Viral and bacterial dynamics in response to drastic and short dietary shifts in creole Colombian cattle

Viral and bacterial dynamics in response to drastic and short dietary shifts in creole Colombian cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Viral and bacterial dynamics in response to drastic and short dietary shifts in creole Colombian cattle Ruth Hernández, Laura Forero-Junco, Yesid Avellaneda, Diego Velasco, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7160638/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract The inclusion of grains in cattle diets aims to boost milk and meat production but can reduce ruminal pH and disrupt normal rumen function, mainly affecting fiber-degrading microbes. While bacterial responses to pH shifts are well documented, the effects on rumen bacteriophage (phage) communities remain unclear. This study aimed at characterizing the viral and bacterial dynamics upon a short-term acidotic challenge induced by adding corn to the diets of three Colombian BON (Blanco Orejinegro) cows, ( Bos Taurus ), usually fed on pasture. Ruminal fluid samples were analyzed to monitor viral and bacterial diversity, pH, and short-chain fatty acids (SCFAs). Viral alpha diversity indices declined significantly with pH reduction, possibly due to lysogeny or phages with broad host ranges. In contrast, bacterial diversity stayed relatively stable, suggesting higher resilience to short-term pH shifts. Beta diversity analysis indicated that individual animal variation, rather than diet, was the main driver shaping both viral and bacterial communities. Moreover, distinct viral abundance patterns were strongly associated with pH fluctuations, highlighting the potential of phages as sensitive early indicators of ruminal acidification. These findings emphasize the important ecological role of the rumen virome and suggest that monitoring phage dynamics could improve understanding of ruminal pH-related disturbances in cattle. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Microbiology bacteriophages ruminal pH shifts microbial dynamics diet shift virome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Ruminants are herbivores that obtain energy by fermenting plant materials such as grasses and small shrubs 1 . Since they lack the enzymes necessary to digest fibrous materials, they rely entirely on a diverse community of anaerobic, symbiotic microorganisms – including bacteria, archaea, protozoa, and viruses - to break down plant fibers. The composition of this microbial community is influenced by several factors, including diet, breed, age, and geographical location 2 Diet is one of the most significant factors shaping the rumen microbiome. The inclusion of rapidly fermentable carbohydrates, such as corn, into cattle diets is a widespread practice to enhance milk production. However, this dietary shift can lead, a condition characterized by the accumulation of short-chain fatty acids (SCFAs) and a decrease in ruminal pH to values as low as 5.5 for several hours a day 3 , 4 . Such decreases in pH can adversely affect animal health contributing body weight loss, increased lameness, fluctuations in feed intake, and reduced milk 3 , 5 . Most studies investigating the effects of pH reduction on the rumen microbiome use animal models and span several weeks 4 , 6 , 7 , 8 . However, some researchers have focused on short-term challenges to assess immediate microbial responses. For example, A research induced a rapid decrease in ruminal pH within 24 hours using high doses of barley grain, while another study 9 lowered the ruminal pH in steers within five days, revealing its effects on rumen bacteria and ciliates. Extensive research has examined the bacterial response to decreases in ruminal pH, particularly among fiber-degrading taxa. Bacterial composition, abundance, growth, and diversity shift significantly when animals consume high concentrations of rapidly digestible carbohydrates 4 , 6 , 10 , 11 . A decline in ruminal pH inhibits cellulolytic bacteria, such as Fibrobacter and Ruminococcus spp., reducing their survival rates. Other microbial groups, including fungi 8 , 12 , 13 , archaea 6 , 14 , and protozoa 8 , 11 , 15 , also exhibit distinct responses to fluctuation in ruminal pH. Among the viruses in the rumen, bacteriophages (phages) play a crucial role in bacterial lysis, nutrient recycling, and substrate availability. They influence microbial turnover by facilitating horizontal gene transfer and modifying bacterial metabolism through auxiliary metabolic genes 16 . As a result, phages are highly responsive to environmental changes and may serve as early indicators of ecosystem disturbances, including dietary shifts 1 , 17 . In other ecosystems, phages have been shown to respond sensitively to environmental stress. For instance, phage abundance declines in chromium-contaminated soils 18 , shifts along pH and moisture gradients in thawing permafrost 19 , and increases under phosphorus and water stress in the potato rhizosphere 20 . Similarly, viral diversity in anaerobic digestion food waste reactors is influenced by pH, methane, and ammonium levels 21 , while in industrial-scale composting systems, viral abundance correlates with nutrient turnover 22 . These findings suggest that phages could serve as sensitive indicators of environmental conditions in the rumen as well. Despite this potential, research on the viral response to dietary changes in the rumen remains limited. Few global studies have explored ruminal virome ecology, phage-host interactions, and their functional impact on animal nutrition 1 . A deeper understanding of these dynamics could provide new insights into microbial responses to diet-induced stress and identify specific phage groups as potential biomarkers for rumen health. In this context, our study addresses the following research question: How does the ruminal the bacterial and viral community respond to decreases in ruminal pH resulting from dietary changes? In this study, we characterize the response of viral and bacterial communities to a short-term dietary challenge in BON (Blanco Orejinegro) cattle, a breed typically maintained on pasture-based diets. We hypothesize that phage communities are more sensitive to pH fluctuations induced by dietary changes than bacterial communities. Consequently, phages may serve as early indicators of ruminal pH shifts, potentially offering new diagnostic tools for monitoring rumen health. Results The incorporation of corn in the diet induced a decrease in the ruminal pH We successfully reduced ruminal pH in the three animals for several hours by introducing corn into the rumen. In Cow A, pH did not drop below 5.5, even after the second corn dose was introduced (pH = 5.79); however, an overall reduction of approximately one unit was observed throughout the experiment. Comparable results were observed in Cow B and Cow C, with pH values ranging between 5.6 and 5.34 for 21 to 24 hours. After the animals resumed grazing on grass, pH recovered to normal values (Fig. 1 ). Regarding SCFAs dynamics, as expected, acetic and propionic acid were the most abundant, with valeric and isovaleric acids near the detection limit. A general positive correlation was observed among SCFA concentrations at different time points ( Suppl. Figure 1). However, an interesting pattern emerged when examining the acetic-to-propionic acid ratio, which decreased following corn introduction compared to time points when the animals were fed grass (Fig. 2 ), this trend was consistent in all 3 animals and showed a significant correlation with the decrease in pH (ρ = 0.4024, p = 0.04151, spearman correlation). This suggests that SCFA production increased due to corn consumption, primarily driven by propionate. Characterizing the bacterial and phage community in the rumen of BON cows To assess bacterial community structure and the impact of pH reduction, we sequenced the V4 region of the 16S rRNA gene. On average, each sample yielded a sequence depth of 31,672 ± 15,789 reads (mean ± SD). ASVs were determined using DADA2, and rarefaction curves were generated for all samples ( Suppl. Figure 2 ). Most samples exhibited diversity saturation around 10,000 reads, so a rarefaction threshold of 10,884 sequences was applied to avoid sample loss and was used for subsequent analyses. A total of 3,461 ASVs were identified. Taxonomic assignment at the family level for each animal is shown in Fig. 3 . The most abundant families were Prevotellaceae, Christensenellaceae, Lachnospiraceae, Anaerovoraceae, Methanobacteriaceae, Oscillospiraceae, and Ruminococcaceae. For the viral community, metagenomic assembly produced a total of 5,644,383 contigs, which were dereplicated into 3,010,564 contigs. Clustering contigs into vOTUs resulted in 697,594 vOTUs. After filtering to retain contigs longer than 4,000 bp or labeled as high-quality by CheckV, a final collection of 92,669 vOTUs remained. These included 990 complete genomes, 2,258 high-quality genomes, and 4,553 medium-quality genomes—an order of magnitude greater than the number of bacterial ASVs identified. Most of the vOTUs could not be taxonomically assigned; however, those that were assigned at the family level belonged to Salasmaviridae, Straboviridae, Herelleviridae, Casjenviridae, Peduoviridae, Drexlerviridae, Mezyanzhinovviridae, Ackermannviridae, Kyanoviridae and Chaseviridae ( Suppl. Figure 3 ). The taxonomic classification observed in this study is very consistent with that reported by Yan and Yu (2024). Our results indicate that viral Shannon and Evenness diversity indices reached their lowest values during the pH decline induced by corn introduction (Fig. 4 ). Although viral richness did not follow a uniform trend across all animals, cows B and C exhibited a reduction in richness correlating with the pH decrease. Spearman correlation analysis did not reveal significant relationships between pH and viral alpha diversity indices (Shannon index, Pielou's evenness, and richness). However, when analyzing the variation between consecutive time points (ΔShannon index and ΔpH), a significant correlation was observed (ρ = 0.7301, p-value = 7.661 × 10 − 5 ). A similar result was found for ΔPielou's evenness (ρ = 0.7337, p-value = 6.762 × 10^-5), while no significant correlation was detected for Δrichness and ΔpH (ρ = 0.2951, p-value = 0.1716). For cow A, although never experienced pH levels below 5.6, displayed a pattern of viral richness closely mirroring the pH fluctuations during the experiment. This highlights the high sensitivity of the viral community to pH changes in the rumen of this animal, particularly during fasting conditions (T1), where an increase of 11,844 vOTUs was recorded compared to the initial time point of the experiment (T0). Conversely, no clear trends were observed in bacterial alpha diversity indices in response to the pH decrease (Fig. 4 ). Neither ΔPielou's evenness (ρ = 0.2550, p-value = 0.3232) nor Δrichness (ρ = 0.4107, p-value = 0.1014) showed significant correlations with ΔpH. However, a significant correlation was found between ΔShannon index and ΔpH (ρ = 0.4929, p-value = 0.044). Beta Diversity and the Influence of Diet and Animal on Microbial Communities Principal Coordinates Analysis (PCoA) based on Bray-Curtis distance was conducted to identify factors influencing bacterial and viral community distributions during dietary perturbation. The results indicated that for both communities, the primary factor shaping community similarity was the individual animal (Fig. 5 ). To further assess the impact of diet and individual animal on beta diversity, we performed a permutational multivariate analysis (PERMANOVA) using the Adonis2 function. The transition from a grass-based to a corn-based diet did not exert a direct and significant effect on the distribution of viral (F = 0.7352, p = 0.685) or bacterial communities (F = 3.10504, p = 0.359). However, when grouping by individual animals, the test demonstrated statistical significance for both viral (F = 6.103, p = 0.001) and bacterial (F = 1.855, p = 0.04) communities. Analysis of microbial community dissimilarities revealed that bacterial communities exhibited greater variation across time points. In all three animals, the largest variation occurred during the transition from a grass-based to a corn-based diet, followed by community restoration upon returning to a grass diet (Fig. 5 A). Meanwhile, for viral communities, the individual animal had a stronger effect on dissimilarity than diet. The shift to a corn-based diet drove viral communities further apart, with slight convergence at the final time points upon returning to a grass diet (Fig. 5 B). Given the strong influence