MAP2K2 mutation as a cause of cardio‐facio‐cutaneous syndrome in an infant with a severe and fatal course of the disease

Cardio‐facio‐cutaneous syndrome (CFCS), a rare congenital disorder of RASopathies, displays high phenotypic variability. Complications during pregnancy and in the perinatal period are commonly reported. Polyhydramnios is observed in over half of pregnancies and might occur with fetal macrocephaly, macrosomia, and/or heart defects. Premature birth is not uncommon and any complications like respiratory insufficiency, edema, and feeding difficulties are present and might delay accurate clinical diagnosis. Besides neonatal complications, CFCS newborns and later infants have distinctive dysmorphic features usually accompanied by neurological (hypotonia with motor delay, neurocognitive delay) findings. Also, heart defects usually present at birth. Herein, we present the case of a female baby born prematurely from a pregnancy complicated with polyhydramnios, presenting at birth with craniofacial features typical for RASopathies, heart defects, neurological abnormalities, and hyperkeratosis unusual for a neonatal period. Due to the presence of a heart defect and other complications related to premature birth, the course of the disease was severe with a fatal outcome at the age of 9 months. The RASopathy, particularly CFCS, clinical diagnosis was confirmed and de novo p.Phe57Ile mutation in MAP2K2 was identified.     

Phylogenomics with paralogs

Phylogenomics heavily relies on well-curated sequence data sets that comprise, for each gene, exclusively 1:1 orthologos. Paralogs are treated as a dangerous nuisance that has to be detected and removed. We show here that this severe restriction of the data sets is not necessary. Building upon recent advances in mathematical phylogenetics, we demonstrate that gene duplications convey meaningful phylogenetic information and allow the inference of plausible phylogenetic trees, provided orthologs and paralogs can be distinguished with a degree of certainty. Starting from tree-free estimates of orthology, cograph editing can sufficiently reduce the noise to find correct event-annotated gene trees. The information of gene trees can then directly be translated into constraints on the species trees. Although the resolution is very poor for individual gene families, we show that genome-wide data sets are sufficient to generate fully resolved phylogenetic trees, even in the presence of horizontal gene transfer.Molecular phylogenetics is primarily concerned with the reconstruction of evolutionary relationships between species based on sequence information. To this end, alignments of protein or DNA sequences are used, whose evolutionary history is believed to be congruent to that of the respective species. This property can be ensured most easily in the absence of gene duplications and horizontal gene transfer (HGT). Phylogenetic studies judiciously select families of genes that rarely exhibit duplications (such as rRNAs, most ribosomal proteins, and many of the housekeeping enzymes). In phylogenomics, elaborate automatic pipelines such as HaMStR (1), are used to filter genome-wide data sets to at least deplete sequences with detectable paralogs (homologs in the same species).In the presence of gene duplications, however, it becomes necessary to distinguish between the evolutionary history of genes (gene trees) and the evolutionary history of the species (species trees) in which these genes reside. Leaves of a gene tree represent genes. Their inner nodes represent two kinds of evolutionary events, namely the duplication of genes within a genome—giving rise to paralogs—and speciations, in which the ancestral gene complement is transmitted to two daughter lineages. Two genes are (co)orthologous if their last common ancestor in the gene tree represents a speciation event, whereas they are paralogous if their last common ancestor is a duplication event; see refs. 2 and 3 for a more recent discussion on orthology and paralogy relationships. Speciation events, in turn, define the inner vertices of a species tree. However, they depend on both the gene and the species phylogeny, as well as the reconciliation between the two. The latter identifies speciation vertices in the gene tree with a particular speciation event in the species tree and places the gene duplication events on the edges of the species tree. Intriguingly, it is nevertheless possible in practice to distinguish orthologs with acceptable accuracy without constructing either gene or species trees (4). Many tools of this type have become available over the last decade; see refs. 5 and 6 for a recent review. The output of such methods is an estimate Θ of the true orthology relation Θ^?, which can be interpreted as a graph G_Θ whose vertices are genes and whose edges connect estimated (co)orthologs.Recent advances in mathematical phylogenetics suggest that the estimated orthology relation Θ contains information on the structure of the species tree. To make this connection, we combine here three abstract mathematical results that are made precise in Materials and Methods below.

