Dyslexia polygenic scores show heightened prediction of verbal working memory and arithmetic

Purpose: The aim of this study is to establish which specific cognitive abilities are phenotypically related to reading skill in adolescence and determine whether this phenotypic correlation is explained by polygenetic overlap. Method: In an Australian population sample of twins and non-twin siblings of European ancestry (734 ≤ N ≤ 1542 [50.7% < F < 66%], mean age = 16.7, range = 11–28 years) from the Brisbane Adolescent Twin Study, mixedeffects models were used to test the association between a dyslexia polygenic score (based on genome-wide association results from a study of 51,800 dyslexics versus >1 million controls) and quantitative cognitive measures. The variance in the cognitive measure explained by the polygenic score was compared to that explained by a reading difficulties phenotype (scores that were lower than 1.5 SD below the mean reading skill) to derive the proportion of the association due to genetic influences. Results: The strongest phenotypic correlations were between poor reading and verbal tests (R2 up to 6.2%); visuo-spatial working memory was the only measure that did not show association with poor reading. Dyslexia polygenic scores could completely explain the phenotypic covariance between poor reading and most working memory tasks and were most predictive of performance on a test of arithmetic (R2 ¼ 2:9%). Conclusion: Shared genetic pathways are thus highlighted for the commonly found association between reading and mathematics abilities, and for the verbal short-term/working memory deficits often observed in dyslexia

Non-invasive selection for euploid embryos: prospects and pitfalls of the three most promising approaches.

The objective of this review was to evaluate the efficacy of three promising technologies for assessment of ploidy status in IVF embryos [i.e. preimplantation genetic testing for aneuploidy (PGT-A)]: artificial intelligence (AI), non-invasive PGT-A (niPGT-A) and metabolomics. Publications where >80% correlation with blastocyst biopsies could be demonstrated in ≥50 cycles were prioritized. AI was found to classify the chance of an embryo implanting with an average area under the curve (AUC) of 0.7. AI is thus a superior selection method compared with morphological selection alone, but is still inferior to invasive PGT-A. Some niPGT-A studies have up to 100% concordance with PGT-A, but a multicentre study showed 78% concordance due to maternal contamination, which can improve with specific changes in culture conditions. niPGT-A has thus improved significantly and has the potential to reach 100% with PGT-A if the issue of maternal contamination is solved; however, >30% of euploid embryos never implant. Finally, metabolomics is the least developed technique of the three, but some preliminary data show >90% concordance with implantation and with PGT-A without changing culture conditions. Metabolomics thus has the potential to identify euploid embryos that, metabolically, are incapable of implanting. A combination of two or all of these approaches is possible

Nutlin-3a efficacy in sarcoma predicted by transcriptomic and epigenetic profiling

Nutlin-3a is a small-molecule antagonist of p53/MDM2 that is being explored as a treatment for sarcoma. In this study, we examined the molecular mechanisms underlying the sensitivity of sarcomas to Nutlin-3a. In an ex vivo tissue explant system, we found that TP53 pathway alterations (TP53 status, MDM2/MDM4 genomic amplification/mRNA overexpression, MDM2 SNP309, and TP53 SNP72) did not confer apoptotic or cytostatic responses in sarcoma tissue biopsies (n = 24). Unexpectedly, MDM2 status did not predict Nutlin-3a sensitivity. RNA sequencing revealed that the global transcriptomic profiles of these sarcomas provided a more robust prediction of apoptotic responses to Nutlin-3a. Expression profiling revealed a subset of TP53 target genes that were transactivated specifically in sarcomas that were highly sensitive to Nutlin-3a. Of these target genes, the GADD45A promoter region was shown to be hypermethylated in 82% of wild-type TP53 sarcomas that did not respond to Nutlin-3a, thereby providing mechanistic insight into the innate ability of sarcomas to resist apoptotic death following Nutlin-3a treatment. Collectively, our findings argue that the existing benchmark biomarker for MDM2 antagonist efficacy (MDM2 amplification) should not be used to predict outcome but rather global gene expression profiles and epigenetic status of sarcomas dictate their sensitivity to p53/MDM2 antagonists.Kathleen I. Pishas, Susan J. Neuhaus, Mark T. Clayer, Andreas W. Schreiber, David M. Lawrence, Michelle Perugini, Robert J. Whitfield, Gelareh Farshid, Jim Manavis, Steve Chryssidis, Bronwen J. Mayo, Rebecca C. Haycox, Kristen Ho, Michael P. Brown, Richard J. D'Andrea, Andreas Evdokiou, David M. Thomas, Jayesh Desai, David F. Callen and Paul M. Neilse

