Mosaic human preimplantation embryos and their developmental potential in a prospective, non-selection clinical trial.

Chromosome imbalance (aneuploidy) is the major cause of pregnancy loss and congenital disorders in humans. Analyses of small biopsies from human embryos suggest that aneuploidy commonly originates during early divisions, resulting in mosaicism. However, the developmental potential of mosaic embryos remains unclear. We followed the distribution of aneuploid chromosomes across 73 unselected preimplantation embryos and 365 biopsies, sampled from four multifocal trophectoderm (TE) samples and the inner cell mass (ICM). When mosaicism impacted fewer than 50% of cells in one TE biopsy (low-medium mosaicism), only 1% of aneuploidies affected other portions of the embryo. A double-blinded prospective non-selection trial (NCT03673592) showed equivalent live-birth rates and miscarriage rates across 484 euploid, 282 low-grade mosaic, and 131 medium-grade mosaic embryos. No instances of mosaicism or uniparental disomy were detected in the ensuing pregnancies or newborns, and obstetrical and neonatal outcomes were similar between the study groups. Thus, low-medium mosaicism in the trophectoderm mostly arises after TE and ICM differentiation, and such embryos have equivalent developmental potential as fully euploid ones.

Implementing preconception expanded carrier screening in a universal health care system: A model-based cost-effectiveness analysis.

PURPOSE: The limited evidence available on the cost-effectiveness (CE) of expanded carrier screening (ECS) prevents its widespread use in most countries, including Italy. Herein, we aimed to estimate the CE of 3 ECS panels (ie, American College of Medical Genetics and Genomics [ACMG] Tier 1 screening, "Focused Screening," testing 15 severe, highly penetrant conditions, and ACMG Tier 3 screening) compared with no screening, the health care model currently adopted in Italy. METHODS: The reference population consisted of Italian couples seeking pregnancy with no increased personal/familial genetic risk. The CE model was developed from the perspective of the Italian universal health care system and was based on the following assumptions: 100% sensitivity of investigated screening strategies, 77% intervention rate of at-risk couples (ARCs), and no risk to conceive an affected child by risk-averse couples opting for medical interventions. RESULTS: The incremental CE ratios generated by comparing each genetic screening panel with no screening were: -14,875 ± 1,208 €/life years gained (LYG) for ACMG1S, -106,863 ± 2,379 €/LYG for Focused Screening, and -47,277 ± 1,430 €/LYG for ACMG3S. ACMG1S and Focused Screening were dominated by ACMG3S. The parameter uncertainty did not significantly affect the outcome of the analyses. CONCLUSION: From a universal health care system perspective, all the 3 ECS panels considered in the study would be more cost-effective than no screening.

The use of copy number loads to designate mosaicism in blastocyst stage PGT-A cycles: fewer is better.

