<figure><img src="image_1.png" /></figure>

# Late-gestation heat stress alters placental structure and function in multiparous dairy cows

L. T. Casarotto,<fnref n="1" /> Ⓓ H. N. Jones,<fnref n="2" /> P. Chavatte-Palmer,<fnref n="3" />,<fnref n="4" /> J. Laporta,<fnref n="5" /> Ⓓ F. Peñagaricano,<fnref n="5" /> Ⓓ V. Ouellet,<fnref n="6" />

J. Bromfield,<fnref n="1" /> Ⓓ and G. E. Dahl<fnref n="1" />* Ⓓ

## ABSTRACT

The placenta plays a pivotal role in fetal development and the dam's subsequent lactation performance, because it facilitates nutrient transfer, heat dissipation, and gas exchange with the growing fetus, and regulates key hormones essential for mammary gland development. Heat stress experienced during gestation and lactation can significantly reduce the placenta's capacity to perform these critical functions. To investigate the impact of heat stress, trials were conducted over the summer months of 2020, 2021, and 2022 in Florida. Multiparous pregnant Holstein cows were dried off 54 ± 5 d before their expected calving date and randomly assigned to 1 of 2 treatments for the entire dry period: active cooling (CL; access to barn shade, natural ventilation plus forced air circulation via fans, and water soakers; n = 20) or heat stress (HT; access to barn shade and natural ventilation; n = 20). Gestation length and calf birth weights were recorded. Placentas were collected from a subset of cows shortly after calving (4.00 ± 1.54 h; n = 10/treatment) and analyzed for total placental weight, as well as cotyledon weight, number, and surface area within 1 h after expulsion. A representative cotyledon sample was isolated for histological analysis. Tissues were also processed for RNA sequencing and DNA methylation analysis. DNA methylation was analyzed by double restriction enzyme reduced representation bisulfate sequencing. Differentially methylated cytosines between HT and CL were identified via logistic regression with a cut-off value of 15% methylation difference and a q-value <0.2. Morphological and histological data were analyzed using generalized linear mixed models. Results indicate that gesta-

tion length was shorter in HT cows compared with CL cows (274.2 vs. 277.2 ± 1.46 d), and heifers born to HT dams were lighter at birth (31.4 vs. 34.8 ± 1.59 kg). Placentas from HT dams tended to have lower total weight (3.54 vs. 4.54 ± 0.38 kg) and fewer cotyledons (66.2 vs. 103.3 ± 8.65). However, placental efficiency was higher in the HT versus CL group (11.5 vs. 8.52 ± 0.91%). Cotyledons from HT cows had greater vascular area (43.1% vs. 31.8% ± 10.4% of total area) and a tendency for less connective tissue (52.7% vs. 65.8% ± 5.39% of total area). A total of 289 differentially expressed genes were identified between HT and CL placentas, with 179 up-regulated and 110 downregulated in the HT group. Key genes affected included *NPSR1*, *SPATC1L*, *PGF*, *HSPB8*, *IL6*, *HBA/HBB*, *MMP12*, *PAPPA2*, *PAG14*, and *SLC7A10*. Dysregulated pathways in HT placentas involved gas and oxygen transport, nutrient transport, inflammatory response, and cortisol biosynthesis. Heat stress induced hypermethylation of regulatory pathways, including collagen biosynthesis and degradation, extracellular matrix structural components, and placental tissue organization. Our findings demonstrate that late-gestation HT causes significant transcript alterations in the placenta, leading to adaptations for thermoregulation and morphological changes. These alterations negatively affect birth weight, health, and dam lactation performance, underscoring the need to address HT during late gestation to ensure optimal fetal development and postnatal outcomes. Addressing these issues can help improve dairy cow resilience to climate change, enhancing animal welfare and productivity.

**Key words:** heat stress, prenatal, placental function

## INTRODUCTION

Dairy cows often experience high levels of stress during gestation and lactation due to exposure to excessive

<footnote n="1">¹Department of Animal Sciences, University of Florida, Gainesville, FL 31608</footnote>

<footnote n="2">²Department of Physiology and Aging, University of Florida College of Medicine, Gainesville, FL 32603</footnote>

<footnote n="3">³Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, 78350, France</footnote>

<footnote n="4">⁴France Ecole Nationale Vétérinaire d'Alfort, BREED, Maisons-Alfort, 94700, France</footnote>

<footnote n="5">⁵Department of Animal and Dairy Sciences, University of Wisconsin–Madison, Madison, WI, 53706</footnote>

<footnote n="6">⁶Department of Animal Sciences, Université Laval, Québec City, QC, G1V DA6, Canada</footnote>
temperatures and humidity, also known as heat stress. When the temperature-humidity index (THI) exceeds 68 (Zimbelman et al., 2009), dairy cattle use evaporative mechanisms, such as sweating and panting, to cope with the heat stress (Kadzere et al., 2002; West, 2003; Collier et al., 2019). Given the physiological challenges faced by dairy cows, it is essential to manage environmental factors to minimize any negative effects during the lactating and the nonlactating (dry) period to maximize health and productivity (Tao and Dahl, 2013). Exposure of dairy cows to high temperatures and humidity can increase core body temperature by 0.4°C to 1.0°C, alter behavioral activity, and change physiological and metabolic responses to increase heat dissipation or minimize heat production, or both (West, 2003; Ouellet et al., 2021). Indeed, lactating animals exposed to heat stress tend to increase their standing time and bunching behavior, while reducing activity, movement, and rumination, and exhibiting changes in feeding patterns (West, 2003; Wheelock et al., 2010; Soriani et al., 2013; Nordlund et al., 2019). Similar physiological adaptations to heat stress are observed in both nonlactating or dry cows in late gestation (Tao and Dahl, 2013). During the dry period, which aligns with late gestation, fetal growth and development accelerates. Heat stress during this period could result in long-term negative effects on dairy heifers through a process known as in utero programming (Barker, 1998). The programming effects also extend to the maternal mammary gland, because the placenta secretes hormones such as placental lactogen and estrogens, which play a crucial role in mammary gland development in dairy cows (Guilbault et al., 1985; Rhoads et al., 2008).

