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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.

International audience; The causative agent of Huntington's disease, the poly-Q homo-repeat in the N-terminal region of huntingtin (httex1), is flanked by a 17-residue-long fragment (N17) and a proline-rich region (PRR), which promote and inhibit the aggregation propensity of the protein, respectively, by poorly understood mechanisms. Based on experimental data obtained from site-specifically labeled NMR samples, we derived an ensemble model of httex1 that identified both flanking regions as opposing poly-Q secondary structure promoters. While N17 triggers helicity through a promiscuous hydrogen bond network involving the side chains of the first glutamines in the poly-Q tract, the PRR promotes extended conformations in neighboring glutamines. Furthermore, a bioinformatics analysis of the human proteome showed that these structural traits are present in many human glutamine-rich proteins and that they are more prevalent in proteins with longer poly-Q tracts. Taken together, these observations provide the structural bases to understand previous biophysical and functional data on httex1.

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.

RNA modifications have recently emerged as an important layer of gene regulation. N6-methyladenosine (m6A) is the most prominent modification on eukaryotic messenger RNA and has also been found on noncoding RNA, including ribosomal and small nuclear RNA. Recently, several m6A methyltransferases were identified, uncovering the specificity of m6A deposition by structurally distinct enzymes. In order to discover additional m6A enzymes, we performed an RNAi screen to deplete annotated orthologs of human methyltransferase-like proteins (METTLs) in Drosophila cells and identified CG9666, the ortholog of human METTL5. We show that CG9666 is required for specific deposition of m6A on 18S ribosomal RNA via direct interaction with the Drosophila ortholog of human TRMT112, CG12975. Depletion of CG9666 yields a subsequent loss of the 18S rRNA m6A modification, which lies in the vicinity of the ribosome decoding center; however, this does not compromise rRNA maturation. Instead, a loss of CG9666-mediated m6A impacts fly behavior, providing an underlying molecular mechanism for the reported human phenotype in intellectual disability. Thus, our work expands the repertoire of m6A methyltransferases, demonstrates the specialization of these enzymes, and further addresses the significance of ribosomal RNA modifications in gene expression and animal behavior., SCOPUS: ar.j, info:eu-repo/semantics/published

Tandem Repeat Proteins (TRPs) are ubiquitous in cells and are enriched in eukaryotes. They contributed to the evolution of organism complexity, specializing for functions that require quick adaptability such as immunity-related functions. To investigate the hypothesis of repeat protein evolution through exon duplication and rearrangement, we designed a tool to analyze the relationships between exon/intron patterns and structural symmetries. The tool allows comparison of the structure fragments as defined by exon/intron boundaries from Ensembl against the structural element repetitions from RepeatsDB. The all-against-all pairwise structural alignment between fragments and comparison of the two definitions (structural units and exons) are visualized in a single matrix, the "repeat/exon plot". An analysis of different repeat protein families, including the solenoids Leucine-Rich, Ankyrin, Pumilio, HEAT repeats and the β propellers Kelch-like, WD40 and RCC1, shows different behaviors, illustrated here through examples. For each example, the analysis of the exon mapping in homologous proteins supports the conservation of their exon patterns. We propose that when a clear-cut relationship between exon and structural boundaries can be identified, it is possible to infer a specific "evolutionary pattern" which may improve TRPs detection and classification.

Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult to appreciate visually when many protein sequences are aligned. This new tool (MAGA; http://cbdm-01.zdv.uni-mainz.de/~munoz/maga/ ) locates positions in multiple sequence alignments differentially conserved in manually defined groups of sequences.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Several sources of variation can affect the reproducibility of a particular ChIP-seq assay, which can lead to a misinterpretation of where the protein under investigation binds to the genome in a particular cell type. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate these regions, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7, and in mouse embryonic stem cells, defined as protein binding regions with non-reproducible results across replicated experiments. These variable-occupancy target (VOT) regions are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Interestingly, among various genomic features, DNA accessibility is a better predictor of VOTs than CpG islands or epigenetic marks. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow up experiments.

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.

Long intergenic non-coding RNAs (lincRNAs) are non-coding transcripts >200 nucleotides long that do not overlap protein-coding sequences. Importantly, such elements are known to be tissue-specifically expressed and to play a widespread role in gene regulation across thousands of genomic loci. However, very little is known of the mechanisms for the evolutionary biogenesis of these RNA elements, especially given their poor conservation across species. It has been proposed that lincRNAs might arise from pseudogenes. To test this systematically, we developed a novel method that searches for remnants of protein-coding sequences within lincRNA transcripts; the hypothesis is that we can trace back their biogenesis from protein-coding genes or posterior transposon/retrotransposon insertions. Applying this method, we found 203 human lincRNA genes with regions significantly similar to protein-coding sequences. Our method provides a visualization tool to trace the evolutionary biogenesis of lincRNAs with respect to protein-coding genes by sequence divergence. Subsequently, we show the expression correlation between lincRNAs and their identified parental protein-coding genes using public RNA-seq repositories, hinting at novel gene regulatory relationships. In summary, we developed a novel computational methodology to study non-coding gene sequences, which can be applied to identify the evolutionary biogenesis and function of lincRNAs.

N6-methyladenosine (m6A) is the most abundant mRNA modification in eukaryotes, playing crucial roles in multiple biological processes. m6A is catalyzed by the activity of methyltransferase-like 3 (Mettl3), which depends on additional proteins whose precise functions remain poorly understood. Here we identified Zc3h13 (zinc finger CCCH domain-containing protein 13)/Flacc [Fl(2)d-associated complex component] as a novel interactor of m6A methyltransferase complex components in Drosophila and mice. Like other components of this complex, Flacc controls m6A levels and is involved in sex determination in Drosophila. We demonstrate that Flacc promotes m6A deposition by bridging Fl(2)d to the mRNA-binding factor Nito. Altogether, our work advances the molecular understanding of conservation and regulation of the m6A machinery.

Ribosome profiling revealed widespread translational activity at upstream open reading frames (uORFs) and validated uORF-mediated translational control as a commonly repressive mechanism of gene expression. Translational activation of proto-oncogenes through loss-of-uORF mutations has been demonstrated, yet a systematic search for cancer-associated genetic alterations in uORFs is lacking. Here, we applied a PCR-based, multiplex identifier-tagged deep sequencing approach to screen 404 uORF translation initiation sites of 83 human tyrosine kinases and 49 other proto-oncogenes in 308 human malignancies. We identified loss-of-function uORF mutations in EPHB1 in two samples derived from breast and colon cancer, and in MAP2K6 in a sample of colon adenocarcinoma. Both mutations were associated with enhanced translation, suggesting that loss-of-uORF-mediated translational induction of the downstream main protein coding sequence may have contributed to carcinogenesis. Computational analysis of whole exome sequencing datasets of 464 colon adenocarcinomas subsequently revealed another 53 non-recurrent somatic mutations functionally deleting 22 uORF initiation and 31 uORF termination codons, respectively. These data provide evidence for somatic mutations affecting uORF initiation and termination codons in human cancer. The insufficient coverage of uORF regions in current whole exome sequencing datasets demands for future genome-wide analyses to ultimately define the contribution of uORF-mediated translational deregulation in oncogenesis.

Advances in organ-on-chip technologies for the application in in vitro drug development provide an attractive alternative approach to replace ethically controversial animal testing and to establish a basis for accelerated drug development. In recent years, various chip-based tissue culture systems have been developed, which are mostly optimized for cultivation of one single cell type or organoid structure and lack the representation of multi organ interactions. Here we present an optimized microfluidic chip design consisting of interconnected compartments, which provides the possibility to mimic the exchange between different organ specific cell types and enables to study interdependent cellular responses between organs and demonstrate that such tandem system can greatly improve the reproducibility and efficiency of toxicity studies. In a simplified liver-kidney-on-chip model, we showed that hepatic cells that grow in microfluidic conditions abundantly and stably expressed metabolism-related biomarkers. Moreover, we applied this system for investigating the biotransformation and toxicity of Aflatoxin B1 (AFB1) and Benzoalphapyrene (BαP), as well as the interaction with other chemicals. The results clearly demonstrate that the toxicity and metabolic response to drugs can be evaluated in a flow-dependent manner within our system, supporting the importance of advanced interconnected multiorgans in microfluidic devices for application in in vitro toxicity testing and as optimized tissue culture systems for in vitro drug screening.

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. This article is protected by copyright. All rights reserved.

Gold compounds have a long history of use as immunosuppressants, but their precise mechanism of action is not completely understood. Using our recently developed liver-on-a-chip platform we now show that gold compounds containing planar N-heterocyclic carbene (NHC) ligands are potent ligands for the aryl hydrocarbon receptor (AHR). Further studies showed that the lead compound (MC3) activates TGFβ1 signaling and suppresses CD4+ T-cell activation in vitro, in human and mouse T cells. Conversely, genetic knockdown or chemical inhibition of AHR activity or of TGFβ1-SMAD-mediated signaling offsets the MC3-mediated immunosuppression. In scurfy mice, a mouse model of human immunodysregulation polyendocrinopathy enteropathy X-linked syndrome, MC3 treatment reduced autoimmune phenotypes and extended lifespan from 24 to 58 days. Our findings suggest that the immunosuppressive activity of gold compounds can be improved by introducing planar NHC ligands to activate the AHR-associated immunosuppressive pathway, thus expanding their potential clinical application for autoimmune diseases., Cheng, Haeberle et al. identify NHC–gold complexes as ligands of the aryl hydrocarbon receptor, which triggers TGFβ production for immune suppression. They find that the MC3 compound activates TGFβ signalling while reducing Il-2 expression and CD4+ T-cell activation and suppresses autoimmune phenotypes in a mouse model of IPEX syndrome.

Background Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat. Results We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest. Conclusions The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes.

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.

Summary Traitpedia is a collaborative database aimed to collect binary traits in a tabular form for a growing number of species. Availability and implementation Traitpedia can be accessed from http://cbdm-01.zdv.uni-mainz.de/~munoz/traitpedia. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors.ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real data. Results on simulated data show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways. An imputation method that allows the interpretation of the underlying models facilitates users to gain an even deeper understanding of individual cells and, consequently, of sparse scChIP-seq datasets.Availability and implementationOur interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA

Motivation Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday events and personal ailments. However, labeling this noisy data is a difficult problem because labeled training data is sparse and automatic labeling is error-prone. Crowd sourcing can help in such a scenario to obtain more reliable labels, but is expensive in comparison because workers have to be paid. To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information. Results We extracted data from Twitter using the public API. We subsequently use Amazon Mechanical Turk in combination with a state-of-the-art semi-supervised active learning method to label tweets with their associated drugs and side effects in two stages. Our results show that our method is an effective way of discovering side effects in tweets with an improvement from 53% F-measure to 67% F-measure as compared to a one stage work flow. Additionally, we show the effectiveness of the active learning scheme in reducing the labeling cost in comparison to a non-active baseline. Availability Code and data will be published on https://github.com/kramerlab.

Controlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following URL:https://github.com/salbrec/seqQscorer.

Background Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. Results We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed. Conclusions This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).

The authors present a novel method for obtaining chromatin profiles from low cell numbers without prior nuclei isolation. The method is successfully implemented in generating epigenetic profile from 100 cells with high signal-to-noise ratio., Chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) is a powerful technique to study transcriptional regulation. However, the requirement of millions of cells to generate results with high signal-to-noise ratio precludes it in the study of small cell populations. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality histone profiles from low cell numbers. The data obtained from the TAF-ChIP approach are amenable to standard tools for ChIP-Seq analysis, owing to its high signal-to-noise ratio. The epigenetic profiles from TAF-ChIP approach showed high agreement with conventional ChIP-Seq datasets, thereby underlining the utility of this approach.

AnABlast is a computational tool that highlights protein-coding regions within intergenic and intronic DNA sequences which escape detection by standard gene prediction algorithms. DNA sequences with small protein-coding genes or exons, complex intron-containing genes, or degenerated DNA fragments are efficiently targeted by AnABlast. Furthermore, this algorithm is particularly useful in detecting protein-coding sequences with nonsignificant homologs to sequences in databases. AnABlast can be executed online at http://www.bioinfocabd.upo.es/anablast/ .

