a-reporter-assay-to-analyze-intronic-microrna-maturation-mammalian

A Reporter Assay to Analyze Intronic microRNA Maturation in Mammalian Cells

MicroRNAs (miRNAs) are short RNA molecules that are widespread in eukaryotes. Most miRNAs are transcribed from introns, and their maturation involves ...

Universidade De São Paulo (USP)

Universidade de São Paulo

The network structure of biological systems provides information on the underlying processes shaping their organization and dynamics. Here we examined the structure of the network depicting protein interactions within the spliceosome, the macromolecular complex responsible for splicing in eukaryotic cells. We show the interactions of less connected spliceosome proteins are nested subsets of the connections of the highly connected proteins. At the same time, the network has a modular structure with groups of proteins sharing similar interaction patterns. We then investigated the role of affinity and specificity in shaping the spliceosome network by adapting a probabilistic model originally designed to reproduce food webs. This food-web model was as successful in reproducing the structure of protein interactions as it is in reproducing interactions among species. The good performance of the model suggests affinity and specificity, partially determined by protein size and the timing of association to the complex, may be determining network structure. Moreover, because network models allow building ensembles of realistic networks while encompassing uncertainty they can be useful to examine the dynamics and vulnerability of intracelullar processes. Unraveling the mechanisms organizing the spliceosome interactions is important to characterize the role of individual proteins on splicing catalysis and regulation. 

The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models.

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.

Nestedness across biological scales.

Thyroid cancer is one of the most frequent types of endocrine cancers. In most cases, thyroid cancers are caused by deregulated miRNA expression, especially involving the miR17-92 cluster. miR17-92 transcription is altered in several different tumor types including lymphoma, leukemia, and of the breast and thyroid. As an intronic cluster, miR17-92 must be processed during splicing and therefore interaction between microprocessor and spliceosome machineries is of major importance in understanding its expression.

miR18a and miR19a Recruit Specific Proteins for Splicing in Thyroid Cancer Cells.

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits, which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects.

The unicellular protists of the group Kinetoplastida include the genera Leishmania and Trypanosoma, which are pathogens of invertebrate and vertebrate animals. Despite their medical and economical importance, critical aspects of their biology such as specific molecular characteristics of gene expression regulation are just beginning to be deciphered. Gene expression regulation also depends on post-transcriptional processing steps, such as the trans-splicing process. Despite being widely used in trypanosomes, trans-splicing is a rare event in other eukaryotes. We sought to describe the protein composition of spliceosomes in epimastigotes of T. cruzi, the etiological agent of Chagas disease. We used two TAP-tagged proteins to affinity purify spliceosomes and analyzed their composition by mass spectrometry. Among the 115 identified proteins we detected conserved spliceosome components, as Sm and LSm proteins, RNA helicases, U2- and U5-snRNP specific proteins. Importantly, by comparing our data with proteomic data of human and T. brucei spliceosome complexes, we observed a core group of proteins common to all spliceosomes. By using amino acid sequence comparisons, we identified RNA-associated proteins that might be involved with splicing regulation in T. cruzi, namely the orthologous of WDR33, PABPCL1 and three different HNRNPs. Data are available via ProteomeXchange with identifier PXD018776.

Proteomic analysis of Trypanosoma cruzi spliceosome complex.

Pre-mRNA splicing is an essential step in eukaryotic gene expression regulation. Genes are composed of exons that remain in the mature mRNAs and intervening sequences named introns. Splicing is the removal of introns and ligation of exons in a mature transcript. Splice site or spliceosome component mutations can lead to different diseases, including neurodegenerative diseases and several cancer types. HuR is an RNA-binding protein that preferentially binds to U- and AU-rich elements, usually found at the 3' UTRs of some mRNAs. We previously observed HuR specifically associated with spliceosomes assembled on introns containing miR-18a and miR-19a. miR-18a and miR-19a are components of the intronic miR-17-92 cluster, along with other five miRNAs. This cluster has been reported to regulate proliferation, migration, and angiogenesis in cells. In this context, we reasoned HuR could be controlling the splicing and processing of these miRNAs, leading to altered cellular phenotypes.

Article	Year
The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models. Scientific reports\| PubMed ID: 26443080	2015
Nestedness across biological scales. PloS one\| PubMed ID: 28166284	2017
miR18a and miR19a Recruit Specific Proteins for Splicing in Thyroid Cancer Cells. Cancer genomics & proteomics\| PubMed ID: 28871004	2017
Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects. Scientific reports\| PubMed ID: 30487551	2018
Proteomic analysis of Trypanosoma cruzi spliceosome complex. Journal of proteomics\| PubMed ID: 32422275	2020
Human Antigen R (HuR) Facilitates miR-19 Synthesis and Affects Cellular Kinetics in Papillary Thyroid Cancer. Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology\| PubMed ID: 35352515	2022

Patricia P. Coltri

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