Maternal hyperthermia can affect the development and function of the placenta. The severity of the placenta dysfunction changes depends on the stage of pregnancy in which the hyperthermia occurs (van Wettere et al., 2021). Changes in maternal core temperature can affect fetal development as the heat load is transferred via placental perfusion (Regnault et al., 2003). Note that placental diffusion capacity decreases in proportion to fetal and placental weight, and thus with advancing gestation (Regnault et al., 2003; Ouellet et al., 2020). Previous studies by Bell et al. (1989) and Early et al. (1991) revealed a reduction in placental weight associated with fewer placentomes and reduced placental cell division associated with heat stress in ewes. More recently, Silva et al. (2021) found that goats exposed to heat stress took longer to deliver the placenta and had more cotyledons, but also had decreased placental efficiency compared with those in thermoneutral conditions. Other studies have shown that environmental factors also adversely affect placental efficiency (Galan et al., 1999), binucleate cell number (Ward et al., 2002), cotyledon number and placenta weight (Dwyer et al., 2005; Konyali et al.,

2007), and placental surface area in small ruminants (Ocak et al., 2015). But the responses of cows are not clear.

The aim of this study was to evaluate the effects of heat stress during late gestation on placental macro- and microstructure and transcriptomic and methylation patterns of the placenta in dairy cows. Our hypothesis was that exposure to heat stress during late gestation causes changes in placental morphology, microstructure, gene expression and DNA methylation, leading to impaired function and fetal development.

## MATERIALS AND METHODS

### Animals and Experiment Design

Experiments were conducted over the summer months (July to October) of 2020, 2021, and 2022 at a commercial dairy farm (study 1; Trenton, FL) and the University of Florida Dairy Unit (study 2 and 3; Hague, FL). All procedures were approved by the University of Florida Institutional Animal Care and Use Committee.

Multiparous pregnant Holstein cows (average ± SD, 1.3 ± 0.8 parity) were dried off, 54 ± 5 d before their expected calving and randomly assigned to 1 of 2 treatments (cooling or heat stress) blocked by their expected calving date and mature equivalent milk production. Before enrollment, dams were managed identically with access to the shade of a freestall barn, fans, and soakers during their lactation. Upon enrollment, cows were housed in a compost-bedded (study 1) or sand-bedded (study 2 and 3) freestall barn in separate adjacent pens and either cooling (CL; access to the shade of a barn plus forced ventilation via fans and water soakers over the feed lane; n = 20) or heat stress (HT; access to the shade of a barn and natural ventilation, no active cooling devices; n = 20) for the entire duration of their dry period. In the CL group, fans ran continuously, and water soakers turned on for 1 min in 5 min intervals when ambient temperature rose above 22°C. Environmental temperature and relative humidity were collected daily from the Florida Automated Weather Network for the farm locations. The THI (Dikmen et al., 2008) of both pens of the barn was above 70 for the duration of all experiments. To assess the physiological changes due to HT, respiration rates (RR, bpm), skin temperatures (°C; study 1) or rectal temperatures (RT, °C; studies 2 and 3) were measured thrice weekly at 1400 h. Cows were fed far-off and close-up TMR ad libitum for the first 3 wk and the following 4 wk before expected calving, respectively, according to the standard operating procedures of both dairy farms. Closer to the expected calving date, cows were monitored closely for signs of parturition. A birth, calf and dam were separated, and the dam was milked for colostrum and calves were weighed.
Gestation length was calculated by subtracting the calving date from the breeding date.

## Placenta Collection

Intact placentas from female calves were collected from a subset of cows following spontaneous expulsion (4.00 ± 1.54 h; n = 10 per treatment). Collected fetal membranes were immediately checked for completeness after expulsion, and damaged or incomplete membranes were excluded. Fetal membranes expelled after 12 h were also excluded because they were considered clinically retained membranes. Once fully expelled, placentas were kept in labeled containers with the corresponding cow ID and respective treatment. All measurements were completed within 1 h of expulsion. The identity of the dam, date, time of expulsion, and all measurements were recorded in a corresponding template.

Entire fetal membranes (i.e., amnion and chorioallantois) were weighed using a digital scale with an accuracy of 0.1 kg (VS660 heavy duty scale, A and A Scales LLC, Prospect Park, NJ; maximum capacity = 300 kg; precision = 0.10 kg). Cotyledons were then separated from the chorioallantois tissue, after which the remaining membranes and the cotyledons were weighed separately. Cotyledons were laid on a flat surface for counting, and photographs were captured. The efficiency of the placenta was determined by calculating the ratio of calf BW (kg) to placenta weight (kg; Banchero et al., 2010).

