New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Core and conditionally rare taxa as indicators of agricultural drainage ditch and stream health and function.

BACKGROUND: The freshwater microbiome regulates aquatic ecological functionality, nutrient cycling, pathogenicity, and has the capacity to dissipate and regulate pollutants. Agricultural drainage ditches are ubiquitous in regions where field drainage is necessary for crop productivity, and as such, are first-line receptors of agricultural drainage and runoff. How bacterial communities in these systems respond to environmental and anthropogenic stressors are not well understood. In this study, we carried out a three year study in an agriculturally dominated river basin in eastern Ontario, Canada to explore the spatial and temporal dynamics of the core and conditionally rare taxa (CRT) of the instream bacterial communities using a 16S rRNA gene amplicon sequencing approach. Water samples were collected from nine stream and drainage ditch sites that represented the influence of a range of upstream land uses. RESULTS: The cross-site core and CRT accounted for 5.6% of the total number of amplicon sequence variants (ASVs), yet represented, on average, over 60% of the heterogeneity of the overall bacterial community; hence, well reflected the spatial and temporal microbial dynamics in the water courses. The contribution of core microbiome to the overall community heterogeneity represented the community stability across all sampling sites. CRT was primarily composed of functional taxa involved in nitrogen (N) cycling and was linked to nutrient loading, water levels, and flow, particularly in the smaller agricultural drainage ditches. Both the core and the CRT were sensitive responders to changes in hydrological conditions. CONCLUSIONS: We demonstrate that core and CRT can be considered as holistic tools to explore the temporal and spatial variations of the aquatic microbial community and can be used as sensitive indicators of the health and function of agriculturally dominated water courses. This approach also reduces computational complexity in relation to analyzing the entire microbial community for such purposes.

All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs.

Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 1-FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn's disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci.

Symptom-driven idiopathic disease gene identification.

PURPOSE: Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity. METHODS: We describe a genetic network-based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene. RESULTS: We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pinrank/. CONCLUSION: We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects.

Gain-of-function ADCY5 mutations in familial dyskinesia with facial myokymia.

OBJECTIVE: To identify the cause of childhood onset involuntary paroxysmal choreiform and dystonic movements in 2 unrelated sporadic cases and to investigate the functional effect of missense mutations in adenylyl cyclase 5 (ADCY5) in sporadic and inherited cases of autosomal dominant familial dyskinesia with facial myokymia (FDFM). METHODS: Whole exome sequencing was performed on 2 parent-child trios. The effect of mutations in ADCY5 was studied by measurement of cyclic adenosine monophosphate (cAMP) accumulation under stimulatory and inhibitory conditions. RESULTS: The same de novo mutation (c.1252C>T, p.R418W) in ADCY5 was found in both studied cases. An inherited missense mutation (c.2176G>A, p.A726T) in ADCY5 was previously reported in a family with FDFM. The significant phenotypic overlap with FDFM was recognized in both cases only after discovery of the molecular link. The inherited mutation in the FDFM family and the recurrent de novo mutation affect residues in different protein domains, the first cytoplasmic domain and the first membrane-spanning domain, respectively. Functional studies revealed a statistically significant increase in ß-receptor agonist-stimulated intracellular cAMP consistent with an increase in adenylyl cyclase activity for both mutants relative to wild-type protein, indicative of a gain-of-function effect. INTERPRETATION: FDFM is likely caused by gain-of-function mutations in different domains of ADCY5-the first definitive link between adenylyl cyclase mutation and human disease. We have illustrated the power of hypothesis-free exome sequencing in establishing diagnoses in rare disorders with complex and variable phenotype. Mutations in ADCY5 should be considered in patients with undiagnosed complex movement disorders even in the absence of a family history.

Auto-DRRAFTER: Automated RNA Modeling Based on Cryo-EM Density.

RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

The great divide: rhamnolipids mediate separation between P. aeruginosa and S. aureus.

The interactions between bacterial species during infection can have significant impacts on pathogenesis. Pseudomonas aeruginosa and Staphylococcus aureus are opportunistic bacterial pathogens that can co-infect hosts and cause serious illness. The factors that dictate whether one species outcompetes the other or whether the two species coexist are not fully understood. We investigated the role of surfactants in the interactions between these two species on a surface that enables P. aeruginosa to swarm. We found that P. aeruginosa swarms are repelled by colonies of clinical S. aureus isolates, creating physical separation between the two strains. This effect was abolished in mutants of S. aureus that were defective in the production of phenol-soluble modulins (PSMs), which form amyloid fibrils around wild-type S. aureus colonies. We investigated the mechanism that establishes physical separation between the two species using Imaging of Reflected Illuminated Structures (IRIS), which is a non-invasive imaging method that tracks the flow of surfactants produced by P. aeruginosa. We found that PSMs produced by S. aureus deflected the surfactant flow, which in turn, altered the direction of P. aeruginosa swarms. These findings show that rhamnolipids mediate physical separation between P. aeruginosa and S. aureus, which could facilitate coexistence between these species. Additionally, we found that a number of molecules repelled P. aeruginosa swarms, consistent with a surfactant deflection mechanism. These include Bacillus subtilis surfactant, the fatty acids oleic acid and linoleic acid, and the synthetic lubricant polydimethylsiloxane. Lung surfactant repelled P. aeruginosa swarms and inhibited swarm expansion altogether at higher concentration. Our results suggest that surfactant interactions could have major impacts on bacteria-bacteria and bacteria-host relationships. In addition, our findings uncover a mechanism responsible for P. aeruginosa swarm development that does not rely solely on sensing but instead is based on the flow of surfactant.

