Altered morphokinetics and differential reproductive outcomes associated with cell exclusion events in human embryos.

RESEARCH QUESTION: Can embryos harbouring cell exclusion and their reproductive outcomes be classified based on morphokinetic profiles? DESIGN: A total of 469 time-lapse videos of embryos transferred between 2013 and 2019 from a single clinic were analysed. Videos were assessed and grouped according to the presence or absence of one or more excluded cells before compaction. Cell division timings, intervals between subsequent cell divisions and dynamic intervals were analysed to determine the morphokinetic profiles of embryos with cell exclusion (CE+), compared with fully compacted embryos without cell exclusion or extrusion (CE-). RESULTS: Transfer of CE+ embryos resulted in lower proportions of fetal heartbeat (FHB) and live birth compared with CE- embryos (both, P < 0.001). CE+ embryos were associated with delays in t2 (P = 0.030), t6 (P = 0.018), t7 (P < 0.001), t8 (P = 0.001), tSC (P < 0.001) and tM (P < 0.001). Earlier timings for t3 (P = 0.014) and t5 (P < 0.001) were positively associated with CE+; CE+ embryos indicated prolonged S2, S3, ECC3, cc2 and cc4. Logistic regression analysis revealed that t5, tM, S2 and ECC3 were the strongest predictive indicators of cell exclusion. Timings for S2 and ECC3 were useful in identifying increased odds of FHB when a cell exclusion event was present. CONCLUSION: Embryos harbouring cell exclusion indicated altered morphokinetic profiles. Their overall lower reproductive success was associated with two morphokinetic parameters. Morphokinetic profiles could be used as adjunct indicators for reproductive success during cycles producing few, low-quality embryos. This may allow more objective identification of cell exclusion and refinement of embryo ranking procedures before transfer.

Standards in semen examination: publishing reproducible and reliable data based on high-quality methodology.

Biomedical science is rapidly developing in terms of more transparency, openness and reproducibility of scientific publications. This is even more important for all studies that are based on results from basic semen examination. Recently two concordant documents have been published: the 6th edition of the WHO Laboratory Manual for the Examination and Processing of Human Semen, and the International Standard ISO 23162:2021. With these tools, we propose that authors should be instructed to follow these laboratory methods in order to publish studies in peer-reviewed journals, preferable by using a checklist as suggested in an Appendix to this article.

Two new loci and gene sets related to sex determination and cancer progression are associated with susceptibility to testicular germ cell tumor.

Genome-wide association (GWA) studies have reported 19 distinct susceptibility loci for testicular germ cell tumor (TGCT). A GWA study for TGCT was performed by genotyping 610 240 single-nucleotide polymorphisms (SNPs) in 1326 cases and 6687 controls from Sweden and Norway. No novel genome-wide significant associations were observed in this discovery stage. We put forward 27 SNPs from 15 novel regions and 12 SNPs previously reported, for replication in 710 case-parent triads and 289 cases and 290 controls. Predefined biological pathways and processes, in addition to a custom-built sex-determination gene set, were subject to enrichment analyses using Meta-Analysis Gene Set Enrichment of Variant Associations (M) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (I). In the combined meta-analysis, we observed genome-wide significant association for rs7501939 on chromosome 17q12 (OR = 0.78, 95% CI = 0.72-0.84, P = 1.1 × 10(-9)) and rs2195987 on chromosome 19p12 (OR = 0.76, 95% CI: 0.69-0.84, P = 3.2 × 10(-8)). The marker rs7501939 on chromosome 17q12 is located in an intron of the HNF1B gene, encoding a member of the homeodomain-containing superfamily of transcription factors. The sex-determination gene set (false discovery rate, FDRM < 0.001, FDRI < 0.001) and pathways related to NF-κB, glycerophospholipid and ether lipid metabolism, as well as cancer and apoptosis, was associated with TGCT (FDR < 0.1). In addition to revealing two new TGCT susceptibility loci, our results continue to support the notion that genes governing normal germ cell development in utero are implicated in the development of TGCT.

Investigation of six testicular germ cell tumor susceptibility genes suggests a parent-of-origin effect in SPRY4.

Recent genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) associated with testicular germ cell tumor (TGCT) risk in the genes ATF7IP, BAK1, DMRT1, KITLG, SPRY4 and TERT. In the present study, we validate these associations in a Scandinavian population, and explore effect modification by parental sex and differences in associations between the major histological subtypes seminoma and non-seminoma. A total of 118 SNPs in the six genes were genotyped in a population-based Swedish-Norwegian sample comprising 831 TGCT case-parent triads, 474 dyads, 712 singletons and 3919 population controls. Seven hundred and thirty-four additional SNPs were imputed using reference haplotypes from the 1000 genomes project. SNP-TGCT association was investigated using a likelihood-based association test for nuclear families and unrelated subjects implemented in the software package UNPHASED. Forward stepwise regression within each gene was applied to determine independent association signals. Effect modifications by parent-of-origin and effect differences between histological subtypes were explored. We observed strong association between SNPs in all six genes and TGCT (lowest P-value per gene: ATF7IP 6.2 × 10(-6); BAK1 2.1 × 10(-10); DMRT1 6.7 × 10(-25); KITLG 2.1 × 10(-48); SPRY4 1.4 × 10(-29); TERT 1.8 × 10(-18)). Stepwise regression indicated three independent signals for BAK1 and TERT, two for SPRY4 and one each for DMRT1, ATF7IP and KITLG. A significant parent-of-origin effect was observed for rs10463352 in SPRY4 (maternal odds ratio = 1.72, paternal odds ratio = 0.99, interaction P = 0.0013). No significant effect differences between seminomas and non-seminomas were found. In summary, we validated previously reported genetic associations with TGCT in a Scandinavian population, and observed suggestive evidence of a parent-of-origin effect in SPRY4.

Sperm motility assessed by deep convolutional neural networks into WHO categories.

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson's correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson's r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson's r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

VISEM-Tracking, a human spermatozoa tracking dataset.

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

Diverse Roles and Targets of miRNA in the Pathogenesis of Testicular Germ Cell Tumour.

Testicular germ cell tumour (TGCT) is the most common cancer type among young adults in many parts of the world. Although the pathogenesis of TGCT is not well understood, the involvement of heritable components is evident, and the risk is polygenic. Genome-wide association studies have so far found 78 susceptibility loci for TGCT, and many of the loci are in non-coding regions indicating the involvement of non-coding RNAs in TGCT pathogenesis. MicroRNAs (miRNAs), a class of non-coding RNAs, have emerged as important gene regulators at the post-transcriptional level. They are crucial in controlling many cellular processes, such as proliferation, differentiation, and apoptosis, and an aberrant miRNA expression may contribute to the pathogenesis of several cancers, including TGCT. In support of this notion, several studies reported differential expression of miRNAs in TGCTs. We previously demonstrated that miRNAs were the most common group of small non-coding RNAs in TGCTs, and several functional studies of miRNAs in TGCTs suggest that they may act as either oncogene or tumour suppressors. Moreover, individual miRNA targets and downstream pathways in the context of TGCT development have been explored. In this review, we will focus on the diverse roles and targets of miRNAs in TGCT pathogenesis.

A Role of the TEX101 Interactome in the Common Aetiology Behind Male Subfertility and Testicular Germ Cell Tumor.

Patients who develop testicular germ cell tumours (TGCT) are at higher risk to be subfertile than the general population. The conditions are believed to originate during foetal life, however, the mechanisms behind a common aetiology of TGCT and male subfertility remains unknown. Testis-expressed 101 (TEX101) is a glycoprotein that is related to male fertility, and downregulation of the TEX101 gene was shown in pre-diagnostic TGCT patients. In this review, we summarize the current knowledge of TEX101 and its interactome related to fertility and TGCT development. We searched literature and compilation of data from curated databases. There are studies from both human and animals showing that disruption of TEX101 result in abnormal semen parameters and sperm function. Members of the TEX101 interactome, like SPATA19, Ly6k, PICK1, and ODF genes are important for normal sperm function. We found only two studies of TEX101 related to TGCT, however, several genes in its interactome may be associated with TGCT development, such as PLAUR, PRSS21, CD109, and ALP1. Some of the interactome members are related to both fertility and cancer. Of special interest is the presence of the glycosylphosphatidylinositol anchored proteins TEX101 and PRSS21 in basophils that may be coupled to the immune response preventing further development of TGCT precursor cells. The findings of this review indicate that members of the TEX101 interactome could be a part of the link between TGCT and male subfertility.

Levels of L-carnitine in human seminal plasma are associated with sperm fatty acid composition.

The fatty acid composition of spermatozoa has been shown to be important for their function, and L-carnitine is crucial for fatty acid metabolism. Its levels in the seminal plasma positively correlate with semen quality, whereas high body mass index (BMI) is associated with both reduced semen quality and altered sperm fatty acid composition. Here, we examined the associations between free seminal L-carnitine levels and sperm fatty acid composition as well as BMI. Semen samples were collected and analyzed from 128 men with unknown fertility status and with BMI ranging from 19 kg m-2 to 63 kg m-2. Sperm fatty acid composition was assessed by gas chromatography, while free seminal L-carnitine analysis was performed using high-performance liquid chromatography. Multiple linear regression analysis showed a positive correlation of free seminal L-carnitine levels with the amount of sperm palmitic acid (ß = 0.21; P = 0.014), docosahexaenoic acid (DHA; ß = 0.23; P = 0.007), and total n-3 polyunsaturated fatty acids (ß = 0.23; P = 0.008) and a negative correlation of free seminal L-carnitine levels with lignoceric acid (ß = -0.29; P = 0.001) and total n-6 polyunsaturated fatty acids (ß = -0.24; P = 0.012) when adjusted for covariates. There was no relationship between free seminal L-carnitine levels and BMI. Since free seminal L-carnitine levels are associated with semen quality, the absence of a correlation with BMI suggests that reduced semen quality in obese men is independent of seminal L-carnitine.