## Histological Analysis

A representative cotyledon sample was collected in the third study for histological analysis. Cotyledonary tissues were washed in sterile saline, trimmed, dissected, and fixed in 4% paraformaldehyde + 2.5% polyvinylpyrrolidone for 48 h followed by washing in 1 × PBS solution for 48 h and storage in 70% ethanol at 4°C until processing. Tissues were paraffin-embedded, sectioned at 5 µm, and mounted on slides. Sections were stained with hematoxylin and eosin (H&E), Masson's trichrome, or picrosirius red (PSR) using an automat (Varistain, Thermofisher, Waltham, MA) for stereological analysis and scanned using NanoZoomer Digital Pathology (Hamamatsu Photonics, Hamamatsu, Japan) for analysis at a 40× magnification. Three random fields of view across 1 slide per staining from each cow:calf placental unit were analyzed with a threshold on ImageJ software (version 1.47, National Institutes of Health) for each cotyledon sample. The H&E staining has been unchanged for many years because it works well with a variety of fixatives and displays a broad range of cytoplasmic, nuclear, and extracellular matrix features (Fischer et al., 2008), we mainly focused on the vascular area by identifying the

red blood cells enclosed by endothelial cells in the cotyledonary tissue. Masson's trichrome is a classical method of visualization with several variants: the nuclear dye can be Masson's hemalum or several hematoxylin solutions, the cytoplasmic dye is fuchsin, and an aniline-derived dye allows the differentiation of collagen fibers (Exbrayat, 2016). Emphasis was placed on the connective tissue components, such as collagens, laminin, fibronectin, vitronectin, elastin, and proteoglycans. Finally, PSR is one of the most important stains for studying collagen networks in different tissues. Under polarized light, collagen bundles appear green, red, or yellow, and are easily differentiated from the black background, thus allowing for quantitative morphometric analysis (Lattouf et al., 2014). We focused on the extracellular matrix components, specifically the perivascular collagen.

All data were analyzed in SAS (v. 9.4, SAS Institute, Cary, NC). Variables were analyzed by PROC GLIMMIX, as a generalized linear mixed model, with the main effects of cow ID, treatment, parity, year, mature equivalent milk production, gestation days, and treatment by year interaction. Normality of residuals and homogeneity of variance were examined with PROC UNIVARIATE, for each continuous dependent variable analyzed after fitting the statistical model. Data are presented as LSM ± SEM, unless otherwise stated. Significance was declared at *P* ≤ 0.05 and tendencies at 0.10 ≥ *P* > 0.05.

## RNA Sequencing: Sample Collection and Preparation, RNA Quantification

From the selected cotyledon, a tissue sample was cut and placed in RNA later solution and stored frozen at -80°C until further processing. RNA extraction from cotyledonary tissue was performed using the RNeasy Micro Kit (Qiagen, Hilden, Germany). RNA integrity was assessed using the RNA Nano 6000 Assay Kit and the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA). Samples with RIN >4 passed the quality control to proceed to sequencing (n = 4–5/treatment) at Novogene Co. (Sacramento, CA).

## RNA Sequencing: Library Preparation, Clustering, and Sequencing

Total RNA was used as input material for the RNA sample preparations. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in First Strand Synthesis Reaction Buffer(5×). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV reverse transcriptase (RNase H-). Second-strand cDNA synthesis was subsequently performed using DNA
polymerase I and RNase H-. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3' ends of DNA fragments, adapters with hairpin loop structures were ligated to prepare for hybridization. To select cDNA fragments of preferentially 370~420 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, CA). Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Finally, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE cluster kit v3-cBot-HS (Illumia) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated.

## RNA Sequencing: Quality Control, Mapping

Raw data (raw reads, FASTQ files) were first processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N, and low-quality reads from raw data. At the same time, the quality scores indicating the percentage of bases with a Phred score of 98% (Q20), 99.9% (Q30), and the percentage of guanine and cytosine bases in the quality-controlled data were calculated. All the downstream analyses were based on clean data with high quality. Reference genome and gene model annotation files were downloaded from the genome website directly (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002263795.1/). An index of the reference genome was built using Hisat2 v2.0.5 (Kim et al., 2015) and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping tool because Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus a better mapping result than other nonsplice mapping tools.

## RNA Sequencing: Data Analysis

The mapped reads of each sample were assembled by StringTie (v1.3.3b; Pertea et al., 2015) in a reference-based approach. StringTie uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Feature Counts v1.5.0-p3 (part of the Subread package; http://subread.sourceforge.net) was used to count the reads

numbers mapped to each gene. It provides the expected number of fragments per kilobase million (FPKM) of transcript sequence per millions of base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most common method for estimating gene expression levels. The FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis (2 biological replicates per condition) was performed using the DESeq2 R package (version 1.20.0, Bioconductor). The DESeq2 package provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted P-value ≤ 0.05 found by DESeq2 were assigned as differentially expressed.

