Evaluating association and transmission of eight inflammatory genes with Viliuisk encephalomyelitis susceptibility

Gut microbiome signature of Viliuisk encephalomyelitis in Yakuts includes an increase in microbes linked to lean body mass and eating behaviour

BACKGROUND

Viliuisk encephalomyelitis (VE) is a rare endemic neurodegenerative disease occurring in the Yakut population of Northeastern Siberia. The main clinical features of VE are spasticity, dysarthria, dementia, central paresis and paralysis, and cortical atrophy observed via MRI. Many hypotheses have been proposed regarding its etiology, including infectious agents, genetics, environmental factors, and immunopathology. Each of these hypotheses has been supported to some extent by epidemiological and experimental data. Nevertheless, none of them has been decisively proven. Gut microbiome is one of the factors that might be involved in VE pathogenesis.

RESULTS

Here we performed a pilot survey of the stool microbiomes of Yakut subjects with VE (n = 6) and without VE (n = 11). 16S rRNA sequencing showed that in comparison with the control group, the Yakuts with VE had increased proportions of Methanobrevibacter and Christensenella, which are reported to be linked to body mass index, metabolism, dietary habits and potentially to neurodegenerative disorders. The identified associations suggest that the microbiome may be involved in VE. Overall, the Yakut microbiome was quite specific in comparison with other populations, such as metropolitan Russians and native inhabitants of the Canadian Arctic.

CONCLUSIONS

Describing the gut microbiome of indigenous human populations will help to elucidate the impact of dietary and environmental factors on microbial community structure and identify risks linked to the lifestyles of such groups as well as endemic diseases.

Greyhound meningoencephalitis: PCR-based detection methods highlight an absence of the most likely primary inducing agents

Greyhound meningoencephalitis is currently classified as a breed-associated idiopathic central nervous system inflammatory disorder. The non-suppurative inflammatory response can be distinguished from the other breed-associated disorders based on histopathology and lesion topography, however the nature of the response primarily suggests a viral infection. In the present study PCR and RT-PCR technologies were employed on frozen cerebral tissue from confirmed cases of meningoencephalitis to target specific viruses and protozoa likely to be implicated and to exclude the presence of bacterial 16SrRNA. Secondly, degenerate primers were used to detect viruses of the herpesvirus and flavivirus families. In addition cerebral tissues were probed for West Nile Virus. Viral nucleic acid sequences to Borna disease virus, to louping ill, tick borne encephalitis, West Nile and other flaviviruses were not detected. Canine distemper virus was detected in one animal with 97% homology to strain A75/15. Degenerate PCR for herpesviruses detected viral amplification products in one animal with 90% homology to canine herpesvirus DNA polymerase gene. Protozoal amplification products were only detected in a single dog with pathological confirmation of a combination of lesions of greyhound meningoencephalitis and a protozoal encephalomyelitis. Neospora was confirmed with sequence homology to Austrian strain 1. Bacterial 16SrRNA was not detected. The present study supports previous observations that many of the known microbial causes of canine meningoencephalitis are not involved. Findings could reflect that the causal agent was not specifically targeted for detection, or that the agent is at undetectable levels or has been eliminated from brain tissue. The potential roles of genetics and of molecular mimicry also cannot be discounted.

Unknown biological mixtures evaluation using STR analytical quantification

Allelic quantification of STRs, where the presence of three or more alleles represents mixtures, provides a novel method to identify mixtures from unknown biological sources. The allelic stutters resulting in slightly different repeat containing products during fragment amplification can be mistaken for true alleles complicating a simple approach to mixture analysis. An algorithm based on the array of estimated stutters from known samples was developed and tuned to maximize the identification of true non-mixtures through the analysis of three pentanucleotide STRs. Laboratory simulated scenarios of needle sharing generated 58 mixture and 38 non-mixture samples that were blinded for determining the number of alleles. Through developing and applying an algorithm that additively estimates stuttering around the two highest peaks, mixtures and non-mixtures were characterized with sensitivity of 77.5, 82.7 and 58% while maintaining the high specificity of 100, 97.4 and 100 for the W, X, and Z STRs individually. When all three STRs were used collectively, the resulting sensitivity and specificity was 91.4 and 97.4%, respectively. The newly validated approach of using multiple STRs as highly informative biomarkers in unknown sample mixture analyses has potential applications in genetics, forensic science, and epidemiological studies.