Association Study between Polymorphisms in DNA Methylation-Related Genes and Testicular Germ Cell Tumor Risk.

BACKGROUND: Testicular germ cell tumors (TGCT), histologically classified as seminomas and nonseminomas, are believed to arise from primordial gonocytes, with the maturation process blocked when they are subjected to DNA methylation reprogramming. SNPs in DNA methylation machinery and folate-dependent one-carbon metabolism genes have been postulated to influence the proper establishment of DNA methylation. METHODS: In this pathway-focused investigation, we evaluated the association between 273 selected tag SNPs from 28 DNA methylation-related genes and TGCT risk. We carried out association analysis at individual SNP and gene-based level using summary statistics from the Genome Wide Association Study meta-analysis recently conducted by the international Testicular Cancer Consortium on 10,156 TGCT cases and 179,683 controls. RESULTS: In individual SNP analyses, seven SNPs, four mapping within MTHFR, were associated with TGCT risk after correction for multiple testing (q ≤ 0.05). Queries of public databases showed that three of these SNPs were associated with MTHFR changes in enzymatic activity (rs1801133) or expression level in testis tissue (rs12121543, rs1476413). Gene-based analyses revealed MTHFR (q = 8.4 × 10-4), methyl-CpG-binding protein 2 (MECP2; q = 2 × 10-3), and ZBTB4 (q = 0.03) as the top TGCT-associated genes. Stratifying by tumor histology, four MTHFR SNPs were associated with seminoma. In gene-based analysis MTHFR was associated with risk of seminoma (q = 2.8 × 10-4), but not with nonseminomatous tumors (q = 0.22). CONCLUSIONS: Genetic variants within MTHFR, potentially having an impact on the DNA methylation pattern, are associated with TGCT risk. IMPACT: This finding suggests that TGCT pathogenesis could be associated with the folate cycle status, and this relation could be partly due to hereditary factors.

Identification of 22 susceptibility loci associated with testicular germ cell tumors.

Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95th percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation.

Serum RNA Profiling in the 10-Years Period Prior to Diagnosis of Testicular Germ Cell Tumor.

Although testicular germ cell tumor (TGCT) overall is highly curable, patients may experience late effects after treatment. An increased understanding of the mechanisms behind the development of TGCT may pave the way for better outcome for patients. To elucidate molecular changes prior to TGCT diagnosis we sequenced small RNAs in serum from 69 patients who were later diagnosed with TGCT and 111 matched controls. The deep RNA profiles, with on average 18 million sequences per sample, comprised of nine classes of RNA, including microRNA. We found that circulating RNA signals differed significantly between cases and controls regardless of time to diagnosis. Different levels of TSIX related to X-chromosome inactivation and TEX101 involved in spermatozoa production are among the interesting findings. The RNA signals differed between seminoma and non-seminoma TGCT subtypes, with seminoma cases showing lower levels of RNAs and non-seminoma cases showing higher levels of RNAs, compared with controls. The differentially expressed RNAs were typically associated with cancer related pathways. Our results indicate that circulating RNA profiles change during TGCT development according to histology and may be useful for early detection of this tumor type.

miRNA-302s may act as oncogenes in human testicular germ cell tumours.

Testicular germ cell tumour (TGCT) represents the most common malignancy in young men in large parts of the world, but the aetiology is yet unclear. Multiple TGCT susceptibility loci have been identified, and we have shown that one of these, SPRY4, may act as a TGCT oncogene. Furthermore, many of the loci are in non-coding regions of the genome. miRNAs, a class of non-coding RNAs may play a crucial role in cell proliferation, differentiation, and apoptosis, and alteration in their expression may lead to oncogenesis. Differential expression of miRNAs in TGCT and normal testis has been reported in previous studies. In this study, we used qPCR to analyse, in normal and malignant testis tissue, the expression of the ten miRNAs that we had previously identified by sequencing to be the most upregulated in TGCT. We found high expression of these miRNAs also by qPCR analysis. The levels of miR-302a-3p, miR-302b-3p, and miR-302c-3p were downregulated after treatment of the TGCT cell lines NT2-D1 and 833 K with the chemotherapy drug cisplatin. By using miRNA inhibitor-mediated transient transfection, we inhibited the expression of the three members of miR-302 family (miR-302s). Inhibition of miR-302s resulted in a decreased cell proliferation in NT2-D1 cells, but not in 833 K cells. In both cell lines, inhibition of miR-302s resulted in decreased expression of SPRY4, which we have previously shown to regulate MAPK/ERK and PI3K/Akt signalling pathways in these cells. Inhibition of miR-302b-3p and miR-302c-3p decreased phosphorylation of ERK1/2, whereas inhibition of miR-302a-3p and miR-302b-3p led to decreased expression of the apoptosis inhibitor, survivin. Our findings suggest that miR-302s act as TGCT oncogenes by inducing the expression of SPRY4 and activating MAPK/ERK pathway while inhibiting apoptosis via increased survivin expression.

Cisplatin treatment of testicular cancer patients introduces long-term changes in the epigenome.

BACKGROUND: Cisplatin-based chemotherapy (CBCT) is part of standard treatment of several cancers. In testicular cancer (TC) survivors, an increased risk of developing metabolic syndrome (MetS) is observed. In this epigenome-wide association study, we investigated if CBCT relates to epigenetic changes (DNA methylation) and if epigenetic changes render individuals susceptible for developing MetS later in life. We analyzed methylation profiles, using the MethylationEPIC BeadChip, in samples collected ~ 16 years after treatment from 279 Norwegian TC survivors with known MetS status. Among the CBCT treated (n = 176) and non-treated (n = 103), 61 and 34 developed MetS, respectively. We used two linear regression models to identify if (i) CBCT results in epigenetic changes and (ii) epigenetic changes play a role in development of MetS. Then we investigated if these changes in (i) and (ii) links to genes, functional networks, and pathways related to MetS symptoms. RESULTS: We identified 35 sites that were differentially methylated when comparing CBCT treated and untreated TC survivors. The PTK6-RAS-MAPk pathway was significantly enriched with these sites and infers a gene network of 13 genes with CACNA1D (involved in insulin release) as a network hub. We found nominal MetS-associations and a functional gene network with ABCG1 and NCF2 as network hubs. CONCLUSION: Our results suggest that CBCT has long-term effects on the epigenome. We could not directly link the CBCT effects to the risk of developing MetS. Nevertheless, since we identified differential methylation occurring in genes associated with conditions pertaining to MetS, we hypothesize that epigenomic changes may also play a role in the development of MetS in TC survivors. Further studies are needed to validate this hypothesis.

Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction.

Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. Adding participant data did not improve the algorithms performance. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.

Subfertility among parents of men diagnosed with testicular cancer.

It is well known that men with testicular cancer also have reduced fertility before diagnosis. It is unclear, however, whether their parents also have reduced fertility. We performed a population-based record linkage study comparing parental fertility among 3711 testicular cancer cases and 371 100 control males. The cases were diagnosed from 1961 to 2001, and the data were analysed by logistic regression. Included indicators of parental fertility were number of children, rate of unlike-sex twins as a proxy for dizygotic twinning rate, and proportion of boys. The number of children was reduced across increasing sibship size among both mothers [odds ratio (OR) = 0.95, p(trend) = 0.003] and fathers [OR = 0.97, p(trend) = 0.057] of subjects with testicular cancer. The proportion of unlike-sex twins was also reduced among their mothers [(OR = 0.56, p = 0.049 (adjusted for year of birth)] and fathers [(OR = 0.56, p = 0.049 (adjusted for year of birth)]. The results were only marginally changed when also adjusting for respective parental age. Our study indicates that parents of testicular cancer cases have reduced fertility. This suggests that genetic factors are important in the association between testicular cancer and reduced fertility.

[The information contained in a semen analysis]. / Hva forteller en saedanalyse?

BACKGROUND: Semen analysis is an important part of the infertility investigation, but its usefulness as a predictor of fertility is under debate. MATERIAL AND METHODS: The article is based on the authors' experience from andrology laboratories and participation in the WHO's editorial committee for standardization of semen analysis and literature retrieved through non-systematic searches of Medline and PubMed. RESULTS AND INTERPRETATION: There is currently no simple and reliable method to predict the fertility of a man. Most laboratories still use semen analyses that are based on methodology introduced in the 1950s. To allow meaningful comparison of results with previous investigations, results from other laboratories or with reference intervals, it is essential that the methods are standardized and evidence-based. Several studies have shown a correlation between semen variables such as sperm concentration, progressive motility and sperm morphology, and time to pregnancy. The results of semen analysis are often difficult to interpret without patient history and other investigations and do not provide a diagnosis by themselves; they must therefore be interpreted in conjunction with other medical investigations of the couple.