Accurate staging of non-metastatic colon cancer with CT: the importance of training and practice for experienced radiologists and analysis of incorrectly staged cases.

PURPOSE: To investigate whether locoregional staging of colon cancer by experienced radiologists can be improved by training and feedback to minimize the risk of over-staging into the context of patient selection for neoadjuvant therapy and to identify potential pitfalls of CT staging by characterizing pathologic traits of tumors that remain challenging for radiologists. METHODS: Forty-five cases of stage I-III colon cancer were included in this retrospective study. Five experienced radiologists evaluated the CTs; 5 baseline scans followed by 4 sequential batches of 10 scans. All radiologists were trained after baseline scoring and 2 radiologists received feedback. The learning curve, diagnostic performance, reader confidence, and reading time were evaluated with pathologic staging as reference. Pathology reports and H&E slides of challenging cases were reviewed to identify potential pitfalls. RESULTS: Diagnostic performance in distinguishing T1-2 vs. T3-4 improved significantly after training and with increasing number of reviewed cases. Inaccurate staging was more frequently related to under-staging rather than over-staging. Risk of over-staging was minimized to 7% in batch 3-4. N-staging remained unreliable with an overall accuracy of 61%. Pathologic review identified two tumor characteristics causing under-staging for T-stage in 5/7 cases: (1) very limited invasive part beyond the muscularis propria and (2) mucinous composition of the invading part. CONCLUSION: The high accuracy and specificity of T-staging reached in our study indicate that sufficient training and practice of experienced radiologists can ensure high validity for CT staging in colon cancer to safely use neoadjuvant therapy without significant risk of over-treatment, while N-staging remained unreliable.

Reducing greenhouse gas emissions through genetic selection in the Australian dairy industry.

This research explores possible options to reduce greenhouse gas (GHG) emissions in the Australian dairy industry by (1) including an environmental component in the national breeding program and (2) estimating the economic and environmental impacts of implementation of the subsequent indexes. A total of 12 possible selection indexes were considered. These indexes were developed to predict changes in gross per-animal methane production (using 3 scenarios depending on availability and efficacy of a direct methane trait breeding value prediction) with 4 different carbon prices, integrating them into an augmentation of the current conventional national selection index. Although some economic response is lost with inclusion of the GHG subindexes in the Balanced Performance Index, options do exist where this loss is marginal and, even in scenarios where all selection pressure is based on the environmental weighting, economic progress is still made in all cases. When including environmental traits within an index, if a relatively low percentage of economic gain or index progression is sacrificed, then approximately 40 to 50% of the maximum possible reductions in emissions may be achieved. This concurrent selection of estimated breeding values that have a correlated favorable response in emissions in addition to direct selection on a residual methane trait allows a high level of methane reduction to be achieved with a realized cost to farmers that is far lower than the economic value placed on carbon. By implementing a GHG subindex in the national breeding program, we can achieve up to a 7.9% decrease in residual methane and 9 times the reduction in gross emissions in 10 yr, compared with the current breeding program, with little to no cost to farmers. By 2050, selection based on one of the more moderate index scenarios at a carbon price of AUD$250/t (AUD$1 = US$0.71), or opportunity cost to farmers of AUD$87.22, will reduce gross emissions by 8.23% and emissions intensity by 21.25%, therefore offering a mitigation strategy that will be effective at reducing emissions with little compromise to profit.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program.

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO2-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO2-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Daily practice in guideline adherence to adjuvant chemotherapy in stage III colon cancer and predictors of outcome.

