Theoretical Assessment of Indistinguishable Peptides in Mass Spectrometry-Based Proteomics.

Mass-spectrometry-based proteomics has advanced with the integration of experimental and predicted spectral libraries, which have significantly improved peptide identification in complex search spaces. However, challenges persist in distinguishing some peptides with close retention times and nearly identical fragmentation patterns. In this study, we conducted a theoretical assessment to quantify the prevalence of indistinguishable peptides within the human canonical proteome and immunopeptidome using state-of-the-art retention time and spectrum prediction models. By quantifying the proportion of peptides posing challenges to unequivocal identification, we set the theoretical nonaccessible portion within a given proteome, and underscore the effectiveness of contemporary analytical methodologies in resolving the complexity of the human proteome and immunopeptidome via mass spectrometry.

Quantitative proteome analysis of LAP1-deficient human fibroblasts: A pilot approach for predicting the signaling pathways deregulated in LAP1-associated diseases.

Lamina-associated polypeptide 1 (LAP1), a ubiquitously expressed nuclear envelope protein, appears to be essential for the maintenance of cell homeostasis. Although rare, mutations in the human LAP1-encoding TOR1AIP1 gene cause severe diseases and can culminate in the premature death of affected individuals. Despite there is increasing evidence of the pathogenicity of TOR1AIP1 mutations, the current knowledge on LAP1's physiological roles in humans is limited; hence, investigation is required to elucidate the critical functions of this protein, which can be achieved by uncovering the molecular consequences of LAP1 depletion, a topic that remains largely unexplored. In this work, the proteome of patient-derived LAP1-deficient fibroblasts carrying a pathological TOR1AIP1 mutation (LAP1 E482A) was quantitatively analyzed to identify global changes in protein abundance levels relatively to control fibroblasts. An in silico functional enrichment analysis of the mass spectrometry-identified differentially expressed proteins was also performed, along with additional in vitro functional assays, to unveil the biological processes that are potentially dysfunctional in LAP1 E482A fibroblasts. Collectively, our findings suggest that LAP1 deficiency may induce significant alterations in various cellular activities, including DNA repair, messenger RNA degradation/translation, proteostasis and glutathione metabolism/antioxidant response. This study sheds light on possible new functions of human LAP1 and could set the basis for subsequent in-depth mechanistic investigations. Moreover, by identifying deregulated signaling pathways in LAP1-deficient cells, our work may offer valuable molecular targets for future disease-modifying therapies for TOR1AIP1-associated nuclear envelopathies.

Machine learning-based health environmental-clinical risk scores in European children.

BACKGROUND: Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data. METHODS: In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions. RESULTS: ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health. CONCLUSIONS: Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Growing up in different environments can greatly affect children's health later in life. This research looked at how living in cities, being exposed to chemicals, and other experiences before birth and during childhood, work together to influence children's mental, cardiovascular and respiratory health. We used advanced computer programs to help us understand these effects and estimate health risk scores. These scores are simple numerical measures that help us quantify the likelihood of children developing health issues based on their environmental exposures. Using those scores, the study identified key factors impacting children's health, in particular psycho-social, perceived environmental and prenatal pollutant exposures for mental health. It also revealed complex patterns and interactions between environmental factors. The results highlighted the potential of such risk scores to support the identification of actionable factors in high-risk children, informing tailored prevention measures in healthcare.

Spectral libraries from nucleobases and deoxyribonucleosides facilitate the identification of ribonucleosides by nano-flow liquid chromatography-tandem mass spectrometry.

