Publications by authors named "Michael Snyder"

753 Publications

COVID-19-Induced New-Onset Diabetes: Trends and Technologies.

Diabetes 2021 Oct 22. Epub 2021 Oct 22.

Department of Genetics, Stanford University, Stanford, CA

The coronavirus disease 2019 (COVID-19) global pandemic continues to spread worldwide with approximately 216 million confirmed cases and 4.49 million deaths to date. Intensive efforts are ongoing to combat this disease by suppressing viral transmission, understanding its pathogenesis, developing vaccination strategies, and identifying effective therapeutic targets. Individuals with preexisting diabetes also show higher incidence of COVID-19 illness and poorer prognosis upon infection. Likewise, an increased frequency of diabetes onset and diabetes complications has been reported in patients following COVID-19 diagnosis. COVID-19 may elevate the risk of hyperglycemia and other complications in patients with and without prior diabetes history. It is unclear whether the virus induces type 1 or type 2 diabetes or instead causes a novel atypical form of diabetes. Moreover, it remains unknown if recovering COVID-19 patients exhibit a higher risk of developing new-onset diabetes or its complications going forward. The aim of this review is to summarize what is currently known about the epidemiology and mechanisms of this bidirectional relationship between COVID-19 and diabetes. We highlight major challenges that hinder the study of COVID-19-induced new-onset of diabetes and propose a potential framework for overcoming these obstacles. We also review state-of-the-art wearables and microsampling technologies that can further study diabetes management and progression in new-onset diabetes cases. We conclude by outlining current research initiatives investigating the bidirectional relationship between COVID-19 and diabetes, some with emphasis on wearable technology.
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http://dx.doi.org/10.2337/dbi21-0029DOI Listing
October 2021

Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy.

Circulation 2021 Oct 21. Epub 2021 Oct 21.

Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; Cardiovascular Research Institute, Stanford University School of Medicine, Stanford, CA.

Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant advances in elucidating sarcomeric structure-function relationships, there is still much to be learned about the mechanisms that link altered cardiac energetics to HCM phenotypes. In this work, we test the hypothesis that changes in cardiac energetics represent a common pathophysiologic pathway in HCM. We performed a comprehensive multi-omics profile of the molecular (transcripts, metabolites, and complex lipids), ultrastructural, and functional components of HCM energetics using myocardial samples from 27 HCM patients and 13 normal controls (donor hearts). Integrated omics analysis revealed alterations in a wide array of biochemical pathways with major dysregulation in fatty acid metabolism, reduction of acylcarnitines, and accumulation of free fatty acids. HCM hearts showed evidence of global energetic decompensation manifested by a decrease in high energy phosphate metabolites [ATP, ADP, and phosphocreatine (PCr)] and a reduction in mitochondrial genes involved in creatine kinase and ATP synthesis. Accompanying these metabolic derangements, electron microscopy showed an increased fraction of severely damaged mitochondria with reduced cristae density, coinciding with reduced citrate synthase (CS) activity and mitochondrial oxidative respiration. These mitochondrial abnormalities were associated with elevated reactive oxygen species (ROS) and reduced antioxidant defenses. However, despite significant mitochondrial injury, HCM hearts failed to upregulate mitophagic clearance. Overall, our findings suggest that perturbed metabolic signaling and mitochondrial dysfunction are common pathogenic mechanisms in patients with HCM. These results highlight potential new drug targets for attenuation of the clinical disease through improving metabolic function and reducing mitochondrial injury.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.121.053575DOI Listing
October 2021

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation.

Nat Genet 2021 Oct 14. Epub 2021 Oct 14.

Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA.

Transcription factors bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multiomic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep-learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter assay analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach shows the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation.
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http://dx.doi.org/10.1038/s41588-021-00947-3DOI Listing
October 2021

A DMS Shotgun Lipidomics Workflow Application to Facilitate High-Throughput, Comprehensive Lipidomics.

J Am Soc Mass Spectrom 2021 Oct 12. Epub 2021 Oct 12.

Department of Biological Chemistry, University of California, Los Angeles, California 90095, United States.