Dissecting signalling contributions of the alpha and beta subunits of the GM-CSF receptor

Normal tissue homeostasis and appropriate responses to injury and infection are dependent on cellular communication mediated by cell surface receptors that respond to extrinsic stimuli. The GM-CSF receptor was the major focus of this project. This receptor shares a common signalling subunit, β [subscript c], with the IL-3 and IL-5 receptors. The unique GM-CSF receptor α-subunit ( GMRα ) confers ligand binding specificity to the complex and is essential for GM-CSF receptor signalling, although the full complement of signalling events mediated by GMRα remains elusive. Through cloning of candidate interacting proteins, expression and co-immunoprecipitation studies, we have confirmed interactions for two proteins previously reported to interact with the GMRα, p85 and IKKβ. Additionally, we identified the Src family kinase, Lyn, as a novel direct interacting partner of GMRα and provide insights into possible roles of this kinase in initiating signalling from the GM-CSF receptor. In addition to GMRα associated events we aimed to further characterise the role of the common β [subscript c] subunit in GM-CSF mediated signalling. We utilised two classes of consitutively active β [subscript c] mutants ( extracellular or transmembrane ) which transform the bi-potential myeloid FDB1 cell line to either factor-independent growth and survival, or granulocyte-macrophage differentiation, respectively. Here we report a comprehensive biochemical analysis of signalling by these two classes of mutants in this cell line. The two activated GMR mutants displayed distinct and non-overlapping signalling capacity. In particular, expression of a mutant with a substitution in the transmembrane domain ( V449E ) selectively activated JAK / STAT5 and MAPK pathways resulting in a high level of sensitivity to JAK and MEK inhibitors. In contrast, expression of a mutant with a 37 amino acid duplication in its extracellular domain ( FI Δ ) selectively activates the PI3K / AKT and IKKβ / NFkB pathways. Cells responding to this mutant display a relative high level of sensitivity to two independent PI3K inhibitors and relative resistance to inhibition of MEK and JAK2. The non-overlapping nature of signalling by these two activated mutants suggests that there are alternative modes of receptor activation that differentially dependent on JAK2 and that act synergistically in the mature liganded cytokine receptor complex. Further detailed analysis of these mutants will facilitate the dissection of the signalling pathways involved in the GM-CSF response that mediate proliferation, survival and differentiation.Thesis (Ph.D.)--University of Adelaide, School of Medicine, 2007

#91 : Development of a Combined Artificial Intelligence Score for Evaluating Both Embryo Ploidy and Viability to Aid in Embryo Selection During IVF

Background and Aims: Artificial intelligence (AI) is being increasingly used for non-invasive evaluation of embryo quality during IVF. Previous studies described development of AI for selecting embryos likely to be euploid (genetics AI), or likely to lead to clinical pregnancy (viability AI), based on analysis of images of blastocysts on day 5 of development. The aim of this study was to determine if a combination of these AI scores could be used to effectively evaluate both outcomes. Method: 936 embryo images with pre-implantation genetic testing for aneuploidies (PGT-A) outcomes, and 479 embryo images with clinical pregnancy outcomes, were retrospectively obtained from 12 IVF clinics in 5 countries. Performance was evaluated for each AI score alone, and the average score of the two AIs. The ability to select euploid or viable embryos was evaluated using ROC-AUC analyses, and a simulated cohort ranking method reported in the literature. Results: The average score of the two AIs was generally as effective at selecting euploid embryos as the genetics AI, and just as effective at selecting viable embryos as the viability AI. Results for both analyses are presented below. Conclusion: An AI score that can evaluate both embryo ploidy and viability simultaneously is useful for selecting preferred embryos for analysis or transfer. These results suggest that it is feasible to generate a single score for evaluating overall embryo quality using a non-invasive approach