RATIONALE: The study addresses the challenge of identifying RNA post-transcriptional modifications when commercial standards are not available to generate reference spectral libraries. It proposes employing homologous nucleobases and deoxyribonucleosides as alternative reference spectral libraries to aid in identifying modified ribonucleosides and distinguishing them from their positional isomers when the standards are unavailable. METHODS: Complete sets of ribonucleoside, deoxyribonucleoside and nucleobase standards were analyzed using high-performance nano-flow liquid chromatography coupled to an Orbitrap Eclipse Tribrid mass spectrometer. Spectral libraries were constructed from homologous nucleobases and deoxyribonucleosides using targeted MS2 and neutral-loss-triggered MS3 methods, and collision energies were optimized. The feasibility of using these libraries for identifying modified ribonucleosides and their positional isomers was assessed through comparison of spectral fragmentation patterns. RESULTS: Our analysis reveals that both MS2 and neutral-loss-triggered MS3 methods yielded rich spectra with similar fragmentation patterns across ribonucleosides, deoxyribonucleosides and nucleobases. Moreover, we demonstrate that spectra from nucleobases and deoxyribonucleosides, generated at optimized collision energies, exhibited sufficient similarity to those of modified ribonucleosides to enable their use as reference spectra for accurate identification of positional isomers within ribonucleoside families. CONCLUSIONS: The study demonstrates the efficacy of utilizing homologous nucleobases and deoxyribonucleosides as interchangeable reference spectral libraries for identifying modified ribonucleosides and their positional isomers. This approach offers a valuable solution for overcoming limitations posed by the unavailability of commercial standards, enhancing the analysis of RNA post-transcriptional modifications via mass spectrometry.

IκB kinase-α coordinates BRD4 and JAK/STAT signaling to subvert DNA damage-based anticancer therapy.

Activation of the IκB kinase (IKK) complex has recurrently been linked to colorectal cancer (CRC) initiation and progression. However, identification of downstream effectors other than NF-κB has remained elusive. Here, analysis of IKK-dependent substrates in CRC cells after UV treatment revealed that phosphorylation of BRD4 by IKK-α is required for its chromatin-binding at target genes upon DNA damage. Moreover, IKK-α induces the NF-κB-dependent transcription of the cytokine LIF, leading to STAT3 activation, association with BRD4 and recruitment to specific target genes. IKK-α abrogation results in defective BRD4 and STAT3 functions and consequently irreparable DNA damage and apoptotic cell death upon different stimuli. Simultaneous inhibition of BRAF-dependent IKK-α activity, BRD4, and the JAK/STAT pathway enhanced the therapeutic potential of 5-fluorouracil combined with irinotecan in CRC cells and is curative in a chemotherapy-resistant xenograft model. Finally, coordinated expression of LIF and IKK-α is a poor prognosis marker for CRC patients. Our data uncover a functional link between IKK-α, BRD4, and JAK/STAT signaling with clinical relevance.

Stepwise emergence of the neuronal gene expression program in early animal evolution.

The assembly of the neuronal and other major cell type programs occurred early in animal evolution. We can reconstruct this process by studying non-bilaterians like placozoans. These small disc-shaped animals not only have nine morphologically described cell types and no neurons but also show coordinated behaviors triggered by peptide-secreting cells. We investigated possible neuronal affinities of these peptidergic cells using phylogenetics, chromatin profiling, and comparative single-cell genomics in four placozoans. We found conserved cell type expression programs across placozoans, including populations of transdifferentiating and cycling cells, suggestive of active cell type homeostasis. We also uncovered fourteen peptidergic cell types expressing neuronal-associated components like the pre-synaptic scaffold that derive from progenitor cells with neurogenesis signatures. In contrast, earlier-branching animals like sponges and ctenophores lacked this conserved expression. Our findings indicate that key neuronal developmental and effector gene modules evolved before the advent of cnidarian/bilaterian neurons in the context of paracrine cell signaling.

Expression of NORAD correlates with breast cancer aggressiveness and protects breast cancer cells from chemotherapy.

The recently discovered human lncRNA NORAD is induced after DNA damage in a p53-dependent manner. It plays a critical role in the maintenance of genomic stability through interaction with Pumilio proteins, limiting the repression of their target mRNAs. Therefore, NORAD inactivation causes chromosomal instability and aneuploidy, which contributes to the accumulation of genetic abnormalities and tumorigenesis. NORAD has been detected in several types of cancer, including breast cancer, which is the most frequently diagnosed and the second-leading cause of cancer death in women. In the present study, we confirmed upregulated NORAD expression levels in a set of human epithelial breast cancer cell lines (MDA-MB-231, MDA-MB-436, and MDA-MB-468), which belong to the most aggressive subtypes (triple-negative breast cancer). These results are in line with previous data showing that high NORAD expression levels in basal-like tumors were associated with poor prognosis. Here, we demonstrate that NORAD downregulation sensitizes triple-negative breast cancer cells to chemotherapy, through a potential accumulation of genomic aberrations and an impaired capacity to signal DNA damage. These results show that NORAD may represent an unexploited neoadjuvant therapeutic target for chemotherapy-unresponsive breast cancer.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification.