Differential mobility spectrometry (DMS) is highly useful for shotgun lipidomic analysis because it overcomes difficulties in measuring isobaric species within a complex lipid sample and allows for acyl tail characterization of phospholipid species. Despite these advantages, the resulting workflow presents technical challenges, including the need to tune the DMS before every batch to update compensative voltages settings within the method. The Sciex Lipidyzer platform uses a Sciex 5500 QTRAP with a DMS (SelexION), an LC system configured for direction infusion experiments, an extensive set of standards designed for quantitative lipidomics, and a software package (Lipidyzer Workflow Manager) that facilitates the workflow and rapidly analyzes the data. Although the Lipidyzer platform remains very useful for DMS-based shotgun lipidomics, the software is no longer updated for current versions of Analyst and Windows. Furthermore, the software is fixed to a single workflow and cannot take advantage of new lipidomics standards or analyze additional lipid species. To address this multitude of issues, we developed Shotgun Lipidomics Assistant (SLA), a Python-based application that facilitates DMS-based lipidomics workflows. SLA provides the user with flexibility in adding and subtracting lipid and standard MRMs. It can report quantitative lipidomics results from raw data in minutes, comparable to the Lipidyzer software. We show that SLA facilitates an expanded lipidomics analysis that measures over 1450 lipid species across 17 (sub)classes. Lastly, we demonstrate that the SLA performs isotope correction, a feature that was absent from the original software.
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http://dx.doi.org/10.1021/jasms.1c00203DOI Listing
October 2021

In-depth triacylglycerol profiling using MS Q-Trap mass spectrometry.

Anal Chim Acta 2021 Nov 3;1184:339023. Epub 2021 Sep 3.

Department of Genetics, Stanford University, Stanford, CA, USA. Electronic address:

Total triacylglycerol (TAG) level is a key clinical marker of metabolic and cardiovascular diseases. However, the roles of individual TAGs have not been thoroughly explored in part due to their extreme structural complexity. We present a targeted mass spectrometry-based method combining multiple reaction monitoring (MRM) and multiple stage mass spectrometry (MS) for the comprehensive qualitative and semiquantitative profiling of TAGs. This method referred as TriP-MS3 - triacylglycerol profiling using MS - screens for more than 6,700 TAG species in a fully automated fashion. TriP-MS3 demonstrated excellent reproducibility (median interday CV ∼ 0.15) and linearity (median R = 0.978) and detected 285 individual TAG species in human plasma. The semiquantitative accuracy of the method was validated by comparison with a state-of-the-art reverse phase liquid chromatography (RPLC)-MS (R = 0.83), which is the most commonly used approach for TAGs profiling. Finally, we demonstrate the utility and the versatility of the method by characterizing the effects of a fatty acid desaturase inhibitor on TAG profiles in vitro and by profiling TAGs in Caenorhabditis elegans.
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http://dx.doi.org/10.1016/j.aca.2021.339023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8502232PMC
November 2021

A scalable, secure, and interoperable platform for deep data-driven health management.

Nat Commun 2021 10 1;12(1):5757. Epub 2021 Oct 1.

Department of Genetics, Stanford University, Stanford, CA, USA.

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.
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http://dx.doi.org/10.1038/s41467-021-26040-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486823PMC
October 2021

Chromatin accessibility associates with protein-RNA correlation in human cancer.

Nat Commun 2021 09 30;12(1):5732. Epub 2021 Sep 30.

Department of Genetics, Stanford University, Stanford, CA, USA.

Although alterations in chromatin structure are known to exist in tumors, how these alterations relate to molecular phenotypes in cancer remains to be demonstrated. Multi-omics profiling of human tumors can provide insight into how alterations in chromatin structure are propagated through the pathway of gene expression to result in malignant protein expression. We applied multi-omics profiling of chromatin accessibility, RNA abundance, and protein abundance to 36 human thyroid cancer primary tumors, metastases, and patient-match normal tissue. Through quantification of chromatin accessibility associated with active transcription units and global protein expression, we identify a local chromatin structure that is highly correlated with coordinated RNA and protein expression. In particular, we identify enhancers located within gene-bodies as predictive of correlated RNA and protein expression, that is independent of overall transcriptional activity. To demonstrate the generalizability of these findings we also identify similar results in an independent cohort of human breast cancers. Taken together, these analyses suggest that local enhancers, rather than distal enhancers, are likely most predictive of cancer gene expression phenotypes. This allows for identification of potential targets for cancer therapeutic approaches and reinforces the utility of multi-omics profiling as a methodology to understand human disease.
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http://dx.doi.org/10.1038/s41467-021-25872-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484618PMC
September 2021

Divergent patterns of selection on metabolite levels and gene expression.