Commercial and Instant Coffees Effectively Lower Aß1-40 and Aß1-42 in N2a/APPswe Cells.

Background: Alzheimer's disease (AD) is a multifactorial neurological disease with neurofibrillary tangles and neuritic plaques as histopathological markers. Due to this, although AD is the leading cause of dementia worldwide, clinical AD dementia cannot be certainly diagnosed until neuropathological post-mortem evaluation. Coffee has been reported to have neurologically protective factors, particularly against AD, but coffee brand and type have not been taken into consideration in previous studies. We examined the discrepancies among popular commercial and instant coffees in limiting the development and progression through Aß1-40 and Aß1-42 production, and hypothesized that coffee consumption, regardless of brand or type, is beneficial for stalling the progression and development of Aß-related AD. Methods: Coffee samples from four commercial coffee brands and four instant coffees were purchased or prepared following given instructions and filtered for the study. 5, 2.5, and 1.25% concentrations of each coffee were used to treat N2a/APPswe cell lines. MTT assay was used to assess cell viability for coffee concentrations, as well as pure caffeine concentrations. Sandwich ELISA assay was used to determine Aß concentration for Aß1-40 and Aß1-42 peptides of coffee-treated cells. Results: Caffeine concentrations were significantly varied among all coffees (DC vs. MDC, PC, SB, NIN, MIN p < 0.05). There was no correlation between caffeine concentration and cell toxicity among brands and types of coffee, with no toxicity at 0.5 mg/ml caffeine and lower. Most coffees were toxic to N2a/APPswe cells at 5% (p < 0.05), but not at 2.5%. Most coffees at a 2.5% concentration reduced Aß1-40 and Aß1-42 production, with comparable results between commercial and instant coffees. Conclusion: All coffees tested have beneficial health effects for AD through lowering Aß1-40 and Aß1-42 production, with Dunkin' Donuts® medium roast coffee demonstrating the most consistent and optimal cell survival rates and Aß concentration. On the other hand, Starbucks® coffee exhibited the highest cell toxicity rates among the tested coffees.

A stromal progenitor and ILC2 niche promotes muscle eosinophilia and fibrosis-associated gene expression.

Despite the well-accepted view that chronic inflammation contributes to the pathogenesis of Duchenne muscular dystrophy (DMD), the function and regulation of eosinophils remain an unclear facet of type II innate immunity in dystrophic muscle. We report the observation that group 2 innate lymphoid cells (ILC2s) are present in skeletal muscle and are the principal regulators of muscle eosinophils during muscular dystrophy. Eosinophils were elevated in DMD patients and dystrophic mice along with interleukin (IL)-5, a major eosinophil survival factor that was predominantly expressed by muscle ILC2s. We also find that IL-33 was upregulated in dystrophic muscle and was predominantly produced by fibrogenic/adipogenic progenitors (FAPs). Exogenous IL-33 and IL-2 complex (IL-2c) expanded muscle ILC2s and eosinophils, decreased the cross-sectional area (CSA) of regenerating myofibers, and increased the expression of genes associated with muscle fibrosis. The deletion of ILC2s in dystrophic mice mitigated muscle eosinophilia and impaired the induction of IL-5 and fibrosis-associated genes. Our findings highlight a FAP/ILC2/eosinophil axis that promotes type II innate immunity, which influences the balance between regenerative and fibrotic responses during muscular dystrophy.

An Immunomodulatory Therapeutic Vaccine Targeting Oligomeric Amyloid-ß.