## Bisulfite Sequencing: DNA Methylation Analysis

Frozen tissue samples were sent to Novogene Co. (Sacramento, CA) for DNA extraction and methylation sequencing (n = 5/treatment). DNA methylation was analyzed by double restriction enzyme reduced representation bisulfite sequencing (RRBS), which provides methylation resolution at the single-nucleotide level from a subset of the genome. Despite low genome coverage, on average 2%, RRBS enriches CpG-rich areas (areas where a cytosine is followed by a guanine in a specific direction), including the majority of CpG islands and gene promoters and, to a lesser extent, introns, exons, CpG shores, and CpG shelves. Thus, RRBS is a powerful method broadly used for assessing DNA methylation patterns. First, DNA was digested with MspI restriction enzyme for 2 h at 37°C, followed by digestion with TaqαI restriction endonuclease at 65°C. DNA fragments <300 bp were selected and used for bisulfite treatment with the Methylamp DNA conversion kit (P-1001, Epigentek, Farmingdale, NY). Conversion efficiency was determined by real-time PCR using 2 primer pairs; the first for β-actin against bisulfite-converted DNA and the second for GAPDH against unconverted DNA. The same bisulfite-converted DNA sample was run with both primer pairs. DNA was >98% converted. Standard Illumina sequencing adapters were ligated onto the bisulfite-treated DNA fragments using a random probing method. Library amplification was achieved using indexed primers and analyzed on a Bioanalyzer and with a KAPA Library Quantification Kit according to the manufacturer's protocol (Kapa Biosystems, Wilmington, MA). Purified DNA (15 nM sample) libraries were sequenced using the Illumina HiSeq 4000 system, generating 50-bp single end reads.
Quality control of the Illumina raw reads was performed using FastQC software (version 0.11.2, Babraham Bioinformatics, UK). Low-quality base removal and adapter trimming was carried out using Trim Galore (version 0.2.7, Babraham Bioinformatics, UK) in the RRBS-specific mode. A Sanger Phred score of 20 was used as the cut-off criterion for removal of low-quality reads. The 3' Illumina adapter was trimmed and any reads shorter than 20 bp were removed. Trimmed reads were mapped to the bovine genome assembly ARS-UCD1.2 using Bismark software (version 0.16.1, Babraham Bioinformatics, Cambridge, UK), which uses the Bowtie short read aligner. The options -n 1 and pcat were selected, which permits up to one mismatch in the seed region and indicates the sequencing library was constructed following a post-bisulfite adapter tagging (PBAT) protocol, respectively. Methylation information was extracted using the Bismark methylation extractor (version 0.16.1, Babraham Bioinformatics) at the base resolution. The ignore 6 option was selected to ignore the first 6 bp of each read to reduce methylation bias typically observed in PBAT libraries. A minimum read coverage of 5 and minimum quality score of 20 at each base position was applied. Sequencing read counts and levels of methylation were calculated with the methylKit package in R (https://www.r-project.org/). Only cytosines with more than 10 reads were analyzed further. Differentially methylated cytosines (DMC) between HT and CL were identified via logistic regression with a cut-off value of 15% methylation difference and a q-value <0.2. The DMC that had a lower level of methylation in the HT group relative to the CL group were hypomethylated. Conversely, DMC that had a higher level of methylation in the HT group relative to the CL group were hypermethylated. Principal component analysis was used to distinguish groups based on intra- and intergroup variability in methylation levels. Differentially methylated genes (DMG) have at least 1 differentially methylated cytosine. Pathways, biological processes, and molecular functions of DMG were identified using the Kyoto Encyclopedia for Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/

<figure><img src="image_2.png" /><figcaption>**Figure 1.** (A) Heifers born from the HT dams were lighter at birth (31.4 vs. 34.8 ± 1.59 kg; *P* = 0.03) and (B) the gestation length was shorter on the animals exposed to HT compared with the CL-exposed animals (274.2 vs. 277.2 ± 1.46 d; *P* = 0.04) during the dry period (54 ± 5 d). Data are presented as LSM ± SE.</figcaption></figure>

pathway.html), Medical Subject Headings (MeSH) database (https://www.ncbi.nlm.nih.gov/mesh/), and Gene Ontology database (GO; Gene Ontology Consortium; https://geneontology.org/).

## RESULTS

### Fetal and Placental Characterization

Animals were exposed to an average environmental THI of 79 (study 1), 77 (study 2), and 77 (study 3) during the late-gestation period. Confirming that treatment was effective, cows under HT had greater RR (53.2 vs. 67.9 ± 2.39 bpm; *P* < 0.001; n = 20) compared with the CL group during late gestation. In addition, HT induced increases in skin temperature (34.0°C vs. 36.0°C ± 0.26°C; *P* < 0.001; study 1; n = 6) and RT (38.53°C vs. 38.90°C ± 0.08°C; *P* < 0.0003; studies 2 and 3; n = 14). Heifers born from the HT dams were lighter at birth (31.4 vs. 34.8 ± 1.59 kg; *P* = 0.03; Figure 1A) and the gestation length was shorter for the cows exposed to HT compared with CL (274.2 vs. 277.2 ± 1.46 d; *P* = 0.04; Figure 1B). Placentas from cows that were under an active cooling system during late gestation tended to have a greater total

<table><caption>**Table 1.** Placentas from female calves were collected from a subset of cows following spontaneous expulsion (4.00 ± 1.54 h; n = 10 per treatment) exposed to CL or HT during the dry period (54 ± 5 d)<fnref n="1" /></caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Treatment</th><th colspan="3">P-value</th></tr><tr><th>CL</th><th>HT</th><th>± SE</th><th>TRT</th><th>Year</th><th>TRT × year</th></tr></thead><tbody><tr><td>Total cotyledon weight, kg</td><td>2.41</td><td>1.96</td><td>0.24</td><td>0.21</td><td>0.48</td><td>0.49</td></tr><tr><td>Membrane weight, kg</td><td>1.88</td><td>1.52</td><td>0.21</td><td>0.26</td><td>0.02</td><td>0.64</td></tr><tr><td>Time to expulsion, min</td><td>266</td><td>207</td><td>30.9</td><td>0.19</td><td>0.30</td><td>0.33</td></tr></tbody></table>

<footnote n="1">¹Data are presented as LSM ± SE. TRT = treatments HT or CL.</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Photographic representation of cotyledons of bovine placentas collected within 4 h of spontaneous expulsion (n = 10/treatment) with the criteria of placenta only of cows delivering female calves and integrity of the fetal membranes of animals exposed to HT (left) or CL (right) during the late-gestation period.</figcaption></figure>

placenta weight relative to the animals exposed to HT (4.54 vs. 3.54 ± 0.38 kg; *P* = 0.08; CL vs. HT respectively; Figure 2 and Figure 3A). The cotyledon number was greater in the CL placentas compared with the HT placentas (103.3 vs. 66.2 ± 8.65; *P* = 0.01; Figure 2 and Figure 3B). Furthermore, there was a higher placenta efficiency in the HT group (8.52% vs. 11.5% ± 0.91%; *P* = 0.02; Figure 3C) relative to the CL group. However, no differences were observed between the treatments for

total cotyledon weight, membrane weight, calf BW, and time of placenta expulsion (Table 1).

