Chromosomal microarray analysis supplements exome sequencing to diagnose children with suspected inborn errors of immunity.

Purpose: Though copy number variants (CNVs) have been suggested to play a significant role in inborn errors of immunity (IEI), the precise nature of this role remains largely unexplored. We sought to determine the diagnostic contribution of CNVs using genome-wide chromosomal microarray analysis (CMA) in children with IEI. Methods: We performed exome sequencing (ES) and CMA for 332 unrelated pediatric probands referred for evaluation of IEI. The analysis included primary, secondary, and incidental findings. Results: Of the 332 probands, 134 (40.4%) received molecular diagnoses. Of these, 116/134 (86.6%) were diagnosed by ES alone. An additional 15/134 (11.2%) were diagnosed by CMA alone, including two likely de novo changes. Three (2.2%) participants had diagnostic molecular findings from both ES and CMA, including two compound heterozygotes and one participant with two distinct diagnoses. Half of the participants with CMA contribution to diagnosis had CNVs in at least one non-immune gene, highlighting the clinical complexity of these cases. Overall, CMA contributed to 18/134 diagnoses (13.4%), increasing the overall diagnostic yield by 15.5% beyond ES alone. Conclusion: Pairing ES and CMA can provide a comprehensive evaluation to clarify the complex factors that contribute to both immune and non-immune phenotypes. Such a combined approach to genetic testing helps untangle complex phenotypes, not only by clarifying the differential diagnosis, but in some cases by identifying multiple diagnoses contributing to the overall clinical presentation.

Clinical exome sequencing of 1000 families with complex immune phenotypes: Toward comprehensive genomic evaluations.

BACKGROUND: Prospective genetic evaluation of patients at this referral research hospital presents clinical research challenges. OBJECTIVES: This study sought not only a single-gene explanation for participants' immune-related presentations, but viewed each participant holistically, with the potential to have multiple genetic contributions to their immune phenotype and other heritable comorbidities relevant to their presentation and health. METHODS: This study developed a program integrating exome sequencing, chromosomal microarray, phenotyping, results return with genetic counseling, and reanalysis in 1505 individuals from 1000 families with suspected or known inborn errors of immunity. RESULTS: Probands were 50.8% female, 71.5% were ≥18 years, and had diverse immune presentations. Overall, 327 of 1000 probands (32.7%) received 361 molecular diagnoses. These included 17 probands with diagnostic copy number variants, 32 probands with secondary findings, and 31 probands with multiple molecular diagnoses. Reanalysis added 22 molecular diagnoses, predominantly due to new disease-gene associations (9 of 22, 40.9%). One-quarter of the molecular diagnoses (92 of 361) did not involve immune-associated genes. Molecular diagnosis was correlated with younger age, male sex, and a higher number of organ systems involved. This program also facilitated the discovery of new gene-disease associations such as SASH3-related immunodeficiency. A review of treatment options and ClinGen actionability curations suggest that at least 251 of 361 of these molecular diagnoses (69.5%) could translate into ≥1 management option. CONCLUSIONS: This program contributes to our understanding of the diagnostic and clinical utility whole exome analysis on a large scale.

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our model. The document to be analyzed is first split into sentences and annotated with a base method to generate candidate concepts. The sentences, along with the candidate concepts, are then fed into the pre-trained model for re-ranking. Our model comprises the pre-trained BlueBERT and a feature selection module, followed by a contrastive loss. We re-ranked the results generated by three robust HPO annotation tools and compared the performance against most of the existing approaches. The experimental results show that our model can improve the performance of the existing methods. Significantly, it boosted 3.0% and 5.6% in F1 score on the two evaluated datasets compared with the base methods. It removed more than 80% of the false positives predicted by the base methods, resulting in up to 18% improvement in precision. Our model utilizes the descriptive data in the ontology and the contextual information in the sentences for re-ranking. The results indicate that the additional information and the re-ranking model can significantly enhance the precision of HPO concept recognition compared with the base method.

PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.

MOTIVATION: Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. RESULTS: In this article, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods. AVAILABILITYAND IMPLEMENTATION: The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structure and function of a malaria transmission blocking vaccine targeting Pfs230 and Pfs230-Pfs48/45 proteins.