BMC Ecol Evol 2021 09 29;21(1):185. Epub 2021 Sep 29.

Department of Biology, Stanford University, Stanford, CA, USA.

Background: Natural selection can act on multiple genes in the same pathway, leading to polygenic adaptation. For example, adaptive changes were found to down-regulate six genes involved in ergosterol biosynthesis-an essential pathway targeted by many antifungal drugs-in some strains of the yeast Saccharomyces cerevisiae. However, the impact of this polygenic adaptation on metabolite levels was unknown. Here, we performed targeted mass spectrometry to measure the levels of eight metabolites in this pathway in 74 yeast strains from a genetic cross.

Results: Through quantitative trait locus (QTL) mapping we identified 19 loci affecting ergosterol pathway metabolite levels, many of which overlap loci that also impact gene expression within the pathway. We then used the recently developed v-test, which identified selection acting upon three metabolite levels within the pathway, none of which were predictable from the gene expression adaptation.

Conclusions: These data showed that effects of selection on metabolite levels were complex and not predictable from gene expression data. This suggests that a deeper understanding of metabolism is necessary before we can understand the impacts of even relatively straightforward gene expression adaptations on metabolic pathways.
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http://dx.doi.org/10.1186/s12862-021-01915-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482673PMC
September 2021

Adapting skills from genetic counseling to wearables technology research during the COVID-19 pandemic: Poised for the pivot.

J Genet Couns 2021 Sep 27. Epub 2021 Sep 27.

Department of Genetics, Stanford University, Stanford, CA, USA.

Genetic counselors have shown themselves to be adaptable in an evolving profession, with expansion into new sub-specialties, various non-clinical settings, and research roles. The COVID-19 pandemic caused a sudden and drastic shift in healthcare priorities. In an effort to contribute meaningfully to the COVID-19 crisis, and to adapt to a remote- and essential-only research environment, our workplace and thus our roles pivoted from genomics research to remote COVID-19 research using wearables technologies. With a deep understanding of genomic data, we were quickly able to apply similar concepts to wearables data including considering privacy implications, managing uncertain findings, and acknowledging the lack of ethnic diversity in many datasets. By sharing our own experience as an example, we hope individuals trained in genetic counseling may see opportunities for adaptation of their skills into expanding roles.
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http://dx.doi.org/10.1002/jgc4.1509DOI Listing
September 2021

Simultaneous Optimization of Radiation-Imaging Coincidence for a Multi-Energy Linac.

J Med Phys 2021 Apr-Jun;46(2):105-110. Epub 2021 Aug 7.

Department of Radiation Oncology, Beaumont Health, Royal Oak, Michigan, USA.

Introduction: Medical physics guidelines stress the importance of radiation-imaging coincidence, especially for stereotactic treatments. However, multi-energy linear accelerators may only allow a single imaging isocenter. A procedure was developed to simultaneously optimize radiation-imaging isocenter coincidence for all linac photon energies on a Versa HD.

Materials And Methods: First, the radiation beam center of each energy was adjusted to match the collimator rotation axis using a novel method that combined ion chamber measurements with a modified Winston-Lutz (WL) test using images only at gantry, couch, and collimator angles of 0°. With all energies properly steered, an 8-field WL test was performed to determine average linac isocenter position across all energies, gantry, and collimator angles. Lasers and the kV imaging isocenter were calibrated to the average linac isocenter of all photon energies. Finally, A 12-field WL test consisting of gantry, couch, and collimator rotations was used to adjust the couch rotation axis to the average linac isocenter, thereby minimizing overall radiation-imaging isocentricity of the system.

Results: Using this method, the beam centers were calibrated within 0.10 mm of collimator rotation axis, and linac isocenter coincidence was within 0.20 mm for all energies. Couch isocenter coincidence was adjusted within 0.20 mm of average linac isocenter. Average radiation-imaging isocentricity for all energies was 0.89 mm (0.80-0.98 mm) for a single imaging isocenter.

Conclusion: This work provides a method to adjust radiation-imaging coincidence within 1.0 mm for all energies on Elekta's Versa HD.
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http://dx.doi.org/10.4103/jmp.JMP_7_21DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415251PMC
August 2021

Comment on: Management of common iatrogenic iris defects induced by cataract surgery.

Authors:
Michael E Snyder

J Cataract Refract Surg 2021 09;47(9):1248-1249

Cincinnati Eye Institute, Cincinnati, Ohio.

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http://dx.doi.org/10.1097/j.jcrs.0000000000000760DOI Listing
September 2021

The Exposome in the Era of the Quantified Self.

Annu Rev Biomed Data Sci 2021 07;4:255-277

Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; email:

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.
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http://dx.doi.org/10.1146/annurev-biodatasci-012721-122807DOI Listing
July 2021

Time-Course Transcriptome Profiling of a Poxvirus Using Long-Read Full-Length Assay.

Pathogens 2021 Jul 21;10(8). Epub 2021 Jul 21.

Department of Medical Biology, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary.

Viral transcriptomes that are determined using first- and second-generation sequencing techniques are incomplete. Due to the short read length, these methods are inefficient or fail to distinguish between transcript isoforms, polycistronic RNAs, and transcriptional overlaps and readthroughs. Additionally, these approaches are insensitive for the identification of splice and transcriptional start sites (TSSs) and, in most cases, transcriptional end sites (TESs), especially in transcript isoforms with varying transcript ends, and in multi-spliced transcripts. Long-read sequencing is able to read full-length nucleic acids and can therefore be used to assemble complete transcriptome atlases. Although vaccinia virus (VACV) does not produce spliced RNAs, its transcriptome has a high diversity of TSSs and TESs, and a high degree of polycistronism that leads to enormous complexity. We applied single-molecule, real-time, and nanopore-based sequencing methods to investigate the time-lapse transcriptome patterns of VACV gene expression.
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http://dx.doi.org/10.3390/pathogens10080919DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8398953PMC
July 2021

Structured elements drive extensive circular RNA translation.

Mol Cell 2021 Oct 25;81(20):4300-4318.e13. Epub 2021 Aug 25.

Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA; Departments of Dermatology and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address:

The human genome encodes tens of thousands circular RNAs (circRNAs) with mostly unknown functions. Circular RNAs require internal ribosome entry sites (IRES) if they are to undergo translation without a 5' cap. Here, we develop a high-throughput screen to systematically discover RNA sequences that can direct circRNA translation in human cells. We identify more than 17,000 endogenous and synthetic sequences as candidate circRNA IRES. 18S rRNA complementarity and a structured RNA element positioned on the IRES are important for driving circRNA translation. Ribosome profiling and peptidomic analyses show extensive IRES-ribosome association, hundreds of circRNA-encoded proteins with tissue-specific distribution, and antigen presentation. We find that circFGFR1p, a protein encoded by circFGFR1 that is downregulated in cancer, functions as a negative regulator of FGFR1 oncoprotein to suppress cell growth during stress. Systematic identification of circRNA IRES elements may provide important links among circRNA regulation, biological function, and disease.
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http://dx.doi.org/10.1016/j.molcel.2021.07.042DOI Listing
October 2021

Statins Are Associated With Increased Insulin Resistance and Secretion.

Arterioscler Thromb Vasc Biol 2021 Aug 26:ATVBAHA121316159. Epub 2021 Aug 26.

Division of Cardiovascular Medicine, Stanford University, CA. (F. Abbasi, C.L., C.S.H., V.H., P.T., F. Abbas, G.R., J.W.K.).

