in a recent issue of critical care , parnell and colleagues examined host gene expression changes in whole blood from critically ill patients infected with pandemic h1n1 influenza a infection .
the measurable genomic response of the host to influenza may provide important information not only on viral pathogenesis but also on the immune response , its dissemination within the infected host , and its transmission in a population .
the authors evaluated transcriptional changes in blood samples obtained from 39 patients and 18 healthy controls .
of the 39 patients , eight had polymerase chain reaction ( pcr)-confirmed pandemic h1n1 influenza a virus , 16 had culture - documented bacterial pneumonia , and 12 had systemic inflammatory response syndrome of undefined etiology .
a support vector machine class predictor identified a blood - based h1n1 gene expression signature , but a separate unique bacterial signature , or secondary bacterial infection , could not be generated . distinguishing bacterial pneumonia from other causes of upper and lower respiratory tract infection ,
improvements in the timely and accurate classification of the causative agent(s ) of respiratory infection could have a profound impact on health and quality of life by improving appropriate antibiotic use and infection control interventions .
the development of inexpensive and easy - to - use rapid diagnostics to accurately distinguish bacterial pneumonia from viral pneumonia in routine clinical specimens ( for example , blood ) would represent a major advance in health care and have a significant impact on improving the clinical management and outcome of acute pneumonias . at present ,
infectious disease diagnostics focus on one or more pathogens of interest ( for example , the urinary pneumococcal antigen test or multiplex pcr for respiratory viruses ) or one - analyte - at - a - time biomarkers ( for example , c - reactive protein or procalcitonin elevation as a marker for severe bacterial infections ) .
in other fields , high - dimensional gene expression data offer a higher level of precision in the diagnosis of acute and chronic diseases .
infectious disease lends itself well to these analyses since the host response to pathogens entails a cascade of cellular events and expressed molecules that can be readily measured on a genome scale .
the dimension and complexity of such data provide unique opportunities to uncover patterns that can distinguish subtle phenotypes in ways that traditional single - molecule techniques can not and to use these patterns in models to predict with precision the way the genome interacts with environmental stimuli , such as infectious or other noxious agents .
recently , strategies have been developed by several groups to use host gene expression to classify viral , bacterial , and fungal   bloodstream infections - a strategy termed a ' paradigm shift ' by some investigators in the field .
the rationale for this approach is clear - immune effector cells respond to pathogens with characteristic gene expression patterns reflecting the interaction of the pathogen with pattern recognition receptor signaling .
these patterns , captured by microarray or alternative technologies , offer robust classification of infectious pathogens . in vitro
[ 11 - 13 ] and ex vivo   data provide proof - of - concept that gene expression patterns exhibited by host immune cells have , in addition to common cellular programs , pathogen - specific components .
parnell and colleagues should be applauded for their work in this area and extending the paradigm of hostbased classification of infectious disease .
however , the study has some limitations that need to be recognized so that future investigations of this type continue to move this important area of investigation forward .
the sample size is relatively small and thus the generalizability of the findings remains to be elucidated .
the authors recognize that additional validations with expanded cohorts are necessary to determine whether these findings represent an h1n1-specific molecular signature or one that is more representative of influenza viruses or other respiratory pathogens .
in addition , the question of whether these results would be generalizable to population extremes ( such as the very young or the aged and immune - compromised ) and the impact of medications , particularly antiviral and other antimicrobial drugs , on the accuracy of the classifier would absolutely need to be addressed .
has limited utility for clinical infectious disease diagnostics , and the signatures developed here need to be migrated to a more conventional platform for diagnostics , such as reverse transcription - pcr , and revalidated . critical work on sample processing , turnaround time , and sensitivity , specificity , and negative and positive predictive values also would need to be assessed prior to any claims about the suitability of the signatures as a diagnostic .
it is interesting that the investigators were unable to develop a signature for the bacterial pneumonia where others have had some success .
perhaps this reflects the heterogeneity of the subjects , issues with accurate phenotyping , or statistical methodology limitations .
bacterial pneumonias are heterogeneous , and the details of the case definition and adjudication of the phenotypes in these types of studies are critical components of the study design
. the development of a specific bacterial host response signature will be essential to the advancement of this technology to clinical application .
nonetheless , the results of this work are a significant step forward in the use of genomics to understanding the host response to an important pathogen in global health .
both authors are applicants for a patent to apply gene expression signatures for the diagnosis of viral infection . in addition both authors receive us government funding to study host based genomic biomarkers of infectious disease .
 capturing the host response by using genomic technologies such as transcriptional profiling provides a new paradigm for classifying and diagnosing infectious disease and for potentially distinguishing infection from other causes of serious respiratory illness . 
 this strategy has been used to define a blood - based rna signature as a classifier for pandemic h1n1 influenza infection that is distinct from bacterial pneumonia and other inflammatory causes of respiratory disease . to realize the full potential of this approach as a diagnostic test will require additional independent validation of the results and studies to examine the specificity of this signature for viral versus bacterial infection or co - infection . 
None
Competing interests