Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer’s Disease Mice

Background: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer’s disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord. Objective: Analysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition. Methods: T-tests with Bonferroni correction (significance = p < 0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t–) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t–, t0), mouse age, and mouse gender (male = g_m; female = g_f, mixed gender = g_mi). Results: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t–, g_f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]. Conclusion: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.

Dominant Effect Of Host Genetics On Skin Microbiota Composition In Homeostasis And Wound Healing

Animal microbiota are shaped and maintained not only through microbiota-environmental interactions but also through host-microbiota interactions. The effects of the microbiota on the host has been the source of intense research in recent years, indicating a role for resident microbes in a range of conditions from obesity and mood disorders to atopic dermatitis and chronic wounds. Yet the ability of hosts to determine their microbiota composition is less well studied. In this study, we investigated the role host genetics plays in determining skin microbiota. We used 30 different mouse strains from the advanced recombinant inbred mouse panel, the Collaborative Cross, with PERMANOVA, GWAS and PCA-based GWAS analyses to demonstrate that murine skin microbiota composition is strongly dependent on murine strain. In particular, a quantitative trait locus on chromosome 4 associates both with Staphylococcus abundance and principal-component multi-trait analyses. Additionally, we used a full thickness excisional wound healing model to investigate the relative contributions from the skin microbiota and/or host genetics on wound healing speed. Wound associated changes in skin microbiota composition were observed and were in many instances host-specific. Despite reaching statistical significance, the wound-associated changes in skin microbiota accounted for only a small amount of the variance in wound healing speeds, with the majority attributable to mouse genotype (strain) and age. Host genetics has a significant impact on the skin microbiota composition during both homeostasis and wound healing. These findings have long reaching implications in our understanding of associations between microbiota dysbiosis and disease.

Response of immunocompetent cells of bone marrow and spleen of mouse males of several strains to stress and to pyrazine containing chemosignals

The quantity of antibody producing cells and mitotic disturbances in dividing bone marrow cells of mice were studied after exposure of animals to a physical stressor or various pyrazinecontaining chemosignals. Several different strains of mice were used. We demonstrate that immune suppression and destabilization of the chromosome apparatus in dividing cells depend on: а) mouse genotype and b) side chains position in the pyrazine ring. Importance of this effects in the light of wide usage of pyrazine containing substances in perfume industry, food production and pharmacology is discussed.

FVB mouse genotype confers susceptibility to OVE26 diabetic albuminuria

OVE26 (OVE) diabetic mice on the inbred strain FVB are a valuable model of diabetic nephropathy that excretes the highest amount of urine albumin of all diabetic mouse models. Crossing of OVE mice to C57BL6 or DBA2 mice reduced albuminuria 17-fold in F1 diabetic offspring without reducing diabetes. When comparing renal histology of OVE mice on the FVB background to F1 C57BL6 crosses, we found that the F1 kidneys had significantly smaller glomeruli, much less albumin accumulation in tubules, reduced mesangial matrix expansion, and less interstitial fibrosis. A genome scan of 108 OVE-positive N2 offspring for albuminuria revealed one significant peak on chromosome 11 and nearly significant peaks on chromosomes 9, 13, and 19. Homozygosity for the FVB genotype for peaks on chromosomes 11, 13, or 19 increased albuminuria. Homozygosity for the chromosome 9 peak reduced albuminuria. Combined homozyogosity for the peaks on chromosomes 11, 13, and 19 increased albuminuria over 12-fold and accounted for >70% of the difference between OVE mice on the FVB vs. the F1 background. These loci contain sequences important to susceptibility to diabetic albuminuria.

Cryptosporidium spp. in Wild, Laboratory, and Pet Rodents in China: Prevalence and Molecular Characterization

ABSTRACT To understand the prevalence of Cryptosporidium infection in rodents in China and to assess the potential role of rodents as a source for human cryptosporidiosis, 723 specimens from 18 rodent species were collected from four provinces of China and examined between August 2007 and December 2008 by microscopy after using Sheather's sugar flotation and modified acid-fast staining. Cryptosporidium oocysts were detected in 83 specimens, with an overall prevalence of 11.5%. Phodopus sungorus, Phodopus campbelli, and Rattus tanezumi were new reported hosts of Cryptosporidium. The genotypes and subtypes of Cryptosporidium strains in microscopy-positive specimens were further identified by PCR and sequence analysis of the small subunit rRNA and the 60-kDa glycoprotein (gp60) genes. In addition to Cryptosporidium parvum, C. muris, C. andersoni, C. wrairi, ferret genotype, and mouse genotype I, four new Cryptosporidium genotypes were identified, including the hamster genotype, chipmunk genotype III, and rat genotypes II and III. Mixed Cryptosporidium species/genotypes were found in 10.8% of Cryptosporidium-positive specimens. Sequence analysis of the gp60 gene showed that C. parvum strains in pet Siberian chipmunks and hamsters were all of the subtype IIdA15G1, which was found previously in a human isolate in The Netherlands and lambs in Spain. The gp60 sequences of C. wrairi and the Cryptosporidium ferret genotype and mouse genotype I were also obtained. These findings suggest that pet rodents may be potential reservoirs of zoonotic Cryptosporidium species and subtypes.

Phylogenetic analysis ofCryptosporidiumisolates in Henan

The functional mitochondrial protein alternative oxidase (AOX) gene was used as a marker to analyse the phylogenetic relationship betweenCryptosporidiumisolates. This gene was characterized, and the phylogentic tree was established fromCryptosporidiumisolates and compared to those generated from 18S rRNA and heat-shock protein 70 (HSP70) gene sequences. The present trial aimed at finding out whether the AOX gene is suitable for phylogenetic analysis of theCryptosporidiumgenus. The results revealed that the genusCryptosporidiumcontained the phylogenetically distinct speciesC. parvum,C. hominis,C. suisandC. baileyi, which were consistent with the biological characterization and host specificity reported earlier.Cryptosporidiumspecies formed two clades: one includedC. hominis,C. suis,C. parvumcattle genotypes andC. parvummouse genotype; and the other comprisedC. meleagridisandC. baileyiisolates. WithinC. parvum, both the mouse genotype and the pig genotype I (also known asC. suis) isolates differed from cattle and human (also known asC. hominis) genotypes, based on the aligned nucleotide sequences. The sequence identity of the AOX gene was higher betweenC. meleagridisandC. baileyithan betweenC. meleagridisandC. parvum. The phylogenetic trees showed thatC. meleagridiswas closer toC. baileyithan toC. parvum. This result was inconsistent with the phylogenetic analysis deduced from 18S rRNA and HSP70 gene sequences, respectively. The present results suggest that the AOX gene is not only equally suitable for the phylogenetic analysis ofCryptosporidium, but also provides an outstanding and new approach in determiningCryptosporidiumheredity.