APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants.

Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2's ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Dissecting the Genome for Drug Response Prediction.

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.

Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types.

Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3'UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types. We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples. We show how more than 90% of these miRNA-mRNA interactions show a loss of regulation in the tumoral samples compared with their healthy counterparts. As expected, we found shared miRNA-mRNA dysregulated pairs among different tumors of the same tissue. However, anatomically different cancers also share multiple dysregulated interactions, suggesting that some cancer-related mechanisms are not tumor-specific. 2865 unique miRNA-mRNA pairs were identified across 13 cancer types, ≈ 40% of these pairs showed a loss of correlation in the tumoral samples in at least 2 out of the 13 analyzed cancers. Specifically, miR-200 family, miR-155 and miR-1 were identified, based on the computational analysis described below, as the miRNAs that potentially lose the highest number of interactions across different samples (only literature-based interactions were used for this analysis). Moreover, the miR-34a/ALDH2 and miR-9/MTHFD2 pairs show a switch in their correlation between healthy and tumor kidney samples suggesting a possible change in the regulation exerted by the miRNAs. Interestingly, the expression of these mRNAs is also associated with the overall survival. The disruption of miRNA regulation on its target, therefore, suggests the possible involvement of these pairs in cell malignant functions. The analysis reported here shows how the regulation of miRNA-mRNA interactions strongly differs between healthy and tumoral cells, based on the strong correlation variation between miRNA and its target that we obtained by analyzing the expression data of healthy and tumor tissue in highly reliable miRNA-target pairs. Finally, a go term enrichment analysis shows that the critical pairs identified are involved in cellular adhesion, proliferation, and migration.

Conserved exchange of paralog proteins during neuronal differentiation.

Gene duplication enables the emergence of new functions by lowering the evolutionary pressure that is posed on the ancestral genes. Previous studies have highlighted the role of specific paralog genes during cell differentiation, for example, in chromatin remodeling complexes. It remains unexplored whether similar mechanisms extend to other biological functions and whether the regulation of paralog genes is conserved across species. Here, we analyze the expression of paralogs across human tissues, during development and neuronal differentiation in fish, rodents and humans. Whereas ∼80% of paralog genes are co-regulated, a subset of paralogs shows divergent expression profiles, contributing to variability of protein complexes. We identify 78 substitutions of paralog pairs that occur during neuronal differentiation and are conserved across species. Among these, we highlight a substitution between the paralogs SEC23A and SEC23B members of the COPII complex. Altering the ratio between these two genes via RNAi-mediated knockdown is sufficient to influence neuron differentiation. We propose that remodeling of the vesicular transport system via paralog substitutions is an evolutionary conserved mechanism enabling neuronal differentiation.

Mapping protein carboxymethylation sites provides insights into their role in proteostasis and cell proliferation.

Posttranslational mechanisms play a key role in modifying the abundance and function of cellular proteins. Among these, modification by advanced glycation end products has been shown to accumulate during aging and age-associated diseases but specific protein targets and functional consequences remain largely unexplored. Here, we devise a proteomic strategy to identify sites of carboxymethyllysine modification, one of the most abundant advanced glycation end products. We identify over 1000 sites of protein carboxymethylation in mouse and primary human cells treated with the glycating agent glyoxal. By using quantitative proteomics, we find that protein glycation triggers a proteotoxic response and indirectly affects the protein degradation machinery. In primary endothelial cells, we show that glyoxal induces cell cycle perturbation and that carboxymethyllysine modification reduces acetylation of tubulins and impairs microtubule dynamics. Our data demonstrate the relevance of carboxymethyllysine modification for cellular function and pinpoint specific protein networks that might become compromised during aging.

A comparative benchmark of classic DNA motif discovery tools on synthetic data.

Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.

Phosphorylation within Intrinsic Disordered Region Discriminates Histone Variant macroH2A1 Splicing Isoforms-macroH2A1.1 and macroH2A1.2.

