Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis.

BACKGROUND: Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms. METHODS: We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms. RESULTS: Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS. CONCLUSION: This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

Genetic risk factors for ME/CFS identified using combinatorial analysis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients. METHODS: We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case-control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility. RESULTS: Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10-10 to 1.6 × 10-72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS. CONCLUSIONS: This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients.

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics.

Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.

Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients.

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Per- and polyfluoroalkyl substances in Northern California cats: Temporal comparison and a possible link to cat hyperthyroidism.

The indoor environment and dietary intake are considered to be major human exposure pathways to per- and polyfluoroalkyl substances (PFASs). Cats have similar exposures to humans by sharing their residential environments, although they have different diet, body sizes, and indoor activities. In the present study, we report PFAS levels in the serum of 2 groups of Northern California cats (>10 yr old) collected during 2 time periods: 2008 to 2010 (n = 21) and 2012 to 2013 (n = 22). Levels of ∑PFAS (geometric mean) were lower in the second period (geometric mean = 8.10 ng/mL) than the first time period (geometric mean = 15.8 ng/mL), although PFAS profiles remained similar. We also analyzed PFAS levels in human serum collected in the same time period (2008-2010) and geographic area, and compared the profiles and ∑PFAS levels (15.8 vs 14.3 ng/mL for cat and human, respectively). Long chain perfluorinated carboxylic acids, especially perfluorononanoic acid and perfluoroundecanoic acid, were significantly higher in cat serum than in humans. Furthermore, serum from hyperthyroid cats in the second time period showed higher ∑PFAS level (9.50 ng/mL) compared to nonhyperthyroid cats (7.24 ng/mL), and it is the perfluorooctanoic acid levels that were statistically significantly higher in hyperthyroid cats' serum (p < 0.05). This result may indicate a possible link between PFAS levels and cat hyperthyroid, warranting a larger study for further investigation. Environ Toxicol Chem 2018;37:2523-2529. © 2018 SETAC.

High polybrominated diphenyl ether levels in California house cats: house dust a primary source?

Polybrominated diphenyl ethers (PBDEs) are brominated flame retardants that act as endocrine disruptors, affecting thyroid hormone homeostasis. As a follow-up to a recent study showing high PBDE levels in household cats and linking PBDE levels with cat hyperthyroidism, we measured PBDEs, polychlorinated biphenyls (PCBs), and organochlorinated pesticides (OCPs) in serum samples from 26 California household cats (16 hyperthyroid, 10 controls) using liquid-liquid extraction and high-resolution gas chromatography/high-resolution mass spectrometry. In the present pilot study, we found that PBDE levels in California house cats were extremely high (ΣPBDEs median = 2,904 ng/g lipid; range, 631-22,537 ng/g lipid). This is approximately 50 times higher than levels in California residents (ΣPBDEs geomean = 62 ± 8.9 ng/g lipid, National Health and Nutrition Examination Survey), who have among the highest human levels in the world. Polybrominated diphenyl ethers congener patterns (BDE-99 major congener, BDE-209 significant) differed markedly from patterns found in California residents (BDE-47 major) or wildlife but resembled patterns found in house dust. Polychlorinated biphenyls and OCPs in cats were highly correlated, consistent with a shared dietary source or pathway of exposure, but did not correlate with PBDEs. This suggests a different source or pathway of exposure for PBDEs, which was most likely house dust. The authors found no evidence that linked levels of PBDEs, PCBs, or OCPs with hyperthyroidism. This may be because of the small sample size, competing or confounding risk factors, or complicated causal mechanisms.

An ESID case study at the federal level.

The D (dissemination) phase of the ESID model has been often overlooked in our efforts to create innovative and widespread social change. The process of replicating successful social innovations is both a prerequisite for dissemination (in order to assess the consistency of effects) and an obvious outcome of a successful dissemination effort. Fidelity, the extent to which a replicated program is implemented in a manner consistent with the original program model, is an important dimension of replication. This study was designed to provide empirical data related to three questions. Can complex social programs be implemented with fidelity? How much fidelity is appropriate or desired? What are the organizational dynamics of adoption with fidelity? Data were collected from grantees of a national replication initiative funded by the Center for Substance Abuse Prevention. Data suggest that high fidelity can be achieved, at least in the context in which programs are mandated to do so as part of the funding agreement and are given technical assistance in achieving fidelity. Secondly, programs perceived high fidelity as having positive effects on the program and its participants, a finding consistent with a limited assessment of the relationship of program outcomes and fidelity. Finally, much was learned about the human and organizational dynamics of replicating with fidelity. Implications for policy and direction regarding replication are discussed.