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

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

Zc3h13/Flacc is required for adenosine methylation by bridging the mRNA-binding factor Rbm15/Spenito to the m6A machinery component Wtap/Fl(2)d.

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.

Expression and subcellular localization of USH1C/harmonin in human retina provides insights into pathomechanisms and therapy.

Usher syndrome (USH) is the most common form of hereditary deaf-blindness in humans. USH is a complex genetic disorder, assigned to three clinical subtypes differing in onset, course and severity, with USH1 being the most severe. Rodent USH1 models do not reflect the ocular phenotype observed in human patients to date; hence, little is known about the pathophysiology of USH1 in the human eye. One of the USH1 genes, USH1C, exhibits extensive alternative splicing and encodes numerous harmonin protein isoforms that function as scaffolds for organizing the USH interactome. RNA-seq analysis of human retinae uncovered harmonin_a1 as the most abundant transcript of USH1C. Bulk RNA-seq analysis and immunoblotting showed abundant expression of harmonin in Müller glia cells (MGCs) and retinal neurons. Furthermore, harmonin was localized in the terminal endfeet and apical microvilli of MGCs, presynaptic region (pedicle) of cones and outer segments (OS) of rods as well as at adhesive junctions between MGCs and photoreceptor cells (PRCs) in the outer limiting membrane (OLM). Our data provide evidence for the interaction of harmonin with OLM molecules in PRCs and MGCs and rhodopsin in PRCs. Subcellular expression and colocalization of harmonin correlate with the clinical phenotype observed in USH1C patients. We also demonstrate that primary cilia defects in USH1C patient-derived fibroblasts could be reverted by the delivery of harmonin_a1 transcript isoform. Our studies thus provide novel insights into PRC cell biology, USH1C pathophysiology and development of gene therapy treatment(s).

DNMT1 Deficiency Impacts on Plasmacytoid Dendritic Cells in Homeostasis and Autoimmune Disease.

Dendritic cells (DCs) are heterogeneous immune regulators involved in autoimmune diseases. Epigenomic mechanisms orchestrating DC development and DC subset diversification remain insufficiently understood but could be important to modulate DC fate for clinical purposes. By combining whole-genome methylation assessment with the analysis of mice expressing reduced DNA methyltransferase 1 levels, we show that distinct DNA methylation levels and patterns are required for the development of plasmacytoid DC and conventional DC subsets. We provide clonal in vivo evidence for DC lineage establishment at the stem cell level, and we show that a high DNA methylation threshold level is essential for Flt3-dependent survival of DC precursors. Importantly, reducing methylation predominantly depletes plasmacytoid DC and alleviates systemic lupus erythematosus in an autoimmunity mouse model. This study shows how DNA methylation regulates the production of DC subsets and provides a potential rationale for targeting autoimmune disease using hypomethylating agents.

Structured Tandem Repeats in Protein Interactions.

Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.

Protein repeats evolve and emerge in giant viruses.

Nucleocytoplasmatic large DNA viruses (NCLDVs or giant viruses) stand out because of their relatively large genomes encoding hundreds of proteins. These species give us an unprecedented opportunity to study the emergence and evolution of repeats in protein sequences. On the one hand, as viruses, these species have a restricted set of functions, which can help us better define the functional landscape of repeats. On the other hand, given the particular use of the genetic machinery of the host, it is worth asking whether this allows the variations of genetic material that lead to repeats in non-viral species. To support research in the characterization of repeat protein evolution and function, we present here an analysis focused on the repeat proteins of giant viruses, namely tandem repeats (TRs), short repeats (SRs), and homorepeats (polyX). Proteins with large and short repeats are not very frequent in non-eukaryotic organisms because of the difficulties that their folding may entail; however, their presence in giant viruses remarks their advantage for performance in the protein environment of the eukaryotic host. The heterogeneous content of these TRs, SRs and polyX in some viruses hints at diverse needs. Comparisons to homologs suggest that the mechanisms that generate these repeats are extensively used by some of these viruses, but also their capacity to adopt genes with repeats. Giant viruses could be very good models for the study of the emergence and evolution of protein repeats.

A STRP-ed definition of Structured Tandem Repeats in Proteins.

Tandem Repeat Proteins (TRPs) are a class of proteins with repetitive amino acid sequences that have been studied extensively for over two decades. Different features at the level of sequence, structure, function and evolution have been attributed to them by various authors. And yet many of its salient features appear only when looking at specific subclasses of protein tandem repeats. Here, we attempt to rationalize the existing knowledge on Tandem Repeat Proteins (TRPs) by pointing out several dichotomies. The emerging picture is more nuanced than generally assumed and allows us to draw some boundaries of what is not a "proper" TRP. We conclude with an operational definition of a specific subset, which we have denominated STRPs (Structural Tandem Repeat Proteins), which separates a subclass of tandem repeats with distinctive features from several other less well-defined types of repeats. We believe that this definition will help researchers in the field to better characterize the biological meaning of this large yet largely understudied group of proteins.

A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities.

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

The sequence context in poly-alanine regions: structure, function and conservation.