Mass spectrometry coupled to liquid chromatography is one of the most powerful technologies for proteome quantification in biomedical samples. In peptide-centric workflows, protein mixtures are enzymatically digested to peptides prior their analysis. However, proteome-wide quantification studies rarely identify all potential peptides for any given protein, and targeted proteomics experiments focus on a set of peptides for the proteins of interest. Consequently, proteomics relies on the use of a limited subset of all possible peptides as proxies for protein quantitation. In this work, we evaluated the stability of the human proteotypic peptides during 21 days and trained a deep learning model to predict peptide stability directly from tryptic sequences, which together constitute a resource of broad interest to prioritize and select peptides in proteome quantification experiments.

Peptidomics Methods Applied to the Study of Flower Development.

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

Molecular signature associated with cladribine treatment in patients with multiple sclerosis.

Introduction: Little is known about the molecular profiling associated with the effect of cladribine in patients with multiple sclerosis (MS). Here, we aimed first to characterize the transcriptomic and proteomic profiles induced by cladribine in blood cells, and second to identify potential treatment response biomarkers to cladribine in patients with MS. Methods: Gene, protein and microRNA (miRNA) expression profiles were determined by microarrays (genes, miRNAs) and mass spectrometry (proteins) in peripheral blood mononuclear cells (PBMCs) from MS patients after in vitro treatment with cladribine in its active and inactive forms. Two bioinformatics approaches to integrate the three obtained datasets were applied: (i) a multiomics discriminant analysis (DIABLO - Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies); and (ii) a multi-stage integration of features selected in differential expression analysis on each dataset and then merged. Selected molecules from the in vitro study were quantified by qPCR ex vivo in PBMCs from MS patients receiving cladribine. Results: PBMCs treated in vitro with cladribine were characterized by a major downregulation of gene, protein, and miRNA expression compared with the untreated cells. An intermediate pattern between the cladribine-treated and untreated conditions was observed in PBMCs treated with cladribine in its inactive form. The differential expression analysis of each dataset led to the identification of four genes and their encoded proteins, and twenty-two miRNAs regulating their expression, that were associated with cladribine treatment. Two of these genes (PPIF and NHLRC2), and three miRNAs (miR-21-5p, miR-30b-5p, and miR-30e-5p) were validated ex vivo in MS patients treated with cladribine. Discussion: By using a combination of omics data and bioinformatics approaches we were able to identify a multiomics molecular profile induced by cladribine in vitro in PBMCs. We also identified a number of biomarkers that were validated ex vivo in PBMCs from patients with MS treated with cladribine that have the potential to become treatment response biomarkers to this drug.

Thymine DNA glycosylase regulates cell-cycle-driven p53 transcriptional control in pluripotent cells.

Cell cycle progression is linked to transcriptome dynamics and variations in the response of pluripotent cells to differentiation cues, mostly through unknown determinants. Here, we characterized the cell-cycle-associated transcriptome and proteome of mouse embryonic stem cells (mESCs) in naive ground state. We found that the thymine DNA glycosylase (TDG) is a cell-cycle-regulated co-factor of the tumor suppressor p53. Furthermore, TDG and p53 co-bind ESC-specific cis-regulatory elements and thereby control transcription of p53-dependent genes during self-renewal. We determined that the dynamic expression of TDG is required to promote the cell-cycle-associated transcriptional heterogeneity. Moreover, we demonstrated that transient depletion of TDG influences cell fate decisions during the early differentiation of mESCs. Our findings reveal an unanticipated role of TDG in promoting molecular heterogeneity during the cell cycle and highlight the central role of protein dynamics for the temporal control of cell fate during development.

New Ruthenium-Cyclopentadienyl Complexes Affect Colorectal Cancer Hallmarks Showing High Therapeutic Potential.