Background: Gene expression in eukaryotic cells can be governed by histone variants, which replace replication-coupled histones, conferring unique chromatin properties. MacroH2A1 is a histone H2A variant containing a domain highly similar to H2A and a large non-histone (macro) domain. MacroH2A1, in turn, is present in two alternatively exon-spliced isoforms: macroH2A1.1 and macroH2A1.2, which regulate cell plasticity and proliferation in a remarkably distinct manner. The N-terminal and the C-terminal tails of H2A histones stem from the nucleosome core structure and can be target sites for several post-translational modifications (PTMs). MacroH2A1.1 and macroH2A1.2 isoforms differ only in a few amino acids and their ability to bind NAD-derived metabolites, a property allegedly conferring their different functions in vivo. Some of the modifications on the macroH2A1 variant have been identified, such as phosphorylation (T129, S138) and methylation (K18, K123, K239). However, no study to our knowledge has analyzed extensively, and in parallel, the PTM pattern of macroH2A1.1 and macroH2A1.2 in the same experimental setting, which could facilitate the understanding of their distinct biological functions in health and disease. Methods: We used a mass spectrometry-based approach to identify the sites for phosphorylation, acetylation, and methylation in green fluorescent protein (GFP)-tagged macroH2A1.1 and macroH2A1.2 expressed in human hepatoma cells. The impact of selected PTMs on macroH2A1.1 and macroH2A1.2 structure and function are demonstrated using computational analyses. Results: We identified K7 as a new acetylation site in both macroH2A1 isoforms. Quantitative comparison of histone marks between the two isoforms revealed significant differences in the levels of phosphorylated T129 and S170. Our computational analysis provided evidence that the phosphorylation status in the intrinsically disordered linker region in macroH2A1 isoforms might represent a key regulatory element contributing to their distinct biological responses. Conclusions: Taken together, our results report different PTMs on the two macroH2A1 splicing isoforms as responsible for their distinct features and distribution in the cell.

Variants in MHY7 Gene Cause Arrhythmogenic Cardiomyopathy.

BACKGROUND: Arrhythmogenic Cardiomyopathy (ACM) is a disease of the cardiac muscle, characterized by frequent ventricular arrhythmias and functional/ structural abnormalities, mainly of the right ventricle. To date, 20 different genes have been associated with ACM and the majority of them encode for desmosomal proteins. In this study, we describe the characterization of two novel variants in MHY7 gene, segregating in two ACM families. MYH7 encodes for myosin heavy chain ß (MHC-ß) isoform, involved in cardiac muscle contractility. METHOD AND RESULTS: In family A, the autopsy revealed ACM with biventricular involvement in both the proband and his father. In family B, the proband had been diagnosed as affected by ACM and implanted with implantable cardioverter defibrillator (ICD), due to ECG evidence of monomorphic ventricular tachycardia after syncope. After clinical evaluation, a molecular diagnosis was performed using a NGS custom panel. The two novel variants identified predicted damaging, located in a highly conserved domain: c. 2630T>C is not described while c.2609G>A has a frequency of 0.00000398. In silico analyses evaluated the docking characteristics between proteins using the Haddock2.2 webserver. CONCLUSIONS: Our results reveal two variants in sarcomeric genes to be the molecular cause of ACM, further increasing the genetic heterogeneity of the disease; in fact, sarcomeric variants are usually associated with HCM phenotype. Studies on the role of sarcomere genes in the pathogenesis of ACM are surely recommended in those ACM patients negative for desmosomal mutation screening.

MicroRNAs and Long Non-Coding RNAs as Potential Candidates to Target Specific Motifs of SARS-CoV-2.