Background Our understanding of the nuclear chromatin structure has increased hugely during the last years mainly as a consequence of the advances in chromatin conformation capture methods like Hi-C. The unprecedented resolution of genome-wide interaction maps shows functional consequences that extend the initial thought of an efficient DNA packaging mechanism: gene regulation, DNA repair, chromosomal translocations and evolutionary rearrangements seem to be only the peak of the iceberg. One key concept emerging from this research is the topologically associating domains (TADs) whose functional role in gene regulation and their association with disease is not fully untangled. Results We report that the lower the number of protein coding genes inside TADs, the higher the tendency of those genes to be associated with disease (p-value = 4 × $$10^{-54}$$10-54). Moreover, housekeeping genes are less associated with disease than other genes. Accordingly, they are depleted in TADs containing less than three protein coding genes (p-value = 3.9 × $$10^{-34}$$10-34). We observed that TADs with higher ratios of enhancers versus genes contained higher numbers of disease-associated genes. We interpret these results as an indication that sharing enhancers among genes reduces their involvement in disease. Larger TADs would have more chances to accommodate many genes and select for enhancer sharing along evolution. Conclusions Genes associated with human disease do not distribute randomly over the TADs. Our observations suggest general rules that confer functional stability to TADs, adding more evidence to the role of TADs as regulatory units.

Embryonic development is arguably the most complex process an organism undergoes during its lifetime, and understanding this complexity is best approached with a systems-level perspective. The sea urchin has become a highly valuable model organism for understanding developmental specification, morphogenesis, and evolution. As a non-chordate deuterostome, the sea urchin occupies an important evolutionary niche between protostomes and vertebrates.Lytechinus variegatus(Lv) is an Atlantic species that has been well studied, and which has provided important insights into signal transduction, patterning, and morphogenetic changes during embryonic and larval development. The Pacific species,Strongylocentrotus purpuratus(Sp), is another well-studied sea urchin, particularly for gene regulatory networks (GRNs) andcis-regulatory analyses. A well-annotated genome and transcriptome for Sp are available, but similar resources have not been developed for Lv. Here, we provide an analysis of the Lv transcriptome at 11 timepoints during embryonic and larval development. The data indicate that the gene regulatory networks that underlie specification are well-conserved among sea urchin species. We show that the major transitions in variation of embryonic transcription divide the developmental time series into four distinct, temporally sequential phases. Our work shows that sea urchin development occurs via sequential intervals of relatively stable gene expression states that are punctuated by abrupt transitions.

Low complexity regions (LCRs) in proteins are characterized by amino acid frequencies that differ from the average. These regions evolve faster and tend to be less conserved between homologs than globular domains. They are not common in bacteria, as compared to their prevalence in eukaryotes. Studying their conservation could help provide hypotheses about their function. To obtain the appropriate evolutionary focus for this rapidly evolving feature, here we study the conservation of LCRs in bacterial strains and compare their high variability to the closeness of the strains. For this, we selected 20 taxonomically diverse bacterial species and obtained the completely sequenced proteomes of two strains per species. We calculated all orthologous pairs for each of the 20 strain pairs. Per orthologous pair, we computed the conservation of two types of LCRs: compositionally biased regions (CBRs) and homorepeats (polyX). Our results show that, in bacteria, Q-rich CBRs are the most conserved, while A-rich CBRs and polyA are the most variable. LCRs have generally higher conservation when comparing pathogenic strains. However, this result depends on protein subcellular location: LCRs accumulate in extracellular and outer membrane proteins, with conservation increased in the extracellular proteins of pathogens, and decreased for polyX in the outer membrane proteins of pathogens. We conclude that these dependencies support the functional importance of LCRs in host–pathogen interactions.

