rosa et al. [12, 13] demonstrated that even under nondiarrheic conditions, RNA isolated from neonatal dairy calf feces is derived from a considerable number of IECs as evidenced by mRNA expression of KRT8. This held true in our study in that KRT8 mRNA abundance exceeded that from other genes of interest, suggesting that a considerable amount of fecal RNA originated from IECs. Keratins are the largest family of cellular intermediate filament proteins and are predominant in epithelial cells [44]. Specifically, keratin 8 has been associated with mammary epithelial cells and small intestinal mucosa but not immune cells in general or PMNs in particular [12, 45]. Importantly, various lines of evidence suggest that keratins are key regulators of epithelial integrity [46].

The results from our study suggested a tendency for reduced KRT8 expression in fecal RNA from healthy Holstein as compared to Jersey calves. Although not noteworthy in every situation, KRT8 was the only gene demonstrating downregulation in every comparison of diseased versus healthy calves conducted within this study. More specifically, there was a tendency for KRT8 downregulation in Jerseys with scours and substantial evidence of downregulation in systemically affected Jerseys. Studies conducted in humans and mice suggest that keratin 8 is involved in intestinal barrier protection, colonic active ion transport, colorectal hyperplasia, and inflammatory regulation [46]. In fact, indications are that keratin 8 may help suppress NFKB signaling and TLR-dependent inflammation [47], while protecting colonic integrity in support of gut microbiota homeostasis [46]. This aligns with a previous demonstration of associations between the microbiome and the expression of genes regulating the mucosal barrier and innate immunity in neonatal cattle [48]. The interrelatedness and crosstalk between the various components of the GIT (i.e., epithelium, diverse and robust immune arm, and commensal bacteria) cumulatively make the gut the basis for the health and productivity of animals, highlighting that the GIT is responsible for regulating the physiological homeostasis underlying the ability to withstand infectious and noninfectious stressors [49].

The Holstein and Jersey calves in this study were sourced from different dairies. Consequently, there undoubtedly were undocumented differences in dry cow and maternity management impacting calf health. Nonetheless, Jersey calves demonstrated increased levels of TSP relative to Holsteins suggestive of higher maternally derived serum immunoglobulin G concentrations [50]. Even so, it appeared that despite having received enhanced immunological protection from disease, Jersey calves still may have had reduced immune capacity. Holstein calves experiencing systemic enteritis mounted a more robust and potentially more acute DGE response than systemically affected Jerseys to genes that play key roles in the inflammation and cell membrane or cytoplasmic transport. Furthermore, Jersey calves with GI disease had evidence of KRT8 downregulation, which may have adversely affected intestinal barrier protection and inflammatory regulation. These findings align with our previous observations that a greater number of cases of GI disease can progress to necrotizing enteritis with peritonitis in preweaned Jersey as compared to Holstein calves (unpublished data). In addition, Holstein as compared to Jersey calves were less affected by scours with fewer delayed DGE effects. These findings support previous observations by Johnston et al. [26] of breed-specific transcriptional activity suggesting Holstein calves may be better equipped than Jersey calves to rapidly fight pathogen invasion before pathogens can cause disease. That study demonstrated differences between these breeds in the expression of genes involved in immune responses and cell signaling activity. More specifically, Jersey calves demonstrated decreased cellular movement, chemotaxis, and phagocytic functionality relative to Holstein calves [26]. Breed-specific cellular effects may be diverse depending on the pathogens and the nature of inflammation which influence the expression of associated resistance and tolerance mechanisms [51]. Ultimately, a timely and properly controlled immune response is the key to an animal’s resilience based on balancing pathogen elimination (resistance) and tissue damage (tolerance) [52, 53].

Conclusions

This study investigated transcriptional changes in fecal RNA from neonatal Holstein and Jersey calves with GI disease of varying severity. Both breeds demonstrated different levels of transcription for important genes that selectively enhance or alter host innate immune defense mechanisms and modulate pathogen-induced inflammatory responses. These findings indicate divergent susceptibilities and immune capacity that provide insight into individual and population-level markers of disease resilience (resistance and tolerance). Although additional research is required to explain functional mechanisms and bioactivity across dairy populations, defining relationships between molecular regulatory features and phenotypic traits may help reduce disease incidence and improve animal welfare. This is particularly relevant to the preweaned period with its metabolic and physiologic changes and associated increase in disease susceptibility. Ultimately, knowledge of breed-specific immune responses may facilitate targeted prophylactic and therapeutic veterinary interventions, guide improvements in host genetic selection, and clarify the burden of disease on animal wellbeing.

Supporting information

S1 Table. Probe sequences.

(XLSX)

Click here for additional data file.^{(12.7KB, xlsx)}

S2 Table. Raw counts for endogenous, housekeeping, negative, and positive probes.

(XLSX)

Click here for additional data file.^{(39.5KB, xlsx)}

Acknowledgments

The authors thank the participating calf ranch and associated personnel for their invaluable assistance with this project.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2019-68008-29897 from the USDA National Institute of Food and Agriculture, the USDA National Institute of Food and Agriculture, Animal Health & Disease Research Capacity Grant project 1014680, the WSU CVM Caldwell Endowment, and the American Jersey Cattle Association. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PONE-D-22-02112Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severityPLOS ONE

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Reviewer #2: Yes

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Reviewer #1: The article titled “Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity” by C.S. McConnel and collaborators explores an important topic in the dairy industry, focusing the changes in the transcriptome of preweaning calves in response to mild or clinical GI events. The comparison between breeds, as well as the strategy chosen to quantify gene expression, are novel and interesting. However, the manuscript presents several areas that must be improved before it can be deemed suitable for publication. I will list a few broad observations, followed by more precise corrections.