STUDY QUESTION: How well can whole chromosome copy number analysis from a single trophectoderm (TE) biopsy predict true mosaicism configurations in human blastocysts? SUMMARY ANSWER: When a single TE biopsy is tested, wide mosaicism thresholds (i.e. 20-80% of aneuploid cells) increase false positive calls compared to more stringent ones (i.e. 30-70% of aneuploid cells) without improving true detection rate, while binary classification (aneuploid/euploid) provides the highest diagnostic accuracy. WHAT IS KNOWN ALREADY: Next-generation sequencing-based technologies for preimplantation genetic testing for aneuploidies (PGT-A) allow the identification of intermediate chromosome copy number alterations potentially associated with chromosomal mosaicism in TE biopsies. Most validation studies are based on models mimicking mosaicism, e.g. mixtures of cell lines, and cannot be applied to the clinical interpretation of TE biopsy specimens. STUDY DESIGN, SIZE, DURATION: The accuracy of different mosaicism diagnostic thresholds was assessed by comparing chromosome copy numbers in multiple samples from each blastocyst. Enrolled embryos were donated for research between June 2019 and September 2020. The Institutional Review Board at the Near East University approved the study (project: YDU/2019/70-849). Embryos showing euploid/aneuploid mosaicism (n = 53), uniform chromosomal alterations (single or multiple) (n = 25), or uniform euploidy (n = 39) in their clinical TE biopsy were disaggregated into five portions: the inner cell mass (ICM) and four TE segments. Collectively, 585 samples from 117 embryos were analysed. PARTICIPANTS/MATERIALS, SETTING, METHODS: Donated blastocysts were warmed, allowed to re-expand, and disaggregated in TE portions and ICM. PGT-A analysis was performed using Ion ReproSeq PGS kit and Ion S5 sequencer (ThermoFisher). Sequencing data were blindly analysed with Ion Reporter software to estimate raw chromosome copy numbers. Intra-blastocyst comparison of copy number data was performed employing different thresholds commonly used for mosaicism detection. From copy number data, different case scenarios were created using more stringent (30-70%) or less stringent criteria (20-80%). Categorical variables were compared using the two-sample z test for proportions. MAIN RESULTS AND THE ROLE OF CHANCE: When all the five biopsies from the same embryo were analysed with 30-70% thresholds, only 8.4% (n = 14/166) of patterns abnormal in the original analysis revealed a true mosaic configuration, displaying evidence of reciprocal events (3.6%, n = 6/166) or confirmation in additional biopsies (4.8%, n = 8/166), while most mosaic results (87.3% of total predicted mosaic patterns) remained confined to a single TE specimen. Conversely, uniform whole chromosome aneuploidies (28.3% of total patterns, n = 47/166) were confirmed in all subsequent biopsies in 97.9% of cases (n = 46/47). When 20-80% thresholds were employed (instead of 30-70%), the overall mosaicism rate per biopsy increased from 20.2% (n = 114/565) to 40.2% (n = 227/565). However, the use of a wider threshold range did not contribute to the detection of additional true mosaic patterns, while significantly increasing false positive mosaic patterns from 57.8% to 79.5% (n = 96/166; 95% CI = 49.9-65.4 vs n = 271/341; 95% CI = 74.8-83.6, respectively) (P < 0.00001). Moreover, the shift of the aneuploid cut-off from 70% to 80% of aneuploid cells resulted in mosaicism overcalling in the high range (50-80% of aneuploid cells), impacting the accuracy of uniform aneuploid classification. Parametric analysis of thresholds, based on multifocal analysis, revealed that a binary classification scheme with a single cut-off at a 50% level provided the highest sensitivity and specificity rates. Further analysis on technical noise distribution at the chromosome level revealed a greater impact on smaller chromosomes. LIMITATIONS, REASONS FOR CAUTION: While enrolment of a population enriched in embryos showing intermediate chromosome copy numbers enhanced the evaluation of the mosaicism category compared with random sampling such study population selection is likely to lead to an overall underestimation of PGT-A accuracy compared to a general assessment of unselected clinical samples. This approach involved the analysis of aneuploidy chromosome copy number thresholds at the embryo level; future studies will need to evaluate these criteria in relation to clinical predictive values following embryo transfers for different PGT-A assays. Moreover, the study lacked genotyping-based confirmation analysis. Finally, aneuploid embryos with known meiotic partial deletion/duplication were not included. WIDER IMPLICATIONS OF THE FINDINGS: Current technologies can detect low-intermediate chromosome copy numbers in preimplantation embryos but their identification is poorly correlated with consistent propagation of the anomaly throughout the embryo or with negative clinical consequences when transferred. Therefore, when a single TE biopsy is analysed, diagnosis of chromosomal mosaicism should be evaluated carefully. Indeed, the use of wider mosaicism thresholds (i.e. 20-80%) should be avoided as it reduces the overall PGT-A diagnostic accuracy by increasing the risk of false positive mosaic classification and false negative aneuploid classification. From a clinical perspective, this approach has negative consequences for patients as it leads to the potential deselection of normal embryos for transfer. Moreover, a proportion of uniform aneuploid embryos may be inaccurately categorized as high-level mosaic, with a consequent negative outcome (i.e. miscarriage) when inadvertently selected for transfer. Clinical outcomes following PGT-A are maximized when a 50% threshold is employed as it offers the most accurate diagnostic approach. STUDY FUNDING/COMPETING INTEREST(S): The study was supported by Igenomix. The authors not employed by Igenomix have no conflicts of interest to declare. TRIAL REGISTRATION NUMBER: N/A.

Maternal exome analysis for the diagnosis of oocyte maturation defects and early embryonic developmental arrest.