Low complexity regions (LCRs) are very frequent in protein sequences, generally having a lower propensity to form structured domains and tending to be much less evolutionarily conserved than globular domains. Their higher abundance in eukaryotes and in species with more cellular types agrees with a growing number of reports on their function in protein interactions regulated by post-translational modifications. LCRs facilitate the increase of regulatory and network complexity required with the emergence of organisms with more complex tissue distribution and development. Although the low conservation and structural flexibility of LCRs complicate their study, evolutionary studies of proteins across species have been used to evaluate their significance and function. To investigate how to apply this evolutionary approach to the study of LCR function in protein–protein interactions, we performed a detailed analysis for Huntingtin (HTT), a large protein that is a hub for interaction with hundreds of proteins, has a variety of LCRs, and for which partial structural information (in complex with HAP40) is available. We hypothesize that proteins RASA1, SYN2, and KAT2B may compete with HAP40 for their attachment to the core of HTT using similar LCRs. Our results illustrate how evolution might favor the interplay of LCRs with domains, and the possibility of detecting multiple modes of LCR-mediated protein–protein interactions with a large hub such as HTT when enough protein interaction data is available.

N6-methyladenosine RNA (m6A) is a prevalent messenger RNA modification in vertebrates. Although its functions in the regulation of post-transcriptional gene expression are beginning to be unveiled, the precise roles of m6A during development of complex organisms remain unclear. Here we carry out a comprehensive molecular and physiological characterization of the individual components of the methyltransferase complex, as well as of the YTH domain-containing nuclear reader protein in Drosophila melanogaster. We identify the member of the split ends protein family, Spenito, as a novel bona fide subunit of the methyltransferase complex. We further demonstrate important roles of this complex in neuronal functions and sex determination, and implicate the nuclear YT521-B protein as a main m6A effector in these processes. Altogether, our work substantially extends our knowledge of m6A biology, demonstrating the crucial functions of this modification in fundamental processes within the context of the whole animal.

Targeted proteomics utilizing antibody-based proximity extension assays provides sensitive and highly specific quantifications of plasma protein levels. Multivariate analysis of this data is hampered by frequent missing values (random or left censored), calling for imputation approaches. While appropriate missing-value imputation methods exist, benchmarks of their performance in targeted proteomics data are lacking. Here, we assessed the performance of two methods for imputation of values missing completely at random, the previously top-benchmarked ‘missForest’ and the recently published ‘GSimp’ method. Evaluation was accomplished by comparing imputed with remeasured relative concentrations of 91 inflammation related circulating proteins in 86 samples from a cohort of 645 patients with venous thromboembolism. The median Pearson correlation between imputed and remeasured protein expression values was 69.0% for missForest and 71.6% for GSimp (p = 5.8e-4). Imputation with missForest resulted in stronger reduction of variance compared to GSimp (median relative variance of 25.3% vs. 68.6%, p = 2.4e-16) and undesired larger bias in downstream analyses. Irrespective of the imputation method used, the 91 imputed proteins revealed large variations in imputation accuracy, driven by differences in signal to noise ratio and information overlap between proteins. In summary, GSimp outperformed missForest, while both methods show good overall imputation accuracy with large variations between proteins.

Background: Several experimental models of polyglutamine (polyQ) diseases have been previously developed that are useful for studying disease progression in the primarily affected central nervous system. However, there is a missing link between cellular and animal models that would indicate the molecular defects occurring in neurons and are responsible for the disease phenotype in vivo. Methods: Here, we used a computational approach to identify dysregulated pathways shared by an in vitro and an in vivo model of ATXN1(Q82) protein aggregation, the mutant protein that causes the neurodegenerative polyQ disease spinocerebellar ataxia type-1 (SCA1). Results: A set of common dysregulated pathways were identified, which were utilized to construct cerebellum-specific protein-protein interaction (PPI) networks at various time-points of protein aggregation. Analysis of a SCA1 network indicated important nodes which regulate its function and might represent potential pharmacological targets. Furthermore, a set of drugs interacting with these nodes and predicted to enter the blood&ndash, brain barrier (BBB) was identified. Conclusions: Our study points to molecular mechanisms of SCA1 linked from both cellular and animal models and suggests drugs that could be tested to determine whether they affect the aggregation of pathogenic ATXN1 and SCA1 disease progression.