of the individual animal on viral communities, we investigated whether specific viral contigs displayed abundance changes in response to diet transition. For each animal, a time-series bioinformatic analysis was applied. This analysis identified approximately nine distinct patterns of viral contig abundance throughout the experiment. One common trend observed across all animals was an increase in certain vOTUs when pH rose during fasting (t1) (Fig. 6 A, B, C). Additional patterns showed a decrease in viral contig abundance as pH dropped (Fig. 6 D, E), while others exhibited an inverse relationship, with abundance increasing as pH declined (Fig. 6 F, G, H). Furthermore, several trends in viral contig abundance were unique to individual animals and can be found in https://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540 . Discussion This is the first study to investigate the response of the rumen virome to a pH decrease induced by a dietary shift from high-fiber forage to readily fermentable carbohydrates such as corn. We successfully reduced ruminal pH in all three cows following corn introduction, with only two doses, it was possible to observe two animals experiencing pH values below 5.6 for over 20h, a threshold associated with subacute acidosis 24 . The decline in pH correlated with an increase in SCFA concentrations. Transitioning animals from forage-based diets to those high in fermentable carbohydrates, such as corn, intensifies ruminal fermentation and promotes the production of SCFAs. When SCFAs production exceeds the rumen’s absorption and buffering capacity, ruminal pH can drop below 5.6, which may lead to disruption of the ruminal microbiota and a range of metabolic disorders in the animals over the time 6 , 11 . Despite individual variation in pH trends, the Shannon and Pielou’s Evenness indices followed similar patterns across animals, suggesting they could serve as early indicators of pH fluctuations in the rumen. However, larger sample sizes and different cattle breeds should be included in future studies to confirm this observation. During fasting (T1), an increase in Pielou’s evenness was observed, coinciding with a rise in pH. This could be due to bacterial stress caused by feed deprivation, SCFA fluctuations, or pH shifts, potentially triggering prophage induction and lytic phage replication 24 , 25 . As pH remained low for ~ 20 hours, a decline in both Shannon diversity and Pielou’s evenness occurred, likely due to the dominance of certain phages at the expense of others. Similar findings have been reported in high-concentrate-fed animals 26 . Other factors, such as total digestible nutrients, dietary zinc and microbial functional diversity, may also affect viral communities during dietary changes 27 , though these were not explored in the current study. One potential explanation for this reduced viral diversity is the expansion of phage host ranges, allowing them to infect multiple bacterial taxa 28 . In rumen environments, broad-host-range phages are more prevalent than in the human gut, suggesting an adaptation to fluctuating conditions 26 . Such adaptations may help phages endure adverse physicochemical conditions, including elevated osmolarity associated with increased concentrations of SCFAs in the rumen 17 . These osmotic or osmolarity changes could physically impair phages’ ability to infect their primary bacterial hosts 28 . Another factor could be an increase in lysogeny. Most phages in the ruminal environment exist in a lysogenic state, representing a symbiotic coexistence with their microbial 17 , 29 , 30 . Under certain environmental stress, lysogenic phages are more likely to integrate into the genomes of their bacterial hosts as prophages 23 . While viral diversity indices responded significantly to pH changes, bacterial alpha diversity metrics did not show clear trends, except for a correlation between the Shannon index and pH. This suggests that the duration and magnitude of the pH perturbation in this experiment were insufficient to cause large-scale shifts in the bacterial community, as observed in previous studies 7 . However, specific taxonomic groups, such as cellulolytic Fibrobacter and Ruminococcus species, may be more affected by acidosis than overall bacterial diversity 11 , 31 . PCoA analysis indicated that individual variation was the primary driver of viral and bacterial community composition, rather than diet (Fig. 5 ). This finding aligns with previous studies showing that rumen viromes are highly individualized 26 , 32 similar to human gut viromes 33 , 34 , 35 . Despite this strong individual effect, time-series analysis revealed shared viral abundance patterns across animals. Notably, vOTUs increased in abundance during fasting (T1) when pH rose, suggesting the presence of phages sensitive to high pH or dependent on bacterial hosts that proliferate under these conditions. Conversely, some vOTUs increased when pH declined, possibly due to stress-induced prophage induction or shifts in host bacterial populations. Other vOTUs decreased in abundance during acidosis, potentially reflecting reduced bacterial hosts or increased lysogeny. Our results suggest that viral communities in the rumen respond more rapidly and distinctly to pH fluctuations while bacterial communities remain stable and resilient, similarly to another research demonstrated in the mouse intestine upon dietary perturbations 36 . In our study, 15 days after an acidotic challenge, the viral community within the same animal displayed a slightly different structure, as shown by the PCoA (Fig. 5 ). However, this trend was not consistently observed across the bacterial communities of the three animals. It has been suggested that certain phages, like some bacterial members of the gut microbiota, may fail to recover following dietary disturbances, which display hysteresis after dietary changes 36 . Although these researchers noted shifts in the bacterial community’s composition and abundance, its functional profile remained stable, as redundant functions across different bacterial species contribute to its resilience in the mouse intestine. We investigated the response of viral and bacterial communities to transient changes in the ruminal environment caused by the inclusion of corn in the diet of BON ( Blanco Orejinegro ) cattle, which are typically maintained on pasture-based diets. This dietary change led to a decrease in ruminal pH and our results showed that the viral community responded more rapidly and significantly to these fluctuations than the bacterial community. Despite the diet-induced pH changes, the composition of viral and bacterial communities was primarily shaped by individual variation rather than diet type, a finding consistent with previous studies on the gut microbiome in ruminants and humans. Furthermore, analysis of vOTU abundance dynamics revealed distinct viral responses to both pH increases and decreases, whereas bacterial communities remained more stable. This suggests that phages play an active role in the rumen ecosystem’s response to dietary and environmental perturbations and could serve as early indicators of pH fluctuation, preceding significant shifts in the bacterial community. Although this study provides insights into microbial dynamics in cattle, it is limited by the number of individuals, the duration and intensity of the dietary treatment, and the focus on a single cattle breed. However, our findings lay the foundation for future research exploring microbial responses to dietary shifts in larger and more diverse populations. Additionally, similar studies should be conducted in other ruminant species to evaluate whether these patterns can be generalized. In summary, phages have the potential to serve as early biomarkers and diagnostic indicators of ruminal pH fluctuations resulting from dietary changes in cattle. Additionally, a deeper understanding of phage-bacteria interactions could enable the targeted modification of microbial communities, fostering greater resilience to environmental changes. Identifying microbial markers and key factors driving community responses to diet-induced stress is crucial, as it may lead to new diagnostic and treatment strategies for conditions such as ruminal pH decline—benefiting both farmers and livestock production. Methodology Sampling A protocol to induce a period of reduced ruminal pH was applied to all three animals. Importantly, these cows had been fed exclusively on native grasses throughout their lives; this natural grazing condition served as the baseline (control) against which changes following corn introduction were evaluated. Initially (day 0), the cows grazed on a pasture dominated by Kikuyu grass ( Cenchrus clandestinus ) with 60 days of regrowth and had ad libitum access to water in the paddock. Subsequently, the animals underwent a 16-hour fasting period, after which ground corn was administered directly into the rumen via nasogastric tube at a rate of 0.5 kg per 100 kg body weight, once daily for two consecutive days, as their sole food source. After the induction phase, the animals returned to grazing pasture during the recovery phase. All procedures were supervised by an experienced veterinarian and zootechnician from AGROSAVIA. Ruminal fluid samples were collected via rumenocentesis. An anesthetic (lidocaine) was used to perform this procedure in the animals. Ruminal pH was measured at multiple time points throughout the experiment. When possible, up to nine samples per animal were selected for subsequent laboratory and microbial analysis. The experiment involved the random selection of three cows from a bovine herd. Each cow was an experimental unit from which rumen fluid samples were collected before, during and after the induction of ruminal pH reduction. This sample size was chosen to be representative while minimizing stress and adverse effects on the animals. The number of individuals was also based on availability and ethical considerations regarding the stress caused by the treatment. The cows were matched by age, weight, breed and habitat. They were healthy and housed at the AGROSAVIA headquarters in Tibaitatá Mosquera, where the experiment was conducted. The animal care and management procedures were approved by the Ethical Committee of AGROSAVIA headquarters in Tibaitatá, Mosquera (Minute 003, September 8, 2017).All methods were performed in accordance with the relevant guidelines and regulations, including the ARRIVE guidelines. Given the study was designed as a small‑scale, exploratory time‑series (three animals sampled repeatedly across nine time points), no statistical analysis was performed across all three animals. Furthermore, an exploratory PCoA of the microbial data showed that the main factor of variation was the individual. As usual with ecological compositional data, non-parametric statistical tests were used when required. Reported PERMANOVA-Adonis pseudo‑F values and associated R² serve as measures of effect magnitude, and no additional effect‑size estimates or confidence intervals were calculated because there was only one observation per animal–time combination, precluding meaningful interval estimation. Ruminal fluid samples were filtered through sterile gauze before being stored in triplicate in 50 mL Falcon tubes for each animal at each time point. Samples designated for viral analysis were preserved in SM Buffer (5.8 gr NaCl, 2gr MgS0 4 .7H 2 O, 50 ml Tris HCl 1M pH 7.5 and 950 ml dH 2 O) without gelatin at a 9:1 v/v ratio. Samples intended for bacterial analysis were preserved in a 1X PBS buffer using the same ratio. All rumen fluid samples were stored at 4°C. Additionally, 10 mL of ruminal fluid samples designated for SCFAs analysis were transferred to 20 ml Falcon tubes and frozen at -20°C. Bacterial DNA Extraction and 16S rRNA gene library construction Bacterial DNA extraction from the rumen microbiota was performed as follows: a Falcon tube containing ruminal fluid mixed with PBS buffer was homogenized using a vortex and manual stirring for 30 seconds. A 500 µL aliquot was then transferred to a 2 mL Eppendorf tube. This aliquot underwent three cycles of rapid freezing in liquid nitrogen for five minutes, followed by immediate heating in a water bath at 65°C for five minutes. Next, the ruminal fluid was vortexed in a bead-beating machine for 5 minutes and DNA was extracted using the ZR Fungal/Bacterial DNA MiniPrep ™ from Zymo following the manufacturer's instructions. DNA integrity was assessed by electrophoresis on a 2% (w/v) agarose gel, and DNA concentration was quantified using a Qubit fluorometer (Thermo fisher Scientific, United States). The DNA concentration of all samples was adjusted