1) Presentation of the experimental design, and statistical soundness: the way the experimental design is explained is confusing. The cohort was housed on a single facility, to which the animals were transported; later, the authors mention that the calves were sourced from different farms (I can’t tell if each breed came from a separate farm, or if every farm provided equal numbers of calves of both breeds). Because of the abstract, I assume all calves were heifers, but the authors do not mention it in the methods. It’s unclear if calf weight and daily grain intake (when offered) were measured, and it appears that the milk intake was only assessed qualitatively. I’m also still unsure as to when the fecal samples were collected: I understand that calves were 4 to 12 days old at collection #1, then were recollected for DEG 10 days later. Except if they were already showing signs of scours, then they were sampled whenever they first presented symptoms + 10 days later? Another reading is: all were sampled initially, then again if they presented scours before d +10, and then twice more (at collection#1 + 10 days and at first symptoms + 10 days). This is very confusing, especially since I can’t understand which sample was ultimately used for DGE in the scours or systemic group, and I have a suspicion that healthy vs non-healthy samples were collected at different time points.

If we account for disease status and time, there should be 5 groups: healthy throughout, healthy to scours, scours to healthy, systemic to healthy and healthy to systemic; other groups, (systemic to scours, scours throughout, systemic throughout) are absent or have too few samples (and they often received different treatment). All of this separated by breed, so 10 groups for pairwise analysis. However, in the presentation of the results, several contrasts are presented, some of which I do not fully understand: what is the comparison in table 6? Is it each non-healthy group vs healthy at DEG? Healthy throughout? In table 8: the comparisons are based on the time of systemic enteritis and are compared to “healthy calves of the same breed”. I assume “prior” means systemic to healthy ; does that mean that they’re compared to all calves that stated out as healthy, regardless of whether they were healthy throughout, or healthy to scours? Or does it imply healthy throughout?

Finally, the groups are also different in size, and I’m not sure if the authors made any adjustments to their statistical approach to account for unequal group sizes. As it stands, a better explanation of sample collection schedule, group assignment and statistical analysis is required. I suggest analyzing based on a combination of disease progression and breed, leading to the aforementioned 10 groups, though I’m not certain that the statistical power will be maintained.

2) Presentation of results: tables are great for a detailed presentation, but virtually impossible to use for cross-group comparisons. I strongly suggest the table be added to the supplementary files, and be replaced with a grouped column graph: the x axis containing the name of the gene of interest, a group of 3 columns per gene (Holstein, jersey, combined), one separate panel per contrast, and some kind of symbol to indicate significance. I’m partial to fold change, rather than log2FC, but both are acceptable; although, please ensure that whatever is chosen is referenced consistently throughout the text (see 242, 311, 326, 333 and other points where it's called “log fold change”). The heat maps are fine, but a per-sample view does easily display group means and is not terribly useful in this case. I suggest to move that to supplementary as well.

3) Reference to microbiome study: the authors refer to a 2022 microbiome study, also published in PLOS, which as I understand was performed on the same samples/animals. The authors should try to combine these findings with those and see if there are any links.

Other notes (with line number)

57: the statement about how information is “wasted” is subjective. Please rephrase

60-61: I’d argue that the welfare of animals is a more relevant implication than the reputation of the dairy industry…

78-79: depending on how the statistical approach is modified, it may not be worth mentioning the therapeutic approach, since it was not treated as a source of variation.

90-95: please specify number of calves enrolled, how many came from which farm, whether they were all female or not.

120: how much grain was offered at the start?

127: missing comma between Tulare and CA

139: can the raw data for rectal temperatures be provided in the supplementary?

147 and elsewhere: please add catalog number for solutions/reagents used

182: homogenized how?

187: “was” should be “were”

188: why the PBS? Also, homogenized how?

189-192: I don’t understand the strategy; first they were vortexed and the solids were removed, then the supernatant was centrifuged again and discarded? Why was this done instead of bead-homogenization and removal of solids, then lysis?

196: report average yield, 260/280 and 260/230 ratios.

198: report mean RIN or equivalent

202: how was this threshold selected?

205-208: were the re-extracted samples quantified for DV200 before submission?

210: I’m unfamiliar with the system, but is it possible to get more detail on probe design?

263: I’m not sure this whole table needs to be here. Maybe a per-group summary of TSP and ages, and then this table could go in supplementary files.

333 and other tables: check consistency on tables. E.g. for table 7, the timing of disease is indicated as day 1 or day 10, while for systemic it’s prior and current. I know they’re not exactly the same in terms of time, but it’s very confusing.

377 and elsewhere: you can use “p-adj” or “adjusted p” instead of “B-Y P”

379 and elsewhere: the fold change here is missing the unit

469-418: the discrepancy between the presented results and the Rosa et al. is not completely addressed. Add a sentence after 481 that ties it together

494: is there evidence in the literature of PMN transmigration in response to scours/diarrhea? Please cite

512 and elsewhere: note a recent follow-up paper by Rosa et al. on the same calves as [6] that looks at expression of the same genes throughout the GI tract using tissue samples https://www.sciencedirect.com/science/article/pii/S0022030220309115.

520: does “not noteworthy” mean “not statistically significant”?

535-554: I’m not sure I agree with the whole argument here. By the logic applied in ~494, KRT8 could indicate larger degree of PMN migration into the lumen, as Rosa and Osorio in ref. 12 noted that KRT8 was downregulated in PMN; at the same time, the study in ref. 25 demonstrates decreased leukocyte migration and chemotaxis. Additionally, could breed differences have been skewed by unequal sample sizes and the contrasts that were chosen? The differences between the two breeds seem minimal (no significant p-adj, only unadjusted p).

565: What markers of disease resilience? Any insight on resilience could be assessed by looking at animals that recovered (scours to healthy and systemic to healthy) vs those that didn’t (scours to scours and systemic to systemic, which are both absent).

Reviewer #2: Dear Authors,

Congratulations on the manuscript. It is well written and easy to understand.

I will list just a few comments:

L21-23 would be "observed by Rosa et al. in fecal..."?

L91 how many animals were previously evaluated in your study? And what are the selection criteria for the animals that you collected fecal samples from?

L91-93 How much colostrum was offered? And what was the concentration of IgG?

L93-95 did the calves have adequate transfer of passive immunity? What is the average value of TSP? Did any calves have failure in transfer of passive immunity?

L113-114 what was the mixture ratio? What is the composition of the liquid diet?

L118 what is the composition of the oral electrolyte solution?

L120-121 what is the composition of the concentrate?

L129-140 what are the references used for such measurements?

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PLoS One. 2022 Dec 1;17(12):e0278664. doi: 10.1371/journal.pone.0278664.r002

Author response to Decision Letter 0

15 Sep 2022

Thank you for your insightful questions and comments. Your commentary has led to changes that we believe make the manuscript a stronger addition to the literature. Please find responses to specific questions below.

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Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

AU: Thank you for pointing out our editorial mistakes. We have corrected various file names, amended the Funding Statement and included it within the Cover Letter, and updated the caption for the Supporting Information.

Review 1 Queries:

The article titled “Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity” by C.S. McConnel and collaborators explores an important topic in the dairy industry, focusing the changes in the transcriptome of preweaning calves in response to mild or clinical GI events. The comparison between breeds, as well as the strategy chosen to quantify gene expression, are novel and interesting. However, the manuscript presents several areas that must be improved before it can be deemed suitable for publication. I will list a few broad observations, followed by more precise corrections.

AU: Thank you for identifying points in need of clarification. Calf weights and daily grain intakes were not measured due to logistics and housing constraints on the ranch. Milk intake was assessed qualitatively as stated ((good appetite; did not finish the milk or did not consume any of what was offered; orogastric intubation). We have added clarifying text as follows:

1. Breed differences were variable within the source dairies ranging from Holstein and Jersey, Holstein or Jersey only, or crossbred from a single source..

2. Heifer calves were randomly enrolled.

3. The section “Fecal samples for differential gene expression (DGE)” and Fig 1 both have been edited in an attempt to more clearly define the following:

a. Days of age 4-12

i. Period of age during which calves were randomly enrolled and an initial (day 1) fecal sample was collected for both a fecal consistency score and microbiome analysis.

ii. Calves that were initially sampled when healthy or with scours but progressed to systemic enteritis (i.e., diarrhea plus a behavioral score ≥1) during this period were sampled again on the day that systemic enteritis was diagnosed.

iii. Calves with systemic enteritis prior to a random sample were sampled once during this period at the time of initial clinical diagnosis irrespective of the randomized sampling scheme.

b. Days of age 13-21

i. Period of age during which follow-up (day 10) fecal samples were collected for both a fecal consistency score and DGE analysis.

ii. Calves that were initially sampled when healthy or with scours but progressed to systemic enteritis by 12 days of age were sampled twice during this period—9 days following both the first (healthy or scours) and second (systemic enteritis) samples to provide 2 day 10 samples.

A key point here is that calves that were randomly enrolled were identified as having scours (or not) when initially sampled whereas systemic enteritis was diagnosed based on clinical presentation. Calves with systemic signs were sampled at the time of diagnosis during the 4-12 days of age window regardless of whether they had already been sampled based on their random allocation. Ultimately one of sample was analyzed for DGE with preference given to the systemic enteritis sample. Contrary to suspicions, healthy and non-healthy samples were collected at the same time.

AU: Indeed there are a number of possible groups—including systemic to scours and scours throughout which are demonstrated in Figs 2 and 3 (no calves survived to populate a group of systemic throughout). To help alleviate confusion, the Table 6 legend has been moved into the title to explain that Table 6 is presenting log fold-changes for fecal mRNA gene expression in calves with scours or systemic enteritis at any point from enrollment until the day 10 DGE sample as compared to healthy calves of the same breed(s).

Thank you for raising the question regarding Table 8, as it highlighted that we should add “consistently” to the descriptor for healthy calves within the titles of Tables 5-8 (i.e., healthy to healthy category), and additional text to Table 6 clarifying categories for comparison (e.g., scours to healthy and healthy to scours). Within the title for Table 8 specifically, we have also included text clarifying that calves with systemic enteritis ≥7 days prior to the day 10 DGE fecal sample were within the “systemic to healthy” groups, whereas calves with systemic enteritis on the day of the DGE fecal sample were within the “healthy to systemic” category.

We discussed how best to present this data given that less useful information was garnered from the collapsed data (i.e., comparing calves with clinical symptoms of GI disease at any point from enrollment until the day 10 DGE) than from the more nuanced analyses of disease severity culminating in the findings presented in Table 8 for systemic disease. In the end, we decided it was important to explore the data in a stepwise fashion in an effort to peel back the layers from broad disease and combined breeds to refined disease and breed-specific outcomes. Although in the grand scheme of things those outer "layers" of the analyses were overly generalized given the final specific outcomes, we felt that omitting those general assessments would lead readers to overlook the importance of the disease classifications. As we stated in the Discussion, these findings highlight the importance of discriminating between breed-specific GI disease phenotypes when describing, comparing, and investigating the overall consequences of disease on calf health and wellbeing. In other words, we are hoping that this approach highlights the fact that scours (diarrhea) should not be considered a singular disease state without consideration for other contextual aspects such as breed and severity.