AnABlast is a computational tool that highlights protein-coding regions within intergenic and intronic DNA sequences which escape detection by standard gene prediction algorithms. DNA sequences with small protein-coding genes or exons, complex intron-containing genes, or degenerated DNA fragments are efficiently targeted by AnABlast. Furthermore, this algorithm is particularly useful in detecting protein-coding sequences with nonsignificant homologs to sequences in databases. AnABlast can be executed online at http://www.bioinfocabd.upo.es/anablast/ .

Self-propagating amyloid-β (Aβ) aggregates or seeds possibly drive pathogenesis of Alzheimer's disease (AD). Small molecules targeting such structures might act therapeutically in vivo. Here, a fluorescence polarization assay was established that enables the detection of compound effects on both seeded and spontaneous Aβ42 aggregation. In a focused screen of anti-amyloid compounds, we identified Disperse Orange 1 (DO1) ([4-((4-nitrophenyl)diazenyl)-N-phenylaniline]), a small molecule that potently delays both seeded and non-seeded Aβ42 polymerization at substoichiometric concentrations. Mechanistic studies revealed that DO1 disrupts preformed fibrillar assemblies of synthetic Aβ42 peptides and decreases the seeding activity of Aβ aggregates from brain extracts of AD transgenic mice. DO1 also reduced the size and abundance of diffuse Aβ plaques and decreased neuroinflammation-related gene expression changes in brains of 5xFAD transgenic mice. Finally, improved nesting behavior was observed upon treatment with the compound. Together, our evidence supports targeting of self-propagating Aβ structures with small molecules as a valid therapeutic strategy.

Sleeping Beauty (SB) is a synthetic Tc1/mariner transposon that is widely used for genetic engineering in vertebrates, including humans. Its sequence was derived from a consensus of sequences found in fish species including the Atlantic salmon (Salmo salar). One of the functional components of SB, the transposase enzyme, has been subject to extensive mutagenesis yielding hyperactive protein variants for advanced applications. The second functional component, the transposon inverted terminal repeats (ITRs), has so far not been extensively modified, mainly due to a lack of natural sequence information. Importantly, as genome sequences become available, they can provide a rich source of information for a refined molecular definition of the functional components of these transposons. Here we have mined the Salmo salar genome for a comprehensive set of transposon sequences that were used to build a refined consensus sequence. We synthetically produced the new consensus ITR sequences and used them to build a new transposon, the performance of which has been tested in cell-based transposition assays. The consensus sequence did not support enhanced transposition, suggesting alternative mechanisms responsible for the preferential amplification of these sequence variants in the salmon genome.

Chromatin immunoprecipitation (ChIP) followed by next generation sequencing is an invaluable and powerful technique to understand transcriptional regulation. However, ChIP is currently limited by the requirement of large amount of starting material. This renders studying rare cell populations very challenging, or even impossible. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality datasets from low cell numbers. The method relies on Tn5 transposon activity to fragment the chromatin that is immunoprecipitated, thus circumventing the need for sonication or MNAse digestion to fragment. Furthermore, Tn5 adds the sequencing adaptors while fragmenting the chromatin resulting in one-step generation of PCR ready library, which makes it an extremely simple approach with minimum hands-on time. The method can be directly implemented on sorted cells, and does not require de-multiplexing strategies to generate the profile from limited cell numbers. This can be very useful when the access to the starting material is restricted. Using our approach we generated the H3K4Me3 and H3K9Me3 profiles from 100 K562 cells and 1000 sorted neural stem cells (NSC) from Drosophila. We benchmarked our TAF-ChIP datasets from K562 cells against the ENCODE datasets. For validating the TAF-ChIP datasets obtained from Drosophila NSCs we performed conventional ChIP-Seq experiments in identical cell types. The epigenetic profiles obtained from both conventional ChIP-Seq and TAF-ChIP showed high degree of agreement, thereby underlining the utility of this approach for generating ChIP-Seq profiles from very low cell numbers.

