The Department of Genomics and Computational Biology (GCB) at UMass Chan Medical School was originally established as the Program in Bioinformatics and Integrative Biology in 2008. The group evolved into a full-fledged department in 2023, reflecting their growth and the expanding scope of their research. The department embodies the convergence of Computational Biology, Evolutionary Biology, and Genomics and is committed to advancing understanding of biological complexity through cutting-edge computational methods, evolutionary theory, and genomic technologies. This collection showcases journal articles and other publications produced by faculty and researchers of the Department of Genomics and Computational Biology.

Recently Published

  • Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

    Wirthlin, Morgan E; Schmid, Tobias A; Elie, Julie E; Zhang, Xiaomeng; Kowalczyk, Amanda; Redlich, Ruby; Shvareva, Varvara A; Rakuljic, Ashley; Ji, Maria B; Bhat, Ninad S; et al. (2024-03-29)
    Vocal production learning ("vocal learning") is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.
  • Tunable DNMT1 degradation reveals DNMT1/DNMT3B synergy in DNA methylation and genome organization

    Scelfo, Andrea; Barra, Viviana; Abdennur, Nezar; Spracklin, George; Busato, Florence; Salinas-Luypaert, Catalina; Bonaiti, Elena; Velasco, Guillaume; Bonhomme, Frédéric; Chipont, Anna; et al. (2024-02-20)
    DNA methylation (DNAme) is a key epigenetic mark that regulates critical biological processes maintaining overall genome stability. Given its pleiotropic function, studies of DNAme dynamics are crucial, but currently available tools to interfere with DNAme have limitations and major cytotoxic side effects. Here, we present cell models that allow inducible and reversible DNAme modulation through DNMT1 depletion. By dynamically assessing whole genome and locus-specific effects of induced passive demethylation through cell divisions, we reveal a cooperative activity between DNMT1 and DNMT3B, but not of DNMT3A, to maintain and control DNAme. We show that gradual loss of DNAme is accompanied by progressive and reversible changes in heterochromatin, compartmentalization, and peripheral localization. DNA methylation loss coincides with a gradual reduction of cell fitness due to G1 arrest, with minor levels of mitotic failure. Altogether, this system allows DNMTs and DNA methylation studies with fine temporal resolution, which may help to reveal the etiologic link between DNAme dysfunction and human disease.
  • Bigtools: a high-performance BigWig and BigBed library in Rust [preprint]

    Huey, Jack; Abdennur, Nezar (2024-02-08)
    The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications. Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python. Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.
  • Genome-wide association study identifies human genetic variants associated with fatal outcome from Lassa fever

    Kotliar, Dylan; Raju, Siddharth; Tabrizi, Shervin; Odia, Ikponmwosa; Goba, Augustine; Momoh, Mambu; Sandi, John Demby; Nair, Parvathy; Phelan, Eric; Tariyal, Ridhi; et al. (2024-02-07)
    Infection with Lassa virus (LASV) can cause Lassa fever, a haemorrhagic illness with an estimated fatality rate of 29.7%, but causes no or mild symptoms in many individuals. Here, to investigate whether human genetic variation underlies the heterogeneity of LASV infection, we carried out genome-wide association studies (GWAS) as well as seroprevalence surveys, human leukocyte antigen typing and high-throughput variant functional characterization assays. We analysed Lassa fever susceptibility and fatal outcomes in 533 cases of Lassa fever and 1,986 population controls recruited over a 7 year period in Nigeria and Sierra Leone. We detected genome-wide significant variant associations with Lassa fever fatal outcomes near GRM7 and LIF in the Nigerian cohort. We also show that a haplotype bearing signatures of positive selection and overlapping LARGE1, a required LASV entry factor, is associated with decreased risk of Lassa fever in the Nigerian cohort but not in the Sierra Leone cohort. Overall, we identified variants and genes that may impact the risk of severe Lassa fever, demonstrating how GWAS can provide insight into viral pathogenesis.
  • Bioframe: operations on genomic intervals in Pandas dataframes

    Abdennur, Nezar; Fudenberg, Geoffrey; Flyamer, Ilya M; Galitsyna, Aleksandra A; Goloborodko, Anton; Imakaev, Maxim; Venev, Sergey (2024-02-01)
    Motivation: Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments. Results: Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features. Availability and implementation: Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.
  • Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing

    Levine, Zoë C; Sene, Aita; Mkandawire, Winnie; Deme, Awa B; Ndiaye, Tolla; Sy, Mouhamad; Gaye, Amy; Diedhiou, Younouss; Mbaye, Amadou M; Ndiaye, Ibrahima M; et al. (2024-01-25)
    The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.
  • Dog size and patterns of disease history across the canine age spectrum: Results from the Dog Aging Project

    Nam, Yunbi; White, Michelle; Karlsson, Elinor K; Creevy, Kate E; Promislow, Daniel E L; McClelland, Robyn L (2024-01-17)
    Age in dogs is associated with the risk of many diseases, and canine size is a major factor in that risk. However, the size patterns are complex. While small size dogs tend to live longer, some diseases are more prevalent among small dogs. In this study we seek to quantify how the pattern of disease history varies across the spectrum of dog size, dog age, and their interaction. Utilizing owner-reported data on disease history from a substantial number of companion dogs enrolled in the Dog Aging Project, we investigate how body size, as measured by weight, associates with the lifetime prevalence of a reported condition and its pattern across age for various disease categories. We found significant positive associations between dog size and the lifetime prevalence of skin, bone/orthopedic, gastrointestinal, ear/nose/throat, cancer/tumor, brain/neurologic, endocrine, and infectious diseases. Similarly, dog size was negatively associated with lifetime prevalence of ocular, cardiac, liver/pancreas, and respiratory disease categories. Kidney/urinary disease prevalence did not vary by size. We also found that the association between age and lifetime disease prevalence varied by dog size for many conditions including ocular, cardiac, orthopedic, ear/nose/throat, and cancer. Controlling for sex, purebred vs. mixed-breed status, and geographic region made little difference in all disease categories we studied. Our results align with the reduced lifespan in larger dogs for most of the disease categories and suggest potential avenues for further examination.
  • Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers

    Bearnot, Courtney J; Mbong, Eta N; Muhayangabo, Rigo F; Laghari, Razia; Butler, Kelsey M.; Gainey, Monique; Perera, Shiromi M; Michelow, Ian C; Tang, Oliver Y; Levine, Adam C; et al. (2024-01-05)
    Background: Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods: This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results: Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69-.80); days 1 and 2, 0.84 (95% CI, .73-.94); days 3 and 4, 0.94 (95% CI, .88-1.0); and days 5 and 6, 0.96 (95% CI, .90-1.0). Conclusions: This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care.
  • A Burden of Rare Copy Number Variants in Obsessive-Compulsive Disorder [preprint]

    Halvorsen, Matthew; de Schipper, Elles; Boberg, Julia; Strom, Nora; Hagen, Kristen; Lindblad-Toh, Kerstin; Karlsson, Elinor K; Pedersen, Nancy; Bulik, Cynthia; Fundín, Bengt; et al. (2024-01-03)
    Current genetic research on obsessive-compulsive disorder (OCD) supports contributions to risk specifically from common single nucleotide variants (SNVs), along with rare coding SNVs and small insertion-deletions (indels). The contribution to OCD risk from large, rare copy number variants (CNVs), however, has not been formally assessed at a similar scale. Here we describe an analysis of rare CNVs called from genotype array data in 2,248 deeply phenotyped OCD cases and 3,608 unaffected controls from Sweden and Norway. We found that in general cases carry an elevated burden of large (>30kb, at least 15 probes) CNVs (OR=1.12, P=1.77×10-3). The excess rate of these CNVs in cases versus controls was around 0.07 (95% CI 0.02-0.11, P=2.58×10-3). This signal was largely driven by CNVs overlapping protein-coding regions (OR=1.19, P=3.08×10-4), particularly deletions impacting loss-of-function intolerant genes (pLI>0.995, OR=4.12, P=2.54×10-5). We did not identify any specific locus where CNV burden was associated with OCD case status at genome-wide significance, but we noted non-random recurrence of CNV deletions in cases (permutation P = 2.60×10-3). In cases where sufficient clinical data were available (n=1612) we found that carriers of neurodevelopmental duplications were more likely to have comorbid autism (P<0.001), and that carriers of deletions overlapping neurodevelopmental genes had lower treatment response (P=0.02). The results demonstrate a contribution of large, rare CNVs to OCD risk, and suggest that studies of rare coding variation in OCD would have increased power to identify risk genes if this class of variation were incorporated into formal tests.
  • Mutational spectrum and phenotypic variability of Duchenne muscular dystrophy and related disorders in a Bangladeshi population

