5,10-methylenetetrahydrofolate reductase (MTHFR) deficiency is a rare hereditary disease characterized by defects in folate and homocysteine metabolism. Individuals with inherited
MTHFR
gene mutations have a higher tendency to develop neurodegeneration disease as Alzheimer’ disease and atherosclerosis. MTHFR is a rate-limiting enzyme catalyzing folate production, various SNPs/mutations in the
MTHFR
gene have been correlated to MTHFR deficiency. However, the molecular mechanisms underpinning the pathogenic effects of these SNPs/mutations have not been clearly understood. In the present study, we reported a severe MTHFR deficiency patient with late-onset motor dysfunction and sequenced
MTHFR
gene exons of the family. The patient carries an MD-associating SNP (rs748289202) in one
MTHFR
allele and the rs545086633 SNP with unknown disease relevance in the other. The rs545086633 SNP (p.Leu439Pro) results in an L439P substitution in MTHFR protein, and drastically decreases mutant protein expression by promoting proteasomal degradation. L
439
in MTHFR is highly conserved in vertebrates. Our study demonstrated that p.Leu439Pro in
MTHFR
is the first mutation causing significant intracellular defects of MTHFR, and rs545086633 should be examined for the in-depth diagnosis and treatment of MD.
The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.
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