Identification of large offspring syndrome during pregnancy through ultrasonography and maternal blood transcriptome analyses

Rivera, Rocío Melissa; Goldkamp, Anna Katherine; Patel, Bhaumik Narendrabhai; Hagen, Darren Erich; Soto-Moreno, Edgar Joel; Li, Yahan; Kim, Chris Nayoon; Miller, Cliff; Williams, Fred; Jannaman, Elizabeth; Xiao, Yao; Tribulo, Paula; Estrada-Cortés, Eliab; Brau-Rodríguez, Astrid Roshealy; Hansen, Peter James; Wu, Zhoulin; Spinka, Christine Marie; Martin, Neal; Elsik, Christine G.

doi:10.1038/s41598-022-14597-w

Download PDF

Article
Open access
Published: 22 June 2022

Identification of large offspring syndrome during pregnancy through ultrasonography and maternal blood transcriptome analyses

Rocío Melissa Rivera¹,
Anna Katherine Goldkamp²,
Bhaumik Narendrabhai Patel¹,
Darren Erich Hagen²,
Edgar Joel Soto-Moreno¹,
Yahan Li¹,
Chris Nayoon Kim¹,
Cliff Miller³,
Fred Williams III⁴,
Elizabeth Jannaman⁵,
Yao Xiao⁵,
Paula Tribulo⁵^nAff8,
Eliab Estrada-Cortés⁵^nAff9,
Astrid Roshealy Brau-Rodríguez¹,
Peter James Hansen⁵,
Zhoulin Wu¹^nAff10,
Christine Marie Spinka⁶,
Neal Martin⁷ &
…
Christine G. Elsik¹

Scientific Reports volume 12, Article number: 10540 (2022) Cite this article

2372 Accesses
6 Citations
12 Altmetric
Metrics details

Subjects

Abstract

In vitro production (IVP) of embryos in cattle can result in large/abnormal offspring syndrome (LOS/AOS) which is characterized by macrosomia. LOS can cause dystocia and lead to the death of dam and calf. Currently, no test exists to identify LOS pregnancies. We hypothesized that fetal ultrasonography and/or maternal blood markers are useful to identify LOS. Bovine fetuses were generated by artificial insemination (control) or IVP. Fetal ultrasonographies were taken on gestation D55 (D55) and fetal collections performed on D56 or D105 (gestation in cattle ≈ D280). IVP fetuses weighing ≥ 97 percentile of the control weight were considered LOS. Ultrasonography results show that the product of six D55 measurements can be used to identify extreme cases of LOS. To determine whether maternal blood can be used to identify LOS, leukocyte mRNA from 23 females was sequenced. Unsupervised hierarchical clustering grouped the transcriptomes of the two females carrying the two largest LOS fetuses. Comparison of the leukocyte transcriptomes of these two females to the transcriptome of all other females identified several misregulated transcripts on gestation D55 and D105 with LOC783838 and PCDH1 being misregulated at both time-points. Together our data suggest that LOS is identifiable during pregnancy in cattle.

Paternal microbiome perturbations impact offspring fitness

Article Open access 01 May 2024

Tracking single-cell evolution using clock-like chromatin accessibility loci

Article Open access 09 May 2024

Early pregnancy ultrasound measurements and prediction of first trimester pregnancy loss: A logistic model

Article Open access 31 January 2020

Introduction

Large offspring syndrome (LOS) is an overgrowth condition observed in ruminant fetuses and neonates^1,2. LOS was first reported in 1991 in a cloned calf produced via nuclear transfer³. Later, in 1995, overgrowth was reported as a result of non-invasive in vitro production (IVP) procedures⁴. At that time, the overgrown animals were called “large calves” and the syndrome was coined LOS⁵. As the use of IVP in ruminants increased during the next decade, so did the number of LOS reports^{6,7,8,9,10,11}. The main characteristic of LOS is overgrowth, which in some instances can result in calves weighing twice the average birthweight of their breed⁴. However, LOS in ruminants is a complex disorder with other phenotypes observed including visceromegaly, macroglossia, increased incidence of hydro-allantois, abnormal limbs and spinal cord, ear malformation, hypoglycemia, and umbilical hernia^{6,7,9,10,11,12,13,14}. Because of these varied phenotypes, this syndrome is also known as abnormal offspring syndrome (AOS)¹¹.

Even though it is now clear that LOS/AOS is a multi-locus loss-of-imprinting (i.e., epigenetic) condition¹⁴, it is still not known what triggers LOS and which assisted reproductive technology procedure (e.g., in vitro maturation of oocytes, in vitro fertilization, in vitro embryo culture of embryos or embryo transfer) is involved. Several reported cases of LOS in the literature were produced using serum supplementation during oocyte maturation and/or during embryo culture, which suggest that serum may be a factor promoting the syndrome^1,2,4,15,16. Serum has been experimentally determined to cause LOS in sheep^16,17,18 and bovine offspring derived from embryos cultured in serum containing medium can develop LOS². In addition, the syndrome can also occur in fetuses and calves derived from embryos cultured without serum supplementation^19,20 and, more recently, we have documented that this syndrome occurs spontaneously in cattle produced by natural or artificial insemination^21,22,23. The latter is of interest as there is a similar loss-of-imprinting overgrowth syndrome in humans, namely Beckwith–Wiedemann syndrome, which occurs naturally, and its incidence is increased in children conceived by assisted reproduction²⁴.

Due to its large size, LOS can cause dystocia and, sometimes, cesarean section is needed for delivery²⁵. Even if the newborn calf survives the difficult birth, the enlarged tongue or extreme body weight make suckling difficult, thus increasing the chances of postnatal death²⁶. In addition to the possible death of calves and cows, other financial losses are incurred due to veterinary costs²² and the associated negative economic impact in terms of losses in milk, fat, and protein yields in the subsequent lactation^27,28. For example, two independent LOS cases have been recently reported with total estimated losses of approximately $30,000 each²². These monetary losses could have been minimized if the early identification of LOS was possible. To date, however, no test exists to predict LOS pregnancies in cattle. As IVP is the current method of choice to improve genetic merit of the offspring in the cattle industry^29,30 it is of particular importance to find biomarkers to identify fetal overgrowth early during gestation to help producers decide whether to terminate the pregnancy or prepare for a difficult birth.

Ultrasound is a valuable non-invasive and repeatable tool that has been widely used in cattle to determine fetal growth^7,31,32, fetal age³³, fetal sex³⁴ and clinical pathologies such as mummified fetuses or endometritis³⁵. In addition, blood biomarkers have been successfully used as a non-invasive method to determine pregnancy status. For example, ISG-15 mRNA from pregnant cattle leukocytes³⁶ and pregnancy associated glycoproteins from bovine maternal blood serum are useful markers of early pregnancy³⁷. Whether maternal blood components can be used to identify LOS early in pregnancy is not known.

For our study, we hypothesized that LOS can be identified during pregnancy in cattle by use of ultrasonography and/or maternal blood leukocyte mRNA biomarkers. The approach was to generate embryos by artificial insemination (AI; control) or by in vitro procedures previously shown by us to generate overgrown fetuses and to perform ultrasonographic measurements of those fetuses at day 55 (D55) of gestation and transcriptome analysis of maternal blood leukocytes on D55 and D105 of gestation.

Materials and methods

The study is reported in accordance with ARRIVE guidelines³⁸.

Heifers

All animal procedures were conducted in accordance with the Guide for the Care and Use of Agricultural Animals in Research and Teaching and approved by the Institutional Animal Care and Use Committee of the University of Missouri (Protocol #9455). All animals were kept at the University of Missouri South Farm Research Center in Columbia.

Angus crossbred heifers of approximately 18–20 months of age were synchronized and selected for breeding using the 14-day controlled internal drug release (CIDR^®, Zoetis, Kalamazoo, MI) prostaglandin and timed artificial insemination protocol (Supplemental Fig. 1).

