The transcriptional co-activator Yap1 promotes adult hippocampal neural stem cell activation.

Most adult hippocampal neural stem cells (NSCs) remain quiescent, with only a minor portion undergoing active proliferation and neurogenesis. The molecular mechanisms that trigger the transition from quiescence to activation are still poorly understood. Here, we found the activity of the transcriptional co-activator Yap1 to be enriched in active NSCs. Genetic deletion of Yap1 led to a significant reduction in the relative proportion of active NSCs, supporting a physiological role of Yap1 in regulating the transition from quiescence to activation. Overexpression of wild-type Yap1 in adult NSCs did not induce NSC activation, suggesting tight upstream control mechanisms, but overexpression of a gain-of-function mutant (Yap1-5SA) elicited cell cycle entry in NSCs and hilar astrocytes. Consistent with a role of Yap1 in NSC activation, single cell RNA sequencing revealed a partial induction of an activated NSC gene expression program. Furthermore, Yap1-5SA expression also induced expression of Taz and other key components of the Yap/Taz regulon that were previously identified in glioblastoma stem cell-like cells. Consequently, dysregulated Yap1 activity led to repression of hippocampal neurogenesis, aberrant cell differentiation, and partial acquisition of a glioblastoma stem cell-like signature.

Initiation of a conserved trophectoderm program in human, cow and mouse embryos.

Current understandings of cell specification in early mammalian pre-implantation development are based mainly on mouse studies. The first lineage differentiation event occurs at the morula stage, with outer cells initiating a trophectoderm (TE) placental progenitor program. The inner cell mass arises from inner cells during subsequent developmental stages and comprises precursor cells of the embryo proper and yolk sac1. Recent gene-expression analyses suggest that the mechanisms that regulate early lineage specification in the mouse may differ in other mammals, including human2-5 and cow6. Here we show the evolutionary conservation of a molecular cascade that initiates TE segregation in human, cow and mouse embryos. At the morula stage, outer cells acquire an apical-basal cell polarity, with expression of atypical protein kinase C (aPKC) at the contact-free domain, nuclear expression of Hippo signalling pathway effectors and restricted expression of TE-associated factors such as GATA3, which suggests initiation of a TE program. Furthermore, we demonstrate that inhibition of aPKC by small-molecule pharmacological modulation or Trim-Away protein depletion impairs TE initiation at the morula stage. Our comparative embryology analysis provides insights into early lineage specification and suggests that a similar mechanism initiates a TE program in human, cow and mouse embryos.

Frequent loss of heterozygosity in CRISPR-Cas9-edited early human embryos.

CRISPR-Cas9 genome editing is a promising technique for clinical applications, such as the correction of disease-associated alleles in somatic cells. The use of this approach has also been discussed in the context of heritable editing of the human germ line. However, studies assessing gene correction in early human embryos report low efficiency of mutation repair, high rates of mosaicism, and the possibility of unintended editing outcomes that may have pathologic consequences. We developed computational pipelines to assess single-cell genomics and transcriptomics datasets from OCT4 (POU5F1) CRISPR-Cas9-targeted and control human preimplantation embryos. This allowed us to evaluate on-target mutations that would be missed by more conventional genotyping techniques. We observed loss of heterozygosity in edited cells that spanned regions beyond the POU5F1 on-target locus, as well as segmental loss and gain of chromosome 6, on which the POU5F1 gene is located. Unintended genome editing outcomes were present in ∼16% of the human embryo cells analyzed and spanned 4-20 kb. Our observations are consistent with recent findings indicating complexity at on-target sites following CRISPR-Cas9 genome editing. Our work underscores the importance of further basic research to assess the safety of genome editing techniques in human embryos, which will inform debates about the potential clinical use of this technology.

The latent geometry of the human protein interaction network.

Motivation: A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins. Results: We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperbolic plane offers a novel and informative way to visualize proteins and their interactions. We then used these coordinates to compute hyperbolic distances between proteins, which served as likelihood scores for the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routing process, guided by the latent geometry of the hPIN. We show that these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency. Availability and implementation: An R implementation of our network embedder is available at https://github.com/galanisl/NetHypGeom. Also, a web tool for the geometric analysis of the hPIN accompanies this text at http://cbdm-01.zdv.uni-mainz.de/~galanisl/gapi. Supplementary information: Supplementary data are available at Bioinformatics online.

HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks.

The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein-protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein-Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.

Context characterization of amino acid homorepeats using evolution, position, and order.

Amino acid repeats, or homorepeats, are low complexity protein motifs consisting of tandem repetitions of a single amino acid. Their presence and relative number vary in different proteomes, and some studies have tried to address this variation, proteome by proteome. In this work, we present a full characterization of amino acid homorepeats across evolution. We studied the presence and differential usage of each possible homorepeat in proteomes from various taxonomic groups, using clusters of very similar proteins to eliminate redundancy. The position of each amino acid repeat within proteins, and the order of co-occurring amino acid repeats were also addressed. As a result, we present evidence about the unevenly evolution of homorepeats, as well as the functional implications of their relative position in proteins. We discuss some of these cases in their taxonomic context. Collectively, our results show evolutionary and positional signals that suggest that homorepeats have biological function, likely creating unspecific protein interactions or modulating specific interactions in a context dependent manner. In conclusion, our work supports the functional importance of homorepeats and establishes a basis for the study of other low complexity repeats. Proteins 2017; 85:709-719. © 2016 Wiley Periodicals, Inc.