to 20 ng/µL. 16S rRNA gene libraries were constructed following the methodology described 38 . Briefly, a two-step PCR was performed aimed to amplify the V4 hypervariable region of the 16S rRNA gene. In the first step, PCR amplification was carried out using primers 515F and 806R. The PCR was performed in triplicate for each sample, and the resulting PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA) and verified by electrophoresis. In the second step, the triplicate amplification products were pooled and used as a template for the second PCR, during which indexes and adaptors were added to the V4 amplicons. The PCR products were purified, and a pooled library was prepared at a concentration of 10 µM for each sample. Pair-end libraries were sequenced using the Illumina MiSeq platform at Universidad del Bosque in Bogotá, Colombia. Samples BAt2 from cow A, BBt3 from cow B and samples BCt2 from cow C were removed from the analysis due to poor sequence quality. Sample BCt8 from cow C was contaminated with blood and was discarded ( Supp. Table 1 ). Viral DNA Extraction To extract viral DNA from ruminal fluid samples, plant particles and other microorganisms were removed by centrifugation. Ruminal fluid samples preserved in SM buffer were centrifuged three times: First, 20 mL of ruminal fluid was centrifuged at 5000 x g for 30 min at 4°C. The supernatant was collected in a Falcon tube, and the pellet was discarded. This process was repeated once. Finally, the supernatant was centrifuged at 8000 x g for 15 min at 4°C. The resulting supernatant was filtered through 0.45 µm and 0.22 µm sterile vacuum filter systems (Corning) and sterile syringe filters. The filtered sample was ultracentrifugated at 130,000 x g for 2 hours at 4°C. After centrifugation, the supernatant was discarded, and the pellet was resuspended in 500 µL of SM buffer. Viral DNA extraction was performed using the Stractec DNA Viral Kit, Berlin, Germany. The presence of viral DNA in the sample was confirmed in a 2% (w/v) agarose gel, and DNA concentration was measured using Qubit fluorometer (Thermo Fisher Scientific, USA). Viral DNA was sent to the Genome Sequencing Center at the Edison Family Center for Genome Sciences and Systems Biology Washington University in St. Louis, MO, USA. Shotgun libraries were constructed using the standard Illumina Nextera protocol and sequenced on a NextSeq platform, generating 2x150 pair-end reads. Sample VCt8 from cow C was contaminated with blood and was discarded and sample VBt7 also was removed due to poor sequence quality ( Suppl. Table 1 ). Short-Chain Fatty Acids (SCFAs) analysis Standards: Acetic acid (≥ 99.7%), propionic acid (≥ 99.5%), butyric acid (≥ 99.8%), isovaleric acid (≥ 99.5%), and valeric acid (≥ 99.0%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Methyl tert-butyl ether (MTBE) and H 2 SO 4 were purchased from J.T. Baker (Phillipsburg, NJ, USA) and Merck (Darmstadt, Germany), respectively. HPLC-grade water was produced using a Heal Force Smart-Mini water purification system (Shanghai, China). A stock standard solution containing all SCFAs dissolved in MTBE was prepared at concentrations ranging from 10 to 15 mmol/L for each acid. The SCFAs standard solution was stored at -80°C and used within three days. Calibration curves were generated using ten concentration points (0.050-5.000 mmol/L) for each SCFA, with three replicates per concentration, following previously reported conditions 40 . The linear regression equations for each SCFA were as follows: y = 1.7179x – 0.2532; R 2 = 0.993 (acetic acid), y = 3.1251x – 0.2268; R 2 = 0.990 (propionic acid), y = 4.1944x – 0.4421; R 2 = 0.993 (butyric acid), y = 5.3173x – 0.2381; R 2 = 0.993 (isovaleric acid), and y = 5.3913x – 0.4161; R 2 = 0.990 (valeric acid). Limits of detection (LODs) and quantification (LOQs) were determined at 0.008 and 0.025 mmol/L, respectively, based on signal-to-noise ratios of three (S/N = 3) and ten (S/N = 10), consistent with previous reports 39 . The concentration of SCFAs (acetic acid, propionic acid, butyric acid, isovaleric acid, valeric acid and total SCFAs) in ruminal fluid samples was measured as follows. Frozen samples were thawed at 4°C over 24 hours. The thawed samples were shaken for 1 minute using a vortex and then centrifuged at 6000 rpm at 4°C for 15 minutes. After centrifugation, 1 mL of the supernatant was collected and centrifuged at 12,000 rpm for 10 minutes at 4°C. Next, 500 µL of the supernatant was filtered through 0.45 µm membranes by centrifugation at 14,000 rpm for 10 minutes at 4°C. Finally, 50 µL of 10% NaOH solution and 50 µL of internal standard (2-ethylbutyric acid 25 mM in 25% metaphosphoric acid) were added to 400 µL of the filtered sample. Samples were stored at 4°C. SCFA extraction was performed by mixing 100 µL of the ruminal fluid sample with 25 µL of 50% H 2 SO 4 , and 125 µL of MTBE. The mixture was shaken for 5 min using a vortex and then centrifuged for 15 min at 10,500 rpm. The organic solvent was transferred to a new tube, and anhydrous Na 2 SO 4 was added to remove residual water. The tube was vortexed for one minute, centrifuged for one minute, and the supernatant was filtered through a 0.22 µm, before chromatographic analysis. Identification of SCFAs was performed using a Trace 1300 gas chromatograph (GC) coupled to an ISQ-LT Single Quadrupole Mass Spectrometer (MS), using previously published conditions 40 . Raw data were acquired and processed using XCalibur 3.0 software. Both equipment and software were Thermo Scientific, San Jose, CA, USA. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. Manual injection of 1 µL of sample was performed in split mode (1:10), with the injector temperature set to 250°C. Separation was achieved using an HP-FFAP column (30m × 0.53mm × 1µm; Agilent Technologies, Santa Clara, CA, USA). The oven program was set at an initial temperature of 50°C for 1 min, increased to 150°C at a rate of 6°C/min, then increased to 220°C at 20°C/min, and held for 4.5 min. An Electronic Ionization (EI) system was operated at 70 eV, and the MS detector was set to full-scan mode, covering an m/z range from 20 to 150 Da, with an initial scan time of 5.5 min. The ion source and transfer line temperatures were 220°C and 230°C, respectively. Total ion chromatogram mode was used to confirm characteristic ions for each SCFA. Suppl. Figure 1 shows the chromatograms obtained by GC-MS analysis of the standard mix (a) and a ruminal fluid sample (b). Quantitative SCFA analysis was performed using a Trace 1300 GC equipped with a flame ionization detector (FID) and an AI 1310 automatic injector. Helium was used as the carrier gas at 1.4 mL/min. Raw data were acquired and processed using Chromeleon 7.2 software. Both equipment and software were Thermo Scientific, San Jose, CA, USA. SCFA separation was achieved using an HP-FFAP column (30m×0.53mm×1µm, Agilent Technologies, Santa Clara, CA, USA). The injector and detector temperatures were both set at 250°C. The air, nitrogen, and hydrogen flow rates were 350, 40, and 35mL/min, respectively. A volume of 1 µL of each sample was injected in split mode (1:20). The oven program was set at an initial temperature of 120°C during 2 min, increased to 140°C at a rate of 6°C/min and held at 140°C for 1 min, then increased to 150°C at a rate of 1.5°C/min and maintaining this temperature for 1 min for a total run time of 15 min. SCFAs concentrations were measured in mmol/L, and the acetic-to-propionic acid ratio was calculated for each time point and each animal. Metataxonomic analysis of bacteria Demultiplexing of the samples was performed using an internal script, which is available upon request. Sequence integrity and quality were assessed using FastQC v0.11.7 41 . Adapter and linker sequences were removed from partial 16S rRNA gene sequences using cutadapt v1.12 42 . Low-quality sequences with an average Phred score < 20 and a read length < 200 nt were detected and removed using Trimmomatic v0.38 43 . Consensus sequences were generated using FLASH program 44 and imported to Qiime2 pipeline v 2020for 16S rRNA gene amplicon analysis 45 . DADA2 46 was used to denoise and generate Amplicon Sequence Variants (ASVs) from pair-end sequences, applying a truncation length of 200 bp. ASVs classified as singletons or containing fewer than 4 sequences across all samples in the feature table were excluded from further analyses. Taxonomic assignment of ASVs was performed using the Silva Database version 138_99 47 . Assembly and coverage of the viral community Trimmomatic 0.39 43 was used to remove low-quality bases and adapters. high-quality reads were taxonomically classified using Kraken v2.1.2 48 , 49 against the Plus PFP database. Reads classified as eukaryotic were removed. Additionally, reads mapping to ΦX174 (GCF_000819615.1) were removed using bbduk, part of BBtools suite v39.01 50 . FastQC 0.11.9 51 and MultiQC 1.11 52 were used to generate an HTML report summarizing the quality profiles of the fastq files before and after quality control. Reads from all samples were for cross-assembly. Clean reads were subsampled at incremental levels (10–100%) using the reformat script from BBtools. BBNorm, was used to down-normalize each subsample to a maximum depth of 100x. Down-normalized reads were assembled using metaSPAdes 3.15.5 53 and assemblies were filtered to retain contigs > 1000 bp with a minimum fold coverage of 2. Pre-filtering of non-viral sequences was performed using geNomad 1.5.0 54 with the relaxed before vOTU clustering. Redundant contigs were dereplicated using MMseqs v14.7e284 55 . Contigs were clustered into viral operational taxonomic units (vOTUs) at 95% nucleotide identity and 85% coverage using the anicalc and aniclust scripts from CheckV 56 . The vOTU representative was selected as the contig with the highest completeness percentage and the shortest length, as determined by CheckV. To remove non-viral sequences, viral contigs were identified using VIBRANT 1.2.1 57 , VirSorter 2.2.4 58 and geNomad in its defaultmode 59 . A contig was classified as viral only if all three tools identified it as such. Only sequences ≥ 4000 bp or classified as high-quality by CheckV were included in the final vOTU table. Quality-controlled Illumina reads were mapped against the filtered vOTUs using Bowtie2 v2.4.4 60 retrieving all the possible alignments. CoverM v0.6.1 ( https://github.com/wwood/CoverM ) was used to filter mapped reads, removing with < 85% coverage and < 95% identity. BAM and flagstats files were generated using SAMtools 1.14 61 . Genome breadth coverage (C A ) was calculated using genomecov from BEDtools v2.29.2.0 62 . Additional mapping statistics including read count, reads per kilobase per million mapped reads (RPKM), trimmed mean, and variance, were estimated using CoverM. For normalized counts, BAM files were filtered with SAMtools to remove non-unique alignments. CoverM was used to calculate the number of reads uniquely mapped to a contig (R U ). BEDtools v2.29.2.0 was used to determine the number of bases covered by those unique alignments (C U ). The mean depth coverage of unique reads (D U ) was calculated as D u = R U /C U . Normalized counts (R N ) were calculated using: R N = R U + (D U *(C A -C U )) The length of the contig (L) and the sum of the normalized counts (∑R N ) is used to estimate normalized RPKM value as follows: Finally, we used the coverage statistics to filter the vOTUs abundance table, keeping only those with at least 5% breadth coverage in at least one sample. Taxonomic classification of Viral Operational Taxonomic Units Viral taxonomy was assigned using PhaGCN 63 in accordance with the latest International Committee on Taxonomy of Viruses (ICTV) guidelines. Additionally, nucleotide BLAST searches against the Viral RefSeq 64 were performed to identify closely related sequences. Evaluating diversity in the bacterial and viral communities Rarefaction curves for bacterial samples were generated using Qiime2 v2020 45 at a depth of 10,880 sequences, representing the minimum read count observed across all samples. Alpha diversity was assessed using Shannon, richness and Pielou's evenness indices for both viral and bacterial communities. To evaluate beta diversity, correlations between diversity indexes and pH to were analyzed using the Bray-Curtis metric for both bacterial and viral communities. Principal Coordinate Analysis (PCoA) was used to visualize beta diversity and examine the effects of diet, individual variation, and sampling time. Statistical analysis of bacterial and viral abundances A permutational multivariate analysis of variance (ADONIS) 65 was conducted using the Bray-Curtis distance-matrix to determine whether diet (corn