#92 : An Artificial Intelligence Algorithm Outperforms Highly Variable Embryologist Grading for Predicting the Likelihood of Pregnancy Outcome from Embryo Images

Background and Aims: Embryologist evaluation of embryos is critical for ensuring successful pregnancy outcomes. Standard, manual evaluation is variable, subjective, and time-consuming. The aim of this study was to evaluate whether an artificial intelligence (AI) algorithm can standardize and improve embryo evaluation during IVF. Method: 20 images of blastocyst-stage embryos on day 5 of in vitro development were selected to represent a range of morphological qualities. All embryos had been transferred and the clinical pregnancy outcome was known for each embryo based on detection of fetal heartbeat at first ultrasound scan (∼7-9 weeks gestation). 50% of embryos in the dataset resulted in pregnancy. 158 embryologists made a total of 236 attempts at providing their evaluation of the morphological quality of the 20 embryo images using the Gardner system. The embryologist-assigned grades were then used to generate their prediction of whether that embryo would lead to pregnancy or not (≥ 3BB indicated a pregnancy prediction, and <3BB indicated a non-pregnancy prediction). The same 20 embryo images were also assessed by a previously developed viability AI algorithm for evaluating the likelihood of clinical pregnancy based on embryo images. An AI score of ≥5.0/10 indicated a pregnancy prediction, and <5.0/10 indicated a non-pregnancy prediction. The AI algorithm provided the same score for each embryo image regardless of how many times the analysis was performed. Results: The AI algorithm correctly predicted pregnancy outcome for 14/20 embryo images (70%). Embryologists also correctly predicted 14/20 images in 14/236 attempts (6%), and in 1 attempt correctly predicted 15/20 images. In the remaining 221 attempts (94%) embryologists correctly predicted between 6-13 images, representing a range of accuracies from 30-75%. Conclusion: This study demonstrates the inherent variability and lack of objectivity associated with an embryologist’s evaluation of embryos. It highlights the benefits of accurate AI algorithms for standardizing embryo assessmen

Efficient automated error detection in medical data using deep-learning and label-clustering

Abstract Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for detecting errors in data typically focus only on minimizing the effects of random classification noise. More recent progress has focused on using deep-learning to capture errors stemming from subjective labelling and confounding variables, however, such methods can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. Results demonstrated high performance and efficiency on both image- and record-based datasets. Errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete. The resulting trained AI models exhibited greater stability and up to a 45% improvement in accuracy, from 69% to over 99%. These results indicate that practical, automated detection of errors in medical data is possible without human oversight.</jats:p

#316 : Improved Time to Pregnancy When Combining an Artificial Intelligence Score and Morphology Grading for Embryo Selection During IVF

