QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations-case studies in forage breeding.

Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enables the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross-pollinated species. Each strategy was compared across three levels of half-sib family mean heritability (0.1, 0.5, and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.

Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing.

KEY MESSAGE: Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.

Using non-targeted direct analysis in real time-mass spectrometry (DART-MS) to discriminate seeds based on endogenous or exogenous chemicals.

Forage seeds are a highly traded agricultural commodity, and therefore, quality control and assurance is high priority. In this study, we have used direct analysis in real time-mass spectrometry (DART-MS) as a tool to discriminate forage seeds based on their non-targeted chemical profiles. In the first experiment, two lots of perennial ryegrass (Lolium perenne L.) seed were discriminated based on exogenous residues of N-(3, 4-dichlorophenyl)-N,N-dimethylurea (Diuron(TM)), a herbicide. In a separate experiment, washed and unwashed seeds of the forage legumes white clover (Trifolium repens L.) and alfalfa (Medicago sativa L.) were discriminated based on the presence or absence of oxylipins, a class of endogenous antimicrobial compounds. Unwashed seeds confer toxicity towards symbiotic, nitrogen-fixing rhizobia which are routinely coated on legume seeds before planting, resulting in reduced rhizobial count. This is the first report of automatic introduction of intact seeds in the DART ion source and detecting oxylipins using DART-MS. Apart from providing scope to investigate legume-rhizobia symbiosis further in the context of oxylipins, the results presented here will enable future studies aimed at classification of seeds based on chemicals bound to the seed coat, thereby offering an efficient screening device for industry.

An integrated genetic linkage map for white clover (Trifolium repens L.) with alignment to Medicago.

BACKGROUND: White clover (Trifolium repens L.) is a temperate forage legume with an allotetraploid genome (2n=4×=32) estimated at 1093 Mb. Several linkage maps of various sizes, marker sources and completeness are available, however, no integrated map and marker set has explored consistency of linkage analysis among unrelated mapping populations. Such integrative analysis requires tools for homoeologue matching among populations. Development of these tools provides for a consistent framework map of the white clover genome, and facilitates in silico alignment with the model forage legume, Medicago truncatula. RESULTS: This is the first report of integration of independent linkage maps in white clover, and adds to the literature on methyl filtered GeneThresher®-derived microsatellite (simple sequence repeat; SSR) markers for linkage mapping. Gene-targeted SSR markers were discovered in a GeneThresher® (TrGT) methyl-filtered database of 364,539 sequences, which yielded 15,647 SSR arrays. Primers were designed for 4,038 arrays and of these, 465 TrGT-SSR markers were used for parental consensus genetic linkage analysis in an F1 mapping population (MP2). This was merged with an EST-SSR consensus genetic map of an independent population (MP1), using markers to match homoeologues and develop a multi-population integrated map of the white clover genome. This integrated map (IM) includes 1109 loci based on 804 SSRs over 1274 cM, covering 97% of the genome at a moderate density of one locus per 1.2 cM. Eighteen candidate genes and one morphological marker were also placed on the IM. Despite being derived from disparate populations and marker sources, the component maps and the derived IM had consistent representations of the white clover genome for marker order and genetic length. In silico analysis at an E-value threshold of 1e-20 revealed substantial co-linearity with the Medicago truncatula genome, and indicates a translocation between T. repens groups 2 and 6 relative to M. truncatula. CONCLUSIONS: This integrated genetic linkage analysis provides a consistent and comprehensive linkage analysis of the white clover genome, with alignment to a model forage legume. Associated marker locus information, particularly the homoeologue-specific markers, offers a new resource for forage legume research to enable genetic analysis and improvement of this forage and grassland species.

An improved reference genome for Trifolium subterraneum L. provides insight into molecular diversity and intra-specific phylogeny.

