Seasonal influenza vaccine protects 60 to 90% of healthy young adults from influenza infection. The immunological events that lead to the induction of protective antibody responses remain poorly understood in humans. We identified the type of CD4⁺ T cells associated with protective antibody responses after seasonal influenza vaccinations. The administration of trivalent split-virus influenza vaccines induced a temporary increase of CD4⁺ T cells expressing ICOS, which peaked at day 7, as did plasmablasts. The induction of ICOS was largely restricted to CD4⁺ T cells coexpressing the chemokine receptors CXCR3 and CXCR5, a subpopulation of circulating memory T follicular helper cells. Up to 60% of these ICOS⁺CXCR3⁺CXCR5⁺CD4⁺ T cells were specific for influenza antigens and expressed interleukin-2 (IL-2), IL-10, IL-21, and interferon-γ upon antigen stimulation. The increase of ICOS⁺CXCR3⁺CXCR5⁺CD4⁺ T cells in blood correlated with the increase of preexisting antibody titers, but not with the induction of primary antibody responses. Consistently, purified ICOS⁺CXCR3⁺CXCR5⁺CD4⁺ T cells efficiently induced memory B cells, but not naïve B cells, to differentiate into plasma cells that produce influenza-specific antibodies ex vivo. Thus, the emergence of blood ICOS⁺CXCR3⁺CXCR5⁺CD4⁺ T cells correlates with the development of protective antibody responses generated by memory B cells upon seasonal influenza vaccination.

Respiratory syncytial virus (RSV) is the leading cause of viral lower respiratory tract infection (LRTI) and hospitalization in infants. Mostly because of the incomplete understanding of the disease pathogenesis, there is no licensed vaccine, and treatment remains symptomatic. We analyzed whole blood transcriptional profiles to characterize the global host immune response to acute RSV LRTI in infants, to characterize its specificity compared with influenza and human rhinovirus (HRV) LRTI, and to identify biomarkers that can objectively assess RSV disease severity. This was a prospective observational study over six respiratory seasons including a cohort of infants hospitalized with RSV (n = 135), HRV (n = 30), and influenza (n = 16) LRTI, and healthy age- and sex-matched controls (n = 39). A specific RSV transcriptional profile was identified in whole blood (training cohort, n = 45 infants; Dallas, Texas, US) and validated in three different cohorts (test cohort, n = 46, Dallas, Texas, US; validation cohort A, n = 16, Turku, Finland; validation cohort B, n = 28, Columbus, Ohio, US) with high sensitivity (94% [95% CI 87%–98%]) and specificity (98% [95% CI 88%–99%]). It classified infants with RSV LRTI versus HRV or influenza LRTI with 95% accuracy. The immune dysregulation induced by RSV (overexpression of neutrophil, inflammation, and interferon genes, and suppression of T and B cell genes) persisted beyond the acute disease, and immune dysregulation was greatly impaired in younger infants (<6 mo). We identified a genomic score that significantly correlated with outcomes of care including a clinical disease severity score and, more importantly, length of hospitalization and duration of supplemental O₂. Blood RNA profiles of infants with RSV LRTI allow specific diagnosis, better understanding of disease pathogenesis, and assessment of disease severity. This study opens new avenues for biomarker discovery and identification of potential therapeutic or preventive targets, and demonstrates that large microarray datasets can be translated into a biologically meaningful context and applied to the clinical setting. Please see later in the article for the Editors' Summary Lower respiratory tract infections (LRTIs)—bacterial and viral infections of the lungs and airways (the tubes that take oxygen-rich air to the lungs)—are major causes of illness and death in children worldwide. Pneumonia (infection of the lungs) alone is responsible for 14% of all child deaths. The leading cause of viral LTRIs in children is respiratory syncytial virus (RSV), which is readily transmitted from person to person by direct contact with nasal fluids or airborne droplets. Almost all children have an RSV infection before their second birthday, but most have only minor symptoms similar to those of a common cold and are cared for at home. Unfortunately, some children develop more serious conditions when they become infected with RSV, such as pneumonia or bronchiolitis (swelling and mucus build-up in the bronchioles, the smallest air passages in the lungs). These children have to be admitted to the hospital for supportive care—there is no specific treatment for RSV infection—such as the provision of supplemental oxygen. The lack of a treatment (and of a vaccine) for RSV is largely due to our incomplete understanding of the cellular events and reactions, including the host immune response, that occur during the development of an RSV infection (disease pathogenesis). Moreover, based on physical examination and available diagnostic tools, it is impossible to predict which children infected with RSV will develop a serious condition that requires hospitalization and which ones can be safely nursed at home. Here, the researchers use microarrays to analyze the global host response to acute RSV LTRI in infants, to define gene expression patterns that are specific to RSV infection rather than infection with other common respiratory viruses, and to identify biomarkers that indicate the severity of RSV infection. “Microarray” analysis allows researchers to examine gene expression patterns (“RNA transcriptional profiles”) in, for example, whole blood; a biomarker is a molecule whose level in bodily fluids or tissues indicates how a disease might develop and helps with patient classification. The researchers compared the RNA transcriptional profile in whole blood taken from children less than two years old hospitalized with RSV, human rhinovirus, or influenza virus infection (rhinovirus and influenza are two additional viral causes of LRTI), and from healthy infants. Using “statistical group comparisons,” they identified more than 2,000 transcripts that were differentially expressed in blood from 45 infants with RSV infection and from 14 healthy matched controls. Genes related to interferon function (interferons are released by host cells in response to the presence of disease-causing organisms) and neutrophil function (neutrophils are immune system cells that, like interferons, are involved in the innate immune response, the body's first line of defense against infection) were among the most overexpressed genes in infants infected with RSV. Genes regulating T and B cells (components of the adaptive immune response, the body's second-line of defense against infection) were among the most underexpressed genes. This specific transcriptional profile, which was validated in three additional groups of infants, accurately distinguished between infants infected with RSV and those infected with human rhinovirus or influenza virus. Finally, a “molecular distance to health” score (a numerical score that quantifies the transcriptional perturbation associated with an illness) was correlated with the clinical disease severity score of the study participants, with how long they needed supplemental oxygen, and with their duration of hospitalization. These findings suggest that it might be possible to use whole blood RNA transcriptional profiles to distinguish between infants infected with RSV and those with other viruses that commonly cause LRTI. Moreover, if these findings can be replicated in more patients (including non-hospitalized children), gene expression profiling might provide a strategy for triaging patients with RSV infections when they first present to an emergency department and for monitoring clinical changes during the course of the infection, particularly given the development of molecular tools that might soon enable the “real time” acquisition of transcriptional profiles in the clinical setting. Finally, although certain aspects of the study design limit the accuracy and generalizability of the study's findings, these data provide new insights into the pathogenesis of RSV infection and open new avenues for the discovery of biomarkers for RSV infection and for the identification of therapeutic and preventative targets. Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001549.