vs. grass) and individual animals significantly influenced microbial community composition. The analysis was performed using the Adonis2 function from the Vegan package in R. The dynamics of ruminal pH changes The variation in vOTU abundance during ruminal pH decline, was assessed for each animal using the time series analysis 66 . Before clustering analysis, vOTU abundance values were scaled by a factor of 10³ and rounded to integers while preserving the relative patterns. A custom script was used to generate vOTU clusters using the k/medoids clustering algorithm. Contigs were grouped based on similar abundance trends throughout the experiment. Taxonomic identification of contigs in each cluster was assigned at the family level. Modifications to the Coenen et al. (2020) scripts, commands and the complete results for each animal can be found in https://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540 . Declarations Competing interests statement The authors declare no competing interests. Funding statement declaration No funding Author Contribution Authors' contribution RH, AR, HJ and AC conceptualized and structured the idea for this research. RH, AC, and HJ conducted the experiments at the Agrosavia facilities. DV and YA managed the care and welfare of the animals. RH, GL, and CC were responsible for sampling, processing, and measuring SCFAs in the rumen. LF performed the computational analysis to identify viral contigs and generate the vOTU table. RH carried out the computational analysis of bacterial communities, as well as diversity and statistical analyses for both bacterial and viral communities. Additionally, RH, LF, CV, and HJ conducted computational analyses to investigate the dynamics of viral contig abundance throughout the experiment. RH wrote the first draft of the manuscript, while AR supervised all stages of the research. All authors reviewed the manuscript. Acknowledgement We extend our gratitude to Fernando Rodriguez, whose expertise in the rumen ecosystem highlighted the importance of studying the behavior of viral communities in response to dietary changes and pH reduction. We also sincerely thank the Agrosavia staff for their dedicated care and handling of the animals throughout the experiment. Data Availability All raw and assembled viral data are available from the ENA with project accession number PRJEB54786. 16S rRNA gene amplicons are also available from ENA with project accession number PRJEB54691 References Puniya, A. K., Singh, R. & Kamra, D. N. (eds) Rumen Microbiology: From Evolution to Revolution. Springer India (2015). Henderson, G. et al. 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A primer for computational microbiome analysis in natural systems. mSystems 5 , e00917–e00920 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Supplementary table 1. Metadata showing the time points at which ruminal samples were collected throughout the experiment, along with alpha diversity indices for both bacterial and viral communities. Short-chain fatty acid (SCFA) concentrations are also included. FigureS1.pdf Supplementary figure 1. GC-MS chromatograms of the mix standard (A) and cow rumen sample (B). Peak identification: 1, acetic acid; 2, propionic acid; 3, isobutyric acid; 4, butyric acid; 5, valeric acid. FigureS2.pdf Supplementary Figure.2 Rarefaction curves for all bacterial samples in the rumen of the BON cows during the experiment. FigureS3.pdf Supplementary figure 3. Most common viral families found in the rumen of the BON cows during the experiment. 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07:25:56","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":229125,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/fdd6111641eafd4807c97f19.pdf"},{"id":96919978,"identity":"112667f8-6b6a-455d-ac7f-00459de84fd0","added_by":"auto","created_at":"2025-11-27 14:14:39","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78207,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/44e5c3b3e6c1cdc55ffc1483.pdf"},{"id":96882778,"identity":"003b1016-2522-40f4-be04-05fbafa5fb0a","added_by":"auto","created_at":"2025-11-27 07:25:57","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92089,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/34a4d46bef4e7be32bad9e25.pdf"},{"id":96882773,"identity":"10d3c976-9d40-4dc3-9918-c002626eb196","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"pdf","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76498,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/fa603266ae35a2798d9cb50c.pdf"},{"id":96882769,"identity":"65473555-c2dd-4b1e-806a-ef8e1c7a45ec","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3950,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/5e6ed4910c1b204d7faf7551.png"},{"id":96919113,"identity":"1f2c9c91-ec55-47fd-b432-74301a868266","added_by":"auto","created_at":"2025-11-27 14:13:09","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3129,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/43a00b1d5a243409cc2e2378.png"},{"id":96919872,"identity":"bbe3d3ee-cc27-4128-9c5e-3de7c379ad66","added_by":"auto","created_at":"2025-11-27 14:14:34","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143498,"visible":true,"origin":"","legend":"","description":"","filename":"264e78709d6f4e8d9b4292165db9ff021structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/5a5d401e20f3060b115dc6e2.xml"},{"id":96919915,"identity":"e06fcc22-7516-4388-9c66-18c2a895cb4b","added_by":"auto","created_at":"2025-11-27 14:14:36","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164843,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/6040e5abe800bd0b2f2f197d.html"},{"id":96882755,"identity":"74b4f2d9-28d3-439a-8b40-985603e2b0e9","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":301115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRuminal pH values for each of the three animals during the experiment.\u003c/strong\u003eThe panel A, B and C correspond to Cow A, B and C, respectively. The green dots indicate the time of the experiment in which rumen fluid samples were taken for analysis of the viral and bacterial community, Panel A has the time point convention used throughout the manuscript (t0 – t8). Down pointing arrows indicate the time where the corn was introduced, yellow shaded areas represent the time while in corn diet.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/fb45112eac523cdf20f832b8.jpg"},{"id":96882754,"identity":"68e77680-c7a2-47d9-813a-31a6858d524e","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamics of the Acetic-to-Propionic acid ratio and ruminal pH.\u003c/strong\u003eChanges in the acetic-to-propionic acid ratio (left y-axis, red continuous line) and ruminal pH (right y-axis, dotted line) for cow A, B and C (Panel A, B and C, respectively) as a function of time. Data illustrate the relationship between short chain fatty acid composition and pH fluctuations in response to dietary changes.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/4e985d7ab199a85c7ec5d82b.jpg"},{"id":96919701,"identity":"1649e054-0cf1-4f51-8b41-fbfefdaad903","added_by":"auto","created_at":"2025-11-27 14:14:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":402649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmaps representing the relative abundance of the most abundant bacterial families for each animal across different sampling times.\u003c/strong\u003e The relative abundance (percentage) is presented on a logarithmic scale to enhance the visualization of shifts in community composition, particularly for less abundant families. The sampling times correspond to specific dietary phases as shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/eb0fb6552854be873e68cb96.jpg"},{"id":96882757,"identity":"10304c43-821c-4594-8dc4-d437a66aa7a5","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamics of alpha diversity indices for viral and bacterial communities.\u003c/strong\u003e Different alpha diversity indices were calculated for both the viral (continuous colored line) and bacterial (dotted colored line) communities. Shannon index (Panels A, B, C), evenness (Panels D, E, F), due to the richness range variation for viral and bacterial communities, it is displayed separately for viruses (Panels G, H, I) and bacteria (Panels J, K, L). Right y-axis displays the pH variation (black smooth line) throughout the experiment. Individual animals are displayed in different colors, Cow A in red, Cow B in blue and Cow C in purple.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/a23c924cd186f7aefce02c24.jpg"},{"id":96882759,"identity":"a21916b6-ca8c-4b2a-90b6-a2147922b1d5","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198928,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinate Analysis (PCoA) of bacterial (A) and viral (B) for the three cows (A: red, B: blue, C: purple). Sample labels are according to the type of community (B or V), the animal (A, B, C) and the time point (t\u003csub\u003e0\u003c/sub\u003e – t\u003csub\u003e8\u003c/sub\u003e), i.e. BBt5 represents the sample from the Bacterial community of animal B at time point t5. Time point convention as shown in Figure 1. The arrows in panel (A) indicate the sequence of sampling times from the beginning (t0) to the end of the experiment (t8).\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/03b0da502ecf648691541faf.jpg"},{"id":96919038,"identity":"a3057512-105a-4173-a7d4-a9099178c983","added_by":"auto","created_at":"2025-11-27 14:13:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":313664,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent patterns of viral contig abundance in response to the variation in ruminal pH (right y-axis, dotted red lines). Time point (x-axis) convention as shown in Figure 1. The left y-axis represents the mean z-score of the abundance of the contigs clustered with similar abundance distribution over time. Organized by columns are whether the patterns are observed in animal A (Panel A, D, F), B (Panel B, E, G) or C (Panel C, H). It can be distinguished three distinct behaviors, 1) where abundance rise during the fasting period (t1) in panels A, B, C, 2) where the abundance closely resembles the change in pH in panels D and E, and 3) where the viral abundance has an inverse relationship with the pH in panels F, G and H\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/9548af6ff0f9304ce7b09cb4.jpg"},{"id":97136306,"identity":"92fb1589-21c5-420d-a525-4ba3b97ef786","added_by":"auto","created_at":"2025-12-01 09:56:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2820167,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/c2e1f6e4-cbb3-47b2-b38c-8f28d0aac493.pdf"},{"id":96919364,"identity":"f00dec89-855e-4256-bbaa-5f96340599de","added_by":"auto","created_at":"2025-11-27 14:13:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary table 1. \u003c/strong\u003eMetadata showing the time points at which ruminal samples were collected throughout the experiment, along with alpha diversity indices for both bacterial and viral communities. Short-chain fatty acid (SCFA) concentrations are also included.\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/20c4991f3500b142c74e46fc.xlsx"},{"id":96882756,"identity":"b12e42a9-ae3e-422f-bda9-ee8805bfb5b5","added_by":"auto","created_at":"2025-11-27 07:25:56","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 1. \u003c/strong\u003eGC-MS chromatograms of the mix standard (A) and cow rumen sample (B). Peak identification: 1, acetic acid; 2, propionic acid; 3, isobutyric acid; 4, butyric acid; 5, valeric acid.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/3fd94916a72c6f82d79d7639.pdf"},{"id":96918828,"identity":"9560f17c-b1cf-46df-afd2-102a742fdc61","added_by":"auto","created_at":"2025-11-27 14:12:39","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":413835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure.2\u003c/strong\u003e Rarefaction curves for all bacterial samples in the rumen of the BON cows during the experiment.\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/3d3d1d8ff6f137ff052ec5ce.pdf"},{"id":96919213,"identity":"a402eef2-6541-49c1-a3f9-86d16fbe85c0","added_by":"auto","created_at":"2025-11-27 14:13:23","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":84818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 3\u003c/strong\u003e. Most common viral families found in the rumen of the BON cows during the experiment.