Subterranean clover (Trifolium subterraneum L., Ts) is a geocarpic, self-fertile annual forage legume with a compact diploid genome (n = x = 8, 544 Mb/1C). Its resilience and climate adaptivity have made it an economically important species in Mediterranean and temperate zones. Using the cultivar Daliak, we generated higher resolution sequence data, created a new genome assembly TSUd_3.0, and conducted molecular diversity analysis for copy number variant (CNV) and single-nucleotide polymorphism (SNP) among 36 cultivars. TSUd_3.0 substantively improves prior genome assemblies with new Hi-C and long-read sequence data, covering 531 Mb, containing 41,979 annotated genes and generating a 94.4% BUSCO score. Comparative genomic analysis among select members of the tribe Trifolieae indicated TSUd 3.0 corrects six assembly-error inversion/duplications and confirmed phylogenetic relationships. Its synteny with T. pratense, T. repens, Medicago truncatula and Lotus japonicus genomes were assessed, with the more distantly related T. repens and M. truncatula showing higher levels of co-linearity with Ts than between Ts and its close relative T. pratense. Resequencing of 36 cultivars discovered 7,789,537 SNPs subsequently used for genomic diversity assessment and sequence-based clustering. Heterozygosity estimates ranged from 1% to 21% within the 36 cultivars and may be influenced by admixture. Phylogenetic analysis supported subspecific genetic structure, although it indicates four or five groups, rather than the three recognized subspecies. Furthermore, there were incidences where cultivars characterized as belonging to a particular subspecies clustered with another subspecies when using genomic data. These outcomes suggest that further investigation of Ts sub-specific classification using molecular and morpho-physiological data is needed to clarify these relationships. This upgraded reference genome, complemented with comprehensive sequence diversity analysis of 36 cultivars, provides a platform for future gene functional analysis of key traits, and genome-based breeding strategies for climate adaptation and agronomic performance. Pangenome analysis, more in-depth intra-specific phylogenomic analysis using the Ts core collection, and functional genetic and genomic studies are needed to further augment knowledge of Trifolium genomes.

Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

IVF and embryo development subsequent to ovarian torsion occurring during the resumption of meiosis.

This report describes an unusual case of ovarian torsion during an IVF cycle prior to vaginal oocyte retrieval and the subsequent embryo development. A 27-year-old, whose husband carries a balanced translocation, presented on stimulation day 11 (day after human chorionic gonadotrophin administration) with signs of right ovarian torsion. Transvaginal ultrasound identified decreased right ovarian venous flow but preservation of right ovarian arterial flow. She underwent emergency laparoscopic right ovarian detorsion followed by vaginal oocyte retrieval on postoperative day 1. Ten oocytes were retrieved from the right detorted ovary, 4/10 (40%) were fertilized and 3/4 (75%) became blastocysts. Fifteen oocytes were retrieved from the left ovary, 14/15 (93%) were fertilized and 9/14 (64%) became blastocysts. All 18 embryos biopsied for preimplantation genetic diagnosis carried unbalanced translocations and none were transferred. The markedly reduced fertilization rate of the oocytes from the previously torted ovary is similar to the rate described in a prior report and likely related to decreased but maintained ovarian arterial flow. This report is unique because not only was the patient's ovarian torsion surgically corrected prior to oocyte retrieval but also the embryos originating from the previously torted ovary had excellent development with 75% progressing to the blastocyst stage.

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass.

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.).

BACKGROUND: In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass (Lolium perenne L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations. RESULTS: The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant (P < 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R2 = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R2 = 0.79 for 1008 observations made across seven different harvests between March and December 2018. CONCLUSIONS: Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass.

Untargeted Metabotyping Lolium perenne Reveals Population-Level Variation in Plant Flavonoids and Alkaloids.