The respiratory system is one of the most affected targets of SARS-CoV-2. Various therapies have been utilized to counter viral-induced inflammatory complications, with diverse success rates. Pending the distribution of an effective vaccine to the whole population and the achievement of "herd immunity", the discovery of novel specific therapies is to be considered a very important objective. Here, we report a computational study demonstrating the existence of target motifs in the SARS-CoV-2 genome suitable for specific binding with endogenous human micro and long non-coding RNAs (miRNAs and lncRNAs, respectively), which can, therefore, be considered a conceptual background for the development of miRNA-based drugs against COVID-19. The SARS-CoV-2 genome contains three motifs in the 5'UTR leader sequence recognized by selective nucleotides within the seed sequence of specific human miRNAs. The seed of 57 microRNAs contained a "GGG" motif that promoted leader sequence-recognition, primarily through offset-6mer sites able to promote microRNAs noncanonical binding to viral RNA. Similarly, lncRNA H19 binds to the 5'UTR of the viral genome and, more specifically, to the transcript of the viral gene Spike, which has a pivotal role in viral infection. Notably, some of the non-coding RNAs identified in our study as candidates for inhibiting SARS-CoV-2 gene expression have already been proposed against diverse viral infections, pulmonary arterial hypertension, and related diseases.

MitImpact 3: modeling the residue interaction network of the Respiratory Chain subunits.

Numerous lines of evidence have shown that the interaction between the nuclear and mitochondrial genomes ensures the efficient functioning of the OXPHOS complexes, with substantial implications in bioenergetics, adaptation, and disease. Their interaction is a fascinating and complex trait of the eukaryotic cell that MitImpact explores with its third major release. MitImpact expands its collection of genomic, clinical, and functional annotations of all non-synonymous substitutions of the human mitochondrial genome with new information on putative Compensated Pathogenic Deviations and co-varying amino acid sites of the Respiratory Chain subunits. It further provides evidence of energetic and structural residue compensation by techniques of molecular dynamics simulation. MitImpact is freely accessible at http://mitimpact.css-mendel.it.

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

BACKGROUND: Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. RESULTS: We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. CONCLUSIONS: We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

Transcriptome and Gene Fusion Analysis of Synchronous Lesions Reveals lncMRPS31P5 as a Novel Transcript Involved in Colorectal Cancer.

Fusion genes and epigenetic regulators (i.e., miRNAs and long non-coding RNAs) constitute essential pieces of the puzzle of the tumor genomic landscape, in particular in mechanisms behind the adenoma-to-carcinoma progression of colorectal cancer (CRC). In this work, we aimed to identify molecular signatures of the different steps of sporadic CRC development in eleven patients, of which synchronous samples of adenomas, tumors, and normal tissues were analyzed by RNA-Seq. At a functional level, tumors and adenomas were all characterized by increased activity of the cell cycle, cell development, cell growth, and biological proliferation functions. In contrast, organic survival and apoptosis-related functions were inhibited both in tumors and adenomas at different levels. At a molecular level, we found that three individuals shared a tumor-specific fusion named MRPS31-SUGT1, generated through an intra-chromosomal translocation on chromosome 13, whose sequence resulted in being 100% identical to the long non-coding RNA (lncRNA) MRPS31P5. Our analyses suggest that MRPS31P5 could take part to a competitive endogenous (ce)RNA network by acting as a miRNA sponge or/and as an interactor of other mRNAs, and thus it may be an important gene expression regulatory factor and could be used as a potential biomarker for the detection of early CRC events.

Mechanisms of pathogenesis of missense mutations on the KDM6A-H3 interaction in type 2 Kabuki Syndrome.

Mutations in genes encoding for histone methylation proteins are associated with several developmental disorders. Among them, KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease. While nonsense mutations and short insertions/deletions are known to trigger pathogenic mechanisms, the functional effects of missense mutations are still uncharacterized. In this study, we demonstrate that a selected set of missense mutations significantly hamper the interaction between KDM6A and the histone H3, by modifying the dynamics of the linker domain, and then causing a loss of function effect.

Modeling cancer drug response through drug-specific informative genes.