Reprogramming somatic cells into induced-pluripotent cells (iPSCs) provides new access to all somatic cell types for clinical application without any ethical controversy arising from the use of embryonic stem cells (ESCs). Established protocols for iPSCs generation based on viral transduction with defined factors are limited by low efficiency and the risk of genetic abnormality. Several small molecules have been reported as replacements for defined transcriptional factors, but a chemical able to replace Oct3/4 allowing the generation of human iPSCs is still unavailable. Using a cell-based High Throughput Screening (HTS) campaign, we identified that 2-[4-[(4-methoxyphenyl)methoxy]phenyl]acetonitrile (1), termed O4I1, enhanced Oct3/4 expression. Structural verification and modification by chemical synthesis showed that O4I1 and its derivatives not only promoted expression and stabilization of Oct3/4 but also enhanced its transcriptional activity in diverse human somatic c ells, implying the possible benefit from using this class of compounds in regenerative medicine.

Clinical evaluation of patients and diagnosis of disorder is crucial to make decisions on appropriate therapies. In addition, in the case of genetic disorders resulting from gene abnormalities, phenotypic effects may guide basic research on the mechanisms of a disorder to find the mutated gene and therefore to propose novel targets for drug therapy. However, this approach is complicated by two facts. First, the relationship between genes and disorders is not simple: one gene may be related to multiple disorders and a disorder may be caused by mutations in different genes. Second, recognizing relevant phenotypes might be difficult for clinicians working with patients of closely related complex disorders. Neuropsychiatric disorders best illustrate these difficulties since phenotypes range from metabolic to behavioral aspects, the latter extremely complex. Based on our clinical expertise on five neurodegenerative disorders, and from the wealth of bibliographical data on neuropsychiatric disorders, we have built a resource to infer associations between genes, chemicals, phenotypes for a total of 31 disorders. An initial step of automated text mining of the literature related to 31 disorders returned thousands of enriched terms. Fewer relevant phenotypic terms were manually selected by clinicians as relevant to the five neural disorders of their expertise and used to analyze the complete set of disorders. Analysis of the data indicates general relationships between neuropsychiatric disorders, which can be used to classify and characterize them. Correlation analyses allowed us to propose novel associations of genes and drugs with disorders. More generally, the results led us to uncovering mechanisms of disease that span multiple neuropsychiatric disorders, for example that genes related to synaptic transmission and receptor functions tend to be involved in many disorders, whereas genes related to sensory perception and channel transport functions are associated with fewer disorders. Our study shows that starting from expertise covering a limited set of neurological disorders and using text and data mining methods, meaningful and novel associations regarding genes, chemicals and phenotypes can be derived for an expanded set of neuropsychiatric disorders. Our results are intended for clinicians to help them evaluate patients, and for basic scientists to propose new gene targets for drug therapies. This strategy can be extended to virtually all diseases and takes advantage of the ever increasing amount of biomedical literature.

SummaryInduced pluripotent stem cells (iPS) and direct lineage programming offer promising autologous and patient-specific sources of cells for personalized drug-testing and cell-based therapy. Before these engineered cells can be widely used, it is important to evaluate how well the engineered cell types resemble their intended target cell types. We have developed a method to generate CellScore, a cell identity score that can be used to evaluate the success of an engineered cell type in relation to both its initial and desired target cell type, which are used as references. Of 20 cell transitions tested, the most successful transitions were the iPS cells (CellScore > 0.9), while other transitions (e.g. induced hepatocytes or motor neurons) indicated incomplete transitions (CellScore < 0.5). In principle, the method can be applied to any engineered cell undergoing a cell transition, where transcription profiles are available for the reference cell types and the engineered cell type.HighlightsA curated standard dataset of transcription profiles from normal cell types was created.CellScore evaluates the cell identity of engineered cell types, using the curated dataset.CellScore considers the initial and desired target cell type.CellScore identifies the most successfully engineered clones for further functional testing.

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.

In this study, Knuckles et al. identified Flacc/Zc3h13 as a novel interactor of m6A methyltransferase complex components in Drosophila and mice. They show that Flacc promotes the recruitment of the methyltransferase to mRNA by bridging Fl(2)d to the mRNA-binding factor Nito, providing novel insights into the conservation and regulation of the m6A machinery.