• i)Building upon the theory of symbolic ultrametrics (7), we showed that in the absence of horizontal gene transfer, the orthology relation of each gene family is a cograph (8). Cographs can be generated from the single-vertex graph K₁ by complementation and disjoint union (9). This special structure of cographs imposes very strong constraints that can be used to reduce the noise and inaccuracies of empirical estimates of orthology from pairwise sequence comparison. To this end, the initial estimate of G_Θ is modified to the closest correct orthology relation G_Θ^? in such a way that a minimal number of edges (i.e., orthology assignments) are introduced or removed. This amounts to solving the cograph-editing problem (10, 11).
• ii)It is well known that each cograph is equivalently represented by its cotree (9). The cotree is easily computed for a given cograph. In our context, the cotree of G_Θ^? is an incompletely resolved event-labeled gene tree. That is, in addition to the tree topology, we know for each internal branch point whether it corresponds to a speciation or a duplication event. Even though adjacent speciations or adjacent duplications cannot be resolved, the tree faithfully encodes the relative order of any pair of duplication and speciation (8). In the presence of horizontal gene transfer, G_Θ may deviate from the structural requirements of a cograph. Still, the situation can be described in terms of edge-colored graphs whose subgraphs are cographs (7, 8), so that the cograph structure remains an acceptable approximation.
• iii)Every triple (rooted binary tree on three leaves) in the cotree that has leaves from three species and is rooted in a speciation event also appears in the underlying species tree (12). Thus, the estimated orthology relation, after editing to a cograph and conversion to the equivalent event-labeled gene tree, provides much information on the species tree. This result allows us to collect, from the cotrees for each gene family, partial information on the underlying species tree. Interestingly, only gene families that harbor duplications, and thus have a nontrivial cotree, are informative. If no paralogs exist, then the orthology relation G_Θ is a clique (i.e., every family member is orthologous to every other family member) and the corresponding cotree is completely unresolved, and hence contains no triple. On the other hand, full resolution of the species tree is guaranteed if at least one duplication event between any two adjacent speciations is observable. The achievable resolution therefore depends on the frequency of gene duplications and the number of gene families.

Despite the variance reduction due to cograph editing, noise in the data, as well as the occasional introduction of contradictory triples as a consequence of horizontal gene transfer, is unavoidable. The species triples collected from the individual gene families thus will not always be congruent. A conceptually elegant way to deal with such potentially conflicting information is provided by the theory of supertrees in the form of the largest set of consistent triples (13, 14). The data will not always contain a sufficient set of duplication events to achieve full resolution. To this end, we consider trees with the property that the contraction of any edge leads to the loss of an input triple. There may be exponentially many alternative trees of this type. They can be listed efficiently using Semple’s algorithms (15). To reduce the solution space further, we search for a least resolved tree in the sense of ref. 16, i.e., a tree that has the minimum number of inner vertices. It constitutes one of the best estimates of the phylogeny without pretending a higher resolution than actually supported by the data. In SI Appendix, we discuss alternative choices.The mathematical reasoning summarized above, outlined in Materials and Methods, and presented in full detail in SI Appendix, directly translates into a computational workflow, Fig. 1. It entails three NP-hard combinatorial optimization problems: cograph editing (11), maximal consistent triple set (17–19), and least resolved supertree (16). We show here that they are nevertheless tractable in practice by formulating them as Integer Linear Programs (ILP) that can be solved for both artificial benchmark data sets and real-life data sets, comprising genome-scale protein sets for dozens of species, even in the presence of horizontal gene transfer.Open in a separate windowFig. 1.Outline of the computational framework. Starting from an estimated orthology relation Θ, its graph representation G_Θ is edited to obtain the closest cograph G_Θ^*, which, in turn, is equivalent to a (not necessarily fully resolved) gene tree T and an event labeling t. From (T, t), we extract the set ?? of all relevant species triples. As the triple set ?? need not be consistent, we compute the maximal consistent subset ??^? of ??. Finally, we construct a least resolved species tree from ??^?.     

Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression

The evolutionary forces that maintain genetic variation in quantitative traits within populations remain poorly understood. One hypothesis suggests that variation is under purifying selection, resulting in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients. Here, we test these predictions using the genetic loci associated with total expression variation (eQTLs) and allele-specific expression variation (aseQTLs) mapped within a single population of the plant Capsella grandiflora. In addition to finding eQTLs and aseQTLs for a large fraction of genes, we show that alleles at these loci are rarer than expected and exhibit a negative correlation between phenotypic effect size and frequency. Overall, our results show that the distribution of frequencies and effect sizes of the loci responsible for local expression variation within a single outcrossing population are consistent with the effects of purifying selection.Genetic variation for quantitative traits persists within populations despite the expectation that prevalent stabilizing selection will reduce genetic variance (1). One hypothesis suggests that variation is under purifying selection, resulting in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients (2). Although studies of allele frequency spectra show that purifying selection on functional DNA sequences is prevalent (3–5), little is known about how the genetic variants under selection relate to phenotype, and ultimately, how phenotypic variation is maintained within populations. Association mapping can identify specific loci influencing phenotypes, providing candidates for further analysis of selection (6). In particular, mapping the local regulatory variants that affect gene expression can identify a large number of genetic loci that affect a phenotype. Additionally, mapping the genetic basis of gene expression may answer questions about the basic biology of gene regulation, for example, by testing predictions that conserved noncoding sequences (“CNSs”) are constrained because they have regulatory function (7).Early eQTL studies mapped expression divergence between two lines, finding that many genes have local expression QTL (8, 9). These studies have provided insight into selection on eQTLs; for example, a correlation between recombination rate and eQTL density implied that background selection is a dominant force acting on expression variation in Caenorhabditis elegans (10), and a skew toward rare allele frequencies in promoters of genes with eQTLs suggests that purifying selection may act on expression variation (11). However, eQTL studies of population-level genetic variation have thus far been limited to a few study systems (12–16) and only one study, in humans, has identified a negative correlation between phenotypic effect size and frequency (15). In addition, human eQTL studies have shown that loci expected to be involved in selective sweeps are more likely to be eQTLs than other loci (17), allele frequencies of eQTLs that increase expression of a potentially deleterious coding SNP are under stronger purifying selection than those that do not (18), and eQTL allele frequencies within populations are correlated with local adaptation (19, 20). To date, eQTL studies in plants have used genetic crosses (21–23) or species-wide samples (24–26), making it difficult to distinguish evolutionary forces acting within and between populations. In sum, we currently lack comprehensive tests of selection on within-population eQTLs in any system, especially in plants.Here, we map local regulatory loci affecting expression in 99 members of a single large population of Capsella grandiflora (Brassicaceae), an obligate outcrosser. As might be expected from its large effective population size (N_e) and relative lack of population structure, purifying and positive selection are prevalent in C. grandiflora (4, 27), making it an ideal system for investigating the maintenance of genetic variation in the face of selection.     