In vitro and in vivo production of embryos and embryo transfer

Media and procedures were as previously described by us^2,39. Briefly, Bos taurus taurus (B. t. taurus; Angus/Angus-Crossbred) oocytes were harvested from slaughterhouse ovaries. Oocytes were removed from maturation medium after ~ 21 h of culture and inseminated with semen from one B. t. indicus male (Brahman breed [JDH MR MANSO 7 960958 154BR599 11200 EBS/INC CSS 2]). Putative zygotes were stripped of cumulus cells by five minutes of vigorous vortexing at approximately 18 h after insemination and then cultured in KSOM supplemented with amino acids in a humidified atmosphere containing 5% O₂, 5% CO₂, and 90% N₂. On D5 after insemination, the culture medium was supplemented with 10% (v/v) estrus cow serum (collected and prepared in house and previously used in Ref.²) and embryos returned to the incubator. On D7, blastocyst-stage IVP embryos were selected, washed in BioLife Holding & Transfer Medium (AgTech; Manhattan, KS), and loaded in groups of two into 0.25 cc yellow, direct transfer and irradiated straws (AgTech). Blastocysts were transferred to synchronized recipient females on D7 after estrus (Supplemental Fig. 1).

Rationale for experimental design

This experiment is a part of a large-scale study aimed at the identification of epigenetic misregulations in LOS (as defined by IVP fetuses weighing ≥ 97 percentile of the weight of the control fetuses). Based on our previous results for pregnancy rates from IVP embryos, percent LOS on D105, and transcriptome and methylome analyses⁴⁰, we calculated that four LOS males and four LOS females would be required to achieve 90% power. Therefore, we transferred two embryos per heifer (as we did in Ref.²) to achieve that goal. In our previous study², body weight did not differ between singletons and twins on D105 of gestation. It should be noted that twinning occurs in cattle⁴¹. In addition, the rationale for generating Bos taurus indicus × Bos taurus taurus F1 fetuses in this study is that since LOS is a loss-of-imprinting condition, allele sequence differences are required to characterize parental-specific genomic (mis)regulation in the F1. This has been a useful breeding scheme to enhance the list of imprinted genes in bovine and to identify loss-of-imprinting in LOS^40,42.

D55 and D77 fetal ultrasound morphometry

At D55 of pregnancy, presumed pregnant animals were checked for the presence of a fetus/es by transrectal ultrasonography using a SonoSite EDGE ultrasound machine equipped with a L52 10.0–5.0 megahertz linear-array transducer (Supplemental Fig. 1). Images acquired by ultrasound were analyzed using software resident on the machine. Ultrasound measurements taken were abdominal height, abdominal diameter, thoracic diameter, thoracic height, crown rump length, head length, and biparietal diameter (Fig. 1A). Fetal sex was determined by ultrasonography on D77 of gestation (Supplemental Fig. 1). Furthermore, fetal morphometry was assessed on D77 in a subset of 18 animals (6 AI and 12 IVP animals). Ultrasound measurements taken on D77 were abdominal height, abdominal diameter, thoracic diameter, thoracic height, crown rump length, head length, and biparietal diameter.

Surgical fetal collections of D56 and D105 fetuses

Heifers (n = 51 for D56; n = 48 for D105) were fasted at least 12 h prior to surgery. Fetuses were surgically retrieved to preserve nucleic acid integrity. All surgical procedures were performed by a licensed veterinarian.

Collection of fetal tissues and fetal measurements of D56 and D105 fetuses

Collected fetuses and their fetal membranes were weighed, fetal morphometry was assessed, and abnormal phenotypes were noted. Measurements were crown-rump length, heart girth, forelimb length, biparietal length, abdominal height, head length and thoracic height. Subsequently, all tissues (i.e. pancreas, kidney, liver, ears, skeletal muscle, heart, diaphragm, tongue, buccal mucosa, umbiliculs, placenta [cotyledons and intercotyledon], brain, reproductive tract, gonad, skin, stomach, intestine, lung, spleen, tail, leftover carcass) were dissected and divided in two and immediately frozen in liquid nitrogen. For all collections, the same person measured and weighed all fetuses and fetal membranes, and another person (veterinary anatomic pathologist) dissected all the tissues. The average time from excision of the fetus from the uterus to when all tissues were frozen in liquid nitrogen was approximately 18 min. All tissues were stored at − 86 °C until further use.

Image analysis of crown rump length and umbilicus diameter in D105 fetuses

Measurement of D105 fetuses’ crown rump length (from the top of the head to base of the tail) and diameter of the umbilicus (at the base of the umbilicus where it protrudes from the body) were measured using the ImageJ’s⁴³ freehand line function using a lateral side picture of the D105 fetuses. The surface where the fetuses laid was squared (each square = 2.54 cm) and was used to convert all measurements to cm, and then a ratio of umbilicus diameter to crown rump length was determined.

Maternal blood collection and processing

Maternal blood was collected via tail venipuncture on D55 and D105 of pregnancy into K3 EDTA tubes (BD, Franklin Lakes). Blood processing was done as described earlier in Ref.⁴⁴. Briefly, blood containing tubes were centrifuged at 1200×g for 20 min at 4 °C. The buffy coat was transferred to 15 ml centrifuge tubes containing 12 ml of red blood cell lysis buffer (150 mM NH₄Cl, 10 mM NaHCO₃, 1 mM EDTA, pH 7.0). White blood cells (WBC = leukocytes) containing tubes were briefly vortexed, incubated at room temperature for 5 min, and later centrifuged at 300×g for 10 min at 4 °C. After discarding the supernatant, the WBC pellet was washed once in 5 ml red blood cell lysis buffer and then with 5 ml ice-cold 1× Dulbecco’s phosphate buffered saline, with centrifugation at 300×g at 4 °C for 5 min at each wash. After discarding the supernatant, the WBC pellet containing tubes were placed immediately on dry ice and then stored at − 86 °C until use.

Selection of samples for RNA sequencing (RNAseq)

The WBC samples of 23 D105 pregnant heifers were selected for RNAseq on the basis of their group (AI or IVP), weight of IVP fetuses (≥ 97 percentile of AI weight = IVP-LOS or < 97 percentile of AI weight = IVP-Normal), fetal sex, and whether the females carried one or two fetuses in the IVP group. In addition, the D55 WBC samples of the same 23 females were also used for transcriptome analyses (Supplemental Fig. 2).

RNA isolation and transcriptome analyses

Total RNA was isolated using Trizol™ reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions and stored at − 86 °C until use.

Library preparation and transcriptome sequencing

RNA processing and sequencing was performed by BGI Americas Corporation (Cambridge, MA). Libraries were sequenced using the DNBSEQ-G400 platform to generate 20 million 100 bp paired end reads.

RNAseq data analysis

Base quality and adapter contamination was assessed with FastQC and low quality (Pred < 20) raw reads trimmed using DynamicTrim. Trimmed reads less than 60 bases in length were removed with SolexaQA++ LengthSort function⁴⁵. Retained paired-end reads were aligned to the bovine reference genome, ARS-UCD1.2⁴⁶ with HISAT2 v2.1.0⁴⁷ with the parameter adjustment (–mp 6,6; –score-min L,0,-0.2; –known-splicesite-infile) included to improve specificity. Total read counts for each gene were calculated by using the HTseq-count default union-counting module⁴⁸ using NCBI (GCF_002263795.1_ARS-UCD1.2) RefSeq gene set.

Hierarchical clustering

Raw read counts were normalized and the dist(method = ‘euclidean’) function was used to compute the distances between the rows of normalized count data. Finally, unsupervised hierarchical clustering was applied using the average linkage method and the base R hclust function to build the dendrogram.