\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160638/v1/8726ebeb235d9f75faf4c3aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Viral and bacterial dynamics in response to drastic and short dietary shifts in creole Colombian cattle","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRuminants are herbivores that obtain energy by fermenting plant materials such as grasses and small shrubs\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Since they lack the enzymes necessary to digest fibrous materials, they rely entirely on a diverse community of anaerobic, symbiotic microorganisms \u0026ndash; including bacteria, archaea, protozoa, and viruses - to break down plant fibers. The composition of this microbial community is influenced by several factors, including diet, breed, age, and geographical location\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDiet is one of the most significant factors shaping the rumen microbiome. The inclusion of rapidly fermentable carbohydrates, such as corn, into cattle diets is a widespread practice to enhance milk production. However, this dietary shift can lead, a condition characterized by the accumulation of short-chain fatty acids (SCFAs) and a decrease in ruminal pH to values as low as 5.5 for several hours a day\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Such decreases in pH can adversely affect animal health contributing body weight loss, increased lameness, fluctuations in feed intake, and reduced milk\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMost studies investigating the effects of pH reduction on the rumen microbiome use animal models and span several weeks\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However, some researchers have focused on short-term challenges to assess immediate microbial responses. For example, A research induced a rapid decrease in ruminal pH within 24 hours using high doses of barley grain, while another study\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e lowered the ruminal pH in steers within five days, revealing its effects on rumen bacteria and ciliates.\u003c/p\u003e\u003cp\u003eExtensive research has examined the bacterial response to decreases in ruminal pH, particularly among fiber-degrading taxa. Bacterial composition, abundance, growth, and diversity shift significantly when animals consume high concentrations of rapidly digestible carbohydrates\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. A decline in ruminal pH inhibits cellulolytic bacteria, such as \u003cem\u003eFibrobacter\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e spp., reducing their survival rates. Other microbial groups, including fungi\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, archaea\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, and protozoa\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, also exhibit distinct responses to fluctuation in ruminal pH.\u003c/p\u003e\u003cp\u003eAmong the viruses in the rumen, bacteriophages (phages) play a crucial role in bacterial lysis, nutrient recycling, and substrate availability. They influence microbial turnover by facilitating horizontal gene transfer and modifying bacterial metabolism through auxiliary metabolic genes\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. As a result, phages are highly responsive to environmental changes and may serve as early indicators of ecosystem disturbances, including dietary shifts\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn other ecosystems, phages have been shown to respond sensitively to environmental stress. For instance, phage abundance declines in chromium-contaminated soils\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, shifts along pH and moisture gradients in thawing permafrost\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, and increases under phosphorus and water stress in the potato rhizosphere\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Similarly, viral diversity in anaerobic digestion food waste reactors is influenced by pH, methane, and ammonium levels\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, while in industrial-scale composting systems, viral abundance correlates with nutrient turnover\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These findings suggest that phages could serve as sensitive indicators of environmental conditions in the rumen as well.\u003c/p\u003e\u003cp\u003eDespite this potential, research on the viral response to dietary changes in the rumen remains limited. Few global studies have explored ruminal virome ecology, phage-host interactions, and their functional impact on animal nutrition\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. A deeper understanding of these dynamics could provide new insights into microbial responses to diet-induced stress and identify specific phage groups as potential biomarkers for rumen health. In this context, our study addresses the following research question: How does the ruminal the bacterial and viral community respond to decreases in ruminal pH resulting from dietary changes?\u003c/p\u003e\u003cp\u003eIn this study, we characterize the response of viral and bacterial communities to a short-term dietary challenge in BON (Blanco Orejinegro) cattle, a breed typically maintained on pasture-based diets. We hypothesize that phage communities are more sensitive to pH fluctuations induced by dietary changes than bacterial communities. Consequently, phages may serve as early indicators of ruminal pH shifts, potentially offering new diagnostic tools for monitoring rumen health.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eThe incorporation of corn in the diet induced a decrease in the ruminal pH\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe successfully reduced ruminal pH in the three animals for several hours by introducing corn into the rumen. In Cow A, pH did not drop below 5.5, even after the second corn dose was introduced (pH\u0026thinsp;=\u0026thinsp;5.79); however, an overall reduction of approximately one unit was observed throughout the experiment. Comparable results were observed in Cow B and Cow C, with pH values ranging between 5.6 and 5.34 for 21 to 24 hours. After the animals resumed grazing on grass, pH recovered to normal values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding SCFAs dynamics, as expected, acetic and propionic acid were the most abundant, with valeric and isovaleric acids near the detection limit. A general positive correlation was observed among SCFA concentrations at different time points (\u003cb\u003eSuppl. Figure\u0026nbsp;1).\u003c/b\u003e However, an interesting pattern emerged when examining the acetic-to-propionic acid ratio, which decreased following corn introduction compared to time points when the animals were fed grass (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), this trend was consistent in all 3 animals and showed a significant correlation with the decrease in pH (ρ\u0026thinsp;=\u0026thinsp;0.4024, p\u0026thinsp;=\u0026thinsp;0.04151, spearman correlation). This suggests that SCFA production increased due to corn consumption, primarily driven by propionate.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterizing the bacterial and phage community in the rumen of BON cows\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess bacterial community structure and the impact of pH reduction, we sequenced the V4 region of the 16S rRNA gene. On average, each sample yielded a sequence depth of 31,672\u0026thinsp;\u0026plusmn;\u0026thinsp;15,789 reads (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). ASVs were determined using DADA2, and rarefaction curves were generated for all samples (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e). Most samples exhibited diversity saturation around 10,000 reads, so a rarefaction threshold of 10,884 sequences was applied to avoid sample loss and was used for subsequent analyses. A total of 3,461 ASVs were identified. Taxonomic assignment at the family level for each animal is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The most abundant families were Prevotellaceae, Christensenellaceae, Lachnospiraceae, Anaerovoraceae, Methanobacteriaceae, Oscillospiraceae, and Ruminococcaceae.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the viral community, metagenomic assembly produced a total of 5,644,383 contigs, which were dereplicated into 3,010,564 contigs. Clustering contigs into vOTUs resulted in 697,594 vOTUs. After filtering to retain contigs longer than 4,000 bp or labeled as high-quality by CheckV, a final collection of 92,669 vOTUs remained. These included 990 complete genomes, 2,258 high-quality genomes, and 4,553 medium-quality genomes\u0026mdash;an order of magnitude greater than the number of bacterial ASVs identified. Most of the vOTUs could not be taxonomically assigned; however, those that were assigned at the family level belonged to Salasmaviridae, Straboviridae, Herelleviridae, Casjenviridae, Peduoviridae, Drexlerviridae, Mezyanzhinovviridae, Ackermannviridae, Kyanoviridae and Chaseviridae (\u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e). The taxonomic classification observed in this study is very consistent with that reported by Yan and Yu (2024).\u003c/p\u003e\u003cp\u003eOur results indicate that viral Shannon and Evenness diversity indices reached their lowest values during the pH decline induced by corn introduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although viral richness did not follow a uniform trend across all animals, cows B and C exhibited a reduction in richness correlating with the pH decrease. Spearman correlation analysis did not reveal significant relationships between pH and viral alpha diversity indices (Shannon index, Pielou's evenness, and richness). However, when analyzing the variation between consecutive time points (ΔShannon index and ΔpH), a significant correlation was observed (ρ\u0026thinsp;=\u0026thinsp;0.7301, p-value\u0026thinsp;=\u0026thinsp;7.661 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). A similar result was found for ΔPielou's evenness (ρ\u0026thinsp;=\u0026thinsp;0.7337, p-value\u0026thinsp;=\u0026thinsp;6.762 \u0026times; 10^-5), while no significant correlation was detected for Δrichness and ΔpH (ρ\u0026thinsp;=\u0026thinsp;0.2951, p-value\u0026thinsp;=\u0026thinsp;0.1716). For cow A, although never experienced pH levels below 5.6, displayed a pattern of viral richness closely mirroring the pH fluctuations during the experiment. This highlights the high sensitivity of the viral community to pH changes in the rumen of this animal, particularly during fasting conditions (T1), where an increase of 11,844 vOTUs was recorded compared to the initial time point of the experiment (T0).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConversely, no clear trends were observed in bacterial alpha diversity indices in response to the pH decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Neither ΔPielou's evenness (ρ\u0026thinsp;=\u0026thinsp;0.2550, p-value\u0026thinsp;=\u0026thinsp;0.3232) nor Δrichness (ρ\u0026thinsp;=\u0026thinsp;0.4107, p-value\u0026thinsp;=\u0026thinsp;0.1014) showed significant correlations with ΔpH. However, a significant correlation was found between ΔShannon index and ΔpH (ρ\u0026thinsp;=\u0026thinsp;0.4929, p-value\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBeta Diversity and the Influence of Diet and Animal on Microbial Communities\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrincipal Coordinates Analysis (PCoA) based on Bray-Curtis distance was conducted to identify factors influencing bacterial and viral community distributions during dietary perturbation. The results indicated that for both communities, the primary factor shaping community similarity was the individual animal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further assess the impact of diet and individual animal on beta diversity, we performed a permutational multivariate analysis (PERMANOVA) using the Adonis2 function. The transition from a grass-based to a corn-based diet did not exert a direct and significant effect on the distribution of viral (F\u0026thinsp;=\u0026thinsp;0.7352, p\u0026thinsp;=\u0026thinsp;0.685) or bacterial communities (F\u0026thinsp;=\u0026thinsp;3.10504, p\u0026thinsp;=\u0026thinsp;0.359). However, when grouping by individual animals, the test demonstrated statistical significance for both viral (F\u0026thinsp;=\u0026thinsp;6.103, p\u0026thinsp;=\u0026thinsp;0.001) and bacterial (F\u0026thinsp;=\u0026thinsp;1.855, p\u0026thinsp;=\u0026thinsp;0.04) communities.