Proteins Pfs230 and Pfs48/45 are Plasmodium falciparum transmission-blocking (TB) vaccine candidates that form a membrane-bound protein complex on gametes. The biological role of Pfs230 or the Pfs230-Pfs48/45 complex remains poorly understood. Here, we present the crystal structure of recombinant Pfs230 domain 1 (Pfs230D1M), a 6-cysteine domain, in complex with the Fab fragment of a TB monoclonal antibody (mAb) 4F12. We observed the arrangement of Pfs230 on the surface of macrogametes differed from that on microgametes, and that Pfs230, with no known membrane anchor, may exist on the membrane surface in the absence of Pfs48/45. 4F12 appears to sterically interfere with Pfs230 function. Combining mAbs against different epitopes of Pfs230D1 or of Pfs230D1 and Pfs48/45, significantly increased TB activity. These studies elucidate a mechanism of action of the Pfs230D1 vaccine, model the functional activity induced by a polyclonal antibody response and support the development of TB vaccines targeting Pfs230D1 and Pfs230D1-Pfs48/45.

Global distribution of mating types shows limited opportunities for mating across populations of fungi causing boxwood blight disease.

Boxwood blight is a disease threat to natural and managed landscapes worldwide. To determine mating potential of the fungi responsible for the disease, Calonectria pseudonaviculata and C. henricotiae, we characterized their mating-type (MAT) loci. Genomes of C. henricotiae, C. pseudonaviculata and two other Calonectria species (C. leucothoes, C. naviculata) were sequenced and used to design PCR tests for mating-type from 268 isolates collected from four continents. All four Calonectria species have a MAT locus that is structurally consistent with the organization found in heterothallic ascomycetes, with just one idiomorph per individual isolate. Mating type was subdivided by species: all C. henricotiae isolates possessed the MAT1-1 idiomorph, whereas all C. pseudonaviculata isolates possessed the MAT1-2 idiomorph. To determine the potential for divergence at the MAT1 locus to present a barrier to interspecific hybridization, evolutionary analysis was conducted. Phylogenomic estimates showed that C. henricotiae and C. pseudonaviculata diverged approximately 2.1 Mya. However, syntenic comparisons, phylogenetic analyses, and estimates of nucleotide divergence across the MAT1 locus and proximal genes identified minimal divergence in this region of the genome. These results show that in North America and parts of Europe, where only C. pseudonaviculata resides, mating is constrained by the absence of MAT1-1. In regions of Europe where C. henricotiae and C. pseudonaviculata currently share the same host and geographic range, it remains to be determined whether or not these two recently diverged species are able to overcome species barriers to mate.

Hypomorphic caspase activation and recruitment domain 11 (CARD11) mutations associated with diverse immunologic phenotypes with or without atopic disease.

BACKGROUND: Caspase activation and recruitment domain 11 (CARD11) encodes a scaffold protein in lymphocytes that links antigen receptor engagement with downstream signaling to nuclear factor κB, c-Jun N-terminal kinase, and mechanistic target of rapamycin complex 1. Germline CARD11 mutations cause several distinct primary immune disorders in human subjects, including severe combined immune deficiency (biallelic null mutations), B-cell expansion with nuclear factor κB and T-cell anergy (heterozygous, gain-of-function mutations), and severe atopic disease (loss-of-function, heterozygous, dominant interfering mutations), which has focused attention on CARD11 mutations discovered by using whole-exome sequencing. OBJECTIVES: We sought to determine the molecular actions of an extended allelic series of CARD11 and to characterize the expanding range of clinical phenotypes associated with heterozygous CARD11 loss-of-function alleles. METHODS: Cell transfections and primary T-cell assays were used to evaluate signaling and function of CARD11 variants. RESULTS: Here we report on an expanded cohort of patients harboring novel heterozygous CARD11 mutations that extend beyond atopy to include other immunologic phenotypes not previously associated with CARD11 mutations. In addition to (and sometimes excluding) severe atopy, heterozygous missense and indel mutations in CARD11 presented with immunologic phenotypes similar to those observed in signal transducer and activator of transcription 3 loss of function, dedicator of cytokinesis 8 deficiency, common variable immunodeficiency, neutropenia, and immune dysregulation, polyendocrinopathy, enteropathy, X-linked-like syndrome. Pathogenic variants exhibited dominant negative activity and were largely confined to the CARD or coiled-coil domains of the CARD11 protein. CONCLUSION: These results illuminate a broader phenotypic spectrum associated with CARD11 mutations in human subjects and underscore the need for functional studies to demonstrate that rare gene variants encountered in expected and unexpected phenotypes must nonetheless be validated for pathogenic activity.