INTRODUCTION: Although guidelines recommend adjuvant chemotherapy for stage III colon cancer patients, many patients do not receive adjuvant chemotherapy. The aim of this study was to identify reasons for guideline non-adherence and assess the effect on patient outcomes in a multicenter cohort of stage III colon cancer patients who received surgery plus adjuvant chemotherapy or surgery alone. METHODS: Patients who underwent surgery between 2007 and 2017 were included. Reasons for non-adherence were determined. Propensity score analyses with inverse probability weighting were performed to adjust for confounding factors. Cox proportional hazards regression and risk stratified analyses were performed to assess the association of guideline adherence and other potential predictors with recurrence free survival (RFS). RESULTS: Data of 575 patients were included of whom 61% received adjuvant chemotherapy. In 87 of 222 patients (39%) who did not receive adjuvant chemotherapy, no reason was documented. Only age was predictive for receiving chemotherapy. Patients who received adjuvant chemotherapy had longer RFS (HR 0.42, 95%CI 0.29-0.62, p < 0.001). High T- and N-stage were associated with poorer RFS HR 2.0 (95%CI 1.58-2.71, p < 0.001) and HR 2.19 (95%CI 1.60-2.99, p < 0.001) respectively. Risk groups were identified with distinct prognosis and treatment effect and a nomogram is presented to visualize individualized RFS differences. CONCLUSION: This study shows considerable variation in guideline adherence to adjuvant chemotherapy and poor documentation on reasons for non-adherence. Optimizing adherence and gaining insight in reasons for non-adherence is advocated as this can lead to significant RFS benefit, especially in patients with high T-and N-stage tumors.

Genetic parameters for mid-infrared spectroscopy-predicted fertility.

Female fertility is a challenging trait to improve genetically because of its low heritability, its unfavorable genetic correlation with milk yield, and its relatively small number of records. The MFERT trait is the probability of conception to first insemination predicted using mid-infrared (MIR) spectroscopy of a milk sample collected during lactation as part of routine milk recording, age at calving, days in milk, and milk production. As such, MFERT could become available on many more cows than traditional fertility traits. Our objectives were (1) to estimate the heritability of MFERT; (2) to estimate genetic correlations between MFERT, traditional fertility traits, and milk production traits; and (3) to assess the potential of MFERT to be used as an indicator trait for fertility in a selection index. The MFERT trait had a heritability of 0.16, which was higher than that (0.05) obtained for traditional fertility traits. Genetic correlations between MFERT and traditional fertility traits were low to moderate. The weakest and strongest correlations (mean ± standard error) were with pregnancy at the end of the mating season (0.13 ± 0.05) and calving to first service (-0.61 ± 0.03), respectively. Based on our estimates, including MFERT in a fertility index will only substantially increase the accuracy of the index when there are many more records available for MFERT than for the traditional fertility traits. This is likely to be the case because the number of milk samples from commercial machines belonging to milk recording companies in Australia that are capable of generating MIR spectra is growing. Hence, the number of records for MFERT is expected to increase substantially in the near future.

Genetic parameters for methane emission traits in Australian dairy cows.

Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF6 tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correlations were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers.

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as ß-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.

Improving clinical management of colon cancer through CONNECTION, a nation-wide colon cancer registry and stratification effort (CONNECTION II trial): rationale and protocol of a single arm intervention study.

BACKGROUND: It is estimated that around 15-30% of patients with early stage colon cancer benefit from adjuvant chemotherapy. We are currently not capable of upfront selection of patients who benefit from chemotherapy, which indicates the need for additional predictive markers for response to chemotherapy. It has been shown that the consensus molecular subtypes (CMSs), defined by RNA-profiling, have prognostic and/or predictive value. Due to postoperative timing of chemotherapy in current guidelines, tumor response to chemotherapy per CMS is not known, which makes the differentiation between the prognostic and predictive value impossible. Therefore, we propose to assess the tumor response per CMS in the neoadjuvant chemotherapy setting. This will provide us with clear data on the predictive value for chemotherapy response of the CMSs. METHODS: In this prospective, single arm, multicenter intervention study, 262 patients with resectable microsatellite stable cT3-4NxM0 colon cancer will be treated with two courses of neoadjuvant and two courses of adjuvant capecitabine and oxaliplatin. The primary endpoint is the pathological tumor response to neoadjuvant chemotherapy per CMS. Secondary endpoints are radiological tumor response, the prognostic value of these responses for recurrence free survival and overall survival and the differences in CMS classification of the same tumor before and after neoadjuvant chemotherapy. The study is scheduled to be performed in 8-10 Dutch hospitals. The first patient was included in February 2020. DISCUSSION: Patient selection for adjuvant chemotherapy in early stage colon cancer is far from optimal. The CMS classification is a promising new biomarker, but a solid chemotherapy response assessment per subtype is lacking. In this study we will investigate whether CMS classification can be of added value in clinical decision making by analyzing the predictive value for chemotherapy response. This study can provide the results necessary to proceed to future studies in which (neo) adjuvant chemotherapy may be withhold in patients with a specific CMS subtype, who show no benefit from chemotherapy and for whom possible new treatments can be investigated. TRIAL REGISTRATION: This study has been registered in the Netherlands Trial Register (NL8177) at 11-26-2019, https://www.trialregister.nl/trial/8177 . The study has been approved by the medical ethics committee Utrecht (MEC18/712).