Colorectal cancer (CRC) is among the most deadly cancers worldwide. Current therapeutic strategies have low success rates and several side effects. This relevant clinical problem requires the discovery of new and more effective therapeutic alternatives. Ruthenium drugs have arisen as one of the most promising metallodrugs, due to their high selectivity to cancer cells. In this work we studied, for the first time, the anticancer properties and mechanisms of action of four lead Ru-cyclopentadienyl compounds, namely PMC79, PMC78, LCR134 and LCR220, in two CRC-derived cell lines (SW480 and RKO). Biological assays were performed on these CRC cell lines to evaluate cellular distribution, colony formation, cell cycle, proliferation, apoptosis, and motility, as well as cytoskeleton and mitochondrial alterations. Our results show that all the compounds displayed high bioactivity and selectivity, as shown by low half-maximal inhibitory concentrations (IC50) against CRC cells. We observed that all the Ru compounds have different intracellular distributions. In addition, they inhibit to a high extent the proliferation of CRC cells by decreasing clonogenic ability and inducing cell cycle arrest. PMC79, LCR134, and LCR220 also induce apoptosis, increase the levels of reactive oxygen species, lead to mitochondrial dysfunction, induce actin cytoskeleton alterations, and inhibit cellular motility. A proteomic study revealed that these compounds cause modifications in several cellular proteins associated with the phenotypic alterations observed. Overall, we demonstrate that Ru compounds, especially PMC79 and LCR220, display promising anticancer activity in CRC cells with a high potential to be used as new metallodrugs for CRC therapy.

Associations of four biological age markers with child development: A multi-omic analysis in the European HELIX cohort.

Background: While biological age in adults is often understood as representing general health and resilience, the conceptual interpretation of accelerated biological age in children and its relationship to development remains unclear. We aimed to clarify the relationship of accelerated biological age, assessed through two established biological age indicators, telomere length and DNA methylation age, and two novel candidate biological age indicators, to child developmental outcomes, including growth and adiposity, cognition, behavior, lung function and the onset of puberty, among European school-age children participating in the HELIX exposome cohort. Methods: The study population included up to 1173 children, aged between 5 and 12 years, from study centres in the UK, France, Spain, Norway, Lithuania, and Greece. Telomere length was measured through qPCR, blood DNA methylation, and gene expression was measured using microarray, and proteins and metabolites were measured by a range of targeted assays. DNA methylation age was assessed using Horvath's skin and blood clock, while novel blood transcriptome and 'immunometabolic' (based on plasma proteins and urinary and serum metabolites) clocks were derived and tested in a subset of children assessed six months after the main follow-up visit. Associations between biological age indicators with child developmental measures as well as health risk factors were estimated using linear regression, adjusted for chronological age, sex, ethnicity, and study centre. The clock derived markers were expressed as Δ age (i.e. predicted minus chronological age). Results: Transcriptome and immunometabolic clocks predicted chronological age well in the test set (r=0.93 and r=0.84 respectively). Generally, weak correlations were observed, after adjustment for chronological age, between the biological age indicators.Among associations with health risk factors, higher birthweight was associated with greater immunometabolic Δ age, smoke exposure with greater DNA methylation Δ age, and high family affluence with longer telomere length.Among associations with child developmental measures, all biological age markers were associated with greater BMI and fat mass, and all markers except telomere length were associated with greater height, at least at nominal significance (p<0.05). Immunometabolic Δ age was associated with better working memory (p=4 e-3) and reduced inattentiveness (p=4 e-4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviors (p=0.01). Shorter telomere length was also associated with poorer externalizing behaviors (p=0.03). Conclusions: In children, as in adults, biological aging appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological aging. Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological aging, apparent even in children. Funding: UK Research and Innovation (MR/S03532X/1); European Commission (grant agreement numbers: 308333; 874583).

Although age is generally measured by the number of years since birth, many factors contribute to the rate at which a person physically ages. In adults, linking these measurements to age gives a measure of overall health and resilience. This 'biological age' offers a better prediction of remaining life and disease risk than the number of years lived. Multiple factors can be used to calculate biological age, such as measuring the length of telomeres ­ protective caps on the end of chromosomes ­ which shorten as people age. The rate at which they shorten can give an indication of how quickly someone is ageing. Researchers can also study epigenetic factors: these mechanisms lead to certain genes being switched on or off, and they can be combined into a 'epigenetic clock' to assess biological age. However, compared with adults, the relationship between biological age and child health and developmental maturity is less well understood. Robinson et al. studied 1,173 school-aged children from six European countries, measuring telomere length, epigenetic factors and other biological indicators related to metabolism and the immune system. The relationships between these factors and an array of child developmental measures such as height, weight, behaviour and the age of onset of puberty were established. The findings showed that biological age indicators are only weakly linked to each other in children. Despite this, biological age was related to greater amount of body fat across all tested indicators ­ which is also associated with biological age in adults and is an important determinant of lifespan. Among several observed effects on development, analysis found that shorter telomere length and older epigenetic age were associated with greater behavioural problems, suggesting they may be detrimental to child development. On the other hand, a greater age due to metabolic and immune related changes was associated with greater cognitive and behavioural maturity. Environmental factors were also linked to biological ageing, with children exposed to smoking in their homes or while their mother was pregnant displaying an older epigenetic age. Robinson et al. showed that biological ageing in children is multifaceted and can have both beneficial and harmful impacts on development. This knowledge is important for identifying early life risk factors that might influence healthy ageing in later life. Future work will help researchers to understand these complex interactions and the long-term consequences for health and well-being.