Objective: Statin treatment reduces the risk of atherosclerotic cardiovascular disease but is associated with a modest increased risk of type 2 diabetes, especially in those with insulin resistance or prediabetes. Our objective was to determine the physiological mechanism for the increased type 2 diabetes risk. Approach and Results: We conducted an open-label clinical trial of atorvastatin 40 mg daily in adults without known atherosclerotic cardiovascular disease or type 2 diabetes at baseline. The co-primary outcomes were changes at 10 weeks versus baseline in insulin resistance as assessed by steady-state plasma glucose during the insulin suppression test and insulin secretion as assessed by insulin secretion rate area under the curve (ISR) during the graded-glucose infusion test. Secondary outcomes included glucose and insulin, both fasting and during oral glucose tolerance test. Of 75 participants who enrolled, 71 completed the study (median age 61 years, 37% women, 65% non-Hispanic White, median body mass index, 27.8 kg/m). Atorvastatin reduced LDL (low-density lipoprotein)-cholesterol (median decrease 53%, <0.001) but did not change body weight. Compared with baseline, atorvastatin increased insulin resistance (steady-state plasma glucose) by a median of 8% (=0.01) and insulin secretion (ISR) by a median of 9% (<0.001). There were small increases in oral glucose tolerance test glucose (median increase, 0.05%; =0.03) and fasting insulin (median increase, 7%; =0.01).

Conclusions: In individuals without type 2 diabetes, high-intensity atorvastatin for 10 weeks increases insulin resistance and insulin secretion. Over time, the risk of new-onset diabetes with statin use may increase in individuals who become more insulin resistant but are unable to maintain compensatory increases in insulin secretion.

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02437084.
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http://dx.doi.org/10.1161/ATVBAHA.121.316159DOI Listing
August 2021

metID: a R package for automatable compound annotation for LC-MS-based data.

Bioinformatics 2021 Aug 25. Epub 2021 Aug 25.

Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94304, USA.

Summary: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.

Availability And Implementation: https://jaspershen.github.io/metID.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab583DOI Listing
August 2021

Mutability of mononucleotide repeats, not oxidative stress, explains the discrepancy between laboratory-accumulated mutations and the natural allele-frequency spectrum in .

Genome Res 2021 Sep 17;31(9):1602-1613. Epub 2021 Aug 17.

Department of Biology, University of Florida, Gainesville, Florida 32611, USA.

Important clues about natural selection can be gleaned from discrepancies between the properties of segregating genetic variants and of mutations accumulated experimentally under minimal selection, provided the mutational process is the same in the laboratory as in nature. The base-substitution spectrum differs between laboratory mutation accumulation (MA) experiments and the standing site-frequency spectrum, which has been argued to be in part owing to increased oxidative stress in the laboratory environment. Using genome sequence data from MA lines carrying a mutation (-) that increases the cellular titer of reactive oxygen species (ROS), leading to increased oxidative stress, we find the base-substitution spectrum is similar between -, its wild-type progenitor (N2), and another set of MA lines derived from a different wild strain (PB306). Conversely, the rate of short insertions is greater in -, consistent with studies in other organisms in which environmental stress increased the rate of insertion-deletion mutations. Further, the mutational properties of mononucleotide repeats in all strains are different from those of nonmononucleotide sequence, both for indels and base-substitutions, and whereas the nonmononucleotide spectra are fairly similar between MA lines and wild isolates, the mononucleotide spectra are very different, with a greater frequency of A:T → T:A transversions and an increased proportion of ±1-bp indels. The discrepancy in mutational spectra between laboratory MA experiments and natural variation is likely owing to a consistent (but unknown) effect of the laboratory environment that manifests itself via different modes of mutability and/or repair at mononucleotide loci.
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http://dx.doi.org/10.1101/gr.275372.121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415377PMC
September 2021

Temporal changes in soluble angiotensin-converting enzyme 2 associated with metabolic health, body composition, and proteome dynamics during a weight loss diet intervention: a randomized trial with implications for the COVID-19 pandemic.

Am J Clin Nutr 2021 Aug 10. Epub 2021 Aug 10.

Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.

Background: Angiotensin-converting enzyme 2 (ACE2) serves protective functions in metabolic, cardiovascular, renal, and pulmonary diseases and is linked to COVID-19 pathology. The correlates of temporal changes in soluble ACE2 (sACE2) remain understudied.

Objectives: We explored the associations of sACE2 with metabolic health and proteome dynamics during a weight loss diet intervention.

Methods: We analyzed 457 healthy individuals (mean ± SD age: 39.8 ± 6.6 y) with BMI 28-40 kg/m2 in the DIETFITS (Diet Intervention Examining the Factors Interacting with Treatment Success) study. Biochemical markers of metabolic health and 236 proteins were measured by Olink CVDII, CVDIII, and Inflammation I arrays at baseline and at 6 mo during the dietary intervention. We determined clinical and routine biochemical correlates of the diet-induced change in sACE2 (ΔsACE2) using stepwise linear regression. We combined feature selection models and multivariable-adjusted linear regression to identify protein dynamics associated with ΔsACE2.

Results: sACE2 decreased on average at 6 mo during the diet intervention. Stronger decline in sACE2 during the diet intervention was independently associated with female sex, lower HOMA-IR and LDL cholesterol at baseline, and a stronger decline in HOMA-IR, triglycerides, HDL cholesterol, and fat mass. Participants with decreasing HOMA-IR (OR: 1.97; 95% CI: 1.28, 3.03) and triglycerides (OR: 2.71; 95% CI: 1.72, 4.26) had significantly higher odds for a decrease in sACE2 during the diet intervention than those without (P ≤ 0.0073). Feature selection models linked ΔsACE2 to changes in α-1-microglobulin/bikunin precursor, E-selectin, hydroxyacid oxidase 1, kidney injury molecule 1, tyrosine-protein kinase Mer, placental growth factor, thrombomodulin, and TNF receptor superfamily member 10B. ΔsACE2 remained associated with these protein changes in multivariable-adjusted linear regression.

Conclusions: Decrease in sACE2 during a weight loss diet intervention was associated with improvements in metabolic health, fat mass, and markers of angiotensin peptide metabolism, hepatic and vascular injury, renal function, chronic inflammation, and oxidative stress. Our findings may improve the risk stratification, prevention, and management of cardiometabolic complications.This trial was registered at clinicaltrials.gov as NCT01826591.
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http://dx.doi.org/10.1093/ajcn/nqab243DOI Listing
August 2021

Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study.

JAMIA Open 2021 Jul 2;4(3):ooab054. Epub 2021 Aug 2.

Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.

Objectives: Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.

Materials And Methods: Comparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.

Results: At 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult's and teen's sleeping and step counts were correlated, possibly due to coordinated behaviors.

Discussion: We end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.

Conclusion: With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.
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http://dx.doi.org/10.1093/jamiaopen/ooab054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327370PMC
July 2021

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

Clin Cancer Res 2021 Aug;27(15):4265-4276

Personalis, Inc., Menlo Park, California.

Purpose: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).

Experimental Design: Tumors from a cohort of patients with late-stage melanoma ( = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB.

Results: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders ( = 0.016) than TMB alone ( = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response ( = 0.002).

Conclusions: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients ( = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-4314DOI Listing
August 2021

Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment.

Genome Res 2021 Aug 22;31(8):1433-1446. Epub 2021 Jul 22.

Department of Genetics, Stanford University, Stanford, California 94305, USA.

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here, we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.
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http://dx.doi.org/10.1101/gr.265058.120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327913PMC
August 2021

Combined nanopore and single-molecule real-time sequencing survey of human betaherpesvirus 5 transcriptome.

Sci Rep 2021 07 14;11(1):14487. Epub 2021 Jul 14.

Department of Medical Biology, Faculty of Medicine, University of Szeged, Somogyi B. u. 4, 6720, Szeged, Hungary.

Long-read sequencing (LRS), a powerful novel approach, is able to read full-length transcripts and confers a major advantage over the earlier gold standard short-read sequencing in the efficiency of identifying for example polycistronic transcripts and transcript isoforms, including transcript length- and splice variants. In this work, we profile the human cytomegalovirus transcriptome using two third-generation LRS platforms: the Sequel from Pacific BioSciences, and MinION from Oxford Nanopore Technologies. We carried out both cDNA and direct RNA sequencing, and applied the LoRTIA software, developed in our laboratory, for the transcript annotations. This study identified a large number of novel transcript variants, including splice isoforms and transcript start and end site isoforms, as well as putative mRNAs with truncated in-frame ORFs (located within the larger ORFs of the canonical mRNAs), which potentially encode N-terminally truncated polypeptides. Our work also disclosed a highly complex meshwork of transcriptional read-throughs and overlaps.
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http://dx.doi.org/10.1038/s41598-021-93593-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280142PMC
July 2021

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions.

