Browsing by Subject "gene expression"

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    A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses.
    (Open Forum Infect Dis, 2016-01) McClain, Micah T; Nicholson, Bradly P; Park, Lawrence P; Liu, Tzu-Yu; Hero, Alfred O; Tsalik, Ephraim L; Zaas, Aimee K; Veldman, Timothy; Hudson, Lori L; Lambkin-Williams, Robert; Gilbert, Anthony; Burke, Thomas; Nichols, Marshall; Ginsburg, Geoffrey S; Woods, Christopher W
    Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.
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    An age-independent gene signature for monitoring acute rejection in kidney transplantation.
    (Theranostics, 2020-01) Shaw, Brian I; Cheng, Daniel K; Acharya, Chaitanya R; Ettenger, Robert B; Lyerly, Herbert Kim; Cheng, Qing; Kirk, Allan D; Chambers, Eileen T
    Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.
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    Comparing the Diagnostic Accuracy of Clinician Judgment to a Novel Host Response Diagnostic for Acute Respiratory Illness.
    (Open forum infectious diseases, 2021-12) Jaffe, Ian S; Jaehne, Anja K; Quackenbush, Eugenia; Ko, Emily R; Rivers, Emanuel P; McClain, Micah T; Ginsburg, Geoffrey S; Woods, Christopher W; Tsalik, Ephraim L

    Background

    Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated.

    Methods

    Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors.

    Results

    Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P<.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%).

    Conclusions

    Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis.
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    Invited Commentary: Integrating Genomics and Social Epidemiology-Analysis of Late-Life Low Socioeconomic Status and the Conserved Transcriptional Response to Adversity.
    (Am J Epidemiol, 2017-09-01) Belsky, Daniel W; Snyder-Mackler, Noah
    Socially disadvantaged children face increased morbidity and mortality as they age. Understanding mechanisms through which social disadvantage becomes biologically embedded and devising measurements that can track this embedding are critical priorities for research to address social gradients in health. The analysis by Levine et al. (Am J Epidemiol. 2017;186(5):503-509) of genome-wide gene expression in a subsample of US Health and Retirement Study participants suggests important new directions for the field. Specifically, findings suggest promise in integrating gene expression data into population studies and provide further evidence for the conserved transcriptional response to adversity as a marker of biological embedding of social disadvantage. The study also highlights methodological issues related to the analysis of gene expression data and social gradients in health and a need to examine the conserved transcriptional response to adversity alongside other proposed measurements of biological embedding. Looking to the future, advances in genome science are opening new opportunities for sociogenomic epidemiology.
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    Morphological and genomic shifts in mole-rat 'queens' increase fecundity but reduce skeletal integrity.
    (eLife, 2021-04-12) Johnston, Rachel A; Vullioud, Philippe; Thorley, Jack; Kirveslahti, Henry; Shen, Leyao; Mukherjee, Sayan; Karner, Courtney M; Clutton-Brock, Tim; Tung, Jenny
    In some mammals and many social insects, highly cooperative societies are characterized by reproductive division of labor, in which breeders and nonbreeders become behaviorally and morphologically distinct. While differences in behavior and growth between breeders and nonbreeders have been extensively described, little is known of their molecular underpinnings. Here, we investigate the consequences of breeding for skeletal morphology and gene regulation in highly cooperative Damaraland mole-rats. By experimentally assigning breeding 'queen' status versus nonbreeder status to age-matched littermates, we confirm that queens experience vertebral growth that likely confers advantages to fecundity. However, they also upregulate bone resorption pathways and show reductions in femoral mass, which predicts increased vulnerability to fracture. Together, our results show that, as in eusocial insects, reproductive division of labor in mole-rats leads to gene regulatory rewiring and extensive morphological plasticity. However, in mole-rats, concentrated reproduction is also accompanied by costs to bone strength.
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    Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population.
    (Open forum infectious diseases, 2020-06) Tillekeratne, L Gayani; Suchindran, Sunil; Ko, Emily R; Petzold, Elizabeth A; Bodinayake, Champica K; Nagahawatte, Ajith; Devasiri, Vasantha; Kurukulasooriya, Ruvini; Nicholson, Bradly P; McClain, Micah T; Burke, Thomas W; Tsalik, Ephraim L; Henao, Ricardo; Ginsburg, Geoffrey S; Reller, Megan E; Woods, Christopher W
    Background:Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort. Methods:Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious). Results:Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively. Conclusions:Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens.
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    Transcriptomic Analysis of the Host Response and Innate Resilience to Enterotoxigenic Escherichia coli Infection in Humans.
    (J Infect Dis, 2016-05-01) Yang, William E; Suchindran, Sunil; Nicholson, Bradly P; McClain, Micah T; Burke, Thomas; Ginsburg, Geoffrey S; Harro, Clayton D; Chakraborty, Subhra; Sack, David A; Woods, Christopher W; Tsalik, Ephraim L
    BACKGROUND: Enterotoxigenic Escherichia coli (ETEC) is a globally prevalent cause of diarrhea. Though usually self-limited, it can be severe and debilitating. Little is known about the host transcriptional response to infection. We report the first gene expression analysis of the human host response to experimental challenge with ETEC. METHODS: We challenged 30 healthy adults with an unattenuated ETEC strain, and collected serial blood samples shortly after inoculation and daily for 8 days. We performed gene expression analysis on whole peripheral blood RNA samples from subjects in whom severe symptoms developed (n = 6) and a subset of those who remained asymptomatic (n = 6) despite shedding. RESULTS: Compared with baseline, symptomatic subjects demonstrated significantly different expression of 406 genes highlighting increased immune response and decreased protein synthesis. Compared with asymptomatic subjects, symptomatic subjects differentially expressed 254 genes primarily associated with immune response. This comparison also revealed 29 genes differentially expressed between groups at baseline, suggesting innate resilience to infection. Drug repositioning analysis identified several drug classes with potential utility in augmenting immune response or mitigating symptoms. CONCLUSIONS: There are statistically significant and biologically plausible differences in host gene expression induced by ETEC infection. Differential baseline expression of some genes may indicate resilience to infection.
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    Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.
    (Kidney international, 2024-01) Reznichenko, Anna; Nair, Viji; Eddy, Sean; Fermin, Damian; Tomilo, Mark; Slidel, Timothy; Ju, Wenjun; Henry, Ian; Badal, Shawn S; Wesley, Johnna D; Liles, John T; Moosmang, Sven; Williams, Julie M; Quinn, Carol Moreno; Bitzer, Markus; Hodgin, Jeffrey B; Barisoni, Laura; Karihaloo, Anil; Breyer, Matthew D; Duffin, Kevin L; Patel, Uptal D; Magnone, Maria Chiara; Bhat, Ratan; Kretzler, Matthias
    Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.