A metabolomics pipeline highlights microbial metabolism in bloodstream infections.

The growth of antimicrobial resistance (AMR) highlights an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe infections profoundly alter host metabolism, prior studies have largely ignored microbial metabolism in this context. Here, we describe an iterative, comparative metabolomics pipeline to uncover microbial metabolic features in the complex setting of a host and apply it to investigate gram-negative bloodstream infection (BSI) in patients. We find elevated levels of bacterially derived acetylated polyamines during BSI and discover the enzyme responsible for their production (SpeG). Blocking SpeG activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity also enhances bacterial membrane permeability and increases intracellular antibiotic accumulation, allowing us to overcome AMR in culture and in vivo. This study highlights how tools to study pathogen metabolism in the natural context of infection can reveal and prioritize therapeutic strategies for addressing challenging infections.

Clinical Efficacy and Adverse Effects of Bevacizumab in Combination with Chemotherapy for Metastatic Colorectal Cancer.

Objective: This study aims to investigate the efficacy of bevacizumab (BEV) in combination with chemotherapy for metastatic colorectal cancer (mCRC). Methods: A cohort of 121 patients diagnosed with mCRC and admitted to our hospital from May 2018 to October 2019 were selected for the study. The control group, comprising 64 patients, received chemotherapy alone, while the research group, consisting of 57 patients, underwent a combination of BEV and chemotherapy. Comparative analyses included an assessment of clinical outcomes, monitoring of tumor markers including Carcinoembryonic Antigen (CEA), Cancer Antigen 74-2 (CA74-2), and Carbohydrate Antigen 19-9 (CA19-9) before and after treatment, and a count of adverse effects during the treatment phase. A 3-year post-discharge follow-up was conducted to compare the survival prognosis between the two groups. Results: The research group exhibited a significantly higher objective response rate (ORR) and clinical benefit rate (CBR) compared to the control group (P < .05). Furthermore, CEA, CA74-2, and CA19-9 post-treatment levels were markedly lower in the research group (P < .05). No notable difference in the incidence of adverse reactions was observed between the two groups (P > .05). Importantly, the 3-year overall survival prognosis was superior in the research group (P < .05). Within the research group, patients treated with BEV combined with the XELIRI regimen chemotherapy demonstrated a higher CBR rate (P < .05). Conclusions: The combination of BEV and chemotherapy proves to be highly effective in treating mCRC, significantly enhancing the prognostic survival cycle of patients. This treatment modality holds promise for future clinical applications in managing patients with mCRC.

Identification and targeting of microbial putrescine acetylation in bloodstream infections.

The growth of antimicrobial resistance (AMR) has highlighted an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe bacterial infections profoundly alter host metabolism, prior studies have largely ignored alterations in microbial metabolism in this context. Performing metabolomics on patient and mouse plasma samples, we identify elevated levels of bacterially-derived N-acetylputrescine during gram-negative bloodstream infections (BSI), with higher levels associated with worse clinical outcomes. We discover that SpeG is the bacterial enzyme responsible for acetylating putrescine and show that blocking its activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity enhances bacterial membrane permeability and results in increased intracellular accumulation of antibiotics, allowing us to overcome AMR of clinical isolates both in culture and in vivo. This study highlights how studying pathogen metabolism in the natural context of infection can reveal new therapeutic strategies for addressing challenging infections.

Global Research Trends of Exosomes in the Central Nervous System: A Bibliometric and Visualized Analysis.

OBJECTIVE: Exosomes in the central nervous system (CNS) have become an attractive area of research with great value. However, few bibliometric analysis has been conducted. The study aimed to visualize the scientific trends and research hotspots of exosomes in the CNS by bibliometric analysis. METHODS: All potential articles and reviews on exosomes in the CNS published in English from 2001 to 2021 were extracted from the Web of Science Core Collection. The visualization knowledge maps of critical indicators, including countries/regions, institutions, authors, journals, references, and keywords, were generated by CiteSpace and VOSviewer software. Besides, each domain's quantitative and qualitative analysis was also considered. RESULTS: A total of 2,629 papers were included. The number of exosomes-related publications and citations regarding CNS increased yearly. These publications came from 2,813 institutions in 77 countries/regions, led by the United States and China. Harvard University was the most influential institution, while the National Institutes of Health was the most critical funding source. We identified 14,468 authors, among which Kapogiannis D had the most significant number of articles and the highest H-index, while Théry C was the most frequently co-cited. The cluster analysis of keywords generated 13 clusters. In summary, the topic of biogenesis, biomarker, and drug delivery will serve as hotspots in future research. CONCLUSION: Exosomes-related CNS research has gained considerable attention in the past 20 years. The sources and biological functions of exosomes and their promising role in diagnosing and treating CNS diseases are considered hotspots in this field. The clinical translation of the results from exosomes-related CNS research will be of great importance in the future.

Power of testing for exposure effects under incomplete mediation.

Mediation analysis studies situations where an exposure may affect an outcome both directly and indirectly through intervening variables called mediators. It is frequently of interest to test for the effect of the exposure on the outcome, and the standard approach is simply to regress the latter on the former. However, it seems plausible that a more powerful test statistic could be achieved by also incorporating the mediators. This would be useful in cases where the exposure effect size might be small, which for example is common in genomics applications. Previous work has shown that this is indeed possible under complete mediation, where there is no direct effect. In most applications, however, the direct effect is likely nonzero. In this paper we study linear mediation models and find that under certain conditions, power gain is still possible under this incomplete mediation setting for testing the null hypothesis that there is neither a direct nor an indirect effect. We study a class of procedures that can achieve this performance and develop their application to both low- and high-dimensional mediators. We then illustrate their performances in simulations as well as in an analysis using DNA methylation mediators to study the effect of cigarette smoking on gene expression.

Estimation of the proportion of treatment effect explained by a high-dimensional surrogate.

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.

Estimation and inference for the indirect effect in high-dimensional linear mediation models.

Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.