\u003c/p\u003e\u003cp\u003eAnalysis of microbial community dissimilarities revealed that bacterial communities exhibited greater variation across time points. In all three animals, the largest variation occurred during the transition from a grass-based to a corn-based diet, followed by community restoration upon returning to a grass diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Meanwhile, for viral communities, the individual animal had a stronger effect on dissimilarity than diet. The shift to a corn-based diet drove viral communities further apart, with slight convergence at the final time points upon returning to a grass diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGiven the strong influence of the individual animal on viral communities, we investigated whether specific viral contigs displayed abundance changes in response to diet transition. For each animal, a time-series bioinformatic analysis was applied. This analysis identified approximately nine distinct patterns of viral contig abundance throughout the experiment. One common trend observed across all animals was an increase in certain vOTUs when pH rose during fasting (t1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, C). Additional patterns showed a decrease in viral contig abundance as pH dropped (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E), while others exhibited an inverse relationship, with abundance increasing as pH declined (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, G, H). Furthermore, several trends in viral contig abundance were unique to individual animals and can be found in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540\u003c/span\u003e\u003cspan address=\"https://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to investigate the response of the rumen virome to a pH decrease induced by a dietary shift from high-fiber forage to readily fermentable carbohydrates such as corn. We successfully reduced ruminal pH in all three cows following corn introduction, with only two doses, it was possible to observe two animals experiencing pH values below 5.6 for over 20h, a threshold associated with subacute acidosis\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The decline in pH correlated with an increase in SCFA concentrations. Transitioning animals from forage-based diets to those high in fermentable carbohydrates, such as corn, intensifies ruminal fermentation and promotes the production of SCFAs. When SCFAs production exceeds the rumen’s absorption and buffering capacity, ruminal pH can drop below 5.6, which may lead to disruption of the ruminal microbiota and a range of metabolic disorders in the animals over the time\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite individual variation in pH trends, the Shannon and Pielou’s Evenness indices followed similar patterns across animals, suggesting they could serve as early indicators of pH fluctuations in the rumen. However, larger sample sizes and different cattle breeds should be included in future studies to confirm this observation.\u003c/p\u003e\u003cp\u003eDuring fasting (T1), an increase in Pielou’s evenness was observed, coinciding with a rise in pH. This could be due to bacterial stress caused by feed deprivation, SCFA fluctuations, or pH shifts, potentially triggering prophage induction and lytic phage replication\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. As pH remained low for ~ 20 hours, a decline in both Shannon diversity and Pielou’s evenness occurred, likely due to the dominance of certain phages at the expense of others. Similar findings have been reported in high-concentrate-fed animals \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Other factors, such as total digestible nutrients, dietary zinc and microbial functional diversity, may also affect viral communities during dietary changes\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, though these were not explored in the current study.\u003c/p\u003e\u003cp\u003eOne potential explanation for this reduced viral diversity is the expansion of phage host ranges, allowing them to infect multiple bacterial taxa\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. In rumen environments, broad-host-range phages are more prevalent than in the human gut, suggesting an adaptation to fluctuating conditions\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Such adaptations may help phages endure adverse physicochemical conditions, including elevated osmolarity associated with increased concentrations of SCFAs in the rumen\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These osmotic or osmolarity changes could physically impair phages’ ability to infect their primary bacterial hosts\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Another factor could be an increase in lysogeny. Most phages in the ruminal environment exist in a lysogenic state, representing a symbiotic coexistence with their microbial \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Under certain environmental stress, lysogenic phages are more likely to integrate into the genomes of their bacterial hosts as prophages \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile viral diversity indices responded significantly to pH changes, bacterial alpha diversity metrics did not show clear trends, except for a correlation between the Shannon index and pH. This suggests that the duration and magnitude of the pH perturbation in this experiment were insufficient to cause large-scale shifts in the bacterial community, as observed in previous studies\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However, specific taxonomic groups, such as cellulolytic Fibrobacter and Ruminococcus species, may be more affected by acidosis than overall bacterial diversity\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePCoA analysis indicated that individual variation was the primary driver of viral and bacterial community composition, rather than diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding aligns with previous studies showing that rumen viromes are highly individualized\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e similar to human gut viromes\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite this strong individual effect, time-series analysis revealed shared viral abundance patterns across animals. Notably, vOTUs increased in abundance during fasting (T1) when pH rose, suggesting the presence of phages sensitive to high pH or dependent on bacterial hosts that proliferate under these conditions. Conversely, some vOTUs increased when pH declined, possibly due to stress-induced prophage induction or shifts in host bacterial populations. Other vOTUs decreased in abundance during acidosis, potentially reflecting reduced bacterial hosts or increased lysogeny.\u003c/p\u003e\u003cp\u003eOur results suggest that viral communities in the rumen respond more rapidly and distinctly to pH fluctuations while bacterial communities remain stable and resilient, similarly to another research demonstrated in the mouse intestine upon dietary perturbations\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. In our study, 15 days after an acidotic challenge, the viral community within the same animal displayed a slightly different structure, as shown by the PCoA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, this trend was not consistently observed across the bacterial communities of the three animals. It has been suggested that certain phages, like some bacterial members of the gut microbiota, may fail to recover following dietary disturbances, which display hysteresis after dietary changes\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Although these researchers noted shifts in the bacterial community’s composition and abundance, its functional profile remained stable, as redundant functions across different bacterial species contribute to its resilience in the mouse intestine.\u003c/p\u003e\u003cp\u003eWe investigated the response of viral and bacterial communities to transient changes in the ruminal environment caused by the inclusion of corn in the diet of BON (\u003cem\u003eBlanco Orejinegro\u003c/em\u003e) cattle, which are typically maintained on pasture-based diets. This dietary change led to a decrease in ruminal pH and our results showed that the viral community responded more rapidly and significantly to these fluctuations than the bacterial community.\u003c/p\u003e\u003cp\u003eDespite the diet-induced pH changes, the composition of viral and bacterial communities was primarily shaped by individual variation rather than diet type, a finding consistent with previous studies on the gut microbiome in ruminants and humans. Furthermore, analysis of vOTU abundance dynamics revealed distinct viral responses to both pH increases and decreases, whereas bacterial communities remained more stable. This suggests that phages play an active role in the rumen ecosystem’s response to dietary and environmental perturbations and could serve as early indicators of pH fluctuation, preceding significant shifts in the bacterial community.\u003c/p\u003e\u003cp\u003eAlthough this study provides insights into microbial dynamics in cattle, it is limited by the number of individuals, the duration and intensity of the dietary treatment, and the focus on a single cattle breed. However, our findings lay the foundation for future research exploring microbial responses to dietary shifts in larger and more diverse populations. Additionally, similar studies should be conducted in other ruminant species to evaluate whether these patterns can be generalized.\u003c/p\u003e\u003cp\u003eIn summary, phages have the potential to serve as early biomarkers and diagnostic indicators of ruminal pH fluctuations resulting from dietary changes in cattle. Additionally, a deeper understanding of phage-bacteria interactions could enable the targeted modification of microbial communities, fostering greater resilience to environmental changes. Identifying microbial markers and key factors driving community responses to diet-induced stress is crucial, as it may lead to new diagnostic and treatment strategies for conditions such as ruminal pH decline—benefiting both farmers and livestock production.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cb\u003eSampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA protocol to induce a period of reduced ruminal pH was applied to all three animals. Importantly, these cows had been fed exclusively on native grasses throughout their lives; this natural grazing condition served as the baseline (control) against which changes following corn introduction were evaluated. Initially (day 0), the cows grazed on a pasture dominated by Kikuyu grass (\u003cem\u003eCenchrus clandestinus\u003c/em\u003e) with 60 days of regrowth and had ad libitum access to water in the paddock. Subsequently, the animals underwent a 16-hour fasting period, after which ground corn was administered directly into the rumen via nasogastric tube at a rate of 0.5 kg per 100 kg body weight, once daily for two consecutive days, as their sole food source. After the induction phase, the animals returned to grazing pasture during the recovery phase. All procedures were supervised by an experienced veterinarian and zootechnician from AGROSAVIA. Ruminal fluid samples were collected via rumenocentesis. An anesthetic (lidocaine) was used to perform this procedure in the animals. Ruminal pH was measured at multiple time points throughout the experiment. When possible, up to nine samples per animal were selected for subsequent laboratory and microbial analysis.\u003c/p\u003e\u003cp\u003eThe experiment involved the random selection of three cows from a bovine herd. Each cow was an experimental unit from which rumen fluid samples were collected before, during and after the induction of ruminal pH reduction. This sample size was chosen to be representative while minimizing stress and adverse effects on the animals. The number of individuals was also based on availability and ethical considerations regarding the stress caused by the treatment. The cows were matched by age, weight, breed and habitat. They were healthy and housed at the AGROSAVIA headquarters in Tibaitatá Mosquera, where the experiment was conducted. The animal care and management procedures were approved by the Ethical Committee of AGROSAVIA headquarters in Tibaitatá, Mosquera (Minute 003, September 8, 2017).All methods were performed in accordance with the relevant guidelines and regulations, including the ARRIVE guidelines.\u003c/p\u003e\u003cp\u003eGiven the study was designed as a small‑scale, exploratory time‑series (three animals sampled repeatedly across nine time points), no statistical analysis was performed across all three animals. Furthermore, an exploratory PCoA of the microbial data showed that the main factor of variation was the individual. As usual with ecological compositional data, non-parametric statistical tests were used when required. Reported PERMANOVA-Adonis pseudo‑F values and associated R² serve as measures of effect magnitude, and no additional effect‑size estimates or confidence intervals were calculated because there was only one observation per animal–time combination, precluding meaningful interval estimation.