The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Saturation mutagenesis-reinforced functional assays for disease-related genes.

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single-nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.

Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy.

The December 2019 outbreak of a novel respiratory virus, SARS-CoV-2, has become an ongoing global pandemic due in part to the challenge of identifying symptomatic, asymptomatic, and pre-symptomatic carriers of the virus. CRISPR diagnostics can augment gold-standard PCR-based testing if they can be made rapid, portable, and accurate. Here, we report the development of an amplification-free CRISPR-Cas13a assay for direct detection of SARS-CoV-2 from nasal swab RNA that can be read with a mobile phone microscope. The assay achieved ∼100 copies/µL sensitivity in under 30 min of measurement time and accurately detected pre-extracted RNA from a set of positive clinical samples in under 5 min. We combined crRNAs targeting SARS-CoV-2 RNA to improve sensitivity and specificity and directly quantified viral load using enzyme kinetics. Integrated with a reader device based on a mobile phone, this assay has the potential to enable rapid, low-cost, point-of-care screening for SARS-CoV-2.

Mitochondria in disease: changes in shapes and dynamics.

Mitochondrial structure often determines the function of these highly dynamic, multifunctional, eukaryotic organelles, which are essential for maintaining cellular health. The dynamic nature of mitochondria is apparent in descriptions of different mitochondrial shapes [e.g., donuts, megamitochondria (MGs), and nanotunnels] and crista dynamics. This review explores the significance of dynamic alterations in mitochondrial morphology and regulators of mitochondrial and cristae shape. We focus on studies across tissue types and also describe new microscopy techniques for detecting mitochondrial morphologies both in vivo and in vitro that can improve understanding of mitochondrial structure. We highlight the potential therapeutic benefits of regulating mitochondrial morphology and discuss prospective avenues to restore mitochondrial bioenergetics to manage diseases related to mitochondrial dysfunction.

Characterizing metal-biomolecule interactions by mass spectrometry.

Metal micronutrients are essential for life and exist in a delicate balance to maintain an organism's health. The labile nature of metal-biomolecule interactions clouds the understanding of metal binders and metal-mediated conformational changes that are influential to health and disease. Mass spectrometry (MS)-based methods and technologies have been developed to better understand metal micronutrient dynamics in the intra- and extracellular environment. In this review, we describe the challenges associated with studying labile metals in human biology and highlight MS-based methods for the discovery and study of metal-biomolecule interactions.

Genomic Answers for Kids: Toward more equitable access to genomic testing for rare diseases in rural populations.

Next-generation sequencing has revolutionized the speed of rare disease (RD) diagnoses. While clinical exome and genome sequencing represent an effective tool for many RD diagnoses, there is room to further improve the diagnostic odyssey of many RD patients. One recognizable intervention lies in increasing equitable access to genomic testing. Rural communities represent a significant portion of underserved and underrepresented individuals facing additional barriers to diagnosis and treatment. Primary care providers (PCPs) at local clinics, though sometimes suspicious of a potential benefit of genetic testing for their patients, have significant constraints in pursuing it themselves and rely on referrals to specialists. Yet, these referrals are typically followed by long waitlists and significant delays in clinical assessment, insurance clearance, testing, and initiation of diagnosis-informed care management. Not only is this process time intensive, but it also often requires multiple visits to urban medical centers for which distance may be a significant barrier to rural families. Therefore, providing early, "direct-to-provider" (DTP) local access to unrestrictive genomic testing is likely to help speed up diagnostic times and access to care for RD patients in rural communities. In a pilot study with a PCP clinic in rural Kansas, we observed a minimum 5.5 months shortening of time to diagnosis through the DTP exome sequencing program as compared to rural patients receiving genetic testing through the "traditional" PCP-referral-to-specialist scheme. We share our experience to encourage future partnerships beyond our center. Our efforts represent just one step in fostering greater diversity and equity in genomic studies.

Toward clinical exomes in diagnostics and management of male infertility.

