BACKGROUND: Viruses are exceedingly diverse in their evolved strategies to manipulate hosts for viral replication. However, despite these differences, most virus populations will occasionally experience two commonly-encountered challenges: growth in variable host environments, and growth under fluctuating population sizes. We used the segmented RNA bacteriophage ϕ6 as a model for studying the evolutionary genomics of virus adaptation in the face of host switches and parametrically varying population sizes. To do so, we created a bifurcating deme structure that reflected lineage splitting in natural populations, allowing us to test whether phylogenetic algorithms could accurately resolve this ‘known phylogeny’. The resulting tree yielded 32 clones at the tips and internal nodes; these strains were fully sequenced and measured for phenotypic changes in selected traits (fitness on original and novel hosts).
RESULTS: We observed that RNA segment size was negatively correlated with the extent of molecular change in the imposed treatments; molecular substitutions tended to cluster on the Small and Medium RNA chromosomes of the virus, and not on the Large segment. Our study yielded a very large molecular and phenotypic dataset, fostering possible inferences on genotype-phenotype associations. Using further experimental evolution, we confirmed an inference on the unanticipated role of an allelic switch in a viral assembly protein, which governed viral performance across host environments.
CONCLUSIONS: Our study demonstrated that varying complexities can be simultaneously incorporated into experimental evolution, to examine the combined effects of population size, and adaptation in novel environments. The imposed bifurcating structure revealed that some methods for phylogenetic reconstruction failed to resolve the true phylogeny, owing to a paucity of molecular substitutions separating the RNA viruses that evolved in our study.
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Rare mutations in the gene encoding for tau (MAPT, microtubule-associated protein tau) cause frontotemporal dementia-spectrum (FTD-s) disorders, including FTD, progressive supranuclear palsy (PSP) and corticobasal syndrome, and a common extended haplotype spanning across the MAPT locus is associated with increased risk of PSP and Parkinson’s disease. We identified a rare tau variant (p.A152T) in a patient with a clinical diagnosis of PSP and assessed its frequency in multiple independent series of patients with neurodegenerative conditions and controls, in a total of 15 369 subjects. Tau p.A152T significantly increases the risk for both FTD-s (n = 2139, OR = 3.0, CI: 1.6-5.6, P = 0.0005) and Alzheimer’s disease (AD) (n = 3345, OR = 2.3, CI: 1.3-4.2, P = 0.004) compared with 9047 controls. Functionally, p.A152T (i) decreases the binding of tau to microtubules and therefore promotes microtubule assembly less efficiently; and (ii) reduces the tendency to form abnormal fibers. However, there is a pronounced increase in the formation of tau oligomers. Importantly, these findings suggest that other regions of the tau protein may be crucial in regulating normal function, as the p.A152 residue is distal to the domains considered responsible for microtubule interactions or aggregation. These data provide both the first genetic evidence and functional studies supporting the role of MAPT p.A152T as a rare risk factor for both FTD-s and AD and the concept that rare variants can increase the risk for relatively common, complex neurodegenerative diseases, but since no clear significance threshold for rare genetic variation has been established, some caution is warranted until the findings are further replicated.
OBJECTIVE: To use values of cerebrospinal fluid tau and β-amyloid obtained from 2 different analytical immunoassays to differentiate Alzheimer disease (AD) from frontotemporal lobar degeneration (FTLD).
DESIGN: Cerebrospinal fluid values of total tau (T-tau) and β-amyloid 1-42 (Aβ 1-42) obtained using the Innotest enzyme-linked immunosorbent assay were transformed using a linear regression model to equivalent values obtained using the INNO-BIA AlzBio3 (xMAP; Luminex) assay. Cutoff values obtained from the xMAP assay were developed in a series of autopsy-confirmed cases and cross validated in another series of autopsy-confirmed samples using transformed enzyme-linked immunosorbent assay values to assess sensitivity and specificity for differentiating AD from FTLD.
SETTING: Tertiary memory disorder clinics and neuropathologic and biomarker core centers.
PARTICIPANTS: Seventy-five samples from patients with cerebrospinal fluid data obtained from both assays were used for transformation of enzyme-linked immunosorbent assay values. Forty autopsy-confirmed cases (30 with AD and 10 with FTLD) were used to establish diagnostic cutoff values and then cross validated in a second sample set of 21 autopsy-confirmed cases (11 with AD and 10 with FTLD) with transformed enzyme-linked immunosorbent assay values.
MAIN OUTCOME MEASURE: Diagnostic accuracy using transformed biomarker values.
RESULTS: Data obtained from both assays were highly correlated. The T-tau to Aβ 1-42 ratio had the highest correlation between measures (r = 0.928, P < .001) and high reliability of transformation (intraclass correlation coefficient= 0.89). A cutoff of 0.34 for the T-tau to Aβ 1-42 ratio had 90% and 100% sensitivity and 96.7% and 91% specificity to differentiate FTLD cases in the validation and cross-validation samples, respectively.
CONCLUSIONS: Values from 2 analytical platforms can be transformed into equivalent units, which can distinguish AD from FTLD more accurately than the clinical diagnosis.
RNA secondary structure is required for the proper regulation of the cellular transcriptome. This is because the functionality, processing, localization and stability of RNAs are all dependent on the folding of these molecules into intricate structures through specific base pairing interactions encoded in their primary nucleotide sequences. Thus, as the number of RNA sequencing (RNA-seq) data sets and the variety of protocols for this technology grow rapidly, it is becoming increasingly pertinent to develop tools that can analyze and visualize this sequence data in the context of RNA secondary structure. Here, we present Sequencing Annotation and Visualization of RNA structures (SAVoR), a web server, which seamlessly links RNA structure predictions with sequencing data and genomic annotations to produce highly informative and annotated models of RNA secondary structure. SAVoR accepts read alignment data from RNA-seq experiments and computes a series of per-base values such as read abundance and sequence variant frequency. These values can then be visualized on a customizable secondary structure model. SAVoR is freely available at http://tesla.pcbi.upenn.edu/savor.
MOTIVATION: While phylogenetic analyses of datasets containing 1000-5000 sequences are challenging for existing methods, the estimation of substantially larger phylogenies poses a problem of much greater complexity and scale.
METHODS: We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATé and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000-27 643 taxa.
RESULTS: Our studies show that on average DACTAL yields more accurate trees than the two-phase methods we studied on very large datasets that are difficult to align, and has approximately the same accuracy on the easier datasets. The comparison to SATé shows that both have the same accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SATé, including a dataset with almost 28 000 sequences.
AVAILABILITY: DACTAL source code and results of dataset analyses are available at www.cs.utexas.edu/users/phylo/software/dactal.
Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.
Human neurodegenerative diseases have the temporal hallmark of afflicting the elderly population. Ageing is one of the most prominent factors to influence disease onset and progression, yet little is known about the molecular pathways that connect these processes. To understand this connection it is necessary to identify the pathways that functionally integrate ageing, chronic maintenance of the brain and modulation of neurodegenerative disease. MicroRNAs (miRNA) are emerging as critical factors in gene regulation during development; however, their role in adult-onset, age-associated processes is only beginning to be revealed. Here we report that the conserved miRNA miR-34 regulates age-associated events and long-term brain integrity in Drosophila, providing a molecular link between ageing and neurodegeneration. Fly mir-34 expression exhibits adult-onset, brain-enriched and age-modulated characteristics. Whereas mir-34 loss triggers a gene profile of accelerated brain ageing, late-onset brain degeneration and a catastrophic decline in survival, mir-34 upregulation extends median lifespan and mitigates neurodegeneration induced by human pathogenic polyglutamine disease protein. Some of the age-associated effects of miR-34 require adult-onset translational repression of Eip74EF, an essential ETS domain transcription factor involved in steroid hormone pathways. Our studies indicate that miRNA-dependent pathways may have an impact on adult-onset, age-associated events by silencing developmental genes that later have a deleterious influence on adult life cycle and disease, and highlight fly miR-34 as a key miRNA with a role in this process.