## Placental Histology

The H&E staining showed that cotyledons from dams exposed to HT during late gestation had a higher vascular area, compared with the CL group (43.1% vs. 31.8% ± 10.4% of total area; *P* = 0.04; Figures 4A and 4B). Un-

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Placenta parameters and efficiency of cows (n = 10/treatment) exposed to CL or HT during late gestation. (A) Placentas from CL animals that were under an active cooling system (i.e., shade, fans, and water soakers) during late gestation tended to have a greater total placenta weight relative to the animals without a cooling system and therefore exposed to HT (4.54 vs. 3.54 ± 0.38 kg; *P* = 0.08). (B) Cotyledon number was greater in the CL placenta compared with the HT placenta (103.3 vs. 66.2 ± 8.65; *P* = 0.01). (C) Furthermore, the HT group had a higher placenta efficiency (8.52% vs. 11.5% ± 0.91%; *P* = 0.02) relative to the CL group. The efficiency of the placenta was calculated by calf BW per placenta weight (both in kilograms). Data are presented as LSM ± SE.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Histological evaluation of cotyledons exposed to HT during late gestation. Photomicrography of H&E stained cotyledonary tissue at 5× magnitude (A). The HT cotyledons had a higher vascular area (VS) compared with the CL group with H&E staining (43.1% vs. 31.8% ± 10.4% of total area; *P* = 0.04; B). Photomicrography of the Masson's Trichrome staining on cotyledonary tissue at 5× (C). There was a tendency of greater total area for connective tissue (CT) on the CL group compared with the HT when evaluating the Masson's trichrome staining (65.8% vs. 52.7% ± 5.39% of total area; *P* = 0.09; D). Photomicrography of PSR-stained cotyledonary tissue at 5× (E). No significant differences were found between the groups in the percentage of EXM (47.9% vs. 46.9% ± 9.6%; *P* = 0.87; F). Data are presented as LSM ± SE.</figcaption></figure>

der Masson's trichome staining, there was a tendency of greater total area of connective tissue in cotyledons of the CL group compared with those of the HT group (65.8% vs. 52.7% ± 5.39% of total area; *P* = 0.09; Figures 4C and

4D). However, no significant differences were observed between the groups in extracellular matrix (EXM) components with PSR staining (47.9% vs. 46.9% ± 9.6%; *P* = 0.87; Figures 4E and 4F).

## Gene Expression

Upon analysis one of the CL RNA samples did not meet the requirements for sequencing and was therefore excluded. A PCA plot shows the clustering of transcriptomes of all genes from placenta (Figure 5), while the volcano plot and heat map describe the differentially expressed genes in the HT group (Figure 6). A total of 289 differentially expressed genes (DEG; Supplemental Table S1, see Notes) were identified, where 179 genes were upregulated and 110 were downregulated comparing HT versus CL groups with the DESeq2 *P*-value ≤0.05 and log₂FoldChange ≥0.0. The number of DEG indicates a marked effect of HT on placental cotyledon tissue. We delved deeper into the transcriptional regulation affected by HT, by examining selected genes of interest known to affect placental function. The genes selected were *PGF* (placental growth factor), *HSPB8* (heat shock protein β-8), *IL6* (interleukin-6), *HBA/HBB* (hemoglobin subunit α/β), *IGFGB2* (IGB binding protein 2), *PCK2* (phosphoenolpyruvate carboxykinase), *MMP12* (matrix metallopeptidase 12), *PTGFR* (prostaglandin F2-α receptor), *PAPPA2* (pregnancy associated plasma protein A2), *WNT5A* (protein WNT), *PAG14* (pregnancy associated glycoprotein 14), and *SLC7A10* (solute carrier family 7 member 10; Table 2).

Enrichment analysis of DEG with GO and KEGG revealed changes in some important placental function pathways. Under HT, GO enrichment identified a dysregulation of gas and oxygen transport, detoxification, inflammatory response, and negative regulation of transport and blood circulatory pathways relative to the CL treatment. Under HT, KEGG enrichment identified a dysregulation of synthesis and secretion of cortisol, insulin secretion, AA biosynthesis and cellular senescence pathways relative to the CL treatment (Table 3).

## DNA Methylation

A total of 2,936 DMC were identified between the HT and CL groups, with most of them located in exonic regions of genes. Differentially methylated genes of interest regulating placenta structure and function were selected and the pathways regulating placental function and molecular processes including placental regulation (M16071), angiogenesis (GO:0045765/M160/M22215), and extra cellular matrix regulation (M25883/
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Principal component (PC) analysis and heatmap of the DEG expression levels between the HT and CL groups pooled as the differential gene set showing the overall results of FPKM cluster analysis, clustered using the log2(FPKM+1) value. Red indicates genes with high expression levels, and green indicates genes with low expression levels. The color ranging from red to green indicates that log2(FPKM+1) values from large to small. Individual points represent cow ID with treatment.</figcaption></figure>

GO:0062023/M16395/M26953; Table 4). We identified through enrichment analysis that the DMG are involved in EXM structural components and angiogenesis (COL1A1, COL4A1, COL6A3, COL15A1, FBLN1, GPA4, and TRAM2), inflammatory response and cytokine activation (LRRC32, TNFRSF1B, PLXNA2, SMPDL3B, and SERPINB9) and EXM degradation (MMP11; Figure 7).