Protein-protein interactions can be predicted using coiled coil co-evolution patterns.

Protein-protein interactions are sometimes mediated by coiled coil structures. The evolutionary conservation of interacting orthologs in different species, along with the presence or absence of coiled coils in them, may help in the prediction of interacting pairs. Here, we illustrate how the presence of coiled coils in a protein can be exploited as a potential indicator for its interaction with another protein with coiled coils. The prediction capability of our strategy improves when restricting our dataset to highly reliable, known protein-protein interactions. Our study of the co-evolution of coiled coils demonstrates that pairs of interacting proteins can be distinguished from not interacting pairs by means of their structural information. This hints at the potential of our strategy to predict new protein-protein interactions.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.

MOTIVATION: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. METHODS: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. RESULTS: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. CONCLUSION: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. AVAILABILITY: https://sites.google.com/site/carlovittoriocannistraci/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior.

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks. We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks. Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab.

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

Chronic stress deficits in reward behaviour co-occur with low nucleus accumbens dopamine activity during reward anticipation specifically.

Whilst reward pathologies are major and common in stress-related neuropsychiatric disorders, their neurobiology and treatment are poorly understood. Imaging studies in human reward pathology indicate attenuated BOLD activity in nucleus accumbens (NAc) coincident with reward anticipation but not reinforcement; potentially, this is dopamine (DA) related. In mice, chronic social stress (CSS) leads to reduced reward learning and motivation. Here, DA-sensor fibre photometry is used to investigate whether these behavioural deficits co-occur with altered NAc DA activity during reward anticipation and/or reinforcement. In CSS mice relative to controls: (1) Reduced discriminative learning of the sequence, tone-on + appetitive behaviour = tone-on + sucrose reinforcement, co-occurs with attenuated NAc DA activity throughout tone-on and sucrose reinforcement. (2) Reduced motivation during the sequence, operant behaviour = tone-on + sucrose delivery + sucrose reinforcement, co-occurs with attenuated NAc DA activity at tone-on and typical activity at sucrose reinforcement. (3) Reduced motivation during the sequence, operant behaviour = appetitive behaviour + sociosexual reinforcement, co-occurs with typical NAc DA activity at female reinforcement. Therefore, in CSS mice, low NAc DA activity co-occurs with low reward anticipation and could account for deficits in learning and motivation, with important implications for understanding human reward pathology.

Stress deficits in reward behaviour are associated with and replicated by dysregulated amygdala-nucleus accumbens pathway function in mice.

Reduced reward interest/learning and reward-to-effort valuation are distinct, common symptoms in neuropsychiatric disorders for which chronic stress is a major aetiological factor. Glutamate neurons in basal amygdala (BA) project to various regions including nucleus accumbens (NAc). The BA-NAc neural pathway is activated by reward and aversion, with many neurons being monovalent. In adult male mice, chronic social stress (CSS) leads to reduced discriminative reward learning (DRL) associated with decreased BA-NAc activity, and to reduced reward-to-effort valuation (REV) associated, in contrast, with increased BA-NAc activity. Chronic tetanus toxin BA-NAc inhibition replicates the CSS-DRL effect and causes a mild REV reduction, whilst chronic DREADDs BA-NAc activation replicates the CSS effect on REV without affecting DRL. This study provides evidence that stress disruption of reward processing involves the BA-NAc neural pathway; the bi-directional effects implicate opposite activity changes in reward (learning) neurons and aversion (effort) neurons in the BA-NAc pathway following chronic stress.

Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.

Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG's tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.

Generating CRISPR-Cas9-Mediated Null Mutations and Screening Targeting Efficiency in Human Pluripotent Stem Cells.

CRISPR-Cas9 mutagenesis facilitates the investigation of gene function in a number of developmental and cellular contexts. Human pluripotent stem cells (hPSCs), either embryonic or induced, are a tractable cellular model to investigate molecular mechanisms involved in early human development and cell fate decisions. hPSCs also have broad potential in regenerative medicine to model, investigate, and ameliorate diseases. Here, we provide an optimized protocol for efficient CRISPR-Cas9 genome editing of hPSCs to investigate the functional role of genes by engineering null mutations. We emphasize the importance of screening single guide RNAs (sgRNAs) to identify those with high targeting efficiency for generation of clonally derived null mutant hPSC lines. We provide important considerations for targeting genes that may have a role in hPSC maintenance. We also present methods to evaluate the on-target mutation spectrum and unintended karyotypic changes. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Selecting and ligating sgRNAs into expression plasmids Basic Protocol 2: Validation of sgRNA via in vitro transcription and cleavage assay Basic Protocol 3: Nucleofection of primed human embryonic stem cells Basic Protocol 4: MiSeq analysis of indel mutations Basic Protocol 5: Single cell cloning of targeted hPSCs Basic Protocol 6: Karyotyping of targeted hPSCs.

Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE.

High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.

Visualizing Human Protein-Protein Interactions and Subcellular Localizations on Cell Images Through CellMap.

Visualizing protein data remains a challenging and stimulating task. Useful and intuitive visualization tools may help advance biomolecular and medical research; unintuitive tools may bar important breakthroughs. This protocol describes two use cases for the CellMap (http://cellmap.protein.properties) web tool. The tool allows researchers to visualize human protein-protein interaction data constrained by protein subcellular localizations. In the simplest form, proteins are visualized on cell images that also show protein-protein interactions (PPIs) through lines (edges) connecting the proteins across the compartments. At a glance, this simultaneously highlights spatial constraints that proteins are subject to in their physical environment and visualizes PPIs against these localizations. Visualizing two realities helps in decluttering the protein interaction visualization from "hairball" phenomena that arise when single proteins or groups thereof interact with hundreds of partners. © 2019 The Authors. Basic Protocol 1: Visualizing proteins and their interactions on cell images Basic Protocol 2: Displaying all interaction partners for a protein.

MIPPIE: the mouse integrated protein-protein interaction reference.

Cells operate and react to environmental signals thanks to a complex network of protein-protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein-Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

Evolutionary Study of Disorder in Protein Sequences.

Intrinsically disordered proteins (IDPs) contain regions lacking intrinsic globular structure (intrinsically disordered regions, IDRs). IDPs are present across the tree of life, with great variability of IDR type and frequency even between closely related taxa. To investigate the function of IDRs, we evaluated and compared the distribution of disorder content in 10,695 reference proteomes, confirming its high variability and finding certain correlation along the Euteleostomi (bony vertebrates) lineage to number of cell types. We used the comparison of orthologs to study the function of disorder related to increase in cell types, observing that multiple interacting subunits of protein complexes might gain IDRs in evolution, thus stressing the function of IDRs in modulating protein-protein interactions, particularly in the cell nucleus. Interestingly, the conservation of local compositional biases of IDPs follows residue-type specific patterns, with E- and K-rich regions being evolutionarily stable and Q- and A-rich regions being more dynamic. We provide a framework for targeted evolutionary studies of the emergence of IDRs. We believe that, given the large variability of IDR distributions in different species, studies using this evolutionary perspective are required.

Transdifferentiation of Mouse Embryonic Fibroblasts into Dopaminergic Neurons Reactivates LINE-1 Repetitive Elements.

In mammals, LINE-1 (L1) retrotransposons constitute between 15% and 20% of the genome. Although only a few copies have retained the ability to retrotranspose, evidence in brain and differentiating pluripotent cells indicates that L1 retrotransposition occurs and creates mosaics in normal somatic tissues. The function of de novo insertions remains to be understood. The transdifferentiation of mouse embryonic fibroblasts to dopaminergic neuronal fate provides a suitable model for studying L1 dynamics in a defined genomic and unaltered epigenomic background. We found that L1 elements are specifically re-expressed and mobilized during the initial stages of reprogramming and that their insertions into specific acceptor loci coincides with higher chromatin accessibility and creation of new transcribed units. Those events accompany the maturation of neuronal committed cells. We conclude that L1 retrotransposition is a non-random process correlating with chromatin opening and lncRNA production that accompanies direct somatic cell reprogramming.

Nuclear inclusions of pathogenic ataxin-1 induce oxidative stress and perturb the protein synthesis machinery.

Spinocerebellar ataxia type-1 (SCA1) is caused by an abnormally expanded polyglutamine (polyQ) tract in ataxin-1. These expansions are responsible for protein misfolding and self-assembly into intranuclear inclusion bodies (IIBs) that are somehow linked to neuronal death. However, owing to lack of a suitable cellular model, the downstream consequences of IIB formation are yet to be resolved. Here, we describe a nuclear protein aggregation model of pathogenic human ataxin-1 and characterize IIB effects. Using an inducible Sleeping Beauty transposon system, we overexpressed the ATXN1(Q82) gene in human mesenchymal stem cells that are resistant to the early cytotoxic effects caused by the expression of the mutant protein. We characterized the structure and the protein composition of insoluble polyQ IIBs which gradually occupy the nuclei and are responsible for the generation of reactive oxygen species. In response to their formation, our transcriptome analysis reveals a cerebellum-specific perturbed protein interaction network, primarily affecting protein synthesis. We propose that insoluble polyQ IIBs cause oxidative and nucleolar stress and affect the assembly of the ribosome by capturing or down-regulating essential components. The inducible cell system can be utilized to decipher the cellular consequences of polyQ protein aggregation. Our strategy provides a broadly applicable methodology for studying polyQ diseases.