Empirical inference of circuitry and plasticity in a kinase signaling network

Our understanding of physiology and disease is hampered by the difficulty of measuring the circuitry and plasticity of signaling networks that regulate cell biology, and how these relate to phenotypes. Here, using mass spectrometry-based phosphoproteomics, we systematically characterized the topology of a network comprising the PI3K/Akt/mTOR and MEK/ERK signaling axes and confirmed its biological relevance by assessing its dynamics upon EGF and IGF1 stimulation. Measuring the activity of this network in models of acquired drug resistance revealed that cells chronically treated with PI3K or mTORC1/2 inhibitors differed in the way their networks were remodeled. Unexpectedly, we also observed a degree of heterogeneity in the network state between cells resistant to the same inhibitor, indicating that even identical and carefully controlled experimental conditions can give rise to the evolution of distinct kinase network statuses. These data suggest that the initial conditions of the system do not necessarily determine the mechanism by which cancer cells become resistant to PI3K/mTOR targeted therapies. The patterns of signaling network activity observed in the resistant cells mirrored the patterns of response to several drug combination treatments, suggesting that the activity of the defined signaling network truly reflected the evolved phenotypic diversity.Cell signaling pathways form complex networks of biochemical reactions that integrate and decode extracellular signals into appropriate responses (1). The reconstruction of these networks, and systematic analyses of their properties, is important in the advancement of our molecular understanding of disease at the systems level (2). The topology and plasticity of cell signaling networks play major roles in fundamental (3–5) and disease physiology (6, 7). Attempts to characterize such molecular organization have relied on inference algorithms that obtain information on protein interactions and posttranslational modification (PTMs) from the literature (8–10). The accuracy of network reconstruction using such models is limited by the availability of data (10) and by the fact that signaling events are often cell-type specific. As a result, although they can provide insightful data, models that derive network topologies from studies that have used different cell types and organisms result in composite or averaged networks, which, critically, do not always reflect network structure in specific cell types, at specific stages of cell development, or under defined physiological conditions (10).Reconstruction of signaling networks through the use of a single set of well-defined experimental data is appealing, because this approach does not commit to a preconception of how such networks may be wired in a given cell type under defined conditions (3). The maturation of phosphoproteomics techniques based on mass spectrometry (MS) is now allowing the simultaneous quantification of several thousands of phosphorylation sites per experiment, and approaches to derive kinase activity from these large-scale phosphoproteomics datasets have been reported (11–14). One such approach, named kinase substrate enrichment analysis (KSEA), is based on the premise that, because each phosphorylation site is the result of a kinase’s catalytic activity, phosphoproteomic profiling provides a means by which to capture and measure the activities of all kinases expressed in the system under investigation (14).Here, we first used MS-based phosphoproteomics to define a kinase signaling network by systematically identifying phosphorylation sites downstream of kinases targeted by small-molecule kinase inhibitors of the PI3K/Akt/mTOR and MEK-ERK signaling axes. These two ubiquitous pathways form a network that regulates growth factor, antigen, and insulin signaling while also being deregulated in most cancers (15–17). We then measured the activity and plasticity of different routes within this experimentally defined kinase signaling network in cells chronically treated with small-molecule inhibitors of PI3K and mTORC1/2. We found that remodeling of kinase networks in resistant cells produced patterns of signaling activity linked to their evolved phenotypes.     

From the Cover: Anatomy of funded research in science

Seeking research funding is an essential part of academic life. Funded projects are primarily collaborative in nature through internal and external partnerships, but what role does funding play in the formulation of these partnerships? Here, by examining over 43,000 scientific projects funded over the past three decades by one of the major government research agencies in the world, we characterize how the funding landscape has changed and its impacts on the underlying collaboration networks across different scales. We observed rising inequality in the distribution of funding and that its effect was most noticeable at the institutional level—the leading universities diversified their collaborations and increasingly became the knowledge brokers in the collaboration network. Furthermore, it emerged that these leading universities formed a rich club (i.e., a cohesive core through their close ties) and this reliance among them seemed to be a determining factor for their research success, with the elites in the core overattracting resources but also rewarding in terms of both research breadth and depth. Our results reveal how collaboration networks organize in response to external driving forces, which can have major ramifications on future research strategy and government policy.Higher education institutions are nationally assessed in a periodic manner across the globe [examples include the Research Excellence Framework (www.ref.ac.uk) in the United Kingdom, Excellenzinitiative (mediathek.dfg.de/thema/die-exzellenzinitiative/) in Germany, and Star Metrics (https://www.starmetrics.nih.gov/) in the United States], and tremendous effort has been put in place in maximizing research output, because assessment outcomes often have a direct financial impact on an institution’s revenue (1). Bibliometrics are commonly used for this kind of performance evaluations (2–7), and the volume of grant income is also generally seen as a good indicator of performance. Although many studies have examined the collaboration patterns originating from publication information (8–14), little is known about the characteristics of project collaborations supported by research funding, which is undoubtedly a type of research output in its own right, but also the origin of other research outputs.The volume of funding is often subject to direct and indirect constraints arising from internal research strategies and different levels of policy set out by the funding bodies and ultimately by the national government. This manifests into different emphases on both the research area and mode of collaboration, and potentially influences the way we form a project team. We have already seen examples of adaptive changes in our collaboration practices. For instance, research in the science and engineering sector is said to be increasingly interorganizational (15). In addition, there are different theories on the factors that may affect the establishment of a collaboration and how well a research team operates (13, 16). Elite universities were recognized as catalysts for facilitating large-scale multipartner research collaborations (15), and multidisciplinary collaborations were found to have higher potential to foster research outcomes (17). As a result, the setup of a project consortium for a grant application might require considerable strategic planning, because who and how we collaborate with can potentially affect the outcome of a bid, and we are yet to fully understand the underlying mechanics and dynamics.To shed light into the relations between funding landscapes and scientific collaborations, we here examine over 43,000 projects funded between 1985 and 2013 by the Engineering and Physical Sciences Research Council (EPSRC), the government body in the United Kingdom that provides funding to universities to undertake research in engineering and physical sciences, including mathematics, chemistry, materials science, energy, information and communications technology, and innovative manufacturing. For each year, we constructed two different types of collaboration networks in which the nodes are investigators and their affiliations, respectively, and an edge represents a funded project partnership between two nodes. We applied a network-based approach to analyze the local and global interlinkage in these networks; the former was performed by calculating the degree of brokerage (18–21) of individual nodes, which gauges the connectivity in the neighborhood of a node. As for the global level, we calculated the rich-club coefficient (22, 23) of the network and characterized the members of such core structure using a recently introduced profiling technique (24). In addition, we explored how these patterns evolved over time with the total funding in each year and how they correlated with research performance. Our results allow us to gain an insight into how changes in the funding landscape shaped the way we form research partnerships, providing a case study that is highly reflective of other countries in the European Union and possibly other developed countries worldwide.     