DISCUSSION

In our study we found that HT during late gestation affects placental structure, specifically causing a reduction in the number of cotyledons in the HT animals. However, placentas from cows exposed to HT demonstrated improved efficiency. Similarly, Bell et al. (1989) reported

<table><caption>**Table 2.** Cotyledon transcripts from cows that were exposed to late-gestation HT and CL (n = 5/treatment) with the selected genes involved in placental function with their respective protein, log2 fold change (FCH) quantified in HT relative to CL, and raw P-value<fnref n="1" /></caption><thead><tr><td>Gene</td><td>Protein</td><td>FCH</td><td>P-value</td></tr></thead><tbody><tr><td>SLC7A10</td><td>Solute carrier family 7 member 10</td><td>-5.02</td><td>0.03</td></tr><tr><td>PGF</td><td>Placenta growth factor</td><td>-1.31</td><td>0.02</td></tr><tr><td>PCK2</td><td>Phosphoenolpyruvate carboxykinase</td><td>-0.99</td><td>0.04</td></tr><tr><td>HSPB8</td><td>Heat shock protein beta-8</td><td>-0.56</td><td>0.03</td></tr><tr><td>IGFPB2</td><td>IGF binding protein 2</td><td>1.28</td><td>0.02</td></tr><tr><td>HBA/HBB</td><td>Hemoglobin subunit alpha/beta</td><td>-2.98/-2.28</td><td>0.001/0.01</td></tr><tr><td>PTGFR</td><td>PGF2a receptor</td><td>1.62</td><td>0.03</td></tr><tr><td>IL6</td><td>Interleukin-6</td><td>1.95</td><td>0.001</td></tr><tr><td>WNT5A</td><td>Protein WNT</td><td>2.46</td><td>0.01</td></tr><tr><td>MMP12</td><td>Matrix metallopeptidase 12</td><td>2.78</td><td>0.004</td></tr><tr><td>PAPPA2</td><td>Pregnancy associated plasma protein A2</td><td>3.51</td><td>0.006</td></tr><tr><td>PAG14</td><td>Pregnancy associated glycoprotein 14</td><td>5.43</td><td>0.01</td></tr></tbody></table>

<footnote n="1">¹A negative log2 FCH indicates downregulation of the gene and positive values indicate an upregulation of the gene.</footnote>
<figure><img src="image_7.png" /><figcaption>**Figure 6.** The DEG significantly different between the HT and CL groups, with 289 genes in total, 179 upregulated and 110 downregulated genes (A). The volcano plot shows the distribution of DEG in pairwise comparison. The names of the most affected genes are annotated. The pink dots represent the upregulated genes, green dots show the downregulated genes, and the blue dots mean not significant (*P* < 0.05; B).</figcaption></figure>

reduced placental weights in late pregnancy with smaller cotyledons, and Early et al. (1991) observed a reduction in cell division in ewes. In contrast, Potadle et al. (2019) found that late-gestation cows exposed to heat stress had an increased number of cotyledons, as well as greater cotyledonary weights, surface area, and volume. Similarly,

Silva et al. (2021) reported that goats exposed to HT took longer to deliver their placenta and had a greater number of cotyledons, but exhibited poorer placental efficiency compared with those in thermoneutral conditions. Several studies have shown that HT negatively affects placental cells, cotyledon number, placental weight and area, and

<table><caption>**Table 3.** Enrichment analysis of the DEG between HT and CL (n = 5/treatment) of Holstein cows during the 54 ± 5 d of gestation with pathways affecting placental function<fnref n="1" /></caption><thead><tr><th>ID</th><th>Pathway name</th><th>P-value</th><th>DEG</th><th>Up</th><th>Down</th></tr></thead><tbody><tr><td>GO:0051048</td><td>Negative regulation of secretion</td><td>5.22E-05</td><td>9</td><td>8</td><td>1</td></tr><tr><td>GO:0009636</td><td>Response to toxic substance</td><td>0.00017</td><td>11</td><td>4</td><td>7</td></tr><tr><td>GO:0098754</td><td>Detoxification</td><td>0.000872</td><td>6</td><td>0</td><td>6</td></tr><tr><td>GO:0006954</td><td>Inflammatory response</td><td>0.006435</td><td>12</td><td>9</td><td>3</td></tr><tr><td>GO:0031348</td><td>Negative regulation of defense response</td><td>0.010129</td><td>6</td><td>6</td><td>0</td></tr><tr><td>GO:0051051</td><td>Negative regulation of transport</td><td>0.018694</td><td>9</td><td>8</td><td>1</td></tr><tr><td>GO:0051224</td><td>Negative regulation of protein transport</td><td>0.023228</td><td>5</td><td>4</td><td>1</td></tr><tr><td>GO:0050727</td><td>Regulation of inflammatory response</td><td>0.024436</td><td>7</td><td>6</td><td>1</td></tr><tr><td>GO:0050707</td><td>Regulation of cytokine secretion</td><td>0.025592</td><td>5</td><td>5</td><td>0</td></tr><tr><td>GO:0010765</td><td>Positive regulation of sodium ion transport</td><td>0.034187</td><td>2</td><td>0</td><td>2</td></tr><tr><td>bta04927</td><td>Cortisol synthesis and secretion</td><td>0.055</td><td>3</td><td>2</td><td>1</td></tr><tr><td>bta04911</td><td>Insulin secretion</td><td>0.084</td><td>3</td><td>2</td><td>1</td></tr><tr><td>bta01230</td><td>Biosynthesis of amino acids</td><td>0.092</td><td>3</td><td>0</td><td>3</td></tr><tr><td>bta04218</td><td>Cellular senescence</td><td>0.093</td><td>5</td><td>3</td><td>2</td></tr></tbody></table>