Metabolomics provides a powerful platform to characterize plants at the biochemical level, allowing a search for underlying genes and associations with higher level complex traits such as yield and nutritional value. Efficient and reliable methods to characterize metabolic variation in economically important species are considered of high value to the evaluation and prioritization of germplasm and breeding lines. In this investigation, a large-scale metabolomic survey was performed on a collection of diverse perennial ryegrass (Lolium perenne L.) plants. A total of 2,708 data files, derived from liquid chromatography coupled to high resolution mass spectrometry (LCMS), were selected to assess the effectiveness and efficiency of applying high throughput metabolomics to survey chemical diversity in plant populations. The data set was generated from 23 ryegrass populations, with 3-25 genotypes per population, and five clonal replicates per genotype. We demonstrate an integrated approach to rapidly mine and analyze metabolic variation from this large, multi-batch LCMS data set. After performing quality control, statistical data mining and peak annotation, a wide range of variation for flavonoid glycosides and plant alkaloids was discovered among the populations. Structural variation of flavonoids occurs both in aglycone structures and acetylated/malonylated/feruloylated sugar moieties. The discovery of comprehensive metabolic variation among the plant populations offers opportunities to probe into the genetic basis of the variation, and provides a valuable resource to gain insight into biochemical functions and to relate metabolic variation with higher level traits in the species.

Genome sequence of the clover symbiont Rhizobium leguminosarum bv. trifolii strain CC275e.

Rhizobium leguminosarum bv. trifolii strain CC275e is a highly effective, N2-fixing microsymbiont of white clover (Trifolium repens L.). The bacterium has been widely used in both Australia and New Zealand as a clover seed inoculant and, as such, has delivered the equivalent of millions of dollars of nitrogen into these pastoral systems. R. leguminosarum strain CC275e is a rod-shaped, motile, Gram-negative, non-spore forming bacterium. The genome was sequenced on an Illumina MiSeq instrument using a 2 × 150 bp paired end library and assembled into 29 scaffolds. The genome size is 7,077,367 nucleotides, with a GC content of 60.9 %. The final, high-quality draft genome contains 6693 protein coding genes, close to 85 % of which were assigned to COG categories. This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession JRXL00000000. The sequencing of this genome will enable identification of genetic traits associated with host compatibility and high N2 fixation characteristics in Rhizobium leguminosarum. The sequence will also be useful for development of strain-specific markers to assess factors associated with environmental fitness, competiveness for host nodule occupancy, and survival on legume seeds (New Zealand Ministry of Business, Innovation and Employment program, 'Improving forage legume-rhizobia performance' contract C10X1308 and DairyNZ Ltd.).

Laser optical sensor, a label-free on-plate Salmonella enterica colony detection tool.

UNLABELLED: We investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.9%) was obtained by using scatter signatures collected from colonies grown on XLT4. Further verification used a total of 36 serovars (the top 20 plus 16) comprising 123 strains with classification precision levels of 88 to 100%. The similarities between the optical phenotypes of strains analyzed by BARDOT were in general agreement with the genotypes analyzed by pulsed-field gel electrophoresis (PFGE). BARDOT was evaluated for the real-time detection and identification of Salmonella colonies grown from inoculated (1.2 × 10(2) CFU/30 g) peanut butter, chicken breast, and spinach or from naturally contaminated meat. After a sequential enrichment in buffered peptone water and modified Rappaport Vassiliadis broth for 4 h each, followed by growth on XLT4 (~16 h), BARDOT detected S. Typhimurium with 84% accuracy in 24 h, returning results comparable to those of the USDA Food Safety and Inspection Service method, which requires ~72 h. BARDOT also detected Salmonella (90 to 100% accuracy) in the presence of background microbiota from naturally contaminated meat, verified by 16S rRNA sequencing and PFGE. Prolonged residence (28 days) of Salmonella in peanut butter did not affect the bacterial ability to form colonies with consistent optical phenotypes. This study shows BARDOT's potential for nondestructive and high-throughput detection of Salmonella in food samples. IMPORTANCE: High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h.

Innovative developments for long-term mammalian pest control.

BACKGROUND: Invasive mammalian pests have inflicted substantial environmental and economic damage on a worldwide scale. RESULTS: Over the last 30 years there has been minimal innovation in the development of new control tools. The development of new vertebrate pesticides, for example, has been largely restricted due to the costly and time-consuming processes associated with testing and registration. CONCLUSION: In this article we discuss recent progress and trends in a number of areas of research aimed to achieve long-term population suppression or eradication of mammalian pest species. The examples discussed here are emerging from research being conducted in New Zealand, where invasive mammalian pests are one of the greatest threats facing the national environment and economy.