AU: We truly appreciate your overall insights and identification of issues with clarity. We trust that our edited Fig 1 timeline and additional text throughout has provided the necessary clarity to more easily follow the sampling and analytic sequence. As for adjusting our statistical approach to account for unequal group sizes, nSolver Advanced Analysis runs the R package MASS which accounts for unequal variance when estimating parameters and their variance by estimating a dispersion parameter (ϕ) within the negative binomial model. It accommodates the variance of probe expression within biological replicates, which is not of interest in differential expression analysis. When ϕ = 0, the negative binomial model reduces to the Poisson model. It is worth noting that the unequal group sizes were a function of both on-farm disease incidence and the mandatory exclusion of 99 samples (n = 38 consistently healthy; n = 47 scours; n = 14 systemic enteritis) due to insufficient fecal mRNA raw counts or expression above background. It was unfortunate that we were unable to use that data, but the reality is that fecal RNA extraction is challenging, and we were glad to have as many useful samples as we did even it did mean that we could not look more closely at select groups lacking a baseline number of samples (i.e., scours to scours, n=2; systemic to scours, n=2). In the end, although we had unequal numbers of animals per group (healthy, scours, systemic disease) and by breed (Jersey, Holstein) we were satisfied that the various comparisons met baseline criteria for sample size and the inclusion of all available data was more useful than restricting data in the interest of equal group sizes.

AU: We have edited log fold-change to log2 fold-change where necessary.

Admittedly, the Tables as presented provide a lot of information through which to sift. However, we respectfully disagree that they should be replaced or added to supplementary files. As we stated above, we think it is important to discriminate between breed-specific GI disease phenotypes and we think that these Tables provide a worthwhile mechanism for doing so. Although simplified versions in the form of columnar graphs might be more visually appealing, they would almost certainly limit our ability to display the full range of data including standard and corrected p-values, which we believe are important for exploring “significant” differences as well as plausible trends in the data. Our goal is to be as transparent as possible with regard to the findings from this study, and we think that presenting the data as-is per comparison is the best (albeit dense) mechanism for doing so. We have proceeded in this manner previously (https://doi.org/10.3389/fvets.2020.559279), and the second Reviewer for this manuscript appears to agree given their comment that this manuscript is easy to understand.

We will certainly defer to the Editor, but we would also respectfully disagree with moving the heatmaps to the supplementary files. Our experience discussing this data with veterinarians has been that the heat maps provide one of the more powerful representations of the data as it pertains to both individual variations and groups. As such, we would prefer leaving these Figures within the main body of the text as a visual prompt similar to what might be achieved through the addition of columnar graphs.

AU: The previously published study was a bit more expansive and involved additional samples taken from throughout the summer of 2019. We considered exploring the overlaps between microbiome and DGE findings but have shelved that idea for the time being due to time and monetary constraints.

Other notes (with line number)

57: the statement about how information is “wasted” is subjective. Please rephrase

AU: This has been changed to “unproductive”.

60-61: I’d argue that the welfare of animals is a more relevant implication than the reputation of the dairy industry…

AU: Agreed. That is why we say that these misspecifications of disease processes can result in inappropriate therapeutic and preventive actions that impact lifetime wellbeing. Nonetheless, the reputation of the dairy industry is at risk as well and that is an important consideration.

78-79: depending on how the statistical approach is modified, it may not be worth mentioning the therapeutic approach, since it was not treated as a source of variation.

AU: Although not a major component of the analysis, we think that therapeutic interventions should be mentioned simply because they were a component of the management of systemic enteritis.

90-95: please specify number of calves enrolled, how many came from which farm, whether they were all female or not.

AU: These data now are specified within the first paragraph of the Results including reference to the fact that all enrolled calves were female.

120: how much grain was offered at the start?

AU: There was no set amount initially—it was literally a handful. That has been clarified within the text.

127: missing comma between Tulare and CA

AU: Corrected.

139: can the raw data for rectal temperatures be provided in the supplementary?

AU: We decided to only include rectal temperatures in Table 4 to highlight the variability in rectal temperatures even in those calves with systemic evidence of disease. Our experience with rectal temperatures on dairies/calf ranches in the western US in general and with this project specifically is that they are highly variable and ultimately of limited value in clarifying GI disease severity in calves with scours.

147 and elsewhere: please add catalog number for solutions/reagents used

AU: Solutions such a lactated Ringer’s can be sourced from multiple outlets as can be other of the products used on farm or in the lab. Unless we have overlooked a new PLOS ONE Best Practices in Research Reporting we think that the guidelines mandate that company name and location are the standard practice for reporting solutions and reagents that have a specific origin.

182: homogenized how?

AU: To avoid any confusion here we restated to say that the samples were thoroughly mixed by vortex.

187: “was” should be “were”

AU: Corrected.

188: why the PBS? Also, homogenized how? 189-192: I don’t understand the strategy; first they were vortexed and the solids were removed, then the supernatant was centrifuged again and discarded? Why was this done instead of bead-homogenization and removal of solids, then lysis?

AU: The PBS and strategy was based on a recommendation from Fisher Scientific’s technical help; we wanted host material rather than everything from the sample. Text has been added to explain and to restate the homogenization.

196: report average yield, 260/280 and 260/230 ratios. 198: report mean RIN or equivalent

AU: Due to the instability of fecal RNA and Nanostring’s allowance for DV values as a quality measurement, RNA integrity values were not used for quality assessments. Instead, samples with DV200 (percentage of RNA fragments >200 nucleotides) values >50% and concentrations >20 ng/uL were submitted for diagnostic processing at the Primate Diagnostic Services Laboratory.

202: how was this threshold selected?

AU: Great question. This was based on discussions with Nanostring’s Field Application Scientist, Dr. Lisa Poole. The short answer is that the Nanostring manual suggests the use of DV200. The longer answer is that there are published instances in the literature that show RIN is not indicative of how successful a gene expression experiment with nCounter will be due to the fact their chemistry does not rely on total RNA integrity that is measured by RIN. Instead, since the barcodes only need about 100bp of intact RNA to bind, it only matters that the RNA is at least 200bps in length, hence the dependence on the DV200 value over RIN.

