Phan, Isabelle*
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Two key biological features distinguish Trypanosoma evansi from the T. brucei group: independence from the tsetse fly as obligatory vector, and independence from the need for functional mitochondrial DNA (kinetoplast or kDNA). In an effort to better understand the molecular causes and consequences of these differences, we sequenced the genome of an akinetoplastic T. evansi strain from China and compared it to the T. b. brucei reference strain. The annotated T. evansi genome shows extensive similarity to the reference, with 94.9% of the predicted T. b. brucei coding sequences (CDS) having an ortholog in T. evansi, and 94.6% of the non-repetitive orthologs having a nucleotide identity of 95% or greater. Interestingly, several procyclin-associated genes (PAGs) were disrupted or not found in this T. evansi strain, suggesting a selective loss of function in the absence of the insect life-cycle stage. Surprisingly, orthologous sequences were found in T. evansi for all 978 nuclear CDS predicted to represent the mitochondrial proteome in T. brucei, although a small number of these may have lost functionality. Consistent with previous results, the F1FO-ATP synthase γ subunit was found to have an A281 deletion, which is involved in generation of a mitochondrial membrane potential in the absence of kDNA. Candidates for CDS that are absent from the reference genome were identified in supplementary de novo assemblies of T. evansi reads. Phylogenetic analyses show that the sequenced strain belongs to a dominant group of clonal T. evansi strains with worldwide distribution that also includes isolates classified as T. equiperdum. At least three other types of T. evansi or T. equiperdum have emerged independently. Overall, the elucidation of the T. evansi genome sequence reveals extensive similarity of T. brucei and supports the contention that T. evansi should be classified as a subspecies of T. brucei.
- MeSH
- analýza hlavních komponent MeSH
- fylogeneze * MeSH
- genom protozoální * MeSH
- jednonukleotidový polymorfismus MeSH
- mikrosatelitní repetice MeSH
- protozoální proteiny genetika metabolismus MeSH
- regulace genové exprese MeSH
- Trypanosoma klasifikace genetika MeSH
- trypanosomové variantní povrchové glykoproteiny genetika metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
A significant number of individuals with a rare disorder such as Usher syndrome (USH) and (non-)syndromic autosomal recessive retinitis pigmentosa (arRP) remain genetically unexplained. Therefore, we assessed subjects suspected of USH2A-associated disease and no or mono-allelic USH2A variants using whole genome sequencing (WGS) followed by an improved pipeline for variant interpretation to provide a conclusive diagnosis. One hundred subjects were screened using WGS to identify causative variants in USH2A or other USH/arRP-associated genes. In addition to the existing variant interpretation pipeline, a particular focus was put on assessing splice-affecting properties of variants, both in silico and in vitro. Also structural variants were extensively addressed. For variants resulting in pseudoexon inclusion, we designed and evaluated antisense oligonucleotides (AONs) using minigene splice assays and patient-derived photoreceptor precursor cells. Biallelic variants were identified in 49 of 100 subjects, including novel splice-affecting variants and structural variants, in USH2A or arRP/USH-associated genes. Thirteen variants were shown to affect USH2A pre-mRNA splicing, including four deep-intronic USH2A variants resulting in pseudoexon inclusion, which could be corrected upon AON treatment. We have shown that WGS, combined with a thorough variant interpretation pipeline focused on assessing pre-mRNA splicing defects and structural variants, is a powerful method to provide subjects with a rare genetic condition, a (likely) conclusive genetic diagnosis. This is essential for the development of future personalized treatments and for patients to be eligible for such treatments.
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.
