Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research.

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing.

Excitable Signal Transduction Networks in Directed Cell Migration.

Although directed migration of eukaryotic cells may have evolved to escape nutrient depletion, it has been adopted for an extensive range of physiological events during development and in the adult organism. The subversion of these movements results in disease, such as cancer. Mechanisms of propulsion and sensing are extremely diverse, but most eukaryotic cells move by extending actin-filled protrusions termed macropinosomes, pseudopodia, or lamellipodia or by extension of blebs. In addition to motility, directed migration involves polarity and directional sensing. The hundreds of gene products involved in these processes are organized into networks of parallel and interconnected pathways. Many of these components are activated or inhibited coordinately with stimulation and on each spontaneously extended protrusion. Moreover, these networks display hallmarks of excitability, including all-or-nothing responsiveness and wave propagation. Cellular protrusions result from signal transduction waves that propagate outwardly from an origin and drive cytoskeletal activity. The range of the propagating waves and hence the size of the protrusions can be altered by lowering or raising the threshold for network activation, with larger and wider protrusions favoring gliding or oscillatory behavior over amoeboid migration. Here, we evaluate the variety of models of excitable networks controlling directed migration and outline critical tests. We also discuss the utility of this emerging view in producing cell migration and in integrating the various extrinsic cues that direct migration.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Pathogen perception and signaling in plant immunity.

Plant diseases are a constant and serious threat to agriculture and ecological biodiversity. Plants possess a sophisticated innate immunity system capable of detecting and responding to pathogen infection to prevent disease. Our understanding of this system has grown enormously over the past century. Early genetic descriptions of plant disease resistance and pathogen virulence were embodied in the gene-for-gene hypothesis, while physiological studies identified pathogen-derived elicitors that could trigger defense responses in plant cells and tissues. Molecular studies of these phenomena have now coalesced into an integrated model of plant immunity involving cell surface and intracellular detection of specific pathogen-derived molecules and proteins culminating in the induction of various cellular responses. Extracellular and intracellular receptors engage distinct signaling processes but converge on many similar outputs with substantial evidence now for integration of these pathways into interdependent networks controlling disease outcomes. Many of the molecular details of pathogen recognition and signaling processes are now known, providing opportunities for bioengineering to enhance plant protection from disease. Here we provide an overview of the current understanding of the main principles of plant immunity, with an emphasis on the key scientific milestones leading to these insights.

Complex structures arising from the self-assembly of a simple organic salt.

Molecular self-assembly is the spontaneous association of simple molecules into larger and ordered structures1. It is the basis of several natural processes, such as the formation of colloids, crystals, proteins, viruses and double-helical DNA2. Molecular self-assembly has inspired strategies for the rational design of materials with specific chemical and physical properties3, and is one of the most important concepts in supramolecular chemistry. Although molecular self-assembly has been extensively investigated, understanding the rules governing this phenomenon remains challenging. Here we report on a simple hydrochloride salt of fampridine that crystallizes as four different structures, two of which adopt unusual self-assemblies consisting of polyhedral clusters of chloride and pyridinium ions. These two structures represent Frank-Kasper (FK) phases of a small and rigid organic molecule. Although discovered in metal alloys4,5 more than 60 years ago, FK phases have recently been observed in several classes of supramolecular soft matter6-11 and in gold nanocrystal superlattices12 and remain the object of recent discoveries13. In these systems, atoms or spherical assemblies of molecules are packed to form polyhedra with coordination numbers 12, 14, 15 or 16. The two FK structures reported here crystallize from a dense liquid phase and show a complexity that is generally not observed in small rigid organic molecules. Investigation of the precursor dense liquid phase by cryogenic electron microscopy reveals the presence of spherical aggregates with sizes ranging between 1.5 and 4.6 nanometres. These structures, together with the experimental procedure used for their preparation, invite interesting speculation about their formation and open different perspectives for the design of organic crystalline materials.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

SARS-CoV-2 NSP5 antagonizes MHC II expression by subverting histone deacetylase 2.

SARS-CoV-2 interferes with antigen presentation by downregulating major histocompatibility complex (MHC) II on antigen-presenting cells, but the mechanism mediating this process is unelucidated. Herein, analysis of protein and gene expression in human antigen-presenting cells reveals that MHC II is downregulated by the SARS-CoV-2 main protease, NSP5. This suppression of MHC II expression occurs via decreased expression of the MHC II regulatory protein CIITA. CIITA downregulation is independent of the proteolytic activity of NSP5, and rather, NSP5 delivers HDAC2 to the transcription factor IRF3 at an IRF-binding site within the CIITA promoter. Here, HDAC2 deacetylates and inactivates the CIITA promoter. This loss of CIITA expression prevents further expression of MHC II, with this suppression alleviated by ectopic expression of CIITA or knockdown of HDAC2. These results identify a mechanism by which SARS-CoV-2 limits MHC II expression, thereby delaying or weakening the subsequent adaptive immune response.

An AI-guided screen identifies probucol as an enhancer of mitophagy through modulation of lipid droplets.

Failures in mitophagy, a process by which damaged mitochondria are cleared, results in neurodegeneration, while enhancing mitophagy promotes the survival of dopaminergic neurons. Using an artificial intelligence platform, we employed a natural language processing approach to evaluate the semantic similarity of candidate molecules to a set of well-established mitophagy enhancers. Top candidates were screened in a cell-based mitochondrial clearance assay. Probucol, a lipid-lowering drug, was validated across several orthogonal mitophagy assays. In vivo, probucol improved survival, locomotor function, and dopaminergic neuron loss in zebrafish and fly models of mitochondrial damage. Probucol functioned independently of PINK1/Parkin, but its effects on mitophagy and in vivo depended on ABCA1, which negatively regulated mitophagy following mitochondrial damage. Autophagosome and lysosomal markers were elevated by probucol treatment in addition to increased contact between lipid droplets (LDs) and mitochondria. Conversely, LD expansion, which occurs following mitochondrial damage, was suppressed by probucol and probucol-mediated mitophagy enhancement required LDs. Probucol-mediated LD dynamics changes may prime the cell for a more efficient mitophagic response to mitochondrial damage.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Nanotopography modulates intracellular excitable systems through cytoskeleton actuation.

Cellular sensing of most environmental cues involves receptors that affect a signal-transduction excitable network (STEN), which is coupled to a cytoskeletal excitable network (CEN). We show that the mechanism of sensing of nanoridges is fundamentally different. CEN activity occurs preferentially on nanoridges, whereas STEN activity is constrained between nanoridges. In the absence of STEN, waves disappear, but long-lasting F-actin puncta persist along the ridges. When CEN is suppressed, wave propagation is no longer constrained by nanoridges. A computational model reproduces these experimental observations. Our findings indicate that nanotopography is sensed directly by CEN, whereas STEN is only indirectly affected due to a CEN-STEN feedback loop. These results explain why texture sensing is robust and acts cooperatively with multiple other guidance cues in complex, in vivo microenvironments.

Phenotype-aware prioritisation of rare Mendelian disease variants.

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.

Reverse fountain flow of phosphatidylinositol-3,4-bisphosphate polarizes migrating cells.

The ability of cells to polarize and move toward external stimuli plays a crucial role in development, as well as in normal and pathological physiology. Migrating cells maintain dynamic complementary distributions of Ras activity and of the phospholipid phosphatidylinositol-3,4-bisphosphate (PI(3,4)P2). Here, we show that lagging-edge component PI(3,4)P2 also localizes to retracting leading-edge protrusions and nascent macropinosomes, even in the absence of phosphatidylinositol 3,4,5-trisphosphate (PIP3). Once internalized, macropinosomes break up into smaller PI(3,4)P2-enriched vesicles, which fuse with the plasma membrane at the rear of the cell. Subsequently, the phosphoinositide diffuses toward the front of the cell, where it is degraded. Computational modeling confirms that this cycle gives rise to stable back-to-front gradient. These results uncover a surprising "reverse-fountain flow" of PI(3,4)P2 that regulates polarity.

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology.