    Sarker, Shaoli; Eshaque, Tamannyat Binte; Soorajkumar, Anjana; Nassir, Nasna; Zehra, Binte; Kanta, Shayla Imam; Rahaman, Md Atikur; Islam, Amirul; Akter, Shimu; Ali, Mohammad Kawsar; et al. (2023-12-06)
    Duchenne muscular dystrophy (DMD) is a severe rare neuromuscular disorder caused by mutations in the X-linked dystrophin gene. Several mutations have been identified, yet the full mutational spectrum, and their phenotypic consequences, will require genotyping across different populations. To this end, we undertook the first detailed genotype and phenotype characterization of DMD in the Bangladeshi population. We investigated the rare mutational and phenotypic spectrum of the DMD gene in 36 DMD-suspected Bangladeshi participants using an economically affordable diagnostic strategy involving initial screening for exonic deletions in the DMD gene via multiplex PCR, followed by testing PCR-negative patients for mutations using whole exome sequencing. The deletion mapping identified two critical DMD gene hotspot regions (near proximal and distal ends, spanning exons 8-17 and exons 45-53, respectively) that comprised 95% (21/22) of the deletions for this population cohort. From our exome analysis, we detected two novel pathogenic hemizygous mutations in exons 21 and 42 of the DMD gene, and novel pathogenic recessive and loss of function variants in four additional genes: SGCD, DYSF, COL6A3, and DOK7. Our phenotypic analysis showed that DMD suspected participants presented diverse phenotypes according to the location of the mutation and which gene was impacted. Our study provides ethnicity specific new insights into both clinical and genetic aspects of DMD.
  • An encyclopedia of enhancer-gene regulatory interactions in the human genome [preprint]

    Gschwind, Andreas R; Mualim, Kristy S; Karbalayghareh, Alireza; Sheth, Maya U; Dey, Kushal K; Jagoda, Evelyn; Nurtdinov, Ramil N; Xi, Wang; Tan, Anthony S; Jones, Hank; et al. (2023-11-13)
    Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
  • Single-cell transcriptomic and genomic changes in the aging human brain [preprint]

    Jeffries, Ailsa M; Yu, Tianxiong; Ziegenfuss, Jennifer S; Tolles, Allie K; Kim, Yerin; Weng, Zhiping; Lodato, Michael A (2023-11-07)
    Aging brings dysregulation of various processes across organs and tissues, often stemming from stochastic damage to individual cells over time. Here, we used a combination of single-nucleus RNA-sequencing and single-cell whole-genome sequencing to identify transcriptomic and genomic changes in the prefrontal cortex of the human brain across life span, from infancy to centenarian. We identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, and a common down-regulation of cell-essential homeostatic genes that function in ribosomes, transport, and metabolism during aging across cell types. Conversely, expression of neuron-specific genes generally remains stable throughout life. We observed a decrease in specific DNA repair genes in aging, including genes implicated in generating brain somatic mutations as indicated by mutation signature analysis. Furthermore, we detected gene-length-specific somatic mutation rates that shape the transcriptomic landscape of the aged human brain. These findings elucidate critical aspects of human brain aging, shedding light on transcriptomic and genomics dynamics.
  • Beyond genome-wide association studies: Investigating the role of noncoding regulatory elements in primary sclerosing cholangitis