Differential expression analysis

Differential expression analysis was performed using edgeR v3.32.1⁴⁹ and DESeq2 v1.30.1⁵⁰. Likelihood ratio tests were done using both packages (edgeR using glmLRT and DESeq2 using DESeq(test = LRT)). For edgeR, the raw read counts were normalized using RUVseq⁵¹. The betweenLaneNormalization function of EDAseq v2.24.0 was used to adjust for sequencing depth and then upper quantile normalization was done to remove unwanted variation⁵². The trimmed means of M values (TMM) was also used for edgeR analysis. For DESeq2, the median of ratios method of normalization was used using estimateSizeFactors and assigning the normalization factors back to our count matrix. Pairwise comparisons between each treatment (Control-AI vs IVP-Normal, IVP-Normal vs IVP-LOS, and Control-AI vs IVP-LOS) group at each time point (D55 and D105) were performed. In addition, we compared the transcriptome of the dams carrying the two largest IVP-LOS individuals (#604 and #664) against all other animals for D55 and D105. Furthermore, a pairwise comparison of WBC transcriptomes of females carrying two vs one fetuses was done to account for differential expression due to multiple fetuses (i.e. increased fetal mass). Genes with a false discovery rate (FDR; EdgeR) or adjusted P-value (padj; DEseq2) ≤ 0.05 were classified as significant. Significant genes from both packages were overlapped in order to generate a list of candidate genes for downstream analysis.

cDNA synthesis and quantitative RT-PCR (qRT-PCR)

Total RNA was treated with DNase (Fischer Scientific, Waltham, MA) and used as template to synthesize cDNA in a 20 μl reaction using oligo dT and Superscript IV (Invitrogen, Carlsbad, CA) as recommended by the manufacturer.

Arginine and Serine Rich Coiled-Coil 1 (RSRC1) and Transcription Termination Factor 1 (TTF1) were used as test genes to corroborate RNA sequencing results based on their upregulation in the WBC transcriptome of the two females carrying the two largest LOS fetuses. Endogenous transcripts used as normalizers were: Ecdysoneless Cell Cycle Regulator (ECD), Nuclear Factor of Kappa Light Polypeptide Gene Enhancer in B-Cells Inhibitor, Beta (NFKBIB), and VPS35 Endosomal Protein Sorting Factor Like (VPS35L). These were chosen based on their constancy of expression in the RNAseq result (i.e., EdgeR FDR = 1; DESeq2 padj > 0.8 and coefficient of variation ≤ 0.10 across all 23 D105 WBC samples. In addition, these transcripts were also chosen based on availability of intron-spanning TaqMan probes (ABI, Foster City, CA) for bovine. TaqMan probe information is as follows: ECD (Bt03235022_m1), NFKBIB (Bt03247668_m1), RSRC1 (Bt03236115_m1), TTF1 (Bt03266651_m1), VPS35L (Bt03269522_m1). The mRNA levels of the target genes for the pregnant females carrying IVP-LOS and the two largest LOS fetuses (cow #604 and #664) relative to the combined AI and IVP-Normal groups was calculated using the comparative cycle threshold (C_T) method. Briefly, the C_T for each sample was normalized to the geometric mean of the three endogenous reference genes. The average C_T was calculated by averaging the C_T of all independent samples excluding those from the females carrying the two largest LOS fetuses. The comparative C_T method (ΔΔC_T) was used to compare the values of #604 and #664 against the average C_T for all other samples. Fold difference is used for data representation.

Statistical analyses

All analyses include an IVP-Normal group to remove potential confounding effects of method of conception when analyzing variables. The morphometric data were analyzed by analysis of variance using the general linear model procedure using SAS software v9.4 (SAS Institute, Cary, NC). Dependent variables were all fetal measurements, and the independent variables were the group and day of collection (AI, IVP-Normal, IVP-LOS). Differences were considered statistically significance when p < 0.10.

Results

Pregnancy rate

The overall pregnancy rates on D55 were 61.1% for the AI group and 43.0% for the IVP group. For the D56 fetal collection set, there were 14 singleton pregnancies in the AI group, while, for the IVP group, there were 19 singleton pregnancies and 12 females carried two conceptuses. For D105 fetal collection set, there were 12 singleton pregnancies in the AI group, while, for the IVP group, there were 26 singleton pregnancies and 10 females carried two conceptuses.

D55 fetal ultrasonographies

Fetal ultrasonographies were taken on gestation D55 to determine if LOS could be detected by this stage of pregnancy. Figure 1 shows the average measurements of all fetuses collected in this study (AI = 26, IVP = 89). There were no differences in any of the fetal ultrasonographic measurements between the AI and IVP groups. Head length was greater in male fetuses (n = 69; mean ± SEM; p < 0.04; 2.01 ± 0.02) when compared to female fetuses (n = 43; 1.94 ± 0.03). There was also a sex effect on crown rump length (p = 0.06) with males (4.64 ± 0.05) being longer than females (4.46 ± 0.06). This effect was more pronounced within the IVP group (p < 0.03; 4.62 ± 0.05 and 4.42 ± 0.07, for males (n = 53) and females (n = 34), respectively).

Fetal collections and LOS determination

Fetal overgrowth is the defining phenotype of LOS, and weight at surgical collection was used to define LOS using criteria described earlier by us². A fetus was categorized as overgrown if its weight was ≥ 97 percentile weight of the AI in a sex-specific manner. We chose ≥ 97 percentile of control weight as threshold to ascribe LOS as this has previously been used to describe BWS⁵³, the counterpart syndrome in humans.

D56 fetuses

Fetal weight was not significantly different between singletons and twins (mean ± SEM; 8.94 ± 0.28 g and 8.79 ± 0.23 g, respectively). Fetal weight was similar between the AI and IVP fetuses (mean ± S.D.; 8.66 ± 1.39 and 8.95 ± 1.43, respectively). For the AI group, there were eight males and six females. The average weight for the males was 8.78 ± 1.33 g (weight range: 6.21–10.27 g; Fig. 2) while the average weight for the females was 8.50 ± 1.59 g (weight range: 6.20–10.22 g). For the IVP group, we collected 21 males and 22 females. The average weight for the males was 9.52 ± 1.53 g (weight range = 6.94–13.77 g) while the average weight for the females was 8.40 ± 1.11 g (weight range = 5.55–10.31 g). Fetuses weighing ≥ 97 percentile of controls (male = 10.18 g and female = 10.16 g) in the IVP group were considered LOS (Fig. 2). In total, there were eight IVP-LOS males (weight range = 10.23–13.77 g) and one IVP-LOS female (weight = 10.31 g). All IVP fetuses weighing < 97 percentile weight of controls were referred to “IVP-Normal” (males—n = 13 [weight range: 6.94–9.98 g] females—n = 21 [weight range: 5.55–9.8 g]). Besides heavier body weight, other phenotypes observed in the IVP group were focal hemorrhage on the brain and abdominal wall defects (Fig. 2C). The fetal measurements at collection for the AI, IVP-Normal and IVP-LOS groups are summarized in Fig. 3. Only one female in the IVP group was considered LOS, therefore no comparisons were made between it and the other groups.

D105 fetuses

Fetal weight was not significantly different between singletons and twins (mean ± SEM; 532.78 ± 20.02 g and 494.80 ± 29.69 g, respectively). Fetal weight was similar between the AI and IVP fetuses (mean ± S.D.; 466.00 ± 55.62 and 532.15 ± 134.80 g, respectively). For the AI group, we collected eight males and four females. The average weight for the males was 494.29 ± 44.05 g (mean ± S.D.; weight range: 442–550 g; note: there is a missed observation in this group) while the average weight for the females was 416.50 ± 36.01 g (weight range: 388–468 g; Fig. 4). For the IVP group, we collected 33 males and 13 females. The average weight for the males was 526.39 ± 123.00 g (weight range = 366–1080 g) while the average weight for the females was 546.77 ± 167.77 g (weight range = 318–986 g). Fetuses weighing ≥ 97 percentile of controls (male = 548.92 g and female = 463.14 g) in the IVP group were considered LOS (Fig. 4).

In this study, it appears that female fetuses were preferentially lost between D56 and D105, especially in the IVP group. On D56, we collected 6 females and 8 males in the AI group and 22 females and 21 males in the IVP group. On D105 we collected 4 females and 8 males in the AI group and 13 females and 33 males in the IVP group, which is different than the expected 1:1 ratio (p < 0.01). Although the reason for the loss of female fetuses between D56 and D105 is unknown, this was far more pronounced in the IVP group. The deficiency of female fetuses was not statistically significant in the AI group (p = 0.20) but was highly significant in the IVP group (p < 0.003) when compared to the binomial distribution with the expected 1:1 sex ratio.

