A quantitative and site-specific atlas of the citrullinome reveals widespread existence of citrullination and insights into PADI4 substrates.

Despite the importance of citrullination in physiology and disease, global identification of citrullinated proteins, and the precise targeted sites, has remained challenging. Here we employed quantitative-mass-spectrometry-based proteomics to generate a comprehensive atlas of citrullination sites within the HL60 leukemia cell line following differentiation into neutrophil-like cells. We identified 14,056 citrullination sites within 4,008 proteins and quantified their regulation upon inhibition of the citrullinating enzyme PADI4. With this resource, we provide quantitative and site-specific information on thousands of PADI4 substrates, including signature histone marks and transcriptional regulators. Additionally, using peptide microarrays, we demonstrate the potential clinical relevance of certain identified sites, through distinct reactivities of antibodies contained in synovial fluid from anti-CCP-positive and anti-CCP-negative people with rheumatoid arthritis. Collectively, we describe the human citrullinome at a systems-wide level, provide a resource for understanding citrullination at the mechanistic level and link the identified targeted sites to rheumatoid arthritis.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks.

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

eggNOG 6.0: enabling comparative genomics across 12 535 organisms.

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.

Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMapper.

Post-translational modifications (PTMs) of proteins play crucial roles in defining protein function. They often do not occur alone, leading to a large variety of proteoforms that correspond to different combinations of multiple PTMs simultaneously decorating a protein. Changes of these proteoforms can be quantified via middle-down and top-down mass spectrometry experiments where the simultaneous PTM settings are obtained by measuring long peptides or entire proteins. Data from such experiments poses big challenges in identifying relevant features of biological and clinical importance. Generally, multiple data layers need to be considered such as proteoforms, individual PTMs, and PTM types. Therein, visualization methods are a crucial part of data analysis as they provide, if applied correctly, insights into both general behaviors as well as a deep view into fine-grained behavior. Here, we present a workflow to visualize histone proteins and their myriad of PTMs based on different R visualization modules applied to data from quantitative middle-down experiments. The procedure can be adapted to diverse experimental designs and is applicable to different proteins and PTMs.

Protocol for printing 3D neural tissues using the BIO X equipped with a pneumatic printhead.

3D bioprinting-a type of additive manufacturing-can create 3D tissue constructs resembling in vivo tissues. Here, we present a protocol for 3D printing neural tissues using Axolotl Biosciences' fibrin-based bioink and the CELLINK BIO X bioprinter with a pneumatic printhead. This workflow can be applied to printing 3D tissue models using a variety of cell lines and any chemically crosslinked bioink. These 3D-printed tissue models can be used for applications such as drug screening and disease modeling in vitro.

The effect of SARS-CoV-2 on the nervous system: a review of neurological impacts caused by human coronaviruses.

The COVID-19 pandemic has affected millions of people worldwide. While coronaviruses typically have low rates of neurotropic effects, the massive transmission of SARS-CoV-2 suggests that a substantial population will suffer from potential SARS-CoV-2-related neurological disorders. The rapid and recent emergence of SARS-CoV-2 means little research exists on its potential neurological effects. Here we analyze the effects of similar viruses to provide insight into the potential effects of SARS-CoV-2 on the nervous system and beyond. Seven coronavirus strains (HCoV-OC43, HCoV-HKU1, HCoV-229E, HCoV-NL63, SARS-CoV, MERS-CoV, SARS-CoV-2) can infect humans. Many of these strains cause neurological effects, such as headaches, dizziness, strokes, seizures, and critical illness polyneuropathy/myopathy. Certain studies have also linked coronaviruses with multiple sclerosis and extensive central nervous system injuries. Reviewing these studies provides insight into the anticipated effects for patients with SARS-CoV-2. This review will first describe the effects of other coronaviruses that have caused severe disease (SARS-CoV, MERS-CoV) on the nervous system, as well as their proposed origins, non-neurological effects, and neurological infection mechanisms. It will then discuss what is known about SARS-CoV-2 in these areas with reference to the aforementioned viruses, with the goal of providing a holistic picture of SARS-CoV-2.

Recent advances in the design of microfluidic technologies for the manufacture of drug releasing particles.

Drug releasing particles are valued for their ability to deliver therapeutics to targeted locations and for their controllable release patterns. The development of microfluidic technologies, which are designed specifically to manipulate small amounts of fluids, to manufacture particles for drug delivery applications reflects a recent trend due to the advantages they confer in terms of control over particle size and material composition. This review takes a comprehensive look at the different types of microfluidic devices used to fabricate such particles from different types of biomaterials, and at how the on-chip features enable the production of particles with different types of properties. The review concludes by suggesting avenues for future work that will enable these technologies to fulfill their potential and be used in industrial settings for the manufacture of drug releasing particles with unique capabilities.

Natural Biomaterials and Their Use as Bioinks for Printing Tissues.

The most prevalent form of bioprinting-extrusion bioprinting-can generate structures from a diverse range of materials and viscosities. It can create personalized tissues that aid in drug testing and cancer research when used in combination with natural bioinks. This paper reviews natural bioinks and their properties and functions in hard and soft tissue engineering applications. It discusses agarose, alginate, cellulose, chitosan, collagen, decellularized extracellular matrix, dextran, fibrin, gelatin, gellan gum, hyaluronic acid, Matrigel, and silk. Multi-component bioinks are considered as a way to address the shortfalls of individual biomaterials. The mechanical, rheological, and cross-linking properties along with the cytocompatibility, cell viability, and printability of the bioinks are detailed as well. Future avenues for research into natural bioinks are then presented.

