Lifetime of ground conformational state determines the activity of structured RNA.

Biomolecules continually sample alternative conformations. Consequently, even the most energetically favored ground conformational state has a finite lifetime. Here, we show that, in addition to the 3D structure, the lifetime of a ground conformational state determines its biological activity. Using hydrogen-deuterium exchange nuclear magnetic resonance spectroscopy, we found that Zika virus exoribonuclease-resistant RNA (xrRNA) encodes a ground conformational state with a lifetime that is ~105-107 longer than that of canonical base pairs. Mutations that shorten the apparent lifetime of the ground state without affecting its 3D structure decreased exoribonuclease resistance in vitro and impaired virus replication in cells. Additionally, we observed this exceptionally long-lived ground state in xrRNAs from diverse infectious mosquito-borne flaviviruses. These results demonstrate the biological significance of the lifetime of a preorganized ground state and further suggest that elucidating the lifetimes of dominant 3D structures of biomolecules may be crucial for understanding their behaviors and functions.

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry.

Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.

Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study.

BACKGROUND: Living kidney donation currently constitutes approximately a quarter of all kidney donations. There exist barriers that preclude prospective donors from donating, such as medical ineligibility and costs associated with donation. A better understanding of perceptions of and barriers to living donation could facilitate the development of effective policies, education opportunities, and outreach strategies and may lead to an increased number of living kidney donations. Prior research focused predominantly on perceptions and barriers among a small subset of individuals who had prior exposure to the donation process. The viewpoints of the general public have rarely been represented in prior research. OBJECTIVE: The current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. The resultant data set was made available in the public domain to facilitate further investigation of this topic. METHODS: We collected comments using Python-based web-scraping tools from the New York Times, YouTube, Twitter, and Reddit. We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning. RESULTS: A total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation. CONCLUSIONS: The current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation.

FMRP and MOV10 regulate Dicer1 expression and dendrite development.

Fragile X syndrome results from the loss of expression of the Fragile X Mental Retardation Protein (FMRP). FMRP and RNA helicase Moloney Leukemia virus 10 (MOV10) are important Argonaute (AGO) cofactors for miRNA-mediated translation regulation. We previously showed that MOV10 functionally associates with FMRP. Here we quantify the effect of reduced MOV10 and FMRP expression on dendritic morphology. Murine neurons with reduced MOV10 and FMRP phenocopied Dicer1 KO neurons which exhibit impaired dendritic maturation Hong J (2013), leading us to hypothesize that MOV10 and FMRP regulate DICER expression. In cells and tissues expressing reduced MOV10 or no FMRP, DICER expression was significantly reduced. Moreover, the Dicer1 mRNA is a Cross-Linking Immunoprecipitation (CLIP) target of FMRP Darnell JC (2011), MOV10 Skariah G (2017) and AGO2 Kenny PJ (2020). MOV10 and FMRP modulate expression of DICER1 mRNA through its 3'untranslated region (UTR) and introduction of a DICER1 transgene restores normal neurite outgrowth in the Mov10 KO neuroblastoma Neuro2A cell line and branching in MOV10 heterozygote neurons. Moreover, we observe a global reduction in AGO2-associated microRNAs isolated from Fmr1 KO brain. We conclude that the MOV10-FMRP-AGO2 complex regulates DICER expression, revealing a novel mechanism for regulation of miRNA production required for normal neuronal morphology.

Antiviral Effector RTP4 Bats against Flaviviruses.

Bats harbor diverse viruses and manifest distinct antiviral immune responses. Recently in Cell Host & Microbe, Boys et al. demonstrated that bat receptor transporter protein 4 (RTP4) is an innate antiviral effector that inhibits flavivirus replication, revealing an evolutionary arms race between flaviviruses and their hosts.

Macrophage Tropism in Pathogenic HIV-1 and SIV Infections.

Most myeloid lineage cells express the receptor and coreceptors that make them susceptible to infection by primate lentiviruses (SIVs and HIVs). However, macrophages are the only myeloid lineage cell commonly infected by SIVs and/or HIVs. The frequency of infected macrophages varies greatly across specific host and virus combinations as well as disease states, with infection rates being greatest in pathogenic SIV infections of non-natural hosts (i.e., Asian nonhuman primates (Asian NHPs)) and late in untreated HIV-1 infection. In contrast, macrophages from natural SIV hosts (i.e., African NHPs) are largely resistant to infection due to entry and/or post-entry restriction mechanisms. These highly variable rates of macrophage infection may stem from differences in the host immune environment, entry and post-entry restriction mechanisms, the ability of a virus to adapt to efficiently infect macrophages, and the pleiotropic effects of macrophage-tropism including the ability to infect cells lacking CD4 and increased neutralization sensitivity. Questions remain about the relationship between rates of macrophage infection and viral pathogenesis, with some evidence suggesting that elevated levels of macrophage infection may contribute to greater pathogenesis in non-natural SIV hosts. Alternatively, extensive infection of macrophages may only emerge in the context of high viral loads and immunodeficiency, making it a symptom of highly pathogenic infections, not a primary driver of pathogenesis.

The FMRP-MOV10 complex: a translational regulatory switch modulated by G-Quadruplexes.

The Fragile X Mental Retardation Protein (FMRP) is an RNA binding protein that regulates translation and is required for normal cognition. FMRP upregulates and downregulates the activity of microRNA (miRNA)-mediated silencing in the 3' UTR of a subset of mRNAs through its interaction with RNA helicase Moloney leukemia virus 10 (MOV10). This bi-functional role is modulated through RNA secondary structures known as G-Quadruplexes. We elucidated the mechanism of FMRP's role in suppressing Argonaute (AGO) family members' association with mRNAs by mapping the interacting domains of FMRP, MOV10 and AGO and then showed that the RGG box of FMRP protects a subset of co-bound mRNAs from AGO association. The N-terminus of MOV10 is required for this protection: its over-expression leads to increased levels of the endogenous proteins encoded by this co-bound subset of mRNAs. The N-terminus of MOV10 also leads to increased RGG box-dependent binding to the SC1 RNA G-Quadruplex and is required for outgrowth of neurites. Lastly, we showed that FMRP has a global role in miRNA-mediated translational regulation by recruiting AGO2 to a large subset of RNAs in mouse brain.