In total, there were eight IVP-LOS males (weight range = 552–1080 g) and nine IVP-LOS females (weight range = 468–986 g). All IVP fetuses weighing < 97 percentile weight of controls were referred to “IVP-Normal” (males—n = 25 [weight range: 366–542 g] females—n = 4 [weight range: 318–448 g). Besides heavier body weight, other phenotypes observed in the IVP group were long protruding tongue (Fig. 4C.3), large organs (heart, kidney, lung, pancreas), hepatic cyst, abdominal ascites, gelatinous material in the peritoneal cavity and organs (Fig. 4C.1–2) and skull asymmetry (Fig. 4C.4). In addition, large umbilicus (Fig. 4B.3) were observed at collection in two of the female IVP-LOS fetuses (fetus number 656 and 604B), however, the ratio of the umbilicus to the crown-rump length (as determined by image analysis) was similar between groups, although the largest female (604B) had at least a 40% wider base of the umbilicus when compared to all other fetuses (Fig. 4B.3). There was a group (i.e. AI, IVP-Normal, IVP-LOS) effect for crown-rump length, abdominal height, biparietal diameter, forelimb length, heart girth, thoracic height and head length (p < 0.002) and sex effects for crown-rump length, abdominal height, biparietal diameter, forelimb length, and thoracic height (p < 0.05). The fetal measurements at collections are summarized in Fig. 3.

Associations between D55 fetal ultrasonographies and collection measurements

D55 ultrasonographic measurements on fetuses collected on D56

The summary of D55 ultrasonographic measurements for fetuses collected on D56 may be found in Fig. 5. Female specific analysis for the IVP-LOS group was not performed as only one female in the IVP group weighed more than the ≥ 97 percentile threshold used to ascribe overgrowth. No differences were observed for sex, group, or their interaction for abdominal diameter, abdominal height, biparietal diameter, thoracic diameter, or crown-rump length. Head length was greater in males than females (p < 0.006; Mean ± SEM; 2.05 ± 0.02 and 1.93 ± 0.03 for males and females, respectively). A group by sex interaction was detected for thoracic height in which the males of the IVP-Normal group were smaller than males in the AI and IVP-LOS groups (p < 0.03; Mean ± SEM; 1.60 ± 0.04, 1.75 ± 0.05 and 1.83 ± 0.08 for IVP-Normal, AI and IVP-LOS groups, respectively).

A slight positive correlation was observed between the D55 ultrasonographic measurements and the D56 fetal weight for abdominal diameter (0.40; p < 0.003), abdominal height (0.36; p < 0.007), crown-rump length (0.34; p < 0.01), head length (0.32; p < 0.02), and thoracic height (0.25; p = 0.06), while no correlations were observed between biparietal diameter or thoracic diameter measurements. When males and females were analyzed separately, positive correlations were observed for abdominal diameter (0.57; p < 0.002), abdominal height (0.64; p < 0.0003), crown-rump length (0.36; p = 0.06), and thoracic height (0.54; p < 0.004) in males. However, no correlations were found between any of the D55 ultrasonographic measurements and D56 fetal weight in females.

D56 ultrasonographic measurements on fetuses collected on D105

The summary of D55 ultrasonographic measurements for fetuses collected on D105 may be found in Fig. 5. No differences were observed for sex, group, or their interaction for abdominal diameter and head length. Females were smaller than males in their crown-rump length (p = 0.08), abdominal height (p < 0.04), thoracic diameter (p = 0.07), and thoracic height (p < 0.008).

A moderate positive correlation was observed between the D55 ultrasonographic measurements and the D105 fetal weight for abdominal diameter (0.57; p < 0.0001) and abdominal height (0.58; p < 0.0001). A slight positive correlation was observed between fetal weight and crown-rump length (0.27; p < 0.04), head length (0.33; p < 0.02), thoracic diameter (0.34; p < 0.02), and thoracic height (0.49; p < 0.0002) while no correlation was observed for biparietal diameter. For males, there was a moderate positive correlation between fetal weight and abdominal diameter (0.52; p < 0.0009), abdominal height (0.57; p < 0.0002), and thoracic height (0.56; p < 0.0003) and slight positive correlation for thoracic diameter (0.36; p < 0.03). For females, there was a moderate positive correlation between fetal weight and abdominal diameter (0.67; p < 0.005), abdominal height (0.67; p < 0.005), and head length (0.59; p < 0.02).

D77 ultrasonographic measurements on fetuses collected D105

An attempt was made to determine fetal morphometry on the subset of D77 pregnant group (AI = 6; IVP = 12), however this was not possible or reliable for many of the samples as the fetus was too large to do accurate measurements (data not shown).

Are D55 ultrasonographic measurements useful to identify LOS?

Overall, no single ultrasonographic measurement can explain LOS on D105 of gestation. We tested various combinations of measurements and identified a strong positive correlation between D105 fetal weight and the product of the D55 ultrasonographic measurements for abdominal diameter, abdominal height, crown-rump length, head length, thoracic height, and thoracic diameter (0.76; p < 0.007 and 0.72; p < 0.0001 for AI and IVP fetuses, respectively; Fig. 6). The highest number resulting from the multiplication of the beforementioned ultrasonographic measurements was 79.92 for the AI (control) group. The D105 IVP fetuses were compared to that threshold and all except the two most extreme LOS cases (fetus 604B and 664 weighing 986 and 1080 g, respectively) were on or below the threshold. Similar comparisons were made for the set of fetuses collected on D56. While the correlation was also strong (0.75; p < 0.003), the threshold for the AI was higher (91.32) than for that obtained for the D105 AI fetuses. The correlation decreased to 0.46 (p < 0.03) for the IVP group indicating more variability in fetal weight at this stage and perhaps inclusion of fetuses that will be lost later during pregnancy or will have a differential rate of growth after this stage. In this group, only the heaviest IVP fetus was above the 100-threshold used in the D105 group.

Maternal blood transcriptome analysis

Only reads which aligned to known genes of the bovine reference genome assembly ARS-UCD1.2 using NCBI (GCF_002263795.1_ARS-UCD1.2) were used in the present study. The results of all transcriptome analyses generated in this study may be found in Supplementary Table 1.

Unsupervised hierarchical clustering of the normalized read counts showed 18 of 46 samples (ie. D55 and D105 WBC transcriptomes of the same 23 females) clustered by animal (Supplementary Fig. 3). In other words, the D55 and D105 samples from 9 of the heifers grouped together by individual and this was irrespective of treatment group (AI, IVP-Normal or IVP-LOS). In addition, the analysis separated the females carrying the two largest D105 IVP-LOS fetuses (dam #604 and #664) from the rest of the animals (Supplementary Fig. 3). The day-specific unsupervised hierarchical clustering analyses may be found in Supplementary Fig. 4. Figure 7 shows the results for differentially expressed genes identified during the pairwise comparisons between each treatment group at each time point (D55 and D105). In addition, a pairwise comparison of WBC transcriptomes of females carrying two fetuses vs one was done to account for differential expression due to multiple fetuses (i.e. increased fetal mass). Furthermore, we compared the transcriptome of the dams carrying the two largest IVP-LOS individuals (#604 and #664) against all other animals for D55 and D105. Overall, for the D55 comparison, there were 13 differentially expressed genes identified by EdgeR and 8 identified by DESeq2 and for D105 there were 31 differentially expressed genes identified by EdgeR and 4451 by DESeq2. Data show that a large number of genes identified as differentially expressed are uncharacterized transcripts (“LOC”). Maternal WBC transcriptome analyses found that LOC783838 and PCDH1 were identified as differentially expressed in the extreme cases of LOS on gestation D55 and D105 by both EdgeR and DESeq2 statistical packages (Fig. 7). In addition, transcript levels of ACTA2, KDM5A, MAN1A2, MIR2376, PRRC2C, RSBN1, S100A14, SRPK2, and TTF1 were identified as differentially expressed in the females carrying the two largest fetuses on D105. For qRT-PCR corroborations, we focused on two genes upregulated in the two largest LOS fetuses when compared to all other fetuses, and whose intron-spanning TaqMan probes were readily available, namely TTF1 and RSRC1. TTF1 was identified as differentially expressed by both EdgeR and DESeq2 statistical packages and RSRC1 was identified as differentially expressed only by DESeq2. RSRC1 was used for corroborations even though it was only identified by one package due to the limitation of available Taqman probes of other genes in Fig. 7. Data show that the pattern of expression for these genes is similar between RNAseq and qRT-PCR results (Fig. 8).