RESEARCH QUESTION: Can a methodology be developed for case selection and whole-exome sequencing (WES) analysis of women who are infertile owing to recurrent oocyte maturation defects (OOMD) and/or preimplantation embryo lethality (PREMBL)? DESIGN: Data were collected from IVF patients attending the Istanbul Memorial Hospital (2015-2021). A statistical methodology to identify infertile endophenotypes (recurrent low oocyte maturation rate, low fertilization rate and preimplantation developmental arrest) was developed using a large IVF dataset (11,221 couples). Twenty-eight infertile women with OOMD/PREMBL were subsequently enrolled for WES on their genomic DNA. Pathogenic variants were prioritized using a custom-made bioinformatic pipeline set to minimize false-positive discoveries through resampling in control cohorts (the Human Genome Diversity Project and 1343 whole-exome sequences from oocyte donors). Individual single-cell RNA sequencing data from 18 human metaphase II (MII) oocytes and antral granulosa cells was used for genome-wide validation. WES and bioinformatics were performed at Igenomix and the National Research Council, Italy. RESULTS: Variant prioritization analysis identified 265 unique variants in 248 genes (average 22.4 per sample). Of the genes harbouring high-impact variants 78% were expressed by MII oocytes and/or antral granulosa cells, significantly higher than for random sample of controls (odds ratio = 5, Fisher's exact P = 0.0004). Seven of the 28 women (25%) were homozygous carriers of missense pathogenic variants in known candidate genes for OOMD/PREMBL, including PATL2, NLRP5 (n = 2),TLE6, PADI6, TUBB8 and TRIP13. Furthermore, novel gene-disease associations were identified. In fact, one woman with a low oocyte maturation rate was a homozygous carrier of high-impact variants in ENSA, an essential gene for prophase I meiotic transition in mice. CONCLUSIONS: This analytical framework could reveal known and new genes associated with isolated recurrent OOMD/PREMBL, providing essential indications for scaling this strategy to larger studies.

Molecular tools for the genomic assessment of oocyte's reproductive competence.

The most important factor associated with oocytes' developmental competence has been widely identified as the presence of chromosomal abnormalities. However, growing application of genome-wide sequencing (GS) in population diagnostics has enabled the identification of multifactorial genetic predispositions to sub-lethal pathologies, including those affecting IVF outcomes and reproductive fitness. Indeed, GS analysis in families with history of isolated infertility has recently led to the discovery of new genes and variants involved in specific human infertility endophenotypes that impact the availability and the functionality of female gametes by altering unique mechanisms necessary for oocyte maturation and early embryo development. Ongoing advancements in analytical and bioinformatic pipelines for the study of the genetic determinants of oocyte competence may provide the biological evidence required not only for improving the diagnosis of isolated female infertility but also for the development of novel preventive and therapeutic approaches for reproductive failure. Here, we provide an updated discussion and review of the progresses made in preconception genomic medicine in the identification of genetic factors associated with oocyte availability, function, and competence.

Identification of Protein Functional Regions.

Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.

A multi-scale coevolutionary approach to predict interactions between protein domains.

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?

Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity to accurately predict residue-residue contacts from sequence information alone, and thereby to facilitate tertiary and quaternary protein structure prediction. More recently, they have also been used to predict fitness effects of amino-acid substitutions in proteins, and to predict evolutionary conserved protein-protein interactions. These models are based on two currently unjustified hypotheses: 1) correlations in the amino-acid usage of different positions are resulting collectively from networks of direct couplings; and 2) pairwise couplings are sufficient to capture the amino-acid variability. Here, we propose a highly precise inference scheme based on Boltzmann-machine learning, which allows us to systematically address these hypotheses. We show how correlations are built up in a highly collective way by a large number of coupling paths, which are based on the proteins three-dimensional structure. We further find that pairwise coevolutionary models capture the collective residue variability across homologous proteins even for quantities which are not imposed by the inference procedure, like three-residue correlations, the clustered structure of protein families in sequence space or the sequence distances between homologs. These findings strongly suggest that pairwise coevolutionary models are actually sufficient to accurately capture the residue variability in homologous protein families.

Inverse statistical physics of protein sequences: a key issues review.

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1.

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, for example, of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared with recent large-scale mutagenesis data of the beta-lactamase TEM-1, a protein providing resistance against beta-lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared with approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 Å from the mutated site, reaching thus far beyond residues in direct physical contact.

Probing the Limits to MicroRNA-Mediated Control of Gene Expression.

