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.

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.

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.

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.

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.

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.

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.

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.

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.

IGF1-mediated human embryonic stem cell self-renewal recapitulates the embryonic niche.

Our understanding of the signalling pathways regulating early human development is limited, despite their fundamental biological importance. Here, we mine transcriptomics datasets to investigate signalling in the human embryo and identify expression for the insulin and insulin growth factor 1 (IGF1) receptors, along with IGF1 ligand. Consequently, we generate a minimal chemically-defined culture medium in which IGF1 together with Activin maintain self-renewal in the absence of fibroblast growth factor (FGF) signalling. Under these conditions, we derive several pluripotent stem cell lines that express pluripotency-associated genes, retain high viability and a normal karyotype, and can be genetically modified or differentiated into multiple cell lineages. We also identify active phosphoinositide 3-kinase (PI3K)/AKT/mTOR signalling in early human embryos, and in both primed and naïve pluripotent culture conditions. This demonstrates that signalling insights from human blastocysts can be used to define culture conditions that more closely recapitulate the embryonic niche.

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.

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.

Assessing the reliability of gene expression measurements in very-low-numbers of human monocyte-derived macrophages.

Tumor-derived primary cells are essential for in vitro and in vivo studies of tumor biology. The scarcity of this cellular material limits the feasibility of experiments or analyses and hence hinders basic and clinical research progress. We set out to determine the minimum number of cells that can be analyzed with standard laboratory equipment and that leads to reliable results, unbiased by cell number. A proof-of-principle study was conducted with primary human monocyte-derived macrophages, seeded in decreasing number and constant cell density. Gene expression of cells stimulated to acquire opposite inflammatory states was analyzed by quantitative PCR. Statistical analysis indicated the lack of significant difference in the expression profile of cells cultured at the highest (100,000 cells) and lowest numbers (3,610 cells) tested. Gene Ontology, pathway enrichment and network analysis confirmed the reliability of the data obtained with the lowest cell number. This statistical and computational analysis of gene expression profiles indicates that low cell number analysis is as dependable and informative as the analysis of a larger cell number. Our work demonstrates that it is possible to employ samples with a scarce number of cells in experimental studies and encourages the application of this approach on other cell types.

Detection of condition-specific marker genes from RNA-seq data with MGFR.

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR's predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

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.

Geometric characterisation of disease modules.

There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.

Interaction profiling of RNA-binding ubiquitin ligases reveals a link between posttranscriptional regulation and the ubiquitin system.

RNA-binding ubiquitin ligases (RBULs) have the potential to link RNA-mediated mechanisms to protein ubiquitylation. Despite this, the cellular functions, substrates and interaction partners of most RBULs remain poorly characterized. Affinity purification (AP) combined with quantitative mass spectrometry (MS)-based proteomics is a powerful approach for analyzing protein functions. Mapping the physiological interaction partners of RNA-binding proteins has been hampered by their intrinsic properties, in particular the existence of low-complexity regions, which are prone to engage in non-physiological interactions. Here, we used an adapted AP approach to identify the interaction partners of human RBULs harboring different RNA-binding domains. To increase the likelihood of recovering physiological interactions, we combined control and bait-expressing cells prior to lysis. In this setup, only stable interactions that were originally present in the cell will be identified. We exploit gene function similarity between the bait proteins and their interactors to benchmark our approach in its ability to recover physiological interactions. We reveal that RBULs engage in stable interactions with RNA-binding proteins involved in different steps of RNA metabolism as well as with components of the ubiquitin conjugation machinery and ubiquitin-binding proteins. Our results thus demonstrate their capacity to link posttranscriptional regulation with the ubiquitin system.

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.