Genome-Wide CRISPR Screen Reveals Cancer Cell Resistance to NK Cells Induced by NK-Derived IFN-γ.

The anti-leukemia activity of NK cells helps prevent relapse during hematopoietic stem cell transplantation (HSCT) in leukemia patients. However, the factors that determine the sensitivity or resistance of leukemia cells in the context of NK-mediated cytotoxicity are not well-established. Here, we performed a genome-wide CRISPR screen in the human chronic-myelogenous-leukemia (CML) cell line K562 to identify genes that regulate the vulnerability of leukemia cells to killing by primary human NK cells. The distribution of guide RNAs (gRNAs) in K562 cells that survived co-incubation with NK cells showed that loss of NCR3LG1, which encodes the ligand of the natural cytotoxicity receptor NKp30, protected K562 cells from killing. In contrast, loss of genes that regulate the antigen-presentation and interferon-γ-signaling pathways increased the vulnerability of K562 cells. The addition of IFN-γ neutralizing antibody increased the susceptibility of K562 cells to NK-mediated killing. Upregulation of MHC class I on K562 cells after co-incubation with NK cells was dependent on IFNGR2. Analysis of RNA-seq data from The Cancer Genome Atlas (TCGA) showed that low IFNGR2 expression in cancer tissues was associated with improved overall survival in acute myeloid leukemia (AML) and Kidney Renal Clear Cell Carcinoma (KIRC) patients. Our results, showing that the upregulation of MHC class I by NK-derived IFN-γ leads to resistance to NK cytotoxicity, suggest that targeting IFN-γ responses might be a promising approach to enhance NK cell anti-cancer efficacy.

Genome analysis of the ubiquitous boxwood pathogen Pseudonectria foliicola.

Boxwood (Buxus spp.) are broad-leaved, evergreen landscape plants valued for their longevity and ornamental qualities. Volutella leaf and stem blight, caused by the ascomycete fungi Pseudonectria foliicola and P. buxi, is one of the major diseases affecting the health and ornamental qualities of boxwood. Although this disease is less severe than boxwood blight caused by Calonectria pseudonaviculata and C. henricotiae, its widespread occurrence and disfiguring symptoms have caused substantial economic losses to the ornamental industry. In this study, we sequenced the genome of P. foliicola isolate ATCC13545 using Illumina technology and compared it to other publicly available fungal pathogen genomes to better understand the biology of this organism. A de novo assembly estimated the genome size of P. foliicola at 28.7 Mb (425 contigs; N50 = 184,987 bp; avg. coverage 188×), with just 9,272 protein-coding genes. To our knowledge, P. foliicola has the smallest known genome within the Nectriaceae. Consistent with the small size of the genome, the secretome, CAzyme and secondary metabolite profiles of this fungus are reduced relative to two other surveyed Nectriaceae fungal genomes: Dactylonectria macrodidyma JAC15-245 and Fusarium graminearum Ph-1. Interestingly, a large cohort of genes associated with reduced virulence and loss of pathogenicity was identified from the P. foliicola dataset. These data are consistent with the latest observations by plant pathologists that P. buxi and most likely P. foliicola, are opportunistic, latent pathogens that prey upon weak and stressed boxwood plants.

Genome wide analysis of the transition to pathogenic lifestyles in Magnaporthales fungi.

The rice blast fungus Pyricularia oryzae (syn. Magnaporthe oryzae, Magnaporthe grisea), a member of the order Magnaporthales in the class Sordariomycetes, is an important plant pathogen and a model species for studying pathogen infection and plant-fungal interaction. In this study, we generated genome sequence data from five additional Magnaporthales fungi including non-pathogenic species, and performed comparative genome analysis of a total of 13 fungal species in the class Sordariomycetes to understand the evolutionary history of the Magnaporthales and of fungal pathogenesis. Our results suggest that the Magnaporthales diverged ca. 31 millon years ago from other Sordariomycetes, with the phytopathogenic blast clade diverging ca. 21 million years ago. Little evidence of inter-phylum horizontal gene transfer (HGT) was detected in Magnaporthales. In contrast, many genes underwent positive selection in this order and the majority of these sequences are clade-specific. The blast clade genomes contain more secretome and avirulence effector genes, which likely play key roles in the interaction between Pyricularia species and their plant hosts. Finally, analysis of transposable elements (TE) showed differing proportions of TE classes among Magnaporthales genomes, suggesting that species-specific patterns may hold clues to the history of host/environmental adaptation in these fungi.

