Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

The beauty of the beast: Suggestions to curb the excesses of dog breeding and restore animal welfare - Invited review

Dog. Specifically created to save its master's life. - (The dog is the ideal) Friend of man, (because it is his devoted slave) (source: Gustave Flaubert, Dictionnaire des Idées Reçues). But is man the best friend of the dog? This question is legitimate when we consider living situations to which modern domestic dogs are exposed. They often do not satisfy basic animal needs. In this narrative review, the author revisits the history of the dog's presence alongside humans, in the light of current knowledge. The modern dog (breed standards and their interests in canine research) and its breeding strategy, including extreme breeding, will then be given particular attention. Dysfunctional human psychological processes will be explored to make it possible to grasp why the breeding of the modern dog is undergoing such a transformation. Finally, based on these factual and conceptual insights, suggestions to improve canine welfare will be proposed. To be effective, all these must be assessed against real-world conditions.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Ovarian Cancer: Applications of Chickens to Humans

The lack of preclinical models of spontaneous ovarian cancer (OVCA), a fatal gynecological malignancy, is a significant barrier to generating information on early changes indicative of OVCA. In contrast to rodents, laying hens develop OVCA spontaneously, with remarkable similarities to OVCA in women regarding tumor histology, OVCA dissemination, immune responses, and risk factors. These important features of OVCA will be useful to develop an early detection test for OVCA, which would significantly reduce mortality rates; preventive strategies; immunotherapeutics; prevention of resistance to chemotherapeutics; and exploration of gene therapies. A transvaginal ultrasound (TVUS) imaging method for imaging of hen ovarian tumors has been developed. Hens can be monitored prospectively by using serum markers, together with TVUS imaging, to detect early-stage OVCA, provided that a panel of serum markers can be established and imaging agents developed. Recent sequencing of the chicken genome will further facilitate the hen model to explore gene therapies against OVCA.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

Hereditary spinocerebellar degeneration (SCD) encompasses an expanding list of rare diseases with a broad clinical and genetic heterogeneity, complicating their diagnosis and management in daily clinical practice. Correct diagnosis is a pillar for precision medicine, a branch of medicine that promises to flourish with the progressive improvements in studying the human genome. Discovering the genes causing novel Mendelian phenotypes contributes to precision medicine by diagnosing subsets of patients with previously undiagnosed conditions, guiding the management of these patients and their families, and enabling the discovery of more causes of Mendelian diseases. This new knowledge provides insight into the biological processes involved in health and disease, including the more common complex disorders. This review discusses the evolution of the clinical and genetic approaches used to diagnose hereditary SCD and the potential of new tools for future discoveries.