<footnote n="1">¹Enrichment analysis of DEG was performed through the KEGG database and GO database. The DEG are shown, along with the up- or downregulation of HT relative to CL, as well as the corresponding *P*-value.</footnote>
<table><caption>**Table 4.** Significant pathways, biological functions, and molecular processes of DMG in the HT cotyledonary tissue of cows relative to the CL tissue<fnref n="1" /></caption><thead><tr><th>Pathway</th><th>Total genes</th><th>DMG</th><th>q-value</th></tr></thead><tbody><tr><td>Ensemble of genes encoding core extracellular matrix</td><td>203</td><td>45</td><td>&lt;0.001</td></tr><tr><td>Assembly of collagen fibrils and other multimeric structures</td><td>48</td><td>16</td><td>&lt;0.001</td></tr><tr><td>A collagen-containing extracellular matrix</td><td>75</td><td>21</td><td>&lt;0.001</td></tr><tr><td>Cartilage development to form mature structure</td><td>62</td><td>18</td><td>&lt;0.001</td></tr><tr><td>Extracellular matrix consisting of physical scaffolding for the cellular constituents</td><td>312</td><td>57</td><td>&lt;0.001</td></tr><tr><td>Collagen formation</td><td>68</td><td>18</td><td>&lt;0.001</td></tr><tr><td>Placenta genes</td><td>407</td><td>70</td><td>&lt;0.001</td></tr><tr><td>A protein complex left-handed triple-helix collagen</td><td>61</td><td>15</td><td>0.003</td></tr><tr><td>Collagen chain trimerization</td><td>29</td><td>9</td><td>0.003</td></tr><tr><td>Genes encoding collagen proteins</td><td>29</td><td>9</td><td>0.003</td></tr><tr><td>Collagen type V</td><td>7</td><td>4</td><td>0.004</td></tr><tr><td>Extracellular matrix and collagens</td><td>194</td><td>35</td><td>0.004</td></tr><tr><td>Pregnancy associated plasma protein A</td><td>4</td><td>3</td><td>0.005</td></tr><tr><td>Cartilage development extracellular matrix containing collagen type II</td><td>30</td><td>9</td><td>0.005</td></tr><tr><td>Crosslinking of collagen fibrils</td><td>16</td><td>6</td><td>0.006</td></tr><tr><td>Collagen triple-helix repeat</td><td>62</td><td>14</td><td>0.007</td></tr><tr><td>Collagen fibril organization</td><td>39</td><td>10</td><td>0.009</td></tr><tr><td>Collagen biosynthetic process</td><td>9</td><td>4</td><td>0.012</td></tr><tr><td>Molecular signals initiated by the binding of collagen</td><td>9</td><td>4</td><td>0.013</td></tr><tr><td>Collagen trimer</td><td>36</td><td>9</td><td>0.015</td></tr><tr><td>Progression of a cartilage element by extracellular matrix containing collagen type II</td><td>191</td><td>32</td><td>0.016</td></tr><tr><td>Collagen degradation</td><td>48</td><td>11</td><td>0.017</td></tr><tr><td>Molecular signals initiated by collagen binding to a cell surface receptor</td><td>10</td><td>4</td><td>0.020</td></tr><tr><td>Collagen-activated tyrosine kinase receptor signaling pathway</td><td>11</td><td>4</td><td>0.026</td></tr><tr><td>Collagen-containing extracellular matrix</td><td>88</td><td>16</td><td>0.032</td></tr><tr><td>Collagen type II</td><td>23</td><td>6</td><td>0.035</td></tr><tr><td>Collagen trimer that is part of a basement membrane</td><td>7</td><td>3</td><td>0.036</td></tr><tr><td>Regulation of angiogenesis</td><td>24</td><td>6</td><td>0.043</td></tr><tr><td>Integrins in angiogenesis</td><td>62</td><td>12</td><td>0.045</td></tr><tr><td>The multiplication of blood vessel endothelial cells</td><td>18</td><td>5</td><td>0.046</td></tr><tr><td>Collagen biosynthesis and modifying enzymes</td><td>49</td><td>10</td><td>0.047</td></tr></tbody></table>

placental efficiency (Galan et al., 1999; Ward et al., 2002; Dwyer et al., 2005; Konyali et al., 2007; Ocak et al., 2015). This evidence suggests that exposure to HT during pregnancy may trigger a compensatory mechanism in

the placenta due to nutrient restriction and hyperthermia, resulting in an increase in individual cotyledon surface area. Nonetheless, placentas are working harder to adapt and compensate for these changes and stressors to guar-

<figure><img src="image_8.png" /><figcaption>**Figure 7.** Number and direction of the DMC corresponding to the DMG in the cotyledonary tissue of HT cows compared with those of CL cows during the late-gestation period. The DMG are of the significant pathway of M16071: placental regulation (from Table 4), identified through enrichment analysis. Hypomethylated cytosines are indicated by the black bars. White bars denote hypermethylated cytosines. Hypo- or hypermethylation of cytosines refers to the HT group relative to the CL group.</figcaption></figure>

<footnote n="1">¹Enrichment analysis of DMG was achieved through the KEGG, MeSH, and GO Consortium databases. Differentially methylated cytosines between HT and CL were identified via logistic regression with a cut-off value of 15% methylation difference and a q-value < 0.2.</footnote>
antee the success of the pregnancy and our results support this hypothesis, because we observed greater placental efficiency and only a modest decrease in calf birthweight in the HT group relative to the CL calves.

