Biology subjects

Troncoso, J.

Publications and source records attributed to Troncoso, J..

22 records · Page 2Linked to original sources

bioRxiv2019

Mutations in the SPTLC1 gene are a cause of amyotrophic lateral sclerosis that may be amenable to serine supplementation

Juvenile amyotrophic lateral sclerosis (ALS) is a rare form of childhood motor disorder with a heterogeneous clinical presentation. The underlying causes of this condition are poorly understood, hindering the development of effective therapies. In a whole-exome sequencing trio-family study of three unrelated juvenile patients diagnosed with ALS and failure to thrive, we identified de-novo mutations in SPTLC1 (p.Ala20Ser in two patients and p.Ser331Tyr) not present in their healthy parents or siblings. SPTLC1 encodes a subunit of the serine palmitoyltransferase complex, a key enzyme in sphingolipid biosynthesis. Mutations in this gene are known to cause hereditary sensory autonomic neuropathy, type 1A, with a characteristic increase in plasma levels of neurotoxic deoxymethyl-sphinganine. We found an increase of this metabolite in one of our patients carrying the p.Ala20Ser mutation. Treatment of one of the patients with high dose, oral L-serine led to an increase in body weight, suggesting that serine supplementation may be beneficial among patients carrying mutations in this gene.

genetics↗

bioRxiv2019

Discovery of several thousand highly diverse circular DNA viruses

Although it is suspected that there are millions of distinct viral species, fewer than 9,000 are catalogued in GenBanks RefSeq database. We selectively enriched for and amplified the genomes of circular DNA viruses in over 70 animal samples, ranging from cultured soil nematodes to human tissue specimens. A bioinformatics pipeline, Cenote-Taker, was developed to automatically annotate over 2,500 circular genomes in a GenBank-compliant format. The new genomes belong to dozens of established and emerging viral families. Some appear to be the result of previously undescribed recombination events between ssDNA viruses and ssRNA viruses. In addition, hundreds of circular DNA elements that do not encode any discernable similarities to previously characterized sequences were identified. To characterize these "dark matter" sequences, we used an artificial neural network to identify candidate viral capsid proteins, several of which formed virus-like particles when expressed in culture. These data further the understanding of viral sequence diversity and allow for high throughput documentation of the virosphere.

microbiology↗

bioRxiv2018

Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer’s disease

This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity, together with pose and deformation parameters. Missing data is accommodated via a multiple atlas selection procedure where several atlases may be of homogeneous intensity and correspond to \"background\" or \"artifact\". The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We show results for 3D reconstruction of digital pathology and MRI in standard atlas coordinates. In conjunction with convolutional neural networks, we quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimers disease postmortem specimen.\n\nAuthor summaryOur work in Alzheimers disease (AD) is attempting to connect histopathology at autopsy and longitudinal clinical magnetic resonance imaging (MRI), combining the strengths of each modality in a common coordinate system. We are bridging this gap by using post mortem high resolution MRI to reconstruct digital pathology in 3D. This image registration problem is challenging because it combines images from different modalities in the presence of missing tissue and artifacts. We overcome this challenge by developing a new registration technique that simultaneously classifies each pixel as \"good data\" / \"missing tissue\" / \"artifact\", learns a contrast transformation between modalities, and computes deformation parameters. We name this technique \"(D)eformable (R)egistration and (I)ntensity (T)ransformation with (M)issing (D)ata\", pronounced as \"Dr. It, M.D.\". In conjunction with convolutional neural networks, we use this technique to map the three dimensional distribution of tau tangles in the medial temporal lobe of an AD postmortem specimen.

neuroscience↗

bioRxiv2018

Heritability and genetic variance of dementia with Lewy bodies

Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinsons disease (PD) or Alzheimers disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

neuroscience↗