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.

Discovering sequence and structure landscapes in RNA interaction motifs.

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes.

Motivation: Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind. Results: In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome. Availability and implementation: http://akid.bio.uniroma2.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Quantifying compartment-associated variations of protein abundance in proteomics data.

Quantitative mass spectrometry enables to monitor the abundance of thousands of proteins across biological conditions. Currently, most data analysis approaches rely on the assumption that the majority of the observed proteins remain unchanged across compared samples. Thus, gross morphological differences between cell states, deriving from, e.g., differences in size or number of organelles, are often not taken into account. Here, we analyzed multiple published datasets and frequently observed that proteins associated with a particular cellular compartment collectively increase or decrease in their abundance between conditions tested. We show that such effects, arising from underlying morphological differences, can skew the outcome of differential expression analysis. We propose a method to detect and normalize morphological effects underlying proteomics data. We demonstrate the applicability of our method to different datasets and biological questions including the analysis of sub-cellular proteomes in the context of Caenorhabditis elegans aging. Our method provides a complementary perspective to classical differential expression analysis and enables to uncouple overall abundance changes from stoichiometric variations within defined group of proteins.

Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC).

The interpatient variability of tumor proteomes has been investigated on a large scale but many tumors display also intratumoral heterogeneity regarding morphological and genetic features. It remains largely unknown to what extent the local proteome of tumors intrinsically differs. Here, we used hepatocellular carcinoma as a model system to quantify both inter- and intratumor heterogeneity across human patient specimens with spatial resolution. We defined proteomic features that distinguish neoplastic from the directly adjacent nonneoplastic tissue, such as decreased abundance of NADH dehydrogenase complex I. We then demonstrated the existence of intratumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry-based proteomics to analyze diagnostic tumor specimens with spatial resolution. Data are available via ProteomeXchange with identifier PXD007052.

Systematic identification of phosphorylation-mediated protein interaction switches.

Proteomics techniques can identify thousands of phosphorylation sites in a single experiment, the majority of which are new and lack precise information about function or molecular mechanism. Here we present a fast method to predict potential phosphorylation switches by mapping phosphorylation sites to protein-protein interactions of known structure and analysing the properties of the protein interface. We predict 1024 sites that could potentially enable or disable particular interactions. We tested a selection of these switches and showed that phosphomimetic mutations indeed affect interactions. We estimate that there are likely thousands of phosphorylation mediated switches yet to be uncovered, even among existing phosphorylation datasets. The results suggest that phosphorylation sites on globular, as distinct from disordered, parts of the proteome frequently function as switches, which might be one of the ancient roles for kinase phosphorylation.

Spatiotemporal variation of mammalian protein complex stoichiometries.

BACKGROUND: Recent large-scale studies revealed cell-type specific proteomes. However, protein complexes, the basic functional modules of a cell, have been so far mostly considered as static entities with well-defined structures. The co-expression of their members has not been systematically charted at the protein level. RESULTS: We used measurements of protein abundance across 11 cell types and five temporal states to analyze the co-expression and the compositional variations of 182 well-characterized protein complexes. We show that although the abundance of protein complex members is generally co-regulated, a considerable fraction of all investigated protein complexes is subject to stoichiometric changes. Compositional variation is most frequently seen in complexes involved in chromatin regulation and cellular transport, and often involves paralog switching as a mechanism for the regulation of complex stoichiometry. We demonstrate that compositional signatures of variable protein complexes have discriminative power beyond individual cell states and can distinguish cancer cells from healthy ones. CONCLUSIONS: Our work demonstrates that many protein complexes contain variable members that cause distinct stoichometries and functionally fine-tune complexes spatiotemporally. Only a fraction of these compositional variations is mediated by changes in transcription and other mechanisms regulating protein abundance contribute to determine protein complex stoichiometries. Our work highlights the superior power of proteome profiles to study protein complexes and their variants across cell states.