According to the 'ceRNA hypothesis', microRNAs (miRNAs) may act as mediators of an effective positive interaction between long coding or non-coding RNA molecules, carrying significant potential implications for a variety of biological processes. Here, inspired by recent work providing a quantitative description of small regulatory elements as information-conveying channels, we characterize the effectiveness of miRNA-mediated regulation in terms of the optimal information flow achievable between modulator (transcription factors) and target nodes (long RNAs). Our findings show that, while a sufficiently large degree of target derepression is needed to activate miRNA-mediated transmission, (a) in case of differential mechanisms of complex processing and/or transcriptional capabilities, regulation by a post-transcriptional miRNA-channel can outperform that achieved through direct transcriptional control; moreover, (b) in the presence of large populations of weakly interacting miRNA molecules the extra noise coming from titration disappears, allowing the miRNA-channel to process information as effectively as the direct channel. These observations establish the limits of miRNA-mediated post-transcriptional cross-talk and suggest that, besides providing a degree of noise buffering, this type of control may be effectively employed in cells both as a failsafe mechanism and as a preferential fine tuner of gene expression, pointing to the specific situations in which each of these functionalities is maximized.

RNA-based regulation: dynamics and response to perturbations of competing RNAs.

The observation that, through a titration mechanism, microRNAs (miRNAs) can act as mediators of effective interactions among their common targets (competing endogenous RNAs or ceRNAs) has brought forward the idea (i.e., the ceRNA hypothesis) that RNAs can regulate each other in extended cross-talk networks. Such an ability might play a major role in posttranscriptional regulation to shape a cell's protein repertoire. Recent work focusing on the emergent properties of the cross-talk networks has emphasized the high flexibility and selectivity that may be achieved at stationarity. On the other hand, dynamical aspects, possibly crucial on the relevant timescales, are far less clear. We have carried out a dynamical study of the ceRNA hypothesis on a model of posttranscriptional regulation. Sensitivity analysis shows that ceRNA cross-talk is dynamically extended, i.e., it may take place on timescales shorter than those required to achieve stationarity even in cases where no cross-talk occurs in the steady state, and is possibly amplified. In addition, in the case of large, transfection-like perturbations, the system may develop a strongly nonlinear, threshold response. Finally, we show that the ceRNA effect provides a very efficient way for a cell to achieve fast positive shifts in the level of a ceRNA when necessary. These results indicate that competition for miRNAs may indeed provide an elementary mechanism to achieve system-level regulatory effects on the transcriptome over physiologically relevant timescales.

MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory.

It has recently been suggested that the competition for a finite pool of microRNAs (miRNA) gives rise to effective interactions among their common targets (competing endogenous RNAs or ceRNAs) that could prove to be crucial for posttranscriptional regulation. We have studied a minimal model of posttranscriptional regulation where the emergence and the nature of such interactions can be characterized in detail at steady state. Sensitivity analysis shows that binding free energies and repression mechanisms are the key ingredients for the cross-talk between ceRNAs to arise. Interactions emerge in specific ranges of repression values, can be symmetrical (one ceRNA influences another and vice versa) or asymmetrical (one ceRNA influences another but not the reverse), and may be highly selective, while possibly limited by noise. In addition, we show that nontrivial correlations among ceRNAs can emerge in experimental readouts due to transcriptional fluctuations even in the absence of miRNA-mediated cross-talk.

A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks.

The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample (10(6)) of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.

Local and Global Protein Interactions Contribute to Residue Entrenchment in Beta-Lactamase TEM-1.

Due to their rapid evolution and their impact on healthcare, beta-lactamases, protein degrading beta-lactam antibiotics, are used as generic models of protein evolution. Therefore, we investigated the mutation effects in two distant beta-lactamases, TEM-1 and CTX-M-15. Interestingly, we found a site with a complex pattern of genetic interactions. Mutation G251W in TEM-1 inactivates the protein's function, just as the reciprocal mutation, W251G, does in CTX-M-15. The phylogenetic analysis revealed that mutation G has been entrenched in TEM-1's background: while rarely observed throughout the phylogeny, it is essential in TEM-1. Using a rescue experiment, in the TEM-1 G251W mutant, we identified sites that alleviate the deviation from G to W. While few of these mutations could potentially involve local interactions, most of them were found on distant residues in the 3D structure. Many well-known mutations that have an impact on protein stability, such as M182T, were recovered. Our results therefore suggest that entrenchment of an amino acid may rely on diffuse interactions among multiple sites, with a major impact on protein stability.

An evolution-based model for designing chorismate mutase enzymes.

The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.

Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.

The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences.