Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations.

Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10-9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10-16 and CPA3; p = 2.39 × 10-14) and wound healing (FN1; p = 7.63 × 10-9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes - increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response - that may play a critical role in asthma within the African Diaspora.

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.

Pharmacogenetic studies of long-acting beta agonist and inhaled corticosteroid responsiveness in randomised controlled trials of individuals of African descent with asthma.

BACKGROUND: Pharmacogenetic studies in asthma cohorts, primarily made up of White people of European descent, have identified loci associated with response to inhaled beta agonists and corticosteroids (ICSs). Differences exist in how individuals from different ancestral backgrounds respond to long-acting beta agonist (LABA) and ICSs. Therefore, we sought to understand the pharmacogenetic mechanisms regulating therapeutic responsiveness in individuals of African descent. METHODS: We did ancestry-based pharmacogenetic studies of children (aged 5-11 years) and adolescents and adults (aged 12-69 years) from the Best African Response to Drug (BARD) trials, in which participants with asthma uncontrolled with low-dose ICS (fluticasone propionate 50 µg in children, 100 µg in adolescents and adults) received different step-up combination therapies. The hierarchal composite outcome of pairwise superior responsiveness in BARD was based on asthma exacerbations, a 31-day difference in annualised asthma-control days, or a 5% difference in percentage predicted FEV1. We did whole-genome admixture mapping of 15 159 ancestral segments within 312 independent regions, stratified by the two age groups. The two co-primary outcome comparisons were the step up from low-dose ICS to the quintuple dose of ICS (5 × ICS: 250 µg twice daily in children and 500 µg twice daily in adolescents and adults) versus double dose (2-2·5 × ICS: 100 µg twice daily in children, 250 µg twice daily in adolescents and adults), and 5 × ICS versus 100 µg fluticasone plus a LABA (salmeterol 50 µg twice daily). We used a genome-wide significance threshold of p<1·6 × 10-4, and tested for replication using independent cohorts of individuals of African descent with asthma. FINDINGS: We included 249 unrelated children and 267 unrelated adolescents and adults in the BARD pharmacogenetic analysis. In children, we identified a significant admixture mapping peak for superior responsiveness to 5 × ICS versus 100 µg fluticasone plus salmeterol on chromosome 12 (odds ratio [ORlocal African] 3·95, 95% CI 2·02-7·72, p=6·1 × 10-5) fine mapped to a locus adjacent to RNFT2 and NOS1 (rs73399224, ORallele dose 0·17, 95% CI 0·07-0·42, p=8·4 × 10-5). In adolescents and adults, we identified a peak for superior responsiveness to 5 × ICS versus 2·5 × ICS on chromosome 22 (ORlocal African 3·35, 1·98-5·67, p=6·8 × 10-6) containing a locus adjacent to TPST2 (rs5752429, ORallele dose 0·21, 0·09-0·52, p=5·7 × 10-4). We replicated rs5752429 and nominally replicated rs73399224 in independent African American cohorts. INTERPRETATION: BARD is the first genome-wide pharmacogenetic study of LABA and ICS response in clinical trials of individuals of African descent to detect and replicate genome-wide significant loci. Admixture mapping of the composite BARD trial outcome enabled the identification of novel pharmacogenetic variation accounting for differential therapeutic responses in people of African descent with asthma. FUNDING: National Institutes of Health, National Heart, Lung, and Blood Institute.

Multiethnic genome-wide and HLA association study of total serum IgE level.

BACKGROUND: Total serum IgE (tIgE) is an important intermediate phenotype of allergic disease. Whole genome genetic association studies across ancestries may identify important determinants of IgE. OBJECTIVE: We aimed to increase understanding of genetic variants affecting tIgE production across the ancestry and allergic disease spectrum by leveraging data from the National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine program; the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA); and the Atopic Dermatitis Research Network (N = 21,901). METHODS: We performed genome-wide association within strata of study, disease, and ancestry groups, and we combined results via a meta-regression approach that models heterogeneity attributable to ancestry. We also tested for association between HLA alleles called from whole genome sequence data and tIgE, assessing replication of associations in HLA alleles called from genotype array data. RESULTS: We identified 6 loci at genome-wide significance (P < 5 × 10-9), including 4 loci previously reported as genome-wide significant for tIgE, as well as new regions in chr11q13.5 and chr15q22.2, which were also identified in prior genome-wide association studies of atopic dermatitis and asthma. In the HLA allele association study, HLA-A∗02:01 was associated with decreased tIgE level (Pdiscovery = 2 × 10-4; Preplication = 5 × 10-4; Pdiscovery+replication = 4 × 10-7), and HLA-DQB1∗03:02 was strongly associated with decreased tIgE level in Hispanic/Latino ancestry populations (PHispanic/Latino discovery+replication = 8 × 10-8). CONCLUSION: We performed the largest genome-wide association study and HLA association study of tIgE focused on ancestrally diverse populations and found several known tIgE and allergic disease loci that are relevant in non-European ancestry populations.

Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual's collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity.

Finding and extending ancient simple sequence repeat-derived regions in the human genome.

BACKGROUND: Previously, 3% of the human genome has been annotated as simple sequence repeats (SSRs), similar to the proportion annotated as protein coding. The origin of much of the genome is not well annotated, however, and some of the unidentified regions are likely to be ancient SSR-derived regions not identified by current methods. The identification of these regions is complicated because SSRs appear to evolve through complex cycles of expansion and contraction, often interrupted by mutations that alter both the repeated motif and mutation rate. We applied an empirical, kmer-based, approach to identify genome regions that are likely derived from SSRs. RESULTS: The sequences flanking annotated SSRs are enriched for similar sequences and for SSRs with similar motifs, suggesting that the evolutionary remains of SSR activity abound in regions near obvious SSRs. Using our previously described P-clouds approach, we identified 'SSR-clouds', groups of similar kmers (or 'oligos') that are enriched near a training set of unbroken SSR loci, and then used the SSR-clouds to detect likely SSR-derived regions throughout the genome. CONCLUSIONS: Our analysis indicates that the amount of likely SSR-derived sequence in the human genome is 6.77%, over twice as much as previous estimates, including millions of newly identified ancient SSR-derived loci. SSR-clouds identified poly-A sequences adjacent to transposable element termini in over 74% of the oldest class of Alu (roughly, AluJ), validating the sensitivity of the approach. Poly-A's annotated by SSR-clouds also had a length distribution that was more consistent with their poly-A origins, with mean about 35 bp even in older Alus. This work demonstrates that the high sensitivity provided by SSR-Clouds improves the detection of SSR-derived regions and will enable deeper analysis of how decaying repeats contribute to genome structure.

Inference of transposable element ancestry.

Most common methods for inferring transposable element (TE) evolutionary relationships are based on dividing TEs into subfamilies using shared diagnostic nucleotides. Although originally justified based on the "master gene" model of TE evolution, computational and experimental work indicates that many of the subfamilies generated by these methods contain multiple source elements. This implies that subfamily-based methods give an incomplete picture of TE relationships. Studies on selection, functional exaptation, and predictions of horizontal transfer may all be affected. Here, we develop a Bayesian method for inferring TE ancestry that gives the probability that each sequence was replicative, its frequency of replication, and the probability that each extant TE sequence came from each possible ancestral sequence. Applying our method to 986 members of the newly-discovered LAVA family of TEs, we show that there were far more source elements in the history of LAVA expansion than subfamilies identified using the CoSeg subfamily-classification program. We also identify multiple replicative elements in the AluSc subfamily in humans. Our results strongly indicate that a reassessment of subfamily structures is necessary to obtain accurate estimates of mutation processes, phylogenetic relationships and historical times of activity.

The neuroprotective peptide NAP does not directly affect polymerization or dynamics of reconstituted neural microtubules.

