Mutations in Disordered Regions Can Cause Disease by Creating Dileucine Motifs.

Many disease-causing missense mutations affect intrinsically disordered regions (IDRs) of proteins, but the molecular mechanism of their pathogenicity is enigmatic. Here, we employ a peptide-based proteomic screen to investigate the impact of mutations in IDRs on protein-protein interactions. We find that mutations in disordered cytosolic regions of three transmembrane proteins (GLUT1, ITPR1, and CACNA1H) lead to an increased clathrin binding. All three mutations create dileucine motifs known to mediate clathrin-dependent trafficking. Follow-up experiments on GLUT1 (SLC2A1), the glucose transporter causative of GLUT1 deficiency syndrome, revealed that the mutated protein mislocalizes to intracellular compartments. Mutant GLUT1 interacts with adaptor proteins (APs) in vitro, and knocking down AP-2 reverts the cellular mislocalization and restores glucose transport. A systematic analysis of other known disease-causing variants revealed a significant and specific overrepresentation of gained dileucine motifs in structurally disordered cytosolic domains of transmembrane proteins. Thus, several mutations in disordered regions appear to cause "dileucineopathies."

Transcriptional repression of NFKBIA triggers constitutive IKK- and proteasome-independent p65/RelA activation in senescence.

The IκB kinase (IKK)-NF-κB pathway is activated as part of the DNA damage response and controls both inflammation and resistance to apoptosis. How these distinct functions are achieved remained unknown. We demonstrate here that DNA double-strand breaks elicit two subsequent phases of NF-κB activation in vivo and in vitro, which are mechanistically and functionally distinct. RNA-sequencing reveals that the first-phase controls anti-apoptotic gene expression, while the second drives expression of senescence-associated secretory phenotype (SASP) genes. The rapidly activated first phase is driven by the ATM-PARP1-TRAF6-IKK cascade, which triggers proteasomal destruction of inhibitory IκBα, and is terminated through IκBα re-expression from the NFKBIA gene. The second phase, which is activated days later in senescent cells, is on the other hand independent of IKK and the proteasome. An altered phosphorylation status of NF-κB family member p65/RelA, in part mediated by GSK3ß, results in transcriptional silencing of NFKBIA and IKK-independent, constitutive activation of NF-κB in senescence. Collectively, our study reveals a novel physiological mechanism of NF-κB activation with important implications for genotoxic cancer treatment.

The IκB kinase complex is a regulator of mRNA stability.

The IκB kinase (IKK) is considered to control gene expression primarily through activation of the transcription factor NF-κB. However, we show here that IKK additionally regulates gene expression on post-transcriptional level. IKK interacted with several mRNA-binding proteins, including a Processing (P) body scaffold protein, termed enhancer of decapping 4 (EDC4). IKK bound to and phosphorylated EDC4 in a stimulus-sensitive manner, leading to co-recruitment of P body components, mRNA decapping proteins 1a and 2 (DCP1a and DCP2) and to an increase in P body numbers. Using RNA sequencing, we identified scores of transcripts whose stability was regulated via the IKK-EDC4 axis. Strikingly, in the absence of stimulus, IKK-EDC4 promoted destabilization of pro-inflammatory cytokines and regulators of apoptosis. Our findings expand the reach of IKK beyond its canonical role as a regulator of transcription.

HOT or not: examining the basis of high-occupancy target regions.

High-occupancy target (HOT) regions are segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and consequently associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solely defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed 'hyper-ChIPable' regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers, enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

How pathogens use linear motifs to perturb host cell networks.

Molecular mimicry is one of the powerful stratagems that pathogens employ to colonise their hosts and take advantage of host cell functions to guarantee their replication and dissemination. In particular, several viruses have evolved the ability to interact with host cell components through protein short linear motifs (SLiMs) that mimic host SLiMs, thus facilitating their internalisation and the manipulation of a wide range of cellular networks. Here we present convincing evidence from the literature that motif mimicry also represents an effective, widespread hijacking strategy in prokaryotic and eukaryotic parasites. Further insights into host motif mimicry would be of great help in the elucidation of the molecular mechanisms behind host cell invasion and the development of anti-infective therapeutic strategies.

RCAS: an RNA centric annotation system for transcriptome-wide regions of interest.

