Advances in bacterial DNA sequencing allow for characterization of the human commensal bacterial community (i.e., microbiota) and its corresponding genome (i.e., microbiome). Surveys of healthy adults reveal that each unique body habitat (e.g., gut, skin, oral cavity, vagina) is characterized by a signature composite of bacteria. Aging is accompanied by a myriad of clinical issues, including a basal pro-inflammatory state (i.e.,inflamm-aging), that directly interface with the microbiotaof older adults and enhance susceptibility to disease. Studies in older adults demonstrate that the gut microbiotacorrelates with diet, location of residence (e.g., community dwelling, long term care settings), and basal level of inflammation.Links exist between the microbiotaand a variety of clinical issues plaguing older adults including physical frailty, Clostridium difficile colitis, vulvovaginal atrophy, colorectal carcinoma and atherosclerotic disease. Manipulation of the microbiota andmicrobiome of older adults holds promise as an innovative strategy to affect comorbidities associated with aging.

Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

Use of infection control measures described in the Centers for Disease Control and Prevention's 2012 CRE toolkit was associated with a reduction in the IR of CPE and an interruption in XDR-AB transmission.

The seasonal influenza vaccine is an important public health tool but is only effective in a subset of individuals. The identification of molecular signatures provides a mechanism to understand the drivers of vaccine-induced immunity. Most previously reported molecular signatures of human influenza vaccination were derived from a single age group or season, ignoring the effects of immunosenescence or vaccine composition. Thus, it remains unclear how immune signatures of vaccine response change with age across multiple seasons. In this study we profile the transcriptional landscape of young and older adults over five consecutive vaccination seasons to identify shared signatures of vaccine response as well as marked seasonal differences. Along with substantial variability in vaccine-induced signatures across seasons, we uncovered a common transcriptional signature 28 days postvaccination in both young and older adults. However, gene expression patterns associated with vaccine-induced Ab responses were distinct in young and older adults; for example, increased expression of killer cell lectin-like receptor B1 (KLRB1; CD161) 28 days postvaccination positively and negatively predicted vaccine-induced Ab responses in young and older adults, respectively. These findings contribute new insights for developing more effective influenza vaccines, particularly in older adults.

Objectives Effective and safe COVID‐19 vaccines have been developed and have resulted in decreased incidence and severity of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection and can decrease secondary transmission. However, there are concerns about dampened immune responses to COVID‐19 vaccination among immunocompromised patients, including people living with HIV (PLWH), which may blunt the vaccine's efficacy and durability of protection. This study aimed to assess the qualitative SARS‐CoV‐2 vaccine immunogenicity among PLWH after vaccination. Methods We conducted targeted COVID‐19 vaccination (all received BNT162b2 vaccine) of PLWH (aged ≥ 55 years per state guidelines) at Yale New Haven Health System and established a longitudinal survey to assess their qualitative antibody responses at 3 weeks after the first vaccination (and prior to receipt of the second dose of the COVID‐19 vaccine) (visit 1) and at 2–3 weeks after the second vaccination (visit 2) but excluded patients with prior COVID‐19 infection. Our goal was to assess vaccine‐induced immunity in the population we studied. Qualitative immunogenicity testing was performed using Healgen COVID‐19 anti‐Spike IgG/IgM rapid testing. Poisson regression with robust standard errors was used to determine factors associated with a positive IgG response. Results At visit 1, 45 of 78 subjects (57.7%) tested positive for SARS‐CoV‐2 anti‐Spike IgG after the first dose of COVID‐19 vaccine. Thirty‐nine subjects returned for visit 2. Of these, 38 had positive IgG (97.5%), including 20 of 21 subjects (95.2%) with an initial negative anti‐Spike IgG. Our bivariate analysis suggested that participants on an antiretroviral regimen containing integrase strand transfer inhibitors [relative risk (RR) = 1.81, 95% confidence interval (CI): 0.92–3.56, p = 0.085] were more likely to seroconvert after the first dose of the COVID‐19 vaccine, while those with a CD4 count < 500 cells/μL (RR = 0.59, 95% CI: 0.33–1.05, p = 0.071), and those diagnosed with cancer or another immunosuppressive condition (RR = 0.49, 95% CI: 0.18–1.28, p = 0.15) may have been less likely to seroconvert after the first dose of the COVID‐19 vaccine. The direction of these associations was similar in the multivariate model, although none of these findings reached statistical significance (RR integrase inhibitor = 1.71, 95% CI: 0.90–3.25, p = 0.10; RR CD4 count = 0.68, 95% CI: 0.39–1.19, p = 0.18; RR cancer or another immunosuppressive condition = 0.50, 95% CI: 0.19–1.33, p = 0.16). With regard to immunogenicity, we were able to record very high rates of new seroconversion following the second dose of the COVID‐19 vaccine. Conclusions ...