The secondary structure of RNA is necessary for its maturation, regulation, processing, and function. However, the global influence of RNA folding in eukaryotes is still unclear. Here, we use a high-throughput, sequencing-based, structure-mapping approach to identify the paired (double-stranded RNA [dsRNA]) and unpaired (single-stranded RNA [ssRNA]) components of the Drosophila melanogaster and Caenorhabditis elegans transcriptomes, which allows us to identify conserved features of RNA secondary structure in metazoans. From this analysis, we find that ssRNAs and dsRNAs are significantly correlated with specific epigenetic modifications. Additionally, we find key structural patterns across protein-coding transcripts that indicate that RNA folding demarcates regions of protein translation and likely affects microRNA-mediated regulation of mRNAs in animals. Finally, we identify and characterize 546 mRNAs whose folding pattern is significantly correlated between these metazoans, suggesting that their structure has some function. Overall, our findings provide a global assessment of RNA folding in animals.
The pathophysiology of negative affect states in older adults is complex, and a host of central nervous system and peripheral systemic mechanisms may play primary or contributing roles. We conducted an unbiased analysis of 146 plasma analytes in a multiplex biochemical biomarker study in relation to number of depressive symptoms endorsed by 566 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) at their baseline and 1-year assessments. Analytes that were most highly associated with depressive symptoms included hepatocyte growth factor, insulin polypeptides, pregnancy-associated plasma protein-A and vascular endothelial growth factor. Separate regression models assessed contributions of past history of psychiatric illness, antidepressant or other psychotropic medicine, apolipoprotein E genotype, body mass index, serum glucose and cerebrospinal fluid (CSF) τ and amyloid levels, and none of these values significantly attenuated the main effects of the candidate analyte levels for depressive symptoms score. Ensemble machine learning with Random Forests found good accuracy (~80%) in classifying groups with and without depressive symptoms. These data begin to identify biochemical biomarkers of depressive symptoms in older adults that may be useful in investigations of pathophysiological mechanisms of depression in aging and neurodegenerative dementias and as targets of novel treatment approaches.
The best-studied biomarkers of Alzheimer’s disease (AD) are the pathologically-linked cerebrospinal fluid (CSF) proteins amyloid-β 42 (Aβ(1-42)), total tau (t-tau), and tau phosphorylated on amino acid 181 (p-tau(181)). Many laboratories measure these proteins using enzyme-linked immunosorbent assay (ELISA). Multiplex xMAP Luminex is a semi-automated assay platform with reduced intra-sample variance, which could facilitate its use in CLIA-approved clinical laboratories. CSF concentrations of these three biomarkers reported using xMAP technology differ from those measured by the most commonly used ELISA, confounding attempts to compare results. To develop a model for converting between xMAP and ELISA levels of the three biomarkers, we analyzed CSF samples from 140 subjects (59 AD, 30 controls, 34 with mild cognitive impairment, and 17 with Parkinson’s disease, including 1 with dementia). Log-transformation of ELISA and xMAP levels made the variance constant in all three biomarkers and improved the linear regression: t-tau concentrations were highly correlated (r = 0.94); p-tau(181) concentrations by ELISA can be better predicted using both the t-tau and p-tau(181) xMAP values (r = 0.96) as compared to p-tau(181) concentrations alone (r = 0.82); correlation of Aβ(1-42) concentrations was relatively weaker but still high (r = 0.77). Among all six protein/assay combinations, xMAP Aβ(1-42) had the best accuracy for diagnostic classification (88%) between AD and control subjects. In conclusion, our study demonstrates that multiplex xMAP is an appropriate assay platform providing results that can be correlated with research-based ELISA values, facilitating the incorporation of this diagnostic biomarker into routine clinical practice.