Multivariate associations of flavonoid and biomass accumulation in white clover (Trifolium repens) under drought.

White clover (Trifolium repens L.) is an important pasture legume in temperate regions, but growth is often strongly reduced under summer drought. Cloned individuals from a full-sib progeny of a pair cross between two phenotypically distinct white clover populations were exposed to water deficit in pots under outdoor conditions for 9 weeks, while control pots were maintained at field capacity. Water deficit decreased leaf water potential by more than 50% overall, but increased the levels of the flavonol glycosides of quercetin (Q) and the ratio of quercetin and kaempferol glycosides (QKR) by 111% and by 90%, respectively. Water deficit reduced dry matter (DM) by 21%, with the most productive genotypes in the controls showing the greatest proportional reduction. The full-sib progeny displayed a significant increase in the root:shoot ratio by 53% under water deficit. Drought-induced changes in plant morphology were associated with changes in Q, but not kaempferol (K) glycosides. The genotypes with high QKR levels reduced their DM production least under water deficit and increased their Q glycoside levels and QKR most. These data show, at the individual genotype level, that increased Q glycoside accumulation in response to water deficit stress can be positively associated with retaining higher levels of DM production.

Effect of steroid and antibiotic treatment during assisted hatching on IVF outcomes.

There are limited data on the use of steroids and antibiotics in assisted reproductive technology (ART). Our aim was to evaluate the impact of these treatments on the outcome of IVF cycles in which Assisted Hatching (AH) was performed. We studied a retrospective cohort in a large university-affiliated infertility centre. Data from 1126 AH cycles performed between 2007 and 2009 were reviewed. Cycles were categorized as "treatment" (n = 640) and "no treatment" (n = 486), depending on whether they received steroids and antibiotics. The primary outcome was live birth. Secondary outcomes included implantation, spontaneous abortion, biochemical, clinical and ectopic pregnancy. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). OR were adjusted (AOR) for age, BMI, baseline FSH, peak estradiol, cycle number, number of oocytes retrieved, number of embryos that underwent AH, number of high-implantation potential embryos, number of embryos transferred and physician in charge. The AOR (95% CI) of live birth was 1.91 (1.08-3.38), of clinical pregnancy, 1.75 (1.08-2.83) and of biochemical pregnancy, 0.24 (0.07-0.85). Our study suggests that treatment with steroids and antibiotics during AH cycles significantly increases the odds of live birth.

High levels of mineralocorticoids in preovulatory follicular fluid could contribute to oocyte development.

OBJECTIVE: To identify aldosterone and precursors in ovarian follicles and to relate levels of mineralocorticoids to previously described renin-angiotensin system follicular fluid content. DESIGN: Experimental. SETTING: Academic laboratory and affiliated large private practice. PATIENT(S): Women undergoing oocyte retrieval for in vitro fertilization (IVF). MAIN OUTCOME MEASURE(S): The concentrations of mineralocorticoids were measured in plasma and follicular fluid. Granulosa cell mRNA expression and oocyte receptor content were evaluated. RESULT(S): High concentrations of preovulatory aldosterone and corticosterone were measured in follicular fluid (419.5±122.2 and 218,383±124,143 pg/mL, respectively). Increased mineralocorticoid levels are found in follicular fluid compared with in plasma and in large follicles compared with in small. Plasma aldosterone levels increase before ovulation. Granulosa cell gene expression that promotes aldosterone production and accumulation of corticosterone is increased in younger patients. Aldosterone receptors are localized to the surface of human oocytes. CONCLUSION(S): High levels of aldosterone and its precursor, corticosterone, were found in ovarian follicles. This combined with the presence of aldosterone receptors on oocytes suggests a possible role for aldosterone in oocyte development.