In the field of RNA, the technologies for studying the transcriptome have created a tremendous potential for deciphering the puzzles of the RNA biology. Along with the excitement, the unprecedented volume of RNA related omics data is creating great challenges in bioinformatics analyses. Here, we present the RNA Centric Annotation System (RCAS), an R package, which is designed to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies. The design of RCAS is modular, which enables flexible usage and convenient integration with other bioinformatics workflows. RCAS is an R/Bioconductor package but we also created graphical user interfaces including a Galaxy wrapper and a stand-alone web service. The application of RCAS on published datasets shows that RCAS is not only able to reproduce published findings but also helps generate novel knowledge and hypotheses. The meta-gene profiles, gene-centric annotation, motif analysis and gene-set analysis provided by RCAS provide contextual knowledge which is necessary for understanding the functional aspects of different biological events that involve RNAs. In addition, the array of different interfaces and deployment options adds the convenience of use for different levels of users. RCAS is available at http://bioconductor.org/packages/release/bioc/html/RCAS.html and http://rcas.mdc-berlin.de.

ELM 2016--data update and new functionality of the eukaryotic linear motif resource.

The Eukaryotic Linear Motif (ELM) resource (http://elm.eu.org) is a manually curated database of short linear motifs (SLiMs). In this update, we present the latest additions to this resource, along with more improvements to the web interface. ELM 2016 contains more than 240 different motif classes with over 2700 experimentally validated instances, manually curated from more than 2400 scientific publications. In addition, more data have been made available as individually searchable pages and are downloadable in various formats.

The eukaryotic linear motif resource ELM: 10 years and counting.

The eukaryotic linear motif (ELM http://elm.eu.org) resource is a hub for collecting, classifying and curating information about short linear motifs (SLiMs). For >10 years, this resource has provided the scientific community with a freely accessible guide to the biology and function of linear motifs. The current version of ELM contains ∼200 different motif classes with over 2400 experimentally validated instances manually curated from >2000 scientific publications. Furthermore, detailed information about motif-mediated interactions has been annotated and made available in standard exchange formats. Where appropriate, links are provided to resources such as switches.elm.eu.org and KEGG pathways.

RNA-seq analysis of the C. briggsae transcriptome.

Curation of a high-quality gene set is the critical first step in genome research, enabling subsequent analyses such as ortholog assignment, cis-regulatory element finding, and synteny detection. In this project, we have reannotated the genome of Caenorhabditis briggsae, the best studied sister species of the model organism Caenorhabditis elegans. First, we applied a homology-based gene predictor genBlastG to annotate the C. briggsae genome. We then validated and further improved the C. briggsae gene annotation through RNA-seq analysis of the C. briggsae transcriptome, which resulted in the first validated C. briggsae gene set (23,159 genes), among which 7347 genes (33.9% of all genes with introns) have all of their introns confirmed. Most genes (14,812, or 68.3%) have at least one intron validated, compared with only 3.9% in the most recent WormBase release (WS228). Of all introns in the revised gene set (103,083), 61,503 (60.1%) have been confirmed. Additionally, we have identified numerous trans-splicing leaders (SL1 and SL2 variants) in C. briggsae, leading to the first genome-wide annotation of operons in C. briggsae (1105 operons). The majority of the annotated operons (564, or 51.0%) are perfectly conserved in C. elegans, with an additional 345 operons (or 31.2%) somewhat divergent. Additionally, RNA-seq analysis revealed over 10 thousand small-size assembly errors in the current C. briggsae reference genome that can be readily corrected. The revised C. briggsae genome annotation represents a solid platform for comparative genomics analysis and evolutionary studies of Caenorhabditis species.

Fine tuning of RFX/DAF-19-regulated target gene expression through binding to multiple sites in Caenorhabditis elegans.

In humans, mutations of a growing list of regulatory factor X (RFX) target genes have been associated with devastating genetics disease conditions including ciliopathies. However, mechanisms underlying RFX transcription factors (TFs)-mediated gene expression regulation, especially differential gene expression regulation, are largely unknown. In this study, we explore the functional significance of the co-existence of multiple X-box motifs in regulating differential gene expression in Caenorhabditis elegans. We hypothesize that the effect of multiple X-box motifs is not a simple summation of binding effect to individual X-box motifs located within a same gene. To test this hypothesis, we identified eight C. elegans genes that contain two or more X-box motifs using comparative genomics. We examined one of these genes, F25B4.2, which contains two 15-bp X-box motifs. F25B4.2 expression in ciliated neurons is driven by the proximal motif and its expression is repressed by the distal motif. Our data suggest that two X-box motifs cooperate together to regulate the expression of F25B4.2 in location and intensity. We propose that multiple X-box motifs might be required to tune specific expression level. Our identification of genes with multiple X-box motifs will also improve our understanding of RFX/DAF-19-mediated regulation in C. elegans and in other organisms including humans.

ELM--the database of eukaryotic linear motifs.

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions.