Rapid genome reshaping by multiple-gene loss after whole-genome duplication in teleost fish suggested by mathematical modeling

Whole-genome duplication (WGD) is believed to be a significant source of major evolutionary innovation. Redundant genes resulting from WGD are thought to be lost or acquire new functions. However, the rates of gene loss and thus temporal process of genome reshaping after WGD remain unclear. The WGD shared by all teleost fish, one-half of all jawed vertebrates, was more recent than the two ancient WGDs that occurred before the origin of jawed vertebrates, and thus lends itself to analysis of gene loss and genome reshaping. Using a newly developed orthology identification pipeline, we inferred the post–teleost-specific WGD evolutionary histories of 6,892 protein-coding genes from nine phylogenetically representative teleost genomes on a time-calibrated tree. We found that rapid gene loss did occur in the first 60 My, with a loss of more than 70–80% of duplicated genes, and produced similar genomic gene arrangements within teleosts in that relatively short time. Mathematical modeling suggests that rapid gene loss occurred mainly by events involving simultaneous loss of multiple genes. We found that the subsequent 250 My were characterized by slow and steady loss of individual genes. Our pipeline also identified about 1,100 shared single-copy genes that are inferred to have become singletons before the divergence of clupeocephalan teleosts. Therefore, our comparative genome analysis suggests that rapid gene loss just after the WGD reshaped teleost genomes before the major divergence, and provides a useful set of marker genes for future phylogenetic analysis.The recent rapid growth of genome data has made it possible to clarify major evolutionary events that have shaped eukaryote genomes, such as gene duplication, chromosomal rearrangement, and whole-genome duplication (WGD) (1). In particular, WGD events, known to have occurred in several major lineages of flowering plants (2), budding yeasts (3), and vertebrates (4) (Fig. 1A), are considered to have had a major impact on genomic architecture and consequently organismal features.Open in a separate windowFig. 1.Inferred spatiotemporal process of gene loss and persistence after TGD in teleost ancestors. (A) The estimated numbers of gene loss events in the teleost phylogeny, time-scaled tree of vertebrates (11, 41) with the timing of genome duplication events at the base of vertebrates (VGD1/2) and teleosts (TGD), and the number of extant species (26). Species used in this study are connected by solid branches. The numbers were parsimoniously inferred from the presence or absence of TGD-derived gene lineage pairs belonging to 6,892 orthogroups and mapped onto the time points of TGD (306 Mya), nodes a–g (a: 245 Mya; b: 158; c: 120; d: 105; e: 41; f: 164; g: 86) (11), and h (74 Mya) (28). On the left side of the tree, ortholog arrangements are compared between representatives (connected by bold branches in the tree) by CIRCOS (circos.ca) using orthology information for 5,655 orthogroups belonging to the 1to1 category (Fig. S2). (B) Definition of terms relating to WGD events. An orthogroup is a monophyletic group containing WGD-derived paralogs (gene lineages) of all focal species (Sp1) and orthologs of their sister species (Sp2), ignoring lineage-specific gene duplications (GeneA-1′ and -1″) or gene loss (GeneA-1″). (C) Approximation of the pattern of the number of gene loss and persistence events associated with TGD. The estimated number of retained paired gene lineages at nodes a to h and current teleosts (Ca, Ze, Co, Ti, Pl, Me, St, Te, and Fu) were used to compare the fit of the one-phase [αe^–2_μt (14)] and two-phase models. (D) Region of C detailing the recent pattern of gene loss. The solid and dashed curves have been corrected upward to remove the bias expected to result from parsimony analysis. These approximations are effectively insensitive to fluctuations in the estimated numbers of gene lineage pairs and times for the TGD