MOTIVATION: Poly-alanine (polyA) regions are protein stretches mostly composed of alanines. Despite their abundance in eukaryotic proteomes and their association to nine inherited human diseases, the structural and functional roles exerted by polyA stretches remain poorly understood. In this work we study how the amino acid context in which polyA regions are settled in proteins influences their structure and function. RESULTS: We identified glycine and proline as the most abundant amino acids within polyA and in the flanking regions of polyA tracts, in human proteins as well as in 17 additional eukaryotic species. Our analyses indicate that the non-structuring nature of these two amino acids influences the α-helical conformations predicted for polyA, suggesting a relevant role in reducing the inherent aggregation propensity of long polyA. Then, we show how polyA position in protein N-termini relates with their function as transit peptides. PolyA placed just after the initial methionine is often predicted as part of mitochondrial transit peptides, whereas when placed in downstream positions, polyA are part of signal peptides. A few examples from known structures suggest that short polyA can emerge by alanine substitutions in α-helices; but evolution by insertion is observed for longer polyA. Our results showcase the importance of studying the sequence context of homorepeats as a mechanism to shape their structure-function relationships. AVAILABILITY AND IMPLEMENTATION: The datasets used and/or analyzed during the current study are available from the corresponding author onreasonable request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein expression profiling suggests relevance of noncanonical pathways in isolated pulmonary embolism.

Patients with isolated pulmonary embolism (PE) have a distinct clinical profile from those with deep vein thrombosis (DVT)-associated PE, with more pulmonary conditions and atherosclerosis. These findings suggest a distinct molecular pathophysiology and the potential involvement of alternative pathways in isolated PE. To test this hypothesis, data from 532 individuals from the Genotyping and Molecular Phenotyping of Venous ThromboEmbolism Project, a multicenter prospective cohort study with extensive biobanking, were analyzed. Targeted, high-throughput proteomics, machine learning, and bioinformatic methods were applied to contrast the acute-phase plasma proteomes of isolated PE patients (n = 96) against those of patients with DVT-associated PE (n = 276) or isolated DVT (n = 160). This resulted in the identification of shared molecular processes between PE phenotypes, as well as an isolated PE-specific protein signature. Shared processes included upregulation of inflammation, response to oxidative stress, and the loss of pulmonary surfactant. The isolated PE-specific signature consisted of 5 proteins: interferon-γ, glial cell line-derived neurotrophic growth factor, polypeptide N-acetylgalactosaminyltransferase 3, peptidyl arginine deiminase type-2, and interleukin-15 receptor subunit α. These proteins were orthogonally validated using cis protein quantitative trait loci. External replication in an independent population-based cohort (n = 5778) further validated the proteomic results and showed that they were prognostic for incident primary isolated PE in individuals without history of VTE (median time to event: 2.9 years; interquartile range: 1.6-4.2 years), supporting their possible involvement in the early pathogenesis. This study has identified molecular overlaps and differences between VTE phenotypes. In particular, the results implicate noncanonical pathways more commonly associated with respiratory and atherosclerotic disease in the acute pathophysiology of isolated PE.

RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures.

The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures.

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network.

Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.

BACKGROUND: The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach. RESULTS: We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%). CONCLUSIONS: In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably  corrected statistically in well-designed experiments.

Disentangling the complexity of low complexity proteins.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. SHORT ABSTRACT: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

The 18S ribosomal RNA m6 A methyltransferase Mettl5 is required for normal walking behavior in Drosophila.

RNA modifications have recently emerged as an important layer of gene regulation. N6-methyladenosine (m6 A) 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 m6 A methyltransferases were identified, uncovering the specificity of m6 A deposition by structurally distinct enzymes. In order to discover additional m6 A 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 m6 A 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 m6 A modification, which lies in the vicinity of the ribosome decoding center; however, this does not compromise rRNA maturation. Instead, a loss of CG9666-mediated m6 A impacts fly behavior, providing an underlying molecular mechanism for the reported human phenotype in intellectual disability. Thus, our work expands the repertoire of m6 A methyltransferases, demonstrates the specialization of these enzymes, and further addresses the significance of ribosomal RNA modifications in gene expression and animal behavior.

m6A modulates neuronal functions and sex determination in Drosophila.

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.

Computational identification of cell-specific variable regions in ChIP-seq data.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. 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 ChIP-seq variability, 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 (mESCs). These variable-occupancy target regions (VOTs) 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. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. 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.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity-a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

Analysis of Huntington's Disease Modifiers Using the Hyperbolic Mapping of the Protein Interaction Network.

Huntington's disease (HD) is caused by the production of a mutant huntingtin (HTT) with an abnormally long poly-glutamine (polyQ) tract, forming aggregates and inclusions in neurons. Previous work by us and others has shown that an increase or decrease in polyQ-triggered aggregates can be passive simply due to the interaction of proteins with the aggregates. To search for proteins with active (functional) effects, which might be more effective in finding therapies and mechanisms of HD, we selected among the proteins that interact with HTT a total of 49 pairs of proteins that, while being paralogous to each other (and thus expected to have similar passive interaction with HTT), are located in different regions of the protein interaction network (suggesting participation in different pathways or complexes). Three of these 49 pairs contained members with opposite effects on HD, according to the literature. The negative members of the three pairs, MID1, IKBKG, and IKBKB, interact with PPP2CA and TUBB, which are known negative factors in HD, as well as with HSP90AA1 and RPS3. The positive members of the three pairs interact with HSPA9. Our results provide potential HD modifiers of functional relevance and reveal the dynamic aspect of paralog evolution within the interaction network.

The developmental transcriptome for Lytechinus variegatus exhibits temporally punctuated gene expression changes.

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) and cis-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. Temporal analysis suggests 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.