Discussion

The goal of the present study was to determine the usefulness of D55 of gestation fetal morphometry and D55 and D105 maternal leukocyte transcriptome for the identification of congenital fetal overgrowth in cattle. In total, 20.9% (9/43) of the D56 and 36.9% (17/46) of the D105 collected IVP fetuses were considered LOS (≥ 97 percentile of the sex-specific weight of controls). The study revealed that the product of the D55 ultrasonographic measurements for abdominal diameter, abdominal height, crown-rump length, head length, thoracic height, and thoracic diameter may be used as an indicator of extreme cases of LOS. In addition, we identified that LOS fetuses had a different growth pattern from AI and IVP-Normal fetuses after D55 of gestation and that LOS fetuses had several developmental abnormalities such as hemihyperplasia (asymmetric growth), enlarged tongue, brain hemorrhage, enlarged umbilical cord, abdominal ascites, and abdominal wall defect. Maternal leukocyte transcriptome analyses identified that ADGRE3, LOC107131273, LOC783838, PCDH1, and RAB34, were differentially expressed on D55 in the females carrying the two largest LOS fetuses. On D105 of pregnancy, the leukocyte transcriptomes of the same females had differential expression of ACTA2, KDM5A, LOC783838, MAN1A2, MIR2376, PCDH1, PRRC2C, RSBN1, S100A14, SRPK2, and TTF1 when compared to all other animals.

Collection of fetuses was done on D56 of gestation since organogenesis has been shown to be completed before this day in cattle⁵⁴. The reason behind this decision was to answer other aspects of the project, which are beyond the scope of the current study, such as questions regarding the epigenetic mechanisms associated with abnormal organ formation in LOS; information that will be used to identify etiologies of fetal overgrowth syndrome in cattle and human (i.e. BWS). Furthermore, fetuses were also collected on D105 as we have previously shown that fetal overgrowth is evident at this stage of gestation². In humans, 97 percentile criteria is used to describe macrosomia in newborn babies⁵³. Since macrosomia is the main characteristic of LOS and one characteristic that can result in dystocia, we used this weight to ascribe LOS in the present study, similar to what we have done previously.

A greater than two-times increase in fetal weight was observed in two of the D105 IVP-LOS fetuses in the current study, an observation also reported by others⁴, suggesting that if those fetuses were allowed to go to term, dystocia would be probable and assisted delivery or caesarean section would be required. Dystocia as a result of LOS can lead to neonatal death⁴ and/or death of cows²². Further, dystocia associated with stillbirth⁵⁵, is a major contributor to perinatal mortality in cattle⁵⁶, and has also been shown to increase the chances of metritis⁵⁷, and lameness²⁸. Furthermore, dystocia can have an adverse impact on milk production⁵⁸ and increase the calving to conception interval in dairy cows⁵⁹. Thus, overgrown fetuses can have negative economic consequences to cattle producers²² and identification of those large calves during early pregnancy would help overcome these problems.

In the current study, we performed fetal ultrasonographic measurements at D55 for fetuses that were collected on D56 or D105 of gestation. Based on our findings, it is evident that the ≥ 97 percentile AI weight criteria used to assign a fetus as being LOS while useful at D105, it is not appropriate at gestation D56. From the findings of the correlation analysis between fetal weight and the product of six ultrasound measurements, we hypothesize that fetuses are lost after this day of pregnancy and/or will have subsequent disproportionate growth. This is in accordance to previous work which showed that smaller in vitro or cloned fetuses in the first trimester resulted in heavier fetuses at term^7,60. Further, Bertolini and coinvestigators also suggested that in vitro produced fetuses show early growth retardation and then follow acceleration in fetal growth at later stages of gestation, showing biphasic growth pattern⁷.

Previous research reported that larger biparietal diameter might be a useful measurement to identify LOS at D63 of gestation in cloned LOS fetuses⁶¹ and that a smaller crown rump length might be a useful measurement to identify LOS at D58 of pregnancy in in vitro produced LOS fetuses⁷. However, those measurements were not found to be indicators for LOS in our current study. Possibilities for the discrepancies in our findings, are; (1) different definition of LOS (we used ≥ 97% weight of the control fetuses while Bertolini used fetuses that were 33% heavier than controls for comparisons⁷, (2) improvement in ultrasonography technology allowing for more accurate measurements, and (3) number of observations (113 fetuses in our study vs. 34 fetuses in Ref.⁶⁰).

When we tried to do fetometry at D77 of gestation, we were only able to measure head length with some accuracy. All other measurements were not reliable, indicating that D77 ultrasonography might be too late to try to accurately identify LOS. Given the allometric growth that occurs after D56 and the fact that D77 is too late to predict LOS by ultrasonography, and that fetal sex is most accurately predicted between D60–80³⁴, we suggest that future studies focus on ~ D65 as a target day to identify LOS in a sex-specific manner.

Larger than normal umbilicus and presence of large amounts of fluid-gelatinous material in the abdominal cavity were observed in largest/heaviest D105 IVP-LOS fetuses. This is similar to what Constant et al.⁶⁰ reported in D220 fetuses produced by somatic cell nuclear transfer. In that study, the authors suggested that a large umbilical cord and abdominal ascites were not the result of fetal overgrowth per se, but rather a consequence of placental dysfunction, which in turn led to placental overgrowth. Contrary to this, other studies have shown an association of placental defects in cloned fetuses, with their loss during early pregnancy as a result of growth retardation^62,63. Taken together, these studies suggest that fetuses with severe placental defects may be lost during early pregnancy and if those fetuses with placental defects survive, they could have higher placental and fetal growth at later stages of pregnancy through compensatory mechanisms. In the current study, the conceptuses were surgically removed to allow rapid collection of tissues in order to preserve nucleic acid integrity for other aspects of the project, therefore, even though we collected the placentas, we were not able to make thorough morphological assessments of this tissue. Regardless, no obvious placental abnormalities were evident at collection for IVP conceptuses.

Enlarged tongue was also observed in D105 IVP-LOS which is similar to previous findings in our laboratory^2,64 and comparable to what has been observed in a similar congenital overgrowth condition in humans, namely Beckwith–Wiedemann Syndrome⁶⁵. Large tongues can lead to difficulty in suckling and increase the chances of prenatal death²⁶. In addition, the largest D105 IVP-LOS fetus showed brachycephaly and asymmetrical growth of the cranium, an interesting finding given that one characteristic of BWS is hemihyperplasia⁶⁵. These similarities demonstrates that BWS and LOS are the same syndrome, as previously reported by us², and that they share similar misregulated developmental epi(genetic) mechanisms associated with asymmetrical growth.

In our study, we also had the objective of determining if maternal blood could be used as a biomarker to identify LOS on D55 and/or D105 of pregnancy. For this, we analyzed leukocyte transcriptome of 23 females carrying D105 AI, IVP-Normal and IVP-LOS fetuses. We also analyzed the D55 leukocyte transcriptomes of the same females. Our initial approach was to do an unsupervised hierarchical clustering of de-identified samples to determine if obvious difference existed between the females carrying LOS fetuses when compared to the other two groups. Surprisingly, the transcriptome of 18/46 samples clustered together by animal (i.e., n = 9) regardless of pregnancy stage (i.e., D55 and D105). Another interesting point is that the experiment was done over three seasons (Autumn, Spring and Summer) with a range in temperature of − 22 °C to 33 °C, and this was not detected in the transcriptome given the clustering by individual. Further, given the design of the study, in which we transferred two embryos per recipient heifer, some pregnancies in the D55 and D105 IVP groups had two fetuses, however, the unsupervised hierarchical clustering did not cluster animals by number of fetuses. The unsupervised hierarchical clustering, did however, cluster the D105 transcriptomes of the females carrying the two largest LOS from all other females.