The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

Visualization of the dynamics of histone modifications and their crosstalk using PTM-CrossTalkMapper.

Visualization of large-scale, multi-dimensional omics data is a major challenge. Experimental study designs in proteomics research produce multiple data layers, e.g. relationships between hundreds of proteins, their interactions and abundances as a function of time or perturbation, as well as dynamics of post-translational modifications (PTMs) and allelic variants (proteoforms). These different levels and types of information of proteins and proteoforms complicate data analysis and generation of insightful and comprehensible graphic visualizations. Middle-down mass spectrometry of histone proteins now allows quantifying hundreds of histone proteoforms including co-existing methylation, acetylation and phosphorylation events at distinct amino acid residues within histone molecules. The histone PTM landscape plays a dominant role in the regulation of chromatin activity and transcriptional and epigenetic control. The dynamics of these reversible modifications are governed by reader, writer and eraser enzymes that cooperate to regulate molecular mechanisms that rely on multiple interdependent PTM marks in histones and nucleosomes in chromatin. This PTM crosstalk can be quantified and provides a detailed picture of the underlying rules for setting the histone PTM landscape and chromatin activity, and is available to the community via our CrosstalkDB platform. Here, we developed a new computational method, PTM-CrossTalkMapper, to visualize the dynamics of histone PTMs for different experimental conditions, replicates and proteoforms onto a landscape, thereby describing the crosstalk and interplay between PTMs in a more comprehensible manner. We show the power of combining different levels of information on such crosstalk maps for histone PTM dynamics in mouse organs during the aging process. The PTM-CrossTalkMapper toolkit provides flexible functions to create these maps in various scenarios of multi-dimensional experimental designs, including histone PTM patterns and PTM crosstalk. The source code is available at https://github.com/veitveit/CrossTalkMapper.

A streamlined protocol for the detection of mRNA-sRNA interactions using AMT-crosslinking in vitro.

Until recently, RNA-RNA interactions were mainly identified by crosslinking RNAs with interacting proteins, RNA proximity ligation and deep sequencing. Recently, AMT-based direct RNA crosslinking was established. Yet, several steps of these procedures are rather inefficient, reducing the output of identified interaction partners. To increase the local concentration of RNA ends, interacting RNAs are often fragmented. However, the resulting 2',3'-cyclic phosphate and 5'-OH ends are not accepted by T4 RNA ligase and have to be converted to 3'-OH and 5'-phosphate ends. Using an artificial mRNA/sRNA pair, we optimized the workflow downstream of the crosslinking reaction in vitro. The use of a tRNA ligase allows direct fusion of 2',3'-cyclic phosphate and 5'-OH RNA ends.

Identification and characterization of novel conserved RNA structures in Drosophila.

BACKGROUND: Comparative genomics approaches have facilitated the discovery of many novel non-coding and structured RNAs (ncRNAs). The increasing availability of related genomes now makes it possible to systematically search for compensatory base changes - and thus for conserved secondary structures - even in genomic regions that are poorly alignable in the primary sequence. The wealth of available transcriptome data can add valuable insight into expression and possible function for new ncRNA candidates. Earlier work identifying ncRNAs in Drosophila melanogaster made use of sequence-based alignments and employed a sliding window approach, inevitably biasing identification toward RNAs encoded in the more conserved parts of the genome. RESULTS: To search for conserved RNA structures (CRSs) that may not be highly conserved in sequence and to assess the expression of CRSs, we conducted a genome-wide structural alignment screen of 27 insect genomes including D. melanogaster and integrated this with an extensive set of tiling array data. The structural alignment screen revealed ∼30,000 novel candidate CRSs at an estimated false discovery rate of less than 10%. With more than one quarter of all individual CRS motifs showing sequence identities below 60%, the predicted CRSs largely complement the findings of sliding window approaches applied previously. While a sixth of the CRSs were ubiquitously expressed, we found that most were expressed in specific developmental stages or cell lines. Notably, most statistically significant enrichment of CRSs were observed in pupae, mainly in exons of untranslated regions, promotors, enhancers, and long ncRNAs. Interestingly, cell lines were found to express a different set of CRSs than were found in vivo. Only a small fraction of intergenic CRSs were co-expressed with the adjacent protein coding genes, which suggests that most intergenic CRSs are independent genetic units. CONCLUSIONS: This study provides a more comprehensive view of the ncRNA transcriptome in fly as well as evidence for differential expression of CRSs during development and in cell lines.

Mapping the RNA-Seq trash bin: unusual transcripts in prokaryotic transcriptome sequencing data.

Prokaryotic transcripts constitute almost always uninterrupted intervals when mapped back to the genome. Split reads, i.e., RNA-seq reads consisting of parts that only map to discontiguous loci, are thus disregarded in most analysis pipelines. There are, however, some well-known exceptions, in particular, tRNA splicing and circularized small RNAs in Archaea as well as self-splicing introns. Here, we reanalyze a series of published RNA-seq data sets, screening them specifically for non-contiguously mapping reads. We recover most of the known cases together with several novel archaeal ncRNAs associated with circularized products. In Eubacteria, only a handful of interesting candidates were obtained beyond a few previously described group I and group II introns. Most of the atypically mapping reads do not appear to correspond to well-defined, specifically processed products. Whether this diffuse background is, at least in part, an incidental by-product of prokaryotic RNA processing or whether it consists entirely of technical artifacts of reverse transcription or amplification remains unknown.