Pericytes Extend Survival of ALS SOD1 Mice and Induce the Expression of Antioxidant Enzymes in the Murine Model and in IPSCs Derived Neuronal Cells from an ALS Patient.

Amyotrophic Lateral Sclerosis (ALS) is one of the most common adult-onset motor neuron disease causing a progressive, rapid and irreversible degeneration of motor neurons in the cortex, brain stem and spinal cord. No effective treatment is available and cell therapy clinical trials are currently being tested in ALS affected patients. It is well known that in ALS patients, approximately 50% of pericytes from the spinal cord barrier are lost. In the central nervous system, pericytes act in the formation and maintenance of the blood-brain barrier, a natural defense that slows the progression of symptoms in neurodegenerative diseases. Here we evaluated, for the first time, the therapeutic effect of human pericytes in vivo in SOD1 mice and in vitro in motor neurons and other neuronal cells derived from one ALS patient. Pericytes and mesenchymal stromal cells (MSCs) were derived from the same adipose tissue sample and were administered to SOD1 mice intraperitoneally. The effect of the two treatments was compared. Treatment with pericytes extended significantly animals survival in SOD1 males, but not in females that usually have a milder phenotype with higher survival rates. No significant differences were observed in the survival of mice treated with MSCs. Gene expression analysis in brain and spinal cord of end-stage animals showed that treatment with pericytes can stimulate the host antioxidant system. Additionally, pericytes induced the expression of SOD1 and CAT in motor neurons and other neuronal cells derived from one ALS patient carrying a mutation in FUS. Overall, treatment with pericytes was more effective than treatment with MSCs. Our results encourage further investigations and suggest that pericytes may be a good option for ALS treatment in the future. Graphical Abstract ᅟ.

Competing regression models for longitudinal data.

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.