Optimizing genomic prediction for Australian Red dairy cattle.

The reliability of genomic prediction is influenced by several factors, including the size of the reference population, which makes genomic prediction for breeds with a relatively small population size challenging, such as Australian Red dairy cattle. Including other breeds in the reference population may help to increase the size of the reference population, but the reliability of genomic prediction is also influenced by the relatedness between the reference and validation population. Our objective was to optimize the reference population for genomic prediction of Australian Red dairy cattle. A reference population comprising up to 3,248 Holstein bulls, 48,386 Holstein cows, 807 Jersey bulls, 8,734 Jersey cows, and 3,041 Australian Red cows and a validation population with between 208 and 224 Australian Red Bulls were used, with records for milk, fat, and protein yield, somatic cell count, fertility, and survival. Three different analyses were implemented: single-trait genomic best linear unbiased predictor (GBLUP), multi-trait GBLUP, and single-trait Bayes R, using 2 different medium-density SNP panels: the standard 50K chip and a custom array of variants that were expected to be enriched for causative mutations. Various reference populations were constructed containing the Australian Red cows and all Holstein and Jersey bulls and cows, all Holstein and Jersey bulls, all Holstein bulls and cows, all Holstein bulls, and a subset of the Holstein individuals varying the relatedness between Holsteins and Australian Reds and the number of Holsteins. Varying the relatedness between reference and validation populations only led to small changes in reliability. Whereas adding a limited number of closely related Holsteins increased reliabilities compared with within-breed prediction, increasing the number of Holsteins decreased the reliability. The multi-trait GBLUP, which considered the same trait in different breeds as correlated traits, yielded higher reliabilities than the single-trait GBLUP. Bayes R yielded lower reliabilities than multi-trait GBLUP and outperformed single-trait GBLUP for larger reference populations. Our results show that increasing the size of a multi-breed reference population may result in a reference population dominated by one breed and reduce the reliability to predict in other breeds.

Low Socioeconomic Status Is Associated with Worse Outcomes After Curative Surgery for Colorectal Cancer: Results from a Large, Multicenter Study.

BACKGROUND: Socioeconomic status (SES) has been associated with early mortality in cancer patients. However, the association between SES and outcome in colorectal cancer patients is largely unknown. The aim of this study was to investigate whether SES is associated with short- and long-term outcome in patients undergoing curative surgery for colorectal cancer. METHODS: Patients who underwent curative surgery in the region of Rotterdam for stage I-III colorectal cancer between January 2007 and July 2014 were included. Gross household income and survival status were obtained from a national registry provided by Statistics Netherlands Centraal Bureau voor de Statistiek. Patients were assigned percentiles according to the national income distribution. Logistic regression and Cox proportional hazard regression were performed to assess the association of SES with 30-day postoperative complications, overall survival and cancer-specific survival, adjusted for known prognosticators. RESULTS: For 965 of the 975 eligible patients (99%), gross household income could be retrieved. Patients with a lower SES more often had diabetes, more often underwent an open surgical procedure, and had more comorbidities. In addition, patients with a lower SES were less likely to receive (neo) adjuvant treatment. Lower SES was independently associated with an increased risk of postoperative complications (Odds ratio per percent increase 0.99, 95%CI 0.99-0.998, p = 0.004) and lower cancer-specific mortality (Hazard ratio per percent increase 0.99, 95%CI 0.98-0.99, p = 0.009). CONCLUSION: This study shows that lower SES is associated with increased risk of postoperative complications, and poor cancer-specific survival in patients undergoing surgery for stage I-III colorectal cancer after correcting for known prognosticators.

Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle.

BACKGROUND: Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL. RESULTS: With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants. CONCLUSIONS: Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL.

