1932

Abstract

Recent advances in the field of infectious disease diagnostics have given rise to a number of host- and pathogen-centered diagnostic approaches. Most diagnostic approaches in contemporary infectious disease focus on pathogen detection and characterization. Host-focused diagnostics have recently emerged and are based on detecting the activation of biological pathways that are highly specific to the type of infecting pathogen (e.g., viral, bacterial, protozoan, fungal). Although this progress is encouraging, it is unlikely that any single diagnostic platform will fully address the clinician's need for actionable data with short turnaround times in all settings.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-052716-030320
2018-01-29
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/med/69/1/annurev-med-052716-030320.html?itemId=/content/journals/10.1146/annurev-med-052716-030320&mimeType=html&fmt=ahah

Literature Cited

  1. Woods CW, McClain MT, Chen M. 1.  et al. 2013. A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2. PLOS ONE 8:e52198 [Google Scholar]
  2. Daniels JM, Schoorl M, Snijders D. 2.  et al. 2010. Procalcitonin vs C-reactive protein as predictive markers of response to antibiotic therapy in acute exacerbations of COPD. Chest 138:1108–15 [Google Scholar]
  3. Tsalik EL, Jaggers LB, Glickman SW. 3.  et al. 2012. Discriminative value of inflammatory biomarkers for suspected sepsis. J. Emerg. Med. 43:97–106 [Google Scholar]
  4. Wacker C, Prkno A, Brunkhorst FM, Schlattmann P. 4.  2013. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect. Dis. 13:426–35 [Google Scholar]
  5. West M. 5.  2003. Bayesian factor regression models in the “large p, small n” paradigm. Bayesian Stat 7:723–32 [Google Scholar]
  6. Aittokallio T, Kurki M, Nevalainen O. 6.  et al. 2003. Computational strategies for analyzing data in gene expression microarray experiments. J. Bioinform. Comput. Biol. 1:541–86 [Google Scholar]
  7. Dunkler D, Sanchez-Cabo F, Heinze G. 7.  2011. Statistical analysis principles for omics data. Methods Mol. Biol. 719:113–31 [Google Scholar]
  8. Birney E, Stamatoyannopoulos JA. 8. ENCODE Project Consortium, et al. 2007. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816 [Google Scholar]
  9. Clarke LE, Warf MB, Flake DD 2nd. 9.  et al. 2015. Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J. Cutan. Pathol. 42:244–52 [Google Scholar]
  10. Deng MC, Eisen HJ, Mehra MR. 10.  et al. 2006. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am. J. Transplant. 6:150–60 [Google Scholar]
  11. Crespo-Leiro MG, Stypmann J, Schulz U. 11.  et al. 2015. Performance of gene-expression profiling test score variability to predict future clinical events in heart transplant recipients. BMC Cardiovasc. Disord. 15:120 [Google Scholar]
  12. Cardoso F, van't Veer LJ, Bogaerts J. 12.  et al. 2016. 70-Gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375:717–29 [Google Scholar]
  13. McHugh L, Seldon TA, Brandon RA. 13.  et al. 2015. A molecular host response assay to discriminate between sepsis and infection-negative systemic inflammation in critically ill patients: discovery and validation in independent cohorts. PLOS Med 12:e1001916 [Google Scholar]
  14. Zimmerman JJ, Sullivan E, Yager TD. 14.  et al. 2017. Diagnostic accuracy of a host gene expression signature that discriminates clinical severe sepsis syndrome and infection-negative systemic inflammation among critically ill children. Crit. Care Med. 45:e418–e25 [Google Scholar]
  15. Gilad S, Meiri E, Yogev Y. 15.  et al. 2008. Serum microRNAs are promising novel biomarkers. PLOS ONE 3:e3148 [Google Scholar]
  16. Chaussabel D, Pascual V, Banchereau J. 16.  2010. Assessing the human immune system through blood transcriptomics. BMC Biol 8:84 [Google Scholar]
  17. van Houten CB, de Groot JAH, Klein A. 17.  et al. 2017. A host-protein based assay to differentiate between bacterial and viral infections in preschool children (OPPORTUNITY): a double-blind, multicentre, validation study. Lancet Infect. Dis. 17:431–40 [Google Scholar]
  18. Chung HJ, Castro CM, Im H. 18.  et al. 2013. A magneto-DNA nanoparticle system for rapid detection and phenotyping of bacteria. Nat. Nanotechnol. 8:369–75 [Google Scholar]
  19. Yang WE, Woods CW, Tsalik EL. 19.  2015. Host-based diagnostics for detection and prognosis of infectious diseases. Methods in Microbiology A Sails, Y-W Tang 465–500 Amsterdam, Neth.: Elsevier [Google Scholar]
  20. 20. World Health Organization. 2016. Global Health Estimates 2015: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2015 Geneva: World Health Organ.
  21. Shapiro DJ, Hicks LA, Pavia AT, Hersh AL. 21.  2014. Antibiotic prescribing for adults in ambulatory care in the USA, 2007–09. J. Antimicrob. Chemother. 69:234–40 [Google Scholar]
  22. Lee GC, Reveles KR, Attridge RT. 22.  et al. 2014. Outpatient antibiotic prescribing in the United States: 2000 to 2010. BMC Med 12:96 [Google Scholar]
  23. Ramilo O, Allman W, Chung W. 23.  et al. 2007. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109:2066–77 [Google Scholar]
  24. Hu X, Yu J, Crosby SD, Storch GA. 24.  2013. Gene expression profiles in febrile children with defined viral and bacterial infection. PNAS 110:12792–97 [Google Scholar]
  25. Tsalik EL, Henao R, Nichols M. 25.  et al. 2016. Host gene expression classifiers diagnose acute respiratory illness etiology. Sci. Transl. Med. 8:322ra11 [Google Scholar]
  26. Mejias A, Dimo B, Suarez NM. 26.  et al. 2013. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLOS Med 10:e1001549 [Google Scholar]
  27. Heinonen S, Jartti T, Garcia C. 27.  et al. 2016. Rhinovirus detection in symptomatic and asymptomatic children: value of host transcriptome analysis. Am. J. Respir. Crit. Care Med. 193:772–82 [Google Scholar]
  28. McClain MT, Nicholson BP, Park LP. 28.  et al. 2016. A genomic signature of influenza infection shows potential for presymptomatic detection, guiding early therapy, and monitoring clinical responses. Open Forum Infect. Dis. 3:1ofw007 doi: 10.1093/ofid/ofw007 [Google Scholar]
  29. Suarez NM, Bunsow E, Falsey AR. 29.  et al. 2015. Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults. J. Infect. Dis. 212:213–22 [Google Scholar]
  30. Meaza A, Kebede A, Yaregal Z. 30.  et al. 2017. Evaluation of genotype MTBDRplus VER 2.0 line probe assay for the detection of MDR-TB in smear positive and negative sputum samples. BMC Infect. Dis. 17:280 [Google Scholar]
  31. Berry MP, Graham CM, McNab FW. 31.  et al. 2010. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466:973–77 [Google Scholar]
  32. Koth LL, Solberg OD, Peng JC. 32.  et al. 2011. Sarcoidosis blood transcriptome reflects lung inflammation and overlaps with tuberculosis. Am. J. Respir. Crit. Care Med. 184:1153–63 [Google Scholar]
  33. Koh GC, Schreiber MF, Bautista R. 33.  et al. 2013. Host responses to melioidosis and tuberculosis are both dominated by interferon-mediated signaling. PLOS ONE 8:e54961 [Google Scholar]
  34. Bloom CI, Graham CM, Berry MP. 34.  et al. 2013. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PLOS ONE 8:e70630 [Google Scholar]
  35. Cliff JM, Lee JS, Constantinou N. 35.  et al. 2013. Distinct phases of blood gene expression pattern through tuberculosis treatment reflect modulation of the humoral immune response. J. Infect. Dis. 207:18–29 [Google Scholar]
  36. Afshari A, Schrenzel J, Ieven M, Harbarth S. 36.  2012. Bench-to-bedside review: rapid molecular diagnostics for bloodstream infection—a new frontier?. Crit. Care 16:222 [Google Scholar]
  37. Tenover FC. 37.  2007. Rapid detection and identification of bacterial pathogens using novel molecular technologies: infection control and beyond. Clin. Infect. Dis. 44:418–23 [Google Scholar]
  38. Caliendo AM, Gilbert DN, Ginocchio CC. 38.  et al. 2013. Better tests, better care: improved diagnostics for infectious diseases. Clin. Infect. Dis. 57:Suppl. 3S139–70 [Google Scholar]
  39. Caliendo AM. 39.  2015. Editorial commentary: rapid blood culture identification: the value of a randomized trial. Clin. Infect. Dis. 61:1081–83 [Google Scholar]
  40. Caliendo AM, Hanson KE. 40.  2016. Point-counterpoint: the FDA has a role in regulation of laboratory-developed tests. J. Clin. Microbiol. 54:829–33 [Google Scholar]
  41. Ecker DJ, Sampath R, Li H. 41.  et al. 2010. New technology for rapid molecular diagnosis of bloodstream infections. Expert Rev. Mol. Diagn. 10:399–415 [Google Scholar]