Febrile infants aged 60 days or younger are at substantial risk of serious bacterial infections (defined as bacteremia, urinary tract infections [UTIs], and bacterial meningitis), estimated at 7% to 9% in this population.¹ Current guidelines recommend obtaining blood, urine, and cerebrospinal fluid (CSF) cultures, with consideration of antibiotic treatment and hospitalization until culture results are negative. Although these strategies aim to identify and treat all young febrile infants with serious bacterial infections, the costs and risks are high.² The lack of an optimal management strategy has led to substantial variation in the care for this vulnerable population, unnecessarily exposing many infants to potential harm.

Background. Distinguishing between bacterial and viral lower respiratory tract infection (LRTI) remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods. We performed whole blood transcriptional analysis in 118 patients (median age [interquartile range], 61 [50–76] years) hospitalized with LRTI and 40 age-matched healthy controls (median age, 60 [46–70] years). We applied class comparisons, modular analysis, and class prediction algorithms to identify and validate diagnostic biosignatures for bacterial and viral LRTI. Results. Patients were classified as having bacterial (n = 22), viral (n = 71), or bacterial-viral LRTI (n = 25) based on comprehensive microbiologic testing. Compared with healthy controls, statistical group comparisons (P < .01; multiple-test corrections) identified 3376 differentially expressed genes in patients with bacterial LRTI, 2391 in viral LRTI, and 2628 in bacterial-viral LRTI. Patients with bacterial LRTI showed significant overexpression of inflammation and neutrophil genes (bacterial > bacterial-viral > viral), and those with viral LRTI displayed significantly greater overexpression of interferon genes (viral > bacterial-viral > bacterial). The K–nearest neighbors algorithm identified 10 classifier genes that discriminated between bacterial and viral LRTI with a 95% sensitivity (95% confidence interval, 77%–100%) and 92% specificity (77%–98%), compared with a sensitivity of 38% (18%–62%) and a specificity of 91% (76%–98%) for procalcitonin. Conclusions. Transcriptional profiling is a helpful tool for diagnosis of LRTI.