Updated MS²PIP web server supports cutting-edge proteomics applications.

Interest in the use of machine learning for peptide fragmentation spectrum prediction has been strongly on the rise over the past years, especially for applications in challenging proteomics identification workflows such as immunopeptidomics and the full-proteome identification of data independent acquisition spectra. Since its inception, the MS²PIP peptide spectrum predictor has been widely used for various downstream applications, mostly thanks to its accuracy, ease-of-use, and broad applicability. We here present a thoroughly updated version of the MS²PIP web server, which includes new and more performant prediction models for both tryptic- and non-tryptic peptides, for immunopeptides, and for CID-fragmented TMT-labeled peptides. Additionally, we have also added new functionality to greatly facilitate the generation of proteome-wide predicted spectral libraries, requiring only a FASTA protein file as input. These libraries also include retention time predictions from DeepLC. Moreover, we now provide pre-built and ready-to-download spectral libraries for various model organisms in multiple DIA-compatible spectral library formats. Besides upgrading the back-end models, the user experience on the MS²PIP web server is thus also greatly enhanced, extending its applicability to new domains, including immunopeptidomics and MS3-based TMT quantification experiments. MS²PIP is freely available at https://iomics.ugent.be/ms2pip/.

Beauveria bassiana rewires molecular mechanisms related to growth and defense in tomato.

Plant roots can exploit beneficial associations with soil-inhabiting microbes, promoting growth and expanding the immune capacity of the host plant. In this work, we aimed to provide new information on changes occurring in tomato interacting with the beneficial fungus Beauveria bassiana. The tomato leaf proteome revealed perturbed molecular pathways during the establishment of the plant-fungus relationship. In the early stages of colonization (5-7 d), proteins related to defense responses to the fungus were down-regulated and proteins related to calcium transport were up-regulated. At later time points (12-19 d after colonization), up-regulation of molecular pathways linked to protein/amino acid turnover and to biosynthesis of energy compounds suggests beneficial interaction enhancing plant growth and development. At the later stage, the profile of leaf hormones and related compounds was also investigated, highlighting up-regulation of those related to plant growth and defense. Finally, B. bassiana colonization was found to improve plant resistance to Botrytis cinerea, impacting plant oxidative damage. Overall, our findings further expand current knowledge on the possible mechanisms underlying the beneficial role of B. bassiana in tomato plants.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Dynamic interplay between RPL3- and RPL3L-containing ribosomes modulates mitochondrial activity in the mammalian heart.

The existence of naturally occurring ribosome heterogeneity is now a well-acknowledged phenomenon. However, whether this heterogeneity leads to functionally diverse 'specialized ribosomes' is still a controversial topic. Here, we explore the biological function of RPL3L (uL3L), a ribosomal protein (RP) paralogue of RPL3 (uL3) that is exclusively expressed in skeletal muscle and heart tissues, by generating a viable homozygous Rpl3l knockout mouse strain. We identify a rescue mechanism in which, upon RPL3L depletion, RPL3 becomes up-regulated, yielding RPL3-containing ribosomes instead of RPL3L-containing ribosomes that are typically found in cardiomyocytes. Using both ribosome profiling (Ribo-seq) and a novel orthogonal approach consisting of ribosome pulldown coupled to nanopore sequencing (Nano-TRAP), we find that RPL3L modulates neither translational efficiency nor ribosome affinity towards a specific subset of transcripts. In contrast, we show that depletion of RPL3L leads to increased ribosome-mitochondria interactions in cardiomyocytes, which is accompanied by a significant increase in ATP levels, potentially as a result of fine-tuning of mitochondrial activity. Our results demonstrate that the existence of tissue-specific RP paralogues does not necessarily lead to enhanced translation of specific transcripts or modulation of translational output. Instead, we reveal a complex cellular scenario in which RPL3L modulates the expression of RPL3, which in turn affects ribosomal subcellular localization and, ultimately, mitochondrial activity.