event and ancestral nodes (a to h) (SI Text). The evolutionary scenario is essentially unchanged if the number of gene lineage pairs estimated without the BS 70% criterion or the divergence times estimated by nuclear gene (28)/mitochondrial genome (42) data were used. Note that the two-phase model can be roughly approximated by a double-exponential curve.Duplicate genes generated by WGD are typically assumed to be redundant and therefore subsequently lost in a stochastic manner. Comparative genome studies have suggested that 90% of duplicate genes were rapidly lost (5) by a neutral process (6) after WGD in budding yeast, but 20–30% of them were retained in human (7) even after several hundred million years. However, few genome-wide studies have addressed the temporal pattern of gene loss or persistence after WGD with reference to a reliable timescale (but see refs. 6 and 8). Such examination is indispensable for understanding when duplicate genes were lost and, consequently, genome structures were reshaped, during vertebrate diversification after the WGD (Fig. 1).To examine the detailed process of duplicate gene loss after WGD, one needs to estimate the number (proportion) of remaining duplicates in extant and ancestral species. For this purpose, both (i) reliably time-calibrated phylogenetic trees of species and (ii) well-annotated genomes are required. These two requirements have been met for several vertebrate lineages, including some teleost fishes. Given this, the next step should be to accurately estimate orthology and paralogy relationships of all of the genes that experienced WGD. For the analysis of gene orthology and paralogy, a homology search- or synteny-based approach has usually been used (9). In addition to the homology search-based approach (e.g., COGs and OrthoDB), a phylogenetic tree-based approach has also been introduced (e.g., Ensembl and PhylomeDB) (9). Recent developments of tree search algorithms and increased computing power allow a sophisticated tree-based approach, comparing each gene tree with the species tree. Such an approach is indispensable for the effective analysis of gene orthology and paralogy across many species, providing us with a powerful opportunity to investigate genome evolution after WGD.Here, we aim to investigate the gene loss/persistence pattern using genome-wide data, focusing on what is known as the teleost genome duplication (TGD). TGD is estimated to have occurred in an ancestor of teleosts (Fig. 1A) but after the divergence of tetrapods and teleosts (10). Thus, it is a relatively recent WGD shared by a large vertebrate group, i.e., the Teleostei. For teleosts, reliably time-calibrated phylogenies, including phylogenetic position and timing of the TGD event, are available (e.g., ref. 11). In addition, well-annotated whole-genome data from at least nine phylogenetically representative teleost species (cave fish, zebrafish, cod, tilapia, platyfish, medaka, stickleback, Tetraodon, and fugu) are now available from Ensembl (12). In the present study, we inferred the timing of rapid genome reshaping through gene loss after TGD by estimating the temporal and genomic positional (spatiotemporal) loss/persistence pattern of TGD-derived gene lineage pairs (Fig. 1B) over the past several hundred million years, using accurate tree-based orthology estimation (Fig. S1) and a reliable time-calibrated teleost tree. We investigated the mechanism of rapid gene loss after TGD by fitting a newly developed model for the observed temporal pattern of gene loss. This new model is necessary because standard models, based upon random and independent loss of duplicate genes, fail to fit our data. Our model analysis explicitly includes both the possibility of the loss of multiple genes in single events, and also the known phylogeny of the relevant species. The significance of the inclusion of events that result in the loss of multiple genes is that it reproduces the two phases of loss. The inclusion of known phylogeny allows us to correct for the bias associated with parsimony analysis.