Infertility, affecting ∼10% of men, is predominantly caused by primary spermatogenic failure (SPGF). We screened likely pathogenic and pathogenic (LP/P) variants in 638 candidate genes for male infertility in 521 individuals presenting idiopathic SPGF and 323 normozoospermic men in the ESTAND cohort. Molecular diagnosis was reached for 64 men with SPGF (12%), with findings in 39 genes (6%). The yield did not differ significantly between the subgroups with azoospermia (20/185, 11%), oligozoospermia (18/181, 10%), and primary cryptorchidism with SPGF (26/155, 17%). Notably, 19 of 64 LP/P variants (30%) identified in 28 subjects represented recurrent findings in this study and/or with other male infertility cohorts. NR5A1 was the most frequently affected gene, with seven LP/P variants in six SPGF-affected men and two normozoospermic men. The link to SPGF was validated for recently proposed candidate genes ACTRT1, ASZ1, GLUD2, GREB1L, LEO1, RBM5, ROS1, and TGIF2LY. Heterozygous truncating variants in BNC1, reported in female infertility, emerged as plausible causes of severe oligozoospermia. Data suggested that several infertile men may present congenital conditions with less pronounced or pleiotropic phenotypes affecting the development and function of the reproductive system. Genes regulating the hypothalamic-pituitary-gonadal axis were affected in >30% of subjects with LP/P variants. Six individuals had more than one LP/P variant, including five with two findings from the gene panel. A 4-fold increased prevalence of cancer was observed in men with genetic infertility compared to the general male population (8% vs. 2%; p = 4.4 × 10-3). Expanding genetic testing in andrology will contribute to the multidisciplinary management of SPGF.

Functional characterization of 2,832 JAG1 variants supports reclassification for Alagille syndrome and improves guidance for clinical variant interpretation.

Pathogenic variants in the JAG1 gene are a primary cause of the multi-system disorder Alagille syndrome. Although variant detection rates are high for this disease, there is uncertainty associated with the classification of missense variants that leads to reduced diagnostic yield. Consequently, up to 85% of reported JAG1 missense variants have uncertain or conflicting classifications. We generated a library of 2,832 JAG1 nucleotide variants within exons 1-7, a region with a high number of reported missense variants, and designed a high-throughput assay to measure JAG1 membrane expression, a requirement for normal function. After calibration using a set of 175 known or predicted pathogenic and benign variants included within the variant library, 486 variants were characterized as functionally abnormal (n = 277 abnormal and n = 209 likely abnormal), of which 439 (90.3%) were missense. We identified divergent membrane expression occurring at specific residues, indicating that loss of the wild-type residue itself does not drive pathogenicity, a finding supported by structural modeling data and with broad implications for clinical variant classification both for Alagille syndrome and globally across other disease genes. Of 144 uncertain variants reported in patients undergoing clinical or research testing, 27 had functionally abnormal membrane expression, and inclusion of our data resulted in the reclassification of 26 to likely pathogenic. Functional evidence augments the classification of genomic variants, reducing uncertainty and improving diagnostics. Inclusion of this repository of functional evidence during JAG1 variant reclassification will significantly affect resolution of variant pathogenicity, making a critical impact on the molecular diagnosis of Alagille syndrome.

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C-two conditions presenting with overlapping symptoms-with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.

Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry.

The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.

Biomolecules capturing live bacteria from clinical samples.

Rapid phenotypic antimicrobial susceptibility testing (AST) requires the enrichment of live bacteria from patient samples, which is particularly challenging in the context of life-threatening bloodstream infections (BSIs) due to low bacterial titers. Over two decades, an extensive array of pathogen-specific biomolecules has been identified to capture live bacteria. The prevailing biomolecules are immune proteins of the complement system, antibodies, aptamers, phage proteins, and antimicrobial peptides. These biomolecules differ by their binder generation technologies and exhibit highly variable specificities, ranging from bacterial strains to most pathogenic bacteria. Here, we summarize how these diverse biomolecules were identified, list examples of successfully reported capture assays, and provide an outlook on the use of nanobodies raised against conserved surface-accessible proteins as promising biomolecules for pathogen capture.