205-208: were the re-extracted samples quantified for DV200 before submission?

AU: Re-extracted sample DValues were not quantified. Trizol extracts that were equal to or greater then 35ng/ul were allowed to move forward in the process and all re-extracted samples were well above 35ng/ul. In fact, only re-extracted samples with concentrations >60ng/uL were submitted for diagnostic processing per Nanostring recommendations.

210: I’m unfamiliar with the system, but is it possible to get more detail on probe design?

AU: This is probably more than you care to know but the probe design process used by Nanostring breaks a target transcript down into 100nt windows to profile for probe characteristics. Each 100nt window is profiled for intrinsic sequence makeup – non-canonical bases, GC%, inverted and direct repeat regions, runs of polynucleotides, as well as calculating the Tm for each potential probe to target interaction. Each individual probe is then thermodynamically tuned to determine the optimal probe length (each probe ranges in size from 35-50nt) within the 100nt target region. Next, a cross-hybridization score is calculated for each probe region; BLAST is used to identify potential off-target interactions, and then an internal algorithm is used to determine the cross-hybridization score using a pair-wise comparison of the probes to the potential cross-hybridization target. In addition to a cross-hyb score, a splice isoform coverage score is generated by identifying transcripts that are isoforms to the transcript the probe is being designed to. Once all of this information is compiled, then the final probe is selected by identifying the probe that has the optimal splice form coverage, the best cross-hybridization score, and the most optimal thermodynamic profile.

To ensure that there are no potential intramolecular probe-probe interactions that could cause elevated false-positive or background signal for any individual probe in a CodeSet, a stringent intramolecular screen is run on every collection of probes assembled into a CodeSet. This screen is run using a sensitive algorithm that calculates both the Tm and the free energy potential of every probe compared to every other probe in the project. If a probe is identified having either Tm or free energy profile above a conservative threshold, an alternative probe is selected for that target and the screening is re-run until there are no issues.

We considered including some more of this information in the Materials and Methods, but it seems overkill under the circumstances and we felt the inclusion of a relevant reference was more in line with expectations (https://doi.org/10.1186/1756-0500-2-80).

263: I’m not sure this whole table needs to be here. Maybe a per-group summary of TSP and ages, and then this table could go in supplementary files.

AU: This is a bit of a toss-up between providing excessive background information and doing our best to help set the stage for the comparisons to follow. We would prefer leaving this Table as-is even if it is a bit of overkill, to help readers appreciate the enrollment/sampling scheme that is presented in Fig1 while highlighting the various treatments of systemically affected animals.

AU: Thank you again for pointing out this issue with clarity. We think it has been addressed appropriately per the edits discussed above.

377 and elsewhere: you can use “p-adj” or “adjusted p” instead of “B-Y P”

AU: This is true but we think that B-Y P helps the reader maintain a consistent understanding of where the adjusted p-value originated per the description in the Materials and Methods.

379 and elsewhere: the fold change here is missing the unit

AU: As mentioned above, we have edited log fold-change to log2 fold-change where necessary and included text in the Differential Gene Expression section of the Results indicating that Log2 fold-changes are ratios.

469-418: the discrepancy between the presented results and the Rosa et al. is not completely addressed. Add a sentence after 481 that ties it together

AU: Thank you very much for highlighting this section. The findings from that paper by Rosa et al. were not well represented in this portion of our manuscript. We should have been pointed out that there was a time-dependent IFNG downregulation in the Jersey calves following mild diarrhea; however, Rosa showed a pattern of fecal score and pro-inflammatory blood biomarkers (peaking both between 2 and 3 weeks of age) that indicated a pro-inflammatory response during diarrhea in neonatal dairy calves. This has been corrected within the text and provides a more egalitarian representation of the comparative data.

494: is there evidence in the literature of PMN transmigration in response to scours/diarrhea?

AU: This is a good question. There appears to be little in the way of direct evidence although this paper (https://doi.org/10.1128/IAI.67.9.4950-4954.1999) does suggest that Salmonella-induced enteritis leads to an inflammatory response as evidenced by a rapid and large influx of polymorphonuclear leukocytes into the intestinal mucosa and lumen and a net fluid secretion into the intestinal lumen. Rosa also mentions a good paper by Brazil et al. that speaks to the fact that the migration of PMN across the intestinal epithelium is a common pathological event of many mucosal inflammatory diseases (Brazil et al., 2010). We now have included this reference in our manuscript.

Please cite 512 and elsewhere: note a recent follow-up paper by Rosa et al. on the same calves as [6] that looks at expression of the same genes throughout the GI tract using tissue samples https://www.sciencedirect.com/science/article/pii/S0022030220309115.

AU: Done.

520: does “not noteworthy” mean “not statistically significant”?

AU: More or less—this seemed like an efficient method for pointing out the consistent downregulation without getting mired in B-Y or standard p-value cutoffs.

AU: These are good considerations and worthy of debate. The key from our standpoint is that although Rosa and Osario demonstrated that mRNA expression of AQP3 tended to be greater in PMN than fecal RNA (indicating that the fecal RNA samples in our study may have contained PMN mRNA transcripts), the more important point is that Rosa et al. also demonstrated that even under nondiarrheic conditions RNA isolated from neonatal dairy calf feces is derived from a considerable number of IECs as evidenced by mRNA expression of KRT8. Based on this latter finding we feel comfortable positing that the evidence for KRT8 downregulation in Jersey calves with GI disease may have indicated adversely affected intestinal barrier protection and inflammatory regulation.

As mentioned above, we are confident that nSolver (R package MASS) appropriately accounted for any variance difference and adjusted the estimated model variances for over-dispersed data. That said, we certainly hope that our research and others will continue to explore breed differences in response to GI disease states.