Deep learning improves antimicrobial peptide recognition.

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results: In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation: Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.

Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity. In this paper, we advance research in this direction through a novel method that constructs and selects complex sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveals our method is not only among the top performers, but it also provides transparent summarizations of antibacterial activity at the sequence level. Moreover, this paper demonstrates for the first time the capability not only to recognize that a peptide is an AMP or not but also to predict its target selectivity based on models of activity against only Gram-positive, only Gram-negative, or both types of bacteria. The work described in this paper is a step forward in computational research seeking to facilitate AMP design or modification in the wet laboratory.

SimpleSynteny: a web-based tool for visualization of microsynteny across multiple species.

Defining syntenic relationships among orthologous gene clusters is a frequent undertaking of biologists studying organismal evolution through comparative genomic approaches. With the increasing availability of genome data made possible through next-generation sequencing technology, there is a growing need for user-friendly tools capable of assessing synteny. Here we present SimpleSynteny, a new web-based platform capable of directly interrogating collinearity of local genomic neighbors across multiple species in a targeted manner. SimpleSynteny provides a pipeline for evaluating the synteny of a preselected set of gene targets across multiple organismal genomes. An emphasis has been placed on ease-of-use, and users are only required to submit FASTA files for their genomes and genes of interest. SimpleSynteny then guides the user through an iterative process of exploring and customizing genomes individually before combining them into a final high-resolution figure. Because the process is iterative, it allows the user to customize the organization of multiple contigs and incorporate knowledge from additional sources, rather than forcing complete dependence on the computational predictions. Additional tools are provided to help the user identify which contigs in a genome assembly contain gene targets and to optimize analyses of circular genomes. SimpleSynteny is freely available at: http://www.SimpleSynteny.com.

LAMP Detection Assays for Boxwood Blight Pathogens: A Comparative Genomics Approach.

Rapid and accurate molecular diagnostic tools are critical to efforts to minimize the impact and spread of emergent pathogens. The identification of diagnostic markers for novel pathogens presents several challenges, especially in the absence of information about population diversity and where genetic resources are limited. The objective of this study was to use comparative genomics datasets to find unique target regions suitable for the diagnosis of two fungal species causing a newly emergent blight disease of boxwood. Candidate marker regions for loop-mediated isothermal amplification (LAMP) assays were identified from draft genomes of Calonectria henricotiae and C. pseudonaviculata, as well as three related species not associated with this disease. To increase the probability of identifying unique targets, we used three approaches to mine genome datasets, based on (i) unique regions, (ii) polymorphisms, and (iii) presence/absence of regions across datasets. From a pool of candidate markers, we demonstrate LAMP assay specificity by testing related fungal species, common boxwood pathogens, and environmental samples containing 445 diverse fungal taxa. This comparative-genomics-based approach to the development of LAMP diagnostic assays is the first of its kind for fungi and could be easily applied to diagnostic marker development for other newly emergent plant pathogens.

Polymorphic SSR markers for Plasmopara obducens (Peronosporaceae), the newly emergent downy mildew pathogen of Impatiens (Balsaminaceae).

PREMISE OF THE STUDY: Simple sequence repeat (SSR) markers were developed for Plasmopara obducens, the causal agent of the newly emergent downy mildew disease of Impatiens walleriana. METHODS AND RESULTS: A 202-Mb draft genome assembly was generated from P. obducens using Illumina technology and mined to identify 13,483 SSR motifs. Primers were synthesized for 62 marker candidates, of which 37 generated reliable PCR products. Testing of the 37 markers using 96 P. obducens samples showed 96% of the markers were polymorphic, with 2-6 alleles observed. Observed and expected heterozygosity ranged from 0.000-0.892 and 0.023-0.746, respectively. Just 17 markers were sufficient to identify all multilocus genotypes. CONCLUSIONS: These are the first SSR markers available for this pathogen, and one of the first molecular resources. These markers will be useful in assessing variation in pathogen populations and determining the factors contributing to the emergence of destructive impatiens downy mildew disease.

IMA Genome-F 4: Draft genome sequences of Chrysoporthe austroafricana, Diplodia scrobiculata, Fusarium nygamai, Leptographium lundbergii, Limonomyces culmigenus, Stagonosporopsis tanaceti, and Thielaviopsis punctulata.

The genomes of Chrysoporthe austroafricana, Diplodia scrobiculata, Fusarium nygami, Leptographium lundbergii, Limonomyces culmigenus, Stagonosporopsis tanaceti, and Thielaviopsis punctulata are presented in this genome announcement. These seven genomes are from endophytes, plant pathogens and economically important fungal species. The genome sizes range from 26.6 Mb in the case of Leptographium lundbergii to 44 Mb for Chrysoporthe austroafricana. The availability of these genome data will provide opportunities to resolve longstanding questions regarding the taxonomy of species in these genera, and may contribute to our understanding of the lifestyles through comparative studies with closely related organisms.

Draft Genome Sequence of Dactylonectria macrodidyma, a Plant-Pathogenic Fungus in the Nectriaceae.

Dactylonectria macrodidyma is part of the Nectriaceae, a family containing important plant pathogens. This species possesses the ability to induce disease on grapevine, avocado, and olive. Here, we report the first draft genome of D. macrodidyma isolate JAC15-245. The assembled genome was 58 Mbp and contained an estimated 16,454 genes.

Are nicotinic acetylcholine receptors coupled to G proteins?

It was, until recently, accepted that the two classes of acetylcholine (ACh) receptors are distinct in an important sense: muscarinic ACh receptors signal via heterotrimeric GTP binding proteins (G proteins), whereas nicotinic ACh receptors (nAChRs) open to allow flux of Na+, Ca2+, and K+ ions into the cell after activation. Here we present evidence of direct coupling between G proteins and nAChRs in neurons. Based on proteomic, biophysical, and functional evidence, we hypothesize that binding to G proteins modulates the activity and signaling of nAChRs in cells. It is important to note that while this hypothesis is new for the nAChR, it is consistent with known interactions between G proteins and structurally related ligand-gated ion channels. Therefore, it underscores an evolutionarily conserved metabotropic mechanism of G protein signaling via nAChR channels.

Menthol binding and inhibition of α7-nicotinic acetylcholine receptors.

Menthol is a common compound in pharmaceutical and commercial products and a popular additive to cigarettes. The molecular targets of menthol remain poorly defined. In this study we show an effect of menthol on the α7 subunit of the nicotinic acetylcholine (nACh) receptor function. Using a two-electrode voltage-clamp technique, menthol was found to reversibly inhibit α7-nACh receptors heterologously expressed in Xenopus oocytes. Inhibition by menthol was not dependent on the membrane potential and did not involve endogenous Ca(2+)-dependent Cl(-) channels, since menthol inhibition remained unchanged by intracellular injection of the Ca(2+) chelator BAPTA and perfusion with Ca(2+)-free bathing solution containing Ba(2+). Furthermore, increasing ACh concentrations did not reverse menthol inhibition and the specific binding of [(125)I] α-bungarotoxin was not attenuated by menthol. Studies of α7- nACh receptors endogenously expressed in neural cells demonstrate that menthol attenuates α7 mediated Ca(2+) transients in the cell body and neurite. In conclusion, our results suggest that menthol inhibits α7-nACh receptors in a noncompetitive manner.

Menthol inhibits 5-HT3 receptor-mediated currents.

The effects of alcohol monoterpene menthol, a major active ingredient of the peppermint plant, were tested on the function of human 5-hydroxytryptamine type 3 (5-HT3) receptors expressed in Xenopus laevis oocytes. 5-HT (1 µM)-evoked currents recorded by two-electrode voltage-clamp technique were reversibly inhibited by menthol in a concentration-dependent (IC50 = 163 µM) manner. The effects of menthol developed gradually, reaching a steady-state level within 10-15 minutes and did not involve G-proteins, since GTPγS activity remained unaltered and the effect of menthol was not sensitive to pertussis toxin pretreatment. The actions of menthol were not stereoselective as (-), (+), and racemic menthol inhibited 5-HT3 receptor-mediated currents to the same extent. Menthol inhibition was not altered by intracellular 1,2-bis(o-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid injections and transmembrane potential changes. The maximum inhibition observed for menthol was not reversed by increasing concentrations of 5-HT. Furthermore, specific binding of the 5-HT3 antagonist [(3)H]GR65630 was not altered in the presence of menthol (up to 1 mM), indicating that menthol acts as a noncompetitive antagonist of the 5-HT3 receptor. Finally, 5-HT3 receptor-mediated currents in acutely dissociated nodose ganglion neurons were also inhibited by menthol (100 µM). These data demonstrate that menthol, at pharmacologically relevant concentrations, is an allosteric inhibitor of 5-HT3 receptors.