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1.
IUBMB Life ; 75(5): 411-426, 2023 05.
Article in English | MEDLINE | ID: mdl-36057100

ABSTRACT

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also noncoding RNAs. Here, we introduce the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic noncoding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD, and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to the alteration of interaction sites with proteins involved in phase separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive.


Subject(s)
Adenosine , RNA, Long Noncoding , Humans , Adenosine/chemistry , RNA, Untranslated/genetics , RNA, Messenger/genetics , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , Inosine/metabolism
2.
Nucleic Acids Res ; 48(D1): D511-D516, 2020 01 08.
Article in English | MEDLINE | ID: mdl-31665505

ABSTRACT

Bacterial infections have been on the rise world-wide in recent years and have a considerable impact on human well-being in terms of attributable deaths and disability-adjusted life years. Yet many mechanisms underlying bacterial pathogenesis are still poorly understood. Here, we introduce the BacFITBase database for the systematic characterization of bacterial proteins relevant for host infection aimed to enable the identification of new antibiotic targets. BacFITBase is manually curated and contains more than 90 000 entries with information on the contribution of individual genes to bacterial fitness under in vivo infection conditions in a range of host species. The data were collected from 15 different studies in which transposon mutagenesis was performed, including top-priority pathogens such as Acinetobacter baumannii and Campylobacter jejuni, for both of which increasing antibiotic resistance has been reported. Overall, BacFITBase includes information on 15 pathogenic bacteria and 5 host vertebrates across 10 different tissues. It is freely available at www.tartaglialab.com/bacfitbase.


Subject(s)
Bacterial Infections/microbiology , Databases, Genetic , Genes, Bacterial , Software , Animals , Bacteria/genetics , Bacteria/pathogenicity , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Humans , Virulence/genetics
3.
Cell Mol Life Sci ; 72(24): 4795-805, 2015 Dec.
Article in English | MEDLINE | ID: mdl-26403788

ABSTRACT

Systemic inflammation and infections are associated with neurodegenerative diseases. Unfortunately, the molecular bases of this link are still largely undiscovered. We, therefore, review how inflammatory processes can imbalance membrane homeostasis and theorize how this may have an effect on the aggregation behavior of the proteins implicated in such diseases. Specifically, we describe the processes that generate such imbalances at the molecular level, and try to understand how they affect protein folding and localization. Overall, current knowledge suggests that microglia pro-inflammatory mediators can generate membrane damage, which may have an impact in terms of triggering or accelerating disease manifestation.


Subject(s)
Cell Membrane/metabolism , Inflammation/pathology , Neurodegenerative Diseases/pathology , Alzheimer Disease/metabolism , Alzheimer Disease/pathology , Amyotrophic Lateral Sclerosis/metabolism , Amyotrophic Lateral Sclerosis/pathology , Cell Membrane/ultrastructure , Homeostasis , Humans , Huntington Disease/metabolism , Huntington Disease/pathology , Inflammation/metabolism , Models, Biological , Neurodegenerative Diseases/metabolism , Parkinson Disease/pathology , Protein Aggregation, Pathological , Signal Transduction
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