Predicting the effect of reference population on the accuracy of within, across, and multibreed genomic prediction.

Genomic prediction is widely used to select candidates for breeding. Size and composition of the reference population are important factors influencing prediction accuracy. In Holstein dairy cattle, large reference populations are used, but this is difficult to achieve in numerically small breeds and for traits that are not routinely recorded. The prediction accuracy is usually estimated using cross-validation, requiring the full data set. It would be useful to have a method to predict the benefit of multibreed reference populations that does not require the availability of the full data set. Our objective was to study the effect of the size and breed composition of the reference population on the accuracy of genomic prediction using genomic BLUP and Bayes R. We also examined the effect of trait heritability and validation breed on prediction accuracy. Using these empirical results, we investigated the use of a formula to predict the effect of the size and composition of the reference population on the accuracy of genomic prediction. Phenotypes were simulated in a data set containing real genotypes of imputed sequence variants for 22,752 dairy bulls and cows, including Holstein, Jersey, Red Holstein, and Australian Red cattle. Different reference populations were constructed, varying in size and composition, to study within-breed, multibreed, and across-breed prediction. Phenotypes were simulated varying in heritability, number of chromosomes, and number of quantitative trait loci. Genomic prediction was carried out using genomic BLUP and Bayes R. We used either the genomic relationship matrix (GRM) to estimate the number of independent chromosomal segments and subsequently to predict accuracy, or the accuracies obtained from single-breed reference populations to predict the accuracies of larger or multibreed reference populations. Using the GRM overestimated the accuracy; this overestimation was likely due to close relationships among some of the reference animals. Consequently, the GRM could not be used to predict the accuracy of genomic prediction reliably. However, a method using the prediction accuracies obtained by cross-validation using a small, single-breed reference population predicted the accuracy using a multibreed reference population well and slightly overestimated the accuracy for a larger reference population of the same breed, but gave a reasonably close estimate of the accuracy for a multibreed reference population. This method could be useful for making decisions regarding the size and composition of the reference population.

Review: How to improve genomic predictions in small dairy cattle populations.

This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

Considering Fabry, but Diagnosing MPS I: Difficulties in the Diagnostic Process.

INTRODUCTION: Recent studies have indicated that a proportion of patients with renal failure, left ventricular hypertrophy, or cryptogenic stroke have sequence variants in their aGal A gene (Fabry disease), which has resulted in an increase in diagnostic activities for this disorder. The diagnostic process for lysosomal storage disorders may result in findings of unknown clinical significance. Here we report such an unexpected outcome. CASE: A 32-year-old male presented at the emergency department because of a transient ischemic attack. Extensive investigations revealed no cause and an initial diagnosis of cryptogenic stroke was made. Subsequently, aGal A activity was measured in a bloodspot and was shown to be normal, but the activity of alpha-L-iduronidase (IDUA), used as reference enzyme, was unexpectedly low: 0.5 umol/L (ref = 1.7-14.3). A diagnosis of IDUA deficiency, mucopolysaccharidosis type 1S or Scheie disease was considered. IDUA gene analysis revealed two homozygous sequence alterations: a silent sequence change (979C > T) in exon 7 (N297N) and an unknown missense mutation 875A > T (R263W). Physical examination was completely normal, without clinical signs of mucopolysaccharidosis type I (MPS I). Leukocyte IDUA activity was also low: 2.1 nmol/mg prot/h (ref = 14-40 nmol prot/h), but higher than the patient range of <0.1 nmol/mg prot/h. Urinary glycosaminoglycan levels were normal both quantitatively and qualitatively. It was concluded that there was low IDUA activity without clinical symptoms and the diagnosis of mucopolysaccharidosis I was discarded. CONCLUSION: The diagnostic process for lysosomal storage disorders may result in biochemical abnormalities of unknown clinical significance. Early evaluation by a specialist in inborn errors of metabolism may help to avoid anxiety in patients and unnecessary additional analyses.

XCI in preimplantation mouse and human embryos: first there is remodelling .