NAP (Asn-Ala-Pro-Val-Ser-Ile-Pro-Gln) is a neuroprotective peptide that shows cognitive protection in patients with amnestic mild cognitive impairment, a precursor to Alzheimer's disease. NAP exhibits potent neuroprotective properties in several in vivo and cellular models of neural injury. While NAP has been found in many studies to affect microtubule assembly and/or stability in neuronal and glial cells at fM concentrations, it has remained unclear whether NAP acts directly or indirectly on tubulin or microtubules. We analyzed the effects of NAP (1 fM-1 microM) on the assembly of reconstituted bovine brain microtubules in vitro and found that it did not significantly (p< 0.05) alter polymerization of either purified tubulin or of a mixture of tubulin and unfractionated microtubule-associated proteins. NAP also had no significant effect (p < 0.05) on the growing and shortening dynamics of steady-state microtubules at their plus ends, nor did it alter the polymerization or dynamics of microtubules assembled in the presence of 3-repeat or 4-repeat tau. Thus, the neuroprotective activity of NAP does not appear to involve a direct action on the polymerization or dynamics of purified tubulin or microtubules.

Carbendazim inhibits cancer cell proliferation by suppressing microtubule dynamics.

Carbendazim (methyl 2-benzimidazolecarbamate) is widely used as a systemic fungicide in human food production and appears to act on fungal tubulin. However, it also inhibits proliferation of human cancer cells, including drug- and multidrug-resistant and p53-deficient cell lines. Because of its promising preclinical anti-tumor activity, it has undergone phase I clinical trials and is under further clinical development. Although it weakly inhibits polymerization of brain microtubules and induces G(2)/M arrest in tumor cells, its mechanism of action in human cells has not been fully elucidated. We examined its mechanism of action in MCF7 human breast cancer cells and found that it inhibits proliferation (IC(50), 10 microM) and half-maximally arrests mitosis at a similar concentration (8 microM), in concert with suppression of microtubule dynamic instability without appreciable microtubule depolymerization. It induces mitotic spindle abnormalities and reduces the metaphase intercentromere distance of sister chromatids, indicating reduction of tension on kinetochores, thus leading to metaphase arrest. With microtubules assembled in vitro from pure tubulin, carbendazim also suppresses dynamic instability, reducing the dynamicity by 50% at 10 microM, with only minimal (21%) reduction of polymer mass. Carbendazim binds to mammalian tubulin (K(d), 42.8 +/- 4.0 microM). Unlike some benzimidazoles that bind to the colchicine site in tubulin, carbendazim neither competes with colchicine nor competes with vinblastine for binding to brain tubulin. Thus, carbendazim binds to an as yet unidentified site in tubulin and inhibits tumor cell proliferation by suppressing the growing and shortening phases of microtubule dynamic instability, thus inducing mitotic arrest.

Effects of tetramethoxystilbene on hormone-resistant breast cancer cells: biological and biochemical mechanisms of action.

Secondary resistance to hormonal therapy for breast cancer commonly develops after an initial response to tamoxifen or aromatase inhibitors. Agents to abrogate these adaptive changes would substantially enhance the long-term benefits of hormonal therapy. Our studies with a stilbene derivative called TMS (2,3',4,5'-tetramethoxystilbene) identified unexpected effects with potential utility for treatment of breast tumors secondarily resistant to hormonal therapy. TMS was originally developed as an inhibitor of cytochrome P450 1B1 to block the conversion of estradiol to 4-OH-estradiol. While studying this agent in three models of hormone resistance, we detected direct antitumor effects not related to its role as an inhibitor of catecholestrogens. During examination of the mechanisms involved, we showed that treatment with 3 micromol/L TMS for 24 h inhibited tubulin polymerization and microtubule formation, caused a cell cycle block at the G2-M phase, and induced apoptosis. TMS also inhibited activated focal adhesion kinase (FAK), Akt, and mammalian target of rapamycin (mTOR) and stimulated c-jun-NH2-kinase and p38 mitogen-activated protein kinase activity. With respect to antitumor effects, TMS at a concentrations of 0.2 to 0.3 micromol/L inhibited the growth of long-term tamoxifen-treated MCF-7 cells by 80% and fulvestrant-treated MCF-7 cells by 70%. In vivo studies, involving 8 weeks of treatment with TMS via a 30-mg s.c. implant, reduced tumor volume of tamoxifen-resistant MCF-7 breast cancer xenografts by 53%. Our data suggest that TMS is a promising therapeutic agent because of its unique ability to block several pathways involved in the development of hormone resistance.