Introduction

Lactate is an important signaling molecule with autocrine, paracrine and endocrine properties involved in multiple biological processes including regulation of gene expression and metabolism. Levels of lactate are increased chronically in diseases associated with cardiometabolic disease such as heart failure, type 2 diabetes, and cancer. Using neonatal ventricular myocytes, we tested the hypothesis that chronic lactate exposure could decrease the activity of cardiac mitochondria that could lead to metabolic inflexibility in the heart and other tissues.

Methods

Neonatal rat ventricular myocytes (NRVMs) were treated for 48 h with 5, 10, or 20 mM lactate and CPT I and II activities were tested using radiolabelled assays. The molecular species profile of the major mitochondrial phospholipid, cardiolipin, was determined using electrospray ionization mass spectrometry along with reactive oxygen species (ROS) levels measured by Amplex Red and mitochondrial oxygen consumption using the Seahorse analyzer.

Results

CPT I activity trended downward (p = 0.07) and CPT II activity significantly decreased with lactate exposure (p < 0.001). Cardiolipin molecular species containing four 18 carbon chains (72 carbons total) increased with lactate exposure, but species of other sizes decreased significantly. Furthermore, ROS production was strongly enhanced with lactate (p < 0.001) and mitochondrial ATP production and maximal respiration were both significantly down regulated with lactate exposure (p < 0.05 and p < 0.01 respectively).

Conclusions

Chronic lactate exposure in cardiomyocytes leads to a decrease in fatty acid transport, alterations of cardiolipin remodeling, increases in ROS production and decreases in mitochondrial oxygen consumption that could have implications for both metabolic health and flexibility. The possibility that both intra-, or extracellular lactate levels play roles in cardiometabolic disease, heart failure, and other forms of metabolic inflexibility needs to be assessed in vivo.

Segmental duplications (SDs) are a class of long, repetitive DNA elements whose paralogs share a high level of sequence similarity with each other. SDs mediate chromosomal rearrangements that lead to structural variation in the general population as well as genomic disorders associated with multiple congenital anomalies, including the 7q11.23 (Williams-Beuren Syndrome, WBS), 15q13.3, and 16p12.2 microdeletion syndromes. Population-level characterization of SDs has generally been lacking because most techniques used for analyzing these complex regions are both labor and cost intensive. In this study, we have used a high-throughput technique to genotype complex structural variation with a single molecule, long-range optical mapping approach. We characterized SDs and identified novel structural variants (SVs) at 7q11.23, 15q13.3, and 16p12.2 using optical mapping data from 154 phenotypically normal individuals from 26 populations comprising five super-populations. We detected several novel SVs for each locus, some of which had significantly different prevalence between populations. Additionally, we localized the microdeletion breakpoints to specific paralogous duplicons located within complex SDs in two patients with WBS, one patient with 15q13.3, and one patient with 16p12.2 microdeletion syndromes. The population-level data presented here highlights the extreme diversity of large and complex SVs within SD-containing regions. The approach we outline will greatly facilitate the investigation of the role of inter-SD structural variation as a driver of chromosomal rearrangements and genomic disorders.

Costello syndrome (CS) is a RASopathy caused by activating germline mutations in HRAS. Due to ubiquitous HRAS gene expression, CS affects multiple organ systems and individuals are predisposed to cancer. Individuals with CS may have distinctive craniofacial features, cardiac anomalies, growth and developmental delays, as well as dermatological, orthopedic, ocular, and neurological issues; however, considerable overlap with other RASopathies exists. Medical evaluation requires an understanding of the multifaceted phenotype. Subspecialists may have limited experience in caring for these individuals because of the rarity of CS. Furthermore, the phenotypic presentation may vary with the underlying genotype. These guidelines were developed by an interdisciplinary team of experts in order to encourage timely health care practices and provide medical management guidelines for the primary and specialty care provider, as well as for the families and affected individuals across their lifespan. These guidelines are based on expert opinion and do not represent evidence-based guidelines due to the lack of data for this rare condition.

PURPOSE: Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. METHODS: We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. RESULTS: Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. CONCLUSION: Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of unaffected relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.

The RASopathies are a group of disorders due to variations of genes associated with the Ras/MAPK pathway. Some of the RASopathies include neurofibromatosis type 1 (NF1), Noonan syndrome, Noonan syndrome with multiple lentigines, cardiofaciocutaneous (CFC) syndrome, Costello syndrome, Legius syndrome, and capillary malformation-arteriovenous malformation (CM-AVM) syndrome. In combination, the RASopathies are a frequent group of genetic disorders. This report summarizes the proceedings of the 4th International Symposium on Genetic Disorders of the Ras/MAPK pathway and highlights gaps in the field. © 2016 Wiley Periodicals, Inc.

Purpose

Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.

Methods

We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.

Results

Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.

Conclusion

Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.