Influenza is a major public health concern, and the high mortality rate is largely attributed to secondary bacterial infections. There are several mechanisms through which the virus increases host susceptibility to bacterial colonization, but the micro-environment in lower respiratory tract (LRT) of host, infected with influenza virus, is unclear. To this end, we analyzed the LRT microbiome, transcriptome of lung and metabolome of bronchoalveolar lavage fluid (BALF) in mice inoculated intra-nasally with H1N1 to simulate human influenza, and we observed significant changes in the composition of microbial community and species diversity in the acute (7 days post inoculation or dpi), convalescent (14 dpi) and the recovery (28 dpi) periods. The dominant bacterial class shifted from Alphaproteobacteria to Gammaproteobacteria and Actinobacteria in the infected mice, with a significant increase in the relative abundance of anaerobes and facultative anaerobes like Streptococcus and Staphylococcus. The dysbiosis in the LRT of infected mice was not normalized even in the recovery phase of the infection. In addition, the infected lung transcriptome showed significant differences in the expression levels of genes associated with bacterial infection and immune responses. Finally, the influenza virus infection also resulted in significant changes in the metabolome of the BALF. These alterations in the microbiome, transcriptome, and metabolome of infected lungs were not only appeared at the acute period, but also observed at the recovery period. Furthermore, the infection of influenza virus induced a long-term effect in LRT micro-environmental homeostasis, which may give a chance for the invasion of potential pathogens.

Previous studies showed that TNF-α could activate voltage-gated Na+ channels (VGSCs) in the peripheral nervous system (PNS). Since TNF-α is implicated in many central nervous system (CNS) diseases, we examined potential effects of TNF-α on VGSCs in the CNS. Effects of TNF-α (1–1000 pg/mL, for 4–48 h) on VGSC currents were examined using whole-cell voltage clamp and current clamp techniques in primary culture of mouse cortical neurons. Expression of Nav1.1, Nav1.2, Nav1.3, and Nav1.6 were examined at both the mRNA and protein levels, prior to and after TNF-α exposure. TNF-α increased Na+ currents by accelerating the activation of VGSCs. The threshold for action potential (AP) was decreased and firing rate were increased. VGSCs were up-regulated at both the mRNA and protein levels. The observed effects of TNF-α on Na+ currents were inhibited by pre-incubation with the NF-κB inhibitor BAY 11–7082 (1 μM) or the p38 mitogen-activated protein kinases (MAPK) inhibitor SB203580 (1 μM). TNF-α increases Na+ currents by accelerating the channel activation as well as increasing the expression of VGSCs in a mechanism dependent upon NF-κB and p38 MAPK signal pathways in CNS neurons.

The great heterogeneity of patients with chronic critical illness (CCI) leads to difficulty for intensive care unit (ICU) management. Identifying subphenotypes could assist in individualized care, which has not yet been explored. In this study, we aim to identify the subphenotypes of patients with CCI and reveal the heterogeneous treatment effect of fluid balance for them. In this retrospective study, we defined CCI as an ICU length of stay over 14 days and coexists with persistent organ dysfunction (cardiovascular Sequential Organ Failure Assessment (SOFA) score ≥1 or score in any other organ system ≥2) at Day 14. Data from five electronic healthcare record datasets covering geographically distinct populations (the US, Europe, and China) were studied. These five datasets include (1) subset of Derivation (MIMIC-IV v1.0, US) cohort (2008–2019); (2) subset Derivation (MIMIC-III v1.4 ‘CareVue’, US) cohort (2001–2008); (3) Validation I (eICU-CRD, US) cohort (2014–2015); (4) Validation II (AmsterdamUMCdb/AUMC, Euro) cohort (2003–2016); (5) Validation III (Jinling, CN) cohort (2017–2021). Patients who meet the criteria of CCI in their first ICU admission period were included in this study. Patients with age over 89 or under 18 years old were excluded. Three unsupervised clustering algorithms were employed independently for phenotypes derivation and validation. Extreme Gradient Boosting (XGBoost) was used for phenotype classifier construction. A parametric G-formula model was applied to estimate the cumulative risk under different daily fluid management strategies in different subphenotypes of ICU mortality. We identified four subphenotypes as Phenotype A, B, C, and D in a total of 8145 patients from three countries. Phenotype A is the mildest and youngest subgroup; Phenotype B is the most common group, of whom patients showed the oldest age, significant acid-base abnormality, and low white blood cell count; Patients with Phenotype C have hypernatremia, hyperchloremia, and hypercatabolic status; and in Phenotype D, patients accompany with the most severe multiple organ failure. An easy-to-use classifier showed good effectiveness. Phenotype characteristics showed robustness across all cohorts. The beneficial fluid balance threshold intervals of subphenotypes were different. We identified four novel phenotypes that revealed the different patterns and significant heterogeneous treatment effects of fluid therapy within patients with CCI. A prospective study is needed to validate our findings, which could inform clinical practice and guide future research on individualized care. This study was funded by 333 High Level Talents Training Project of 10.13039/501100002949 Jiangsu Province (BRA2019011), General Program of Medical Research from the Jiangsu Commission of Health (M2020052), and Key Research and Development Program of Jiangsu Province (BE2022823).

Blood microorganisms were once thought to indicate infection. Blood in healthy people appears to be devoid of growing bacteria; nonetheless, intracellular dormant forms of bacteria have been reported previously. With breakthroughs in sequencing and bioinformatics, the presence of bacterial DNA in healthy human blood initiated the controversy of human blood microbiota (HBM). Recently, bacteria-specific DNA and culturable bacteria were found in healthy human blood. Researchers wanted to study the phenomena of a “healthy blood microbiota” by providing a thorough description of bacterially produced nucleic acids using many complementing molecular and traditional microbiological approaches. Because blood is a relatively limited and particular environment, culturability and plate count issues can be overcome using enhanced cultured procedures. However, more evidence is required to confirm that healthy human blood contains normal microbiota. Cavities, mouth and intestinal microbiota, trauma, surgery, and animal/insect bites can introduce bacteria into human blood. All these factors strengthen the concept of transient blood bacteria too. The presence of blood bacteria may be caused by temporary immunological clearance and absorption by dendritic or M cells. This review provides an extensive and comprehensive analysis that suggests that healthy blood bacteria may not be typical microbiota but transient circulatory microorganisms. In this study, we look at how contaminants (Escherichia, Shigella, Pseudomonads, etc.) from the skin, laboratory environments, and reagents can affect the interpretation of blood-derived microbial information and the relationship between the circulating bacteria and non-communicable diseases. Circulating transient bacteria may play a role in the pathogenesis of non-infectious diseases such as diabetes and CVD. Contamination-free hematological studies can aid in understanding the disease mechanisms, therapy, and biomarkers.

The nucleolus is a stress sensor associated with cell cycle progression and a central hub for the replication of pathogenic RNA viruses. However, the role of nucleolus in influenza A virus infection has not been well studied. Here we show that the interaction between NS1 protein of influenza A/Shantou/602/06 (H3N2) and nucleolin, a ubiquitous protein of nucleolus repressed RNA Pol I-dependent transcription via establishing hyper-methylation in the UCE of rRNA gene promoter. NS1 expressed cells showed significant association of ribosomal proteins with MDM2, and p53 accumulation, suggesting induced nucleolar stress. Disruption of the interaction of NS1 with nucleolin or overexpression of nucleolin in NS1 expressed cells revived RNA Pol I-dependent transcription, indicating nucleolin could be one target for NS1 to repress rRNA synthesis of host cells. Our present study suggests that NS1 protein of H3N2 could induce nucleolar stress based on epigenetic alteration of rRNA gene promoter via interaction with nucleolin.