FTD-associated behavioural and transcriptomic abnormalities in 'humanized' progranulin-deficient mice: A novel model for progranulin-associated FTD.

Frontotemporal dementia (FTD) is an early onset dementia characterized by neuropathology and behavioural changes. A common genetic cause of FTD is haploinsufficiency of the gene progranulin (GRN). Mouse models of progranulin deficiency have provided insight into progranulin neurobiology, but the description of phenotypes with preclinical relevance has been limited in the currently available heterozygous progranulin-null mice. The identification of robust and reproducible FTD-associated behavioural, neuropathological, and biochemical phenotypes in progranulin deficient mice is a critical step in the preclinical development of therapies for FTD. In this work, we report the generation of a novel, 'humanized' mouse model of progranulin deficiency that expresses a single, targeted copy of human GRN in the absence of mouse progranulin. We also report the in-depth, longitudinal characterization of humanized progranulin-deficient mice and heterozygous progranulin-null mice over 18 months. Our analysis yielded several novel progranulin-dependent physiological and behavioural phenotypes, including increased marble burying, open field hyperactivity, and thalamic microgliosis in both models. RNAseq analysis of cortical tissue revealed an overlapping profile of transcriptomic dysfunction. Further transcriptomic analysis offers new insights into progranulin neurobiology. In sum, we have identified several consistent phenotypes in two independent mouse models of progranulin deficiency that are expected to be useful endpoints in the development of therapies for progranulin-deficient FTD. Furthermore, the presence of the human progranulin gene in the humanized progranulin-deficient mice will expedite the development of clinically translatable gene therapy strategies.

Fecal Microbiota Transplant in a Pre-Clinical Model of Type 2 Diabetes Mellitus, Obesity and Diabetic Kidney Disease.

Diabetes mellitus (DM) burden encompasses diabetic kidney disease (DKD), the leading cause of end-stage renal disease worldwide. Despite compelling evidence indicating that pharmacological intervention curtails DKD progression, the search for non-pharmacological strategies can identify novel targets for drug development against metabolic diseases. One of those emergent strategies comprises the modulation of the intestinal microbiota through fecal transplant from healthy donors. This study sought to investigate the benefits of fecal microbiota transplant (FMT) on functional and morphological parameters in a preclinical model of type 2 DM, obesity, and DKD using BTBRob/ob mice. These animals develop hyperglycemia and albuminuria in a time-dependent manner, mimicking DKD in humans. Our main findings unveiled that FMT prevented body weight gain, reduced albuminuria and tumor necrosis factor-α (TNF-α) levels within the ileum and ascending colon, and potentially ameliorated insulin resistance in BTBRob/ob mice. Intestinal structural integrity was maintained. Notably, FMT was associated with the abundance of the succinate-consuming Odoribacteraceae bacteria family throughout the intestine. Collectively, our data pointed out the safety and efficacy of FMT in a preclinical model of type 2 DM, obesity, and DKD. These findings provide a basis for translational research on intestinal microbiota modulation and testing its therapeutic potential combined with current treatment for DM.

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence.