Novel in-frame duplication variant characterization in late infantile metachromatic leukodystrophy using whole-exome sequencing and molecular dynamics simulation.

Metachromatic leukodystrophy (MLD) is a neurodegenerative lysosomal storage disease caused by a deficiency in the arylsulfatase A (ARSA). ARSA deficiency leads to sulfatide accumulation, which involves progressive demyelination. The profound impact of early diagnosis on MLD treatment options necessitates the development of new or updated analysis tools and approaches. In this study, to identify the genetic etiology in a proband from a consanguineous family with MLD presentation and low ARSA activity, we employed Whole-Exome Sequencing (WES) followed by co-segregation analysis using Sanger sequencing. Also, MD simulation was utilized to study how the variant alters the structural behavior and function of the ARSA protein. GROMACS was applied and the data was analyzed by RMSD, RMSF, Rg, SASA, HB, atomic distance, PCA, and FEL. Variant interpretation was done based on the American College of Medical Genetics and Genomics (ACMG) guidelines. WES results showed a novel homozygous insertion mutation, c.109_126dup (p.Asp37_Gly42dup), in the ARSA gene. This variant is located in the first exon of ARSA, fulfilling the criteria of being categorized as likely pathogenic, according to the ACMG guidelines and it was also found to be co-segregating in the family. The MD simulation analysis revealed this mutation influenced the structure and the stabilization of ARSA and led to the protein function impairment. Here, we report a useful application of WES and MD to identify the causes of a neurometabolic disorder.

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.

BACKGROUND: The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. METHODS: In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. RESULTS: Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2484 new confirmed and 114 new death cases of COVID-19. CONCLUSION: According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

The effect of TGF-beta2 on MMP-2 production and activity in highly metastatic human bladder carcinoma cell line 5637.

Transforming growth factor-beta (TGF-beta) superfamily regulates matrix metalloproteinases (MMP), which intrinsically regulate various cell behaviors leading to metastasis. We investigated the effect of TGF-beta(2) on MMP-2 regulation in human bladder carcinoma cell line 5637. Zymography, ELISA, and real-time polymerase chain reaction revealed that TGF-beta(2) stimulated MMP-2 production, but the transcription of its gene remained unchanged. Wortmannin could not inhibit MMP-2 secretion and activity and conversely the amount of the protein and its enzymatic activity were increased. These data suggest that TGF-beta(2) increased MMP-2 at the posttranscriptional level and this upregulation was independent of phosphatidylinositol 3-kinase signaling pathway.