AU: Additional calves representing ongoing disease processes such as systemic-systemic, scours-scours, or systemic-scours would have provided a nice comparative group by which to evaluate potential markers of animal’s resilience based on balancing pathogen elimination (resistance) and tissue damage (tolerance). Hopefully we or others can continue with this line of inquiry to understand varying responses to disease more fully. That said, even without those comparative groups the genes that were selected for analysis based on previous work by Rosa et al. provide insight into resilience based on both breed and disease severity as demonstrated through divergent susceptibilities and apparent immune function.

Review 2 Queries:

Congratulations on the manuscript. It is well written and easy to understand.

AU: Thank you. We appreciate your kind words.

I will list just a few comments:

L21-23 would be "observed by Rosa et al. in fecal..."?

AU: Corrected.

L91 how many animals were previously evaluated in your study? And what are the selection criteria for the animals that you collected fecal samples from?

AU: We trust that our edited Fig 1 timeline and additional text throughout have provided the necessary clarity to more easily follow the sampling and analytic sequence. Specifically, heifer calves were randomly enrolled into the sampling scheme and allocated to an initial sampling day (day 1 enrollment sample) between 4 to 12 days of age to have fecal samples collected. This provided the opportunity to sample healthy calves and calves with previously undiagnosed scours but no other clinical abnormalities. Calves that demonstrated systemic enteritis (i.e., diarrhea plus a behavioral score ≥1) during this same period but prior to a random sample were sampled once at the time of initial clinical diagnosis irrespective of the randomized sampling scheme. Nine days after the initial fecal collection each calf was sampled again (day 10 DGE sample; 13-21 days of age) in an effort to align the fecal RNA transcriptomic analysis with previously documented delays in gene expression following evidence of diarrhea. Calves that were initially sampled when healthy or with scours but progressed to systemic enteritis by 12 days of age, were sampled again on the day that systemic enteritis was diagnosed. These calves then were sampled twice more 9 days following both the first (healthy or scours) and second (systemic enteritis) samples to provide 2 day 10 samples. No calf was sampled more than 4 times in total and only one sample ultimately was analyzed for DGE, with preference given to the sample obtained during systemic enteritis. Ultimately, a total of 183 heifer calves were enrolled with RNA extracted from fecal samples and submitted for diagnostic processing.

L91-93 How much colostrum was offered? And what was the concentration of IgG?

AU: Calves were fed colostrum at the dairy of birth which unfortunately means that there were no records available for specific levels of colostrum that were fed to individual calves. However, transfer of passive immunity was assessed via blood samples obtained from 1-day old calves via jugular venipuncture to measure total serum protein (TSP) using a calibrated refractometer (details below).

L93-95 did the calves have adequate transfer of passive immunity? What is the average value of TSP? Did any calves have failure in transfer of passive immunity?

AU: Average TSP was 6.5 ±0.8 g/dL. The individual TSP levels are presented in Table 3 and interestingly the average TSP was the same for healthy (6.5 ±0.9 g/dL) and diseased calves (6.5 ±0.8 g/dL); however, as expected the average TSP for Jerseys (6.9 ±0.7 g/dL) was greater than for Holsteins 6.0 ±0.7 g/dL; P < 0.0001).

L113-114 what was the mixture ratio? What is the composition of the liquid diet?

AU: The milk blend consisted of pasteurized waste milk together with milk replacer and was targeted for an optimal composition of 13% solids, 22-24% fat, and 28% protein.

L118 what is the composition of the oral electrolyte solution?

AU: The oral electrolytes were a proprietary product for the participating calf ranch, which unfortunately meant that we were not privy to specific compositional details.

L120-121 what is the composition of the concentrate?

AU: Similarly, the concentrate was specifically developed for the calf ranch as a proprietary product so we were provided only with the fact that it consisted of pellets, molasses, and whole corn.

L129-140 what are the references used for such measurements?

AU: Thank you for this comment as it highlighted the need for an appropriate reference that was missing: McGuirk SM. Disease management of dairy calves and heifers. Vet Clin North Am Food Anim Pract. 2008;24(1):139-53. Epub 2008/02/27. doi: 10.1016/j.cvfa.2007.10.003. PubMed PMID: 18299036.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0278664.r003

Decision Letter 1

Angel Abuelo

18 Oct 2022

PONE-D-22-02112R1Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severityPLOS ONE

Dear Dr. McConnel,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Dec 02 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Angel Abuelo, DVM, MRes, MSc, PhD, DABVP (Dairy), DECBHM

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

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6. Review Comments to the Author

Reviewer #1: I would like to commend the authors for making pertinent changes to the manuscript, which has improved as a consequence. I still have a few comments to share, which I will list below in no particular order. If line numbers are provided, they refer to the version with track changes.

1) Regarding presentation of timeline and sample collection: I think I now understand how samples were collected. Let me try to lay it out with an example, so that the authors can confirm if I’m correct in my interpretation:

- If a calf is healthy or has scours at enrollment date, and DOES NOT develop systemic enteritis throughout the study � two samples are collected, 9 days apart. Age at collection of sample 1 is 4-12 days, therefore age at sample 2 is 13-21 days. Sample 2 is used for DGE.

- If a calf is healthy or has scours at enrollment date, and DOES develop systemic enteritis throughout the study � sample 1 is collected as above, another sample is collected when symptoms show (let’s call this Sample 1_SE), then sample 2 is collected 9 days after sample 1, and Sample 2_SE is collected 9 days after Sample 1_SE. Ultimately Sample 2_SE is used for DGE.

- If a calf started with systemic enteritis at enrollment date � Sample 1_SE is collected at beginning of symptom, Sample 1 is collected as above, Sample 2 and Sample 2_SE are collected 9 days after corresponding sample. (however, based on Table 3, no animal that started SE had SE at the time the DGE sample was collected).