For the maternal leukocyte transcriptome analysis, we focused on the two females that carried the largest LOS fetuses as those were the ones that separated by hierarchical clustering and the ones that would most likely cause a difficult birth. Analyses identified ADGRE3, LOC107131273, LOC783838, PCDH1, and RAB34, as being different on D55 and ACTA2, KDM5A, LOC783838, MAN1A2, MIR2376, PCDH1, PRRC2C, RSBN1, S100A14, SRPK2, and TTF1 on D105 when compared to all other animals. For qRT-PCR corroborations we focused on two transcripts whose TaqMan probes were readily available, namely TTF1 which was identified as differentially expressed on D105 by EdgeR and DESeq2 statistical packages as well as RSRC1 which was identified as differentially expressed by DESeq2 only. Analyses show consistency of expression between RNAseq and qRT-PCR results. Other genes will be tested in the future as assays become available.

Our study has several limitations. As it pertains to fetal ultrasonographies, published work shows that fetal growth varies among different breeds⁶⁶ and that size difference can be noticeable as early as 3 months of gestation, therefore it is possible that fetal growth patterns may vary as early as D55 of gestation among different breeds; however, there is lack of published data showing that on D55 of gestation, fetuses from different breeds differ in size. Future research would have to address this question. Here, we also did sex-specific analyses for all our variables, including for ultrasonographic measurements. Since in our experiment, the number of male fetuses was unexpectedly higher in the IVP group at D105, female specific ultrasonographic data analysis may be limiting. Finally, and importantly, it should be noted that during transcriptome analysis, we only used the known (mostly coding) group of bovine transcripts for this study and that the non-coding and novel transcript portion of the transcriptome remains unexplored. Future work will focus on these types of transcripts.

In summary, here we document initial efforts to identify LOS during the first trimester of pregnancy in cattle. We found that the product of the D55 ultrasonographic measurements for abdominal diameter, abdominal height, crown-rump length, head length, thoracic height, and thoracic diameter may be useful to identify the largest fetuses, whereas maternal leukocyte transcriptome analyses suggest LOC783838 and PCDH1 as potential markers for extreme cases of LOS on gestation D55 and D105. In addition, transcript levels of ACTA2, KDM5A, MAN1A2, MIR2376, PRRC2C, RSBN1, S100A14, SRPK2, and TTF1 may also serve as biomarkers on D105 of pregnancy for extreme cases. Further, our analysis identified several genes that were misregulated in all LOS fetuses when compared to fetuses of normal weight produced by IVP. Future work will query the usefulness of these genes to predict milder cases of LOS. Finally, the long-term goal of this research is to identify the best time of pregnancy to capture LOS by ultrasonography and train a model using fetuses that will be allowed to go to term to determine the best maternal blood markers to identify fetal overgrowth in cattle.

Data availability

The raw FASTQ files are publicly available at Gene Expression Omnibus (GEO accession no. GSE179946).