One of the key findings from our research indicates that HT has a significant effect on the major genes that regulate the EXM function. Gene expression and DNA methylation data show that HT affects the mechanisms of the genes responsible for expressing structural proteins (such as collagens and elastin), adhesion glycoproteins (such as fibronectin and laminin), and proteoglycans (such as syndecan), which are significantly downregulated compared with placentas from cows with access to active cooling in late gestation. The EXM not only provides structural support to the placental tissue, but also creates an environment conducive to the effective placental cell function (Franczyk et al., 2017). Although there is a lack of evidence regarding the importance of the EXM for the ruminant placenta, in other species, specifically in mice, Kuo et al. (2018) reported that the placental EXM is not only responsible for structural support but also regulates cellular signaling-modulating processes such as proliferation and motility. As observed in our DNA methylation results, the genes that regulate collagen types were mostly found to be hypermethylated with HT, indicating an altered expression of those genes that may reflect an adaptation of the placental structure. This may have contributed to the effect on the EXM architecture and ultrastructure organization, as described by Barreto et al. (2018, 2022), who reported that the overexpression of some specific EXM proteins could exacerbate the remodeling associated with cell proliferation and blood vessels, leading to morphophysiological alterations. The EXM is important for maintaining vascular architecture and presenting basement membrane proteins of the placenta. Another significant structural protein, matrix metalloproteinase 12 (MMP12), was upregulated by HT. This protein plays a major role in EXM biosynthesis and is associated with collagen degradation (Shi et al., 2020). Therefore, despite the limited number of samples available in the present study, the gene expression and methylation patterns observed with HT are consistent with the shifts in placental morphology identified at the structural level.

One major function of the placenta is transporting nutrients and facilitating oxygen diffusion to the developing fetus. This is only possible with proper angiogenesis, which is the growth of new blood vessels. We found that exposure to high temperatures during late pregnancy impaired the expression of genes that regulate angiogenesis. Specifically, placental growth factor (PGF), vascular endothelial growth factor (VEGF), collagen types, and hemoglobin subunits A and B (HBA/HBB) genes were hypermethylated, and transcript abundance was reduced.

Reduced expression of PGF and VEGF can significantly impair placental angiogenesis. A potent angiogenic factor, VGEF promotes endothelial cell proliferation and new vessel formation (Ferrara, 2004). A member of the VEGF family, PGF plays a critical role in placental vascular development and maternal-fetal blood flow (Sahay et al., 2009). Collagen types are vital for maintaining the structural integrity of the placental vasculature, and alterations in collagen gene expression can compromise vascular stability and function (Kirk et al., 2003). Additionally, HBA and HBB are crucial for oxygen transport, and the reduced expression of these genes can lead to decreased oxygen-carrying capacity, further compromising fetal gas exchange (Levy et al., 2004). The degree of vascularization depends on various factors, such as cell proliferation, migration, and differentiation, and these processes are affected by the synthesis and secretion of metabolites or intermediates and mutual coordination or inhibition in these pathways (Huang et al., 2021). Another crucial pathway for angiogenesis is the Wnt signaling pathway. We observed that one of the major proteins involved in this pathway, the *WNT5A* gene, was upregulated by HT. The results of our study suggest that exposure to HT during the late stages of gestation impairs the pathways that regulate angiogenesis and vasculature of the placenta.

The H&E histological staining revealed notable differences in the percentage of the vascular area with HT, indicating that modifications and remodeling of the placental vasculature were occurring to enhance fetal thermoregulation. Fetal thermoregulation is a critical aspect of development, and the placenta's ability to adapt its vasculature is essential for this process. An increased vascular surface area and enhanced blood flow facilitate heat exchange, helping to maintain the fetal temperature within a narrow physiological range (Jansson and Powell, 2007). The placenta regulates blood flow between the dam and fetus, playing a crucial role in fetal thermoregulation. Enhanced blood flow through remodeled vasculature helps dissipate heat generated by the fetus, maintaining optimal temperature (Burton and Fowden, 2015). Our research has critical implications for enhancing our understanding of the effects of HT on vascularization regulation during gestation, enabling adaptation to thermoregulatory insults.

## CONCLUSIONS

In conclusion, our study provides comprehensive insights into the effects of heat stress on placental morphology and gene expression during late gestation in dairy cattle. By elucidating the molecular mechanisms underlying the impact of maternal heat stress exposure and the associated placental alterations, we contribute
to a better understanding of the reproductive challenges posed by environmental stressors in livestock. These findings have implications for livestock management practices aimed at mitigating heat stress and optimizing reproductive outcomes in dairy herds. Furthermore, our study highlights the importance of continued research in this area to develop targeted interventions that safeguard placental health and promote successful pregnancy outcomes in dairy cattle exposed to heat stress. The changes in placental gene expression of key regulators of oxygen diffusion and nutrient transporters identified herein warrant further investigation.

## NOTES

This study received no external funding. Supplemental material for this article is available at https://figshare.com/s/d8e227dca47a5400e446. All procedures were approved by the University of Florida Institutional Animal Care and Use Committee. The authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** CL = active cooling; CT = connective tissue; DEG = differentially expressed genes; DMC = differentially methylated cytosines; DMG = differentially methylated genes; EXM = extracellular matrix; FCH = fold change; FPKM = fragments per kilobase million; GO = Gene Ontology; H&E = hematoxylin and eosin; HBA = hemoglobin subunit A; HBB = hemoglobin subunit B; HT = heat stress; KEGG = Kyoto Encyclopedia of Genes and Genomes; PBAT = post-bisulfite adapter tagging; PC = principal component; PGF = placental growth factor; PSR = picrosirius red stain; RR = respiration rate; RRBS = double restriction enzyme reduced representation bisulfite sequencing; RT = rectal temperature; THI = temperature-humidity index; TRT = treatments HT or CL; VEGF = vascular endothelial growth factor; VS = vascular area.

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ORCIDS

L. T. Casarotto, ● https://orcid.org/0000-0001-9514-387X

J. Laporta, ● https://orcid.org/0000-0002-3186-5360

F. Peñagaricano, ● https://orcid.org/0000-0001-6661-3991

J. Bromfield, ● https://orcid.org/0000-0001-5438-2137

G. E. Dahl ● https://orcid.org/0000-0002-2182-6317