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Alves, L. G. T.

Publications and source records attributed to Alves, L. G. T..

3 recordsLinked to original sources

Distinct roles for MNK1 and MNK2 in social and cognitive behavior through kinase-specific regulation of the synaptic proteome and phosphoproteome

Local mRNA translation is required for adaptive changes in the synaptic proteome. The mitogen-activated protein kinase (MAPK) interacting protein kinases 1 and 2 (MNK1 and MNK2) have emerged as key regulators of neuronal translation, primarily through phosphorylation of the eukaryotic initiation factor 4E (eIF4E). The therapeutic benefits of targeting the MNKs are being investigated for nervous system conditions and disorders that affect translation, including autism, pain, and cancer. However, it is still unclear if MNK1 and MNK2 regulate distinct aspects of neuronal translation and how the activity of each kinase is associated with the synaptic and behavioral features linked to MNK signaling. To examine the individual neurobiological functions of each kinase, we used knockout mice lacking either MNK1 or MNK2. We found that the knockout of MNK1 and MNK2 leads to different social and cognitive behavioral profiles and distinct alterations of the cortical synaptic proteome, transcriptome, and phosphoproteome. Loss of MNK1 was associated with an increase in ribosomal protein expression, whereas deletion of MNK2 decreased the expression and phosphorylation of synaptic proteins. Together, our findings provide evidence for a high degree of functional specialization of the MNKs in synaptic compartments and suggest that pharmacological inhibition of individual MNK may provide more specific targets for neurological disorders.

neuroscience↗

Rapid UPF1 depletion illuminates the temporal dynamics of the NMD-regulated transcriptome in human cells

The helicase UPF1 acts as the central essential factor in human nonsense-mediated mRNA decay (NMD) and is involved in various other mRNA degradation processes. Given its multifunctionality, distinguishing between mRNAs regulated directly and indirectly by UPF1 remains a critical challenge. We engineered two different conditional degron tags into endogenous UPF1 in human cell lines to probe the consequences of UPF1 rapid depletion. UPF1 degradation inhibits NMD within hours and strongly stabilizes endogenous NMD substrates, which can be classified into different groups based on their expression kinetics. Extended UPF1 depletion results in massive transcript and isoform alterations, partially driven by secondary effects. We define a high-confidence UPF1-regulated core set of transcripts, which consists mostly of NMD substrates. NMD-regulated genes are involved in brain development and the integrated stress response, among other biological processes. In summary, UPF1 degron systems rapidly inhibit NMD, providing valuable insights into its roles across various experimental systems.

molecular biology↗

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamsters response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species. Key PointsO_LINeural networks can successfully match disease states between animal models and humans using single-cell data as shown for COVID-19 C_LIO_LIModerately diseased patients best matched Syrian hamster cells; severely diseased patients best matched Roborovski hamster neutrophils C_LI

immunology↗