References

Walker, S. K., Hartwich, K. M. & Seamark, R. F. The production of unusually large offspring following embryo manipulation: Concepts and challenges. Theriogenology 45, 111–120 (1996).
Article Google Scholar
Chen, Z., Robbins, K. M., Wells, K. D. & Rivera, R. M. Large offspring syndrome: A bovine model for the human loss-of-imprinting overgrowth syndrome Beckwith-Wiedemann. Epigenetics 8, 591–601. https://doi.org/10.4161/epi.24655 (2013).
Article CAS PubMed PubMed Central Google Scholar
Willadsen, S. M. et al. The viability of late morulae and blastocysts produced by nuclear transplantation in cattle. Theriogenology 35, 161–170 (1991).
Article Google Scholar
Behboodi, E. et al. Birth of large calves that developed from in vitro-derived bovine embryos. Theriogenology 44, 227–232. https://doi.org/10.1016/0093-691x(95)00172-5 (1995).
Article CAS PubMed Google Scholar
Young, L. E., Sinclair, K. D. & Wilmut, I. Large offspring syndrome in cattle and sheep. Rev. Reprod. 3, 155–163. https://doi.org/10.1530/ror.0.0030155 (1998).
Article CAS PubMed Google Scholar
Bertolini, M. & Anderson, G. B. The placenta as a contributor to production of large calves. Theriogenology 57, 181–187. https://doi.org/10.1016/s0093-691x(01)00665-3 (2002).
Article CAS PubMed Google Scholar
Bertolini, M. et al. Morphology and morphometry of in vivo- and in vitro-produced bovine concepti from early pregnancy to term and association with high birth weights. Theriogenology 58, 973–994. https://doi.org/10.1016/s0093-691x(02)00935-4 (2002).
Article PubMed Google Scholar
Hiendleder, S. et al. Tissue-specific elevated genomic cytosine methylation levels are associated with an overgrowth phenotype of bovine fetuses derived by in vitro techniques. Biol. Reprod. 71, 217–223. https://doi.org/10.1095/biolreprod.103.026062 (2004).
Article CAS PubMed Google Scholar
Hiendleder, S. et al. Tissue-specific effects of in vitro fertilization procedures on genomic cytosine methylation levels in overgrown and normal sized bovine fetuses. Biol. Reprod. 75, 17–23. https://doi.org/10.1095/biolreprod.105.043919 (2006).
Article CAS PubMed Google Scholar
Miles, J. R., Farin, C. E., Rodriguez, K. F., Alexander, J. E. & Farin, P. W. Angiogenesis and morphometry of bovine placentas in late gestation from embryos produced in vivo or in vitro. Biol. Reprod. 71, 1919–1926. https://doi.org/10.1095/biolreprod.104.031427 (2004).
Article CAS PubMed Google Scholar
Farin, P. W., Piedrahita, J. A. & Farin, C. E. Errors in development of fetuses and placentas from in vitro-produced bovine embryos. Theriogenology 65, 178–191. https://doi.org/10.1016/j.theriogenology.2005.09.022 (2006).
Article PubMed Google Scholar
McEvoy, T. G., Sinclair, K. D., Broadbent, P. J., Goodhand, K. L. & Robinson, J. J. Post-natal growth and development of simmental calves derived from in vivo or in vitro embryos. Reprod. Fertil. Dev. 10, 459–464. https://doi.org/10.1071/rd98126 (1998).
Article CAS PubMed Google Scholar
Hiendleder, S. et al. Maternal-fetal transplacental leakage of mitochondrial DNA in bovine nuclear transfer pregnancies: Potential implications for offspring and recipients. Cloning Stem Cells 6, 150–156. https://doi.org/10.1089/1536230041372391 (2004).
Article CAS PubMed Google Scholar
Chen, Z. et al. Characterization of global loss of imprinting in fetal overgrowth syndrome induced by assisted reproduction. Proc. Natl. Acad. Sci. U.S.A. 112, 4618–4623. https://doi.org/10.1073/pnas.1422088112 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
van Wagtendonk-de Leeuw, A. M., Aerts, B. J. & den Daas, J. H. Abnormal offspring following in vitro production of bovine preimplantation embryos: A field study. Theriogenology 49, 883–894. https://doi.org/10.1016/s0093-691x(98)00038-7 (1998).
Article PubMed Google Scholar
Thompson, J. G., Gardner, D. K., Pugh, P. A., McMillan, W. H. & Tervit, H. R. Lamb birth weight is affected by culture system utilized during in vitro pre-elongation development of ovine embryos. Biol. Reprod. 53, 1385–1391. https://doi.org/10.1095/biolreprod53.6.1385 (1995).
Article CAS PubMed Google Scholar
Sinclair, K. D. et al. Aberrant fetal growth and development after in vitro culture of sheep zygotes. J. Reprod. Fertil. 116, 177–186. https://doi.org/10.1530/jrf.0.1160177 (1999).
Article CAS PubMed Google Scholar
Rooke, J. A. et al. Ovine fetal development is more sensitive to perturbation by the presence of serum in embryo culture before rather than after compaction. Theriogenology 67, 639–647. https://doi.org/10.1016/j.theriogenology.2006.09.040 (2007).
Article CAS PubMed Google Scholar
Bonilla, L., Block, J., Denicol, A. C. & Hansen, P. J. Consequences of transfer of an in vitro-produced embryo for the dam and resultant calf. J. Dairy Sci. 97, 229–239. https://doi.org/10.3168/jds.2013-6943 (2014).
Article CAS PubMed Google Scholar
Siqueira, L. G. et al. Colony-stimulating factor 2 acts from days 5 to 7 of development to modify programming of the bovine conceptus at day 86 of gestationdagger. Biol. Reprod. 96, 743–757. https://doi.org/10.1093/biolre/iox018 (2017).
Article PubMed Google Scholar
Li, Y., Donnelly, C. G. & Rivera, R. M. Overgrowth syndrome. Vet. Clin. N. Am. Food Anim. Pract. 35, 265–276. https://doi.org/10.1016/j.cvfa.2019.02.007 (2019).
Article Google Scholar
Rivera, R. M., Donnelly, C. G., Patel, B. N., Li, Y. & Soto-Moreno, E. J. Abnormal offspring syndrome. In Bovine Reproduction (ed. Hopper, R. M.) 876–895 (Wiley, 2021).
Chapter Google Scholar
Li, Y., Sena Lopes, J., Fuster, P. C. & Rivera, R. M. Spontaneous and ART-induced large offspring syndrome: Similarities and differences in DNA methylome. Epigenetics. https://doi.org/10.1080/15592294.2022.2067938 (2022).
Article PubMed Google Scholar
Mussa, A. et al. Assisted reproductive techniques and risk of Beckwith-Wiedemann syndrome. Pediatrics. https://doi.org/10.1542/peds.2016-4311 (2017).
Article PubMed Google Scholar
Kruip, T. A. M. & den Daas, J. H. G. In vitro produced and cloned embryos: Effects on pregnancy, parturition and offspring. Theriogenology 47, 43–52 (1997).
Article Google Scholar
Hansen, P. J. & Block, J. Towards an embryocentric world: The current and potential uses of embryo technologies in dairy production. Reprod Fertil. Dev. 16, 1–14. https://doi.org/10.10371/RD03073 (2004).
Article PubMed Google Scholar
Dematawewa, C. M. & Berger, P. J. Effect of dystocia on yield, fertility, and cow losses and an economic evaluation of dystocia scores for Holsteins. J. Dairy Sci. 80, 754–761. https://doi.org/10.3168/jds.s0022-0302(97)75995-2 (1997).
Article CAS PubMed Google Scholar
Linden, T. C., Bicalho, R. C. & Nydam, D. V. Calf birth weight and its association with calf and cow survivability, disease incidence, reproductive performance, and milk production. J. Dairy Sci. 92, 2580–2588. https://doi.org/10.3168/jds.2008-1603 (2009).
Article CAS PubMed Google Scholar
Kasinathan, P. et al. Acceleration of genetic gain in cattle by reduction of generation interval. Sci. Rep. 5, 8674. https://doi.org/10.1038/srep08674 (2015).
Article CAS PubMed PubMed Central Google Scholar
Fleming, A., Abdalla, E. A., Maltecca, C. & Baes, C. F. Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle. Arch. Anim. Breed. 61, 43–57. https://doi.org/10.5194/aab-61-43-2018 (2018).
Article Google Scholar
Curran, S., Pierson, R. A. & Ginther, O. J. Ultrasonographic appearance of the bovine conceptus from days 20 through 60. J. Am. Vet. Med. Assoc. 189, 1295–1302 (1986).
CAS PubMed Google Scholar
Kastelic, J. P., Curran, S., Pierson, R. A. & Ginther, O. J. Ultrasonic evaluation of the bovine conceptus. Theriogenology 29, 39–54 (1988).
Article Google Scholar
Kahn, W. Sonographic fetometry in the bovine. Theriogenology 31, 1105–1121. https://doi.org/10.1016/0093-691x(89)90494-9 (1989).
Article CAS PubMed Google Scholar
Christmas, R. A. & Colloton, J. Ultrasound Determination of Fetal Gender 2nd edn, 303–307 (Saunders Elsevier, 2007).
Google Scholar
Fissore, R. A., Edmondson, A. J., Pashen, R. L. & Bondurant, R. H. The use of ultrasonography for the study of the bovine reproductive tract. II. Non-pregnant, pregnant and pathological conditions of the uterus. Anim. Reprod. Sci. 12, 167–177 (1986).
Article Google Scholar
Yoshino, H. et al. A predictive threshold value for the diagnosis of early pregnancy in cows using interferon-stimulated genes in granulocytes. Theriogenology 107, 188–193. https://doi.org/10.1016/j.theriogenology.2017.11.014 (2018).
Article CAS PubMed Google Scholar
Green, J. A. et al. The establishment of an ELISA for the detection of pregnancy-associated glycoproteins (PAGs) in the serum of pregnant cows and heifers. Theriogenology 63, 1481–1503. https://doi.org/10.1016/j.theriogenology.2004.07.011 (2005).
Article CAS PubMed Google Scholar
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).
Article CAS PubMed PubMed Central Google Scholar
Tribulo, P., Rivera, R. M., Ortega Obando, M. S., Jannaman, E. A. & Hansen, P. J. Production and culture of the bovine embryo. Methods Mol. Biol. 2006, 115. https://doi.org/10.1007/978-1-4939-9566-0_8 (2019).
Article CAS PubMed Google Scholar
Chen, Z., Hagen, D. E., Ji, T., Elsik, C. G. & Rivera, R. M. Global misregulation of genes largely uncoupled to DNA methylome epimutations characterizes a congenital overgrowth syndrome. Sci. Rep. 7, 12667. https://doi.org/10.1038/s41598-017-13012-z (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Silva del Rio, N., Stewart, S., Rapnicki, P., Chang, Y. M. & Fricke, P. M. An observational analysis of twin births, calf sex ratio, and calf mortality in Holstein dairy cattle. J. Dairy Sci. 90, 1255–1264. https://doi.org/10.3168/jds.s0022-0302(07)71614-4 (2007).
Article CAS PubMed Google Scholar
Chen, Z. et al. Global assessment of imprinted gene expression in the bovine conceptus by next generation sequencing. Epigenetics 11, 501–516. https://doi.org/10.1080/15592294.2016.1184805 (2016).
Article PubMed PubMed Central Google Scholar
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
Article CAS PubMed PubMed Central Google Scholar
Ortega, M. S. et al. Influences of sire conception rate on pregnancy establishment in dairy cattle. Biol. Reprod. 99, 1244–1254. https://doi.org/10.1093/biolre/ioy141 (2018).
Article PubMed PubMed Central Google Scholar
Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinform. 11, 485. https://doi.org/10.1186/1471-2105-11-485 (2010).
Article Google Scholar
Rosen, B. D. et al. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience. https://doi.org/10.1093/gigascience/giaa021 (2020).
Article PubMed PubMed Central Google Scholar
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915. https://doi.org/10.1038/s41587-019-0201-4 (2019).
Article CAS PubMed PubMed Central Google Scholar
Anders, S., Pyl, P. T. & Huber, W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. https://doi.org/10.1093/bioinformatics/btu638 (2015).
Article CAS PubMed Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616 (2010).
Article CAS PubMed Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
Article CAS PubMed PubMed Central Google Scholar
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902. https://doi.org/10.1038/nbt.2931 (2014).
Article CAS PubMed PubMed Central Google Scholar
Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC Bioinform. 12, 480. https://doi.org/10.1186/1471-2105-12-480 (2011).
Article CAS Google Scholar
Weksberg, R., Shuman, C. & Beckwith, J. B. Beckwith-Wiedemann syndrome. Eur. J. Hum. Genet. 18, 8–14. https://doi.org/10.1038/ejhg.2009.106 (2010).
Article PubMed Google Scholar
McGeady, T. A. (ed.) Veterinary Embryology (Blackwell Pub, 2006).
Google Scholar
Lombard, J. E., Garry, F. B., Tomlinson, S. M. & Garber, L. P. Impacts of dystocia on health and survival of dairy calves. J. Dairy Sci. 90, 1751–1760. https://doi.org/10.3168/jds.2006-295 (2007).
Article CAS PubMed Google Scholar
Mee, J. F. Why do so many calves die on modern dairy farms and what can we do about calf welfare in the future? Animals (Basel) 3, 1036–1057. https://doi.org/10.3390/ani3041036 (2013).
Article Google Scholar
Giuliodori, M. J. et al. Metritis in dairy cows: Risk factors and reproductive performance. J. Dairy Sci. 96, 3621–3631. https://doi.org/10.3168/jds.2012-5922 (2013).
Article CAS PubMed Google Scholar
Rajala, P. J. & Grohn, Y. T. Effects of dystocia, retained placenta, and metritis on milk yield in diary cows. J. Dairy Sci. 81, 3172–3181. https://doi.org/10.3168/jds.s0022-0302(98)75883-7 (1998).
Article CAS PubMed Google Scholar
Gaafar, H. M., Shamiah Sh, M., El-Hamd, M. A., Shitta, A. A. & El-Din, M. A. Dystocia in Friesian cows and its effects on postpartum reproductive performance and milk production. Trop. Anim. Health Prod. 43, 229–234. https://doi.org/10.1007/s11250-010-9682-3 (2011).
Article CAS PubMed Google Scholar
Constant, F. et al. Large offspring or large placenta syndrome? Morphometric analysis of late gestation bovine placentomes from somatic nuclear transfer pregnancies complicated by hydrallantois. Biol. Reprod. 75, 122–130. https://doi.org/10.1095/biolreprod.106.051581 (2006).
Article CAS PubMed Google Scholar
Chavatte-Palmer, P. et al. Ultrasound fetal measurements and pregnancy associated glycoprotein secretion in early pregnancy in cattle recipients carrying somatic clones. Theriogenology 66, 829–840. https://doi.org/10.1016/j.theriogenology.2006.01.061 (2006).
Article CAS PubMed Google Scholar
Stice, S. L., Strelchenko, N. S., Keefer, C. L. & Matthews, L. Pluripotent bovine embryonic cell lines direct embryonic development following nuclear transfer. Biol. Reprod. 54, 100–110. https://doi.org/10.1095/biolreprod54.1.100 (1996).
Article CAS PubMed Google Scholar
Hill, J. R. et al. Evidence for placental abnormality as the major cause of mortality in first-trimester somatic cell cloned bovine fetuses. Biol. Reprod. 63, 1787–1794. https://doi.org/10.1095/biolreprod63.6.1787 (2000).
Article CAS PubMed Google Scholar
Li, Y., Lopes, J. S., Fuster, P. C. & Rivera, R. M. Spontaneous and ART-induced large offspring syndrome: Similarities and differences in DNA methylome. BioRxiv. https://doi.org/10.1101/2022.02.07.479430 (2022).
Article PubMed PubMed Central Google Scholar
Brioude, F. et al. Expert consensus document: Clinical and molecular diagnosis, screening and management of Beckwith-Wiedemann syndrome: An international consensus statement. Nat. Rev. Endocrinol. 14, 229–249. https://doi.org/10.1038/nrendo.2017.166 (2018).
Article PubMed PubMed Central Google Scholar
Mao, W. H. et al. Growth- and breed-related changes of fetal development in cattle. Asian-Aust. J. Anim. Sci. 21, 640–647 (2008).
Article Google Scholar