\u003c/p\u003e\u003cp\u003eRuminal fluid samples were filtered through sterile gauze before being stored in triplicate in 50 mL Falcon tubes for each animal at each time point. Samples designated for viral analysis were preserved in SM Buffer (5.8 gr NaCl, 2gr MgS0\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 50 ml Tris HCl 1M pH 7.5 and 950 ml dH\u003csub\u003e2\u003c/sub\u003eO) without gelatin at a 9:1 v/v ratio. Samples intended for bacterial analysis were preserved in a 1X PBS buffer using the same ratio. All rumen fluid samples were stored at 4°C. Additionally, 10 mL of ruminal fluid samples designated for SCFAs analysis were transferred to 20 ml Falcon tubes and frozen at -20°C.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBacterial DNA Extraction and 16S rRNA gene library construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBacterial DNA extraction from the rumen microbiota was performed as follows: a Falcon tube containing ruminal fluid mixed with PBS buffer was homogenized using a vortex and manual stirring for 30 seconds. A 500 µL aliquot was then transferred to a 2 mL Eppendorf tube. This aliquot underwent three cycles of rapid freezing in liquid nitrogen for five minutes, followed by immediate heating in a water bath at 65°C for five minutes. Next, the ruminal fluid was vortexed in a bead-beating machine for 5 minutes and DNA was extracted using the ZR Fungal/Bacterial DNA MiniPrep\u003csup\u003e™\u003c/sup\u003e from Zymo following the manufacturer's instructions. DNA integrity was assessed by electrophoresis on a 2% (w/v) agarose gel, and DNA concentration was quantified using a Qubit fluorometer (Thermo fisher Scientific, United States). The DNA concentration of all samples was adjusted to 20 ng/µL.\u003c/p\u003e\u003cp\u003e16S rRNA gene libraries were constructed following the methodology described\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Briefly, a two-step PCR was performed aimed to amplify the V4 hypervariable region of the 16S rRNA gene. In the first step, PCR amplification was carried out using primers 515F and 806R. The PCR was performed in triplicate for each sample, and the resulting PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA) and verified by electrophoresis.\u003c/p\u003e\u003cp\u003eIn the second step, the triplicate amplification products were pooled and used as a template for the second PCR, during which indexes and adaptors were added to the V4 amplicons. The PCR products were purified, and a pooled library was prepared at a concentration of 10 µM for each sample. Pair-end libraries were sequenced using the Illumina MiSeq platform at Universidad del Bosque in Bogotá, Colombia. Samples BAt2 from cow A, BBt3 from cow B and samples BCt2 from cow C were removed from the analysis due to poor sequence quality. Sample BCt8 from cow C was contaminated with blood and was discarded (\u003cb\u003eSupp. Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eViral DNA Extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo extract viral DNA from ruminal fluid samples, plant particles and other microorganisms were removed by centrifugation. Ruminal fluid samples preserved in SM buffer were centrifuged three times: First, 20 mL of ruminal fluid was centrifuged at 5000 x g for 30 min at 4°C. The supernatant was collected in a Falcon tube, and the pellet was discarded. This process was repeated once. Finally, the supernatant was centrifuged at 8000 x g for 15 min at 4°C. The resulting supernatant was filtered through 0.45 µm and 0.22 µm sterile vacuum filter systems (Corning) and sterile syringe filters. The filtered sample was ultracentrifugated at 130,000 x g for 2 hours at 4°C. After centrifugation, the supernatant was discarded, and the pellet was resuspended in 500 µL of SM buffer.\u003c/p\u003e\u003cp\u003eViral DNA extraction was performed using the Stractec DNA Viral Kit, Berlin, Germany. The presence of viral DNA in the sample was confirmed in a 2% (w/v) agarose gel, and DNA concentration was measured using Qubit fluorometer (Thermo Fisher Scientific, USA). Viral DNA was sent to the Genome Sequencing Center at the Edison Family Center for Genome Sciences and Systems Biology Washington University in St. Louis, MO, USA. Shotgun libraries were constructed using the standard Illumina Nextera protocol and sequenced on a NextSeq platform, generating 2x150 pair-end reads. Sample VCt8 from cow C was contaminated with blood and was discarded and sample VBt7 also was removed due to poor sequence quality (\u003cb\u003eSuppl. Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eShort-Chain Fatty Acids (SCFAs) analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStandards: Acetic acid (≥ 99.7%), propionic acid (≥ 99.5%), butyric acid (≥ 99.8%), isovaleric acid (≥ 99.5%), and valeric acid (≥ 99.0%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Methyl tert-butyl ether (MTBE) and H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e were purchased from J.T. Baker (Phillipsburg, NJ, USA) and Merck (Darmstadt, Germany), respectively. HPLC-grade water was produced using a Heal Force Smart-Mini water purification system (Shanghai, China).\u003c/p\u003e\u003cp\u003eA stock standard solution containing all SCFAs dissolved in MTBE was prepared at concentrations ranging from 10 to 15 mmol/L for each acid. The SCFAs standard solution was stored at -80°C and used within three days. Calibration curves were generated using ten concentration points (0.050-5.000 mmol/L) for each SCFA, with three replicates per concentration, following previously reported conditions\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The linear regression equations for each SCFA were as follows: y = 1.7179x – 0.2532; R\u003csup\u003e2\u003c/sup\u003e = 0.993 (acetic acid), y = 3.1251x – 0.2268; R\u003csup\u003e2\u003c/sup\u003e = 0.990 (propionic acid), y = 4.1944x – 0.4421; R\u003csup\u003e2\u003c/sup\u003e = 0.993 (butyric acid), y = 5.3173x – 0.2381; R\u003csup\u003e2\u003c/sup\u003e = 0.993 (isovaleric acid), and y = 5.3913x – 0.4161; R\u003csup\u003e2\u003c/sup\u003e = 0.990 (valeric acid). Limits of detection (LODs) and quantification (LOQs) were determined at 0.008 and 0.025 mmol/L, respectively, based on signal-to-noise ratios of three (S/N = 3) and ten (S/N = 10), consistent with previous reports\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe concentration of SCFAs (acetic acid, propionic acid, butyric acid, isovaleric acid, valeric acid and total SCFAs) in ruminal fluid samples was measured as follows. Frozen samples were thawed at 4°C over 24 hours. The thawed samples were shaken for 1 minute using a vortex and then centrifuged at 6000 rpm at 4°C for 15 minutes. After centrifugation, 1 mL of the supernatant was collected and centrifuged at 12,000 rpm for 10 minutes at 4°C. Next, 500 µL of the supernatant was filtered through 0.45 µm membranes by centrifugation at 14,000 rpm for 10 minutes at 4°C. Finally, 50 µL of 10% NaOH solution and 50 µL of internal standard (2-ethylbutyric acid 25 mM in 25% metaphosphoric acid) were added to 400 µL of the filtered sample. Samples were stored at 4°C.\u003c/p\u003e\u003cp\u003eSCFA extraction was performed by mixing 100 µL of the ruminal fluid sample with 25 µL of 50% H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, and 125 µL of MTBE. The mixture was shaken for 5 min using a vortex and then centrifuged for 15 min at 10,500 rpm. The organic solvent was transferred to a new tube, and anhydrous Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e was added to remove residual water. The tube was vortexed for one minute, centrifuged for one minute, and the supernatant was filtered through a 0.22 µm, before chromatographic analysis.\u003c/p\u003e\u003cp\u003eIdentification of SCFAs was performed using a Trace 1300 gas chromatograph (GC) coupled to an ISQ-LT Single Quadrupole Mass Spectrometer (MS), using previously published conditions\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Raw data were acquired and processed using XCalibur 3.0 software. Both equipment and software were Thermo Scientific, San Jose, CA, USA. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. Manual injection of 1 µL of sample was performed in split mode (1:10), with the injector temperature set to 250°C. Separation was achieved using an HP-FFAP column (30m × 0.53mm × 1µm; Agilent Technologies, Santa Clara, CA, USA). The oven program was set at an initial temperature of 50°C for 1 min, increased to 150°C at a rate of 6°C/min, then increased to 220°C at 20°C/min, and held for 4.5 min. An Electronic Ionization (EI) system was operated at 70 eV, and the MS detector was set to full-scan mode, covering an \u003cem\u003em/z\u003c/em\u003e range from 20 to 150 Da, with an initial scan time of 5.5 min. The ion source and transfer line temperatures were 220°C and 230°C, respectively. Total ion chromatogram mode was used to confirm characteristic ions for each SCFA. \u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e shows the chromatograms obtained by GC-MS analysis of the standard mix (a) and a ruminal fluid sample (b).\u003c/p\u003e\u003cp\u003eQuantitative SCFA analysis was performed using a Trace 1300 GC equipped with a flame ionization detector (FID) and an AI 1310 automatic injector. Helium was used as the carrier gas at 1.4 mL/min. Raw data were acquired and processed using Chromeleon 7.2 software. Both equipment and software were Thermo Scientific, San Jose, CA, USA. SCFA separation was achieved using an HP-FFAP column (30m×0.53mm×1µm, Agilent Technologies, Santa Clara, CA, USA). The injector and detector temperatures were both set at 250°C. The air, nitrogen, and hydrogen flow rates were 350, 40, and 35mL/min, respectively. A volume of 1 µL of each sample was injected in split mode (1:20). The oven program was set at an initial temperature of 120°C during 2 min, increased to 140°C at a rate of 6°C/min and held at 140°C for 1 min, then increased to 150°C at a rate of 1.5°C/min and maintaining this temperature for 1 min for a total run time of 15 min. SCFAs concentrations were measured in mmol/L, and the acetic-to-propionic acid ratio was calculated for each time point and each animal.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetataxonomic analysis of bacteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemultiplexing of the samples was performed using an internal script, which is available upon request. Sequence integrity and quality were assessed using FastQC v0.11.7\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Adapter and linker sequences were removed from partial 16S rRNA gene sequences using cutadapt v1.12 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Low-quality sequences with an average Phred score \u0026lt; 20 and a read length \u0026lt; 200 nt were detected and removed using Trimmomatic v0.38 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Consensus sequences were generated using FLASH program\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e and imported to Qiime2 pipeline v 2020for 16S rRNA gene amplicon analysis\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. DADA2\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e46\u003c/span\u003e\u003c/sup\u003e was used to denoise and generate Amplicon Sequence Variants (ASVs) from pair-end sequences, applying a truncation length of 200 bp. ASVs classified as singletons or containing fewer than 4 sequences across all samples in the feature table were excluded from further analyses. Taxonomic assignment of ASVs was performed using the Silva Database version 138_99\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssembly and coverage of the viral community\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTrimmomatic 0.39 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e43\u003c/span\u003e\u003c/sup\u003e was used to remove low-quality bases and adapters. high-quality reads were taxonomically classified using Kraken v2.1.2\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e48\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e49\u003c/span\u003e\u003c/sup\u003e against the Plus PFP database. Reads classified as eukaryotic were removed. Additionally, reads mapping to ΦX174 (GCF_000819615.1) were removed using bbduk, part of BBtools suite v39.01\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e50\u003c/span\u003e\u003c/sup\u003e. FastQC 0.11.9 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e51\u003c/span\u003e\u003c/sup\u003e and MultiQC 1.11\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e52\u003c/span\u003e\u003c/sup\u003e were used to generate an HTML report summarizing the quality profiles of the fastq files before and after quality control. Reads from all samples were for cross-assembly. Clean reads were subsampled at incremental levels (10–100%) using the reformat script from BBtools. BBNorm, was used to down-normalize each subsample to a maximum depth of 100x. Down-normalized reads were assembled using metaSPAdes 3.15.5\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e53\u003c/span\u003e\u003c/sup\u003e and assemblies were filtered to retain contigs \u0026gt; 1000 bp with a minimum fold coverage of 2. Pre-filtering of non-viral sequences was performed using geNomad 1.5.0\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e54\u003c/span\u003e\u003c/sup\u003e with the relaxed before vOTU clustering.\u003c/p\u003e\u003cp\u003eRedundant contigs were dereplicated using MMseqs v14.7e284\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Contigs were clustered into viral operational taxonomic units (vOTUs) at 95% nucleotide identity and 85% coverage using the anicalc and aniclust scripts from CheckV\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The vOTU representative was selected as the contig with the highest completeness percentage and the shortest length, as determined by CheckV. To remove non-viral sequences, viral contigs were identified using VIBRANT 1.2.1\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e57\u003c/span\u003e\u003c/sup\u003e, VirSorter 2.2.4\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e58\u003c/span\u003e\u003c/sup\u003e and geNomad in its defaultmode\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. A contig was classified as viral only if all three tools identified it as such. Only sequences ≥ 4000 bp or classified as high-quality by CheckV were included in the final vOTU table.\u003c/p\u003e\u003cp\u003eQuality-controlled Illumina reads were mapped against the filtered vOTUs using Bowtie2 v2.4.4 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e60\u003c/span\u003e\u003c/sup\u003e retrieving all the possible alignments. CoverM v0.6.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wwood/CoverM\u003c/span\u003e\u003cspan address=\"https://github.com/wwood/CoverM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used to filter mapped reads, removing with \u0026lt; 85% coverage and \u0026lt; 95% identity. BAM and flagstats files were generated using SAMtools 1.14 \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Genome breadth coverage (C\u003csub\u003eA\u003c/sub\u003e) was calculated using genomecov from BEDtools v2.29.2.0\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Additional mapping statistics including read count, reads per kilobase per million mapped reads (RPKM), trimmed mean, and variance, were estimated using CoverM.\u003c/p\u003e\u003cp\u003eFor normalized counts, BAM files were filtered with SAMtools to remove non-unique alignments. CoverM was used to calculate the number of reads uniquely mapped to a contig (R\u003csub\u003eU\u003c/sub\u003e). BEDtools v2.29.2.0 was used to determine the number of bases covered by those unique alignments (C\u003csub\u003eU\u003c/sub\u003e). The mean depth coverage of unique reads (D\u003csub\u003eU\u003c/sub\u003e) was calculated as D\u003csub\u003eu\u003c/sub\u003e= R\u003csub\u003eU\u003c/sub\u003e/C\u003csub\u003eU\u003c/sub\u003e. Normalized counts (R\u003csub\u003eN\u003c/sub\u003e) were calculated using:\u003c/p\u003e\u003cp\u003eR\u003csub\u003eN\u003c/sub\u003e = R\u003csub\u003eU\u003c/sub\u003e + (D\u003csub\u003eU\u003c/sub\u003e*(C\u003csub\u003eA\u003c/sub\u003e -C\u003csub\u003eU\u003c/sub\u003e))\u003c/p\u003e\u003cp\u003eThe length of the contig (L) and the sum of the normalized counts (∑R\u003csub\u003eN\u003c/sub\u003e) is used to estimate normalized RPKM value as follows:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"288\" height=\"57.8824\" style=\"width: 288px; height: 57.8824px;\"\u003e\u003c/p\u003e\u003cp\u003eFinally, we used the coverage statistics to filter the vOTUs abundance table, keeping only those with at least 5% breadth coverage in at least one sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTaxonomic classification of Viral Operational Taxonomic Units\u003c/b\u003e\u003c/p\u003e\u003cp\u003eViral taxonomy was assigned using PhaGCN\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e in accordance with the latest International Committee on Taxonomy of Viruses (ICTV) guidelines. Additionally, nucleotide BLAST searches against the Viral RefSeq \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e64\u003c/span\u003e\u003c/sup\u003e were performed to identify closely related sequences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluating diversity in the bacterial and viral communities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRarefaction curves for bacterial samples were generated using Qiime2 v2020\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e45\u003c/span\u003e\u003c/sup\u003e at a depth of 10,880 sequences, representing the minimum read count observed across all samples. Alpha diversity was assessed using Shannon, richness and Pielou's evenness indices for both viral and bacterial communities.\u003c/p\u003e\u003cp\u003eTo evaluate beta diversity, correlations between diversity indexes and pH to were analyzed using the Bray-Curtis metric for both bacterial and viral communities. Principal Coordinate Analysis (PCoA) was used to visualize beta diversity and examine the effects of diet, individual variation, and sampling time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis of bacterial and viral abundances\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA permutational multivariate analysis of variance (ADONIS)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e was conducted using the Bray-Curtis distance-matrix to determine whether diet (corn vs. grass) and individual animals significantly influenced microbial community composition. The analysis was performed using the Adonis2 function from the Vegan package in R.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe dynamics of ruminal pH changes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe variation in vOTU abundance during ruminal pH decline, was assessed for each animal using the time series analysis\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Before clustering analysis, vOTU abundance values were scaled by a factor of 10³ and rounded to integers while preserving the relative patterns. A custom script was used to generate vOTU clusters using the k/medoids clustering algorithm. Contigs were grouped based on similar abundance trends throughout the experiment. Taxonomic identification of contigs in each cluster was assigned at the family level. Modifications to the Coenen et al. (2020) scripts, commands and the complete results for each animal can be found in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540\u003c/span\u003e\u003cspan address=\"https://osf.io/83td6/?view_only=1029383cce304568a67e1aeec8a59540\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests statement\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding statement declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors' contribution RH, AR, HJ and AC conceptualized and structured the idea for this research. RH, AC, and HJ conducted the experiments at the Agrosavia facilities. DV and YA managed the care and welfare of the animals. RH, GL, and CC were responsible for sampling, processing, and measuring SCFAs in the rumen. LF performed the computational analysis to identify viral contigs and generate the vOTU table. RH carried out the computational analysis of bacterial communities, as well as diversity and statistical analyses for both bacterial and viral communities. Additionally, RH, LF, CV, and HJ conducted computational analyses to investigate the dynamics of viral contig abundance throughout the experiment. RH wrote the first draft of the manuscript, while AR supervised all stages of the research. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our gratitude to Fernando Rodriguez, whose expertise in the rumen ecosystem highlighted the importance of studying the behavior of viral communities in response to dietary changes and pH reduction. We also sincerely thank the Agrosavia staff for their dedicated care and handling of the animals throughout the experiment.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll raw and assembled viral data are available from the ENA with project accession number PRJEB54786. 16S rRNA gene amplicons are also available from ENA with project accession number PRJEB54691\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePuniya, A. K., Singh, R. \u0026amp; Kamra, D. N. (eds) Rumen Microbiology: From Evolution to Revolution. Springer India (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 14567 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlaizier, J. C., Krause, D. 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A primer for computational microbiome analysis in natural systems. \u003cem\u003emSystems\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, e00917\u0026ndash;e00920 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bacteriophages, ruminal pH shifts, microbial dynamics, diet shift, virome","lastPublishedDoi":"10.21203/rs.3.rs-7160638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7160638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe inclusion of grains in cattle diets aims to boost milk and meat production but can reduce ruminal pH and disrupt normal rumen function, mainly affecting fiber-degrading microbes. While bacterial responses to pH shifts are well documented, the effects on rumen bacteriophage (phage) communities remain unclear. This study aimed at characterizing the viral and bacterial dynamics upon a short-term acidotic challenge induced by adding corn to the diets of three Colombian BON (Blanco Orejinegro) cows, (\u003cem\u003eBos Taurus\u003c/em\u003e), usually fed on pasture. Ruminal fluid samples were analyzed to monitor viral and bacterial diversity, pH, and short-chain fatty acids (SCFAs). Viral alpha diversity indices declined significantly with pH reduction, possibly due to lysogeny or phages with broad host ranges. In contrast, bacterial diversity stayed relatively stable, suggesting higher resilience to short-term pH shifts. Beta diversity analysis indicated that individual animal variation, rather than diet, was the main driver shaping both viral and bacterial communities. Moreover, distinct viral abundance patterns were strongly associated with pH fluctuations, highlighting the potential of phages as sensitive early indicators of ruminal acidification. These findings emphasize the important ecological role of the rumen virome and suggest that monitoring phage dynamics could improve understanding of ruminal pH-related disturbances in cattle.\u003c/p\u003e","manuscriptTitle":"Viral and bacterial dynamics in response to drastic and short dietary shifts in creole Colombian cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 07:25:51","doi":"10.21203/rs.3.rs-7160638/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T04:15:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T01:52:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207464574116071374503789313593465913697","date":"2026-03-25T05:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283491062991352844786562618511387375502","date":"2026-03-23T15:56:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T00:57:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230113463838336620360123094328391573810","date":"2025-12-03T22:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T14:13:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T05:05:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-04T13:42:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T14:57:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-30T14:53:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22ccc9e5-1f82-4ffd-9380-a30ebea42033","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58645139,"name":"Biological sciences/Ecology"},{"id":58645140,"name":"Earth and environmental sciences/Ecology"},{"id":58645141,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-04-03T04:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 07:25:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7160638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7160638","identity":"rs-7160638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}