Mass spectrometry and ribosome profiling, a perfect combination towards a more comprehensive identification strategy of true in vivo protein forms

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS). Recently, a strategy, termed ribosome profiling, based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. In contrast to routinely employed protein databases in proteomics searches, RIBO-seq derived data gives a more representative expression state and accounts for sequence variation information and alternative translation initiation. To verify the potential of ribosome profiling in providing us with a true snapshot of the translational landscape, we devised a proteogenomic approach generating a database of translation products based on ribosome profiling experiments. The raw and untreated RIBO-seq data is analyzed for both splice isoforms and single nucleotide polymorphisms, as such taking into account transcriptional variation. Next to that, RIBO-seq data for translation start site discovery (treated with harringtonine, lactomidomycin or puromycin) is used to obtain a genome wide blueprint of all possible translation initiation sites and as such taking into account translation variation. By adding protein-DB annotation to the genomic RIBO-seq derived data and after in silico translation a protein database is constructed reflecting the full complexity of the proteome. Using a first version of our proteogenomic approach on an undifferentiated mouse embryonic stem cell line (E14) we could demonstrate an increase of the overall protein identification rate with 2.5% as compared to only searching UniProtKB-SwissProt. Furthermore, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated uORFs

PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration

An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5'-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use

A comprehensive overview of genomic imprinting in breast and its deregulation in cancer

Genomic imprinting plays an important role in growth and development. Loss of imprinting (LOI) has been found in cancer, yet systematic studies are impeded by data-analytical challenges. We developed a methodology to detect monoallelically expressed loci without requiring genotyping data, and applied it on The Cancer Genome Atlas (TCGA, discovery) and Genotype-Tissue expression project (GTEx, validation) breast tissue RNA-seq data. Here, we report the identification of 30 putatively imprinted genes in breast. In breast cancer (TCGA), HM13 is featured by LOI and expression upregulation, which is linked to DNA demethylation. Other imprinted genes typically demonstrate lower expression in cancer, often associated with copy number variation and aberrant DNA methylation. Downregulation in cancer frequently leads to higher relative expression of the (imperfectly) silenced allele, yet this is not considered canonical LOI given the lack of (absolute) re-expression. In summary, our novel methodology highlights the massive deregulation of imprinting in breast cancer

Prevalent, Dynamic, and Conserved R-Loop Structures Associate with Specific Epigenomic Signatures in Mammals.

R-loops are three-stranded nucleic acid structures formed upon annealing of an RNA strand to one strand of duplex DNA. We profiled R-loops using a high-resolution, strand-specific methodology in human and mouse cell types. R-loops are prevalent, collectively occupying up to 5% of mammalian genomes. R-loop formation occurs over conserved genic hotspots such as promoter and terminator regions of poly(A)-dependent genes. In most cases, R-loops occur co-transcriptionally and undergo dynamic turnover. Detailed epigenomic profiling revealed that R-loops associate with specific chromatin signatures. At promoters, R-loops associate with a hyper-accessible state characteristic of unmethylated CpG island promoters. By contrast, terminal R-loops associate with an enhancer- and insulator-like state and define a broad class of transcription terminators. Together, this suggests that the retention of nascent RNA transcripts at their site of expression represents an abundant, dynamic, and programmed component of the mammalian chromatin that affects chromatin patterning and the control of gene expression

Clinically relevant aberrant Filip1l DNA methylation detected in a murine model of cutaneous squamous cell carcinoma

Background: Cutaneous squamous cell carcinomas (cSCC) are among the most common and highly mutated human malignancies. Understanding the impact of DNA methylation in cSCC may provide avenues for new therapeutic strategies. Methods: We used reduced-representation bisulfite sequencing for DNA methylation analysis of murine cSCC. Differential methylation was assessed at the CpG level using limma. Next, we compared with human cSCC Infinium HumanMethylation BeadArray data. Genes were considered to be of major relevance when they featured at least one significantly differentially methylated CpGs (RRBS) / probes (Infinium) with at least a 30% difference between tumour vs. control in both a murine gene and its human orthologue. The human EPIC Infinium data were used to distinguish two cSCC subtypes, stem-cell-like and keratinocyte-like tumours. Findings: We found increased average methylation in mouse cSCC (by 12.8%, p = 0.0011) as well as in stem-cell like (by 3.1%, p=0.002), but not keratinocyte-like (0.2%, p = 0.98), human cSCC. Comparison of differentially methylated genes revealed striking similarities between human and mouse cSCC. Locus specific methylation changes in mouse cSCC often occurred in regions of potential regulatory function, including enhancers and promoters. A key differentially methylated region was located in a potential enhancer of the tumour suppressor gene Filip1l and its expression was reduced in mouse tumours. Moreover, the FILIP1L, locus showed hypermethylation in human cSCC and lower expression in human cSCC cell lines. Interpretation: Deregulation of DNA methylation is an important feature of murine and human cSCC that likely contributes to silencing of tumour suppressor genes, as shown for Filip1l. 2021 The Author(s). Published by Elsevier B.V

A genome-wide search for epigenetically regulated genes in zebra finch using MethylCap-seq and RNA-seq

Learning and memory formation are known to require dynamic CpG (de)methylation and gene expression changes. Here, we aimed at establishing a genome-wide DNA methylation map of the zebra finch genome, a model organism in neuroscience, as well as identifying putatively epigenetically regulated genes. RNA-and MethylCap-seq experiments were performed on two zebra finch cell lines in presence or absence of 5-aza-2'-deoxycytidine induced demethylation. First, the MethylCap-seq methodology was validated in zebra finch by comparison with RRBS-generated data. To assess the influence of (variable) methylation on gene expression, RNA-seq experiments were performed as well. Comparison of RNA-seq and MethylCap-seq results showed that at least 357 of the 3,457 AZA-upregulated genes are putatively regulated by methylation in the promoter region, for which a pathway analysis showed remarkable enrichment for neurological networks. A subset of genes was validated using Exon Arrays, quantitative RT-PCR and CpG pyrosequencing on bisulfite-treated samples. To our knowledge, this study provides the first genome-wide DNA methylation map of the zebra finch genome as well as a comprehensive set of genes of which transcription is under putative methylation control

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming “selfie” videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model’s performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was −0.256 mm; and 95% limits of agreement (LOA) were −0.214–1.768 mm. Model performance showed no improvement when test data were gated to exclude “poor” quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1

(Photo-)crosslinkable gelatin derivatives for biofabrication applications

Over the recent decades gelatin has proven to be very suitable as an extracellular matrix mimic for bio-fabrication and tissue engineering applications. However, gelatin is prone to dissolution at typical cell culture conditions and is therefore often chemically modified to introduce (photo-)crosslinkable functionalities. These modifications allow to tune the material properties of gelatin, making it suitable for a wide range of biofabrication techniques both as a bioink and as a biomaterial ink (component). The present review provides a non-exhaustive overview of the different reported gelatin modification strategies to yield crosslinkable materials that can be used to form hydrogels suitable for biofabrication applications. The different crosslinking chemistries are discussed and classified according to their mechanism including chain-growth and step-growth polymerization. The step-growth polymerization mechanisms are further classified based on the specific chemistry including different (photo-)click chemistries and reversible systems. The benefits and drawbacks of each chemistry are also briefly discussed. Furthermore, focus is placed on different biofabrication strategies using either inkjet, deposition or light-based additive manufacturing techniques, and the applications of the obtained 3D constructs

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock