Biology subjects

Frenkel-Morgenstern, M.

Publications and source records attributed to Frenkel-Morgenstern, M..

7 recordsLinked to original sources

bioRxiv2022

tRNA Methylation Resolves Codon Usage Bias at the Limit of Cell Viability

The codon usage of each genome is closely correlated with the abundance of tRNA isoacceptors. How codon-usage bias is resolved by tRNA post-transcriptional modifications is largely unknown. Here we demonstrate that the N1-methylation of guanosine at position 37 (m1G37) on the 3-side of the anticodon, while not directly responsible for reading of codons, is a neutralizer that resolves differential decoding of proline codons. A genome-wide suppressor screen of a non-viable E. coli strain, m1G37-lacking, identifies proS suppressor mutations, indicating a coupling of methylation with tRNA prolyl-aminoacylation that sets the limit of cell viability. Using these suppressors, where prolyl-aminoacylation is decoupled from tRNA methylation, we show that m1G37 neutralizes differential translation of proline codons by the major isoacceptor. Lacking of m1G37 inactivates this neutralization and exposes the need for a minor isoacceptor for cell viability. This work has medical implications for bacterial genomes that exclusively use the major isoacceptor for survival.

microbiology↗

bioRxiv2022

Mutated Tumor Suppressors Follow Oncogenes Profile by the Gene Hypermethylation of Partners in the Protein Interaction Networks

As a result of current advances in the analysis of patient sequencing data, many tumors have been characterized in a personalized manner. Such data can also be used to characterize genes that act as either oncogenes or tumor suppressors. These include "defective" tumor suppressor genes which may function as driver oncogenes that play a key role in cancer proliferation due to various genetic alterations, specifically, chromosomal translocations. In this study, we considered protein networks, mutations, methylation data and cancer fusions to classify tumor suppressors that may convert into oncogenes. Moreover, we developed a novel network-based parameter called the preferential attachment score to categorize genes as oncogenes and/or tumor suppressors. Such classification was achieved using a naive Bayes computation approach. We used an ABC-MCMC method for selecting features for training our classification algorithm. We then performed a survey of tumor suppressors and oncogenes from the perspective of somatic mutations and network properties for 691 TCGA cases. For comparative purposes, we chose currently well-established methods, such as MutSigCV, OncodriveCLUST, Oncodrive-FM, 20/20+, ActiveDriver, MuSiC, TUSON, OncodriveFML, and found that our algorithm outperformed these other tolls, with 93.3% efficiency. Based on 691 TCGA cohorts, we found that tumor suppressors presented the highest mutation frequency in most tumor types, relative to oncogenes. Using protein-protein interaction data, we found that essential proteins, tumor suppressors and oncogenes had higher degrees of connectivity and betweenness centrality, relative to normal proteins. Similarly, tumor suppressors and oncogenes had lower clustering coefficients, as well as shortest path distances (FDR < 0.05). Finally, most mutated tumor suppressors integrate hyper-methylated partners in the protein interaction networks of 3091 fusions, following the patterns of oncogenes (43%). Thus, these results further characterize cancer oncogenes and tumor suppressors in the context of deep analysis of cancer network alterations. AvailabilitySource scripts are available at https://github.com/somnathtagore/NBC and the resource is available at http://ontum.md.biu.ac.il/index.html

systems biology↗

bioRxiv2022

Improving patient survival by direct targeting of chimeric protein-protein interaction networks

Protein fusions produced by the "slippage" of two genes or by chromosomal translocations are essential diagnostic biomarkers of cancer. Fusions produce novel protein-protein interactions, which eliminate other interactions by changing protein domains in such fusions. The impact of these changes disseminates along protein-protein interaction networks, thereby altering cancer-promoting activity and creating cancer phenotypes. Currently, for most patients, the determination of appropriate drugs is totally empirical. As such, a personalized therapy approach based on unique patient genomic markers is needed. In this study, we considered 672 aliquot IDs containing 3091 fusions from The Cancer Genome Atlas (TCGA), accounting for 25 cancer sub-types assigned as leukemias, lymphomas, sarcomas, melanoma, glioblastoma and carcinomas. Protein-protein interaction maps showed distinct patterns according to cancer sub-type, reflected as different phenotypic traits. We induced site-directed percolations, i.e., critical transitions, by selective knockouts of genes encoding proteins in a given interaction network so as to identify breakdown points. The number of genes that needed to be knocked out in a site-directed manner before inducing a breakdown ranged from 1-7 based on whether the fusion protein was part of a network hub. Quantitatively, in leukemia, lymphoma, melanoma and glioblastoma, breakdown was achieved in 218 fusion networks when only higher degree hubs were percolated, and in 280 networks when a combination of higher and lower degree hubs was targeted, with an FDR < 0.05. These were subsequently addressed in survival studies. We found that patient survival may be improved by considering breakdown points as drug targets.