Web-accessible application for identifying pathogenic transcripts with RNA-seq: Increased sensitivity in diagnosis of neurodevelopmental disorders.

For neurodevelopmental disorders (NDDs), a molecular diagnosis is key for management, predicting outcome, and counseling. Often, routine DNA-based tests fail to establish a genetic diagnosis in NDDs. Transcriptome analysis (RNA sequencing [RNA-seq]) promises to improve the diagnostic yield but has not been applied to NDDs in routine diagnostics. Here, we explored the diagnostic potential of RNA-seq in 96 individuals including 67 undiagnosed subjects with NDDs. We performed RNA-seq on single individuals' cultured skin fibroblasts, with and without cycloheximide treatment, and used modified OUTRIDER Z scores to detect gene expression outliers and mis-splicing by exonic and intronic outliers. Analysis was performed by a user-friendly web application, and candidate pathogenic transcriptional events were confirmed by secondary assays. We identified intragenic deletions, monoallelic expression, and pseudoexonic insertions but also synonymous and non-synonymous variants with deleterious effects on transcription, increasing the diagnostic yield for NDDs by 13%. We found that cycloheximide treatment and exonic/intronic Z score analysis increased detection and resolution of aberrant splicing. Importantly, in one individual mis-splicing was found in a candidate gene nearly matching the individual's specific phenotype. However, pathogenic splicing occurred in another neuronal-expressed gene and provided a molecular diagnosis, stressing the need to customize RNA-seq. Lastly, our web browser application allowed custom analysis settings that facilitate diagnostic application and ranked pathogenic transcripts as top candidates. Our results demonstrate that RNA-seq is a complementary method in the genomic diagnosis of NDDs and, by providing accessible analysis with improved sensitivity, our transcriptome analysis approach facilitates wider implementation of RNA-seq in routine genome diagnostics.

Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index.

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.

Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.

Automating hybrid collective intelligence in open-ended medical diagnostics.

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.

Antibiotic tolerance among clinical isolates: mechanisms, detection, prevalence, and significance.

SUMMARYAntibiotic treatment failures in the absence of resistance are not uncommon. Recently, attention has grown around the phenomenon of antibiotic tolerance, an underappreciated contributor to recalcitrant infections first detected in the 1970s. Tolerance describes the ability of a bacterial population to survive transient exposure to an otherwise lethal concentration of antibiotic without exhibiting resistance. With advances in genomics, we are gaining a better understanding of the molecular mechanisms behind tolerance, and several studies have sought to examine the clinical prevalence of tolerance. Attempts have also been made to assess the clinical significance of tolerance through in vivo infection models and prospective/retrospective clinical studies. Here, we review the data available on the molecular mechanisms, detection, prevalence, and clinical significance of genotypic tolerance that span ~50 years. We discuss the need for standardized methodology and interpretation criteria for tolerance detection and the impact that methodological inconsistencies have on our ability to accurately assess the scale of the problem. In terms of the clinical significance of tolerance, studies suggest that tolerance contributes to worse outcomes for patients (e.g., higher mortality, prolonged hospitalization), but historical data from animal models are varied. Furthermore, we lack the necessary information to effectively treat tolerant infections. Overall, while the tolerance field is gaining much-needed traction, the underlying clinical significance of tolerance that underpins all tolerance research is still far from clear and requires attention.

An updated list of eumycetoma causative agents and their differences in grain formation and treatment response.