Stat Appl Genet Mol Biol 2021 Jul 13;20(2):51-71. Epub 2021 Jul 13.

Department of Genetics, Stanford University, Stanford, 94305, USA.

The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on -density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. . 99: 2053-2081 and Windham, M.P. (1995). Robustifying model fitting. : 599-609, where is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive -selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various 's in average performance has similar capability to capture minimizer as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.
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http://dx.doi.org/10.1515/sagmb-2020-0042DOI Listing
July 2021

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Nat Methods 2021 07 8;18(7):747-756. Epub 2021 Jul 8.

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.
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http://dx.doi.org/10.1038/s41592-021-01197-1DOI Listing
July 2021

Correspondence.

Retina 2021 09;41(9):e66-e67

Cincinnati Eye Institute & University of Cincinnati SOM, Professor of Ophthalmology, Cincinnati, Ohio.

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http://dx.doi.org/10.1097/IAE.0000000000003245DOI Listing
September 2021

Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity.

medRxiv 2021 Jun 21. Epub 2021 Jun 21.

The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.
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http://dx.doi.org/10.1101/2021.06.15.21258703DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240695PMC
June 2021

Real-time Alerting System for COVID-19 Using Wearable Data.

medRxiv 2021 Jun 21. Epub 2021 Jun 21.

Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.
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http://dx.doi.org/10.1101/2021.06.13.21258795DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240687PMC
June 2021

Time-course transcriptome analysis of host cell response to poxvirus infection using a dual long-read sequencing approach.

BMC Res Notes 2021 Jun 24;14(1):239. Epub 2021 Jun 24.

Department of Medical Biology, Faculty of Medicine, University of Szeged, Szeged, Hungary.

Objective: In this study, we applied two long-read sequencing (LRS) approaches, including single-molecule real-time and nanopore-based sequencing methods to investigate the time-lapse transcriptome patterns of host gene expression as a response to Vaccinia virus infection. Transcriptomes determined using short-read sequencing approaches are incomplete because these platforms are inefficient or fail to distinguish between polycistronic RNAs, transcript isoforms, transcriptional start sites, as well as transcriptional readthroughs and overlaps. Long-read sequencing is able to read full-length nucleic acids and can therefore be used to assemble complete transcriptome atlases.

Results: In this work, we identified a number of novel transcripts and transcript isoforms of Chlorocebus sabaeus. Additionally, analysis of the most abundant 768 host transcripts revealed a significant overrepresentation of the class of genes in the "regulation of signaling receptor activity" Gene Ontology annotation as a result of viral infection.
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http://dx.doi.org/10.1186/s13104-021-05657-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223271PMC
June 2021

AdaTiSS: A Novel Data-Adaptive Robust Method for Identifying Tissue Specificity Scores.

Bioinformatics 2021 Jun 19. Epub 2021 Jun 19.

Department of Genetics, Stanford University, Stanford, 94305, CA, USA.

Motivation: Accurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level. The Genotype-Tissue Expression project is one of the publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons and heterogeneous tissue expression make it challenging to accurately identify tissue specific gene expression. How to distinguish the inlier expression from the outlier expression becomes important to build the population level information and further quantify the TS. There still lacks a robust and data-adaptive TS method taking into account heterogeneities of the data.

Methods: We found that the key to identify tissue specific gene expression is to properly define a concept of expression population. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion. The Gaussian-population mixture model was considered in the setting of identifying TS. We took into account heterogeneities of gene expression and applied the robust data-adaptive procedure to estimate the population parameters. With the well-estimated population parameters, we constructed the AdaTiSS algorithm.

Results: Our AdaTiSS profiled TS for each gene and each tissue, which standardized the gene expression in terms of TS. We provided a new robust and powerful tool to the literature of defining tissue specificity.

Availability: https://github.com/mwgrassgreen/AdaTiSS.
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http://dx.doi.org/10.1093/bioinformatics/btab460DOI Listing
June 2021

Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation.

Mol Cell Proteomics 2021 Jun 12;20:100111. Epub 2021 Jun 12.

Personalis, Inc, Menlo Park, California, USA. Electronic address:

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.
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http://dx.doi.org/10.1016/j.mcpro.2021.100111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318994PMC
June 2021
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