CD44 glycoprotein is ubiqutously present and situated at the extracellular surface of cell. Its role as an adhesion recptor was modified depending upon the state of post addition of glycomoeties under varied microenvironment. Many alternative spliced variants of CD44 were expressed by human cells depending upon the attainment during mutifunctional signal disposition. The function of splice factors, and hnRNPA family (Heterogenous ribonucleoproteins A) were dicussed in context to CD44 alternative variants. Glioma cell cline U87 MG were used in this study to search more about the affect of hnRNPA1 and hnRNPA2/B1 knockdown in CD44 V6 and CD44V10 expression. We found in preliminary study that splice factor knock down reduces the alternative splice variants of CD44 such as V6 & V10. Therefore, potential of this study to elaborate further the mechanism in vivo model, to validate the alternative spliced variant in context to interaction with extracellular microenvironment.

PitNETs, although typically benign, comprise a small percentage of therapy-resistant aggressive, occasionally metastatic tumors, underpinning a need to identify novel therapeutic targets. PitNETs carry few mutations but associate with conditions that promote alternative splicing as an oncogenic alternative and express the TrkA neurotrophin receptor, which exhibits oncogenic alternative TrkAIII splicing in other tumors. Alternative TrkAIII splicing was assessed by RT-PCR in 53 PitNETs and compared to HIF1a, HIF2a, SF3B1, SRSF2, U2AF1 and JCPyV large T antigen mRNA expression, unconventional Xbp1 splicing, SF3B1 mutation, and TrkA isoform(s) immunoreactivity by confocal immunofluorescence (IF). Alternative TrkAIII mRNA splicing was detected in all invasive PitNET phenotypes, was significantly elevated in invasive compared to non-invasive PIT1 PitNETs, relatively high in invasive and non-invasive SF1 and TPIT PitNETs, and was associated with IF consistent with intracellular TrkAIII activation but not with hotspot SF3B1 mutations, altered SF3B1, SRSF2 and U2AF1 splice factor mRNA expression, JCPyV large T antigen expression or unconventional Xbp1 splicing. A potential role for hypoxia in TrkAIII mRNA splicing was supported by significant elevation of HIF2a mRNA expression in invasive PIT1 PitNETs. These data demonstrate that alternative TrkAIII splicing, potentially promoted by hypoxia, occurs frequently in PitNETs and may associate with IF consistent with intracellular TrkAIII activation, supporting a potential role for TrkAIII in PitNET pathogenesis and progression, identifying TrkAIII as a novel potential target in refractory PitNETs.

The androgen/androgen receptor (AR)-signaling axis plays a central role in the development and growth of prostate cancer (PCa) cells. Upon androgen-binding the AR dimerizes with another AR, translocates into the nucleus where the AR-dimer activates/inactivates androgen-dependent genes. In consequence treatments for locally advanced or metastatic PCa are commonly based on androgen deprivation therapies (ADT). Unfortunately, the clinical benefits of ADT are only transitory and most tumors develop mechanisms allowing the AR to bypass its need for physiological levels of circulating androgens. In the clinic failure of ADT is often characterized by the synthesis of a C-terminally truncated, constitutively active AR splice variant, termed AR-V7. In contrast to AR, the constitutively active AR-V7 does no longer need androgenic stimuli for nuclear entry and/or dimerization. The goal of the present study was to mechanistically decipher the interaction between full-length AR (AR-FL) and AR-V7 in AR-null HEK-293 cells using the NanoLuc Binary Technology (NanoBiT) structural complementation assay under androgen stimulation and deprivation conditions. Our data point toward a hypothesis that AR-FL/AR-FL homodimers form in the cytoplasm, whereas AR-V7/AR-V7 localize in the nucleus. However, after 15 min of androgen stimulation, all AR-FL/AR-FL, AR-FL/AR-V7 and AR-V7/AR-V7 dimers localized in the nucleus. In this way, we can show an androgen-regulated interaction between AR-FL and AR-V7 at forming heterodimers that localize in the nucleus, whereas AR-V7/AR-V7 dimers were found to localize in the absence of androgens in the nucleus. Treatment with enzalutamide diminished the luminescence of AR-FL homodimers and AR-FL/AR-V7 heterodimers but not AR-V7/AR-V7 homodimers.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

Spinal and bulbar muscular atrophy (SBMA), also known as Kennedy’s disease, is a debilitating neuromuscular disease characterized by progressive muscular weakness and neuronal degeneration, affecting 1-2 individuals per 100,000 globally. While SBMA is relatively rare, recent studies have shown a significantly higher prevalence of the disease within the indigenous population of Western Canada compared to the general population. The disease is caused by a pathogenic expansion of polyglutamine residues in the androgen receptor protein, which acts as a key transcriptional regulator for numerous genes. SBMA has no cure, and current treatments are primarily supportive and focused on symptom management. Recently, a form of precision medicine known as antisense therapy has gained traction as a promising therapeutic option for numerous neuromuscular diseases. Antisense therapy uses small synthetic oligonucleotides to confer therapeutic benefit by acting on pathogenic mRNA molecules, serving to either degrade pathogenic mRNA transcripts or helping to modulate splicing. Recent studies have explored the suitability of antisense therapy for the treatment of SBMA, primarily focused on antisense-mediated mRNA knockdown approaches. Advancements in understanding the pathogenesis of SBMA and the development of targeted therapies offer hope for improved quality of life for individuals affected by this debilitating condition. Continued research is essential to optimize these genetic approaches, ensuring their safety and efficacy.

Antisense oligonucleotide (ASO)-mediated exon skipping has become a valuable tool for investigating gene function and developing gene therapy. Machine learning-based computational methods such as eSkip-Finder have been developed to predict the efficacy of ASOs via exon skipping. However, these methods are computationally demanding, and the accuracy of predictions remains suboptimal. In this study, we propose a new approach to reduce computational burden and improve prediction performance by using feature selection within machine learning algorithms and ensemble learning techniques. We evaluated our approach using a dataset of experimentally validated exon skipping events, dividing it into training and testing sets. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. Our findings suggest that our approach has the potential to enhance the accuracy and efficiency of predicting ASO efficacy via exon skipping. It could also facilitate the development of novel therapeutic strategies. This study could contribute to the ongoing efforts to improve ASO design and optimize gene therapy approaches.

Antisense oligonucleotide (ASO)-mediated exon skipping has emerged as a powerful tool for examining the function of genes and exons in basic research, as well as gene therapy. Computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping using machine learning. However, these methods can be computationally demanding and the prediction accuracy of the tool is not yet optimal. In this study, we propose an approach to reduce computational burden and improve prediction performance by utilizing feature selection within machine learning algorithms and employing ensemble learning techniques. The method was evaluated using a dataset of genes with experimentally validated exon skipping events. The dataset was divided into training and testing sets to assess the accuracy of the algorithm. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. These findings suggest that this approach has the potential to enhance the efficiency and accuracy of predicting ASO efficacy via exon skipping, facilitating the development of novel therapeutic strategies.