If the explanation outlined above is correct (and do please correct me if I’m wrong), I have a few comments:

- The explanation of the collection timepoints can be more effectively outlined with something like a flow chart that looks at initial enrollment state, condition throughout the 10 days (healthy, scours, SE), and state at DGE sample. The actual days of age of the calf can be added on a vertical bar next to the graph. In my opinion, the figure that was added in the first round does little to clarify the timeline, as it’s still based on the written description provided with the figure.

- Does table 3 discriminate based on disease progression? For example: if a calf started healthy, quickly developed SE, and was then treated and recovered before the DGE sample, this would show as healthy-healthy, just like a calf that started healthy and ended healthy without change? Maybe there are no such samples? Or does “enrollment sample” in Table 3 refer to “early sample” which would be Sample 1 or Sample 1_SE (see above) depending on whether they developed systemic enteritis or not?

- Since the sample used for DGE are +9 days from sample 1, but sample 1 was collected later for animals that developed systemic enteritis, would this mean that animals that developed systemic enteritis are always slightly older than healthy/scours animals at the DGE sample?

2) Regarding the catalog numbers: both the authors and myself will agree that for common reagents such as lactated Ringer’s, PBS, or certain types of cell culture media adding the catalogue number may be superfluous. However, several nucleic acid extraction and processing kits have been cited within the manuscript, for which many formats (with slightly different chemistries) exist. A few examples: MagMAX Total RNA comes as a 96-preps kit in deep well plates, or as mirVana (still under the “Total RNA” umbrella) which maintains small RNAs. There are four commercial versions of the Qubit fluorimeter available. Invitrogen’s “RNA Storage Solution” (line 205) could be “THE RNA Storage Solution” from Thermo Fisher, or it could be RNA Later which the authors cite multiple times. Whirl Pak bags come in several formats (filtered, unfiltered, in different sizes etc.). The RNA Clean & Concentrator Kit from zymo comes as spin columns, loaded plates or magnetic beads…

Having worked with fecal RNA multiple times in the past, I have first hand experience with how sample collection vessels, storage temperature and solution, and extraction and cleanup can affect quality and integrity of the resulting product. I think it is more than reasonable to request that the author add catalog numbers to these products. The PLOS guidelines are not specific on this, but do request that catalog number be used when antibodies or cell lines are cited in the manuscript, which I think is a rather similar situation to the one we’re discussing here. Please, add the catalog number.

3) Regarding probe design: my question aimed at the possibility of obtaining actual sequences for the probes. If the method used to design them follows the manufacturer’s design and is openly available online, then I will agree that information on the design strategy does not need to be included; the probe sequence, however, would be desirable. One additional note: I could not identify, in the extensive explanation of the probe design provided by the authors, if the probes are designed to span two exons (that is, to straddle an exon-exon junction). If that is not the case, please specify if the TURBO DNAse step of MagMAX was executed or omitted.

4) RNA integrity vs DV200: the requirement for RIN or equivalent in gene expression studies is a debated topic, especially for methods that rely on amplification of short sequences such as RT-qPCR. The crux of the matter is as the authors indicate, amplified sequences are rather short, and as such are less likely to be impacted by degradation. This is true of both qPCR and the nanostring technology employed here, provided the amplicons in qPCR are <200 in length (which they often are). Which is to say, the conversation about RIN requirements is as pertinent to this manuscript as it is to qPCR. The authors mentioned in the response to my feedback that there are “published instances” in the literature where RIN was not indicative of successful quantification. I invite the authors to include a statement about the use of RIN vs DV200 to quantify transcript integrity, citing said manuscripts as support for their strategy. Additionally, the fact that re-extracted samples were not quantified for DV values is a limitation of this study and should be stated or justified, as there can be no guarantee on the quality of those samples, regardless of the method to quantify said quality.

5) The authors indicate that PBS was used to “preserve host material”; is there a reference for this? As considerable or at least some lysis presumably happens before this step (due to vortexing), any lysed pathogenic/microbial nucleic acid material is preserved.

6) Line 193: at what temperature were the samples stored?

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

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PLoS One. 2022 Dec 1;17(12):e0278664. doi: 10.1371/journal.pone.0278664.r004

Author response to Decision Letter 1

18 Nov 2022

Thank you for your additional input. questions and comments. Please find responses to specific questions below.

- If a calf is healthy or has scours at enrollment date and DOES NOT develop systemic enteritis throughout the study, two samples are collected, 9 days apart. Age at collection of sample 1 is 4-12 days, therefore age at sample 2 is 13-21 days. Sample 2 is used for DGE.

AU: Correct.

- If a calf is healthy or has scours at enrollment date, and DOES develop systemic enteritis throughout the study, sample 1 is collected as above, another sample is collected when symptoms show (let’s call this Sample 1_SE), then sample 2 is collected 9 days after sample 1, and Sample 2_SE is collected 9 days after Sample 1_SE. Ultimately Sample 2_SE is used for DGE.

AU: Correct.

- If a calf started with systemic enteritis at enrollment date, Sample 1_SE is collected at beginning of symptom, Sample 1 is collected as above, Sample 2 and Sample 2_SE are collected 9 days after corresponding sample. (however, based on Table 3, no animal that started SE had SE at the time the DGE sample was collected).

AU: Calves with systemic enteritis prior to selection for a random sample were sampled only once during this period at the time of initial clinical diagnosis. I.e., there was no Sample 1 collected if a Sample 1_SE preceded the anticipated randomized sample. We have added the word ‘only’ to the text and Fig1 to help clarify this point.

You are correct that no surviving calves that started SE remained SE 9 days later.

If the explanation outlined above is correct (and do please correct me if I’m wrong), I have a few comments:

AU: Figure 1 has been updated to include a flowchart indicating observed enrollment and DGE sample classifications.

AU: You are correct that ‘enrollment sample’ refers to the sample preceding the DGE sample. E.g., for the purposes of our analysis it was the sample used to enroll a calf into the study. Only one sample ultimately was analyzed for DGE. E.g., no samples from a calf with systemic enteritis were used for anything other than evaluating healthy to systemic, systemic to healthy, or systemic to scours. This might not have been clear given our previous word choice stating that preference was given to the sample obtained during systemic enteritis. This has been corrected within the text to clarify that preference was given to the sample obtained during or following systemic enteritis.

AU: Correct. Ages were slightly different. Per Table 3, calves that survived following systemic enteritis were initially identified between 8-12 days of age, leading to a DGE sample obtained between 17-21 (average 19.6) days of age. All other DGE samples, including those obtained from calves that were initially healthy but developed systemic enteritis by the time of their DGE sample, were between 13-21 (average 16.4) days of age. Prior to conducting this study, it was decided that the sampling timeframe for DGE between 13-21 days of age would be analyzed in bloc given the relatively narrow spectrum of development. Ongoing research breaking down transcriptomic and DGE changes by day of age may ultimately prove enlightening as we continue to try and understand developmental aspects underlying immunologic resilience (resistance and tolerance).

AU: Done.

AU: An additional Supplementary Table 1 (S1 Table) has been added to provide the actual sequences for the probes. Per Nanostring’s Field Application Scientist, the probes are designed based on transcriptomics data with the introns spliced out. Some may fall across exon-exon junctions, although others may not for targeting the RNA from that gene. So, if the question is how do we know the probe is not targeting DNA, if it is not covering a exon-exon junction unique to RNA? Then the answer is that the probe hybridization assay takes place at 65°C, and the temperature in the PCR block never rises past this so all genomic DNA remains double stranded and inaccessible to the probes, only single-stranded RNA should be binding.

AU: I think it is important to note that the NanoString technology does not used any “amplified” sequences. There are no enzymatic steps, they are just counting the original number of RNA molecules in the sample. So in this way, the relationship of RIN to qPCR is not the same as RIN to NanoString nCounter assays. Many NanoString applications also use FFPE samples for their RNA, and RIN is not considered a sensitive measure of RNA quality for FFPE samples.

That said, if there is a DV200 less than 50%, Nanostring mostly recommends increasing the sample input. It turns out that according to Nanostring there are MANY (the majority) of groups who do not even check the DV200, they just look at concentration, A260/A280 (1.7-2.3) and A260/A230 (1.8-2.3). They recommend including the DV200 metric if you think there is a reason your samples will be degraded (FFPE). There are many positive and negative controls within in the assay, as well as QC measurements of the data’s signal and quality after running the assay. If there are a lot of QC flags, then it’s possible that something is off and it is recommended to go back and run the sample on a bioanalyzer to double-check the DV200. Otherwise, the assay worked as expected. If the DV200 was poor, we would have expected to get poor signal/background ratios that raise a QC flag.

AU: Correct, during vortexing some mechanical lysis can happen and microbial material will carry over. Of course, that is why we run housekeeping genes to ensure host material was present. Our use of PBS to aid in removal of RNAlater was informed by methods presented in a manuscript entitled, “Variation of glucoraphanin metabolism in vivo and ex vivo by human gut bacteria.” Fei Li et al. PMID: 21342607 PMCID: PMC3137642 DOI: 10.1017/S0007114511000274. This reference has been added to the manuscript.

6) Line 193: at what temperature were the samples stored?

AU: -80°C. This has been added to the text.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0278664.r005

Decision Letter 2

Angel Abuelo

22 Nov 2022

Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity

PONE-D-22-02112R2

Dear Dr. McConnel,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Angel Abuelo, DVM, MRes, MSc, PhD, DABVP (Dairy), DECBHM

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0278664.r006

Acceptance letter

Angel Abuelo

24 Nov 2022

PONE-D-22-02112R2

Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity

Dear Dr. McConnel:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Angel Abuelo

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Probe sequences.

(XLSX)

Click here for additional data file.^{(12.7KB, xlsx)}

S2 Table. Raw counts for endogenous, housekeeping, negative, and positive probes.

(XLSX)

Click here for additional data file.^{(39.5KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(37KB, docx)}

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Transcriptional changes detected in fecal RNA from neonatal dairy calves of different breeds following gastrointestinal disease of varying severity

C S McConnel

G S Slanzon

L M Parrish

S C Trombetta

L F Shaw

D A Moore

W M Sischo

Roles

Abstract

Introduction

Materials and methods

Ethics statement

Study design and calf enrollment

Animal husbandry

Clinical health assessments and therapeutic interventions

Table 1. Behavioral assessment scoring matrix for preweaned Holstein and Jersey calves.

Fecal samples for differential gene expression (DGE)

Fig 1. Timeline for fecal collections and fecal consistency scores, and observed progression of health and disease states included in this analysis.

RNA extraction

Gene selection and gene expression analysis

Table 2. Genes examined for differential expression from fecal mRNA obtained from Holstein and Jersey calves.

Sample size

Results

Calves and clinical outcomes

Table 3. Details related to enrolled Holstein and Jersey calves.

Table 4. Timing and duration of clinical signs for calves with systemic GI disease.

Differential gene expression

Table 9. Log2 fold-changes for fecal mRNA gene expression in healthy Holstein (n = 20) vs healthy Jersey (n = 13) calves.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Angel Abuelo

Roles

Author response to Decision Letter 0

Decision Letter 1

Angel Abuelo

Roles

Author response to Decision Letter 1

Decision Letter 2

Angel Abuelo

Roles

Acceptance letter

Angel Abuelo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Table 9. Log₂ fold-changes for fecal mRNA gene expression in healthy Holstein (n = 20) vs healthy Jersey (n = 13) calves.