BACKGROUND: Aging is considered the most important risk factor for Alzheimer's disease (AD). Recent research supports the theory that immunotherapy targeting the "oligomeric" forms of amyloid-ß (Aß) may halt the progression of AD. However, previous clinical trial of the vaccine against Aß, called AN1792, was suspended due to cases of meningoencephalitis in patients. OBJECTIVE: To develop a peptide sensitized dendritic cells (DCs) vaccine that would target oligomer Aß and prevent an autoimmune response. METHODS: Double transgenic APPswe/PS1ΔE9 (Tg) and C57BL/6J control mice were used in this study. Cytokine expression profile detection, characterization of antisera, brain GSK-3ß, LC3 expression, and spatial working memory testing before and post-vaccination were obtained. RESULTS: Epitope prediction indicated that E22W42 could generate 13 new T cell epitopes which can strengthen immunity in aged subjects and silence several T cell epitopes of the wild type Aß. The silenced T cell epitope could help avoid the autoimmune response that was seen in some patients of the AN-1792 vaccine. The E22W42 not only helped sensitize bone marrow-derived DCs for the development of an oligomeric Aß-specific antibody, but also delayed memory impairment in the APP/PS1 mouse model. Most importantly, this E22W42 peptide will not alter the DC's natural immunomodulatory properties. CONCLUSION: The E22W42 vaccine is possibly safer for patients with impaired immune systems. Since there is increasing evidence that oligomeric form of Aß are the toxic species to neurons, the E22W42 antibody's specificity for these "oligomeric" Aß species could provide the opportunity to produce some clinical benefits in AD subjects.

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology.

Scripps Genome ADVISER: Annotation and Distributed Variant Interpretation SERver.

Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease--without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER.

Chip-based direct genotyping of coding variants in genome wide association studies: utility, issues and prospects.

There is considerable debate about the most efficient way to interrogate rare coding variants in association studies. The options include direct genotyping of specific known coding variants in genes or, alternatively, sequencing across the entire exome to capture known as well as novel variants. Each strategy has advantages and disadvantages, but the availability of cost-efficient exome arrays has made the former appealing. Here we consider the utility of a direct genotyping chip, the Illumina HumanExome array (HE), by evaluating its content based on: 1. functionality; and 2. amenability to imputation. We explored these issues by genotyping a large, ethnically diverse cohort on the HumanOmniExpressExome array (HOEE) which combines the HE with content from the GWAS array (HOE). We find that the use of the HE is likely to be a cost-effective way of expanding GWAS, but does have some drawbacks that deserve consideration when planning studies.

Multisensory encoding improves auditory recognition.

Recent studies have challenged the long-held belief that recognition is unfailingly degraded by contextual differences between study and test items. In these studies, recognition of pictures presented in silence was better when during study or initial exposure the images were accompanied by a semantically congruent sound rather than silence. In the present study, we sought to examine the generalization of this phenomenon to auditory recognition and found a significant improvement in the recognition of auditory items when coupled with a congruent picture. We discuss these findings within the framework of the redintegration hypothesis of memory retrieval as well as Bayesian inference and learning.

Clinical implications of human population differences in genome-wide rates of functional genotypes.

There have been a number of recent successes in the use of whole genome sequencing and sophisticated bioinformatics techniques to identify pathogenic DNA sequence variants responsible for individual idiopathic congenital conditions. However, the success of this identification process is heavily influenced by the ancestry or genetic background of a patient with an idiopathic condition. This is so because potential pathogenic variants in a patient's genome must be contrasted with variants in a reference set of genomes made up of other individuals' genomes of the same ancestry as the patient. We explored the effect of ignoring the ancestries of both an individual patient and the individuals used to construct reference genomes. We pursued this exploration in two major steps. We first considered variation in the per-genome number and rates of likely functional derived (i.e., non-ancestral, based on the chimp genome) single nucleotide variants and small indels in 52 individual whole human genomes sampled from 10 different global populations. We took advantage of a suite of computational and bioinformatics techniques to predict the functional effect of over 24 million genomic variants, both coding and non-coding, across these genomes. We found that the typical human genome harbors ∼5.5-6.1 million total derived variants, of which ∼12,000 are likely to have a functional effect (∼5000 coding and ∼7000 non-coding). We also found that the rates of functional genotypes per the total number of genotypes in individual whole genomes differ dramatically between human populations. We then created tables showing how the use of comparator or reference genome panels comprised of genomes from individuals that do not have the same ancestral background as a patient can negatively impact pathogenic variant identification. Our results have important implications for clinical sequencing initiatives.

Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.

Supercentenarians (age 110+ years old) generally delay or escape age-related diseases and disability well beyond the age of 100 and this exceptional survival is likely to be influenced by a genetic predisposition that includes both common and rare genetic variants. In this report, we describe the complete genomic sequences of male and female supercentenarians, both age >114 years old. We show that: (1) the sequence variant spectrum of these two individuals' DNA sequences is largely comparable to existing non-supercentenarian genomes; (2) the two individuals do not appear to carry most of the well-established human longevity enabling variants already reported in the literature; (3) they have a comparable number of known disease-associated variants relative to most human genomes sequenced to-date; (4) approximately 1% of the variants these individuals possess are novel and may point to new genes involved in exceptional longevity; and (5) both individuals are enriched for coding variants near longevity-associated variants that we discovered through a large genome-wide association study. These analyses suggest that there are both common and rare longevity-associated variants that may counter the effects of disease-predisposing variants and extend lifespan. The continued analysis of the genomes of these and other rare individuals who have survived to extremely old ages should provide insight into the processes that contribute to the maintenance of health during extreme aging.