Background and Aims: Embryo selection is critical in determining IVF success yet continues to be challenging due to the subjectivity of morphology grading methods, especially when grading fair/average quality embryos. Improving embryo selection could optimise implantation rates and minimise financial/emotional burden on patients. Artificial Intelligence (AI) algorithms represent promising, non-invasive methods of standardising embryo grading and potentially increasing IVF success rates. This study assessed whether an AI algorithm (Life Whisperer Viability) for evaluating the likelihood of clinical pregnancy improves time to pregnancy (TTP) when compared to or combined with standard morphology grading. Method: 305 de-identified 2D images of day 5 blastocysts (121 fresh/184 frozen) with matched clinical pregnancy outcomes (fetal heartbeat at first scan) from women who underwent IVF treatment from 2020-2023 were retrospectively assessed. All images were taken prior to transfer/freezing. TTP was assessed using a simulated cohort ranking method, with TTP being defined as the average number of transfers needed to obtain a clinical pregnancy. Results: A positive linear correlation of LWV scores with pregnancy outcomes was observed (p<0.001). ROC-AUC results indicate that LWV is selecting embryos leading to pregnancy at least as well, if not better, than Gardner morphology grading (0.641 vs 0.624), with further improvement observed when LWV and Gardner grading were combined. The TTP analysis showed a 7.3% reduction in TTP when using LWV over Gardner grading. Combined use of LWV+Gardner grading reduced TTP by up to 10.8%, with the largest improvement (5.3%) seen in the frozen group, where there was a higher distribution of average quality embryos. Conclusion: LWV showed improved embryo rankingand reduction in the estimated average number oftransfers needed to achieveclinical pregnancy. Furthermore, evaluation of TTP supports the combined use of LWV+Gardner grading, showing that they work synergistically to further improve ranking performance when selecting average quality embryos

Common Leukemic Signaling Pathways Identified by Comparative Analysis of GM-CSF and FLT3 Activated Receptor Mutations.

Abstract As a strategy to identify key targets in myeloid leukemia we examined directly the overlap in signaling and downstream events induced by several activated receptor mutants associated with AML. We compared the events induced by a leukemic mutant (V449E) of the GM-CSF receptor with events associated with the activating FLT3 internal tandem duplication mutant (FLT3-ITD) and the D835Y kinase domain mutant (FLT3-TKD). Receptor mutants were introduced by retroviral transduction into the FDB-1 bi-potential myeloid cell line model (McCormack MP et al., Blood.95:120–127, 2000). We used biochemical analysis and pathway inhibitors to demonstrate that the GMR V449E mutant selectively activates the JAK2-STAT5 and p44/42 MAPK pathways which are central to the ability of this mutant to confer continued growth and arrested differentiation in the absence of growth factor. Like the GMR V449E mutant, the FLT3-ITD and -TKD mutants arrest granulocyte-macrophage differentiation and support continued growth in the absence of added growth factor. For both of these mutants we also observed constitutive JAK2 and STAT5 phosphorylation and sensitivity to a selective JAK2 inhibitor (JAK2 Inhibitor II, Merck) suggesting a potential key role for JAK2 in signalling from FLT3 activating mutants. Both FLT3 mutants activated the p44/42 MAPK pathway, resulted in Akt phosphorylation and were sensitive to PI3-kinase and MEK inhibitors. As activation of the p44/42 MAPK pathway is common to all mutants, and has recently been associated with suppressed differentiation in AML (Radomska HS et al, J Exp Med. 203:371–381, 2006), we used the selective MEK inhibitor, U0126 (Merck), to investigate the contribution of this pathway to maintenance of the differentiation block. Treatment of factor-independent FDB-1 cells expressing the GMR V449E mutant, or either FLT3 mutant, with U0126 for 48 hours induced morphological differentiation suggesting that the sustained activation of p44/42 MAPK contributes to maintaining the arrest in myeloid differentiation. To explore further the overlapping nature of signalling by the GMRV449E and FLT3-ITD activated mutants we utilised the Wilcoxan rank sum test to perform gene-set enrichment analysis. Analysis of 119 FLT3-ITD associated genes (126 probes) (Mizuki M et al, Blood101: 3164–3173, 2003) revealed that this gene-set shows evidence of differential expression in response to GMR V449E signaling (p=2.8x10−7). In particular, we identify the tumor suppressor gene, Gadd45α, as a common gene, down-regulated in response to signalling via the FLT3-ITD, FLT3-TKD and GMR V449E activated receptors. Analysis of AML gene expression data (Valk PJ et al, New Engl. J. Med: 350:1617–1628, 2004) indicates that activation of Gadd45α maybe suppressed by multiple pathways in AML blasts.</jats:p