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

genBlastG: using BLAST searches to build homologous gene models.

MOTIVATION: BLAST users frequently expect to obtain homologous genes with certain similarity to their query genes. But what they get from BLAST searches are often collections of local alignments called high-scoring segment pairs (HSPs). On the other hand, most homology-based gene finders have been built using computation-intensive algorithms, without taking full advantage of BLAST searches that have been perfected over the last decades. RESULTS: Here we report an efficient algorithm, genBlastG that directly uses the HSPs reported by BLAST to define high-quality gene models. AVAILABILITY: http://genome.sfu.ca/genblast/download.html

Multi-Omics Alleviates the Limitations of Panel Sequencing for Cancer Drug Response Prediction.

Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels do not necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

Identifying tumor cells at the single-cell level using machine learning.

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

SARS-CoV-2 infection dynamics revealed by wastewater sequencing analysis and deconvolution.

The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.

Parallel genetics of regulatory sequences using scalable genome editing in vivo.

How regulatory sequences control gene expression is fundamental for explaining phenotypes in health and disease. Regulatory elements must ultimately be understood within their genomic environment and development- or tissue-specific contexts. Because this is technically challenging, few regulatory elements have been characterized in vivo. Here, we use inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3' UTRs of 16 genes in C. elegans. Our software crispr-DART analyzes indel mutations in targeted DNA sequencing. We quantify the impact of mutations on expression and fitness by targeted RNA sequencing and DNA sampling. When applying our approach to the lin-41 3' UTR, generating hundreds of mutants, we find that the two adjacent binding sites for the miRNA let-7 can regulate lin-41 expression independently of each other. Finally, we map regulatory genotypes to phenotypic traits for several genes. Our approach enables parallel analysis of regulatory sequences directly in animals.

Single-cell analyses of aging, inflammation and senescence.

Single-cell gene expression (transcriptomics) data are becoming robust and abundant, and are increasingly used to track organisms along their life-course. This allows investigation into how aging affects cellular transcriptomes, and how changes in transcriptomes may underlie aging, including chronic inflammation (inflammaging), immunosenescence and cellular senescence. We compiled and tabulated aging-related single-cell datasets published to date, collected and discussed relevant findings, and inspected some of these datasets ourselves. We specifically note insights that cannot (or not easily) be based on bulk data. For example, in some datasets, the fraction of cells expressing p16 (CDKN2A), one of the most prominent markers of cellular senescence, was reported to increase, in addition to its upregulated mean expression over all cells. Moreover, we found evidence for inflammatory processes in most datasets, some of these driven by specific cells of the immune system. Further, single-cell data are specifically useful to investigate whether transcriptional heterogeneity (also called noise or variability) increases with age, and many (but not all) studies in our review report an increase in such heterogeneity. Finally, we demonstrate some stability of marker gene expression patterns across closely similar studies and suggest that single-cell experiments may hold the key to provide detailed insights whenever interventions (countering aging, inflammation, senescence, disease, etc.) are affecting cells depending on cell type.

The conserved histone chaperone LIN-53 is required for normal lifespan and maintenance of muscle integrity in Caenorhabditis elegans.

Whether extension of lifespan provides an extended time without health deteriorations is an important issue for human aging. However, to which degree lifespan and aspects of healthspan regulation might be linked is not well understood. Chromatin factors could be involved in linking both aging aspects, as epigenetic mechanisms bridge regulation of different biological processes. The epigenetic factor LIN-53 (RBBP4/7) associates with different chromatin-regulating complexes to safeguard cell identities in Caenorhabditis elegans as well as mammals, and has a role in preventing memory loss and premature aging in humans. We show that LIN-53 interacts with the nucleosome remodeling and deacetylase (NuRD) complex in C. elegans muscles to ensure functional muscles during postembryonic development and in adults. While mutants for other NuRD members show a normal lifespan, animals lacking LIN-53 die early because LIN-53 depletion affects also the histone deacetylase complex Sin3, which is required for a normal lifespan. To determine why lin-53 and sin-3 mutants die early, we performed transcriptome and metabolomic analysis revealing that levels of the disaccharide trehalose are significantly decreased in both mutants. As trehalose is required for normal lifespan in C. elegans, lin-53 and sin-3 mutants could be rescued by either feeding with trehalose or increasing trehalose levels via the insulin/IGF1 signaling pathway. Overall, our findings suggest that LIN-53 is required for maintaining lifespan and muscle integrity through discrete chromatin regulatory mechanisms. Since both LIN-53 and its mammalian homologs safeguard cell identities, it is conceivable that its implication in lifespan regulation is also evolutionarily conserved.