Ribosomes are macromolecular machines responsible for protein synthesis in all living beings. Recent studies have shown that ribosomes can be heterogeneous in their structure, possibly leading to a specialized function. Here, we focus on RPL3L, a ribosomal protein expressed exclusively in striated muscles. We find that the deletion of the Rpl3l gene in a mouse model triggers a compensation mechanism, in which the missing RPL3L protein is replaced by its paralogue, RPL3. Furthermore, we find that RPL3-containing ribosomes establish closer interactions with mitochondria, cellular organelles responsible for energy production, leading to higher energy production when compared with RPL3L-containing ribosomes. Finally, we show that the RPL3­RPL3L compensation mechanism is also triggered in heart disease conditions, such as hypertrophy and myocardial infarction.

Childhood exposure to non-persistent endocrine disrupting chemicals and multi-omic profiles: A panel study.

BACKGROUND: Individuals are exposed to environmental pollutants with endocrine disrupting activity (endocrine disruptors, EDCs) and the early stages of life are particularly susceptible to these exposures. Previous studies have focused on identifying molecular signatures associated with EDCs, but none have used repeated sampling strategy and integrated multiple omics. We aimed to identify multi-omic signatures associated with childhood exposure to non-persistent EDCs. METHODS: We used data from the HELIX Child Panel Study, which included 156 children aged 6 to 11. Children were followed for one week, in two time periods. Twenty-two non-persistent EDCs (10 phthalate, 7 phenol, and 5 organophosphate pesticide metabolites) were measured in two weekly pools of 15 urine samples each. Multi-omic profiles (methylome, serum and urinary metabolome, proteome) were measured in blood and in a pool urine samples. We developed visit-specific Gaussian Graphical Models based on pairwise partial correlations. The visit-specific networks were then merged to identify reproducible associations. Independent biological evidence was systematically sought to confirm some of these associations and assess their potential health implications. RESULTS: 950 reproducible associations were found among which 23 were direct associations between EDCs and omics. For 9 of them, we were able to find corroborating evidence from previous literature: DEP - serotonin, OXBE - cg27466129, OXBE - dimethylamine, triclosan - leptin, triclosan - serotonin, MBzP - Neu5AC, MEHP - cg20080548, oh-MiNP - kynurenine, oxo-MiNP - 5-oxoproline. We used these associations to explore possible mechanisms between EDCs and health outcomes, and found links to health outcomes for 3 analytes: serotonin and kynurenine in relation to neuro-behavioural development, and leptin in relation to obesity and insulin resistance. CONCLUSIONS: This multi-omics network analysis at two time points identified biologically relevant molecular signatures related to non-persistent EDC exposure in childhood, suggesting pathways related to neurological and metabolic outcomes.

Loss of dyskerin facilitates the acquisition of metastatic traits by altering the mevalonate pathway.

The initial dissemination of cancer cells from many primary tumors implies intravasation to lymphatic nodes or blood vessels. To investigate the mechanisms involved, we analyzed the expression of small non-coding RNAs in cutaneous squamous cell carcinoma (cSCC), a prevalent tumor that mainly spreads to lymph nodes. We report the reduced expression of small nucleolar RNAs in primary cSCCs that metastasized when compared to non-metastasizing cSCCs, and the progressive loss of DKC1 (dyskerin, which stabilizes the small nucleolar RNAs) along the metastasis. DKC1 depletion in cSCC cells triggered lipid metabolism by altering the mevalonate pathway and the acquisition of metastatic traits. Treatment of DKC1-depleted cells with simvastatin, an inhibitor of the mevalonate pathway, blocked the expression of proteins involved in the epithelial-to-mesenchymal transition. Consistently, the expression of the enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 1 was associated with pathological features of high metastatic risk in cSCC patients. Our data underpin the relevance of the mevalonate metabolism in metastatic dissemination and pave the possible incorporation of therapeutic approaches among the antineoplastic drugs used in routine patient care.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments.

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).