    Pratt, Henry E; Wu, Tong; Elhajjajy, Shaimae I; Zhou, Jeffrey Y.; Fitzgerald, Kate; Fazzio, Tom; Weng, Zhiping; Pratt, Daniel S (2023-09-27)
    Background: Genome-wide association studies (GWAS) have identified 30 risk loci for primary sclerosing cholangitis (PSC). Variants within these loci are found predominantly in noncoding regions of DNA making their mechanisms of conferring risk hard to define. Epigenomic studies have shown noncoding variants broadly impact regulatory element activity. The possible association of noncoding PSC variants with regulatory element activity has not been studied. We aimed to (1) determine if the noncoding risk variants in PSC impact regulatory element function and (2) if so, assess the role these regulatory elements have in explaining the genetic risk for PSC. Methods: Available epigenomic datasets were integrated to build a comprehensive atlas of cell type-specific regulatory elements, emphasizing PSC-relevant cell types. RNA-seq and ATAC-seq were performed on peripheral CD4+ T cells from 10 PSC patients and 11 healthy controls. Computational techniques were used to (1) study the enrichment of PSC-risk variants within regulatory elements, (2) correlate risk genotype with differences in regulatory element activity, and (3) identify regulatory elements differentially active and genes differentially expressed between PSC patients and controls. Results: Noncoding PSC-risk variants are strongly enriched within immune-specific enhancers, particularly ones involved in T-cell response to antigenic stimulation. In total, 250 genes and >10,000 regulatory elements were identified that are differentially active between patients and controls. Conclusions: Mechanistic effects are proposed for variants at 6 PSC-risk loci where genotype was linked with differential T-cell regulatory element activity. Regulatory elements are shown to play a key role in PSC pathophysiology.
  • Reliable multiplex generation of pooled induced pluripotent stem cells

    Smullen, Molly; Olson, Meagan N; Reichert, Julia M; Dawes, Pepper; Murray, Liam F; Baer, Christina E; Wang, Qi; Readhead, Benjamin; Church, George M; Lim, Elaine T; et al. (2023-08-31)
    Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.
  • Improving diagnosis of non-malarial fevers in Senegal: Borrelia and the contribution of tick-borne bacteria [preprint]

    Levine, Zoë C; Sene, Aita; Mkandawire, Winnie; Deme, Awa B; Ndiaye, Tolla; Sy, Mouhamad; Gaye, Amy; Diedhiou, Younouss; Mbaye, Amadou M; Ndiaye, Ibrahima; et al. (2023-08-25)
    The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata from febrile patients and healthy controls in a low malaria burden area. Using 16S and unbiased sequencing, we detected viral, bacterial, or eukaryotic pathogens in 29% of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15% and 3.7% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model to distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs. These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.
  • Using evolutionary constraint to define novel candidate driver genes in medulloblastoma

    Roy, Ananya; Sakthikumar, Sharadha; Kozyrev, Sergey V; Nordin, Jessika; Pensch, Raphaela; Mäkeläinen, Suvi; Pettersson, Mats; Karlsson, Elinor K; Lindblad-Toh, Kerstin; Forsberg-Nilsson, Karin (2023-08-07)
    Current knowledge of cancer genomics remains biased against noncoding mutations. To systematically search for regulatory noncoding mutations, we assessed mutations in conserved positions in the genome under the assumption that these are more likely to be functional than mutations in positions with low conservation. To this end, we use whole-genome sequencing data from the International Cancer Genome Consortium and combined it with evolutionary constraint inferred from 240 mammals, to identify genes enriched in noncoding constraint mutations (NCCMs), mutations likely to be regulatory in nature. We compare medulloblastoma (MB), which is malignant, to pilocytic astrocytoma (PA), a primarily benign tumor, and find highly different NCCM frequencies between the two, in agreement with the fact that malignant cancers tend to have more mutations. In PA, a high NCCM frequency only affects the BRAF locus, which is the most commonly mutated gene in PA. In contrast, in MB, >500 genes have high levels of NCCMs. Intriguingly, several loci with NCCMs in MB are associated with different ages of onset, such as the HOXB cluster in young MB patients. In adult patients, NCCMs occurred in, e.g., the WASF-2/AHDC1/FGR locus. One of these NCCMs led to increased expression of the SRC kinase FGR and augmented responsiveness of MB cells to dasatinib, a SRC kinase inhibitor. Our analysis thus points to different molecular pathways in different patient groups. These newly identified putative candidate driver mutations may aid in patient stratification in MB and could be valuable for future selection of personalized treatment options.
  • Aub, Vasa and Armi localization to phase separated nuage is dispensable for piRNA biogenesis and transposon silencing in Drosophila [preprint]