Female eutherians silence one of their X chromosomes to accomplish an equal dose of X-linked gene expression compared with males. The mouse is the most widely used animal model in XCI research and has proven to be of great significance for understanding the complex mechanism of X-linked dosage compensation. Although the basic principles of XCI are similar in mouse and humans, differences exist in the timing of XCI initiation, the genetic elements involved in XCI regulation and the form of XCI in specific tissues. Therefore, the mouse has its limitations as a model to understand early human XCI and analysis of human tissues is required. In this review, we describe these differences with respect to initiation of XCI in human and mouse preimplantation embryos, the extra-embryonic tissues and the in vitro model of the epiblast: the embryonic stem cells.

Defective deacetylation of histone 4 K12 in human oocytes is associated with advanced maternal age and chromosome misalignment.

BACKGROUND: Chromosome segregation errors during human oocyte meiosis are associated with low fertility in humans and the incidence of these errors increases with advancing maternal age. Studies of mitosis and meiosis suggest that defective remodeling of chromatin plays a causative role in aneuploidy. We analyzed the histone deacetylation pattern during the final stages of human oocyte maturation to investigate whether defective epigenetic regulation of chromatin remodeling in human oocytes is related to maternal age and leads to segregation errors. METHODS: Human surplus oocytes of different meiotic maturation stages [germinal vesicle (GV), metaphase (M)I and MII] were collected from standard IVF/ICSI treatments. Oocytes were analyzed for acetylation of different lysines of histone 4 (H4K5, H4K8, H4K12 and H4K16) and for α-tubulin. RESULTS: Human GV oocytes had an intense staining of the chromatin for all four histone 4 lysine acetylations. MI and MII stage oocytes showed either normal deacetylation or various amounts of defective histone deacetylation. Residual H4K12 acetylation was more frequently found in oocytes obtained from older women, with a significant correlation between defective deacetylation and maternal age (r = 0.185, P = 0.007). Eighty-eight percent of the oocytes with residual acetylation had misaligned chromosomes, whereas only 33% of the oocytes that showed correct deacetylated chromatin had misaligned chromosomes (P < 0.001). CONCLUSIONS: We conclude that defective deacetylation during human female meiosis increases with maternal age and is correlated with misaligned chromosomes. As chromosome misalignment predisposes to segregation errors, our data imply that defective regulation of histone deacetylation could be an important factor in age-related aneuploidy.

Novel mutations in 3-phosphoglycerate dehydrogenase (PHGDH) are distributed throughout the protein and result in altered enzyme kinetics.

Three-phosphoglycerate dehydrogenase (3-PGDH) deficiency is a rare recessive inborn error in the biosynthesis of the amino acid L-serine characterized clinically by congenital microcephaly, psychomotor retardation, and intractable seizures. The biochemical abnormalities associated with this disorder are low concentrations of L-serine, D-serine, and glycine in cerebrospinal fluid (CSF). Only two missense mutations (p.V425M and p.V490M) have been identified in PHGDH, the gene encoding 3-PGDH, but it is currently unclear how these mutations in the carboxy-terminal regulatory domain of the protein affect enzyme function. We now describe five novel mutations in five patients with 3-PGDH deficiency; one frameshift mutation (p.G238fsX), and four missense mutations (p.R135W, p.V261M, p.A373T, and p.G377S). The missense mutations were located in the nucleotide binding and regulatory domains of 3-PGDH and did not affect steady-state expression, protein stability, and protein degradation rates. Patients' fibroblasts displayed a significant, but incomplete, reduction in maximal enzyme activities associated with all missense mutations. In transient overexpression studies in HEK293T cells, the p.A373T, p.V425M, and p.V490M mutations resulted in almost undetectable enzyme activities. Molecular modeling of the p.R135W and p.V261M mutations onto the partial crystal structure of 3-PGDH predicted that these mutations affect substrate and cofactor binding. This prediction was confirmed by the results of kinetic measurements in fibroblasts and transiently transfected HEK293T cells, which revealed a markedly decreased V(max) and an increase in K(m) values, respectively. Taken together, these data suggest that missense mutations associated with 3-PGDH deficiency either primarily affect substrate binding or result in very low residual enzymatic activity.

Parental origin of chromatin in human monopronuclear zygotes revealed by asymmetric histone methylation patterns, differs between IVF and ICSI.

In mouse zygotes, many post-translational histone modifications are asymmetrically present in male and female pronuclei. We investigated whether this principle could be used to determine the genetic composition of monopronuclear human zygotes in conventional IVF and ICSI. First we determined whether male female asymmetry is conserved from mouse to human by staining polypronuclear zygotes with antibodies against a subset of histone N-tail post-translational modifications. To analyze human monopronuclear zygotes, a modification, H3K9me3, was selected that is present in the maternal chromatin. After IVF a total of 45 monopronuclear zygotes were obtained. In 39 (87%) of zygotes a nonuniform staining pattern was observed, proof of a bi-parental origin and assumed to result into a diploid conception. Two zygotes showed no staining for the modification, indicating that the single pronucleus was of paternal origin. Four zygotes contained only maternally derived chromatin. ICSI-derived monopronuclear zygotes (n = 33) could also be divided into three groups based on the staining pattern of their chromatin: (1) of maternal origin (n = 15), (2) of paternal origin (n = 8) or (3) consisting of two chromatin domains as dominating in IVF (n = 10). Our data show that monopronuclear zygotes originating from IVF generally arise through fusion of parental chromatin after sperm penetration. Monopronuclear zygotes derived from ICSI in most cases contain uni-parental chromatin. The fact that chromatin was of paternal origin in 24% of ICSI and in 4% of the IVF zygotes confirms earlier results obtained by FISH on cleavage stages. Our findings are of clinical importance in IVF and ICSI practice.

Mesh fixation using staples in Lichtenstein's inguinal hernioplasty: fewer complications and fewer recurrences.

BACKGROUND: Fixation of the mesh in Lichtenstein's inguinal hernioplasty is traditionally performed with polypropylene sutures. A modification of this technique uses staples for securing of the mesh. METHOD: A retrospective comparative study of 149 elective repairs of a primary inguinal hernia was performed: a control group of 67 patients undergoing mesh fixation using sutures and a study group of 82 patients undergoing staple fixation. Operating time, recurrence, postoperative pain, complications and costs were studied. RESULTS: Seven recurrences (11%) occurred in the polypropylene group as compared to one recurrence (1%) in the staple group (P < 0.01). There was a trend of fewer complications in the staple group. Operative time and long-term postoperative pain did not differ significantly between the two groups. The costs per surgery for mesh fixation and skin closure were euro 11.13 for the suture group and euro 24.35 for the staple group. CONCLUSION: Staple fixation of the mesh in Lichtenstein's inguinal hernioplasty can be considered equal to traditional fixation with sutures with regard to operating time and postoperative pain. However, staple fixation seems to show fewer recurrences and fewer complications.

FISH analysis of 15 chromosomes in human day 4 and 5 preimplantation embryos: the added value of extended aneuploidy detection.

OBJECTIVE: Screening for an increased number of chromosomes may improve the detection of abnormal embryos and thus contribute to the capability of preimplantation genetic screening (PGS) to detect the embryo(s) for transfer in IVF with the best chance for a healthy child. Good-quality day 4 and 5 embryos were analyzed after cryopreservation for the nine chromosomes mostly recommended for screening (13, 14, 15, 16, 18, 21, 22, X and Y), next to six additional chromosomes which are less well studied in this context (1, 2, 7, 6, 10 and 17). METHOD: The copy numbers of 15 chromosomes were investigated by fluorescence in situ hybridization (FISH) in three consecutive rounds. The proportion of aneuploid and mosaic embryos was determined and compared in retrospect to results in case only the recommended probe set had been analyzed. RESULTS: A total of 52 embryos from 29 infertile women were analyzed. Screening the embryos for six additional chromosomes increased the proportion of abnormal embryos from 67 to 81% (P = 0.03), owing to an increase in mosaic embryos. CONCLUSION: All but one of the meiotic aneuploidies found in this study would have been detected by the probe set most frequently used in PGS clinics. However, aneuploid cell lines originating from mitotic errors could be detected for almost all chromosomes, so screening of six additional chromosomes mainly increased the proportion of mosaic embryos. The added value of screening for six additional chromosomes in PGS for clinical practice will remain undetermined as long as the fate of mosaic embryos after transfer is unclear.