Projects at the MFM Lab:
We are looking for excellent PhD and Masters students in Computational and Molecular Biology. Contact: Dr. Milana Frenkel-Morgenstern, email: milana.morgenstern 'at' biu.ac.il. Our address: Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed.
We established a novel Liquid Biopsy platform for monitoring patients with glioblastoma, cervical and oral cavity cancers. Glioblastoma is the most prevalent and lethal type of malignant brain tumour. Our platform uses circulating cell-free DNA (cfDNA) from blood plasma. We have developed a method for analyzing the extremely low levels of cfDNA in glioblastoma. We identified novel chimeric genes (gene-gene fusions) in both tumours and cfDNA samples. These fusions are predicted to alter cellular processes, by removing tumour suppressors and accumulating onco-proteins. For example for gliomas, several fusions are potential drug targets, particularly NTRK or ROS1 fusions, for crizotinib analogues (like entrectinib and larotrectinib). We are working on the non-invasive liquid biopsy platform to follow patients response to treatments. We succeeded to identify gene-gene fusions as biomarkers and potential drug targets. We developed also a computational model for predicting disrupted cellular networks in cancer cells. Our results open new perspectives and capabilities for precision medicine in cancer patients, including supportive care and pain relief after chemotherapy.
Chimeric RNAs and gene-gene fusions
Gene fusions can give rise to somatic alterations in cancers. Fusion genes have the potential to create chimeric RNAs, which can generate the phenotypic diversity of cancer cells, and could be associated with novel molecular functions related to cancer cell survival and proliferation. The expression of chimeric RNAs in cancer cells might impact diverse cancer-related functions, including loss of apoptosis and cancer cell plasticity, and promote oncogenesis. Due to their recurrence in cancers and functional association with oncogenic processes, chimeric RNAs are considered biomarkers for cancer diagnosis. Several recent studies demonstrated that chimeric RNAs could lead to the generation of new functionality for the resistance of cancer cells against drug therapy. Therefore, targeting chimeric RNAs in drug resistance cancer could be useful for developing precision medicine. So, understanding the functional impact of chimeric RNAs in cancer cells from an evolutionary perspective will be helpful to elucidate cancer evolution, which could provide a new insight to design more effective therapies for cancer patients in a personalized manner.
Evolution model by chromosomal translocations
Our 'Evolution of Protein Domains' (EvoProDom) model describes the evolution of proteins based on the 'mix and merge' of protein domains. We assembled and integrated genomic and proteomic data comprising protein domain content and orthologous proteins from 109 organisms. In EvoProDom, we characterized evolutionary events, particularly, translocations, as reciprocal exchanges of protein domains between orthologous proteins in different organisms. We showed that protein domains that translocate with highly frequency are generated by transcripts enriched in trans-splicing events, that is, the generation of novel transcripts from the fusion of two distinct genes. In EvoProDom, we describe a general method to collate orthologous protein annotation from KEGG, and protein domain content from protein sequences using tools such as KoFamKOAL and Pfam. EvoProDom presents a novel model for protein evolution based on the 'mix and merge' of protein domains rather than DNA-based evolution models. This confers the advantage of considering chromosomal alterations as drivers of protein evolutionary events.
COVID-19 and Vitamin D
Several recent studies have demonstrated that low plasma 25(OH) vitamin D levels are associated with the risk of COVID-19 infection. The primary source of vitamin D production in humans is environmental UV radiation. In many viral respiratory diseases, peak infection rates are observed during winter due to reduced UV exposure and low temperatures. In Europe, the second wave of COVID-19 began early in the winter of 2020. Investigating the impact of seasonal temperature and UV exposure on COVID-19 transmission could thus aid in prevention and intervention. As such, we first performed a comprehensive meta-analysis of all related published literature based on the association between vitamin D and COVID-19, which supported the hypothesis that the low vitamin D level is a critical risk factor for COVID-19 infection. Next, to understand the potential impact of seasonal UV and temperature levels on COVID-19 cases, we analyzed meteorological data and daily COVID-19 cases per million in the populations of 26 European countries. We observed that low temperature, UV index, and cloud-free vitamin D UV dose (UVDVF) levels are negatively correlated with COVID-19 prevalence in Europe. Furthermore, a distributed lag nonlinear model was used to assess the nonlinear delayed effects of individual seasonal factors on COVID-19 cases. Such analysis highlighted the significantly delayed impact of UVDVF on the cumulative relative risk of COVID-19 infection. The findings of this study suggest that low UV exposure can affect the required production of vitamin D in the body, which substantially influences the dynamics of COVID-19 transmission and severity.
Non-optimal codon usage in cell-cycle
The cell cycle is a temporal program that regulates DNA synthesis and cell division. When we compared the codon usage of cell cycle-regulated genes with that of other genes, we discovered that there is a significant preference for non-optimal codons. Moreover, genes encoding proteins that cycle at the protein level exhibit non-optimal codon preferences. Remarkably, cell cycle-regulated genes expressed in different phases display different codon preferences. Here, we show empirically that transfer RNA (tRNA) expression is indeed highest in the G2 phase of the cell cycle, consistent with the non-optimal codon usage of genes expressed at this time, and lowest toward the end of G1, reflecting the optimal codon usage of G1 genes. Accordingly, protein levels of human glycyl-, threonyl-, and glutamyl-prolyl tRNA synthetases were found to oscillate, peaking in G2/M phase. In light of our findings, we propose that non-optimal (wobbly) matching codons influence protein synthesis during the cell cycle. Our data indicate that cells exploit wobbling to generate cell cycle-dependent dynamics of proteins.