SUMMARYIn 2023, the World Health Organization designated eumycetoma causative agents as high-priority pathogens on its list of fungal priority pathogens. Despite this recognition, a comprehensive understanding of these causative agents is lacking, and potential variations in clinical manifestations or therapeutic responses remain unclear. In this review, 12,379 eumycetoma cases were reviewed. In total, 69 different fungal species were identified as causative agents. However, some were only identified once, and there was no supporting evidence that they were indeed present in the grain. Madurella mycetomatis was by far the most commonly reported fungal causative agent. In most studies, identification of the fungus at the species level was based on culture or histology, which was prone to misidentifications. The newly used molecular identification tools identified new causative agents. Clinically, no differences were reported in the appearance of the lesion, but variations in mycetoma grain formation and antifungal susceptibility were observed. Although attempts were made to explore the differences in clinical outcomes based on antifungal susceptibility, the lack of large clinical trials and the inclusion of surgery as standard treatment posed challenges in drawing definitive conclusions. Limited case series suggested that eumycetoma cases caused by Fusarium species were less responsive to treatment than those caused by Madurella mycetomatis. However, further research is imperative for a comprehensive understanding.

Harnessing the host response for precision infectious disease diagnosis.

SUMMARYDetection of the presence of infection and its etiology must be accurate and timely to facilitate appropriate antimicrobial use. Diagnostic strategies that rely solely on pathogen detection often are insufficient due to poor test characteristics, inability to differentiate colonization from infection, or protracted delay to result. Understanding the human response across different pathogens on a clinical and molecular level can provide more accurate, timely, and useful answers, especially in critical illness and diagnostic uncertainty. Improvements in understanding the human immune response including genomics, protein analysis, gene expression, and cellular morphology have led to rapid innovation of new host response-based diagnostic tests. This review describes the limitations of pathogen-focused technology and the benefits of examining the breadth of immune response to diagnose infection. It then explores biomarkers that have been studied for this purpose and scrutinizes the performance of host-based multianalyte testing. Currently cleared diagnostics and those in late-stage development are described in depth, with a focus on the purpose of testing and its utility for clinicians. Finally, it concludes by examining opportunities for further host response-derived diagnostic innovation.

Advances in the molecular biology of the solitary fibrous tumor and potential impact on clinical applications.

Solitary fibrous tumor (SFT) is a rare fibroblastic mesenchymal neoplasm. The current classification has merged SFT and hemangiopericytoma (HPC) into the same tumor entity, while the risk stratification models have been developed to compensate for clinical prediction. Typically, slow-growing and asymptomatic, SFT can occur in various anatomical sites, most commonly in the pleura. Histologically, SFT consists of spindle to oval cells with minimal patterned growth, surrounded by stromal collagen and unique vascular patterns. Molecularly, SFT is defined by the fusion of NGFI-A-binding protein 2 (NAB2) and signal transducer and activator of transcription 6 (STAT6) genes as NAB2-STAT6. This fusion transforms NAB2 into a transcriptional activator, activating early growth response 1 (EGR1) and contributing to SFT pathogenesis and development. There are several fusion variants of NAB2-STAT6 in tumor tissues, with the most frequent ones being NAB2ex4-STAT6ex2 and NAB2ex6-STAT6ex16/ex17. Diagnostic methods play a crucial role in SFT clinical practice and basic research, including RT-PCR, next-generation sequencing (NGS), FISH, immunohistochemistry (IHC), and Western blot analysis, each with distinct capabilities and limitations. Traditional treatment strategies of SFT encompass surgical resection, radiation therapy, and chemotherapy, while emerging management regimes include antiangiogenic agents, immunotherapy, RNA-targeting technologies, and potential targeted drugs. This review provides an update on SFT's clinical and molecular aspects, diagnostic methods, and potential therapies.

Complex genomic rearrangements: an underestimated cause of rare diseases.

Complex genomic rearrangements (CGRs) are known contributors to disease but are often missed during routine genetic screening. Identifying CGRs requires (i) identifying copy number variants (CNVs) concurrently with inversions, (ii) phasing multiple breakpoint junctions incis, as well as (iii) detecting and resolving structural variants (SVs) within repeats. We demonstrate how combining cytogenetics and new sequencing methodologies is being successfully applied to gain insights into the genomic architecture of CGRs. In addition, we review CGR patterns and molecular features revealed by studying constitutional genomic disorders. These data offer invaluable lessons to individuals interested in investigating CGRs, evaluating their clinical relevance and frequency, as well as assessing their impact(s) on rare genetic diseases.