MOTIVATION: Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing adoption of large language models (LLMs) for natural language understanding has led to several LLM-based solutions, we argue that their intrinsic resource-intensive nature is not suitable for realistic management of the phenotype CR lifecycle. Consequently, we propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate refresh of the ontological concepts as well as efficient re-analysis of past data. RESULTS: We developed a dictionary-based approach using a pre-built large collection of clusters of morphologically equivalent tokens-to address lexical variability and a more effective CR step by reducing the entity boundary detection strictly to candidates consisting of tokens belonging to ontology concepts. Our method achieves state-of-the-art results (0.76 F1 on the GSC+ corpus) and a processing efficiency of 10 000 publication abstracts in 5 s. AVAILABILITY AND IMPLEMENTATION: FastHPOCR is available as a Python package installable via pip. The source code is available at https://github.com/tudorgroza/fast_hpo_cr. A Java implementation of FastHPOCR will be made available as part of the Fenominal Java library available at https://github.com/monarch-initiative/fenominal. The up-to-date GCS-2024 corpus is available at https://github.com/tudorgroza/code-for-papers/tree/main/gsc-2024.

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.

MOTIVATION: Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. RESULTS: Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM's native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. AVAILABILITY AND IMPLEMENTATION: SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Two kinds of memory signals in neurons of the human hippocampus.

Prior studies of the neural representation of episodic memory in the human hippocampus have identified generic memory signals representing the categorical status of test items (novel vs. repeated), whereas other studies have identified item specific memory signals representing individual test items. Here, we report that both kinds of memory signals can be detected in hippocampal neurons in the same experiment. We recorded single-unit activity from four brain regions (hippocampus, amygdala, anterior cingulate, and prefrontal cortex) of epilepsy patients as they completed a continuous recognition task. The generic signal was found in all four brain regions, whereas the item-specific memory signal was detected only in the hippocampus and reflected sparse coding. That is, for the item-specific signal, each hippocampal neuron responded strongly to a small fraction of repeated words, and each repeated word elicited strong responding in a small fraction of neurons. The neural code was sparse, pattern-separated, and limited to the hippocampus, consistent with longstanding computational models. We suggest that the item-specific episodic memory signal in the hippocampus is fundamental, whereas the more widespread generic memory signal is derivative and is likely used by different areas of the brain to perform memory-related functions that do not require item-specific information.

Impaired interferon response in plasmacytoid dendritic cells from children with persistent wheeze.

BACKGROUND: Impaired interferon response and allergic sensitization may contribute to virus-induced wheeze and asthma development in young children. Plasmacytoid dendritic cells (pDCs) play a key role in antiviral immunity as critical producers of type I interferons. pDCs also express the high-affinity IgE receptor through which type I interferon production may be negatively regulated. Whether antiviral function of pDCs is associated with recurrent episodes of wheeze in young children is not well understood. OBJECTIVE: We sought to evaluate the phenotype and function of circulating pDCs in children with a longitudinally defined wheezing phenotype. METHODS: We performed multiparameter flow cytometry on PBMCs from 38 children presenting to the emergency department with an acute episode of respiratory wheeze and 19 controls. RNA sequencing on isolated pDCs from the same individuals was also performed. For each subject, their longitudinal exacerbation phenotype was determined using the Western Australia public hospital database. RESULTS: We observed a significant depletion of circulating pDCs in young children with a persistent phenotype of wheeze. The same individuals also displayed upregulation of the FcεRI on their pDCs. Based on transcriptomic analysis, pDCs from these individuals did not mount a robust systemic antiviral response as observed in children who displayed a nonrecurrent wheezing phenotype. CONCLUSIONS: Our data suggest that circulating pDC phenotype and function are altered in young children with a persistent longitudinal exacerbation phenotype. Expression of high-affinity IgE receptor is increased and their function as major interferon producers is impaired during acute exacerbations of wheeze.

Comparative Analysis of the Avirulence Effectors Produced by the Fungal Stem Rust Pathogen of Wheat.

Crops are constantly exposed to pathogenic microbes. Rust fungi are examples of these harmful microorganisms, which have a major economic impact on wheat production. To protect themselves from pathogens like rust fungi, plants employ a multilayered immune system that includes immunoreceptors encoded by resistance genes. Significant efforts have led to the isolation of numerous resistance genes against rust fungi in cereals, especially in wheat. However, the evolution of virulence of rust fungi hinders the durability of resistance genes as a strategy for crop protection. Rust fungi, like other biotrophic pathogens, secrete an arsenal of effectors to facilitate infection, and these are the molecules that plant immunoreceptors target for pathogen recognition and mounting defense responses. When recognized, these effector proteins are referred to as avirulence (Avr) effectors. Despite the many predicted effectors in wheat rust fungi, only five Avr genes have been identified, all from wheat stem rust. Knowledge of the Avr genes and their variation in the fungal population will inform deployment of the most appropriate wheat disease-resistance genes for breeding and farming. The review provides an overview of methodologies as well as the validation techniques that have been used to characterize Avr effectors from wheat stem rust. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Genome-Enabled Analysis of Population Dynamics and Virulence-Associated Loci in the Oat Crown Rust Fungus Puccinia coronata f. sp. avenae.

Puccinia coronata f. sp. avenae (Pca) is an important fungal pathogen causing crown rust that impacts oat production worldwide. Genetic resistance for crop protection against Pca is often overcome by the rapid virulence evolution of the pathogen. This study investigated the factors shaping adaptive evolution of Pca using pathogen populations from distinct geographic regions within the United States and South Africa. Phenotypic and genome-wide sequencing data of these diverse Pca collections, including 217 isolates, uncovered phylogenetic relationships and established distinct genetic composition between populations from northern and southern regions from the United States and South Africa. The population dynamics of Pca involve a bidirectional movement of inoculum between northern and southern regions of the United States and contributions from clonality and sexuality. The population from South Africa is solely clonal. A genome-wide association study (GWAS) employing a haplotype-resolved Pca reference genome was used to define 11 virulence-associated loci corresponding to 25 oat differential lines. These regions were screened to determine candidate Avr effector genes. Overall, the GWAS results allowed us to identify the underlying genetic factors controlling pathogen recognition in an oat differential set used in the United States to assign pathogen races (pathotypes). Key GWAS findings support complex genetic interactions in several oat lines, suggesting allelism among resistance genes or redundancy of genes included in the differential set, multiple resistance genes recognizing genetically linked Avr effector genes, or potentially epistatic relationships. A careful evaluation of the composition of the oat differential set accompanied by the development or implementation of molecular markers is recommended. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Enzyme-like Acyl Transfer Catalysis in a Bifunctional Organic Cage.

Amide-based organic cage cavities are, in principle, ideal enzyme active site mimics. Yet, cage-promoted organocatalysis has remained elusive, in large part due to synthetic accessibility of robust and functional scaffolds. Herein, we report the acyl transfer catalysis properties of robust, hexaamide cages in organic solvent. Cage structural variation reveals that esterification catalysis with an acyl anhydride acyl carrier occurs only in bifunctional cages featuring internal pyridine motifs and two crucial antipodal carboxylic acid groups. 1H NMR data and X-ray crystallography show that the acyl carrier is rapidly activated inside the cavity as a covalent mixed-anhydride intermediate with an internal hydrogen bond. Michaelis-Menten (saturation) kinetics suggest weak binding (KM = 0.16 M) of the alcohol pronucleophile close to the internal anhydride. Finally, activation and delivery of the alcohol to the internal anhydride by the second carboxylic acid group forms ester product and releases the cage catalyst. Eyring analysis indicates a strong enthalpic stabilization of the transition state (5.5 kcal/mol) corresponding to a rate acceleration of 104 over background acylation, and an ordered, associative rate-determining attack by the alcohol, supported by DFT calculations. We conclude that internal bifunctional organocatalysis specific to the cage structural design is responsible for the enhancement over the background reaction. These results pave the way for organic-phase enzyme mimicry in self-assembled cavities with the potential for cavity elaboration to enact selective acylations.