  42. Warhurst G, Dunn G, Chadwick P. 42.  et al. 2015. Rapid detection of health-care-associated bloodstream infection in critical care using multipathogen real-time polymerase chain reaction technology: a diagnostic accuracy study and systematic review. Health Technol. Assess. 19:351–142 [Google Scholar]
  43. Evans SR, Hujer AM, Jiang H. 43.  et al. 2016. Rapid molecular diagnostics, antibiotic treatment decisions, and developing approaches to inform empiric therapy: PRIMERS I and II. Clin. Infect. Dis. 62:181–89 [Google Scholar]
  44. Viau R, Frank KM, Jacobs MR. 44.  et al. 2016. Intestinal carriage of carbapenemase-producing organisms: current status of surveillance methods. Clin. Microbiol. Rev. 29:1–27 [Google Scholar]
  45. Yamamoto N, Hamaguchi S, Akeda Y. 45.  et al. 2015. Clinical specimen-direct LAMP: a useful tool for the surveillance of blaOXA-23-positive carbapenem-resistant Acinetobacter baumannii. PLOS ONE 10:e0133204 [Google Scholar]
  46. Bachmann LH, Johnson RE, Cheng H. 46.  et al. 2009. Nucleic acid amplification tests for diagnosis of Neisseria gonorrhoeae oropharyngeal infections. J. Clin. Microbiol. 47:902–7 [Google Scholar]
  47. Huang S, Do J, Mahalanabis M. 47.  et al. 2013. Low cost extraction and isothermal amplification of DNA for infectious diarrhea diagnosis. PLOS ONE 8:e60059 [Google Scholar]
  48. Endimiani A, Hujer AM, Hujer KM. 48.  et al. 2010. Evaluation of a commercial microarray system for detection of SHV-, TEM-, CTX-M-, and KPC-type beta-lactamase genes in Gram-negative isolates. J. Clin. Microbiol. 48:2618–22 [Google Scholar]
  49. Fishbain JT, Sinyavskiy O, Riederer K. 49.  et al. 2012. Detection of extended-spectrum beta-lactamase and Klebsiella pneumoniae carbapenemase genes directly from blood cultures by use of a nucleic acid microarray. J. Clin. Microbiol. 50:2901–4 [Google Scholar]
  50. Lupo A, Papp-Wallace KM, Sendi P. 50.  et al. 2013. Non-phenotypic tests to detect and characterize antibiotic resistance mechanisms in Enterobacteriaceae. Diagn. Microbiol. Infect. Dis. 77:179–94 [Google Scholar]
  51. Wright MS, Stockwell TB, Beck E. 51.  et al. 2015. SISPA-Seq for rapid whole genome surveys of bacterial isolates. Infect. Genet. Evol. 32:191–98 [Google Scholar]
  52. Buchan BW, Ledeboer NA. 52.  2014. Emerging technologies for the clinical microbiology laboratory. Clin. Microbiol. Rev. 27:783–822 [Google Scholar]
  53. Mylonakis E, Clancy CJ, Ostrosky-Zeichner L. 53.  et al. 2015. T2 magnetic resonance assay for the rapid diagnosis of candidemia in whole blood: a clinical trial. Clin. Infect. Dis. 60:892–99 [Google Scholar]
  54. Farrell JJ, Sampath R, Ecker DJ, Bonomo RA. 54.  2013. “Salvage microbiology”: detection of bacteria directly from clinical specimens following initiation of antimicrobial treatment. PLOS ONE 8:e66349 [Google Scholar]
  55. Vincent JL, Brealey D, Libert N. 55.  et al. 2015. Rapid diagnosis of infection in the critically ill, a multicenter study of molecular detection in bloodstream infections, pneumonia, and sterile site infections. Crit. Care Med. 43:2283–91 [Google Scholar]
  56. Endimiani A, Hujer KM, Hujer AM. 56.  et al. 2011. Are we ready for novel detection methods to treat respiratory pathogens in hospital-acquired pneumonia?. Clin. Infect. Dis. 52:Suppl. 4S373–83 [Google Scholar]
  57. Banerjee R, Teng CB, Cunningham SA. 57.  et al. 2015. Randomized trial of rapid multiplex polymerase chain reaction–based blood culture identification and susceptibility testing. Clin. Infect. Dis. 61:71071–80 [Google Scholar]
/content/journals/10.1146/annurev-med-052716-030320
Loading
/content/journals/10.1146/annurev-med-052716-030320
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error