Cdx1 and Gsc distinctly regulate the transcription of BMP4 target gene ventx3.2 by directly binding to the proximal promoter region in Xenopus gastrulae.

A comprehensive regulatory network of transcription factors controls the dorsoventral patterning of the body axis in developing vertebrate embryos. Bone morphogenetic protein signaling is essential for activating the Ventx family of homeodomain transcription factors, which regulates embryonic patterning and germ layer identity during Xenopus gastrulation. Although Ventx1.1 and Ventx2.1 of the Xenopus Ventx family have been extensively investigated, Ventx3.2 remains largely understudied. Therefore, this study aimed to investigate the transcriptional regulation of ventx3.2 during the embryonic development of Xenopus. We used goosecoid (Gsc) genome-wide chromatin immunoprecipitation-sequencing data to isolate and replicate the promoter region of ventx3.2. Serial deletion and site-directed mutagenesis were used to identify the cis-acting elements for Gsc and caudal type homeobox 1 (Cdx1) within the ventx3.2 promoter. Cdx1 and Gsc differentially regulated ventx3.2 transcription in this study. Additionally, positive cis-acting and negative response elements were observed for Cdx1 and Gsc, respectively, within the 5' flanking region of the ventx3.2 promoter. This result was corroborated by mapping the active Cdx1 response element (CRE) and Gsc response element (GRE). Moreover, a point mutation within the CRE and GRE completely abolished the activator and repressive activities of Cdx1 and Gsc, respectively. Furthermore, the chromatin immunoprecipitation-polymerase chain reaction confirmed the direct binding of Cdx1 and Gsc to the CRE and GRE, respectively. Inhibition of Cdx1 and Gsc activities at their respective functional regions, namely, the ventral marginal zone and dorsal marginal zone, reversed their effects on ventx3.2 transcription. These results indicate that Cdx1 and Gsc modulate ventx3.2 transcription in the ventral marginal zone and dorsal marginal zone by directly binding to the promoter region during Xenopus gastrulation.

Xbra modulates the activity of linker region phosphorylated Smad1 during Xenopus development.

The Bmp/Smad1 pathway plays a crucial role in developmental processes and tissue homeostasis. Mitogen-activated protein kinase (Mapk)/Erk mediated phosphorylation of Smad1 in the linker region leads to Smad1 degradation, cytoplasmic retention and inhibition of Bmp/Smad1 signaling. While Fgf/Erk pathway has been documented to inhibit Bmp/Smad1 signaling, several studies also suggests the cooperative interaction between these two pathways in different context. However, the precise role and molecular pathway of this collaborative interaction remain obscure. Here, we identified Xbra induced by Fgf/Erk signaling as a factor in a protective mechanism for Smad1. Xbra physically interacted with the linker region phosphorylated Smad1 to make Xbra/Smad1/Smad4 trimeric complex, leading to Smad1 nuclear localization and protecting it from ubiquitin-mediated proteasomal degradation. This interaction of Xbra/Smad1/Smad4 led to sustained nuclear localization of Smad1 and the upregulation of lateral mesoderm genes, while concurrently suppression of neural and blood forming genes. Taken together, the results suggests Xbra-dependent cooperative interplays between Fgf/Erk and Bmp/Smad1 signaling during lateral mesoderm specification in Xenopus embryos.

Sizzled (Frzb3) physically interacts with noncanonical Wnt ligands to inhibit gastrulation cell movement.

The coordinated movement of germ layer progenitor cells reaches its peak at the dorsal side, where the Bmp signaling gradient is low, and minimum at the ventral side, where the Bmp gradient is high. This dynamic cell movement is regulated by the interplay of various signaling pathways. The noncanonical Wnt signaling cascade serves as a pivotal regulator of convergence and extension cell movement, facilitated by the activation of small GTPases such as Rho, Rab, and Rac. However, the underlying cause of limited cell movement at the ventral side remains elusive. To explore the functional role of a key regulator in constraining gastrulation cell movement at the ventral side, we investigated the Bmp4-direct target gene, sizzled (szl), to assess its potential role in inhibiting noncanonical Wnt signaling. In our current study, we demonstrated that ectopic expression of szl led to gastrulation defects in a dose-dependent manner without altering cell fate specification. Overexpression of szl resulted in decreased elongation of Activin-treated animal cap and Keller explants. Furthermore, our immunoprecipitation assay unveiled the physical interaction of Szl with noncanonical Wnt ligand proteins (Wnt5 and Wnt11). Additionally, the activation of small GTPases involved in Wnt signaling mediation (RhoA and Rac1) was diminished upon szl overexpression. In summary, our findings suggest that Bmp4 signaling negatively modulates cell movement from the ventral side of the embryo by inducing szl expression during early Xenopus gastrulation.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.