Download references

Acknowledgements

The authors would like to thank Drs. Martha Sofía Ortega, Dave Patterson, Jordan Thomas, and Mike Smith for their help during estrous synchronization and technical assistance through the project and Kenneth Ladyman and Reinhard Van Zyl for farm management and related help. We also thank other members of the Rivera laboratory (Ali Patten, Amanda Moreno, Monique Ferrell, Casper Safranski, Olivia Styron, and Carla Reyes-Flores) for help with preparations and tissue collections. We would also like to thank Cooper Stansberry and Michael Campbell from the Hagen laboratory for their help during oocyte collection and Dr. Ky Pohler for facilitating the donation of the Brahman semen used in the project. We would also like to thank Drs. Cliff Miller and Dr. Neal Martin’s veterinary technicians Sarah K., Carolyn W. and Chase H for veterinary assistance. This project is supported by the Agriculture and Food Research Initiative competitive Grants Nos. 2018-67015-27598 and 2021-67016-33417 from the United States Department of Agriculture National Institute of Food and Agriculture and the L.E. “Red” Larson Endowment (PJH).

Author information

Paula Tribulo
Present address: Instituto de Reproducción Animal, Córdoba, and CONICET, Córdoba, Argentina
Eliab Estrada-Cortés
Present address: Centro Altos de Jalisco INIFAP, Querétaro, Mexico
Zhoulin Wu
Present address: Key Lab of Meat Processing of Sichuan Province, Chengdu University, Chengdu, China

Authors and Affiliations

Division of Animal Sciences, University of Missouri, 164 ASRC, 920 East Campus Drive, Columbia, MO, 65211, USA
Rocío Melissa Rivera, Bhaumik Narendrabhai Patel, Edgar Joel Soto-Moreno, Yahan Li, Chris Nayoon Kim, Astrid Roshealy Brau-Rodríguez, Zhoulin Wu & Christine G. Elsik
Department of Animal and Food Sciences, Oklahoma State University, Stillwater, USA
Anna Katherine Goldkamp & Darren Erich Hagen
Green Hills Veterinary Clinic, Moberly, MO, USA
Cliff Miller
Department of Veterinary Pathobiology, University of Missouri, Columbia, MO, USA
Fred Williams III
Department of Animal Sciences, University of Florida, Gainesville, FL, USA
Elizabeth Jannaman, Yao Xiao, Paula Tribulo, Eliab Estrada-Cortés & Peter James Hansen
Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA
Christine Marie Spinka
Martin Veterinary Services, Centralia, MO, USA
Neal Martin

Authors

Rocío Melissa Rivera
View author publications
You can also search for this author in PubMed Google Scholar
Anna Katherine Goldkamp
View author publications
You can also search for this author in PubMed Google Scholar
Bhaumik Narendrabhai Patel
View author publications
You can also search for this author in PubMed Google Scholar
Darren Erich Hagen
View author publications
You can also search for this author in PubMed Google Scholar
Edgar Joel Soto-Moreno
View author publications
You can also search for this author in PubMed Google Scholar
Yahan Li
View author publications
You can also search for this author in PubMed Google Scholar
Chris Nayoon Kim
View author publications
You can also search for this author in PubMed Google Scholar
Cliff Miller
View author publications
You can also search for this author in PubMed Google Scholar
Fred Williams III
View author publications
You can also search for this author in PubMed Google Scholar
Elizabeth Jannaman
View author publications
You can also search for this author in PubMed Google Scholar
Yao Xiao
View author publications
You can also search for this author in PubMed Google Scholar
Paula Tribulo
View author publications
You can also search for this author in PubMed Google Scholar
Eliab Estrada-Cortés
View author publications
You can also search for this author in PubMed Google Scholar
Astrid Roshealy Brau-Rodríguez
View author publications
You can also search for this author in PubMed Google Scholar
Peter James Hansen
View author publications
You can also search for this author in PubMed Google Scholar
Zhoulin Wu
View author publications
You can also search for this author in PubMed Google Scholar
Christine Marie Spinka
View author publications
You can also search for this author in PubMed Google Scholar
Neal Martin
View author publications
You can also search for this author in PubMed Google Scholar
Christine G. Elsik
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

R.M.R. designed the experiment. R.M.R., B.N.P., A.K.G., D.E.H., E.J.S.M., Y.L., C.K., C.M., F.W.I.I.I., E.J., Y.X., P.T., E.E.C., A.R.B.R., P.J.H., Z.W., C.M.S., N.M., C.G.E. conducted the experiment and collected data. R.M.R. wrote the original draft and all authors contributed to the manuscript.

Corresponding author

Correspondence to Rocío Melissa Rivera.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Figures.

Supplementary Tables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Rivera, R.M., Goldkamp, A.K., Patel, B.N. et al. Identification of large offspring syndrome during pregnancy through ultrasonography and maternal blood transcriptome analyses. Sci Rep 12, 10540 (2022). https://doi.org/10.1038/s41598-022-14597-w

Download citation

Received: 31 March 2022
Accepted: 09 June 2022
Published: 22 June 2022
DOI: https://doi.org/10.1038/s41598-022-14597-w

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.