    Ho, Samantha; Rice, Nicholas P; Yu, Tianxiong; Weng, Zhiping; Theurkauf, William E (2023-07-26)
    From nematodes to placental mammals, key components of the germline transposon silencing piRNAs pathway localize to phase separated perinuclear granules. In Drosophila, the PIWI protein Aub, DEAD box protein Vasa and helicase Armi localize to nuage granules and are required for ping-pong piRNA amplification and phased piRNA processing. Drosophila piRNA mutants lead to genome instability and Chk2 kinase DNA damage signaling. By systematically analyzing piRNA pathway organization, small RNA production, and long RNA expression in single piRNA mutants and corresponding chk2/mnk double mutants, we show that Chk2 activation disrupts nuage localization of Aub and Vasa, and that the HP1 homolog Rhino, which drives piRNA precursor transcription, is required for Aub, Vasa, and Armi localization to nuage. However, these studies also show that ping-pong amplification and phased piRNA biogenesis are independent of nuage localization of Vasa, Aub and Armi. Dispersed cytoplasmic proteins thus appear to mediate these essential piRNA pathway functions.
  • Knowledge, attitudes and practices regarding the use of mobile travel health apps

    Machoko, Munashe M P; Dong, Yinan; Grozdani, Andonaq; Hong, Hung; Oliver, Elizabeth; Hyle, Emily P; Ryan, Edward T; Colubri, Andrés; LaRocque, Regina C (2023-07-06)
    We performed a survey of U.S. international travellers to evaluate their knowledge, attitudes and practices regarding mobile technologies related to health. We found that many international travellers carry smartphones and are interested in receiving health information from a mobile app when they travel abroad.
  • Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study

    Soni, Apurv; Herbert, Carly; Lin, Honghuang; Yan, Yi; Pretz, Caitlin; Stamegna, Pamela; Wang, Biqi; Orwig, Taylor; Wright, Colton; Tarrant, Seanan; et al. (2023-07-04)
    Background: The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. Objective: To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. Design: This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. Setting: Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. Participants: Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. Measurements: The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. Results: Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. Limitation: Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. Conclusion: The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. Primary funding source: National Institutes of Health RADx Tech program.
  • Modeling of mitochondrial genetic polymorphisms reveals induction of heteroplasmy by pleiotropic disease locus 10398A>G

    Smullen, Molly; Olson, Meagan N; Murray, Liam F; Suresh, Madhusoodhanan; Yan, Guang; Dawes, Pepper; Barton, Nathaniel J; Mason, Jivanna N; Zhang, Yucheng; Fernandez-Fontaine, Aria A; et al. (2023-06-27)
    Mitochondrial (MT) dysfunction has been associated with several neurodegenerative diseases including Alzheimer's disease (AD). While MT-copy number differences have been implicated in AD, the effect of MT heteroplasmy on AD has not been well characterized. Here, we analyzed over 1800 whole genome sequencing data from four AD cohorts in seven different tissue types to determine the extent of MT heteroplasmy present. While MT heteroplasmy was present throughout the entire MT genome for blood samples, we detected MT heteroplasmy only within the MT control region for brain samples. We observed that an MT variant 10398A>G (rs2853826) was significantly associated with overall MT heteroplasmy in brain tissue while also being linked with the largest number of distinct disease phenotypes of all annotated MT variants in MitoMap. Using gene-expression data from our brain samples, our modeling discovered several gene networks involved in mitochondrial respiratory chain and Complex I function associated with 10398A>G. The variant was also found to be an expression quantitative trait loci (eQTL) for the gene MT-ND3. We further characterized the effect of 10398A>G by phenotyping a population of lymphoblastoid cell-lines (LCLs) with and without the variant allele. Examination of RNA sequence data from these LCLs reveal that 10398A>G was an eQTL for MT-ND4. We also observed in LCLs that 10398A>G was significantly associated with overall MT heteroplasmy within the MT control region, confirming the initial findings observed in post-mortem brain tissue. These results provide novel evidence linking MT SNPs with MT heteroplasmy and open novel avenues for the investigation of pathomechanisms that are driven by this pleiotropic disease associated loci.

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