systems biology↗

bioRxiv2020

A liquid biopsy platform for detecting gene-gene fusions as glioma diagnostic biomarkers and drug targets

Gliomas account for about 80% of all malignant brain tumours. Diagnosis is achieved by radiographic imaging followed by tumour resection, to determine tumour cell type, grade and molecular characteristics. Glioblastoma multiforme (GBM) is the most common type of glioma, and is uniformly fatal. The median survival of treated GBM patients is 12-15 months. Standard modalities of therapy are unselective and include surgery, radiation therapy and chemotherapy, while precision medicine has yet to demonstrate improvements in disease outcome. We therefore selected GBM as a model to develop a precision medicine methodology for monitoring patients using blood plasma circulating cell-free DNA (cfDNA). Currently, tumour heterogeneity, clonal diversity and mutation acquisition are the major impedances for tailoring personalized therapy in gliomas in general, and particularly in GBM. Thus, a liquid biopsy diagnostics platform based on cfDNA sequencing may improve treatment outcome for GBM patients, by guiding therapy selection. In this study, we processed from 27 patients with glioma, 27 plasma samples for cfDNA isolation and 5 tissue biopsy samples for tumour DNA isolation. From a control group of 14 healthy individuals, 14 plasma samples were processed for cfDNA isolation. In glioma patients, cfDNA concentration was elevated compared to controls. Point mutations found in glioma tissue biopsies were also found in the cfDNA samples (95% identity). Finally, we identified novel chimeric genes (gene-gene fusions) in both tumour and cfDNA samples. These fusions are predicted to alter protein interaction networks, by removing tumour suppressors and adding oncoproteins. Indeed, several of these fusions are potential drug targets, particularly, NTRK or ROS1 fusions, specifically for crizotinib analogues (like entrectinib and larotrectinib) with enhanced penetration of the central nervous system. Taken together, our results demonstrate that novel druggable targets in gliomas can be identified by liquid biopsy using cfDNA in patient plasma. These results open new perspectives and abilities of precision medicine in GBM.

cancer biology↗

bioRxiv2020

EvoProDom: Evolutionary model of protein families by means of translocations of protein domains

Here, we developed a novel evolution of protein domains (EvoProDom) model for evolution of proteins, which was based on mix and merge of protein domains. We collected and integrated genomic and proteome data for 109 organisms. These data include protein domain content and orthologous protein families. In EvoProDom, we defined evolutionary events, such as translocations, as reciprocal exchanges of protein domains between orthologous proteins of different organisms. We found that protein domains, which frequently appear in translocation events, were enriched in trans-splicing events, i.e., producing novel transcripts fused from two distinct genes. We presented in EvoProDom, a general method to obtain protein domain content and orthologous protein annotation, by predicting these data from protein sequences using the Pfam search tool and KoFamKOALA, respectively. This method can be implemented in other research such as proteomics, protein design and host-virus interactions.

evolutionary biology↗

bioRxiv2018

A Comprehensive Approach Characterizing Fusion Proteins and Their Interactions Using Biomedical Literature

Todays increase in scientific literature requires the efficient methods of data mining for improving the extraction of the useful information from texts. In this manuscript, we used a data and text mining method to identify fusions and their protein-protein interactions from published biomedical text. The extracted fusion proteins and their protein-protein interactions are used as a training set for a Naive Bayes classifier that is further used for final identification of testing dataset, consisting of 1817 fusions. Our method has a literature corpus, text and annotation mappers; keywords, rule bases, negative tokens, and pattern extractor; synonym tagger, normalization, regular expression mapper; and Naive Bayes classifier. We classified 1817 unique fusion proteins and their corresponding 2908 protein-protein interactions for 18 cancer types. Therefore, it can be used for screening literature for identifying mentions unique cases of fusions that can be further used for downstream analysis. It is available at http://protfus.md.biu.ac.il/.

bioinformatics↗

bioRxiv2017

Pan-cancer study of heterogeneous RNA aberrations

We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called \"bridged\" fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

genomics↗